diff --git "a/5279.jsonl" "b/5279.jsonl"
new file mode 100644--- /dev/null
+++ "b/5279.jsonl"
@@ -0,0 +1,717 @@
+{"seq_id":"27530244036","text":"import glob\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport os\nimport tensorflow as tf\n\nfrom PIL import Image\n\nimport model\n\nIMG_SIZE = 28\nOUTPUT_SIZE = 10\nDATA_SIZE = 500\nMAXIMUM_ANALYSIS_SIZE = 30\n\ndataImages = np.zeros((DATA_SIZE, IMG_SIZE*IMG_SIZE))\ndataLabels = np.zeros((DATA_SIZE, OUTPUT_SIZE))\n\ndef read_data():\n    fileImg = open('./data/testImage.txt', 'r')\n    for i in range(DATA_SIZE):\n        line = fileImg.readline()\n        val = line.split(',')\n        dataImages[i, :] = val[1:IMG_SIZE*IMG_SIZE + 1]\n    \nif __name__=='__main__':    \n    #with tf.device(\"/gpu:0\"):\n        with tf.Graph().as_default():\n            read_data()\n\n            graph = tf.Graph()\n            sess = tf.InteractiveSession(graph = graph)\n\n            with open('frozen_graph.pb', 'rb') as f:\n                graph_def = tf.GraphDef()\n                graph_def.ParseFromString(f.read())\n                tf.import_graph_def(graph_def, name='') #, {'x':input})\n\n            print('=' * 60)\n            for op in tf.get_default_graph().get_operations():\n                print(op.name)\n                for output in op.outputs:\n                    print('  ', output.name)\n            print('=' * 60)\n\n            if not os.path.exists('./result'):\n                os.mkdir('./result')\n            remaining_size = DATA_SIZE\n            ANALYSIS_SIZE = MAXIMUM_ANALYSIS_SIZE\n            start = 0\n            counter = 0\n            while True:\n                if(remaining_size > 0 and remaining_size - MAXIMUM_ANALYSIS_SIZE >= 0):\n                    ANALYSIS_SIZE = MAXIMUM_ANALYSIS_SIZE\n                elif(remaining_size > 0 and remaining_size - MAXIMUM_ANALYSIS_SIZE < 0):\n                    ANALYSIS_SIZE = remaining_size\n                else:\n                    break\n\n                decoded_imgs = sess.run('y:0', feed_dict={'x:0': dataImages[0:30,:], 'y_:0': dataLabels[0:30, :], 'keep_prob:0': 1.0})\n                decoded_imgs = decoded_imgs.reshape([-1, OUTPUT_SIZE])\n                \n                for i in range(ANALYSIS_SIZE):\n                    filename = './result/data' + str(counter) + '.txt'\n                    print('writing ' + filename)\n                    np.savetxt(filename, decoded_imgs[i, :])\n                    counter += 1\n\n                remaining_size -= ANALYSIS_SIZE \n                start = start + ANALYSIS_SIZE\n        \n            for k in range(DATA_SIZE):\n                plt.figure(figsize=(7, 4))\n                plt.subplot(1, 2, 1)\n                fig = plt.imshow(dataImages[k, :].reshape([IMG_SIZE, IMG_SIZE]), vmin=0, vmax=255, cmap='gray')\n                fig.axes.get_xaxis().set_visible(False)\n                fig.axes.get_yaxis().set_visible(False)\n\n                output = np.loadtxt('./result/data' + str(k) + '.txt')\n                plt.subplot(1, 2, 2)\n                n = np.linspace(0, 9, 10)\n                fig = plt.plot(output.reshape([OUTPUT_SIZE, 1]), n)\n                plt.show()\n                \n\n\n","repo_name":"changGitHubJ/mnist_inference_using_pb","sub_path":"apply_model_pb.py","file_name":"apply_model_pb.py","file_ext":"py","file_size_in_byte":2978,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"24422384737","text":"from flask import Flask, jsonify, request\nfrom pprint import pprint as pp\nimport logging\n\n# Understanding how to develop API's in Python. \n#Jasonify -  Flask offers the convenient jsonify() function, which returns a JSON object from Python variables:\n\napp = Flask(__name__)\n\n# My first API in Python\nincomes = [\n  { 'description': 'salary', 'amount': 5000 }\n]\n\nbooks = [\n    {'id': 0,\n     'title': 'A Fire Upon the Deep',\n     'author': 'Vernor Vinge',\n     'first_sentence': 'The coldsleep itself was dreamless.',\n     'year_published': '1992'},\n    {'id': 1,\n     'title': 'The Ones Who Walk Away From Omelas',\n     'author': 'Ursula K. Le Guin',\n     'first_sentence': 'With a clamor of bells that set the swallows soaring, the Festival of Summer came to the city Omelas, bright-towered by the sea.',\n     'published': '1973'},\n    {'id': 2,\n     'title': 'Dhalgren',\n     'author': 'Samuel R. Delany',\n     'first_sentence': 'to wound the autumnal city.',\n     'published': '1975'}\n]\n\n\n@app.route('/incomes')\ndef get_incomes():\n  return jsonify(incomes)\n\n# @app.route('/')\n# def json_hello():\n#    return jsonify({x:x*x for x in range(5)}), 200\n\n\n@app.route('/', methods=['GET'])\ndef home():\n    return '''<h1>Distant Reading Archive</h1><p>A prototype API for distant reading of science fiction novels.</p>'''\n\n\n@app.route('/incomes', methods=['POST'])\ndef add_income():\n  incomes.append(request.get_json())\n  return '', 204\n\n# Sample Call - http://127.0.0.1:5000/api/v1/resources/books?id=1\n\n@app.route('/api/v1/resources/books', methods=['GET'])\ndef api_id():\n    # Check if an ID was provided as part of the URL.\n    # If ID is provided, assign it to a variable.\n    # If no ID is provided, display an error in the browser.\n    if 'id' in request.args:\n        id = int(request.args['id'])\n    else:\n        return \"Error: No id field provided. Please specify an id.\"\n\n    # Create an empty list for our results\n    results = []\n\n    # Loop through the data and match results that fit the requested ID.\n    # IDs are unique, but other fields might return many results\n    for book in books:\n        if book['id'] == id:\n            results.append(book)\n\n    # Use the jsonify function from Flask to convert our list of\n    # Python dictionaries to the JSON format.\n    return jsonify(results)\n\n\n\n@app.route('/LongAO')\ndef QuantScreenRouter():\n\n    try:\n\n      logging.warning(\"before_request\")\n\n      cnxn = pyodbc.connect(\n              r'Driver={SQL Server Native Client 11.0};'\n              r'SERVER=f6iq6q5hoj.database.windows.net;'\n              r'DATABASE=QuantValue;'\n              r'UID=@f6iq6q5hoj;'\n              r'PWD=!'\n          )\n\n      cursor = cnxn.cursor()\n      cursor.execute(\"exec [dbo].[QuantScreenRouter] 'LONG_AO','Toronto'\")\n      logging.info(\"has data\")\t\n      results = cursor.fetchall()\n      entries = [dict(title=row['title'], text=row[1]) for row in results]\n\n      #for row in cursor:\n      #    print(row.Symbol)\n\n      cnxn.close(cursor)\n      cursor.close()\n\n      return entries\n\n    except ValueError:\n        pass\n\n\nif __name__ == '__main__':\n    app.run(debug=True)\n    \n\n","repo_name":"nlgandhi/LearnPython","sub_path":"Learn/api1.py","file_name":"api1.py","file_ext":"py","file_size_in_byte":3121,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"24967532141","text":"#Input text file to read. If it can't be opened then it will stop.\nfilename = input('Enter file name: ')\ntry:\n    fhandle = open(filename)\nexcept:\n    print('Could not open file.')\n    quit()\ntopwords = input('How many words do you want to return? (Only input integers) ')\ntry:\n    top = int(topwords)\nexcept:\n    print('Not a number.')\n    quit()\n    \n#Setup empty dictionary. For each line in the opened file, split the line into individual words based on spaces. For each word in the list of words, the get method will assign 0 as default and increase by 1 whenever it encounters new word.\ncounts = dict()\nfor line in fhandle:\n    wordlist = line.split()\n    for iword in wordlist:\n        #Idiom: retrieve, create, update counter\n        counts[iword] = counts.get(iword,0)+1\n\ntmplist = list()\n#Assigning the tuple to another variable 'oldtup'\noldtup = counts.items()\n#For each pair of key and value in oldtup, switch the placements and put them into newtup. Then append it to the newly created list tmplist.\nfor k, v in oldtup:\n    newtup = (v,k)\n    tmplist.append(newtup)\n#Using newtup, sort from highest to lowest and put it into newcounts. Now the list is sorted from highest frequency to lowest frequency.\nnewcounts = sorted(tmplist, reverse=True)\n\nfor v,k in newcounts[:top]:\n    print(k,v)","repo_name":"Silijet/Projects","sub_path":"Most Common Words/Most Common Words.py","file_name":"Most Common Words.py","file_ext":"py","file_size_in_byte":1301,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"3661785183","text":"class VirtualCoinPrice(object):\n    \"\"\"\n    Class for virtual coin price data for a specific date (day)\n    Attributes:\n        price: virtual coin price for a specific date (day)\n        open: open price\n        high: high price\n        low: low price\n        date:\n        change: change from previous day in percentage\n    \"\"\"\n\n    def __init__(self,date,price,open=None,high=None,low=None,change=None):\n        self.date = date\n        self.price = float(price.replace(',',''))\n        self.open = float(open.replace(',',''))\n        self.high = float(high.replace(',',''))\n        self.low = float(low.replace(',',''))\n        self.change = float(change.strip('%'))\n\n    def init_with_dict(self,data):\n        self.date = data['date']\n        self.price = data['price']\n        self.open = data['open']\n        self.high = data['high']\n        self.low = data['low']\n        self.change =  data['change']\n\n    def calculate_change (self, last_day_price):\n        if self.price:\n            self.change = (self.price - last_day_price)/last_day_price\n            return self.change\n        else: return None\n\n","repo_name":"eladnachum/HTML_Scrapper","sub_path":"virtual_coin/model/VirtualCoinPrice.py","file_name":"VirtualCoinPrice.py","file_ext":"py","file_size_in_byte":1112,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"11155487730","text":"a=int(input())\nls=list(input().split())\no=0\nans=0\nfor i in range(0,a):\n    b=int(ls[i])\n    if b > 0:\n        o+=b\n    elif b < 0 and o==0:\n        ans+=b\n    else:\n        o-=1\nprint(ans*-1)","repo_name":"PROG-007/coding-shoding","sub_path":"codeforces/427a.py","file_name":"427a.py","file_ext":"py","file_size_in_byte":191,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"30203288964","text":"import asyncio\nimport json\n\nimport dpath.util\nimport pytest\n\nfrom .utils import subscribe_to_queue\n\n\n@pytest.mark.asyncio\nasync def test_get_queued_payment_tokens(stan_server, event_loop, client_id, entity_stan, future):\n    \"\"\"Assert that payment tokens can be retrieved and decoded from the Queue.\"\"\"\n    # Call back for the subscription\n    msgs = []\n\n    async def cb(msg):\n        nonlocal msgs\n        nonlocal future\n        msgs.append(msg)\n        if len(msgs) == 10:\n            future.set_result(True)\n\n    entity_subject = await subscribe_to_queue(entity_stan, cb)\n\n    # add payment tokens to queue\n    for i in range(0, 5):\n        payload = {'paymentToken': {'id': 1234 + i, 'statusCode': 'COMPLETED'}}\n        await entity_stan.publish(subject=entity_subject,\n                                  payload=json.dumps(payload).encode('utf-8'))\n    try:\n        await asyncio.wait_for(future, 2, loop=event_loop)\n    except Exception as err:\n        print(err)\n\n    # check the payment tokens were retrieved from the queue\n    assert len(msgs) == 5\n    for i in range(0, 5):\n        m = msgs[i]\n        assert 'paymentToken' in m.data.decode('utf-8')\n        assert 'COMPLETED' == dpath.util.get(json.loads(m.data.decode('utf-8')),\n                                             'paymentToken/statusCode')\n        assert m.sequence == i + 1\n","repo_name":"bcgov/lear","sub_path":"queue_services/common/tests/integration/test_queue_payment_token.py","file_name":"test_queue_payment_token.py","file_ext":"py","file_size_in_byte":1349,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"48"}
+{"seq_id":"40897916437","text":"#from PySide.QtCore import *\nfrom PySide.QtGui import *\nfrom sys import exit, argv\n\nclass Window(QWidget):\n\t\"\"\"docstring for Window\"\"\"\n\tdef __init__(self):\n\t\tQWidget.__init__(self)\n\t\tself.btnSumar = QPushButton(\"sumar\")\n\t\tlayout = QVBoxLayout()\n\napp = QApplication(argv)\nwindow = Window()\nwindow.setWindowTitle(\"primera app PySide\")\nwindow.show()\nexit(app.exec_())\n","repo_name":"ArnoldoRicardo/test-python","sub_path":"pyside/app1.py","file_name":"app1.py","file_ext":"py","file_size_in_byte":365,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"36327054985","text":"import sys\nsys.path.append('..\\src')\n\nfrom nia.algorithms import ArtificialBeeColony\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib import cm\nfrom mpl_toolkits.mplot3d import Axes3D\n\ndef ackley(X):\n    x = X[0]\n    y = X[1]\n    return -20*np.exp(-0.2*np.sqrt(0.5*(x**2+y**2)))-np.exp(0.5*(np.cos(2*np.pi*x)+np.cos(2*np.pi*y)))+np.e+20\n\n#create plot\nfig = plt.figure()\nax = fig.add_subplot(projection='3d')\nfits = []\ni = 0\nx = np.arange(-10, 10, 0.5)\ny = np.arange(-10, 10, 0.5)\nx, y = np.meshgrid(x, y)\nz = -20*np.exp(-0.2*np.sqrt(0.5*(x**2+y**2)))-np.exp(0.5*(np.cos(2*np.pi*x)+np.cos(2*np.pi*y)))+np.e+20\n\ndef log(abc):\n    #clear \n    plt.cla()\n    # Plot the surface.\n    surf = ax.plot_surface(x, y, z, cmap=cm.coolwarm,\n                        linewidth=0, antialiased=False, alpha=0.2)\n    # Add a color bar which maps values to colors.\n    for food in abc.food_sources:\n        ax.scatter(food.solution[0], food.solution[1], food.fitness, marker='o')\n    fig.canvas.draw()\n    plt.pause(0.001)\n\nlower = np.array([-5,-5])\nupper = np.array([5,5])\n\nnia = ArtificialBeeColony(cost_function=ackley,\n                                iteration_function=log,\n                                lower_bond=lower,\n                                upper_bond=upper,\n                                quit_criteria = 0.0001,\n                                num_variable = 2,\n                                )\nnia.run()\nprint(nia.message);\n","repo_name":"salar-shdk/nia","sub_path":"examples/test-abc.py","file_name":"test-abc.py","file_ext":"py","file_size_in_byte":1448,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"48"}
+{"seq_id":"25872813841","text":"import sys\nimport math\nsys.setrecursionlimit(10005)\ndef input():\n    return sys.stdin.readline().rstrip()\n\ndef shuffle(arr,cnt):\n    arr = arr[N-2**cnt:] + arr[:N-2**cnt]\n    cnt -= 1\n    while cnt>=0:\n        prev = 2**(cnt+1)\n        move_arr = arr[:prev]\n        fix_arr = arr[prev:]\n        move = 2**cnt\n        arr = move_arr[prev-move:] + move_arr[:prev-move] + fix_arr[:]\n        cnt -= 1\n\n    return arr\ndef solve():\n    for one_k in range(1,max_K+1):\n        shuffled = shuffle(list(range(1,N+1)),one_k)\n        for two_k in range(1,max_K+1):\n            shuffled = shuffle(shuffled[:],two_k)\n            for ind in range(N):\n                if shuffled[ind] != cards[ind]:\n                    break\n            else:\n                print(one_k,two_k)\n                return\nN = int(input())\n\ncards = list(map(int,input().split()))\n\nmax_K = 0\nwhile 2**max_K<N:\n    max_K += 1\nmax_K -= 1\nsolve()\n","repo_name":"gkgg123/TIL_new","sub_path":"알고리즘/백준/21315_카드_섞기_version1.py","file_name":"21315_카드_섞기_version1.py","file_ext":"py","file_size_in_byte":906,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"27325096566","text":"\"\"\"\nPowder line intensity - temperature dependence\n\"\"\"\nfrom numpy import *\nw=Load('/SNS/SEQ/IPTS-4273/data/SEQ_10235_event.nxs+/SNS/SEQ/IPTS-4273/data/SEQ_10236_event.nxs')\ntomask=[]\n\nangmin=19\nangmax=21\n\nfor i in range(w.getNumberHistograms()):\n\tang=degrees(w.detectorTwoTheta(w.getDetector(i)))\n\tif (ang<angmin) or (ang>angmax):\n\t\ttomask.append(i)\n\t\t\nMaskDetectors(w,tomask)\n\nGenerateEventsFilter(InputWorkspace='w',OutputWorkspace='temp_split',InformationWorkspace='info',UnitOfTime='Nanoseconds',LogName='SampleTemp',MinimumLogValue='7',MaximumLogValue='300',LogValueInterval='5')\nFilterEvents(InputWorkspace='w',OutputWorkspaceBaseName='split',InformationWorkspace='info',SplitterWorkspace='temp_split',FilterByPulseTime='1',GroupWorkspaces='1')\nDeleteWorkspace('split_unfiltered')\nsum=SumSpectra('split')\nsum=NormaliseByCurrent(sum)\n\ntemperature=[]\nintensity=[]\nerror=[]\n\nfor name in sum.getNames():\n\tsi=mtd[name]\n\tif si.run().getProtonCharge()>1:\n\t\tintensity.append(si.extractY()[0][0])\n\t\terror.append(si.extractE()[0][0])\n\t\ttemperature.append(si.run()['SampleTemp'].getStatistics().mean)\n\nfinal=CreateWorkspace(temperature,intensity,error)\n\nplotSpectrum(final,0,1)\nSaveAscii(final,'/SNS/SEQ/IPTS-4273/shared/int_OP.dat')\n","repo_name":"mantidproject/scripts","sub_path":"inelastic/contrib/SNS-scripts/powderTdep_OP.py","file_name":"powderTdep_OP.py","file_ext":"py","file_size_in_byte":1229,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"48"}
+{"seq_id":"73130444307","text":"import numpy as np\r\nfrom osgeo import gdal\r\nimport rasterio as rio\r\nimport tkinter\r\nfrom tkinter.filedialog import askopenfilename\r\n\r\n'''\r\nCreated on 23 December 2020 by Oanh Thi La\r\nPurposes: \r\n1. Calculate cosine of Relative Azimuth Angle (RAA) for each pixel in the image scene from Sun-Azimuth Angle and Sensor Azimuth Angle\r\n2. calculate cosine of Sun zenith angle (SZA) and Sensor zenith angle (VZA)\r\n'''\r\n'''1. COSINE OF RAA'''\r\n# Read sun azimuth angle (SAA) and sensor azimuth angle (VAA)\r\n\r\nRoot = tkinter.Tk() # Create a Tkinter.Tk() instance\r\nRoot.withdraw() # Hide the Tkinter.Tk() instance\r\nSAA_path = askopenfilename(title=u'Open SAA file', filetypes=[(\"TIF\", \".tif\")]) #choose your SAA angle\r\nVAA_path = askopenfilename(title=u'Open VAA file', filetypes=[(\"TIF\", \".tif\")]) # choose your VAA angle\r\n## read raster image as array\r\nSAA = gdal.Open(SAA_path).ReadAsArray()\r\nVAA = gdal.Open(VAA_path).ReadAsArray()\r\nsun_azi_angle = SAA/100\r\nsen_azi_angle = VAA/100\r\n\r\n## CALCULATE RELATIVE AZIMUTH ANGLE FROM SUN AZIMUTH AND SENSOR AZIMUTH ANGLE\r\ndef relative_azimuth_angle(sun_azi_angle, sen_azi_angle):\r\n    difference_value = abs(sun_azi_angle - sen_azi_angle) #abs(Sensor Azimuth - 180.0 - Solar azimuth)\r\n    dif_row = difference_value.shape[0]\r\n    dif_col = difference_value.shape[1]\r\n    rel_azi_angle = np.zeros([dif_row, dif_col])\r\n    for i in range(dif_row):\r\n        for j in range(dif_col):\r\n            if difference_value[i, j] > 180.0:\r\n                rel_azi_angle[i, j] = 360.0 - difference_value[i, j]\r\n            elif difference_value[i, j] == 0:\r\n                rel_azi_angle[i, j] = 0.0\r\n            else:\r\n                rel_azi_angle[i, j] = 180.0 - difference_value[i, j]\r\n    return rel_azi_angle, difference_value\r\n\r\n[rel_azi_angle, difference_value] = relative_azimuth_angle(sun_azi_angle, sen_azi_angle)\r\n\r\n#calculating cosine of RAA angle\r\nrel_azi_angle[rel_azi_angle==0] = np.nan # convert 0 value to nan to avoid calculate cosine for pixel have 0 value\r\ncos_RAA = np.cos(rel_azi_angle)\r\ncos_RAA[np.isnan(cos_RAA)] = 0 # convert nan back to 0 value\r\ncos_RAA = np.float32(cos_RAA) # convert float64 to float32\r\n# save cos_RAA to TIF file\r\nwith rio.open(SAA_path) as src: # choose one image\r\n    ras_data = src.read()\r\n    ras_meta = src.profile\r\n\r\nras_meta['dtype'] = \"float32\"\r\nras_meta['No Data'] = 0.0\r\n\r\nFname1 = tkinter.filedialog.asksaveasfilename(title=u'Save RAA file', filetypes=[(\"TIF\", \".tif\")])\r\nwith rio.open(Fname1, 'w', **ras_meta) as dst: # write image with the same shape with the selected image aboved\r\n    dst.write(cos_RAA, 1)\r\n\r\n\r\n\r\n\r\n'''2. COSINE OF SZA and VZA'''\r\n\r\nRoot = tkinter.Tk() # Create a Tkinter.Tk() instance\r\nRoot.withdraw() # Hide the Tkinter.Tk() instance\r\nSZA_path = askopenfilename(title=u'Open SZA file', filetypes=[(\"TIF\", \".tif\")]) #choose your SZA angle\r\nVZA_path = askopenfilename(title=u'Open VZA file', filetypes=[(\"TIF\", \".tif\")]) # choose your VZA angle\r\n## read raster image as array\r\nSZA = gdal.Open(SZA_path).ReadAsArray()\r\nVZA = gdal.Open(VZA_path).ReadAsArray()\r\nsun_ze_angle = SZA/100\r\nsen_ze_angle = VZA/100\r\n\r\nsun_ze_angle[sun_ze_angle==0] = np.nan # convert 0 value to nan to avoid calculate cosine for pixel have 0 value\r\ncos_SZA = np.cos(sun_ze_angle)\r\ncos_SZA[np.isnan(cos_SZA)] = 0 # convert nan back to 0 value\r\ncos_SZA = np.float32(cos_SZA) # convert float64 to float32\r\n\r\nsen_ze_angle[sen_ze_angle==0] = np.nan # convert 0 value to nan to avoid calculate cosine for pixel have 0 value\r\ncos_VZA = np.cos(sen_ze_angle)\r\ncos_VZA[np.isnan(cos_VZA)] = 0 # convert nan back to 0 value\r\ncos_VZA = np.float32(cos_VZA) # convert float64 to float32\r\n\r\n# save cos_SZA to TIF file\r\nwith rio.open(SZA_path) as src: # choose one image\r\n    ras_data = src.read()\r\n    ras_meta = src.profile\r\n\r\nras_meta['dtype'] = \"float32\"\r\nras_meta['No Data'] = 0.0\r\n\r\nFname2 = tkinter.filedialog.asksaveasfilename(title=u'Save SZA file', filetypes=[(\"TIF\", \".tif\")])\r\nwith rio.open(Fname2, 'w', **ras_meta) as dst: # write image with the same shape with the selected image aboved\r\n    dst.write(cos_SZA, 1)\r\n\r\n# save cos_VZA to TIF file\r\nwith rio.open(VZA_path) as src:  # choose one image\r\n    ras_data = src.read()\r\n    ras_meta = src.profile\r\n\r\nras_meta['dtype'] = \"float32\"\r\nras_meta['No Data'] = 0.0\r\n\r\nFname3 = tkinter.filedialog.asksaveasfilename(title=u'Save VZA file', filetypes=[(\"TIF\", \".tif\")])\r\nwith rio.open(Fname3, 'w', **ras_meta) as dst:  # write image with the same shape with the selected image aboved\r\n    dst.write(cos_VZA, 1)\r\n","repo_name":"LaOanh/GeoAngles-Calculation","sub_path":"GeoAnglesCalculation.py","file_name":"GeoAnglesCalculation.py","file_ext":"py","file_size_in_byte":4530,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"48"}
+{"seq_id":"39605557052","text":"# last revision: 2015-06-10 21:30\n# hyper-closest.py\n\nimport nltk, os\nfrom nltk.corpus import wordnet as wn, brown, gutenberg, stopwords\n\nNUMBER_TO_LOOK = 300\n\ndef dist(IL1, IL2):\n\td = 0\n\tL1 = IL1[:NUMBER_TO_LOOK]\n\tL2 = IL2[:NUMBER_TO_LOOK]\n\tfor e in L1:\n\t\td += (L1.index(e) - L2.index(e))**2 if e in L2 else len(L2)**2\n\treturn d\n\n# Load Categories...\nD = {}\nDcorrect = {}\nfor ctg in brown.categories():\n\tfile_pop = open('category_'+ctg+'_popular.txt', 'r')\n\tpopL = []\n\tfor line in file_pop:\n\t\tc, s = line[:-1].split('\\t')\n\t\tpopL.append(s)\n\tD[ctg] = popL\n\tfile_pop.close()\n\nresult_file = open('dist_result.txt', 'w')\ncorrect_file = open('dist_correct.txt', 'w')\ncorrect_file.write(\"NUMBER_TO_LOOK:%d\\n\"%(NUMBER_TO_LOOK))\nfor nctg in brown.categories():\n\tprint(nctg)\n\tflist = brown.fileids(categories=nctg)\n\tnumber_of_files = 0\n\tnumber_of_correct = 0\n\tfor f in flist:\n\t\tfile_inf = open('./'+nctg+'/net-' + nctg + '-' + f + '-info.txt', 'r')\n\t\tnumber_of_files += 1\n\t\tfname = file_inf.readline()[len('#FILE:'):-1]\n\t\tvertex = int(file_inf.readline()[len('#Vertices '):-1])\n\t\tarcs = int(file_inf.readline()[len('#Arcs '):-1])\n\t\tmaxpath = int(file_inf.readline()[len('#MaxPath '):-1])\n\t\tleafn = int(file_inf.readline()[len('#LeafN '):-1])\n\t\trcs = file_inf.readline()\n\n\t\tDL = []\n\t\t#Rank, Count, Synset = file_inf[5]\n\t\tfor line in file_inf:\n\t\t\tr, c, s = line[:-1].split('\\t')\n\t\t\tDL.append(s)\n\n\t\tthatctg = ''\n\t\tthatdist = -1\n\n\t\tcaldist = [(dist(D[actg], DL), actg) for actg in brown.categories()]\n\t\tcaldist.sort()\n\t\tthatctg = caldist[0][1]\n\t\tprint(nctg + '-' + f + '\\t' + thatctg + '\\t' + str(1 if nctg == thatctg else 0))\n\t\tnumber_of_correct += 1 if nctg == thatctg else 0\n\t\tresult_file.write(nctg + '-' + f + '\\t' + thatctg + '\\t' + str(1 if nctg == thatctg else 0) + '\\n')\n\tcorrect_file.write(nctg + '\\t' + str(float(number_of_correct)*100/number_of_files) + '\\n')\ncorrect_file.close()\nresult_file.close()\n","repo_name":"SuminHan/CS372-Hypernym-Tree","sub_path":"source_code/hyper-closest.py","file_name":"hyper-closest.py","file_ext":"py","file_size_in_byte":1900,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"7159274763","text":"# <그림1>과 같이 정사각형 모양의 지도가 있다.\n# 1은 집이 있는곳 0은 집이 없는곳이다.\n# 철수는 이 지도를 가지고 연결된 모임인 단지를 정의하고 단지에 번호를 붙히려고 한다.\n# 여기서 연결되어있다는것은 어떤 집이 좌우, 혹은 아래위로 다른 집이 있는경우를 말한다. ( 대각선에 있는경우 집이 아님)\n# 지도를 입력하여 단지수를 출력하고, 단지에 속하는 집을 수를 오름차순으로 정렬하여 출력하는 프로그램을 작성하시오.\n\nN = int(input())\narr = []\nfor _ in range(N):\n    arr.append(list(map(int , input())))\n\nvisited = [[False] * N for _ in range(N)]\ncount = 0\ngroup = []\ndef dfs(i, j, count):\n    \n    if (not visited[i][j]) and arr[i][j] == 1:\n        group.append(count)\n        visited[i][j] = True\n        if(N > i+1 >= 0):\n            dfs(i+1,j, count)\n        if(N > i-1 >= 0):\n            dfs(i-1,j, count)\n        if(N > j+1 >= 0):\n            dfs(i,j+1, count)\n        if(N > j-1 >= 0):\n            dfs(i,j-1, count)\n    else :\n        return False\n    \n    return True\n\nfor i in range(N):\n    for j in range(N):\n        if(dfs(i,j,count) == True):\n            count += 1\n            \nprint(count)\nnewarr = []\nfor i in range(count):\n    newarr.append(group.count(i))\n\nnewarr.sort()\nfor data in newarr:\n    print(data)","repo_name":"rhkdguskim/Study","sub_path":"알고리즘/이것이코딩테스트다스터디/탐색/단지번호붙이기.py","file_name":"단지번호붙이기.py","file_ext":"py","file_size_in_byte":1372,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"38670227907","text":"import pytest_mock\nfrom src.scraper import Scraper\n\n\nclass TestScraper():\n\n    def test_scraper(self, mocker: pytest_mock.MockFixture, html_page: str):\n        browser = mocker.patch('src.browser.Browser')\n        scraper = Scraper(browser, 'fake_url.com')\n        scraper.get_estates_quantity = mocker.MagicMock(return_value=20)\n        browser.get_text = mocker.MagicMock(return_value=html_page)\n        estates = scraper.scrap_website()\n        assert len(estates) == 20\n","repo_name":"Sotrosca/zona-prop-scraper","sub_path":"test/test_scraper.py","file_name":"test_scraper.py","file_ext":"py","file_size_in_byte":474,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"48"}
+{"seq_id":"13134327053","text":"import os\nimport threading\nfrom pathlib import Path\nfrom threading import Lock, Thread\n\nfrom learning_site import settings\nfrom sl_ai.dataset import GestureDataset\nfrom sl_ai.gesture_classifier import GestureClassifier\nfrom sl_ai.utils import clean_listdir\n\n# MAIN_GESTURE_DATASET_PATH = Path(\"sl_ai/gestures_dataset.csv\")\n# DATASET_LOCATION = Path(\"ai_data/vgt-all\")\n# MODEL_FILE_PATH = Path(\"sl_ai/model.h5\")\n\n\nclass SingletonMeta(type):\n    _instances = {}\n\n    _lock: Lock = Lock()\n\n    def __call__(cls, *args, **kwargs):\n        with cls._lock:\n            if cls not in cls._instances:\n                instance = super().__call__(*args, **kwargs)\n                cls._instances[cls] = instance\n        return cls._instances[cls]\n\n\nclass Classifier(metaclass=SingletonMeta):\n    gesture_dataset: GestureDataset = GestureDataset()\n    gesture_classifier: GestureClassifier\n\n    def __init__(self):\n        print(\"Created a Classifier instance.\")\n        # self.load_dataset()\n        self.gesture_classifier: GestureClassifier = GestureClassifier(\n            gesture_dataset=self.gesture_dataset\n        )\n\n    def load_dataset(self):\n        handedness_data = {}\n        for gesture_folder in clean_listdir(settings.VGT_GESTURES_ROOT):\n            *gestures_name_parts, handedness_string = gesture_folder.split(\"_\")\n            gesture_name = \"_\".join(gestures_name_parts)\n            handedness_data[gesture_name] = (\n                handedness_string[0] == \"1\",\n                handedness_string[1] == \"1\",\n            )\n        self.gesture_dataset.scan_videos(dataset_location=settings.VGT_GESTURES_ROOT, handedness_data=handedness_data)\n        self.gesture_dataset.load_gestures_from_csv()\n        print(\"Searching for user uploaded datasets...\")\n        for user_folder in clean_listdir(settings.UPLOADED_GESTURES_ROOT):\n            for gesture_folder in clean_listdir(settings.UPLOADED_GESTURES_ROOT / user_folder):\n                csv_files = list(\n                    filter(\n                        lambda file: file.endswith(\".csv\"),\n                        clean_listdir(\n                            settings.UPLOADED_GESTURES_ROOT / user_folder / gesture_folder\n                        ),\n                    )\n                )\n                if not csv_files:\n                    print(\n                        \"Warning: Found a uploaded gesture folder without a dataset.csv file.\"\n                    )\n                    continue\n                dataset_file = csv_files[0]\n                print(\n                    f\"Loading dataset from {settings.UPLOADED_GESTURES_ROOT / user_folder / gesture_folder / dataset_file}\"\n                )\n                *gestures_words, handedness_string = gesture_folder.split(\"_\")\n                gesture_name = \"_\".join(gestures_words)\n                gesture_dataset = GestureDataset(single_gesture=True)\n                gesture_dataset.scan_videos(\n                    dataset_location=settings.UPLOADED_GESTURES_ROOT\n                    / user_folder\n                    / gesture_folder,\n                    handedness_data={\n                        gesture_name: (\n                            handedness_string[0] == \"1\",\n                            handedness_string[1] == \"1\",\n                        )\n                    },\n                )\n                gesture_dataset.load_from_csv(\n                    csv_path=settings.UPLOADED_GESTURES_ROOT\n                    / user_folder\n                    / gesture_folder\n                    / dataset_file\n                )\n                self.gesture_dataset.append_dataset(gesture_dataset)\n\n        if settings.SAVED_MODEL_PATH.exists():\n            self.gesture_classifier.load_saved_model(model_path=settings.SAVED_MODEL_PATH)\n        else:\n            print(\"No model file found. Training a new model.\")\n            self.gesture_classifier.train(save_path=settings.SAVED_MODEL_PATH)\n        self.gesture_classifier.summary()\n\n","repo_name":"ArthurVanRemoortel/Sign-Language-Learning-Tool","sub_path":"sign_language_app/classifier.py","file_name":"classifier.py","file_ext":"py","file_size_in_byte":3956,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"7009191591","text":"#!/usr/bin/python3\n\nimport sys\nimport json\nimport networkx as nx\nimport numpy as np\nimport networkit as nk\nfrom collections import defaultdict\n\ndef make_graph_from_json(json_data):\n    G = nk.Graph()\n\n    for node in json_data:\n        for neighbor in json_data[node]:\n            G.addEdge(int(node), int(neighbor), addMissing=True)\n\n    return G\n\ndef get_seed_nodes(graph_data, n_players, n_seeds, n_rounds):\n    G = make_graph_from_json(graph_data)\n    N = G.numberOfNodes()\n\n    print(\"[INFO]: |V|={}, |E|={}, seeds={}, iters={}\".format(\n        N, G.numberOfEdges(), n_seeds, n_rounds))\n\n    # this is O(m)\n    bc = nk.centrality.EstimateBetweenness(G, nSamples=8, normalized=True)\n    bc.run()\n\n    nodes = [x[0] for x in bc.ranking()[:n_seeds]]\n    # scores = tc.topkScoresList()\n\n    seeds = []\n    for _ in range(n_rounds):\n        seeds.append(nodes)\n\n    return [[str(node) for node in round] for round in seeds]\n","repo_name":"jyyeo/Pandamaniac","sub_path":"top_betweenness.py","file_name":"top_betweenness.py","file_ext":"py","file_size_in_byte":924,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"8903675203","text":"import numpy as np\nimport seaborn as sns\nimport matplotlib.pylab as plt\nfrom matplotlib.ticker import FuncFormatter\n\n\ndef heatmap_speaker_action_dataframe(full_utterance_probabilities_df, speaker_one, rewards, speaker_two=None, vmax=None,\n                                     vmin=None, cmap=\"Purples\"):\n    \"\"\"Visualize the probability of a speaker choosing an utterance.\"\"\"\n\n    plt.figure()\n    collapsed_df = full_utterance_probabilities_df.groupby([\"value\", \"feature\"]).agg(np.mean).reset_index()\n\n    collapsed_df[\"label\"] = collapsed_df.truthful.apply(lambda x: \"X\" if x else \"\")\n\n    speaker_one_col = speaker_one.name + \"_prob\"\n    if speaker_two is None:\n        to_plot_col = speaker_one_col\n        title = \"Utterance Probabilities for {} Speaker\".format(speaker_one.name)\n    else:\n        cmap = \"PuOr_r\"\n        title = \"Difference in Probabilities: {} (gold) vs {} (purple)\".format(speaker_one.name, speaker_two.name)\n        collapsed_df[\"speaker_diff\"] = collapsed_df[speaker_one_col] - collapsed_df[speaker_two.name + \"_prob\"]\n        to_plot_col = \"speaker_diff\"\n\n    utterance_selection = collapsed_df.pivot(\"value\", \"feature\", to_plot_col)\n    labels = collapsed_df.pivot(\"value\", \"feature\", \"label\")\n\n    # Sort columns in descending order of features\n    features_in_descending_order = rewards[utterance_selection.columns].sort_values(ascending=False).index\n    utterance_selection = utterance_selection.reindex(features_in_descending_order, axis=1)\n    labels = labels.reindex(features_in_descending_order, axis=1)\n\n    if vmax is None:\n        vmax = collapsed_df[to_plot_col].max()\n    if vmin is None:\n        vmin = collapsed_df[to_plot_col].min()\n\n    formatter = FuncFormatter(_format_positive)\n\n    ax = sns.heatmap(utterance_selection, annot=labels, fmt='', linewidths=.5, cmap=cmap, vmin=vmin, vmax=vmax,\n                     cbar_kws={'format': formatter})\n\n    ax.invert_yaxis()\n\n    plt.title(title)\n\n\ndef _format_positive(x, pos):\n\n    return '%0.2f' % abs(x)\n","repo_name":"tsumers/signaling-bandits","sub_path":"visualizations.py","file_name":"visualizations.py","file_ext":"py","file_size_in_byte":1998,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"24176809186","text":"from typing import List\nimport pandas as pd\nimport xarray as xr\nimport numpy as np\n\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\n\nimport sortingview.views as vv\nfrom visualization_1D_view import create_1D_decode_view\n\nposition_info_fname = 'position_info_df.csv'\nlinear_position_info_fname = 'linear_position_info_df.csv'\nmarks_fname = 'marks_xarray.nc'\nresults_fname = 'results_xarray.nc'\n\nunit_spike_times_fname = 'unit_spike_times.npy'\n\nposterior_type='acausal_posterior'\nposition_name='linear_position'\nspeed_name='head_speed'\n\ndef main():\n    position_info = pd.read_csv(position_info_fname)\n    linear_position_info = pd.read_csv(linear_position_info_fname)\n    print(position_info.columns)\n    print(linear_position_info.columns)\n\n    marks = xr.load_dataset(marks_fname)\n    results = xr.load_dataset(results_fname)\n\n    ref_time_sec = position_info['time'][0]\n    end_time_sec = np.max(position_info['time'])\n\n    #####################################################################################################\n    print('Creating 1D decode view')\n    view_1d_decode = create_1D_decode_view(\n        ref_time_sec=ref_time_sec,\n        posterior=results[posterior_type].sum(\"state\"),\n        linear_position=linear_position_info[position_name],\n    )\n\n    # #####################################################################################################\n    # print('Creating probability view')\n    # view_probability = vv.TimeseriesGraph()\n    # COLOR_CYCLE = [\n    #     \"#1f77b4\",\n    #     \"#ff7f0e\",\n    #     \"#2ca02c\",\n    #     \"#d62728\",\n    #     \"#9467bd\",\n    #     \"#8c564b\",\n    #     \"#e377c2\",\n    #     \"#7f7f7f\",\n    #     \"#bcbd22\",\n    #     \"#17becf\",\n    # ]\n    # for state, color in zip(results.state.values, COLOR_CYCLE):\n    #     view_probability.add_line_series(\n    #         name=state,\n    #         t=np.asarray(results['time'] - ref_time_sec).astype(np.float32),\n    #         y=np.asarray(\n    #             results[posterior_type].sel(state=state).sum(\"position\"),\n    #             dtype=np.float32,\n    #         ),\n    #         color=color,\n    #         width=1,\n    #     )\n\n    #####################################################################################################\n    print('Creating speed view')\n    view_speed = vv.TimeseriesGraph().add_line_series(\n        name=\"Speed [cm/s]\",\n        t=np.asarray(position_info['time'] - ref_time_sec).astype(np.float32),\n        y=np.asarray(position_info[speed_name], dtype=np.float32),\n        color=\"black\",\n        width=1,\n    )\n\n    #####################################################################################################\n    print('Creating raster plot')\n    unit_spike_times = np.load(unit_spike_times_fname, allow_pickle=True)\n    plot_items: List[vv.RasterPlotItem] = []\n    for i in range(len(unit_spike_times)):\n        spike_times_sec = unit_spike_times[i] - ref_time_sec\n        if len(spike_times_sec) > 0:\n            # for unit_id in sorting.get_unit_ids():\n            #     spike_times_sec = np.array(sorting.get_unit_spike_train(segment_index=0, unit_id=unit_id)) / sorting.get_sampling_frequency()\n            plot_items.append(\n                vv.RasterPlotItem(\n                    unit_id=f'{i}',\n                    spike_times_sec=spike_times_sec.astype(np.float32)\n                )\n            )\n    view_raster_plot = vv.RasterPlot(\n        start_time_sec=0,\n        end_time_sec=end_time_sec - ref_time_sec,\n        plots=plot_items\n    )\n    view_units_table = vv.UnitsTable(columns=[], rows=[vv.UnitsTableRow(unit_id=f'{i}', values={}) for i in range(len(unit_spike_times)) if len(unit_spike_times) > 0])\n\n    #####################################################################################################\n    print('Creating place fields view')\n    unit_firing_rates_over_linear_position_bins = np.load('unit_firing_rates_over_linear_position_bins.npy', allow_pickle=True)\n    linear_position_bins_cm = np.load('linear_position_bins_cm.npy', allow_pickle=True)\n    image_items = []\n    for i in range(len(unit_firing_rates_over_linear_position_bins)):\n        y = unit_firing_rates_over_linear_position_bins[i]\n        fig, ax = plt.subplots(nrows=1, ncols=1)\n        fig.set_size_inches(5, 3)\n        ax.plot(linear_position_bins_cm, y)\n        image_items.append(vv.UnitImagesItem(\n            unit_id=f'{i}',\n            figure=fig,\n            dpi=100\n        ))\n        plt.close(fig)\n    view_place_fields = vv.UnitImages(items=image_items, item_width=500, item_height=300)\n\n\n    #####################################################################################################\n    print('Creating timeseries view')\n    v_timeseries = vv.Box(\n        direction='vertical',\n        items=[\n            vv.LayoutItem(view_1d_decode, stretch=1, title='Decode', collapsible=True),\n            # vv.LayoutItem(view_probability, stretch=1, title='Probability of state', collapsible=True),\n            vv.LayoutItem(view_speed, stretch=1, title='Speed', collapsible=True),\n            vv.LayoutItem(view_raster_plot, stretch=1, title='Raster', collapsible=True)\n        ],\n        show_titles=True\n    )\n\n    v_right_window = vv.Box(\n        direction='horizontal',\n        items=[\n            vv.LayoutItem(view_units_table, max_size=180),\n            vv.LayoutItem(view_place_fields)\n        ]\n    )\n\n    v_splitter = vv.Splitter(\n        direction='horizontal',\n        item1=vv.LayoutItem(v_timeseries, stretch=2),\n        item2=vv.LayoutItem(v_right_window, stretch=1)\n    )\n\n    view = v_splitter\n    \n    #####################################################################################################\n    print('Creating figURL')\n    url = view.url(label='alison example')\n    print(url)\n\n\nif __name__ == '__main__':\n    main()","repo_name":"dcmnts/misc","sub_path":"scratchspace/alison_example.py","file_name":"alison_example.py","file_ext":"py","file_size_in_byte":5838,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"13773468365","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Apr 12 00:15:15 2020\r\n\r\n@author: Mrityunjay1.Pandey\r\n\"\"\"\r\npath='./ProbabLoanDefaulters.csv'\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\n\r\ndf=pd.read_csv(path)\r\n#probability p(A),in variable p_a ,for the event that fico credit score is greater than 700.\r\np_a=len(df[df.fico>700])/len(df)\r\n\r\n#probabilityp(B) for the event that purpose == 'debt_consolidation'\r\np_b=len(df[df.purpose== 'debt_consolidation'])/len(df)\r\n\r\n#Calculate the purpose == 'debt_consolidation' and store it in dataframe df1.\r\ndf1=df[df.purpose== 'debt_consolidation']\r\n\r\n#Calculate the probablityp(B|A) for the event purpose == 'debt_consolidation' given \r\n#'fico' credit score is greater than 700 and store it in variable p_a_b.\r\n\r\np_a_b=(len(df1[df1.fico>700])/len(df1))/p_a\r\n\r\n#check the independency p_a_b==p_a:\r\n\r\nresult=p_a_b==p_a\r\nprint(\"Dependency Check result is:\",result)\r\n\r\n#probability p(A) for the event that paid.back.loan == Yes\r\nprob_lp=len(df[df['paid.back.loan']=='Yes'])/len(df)\r\n\r\n# probability p(B) for the event that credit.policy == Yes\r\nprob_cs=len(df[df['credit.policy']=='Yes'])/len(df)\r\n\r\n#Taking DataFrame where ['paid.back.loan'] == 'Yes'\r\n\r\nnew_df=df[df['paid.back.loan'] == 'Yes']\r\n\r\n#probablityp(B|A) for the event paid.back.loan == 'Yes' given credit.policy == 'Yes'\r\nprob_pd_cs=len(new_df[new_df['credit.policy'] == 'Yes'])/len(new_df)\r\n\r\n#Calculating conditional probability \r\n\r\nbayes=(prob_pd_cs*prob_lp)/prob_cs\r\n\r\nprint(\"Bayes Value is:\",bayes)\r\n\r\n#Visualize the bar plot for the feature purpose\r\ndf.purpose.value_counts().plot(kind='bar')\r\n#Creating new data frame with paid back loan as No\r\ndf1=df[df['paid.back.loan'] == 'No']\r\n\r\n#Visualize the bar plot for the feature purpose of df1\r\ndf1.purpose.value_counts().plot(kind='bar')\r\nplt.show()\r\n\r\n\r\n# median for installment\r\ninst_median=df.installment.median()\r\n\r\n# mean for installment\r\ninst_mean=df.installment.mean()\r\n\r\n#histogram for installment\r\nplt.hist(df.installment)\r\nplt.axvline(inst_mean, color='k', linestyle='dashed', linewidth=1)\r\nmin_ylim, max_ylim = plt.ylim()\r\nplt.text(inst_mean*1.1,max_ylim*0.9,'Mean: {:.2f}'.format(inst_mean))\r\nplt.axvline(inst_median, color='k', linestyle='dashed', linewidth=1)\r\nplt.text(inst_median*0.7,max_ylim*0.7,'\\nMedian: {:.2f}'.format(inst_median))\r\nplt.show()\r\n\r\n#histogram for log annual income\r\nplt.hist(df['log.annual.inc'])\r\nplt.show()","repo_name":"Mrityunjay87/DSMP19_GA","sub_path":"GreyAtom/Loan_Probablity/ProbabLoanDefaulters.py","file_name":"ProbabLoanDefaulters.py","file_ext":"py","file_size_in_byte":2425,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"15518296510","text":"import os\nimport config\nfrom DQXTableUtils import VTTable\n\n\ndef response(returndata):\n\n    filename = os.path.join(config.BASEDIR, 'Uploads', returndata['fileid'])\n    tb = VTTable.VTTable()\n    tb.allColumnsText = True\n    try:\n        tb.LoadFile(filename, 100)\n        columns = tb.GetColList()\n        returndata['columns'] = ';'.join(columns)\n    except Exception as e:\n        returndata['Error'] = str(e)\n        return returndata\n\n    return returndata","repo_name":"TAlexPerkins/panoptes","sub_path":"servermodule/panoptesserver/gettabfileinfo.py","file_name":"gettabfileinfo.py","file_ext":"py","file_size_in_byte":460,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"48"}
+{"seq_id":"25473631349","text":"import typing as t\n\nfrom flask import Markup\n\n\ndef loader(app):\n    @app.context_processor\n    def author_link_from_user_proc():\n        from app.models.user import User\n\n        def author_link_from_user(user_id):\n            return User.get_by_id(user_id).author_link or \"\"\n\n        return dict(author_link_from_user=author_link_from_user)\n\n    @app.context_processor\n    def og_tags_proc():\n        def og_tags(\n                title: t.Optional[str] = None,\n                description: t.Optional[str] = None,\n                url: t.Optional[str] = None,\n                image: t.Optional[str] = None,\n                type_: t.Optional[str] = None,\n                locale: t.Optional[str] = \"en_GB\",\n        ):\n            ogs = []\n            if title:\n                ogs.append(f'<meta property=\"og:title\" content=\"{title}\" />')\n            if description:\n                ogs.append(f'<meta property=\"og:description\" content=\"{description}\" />')\n            if url:\n                ogs.append(f'<meta property=\"og:url\" content=\"{url}\" />')\n            if image:\n                ogs.append(f'<meta property=\"og:image\" content=\"{image}\" />')\n            if type_:\n                ogs.append(f'<meta property=\"og:type\" content=\"{type_}\" />')\n            if locale:\n                ogs.append(f'<meta property=\"og:locale\" content=\"{locale}\" />')\n\n            return Markup(\"\\n\".join(ogs))\n\n        return dict(og_tags=og_tags)\n","repo_name":"Flask-Planet/flask-planet.org","sub_path":"app/builtins/context_processors.py","file_name":"context_processors.py","file_ext":"py","file_size_in_byte":1432,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"14096343687","text":"class Solution(object):\n    def canVisitAllRooms(self, rooms):\n        visited = [False] * len(rooms)\n        stack = [0]\n        count = 0\n        while stack:\n            v = stack.pop()\n            visited[v] = True\n            count += 1\n            for neighbor in rooms[v]:\n                if not visited[neighbor] and neighbor not in stack:\n                    stack.append(neighbor)\n\n        return count == len(rooms)\n\n\nrooms = [[1, 2, 3],[3, 0],[1, 3],[0]]\nprint(Solution().canVisitAllRooms(rooms))\nrooms = [[1,3],[3,0,1],[2],[0]]\nprint(Solution().canVisitAllRooms(rooms))","repo_name":"dvc0310/Interview-prep-stuff","sub_path":"leetcode/rooms.py","file_name":"rooms.py","file_ext":"py","file_size_in_byte":582,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"36805466173","text":"import re\nimport time\n\ns = \"python\"\n\ndef morse(text):\n    encrypt  = {'A':'.-', 'B':'-...', 'C':'-.-.', 'D':'-..', 'E':'.', 'F':'..-.', 'G':'--.', 'H':'....', 'I':'..',\n                'J':'.---', 'K':'-.-', 'L':'.-..', 'M':'--', 'N':'-.', 'O':'---', 'P':'.--', 'Q':'--.-', 'R':'.-.',\n                'S':'...', 'T':'-', 'U':'..-', 'V':'...-', 'W':'.--', 'X':'-..-', 'Y':'-.--', 'Z':'--..', ' ':'.....'}\n\n    decrypt  = {value: key for key, value in encrypt. items()}\n\n    if re.match('(\\s|-|\\.)+', text):\n        return ''.join(decrypt[i] for i in text.split())\n    return ' '.join(encrypt[i] for i in text.upper())\n\ndef phonetic(text):\n    encrypt = {'A':'Alpha', 'B':'Bravo', 'C':'Charlie', 'D':'Delta', 'E':'Echo', 'F':'Foxtrot', 'G':'Golf', 'H':'Hotel',\n               'I':'India','J':'Juliet', 'K':'Kilo', 'L':'Lima', 'M':'Mike', 'N':'November', 'O':'Oscar', 'P':'Papa',\n               'Q':'Quebec', 'R':'Romeo', 'S':'Sierra', 'T':'Tango', 'U':'Uniform', 'V':'Victor', 'W':'Whiskey',\n               'X':'Xray', 'Y':'Yankee', 'Z':'Zulu', ' ':'     '}\n\n    decrypt = {value: key for key, value in encrypt.items()}\n\n    if re.match('(\\s|-|\\.)+', text):\n        return ''.join(decrypt[i] for i in text.split())\n    return ' '.join(encrypt[i] for i in text.upper())\n\ndef vigenere_key(string, key):\n    key = list(key)\n    if len(string) == len(key):\n        return(key)\n    else:\n        for i in range(len(string) - len(key)):\n            key.append(key[i % len(key)])\n    return (\"\".join(key))\n\ndef vigenere_text(string, key):\n    vigenereText = []\n    for i in range(len(string)):\n        x = (ord(string[i]) +ord(key[i])) % 26\n        x += ord('A')\n        vigenereText.append(chr(x))\n    return (\"\".join(vigenereText))\n\ndef vigenere():\n    if __name__ == \"__main__\":\n        string = input(\"Enter text: \").upper()\n        string = string.replace(\" \", \"\")\n        keyword = input(\"Enter key: \").upper()\n        key = vigenere_key(string, keyword)\n        vigenereText = vigenere_text(string, key)\n        print(\"Vigerene Code:\", vigenereText)\n\ndef binary(text):\n    encrypt = {'A': '01000001', 'B': '01000010', 'C': '01000011', 'D': '01000100', 'E': '01000101', 'F': '01000110',\n               'G': '01000111', 'H': '01001000', 'I': '01001001', 'J': '01001010', 'K': '01001011', 'L': '01001100',\n               'M': '01001101', 'N': '01001110', 'O': '01001111', 'P': '01010000', 'Q': '01010001', 'R': '01010010',\n               'S': '01010011', 'T': '01010100', 'U': '01010101', 'V': '01010110', 'W': '01010111', 'X': '01011000',\n               'Y': '01011001', 'Z': '01011010', ' ': '.....'}\n\n    decrypt = {value: key for key, value in encrypt.items()}\n\n    if re.match('(\\s|-|\\.)+', text):\n        return ''.join(decrypt[i] for i in text.split())\n    return ' '.join(encrypt[i] for i in text.upper())\n\nwhile True:\n    print(\"\")\n    print(\"********** MENU **********\")\n    print(\"(1) Morse Code\")\n    print(\"(2) Phonetic Alphabet\")\n    print(\"(3) Vigenere Cipher\")\n    print(\"(4) Binary Code\")\n    print(\"(5) Exit\")\n    print(\"***************************\")\n    print(\"\")\n\n    menu_option = int(input(\"What would you like to do (1-5)? \"))\n    print(\"\")\n\n    if menu_option == 1:\n        morseTxt = str(input(\"Enter text: \")).upper()\n        print(\"Morse Code:\", morse(morseTxt))\n        time.sleep(5)\n    if menu_option == 2:\n        phoneticTxt = str(input(\"Enter text: \")).upper()\n        print(\"Phonetics:\", phonetic(phoneticTxt))\n        time.sleep(5)\n    if menu_option == 3:\n        vigenere()\n        time.sleep(5)\n    if menu_option == 4:\n        binaryTxt = str(input(\"Enter text: \")).upper()\n        print(\"Binary Code:\", binary(binaryTxt))\n        time.sleep(5)\n    if menu_option == 5:\n        exit = str(input(\"Are you sure you want to exit (y/n)? \")).lower()\n        if exit == \"y\":\n            break\n\n","repo_name":"SenyoGela/Cipher-Decoder","sub_path":"codeDecoder.py","file_name":"codeDecoder.py","file_ext":"py","file_size_in_byte":3822,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"22095133919","text":"import torch.nn as nn\nimport torch\nfrom torchsummary import summary\n\n__all__ = ['Cfcn']\n\n\nclass Conv1x1(nn.Module):\n    def __init__(self, in_channel, out_channel):\n        super(Conv1x1, self).__init__()\n        self.conv = nn.Conv2d(in_channel, out_channel, kernel_size=1, stride=1)\n        self.bn = nn.BatchNorm2d(out_channel)\n        self.relu = nn.ReLU(inplace=True)\n\n    def forward(self, x):\n        x = self.conv(x)\n        x = self.bn(x)\n        x = self.relu(x)\n\n        return x\n\n\nclass Conv3x3(nn.Module):\n    def __init__(self, _in, _out, stride=1, dilation=1):\n        super(Conv3x3, self).__init__()\n        self.conv = nn.Conv2d(_in, _out, kernel_size=3, stride=stride, padding=dilation, dilation=dilation)\n        self.bn = nn.BatchNorm2d(_out)\n        self.relu = nn.ReLU(inplace=True)\n\n    def forward(self, x):\n        x = self.conv(x)\n        x = self.bn(x)\n        x = self.relu(x)\n\n        return x\n\n\nclass Deconv3x3(nn.Module):\n    def __init__(self, _in, _out, stride=2, dilation=1):\n        super(Deconv3x3, self).__init__()\n        self.conv = nn.ConvTranspose2d(_in, _out, kernel_size=3, stride=stride, padding=dilation)\n        self.bn = nn.BatchNorm2d(_out)\n        self.relu = nn.ReLU(inplace=True)\n\n    def forward(self, x):\n        x = self.conv(x)\n        x = self.bn(x)\n        x = self.relu(x)\n\n        return x\n\n\nclass BasicBlock(nn.Module):\n    expansion: int = 1\n\n    def __init__(self, in_channels, out_channels, downsample=None, stride=1):\n        super(BasicBlock, self).__init__()\n        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1)\n        self.bn1 = nn.BatchNorm2d(out_channels)\n        self.relu = nn.ReLU(inplace=True)\n        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)\n        self.bn2 = nn.BatchNorm2d(out_channels)\n        self.downsample = downsample\n\n    def forward(self, x):\n        identity = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n\n        if self.downsample is not None:\n            identity = self.downsample(x)\n\n        out += identity\n        out = self.relu(out)\n\n        return out\n\n\nclass CSP_layer(nn.Module):\n    def __init__(self, in_channel, out_channel, block, blocks, stride):\n        super(CSP_layer, self).__init__()\n        self.channels = in_channel\n        self.layer = self.make_layer(block, out_channel, blocks)\n        self.conv1 = Conv1x1(out_channel, out_channel)\n        self.conv2 = Conv1x1(out_channel*2, out_channel)\n        self.downsample = Conv3x3(in_channel, out_channel, stride=stride)\n\n    def make_layer(self, block, channel, blocks):\n        layers = []\n        downsample = None\n\n        front = nn.Sequential(\n            nn.Conv2d(channel, channel, kernel_size=1),\n            nn.BatchNorm2d(channel),\n            nn.ReLU(inplace=True)\n        )\n\n        back = nn.Sequential(\n            nn.Conv2d(channel, channel, kernel_size=1),\n            nn.BatchNorm2d(channel),\n            nn.ReLU(inplace=True)\n        )\n\n        layers.append(front)\n\n        for _ in range(blocks):\n            layers.append(block(channel, channel))\n\n        layers.append(back)\n\n        return nn.Sequential(*layers)\n\n    def forward(self, x):\n        x = self.downsample(x)\n        identity = x\n\n        x = self.layer(x)\n\n        identity = self.conv1(identity)\n        x = torch.cat([x, identity], dim=1)\n        out = self.conv2(x)\n\n        return out\n\n\nclass Cfcn(nn.Module):\n    def __init__(self, block, layers, class_num):\n        super(Cfcn, self).__init__()\n        self.channels = 32\n\n        self.conv1 = nn.Conv2d(3, self.channels, kernel_size=3, stride=1, padding=1)\n        self.bn1 = nn.BatchNorm2d(self.channels)\n        self.relu = nn.ReLU(inplace=True)\n\n        self.conv_layer1 = self.make_layer(block, 32, layers[0])\n        self.conv_layer2 = self.make_layer(block, 64, layers[1], stride=2)\n        self.conv_layer3 = self.make_layer(block, 128, layers[2], stride=2)\n        self.conv_layer4 = self.make_layer(block, 256, layers[3], stride=2)\n\n        self.dilconv1_1 = Conv3x3(256, 256, dilation=1)\n        self.dilconv1_2 = Conv3x3(256, 256, dilation=2)\n        self.dilconv1_5 = Conv3x3(256, 256, dilation=4)\n        self.dilconv2_1 = Conv3x3(256, 256, dilation=1)\n        self.dilconv2_2 = Conv3x3(256, 256, dilation=2)\n        self.dilconv3_1 = Conv3x3(256, 256, dilation=1)\n\n        self.up_conv4 = Conv1x1(256, 256)\n        self.up_deconv3 = nn.ConvTranspose2d(256, 128, kernel_size=3, stride=2, padding=1)\n        self.up_conv3 = Conv1x1(128, 128)\n        self.up_deconv2 = nn.ConvTranspose2d(128, 64, kernel_size=3, stride=2, padding=1)\n        self.up_conv2 = Conv1x1(64, 64)\n\n        self.deconv3 = nn.ConvTranspose2d(256, 128, kernel_size=3, stride=2, padding=1)\n        self.deconv2 = nn.ConvTranspose2d(256, 64, kernel_size=3, stride=2, padding=1)\n        self.deconv1 = nn.ConvTranspose2d(128, 32, kernel_size=3, stride=2, padding=1)\n        self.conv2 = nn.Conv2d(32, class_num, kernel_size=3, padding=1)\n\n        for m in self.modules():\n            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):\n                nn.init.kaiming_normal_(m.weight)\n\n    def make_layer(self, block, channel, blocks, stride=1):\n        layers = [CSP_layer(self.channels, channel, block, blocks, stride)]\n        self.channels = channel\n        return nn.Sequential(*layers)\n\n    def forward(self, _x):\n        x = self.conv1(_x)\n        x = self.bn1(x)\n        x = self.relu(x)\n\n        x = self.conv_layer1(x)\n        x1 = x\n\n        x = self.conv_layer2(x)\n        x2 = self.up_conv2(x)\n\n        x = self.conv_layer3(x)\n        x3 = self.up_conv3(x)\n\n        x = self.conv_layer4(x)\n\n        d1x = self.dilconv1_1(x)\n        d1x = self.dilconv1_2(d1x)\n        d1x = self.dilconv1_5(d1x)\n        d2x = self.dilconv2_1(x)\n        d2x = self.dilconv2_2(d2x)\n        d3x = self.dilconv3_1(x)\n        x4 = d1x + d2x + d3x + x\n        x4 = self.up_conv4(x4)\n\n        up_x = self.up_deconv3(x4, output_size=x3.size())\n        up_x = self.relu(up_x)\n        x3 = x3 + up_x\n        up_x = self.up_deconv2(x3, output_size=x2.size())\n        up_x = self.relu(up_x)\n        x2 = x2 + up_x\n\n        x = self.deconv3(x, output_size=x3.size())\n        x = self.relu(x)\n        x = torch.cat([x, x3], dim=1)\n\n        x = self.deconv2(x, output_size=x2.size())\n        x = self.relu(x)\n        x = torch.cat([x, x2], dim=1)\n        x = self.relu(x)\n\n        x = self.deconv1(x, output_size=x1.size())\n        x = self.relu(x)\n\n        out = self.conv2(x)\n\n        return out\n\n\ndef cfcn(num_class):\n    return Cfcn(BasicBlock, [9, 9, 9, 9], num_class)\n\n\nif __name__ == '__main__':\n    model = cfcn(2)\n    print(summary(model, (3, 256, 256), batch_size=32, device='cpu'))\n","repo_name":"BYSora/CFCN","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":6851,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"22085172790","text":"import tqdm\r\nimport sys\r\nimport os\r\nimport torch\r\nimport shutil\r\n\r\nif __name__ == '__main__':\r\n    args = sys.argv[1:]\r\n    imgdir = args[1]\r\n    images = [i for i in os.listdir(imgdir)]\r\n    path_model = args[0]\r\n    outdir = args[2]\r\n    if os.path.exists(outdir):\r\n        shutil.rmtree(outdir, ignore_errors=True)\r\n    os.mkdir(outdir)\r\n    model = torch.hub.load('ultralytics/yolov5', 'custom', path=path_model)\r\n    for img in tqdm.tqdm(images):\r\n        out = model(os.path.join(imgdir, img))\r\n        df = out.pandas().xyxy[0]\r\n        df['x'] = (df['xmax'] + df['xmin']) // 2\r\n        df['y'] = (df['ymax'] + df['ymin']) // 2\r\n        df['x'] = df['x'].astype(int)\r\n        df['y'] = df['y'].astype(int)\r\n        df.to_csv(os.path.join(outdir, img[:-3]+'csv'), index=False, columns = ['x', 'y'])\r\n","repo_name":"eles13/WhileTrue_walRUSfinder","sub_path":"inferenceYOLO.py","file_name":"inferenceYOLO.py","file_ext":"py","file_size_in_byte":806,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"1004751130","text":"LOG_STDOUT = False\nLOGGING_LEVEL = 'DEBUG'\n\n# This the URL to resolver_service api\nGATEWAY_RESOLVER_SERVICE_URL = 'https://dev.adsabs.harvard.edu/v1/resolver/%s'\n\n# This is a URL to adsws account info service\nGATEWAY_SERVICE_ACCOUNT_INFO_URL = ''\n\n# gateway token\nGATEWAY_TOKEN = 'this is a secret api token!'\n\n# For caching\nREDIS_URL = \"redis://localhost:6379/0\"\nREDIS_NAME_PREFIX = \"link_gateway_\"\n# save to cache for a week\nREDIS_EXPIRATION_TIME = 604800\n\nGATEWAY_SERVICE_REFERRED_DOMAIN = 'adsabs.harvard.edu'\n\nGATEWAY_ADS_ABSTRACT_PAGE = '/abs/%s/abstract'\n","repo_name":"adsabs/resolver_gateway","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":562,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"17413901712","text":"#!/usr/bin/env python\n\"\"\"\nThis script is used to check if the commit message adheres to the conventional commit rules.\n    1. Optional \"revert:\" tag before commit msg\n    2. A type from the config followed by a semi colon and a whitespace\n    3. A commit body within a min and max length\nCommit-msg.config.json is used to configure the script.\n\"\"\"\n\nimport re\nimport json\nimport sys\n\n\ndef checkCommitMsg() -> None:\n    \"\"\"\n    Checks the commit message for the conventional commit rules.\n    \"\"\"\n\n    with open(sys.argv[1], encoding=\"utf-8\") as msg:\n        commitMsg = msg.read()\n\n    with open(\"./.hooks/commit-msg.config.json\", encoding=\"utf-8\") as configFile:\n        config = json.load(configFile)\n\n    regexp = r\"^(revert: )?(\"\n    for msgType in config[\"types\"]:\n        regexp += f\"{msgType}|\"\n    regexp = regexp[0:-1] + f\"): .{{{config['length']['min']},{config['length']['max']}}}$\"\n\n    if re.match(regexp, commitMsg) is None:\n        print(\"please enter a commit message in the conventional format.\")\n        print(\"commit message must follow (revert: )?<type>: <commit message>\")\n        sys.exit(1)\n    sys.exit(0)\n\n\nif __name__ == \"__main__\":\n    checkCommitMsg()\n","repo_name":"ahmedemad242/secure-shared-file-storage","sub_path":".hooks/commit-msg.py","file_name":"commit-msg.py","file_ext":"py","file_size_in_byte":1179,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"715796713","text":"#Find if it is a perfect number\r\ndef findDivisors(num):\r\n    divisior=list()\r\n    for i in range(1,num):\r\n        if num%i==0:\r\n            divisior.append(i)\r\n        else:\r\n            continue\r\n    return divisior\r\n\r\ndef isItPerfectNumber(num):\r\n    list=findDivisors(num)\r\n    total=0\r\n    for i in range(0,len(list)):\r\n        total +=list[i]\r\n    \r\n    if(total==num):\r\n        print(\"{} is a perfect number\".format(num))\r\n    else:\r\n        print(\"{} is not a perfect number\".format(num))\r\n        \r\n\r\nisItPerfectNumber(6)\r\nisItPerfectNumber(7)","repo_name":"Mertumul/PythonBasic","sub_path":"perfectnuöber.py","file_name":"perfectnuöber.py","file_ext":"py","file_size_in_byte":551,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"38268094427","text":"import numpy as np\nfrom mymain import my_main_3, preparation\nfrom sim_events_3 import create_sumHist, create_allsumHist, save_sumHist\n\nev_dir  = ['rootfiles_noguard_06_09_2018', \n\t\t'rootfiles_zcut_only_06_09_2018', \n\t\t'rootfiles_guard_only_06_09_2018', \n\t\t'rootfiles_guard+zcut_06_09_2018'\n\t\t]\n\nsavedir = ['noguard',\n\t\t\t'zcut_only',\n\t\t\t'guard_only',\n\t\t\t'guard+zcut'\n\t\t\t]\n\nfor i_dir in range(len(ev_dir)):\n\teventfiles, background, scale, x_range = preparation(ev_dir[i_dir])\n\teventfile_czt = eventfiles[2]\n\tscale_czt = scale[2]\n\tx_range_czt = x_range[2]\n\t\n\teventfile_coating = eventfiles[1]\n\tscale_coating = scale[1]\n\tx_range_coating = x_range[1]\n\t\n\teventfile_plastic = eventfiles[0]\n\tscale_plastic = scale[0]\n\tx_range_plastic = x_range[0]\n\t\n\tall_hists = []\n\tall_hists.append(my_main_3(eventfile_plastic, scale_plastic, x_range_coating, background[0], returns=True, savedir=savedir[i_dir]))\n\tall_hists.append(my_main_3(eventfile_coating, scale_coating, x_range_coating, background[1], returns=True, savedir=savedir[i_dir]))\n\tall_hists.append(my_main_3(eventfile_czt, scale_czt, x_range_czt, background[2], returns=True, savedir=savedir[i_dir]))\n\t\n\thists = create_sumHist(all_hists, i_dir)\n\tall_sumhists = create_allsumHist(all_hists, i_dir, these_bins=250)\n\tsave_sumHist(hists, all_sumhists, background, save_dir=savedir[i_dir])\n","repo_name":"chris2503/MA","sub_path":"Calculations/main_3.py","file_name":"main_3.py","file_ext":"py","file_size_in_byte":1328,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"38122858640","text":"from typing import List\ndef restoreMatrix(rowSum: List[int], colSum: List[int]) -> List[List[int]]:\n    col_sum = colSum\n    row_sum = rowSum\n\n    mat = [[0] * len(col_sum) for i in range(len(row_sum))]\n    i = 0\n    j = 0\n    while i < len(row_sum) and j < len(col_sum):\n        mat[i][j] = min(row_sum[i], col_sum[j])\n        if row_sum[i] == col_sum[j]:\n            i += 1\n            j += 1\n        elif row_sum[i] > col_sum[j]:\n            row_sum[i] -= col_sum[j]\n            j += 1\n        else:\n            col_sum[j] -= row_sum[i]\n            i += 1\n\n    return mat\n\ndef restoreMatrix1(rowSum: List[int], colSum: List[int]) -> List[List[int]]:\n    n, m = len(rowSum), len(colSum)\n    matrix = [[0] * m for i in range(n)]\n    for i in range(n):\n        for j in range(m):\n            matrix[i][j] = min(rowSum[i], colSum[j])\n            rowSum[i] -= matrix[i][j]\n            colSum[j] -= matrix[i][j]\n    return matrix\n\ndef test():\n    test_cases = [\n        {\n            \"name\": \"simple case 1\",\n            \"input1\": [3,8], \n            \"input2\": [4,7],\n            \"expected\": [[3,0], [1,7]]\n        }\n    ]\n\n    for test_case in test_cases:\n        assert test_case[\"expected\"] == restoreMatrix1(test_case[\"input1\"], test_case[\"input2\"]), test_case[\"name\"]\nfrom datetime import datetime\nstart_time = datetime.now()\ntest()\nprint(\"Everything passed\")\nend_time = datetime.now()\nprint('Duration: {}'.format(end_time - start_time))","repo_name":"0xspringtime/leetcode","sub_path":"1605.py","file_name":"1605.py","file_ext":"py","file_size_in_byte":1439,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"12246476329","text":"# Kelly Peterson\n# LING 575\n# Pulling certain labels out of enron \n\nimport sys\nimport os\n\nformalityDict = {}\n\nformalityLabels = ['misspelling', 'colloquial', 'vulgar', 'offensive', 'eye dialect' ]\npartsOfSpeech = ['interjection']\n# some of these were returning ridiculous results...\n#formalityLabels.append('informal')\n#formalityLabels.append('slang')\n\ndef ReadFile(filename):\n\tfile = open(filename, 'r')\n\t\n\tfor line in file:\n\t\tline = line.strip()\n\t\tequalsIndex = line.find('=')\n\t\tword = line[:equalsIndex]\n\t\tprint(word)\n\t\twordDefString = line[equalsIndex+1:]\n\t\twordDef = eval(wordDefString)\n\t\ttempLabelDict = {}\n\t\ttempPosDict = {}\n\t\tfor defPart in wordDef:\n\t\t\tlabelKey = 'labels'\n\t\t\tif labelKey in defPart:\n\t\t\t\tlabelDict = defPart[labelKey]\n\t\t\t\tfor labelEntry in labelDict:\n\t\t\t\t\ttextKey = 'text'\n\t\t\t\t\tif textKey in labelEntry:\n\t\t\t\t\t\tlabelText = labelEntry[textKey].lower()\n\t\t\t\t\t\t#print('LABEL TEXT ENTRY : ' + labelText)\n\t\t\t\t\t\tif labelText in formalityLabels:\n\t\t\t\t\t\t\tprint('Adding label : ' + labelText)\n\t\t\t\t\t\t\tif labelText not in tempLabelDict:\n\t\t\t\t\t\t\t\ttempLabelDict[labelText] = 1\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\ttempLabelDict[labelText] += 1\n\t\t\tposKey = 'partOfSpeech'\n\t\t\tif posKey in defPart:\n\t\t\t\tpos = defPart[posKey]\n\t\t\t\tif pos in partsOfSpeech:\n\t\t\t\t\tif pos not in tempPosDict:\n\t\t\t\t\t\ttempPosDict[pos] = 1\n\t\t\t\t\telse:\n\t\t\t\t\t\ttempPosDict[pos] += 1\n\t\t# now that we have raw counts, let's see if they are significant enough...\n\t\ttargetLabelCount = 0\n\t\tfor label in tempLabelDict:\n\t\t\ttargetLabelCount += tempLabelDict[label]\n\t\tlabelThreshold = 0.5\n\t\t# see how many we saw over the total definitions\n\t\tactualRatio = targetLabelCount / float(len(wordDef))\n\t\tif actualRatio > labelThreshold:\n\t\t\tprint('actual ratio : ' + str(actualRatio))\n\t\t\tif word not in formalityDict:\n\t\t\t\t# set up a new dictionary we can add labels to\n\t\t\t\tformalityDict[word] = {}\n\t\t\tfor label in tempLabelDict:\n\t\t\t\tformalityDict[word][label] = 1\n\t\t\t\t\n\t\t# now let's see if any of our parts of speech are the same count as total definitions (unanimous voting)\n\t\ttargetPosCount = 0\n\t\tfor pos in tempPosDict:\n\t\t\ttargetPosCount += tempPosDict[pos]\n\t\t\n\t\tposThreshold = 0.6\n\t\t# see how many we saw over the total definitions\n\t\tactualRatio = targetPosCount / float(len(wordDef))\n\t\t\n\t\tif actualRatio > posThreshold:\n\t\t\tif word not in formalityDict:\n\t\t\t\t# set up a new dictionary we can add labels to\n\t\t\t\tformalityDict[word] = {}\n\t\t\tfor pos in tempPosDict:\n\t\t\t\tformalityDict[word][pos] = 1\n\t\t\t\ndef WriteDict(outputFilename):\n\tprint('Writing to file : ' + outputFilename)\n\toutputFile = open(outputFilename, 'w')\n\t# let's sort these for quick lookups if needed\n\tsortedWords = formalityDict.keys()\n\tsortedWords.sort()\n\tfor word in sortedWords:\n\t\tif len(formalityDict[word]) > 0:\n\t\t\toutputFile.write(word + '=' + str(formalityDict[word]) + '\\n')\n\toutputFile.close()\n\nif len(sys.argv) != 3:\n\tprint('Usage : python wordnik_formality.py [sourceDir] [outputFile]')\n\texit(-1)\n\t\t\t\nsourceFile = sys.argv[1]\noutputFile = sys.argv[2]\n\nReadFile(sourceFile)\nWriteDict(outputFile)","repo_name":"burgersmoke/enron-formality","sub_path":"scripts/wordnik_readers/wordnik_formality.py","file_name":"wordnik_formality.py","file_ext":"py","file_size_in_byte":3012,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"48"}
+{"seq_id":"4715347889","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Sep 23 15:37:01 2018\n\n@author: jasleenarora\n\"\"\"\n\n# define our alphabet and create a list of both upper and lower case to handle it in the message\nalphabet = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'\ncaps = list(alphabet)\nlower = list(alphabet.lower())\n\n# Open the message from the input file\nmessage = open('input.txt','r')\n\n#read lines and append to string\nline1 = ''\nfor newline in message.readlines():\n    line1 = line1 + str(newline)\nprint(\"The encrypted text: \")\nprint(line1)\n    \n# close the opened file\nmessage.close()\n    \n# Convert the input string into a list\nletters = list(line1)\n\ndictionary = open('dictionary.txt','r')\nwords = []\n#separate words by line and append them to dictionary\nfor word in dictionary.read().split('\\n'):\n    words.append(word)\ndictionary.close()\n\n# For the decryption function, the steps are as follows:\n# 1. Loop through all the possible 26 keys\n# 2. Subtract each key from each letter\n# 3. Combine to form words\n# 4. Parse each word through the dictionary and find whether the word is in the dictionary or not\n# 5. Calculate the ratio, I have taken alpha as 0.2. If it is more than 0.2, then I have taken it as decrypted plaintext\n\ndef decrypt(line1):\n    letters_d = list(line1)\n    for key in range(0,26):\n        dec = []\n        for letter in line1:\n            if letter in lower:\n                num = lower.index(letter)\n                num = num - key\n                num = num%26\n                letter = lower[num]\n            elif letter in caps:\n                num = caps.index(letter)\n                num = num - key\n                num = num%26\n                letter = caps[num]\n            dec.append(letter)\n        text = ''.join(dec)\n        text = text.upper()\n        temp = text.split(' ')\n        y=0\n        n=0\n        for t in temp:\n            if t in words:\n                y+=1\n            else:\n                n+=1\n\n        if(n==0):\n            n=0.1\n            \n        d = y/n\n        if(d>0.2):\n            temp = ' '.join(temp)\n            print(\"This is the decrypted text: \")\n            print(temp)\n            f = open('output.txt','w')\n            f.write(temp)\n        \n# Call the function    \ndecrypt(line1)\n","repo_name":"JasleenUT/Encryption","sub_path":"Caeser Cipher/caeser_cipher_dec.py","file_name":"caeser_cipher_dec.py","file_ext":"py","file_size_in_byte":2245,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"30603741111","text":"import discord\nfrom discord.ext import commands\nfrom BballBot.assets.dbconnection import db_cursor\nfrom BballBot.assets.teams.aliases import team_aliases\nfrom BballBot.assets.teams.colors import team_colors\nfrom BballBot.assets.teams.logos import team_logos\n\nclass Roster(commands.Cog):\n  def __init__(self, client) -> None:\n    self.client = client\n  \n  \n  @commands.command()\n  async def roster(self,\n  ctx: discord.ApplicationContext,\n  team: str):\n    if team.upper() not in team_aliases:\n      await ctx.send(f\"`{team}` is not a valid abbreviation, location, or team name\")\n      return\n    \n    team = team_aliases[team.upper()]\n    db_cursor.execute(f\"SELECT player_name, jersey, pos,exp from rostered_players where team  = \\\"{team}\\\";\")\n\n    \n    data = db_cursor.fetchall()\n    max_len = max([len(x[0]) for x in data])\n    text = f\"`#  Pos {'Name'.ljust(max_len)}`\\n\"\n    for name,jersey,pos,exp in data:\n      text += f\" `{jersey.ljust(2)} {pos.center(3)} {name.ljust(max_len)}`\\n\"\n    e = discord.Embed(color= discord.Colour.from_rgb(*team_colors[team]),)\\\n      .add_field(name = f\"2022-2023 {team} Roster\", value = text)\\\n      .set_thumbnail(url = team_logos[team])\n    await ctx.send(embed = e)\n\ndef setup(client):\n  client.add_cog(Roster(client))","repo_name":"PhamjaminBen/BballBot","sub_path":"BballBot/commands/roster.py","file_name":"roster.py","file_ext":"py","file_size_in_byte":1262,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"31308096095","text":"from vyper import ast as vy_ast\nfrom vyper.semantics.types.user.struct import StructPrimitive\nfrom vyper.semantics.validation.utils import get_exact_type_from_node\n\nfrom vyro.transpiler.context import ASTContext\nfrom vyro.transpiler.utils import set_parent\nfrom vyro.transpiler.visitor import BaseVisitor\n\n\nclass StructConverterVisitor(BaseVisitor):\n    def visit_Assign(self, node: vy_ast.Assign, ast: vy_ast.Module, context: ASTContext):\n        value_node = node.value\n        if isinstance(value_node, vy_ast.Call):\n            call_typ = get_exact_type_from_node(value_node.func)\n            if not isinstance(call_typ, StructPrimitive):\n                return\n\n            # Unwrap `vy_ast.Dict` node for members into individual `vy_ast.keywords` node\n            args_dict_node = value_node.args[0]\n            value_node.args = []\n            value_node._children.remove(args_dict_node)\n\n            for name_node, v in zip(args_dict_node.keys, args_dict_node.values):\n                kw_node = vy_ast.keyword(\n                    node_id=context.reserve_id(), arg=name_node.id, value=v, ast_type=\"keyword\"\n                )\n                value_node.keywords.append(kw_node)\n                set_parent(v, kw_node)\n                set_parent(kw_node, value_node)\n","repo_name":"tserg/vyro","sub_path":"vyro/transpiler/passes/struct_converter.py","file_name":"struct_converter.py","file_ext":"py","file_size_in_byte":1272,"program_lang":"python","lang":"en","doc_type":"code","stars":30,"dataset":"github-code","pt":"48"}
+{"seq_id":"8372574989","text":"import pandas as pd\nimport numpy as np\nimport mxnet as mx\nimport matplotlib.pyplot as plt\nfrom gluonts.dataset.common import ListDataset\nfrom gluonts.model.deepvar import DeepVAREstimator\nfrom gluonts.model.gpvar import GPVAREstimator\nfrom gluonts.dataset.multivariate_grouper import MultivariateGrouper\nfrom gluonts.mx.trainer import Trainer\nfrom gluonts.evaluation.backtest import make_evaluation_predictions\nfrom gluonts.dataset.field_names import FieldName\nfrom itertools import islice\nfrom gluonts.evaluation.backtest import backtest_metrics\nnp.random.seed(123)\nmx.random.seed(123)\n\ndef create_dataset(file_path):\n    df = pd.read_csv(file_path, header=0, index_col=0)\n    df = df.set_index('Time')\n    df['Diff'] = np.log((df['Open']/df['Close']))\n    #df['Diff'] = df['Close'].sub(df['Open'], axis=0)\n    df = df[['Diff',\"fear\",\"anger\",\"anticipation\",\"trust\",\"suprise\",\"positive\",\"negative\",\n             \"sadness\",\"disgust\",\"joy\",\"Volume_of_tweets\",\"Retweet\",\"Replies\",\"Likes\"]]\n    values = df.values\n    values = values.astype('float32')\n    target = np.transpose(values) #(5,433)\n    return target, df\n\n\ndef train(file_path, P,frac):\n    target, df = create_dataset(file_path)\n    i = 0\n    rolling_test = []\n    train_size = int(frac * df.shape[0])\n    starts = [pd.Timestamp(df.index[0]) for _ in range(len(target))]\n    delay = 0\n    grouper_train = MultivariateGrouper(max_target_dim=df.shape[0])\n    grouper_test = MultivariateGrouper(max_target_dim=df.shape[0])\n\n    train_ds = ListDataset([{FieldName.TARGET: targets, FieldName.START: start}\n                            for (targets, start) in zip(target[:, 0:train_size - P], starts)],freq='1B')\n    train_ds = grouper_train(train_ds)\n\n    while train_size + delay< df.shape[0]:\n        delay = int(P) * i\n        test_ds = ListDataset([{FieldName.TARGET: targets, FieldName.START: start}\n                               for (targets, start) in zip(target[:, 0:train_size+delay], starts)],\n                              freq='1B')\n        test_ds = grouper_test(test_ds)\n        rolling_test.append(test_ds)\n        i+=1\n    estimator = GPVAREstimator(prediction_length=pred_len, context_length=6, freq='1B', target_dim=df.shape[1],\n        trainer=Trainer(ctx=\"cpu\", epochs=200))\n    return train_ds, rolling_test, estimator,train_size\n\n\ndef plot_forecasts(test_ds, predictor):\n    forecast_it, ts_it = make_evaluation_predictions(test_ds, predictor=predictor, num_samples=100)\n    forecasts = list(forecast_it)\n    tss = list(ts_it)\n    for target, forecast in islice(zip(tss, forecasts), 1):\n        return forecast.copy_dim(0).mean_ts, target[0]\n\npred_len = 3\nfrac = 0.8\nfile_path = '/Users/gabriel/PycharmProjects/Finance/Data/5B- grouped final results no ML/FB_1d_final_results_no_ML.csv'\ntrain_ds, rolling_test, estimator,train_size = train(file_path, pred_len, frac)\ntrain_output = estimator.train_model(train_ds,num_workers = None)\npredictor = train_output.predictor\n\n\nfor i, test_set in enumerate(rolling_test):\n    predict_stock, real_stock = plot_forecasts(test_set, predictor)\n    if i == 0:\n        predicted_change_stock = predict_stock\n    else:\n        predicted_change_stock = predicted_change_stock.append(predict_stock)\nprediction = predicted_change_stock.to_frame()\n\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nfig, ax = plt.subplots(2, 1,figsize=(10, 7))\n#First PLOT: Training and Test Set seperated by a red line, test set has the predictions overlayed\nreal_stock.plot(color = 'b', ax = ax[0])\nprediction.plot(color = 'g', ax = ax[0])\nax[0].axvline(real_stock.index[train_size-pred_len-1], color='r') # end of train dataset\nax[0].grid(which=\"both\") #grid lines\nax[0].legend([\"Change in Stock\", 'Predicted Change in stock'], loc=\"upper left\")\n#second Plot: zoomed in version of the test set from the first plot\nreal_stock[train_size:].plot(color = 'b', ax = ax[1])\nprediction.plot(color = 'r', ax = ax[1])\nax[1].grid(which=\"both\")\nplt.show()","repo_name":"GabrielDeza/Twitter-Adversarial-Finance","sub_path":"Forecasting/DeepVAR.py","file_name":"DeepVAR.py","file_ext":"py","file_size_in_byte":3933,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"25239485760","text":"import bpy\nimport re\n\nclass ObjectNavigator:\n\tdef dump(self, obj, text):\n\t\tprint('-'*40, text, '-'*40)\n\t\tfor attr in dir(obj):\n\t\t\tif hasattr(obj, attr):\n\t\t\t\tvalue = getattr(obj, attr)\n\t\t\t\tif isinstance(value, bpy.types.Property):\n\t\t\t\t\tprint(\"obj.%s = %s\" % (attr, value))\n\t\t\t\t\t# print(\"  Data path: %s\" % value.path())\n\t\t\t\telse:\n\t\t\t\t\tprint(\"obj.%s = %s\" % (attr, value))\n\t\t# print(\"Full path: \", bpy.path.abspath(obj.bl_rna.filepath))\n\n\tdef add_driver(self, target, source, prop, dataPath, driverType = 'AVERAGE', func = ''):\n\t\td = target.driver_add( '[\"' + prop + '\"]' ).driver\n\t\t\n\t\td.type = driverType\n\t\td.expression = func \n\t\tv = d.variables.new()\n\t\tv.name                 = prop\n\t\tv.targets[0].id        = source\n\t\tv.targets[0].data_path = dataPath\n\t\n\tdef get_object(self, key):\n\t\t# Use a regular expression to split the key on the period character, except when it's inside square brackets\n\t\tparts = re.split(r'(?<!\\[)\\.(?![^\\[]*\\])', key)\n\n\t\t# Skip the first part of the key, which represents the bpy.data object\n\t\tif len(parts) > 2:\n\t\t\tparts = parts[2:]\n\t\telse:\n\t\t\t# If the key does not contain a period character, return the bpy.data object\n\t\t\treturn bpy.data\n\n\t\t# Initialize a variable to hold the current object\n\t\tobj = bpy.data\n\n\t\t# Iterate through the parts of the key, using getattr or __getitem__ to access the corresponding attribute of the object at each level\n\t\tfor part in parts:\n\t\t\tif '[' in part:\n\t\t\t\t# If the part contains a square bracket, it represents an indexed element, so use __getitem__ to access it\n\t\t\t\t# First, split the part on the '[' character to separate the attribute name from the index\n\t\t\t\tattr_parts = part.split('[')\n\t\t\t\tattr_name = attr_parts[0]  # the attribute name is the part before the '[' character\n\t\t\t\tindex = attr_parts[1][:-1].replace('\"', '')  # the index is the part between the '[' and ']' characters\n\n\t\t\t\t# Check if the index is a string or an integer, and use the appropriate method to access the element\n\t\t\t\ttry:\n\t\t\t\t\tindex = int(index)\n\t\t\t\texcept ValueError:\n\t\t\t\t\tpass\n\n\t\t\t\t# Finally, use __getitem__ to access the indexed element\n\t\t\t\tobj = getattr(obj, attr_name)[index]\n\t\t\telse:\n\t\t\t\t# Otherwise, use getattr to access the attribute\n\t\t\t\tobj =  getattr(obj, part)\n\n\t\t# Return the final object\n\t\treturn obj\n\n\tdef split_string(self, s):\n\t\t# Find the index of the last '[' or '.' character in the string\n\t\tstart_index = max(s.rindex('['), s.rindex('.'))\n\n\t\t# Find the index of the ']' character that corresponds to the '[' character found above, if it exists\n\t\ttry:\n\t\t\tend_index = s.index(']', start_index)\n\t\texcept ValueError:\n\t\t\tend_index = len(s)\n\n\t\t# Extract the part of the string before the '[' or '.' character\n\t\tpath = s[:start_index]\n\n\t\t# Extract the part of the string between the '[' or '.' and ']' characters, if it exists\n\t\tlevel = s[start_index + 1:end_index].replace('\"', '')\n\n\t\t# Determine the type of the level based on whether it is a number or a string\n\t\tif s[start_index:start_index+1] == '.':\n\t\t\tlevel_type = 'property'\n\t\telse:\n\t\t\tlevel_type = 'array'\n\n\t\treturn path, level, level_type\n\n\tdef set_object_value(self, key, value):\n\t\tpath, level, level_type = self.split_string(key)\n\t\tobj = self.get_object(path)\n\t\tif (level_type == 'array'):\n\t\t\tobj[level] = value\n\t\telse:\n\t\t\tsetattr(obj, level,  value)\n\t\treturn obj\n","repo_name":"s-a/easy-ssp","sub_path":"ssp_object_navigator.py","file_name":"ssp_object_navigator.py","file_ext":"py","file_size_in_byte":3286,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"42575508104","text":"import os\nimport random\nimport poe\nfrom ...typing import sha256, Dict, get_type_hints\n\nurl = 'https://poe.com/'\nmodels = {'gpt-3.5-turbo':'capybara','claude-instant':'a2','palm':'acouchy','palm2':'acouchy','bard':'acouchy','google-bard':'acouchy','google-palm':'acouchy','llama-2-70b-chat':'llama_2_70b_chat'}\nmodel = ['gpt-3.5-turbo','claude-instant','palm2','llama-2-70b-chat']\nsupports_stream = True\nneeds_auth = False\nworking = True\ntoken = ['H959lSH8kjQ-b4K8FCrDPg%3D%3D','ACHY1MG7xz1yE0P6EByF5g%3D%3D','a4DoOVnIl3FievhYiQYOJw%3D%3D']\nformkey = ['a40f267a9751c48d34c9f12f56c5c6f8','b65db0a463062fcabe43aa6c6978c344','413b8fa39bfb54f99cb9a4f18d18aab1']\ndef _create_completion(model: str, messages: list, stream: bool, **kwargs):\n    path = os.path.dirname(os.path.realpath(__file__))\n    conversation = '这是一个人和一个语言模型之间的对话。语言模型应该始终作为助理进行响应，如果需要，可以参考过去的消息历史。\\n'\n    for message in messages:\n        conversation += '%s：%s\\n' % (message['role'], message['content'])\n    conversation += '助理: '\n    index = random.randrange(len(token))\n    client = poe.Client(token[index],formkey=formkey[index])\n    for chunk in client.send_message(models[model], conversation, with_chat_break=True):\n        yield chunk[\"text_new\"]\n    client.purge_conversation(models[model], count=3)\n\nparams = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \\\n    '(%s)' % ', '.join(\n        [f\"{name}: {get_type_hints(_create_completion)[name].__name__}\" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])","repo_name":"musabatas/g4f","sub_path":"g4f/Provider/Providers/Poe.py","file_name":"Poe.py","file_ext":"py","file_size_in_byte":1648,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"21958632567","text":"from __future__ import annotations\n\nimport logging\nfrom typing import TYPE_CHECKING\n\nfrom cloudshell.cli.service.session_pool_context_manager import (\n    SessionPoolContextManager,\n)\nfrom cloudshell.cli.service.session_pool_manager import SessionPoolManager\n\nif TYPE_CHECKING:\n    from logging import Logger\n\n    from cloudshell.cli.service.command_mode import CommandMode\n    from cloudshell.cli.types import T_SESSION\n\n\nclass CLI:\n    def __init__(self, session_pool: SessionPoolManager = SessionPoolManager()):\n        self._session_pool = session_pool\n\n    def get_session(\n        self,\n        defined_sessions: list[T_SESSION],\n        command_mode: CommandMode,\n        logger: Logger | None = None,\n    ) -> SessionPoolContextManager:\n        \"\"\"Get session from the pool or create new.\"\"\"\n        if not isinstance(defined_sessions, list):\n            defined_sessions = [defined_sessions]\n\n        if not logger:\n            logger = logging.getLogger(\"cloudshell_cli\")\n        return SessionPoolContextManager(\n            self._session_pool, defined_sessions, command_mode, logger\n        )\n","repo_name":"QualiSystems/cloudshell-cli","sub_path":"cloudshell/cli/service/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":1105,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"48"}
+{"seq_id":"37558703913","text":"#tkinter모듈 가져오기\r\nfrom tkinter import *\r\nfrom tkinter import messagebox\r\n\r\n#포인트 설정\r\npoint = 0\r\n\r\n#플레이어 설정\r\nclass Player():\r\n    def __init__(self, canvas, x, y):\r\n        self.canvas = canvas\r\n        self.id = canvas.create_oval(x * 30, y * 30, x * 30 + 30, y * 30 + 30, fill=\"lightpink\", outline=\"hotpink\")\r\n        self.x, self.y = x, y\r\n        self.nx, self.ny = x, y\r\n    \r\n    def move(self, direction):\r\n        #w, a, s, d로 플레이어 조작\r\n        if direction == 'w':\r\n            self.nx, self.ny = self.x, self.y - 1\r\n        elif direction == 'a':\r\n            self.nx, self.ny = self.x - 1, self.y\r\n        elif direction == 's':\r\n            self.nx, self.ny = self.x, self.y + 1\r\n        elif direction == 'd':\r\n            self.nx, self.ny = self.x + 1, self.y\r\n\r\n        if not self.is_collide():\r\n            self.canvas.move(self.id, (self.nx - self.x) * 30, (self.ny - self.y) * 30)\r\n            self.x, self.y = self.nx, self.ny\r\n\r\n        # 첫 번째 퀴즈: 야구 게임\r\n        if map[self.y][self.x] == 4:\r\n            key = Tk()\r\n            key.title(\"첫 번째 퀴즈\")\r\n            key.option_add(\"*Font\", \"맑은고딕 18\")\r\n            \r\n            width, height = 500, 100\r\n            x, y = (key.winfo_screenwidth() - width) / 2, (key.winfo_screenheight() - height) / 2\r\n            key.geometry(\"%dx%d+%d+%d\" % (width, height, x, y))\r\n\r\n            lab = Label(key)\r\n            lab.config(text = \"숫자 야구 게임\")\r\n            lab.pack()\r\n\r\n            btn = Button(key)\r\n            btn.config(text = \"도전\")\r\n            btn.config(width = 10)\r\n\r\n            def close():\r\n                key.destroy()\r\n\r\n            def ball():\r\n                import random\r\n                secretLen = 3\r\n\r\n                secretList = random.sample(range(10), secretLen)\r\n                secret = ''\r\n                for i in range(secretLen):\r\n                    secret += str(secretList[i])\r\n\r\n\r\n                for chance in range(10, 0, -1):\r\n                    while True:\r\n                        guess = input((\"%d 번의 기회가 남았습니다.\" % chance) + \"세 자리 숫자를 추측해보세요.: \")\r\n                        if len(guess) == secretLen and guess.isdigit():\r\n                            break\r\n                    strike = 0\r\n                    ball = 0\r\n                    for i in range(secretLen):\r\n                        if secret[i] == guess[i]:\r\n                            strike += 1\r\n                        elif secret[i] in guess:\r\n                            ball += 1\r\n\r\n                    if strike == secretLen:\r\n                        print('성공!!')\r\n                        global point\r\n                        point = point + 1\r\n                        canvas.delete(\"item1\")\r\n                        map[self.y][self.x] = 0\r\n                        close()\r\n                        break\r\n                    if strike > 0:\r\n                        if ball > 0:\r\n                            print(\"%d strike(s) and %d ball(s)\\n\" % (strike, ball))\r\n                        else:\r\n                            print(\"%d strike(s)\\n\" % strike)\r\n                    else:\r\n                        if ball > 0:\r\n                            print(\"%d ball(s)\\n\" % ball)\r\n                        else:\r\n                            print(\"Out\\n\")\r\n\r\n                else:\r\n                    print(\"실패했습니다. 다시 시도하세요.\")\r\n    \r\n                    \r\n            btn.config(command = ball)\r\n            btn.pack()\r\n            \r\n            key.mainloop()\r\n\r\n        #두 번째 퀴즈: 사칙연산\r\n        elif map[self.y][self.x] == 5:\r\n            \r\n            key = Tk()\r\n            key.title(\"두 번째 퀴즈\")\r\n            key.option_add(\"*Font\", \"맑은고딕 18\")\r\n            \r\n            width, height = 500, 150\r\n            x, y = (key.winfo_screenwidth() - width) / 2, (key.winfo_screenheight() - height) / 2\r\n            key.geometry(\"%dx%d+%d+%d\" % (width, height, x, y))\r\n\r\n            lab = Label(key)\r\n            lab.config(text = \"사칙연산\")\r\n            lab.pack()\r\n            \r\n            btn = Button(key)\r\n            btn.config(text = \"보기\")\r\n            btn.config(width = 10)\r\n\r\n            def num():\r\n                cal = Tk()\r\n                cal.title(\"사칙연산\")\r\n                cal.option_add(\"*Font\", \"맑은고딕 18\")\r\n                cal.resizable(False, False)\r\n\r\n                width, height = 200, 40\r\n                x, y = (cal.winfo_screenwidth() - width) / 2, (cal.winfo_screenheight() - height) / 2\r\n                cal.geometry(\"%dx%d+%d+%d\" % (width, height, x, y))\r\n\r\n                text = Text(cal)\r\n                text.insert(CURRENT, \"3 + 8 x 9 / 2 = ?\")\r\n                text.pack()\r\n                \r\n            ent = Entry(key)\r\n            ent.pack()\r\n\r\n            def close():\r\n                key.destroy()\r\n\r\n            def answer():\r\n                if ent.get() == '39':\r\n                    global point\r\n                    point = point + 1\r\n                    canvas.delete(\"item2\")\r\n                    map[self.y][self.x] = 0\r\n                    close()\r\n\r\n            btn2 = Button(key)\r\n            btn2.config(text = \"입력\")\r\n            btn2.config(width = 10)\r\n\r\n            btn.config(command = num)\r\n            btn.pack()\r\n\r\n            btn2.config(command = answer)\r\n            btn2.pack()\r\n\r\n            key.mainloop()\r\n\r\n\r\n        #세 번째 퀴즈: 파이썬 퀴즈\r\n        elif map[self.y][self.x] == 6:\r\n            \r\n            key = Tk()\r\n            key.title(\"세 번째 퀴즈\")\r\n            key.option_add(\"*Font\", \"맑은고딕 18\")\r\n            \r\n            width, height = 550, 200\r\n            x, y = (key.winfo_screenwidth() - width) / 2, (key.winfo_screenheight() - height) / 2\r\n            key.geometry(\"%dx%d+%d+%d\" % (width, height, x, y))\r\n\r\n            lab = Label(key)\r\n            lab.config(text = \"파이썬 퀴즈\")\r\n            lab.pack()\r\n\r\n            lab = Label(key)\r\n            lab.config(text = \"함수 안에서 선언된 변수는 전역 변수이다.\")\r\n            lab.option_add(\"*Font\", \"맑은고딕 15\")\r\n            lab.pack()\r\n            \r\n            def close():\r\n                key.destroy()\r\n\r\n            def click():\r\n                    global point\r\n                    point = point + 1\r\n                    btn2 = Button(key)\r\n                    btn2.config(text = \"거짓\")\r\n                    btn2.config(width = 10)\r\n                    canvas.delete(\"item3\")\r\n                    map[self.y][self.x] = 0\r\n                    close()\r\n\r\n            def click2():\r\n                btn = Button(key)\r\n                btn.config(text = \"참\")\r\n                btn.config(width = 10)\r\n                \r\n            btn = Button(key)\r\n            btn.config(text = \"참\")\r\n            btn.config(width = 10)\r\n\r\n            btn2 = Button(key)\r\n            btn2.config(text = \"거짓\")\r\n            btn2.config(width = 10)\r\n\r\n            btn.config(command = click2)\r\n            btn.pack(side = LEFT, padx = 70)\r\n\r\n            btn2.config(command = click)\r\n            btn2.pack(side = RIGHT,  padx = 70)\r\n\r\n            key.mainloop()\r\n\r\n            #str = StringVar()\r\n\r\n            rv = IntVar()\r\n\r\n            rb1 = Radiobutton(key, text = \"참\", value = 1, variable = rv)\r\n            rb1.select()\r\n            rb2 = Radiobutton(key, text = \"거짓\", value = 2, variable = rv)\r\n\r\n            btn = Button(key)\r\n            btn.config(text = \"선택\")\r\n            btn.config(width = 10)\r\n            btn.config(command = click)\r\n\r\n            rb1.pack()\r\n            rb2.pack()\r\n            btn.pack()\r\n\r\n            key.mainloop()\r\n\r\n\r\n        #포인트가 3이 되면 도착지점 도달\r\n        elif point == 3:\r\n            if map[self.y][self.x] == 3:\r\n                messagebox.showinfo(title=\"성공\", message=\"•*.°미로 탈출 성공 °.*•\")\r\n        \r\n    #이동한 곳이 벽인지 아닌지 판별\r\n    def is_collide(self):\r\n        if map[self.ny][self.nx] == 1:\r\n            return True\r\n        else:\r\n            return False\r\n\r\n#키리스너 이벤트\r\ndef keyEvent(event):\r\n    player.move(repr(event.char).strip(\"'\"))\r\n\r\n#tk선언\r\nroot = Tk()\r\nroot.title(\"미로 찾기\")\r\nroot.resizable(False, False)\r\n\r\n#창 너비, 높이, 위치 설정\r\nwidth, height = 690, 690\r\nx, y = (root.winfo_screenwidth() - width) / 2, (root.winfo_screenheight() - height) / 2\r\nroot.geometry(\"%dx%d+%d+%d\" % (width, height, x, y))\r\n\r\n#canvas추가, 키이벤트 부착, 창 모양 및 색깔, 마우스 포인터 모양 등 설정\r\ncanvas = Canvas(root, width=width, height=height, bg=\"white\", bd=10, relief=\"ridge\", highlightthickness=5, highlightcolor=\"thistle\", cursor=\"heart\")\r\ncanvas.bind(\"<Key>\", keyEvent)\r\ncanvas.focus_set()\r\ncanvas.pack()\r\n\r\n#미로 맵 제작(1: 벽, 2:플레이어, 3:도착지점, 4,5,6: 퀴즈)\r\nmap = [\r\n    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\r\n    [1, 2, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\r\n    [1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1],\r\n    [1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1],\r\n    [1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 4, 1, 0, 1],\r\n    [1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1],\r\n    [1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1],\r\n    [1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1],\r\n    [1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1],\r\n    [1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1],\r\n    [1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1],\r\n    [1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1],\r\n    [1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 5, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1],\r\n    [1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1],\r\n    [1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1],\r\n    [1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1],\r\n    [1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1],\r\n    [1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1],\r\n    [1, 0, 1, 1, 1, 1, 1, 0, 1, 6, 1, 0, 1, 1,1, 0, 1, 0, 1, 0, 1, 0,  1],\r\n    [1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1],\r\n    [1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1],\r\n    [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 3, 1],\r\n    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\r\n]\r\n\r\n#canvas에 맵 그리기\r\nfor y in range(len(map[0])):\r\n    for x in range(len(map[y])):\r\n        if map[y][x] == 1:\r\n            canvas.create_rectangle(x * 30, y * 30, x * 30 + 30, y * 30 + 30, fill=\"thistle\", outline = \"darkmagenta\")\r\n        elif map[y][x] == 2:\r\n            player = Player(canvas, x, y)\r\n        elif map[y][x] == 3:\r\n            canvas.create_oval(x * 30, y * 30, x * 30 + 30, y * 30 + 30, fill=\"lightblue\", outline = \"steelblue\")\r\n        elif map[y][x] == 4:\r\n            canvas.create_arc(x * 30, y * 30, x * 30 + 30, y * 30 + 30, fill=\"moccasin\", outline = \"gold\", tag = \"item1\")\r\n        elif map[y][x] == 5:\r\n            canvas.create_arc(x * 30, y * 30, x * 30 + 30, y * 30 + 30, fill=\"moccasin\", outline = \"gold\", tag = \"item2\")\r\n        elif map[y][x] == 6:\r\n            canvas.create_arc(x * 30, y * 30, x * 30 + 30, y * 30 + 30, fill=\"moccasin\", outline = \"gold\", tag = \"item3\")\r\n\r\nroot.mainloop()\r\n","repo_name":"Myeot/2021-","sub_path":"알고리즘과게임콘텐츠 프로그램 파일.py","file_name":"알고리즘과게임콘텐츠 프로그램 파일.py","file_ext":"py","file_size_in_byte":11657,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"15191959163","text":"from django import forms\nfrom django.core.exceptions import ValidationError\n\nimport re\nimport logging\n\nfrom allauth.account.forms import SignupForm\nfrom captcha.fields import CaptchaField\n\n\nlogger = logging.getLogger('db')\n\nclass MySignupForm(SignupForm):\n    first_name = forms.CharField(required=True, max_length=50)\n    last_name = forms.CharField(required=True, max_length=50)\n    church = forms.CharField(required=False, max_length=100)\n    weekend = forms.CharField(\n        required=False,\n        max_length=100,\n        label='Participant weekend',\n        help_text='What was the name of the weekend that you attended as a participant?',\n    )\n    captcha = CaptchaField(help_text=\"Don't worry &mdash; if you get it wrong you can try again. The rest of your input will be saved.\")\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        # hp stands for [Pooh's favorite treat] + [weed synonym]\n        self.hp_hint = None\n\n    def clean(self):\n        cleaned_data = super().clean()\n        first_name = cleaned_data.get(\"first_name\")\n        last_name = cleaned_data.get(\"last_name\")\n\n        # The following is unfortunate and hacky, but this is the current production mole I'm whacking.\n        # If they didn't enter a church or weekend\n        if not cleaned_data.get(\"church\") and not cleaned_data.get(\"weekend\"):\n            # If the first and last name end with the same two capital letters, preceded by any lowercase letter\n            p = re.compile('[a-z]([A-Z]{2})$')\n            m1 = p.search(first_name)\n            m2 = p.search(last_name)\n            if m1 and m2 and m1.group(1) == m2.group(1):\n                logger.debug(\"%s %s (%s) \\\"signed up\\\" ;)\" % (first_name, last_name, cleaned_data.get(\"email\")))\n                self.hp_hint = \"Thank you for signing up! You should receive a verification email at %s soon.\" % (cleaned_data.get(\"email\"))\n                raise ValidationError(self.hp_hint)\n\n    def save(self, request):\n        user = super().save(request)\n\n        user.first_name = self.cleaned_data['first_name']\n        user.last_name = self.cleaned_data['last_name']\n        user.church = self.cleaned_data['church']\n        user.weekend = self.cleaned_data['weekend']\n\n        user.save()\n        return user\n","repo_name":"taylorvance/prayerbanner","sub_path":"accounts/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":2289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"15967916414","text":"#!/user/bin/env python\n#zoe\ndef RMB(Money):\n\n    Money_str = str(Money)\n    if \"-\" not in  Money_str :\n        Money_li = list( Money_str.split('.')[0])\n    else:\n        Money_li= list( Money_str.split('.')[0])[1:]\n    Money_len=len(Money_li)\n    if Money_len < 3:\n        if \"-\" not in str(Money):\n            return (\"$%.2f\" % Money)\n        else:\n            return (\"-$%.2f\" % abs(Money))\n    else:\n        rev = \"%.2f\" % Money\n        n,x = divmod(Money_len, 3)\n        if x != 0:\n            count = n\n        else:\n            count = n - 1\n        i = 0\n        while i < count:\n             Money_li.insert((Money_len - 3), ',')\n             i += 1\n             Money_len -= 3\n\n        if \"-\" not in rev:\n            Money_str2=\"+$\" + ''.join(Money_li) + rev[-3:]\n            return Money_str2\n        else:\n            Money_str2=\"-$\" + ''.join(Money_li) + rev[-3:]\n            return Money_str2\n\nMon = input(\"Please input the money:\\n\")\nmon2 = RMB(Mon)\nprint(mon2)\n\n","repo_name":"zoewei021/Python-exercise","sub_path":"excise/RMB.py","file_name":"RMB.py","file_ext":"py","file_size_in_byte":978,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"20920297947","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Feb  7 16:12:11 2023\r\n\r\n@author: Bram\r\n\"\"\"\r\n\r\nimport argparse\r\nimport cv2\r\nimport copy\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nimport torch.utils.data as Data\r\nimport torchvision\r\nimport torchvision.transforms as trans\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\n# Initialize argument parser for easy\r\nparser = argparse.ArgumentParser(\"MiniImagenet\")\r\n\r\n# Model arguments\r\nparser.add_argument(\"--num_filters\", type=int, default=16)\r\n\r\n# Training arguments\r\nparser.add_argument(\"--lr\", type=float, default=1e-4, help=\"Learning rate hyperparameter for the AdamW optimizer\")\r\n# parser.add_argument(\"--weight_decay\", type=float, default=0.0, help=\"Weight decay hyperparameter for the AdamW optimizer\")\r\nparser.add_argument(\"--num_epochs\", type=int, default=20, help=\"Number of runs over all of the training data\")\r\nparser.add_argument(\"--batch_size\", type=int, default=1, help=\"The batchsize that is fed to the model\")\r\nparser.add_argument(\"--num_workers\", type=int, default=1, help=\"The number of processes that work for the dataloader\")\r\n# parser.add_argument(\"--device\", type=str, default=\"cuda:0\", choices=[\"cuda:0\", \"cpu:0\"], help=\"Specify the device on which you run the code, gpu(cuda) or cpu\")\r\nparser.add_argument(\"--benchmark_cudnn\", type=bool, default=False)\r\n\r\nargs = parser.parse_args()\r\n\r\nprint('Pytorch Version:' , torch.__version__)\r\n\r\ndata_transforms = {\r\n    'train': trans.Compose([\r\n        trans.RandomResizedCrop(224),\r\n        trans.RandomHorizontalFlip(),\r\n        trans.ToTensor(),\r\n        # when do the transform, image pixels are compressed \r\n        # from (0,255) to (0,1) then we do the normalization\r\n        trans.Normalize([0.485, 0.456, 0.406], # mean of RGB\r\n                        [0.229, 0.224, 0.225]) # std of RGB\r\n    ]),\r\n    'val': trans.Compose([\r\n        trans.Resize(256),\r\n        trans.CenterCrop(224),\r\n        trans.ToTensor(),\r\n        trans.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\r\n    ])\r\n}\r\n\r\ntrain_data = torchvision.datasets.CIFAR10(root='./data', train=True,\r\n                                        download=True, transform=data_transforms['train'])\r\n\r\nval_data = torchvision.datasets.CIFAR10(root='./data', train=False,\r\n                                       download=True, transform=data_transforms['val'])\r\n\r\nclass_names = train_data.classes\r\ndataset_sizes = {'train': len(train_data), 'val': len(val_data)}\r\n\r\ndataloaders = {\r\n    'train': Data.DataLoader(\r\n        dataset=train_data,\r\n        batch_size=1,\r\n        shuffle=True,\r\n#         num_workers=4\r\n    ),\r\n\r\n    'val': Data.DataLoader(\r\n        dataset=val_data,\r\n        batch_size=1,\r\n        shuffle=True,\r\n#         num_workers=4\r\n    )\r\n}\r\n\r\n# Set up the device\r\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu:0\")\r\nif(args.benchmark_cudnn):\r\n    torch.backends.cudnn.benchmark = True\r\n\r\n# denormalize and show an image \r\ndef imshow(image, title=None):\r\n    image = image.numpy().transpose((1, 2, 0)) \r\n    mean = np.array([0.485, 0.456, 0.406])\r\n    std = np.array([0.229, 0.224, 0.225])\r\n    image = image * std + mean\r\n    image = np.clip(image, 0, 1)\r\n    plt.imshow(image)\r\n    if title:\r\n        plt.title(title)\r\n        \r\nprint(dataloaders['train'])\r\n\r\n# get a batch of training data\r\nimages, classes = next(iter(dataloaders['train']))\r\n\r\n# make a grid from batch\r\nimages = torchvision.utils.make_grid(images)\r\n\r\nimshow(images, title=[class_names[x] for x in classes])\r\n\r\n\r\nvgg = torchvision.models.vgg16(pretrained=True)\r\nvgg = vgg.to(device)\r\nprint('VGG16 Architecture:\\n', vgg)\r\n\r\n# reconstruct VGG16, i.e. remove the classifier and replace it with GAP\r\nclass VGG(torch.nn.Module):\r\n    def __init__(self):\r\n        super(VGG, self).__init__()\r\n        self.vgg = torchvision.models.vgg16(pretrained=True) \r\n        self.conv = nn.Sequential(\r\n            self.vgg.features, \r\n            self.vgg.avgpool \r\n        )\r\n         \r\n        # self.fc = nn.Linear(512, num_of_class)\r\n        # as we use ImageNet, num_of_class=1000\r\n        self.fc = nn.Linear(512, 1000)\r\n\r\n    \r\n    def forward(self,x):    \r\n        x = self.conv(x) # -> (512, 7, 7)\r\n        \r\n        \r\n        # we use GAP to replace the fc layer, therefore we need to\r\n        # convert (512,7,7) to (512, 7x7)(i.e. each group contains 7x7=49 values), \r\n        # then convert (512, 7x7) to (512, 1x1) by mean(1)(i.e. average 49 values in each group), \r\n        # and finally convert (512, 1) to (1, 512) \r\n        x = x.view(512,7*7).mean(1).view(1,-1) # -> (1, 512)\r\n        \r\n        # FW^T = S\r\n        # where F is the averaged feature maps, which is of shape (1,512)\r\n        # W is the weights for fc layer, which is of shape (1000, 512)\r\n        # S is the scores, which is of shape (1, 1000)\r\n        x = self.fc(x) # -> (1, 1000)\r\n        return x \r\n\r\n\r\ndef train_model(model, loss_fn, optimizer, scheduler, num_epochs=5):\r\n    \"\"\"\r\n    net: the model to be trained\r\n    loss_fn: loss function\r\n    scheduler: torch.optim.lr_scheduler\r\n    \"\"\"\r\n    \r\n    best_model_weights = copy.deepcopy(model.state_dict())\r\n    best_accuracy = 0.0\r\n    train_loss = []\r\n    val_loss = []\r\n    train_acc = []\r\n    val_acc = []\r\n    \r\n    for epoch in range(num_epochs):\r\n        print('Epoch {}/{}'.format(epoch, num_epochs-1))\r\n        print('-'*10)\r\n        \r\n        step_loss = 0.0\r\n        epoch_accuracy = 0.0\r\n        \r\n        # each epoch has a training and validation phase\r\n        for phase in ['train', 'val']:\r\n            if phase == 'train':\r\n                model.train() # set to training mode\r\n            else:\r\n                model.eval() # set to evaluate mode\r\n            \r\n            step_loss = 0.0\r\n            step_corrects = 0\r\n            \r\n            for step, (images, labels) in enumerate(dataloaders[phase]): \r\n                images = images.to(device)\r\n                labels = labels.to(device)\r\n               \r\n                # forward pass, compute loss and make predictions\r\n                outputs = model(images) \r\n                preds = torch.max(outputs, 1)[1]\r\n                loss = loss_fn(outputs, labels)\r\n                \r\n                # backward pass and update weights if in training phase\r\n                if phase == 'train':\r\n                    optimizer.zero_grad()\r\n                    loss.backward()\r\n                    optimizer.step()\r\n\r\n                # compute step loss and step corrects\r\n                step_loss += loss.item() * images.size(0) # loss.item() extracts the loss's value\r\n                step_corrects += torch.sum(preds == labels.data)\r\n            \r\n            \r\n            if phase == 'train':\r\n                scheduler.step()\r\n            \r\n            epoch_loss = step_loss / dataset_sizes[phase]\r\n            epoch_accuracy = step_corrects.double() / dataset_sizes[phase]\r\n            print('{} Loss: {:.4f} Accuracy: {:.4f}'.format(\r\n                phase, epoch_loss, epoch_accuracy))\r\n\r\n            if phase == 'train':\r\n                train_loss.append(epoch_loss)\r\n                train_acc.append(epoch_accuracy)\r\n            else:\r\n                val_loss.append(epoch_loss)\r\n                val_acc.append(epoch_accuracy)\r\n\r\n            # deep copy the model\r\n            if phase == 'val' and epoch_accuracy > best_accuracy:\r\n                best_accuracy = epoch_accuracy\r\n                best_model_weights = copy.deepcopy(model.state_dict())\r\n            \r\n        print()\r\n        \r\n    print('Best Validation Accuracy: {:4f}'.format(best_accuracy))\r\n\r\n    # draw the loss history and accuracy history\r\n    x = np.arange(num_epochs)\r\n    plt.subplot(221)\r\n    plt.plot(x, train_loss, c='red', label='train loss')\r\n    plt.plot(x, val_loss, c='blue', label='val loss')\r\n    plt.legend(loc='best')\r\n\r\n    plt.subplot(222)\r\n    plt.plot(x, train_acc, c='red', label='train acc')\r\n    plt.plot(x, val_acc, c='blue', label='val acc')\r\n    plt.legend(loc='best')\r\n\r\n    plt.show()\r\n    \r\n    # load best model weights\r\n    model.load_state_dict(best_model_weights)\r\n    return model\r\n\r\n# generic function to display predictions for a few images\r\ndef visualize_model(model, num_images=6):\r\n    was_training = model.training # if true, the model is in training mode otherwise in evaluate mode\r\n    model.eval()\r\n    images_so_far = 0\r\n    fig = plt.figure()\r\n    \r\n    for step, (images, labels) in enumerate(dataloaders['val']): \r\n        outputs = model(images)\r\n        preds = torch.max(outputs, 1)[1]\r\n        \r\n        for i in range(images.size(0)):\r\n            images_so_far += 1\r\n            ax = plt.subplot(num_images//2, 2, images_so_far)\r\n            ax.axis('off')\r\n            ax.set_title('predicted: {}'.format(class_names[preds[i]]))\r\n            imshow(images.cpu().data[i])\r\n            \r\n            if images_so_far == num_images:\r\n                model.train(mode=was_training)\r\n                return\r\n    \r\n    model.train(mode=was_training)\r\n    \r\n    \r\nmodel = VGG()\r\n\r\ntrainable_parameters = []\r\nfor name, p in model.named_parameters():\r\n    if \"fc\" in name:\r\n        trainable_parameters.append(p)\r\n\r\nloss_fn = torch.nn.CrossEntropyLoss()\r\n\r\n# all parameters are being optimized\r\noptimizer = torch.optim.SGD(trainable_parameters, lr=0.001, momentum=0.9)\r\n\r\n# decay LR by a factor of 0.1 every 7 epochs\r\nexp_lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)\r\nmodel = train_model(model, loss_fn, optimizer, exp_lr_scheduler, num_epochs=25)\r\n\r\n\r\nvisualize_model(model)\r\n\r\n\r\nparams = list(model.fc.parameters())\r\nweight = np.squeeze(params[0].data.numpy())\r\nprint('weight.shape', weight.shape)\r\n\r\nimage, label = next(iter(dataloaders['val']))\r\n\r\nmodel.eval()\r\nscores = model(image) # get the raw scores\r\nprobs = F.softmax(scores, dim=1).data.squeeze() # use softmax to generate the probability distribution for the scores\r\nprobs, idx = probs.sort(0, True) # sort the probability distribution in descending order, and idx[0] is the predicted class\r\nprint('sum of probabilities: %.0f'%torch.sum(probs).numpy())\r\nprint('true class: ', class_names[label])\r\nprint('predicated class: ', class_names[idx[0].numpy()])\r\n\r\ndef return_CAM(feature_conv, weight, class_idx):\r\n    \"\"\"\r\n    return_CAM generates the CAMs and up-sample it to 224x224\r\n    arguments:\r\n    feature_conv: the feature maps of the last convolutional layer\r\n    weight: the weights that have been extracted from the trained parameters\r\n    class_idx: the label of the class which has the highest probability\r\n    \"\"\"\r\n    size_upsample = (224, 224)\r\n    \r\n    # we only consider one input image at a time, therefore in the case of \r\n    # VGG16, the shape is (1, 512, 7, 7)\r\n    bz, nc, h, w = feature_conv.shape \r\n    output_cam = []\r\n    for idx in class_idx:\r\n        beforeDot =  feature_conv.reshape((nc, h*w))# -> (512, 49)\r\n        cam = np.matmul(weight[idx], beforeDot) # -> (1, 512) x (512, 49) = (1, 49)\r\n        cam = cam.reshape(h, w) # -> (7 ,7)\r\n        cam = cam - np.min(cam)\r\n        cam_img = cam / np.max(cam)\r\n        cam_img = np.uint8(255 * cam_img)\r\n        output_cam.append(cv2.resize(cam_img, size_upsample))\r\n    return output_cam\r\n\r\n\r\nfeature_maps = model.conv(image) # get the feature maps of the last convolutional layer\r\nprint('feature_maps.shape: ', feature_maps.detach().numpy().shape)\r\n\r\nCAMs = return_CAM(feature_maps.detach().numpy(), weight, [idx.numpy()[0]]) # generate the CAM for the input image\r\nheatmap = cv2.applyColorMap(CAMs[0], cv2.COLORMAP_JET)\r\n\r\n\r\nprint('original image shape: ', image.reshape((3, 224, 224)).numpy().transpose((1,2,0)).shape)\r\nprint('heatmap.shape:', heatmap.shape)\r\nimage = image.reshape((3, 224, 224)).numpy().transpose((1, 2, 0)) \r\nmean = np.array([0.485, 0.456, 0.406])\r\nstd = np.array([0.229, 0.224, 0.225])\r\nimage = image * std + mean\r\nimage = np.clip(image, 0, 1)\r\n\r\nplt.imshow(image)\r\nplt.show()\r\nplt.imshow(heatmap)\r\nplt.show()\r\n\r\nresult = 0.5 * heatmap + 0.5 * image\r\ncv2.imwrite('cam.png', result)","repo_name":"bramdortant/thesis","sub_path":"CAM.py","file_name":"CAM.py","file_ext":"py","file_size_in_byte":12030,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"28967927899","text":"import json\nimport boto3\nfrom boto3.dynamodb.conditions import Attr\n\ndef lambda_handler(event, context):\n    \n    \n    dynamodb = boto3.resource('dynamodb', region_name='us-east-1')\n    table = dynamodb.Table('Documents')\n    \n    res = []\n    \n    if('query' in event[\"params\"][\"querystring\"] and event[\"params\"][\"querystring\"][\"query\"]):\n        res = table.scan(FilterExpression=Attr('Text').contains(event[\"params\"][\"querystring\"][\"query\"].lower()))\n    else:\n        res = table.scan()\n        \n    for item in res[\"Items\"]:\n        item[\"Url\"] = 'https://s3.amazonaws.com/' + item['Bucket'] + '/' +  item['Key']\n        \n    return {\n        'statusCode': 200,\n        'count': res[\"Count\"],\n        'res': res[\"Items\"]\n    }\n","repo_name":"GuiSevero/meetup","sub_path":"src/lambda_functions/text_search.py","file_name":"text_search.py","file_ext":"py","file_size_in_byte":732,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"21436667730","text":"from unittest import TestCase\nfrom sbl.syntax.prepro import *\nfrom sbl.vm.compile import *\nfrom sbl.vm.bc import *\nfrom sbl.vm.val import *\n\n\nclass TestCompiler(TestCase):\n    def compile_source(self, source_text: str) -> FunTable:\n        path = 'test'\n        ast = Parser(source_text, path).parse()\n        ast += Preprocess(path, [], ast).preprocess()\n        return Compiler(ast, meta={'file': path}).compile()\n\n    def test_push(self):\n        fun_table = self.compile_source('''\n            foo {\n                a b c;\n            }\n            bar {\n                1 2 3 4 5 6;\n            }\n        ''')\n        self.assertIn('foo', fun_table)\n        foo_fun = fun_table['foo']\n        self.assertEqual(foo_fun.bc, [\n            BC.load(None, Val('a', ValType.IDENT)),\n            BC.load(None, Val('b', ValType.IDENT)),\n            BC.load(None, Val('c', ValType.IDENT)),\n            BC.ret(None),\n        ])\n\n        self.assertIn('bar', fun_table)\n        bar_fun = fun_table['bar']\n        self.assertEqual(bar_fun.bc, [\n            BC.push(None, Val(1, ValType.INT)),\n            BC.push(None, Val(2, ValType.INT)),\n            BC.push(None, Val(3, ValType.INT)),\n            BC.push(None, Val(4, ValType.INT)),\n            BC.push(None, Val(5, ValType.INT)),\n            BC.push(None, Val(6, ValType.INT)),\n            BC.ret(None),\n        ])\n\n    def test_pop(self):\n        fun_table = self.compile_source('''\n            foo {\n                .a .b .c;\n                .@;\n            }\n        ''')\n        self.assertIn('foo', fun_table)\n        foo_fun = fun_table['foo']\n        self.assertEqual(foo_fun.bc, [\n            BC.pop(None, Val('a', ValType.IDENT)),\n            BC.pop(None, Val('b', ValType.IDENT)),\n            BC.pop(None, Val('c', ValType.IDENT)),\n            BC.pop(None, Val(None, ValType.NIL)),\n            BC.ret(None),\n        ])\n","repo_name":"alekratz/sbl-py","sub_path":"sbl/tests/test_compiler.py","file_name":"test_compiler.py","file_ext":"py","file_size_in_byte":1876,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"13001518918","text":"import sys\nsys.stdin = open(\"D5_3135_input.txt\", \"r\")\n\nT = int(input())\nfor test_case in range(T):\n    N = int(input())\n    ans = []\n    words = []\n    for _ in range(N):\n        p, s = input().split()\n        if p == '1':\n            words.append(s)\n        else:\n            cnt = 0\n            for i in words:\n                if s == i[:len(s)]:\n                    cnt += 1\n            ans.append(cnt)\n    print(\"#{}\".format(test_case + 1), *ans)","repo_name":"hongyong3/TIL","sub_path":"Algorithm/Swea/D5_3135.py","file_name":"D5_3135.py","file_ext":"py","file_size_in_byte":450,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"74939128786","text":"from django.shortcuts import render\nfrom django.db.models import Q\nfrom django.utils import timezone\nfrom django.http import HttpResponse, HttpResponseRedirect, Http404\nfrom django.shortcuts import render, get_object_or_404, redirect\nfrom django.urls import reverse\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\n\nfrom django.contrib.auth.decorators import login_required\n\nfrom .models import (\n    Book,\n    Resource,\n    Author,\n)\n\nfrom wishlist.models import (\n    BookCart,\n    ResourceCart\n)\n\nfrom .forms import (\n    BookCartForm,\n    ResourceCartForm\n)\n\n@login_required\ndef book_list(request):\n    today = timezone.now().date()\n    queryset_list = Book.objects.all()\n\n    query = request.GET.get(\"q\")\n    by = request.GET.get(\"by\")   \n\n    if query:\n        if by == 'title':\n            queryset_list = queryset_list.filter(\n                Q(title__icontains=query)\n            ).distinct()\n        elif by == 'author':\n            queryset_list = queryset_list.filter(\n                Q(author__name__icontains=query) |\n                Q(author__nicname__icontains=query)\n            ).distinct()\n        elif by == 'accession_number':\n            queryset_list = queryset_list.filter(\n                Q(accession_number__icontains=query)\n            ).distinct()\n        elif by == 'call_number':\n            queryset_list = queryset_list.filter(\n                Q(call_number__icontains=query)\n            ).distinct()\n        else:\n            queryset_list = queryset_list.filter(\n                Q(isbn__icontains=query)\n            ).distinct()\n    \n    total_found = len(queryset_list)\n\n    paginator = Paginator(queryset_list, 10)  # Show 25 contacts per page\n    page_request_var = \"page\"\n    page = request.GET.get(page_request_var)\n    try:\n        queryset = paginator.page(page)\n    except PageNotAnInteger:\n        # If page is not an integer, deliver first page.\n        queryset = paginator.page(1)\n    except EmptyPage:\n        # If page is out of range (e.g. 9999), deliver last page of results.\n        queryset = paginator.page(paginator.num_pages)\n\n    context = {\n        \"object_list\": queryset,\n        'total_found': total_found,\n        \"title\": \"Books\",\n        \"page_request_var\": page_request_var,\n        \"today\": today,\n    }\n    # print(context)\n    return render(request, \"book_list.html\", context)\n\n\n@login_required\ndef book_detail(request, id):\n    instance = get_object_or_404(Book, id=id)\n    cart_added =  BookCart.objects.all().filter(book=instance, user=request.user).first()\n\n    form = BookCartForm(request.POST or None, request.FILES or None)\n    if form.is_valid() and not cart_added:\n        cart_instance = form.save(commit=False)\n        cart_instance.book = instance\n        cart_instance.user = request.user\n        cart_instance.save()\n        print(\"Submitted BookCart\", form) \n        # message success\n        # messages.success(request, \"Successfully Added to BookCart\")\n\n    context = {\n        \"instance\": instance,\n        \"title\": \"Book Detail\",\n        \"form\": form,\n        'cart_added': cart_added,\n    }\n    return render(request, \"book_detail.html\", context)\n\n\n\n@login_required\ndef resource_list(request):\n    today = timezone.now().date()\n    queryset_list = Resource.objects.all()\n\n    query = request.GET.get(\"rq\")\n    by = request.GET.get(\"by\")\n    res_type = request.GET.get(\"rt\")\n\n    if query:\n        if res_type != 'all':\n            queryset_list = queryset_list.filter(\n                Q(resource_type__icontains=res_type)\n            ).distinct()\n        if by == 'title':\n            queryset_list = queryset_list.filter(\n                Q(title__icontains=query)\n            ).distinct()\n        elif by == 'accession_number':\n            queryset_list = queryset_list.filter(\n                Q(accession_number__icontains=query)\n            ).distinct()\n        elif by == 'call_number':\n            queryset_list = queryset_list.filter(\n                Q(call_number__icontains=query)\n            ).distinct()\n\n    paginator = Paginator(queryset_list, 5)  # Show 25 contacts per page\n    page_request_var = \"page\"\n    page = request.GET.get(page_request_var)\n    try:\n        queryset = paginator.page(page)\n    except PageNotAnInteger:\n        # If page is not an integer, deliver first page.\n        queryset = paginator.page(1)\n    except EmptyPage:\n        # If page is out of range (e.g. 9999), deliver last page of results.\n        queryset = paginator.page(paginator.num_pages)\n\n    context = {\n        \"object_list\": queryset,\n        \"title\": \"Resources\",\n        \"page_request_var\": page_request_var,\n        \"today\": today,\n    }\n    # print(context)\n    return render(request, \"resource_list.html\", context)\n\n@login_required\ndef resource_detail(request, id):\n    instance = get_object_or_404(Resource, id=id)\n\n    cart_added =  ResourceCart.objects.all().filter(resource=instance, user=request.user).first()\n\n    form = ResourceCartForm(request.POST or None, request.FILES or None)\n    if form.is_valid() and not cart_added:\n        cart_instance = form.save(commit=False)\n        cart_instance.resource = instance\n        cart_instance.user = request.user\n        cart_instance.save()\n        print(\"Submitted ResourceCart\", form)\n\n    context = {\n        \"instance\": instance,\n        \"title\": \"Resource Detail\",\n        \"form\": form,\n        'cart_added': cart_added,\n    }\n    return render(request, \"resource_detail.html\", context)\n\n@login_required\ndef resource_doclist(request):\n    return render(request, \"under_constraction.html\", {})\n\n@login_required\ndef resource_newspaperlist(request):\n    return render(request, \"under_constraction.html\", {})\n","repo_name":"belal-bh/CLIC_PUST","sub_path":"src/resource/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5682,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"24253641116","text":"from tkinter import *\n\n\nclass _Frame(Frame):\n    def __init__(self, master):\n        super().__init__(master)\n        self.build()\n\n    def build(self):\n        pass\n\n\nclass RadioButtons(_Frame):\n    def build(self):\n        self.button1 = Radiobutton(\n            self,\n            text='Blue',\n            padx=10,\n            variable=self.master.color_var,\n            value=1,\n            command=self.master.update_label\n        )\n        self.button1.pack(anchor=W)\n        self.button2 = Radiobutton(\n            self,\n            text='Red',\n            padx=10,\n            variable=self.master.color_var,\n            value=2,\n            command=self.master.update_label\n        )\n        self.button2.pack(anchor=W)\n        self.button3 = Radiobutton(\n            self,\n            text='Green',\n            padx=10,\n            variable=self.master.color_var,\n            value=3,\n            command=self.master.update_label\n        )\n        self.button3.pack(anchor=W)\n\n\nclass CheckBoxes(_Frame):\n    def build(self):\n        self.check1 = Checkbutton(\n            self,\n            text='Bold',\n            padx=10,\n            variable=self.master.bold_var,\n            command=self.master.update_label\n        )\n        self.check1.pack()\n        self.check2 = Checkbutton(\n            self,\n            text='Italic',\n            padx=10,\n            variable=self.master.italic_var,\n            command=self.master.update_label\n        )\n        self.check2.pack()\n\n\nclass BottomButtons(_Frame):\n    def build(self):\n        self.button1 = Button(\n            self,\n            text='Verdana',\n            padx=20,\n            command=lambda: self.master.set_font('Verdana')\n        )\n        self.button1.pack(side=LEFT)\n        self.button2 = Button(\n            self,\n            text='Times',\n            padx=20,\n            command=lambda: self.master.set_font('Times')\n        )\n        self.button2.pack(side=LEFT)\n        self.button3 = Button(\n            self,\n            text='Quit',\n            padx=20,\n            command=self.master.master.destroy\n        )\n        self.button3.pack(side=LEFT)\n\n\nclass App(_Frame):\n    def set_font(self, font):\n        self.font = font\n        self.update_label()\n\n    def update_label(self):\n        color_var = self.color_var.get()\n        bold_var = self.bold_var.get()\n        italic_var = self.italic_var.get()\n\n        font = self.font + self.font_size\n\n        if color_var == 1:\n            color = 'blue'\n        elif color_var == 2:\n            color = 'red'\n        elif color_var == 3:\n            color = 'green'\n        else:\n            color = 'black'\n\n        if bold_var:\n            font += ' bold'\n        if italic_var:\n            font += ' italic'\n\n        self.label.config(fg=color, font=font)\n\n    def build(self):\n        self.font = 'Times'\n        self.font_size = ' 16'\n        self.color_var = IntVar()\n        self.bold_var = BooleanVar()\n        self.italic_var = BooleanVar()\n\n        self.label = Label(\n            self,\n            text='Python GUI Programming',\n            font=self.font+self.font_size,\n            padx=30,\n            pady=30\n        )\n        self.bottom_buttons = BottomButtons(self)\n        self.bottom_buttons.pack(side=BOTTOM)\n\n        self.radio_buttons = RadioButtons(self)\n        self.radio_buttons.pack(side=LEFT)\n        self.label.pack(side=LEFT)\n        self.check_boxes = CheckBoxes(self)\n        self.check_boxes.pack(side=LEFT)\n\n\nroot = Tk()\nroot.title('Button Demo')\nApp(root).pack()\nmainloop()\n","repo_name":"rinald/cen213","sub_path":"lab07/button_demo.py","file_name":"button_demo.py","file_ext":"py","file_size_in_byte":3543,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"5777365350","text":"import pandas as pd\nimport requests\nimport os\nfrom tqdm import tqdm\nfrom handle import GetDescription, GetSamples\n\n\nsource = input('What is the file name (with extension): ')\ndelim = input('What is the delimiter: ')\nfile_name = input('What will be the output file name? ')\nsamples_amount = int(input('How many samples do you want to get? '))\n\nos.system('cls' if os.name == 'nt' else 'clear')\n\nprint('Creating a dict to save the datas...')\nresult = {}\nprint('Result was created successfully!')\n\n\ntry:\n    print('Reading the data file...')\n    if delim == 'tab':\n        data = pd.read_csv(f'./data/{source}', sep=\"\\t\")\n    else:\n        data = pd.read_csv(f'./data/{source}', sep=f\"{delim}\")\n    print(f'{source} was read successfully...')\n    words = [x for x in data.index]\n    print(f'there is {len(words)} words in the {source}')\n    del data\nexcept:\n    print('Error to read file')\n\nprint('\\n')\ntry:\n    print('starting to search translations...')\n    for word in tqdm(words):\n        print(f'Getting description and samples about: \"{word}\"')\n        print(f'Getting description to \"{word}\"...')\n        desc = GetDescription(word)\n        res = requests.get(\n            f'https://pt.bab.la/dicionario/ingles-portugues/{word}')\n        print(f'Getting samples to \"{word}\"...')\n        examples = GetSamples(res, samples_amount)\n        print(f'Save \"{word}\" on dict')\n        result[word] = [desc, examples]\n        print(f'\"{word}\" was saved successfully!')\n\n    del res, desc, examples, words, word\nexcept:\n    print('Error to create dictionary')\n\n\ntry:\n    print(f'Creating {file_name} file...')\n    f = open(f\"./file/{file_name}.txt\", \"w+\", encoding=\"utf-8\")\n    for key, value in tqdm(result.items()):\n        print(f'Creating line to \"{key}\"')\n\n        line = f\"{key};\"\n        line += value[0]\n        line += \";\"\n        for x in value[1]:\n            line += \" \".join(x)\n            line += \"/\"\n\n        print(f'\"{key}\" line was created successfully!')\n        print(f'adding \"{key}\" line on {file_name}')\n        f.write(line + '\\n')\n        print(f'\"{key}\" line was added successfully!')\n    print(f'Closing {file_name}')\n    f.close()\n    print('Done!')\nexcept:\n    print('Error to create a txt file')\n","repo_name":"ccr5/Deck-Creator-For-Anki","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2219,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"48"}
+{"seq_id":"37543685559","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time    : 2021/6/8 14:33\n# @Author  : Yanjun Wang\n# @Site    : \n# @File    : demo.py\n# @Software: PyCharm\n\nimport requests\nimport json\nimport xlwt\nimport xlrd\n\n\nsession = requests.Session()\nbook = xlwt.Workbook()\nsheet = book.add_sheet('企业')\nn = 1\nsheet.write(0,0,'name')\nsheet.write(0,1,'lon')\nsheet.write(0,2,'lat')\nsheet.write(0,3,'addr')\nfor i in range(1,100):\n    try:\n        res = session.get('https://restapi.amap.com/v3/place/text?keywords=化工企业&city=taiyuan&output=json&offset=100&page={}&key=8ca198a8ae0f83242d99062676df301f&extensions=all'.format(i))\n        data = json.loads(res.text)\n        print(data)\n        if data['count'] == '0':\n            break\n        for dat in data['pois']:\n            sheet.write(n, 0, dat['name'])\n            sheet.write(n, 1, dat['location'].split(',')[0])\n            sheet.write(n, 2, dat['location'].split(',')[1])\n            sheet.write(n, 3, dat['address'])\n            print(dat['name'],dat['location'],dat['address'])\n            n+=1\n    except:\n        pass\n\nbook.save('太原市企业.xls')","repo_name":"cycle13/demo","sub_path":"common_learn/高德地图/高德地图/demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":1111,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"16920956520","text":"from django.shortcuts import render, get_object_or_404, redirect\nfrom .models import Group, Post, User, Comment, Follow\nfrom django.core.paginator import Paginator\nfrom .forms import PostForm, CommentForm\nfrom django.contrib.auth.decorators import login_required\nfrom yatube.settings import NUM_PAGE_PAGINATOR\n\n\ndef paginator(request, posts):\n    paginator = Paginator(posts, NUM_PAGE_PAGINATOR)\n    page_number = request.GET.get('page')\n    return paginator.get_page(page_number)\n\n\ndef index(request):\n    posts = Post.objects.select_related('group')\n    context = {\n        'page_obj': paginator(request, posts)\n    }\n    return render(request, 'posts/index.html', context)\n\n\ndef group_posts(request, slug):\n    group = get_object_or_404(Group, slug=slug)\n    posts = group.post.all()\n    context = {\n        'page_obj': paginator(request, posts),\n        'group': group\n    }\n    return render(request, 'posts/group_list.html', context)\n\n\ndef profile(request, username):\n    author = get_object_or_404(User, username=username)\n    posts = author.posts.all()\n\n    following = Follow.objects.filter(author=author).exists()\n    context = {'author': author,\n               'page_obj': paginator(request, posts),\n               'posts': posts,\n               'following': following\n               }\n    return render(request, 'posts/profile.html', context)\n\n\ndef post_detail(request, post_id):\n    post = get_object_or_404(Post, pk=post_id)\n    form = CommentForm()\n    comment = Comment.objects.filter(post=post)\n    context = {\n        'posts': post,\n        'form': form,\n        'comments': comment\n    }\n    return render(request, 'posts/post_detail.html', context)\n\n\n@login_required\ndef post_create(request):\n    form = PostForm(request.POST, request.FILES)\n    if form.is_valid():\n        post = form.save(commit=False)\n        post.author = request.user\n        post.save()\n        return redirect('posts:profile', request.user)\n\n    context = {\n        'form': form,\n    }\n    return render(request, 'posts/create_post.html', context)\n\n\n@login_required\ndef post_edit(request, post_id):\n    post = Post.objects.get(pk=post_id)\n    is_edit = True\n    if post.author != request.user:\n        return redirect('posts:post_detail', post_id=post_id)\n\n    form = PostForm(\n        request.POST or None,\n        files=request.FILES or None,\n        instance=post\n    )\n\n    if form.is_valid():\n        post.save()\n        return redirect('posts:post_detail', post_id=post.id)\n\n    context = {\n        'form': form,\n        'is_edit': is_edit\n    }\n    return render(request, 'posts/create_post.html', context)\n\n\n@login_required\ndef add_comment(request, post_id):\n    post = get_object_or_404(Post, pk=post_id)\n    form = CommentForm(request.POST or None)\n    if form.is_valid():\n        comment = form.save(commit=False)\n        comment.author = request.user\n        comment.post = post\n        comment.save()\n    return redirect('posts:post_detail', post_id=post_id)\n\n\n# posts/views.py\n\n@login_required\ndef follow_index(request):\n    posts = Post.objects.select_related(\n        'group'\n    ).filter(author__following__user=request.user)\n\n    context = {\n        'posts': posts,\n        'page_obj': paginator(request, posts),\n    }\n    return render(request, 'posts/follow.html', context)\n\n\n@login_required\ndef profile_follow(request, username):\n    author = get_object_or_404(User, username=username)\n    if request.user != author:\n        Follow.objects.get_or_create(\n            user=request.user,\n            author=author\n        )\n    return redirect('posts:profile', username=username)\n\n\n@login_required\ndef profile_unfollow(request, username):\n    author = get_object_or_404(User, username=username)\n    # Дизлайк, отписка\n    Follow.objects.filter(author=author, user=request.user).delete()\n    return redirect('posts:profile', username=username)\n","repo_name":"korshikovvital/hw05_final","sub_path":"yatube/posts/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3870,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"48"}
+{"seq_id":"11936451140","text":"\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jan  9 20:40:06 2023\n\n@author: Martin\n\"\"\"\n\n##  1. Keeps the original top 5 Exchanges by Volume\n##  2. Takes the next 5 based Top Rank by TradingPair\n\nimport requests\nimport json\nimport numpy as np\nfrom datetime import datetime\nimport config\nfrom config import SETTINGS\nimport os\nstaging_folder = config.SETTINGS['staging_folder']\n\n# Date Value\ndate = datetime.now().strftime(\"%Y%m%d\")\n\npayload={}\nheaders = {}\n\n### Coin Market Function\ndef coin_markets():\n    # Request market data\n    url = \"https://api.coincap.io/v2/markets\"\n    response = requests.request(\"GET\", url, headers=headers, data=payload)\n    \n    # Request the data output\n    info = json.loads(response.text)[\"data\"]\n    \n    # Create a List for Columns and Values\n    market_data = []\n    \n    # Create List for Columns\n    market_columns = []\n    \n    # Retrieve Field Names\n    for column_names in info[0]:\n        market_columns.append(column_names)\n        \n    # Append to Full List\n    market_data.append(market_columns)\n    \n    # Retrieve all information for each grouping\n    x = 0\n    while x < len(info):\n        market_values = []\n        for columns in market_columns:\n            market_values.append(info[x][columns])\n        x += 1\n        market_data.append(market_values) \n        \n    # When Function is called, return the Full List with Column and Values   \n    return market_data\n\n### Coin Assets Function\ndef coin_assets():\n    # Request market APIS\n    url = \"https://api.coincap.io/v2/assets\"\n    response = requests.request(\"GET\", url, headers=headers, data=payload)\n    \n    # Request the data output\n    info2 = json.loads(response.text)[\"data\"]\n    \n    # Create a List for Columns and Values   \n    assets_data = []\n    \n    # Create List for Columns\n    assets_columns = []\n    \n    # Retrieve Field Names\n    for column_names in info2[0]:\n        assets_columns.append(column_names)\n        \n    # Append to Full List    \n    assets_data.append(assets_columns)\n    \n    # Retrieve all information for each grouping\n    x1 = 0\n    while x1 < len(info2):\n        assets_values = []\n        for columns in assets_columns:\n            assets_values.append(info2[x1][columns])\n        x1 += 1\n        assets_data.append(assets_values) \n        \n    # When Function is called, return the Full List with Column and Values           \n    return assets_data\n\ndef coin_exchanges():\n    # Request market APIS\n    url = \"https://api.coincap.io/v2/exchanges\"\n    response = requests.request(\"GET\", url, headers=headers, data=payload)\n    \n    # Request the data output   \n    info3 = json.loads(response.text)[\"data\"]\n\n    # Create a List for Columns and Values \n    exchanges_data = []\n\n    # Create List for Columns\n    exchanges_columns = []\n    \n    # Retrieve Field Names  \n    for column_names in info3[0]:\n        exchanges_columns.append(column_names)\n        \n    # Append to Full List            \n    exchanges_data.append(exchanges_columns)\n    \n    # Retrieve all information for each grouping\n    x2 = 0\n    while x2 < len(info3):\n        exchanges_values = []\n        for columns in exchanges_columns:\n            exchanges_values.append(info3[x2][columns])\n        x2 += 1\n        exchanges_data.append(exchanges_values) \n    \n    # When Function is called, return the Full List with Column and Values\n    return exchanges_data\n\n# Assigns output to variables\n# In this case, returns lists to deisgnated variables\ncoin_market = coin_markets()\ncoin_assets = coin_assets()\ncoin_exchanges = coin_exchanges()\n \n\n\n# Save Files\nnp.savetxt(staging_folder+f\"\\coin_markets_{date}.csv\",coin_market, delimiter =\", \", fmt ='% s')\n#np.savetxt(f\"C:\\dev\\csdev\\coin_assets_info1_{date}.csv\",coin_market, delimiter =\", \", fmt ='% s')\nnp.savetxt(staging_folder+f\"\\coin_assets_{date}.csv\",coin_assets, delimiter =\", \", fmt ='% s')\nnp.savetxt(staging_folder+f\"\\coin_exchanges_{date}.csv\",coin_exchanges,delimiter =\", \", fmt ='% s')\n\n","repo_name":"0devs-do/executive-query-engine","sub_path":"api-data-extraction/CCApiExtractFull.py","file_name":"CCApiExtractFull.py","file_ext":"py","file_size_in_byte":3956,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"19362126286","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nData collection from FinnHub.\n\"\"\"\n\nimport finnhub as fh\nimport pandas as pd\nfrom datetime import datetime\nfrom datetime import timezone\nimport time\n\nfinnhub_client = fh.Client(api_key=\"br4je57rh5r8ufeothr0\")\n\n# Helper functions\ndef DateToUnix(date):\n    return int(date.replace(tzinfo=timezone.utc).timestamp())\n        \ndef FlattenStatementDF(statement_df):\n    list_of_dict = list(statement_df.financials)\n    return pd.DataFrame(list_of_dict)\n\ndef MergeStatementDF(statement_df_list):\n    output = statement_df_list[0].merge(statement_df_list[1],on=\"period\")\n    output = output.merge(statement_df_list[2],on=\"period\")\n    output = output.set_index('period')\n    return output\n\ndef SaveOutput(filename_prefix,**kwargs):\n    for df_name, df in kwargs.items():\n        filename = filename_prefix + '_' + df_name + '.csv'\n        df.to_csv(filename)\n\ndef FinnHubCode(ticker,market):\n    return ticker + '.' + market\n\ndef GetFailedCompanies(failed_ticker_list,ticker_company_table,reason_failed):\n    print(\"Couldn't get\",reason_failed,\"data for\",len(failed_ticker_list),\"companies.\")\n    output = pd.DataFrame(columns=['ticker','company','reason_failed'])\n    for ticker in failed_ticker_list:\n        company = ticker_company_table.company.loc[ticker_company_table.finnhub == ticker]\n        row = {'ticker':ticker,'company':company,'reason_failed':reason_failed}\n        output = output.append(row,ignore_index=True)\n    return output\n    \n# FinnHub API functions\ndef GetPrice(client,ticker,start_unix,end_unix):\n    candles = client.stock_candles(ticker, 'D', start_unix, end_unix)\n    if candles['s'] == 'no_data':\n        print(\"Could not find price data for\",ticker)\n        return None\n    else:\n        return pd.DataFrame(candles)\n\ndef GetFinancials(client,ticker,frequency,backtest_start_date):\n    def _GetRelevantStatementIndex_(statement_df,backtest_start_date):\n        # Flag if date of statement is within backtesting period\n        def CheckDate(date_to_check,date_window):\n            date_to_check = datetime.strptime(date_to_check, '%Y-%m-%d')\n            return date_to_check >= date_window\n        \n        index = 0\n        while index < len(statement_df):\n            statement_date = statement_df.iloc[index][0]['period']\n            if CheckDate(statement_date,backtest_start_date):\n                index += 1\n            else:\n                return index + 1 # Include first statement out of period\n        return index\n    \n    statement_type_list = ['bs','ic','cf']\n    statement_df_list = []\n    for statement_type in statement_type_list:\n        statement_data_full = client.financials(ticker,statement_type,frequency)\n        if statement_data_full['financials'] is None:\n            print(\"Could not find financial data for\",ticker)\n            return None\n        else:\n            statement_data_full = pd.DataFrame(statement_data_full)\n            statement_relevant_index = _GetRelevantStatementIndex_(statement_data_full,backtest_start_date)\n            statement_data_relevant = statement_data_full.iloc[:statement_relevant_index]\n            statement_df_list.append(FlattenStatementDF(statement_data_relevant))\n    return MergeStatementDF(statement_df_list)\n    \n\n# Backtest parameters\nstart_date = datetime(2019,7,1) # 6 months prior to Covid-19\nend_date = datetime(2020,8,7)\n\n# Calculate UNIX Timestamps\n\"\"\"\nAs UNIX timestamps are for midnight, the end_unix will give prices for day BEFORE end_date\n\"\"\"\nstart_unix = DateToUnix(start_date)\nend_unix = DateToUnix(end_date)\n\n# Load finnhub codes for multiple investment universes\nhong_kong = pd.read_excel('investment_universe.xlsx',sheet_name=\"HK\")\nunited_kingdom = pd.read_excel('investment_universe.xlsx',sheet_name=\"GB\")\ndenmark = pd.read_excel('investment_universe.xlsx',sheet_name=\"DK\")\nitaly = pd.read_excel('investment_universe.xlsx',sheet_name=\"IT\")\nunited_states = pd.read_excel('investment_universe.xlsx',sheet_name=\"US\")\n\n# Group by country\nmarkets = {}\nmarkets[\"HK\"] = hong_kong\nmarkets[\"L\"] = united_kingdom\nmarkets[\"CO\"] = denmark\nmarkets[\"MI\"] = italy\nmarkets[\"US\"] = united_states\n\n# Get price data for investment universe\nmaster_price_data = pd.DataFrame(columns=['c', 'h', 'l', 'o', 's', 't', 'v','ticker','market'])\nmaster_financial_data = pd.DataFrame()\nfailed_company_data = pd.DataFrame(columns=['ticker','company','reason_failed'])\n\nfor market, universe in markets.items():\n    ticker_no_price_data = [] # Reconciliation\n    ticker_no_financial_data = [] # Reconciliation\n    \n    # ## Debug\n    # if market == 'HK'or market == 'L' or market == 'CO' or market == 'MI': #  \n    #     continue\n    # ##\n    \n    # Get list of stocks\n    ticker_list = list(universe.finnhub)\n    print(\"\\n\\nExtracting data for\",market,\"\\n\\n\")\n    for ticker in ticker_list:\n        # Get price data\n        print(\"Extracting\",ticker,\"price\")\n        stock_price_data = GetPrice(finnhub_client,ticker,start_unix,end_unix)\n        if stock_price_data is not None:\n            stock_price_data['ticker'] = ticker\n            stock_price_data['market'] = market\n            # Add to master data DF\n            master_price_data = master_price_data.append(stock_price_data)\n        else:\n            ticker_no_price_data.append(ticker)\n        \n        # Get financial statement data\n        print(\"Extracting\",ticker,\"financials\")\n        stock_financial_data = GetFinancials(finnhub_client,ticker,'quarterly',start_date)\n        if stock_financial_data is not None:\n            stock_financial_data['ticker'] = ticker\n            stock_financial_data['market'] = market\n            # Add to master data DF\n            master_financial_data = master_financial_data.append(stock_financial_data)\n        else:\n            ticker_no_financial_data.append(ticker)\n            \n        # Sleep every 10 tickers to avoid timeout\n        if (ticker_list.index(ticker)+1) % 10 == 0:\n            completion_percent = ((ticker_list.index(ticker)+1) / len(ticker_list))*100\n            print(\"\\n\",str(completion_percent),\"% complete\\n\")\n            time.sleep(60)\n        \n    # Save companies that failed to download for price and financials\n    failed_company_data = failed_company_data.append(GetFailedCompanies(ticker_no_price_data,universe,\"price\"))\n    failed_company_data = failed_company_data.append(GetFailedCompanies(ticker_no_financial_data,universe,\"financials\"))\n    SaveOutput(market,failed_company_data = failed_company_data)\n    \n    \n    # Save price data as excel\n    print(\"\\n\\nSaving Checkpoint...\")\n    filename = 'checkpoint_' + market\n    SaveOutput(filename,\n               price_data = master_price_data,\n               financial_data = master_financial_data)\n    time.sleep(60)\n    \n# Save price data as excel\nprint(\"\\n\\nSaving data...\")\nSaveOutput('master',\n           price_data = master_price_data,\n           financial_data = master_financial_data)\n","repo_name":"SJDunkelman/applied-project-msc","sub_path":"Data/market_data/finnhub_data_extract.py","file_name":"finnhub_data_extract.py","file_ext":"py","file_size_in_byte":6910,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"3155693900","text":"class Solution:\n    def groupAnagrams(self, strs: list[str]) -> list[list[str]]:\n        biggerAngList = []\n        angDict = dict()\n        for i in strs:\n            t = \"\".join(sorted(i))\n            if t in angDict:\n                # if i not in angDict[t]:\n                angDict[t].append(i)\n            else:\n                angDict[t] = [i]\n        for value in angDict.values():\n            biggerAngList.append(value)\n        return biggerAngList","repo_name":"mduruji/Blind_75","sub_path":"Arrays_&_Hashing/groupAnagram.py","file_name":"groupAnagram.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"9392429373","text":"import json\nimport os\nimport sys\nimport unittest\n\nimport mox\n\ncwd = os.getcwd()\nos.chdir(\"..\")\nsys.path.append(os.getcwd())\nfrom smartagent import SmartAgent\nfrom smartagent_messaging import MessagingService\nfrom check_mysql_status import MySqlChecker\nfrom command_handler import MysqlCommandHandler\nos.chdir(cwd)\n\n\nclass SmartAgentUnitTest(unittest.TestCase):\n\n    def setUp (self):\n        self.mox = mox.Mox()\n        self.mock_msg_service = self.mox.CreateMock(MessagingService)\n        self.agent = SmartAgent(self.mock_msg_service)\n\n    def test_unsupported_method(self):\n        \"\"\"Test to see that a message with an unsupported method generates\n           the correct response.\"\"\"\n\n        message = r'''{\"method\": \"unsupported\"}'''\n        result = self.agent.process_message(msg=json.loads(message))\n        self.mox.ReplayAll()\n\n        self.assertEqual(result, {'failure': 'unsupported_method', 'result': None})\n        self.mox.VerifyAll()\n\n    def test_missing_method(self):\n        \"\"\"Test to see that a missing method key generates an error.\"\"\"\n\n        message = r'''{\"not_method\": \"test\"}'''\n        result = self.agent.process_message(msg=json.loads(message))\n        self.mox.ReplayAll()\n\n        self.assertEqual(result, {'failure': 'missing_method', 'result': None})\n        self.mox.VerifyAll()\n\n    def test_check_mysql_status_running(self):\n        \"\"\"Test to see that the correct response is returned when a MySQL\n           server is running.\"\"\"\n\n        self.agent.checker = self.mox.CreateMock(MySqlChecker)\n        self.mox.StubOutWithMock(self.agent.checker, \"check_if_running\")\n        self.agent.checker.check_if_running(number_of_checks=5,\n                                            sleep_time_seconds=3).AndReturn(True)\n        self.mox.ReplayAll()\n\n        result = self.agent.check_status()\n        self.assertEqual(result, 1)\n        self.mox.VerifyAll()\n\n    def test_check_mysql_status_not_running(self):\n        \"\"\"Test to see that the correct response is returned when a MySQL\n           server is not running.\"\"\"\n\n        self.agent.checker = self.mox.CreateMock(MySqlChecker)\n        self.mox.StubOutWithMock(self.agent.checker, \"check_if_running\")\n        self.agent.checker.check_if_running(number_of_checks=5,\n                                            sleep_time_seconds=3).AndReturn(False)\n        self.mox.ReplayAll()\n\n        result = self.agent.check_status()\n        self.assertEqual(result, 0)\n        self.mox.VerifyAll()\n\n    def test_create_database(self):\n        \"\"\"Test to see that the correct response is returned from a create\n           database operation.\"\"\"\n\n        msg = json.loads(r\"\"\"{\"args\": {\"database\": \"test\"}}\"\"\")\n        self.agent.handler = self.mox.CreateMock(MysqlCommandHandler)\n        self.mox.StubOutWithMock(self.agent.handler, \"create_database\")\n        self.agent.handler.create_database(msg['args']['database']).AndReturn(\"SUCCESS\")\n        self.mox.ReplayAll()\n\n        result = self.agent.create_database(msg)\n        self.assertEqual(result, \"SUCCESS\")\n        self.mox.VerifyAll()\n\n\n\n    def tearDown(self):\n        self.mox.UnsetStubs()\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"CaptTofu/reddwarf","sub_path":"smartagent/tests/test_smart_agent.py","file_name":"test_smart_agent.py","file_ext":"py","file_size_in_byte":3179,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"48"}
+{"seq_id":"4242088063","text":"from bitcoinaddress import Wallet\nimport requests\nimport json\nimport time\n\ncounter = 0\ndef getRich():\n    wallet = Wallet()\n    wa = str(wallet)\n    wa1 = wa.splitlines(False)\n    wal1 = str(wa1[13])\n    wal2 = wal1.split(sep=\":\")\n    qaddr = wal2[-1]\n    qaddr1 = qaddr.split(sep=\" \")\n    qwerywallet = str(qaddr1[1])\n    print(qwerywallet)\n\n    url = \"https://blockchain.info/rawaddr/\"+qwerywallet\n    btc_addr = requests.get(url)\n    dict_addr = json.loads(btc_addr.text)\n    balance = dict_addr.get(\"final_balance\")\n\n    if balance != \"\":\n        datei = \"wallet\"+str(counter)+\".txt\"\n        dat = open(datei, \"wt\")\n        dat.write(wa)\n        dat.close()\n    else:\n        counter +1\n        print(counter)\n        time.sleep(15) \n        # nicht unter 11 Secunden da sonst die Anfragen von der IP geblockt werden\n        getRich()\ngetRich()","repo_name":"mfgich/walletfinder","sub_path":"wallet_gen.py","file_name":"wallet_gen.py","file_ext":"py","file_size_in_byte":848,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"4971952370","text":"from .text import Text\n\n\nclass Link(Text):\n\n    def __init__(self, link: str, text: str,\n                 name='', priority=1,\n                 x1=0, y1=0,\n                 x2=0, y2=0,\n                 background='', foreground='', **kwargs):\n        super(Link, self).__init__(name=name, priority=priority,\n                                   x1=x1, y1=y1,\n                                   x2=x2, y2=y2,\n                                   background=background, foreground=foreground)\n        self.setTextPropierties(**kwargs)\n\n        self.setText(text)\n        self.link = link\n\n    def render(self, pdf):\n        if pdf.text_color is not self.rgb(self.foreground):\n            pdf.set_text_color(*self.rgb(self.foreground))\n        self.font = self.font.strip().lower()\n        if self.font == 'arial black':\n            self.font = 'arial'\n        style = \"\"\n        for tag in 'B', 'I', 'U':\n            if self.text.startswith(\"<%s>\" % tag) and self.text.endswith(\"</%s>\" % tag):\n                self.text = self.text[3:-4]\n                style += tag\n        if self.bold:\n            style += \"B\"\n        if self.italic:\n            style += \"I\"\n        if self.underline:\n            style += \"U\"\n        pdf.set_font(self.font, style, self.size)\n        #  m_k = 72 / 2.54\n        #  h = (size/m_k)\n\n        pdf.set_xy(self.pointUp[0], self.pointUp[1])\n        pdf.write(5, self.text, self.link)","repo_name":"inprojectspl/pyfpdf","sub_path":"fpdf/Elements/link.py","file_name":"link.py","file_ext":"py","file_size_in_byte":1408,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"764181825","text":"import plotly.express as px\r\nimport pandas as pd\r\nimport streamlit as st\r\n\r\n\r\ndef main():\r\n    st.title(\"Jadual Sifir\")\r\n\r\n    choice = st.sidebar.selectbox('Menu', ['Home', 'Tentang'])\r\n    if choice == 'Home':\r\n        st.subheader('Home')\r\n\r\n        # Layout \r\n        col1, col2 = st.columns([1,2])\r\n\r\n        with col1:\r\n            with st.form(key = 'myform'):\r\n                number = st.text_input('Enter Number')\r\n                end_number = st.number_input('End Number', min_value=12, max_value=200)\r\n                submit_button = st.form_submit_button(label='Submit')\r\n\r\n            if submit_button:\r\n                with col2:\r\n                    with st.expander('Times Table'):\r\n                        for i in range(1, (end_number + 1)):\r\n                            answer = i * int(number)\r\n                            st.write(f\"{i} x {number} = {answer}\")\r\n                    \r\n                    # Times Table as DataFrame\r\n                    range_num = list(range(1, end_number))\r\n                    multiplication = [f\"{i} x {number} = \" for i in range(1, end_number)]\r\n                    answer = [i * int(number) for i in range(1, end_number)]\r\n                    df = pd.DataFrame({'Numbers' : range_num, 'Multiplication' : multiplication, 'Answer' : answer})\r\n                    st.dataframe(df)\r\n\r\n                    # Plot\r\n                    p1 = px.bar(df, x = 'Numbers' , y = 'Answer', color = 'Numbers')\r\n                    st.plotly_chart(p1)\r\n\r\n\r\nif __name__ == '__main__':\r\n    main()","repo_name":"NizarMazlan/python-mini-project","sub_path":"streamlit/sifir.py","file_name":"sifir.py","file_ext":"py","file_size_in_byte":1538,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"4998551174","text":"#!/usr/bin/env  python3\n\n############################################################################################\n#                                                                                          #\n#       Program purpose: Obtain (print) unique values from a dictionary.                   #\n#       Program Author : Happi Yvan <ivensteinpoker@gmail.com>                             #\n#       Creation Date  : December 10, 2019                                                 #\n#                                                                                          #\n############################################################################################\n\nimport random\n\ndef get_random_dict(low: int, high: int, size: int) -> dict:\n    if size < 0:\n        raise ValueError('Invalid size for new dictionary. Must be > 0')\n    rand_keys = [random.randint(low, high) for _ in range(size)]\n    rand_values = [random.randint(low, high) for _ in range(size)]\n\n    return {k: v for (k, v) in zip(rand_keys, rand_values)}\n\ndef dict_empty(some_dict: dict) -> bool:\n    return len(some_dict.keys()) == 0\n\ndef get_unique_values(some_dict: dict) -> list:\n    if dict_empty(some_dict=some_dict):\n        return []\n    unique_values = []\n    for val in some_dict.values():\n        if val not in unique_values:\n            unique_values.append(val)\n\n    return unique_values\n\ndef get_unique_values_from_dicts_list(list_of_dicts: list) -> list:\n    lists_of_uniques = []\n    for sub_dict in list_of_dicts:\n        if not dict_empty(some_dict=sub_dict):\n            lists_of_uniques.append(get_unique_values(some_dict=sub_dict))\n    return lists_of_uniques\n\nif __name__ == \"__main__\":\n\n    new_dict = get_random_dict(low=0, high=20, size=10)\n    print(f'  New dictionary is: {new_dict}')\n    print(f'Original values are: {list(new_dict.values())}')\n    print(f'  Unique values are: {get_unique_values(some_dict=new_dict)}')\n\n    print()\n    print('-' * 15, ' [New Operations] ', '-' * 15)\n\n    dicts_list = [\n        get_random_dict(low=0, high=10, size=10),\n        get_random_dict(low=0, high=10, size=10),\n        get_random_dict(low=0, high=10, size=10),\n    ]\n    print(f'List of dictionaries: {dicts_list}')\n    print(f'List of uniques: {get_unique_values_from_dicts_list(list_of_dicts=dicts_list)}')","repo_name":"ivenpoker/Python-Projects","sub_path":"Projects/Online Workouts/w3resource/Dictionary/program-20.py","file_name":"program-20.py","file_ext":"py","file_size_in_byte":2311,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"42540858210","text":"# Token creates tokens for different characters in user input\r\n# Different token types include number, operation, expression, unexpected\r\n# Different tokens will have a token type and a value associated with it\r\n\r\nimport enum\r\n\r\nclass Token:\r\n    def __init__(self, token_type, value) -> None:\r\n        self.token_type = token_type\r\n        self.value = value\r\n    \r\n    def is_arithmetic_operation(self):\r\n        OPERATIONS = '+-*/'\r\n        if self.value in OPERATIONS:\r\n            return True\r\n        else:\r\n            return False\r\n\r\nclass TokenType(enum):\r\n    NUMBER = 'number'\r\n    OPERATION = 'operation'\r\n    EXPRESSION = 'expression'\r\n    UNEXPECTED = 'unexpected'\r\n\r\n# parses user input into different tokens\r\nclass Tokenizer:\r\n    NUMBERS = '1234567890'\r\n    \r\n    tokens = [] # possible user input tokens\r\n\r\n    # pass user input and set current character and current position\r\n    def __init__(self, text) -> None:\r\n        self.text = text\r\n        self.current_pos = -1\r\n        self.current_char = '\\0'\r\n        self.advance()\r\n    \r\n    # proceeds to next character in input\r\n    def advance(self) -> None:\r\n        self.current_pos += 1\r\n        if self.current_pos < len(self.text): # if there is still character left, advance to next character in input\r\n            self.current_char = self.text.index(self.current_pos) # get the character at the current position (index)\r\n\r\n    # simplifies down one expression at a time according to PEMDAS\r\n    def simplify(input):\r\n        if '(' in input: # input still has ()\r\n            pass\r\n        else:\r\n            pass\r\n\r\n    def make_tokens(self):\r\n        OPERATIONS = '+-*/'\r\n\r\n        if self.current_char == ' ': # skip spaces\r\n            self.advance()\r\n        elif self.current_char in self.NUMBERS: # if the current character is a number\r\n            self.tokens.append(self.make_number())\r\n            self.advance()\r\n        elif self.current_char in OPERATIONS: # if the current character is an operation\r\n            self.tokens.append(Token(TokenType.OPERATION, self.current_char))\r\n            self.advance()\r\n        elif self.current_char == '(': # push until ')' is met\r\n            unedited = self.text[self.current_position] # slicing operation\r\n            # cut off expression into 2 from beginning to this point, pass in ()\r\n            self.simplify(self.text[self.current_postion+1:len(self.text)+1]) # slicing string from 1 after start of ( to end of string\r\n            self.make_expression()\r\n        \r\n\r\n    # continues building a number until terminating character reached\r\n    def make_number(self):\r\n        number = ' '\r\n        while self.current_char in self.NUMBERS and self.current_char != '\\0': # while there is still a number\r\n            number += self.current_char\r\n        return Token(TokenType.NUMBER, number)\r\n    \r\n    # finish ?\r\n    def make_expression(self):\r\n        while self.curr_char != ')':\r\n            return","repo_name":"smtono/python-calculator","sub_path":"token.py","file_name":"token.py","file_ext":"py","file_size_in_byte":2938,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"4022468450","text":"from programme.joueur.joueur import Joueur\nfrom programme.utile.ConfirmRetour import confirmRetour\nfrom programme.utile.colorfull import textcolor\nfrom programme.utile.mrPropre import mrPropre\nfrom programme.utile.saisieNombre import saisieInt\nfrom programme.utile.score import incrementScore\n\n\ndef afficherPlateau(plateau: list[list[str]]):\n    \"\"\"Affiche le plateau de jeu du Puissance 4\n\n    Args:\n        plateau (list[list[str]]): Plateau de Jeu\n    \"\"\"\n    print(\" 1 2 3 4 5 6 7\")\n    for ligne in plateau:\n        print(\"|\", end=\"\")\n        for case in ligne:\n            print(case, end=\"|\")\n        print()\n    print()\n\n\ndef saisirColonne() -> int:\n    \"\"\"\nSaisie d'une colonne\n\n    Returns:\n        int: Colonne saisie\n    \"\"\"\n    colonne: int = saisieInt(\"Choisissez une colonne : \")\n    while colonne < 1 or colonne > 7:\n        print(\"Colonne invalide\")\n        colonne = saisieInt(\"Choisissez une colonne : \")\n    return colonne - 1\n\n\ndef placerPion(plateau: list[list[str]], pion: str):\n    \"\"\"Place un pion dans la colonne donnée\n\n    Args:\n        plateau (list[list[str]]): Plateau\n        pion (str): Chaine affichée en tant que pion\n    \"\"\"\n    i: int = 5\n    colonne: int = saisirColonne()\n\n    while plateau[i][colonne] != \" \":  # Tant que la case est occupée\n        i -= 1\n        if i == -1:\n            print(\"Colonne pleine\")\n            colonne = saisirColonne()\n            i = 5\n        if colonne < 0 or colonne > 6:\n            print(\"Colonne invalide\")\n            colonne = saisirColonne()\n            i = 5\n\n    plateau[i][colonne] = pion\n\n\ndef pionAligne(plateau: list[list[str]], pion: str) -> bool:\n    \"\"\"Vérifie si un pion est aligné\n\n    Args:\n        plateau (list[list[str]]): Plateau de Jeu\n        pion (str): Chaine affichée en tant que pion\n\n    Returns:\n        bool: Vrai si le pion est aligné\n    \"\"\"\n    # Vérification horizontale\n    for ligne in plateau:\n        for i in range(4):\n            if ligne[i] == pion and ligne[i + 1] == pion and ligne[i + 2] == pion and ligne[i + 3] == pion:\n                return True\n\n    # Vérification verticale\n    for j in range(7):\n        for i in range(3):\n            if plateau[i][j] == pion and plateau[i + 1][j] == pion and plateau[i + 2][j] == pion \\\n                    and plateau[i + 3][j] == pion:\n                return True\n\n    # Vérifications diagonales\n    for i in range(3):\n        for j in range(4):\n            if plateau[i][j] == pion and plateau[i + 1][j + 1] == pion and plateau[i + 2][j + 2] == pion and \\\n                    plateau[i + 3][j + 3] == pion:\n                return True\n\n    for i in range(3):\n        for j in range(3, 7):\n            if plateau[i][j] == pion and plateau[i + 1][j - 1] == pion and plateau[i + 2][j - 2] == pion and \\\n                    plateau[i + 3][j - 3] == pion:\n                return True\n\n    return False\n\n\ndef plateauPlein(plateau: list[list[str]]) -> bool:\n    \"\"\"Vérifie si le plateau est plein\n\n    Args:\n        plateau (list[list[str]]): Plateau de jeu\n\n    Returns:\n        bool: Vrai si le plateau est plein\n    \"\"\"\n    for ligne in plateau:\n        for case in ligne:\n            if case == \" \":\n                return False\n    return True\n\n\ndef tourPuissance4(joueur1: Joueur, joueur2: Joueur):\n    \"\"\"Joue une partie de puissance 4 entre deux joueurs\n\n    Args:\n        joueur1 (Joueur): Joueur 1\n        joueur2 (Joueur): Joueur 2\n    \"\"\"\n\n    plateau: list[list[str]] = [[\" \" for _ in range(7)] for _ in range(6)] #Taille de 7*6 \n    pionJ: str = textcolor.YELLOW + 'O' + textcolor.DEFAULT\n    pionR: str = textcolor.RED + 'O' + textcolor.DEFAULT\n    aligne: bool = False\n\n    mrPropre()\n    print(joueur1.pseudo, \"joueur avec les\", pionJ) #On indique à chaue joueur ses pions\n    print(joueur2.pseudo, \"joueur avec les\", pionR)\n\n    afficherPlateau(plateau)\n\n    while not aligne and not plateauPlein(plateau): #On joue tant qu'il n'y a ni égalité ni victoire\n        print(joueur1.pseudo + \", joue !\")\n        placerPion(plateau, pionJ)\n        mrPropre()\n        afficherPlateau(plateau)\n        aligne = pionAligne(plateau, pionJ)\n        if aligne:\n            print(textcolor.GREEN + \"Le joueur \" +\n                  joueur1.pseudo + \" a gagné !\" + textcolor.DEFAULT + \"\\n\")\n            incrementScore(joueur1, \"puissance 4\")\n        if plateauPlein(plateau) or aligne:\n            break\n\n        print(joueur2.pseudo + \", joue !\")\n        placerPion(plateau, pionR)\n        mrPropre()\n        afficherPlateau(plateau)\n        aligne = pionAligne(plateau, pionR)\n        if aligne:\n            print(textcolor.GREEN + \"Le joueur \" +\n                  joueur2.pseudo + \" a gagné !\" + textcolor.DEFAULT + \"\\n\")\n            incrementScore(joueur2, \"puissance 4\")\n\n    if plateauPlein(plateau):\n        print(textcolor.CYAN + \"Match nul !\" + textcolor.DEFAULT + \"\\n\")\n\n    confirmRetour()\n","repo_name":"delmat238/S1.01","sub_path":"programme/puissance4/fonctionPuissance4.py","file_name":"fonctionPuissance4.py","file_ext":"py","file_size_in_byte":4879,"program_lang":"python","lang":"fr","doc_type":"code","stars":2,"dataset":"github-code","pt":"48"}
+{"seq_id":"35033711119","text":"import os\n\nimport argparse\n\nimport input_generation.matrix_generation\nimport input_generation.docter_argument_generation\n\nimport running_utils\n\nfrom config import DATA_DIR, TF_NAME, PT_NAME, NUM_OF_ARGS, NUM_OF_INPUT\n\nARGS_DONE_COLS = ['lib', 'version', 'api']\nARGS_DONE_FILE = \"args_gen_done.csv\"\n\n\ndef main():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"lib\", choices=['tensorflow', 'pytorch'])\n    parser.add_argument(\"version\", type=str)\n    args = parser.parse_args()\n    config = vars(args)\n\n    lib = config['lib']\n    version = config['version']\n\n    src_dir = os.path.join(os.getcwd(), '..')\n    # address for generated inputs\n    out_dir = os.path.join(DATA_DIR, \"generated_input\")\n    if not os.path.exists(out_dir):\n        os.makedirs(out_dir)\n\n    # create a done file if it doesn't exist\n    args_done_set, args_done_out_f = running_utils.setup_done_file(out_dir, ARGS_DONE_FILE, ARGS_DONE_COLS)\n    args_done_out_f.close()\n\n    # load list of APIs to generate inputs for\n    tf_api_list = running_utils.load_list(os.path.join('input_generation', 'input_gen_tensorflow_api_list.txt'))\n    pt_api_list = running_utils.load_list(os.path.join('input_generation', 'input_gen_pytorch_api_list.txt'))\n\n    apis_map = {\n        TF_NAME: tf_api_list,\n        PT_NAME: pt_api_list\n    }\n\n    # generate inputs for each API\n    for api in apis_map[lib]:\n        if (lib, version, api) not in args_done_set:\n            print(\"Generating args for %s %s %s\" % (lib, version, api))\n\n            args_gen_time = input_generation.docter_argument_generation.generate_single_api_argument(\n                out_dir, src_dir, lib, version, api, NUM_OF_ARGS)\n            conf = {\n                'lib': lib,\n                'version': version,\n                'api': api\n            }\n\n            running_utils.write_done(conf, args_gen_time, out_dir, ARGS_DONE_FILE, ARGS_DONE_COLS)\n\n    # generating 4D matrix inputs\n    if not os.path.exists(os.path.join(out_dir, 'input')):\n        print(\"Generating 4D input\")\n        input_generation.matrix_generation.generate_single_api_input(out_dir, NUM_OF_INPUT)\n    \n    if lib == TF_NAME:\n        from gen_tf_model_input import save_model_and_inputs\n    else:\n        from gen_pt_model_input import save_model_and_inputs\n    save_model_and_inputs()\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"lin-tan/eagle","sub_path":"EAGLE/generate_all_input.py","file_name":"generate_all_input.py","file_ext":"py","file_size_in_byte":2350,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"48"}
+{"seq_id":"12083142225","text":"\nimport time\n\nfrom .response import AncillaResponse, AncillaError\nfrom .request import BaseRequest\nfrom .router import Route, Router, lazy_attribute, RouterError, getargspec\n\nfrom ..utils.dict import DictProperty, ConfigDict\n\nfrom ..utils import makelist, cached_property, yields, call_maybe_yield\n\nimport sys\nimport io, base64, cgi, email.utils, functools, hmac, imp, itertools,\\\n        os, re, tempfile, time, warnings\n\n\n\nDEBUG = False\n\n\ndef load(target, **namespace):\n    \"\"\" Import a module or fetch an object from a module.\n\n        * ``package.module`` returns `module` as a module object.\n        * ``pack.mod:name`` returns the module variable `name` from `pack.mod`.\n        * ``pack.mod:func()`` calls `pack.mod.func()` and returns the result.\n\n        The last form accepts not only function calls, but any type of\n        expression. Keyword arguments passed to this function are available as\n        local variables. Example: ``import_string('re:compile(x)', x='[a-z]')``\n    \"\"\"\n    module, target = target.split(\":\", 1) if ':' in target else (target, None)\n    if module not in sys.modules: __import__(module)\n    if not target: return sys.modules[module]\n    if target.isalnum(): return getattr(sys.modules[module], target)\n    package_name = module.split('.')[0]\n    namespace[package_name] = sys.modules[package_name]\n    return eval('%s.%s' % (module, target), namespace)\n\n\n\n\nclass WSGIFileWrapper(object):\n    def __init__(self, fp, buffer_size=1024 * 64):\n        self.fp, self.buffer_size = fp, buffer_size\n        for attr in ('fileno', 'close', 'read', 'readlines', 'tell', 'seek'):\n            if hasattr(fp, attr): setattr(self, attr, getattr(fp, attr))\n\n    # def __iter__(self):\n    #     buff, read = self.buffer_size, self.read\n    #     while True:\n    #         part = read(buff)\n    #         if not part: return\n    #         yield part\n\n    def __aiter__(self):\n        return self\n\n    async def __anext__(self):\n        data = await self.fetch_data()\n        if data is not None:\n            return data\n        else:\n            raise StopAsyncIteration\n\n    async def fetch_data(self):\n        buff, read = self.buffer_size, self.read\n        part = read(buff)\n        if not part: \n            if hasattr(self, \"close\"): self.close()\n            return None\n        return part\n        \n        # return part\n        # while not self.queue.empty():\n        #     self.done.append(self.queue.get_nowait())\n        # if not self.done:\n        #     return None\n        # return self.done.pop(0)\n\n\n# request = LocalRequest()\n\ndef yieldroutes(func):\n    \"\"\" Return a generator for routes that match the signature (name, args)\n    of the func parameter. This may yield more than one route if the function\n    takes optional keyword arguments. The output is best described by example::\n\n        a()         -> '/a'\n        b(x, y)     -> '/b/<x>/<y>'\n        c(x, y=5)   -> '/c/<x>' and '/c/<x>/<y>'\n        d(x=5, y=6) -> '/d' and '/d/<x>' and '/d/<x>/<y>'\n    \"\"\"\n    path = '/' + func.__name__.replace('__', '/').lstrip('/')\n    spec = getargspec(func)\n    argc = len(spec[0]) - len(spec[3] or [])\n    path += ('/<%s>' * argc) % tuple(spec[0][:argc])\n    yield path\n    for arg in spec[0][argc:]:\n        path += '/<%s>' % arg\n        yield path\n\ndef path_shift(script_name, path_info, shift=1):\n    \"\"\" Shift path fragments from PATH_INFO to SCRIPT_NAME and vice versa.\n\n        :return: The modified paths.\n        :param script_name: The SCRIPT_NAME path.\n        :param script_name: The PATH_INFO path.\n        :param shift: The number of path fragments to shift. May be negative to\n          change the shift direction. (default: 1)\n    \"\"\"\n    if shift == 0: return script_name, path_info\n    pathlist = path_info.strip('/').split('/')\n    scriptlist = script_name.strip('/').split('/')\n    if pathlist and pathlist[0] == '': pathlist = []\n    if scriptlist and scriptlist[0] == '': scriptlist = []\n    if 0 < shift <= len(pathlist):\n        moved = pathlist[:shift]\n        scriptlist = scriptlist + moved\n        pathlist = pathlist[shift:]\n    elif 0 > shift >= -len(scriptlist):\n        moved = scriptlist[shift:]\n        pathlist = moved + pathlist\n        scriptlist = scriptlist[:shift]\n    else:\n        empty = 'SCRIPT_NAME' if shift < 0 else 'PATH_INFO'\n        raise AssertionError(\"Cannot shift. Nothing left from %s\" % empty)\n    new_script_name = '/' + '/'.join(scriptlist)\n    new_path_info = '/' + '/'.join(pathlist)\n    if path_info.endswith('/') and pathlist: new_path_info += '/'\n    return new_script_name, new_path_info        \n\nclass App(object):\n    \"\"\" Each Bottle object represents a single, distinct web application and\n        consists of routes, callbacks, plugins, resources and configuration.\n        Instances are callable WSGI applications.\n\n        :param catchall: If true (default), handle all exceptions. Turn off to\n                         let debugging middleware handle exceptions.\n    \"\"\"\n\n    @lazy_attribute\n    def _global_config(cls):\n        cfg = ConfigDict()\n        cfg.meta_set('catchall', 'validate', bool)\n        return cfg\n    def __init__(self, *args, **kwargs):\n        #: A :class:`ConfigDict` for app specific configuration.\n        self.config = self._global_config._make_overlay()\n        self.config._add_change_listener(\n            functools.partial(self.trigger_hook, 'config'))\n\n        self.config.update({\n            \"catchall\": False\n        })\n\n        # if kwargs.get('catchall') is False:\n        #     depr(0, 13, \"Bottle(catchall) keyword argument.\",\n        #                 \"The 'catchall' setting is now part of the app \"\n        #                 \"configuration. Fix: `app.config['catchall'] = False`\")\n        #     self.config['catchall'] = False\n        # if kwargs.get('autojson') is False:\n        #     depr(0, 13, \"Bottle(autojson) keyword argument.\",\n        #          \"The 'autojson' setting is now part of the app \"\n        #          \"configuration. Fix: `app.config['json.enable'] = False`\")\n        #     self.config['json.disable'] = True\n\n        self._mounts = []\n\n        #: A :class:`ResourceManager` for application files\n        # self.resources = ResourceManager()\n\n        self.routes = []  # List of installed :class:`Route` instances.\n        self.router = Router()  # Maps requests to :class:`Route` instances.\n        self.error_handler = {}\n\n        # Core plugins\n        self.plugins = []  # List of installed plugins.\n        # self.install(JSONPlugin())\n        # self.install(TemplatePlugin())\n\n    #: If true, most exceptions are caught and returned as :exc:`HTTPError`\n    catchall = DictProperty('config', 'catchall')\n\n    __hook_names = 'before_request', 'after_request', 'app_reset', 'config'\n    __hook_reversed = {'after_request'}\n\n    @cached_property\n    def _hooks(self):\n        return dict((name, []) for name in self.__hook_names)\n\n    def add_hook(self, name, func):\n        \"\"\" Attach a callback to a hook. Three hooks are currently implemented:\n\n            before_request\n                Executed once before each request. The request context is\n                available, but no routing has happened yet.\n            after_request\n                Executed once after each request regardless of its outcome.\n            app_reset\n                Called whenever :meth:`Bottle.reset` is called.\n        \"\"\"\n        if name in self.__hook_reversed:\n            self._hooks[name].insert(0, func)\n        else:\n            self._hooks[name].append(func)\n\n    def remove_hook(self, name, func):\n        \"\"\" Remove a callback from a hook. \"\"\"\n        if name in self._hooks and func in self._hooks[name]:\n            self._hooks[name].remove(func)\n            return True\n\n    def trigger_hook(self, __name, *args, **kwargs):\n        \"\"\" Trigger a hook and return a list of results. \"\"\"\n        # print(\"INSIDE TRIGGER HOOK\")\n        # print(f\"nm: {__name} and args= {args}\", flush=True)\n        # print(f\"hooks = {self._hooks}\", flush=True)\n        return [hook(*args, **kwargs) for hook in self._hooks[__name][:]]\n\n    def hook(self, name):\n        \"\"\" Return a decorator that attaches a callback to a hook. See\n            :meth:`add_hook` for details.\"\"\"\n\n        def decorator(func):\n            self.add_hook(name, func)\n            return func\n\n        return decorator\n\n\n    def reset_app(self):\n        self.router = Router()\n        self.routes = []\n        # self.plugins = []\n\n    def remount_apps(self, apps):\n        self._mounts = []\n        for app in apps:\n            self._mount_wsgi(app.config['_mount.prefix'], app)\n                \n\n    def _mount_wsgi(self, prefix, app, **options):\n        segments = [p for p in prefix.split('/') if p]\n        if not segments:\n            raise ValueError('WSGI applications cannot be mounted to \"/\".')\n        path_depth = len(segments)\n        self._mounts.append(app)\n        app.config['_mount.prefix'] = prefix\n        app.config['_mount.app'] = self\n\n\n        async def mountpoint_wrapper(request, *args, **kwargs):\n            try:\n                # request.path_shift(path_depth)\n                # print(f\"Path dep {path_depth}\", flush=True)\n                request.environ[\"PATH\"] = \"/\" + request.path[len(prefix):]\n\n                rs = request.response\n\n                \n                body = await app(request.environ, rs)\n                # print(f\"BODY = {body}\", flush=True)\n                # print(f'ResponsHeadAFter = {rs.headerlist}', flush=True)\n                if isinstance(body, AncillaResponse):\n                    return body\n                else:\n                    rs.body = body\n                    return rs\n\n            finally:\n                pass\n                # print(\"mountpoint finall\")\n                # request.path_shift(-path_depth)\n\n        options.setdefault('skip', True)\n        options.setdefault('method', 'PROXY')\n        options.setdefault('mountpoint', {'prefix': prefix, 'target': app})\n        options['callback'] = mountpoint_wrapper\n\n        self.route('/%s/<:re:.*>' % '/'.join(segments), **options)\n        # if not prefix.endswith('/'):\n        #     self.route('/' + '/'.join(segments), **options)\n    \n\n    def _mount_app(self, prefix, app, **options):\n        # if app in self._mounts or '_mount.app' in app.config:\n        #     depr(0, 13, \"Application mounted multiple times. Falling back to WSGI mount.\",\n        #          \"Clone application before mounting to a different location.\")\n            \n\n        # if options:\n        #     depr(0, 13, \"Unsupported mount options. Falling back to WSGI mount.\",\n        #          \"Do not specify any route options when mounting bottle application.\")\n        #     return self._mount_wsgi(prefix, app, **options)\n\n        # if not prefix.endswith(\"/\"):\n        #     depr(0, 13, \"Prefix must end in '/'. Falling back to WSGI mount.\",\n        #          \"Consider adding an explicit redirect from '/prefix' to '/prefix/' in the parent application.\")\n        #     return self._mount_wsgi(prefix, app, **options)\n\n        self._mounts.append(app)\n        app.config['_mount.prefix'] = prefix\n        app.config['_mount.app'] = self\n        for route in app.routes:\n            route.rule = prefix + route.rule.lstrip('/')\n            self.add_route(route)\n\n    def mount(self, prefix, app, **options):\n        \"\"\" Mount an application (:class:`Bottle` or plain WSGI) to a specific\n            URL prefix. Example::\n\n                parent_app.mount('/prefix/', child_app)\n\n            :param prefix: path prefix or `mount-point`.\n            :param app: an instance of :class:`Bottle` or a WSGI application.\n\n            Plugins from the parent application are not applied to the routes\n            of the mounted child application. If you need plugins in the child\n            application, install them separately.\n\n            While it is possible to use path wildcards within the prefix path\n            (:class:`Bottle` childs only), it is highly discouraged.\n\n            The prefix path must end with a slash. If you want to access the\n            root of the child application via `/prefix` in addition to\n            `/prefix/`, consider adding a route with a 307 redirect to the\n            parent application.\n        \"\"\"\n\n        if not prefix.startswith('/'):\n            raise ValueError(\"Prefix must start with '/'\")\n\n        # return self._mount_app(prefix, app, **options)\n        return self._mount_wsgi(prefix, app, **options)\n\n    def merge(self, routes):\n        \"\"\" Merge the routes of another :class:`Bottle` application or a list of\n            :class:`Route` objects into this application. The routes keep their\n            'owner', meaning that the :data:`Route.app` attribute is not\n            changed. \"\"\"\n        if isinstance(routes, App):\n            routes = routes.routes\n        for route in routes:\n            self.add_route(route)\n\n    def install(self, plugin):\n        \"\"\" Add a plugin to the list of plugins and prepare it for being\n            applied to all routes of this application. A plugin may be a simple\n            decorator or an object that implements the :class:`Plugin` API.\n        \"\"\"\n        if hasattr(plugin, 'setup'): plugin.setup(self)\n        if not callable(plugin) and not hasattr(plugin, 'apply'):\n            raise TypeError(\"Plugins must be callable or implement .apply()\")\n        self.plugins.append(plugin)\n        self.reset()\n        return plugin\n\n    def uninstall(self, plugin):\n        \"\"\" Uninstall plugins. Pass an instance to remove a specific plugin, a type\n            object to remove all plugins that match that type, a string to remove\n            all plugins with a matching ``name`` attribute or ``True`` to remove all\n            plugins. Return the list of removed plugins. \"\"\"\n        removed, remove = [], plugin\n        for i, plugin in list(enumerate(self.plugins))[::-1]:\n            if remove is True or remove is plugin or remove is type(plugin) \\\n            or getattr(plugin, 'name', True) == remove:\n                removed.append(plugin)\n                del self.plugins[i]\n                if hasattr(plugin, 'close'): plugin.close()\n        if removed: self.reset()\n        return removed\n\n    def reset(self, route=None):\n        \"\"\" Reset all routes (force plugins to be re-applied) and clear all\n            caches. If an ID or route object is given, only that specific route\n            is affected. \"\"\"\n        if route is None: routes = self.routes\n        elif isinstance(route, Route): routes = [route]\n        else: routes = [self.routes[route]]\n        for route in routes:\n            route.reset()\n        if DEBUG:\n            for route in routes:\n                route.prepare()\n        self.trigger_hook('app_reset')\n\n    def close(self):\n        \"\"\" Close the application and all installed plugins. \"\"\"\n        for plugin in self.plugins:\n            if hasattr(plugin, 'close'): plugin.close()\n\n    # def run(self, **kwargs):\n    #     \"\"\" Calls :func:`run` with the same parameters. \"\"\"\n    #     run(self, **kwargs)\n\n    def match(self, environ):\n        \"\"\" Search for a matching route and return a (:class:`Route` , urlargs)\n            tuple. The second value is a dictionary with parameters extracted\n            from the URL. Raise :exc:`HTTPError` (404/405) on a non-match.\"\"\"\n        return self.router.match(environ)\n\n    # def get_url(self, routename, **kargs):\n    #     \"\"\" Return a string that matches a named route \"\"\"\n    #     scriptname = request.environ.get('SCRIPT_NAME', '').strip('/') + '/'\n    #     location = self.router.build(routename, **kargs).lstrip('/')\n    #     return urljoin(urljoin('/', scriptname), location)\n\n    def add_route(self, route):\n        \"\"\" Add a route object, but do not change the :data:`Route.app`\n            attribute.\"\"\"\n        self.routes.append(route)\n        self.router.add(route.rule, route.method, route, name=route.name)\n        if DEBUG: route.prepare()\n\n    def route(self,\n              path=None,\n              method='GET',\n              callback=None,\n              name=None,\n              apply=None,\n              skip=None, **config):\n        \"\"\" A decorator to bind a function to a request URL. Example::\n\n                @app.route('/hello/<name>')\n                def hello(name):\n                    return 'Hello %s' % name\n\n            The ``<name>`` part is a wildcard. See :class:`Router` for syntax\n            details.\n\n            :param path: Request path or a list of paths to listen to. If no\n              path is specified, it is automatically generated from the\n              signature of the function.\n            :param method: HTTP method (`GET`, `POST`, `PUT`, ...) or a list of\n              methods to listen to. (default: `GET`)\n            :param callback: An optional shortcut to avoid the decorator\n              syntax. ``route(..., callback=func)`` equals ``route(...)(func)``\n            :param name: The name for this route. (default: None)\n            :param apply: A decorator or plugin or a list of plugins. These are\n              applied to the route callback in addition to installed plugins.\n            :param skip: A list of plugins, plugin classes or names. Matching\n              plugins are not installed to this route. ``True`` skips all.\n\n            Any additional keyword arguments are stored as route-specific\n            configuration and passed to plugins (see :meth:`Plugin.apply`).\n        \"\"\"\n        if callable(path): path, callback = None, path\n        plugins = makelist(apply)\n        skiplist = makelist(skip)\n\n        def decorator(callback):\n            if isinstance(callback, str): callback = load(callback)\n            for rule in makelist(path) or yieldroutes(callback):\n                for verb in makelist(method):\n                    verb = verb.upper()\n                    route = Route(self, rule, verb, callback,\n                                  name=name,\n                                  plugins=plugins,\n                                  skiplist=skiplist, **config)\n                    self.add_route(route)\n            return callback\n\n        return decorator(callback) if callback else decorator\n\n    def get(self, path=None, method='GET', **options):\n        \"\"\" Equals :meth:`route`. \"\"\"\n        return self.route(path, method, **options)\n\n    def post(self, path=None, method='POST', **options):\n        \"\"\" Equals :meth:`route` with a ``POST`` method parameter. \"\"\"\n        return self.route(path, method, **options)\n\n    def put(self, path=None, method='PUT', **options):\n        \"\"\" Equals :meth:`route` with a ``PUT`` method parameter. \"\"\"\n        return self.route(path, method, **options)\n\n    def delete(self, path=None, method='DELETE', **options):\n        \"\"\" Equals :meth:`route` with a ``DELETE`` method parameter. \"\"\"\n        return self.route(path, method, **options)\n\n    def patch(self, path=None, method='PATCH', **options):\n        \"\"\" Equals :meth:`route` with a ``PATCH`` method parameter. \"\"\"\n        return self.route(path, method, **options)\n\n    def error(self, code=500, callback=None):\n        \"\"\" Register an output handler for a HTTP error code. Can\n            be used as a decorator or called directly ::\n\n                def error_handler_500(error):\n                    return 'error_handler_500'\n\n                app.error(code=500, callback=error_handler_500)\n\n                @app.error(404)\n                def error_handler_404(error):\n                    return 'error_handler_404'\n\n        \"\"\"\n\n        def decorator(callback):\n            if isinstance(callback, str): callback = load(callback)\n            self.error_handler[int(code)] = callback\n            return callback\n\n        return decorator(callback) if callback else decorator\n\n    # def default_error_handler(self, res):\n    #     return tob(template(ERROR_PAGE_TEMPLATE, e=res, template_settings=dict(name='__ERROR_PAGE_TEMPLATE')))\n\n    async def _handle(self, environ, rs = None):\n\n        environ['route.params'] = environ.get(\"params\", {})\n        environ['bottle.app'] = self\n        request = BaseRequest(environ)\n\n        if not rs:\n            rs = AncillaResponse()\n    \n        request.set_response(rs)\n\n        try:\n            # while True: # Remove in 0.14 together with RouteReset\n                out = None\n                try:\n                    # self.trigger_hook('before_request')\n                    res = self.router.match(environ)\n                    route, args = self.router.match(environ)\n                    \n                    # print(f\"ROUTE = {route}\", flush=True)\n                    environ['route.handle'] = route\n                    environ['bottle.route'] = route\n                    environ['route.url_args'] = args\n                    # print(f\"Route.call = {route.call}\", flush=True)\n                    # result = yield from call_maybe_yield(route.call, *[request], **args)\n                    out = await call_maybe_yield(route.call, *[request], **args)\n                    \n                    if isinstance(out, io.IOBase) and hasattr(out, 'read') and (hasattr(out, 'close') or not hasattr(out, '__aiter__')):\n                        out = WSGIFileWrapper(out)\n\n\n                except RouterError as e:\n                    print(f\"RouterError = {e}\", flush=True)\n\n                    if not self.catchall:\n                        raise e\n                    \n                    re_pattern = re.compile('^(/api/services/(?P<service>[^/]+)/(?P<service_id>[^/]+))(?P<other>.*)$')\n                    re_match = re_pattern.match(request.path)\n\n                    if re_match:\n                        gargs = re_match.groupdict()\n                        # result = yield from call_maybe_yield(self.api.catchUnmountedServices, *[request], **gargs)\n                        out = await call_maybe_yield(self.api.catchUnmountedServices, *[request], **gargs)\n                        # return result\n                    else:\n                        raise e\n                \n                except AncillaResponse as e:\n                    # print(f\"INSIDE ANCilla REspone Error {str(e)}\", flush=True)\n                    raise e\n                except Exception as e:\n                    # print(f\"AppCallException = {e}\", flush=True)\n                    raise AncillaError(400, {\"error\": str(e)})\n                    \n        except (KeyboardInterrupt, SystemExit, MemoryError):\n            raise\n        except Exception as e:\n            # print(f\"INSIDE OTHER EXCEPTION = {str(e)}\", flush=True)\n            # if not self.catchall: raise e\n            raise e\n            # stacktrace = format_exc()\n            # environ['wsgi.errors'].write(stacktrace)\n            # environ['wsgi.errors'].flush()\n\n        if isinstance(out, AncillaResponse):\n            return out\n        else:\n            rs.body = out\n            return rs\n        # return out\n\n    \n    # def _cast(self, out, peek=None):\n    #     \"\"\" Try to convert the parameter into something WSGI compatible and set\n    #     correct HTTP headers when possible.\n    #     Support: False, str, unicode, dict, HTTPResponse, HTTPError, file-like,\n    #     iterable of strings and iterable of unicodes\n    #     \"\"\"\n\n    #     # Empty output is done here\n    #     if not out:\n    #         if 'Content-Length' not in response:\n    #             response['Content-Length'] = 0\n    #         return []\n    #     # Join lists of byte or unicode strings. Mixed lists are NOT supported\n    #     if isinstance(out, (tuple, list))\\\n    #     and isinstance(out[0], (bytes, unicode)):\n    #         out = out[0][0:0].join(out)  # b'abc'[0:0] -> b''\n    #     # Encode unicode strings\n    #     if isinstance(out, unicode):\n    #         out = out.encode(response.charset)\n    #     # Byte Strings are just returned\n    #     if isinstance(out, bytes):\n    #         if 'Content-Length' not in response:\n    #             response['Content-Length'] = len(out)\n    #         return [out]\n    #     # HTTPError or HTTPException (recursive, because they may wrap anything)\n    #     # TODO: Handle these explicitly in handle() or make them iterable.\n    #     if isinstance(out, HTTPError):\n    #         out.apply(response)\n    #         out = self.error_handler.get(out.status_code,\n    #                                      self.default_error_handler)(out)\n    #         return self._cast(out)\n    #     if isinstance(out, HTTPResponse):\n    #         out.apply(response)\n    #         return self._cast(out.body)\n\n    #     # File-like objects.\n    #     if hasattr(out, 'read'):\n    #         if 'wsgi.file_wrapper' in request.environ:\n    #             return request.environ['wsgi.file_wrapper'](out)\n    #         elif hasattr(out, 'close') or not hasattr(out, '__iter__'):\n    #             return WSGIFileWrapper(out)\n\n    #     # Handle Iterables. We peek into them to detect their inner type.\n    #     try:\n    #         iout = iter(out)\n    #         first = next(iout)\n    #         while not first:\n    #             first = next(iout)\n    #     except StopIteration:\n    #         return self._cast('')\n    #     except HTTPResponse as E:\n    #         first = E\n    #     except (KeyboardInterrupt, SystemExit, MemoryError):\n    #         raise\n    #     except Exception as error:\n    #         if not self.catchall: raise\n    #         first = HTTPError(500, 'Unhandled exception', error, format_exc())\n\n    #     # These are the inner types allowed in iterator or generator objects.\n    #     if isinstance(first, HTTPResponse):\n    #         return self._cast(first)\n    #     elif isinstance(first, bytes):\n    #         new_iter = itertools.chain([first], iout)\n    #     elif isinstance(first, unicode):\n    #         encoder = lambda x: x.encode(response.charset)\n    #         new_iter = imap(encoder, itertools.chain([first], iout))\n    #     else:\n    #         msg = 'Unsupported response type: %s' % type(first)\n    #         return self._cast(HTTPError(500, msg))\n    #     if hasattr(out, 'close'):\n    #         new_iter = _closeiter(new_iter, out.close)\n    #     return new_iter\n\n    # def __call__(self, method, path, msg):\n    def __call__(self, environ, rs = None):\n        \"\"\" Each instance of :class:'Bottle' is a WSGI application. \"\"\"\n        return self._handle(environ, rs)\n        # return self.wsgi(environ, start_response)\n\n    # def __enter__(self):\n    #     \"\"\" Use this application as default for all module-level shortcuts. \"\"\"\n    #     default_app.push(self)\n    #     return self\n\n    # def __exit__(self, exc_type, exc_value, traceback):\n    #     default_app.pop()\n\n    # def __setattr__(self, name, value):\n    #     # if name in self.__dict__:\n    #     #     raise AttributeError(\"Attribute %s already defined. Plugin conflict?\" % name)\n    #     self.__dict__[name] = value\n\n\n","repo_name":"frenzylabs/ancilla","sub_path":"ancilla/ancilla/foundation/node/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":26838,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"48"}
+{"seq_id":"25593441198","text":"f = open(\"./data/d7/input.txt\")\nl = [ x for x in ''.join(f.readlines()).strip().split('\\n') ]\n\nclass Dir:\n    def __init__(self, name, parent_dir):\n        self.name=name\n        self.size=0\n        self.sub_dir=[]\n        self.parent_dir=parent_dir\n\n    def find_sub_dir(self, name):\n        for s in self.sub_dir:\n            if s.name==name:\n                return s\n        return None\n    \n    def add_sub_dir(self, dir):\n        self.sub_dir.append(dir)\n    \n    def add_size(self, size):\n        if self.parent_dir!=None:\n            self.parent_dir.add_size(size)\n        self.size+=size\n        \n              \ndef build_dir():\n    i=0\n    dir_list=[]\n    current_dir=None\n    while i<len(l):\n        if l[i][:4]==\"$ cd\":\n            if l[i]==\"$ cd ..\":\n                current_dir=current_dir.parent_dir\n            else:\n                dir_name=l[i].split(\" \")[2]\n                if current_dir!=None:\n                    current_dir=current_dir.find_sub_dir(dir_name)\n                else:\n                    current_dir=Dir(dir_name, None)\n                    dir_list.append(current_dir)\n            i+=1\n\n        if l[i]==\"$ ls\":\n            j=i+1\n            while j<len(l) and l[j][0]!=\"$\":\n                if l[j][0:3]==\"dir\":\n                    dir_name=l[j].split(\" \")[1]\n                    d=Dir(dir_name, current_dir)\n                    current_dir.add_sub_dir(d)\n                    dir_list.append(d)\n                \n                else:\n                    current_dir.add_size(int(l[j].split(\" \")[0]))\n                j+=1\n            i=j\n\n    return dir_list\n\ndef p1():\n    dir_list=build_dir()\n    sizes=[ d.size for d in dir_list ]\n    low_sizes=[ s for s in sizes if s<=100000 ]\n    return sum(low_sizes)\n    \ndef p2():\n    dir_list=build_dir()\n    dir_list.sort(key=lambda d: d.size)\n    total_outermost=dir_list[len(dir_list)-1].size\n    unused_space=70000000-total_outermost\n    size_to_delete=30000000-unused_space\n    s=[ d.size for d in dir_list if d.size>=size_to_delete ][0]\n    return s\n\np1()\np2()","repo_name":"Minifixio/AdventOfCode2022","sub_path":"python/d7/d7.py","file_name":"d7.py","file_ext":"py","file_size_in_byte":2043,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"73734663186","text":"import numpy as np\nimport pandas as pd\nimport cv2\nfrom tqdm import tqdm\nimport mediapipe as mp\nfrom scipy.spatial.transform import Rotation as R\n\nmp_drawing = mp.solutions.drawing_utils\nmp_drawing_styles = mp.solutions.drawing_styles\nmp_face_mesh = mp.solutions.face_mesh\n\ntqdm.pandas()\n\nFERclassName = [\n    'Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral', 'None'\n]\n\nRussellclassName = {\n    'Happy': 'Happy',\n    'Surprise': 'Happy',\n    'Angry': 'Anger',\n    'Fear': 'Anger',\n    'Disgust': 'Anger',\n    'Sad': 'Sadness',\n    'Neutral': 'Relax',\n    'None': 'None'\n}\n\nclass FERdata(object):\n\n    def __init__(self, path_to_csv):\n        print(f'reading csvfile from {path_to_csv}')\n        self.df = pd.read_csv(path_to_csv).sample(frac=1, ignore_index=True)\n        self.len = len(self.df)\n        self.class_name = FERclassName\n\n    def __len__(self):\n        return self.len\n\n    def save_df(self, path_to_save):\n        self.df.to_csv(path_to_save, index=False)\n        print(f'save {path_to_save} done')\n\n    def balance_df(self, mode):\n        return pd.concat([\n            balance_each_usage(self.df[self.df['usage'] == 'train'], mode),\n            self.df[self.df['usage'] == 'val'],\n            self.df[self.df['usage'] == 'test']\n        ],\n                         ignore_index=True)\n\n    def get_df(self,\n               mode='ANN',\n               with_img = False,\n               drawlandmarks=False,\n               rotate=True,\n               repos=True,\n               mapping=False,\n               cmap='GRAY',\n               sample=False,\n               sample_size=10):\n        if sample is False:\n            sample_size = self.len\n        print('Generate df with config')\n        print(\n            f' mode:{mode}\\n drawlandmarks:{drawlandmarks}\\n mapping:{mapping}\\n colormap:{cmap}\\n sample:{sample}\\n sample_size:{sample_size}'\n        )\n        if sample == True:\n            self.df = pd.concat([\n                self.df[self.df[' Usage'] == 'Training'].sample(n=sample_size),\n                self.df[self.df[' Usage'] == 'PublicTest'].sample(\n                    n=sample_size),\n                self.df[self.df[' Usage'] == 'PrivateTest'].sample(\n                    n=sample_size)\n            ])\n            print('Prepare data to img')\n            self.df['img'] = self.df.progress_apply(to_img, axis=1)\n            self.df = getlandmark(self.df, mode, drawlandmarks, mapping, cmap, with_img, rotate, repos)\n            return self.df\n        else:\n            print('Prepare data to img')\n            self.df['img'] = self.df.progress_apply(to_img, axis=1)\n            self.df = getlandmark(self.df, mode, drawlandmarks, mapping, cmap, with_img, rotate, repos)\n            return self.df\n\ndef re_pos(mesh):\n    # print(f'shift from: {mesh[1]}')\n    origin = mesh[1] - np.array([0, 0, 0])\n    mesh = mesh - origin\n    return mesh\n\ndef unit_vector(vector):\n    \"\"\" Returns the unit vector of the vector.  \"\"\"\n    return vector / np.linalg.norm(vector)\n\ndef rot(mesh, u,v,w, x,y,z):\n    r = R.from_matrix([u,v,w])\n    ref_r = R.from_matrix([x,y,z])\n    rotation = ref_r * r\n    # mesh = r.apply(mesh)\n    # mesh = ref_r.apply(mesh)\n    mesh = rotation.apply(mesh)\n    # print(r.as_euler('xyz', degrees=True))\n    return mesh\n\ndef get_ref(mesh):\n    p0 = mesh[1]\n    p1 = mesh[5]\n    p2 = mesh[44]\n    p3 = mesh[274]\n    v1 = p2 - p1\n    v2 = p3 - p1\n    v3 = p0 - p1\n    v4 = p0 - mesh[4]\n    w = unit_vector(np.cross(v1, v2))\n    ref = unit_vector(np.cross(v3, v4))\n    v = unit_vector(np.cross(w, ref))\n    u = unit_vector(np.cross(v, w))\n    return u,v,w\n\ndef balance_each_usage(df, mode):\n    print(f'Balancing dataFrame with {mode}sampling mode')\n    target_list = list(df['target'].value_counts().index)\n    if mode == 'up':\n        count = df['target'].value_counts().max()\n    elif mode == 'down':\n        count = df['target'].value_counts().min()\n    list_df = [\n        df[df['target'] == t].sample(n=count,\n                                     random_state=1,\n                                     replace=(mode == 'up'))\n        for t in target_list\n    ]\n    print(f'target list:{target_list}')\n    print(f'count:{count}')\n    return pd.concat(list_df, ignore_index=True).sample(frac=1,\n                                                        ignore_index=True)\n\n\ndef to_img(row):\n    return np.array(row[' pixels'].split(' ')).reshape(48, 48).astype('uint8')\n\n\ndef prepareforANN(lanmarks, rotation=True, reposition=True):\n    mesh = np.array([[landmark.x, landmark.y, landmark.z] for landmark in lanmarks])\n    if reposition == True:\n        mesh = re_pos(mesh)\n    if rotation == True:\n        u,v,w = get_ref(mesh)\n        x = np.array([1., 0., 0.])\n        y = np.array([0., -1., 0.])\n        z = np.array([0., 0., -1.])\n        mesh = rot(mesh, u,v,w, x,y,z)\n    return mesh\n\ndef drawalllandmark(annotated_image, result):\n    for face_landmarks in result.multi_face_landmarks:\n        # print('face_landmarks:', face_landmarks)\n        mp_drawing.draw_landmarks(\n            image=annotated_image,\n            landmark_list=face_landmarks,\n            connections=mp_face_mesh.FACEMESH_TESSELATION,\n            landmark_drawing_spec=None,\n            connection_drawing_spec=mp_drawing_styles.\n            get_default_face_mesh_tesselation_style())\n        mp_drawing.draw_landmarks(image=annotated_image,\n                                  landmark_list=face_landmarks,\n                                  connections=mp_face_mesh.FACEMESH_CONTOURS,\n                                  landmark_drawing_spec=None,\n                                  connection_drawing_spec=mp_drawing_styles.\n                                  get_default_face_mesh_contours_style())\n        mp_drawing.draw_landmarks(\n            image=annotated_image,\n            landmark_list=face_landmarks,\n            connections=mp_face_mesh.FACEMESH_IRISES,\n            landmark_drawing_spec=None,\n            connection_drawing_spec=mp_drawing_styles.\n            get_default_face_mesh_iris_connections_style())\n\n    return annotated_image\n\n\ndef emotionmapping(emotion):\n    return FERclassName.index(emotion)\n\ndef getlandmark(df, mode, draw, map, cmap, with_img, rotate, repos):\n    new_df = pd.DataFrame(columns=['usage', 'feature', 'target'])\n    draw_img, real_img = [], []\n    usage_map = {\n        'Training': 'train',\n        'PublicTest': 'val',\n        'PrivateTest': 'test',\n    }\n    print('Getting landmarks with mediapipe FaceMesh')\n    with mp_face_mesh.FaceMesh(static_image_mode=True,\n                               max_num_faces=1,\n                               refine_landmarks=True,\n                               min_detection_confidence=0.5) as face_mesh:\n\n        for data in tqdm(df[[' Usage', 'img', 'emotion']].values):\n            # Convert the BGR image to RGB before processing.\n            if cmap == 'GRAY':\n                img = cv2.cvtColor(data[1], cv2.COLOR_GRAY2RGB)\n            elif cmap == 'BGR':\n                img = cv2.cvtColor(data[1], cv2.COLOR_BGR2RGB)\n            else:\n                img = data[1]\n\n            if map == True:\n                print('Currently have only not map')\n            else:\n                emotion = data[2]\n\n            if mode == 'ANN' or mode == 'GNN':\n                result = face_mesh.process(img)\n                if not result.multi_face_landmarks:\n                    continue\n\n                np_landmark = prepareforANN(\n                    result.multi_face_landmarks[0].landmark,rotation=rotate,reposition=repos)\n\n                if draw == True:\n                    annotated_image = cv2.cvtColor(data[1],\n                                                   cv2.COLOR_GRAY2RGB).copy()\n                    annotated_image = drawalllandmark(annotated_image, result)\n                    draw_img.append(annotated_image)\n                \n                if with_img == True:\n                    real_img.append(img)\n                    \n                new_df = pd.concat([\n                    new_df,\n                    pd.DataFrame([[usage_map[data[0]], np_landmark, emotion]],\n                                 columns=['usage', 'feature', 'target'])\n                ],\n                                   ignore_index=True)\n                if mode == 'GNN':\n                    edge_index = list(mp_face_mesh.FACEMESH_TESSELATION)\n                    new_df['edge_index'] = [edge_index for _ in range(len(new_df))]\n\n            elif mode == 'IMG':\n                new_df = pd.concat([\n                    new_df,\n                    pd.DataFrame([[\n                        usage_map[data[0]],\n                        cv2.cvtColor(data[1], cv2.COLOR_GRAY2RGB), emotion\n                    ]],\n                                 columns=['usage', 'feature', 'target'])\n                ],\n                                   ignore_index=True)\n\n            else:\n                print('Currently have only ANN, IMG, and GNN mode')\n                break\n\n        if draw is True:\n            new_df['draw_img'] = draw_img\n        \n        if with_img is True:\n            new_df['img'] = real_img\n\n        print(\n            f'Distribution of Train: \\n{new_df[new_df[\"usage\"] == \"train\"][\"target\"].value_counts()}'\n        )\n        print(\n            f'Distribution of Validation: \\n{new_df[new_df[\"usage\"] == \"val\"][\"target\"].value_counts()}'\n        )\n        print(\n            f'Distribution of Test \\n{new_df[new_df[\"usage\"] == \"test\"][\"target\"].value_counts()}'\n        )\n\n    return new_df","repo_name":"northnpk/FaceMeshEmotions","sub_path":"utils/preprocessFERplus.py","file_name":"preprocessFERplus.py","file_ext":"py","file_size_in_byte":9507,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"74121448466","text":"class UnionFind:\n    def __init__(self, n):\n        self.n = n\n        self.par = [-1] * n\n        self.wei = [0] * n\n\n    def find(self, x):\n        if self.par[x] < 0:\n            return x\n        else:\n            y = self.find(self.par[x])\n            self.wei[x] += self.wei[self.par[x]]\n            self.par[x] = y\n            return y\n\n    def union(self, x, y, w=0):\n        w += self.weight(x) - self.weight(y)\n        x = self.find(x)\n        y = self.find(y)\n        if x == y:\n            return False\n        if self.par[x] > self.par[y]:\n            x, y = y, x\n            w *= -1\n        self.par[x] += self.par[y]\n        self.par[y] = x\n        self.wei[y] = w\n        return True\n\n    def same(self, x, y):\n        return self.find(x) == self.find(y)\n\n    def size(self, x):\n        return -self.par[self.find(x)]\n\n    def weight(self, x):\n        self.find(x)\n        return self.wei[x]\n\n    def diff(self, x, y):\n        return self.weight(y) - self.weight(x)\n\n    def kruskal(self, edge):\n        edge.sort()\n        cost_sum = 0\n        for cost, node1, node2 in edge:\n            if not self.same(node1, node2):\n                cost_sum += cost\n                self.union(node1, node2)\n        return cost_sum\n","repo_name":"happa64/AtCoder_Beginner_Contest","sub_path":"Library/Union_Find.py","file_name":"Union_Find.py","file_ext":"py","file_size_in_byte":1234,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"29116926938","text":"\"\"\"\nGiven 2 arrays, create a function that let's a user know (true/false)\nwhether these two arrays contain any common items\nFor Example:\nconst array1 = ['a', 'b', 'c', 'x'];//const array2 = ['z', 'y', 'i'];\nshould return false.\n-----------\nConst array1 = ['a', 'b', 'c', 'x'];//const array2 = ['z', 'y', 'x'];\nshould return true.\n\n2 parameters - arrays - no size limit\nreturn true or false\n\"\"\"\n\n\ndef common_items1(array1, array2):\n    \"\"\"\n    Finding if any items in each array have similar inputs\n    :param array1:\n    :param array2:\n    :return: True or False\n    \"\"\"\n\n    # Create Dictionary of array1 -\n    # Time Complexity O(n)\n    # Space Complexity O(n)\n    dict1 = {}\n    for item in array1:\n        if item in dict1:\n            updated_value = dict1[item]\n            updated_value += 1\n            dict1[item] = updated_value\n        else:\n            dict1[item] = 1\n\n    # Compare every element of n to dict\n    # Time Complexity O(m)\n    for item in array2:\n        if item in dict1:\n            return True\n\n    # Overall Time Complexity  O(n + m)\n    return False\n\n\n# Press the green button in the gutter to run the script.\nif __name__ == '__main__':\n    array1 = ['a', 'b', 'c', 'x']\n    array2 = ['z', 'y', 'a']\n    array3 = ['z', 'y', 'i']\n    print(common_items1(array1, array2))  # return True\n    print(common_items1(array1, array3))  # return False\n","repo_name":"alexfuoc/DS_ALG","sub_path":"IntroInterview/CommonItems.py","file_name":"CommonItems.py","file_ext":"py","file_size_in_byte":1374,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"23181479660","text":"from django.urls import path\n\nfrom . import views\n\napp_name = 'night_generator'\n\nurlpatterns = [\n  path(\"media/<str:slug>\", views.MediaAPIView, name='media_home'),\n\n  path(\"topRatedMovie/\", views.TopRatedMovieAPI, name='topratedmovie'),\n  path(\"topRatedTvShow/\", views.TopRatedTVShowAPI, name='topratedtvshow'),\n  path(\"mostPopularMovie/\", views.MostPopularMovieAPI, name='mostpopularmovie'),\n  path(\"mostPopularTvShow/\", views.MostPopularTVShowAPI, name='mostpopulartvshow'),\n\n]\n","repo_name":"ricardobohadana/random-night","sub_path":"night_generator/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":480,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"29664043346","text":"from operator import attrgetter\n\nfrom django.contrib.auth.models import User\nfrom django.test import TestCase\n\nfrom ..managers import TradeManager\n\nclass TradeManagerTest(TestCase):\n    \"\"\"Test case for ``TradeManager``.\"\"\"\n    fixtures = ['test.json']\n\n    def setUp(self):\n        self.manager = TradeManager()\n\n    # `latest_offers`\n    def test_latest_offers_count(self):\n        count = 10\n        latest_ten_offers = self.manager.latest_offers(count)\n        self.assertLessEqual(len(latest_ten_offers), count)\n\n    def test_latest_offer_count_when_negative(self):\n        count = -3\n        latest_offers = self.manager.latest_offers(count)\n        self.assertEqual(0, len(latest_offers))\n\n    def test_latest_offers_sorted_by_date(self):\n        latest_offers = self.manager.latest_offers()\n        self.assertEqual(latest_offers, sorted(latest_offers, \n                                               key=attrgetter('date'),\n                                               reverse=True))\n\n\n    # `latest_demands`\n    def test_latest_demands_count(self):\n        count = 10\n        latest_ten_demands = self.manager.latest_demands(count)\n        self.assertLessEqual(len(latest_ten_demands), count)\n\n    def test_latest_demand_count_when_negative(self):\n        count = -3\n        latest_demands = self.manager.latest_demands(count)\n        self.assertEqual(0, len(latest_demands))\n\n    def test_latest_demands_sorted_by_date(self):\n        latest_offers = self.manager.latest_offers()\n        self.assertEqual(latest_offers, sorted(latest_offers, \n                                               key=attrgetter('date'),\n                                               reverse=True))\n\n    # `offers`\n    def test_user_offers(self):\n        alice = User.objects.get(username=u'Alice')\n        offers = self.manager.offers(alice)\n        for offer in offers:\n            self.assertEqual(offer.owner, alice)\n\n    def test_user_offers_sorted_by_date(self):\n        # newest comes first\n        alice = User.objects.get(username=u'Alice')\n        offers = self.manager.offers(alice)\n        sorted_offers = sorted(list(offers),\n                               key=lambda offer: offer.date,\n                               reverse=True)\n        for index, offer in enumerate(sorted_offers):\n            self.assertEqual(offer, offers[index])\n\n    # `demands`\n    def test_user_demands(self):\n        alice = User.objects.get(username=u'Alice')\n        demands = self.manager.demands(alice)\n        for demand in demands:\n            self.assertEqual(demand.owner, alice)\n\n    def test_user_demands_sorted_by_date(self):\n        # newest comes first\n        alice = User.objects.get(username=u'Alice')\n        demands = self.manager.demands(alice)\n        sorted_demands = sorted(list(demands),\n                               key=lambda demand: demand.date,\n                               reverse=True)\n        for index, demand in enumerate(sorted_demands):\n            self.assertEqual(demand, demands[index])\n\n    # `services`\n    def test_user_services(self):\n        alice = User.objects.get(username=u'Alice')\n        services = self.manager.services(alice)\n        for service in services:\n            self.assertEqual(service.owner, alice)\n\n    def test_user_services_sorted_by_date(self):\n        # newest comes first\n        alice = User.objects.get(username=u'Alice')\n        services = self.manager.services(alice)\n        sorted_services = sorted(list(services),\n                               key=lambda service: service.date,\n                               reverse=True)\n        for index, service in enumerate(sorted_services):\n            self.assertEqual(service, services[index])\n\n    # `gifts`\n    def test_user_gifts(self):\n        alice = User.objects.get(username=u'Alice')\n        gifts = self.manager.gifts(alice)\n        for gift in gifts:\n            self.assertEqual(gift.owner, alice)\n\n    def test_user_gifts_sorted_by_date(self):\n        # newest comes first\n        alice = User.objects.get(username=u'Alice')\n        gifts = self.manager.gifts(alice)\n        sorted_gifts = sorted(list(gifts),\n                               key=lambda gift: gift.date,\n                               reverse=True)\n        for index, gift in enumerate(sorted_gifts):\n            self.assertEqual(gift, gifts[index])\n\n    # `loans`\n    def test_user_loans(self):\n        alice = User.objects.get(username=u'Alice')\n        loans = self.manager.loans(alice)\n        for loan in loans:\n            self.assertEqual(loan.owner, alice)\n\n    def test_user_loans_sorted_by_date(self):\n        # newest comes first\n        alice = User.objects.get(username=u'Alice')\n        loans = self.manager.loans(alice)\n        sorted_loans = sorted(list(loans),\n                               key=lambda loan: loan.date,\n                               reverse=True)\n        for index, loan in enumerate(sorted_loans):\n            self.assertEqual(loan, loans[index])\n","repo_name":"tuxskar/mutualismo","sub_path":"apps/red/tests/test_managers.py","file_name":"test_managers.py","file_ext":"py","file_size_in_byte":4961,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"48"}
+{"seq_id":"24056065994","text":"from random import choice, randint, sample\nfrom decimal import *\nfrom collections import namedtuple\nfrom characters import martial_classes, classes, ability_scores, meets_bonus_xp_minimums\n\ngetcontext().prec = 3\n\n\n# todo move this function to globals / rules\ndef get_gender_words(sex):\n    # todo account for neuter creatures\n    if sex == \"male\":\n        return (\"he\", \"him\", \"his\")\n    else:\n        return (\"she\", \"her\", \"her\")\n\n\n# based on Strength\ndef feats(magnitude, c):\n    big_percent = choice([30, 40])\n    small_percent = choice([10, 20])\n    result = \"\"\n    subj, obj, poss = get_gender_words(c[\"sex\"])\n    if magnitude <= -17:\n        result = \"Weak lungs make it impossible to run.\"\n    elif magnitude <= -14:\n        result = \"When stunned, must make save against crushing to end the stun and rejoin combat.\"\n    elif magnitude <= -12:\n        result = \"Character is forever incapable of swimming, regardless of character class or skills.\"\n    elif magnitude <= -11:\n        result = (\n            \"Poor endurance. Character cannot run for more than 1 round at a time, no matter \"\n            + poss\n            + \" Constitution score.\"\n        )\n    elif magnitude <= -10:\n        result = \"One-handed melee weapons must be used two-handed (even daggers); two-handed melee weapons suffer the non-proficiency penalty (if already non-proficient with a given weapon,  add an additional -1 penalty.)\"\n    elif magnitude <= -9:\n        result = \"Encumbrance limits reduced by \" + str(big_percent) + \"%.\"\n        c[\"encumbrance modifier\"] = c[\"encumbrance modifier\"] - Decimal(\n            0.01 * big_percent\n        )\n    elif magnitude <= -8:\n        result = \"Too weak to draw or load any bow or crossbow.\"\n    elif magnitude <= -7:\n        result = \"One-handed melee weapons must be used two-handed (except daggers); two-handed melee weapons have a -1 penalty to attack and damage.\"\n    elif magnitude <= -6:\n        result = \"Encumbrance limits reduced by \" + str(small_percent) + \"%.\"\n        c[\"encumbrance modifier\"] = c[\"encumbrance modifier\"] - Decimal(\n            0.01 * small_percent\n        )\n    elif magnitude <= -5:\n        result = \"Too weak to draw a longbow or load a heavy crossbow.\"\n    elif magnitude <= -4:\n        result = \"Poor cardiovascular health. Character can only hold breath for 1 round (normal: 3).\"\n    elif magnitude <= -2:\n        result = (\n            \"Weak arms and legs. Treat character's Dexterity as half its actual value (round down) for the purpose of no-fault climbing. Danger climbing ability is reduced by \"\n            + str(randint(10, 15))\n            + \"%.\"\n        )\n    elif magnitude <= -1:\n        result = \"Throwing weapons deal 1 less damage (minimum 0.)\"\n    elif magnitude <= 1:\n        result = \"Throwing weapons deal 1 extra damage.\"\n    elif magnitude <= 3:\n        result = \"Strong arms and legs. Treat character's Dexterity as 50% higher (round down) for no-fault climbing.\"\n    elif magnitude == 4:\n        result = \"Strong hands and arms allow a 50% chance to spend 3 fewer AP when loading a crossbow.\"\n    elif magnitude <= 6:\n        result = \"Character is capable of swimming.\"\n    elif magnitude <= 8:\n        result = \"Encumbrance limits increased by \" + str(small_percent) + \"%.\"\n        c[\"encumbrance modifier\"] = c[\"encumbrance modifier\"] + Decimal(\n            0.01 * small_percent\n        )\n    elif magnitude <= 9:\n        result = \"Strong upper body. For slings, bows (not crossbows), and all thrown weapons, increase short range by 1 hex, and medium and long ranges by 2 hexes.\"\n    elif magnitude <= 11:\n        result = \"Can hold breath for three times the normal duration (9 rounds instead of 3.)\"\n    elif magnitude <= 13:\n        result = \"Powerful muscles add +3 on rolls made to escape manacles, ropes, or other constraints.\"\n    elif magnitude <= 15:\n        result = (\n            \"Each day, the first time the character is stunned, after rejoining combat \"\n            + subj\n            + \" gains +2 to melee attack and damage for 10 rounds.\"\n        )\n    else:\n        result = (\n            \"Each day, the first time the character is stunned, after rejoining combat \"\n            + subj\n            + \" gains hysterical strength. Roll 1d3+1: for 10 rounds, character gains that amount as a bonus to melee attack and damage.\"\n        )\n    return result\n\n\nman_at_arms_weapons = [\"dagger\", \"club\", \"quarterstaff\", \"sling\"] * 3 + [\n    \"shortbow\",\n    \"shortsword\",\n    \"longsword\",\n    \"mace\",\n    \"bastard sword\",\n    \"greatsword\",\n]\nman_at_arms_armor = (\n    [\"no armor\"] * 2\n    + [\"gambeson\"] * 4\n    + [\"leather armor\"] * 3\n    + [\"studded leather\"] * 2\n    + [\"haubergeon\"]\n)\n\n\ndef man_at_arms_equipment():\n    num_weapons = 1 if randint(1, 10) < 7 else 2\n    weapons = sample(man_at_arms_weapons, num_weapons)\n    armor = choice(man_at_arms_armor)\n    equipment = namedtuple(\"equipment\", [\"weapons\", \"armor\"])\n    return equipment(weapons, armor)\n\n\ndef stringify_man_at_arms_equipment(equipment):\n    return \"has \" + equipment.armor + \"; carries \" + \", \".join(equipment.weapons)\n\n\ndef man_at_arms_pay():\n    return 4 + randint(1, 4) + randint(2, 5) + randint(2, 5) + randint(2, 5)\n\n\n# based on Wisdom\ndef relationships(magnitude, c):\n    subj, obj, poss = get_gender_words(c[\"sex\"])\n    result = \"\"\n    if magnitude <= -17:\n        result = \"The character inadvertently revealed information to the enemy of the land where they are from, enabling that enemy to invade and seize wealth and property. A death sentence has been laid upon the character.\"\n    elif magnitude == -16:\n        result = (\n            \"Ill-considered opinions and bad intentions caused the character to be excommunicated from \"\n            + poss\n            + \" original religion. \"\n            + subj.capitalize()\n            + \" is marked as an apostate and all sites of organized worship will shun \"\n            + obj\n            + \".\"\n        )\n    elif magnitude <= -14:\n        crimes = [\n            \"murdered a peasant, and is being sought to receive lashes and time in the stocks.\",\n            \"damaged property, and is being sought to pay restitution and receive time in the stocks.\",\n            \"committed petty theft, and is being sought to receive lashes and pay restitution.\",\n            \"murdered a merchant, and is being sought for prison time and a hefty fine.\",\n            \"committed a serious theft, and is being sought to pay restitution and to have a hand chopped off.\",\n            \"murdered a noble, and is being sought for execution.\",\n        ]\n        roll = randint(1, 20)\n        result_start = \"Character has \"\n        if roll <= 3:\n            result = result_start + crimes[0]\n        elif roll <= 7:\n            result = result_start + crimes[1]\n        elif roll <= 11:\n            result = result_start + crimes[2]\n        elif roll <= 14:\n            result = result_start + crimes[3]\n        elif roll <= 18:\n            result = result_start + crimes[4]\n        else:  # 19 and 20\n            result = result_start + crimes[5]\n    elif magnitude == -13:\n        result = \"Character's strange proclivities and morals prevent the character from retaining hirelings or followers for more than a month. Henchmen and retainers (received from leveling up) are not affected.\"\n    elif magnitude <= -11:\n        result = (\n            \"Character is exiled from \"\n            + poss\n            + \" homeland, and will face a life sentence or execution if they return, although \"\n            + poss\n            + \" only 'crime' is that of having powerful enemies.\"\n        )\n    elif magnitude <= -9:\n        enemy_family_member = [\"wife\"] * 9 + [\"daughter\"] * 10 + [\"mother\"] * 1\n        sex_cutoff = 1\n        if c[\"sex\"] == \"female\":\n            sex_cutoff = 11\n        enemy_reasons = [\n            (\n                \"foolishly slept with the enemy's \"\n                + choice(enemy_family_member)\n                + \", making her pregnant.\"\n            ),\n            \"stole a large sum of money from the enemy, and then lost it.\",\n            \"destroyed a possession which the enemy held in great personal significance.\",\n        ]\n        roll = randint(sex_cutoff, 20)\n        result_start = \"The character is pursued by a sworn enemy, who seeks revenge for the character having \"\n        if roll <= 10:\n            result = result_start + enemy_reasons[0]\n        elif roll <= 15:\n            result = result_start + enemy_reasons[1]\n        else:\n            result = result_start + enemy_reasons[2]\n    elif magnitude <= -8:\n        if c[\"has family\"]:\n            result = (\n                \"Character has been ostracized by \"\n                + poss\n                + \" family because of \"\n                + poss\n                + \" poor decisions. The character has no access to any family possessions or talents.\"\n            )\n        else:\n            result = (\n                \"Character has been ostracized by \"\n                + poss\n                + \" mentor because of \"\n                + poss\n                + \" poor decisions. The character's training was incomplete, so \"\n                + subj\n                + \" has no weapon proficiencies.\"\n            )\n    elif magnitude <= -6:\n        result = (\n            \"People in this area treat the character with profound dislike. Thus, \"\n            + subj\n            + \" has been banned from all town establishments save the market.\"\n        )\n    elif magnitude == -5:\n        result = (\n            \"Word has spread that the character's word cannot be trusted. \"\n            + subj.capitalize()\n            + \" will be unable to obtain hirelings or make contracts in this town.\"\n        )\n    elif magnitude == -4:\n        if c[\"has family\"]:\n            result = \"Family members strongly dislike the character, and will provide nothing more than a night's lodging, no more than once every every six months.\"\n        else:\n            result = \"Character's occasional displays of foolishness means that hirelings and followers will take twice as long to earn morale increases for extended service. \"\n    elif magnitude <= -2:\n        num = randint(3, 8)\n        result = (\n            \"A group of \"\n            + str(num)\n            + \" local tough guys have been threatening and harassing the character whenever they can. These toughs certainly have class levels, but aren't too powerful.\"\n        )\n    elif magnitude == -1:\n        result = (\n            \"The character's conduct has led to \"\n            + poss\n            + \" being banned from inns and taverns in this town.\"\n        )\n    elif magnitude == 0:\n        if c[\"has family\"]:\n            result = \"Family members treat the character as hopeless and without prospects, but will provide lodging. Any brothers and sisters will work as hirelings, but will start with a morale of only 7.\"\n        else:\n            result = \"Hireling and followers are made uneasy by character's small-mindedness. Base morale will be 1 point below normal, even if they are gained through this generator.\"\n    elif magnitude == 1:\n        sex = choice([\"he\", \"she\"])\n        result = (\n            \"Character has made friends with a man-at-arms, who has a morale of 8. \"\n            + sex.capitalize()\n            + \" \"\n            + stringify_man_at_arms_equipment(man_at_arms_equipment())\n            + \". \"\n            + sex.capitalize()\n            + \" may be hired now or later; cost is \"\n            + str(man_at_arms_pay())\n            + \" GP/month.\"\n        )\n    elif magnitude <= 3:\n        if c[\"has family\"]:\n            result = (\n                \"Family members treat the character and \"\n                + poss\n                + \" friends well, and look forward to news and visits. Brothers, sisters, and cousins will work as hirelings with a starting morale of 9.\"\n            )\n        else:\n            lover = \"daughter\"\n            if c[\"sex\"] == \"female\":\n                lover = \"son\"\n            result = \"The character is loved by the \" + lover + \" of an artisan.\"\n    elif magnitude == 4:\n        result = (\n            \"Character has made friends with two men-at-arms, who have a morale of 9. One, a \"\n            + choice([\"man\", \"woman\"])\n            + \", \"\n            + stringify_man_at_arms_equipment(man_at_arms_equipment())\n            + \"; the other, a \"\n            + choice([\"man\", \"woman\"])\n            + \", \"\n            + stringify_man_at_arms_equipment(man_at_arms_equipment())\n            + \". Can be hired now or later; wages \"\n            + str(man_at_arms_pay())\n            + \" and \"\n            + str(man_at_arms_pay())\n            + \" GP/mo\"\n        )\n    elif magnitude == 5:\n        result = \"Character is well-known and liked around these parts. Can easily obtain as many hirelings as needed (normally each one requires a reaction roll.) Limits based on town size still apply.\"\n    elif magnitude == 6:\n        if c[\"has family\"]:\n            result = \"Character is the family favorite and will receive regular gifts from home. The family will look forward to news and visits, and treat the character's friends well. All family members will work as hirelings, with starting morale of 10.\"\n        else:\n            result = (\n                \"Character has a particularly warm relationship with the mentor who taught \"\n                + obj\n                + \" class skills. The mentor will help find hirelings or procure items when the character is in town.\"\n            )\n    elif magnitude == 7:\n        result = \"Locals treat the character as a wise teacher, and will seek him or her out for advice. There is a 50% chance for the character to obtain a night's lodging for free each time he or she visits.\"\n    elif magnitude == 8:\n        result = \"Character enjoys the favor of local farmers and peasants, and can count on a night's free lodging on each visit. Prices at nearest market are 10% lower for him or her.\"\n    elif magnitude == 9:\n        result = (\n            \"Character is so popular here that \"\n            + subj\n            + \" can count on free lodging indefinitely no matter how long the stay. This also applies to any area in which \"\n            + subj\n            + \" lives for at least 6 straight months.\"\n        )\n    elif magnitude <= 11:\n        guild = choice(\n            [\n                \"alchemist\",\n                \"brewer\",\n                \"blacksmith\",\n                \"cooper\",\n                \"carpenter\",\n                \"carver\",\n                \"shipwright\",\n                \"chandler\",\n                \"mason\",\n                \"merchant\",\n                \"weaver\",\n            ]\n        )\n        result = (\n            \"Character has made influential contacts in the local \"\n            + guild\n            + \" guild, and can count on a favor from them.\"\n        )\n    elif magnitude == 12:\n        result = \"Character has received the notice of the local government. The character may seek one favor.\"\n    elif magnitude <= 14:\n        num = randint(3, 5)\n        descriptions = []\n        for i in range(1, num + 1):\n            enumerator = \"the last, a\" if i == num else \"one \"\n            sex = choice([\"man\", \"woman\"])\n            desc = (\n                stringify_man_at_arms_equipment(man_at_arms_equipment())\n                + \": wages \"\n                + str(man_at_arms_pay())\n                + \" GP/mo\"\n            )\n            descriptions.append(enumerator + \" \" + sex + \" \" + desc)\n        result = (\n            \"Character has made friends with \"\n            + str(num)\n            + \" men-at-arms, who have a morale of 10. Can be hired now or later. They are as follows:\"\n        )\n        for d in descriptions:\n            result = result + \" \" + d + \".\"\n    elif magnitude <= 16:\n        result = \"Character has been made a member of the Illuminati. Tell nobody.\"\n    else:\n        result = \"Events surrounding the character have caused people to believe that the character had a major part in a religious miracle. The character has received the goodwill of the highest religious figure in the land, and may seek one favor.\"\n    return result\n\n\n# based on Wisdom\ndef tendencies(magnitude, c):\n    subj, obj, poss = get_gender_words(c[\"sex\"])\n    result = \"\"\n    if magnitude <= -17:\n        num = randint(3, 5) * -1\n        result = (\n            \"Character is deeply ignorant and superstitious. Saves against fear and mind-affecting spells suffer a \"\n            + str(num)\n            + \" penalty.\"\n        )\n    elif magnitude == -16:\n        num = randint(2, 3) * -1\n        result = (\n            \"Character is foolish, ignorant, and superstitious. Saves against fear and mind-affecting spells suffer a \"\n            + str(num)\n            + \" penalty.\"\n        )\n    elif magnitude <= -14:\n        result = \"Character is cowardly and lacks confidence. If stunned, a save must be made vs. crushing, or else the character will avoid all combat, including spellcasting, for 1d4 rounds.\"\n    elif magnitude == -13:\n        result = \"Character has an awful temper. If character's weapon breaks or if he or she is hit by friendly fire, a save vs. magic must be made; otherwise, for 1d4+1 rounds, the character will be -3 to hit, +1 to damage, and unaffected by fear, morale, or attempts to communicate.\"\n    elif magnitude <= -11:\n        extra_weight = int(c[\"weight\"] * Decimal(randint(15, 25) * 0.01))\n        result = \"Gluttony and laziness has caused the character to gain fat.\"\n        c[\"conditions\"][\"overweight\"] = dict()\n        c[\"conditions\"][\"overweight\"][\"magnitude\"] = extra_weight\n    elif magnitude <= -9:\n        result = (\n            \"Gullibility causes the character to frivolously spend and give away money. Whenever \"\n            + subj\n            + \" goes to market, if a Wisdom check is not successful, the character will lose 5d4 gold to the likes of confidence men, beggars, and snake-oil salesmen.\"\n        )\n    elif magnitude <= -7:\n        result = (\n            \"Character's Wisdom is treated as 2 points lower when resisting seduction.\"\n        )\n    elif magnitude <= -5:\n        result = \"Character is overly cautious about combat. Must succeed at a Wisdom check before being able to make attacks in a given combat (including discharging offensive spells, but not including backstabbing or assassination before combat proper.) Check can be attempted on character's turn each round.\"\n    elif magnitude <= -3:\n        percent = choice([10, 15])\n        negative_xp = int(classes[c[\"class\"]][\"levels\"][2][\"min XP\"] * percent * 0.01)\n        c[\"XP\"] -= negative_xp\n        result = (\n            \"Character has not quite completed \"\n            + poss\n            + f\" training, and starts the game with -{negative_xp} XP ({percent}% of the number needed to attain 2nd level).\"\n        )\n    elif magnitude <= -1:\n        result = \"Should the character sample an addictive substance, the Wisdom check to determine addiction is made with a -3 penalty.\"\n    elif magnitude == 0:\n        result = (\n            \"A painful love affair has left the character emotionally toughened. \"\n            + poss.capitalize()\n            + \" Wisdom is treated as 2 points higher when resisting seduction.\"\n        )\n    elif magnitude == 1:\n        result = \"Character has a +3 bonus on Wisdom checks to avoid addiction.\"\n    elif magnitude == 2:\n        result = \"Character is able to identify the exact time of day, to the half-hour, when out of doors.\"\n    elif magnitude == 3:\n        result = \"Character has the ability to counsel others, which will give them a +2 bonus on their next check/save against addiction, overcaution in combat, or gullibility.\"\n    elif magnitude <= 5:\n        result = \"The character's good will causes all hirelings and followers within five hexes to have +1 morale.\"\n    elif magnitude == 6:\n        xp = randint(6, 9) * 50\n        if meets_bonus_xp_minimums(c[\"class\"], ability_scores(c)):\n            c[\"XP\"] += xp\n            result = f\"Character begins adventuring with {xp} XP.\"\n        else:\n            result = \"Character gains 10% bonus XP even though their ability scores don't meed the normal bonus thresholds.\"\n    elif magnitude == 7:\n        result = \"When the possibility arises, a successful save vs. poison will reveal to the character the location of a cursed item or location within 50 feet.\"\n    elif magnitude <= 9:\n        result = \"Character can detect secret and concealed doors. Merely passing within 10 feet gives a 1/6 chance to notice it; actively searching an area gives a 2/6 chance.\"\n    elif magnitude == 10:\n        secret = choice(\n            [\n                \"the location of hidden treasure\",\n                \"the location of a shrine to a demon or obscure god\",\n                \"the location of an area which is naturally magic\",\n            ]\n        )\n        result = \"Character possesses secret knowledge: \" + secret + \".\"\n    elif magnitude <= 12:\n        result = \"One time per week, if an item worth less than 20 GP which could have been purchased at the last marketplace the character visited would now be useful, the character can retroactively purchase it.\"\n    elif magnitude <= 15:\n        result = \"Once per day, the character may ignore the effects of being stunned.\"\n    elif magnitude == 16:\n        bonus = randint(1, 3)\n        result = (\n            \"Character is very pious. All saves against fear and mind-affecting spells are made with a +\"\n            + str(bonus)\n            + \" bonus.\"\n        )\n    else:\n        bonus = randint(2, 4)\n        result = (\n            \"Character is very pious. All saves against fear and mind-affecting spells are made with a +\"\n            + str(bonus)\n            + \" bonus.\"\n        )\n    return result\n\n\ndef make_sibling():\n    age_diff = randint(1, 6)\n    sibling = (\n        choice([\"brother\", \"sister\"])\n        + \" who is \"\n        + str(age_diff)\n        + \" years \"\n        + choice([\"older\", \"younger\"])\n    )\n    return sibling\n\n\ndef get_siblings(num):\n    if num == 0:\n        return \"no siblings\"\n    else:\n        acc = []\n        while num > 0:\n            acc.append(make_sibling())\n            num = num - 1\n        res = \", \".join(acc)\n        return res\n\n\ngrandparents = [\n    \"paternal grandfather\",\n    \"paternal grandmother\",\n    \"maternal grandfather\",\n    \"maternal grandmother\",\n]\n\n\ndef get_grandparents(num):\n    if num == 0:\n        return \"no grandparents\"\n    if num == 4:\n        return \"all grandparents\"\n    else:\n        res = \", \".join(sample(grandparents, num)) + \"\"\n        return res\n\n\n# based on Strength\ndef family_composition(magnitude, c):\n    result = \"\"\n    if magnitude <= -7:\n        result = \"Orphan. No known relatives.\"\n        c[\"has family\"] = False\n    elif magnitude <= -1:\n        uncle = randint(0, 1)\n        aunt = randint(0, 1)\n        result_start = \"Few living relations: \"\n        if uncle == 1 and aunt == 1:\n            result = result_start + \"character has an aunt and uncle.\"\n        elif uncle == 1:\n            result = result_start + \"character has an uncle.\"\n        elif aunt == 1:\n            result = result_start + \"character has an aunt.\"\n        else:\n            roll = randint(2, 4)\n            result = result_start + \"character has \" + str(roll) + \" cousins.\"\n    elif magnitude == 0:\n        relative = choice(\n            [\"grandparents\"] * 3\n            + [\"aunt and uncle\"] * 2\n            + [\"grandfather\", \"grandmother\", \"aunt\", \"uncle\"]\n        )\n        mat_pat = choice([\"on father's side\", \"on mother's side\"])\n        raised_by = relative + \" \" + mat_pat\n        num_cousins = randint(1, 4) + randint(1, 4) - 2\n        result = (\n            \"Character raised by \"\n            + (raised_by)\n            + \". Number of first cousins: \"\n            + str(num_cousins)\n            + \".\"\n        )\n    elif magnitude == 1:\n        parent = choice([\"father\", \"mother\"])\n        has_grandparent = choice([True, False])\n        if has_grandparent:\n            grandparent = (\n                choice([\"paternal\", \"maternal\"])\n                + \" \"\n                + choice([\"grandfather\", \"grandmother\"])\n            )\n        else:\n            grandparent = \"no grandparents\"\n        has_sibling = choice([True, False])\n        if has_sibling:\n            sib = make_sibling()\n        else:\n            sib = \"no siblings\"\n        result = (\n            \"Character raised by \" + parent + \". Has \" + grandparent + \"; \" + sib + \".\"\n        )\n    elif magnitude == 2:\n        parents = choice([\"mother and father\", \"mother and father\", \"mother\", \"father\"])\n        grands = get_grandparents(randint(0, 1))\n        has_sib = choice([True, False])\n        if has_sib:\n            sib = make_sibling()\n        else:\n            sib = \"no siblings\"\n        result = \"Character raised by \" + parents + \". Has \" + grands + \"; \" + sib + \".\"\n    elif magnitude <= 4:\n        grands = get_grandparents(randint(0, 3))\n        numsibs = randint(0, 2) + randint(0, 2)\n        sibs = get_siblings(numsibs)\n        result = (\n            \"Character raised by mother and father. Has \" + grands + \"; \" + sibs + \".\"\n        )\n    elif magnitude <= 6:\n        grands = get_grandparents(randint(0, 4))\n        numsibs = randint(1, 2) + randint(0, 2)\n        sibs = get_siblings(numsibs)\n        result = (\n            \"Character raised by mother and father. Has \" + grands + \"; \" + sibs + \".\"\n        )\n    elif magnitude <= 8:\n        grands = get_grandparents(randint(1, 4))\n        numsibs = randint(1, 2) + randint(0, 2)\n        sibs = get_siblings(numsibs)\n        result = \"Character raised by mother and father. Has \" + grands + \"; \"\n    elif magnitude <= 12:\n        grands = get_grandparents(randint(2, 4))\n        numsibs = randint(1, 2) + randint(0, 3)\n        sibs = get_siblings(numsibs)\n        result = (\n            \"Character raised by mother and father. Has \" + grands + \"; \" + sibs + \".\"\n        )\n    elif magnitude <= 14:\n        grands = get_grandparents(randint(2, 4))\n        numsibs = randint(1, 3) + randint(0, 3)\n        sibs = get_siblings(numsibs)\n        result = (\n            \"Character raised by mother and father. Has \" + grands + \"; \" + sibs + \".\"\n        )\n    else:\n        grands = get_grandparents(randint(2, 4))\n        numsibs = randint(1, 3) + randint(1, 3)\n        sibs = get_siblings(numsibs)\n        result = (\n            \"Character raised by mother and father. Has \" + grands + \"; \" + sibs + \".\"\n        )\n    return result\n\n\n# based on Intelligence\ndef choices(magnitude, c):\n    subj, obj, poss = get_gender_words(c[\"sex\"])\n    result = \"\"\n    which_half = choice([\"right\", \"left\"])\n    if magnitude <= -16:\n        result = (\n            \"Character is missing lower half of \"\n            + which_half\n            + \" leg, and has a peg leg. One less AP than normal.\"\n        )\n    elif magnitude <= -14:\n        result = \"Character is missing non-dominant hand. Cannot use two-handed weapons. Can choose hook-hand as a weapon proficiency, but does not start with one to use.\"\n    elif magnitude == -13:\n        result = (\n            \"Character is missing \"\n            + which_half\n            + \" eye. Ranged weapons have additional attack penalty: -1 at close range, -3 at medium, -5 at long (total: -1/-5/-10)\"\n        )\n    elif magnitude == -12:\n        years = randint(1, 8) + randint(1, 8) + randint(1, 8) + randint(1, 8)\n        result = \"Character has spent \" + str(years) + \" miserable years in prison.\"\n        c[\"added age\"][\"prison\"] = years\n    elif magnitude == -11:\n        years = randint(1, 6) + randint(1, 6) + randint(1, 6)\n        result = \"Character has spent \" + str(years) + \" hard years in prison.\"\n        c[\"added age\"][\"prison\"] = years\n    elif magnitude == -10:\n        years = randint(1, 4) + randint(1, 4)\n        result = \"Character has spent \" + str(years) + \" years in prison.\"\n        c[\"added age\"][\"prison\"] = years\n    elif magnitude == -9:\n        num = randint(1, 3)\n        result = (\n            \"Missing \"\n            + str(num)\n            + \" fingers on \"\n            + which_half\n            + \" hand. -2 on attack rolls when using that hand (including two-handed weapons.)\"\n        )\n    elif magnitude == -8:\n        num = randint(2, 3)\n        result = (\n            \"Missing \"\n            + str(num)\n            + \" toes on \"\n            + which_half\n            + \" foot. -1 penalty on all Dexterity checks involving the feet.\"\n        )\n    elif magnitude == -7:\n        months = randint(3, 9)\n        if c[\"sex\"] == \"male\":\n            result = (\n                \"Character has fathered a child out of wedlock. The mother is \"\n                + str(months)\n                + \" months pregnant.\"\n            )\n        else:\n            result = \"Character is pregnant and \" + str(months) + \" months along.\"\n    elif magnitude == -6:\n        result = (\n            \"Character has been swindled of all money and possessions, leaving \"\n            + obj\n            + \" with only a shirt.\"\n        )\n        c[\"money modifier\"] = Decimal(0)\n    elif magnitude == -5:\n        result = \"Trust and generosity to family or others has left the character with little money.\"\n        c[\"money modifier\"] *= Decimal(0.3)\n    elif magnitude == -4:\n        num_cigs = randint(1, 4)\n        grams_per_oz = Decimal(28.3495)\n        # grams per cigar, converted to oz to measure pipe tobacco\n        tobacco = num_cigs * Decimal(2.5) / grams_per_oz\n        num_beers = randint(1, 4)\n        is_cig = choice([True, False])\n        consequence = \"until the addiction is fed each day, the character's Wisdom will be treated as 50% normal, and other ability scores will be treated as 90% normal.\"\n        if is_cig:\n            result = (\n                \"Character is addicted to \"\n                + str(round(num_cigs))\n                + \" cigar(s) per day (or \"\n                + str(round(tobacco, 2))\n                + \" oz pipe tobacco); \"\n                + consequence\n            )\n        else:\n            result = (\n                \"Character is addicted to \"\n                + str(num_beers)\n                + \" beer(s) per day; \"\n                + consequence\n            )\n    elif magnitude == -3:\n        if c[\"sex\"] == \"male\":\n            p = \"fathered\"\n        else:\n            p = \"mothered\"\n        kid_gender = choice([\"son\", \"daughter\"])\n        result = \"Character has \" + p + \" a bastard child, a \" + kid_gender + \".\"\n        # todo prompt player to name the kid - one reason to return a structured result ... then PROCESS that result, including has_family checks, to give the final string...!\n        # todo use he/his she/her instead of \"its (whereabouts)\"\n        # todo when family has become a data structure, choose a family member to care for the child\n        if c[\"has family\"]:\n            result += \" Your family is caring for the child.\"\n        else:\n            result += \" You gave the child up as a foundling, and its whereabouts are unknown.\"\n    elif magnitude == -2:\n        result = (\n            \"Gambling, waste, and foolishness has lost the character half \"\n            + poss\n            + \" money.\"\n        )\n        c[\"money modifier\"] *= Decimal(0.5)\n    elif magnitude == -1:\n        years = randint(2, 5)\n        result = (\n            \"A misspent youth cost the character \"\n            + str(years)\n            + \" extra years to finish training.\"\n        )\n        c[\"added age\"][\"misspent youth\"] = years\n    elif magnitude == 0:\n        scar = randint(3, 8)\n        # todo choose where the scar is, or prompt player (or print a \"fill in the blank\" line!)\n        result = (\n            \"Character has a \"\n            + str(scar)\n            + \"-inch scar on a normally-covered part of \"\n            + poss\n            + \" body, received as a child during a moment of pure stupidity.\"\n        )\n    elif magnitude <= 2:\n        result = \"Good luck has slightly increased the character's money.\"\n        c[\"money modifier\"] *= Decimal(2)\n    elif magnitude <= 4:\n        result = \"Ability to play a musical instrument of the player's choosing.\"\n    elif magnitude == 5:\n        dir = choice([\"north\", \"south\", \"east\", \"west\"])\n        result = (\n            \"Distinguished effort has earned a writ of passage, free of tolls, between this kingdom and the nearest one to the \"\n            + dir\n            + \".\"\n        )\n    elif magnitude <= 7:\n        result = (\n            \"Prudence and savings have significantly increased the character's money.\"\n        )\n        c[\"money modifier\"] *= Decimal(3)\n    elif magnitude <= 9:\n        credit = (randint(1, 6) + randint(1, 6) + randint(1, 6)) * 50\n        already_has = c.get(\"credit\")\n        if already_has:\n            c[\"credit\"][\"gp\"] += credit\n        else:\n            c[\"credit\"] = dict(gp=credit)\n        result = \"Character possesses credit with the local merchant guild.\"\n    elif magnitude <= 11:\n        num = randint(4, 6)\n        result = \"Prudence and savings have greatly increased the character's money.\"\n        c[\"money modifier\"] *= Decimal(num)\n    elif magnitude <= 13:\n        num = randint(6, 9)\n        result = \"Diligent effort has hugely increased the character's money.\"\n        c[\"money modifier\"] *= Decimal(num)\n    elif magnitude == 14:\n        result = \"Character possesses a magic item.\"\n    elif magnitude <= 16:\n        num = randint(9, 12)\n        result = \"Smart and frugal behavior has grown the character's money to an incredible degree.\"\n        c[\"money modifier\"] *= Decimal(num)\n    else:\n        title = choice([\"an academic degree\", \"former advisor to the nobility\"])\n        result = (\n            \"Character has gained a title or honor through work done during training: \"\n            + title\n            + \". The character adds 2d4 points to one skill within \"\n            + poss\n            + \" 1st-level focus, representing the knowledge they cultivated to earn the title.\"\n        )\n    return result\n\n\n# based on Charisma, requires two magnitude rolls\ndef beauty(face_mag, body_mag, c):\n    # to be added on to\n    subj, obj, poss = get_gender_words(c[\"sex\"])\n\n    def face_beauty():\n        which_side = choice([\"right\", \"left\"])\n        if face_mag <= -15:\n            scar_length = randint(3, 7)\n            scar_type = choice([\"a blade\", \"an animal bite\", \"an animal's claws\"])\n            scar = (\n                \"has \"\n                + str(scar_length)\n                + \"-inch scar across the face, received from \"\n                + scar_type\n            )\n            choices = [\n                scar,\n                \"is missing \" + poss + \" \" + which_side + \" ear\",\n                \"is missing \" + poss + \" nose\",\n                \"has a distinctly misshapen head\",\n            ]\n            return choice(choices)\n        elif face_mag <= -8:\n            choices = [\n                \"has a lazy \" + which_side + \" eye\",\n                \"has a whiny, irritating voice\",\n                \"has a crackly, annoying voice\",\n                \"suffers from halitosis if teeth are not brushed 3 times daily\",\n                \"has an always-runny nose\",\n                \"has a greasy, oily face\",\n            ]\n            return choice(choices)\n        elif face_mag <= -1:\n            nose_problem = choice([\"bulbous\", \"squashed\", \"piggish\"])\n            missing_teeth = randint(0, 1) + randint(1, 3)\n            teeth_problem = choice([\"buck\", \"crooked\", str(missing_teeth) + \" missing\"])\n            choices = [\n                \"has bushy eyebrows\",\n                \"has a caveman-like protruding brow\",\n                \"has distinctly large ears\",\n                \"has a \" + nose_problem + \" nose\",\n                \"has \" + teeth_problem + \" teeth\",\n                \"has acne scars\",\n                \"has eyes which are unnervingly close together\",\n            ]\n            return choice(choices)\n        elif face_mag <= 7:\n            choices = [\n                \"has a beautiful aquiline nose\",\n                \"has attractively straight teeth\",\n                \"has a clean and healthy complexion\",\n            ]\n            if c[\"sex\"] == \"male\":\n                choices = choices + [\"has a strong chin\"]\n            else:\n                choices = choices + [\"has lovely full lips\"]\n            return choice(choices)\n        elif face_mag <= 13:\n            choices = [\n                \"perfectly-shaped, brilliantly white teeth\",\n                \"marvelous high cheekbones\",\n            ]\n            if c[\"sex\"] == \"male\":\n                choices = choices + [\"a deep, compelling voice\", \"a strong jawline\"]\n            else:\n                choices = choices + [\n                    \"a throaty, seductive voice\",\n                    \"charmingly delicate features\",\n                ]\n            return \"has \" + choice(choices)\n        else:\n            choices = [\n                \"has a rich, velvety voice\",\n                \"has dazzling eyes\",\n                \"has a flawless complexion\",\n            ]\n            return choice(choices)\n\n    def body_beauty():\n        # redefine which_side so it can be different for face and body results\n        which_side = choice([\"right\", \"left\"])\n        if body_mag <= -16:\n            burn_percent = randint(20, 80)\n            return (\n                \"has nasty burn scars covering \"\n                + str(burn_percent)\n                + \"% of \"\n                + poss\n                + \" body\"\n            )\n        elif body_mag <= -14:\n            scar_length = randint(2, 6)\n            scar_place = choice(\n                [which_side + \" cheek\", \"chin\", \"throat\", \"nose\", \"forehead\"]\n            )\n            scar = (\n                \"has a \"\n                + str(scar_length)\n                + \"-inch scar across \"\n                + poss\n                + \" \"\n                + scar_place\n            )\n            choices = [\n                scar,\n                \"gives off a nasty, unwashable odor\",\n                \"has lumpy limbs\",\n                \"has a misshapen torso\",\n                \"is a hunchback\",\n            ]\n            return choice(choices)\n        elif body_mag <= -8:\n            scar_length = randint(1, 3)\n            scar_place = choice(\n                [which_side + \" arm\", which_side + \" hand\", \"scalp\", \"chin\"]\n            )\n            scar = (\n                \"has a \"\n                + str(scar_length)\n                + \"-inch scar across \"\n                + poss\n                + \" \"\n                + scar_place\n            )\n            choices = [\n                scar,\n                \"is bow-legged\",\n                \"is knock-kneed\",\n                \"moves with an awkward, loping gait\",\n                \"has a sunken chest\",\n            ]\n            return choice(choices)\n        elif body_mag <= -1:\n            choices = [\n                \"has a weak \" + which_side + \" leg and walks with a pronounced limp\",\n                \"has a swayback\",\n                \"has a overlong torso with stumpy legs\",\n            ]\n            return choice(choices)\n        elif body_mag == 0:\n            foot_kind = choice(\n                [\n                    \"very small feet (-5% footwear cost)\",\n                    \"small feet\",\n                    \"large feet\",\n                    \"very large feet (+5% footwear cost)\",\n                ]\n            )\n            freckle_count = choice([\"scattered\", \"many\", \"excessive\"])\n            choices = [\"has \" + foot_kind, \"has \" + freckle_count + \" freckles\"]\n            return choice(choices)\n        elif body_mag <= 7:\n            choices = [\n                \"gives off a pleasant body odor\",\n                \"has well-proportioned legs and arms\",\n                \"has an elegant neck\",\n                \"has healthy, glossy hair\",\n            ]\n            return choice(choices)\n        elif body_mag <= 13:\n            if c[\"sex\"] == \"male\":\n                choices = [\n                    \"has large, muscular shoulders\",\n                    \"has a broad, muscular back\",\n                    \"has large, muscular arms\",\n                    \"has large, muscular legs\",\n                    \"has a six-pack\",\n                ]\n            else:\n                choices = [\n                    \"has a six-pack\",\n                    \"has large, powerful thighs\",\n                    \"has a narrow waist\",\n                    \"has wide, curvy hips\",\n                ]\n            return choice(choices)\n        else:\n            choices = [\n                \"has radiant skin\",\n                \"has luxurious hair\",\n                \"always has perfect posture\",\n            ]\n            return choice(choices)\n\n    # putting it all together\n    face_result = \"Character \" + face_beauty()\n    body_result = \", and also \" + body_beauty()\n    result = face_result + body_result + \".\"\n    return result\n\n\n# helper for health_detail\ndef get_health_condition(roll, sex):\n    # the higher the number, the more severe the condition\n    # the idea is to use these with a random roll in the specified range\n    subj, obj, poss = get_gender_words(sex)\n    if roll <= 2:\n        return \"Motion sickness: repeated waves of nausea and vomiting will occur with the rhythmic motion of any vehicle.\"\n    elif roll <= 4:\n        return \"Flaring pain in joints: character finds outdoor travel difficult, and will suffer an extra point of damage each day when traveling.\"\n    elif roll <= 6:\n        return \"Low tolerance for alcohol: character takes 1d3 damage for each 8 ounces consumed.\"\n    elif roll <= 8:\n        return \"Skin chafes easily: while armored, character will suffer 1 damage per two hours spent walking or riding.\"\n    elif roll <= 10:\n        return \"Weak stomach: character will suffer 1 point of damage per pound of food eaten which is raw or unprocessed.\"\n    elif roll == 11:\n        return \"Muscle pulls: after each combat, character must save vs. explosion or suffer a -1 penalty to attacks for 3 days due to a pulled muscle. Multiple occurrences stack up to -3.\"\n    elif roll == 12:\n        colors = choice([[\"red\", \"orange\", \"green\"], [\"blue\", \"purple\", \"black\"]])\n        sentence_tail = \", \".join(colors)\n        return (\n            \"Color confusion: character cannot distinguish between \"\n            + sentence_tail\n            + \".\"\n        )\n    elif roll == 13:\n        return \"Total color blindness: character cannot distinguish between any colors of the spectrum. Everything appears to be in shades of gray. Ranged attack distance penalties are doubled.\"\n    elif roll <= 15:\n        return \"Tone deafness: character cannot distinguish chords of sound, and thus receives neither positive nor negative effects of music.\"\n    elif roll == 16:\n        return \"Chronic migraines: each day, the character has a 1 in 20 chance of being -4 to hit and -3 to saves and ability checks.\"\n    elif roll <= 18:\n        return (\n            \"Mild hemorrhaging: if character receives a bleeding wound, \"\n            + subj\n            + \" will bleed 1 extra HP per round. If bandages are used, character's wounds must be bound twice in order to stop the bleeding.\"\n        )\n    elif roll == 19:\n        return \"Shortened breath: character is unable to run for more than two rounds. If any strenuous activity (such as combat) continues for more than 10 rounds, the character must succeed at a save vs. poison or else be struck with a coughing spasm, incapacitating them for 1 round (counts as stunned) and causing 3d4 damage.\"\n    elif roll == 20:\n        return \"Brittle bones: all falling damage dice for the character are increased by one step (e.g. d6 -> d8).\"\n    elif roll == 21:\n        return \"Cataracts: character cannot make out any detail further away than 60 feet, and cannot target attacks or spells beyond that range.\"\n    elif roll == 22:\n        return (\n            \"Mild hemorrhaging: if character receives a bleeding wound, \"\n            + subj\n            + \" will bleed 2 extra HP per round. If bandages are used, character's wounds must be bound three times in order to stop the bleeding.\"\n        )\n    elif roll == 23:\n        return \"Complete deafness: character cannot hear any sound at all, and will be unable to respond to or be affected by sound. Character can still sense vibration.\"\n    elif roll == 24:\n        return \"Oversensitive skin: due to extreme discomfort, character cannot wear any kind of armor.\"\n    elif roll == 25:\n        return \"Temporary demonic possession: while talking to strangers, character may suddenly lapse into abusive outbursts. 1 in 20 chance per round.\"\n    elif roll <= 27:\n        return \"Weak heart: if reduced to -6 or fewer HP, character must make save vs. poison or suffer a heart attack and die.\"\n    elif roll == 28:\n        return (\n            \"Ghastly hemorrhaging: if character receives a bleeding wound, \"\n            + subj\n            + \" she will bleed 3 extra HP per round. If bandages are used, character's wounds must be bound four times in order to stop the bleeding.\"\n        )\n    elif roll == 29:\n        return \"Blindness: character cannot see at all, and is altogether unable to sense light. Chief among the consequences is that all attacks are treated as if attacking invisible creatures (-8 normally, -6 if someone spends 1 AP/turn helping to direct your strikes) and that all missile/thrown attacks which miss can cause friendly fire, in any direction.\"\n    else:\n        return \"Crippled legs: while the character's legs appear whole and undamaged, they are in fact entirely without feeling or strength. The character cannot walk under their own power.\"\n\n\ndef robustness(magnitude, c):\n    subj, obj, poss = get_gender_words(c[\"sex\"])\n    # get 4 health conditions, each with different possibilities of being mild or severe\n    condition4 = get_health_condition(randint(24, 30), c[\"sex\"])\n    condition3 = get_health_condition(randint(16, 24), c[\"sex\"])\n    condition2 = get_health_condition(randint(8, 16), c[\"sex\"])\n    condition1 = get_health_condition(randint(1, 8), c[\"sex\"])\n    result = \"\"\n    if magnitude <= -16:\n        result = condition4\n    elif magnitude <= -12:\n        result = condition3\n    elif magnitude <= -7:\n        result = condition2\n    elif magnitude <= -1:\n        result = condition1\n    elif magnitude == 0:\n        num_days = randint(7, 14)\n        result = (\n            \"Character is suffering from a severe cold at the start of the campaign. For the next \"\n            + str(num_days)\n            + \" days, character will be -1 to attack and damage.\"\n        )\n    elif magnitude == 1:\n        result = \"+1 save vs poison.\"\n    elif magnitude == 2:\n        result = \"+1 save vs crushing.\"\n    elif magnitude == 3:\n        result = \"+1 save vs explosion.\"\n    elif magnitude == 4:\n        result = \"+1 save vs magic.\"\n    elif magnitude == 5:\n        source = choice([\"snake or reptile\", \"insect\"])\n        result = (\n            \"Character is resistant against \"\n            + source\n            + \" poison/venom. Damage is reduced by 50%.\"\n        )\n    elif magnitude == 6:\n        source = choice([\"marine creature\", \"spider\"])\n        result = (\n            \"Character is resistant against \"\n            + source\n            + \" poison/venom. Damage is reduced by 50%.\"\n        )\n    elif magnitude <= 8:\n        result = \"Character heals 1 extra HP from a day's rest.\"\n    elif magnitude == 9:\n        result = \"+2 save vs poison.\"\n    elif magnitude == 10:\n        result = \"+2 save vs crushing.\"\n    elif magnitude == 11:\n        result = \"+2 save vs explosion.\"\n    elif magnitude == 12:\n        result = \"+2 save vs magic.\"\n    elif magnitude <= 14:\n        result = \"Character heals 2 extra HP from a day's rest.\"\n    elif magnitude <= 16:\n        result = \"+1 to all saves.\"\n    else:\n        result = \"+2 to all saves.\"\n    return result\n\n\n# governed by Dex\ndef agility_coordination(magnitude, c):\n    subj, obj, poss = get_gender_words(c[\"sex\"])\n    which_side = choice([\"right\", \"left\"])\n    result = \"\"\n    if magnitude <= -17:\n        result = (\n            \"Fused bones in the character's \"\n            + which_side\n            + \" leg causes a severe, dragging limp. Normal AP is reduced by 2.\"\n        )\n    elif magnitude == -16:\n        result = (\n            \"Character must save vs crushing before drawing a weapon; failure indicates \"\n            + subj\n            + \" cannot do it this round. No save is needed for subsequent attempts for the same weapon in the same combat.\"\n        )\n    elif magnitude == -15:\n        result = (\n            \"Character's \"\n            + which_side\n            + \" hand is deformed and useless. Opposite hand is dominant.\"\n        )\n    elif magnitude == -14:\n        result = (\n            \"Character's \"\n            + which_side\n            + \" foot is deformed, and \"\n            + subj\n            + \" has a permanent limp. Normal movement is reduced by 1.\"\n        )\n    elif magnitude == -13:\n        result = (\n            \"Character suffers from severe vertigo. If \"\n            + subj\n            + \" gets within 5 feet of a drop that is at least 15 feet, \"\n            + subj\n            + \" will fall unconscious for five minutes. Once awakened, \"\n            + subj\n            + \" will be nauseated for 2d4 rounds.\"\n        )\n    elif magnitude == -12:\n        result = (\n            \"Each time the character moves among delicate objects, including in a marketplace, \"\n            + subj\n            + \" must make a save vs. explosion to avoid accidentally breaking something.\"\n        )\n    elif magnitude == -11:\n        result = (\n            \"Character is wholly unable to ride any mount of any kind, regardless of \"\n            + poss\n            + \" character class.\"\n        )\n    elif magnitude == -10:\n        result = \"Character is unable to use two-handed weapons or weapons which strike further away than five feet.\"\n    elif magnitude == -9:\n        result = \"Character is incapable of loading or working any kind of bow, crossbow, or similar mechanical device.\"\n    elif magnitude == -8:\n        num = randint(1, 2)\n        result = (\n            \"Character suffers a -\"\n            + str(num)\n            + \" penalty to hit with all ranged weapons.\"\n        )\n    elif magnitude == -7:\n        result = (\n            \"Character requires triple normal time to mount or dismount from an animal.\"\n        )\n    elif magnitude == -6:\n        result = \"Character's armor class has a penalty of 2 when moving at a speed above normal.\"\n    elif magnitude == -5:\n        result = \"Character requires 3 more AP than normal to load any bow.\"\n    elif magnitude == -4:\n        result = \"If character causes friendly fire, it is 50% likely to be in ANY direction.\"\n    elif magnitude == -3:\n        result = \"Character's clumsiness means that dropped weapons will break on 2 in 6 instead of 1 in 6.\"\n    elif magnitude == -2:\n        result = \"Character requires 1 more AP than normal to draw any weapon.\"\n    elif magnitude == -1:\n        result = (\n            \"Character suffers from mild vertigo. If \"\n            + subj\n            + \" gets within 5 feet of a drop that is at least 15 feet, \"\n            + subj\n            + \" will become somewhat nauseated and suffer a -1 to hit. Nausea passes 1 round after moving away.\"\n        )\n    elif magnitude == 0:\n        num = randint(1, 20)\n        if num <= 18:\n            how_hurt = \"maimed\"\n        else:\n            how_hurt = \"killed\"\n        result = (\n            \"Through accidental clumsiness, the character caused a family member to be \"\n            + how_hurt\n            + \".\"\n        )\n    elif magnitude == 1:\n        result = \"Character requires no AP to draw a weapon weighing 2 lbs or less.\"\n    elif magnitude == 2:\n        result = \"Character requires no AP to draw a weapon weighing 3 lbs or less.\"\n    elif magnitude == 3:\n        result = \"The character's penalty for attacks at long range is lessened by 1.\"\n    elif magnitude == 4:\n        result = (\n            \"Character automatically takes a defensive stance when surprised, improving \"\n            + poss\n            + \" AC by 1 until no longer surprised.\"\n        )\n    elif magnitude <= 6:\n        result = (\n            \"Character is quick to find an opening in enemy defenses. \"\n            + subj.capitalize()\n            + \" has an extra +1 modifier to hit opponents from the flank or rear.\"\n        )\n    elif magnitude == 7:\n        result = \"Friendly fire committed by this character is ignored if the affected ally is within 20 feet.\"\n    elif magnitude == 8:\n        result = \"Character has a talent for cheating at cards; base 20% chance plus more favorable of: 2% per point of Dex OR (if a thief) 1/2 of pickpocketing success target.\"\n    elif magnitude == 9:\n        result = \"The character's penalty for attacks at both medium and long ranges is lessened by 1.\"\n    elif magnitude == 10:\n        result = \"Character can climb poles and free-hanging ropes as if climbing an ordinary wall.\"\n    elif magnitude == 11:\n        if c[\"class\"] == \"thief\":\n            result = \"Character notices traps as if one level higher.\"\n        else:\n            result = \"Character has a 15% chance to notice traps.\"\n    elif magnitude == 12:\n        result = (\n            \"Character can catch and handle ordinary snakes if \"\n            + subj\n            + \" successfully saves vs. poison (with a +4 bonus.)\"\n        )\n    elif magnitude == 13:\n        num = randint(1, 2)\n        if num == 1:\n            result = (\n                \"Character gains \"\n                + obj\n                + \" a +1 bonus to hit with bolas, sling, and other weapons which are spun before throwing.\"\n            )\n        else:\n            result = \"Character is +1 to hit with any weapon or object which has a splash effect, including certain orb spells.\"\n    elif magnitude == 14:\n        result = \"Character requires no AP to draw a weapon weighing 5 lbs or less.\"\n    elif magnitude == 15:\n        result = \"Character requires no AP to draw any weapon.\"\n    elif magnitude == 16:\n        body_part = choice([\"hip\", \"shoulder\"])\n        result = (\n            \"Character can dislocate \"\n            + poss\n            + \" \"\n            + which_side\n            + \" \"\n            + body_part\n            + \" at will, though doing so causes 1d4 damage.\"\n        )\n    else:\n        # todo \"with no penalties whatsoever - as if climbing an ordinary wall.\"\n        result = \"Character can climb poles and free-hanging ropes, and walk tightropes, as if climbing an ordinary wall.\"\n    return result\n\n\ndef is_leap_year(year):\n    if (year % 4) != 0:\n        return False\n    else:\n        if (year % 400) == 0:\n            return True\n        else:\n            if (year % 100) == 0:\n                return False\n            else:\n                # divisible by 4; not a 100 or 400 special case\n                return True\n\n\ndef birthday(age, current_year=1650):\n    # TODO clean this ancient noob-designed function up\n    # TODO fix off by one error with birth_year (its either one year too high or too low; the fix is to pass current_day to birthday() -- or use aproper datetime handling function FFS! I'm sure Python datetime library can convert from ordinal day number to date of the year!!!!\n    birth_year = (current_year - age) + 1\n    if is_leap_year(current_year):\n        feb_length = 29\n    else:\n        feb_length = 28\n    end_january = 31\n    end_february = feb_length + end_january\n    end_march = 31 + end_february\n    end_april = 30 + end_march\n    end_may = 31 + end_april\n    end_june = 30 + end_may\n    end_july = 31 + end_june\n    end_august = 31 + end_july\n    end_september = 30 + end_august\n    end_october = 31 + end_september\n    end_november = 30 + end_october\n    end_december = 31 + end_november\n    month_ends = [\n        end_january,\n        end_february,\n        end_march,\n        end_april,\n        end_may,\n        end_june,\n        end_july,\n        end_august,\n        end_september,\n        end_october,\n        end_november,\n        end_december,\n    ]\n    month_names = [\n        \"January\",\n        \"February\",\n        \"March\",\n        \"April\",\n        \"May\",\n        \"June\",\n        \"July\",\n        \"August\",\n        \"September\",\n        \"October\",\n        \"November\",\n        \"December\",\n    ]\n    # initialize the two-way correspondence between final ordinal day of year,\n    # and name of the month whose last day is that day of the year\n    month_relations = {}\n    for x in range(0, len(month_names)):\n        n = month_names[x]\n        end = month_ends[x]\n        month_relations[n] = end\n        month_relations[end] = n\n\n    # end_december is also the number of days in this year\n    day_of_the_year = randint(1, end_december)\n\n    birthday_month_day = \"\"\n    for end in month_ends:\n        if day_of_the_year <= end:\n            m = month_relations[end]\n            d = end - day_of_the_year + 1\n            # the extra 1 added to d is so that days are number 1 to end, instead of 0 to (end-1)\n            birthday_month_day = m + \" \" + str(d)\n            break\n        else:\n            pass\n\n    return \", \".join([birthday_month_day, str(birth_year)])\n\n\nhair_colors = [\"black\"] * 40 + [\"brown\"] * 30 + [\"blonde\"] * 20 + [\"red\"] * 10\n\n\ndef get_base_hair_color():\n    return choice(hair_colors)\n\n\ndef adjust_hair_color_for_aging(base_hair_color, age, constitution):\n    aging = randint(1, 100)  # lower is better\n    hair = namedtuple(\"hair\", [\"color\", \"description\"])\n    base_case = hair(base_hair_color, \"\")\n    if age <= 20:\n        return base_case\n    elif age <= 29:\n        if aging > 7 * constitution:\n            return hair(base_hair_color, \"prematurely graying\")\n        else:\n            return base_case\n    elif age <= 39:\n        if aging > 6 * constitution:\n            return hair(base_hair_color, \"graying\")\n        else:\n            return base_case\n    elif age <= 49:\n        if aging > 5 * constitution:\n            return hair(\"gray\", \"was once \" + base_hair_color)\n        else:\n            return base_case\n    elif age <= 59:\n        if aging > 4 * constitution:\n            return hair(\"gray\", \"was once \" + base_hair_color)\n        else:\n            return hair(base_hair_color, \"graying\")\n    else:\n        # hair is always gray at age 60 and higher\n        return hair(\"gray\", \"was once \" + base_hair_color)\n\n\ndef hair_status_after_aging(age, constitution, sex):\n    aging = randint(1, 100)  # lower is better\n\n    if sex == \"male\":\n        if age <= 20:\n            return \"\"\n        elif age <= 29:\n            if aging > 7 * constitution:\n                return \"bald\"\n            if aging > 6 * constitution:\n                return \"thinning\"\n            else:\n                return \"\"\n        elif age <= 39:\n            if aging > 6 * constitution:\n                return \"bald\"\n            elif aging > 5 * constitution:\n                return \"thinning\"\n            else:\n                return \"\"\n        elif age <= 49:\n            if aging > 5 * constitution:\n                return \"bald\"\n            elif aging > 4 * constitution:\n                return \"thinning\"\n            else:\n                return \"\"\n        elif age <= 59:\n            if aging > 3 * constitution:\n                return \"bald\"\n            elif aging > 2 * constitution:\n                return \"thinning\"\n            else:\n                return \"\"\n        else:\n            if aging > 2 * constitution:\n                return \"bald\"\n            elif aging > constitution:\n                return \"thinning\"\n            else:\n                return \"\"\n    else:\n        # sex is female\n        hair_if_male = hair_status_after_aging(age - 10, constitution, \"male\")\n        if hair_if_male == \"bald\":\n            if aging > 10 * constitution:\n                return \"bald\"\n            else:\n                return \"wispy\"\n        else:\n            return hair_if_male\n\n\ndef get_eye_color(base_hair_color):\n    score = randint(1, 100)\n    if base_hair_color == \"black\":\n        if score < 40:\n            return \"brown\"\n        elif score < 80:\n            return \"black\"\n        else:\n            return \"green\"\n\n    elif base_hair_color == \"red\":\n        if score < 50:\n            return \"green\"\n        else:\n            return \"blue\"\n\n    elif base_hair_color == \"blonde\":\n        if score < 50:\n            return \"blue\"\n        elif score < 75:\n            return \"green\"\n        else:\n            return \"brown\"\n\n    else:\n        # base_hair is brown\n        if score < 15:\n            return \"blue\"\n        elif score < 35:\n            return \"green\"\n        elif score < 60:\n            return \"brown\"\n        else:\n            return \"black\"\n\n\ndef make_final_hair(base_hair, age, con, sex):\n    hair_data = namedtuple(\"hair\", [\"haircolor\", \"hairdesc\", \"haircond\"])\n    hair_color_adj = adjust_hair_color_for_aging(base_hair, age, con)\n    hair_level_adj = hair_status_after_aging(age, con, sex)\n    if hair_level_adj == \"bald\":\n        return hair_data(\"bald\", \"was once \" + base_hair, \"\")\n    else:\n        return hair_data(\n            hair_color_adj.color, hair_color_adj.description, hair_level_adj\n        )\n\n\ndef profession_strength():\n    \"\"\"Return a Strength-based profession.\"\"\"\n    roll = randint(1, 100)\n    if roll <= 20:\n        return \"farmer\"\n    elif roll <= 40:\n        return \"fisherman\"\n    elif roll <= 55:\n        return \"sailor\"\n    elif roll <= 70:\n        return \"teamster\"\n    elif roll <= 75:\n        return \"guardsman\"\n    elif roll <= 80:\n        return \"mercenary\"\n    elif roll <= 85:\n        return \"outrider\"\n    elif roll <= 90:\n        return \"bounty hunter\"\n    elif roll <= 95:\n        return \"gladiator\"\n    else:\n        return \"weapon master\"\n\n\nartisans_food = [\n    \"brewer\",\n    \"vintner\",\n    \"baker\",\n    \"chandler\",\n    \"confectioner\",\n    \"butcher\",\n    \"tobacconist\",\n]\n\nartisans_textiles = [\n    \"tailor\",\n    \"draper\",\n    \"fuller\",\n    \"weaver\",\n    \"furrier\",\n    \"tanner\",\n    \"leatherworker\",\n    \"cobbler\",\n]\n\nartisans_wood = [\n    \"cooper\",\n    \"wainwright\",\n    \"shipwright\",\n    \"furniture maker\",\n    \"instrument maker\",\n    \"papermaker\",\n    \"bookbinder\",\n]\n\nartisans_minerals = [\"potter\", \"glassmaker\", \"glazier\", \"sculptor\"]\n\nartisans_metals = [\n    \"smelter\",\n    \"jeweller\",\n    \"lapidary\",\n    \"toolmaker\",\n    \"diemaker\",\n    \"engraver\",\n]\n\nall_artisans = artisans_food\nall_artisans.extend(artisans_textiles)\nall_artisans.extend(artisans_wood)\nall_artisans.extend(artisans_minerals)\nall_artisans.extend(artisans_metals)\n\n\ndef profession_dexterity():\n    \"\"\"Return a Dexterity-based profession.\"\"\"\n    roll = randint(1, 100)\n    if roll <= 90:\n        selected = choice(all_artisans)\n        return selected\n    elif roll <= 94:\n        return \"juggler\"\n    elif roll <= 98:\n        return \"gambler\"\n    else:\n        return \"monk\"\n\n\ndef profession_constitution():\n    \"\"\"Return a Constitution-based profession.\"\"\"\n    roll = randint(1, 100)\n    if roll <= 5:\n        return \"laborer\"\n    if roll <= 10:\n        return \"rat catcher\"\n    elif roll <= 15:\n        return \"grave robber\"\n    elif roll <= 25:\n        return \"porter\"\n    elif roll <= 35:\n        return \"bricklayer\"\n    elif roll <= 45:\n        return \"drover\"\n    elif roll <= 55:\n        return \"miner\"\n    elif roll <= 60:\n        return \"hermit\"\n    elif roll <= 68:\n        return \"blacksmith\"\n    elif roll <= 76:\n        return \"armorer\"\n    elif roll <= 84:\n        return \"weaponsmith\"\n    elif roll <= 92:\n        return \"mine foreman\"\n    else:\n        return \"explorer\"\n\n\ndef profession_intelligence():\n    roll = randint(1, 100)\n    if roll <= 10:\n        return \"trapper\"\n    elif roll <= 15:\n        return \"scribe\"\n    elif roll <= 20:\n        return \"alchemist's assistant\"\n    elif roll <= 25:\n        return \"boatman\"\n    elif roll <= 30:\n        return \"gamekeeper\"\n    elif roll <= 35:\n        return \"carpenter\"\n    elif roll <= 40:\n        return \"stonemason\"\n    elif roll <= 45:\n        return \"tinker\"\n    elif roll <= 49:\n        return \"bookkeeper\"\n    elif roll <= 51:\n        return \"tomb robber\"\n    elif roll <= 55:\n        return \"artillerist\"\n    elif roll <= 59:\n        return \"cartographer\"\n    elif roll <= 63:\n        return \"veterinarian\"\n    elif roll <= 67:\n        return \"hospitaler\"\n    elif roll <= 71:\n        return \"architect\"\n    elif roll <= 75:\n        return \"spy\"\n    elif roll <= 80:\n        return \"mage's apprentice\"\n    elif roll <= 85:\n        return \"lawyer\"\n    elif roll <= 90:\n        return \"alchemist\"\n    elif roll <= 97:\n        return \"spymaster\"\n    else:\n        return \"political advisor\"\n\n\ndef profession_wisdom():\n    roll = randint(1, 100)\n    if roll <= 15:\n        return \"husbandman\"\n    elif roll <= 25:\n        return \"prospector\"\n    elif roll <= 30:\n        return \"herbalist\"\n    elif roll <= 39:\n        return \"tutor\"\n    elif roll <= 48:\n        return \"surgeon's apprentice\"\n    elif roll <= 57:\n        return \"librarian\"\n    elif roll <= 66:\n        return \"priest\"\n    elif roll <= 75:\n        return \"steward\"\n    elif roll <= 84:\n        return \"village healer\"\n    elif roll <= 88:\n        return \"priest\"\n    elif roll <= 92:\n        return \"mortician\"\n    elif roll <= 87:\n        return \"witch hunter\"\n    elif roll <= 91:\n        return \"professor\"\n    elif roll <= 96:\n        return \"ecclesiastical vicar\"\n    else:\n        return \"ecclesiastial rector\"\n\n\n# creative_artists = [\"painter\", \"illustrator\", \"engraver\", \"sculptor\"]\n# performance_artists = [\"mime\", \"jester\", \"clown\", \"monologist\"]\n# literary_artists = [\"poet\", \"author\"]\n\n\ndef profession_charisma():\n    roll = randint(1, 1000)\n    if roll <= 450:\n        return choice([\"dancer\", \"singer\"])\n    elif roll <= 550:\n        return \"landlord\"\n    elif roll <= 625:\n        return \"buccaneer\"\n    elif roll <= 700:\n        return \"innkeeper\"\n    elif roll <= 775:\n        return \"usurer\"\n    elif roll <= 800:\n        return \"fence\"\n    elif roll <= 875:\n        return \"hitman\"\n    elif roll <= 950:\n        return \"banker\"\n    elif roll <= 960:\n        return \"squire\"\n    elif roll <= 965:\n        return \"knight\"\n    elif roll <= 970:\n        return \"guildmaster \" + choice(choice(all_artisans))\n    elif roll <= 975:\n        return \"landless noble\"\n    elif roll <= 980:\n        return \"crusader\"\n    elif roll <= 985:\n        return \"marshal\"\n    elif roll <= 990:\n        return \"lesser noble\"\n    elif roll <= 994:\n        return \"middle noble\"\n    elif roll <= 998:\n        return \"greater noble\"\n    else:\n        return \"royalty\"\n\n\nfixed_results = {\n    \"farmer\": \"can raise crops; DR 1 vs ice/cold\",\n    \"fisherman\": \"have Fishing skill\",\n    # fisherman and other \"have X skill\" results may also need to include \"(amateur)\"\n    \"sailor\": \"can handle rowboats and crew ships; +1 on checks/saves to keep balance\",\n    \"teamster\": \"have Teamstering skill; party's pack animals have +1 morale\",\n    \"guard\": \"only need 6 hrs of sleep per night; +1 awareness radius\",\n    \"mercenary\": \"gain bonus weapon proficiency\",\n    \"cavalier\": \"can ride a warhorse\",\n    \"bounty hunter\": \"have Tracking skill (from Scouting study)\",\n    \"gladiator\": \"+1 damage against all humanoids\",\n    \"weapon master\": \"bonus weapon proficiency: any weapon; +1 damage with that kind of weapon\",\n    \"baker\": \"DR 1 vs heat/fire\",\n    \"chandler\": \"DR 1 vs heat/fire\",\n    \"confectioner\": \"DR 1 vs heat/fire\",\n    \"butcher\": \"+1 on attacks with dagger\",\n    \"tobacconist\": \"+1 on checks/saves to stave off nausea (usually saves vs poison)\",\n    \"tailor\": \"party's cloth goods have +2 on saves\",\n    \"draper\": \"party's cloth goods have +2 on saves\",\n    \"weaver\": \"party's cloth goods have +2 on saves\",\n    \"furrier\": \"party's leather goods have +2 on saves\",\n    \"tanner\": \"party's leather goods have +2 on saves\",\n    \"leatherworker\": \"party's leather goods have +2 on saves\",\n    \"cobbler\": \"party's leather goods have +2 on saves\",\n    \"cooper\": \"party's wooden goods have +2 on saves\",\n    \"wainwright\": \"party's wooden goods have +2 on saves\",\n    \"shipwright\": \"party's wooden goods have +2 on saves\",\n    \"furniture maker\": \"party's wooden goods have +2 on saves\",\n    \"instrument maker\": \"pick an instrument: you can play that instrument. Crafting skill aplies only to this kind of instrument\",\n    \"potter\": \"strong grip gives 5% bonus when climbing\",\n    \"glassmaker\": \"DR 1 vs heat/fire\",\n    \"glazier\": \"DR 1 vs heat/fire\",\n    \"smelter\": \"DR 1 vs heat/fire\",\n    \"jeweller\": \"appraise gems and jewelry within 25% of actual value\",\n    \"lapidary\": \"appraise gems and jewelry within 25% of actual value\",\n    \"toolmaker\": \"appraise metal goods within 25% of actual value\",\n    \"diemaker\": \"appraise metal goods within 25% of actual value\",\n    \"engraver\": \"appraise metal goods within 25% of actual value\",\n    \"juggler\": \"while concentrating, can juggle 3 objects (0.25 to 1.5 lb each); +1 on attacks with thrown weapons\",\n    \"gambler\": \"can palm an unattended or held item (weight <= 2 oz) into sleeve or pocket\",\n    \"monk\": \"+1 natural AC\",\n    \"rat catcher\": \"+1 save vs poison\",\n    \"grave robber\": \"+1 attack vs undead\",\n    \"drover\": \"reduce daily travel damage by 1 point\",\n    \"alchemist's assistant\": \"+1 save vs explosion\",\n    \"blacksmith\": \"party's metal goods have +2 on saves\",\n    \"armorer\": \"party's armor has +1 on saves\",\n    \"weaponsmith\": \"party's armor has +1 on saves\",\n    \"mine foreman\": \"have Prospecting skill; know direction underground\",\n    \"trapper\": \"have Hunting skill\",\n    \"hermit\": \"have Foraging skill\",\n    \"tinker\": \"have Tinkering skill\",\n    \"carpenter\": \"design wooden structures; party's wooden goods have +2 on saves\",\n    \"stonemason\": \"design stone structures; party's stone goods have +2 on saves\",\n    \"boatman\": \"can handle rowboats and navigate rivers; +1 on checks/saves to keep balance\",\n    \"gamekeeper\": \"+1 attack vs animals; have Hunting skill\",\n    \"tomb robber\": \"possess a magic item\",\n    \"artillerist\": \"gain weapon proficiency with one of: cannon, ballista, catapult, trebuchet\",\n    \"veterinarian\": \"improve recovery of 3 resting animals by +2 HP/day\",\n    \"hospitaler\": \"improve recovery of 2 resting people by +2 HP/day\",\n    \"bookkeeper\": \"have Business Dealing skill\",\n    \"spy\": \"have Passing Through skill\",\n    \"husbandman\": \"have Herding skill\",\n    \"prospector\": \"have Prospecting skill\",\n    \"herbalist\": \"have Floriculture skill\",\n    \"surgeon's apprentice\": \"have Binding Wounds skill\",\n    \"priest\": \"can Turn Undead as if having 5 pts in Dweomercraft\",\n    \"steward\": \"+1 morale for followers and hirelings\",\n    \"village healer\": \"can cast a random 1st level druid or cleric spell\",\n    \"mortician\": \"can embalm corpses; +1 on attacks vs undead\",\n    \"witch hunter\": \"can Turn Undead as if having 10 pts in Dweomercraft; +1 save vs magic\",\n    \"dancer\": \"perform war dance for up to 5 rounds/day; all sighted allies within 60 feet gain +1 attack and morale\",\n    \"singer\": \"sing martial chants for up to 5 rounds/day; all non-deaf allies within 60 feet gain +1 attack and morale\",\n    \"innkeeper\": \"begin game in possession of one-story house currently serving as an inn\",\n    \"fence\": \"have black market connections in nearest market town\",\n    \"hitman\": \"assassinate as a 1st level assassin\",\n    \"landless noble\": \"hold noble title\",\n}\n\n\ndef legacy(c, prof):\n    if prof == \"peasant\":\n        return \"nothing\"\n    ownership_roll = randint(1, 4)\n    ownership = (\n        \"these lands belong to you personally\"\n        if ownership_roll == 4\n        else \"these lands can only belong to you if both your parents, and any siblings older than you, have died\"\n    )\n    if prof == \"usurer\":\n        credit = 200 * randint(1, 4)\n        already_has = c.get(\"credit\")\n        if already_has:\n            c[\"credit\"][\"gp\"] += credit\n        else:\n            c[\"credit\"] = dict(gp=credit)\n        return \"possess credit\"\n    elif prof == \"banker\":\n        credit = 100 * randint(4, 16)\n        already_has = c.get(\"credit\")\n        if already_has:\n            c[\"credit\"][\"gp\"] += credit\n        else:\n            c[\"credit\"] = dict(gp=credit)\n        return \"possess credit\"\n    elif prof == \"cavalier\" and c[\"class\"] in martial_classes:\n        return \"gain bonus weapon proficiency: one of flail, warhammer, or battleaxe\"\n    elif prof == \"bounty hunter\" and c[\"class\"] == \"ranger\":\n        return \"gain bonus weapon proficiency: any missile or throwing weapon\"\n    elif prof in all_artisans:\n        result = (\n            \"create, identify, and repair \"\n            + prof\n            + \" products; \"\n            + fixed_results.get(prof, \"\")\n        )\n        if prof in [\"bookbinder\", \"papermaker\"]:\n            c[\"literate\"] = True\n            result = result + \"; literacy\"\n        return result\n    elif prof == \"laborer\":\n        c[\"encumbrance modifier\"] += Decimal(0.05)\n        return \"increase max encumbrance by 5%\"\n    elif prof == \"porter\":\n        c[\"encumbrance modifier\"] += Decimal(0.1)\n        return \"increase max encumbrance by 10%\"\n    elif prof == \"explorer\":\n        c[\"literate\"] = True\n        return \"literacy; knowledge of a distant region; bonus weapon proficiency with unusual weapon from that region\"\n    elif prof == \"scribe\":\n        c[\"literate\"] = True\n        return (\"literacy; gain additional knowledge field\",)\n    elif prof == \"alchemist\":\n        c[\"literate\"] = True\n        return \"literacy; have Amateur status in Alchemy\"\n    elif prof == \"cartographer\":\n        c[\"literate\"] = True\n        return \"literacy; have Locate Self skill\"\n    elif prof == \"architect\":\n        c[\"literate\"] = True\n        return \"literacy; design structures; +1 attack and +10% damage if directing artillery fire\"\n    elif prof == \"lawyer\":\n        c[\"literate\"] = True\n        return \"literacy; have Solicitor skill\"\n    elif prof == \"mage's apprentice\":\n        c[\"literate\"] = True\n        return \"literacy; can cast a random 1st level mage or illusionist spell\"\n    elif prof == \"political advisor\":\n        c[\"literate\"] = True\n        return \"literacy; +10% reaction bonus on any group of three or more people; have Functionary skill\"\n    elif prof == \"spymaster\":\n        c[\"literate\"] = True\n        return \"literacy; have Amateur status in Guile study\"\n    elif \"guildmaster\" in prof:\n        c[\"literate\"] = True\n        # cut out the \"guildmaster\" part to find out which guild it is\n        x = len(\"guildmaster \")\n        guild = prof[x:]\n        result = \"literacy; \" + profession_effect(c, guild)\n        return result\n    elif prof == \"tutor\":\n        c[\"literate\"] = True\n        return \"literacy; gain additional knowledge field\"\n    elif prof == \"librarian\":\n        c[\"literate\"] = True\n        return \"literacy; gain additional knowledge specialty\"\n    elif prof == \"professor\":\n        c[\"literate\"] = True\n        return \"literacy; gain additional knowledge field and additional specialty within that field\"\n    elif prof == \"ecclesiastical vicar\":\n        c[\"literate\"] = True\n        return \"literacy; +10% reaction with clergymen\"\n    elif prof == \"ecclesiastial rector\":\n        c[\"literate\"] = True\n        return \"literacy; +10% reaction with clergymen; right of free passage throughout this region\"\n    elif prof == \"landlord\":\n        acres = (\n            randint(1, 8)\n            + randint(1, 8)\n            + randint(1, 8)\n            + randint(1, 8)\n            + randint(1, 8)\n        )\n        status = choice([\"rented out\", \"held by family\"])\n        result = (\n            \"own \"\n            + str(acres)\n            + \" acres of personal land, which is currently \"\n            + status\n        )\n        return result\n    elif prof == \"buccaneer\":\n        GP = 2 * randint(250, 500)\n        result = (\n            \"can handle boats and crew ships; begin game with 25-foot sloop, which requires \"\n            + str(GP)\n            + \" GP in repairs to be usable\"\n        )\n        return result\n    elif prof == \"squire\":\n        acres = (randint(1, 4) + randint(1, 4)) * 100\n        result = (\n            \"begin game with \" + str(acres) + \"-acre estate (640 acres = 1 sq mile)\"\n        )\n        return result\n    elif prof == \"knight\":\n        acres = (randint(1, 4) + randint(1, 4) + randint(1, 4)) * 100\n        result = (\n            \"begin game with \" + str(acres) + \"-acre estate (640 acres = 1 sq mile)\"\n        )\n        return result\n    elif prof == \"crusader\":\n        sqmi = randint(2, 4)\n        result = (\n            \"+1 morale for followers and hirelings; family possesses foreign fief of \"\n            + str(sqmi)\n            + \" square miles\"\n        )\n        return result\n    elif prof == \"marshal\":\n        sqmi = randint(3, 8)\n        result = (\n            \"+1 morale for followers and hirelings; family possesses nearby fief of \"\n            + str(sqmi)\n            + \" square miles\"\n        )\n        return result\n    elif prof == \"lesser noble\":\n        sqmi = randint(1, 6) + randint(1, 6) + randint(1, 6)\n        result = (\n            \"noble title; +1 morale for followers and hirelings; family possesses fief of \"\n            + str(sqmi)\n            + \"square miles;\"\n            + ownership\n        )\n        return result\n    elif prof == \"middle noble\":\n        sqmi = 10 * (randint(1, 4) + randint(1, 4))\n        result = (\n            \"noble title; +2 morale for followers and hirelings; family possesses fief of \"\n            + str(sqmi)\n            + \"square miles;\"\n            + ownership\n        )\n        return result\n    elif prof == \"greater noble\":\n        area = \"one map hex (346 sq mi)\"\n        result = (\n            \"noble title; +2 morale for followers and hirelings; family possesses fief of \"\n            + area\n            + \";\"\n            + ownership\n        )\n        return result\n    elif prof == \"royalty\":\n        r1 = randint(1, 4)\n        r2 = 0 if r1 > 1 else randint(1, 4)\n        r3 = 0 if r2 > 1 else randint(1, 4)\n        r4 = 0 if r3 > 1 else randint(1, 4)\n        area = r1 + r2 + r3 + r4\n        result = (\n            \"royal title; +3 morale for followers and hirelings; family possesses fief of \"\n            + str(area)\n            + \" map hexes (346 sq mi each); \"\n            + ownership\n        )\n        return result\n    else:\n        return fixed_results.get(prof, \"ask Maxwell\")\n","repo_name":"maxwelljoslyn/dnd","sub_path":"dnd/details.py","file_name":"details.py","file_ext":"py","file_size_in_byte":78255,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"2911657101","text":"import re\nimport sys\n\n\ndef main():\n    # Prompt the user for input and print the parsed result\n    print(parse(input(\"HTML: \")))\n\n\ndef parse(s):\n    if s.startswith(\"<iframe\") and s.endswith(\"</iframe>\"):\n        src = extract_src(s)\n        if src and \"youtube.com/embed/\" in src:\n            return short_link(src)\n    return None\n\n\ndef extract_src(s):\n    # Extract the URL from the src attribute of the <iframe> tag\n    match = s.split(\"src=\\\"\", 1)[-1].split(\"\\\"\", 1)[0]\n    return match\n\n\ndef short_link(url):\n    # Replace the URL with a short HTTPS URL for YouTube videos\n    url = url.replace(\"http://www.youtube.com/embed\", \"https://youtu.be\")\n    url = url.replace(\"http://youtube.com/embed\", \"https://youtu.be\")\n    url = url.replace(\"https://www.youtube.com/embed\", \"https://youtu.be\")\n    url = url.replace(\"https://youtube.com/embed\", \"https://youtu.be\")\n    return url\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"Always4ukittu/CS50","sub_path":"CS50p/watch/watch.py","file_name":"watch.py","file_ext":"py","file_size_in_byte":924,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"10504967645","text":"from flask import render_template, Blueprint\nfrom app.forms import RequestMatchingTeacherForm, BookingForm\nfrom app.models import Teacher, Goal, RequestLesson, Booking\nfrom wtforms import StringField, RadioField\n\n\nblp = Blueprint('blp', __name__)\n\ndays = {\"mon\": \"Понедельник\",\n        \"tue\": \"Вторник\",\n        \"wed\": \"Среда\",\n        \"thu\": \"Четверг\",\n        \"sun\": \"Воскресенье\",\n        \"fri\": \"Пятница\",\n        \"sat\": \"Суббота\",\n        }\n\n\n@blp.route(\"/\")\ndef index():\n    \"\"\"покажет не более 6 самых высокорейтинговых\n    из доступных сейчас учителей\n\n    can't filter them in query\"\"\"\n    teachers = Teacher.query.order_by(Teacher.rating.desc()).limit(6)\n\n    return render_template(\"index.html\",\n                           teachers=teachers,\n                           goals=Goal.query,\n                           only_free_now=True)\n\n\n@blp.route(\"/all/\")\ndef index_all():\n    \"\"\"покажет 6 самых высокорейтиноговых учителей\"\"\"\n    teachers = Teacher.query.order_by(Teacher.rating.desc()).limit(6)\n    return render_template(\"index.html\",\n                           teachers=teachers,\n                           goals=Goal.query)\n\n\n@blp.route(\"/goals/<goal>/\")\ndef goal_view(goal):\n    goal = Goal.query.filter_by(title=goal).one()\n    return render_template(\"goal.html\", goal=goal,\n                           teachers=goal.teachers)\n\n\n@blp.route(\"/profiles/<int:teacher_id>/\")\ndef profile(teacher_id):\n    teacher = Teacher.query.get(teacher_id)\n    return render_template(\"profile.html\",\n                           teacher=teacher)\n\n\n@blp.route('/booking/<int:teacher_id>/<string:day>/<string:hour>/',\n           methods=[\"GET\", \"POST\"])\ndef booking(teacher_id, day, hour):\n    form = BookingForm()\n    if form.validate_on_submit():\n        bk = Booking(teacher_id=teacher_id, day=day, hour=hour)\n        bk.student_name = form.contact_info.student_name.data\n        bk.student_phone = form.contact_info.student_phone.data\n        teacher = Teacher.query.get(teacher_id)\n        teacher.set_hour_state(day, hour, False)  # теперь время - занято\n        bk.save()\n        return render_template(\n             \"done.html\",\n             title={\"label\": \"Тема\", \"value\": \"Пробный урок\"},\n             time={\"label\": days[bk.day],\n                   \"value\": f\"{bk.hour}:00\"},\n             name=bk.student_name,\n             phone=bk.student_phone\n        )\n\n    return render_template(\"booking.html\",\n                           teacher=Teacher.query.get(teacher_id),\n                           time={\"day\": day, \"hour\": hour},\n                           days=days,\n                           form=form\n                           )\n\n\n@blp.route('/request/', methods=[\"GET\", \"POST\"])\ndef request_view():\n    form = RequestMatchingTeacherForm()\n    req_lesson = RequestLesson()\n    if form.validate_on_submit():\n        req_lesson.student_name = form.contact_info.student_name.data\n        req_lesson.student_phone = form.contact_info.student_phone.data\n        goal_title = form.goal_title.data\n        goal = Goal.query.filter_by(title=goal_title).first()\n        req_lesson.goal = goal\n        req_lesson.time_per_week = form.time_per_week.data\n        req_lesson.save()\n        return render_template(\n            'done.html',\n            title={\"label\": \"Цель занятий\",\n                   \"value\": req_lesson.goal.title_rus},\n            time={\"label\": \"Времени есть\",\n                  \"value\": f\"{req_lesson.time_per_week} часа в неделю\"},\n            name=req_lesson.student_name,\n            phone=req_lesson.student_phone\n        )\n    return render_template('request.html', form=form)\n","repo_name":"Igggr/flsk_repetit","sub_path":"app/blupr_teachers.py","file_name":"blupr_teachers.py","file_ext":"py","file_size_in_byte":3820,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"32816010645","text":"from django.conf.urls import patterns, include, url\nfrom django.contrib import admin\n\nurlpatterns = patterns('',\n    # Examples:\n    # url(r'^$', 'event_subscribe.views.home', name='home'),\n    # url(r'^blog/', include('blog.urls')),\n\n    # user interface views\n    url(r'^$', 'subscribe.views.index', name='index'),\n    url(r'^iscrizione-laboratori/', 'subscribe.views.subscribe', name='subscribe'),\n    url(r'^scelta-laboratori/', 'subscribe.views.choose', name='choose'),\n    url(r'^logout/', 'subscribe.views.logout', name='logout'),\n\n    # API views\n    url(r'^events/', 'base.views.events', name='events'),\n    url(r'^myevents/', 'subscribe.views.myevents', name='myevents'),\n    url(r'^event/(?P<happening_id>\\d+)/subscribe/$', 'subscribe.views.event_subscribe', name='event'),\n    url(r'^event/(?P<happening_id>\\d+)/unsubscribe/$', 'subscribe.views.event_unsubscribe', name='event'),\n    url(r'^units/', 'base.views.units', name='units'),\n    url(r'^validate-chief/', 'subscribe.views.validate', name='validate'),\n    url(r'^rover-list/', 'subscribe.views.get_rover_list', name='rover-list'),\n\n    url(r'^admin/', include(admin.site.urls)),\n)\n","repo_name":"route-nazionale/event_subscribe","sub_path":"event_subscribe/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1151,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"48"}
+{"seq_id":"18829468622","text":"from io import BytesIO\nimport requests\nimport tarfile\nimport shutil\nimport copy\nimport gzip\nimport sys\nimport os\n\n\nAPI_BASE = \"https://api.anaconda.org\"\n\n\ndef download(owner, pkg, ver, base, dst):\n    with requests.get(\"{}/download/{}/{}/{}/{}\".format(API_BASE, owner, pkg, ver, base)) as resp:\n        resp.raise_for_status()\n        with tarfile.open(fileobj=BytesIO(resp.content), mode=\"r:bz2\") as tar:\n            name, = [n for n in tar.getnames() if n.startswith(\"bin/sqm\")]\n            with tar.open(dst, \"w:gz\") as dst:\n                src = tar.extractfile(name)\n                info = tar.getmember(name)\n                info.name = os.path.basename(name)\n                dst.addfile(info, src)\n\n\nvendor = {\n    \"osx\": \"apple\",\n    \"win\": \"pc\",\n}\n\n\ndef main():\n    _, ver, dst = sys.argv\n    owner = \"mordred-descriptor\"\n    pkg = \"sqm\"\n\n    with requests.get(\"https://api.anaconda.org/release/{}/{}/{}\".format(owner, pkg, ver)) as resp:\n        resp.raise_for_status()\n        for dist in resp.json()['distributions']:\n            out_name = \"sqm-{}-{}-{}-{}.tar.gz\".format(\n                dist[\"version\"],\n                dist[\"attrs\"][\"machine\"],\n                vendor.get(dist[\"attrs\"][\"platform\"], \"unknown\"),\n                dist[\"attrs\"][\"subdir\"].replace(\"-\", \"\"),\n                )\n            print(\"downloading {}...\".format(out_name))\n            download(owner, pkg, ver, dist[\"basename\"], os.path.join(dst, out_name))\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"mordred-descriptor/sqm","sub_path":"download_binaries.py","file_name":"download_binaries.py","file_ext":"py","file_size_in_byte":1484,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"37927392538","text":"from odoo import fields, api, models, exceptions\nfrom odoo.exceptions import UserError\n\nclass MrpWorkorder(models.Model):\n    _inherit = 'mrp.workorder'\n\n    derivado = fields.Many2one('mrp.workcenter')\n    devolucion_exigida = fields.Boolean(string='Devolucion exigida', default=False)\n    user_derivado = fields.Boolean(string='Usuario Derivado',default=False,compute=\"_usuario_derivado\")\n    numero_informe = fields.Char(string=\"Numero de Informe\")\n    sale_order_id = fields.Char(string='Nro de Expediente')\n    descripcion_trabajos =fields.Text(compute=\"descripcionesAdicionales\",string=\"Descripción trabajos\")\n    materiales_entregados = fields.Text(string=\"Materiales Entregados\")\n    nombre_retiro= fields.Char(string='Nombre')\n    ci_retiro = fields.Char(string='Nro de C.I')\n    fecha_retiro = fields.Datetime(string=\"Fecha y Hora\")\n    observacion = fields.Text(string=\"Observación\")\n\n    sale_order = fields.Many2one('sale.order', string=\"Nro Expediente\", compute=\"m2o_sale_order\",store=True)\n\n    partner_id = fields.Many2one('res.partner', string=\"Cliente\", store=True, related=\"sale_order.partner_id\")\n    # partner = fields.Many2one('res.partner', string=\"Cliente\",compute='WorkorderPartner')\n\n\n    solicitud_interna_id = fields.Many2one('mrp.workorder.solicitud_interna', string=\"Nro Solicitud Interna\")\n\n    # @api.multi\n    def m2o_sale_order(self):\n        for rec in self:\n            if rec.sale_order_id:\n                sale_order = self.env['sale.order'].search(\n                    [('name','=',rec.sale_order_id)])\n                rec.sale_order = sale_order.id\n\n\n\n    def descripcionesAdicionales(self):\n        for this in self:\n            if this.sale_order_id:\n                expediente = self.env['sale.order'].search([['name', '=', this.production_id.origin]])\n                if expediente.descripcion_trabajos:\n                    this.descripcion_trabajos = expediente.descripcion_trabajos\n                if expediente.materiales_entregados:\n                    this.materiales_entregados = expediente.materiales_entregados\n\n\n\n    # @api.onchange('numero_informe')\n    def numeroInforme(self,sale_order_id,numero_informe,departamento_id):\n            workorders = self.env['mrp.workorder'].search([['production_id', 'ilike', sale_order_id]])\n            for workorder in workorders:\n                # if not workorder.sale_order_id  and workorder.state != 'done':\n                #     workorder.write({'sale_order_id': sale_order_id})\n                if workorder.numero_informe != numero_informe and workorder.state != 'done':\n                    if workorder.product_id.departamento_id.id == departamento_id:\n                        workorder.write({'numero_informe': numero_informe})\n\n    @api.multi\n    def numeroExpediente(self):\n        for this in self:\n            if this.production_id:\n                expediente = self.env['sale.order'].search([['name', '=', this.production_id.origin]])\n                if this.state != 'done':\n                    this.write({'sale_order_id': expediente.name})\n\n    @api.multi\n    def write(self, values):\n        r = super(MrpWorkorder, self).write(values)\n        for this in self:\n           if this._context.get('viene_de_crear') != True:\n               if 'numero_informe' in values:\n                 departamento_id = this.product_id.departamento_id\n                 departamento_id = departamento_id.id\n                 self.env['mrp.workorder'].numeroInforme(this.production_id.origin,this.numero_informe,departamento_id)\n        return r\n\n    def button_start(self):\n        for i in self:\n            if self.env.uid not in self.workcenter_id.user_ids.ids:\n                raise exceptions.ValidationError(\n                    'No tiene permisos para ejecutar Ordenes de Trabajo en éste Centro de Producción')\n            op = i.production_id\n            secuencia = i.operation_id.sequence\n            ots_anteriores = op.workorder_ids.filtered(\n                lambda x: x.operation_id.sequence < secuencia and x.id != i.id)\n            for j in ots_anteriores:\n                if j.state != 'done':\n                    raise exceptions.ValidationError(\n                        'No se puede empezar una Orden de trabajo si existen anteriores no finalizadas')\n            if i.state != 'done':\n                i.date_planned_start = fields.Datetime.now()\n            super(MrpWorkorder, i).button_start()\n\n    def button_derivar(self):\n        view = self.env.ref(\n            'mrp_intn2.mrp_workcenter_derivar_wizard_view')\n        return {\n            'name': 'Derivar a',\n            'type': 'ir.actions.act_window',\n            'view_type': 'form',\n            'view_mode': 'form',\n            'res_model': 'mrp.workorder.derivar',\n            'views': [(view.id, 'form')],\n            'view_id': view.id,\n            'target': 'new',\n            'context': {'default_workorder_id': self.id},\n        }\n\n    def button_solicitud_interna(self):\n        view = self.env.ref(\n            'mrp_intn2.mrp_workcenter_derivar_wizard_view')\n        return {\n            'name': 'Derivar a',\n            'type': 'ir.actions.act_window',\n            'view_type': 'form',\n            'view_mode': 'form',\n            'res_model': 'mrp.workorder.derivar',\n            'views': [(view.id, 'form')],\n            'view_id': view.id,\n            'target': 'new',\n            'context': {'default_workorder_id': self.id,'default_solicitud_interna': True},\n        }\n\n    @api.multi\n    def record_production(self):\n        if self.devolucion_exigida:\n            self.button_pending()\n            self.write(\n                {'state': 'ready', 'workcenter_id': self.derivado.id, 'devolucion_exigida': False, 'derivado': None})\n        elif not self.devolucion_exigida and self.derivado:\n            self.write(\n                {'state': 'ready', 'workcenter_id': self.derivado.id, 'devolucion_exigida': False, 'derivado': None})\n            return super(MrpWorkorder, self).record_production()\n        else:\n            return super(MrpWorkorder, self).record_production()\n\n    def batch_start_orders(self):\n        for this in self:\n            if this.state == 'ready':\n                this.button_start()\n\n    def batch_conclude_orders(self):\n        for this in self:\n            if this.state == 'progress':\n                this.record_production()\n\n    @api.model\n    def button_derivar_multi(self, workorders):\n\n        view = self.env.ref(\n            'mrp_intn2.mrp_workcenter_derivar_wizard_view')\n        return {\n            'name': 'Derivar a',\n            'type': 'ir.actions.act_window',\n            'view_type': 'form',\n            'view_mode': 'form',\n            'res_model': 'mrp.workorder.derivar',\n            'views': [(view.id, 'form')],\n            'view_id': view.id,\n            'target': 'new',\n            'context': {'default_workorder_ids': [(6, 0, workorders.ids)]},\n        }\n\n    def _usuario_derivado(self):\n        for this in self:\n            usuario_actual = self._uid\n            if this.derivado and this.solicitud_interna_id:\n                for users in this.workcenter_id.user_ids:\n                    user_id = users.id\n                    if user_id == usuario_actual:\n                        this.user_derivado = True\n                    else:\n                        this.user_derivado = False\n            else:\n                this.user_derivado = False\n","repo_name":"acostaw/erp_odoo","sub_path":"mrp_intn2/models/mrp_workorder.py","file_name":"mrp_workorder.py","file_ext":"py","file_size_in_byte":7390,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"1810088714","text":"import os\nfrom dotenv import load_dotenv\nimport sqlalchemy\nfrom sqlalchemy import event\nfrom sqlalchemy.orm import relationship\nfrom app.core.database import db, association_table_photo_pet\nfrom app.core.models import BaseModel\nfrom app.core.utils import convert_to_datetime\n\nAPP_ROOT = os.path.join(os.path.dirname(__file__), \"..\")\ndotenv_path = os.path.join(APP_ROOT, \".env\")\nload_dotenv(dotenv_path)\n\n\nclass Pet(BaseModel):\n    __tablename__ = \"pet\"\n    id = db.Column(db.Integer, primary_key=True)\n    name = db.Column(db.String(200), unique=True, nullable=False)\n    weight = db.Column(db.Integer())\n    birthday = db.Column(db.DateTime)\n    color = db.Column(db.String(6))\n    sex = db.Column(db.String(1))\n    intro = db.Column(db.String(1000))\n    photo_id = db.Column(db.Integer, db.ForeignKey(\"photo.id\", ondelete=\"SET NULL\"))\n\n    photos = relationship(\n        \"Photo\", secondary=association_table_photo_pet, back_populates=\"pets\"\n    )\n\n    def __init__(self, name, weight, birthday, color, sex, intro, photo_id):\n        self.name = name\n        self.weight = weight\n        self.birthday = birthday\n        self.color = color\n        self.sex = sex\n        self.intro = intro\n        self.photo_id = photo_id\n\n    def __repr__(self):\n        return self._repr(id=self.id, name=self.name)\n\n    @property\n    def serialize(self):\n        \"\"\"Return object data in easily serializable format\"\"\"\n        from app.core.models import Photo\n\n        photo_url = \"\"\n        if self.photo_id:\n            image_id = Photo.query.get(self.photo_id).image_id\n            photo_url = f\"https://drive.google.com/uc?export=view&id={image_id}\"\n\n        return {\n            \"id\": self.id,\n            \"name\": self.name,\n            \"weight\": self.weight,\n            \"intro\": self.intro,\n            \"sex\": self.sex,\n            \"photo\": {\"id\": self.photo_id, \"url\": photo_url},\n            \"birthday\": self.birthday.isoformat(),\n            \"color\": self.color,\n        }\n\n    @classmethod\n    def create(cls, pet_columns):\n        try:\n            pet = cls(**pet_columns)\n            pet.create()\n            return pet, \"\"\n        except sqlalchemy.exc.IntegrityError as e:\n            return False, f\"Pet name '{pet['name']}' already exists.\"\n\n    @classmethod\n    def get_pets(cls) -> list:\n        return [pet.serialize for pet in cls.query.all()]\n\n    @classmethod\n    def get_by_name(cls, name) -> dict:\n        pet = cls.query.filter_by(name=name).first()\n        return pet.serialize if pet else None\n\n    @classmethod\n    def update_name(cls, id_, name):\n        pet = cls.query.get(id_)\n        pet.name = name\n        db.session.commit()\n\n    @classmethod\n    def get_pet_model_list_from_str_action_ids(cls, str_pet_ids):\n        pet_ids = str_pet_ids.split(\",\")\n        return [cls.query.get(int(pet_id)) for pet_id in pet_ids]\n\n\n@event.listens_for(Pet.__table__, \"after_create\")\ndef insert_initial_values(*args, **kwargs):\n    initial_pets = [\n        Pet(\n            os.getenv(\"SEVI_NAME\"),\n            os.getenv(\"SEVI_WEIGHT\"),\n            convert_to_datetime(os.getenv(\"SEVI_BIRTHDAY\")),\n            os.getenv(\"SEVI_COLOR\"),\n            os.getenv(\"SEVI_SEX\"),\n            os.getenv(\"SEVI_INTRO\"),\n            None,\n        ),\n        Pet(\n            os.getenv(\"ALBI_NAME\"),\n            os.getenv(\"ALBI_WEIGHT\"),\n            convert_to_datetime(os.getenv(\"ALBI_BIRTHDAY\")),\n            os.getenv(\"ALBI_COLOR\"),\n            os.getenv(\"ALBI_SEX\"),\n            os.getenv(\"ALBI_INTRO\"),\n            None,\n        ),\n    ]\n    db.session.add_all(initial_pets)\n    db.session.commit()\n","repo_name":"daehan0226/furfellas_server","sub_path":"flask/app/core/models/pet.py","file_name":"pet.py","file_ext":"py","file_size_in_byte":3597,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"69944316626","text":"\r\n# Import modules\r\nimport os\r\nimport nltk\r\nimport string\r\nimport spacy\r\nnlp_sm = spacy.load('en_core_web_sm')\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\nimport plotnine as pn\r\n\r\nfrom time import time\r\nfrom scipy import stats\r\nfrom plydata.cat_tools import *\r\nfrom funs_support import makeifnot, jaccard, linreg, capture\r\nfrom funs_cipher import encipherer\r\n\r\nletters = [l for l in string.ascii_lowercase]\r\n\r\nn_letters_seq = np.arange(2,26+1,2).astype(int)\r\nholder = []\r\nfor n_letters in n_letters_seq:\r\n    holder.append(encipherer.n_encipher(n_letters))\r\ndf_ncomb = pd.concat(holder).reset_index(drop=True)\r\nprint(df_ncomb)\r\n\r\n\r\n#################################\r\n# --- (1) DOWNLOAD THE DATA --- #\r\n\r\ndir_code = os.getcwd()\r\ndir_data = os.path.join(dir_code, '..', 'data')\r\nmakeifnot(dir_data)\r\ndir_output = os.path.join(dir_code, '..', 'output')\r\nmakeifnot(dir_output)\r\ndir_figures = os.path.join(dir_code, '..', 'figures')\r\nmakeifnot(dir_figures)\r\n\r\n# (1) Download the ngrams data for frequency\r\npath_ngram = os.path.join(dir_data,'words_ngram.txt')\r\nif not os.path.exists(path_ngram):\r\n    os.system('wget -q -O ../data/words_ngram.txt https://norvig.com/ngrams/count_1w.txt')\r\nelse:\r\n    print('words_ngram.txt already exists')\r\n\r\n# (2) Download the curated dataset\r\npath_words = os.path.join(dir_data,'words_corncob.txt')\r\nif not os.path.exists(path_words):\r\n    print('Downloading')\r\n    os.system('wget -q -O ../data/words_corncob.txt http://www.mieliestronk.com/corncob_lowercase.txt')\r\nelse:\r\n    print('corncob_lowercase.txt already exists')\r\n\r\n################################\r\n# --- (2) LOAD THE DATASET --- #\r\n\r\n# (1) Load the Ngrams\r\ndf_ngram = pd.read_csv(path_ngram,sep='\\t',header=None).rename(columns={0:'word',1:'n'})\r\ndf_ngram = df_ngram[~df_ngram['word'].isnull()].reset_index(drop=True)\r\n\r\n# (2) Load the short word set\r\ndf_words = pd.read_csv(path_words,sep='\\n',header=None).rename(columns={0:'word'})\r\ndf_words = df_words[~df_words['word'].isnull()].reset_index(drop=True)\r\n\r\n# Overlap?\r\nn_overlap = df_words.word.isin(df_ngram['word']).sum()\r\nprint('A total of %i short words overlap (out of %i)' % (n_overlap, df_words.shape[0]))\r\n\r\n# Merge datasets in the intersection\r\ndf_merge = df_ngram.merge(df_words,'inner','word')\r\ndf_merge = df_merge.assign(n_sqrt=lambda x: np.sqrt(x['n']), n_log=lambda x: np.log(x['n']))\r\n\r\n# Add on the parts of speech\r\npos_lst = [z[1] for z in nltk.pos_tag(df_merge['word'].to_list())]\r\ndf_merge.insert(1,'pos',pos_lst)\r\n# Get PoS defintions\r\npos_def = pd.Series([capture(nltk.help.upenn_tagset,p) for p in df_merge['pos'].unique()])\r\npos_def = pos_def.str.split('\\\\:\\\\s|\\\\n',expand=True,n=3).iloc[:,:2]\r\npos_def.rename(columns={0:'pos',1:'def'},inplace=True)\r\ndf_merge = df_merge.merge(pos_def, 'left', 'pos')\r\n\r\n\r\n##################################\r\n# --- (3) SUMMARY STATISTICS --- #\r\n\r\n# Examine the score frequency by percentiles\r\np_seq = np.arange(0.01,1,0.01)\r\ndat_n_q = df_merge.melt('word',None,'tt').groupby('tt').value.quantile(p_seq).reset_index()\r\ndat_n_q.rename(columns={'level_1':'qq'}, inplace=True)\r\ndat_n_q.tt = pd.Categorical(dat_n_q.tt,['n','n_sqrt','n_log'])\r\ndi_tt = {'n':'n', 'n_sqrt':'sqrt','n_log':'log'}\r\n\r\n# DISTIRUBTION OF WORD FREQUENCIES\r\ngg_q = (pn.ggplot(dat_n_q, pn.aes(x='qq',y='value')) + pn.geom_path() +\r\n       pn.theme_bw() + pn.facet_wrap('~tt',scales='free_y') +\r\n       pn.labs(y='Weight', x='Quantile') +\r\n       pn.theme(subplots_adjust={'wspace': 0.25}))\r\ngg_q.save(os.path.join(dir_figures,'gg_q.png'),width=9, height=2.5)\r\n\r\nprint('The ten most and least common words in the corpus')\r\nprint(pd.concat([df_merge.head(10)[['word','n']].reset_index(None,True),\r\n           df_merge.tail(10)[['word','n']].reset_index(None,True)],1))\r\n\r\nletter_freq = df_merge['word'].apply(lambda x: list(x),1)\r\nletter_freq = letter_freq.reset_index().explode('word')\r\nletter_freq.rename(columns={'word':'letter','index':'idx'}, inplace=True)\r\ntmp = df_merge.rename_axis('idx')['n'].reset_index()\r\nletter_freq_n = letter_freq.merge(tmp).groupby(['letter']).apply(lambda x: pd.Series({'weight':x['n'].sum(), 'raw':len(x)})).reset_index()\r\nletter_freq_n = letter_freq_n.sort_values('weight',ascending=False).reset_index(drop=True)\r\ntop12_letters = letter_freq_n['letter'].head(12)\r\nprint(', '.join(top12_letters))\r\n\r\n#############################\r\n# --- (4) SANITY CHECKS --- #\r\n\r\n# Do the sanity checks\r\nenc = encipherer(df_merge, 'word')\r\nn_lipogram = enc.n_encipher(n_letters=4)['n_lipogram'][0]\r\n\r\n# (i) Enumerate through all possible letter pairings\r\nholder = []\r\nfor i in range(1, n_lipogram+1):\r\n    if i % 500 == 0:\r\n        print('Iteration %i of %i' % (i, n_lipogram))\r\n    enc.set_letters(n_letters=4, idx_letters=i)\r\n    holder.append(enc.letters)\r\ndf_letters = pd.DataFrame(holder)\r\ndf_letters.columns = ['l'+str(i+1) for i in range(4)]\r\nassert not df_letters.duplicated().any()  # Check that no duplicate values\r\ndf_letters\r\n\r\n# (ii) Enumerate through all possible ciphers\r\nenc = encipherer(df_merge, 'word')\r\nenc.set_letters(n_letters=12, idx_letters=1)\r\nn_encipher = enc.n_encipher(enc.n_letters)['n_encipher'][0]\r\n\r\nholder = []\r\nfor i in range(1, n_encipher+1):\r\n    if i % 500 == 0:\r\n        print('Iteration %i of %i' % (i, n_encipher))\r\n    enc.set_encipher(idx_pairing=i)\r\n    holder.append(enc.mat_pairing.flatten())\r\ndf_encipher = pd.DataFrame(holder)\r\nidx_even = df_encipher.columns % 2 == 0\r\ntmp1 = df_encipher.loc[:,idx_even]\r\ntmp2 = df_encipher.loc[:,~idx_even]\r\ntmp2.columns = tmp1.columns\r\ndf_encipher = tmp1 + ':' + tmp2\r\ndf_encipher.columns = ['sub'+str(i+1) for i in range(6)]\r\nassert not df_encipher.duplicated().any()  # Check that no duplicate values\r\ndf_encipher\r\n\r\n\r\n# (ii) Enumerate through all possible pairing/ciphers\r\nenc = encipherer(df_merge, 'word')\r\nenc.set_letters(n_letters=4, idx_letters=1)\r\nn_encipher = enc.idx_max['n_encipher']\r\nn_lipogram = enc.idx_max['n_lipogram']\r\nn_total = enc.idx_max['n_total']\r\nholder = pd.Series(np.repeat('',n_total))\r\nk = 0\r\nfor j in range(1, n_lipogram+1):\r\n    if j % 500 == 0:\r\n        print('Iteration %i of %i' % (j, n_lipogram))\r\n    enc.set_letters(n_letters=4, idx_letters=j)\r\n    for i in range(1, n_encipher+1):\r\n        enc.set_encipher(idx_pairing=i)\r\n        res = pd.Series(enc.str_pairing)\r\n        holder[k] = res\r\n        k += 1\r\nres_all = holder.reset_index(drop=True).apply(lambda x: x[0])\r\nassert not res_all.duplicated().any()\r\n\r\n# (iv) High-quality mapping\r\nenc = encipherer(df_merge, 'word')\r\nenc.set_letters(letters='etoaisnrlchd')\r\nenc.set_encipher(idx_pairing=1)\r\nenc.get_corpus()\r\nprint(enc.str_pairing)\r\nenc.df_encipher[['word','mirror','pos','def']]\r\n\r\n##############################\r\n# --- (5) CIPHER QUALITY --- #\r\n\r\n# (i) Score the ciphers\r\nenc = encipherer(df_merge, 'word')\r\nenc.set_letters(letters='etoaisnrlchd')\r\nenc.score_ciphers(cn_weight='n_log',set_best=True)\r\n\r\n# (ii) Visualize the distribution\r\ngg_score_w = (pn.ggplot(enc.df_score, pn.aes(x='n_word',y='weight')) + \r\n    pn.theme_bw() + pn.geom_point() + \r\n    pn.ggtitle('Relationship between word count and score') + \r\n    pn.geom_smooth(method='lm',se=False) + \r\n    pn.labs(y='Weight', x='# of words'))\r\ngg_score_w.save(os.path.join(dir_figures,'gg_score_w.png'),width=5,height=3.5)\r\n\r\nlong_score = enc.df_score.melt('idx',None,'metric')\r\ndi_metric = {'n_word':'# of words', 'weight':'Weight'}\r\ngg_dist_score = (pn.ggplot(long_score, pn.aes(x='value',fill='metric')) + \r\n    pn.theme_bw() + pn.guides(fill=False) + \r\n    pn.geom_histogram(bins=25,color='black') + \r\n    pn.ggtitle('Distribution of score and word count') + \r\n    pn.facet_wrap('~metric',labeller=pn.labeller(metric=di_metric),scales='free_x') + \r\n    pn.theme(subplots_adjust={'wspace': 0.20}, axis_title_x=pn.element_blank()) + \r\n    pn.labs(y='Frequency'))\r\ngg_dist_score.save(os.path.join(dir_figures,'gg_dist_score.png'),width=9,height=4)\r\n\r\n\r\n# Add on the cipher pairs to be saved for later\r\nenc2 = encipherer(df_merge, 'word')\r\nenc2.set_letters(letters='etoaisnrlchd')\r\nholder = []\r\nfor i in range(1, n_encipher+1):\r\n    if (i + 1) % 500 == 0:\r\n        print(i+1)\r\n    enc2.set_encipher(idx_pairing=i)\r\n    holder.append(enc2.str_pairing)\r\ndat_mapping = pd.Series(holder).str.split(',',5,True)\r\ndat_mapping = pd.get_dummies(dat_mapping,drop_first=False)\r\ndat_mapping.columns = pd.Series(dat_mapping.columns.values).str.split('_',1,True)[1]\r\ndat_mapping = dat_mapping.rename_axis('idx').reset_index()\r\ndat_mapping = dat_mapping.melt('idx',None,'cn','y')\r\ndat_mapping = dat_mapping.groupby(['idx','cn'])['y'].sum().reset_index()\r\ndat_mapping = dat_mapping.pivot('idx','cn','y')\r\ndat_mapping = pd.concat(objs=[enc.df_score,dat_mapping],axis=1)\r\ndat_mapping.to_csv(os.path.join(dir_output, 'dat_mapping.csv'),index=False)\r\ndat_mapping = pd.read_csv(os.path.join(dir_output, 'dat_mapping.csv'))\r\nXbin = dat_mapping.drop(columns=['idx','n_word','weight'])\r\ncn_drop = Xbin.columns[0]\r\nprint('Dropping column - %s' % cn_drop)\r\nXbin = Xbin.drop(columns=cn_drop)\r\nyval = dat_mapping['weight'].values\r\n\r\nmdl_ols = linreg(inference=True)\r\nmdl_ols.fit(X=Xbin.values, y=yval)\r\ndat_ols = pd.DataFrame({'bhat':mdl_ols.bhat,'se':mdl_ols.se}).assign(z=lambda x: x['bhat']/x['se'])\r\ndat_ols.insert(0,'cn',['Intercept'] + list(Xbin.columns))\r\ndat_ols = dat_ols.assign(pval=lambda x: 2*stats.norm.cdf(-np.abs(x['z'])))\r\ndat_ols = dat_ols.assign(bonfer=lambda x: np.minimum(len(x)*x['pval'], 1))\r\ndat_ols =  dat_ols.assign(is_sig=lambda x: x['bonfer'] < 0.05)\r\ndat_ols = dat_ols.query('cn!=\"Intercept\"').assign(cn=lambda x: cat_reorder(x['cn'], x['bhat']))\r\n\r\ngtit = 'Linear regression on pair to weight\\nDropped column %s' % cn_drop\r\ngg_ols = (pn.ggplot(dat_ols, pn.aes(x='cn',y='bhat',color='is_sig')) +\r\n          pn.theme_bw() + pn.geom_point() + pn.ggtitle(gtit) + \r\n          pn.geom_hline(yintercept=0,linetype='--') +\r\n          pn.labs(y='Coefficient',x='Cipher pairing') +\r\n          pn.scale_color_discrete(name='Statistically significant (Bonferroni correction)') +\r\n          pn.theme(legend_position=(0.50,0.70),axis_text_x=pn.element_text(angle=90,size=10)))\r\ngg_ols.save(os.path.join(dir_figures,'gg_ols.png'), width=12, height=5)\r\n\r\n\r\n# (iii) Look into the distribution\r\npair_range = dat_mapping.drop(columns='idx').melt(['n_word','weight'],None,'pair')\r\npair_range = pair_range.query('value==1').drop(columns='value')\r\npair_ord = pair_range.groupby('pair')['n_word'].mean().sort_values(ascending=False).index.to_list()\r\npair_range['pair'] = pd.Categorical(pair_range['pair'], pair_ord)\r\n\r\ngg_range = (pn.ggplot(pair_range, pn.aes(x='pair',y='n_word')) + \r\n    pn.theme_bw() + pn.geom_boxplot() + \r\n    pn.labs(y='# of words',x='Cipher pairing') + \r\n    pn.theme(axis_text_x=pn.element_text(angle=90,size=10)))\r\ngg_range.save(os.path.join(dir_figures,'gg_range.png'), width=12, height=5)\r\n\r\n\r\n# (iv) Look for hello world\r\nenc3 = encipherer(df_merge, 'word')\r\nenc3.set_letters(letters='etoaisnrlhdw')\r\nn_max = enc3.idx_max['n_encipher']\r\nholder = np.zeros(n_max, dtype=int)\r\nfor j in range(1,n_max+1):\r\n    if (j + 1) % 50 == 0:\r\n        print(j+1)\r\n    enc3.set_encipher(idx_pairing=j)\r\n    enc3.get_corpus()\r\n    n_w = enc3.df_encipher['word'].isin(['hello','world']).sum()\r\n    holder[j-1] = n_w\r\ndf_hw = pd.DataFrame({'idx':range(1,n_max+1),'n':holder})\r\ndf_hw = df_hw.sort_values('n',ascending=False).reset_index(drop=True)\r\nholder = []\r\nfor i in df_hw.query('n>0').idx:\r\n    enc3.set_encipher(idx_pairing=i)\r\n    enc3.get_corpus()\r\n    qq = enc3.df_encipher[enc3.df_encipher['word'].isin(['hello','world'])]\r\n    enc3.df_encipher[['word','mirror']].sort_values('word')\r\n    holder.append(qq[['word','mirror']])\r\n    # word = qq['word'].values[0]\r\n    # mirror = qq['mirror'].values[0]\r\n    # print('word = %s, mirror = %s, hash: %s' % (word, mirror, enc3.str_pairing))\r\nzz = pd.concat(holder).drop_duplicates().sort_values('word').reset_index(drop=True)\r\n\r\n","repo_name":"ErikinBC/mirror_alphabet","sub_path":"explore_12.py","file_name":"explore_12.py","file_ext":"py","file_size_in_byte":11892,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"19540048186","text":"from flask import Flask,render_template,url_for,redirect,session,request,jsonify,url_for\nfrom flask_bootstrap import Bootstrap\nfrom flask_wtf import FlaskForm\nfrom wtforms import FloatField,SubmitField,TextField,BooleanField\nfrom wtforms.validators import InputRequired\nimport numpy as np\nimport joblib\nfrom keras.models import load_model\n\n\ndef return_predictions(model,scaler,sample_json):\n\tmilk=sample_json[\"Milk\"]\n\ttea=sample_json[\"Tea\"]\n\tcoffe=sample_json[\"Coffe\"]\n\tonion=sample_json[\"Onion\"]\n\tyogurt=sample_json[\"Yogurt\"]\n\tbread=sample_json[\"Bread\"]\n\tketchup=sample_json[\"Ketchup\"]\n\tegg=sample_json[\"Egg\"]\n\tfish=sample_json[\"Fish\"]\n\tsoftdrinks=sample_json[\"SoftDrink\"]\n\trice=sample_json[\"Rice\"]\n\tmonths=sample_json[\"periods\"]\n\n\tdata=[[milk,tea,coffe,onion,yogurt,bread,ketchup,egg,fish,softdrinks,rice]]\n\tscaled_data=scaler.transform(data)\n\n\tlength=1\n\tn_features=11\n\tforecast=[]\n\tfirst_eval_batch=scaled_data[-length:]\n\tcurrent_batch=first_eval_batch.reshape((1,length,n_features))\n\n\tfor i in range(months):\n\t\tcurrent_pred=model.predict(current_batch)[0]\n\t\tforecast.append(current_pred)\n\t\tcurrent_batch=np.append(current_batch[:,1:,:],[[current_pred]],axis=1)\n\treturn np.round(scaler.inverse_transform(forecast)).tolist()\n\n\n\n\napp= Flask(__name__)\napp.config['SECRET_KEY']=\"this must be secret\"\nBootstrap(app)\n\n\nclass Form(FlaskForm):\n\tperiods=FloatField('Months',validators=[InputRequired(message='number only')])\n\tmilk=BooleanField(\"Milk\")\n\ttea=BooleanField(\"Tea\")\n\tcoffe=BooleanField(\"Coffee\")\n\tonion=BooleanField(\"Onion\")\n\tyogurt=BooleanField(\"Yogurt\")\n\tbread=BooleanField(\"Bread\")\n\tketchup=BooleanField(\"Ketchup\")\n\tegg=BooleanField(\"Egg\")\n\tfish=BooleanField(\"Fish\")\n\tsoftdrinks=BooleanField(\"SoftDrink\")\n\trice=BooleanField(\"Rice\")\n\tsubmit=SubmitField('Submit')\n\n\n\n@app.route('/form',methods=['GET','POST'])\ndef form():\n\tform=Form()\n\tif form.validate_on_submit():\n\t\tsession['periods']=int(form.periods.data)\n\t\tsession['milk']=float(form.milk.data)\n\t\tsession['tea']=float(form.tea.data)\n\t\tsession['coffe']=float(form.coffe.data)\n\t\tsession['onion']=float(form.onion.data)\n\t\tsession['yogurt']=float(form.yogurt.data)\n\t\tsession['bread']=float(form.bread.data)\n\t\tsession['ketchup']=form.ketchup.data\n\t\tsession['egg']=float(form.egg.data)\n\t\tsession['fish']=float(form.fish.data)\n\t\tsession['softdrinks']=float(form.softdrinks.data)\n\t\tsession['rice']=float(form.rice.data)\n\t\t\n\t\treturn redirect(url_for('predictions'))\n\treturn render_template(\"form.html\",form=form)\n\nmodel=load_model('Data.h5')\nscaler_model=joblib.load('Data.pkl')\n\n\n@app.route('/predictions')\ndef predictions():\n\tcontent={}\n\n\tcontent['periods']=session['periods']\n\tcontent['Milk']=session['milk']\n\tcontent['Tea']=session['tea']\n\tcontent['Coffe']=session['coffe']\n\tcontent['Onion']=session['onion']\n\tcontent['Yogurt']=session['yogurt']\n\tcontent['Bread']=session['bread']\n\tcontent['Ketchup']=session['ketchup']\n\tcontent['Egg']=session['egg']\n\tcontent['Fish']=session['fish']\n\tcontent['SoftDrink']=session['softdrinks']\n\tcontent['Rice']=session['rice']\n\n\tresults=return_predictions(model,scaler_model,content)\n\n\treturn render_template('prediction.html',results=results,content=content)\n\n\n@app.route('/api',methods=['POST'])\ndef api():\n\tcontent=request.json\n\tresults=return_predictions(model,scaler_model,content)\n\treturn jsonify({'results':results})\n\nif __name__ == '__main__':\n\tapp.run(debug=True)\n","repo_name":"yousuf9292/todoappai","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3363,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"23774670390","text":"import socket\nimport glob\n\nIP = socket.gethostname()\nPORT = 8820\n\n\ndef valid_request(request):\n    valid = 1\n    return valid\n\ndef send_request_to_server(my_socket, request):\n    request_len = str(len(request))\n    zfill_len = request_len.zfill(2)\n    request = zfill_len + request\n    my_socket.send(request.encode())\n\n\n\n    if request[2::] == 'screenshot':\n        respons_type = 'image'\n    elif request[2::] == 'dir':\n        respons_type = 'dir'\n    else:\n        respons_type = 'reg'\n\n    return respons_type\n\n\n\ndef handle_server_response(my_socket, request, request_type):\n    \n    if request_type == 'image':\n        f = open('screenshot2.png', 'wb')\n        load_image = my_socket.recv(9999)\n        while load_image[0] != 0:\n            f.write(load_image)\n            load_image = my_socket.recv(9999)\n        print(f)\n    elif request_type == 'dir':\n        data = my_socket.recv(1024).decode()\n        print(data)\n    else:\n        data = my_socket.recv(1024).decode()\n        print(data)\n\n\ndef main():\n    # open socket with the server\n    my_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    my_socket.connect((IP, PORT))\n\n    # print instructions\n    print('Welcome to remote computer application. Available commands are:\\n')\n    print('TAKE_SCREENSHOT\\nSEND_FILE\\nDIR\\nDELETE\\nCOPY\\nEXECUTE\\nEXIT')\n\n    done = False\n    # loop until user requested to exit\n    while not done:\n        request = input(\"Please enter command:\\n\")\n        if valid_request(request):\n            request_type = send_request_to_server(my_socket, request)\n            handle_server_response(my_socket, request, request_type)\n            if request == 'EXIT':\n                done = True\n    my_socket.close()\n\nif __name__ == '__main__':\n    main()","repo_name":"almog674/Network-projects","sub_path":"task 2.7/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":1754,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"72345709905","text":"from datetime import datetime, timedelta\nimport random\nimport re\nimport string\nfrom logger import logger\nimport CONST\nimport hashlib\nimport time\nfrom all_get_repo_func import *\nfrom all_repo_get_func import *\nfrom model.poll_model import get_poll_model\n\ndef get_url_with_no_params(url):\n    return url.split('?')[0]\n\n\ndef safe_get(dict, keys, default=None):\n    if dict is None:\n        return default\n\n    for key in keys:\n        if key not in dict:\n            return default\n        dict = dict[key]\n        if dict is None:\n            return default\n\n    return dict\n\n\ndef get_values_by_key(d, key, value_compare=None):\n    values = []\n\n    if isinstance(d, dict):\n        for k, v in d.items():\n            if k == key:\n                if value_compare is None or value_compare(v):\n                    values.append(d)\n            else:\n                values.extend(get_values_by_key(v, key, value_compare))\n    elif isinstance(d, list):\n        for item in d:\n            values.extend(get_values_by_key(item, key, value_compare))\n    \n    return values\n\n\ndef generate_random_id(length=8):\n    # define the set of characters to use in the random ID\n    characters = string.ascii_letters + string.digits\n\n    # generate a random ID by selecting `length` characters from the set of allowed characters\n    random_id = ''.join(random.choices(characters, k=length))\n\n    # insert dashes or spaces to make the ID more readable\n    random_id = '-'.join([random_id[i:i+4] for i in range(0, len(random_id), 4)])\n\n    return random_id \n\n\ndef save_data_user_name(repo, effective_user_id, effective_user):\n    chat_id_names = repo.compute_if_absent('chat_id_names', lambda k: dict())\n    chat_id_names[effective_user_id] = f'@{effective_user.username}'\n    repo.save()\n    pass\n    \ndef save_data_for_quiz(update, context, message, question, poll_type):\n    msg_poll_id = f'{message.message_id}'\n    logger.info(f'msg_poll_id : {msg_poll_id}')\n    logger.info(f'message.poll.id : {message.poll.id}')\n    repo = get_repo_bot(context)\n    poll_data = dict()\n    payload =  {\n        message.poll.id: {\n            \"timestamp\": time.time(),\n            \"chat_id\": update.effective_chat.id,   \n            \"message_id\": message.message_id,\n            \"user_answers\": {},\n            \"poll_type\": poll_type,\n            \"question\": question[\"question\"],\n            \"options\": question[\"answers\"],\n            \"correct_options_id\": question[\"correct\"]      \n            }\n    }\n    variable_question = question[\"question\"]\n    string_md5_question = hashlib.md5(variable_question.encode()).hexdigest()\n    all_quiz_info_in_one = repo.compute_if_absent(\"all_quiz_info_in_one\", lambda k: dict())\n    payload_quiz_info = {\n                string_md5_question: {\n                    \"timeline_create\": [\n                        {\n                            \"poll_id\": message.poll.id,\n                            \"user_answer_correct\": [],\n                            \"user_answer_wrong\": []\n                        }\n                    ]\n                }\n            }\n    all_quiz_info_in_one.update(payload_quiz_info)       \n    poll_data.update(payload)\n    all_poll_data = repo.compute_if_absent(\"poll_data\", lambda k: dict())\n    all_poll_data.update(poll_data)\n    repo.save()\n\ndef get_datetime_at_midnight():\n    date_now_time_stamp = datetime.now().timestamp()\n    return get_datetime_at_midnight_at_timestamp(date_now_time_stamp)\n\ndef get_datetime_at_midnight_and_add_days(days):\n    date_now = datetime.now()\n    datetime_midnight = datetime(date_now.year, date_now.month, date_now.day, 0, 0)\n    datetime_cal = datetime_midnight + timedelta(days=days)\n    return datetime_cal.strftime(\"%Y-%b-%d\")\n\ndef get_datetime_at_midnight_at_timestamp(timestamps):\n    date_time = datetime.fromtimestamp(timestamps)\n    datetime_midnight = datetime(date_time.year, date_time.month, date_time.day, 0, 0)\n    return datetime_midnight.strftime(\"%Y-%b-%d\")  \n\ndef remove_non_digits(input_string):\n    \"\"\"\n    Removes any non-digit characters from the input string and returns the resulting string\n    \"\"\"\n    output_string = ''.join(filter(str.isdigit, input_string))\n    return output_string\n\n\ndef get_poll_owner_id_message_id(update, context):\n    repo = get_repo_bot(context)\n    message = update.effective_message.reply_to_message\n    poll_data_manager = get_poll_model(update, context, -1, -1, -1)\n    \n    element_poll_history = None\n    if not message or message.poll is None:\n        # logger.info(\"checkbill_hander Poll not found\")\n        # logger.info(f\"checkbill_handler {message}\") #-> expect None\n        # logger.info(f\"checkbill_handler {update.message.text}\")  #-> expect text\n        message_text = update.message.text\n        regex_message_text = r\"^([^ ]+)\\s([\\+\\-]{0,1}\\d+)(.*)$\"\n        if message_text is not None:\n            chat_id = update.effective_chat.id\n            match = re.match(regex_message_text, message_text)\n            if match is not None:\n                part1 = match.group(1)\n                part2 = match.group(2)\n                if part2 is not None:\n                    poll_index = int(part2)\n                    poll_id = poll_data_manager.get_poll_id_by_index(chat_id, poll_index)\n                    element_poll_history = poll_data_manager.get_poll_data_by_poll_id(poll_id)\n                else:\n                    # element_poll_history = repo_get_poll_from_poll_history(repo, -1)\n                    poll_id = poll_data_manager.get_poll_id_by_index(chat_id, -1)\n                    logger.info(f\"get_poll_owner_id_message_id poll_id chat_id 1 {poll_id} {chat_id}\")\n                    element_poll_history = poll_data_manager.get_poll_data_by_poll_id(poll_id)\n            else:\n                poll_id = poll_data_manager.get_poll_id_by_index(chat_id, -1)\n                logger.info(f\"get_poll_owner_id_message_id poll_id chat_id 2 {poll_id} {chat_id}\")\n                element_poll_history = poll_data_manager.get_poll_data_by_poll_id(poll_id)\n        else:\n            logger.info(\"checkbill_handler can't find suitable poll\")\n            return\n\n        message = update.message\n\n    if element_poll_history is None:\n        message_id = f'{message.message_id}'\n    else:\n        message_id = element_poll_history.get_message_id()\n      \n\n    poll_owner_id = f'{update.effective_user.id}'\n    \n    return {\"poll_owner_id\": poll_owner_id, \"message_id\": str(message_id)}\n","repo_name":"thienchidh/shopyfood","sub_path":"util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":6416,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"48"}
+{"seq_id":"30288271365","text":"#! /usr/bin/env python3\n\nimport dataclasses as dc\nfrom typing import List, Dict, Any, Callable\nfrom types import ModuleType\nimport yaml\nimport dacite\nfrom os.path import dirname, basename, splitext, join\nfrom os import listdir\nfrom inspect import getmembers, isfunction\nimport ast\n\nfrom app import cast_data_as\nfrom app import patch_objects\nfrom app.patch_objects import Patch\nfrom app import get_module_at_path\nfrom app import thread_function_executions\nfrom app import error_handler\nfrom app import get_function_definitions\nfrom shared.get_environment import app as get_environment\n\n\nSTORE_RESULTS = []\nEXCLUDE_FUNCTIONS = ['example']\nTHIS_MODULE_PATH = __file__\nENV = get_environment.main(module_path=THIS_MODULE_PATH)\n\n\n@dc.dataclass\nclass Data:\n  values: List[Any] = dc.field(default_factory=lambda: [])\n  cast_as: List[str] = dc.field(default_factory=lambda: [])\n  file_path: str | None = None\n\n\n@dc.dataclass\nclass Coverage:\n  tests_count: int = 0\n  tests_passed_count: int = 0\n  tests_passed_percent: float = 0\n\n\n@dc.dataclass\nclass Case:\n  module_paths: List[str] = dc.field(default_factory=lambda: [])\n  function_name: str | None = None\n  description: List[str] | str = ''\n  patches: List[Patch] | Patch | None = None\n  inputs: Data | None = None\n  expected_outputs: List[Any] = dc.field(default_factory=lambda: [])\n  outputs: Data = dc.field(default_factory=lambda: Data())\n  assertions: List[str] = dc.field(default_factory=lambda: [])\n  results: List[List[bool]] = dc.field(default_factory=lambda: [])\n  coverage: Coverage = dc.field(default_factory=lambda: Coverage())\n\n\n@dc.dataclass\nclass Test:\n  yml_path: str | None = None\n  module_path: str | None = None\n  exclusions: List[str] | str = dc.field(default_factory=lambda: EXCLUDE_FUNCTIONS)\n  test_resources_dir_path: str | None = None\n  test_resources_modules_paths: List[str] | None = None\n  cases: List[Case] = dc.field(default_factory=lambda: [])\n  coverage: Dict[str, Coverage] = dc.field(default_factory=lambda: {})\n\n\n# @error_handler.main\ndef get_module_path_from_yml_path(\n  yml_path: str,\n  module_path: str,\n) -> str:\n  '''Returns the path to a python file associated with a yml file \n  if the path hasn't been set'''\n  if module_path is not None:\n    return module_path\n  return yml_path.replace('.yml', '.py')\n\n\n# @error_handler.main\ndef get_test_resources_dir_path(\n  yml_path: str, \n  test_resources_dir_path: str,\n) -> str:\n  '''Returns the path to the test resources directory associated with a \n  python and yml file if the path hasn't already been set'''\n  if test_resources_dir_path is not None:\n    return test_resources_dir_path\n  parent_dir = dirname(yml_path)\n  return join(parent_dir, 'test_resources')\n\n\n# @error_handler.main\ndef get_test_resources_modules_paths(\n  test_resources_dir_path: str,\n) -> List[str]:\n  '''Returns a list of paths for modules within the test resources directory'''\n  store = []\n  files = listdir(test_resources_dir_path)\n  for file in files:\n    extension = splitext(file)[1]\n    if extension != '.py':\n      continue\n    file_path = join(test_resources_dir_path, file)\n    store.append(file_path)\n  return store\n\n\n# @error_handler.main\ndef get_module_from_path(module_paths: List[str]) -> Dict[str, ModuleType]:\n  '''Returns a dictionary with keys being the name of modules and values\n  being the the modules'''\n  store = {}\n  for module_path in module_paths:\n    module = get_module_at_path.main(data=module_path)\n    store[module.name] = module._object\n  return store\n\n\n# @error_handler.main\ndef format_cases_for_threading(test: Test) -> List[List[Case]]:\n  '''Formats the test cases to facilitate threading downstream'''\n  n = len(test.cases)\n  for i in range(n):\n    # Combine the paths module paths into a single list\n    module_paths = test.test_resources_modules_paths + [test.module_path]    \n    test.cases[i].module_paths = module_paths\n    test.cases[i] = [test.cases[i]]\n  return test.cases\n\n\n# @error_handler.main\ndef cast_data(\n  data: Data, \n  modules: Dict[str, ModuleType], \n) -> List[Any]:\n  '''Returns a list of values cast to specified types'''\n  # No casting needed\n  if data.cast_as in [None, []]:\n    return data.values\n\n  # Store modules as list of `Module` dataclass objects\n  modules_as_dataclasses = []\n  for key, value in modules.items():\n    if key == '__builtins__':\n      key = 'standard_library'\n    module = cast_data_as.Module(\n      name=key,\n      _object=value,\n    )\n    modules_as_dataclasses.append(module)\n  # Setup and cast data\n  data = cast_data_as.Data(\n    values=data.values,\n    constructors=data.cast_as,\n    modules=modules_as_dataclasses,\n  )\n  return cast_data_as.main(data=data)\n\n\n# @error_handler.main\ndef call_function_with_data(\n  function: Callable, \n  inputs: List[Any | None],\n) -> List[Any | None]: \n  '''Returns the results of calling the function to test with input data'''\n  store = []\n  n = len(inputs)\n  for i in range(n):\n    result = None\n    try:\n      result = function(**inputs[i])\n    except:\n      result = function(inputs[i])\n    finally:\n      store.append(result)\n  return store\n\n\n# @error_handler.main\ndef get_patched_objects(\n  modules: Dict[str, ModuleType],\n  patches: List[Patch] | None,\n  _globals: Dict[str, Any] = globals(),\n) -> None:\n  '''Returns a dictionary containing modules, which have objects that\n  have been patched'''\n  if patches in [[], None]:\n    return modules\n\n  # Setup data and patch objects\n  data = patch_object.Data(\n    modules=modules,\n    patches=patches,\n  )\n  return patch_object.main(data=data)\n\n\n# @error_handler.main\ndef setup_assertions(\n  n_output_values: int,\n  assertions: List[str],\n) -> List[str]:\n  '''Returns a list of assertion functions to call to make to validate that \n  a functions output is as expected'''\n  # Equal assertions if no assertions set\n  if assertions in [[], None]:\n    return ['equal' for i in range(n_output_values)]\n\n  n = len(assertions)\n  # Should be a 1 to many or 1 to 1 match \n  # between assertions and output values\n  if n != n_output_values and n > 1:\n    message = ''\n    raise RuntimeError(message)\n  # 1 to many match\n  if n == 1:\n    return [assertions[0] for i in range(n_output_values)]\n  # 1 to 1 match\n  if n == n_output_values:\n    return assertions\n\n\n# @error_handler.main\ndef assert_equal(\n  output: Any, \n  expected_output: Any, \n  param: Any = None,\n) -> bool:\n  '''Returns true if the output equals the expected output'''\n  if output == expected_output:\n    return True\n  return False\n\n\n# @error_handler.main\ndef assert_greater_than(\n  output: Any, \n  expected_output: Any, \n  param: Any = None,\n) -> bool:\n  '''Returns'''\n  if output > expected_output:\n    return True\n  return False\n\n\n# @error_handler.main\ndef assert_less_than(\n  output: Any, \n  expected_output: Any, \n  param: Any = None,\n) -> bool:\n  ''''''\n  if output < expected_output:\n    return True\n  return False\n\n\n# @error_handler.main\ndef assert_has_values(\n  output: Any, \n  expected_output: Any,\n  param: Any = None,\n) -> List[bool]:\n  ''''''\n  store = []\n  if isinstance(output, dict):\n    for key, expected_value in expected_output.items():\n      value = output[key]\n      result = value == expected_value\n      store.append(result)\n\n  if hasattr(output, '__dataclassfields__'):\n    for key, expected_value in expected_output.items():\n      value = getattr()\n      result = value == expected_value\n      store.append(result)\n  \n  return store\n\n\nASSERTION_SWITCHER = {\n  'str': lambda assertion: {assertion: assertion},\n  'dict': lambda assertion: assertion,\n}\n\n\n# @error_handler.main\ndef get_assertion_result(\n  output: Any, \n  expected_output: Any, \n  assertion: dict,\n) -> Any:\n  _type = type(assertion).__name__\n  assertion_switcher = ASSERTION_SWITCHER[_type]\n  assertion = assertion_switcher(assertion)\n  key = list(assertion.keys())[0]\n  function_name = f'assert_{key}'\n  function = globals()[function_name]\n  return function(output, expected_output, assertion[key])\n\n  \n# @error_handler.main\ndef verify_outputs_against_expected_outputs(\n  outputs_values: List[Any],\n  expected_outputs: List[Any],\n  assertions: List[str | List[str]],\n) -> List[bool]:\n  store = []\n  n = len(outputs_values)\n  for i in range(n):\n    assertion_result = get_assertion_result(\n      output=outputs_values[i],\n      expected_output=expected_outputs[i],\n      assertion=assertions[i]\n    )\n    store.append(assertion_result)\n  return store\n\n\n# @error_handler.main\ndef get_case_coverage(\n  coverage: Coverage, \n  results: List[bool],\n) -> Coverage:\n  n = 0\n  store = []\n  for result in results:\n    n += 1\n    if isinstance(result, list) is False:\n      store.append(result)\n      continue\n    if False in result:\n      result = False\n    result = True\n    store.append(result)\n\n  tests_passed_count = sum(store)\n  coverage.tests_count = n\n  coverage.tests_passed_count = tests_passed_count\n  coverage.tests_passed_percent = tests_passed_count / n * 100\n  return coverage\n\n\n# @error_handler.main\ndef run_test_case(case: Case) -> Case:\n  # Get and format module data\n  modules = {}\n  for module_path in case.module_paths:\n    module = get_module_at_path.main(data=module_path)\n    module.name = module.name.replace('.', '_')\n    modules[module.name] = module._object\n  module_names = list(modules.keys())\n  \n  # Patch objects and add to global space\n  patched_objects = get_patched_objects(\n    modules=modules,\n    patches=case.patches,\n  )\n  for key, value in patched_objects.items():\n    globals()[key] = value\n  \n  # Get function to test\n  module = globals()[module_names[-1]]\n  function = getattr(module, case.function_name)\n\n  # Case input data\n  case.inputs.values = cast_data(data=case.inputs, modules=modules)\n  # Get function output data\n  case.outputs.values = call_function_with_data(\n    function=function,\n    inputs=case.inputs.values,\n  )\n\n  # Cast output data\n  case.outputs.values = cast_data(\n    data=case.outputs, \n    modules=modules,\n  )\n  \n  # Setup assertions\n  case.assertions = setup_assertions(\n    assertions=case.assertions,\n    n_output_values=len(case.outputs.values),\n  )\n  # Compare outputs to expected\n  case.results = verify_outputs_against_expected_outputs(\n    outputs_values=case.outputs.values,\n    expected_outputs=case.expected_outputs,\n    assertions=case.assertions,\n  )\n\n  # Get the functions coverage\n  case.coverage = get_case_coverage(\n    coverage=case.coverage, \n    results=case.results,\n  )\n  \n\n  return\n\n\n# @error_handler.main\ndef get_list_of_user_defined_functions_from_module(module_path: str) -> List[str]:\n  '''Returns a list a the user defined functions within a module'''\n  # https://stackoverflow.com/questions/139180/how-to-list-all-functions-in-a-module\n  source = None\n  with open(module_path, 'r') as file:\n    source = file.read()\n  store = []\n  for function in ast.parse(source).body:\n    if not isinstance(function, ast.FunctionDef):\n      continue\n    store.append(function.name)\n  return store\n\n\ndef setup_coverage_data(\n  function_names: List[str],\n  exclusions: List[str],\n) -> Dict[str, Coverage]:\n  store = {}\n  for name in function_names:\n    include = True\n\n    for exclusion in exclusions:\n      if name.find(exclusion) != -1:\n        include = False\n        break\n\n    if include is True:\n      store[name] = Coverage()\n      \n  return store\n  \n  \ndef get_coverage_for_functions(test: Test) -> Dict[str, Coverage]:\n  total = Coverage()\n  for case in test.cases:\n    case = case[0]\n    function_coverage = test.coverage[case.function_name]\n\n    print(case.results)\n\n    function_coverage.tests_count += len(case.results)\n    function_coverage.tests_passed += sum(case.results)\n    total.tests_count += len(case.results)\n    total.tests_passed += sum(case.results)\n    function_coverage.tests_coverage = function_coverage.tests_passed / function_coverage.tests_count\n    function_coverage = test.coverage[case.function_name]\n\n  test.coverage['total'] = total\n  return test.coverage\n\n\ndef get_percent(part: float | int, whole: float | int) -> float:\n  if whole == 0:\n    return 0\n  return part / whole * 100\n\n\ndef aggregate_function_coverages(\n  cases: List[Case], \n  total_coverage: Dict,\n) -> Dict[str, Coverage]:\n  for case in cases:\n    case = case[0]\n    coverage = total_coverage[case.function_name]\n    coverage.tests_count += case.coverage.tests_count\n    coverage.tests_passed_count += case.coverage.tests_passed_count\n    coverage.tests_passed_percent = get_percent(\n      part=coverage.tests_passed_count,\n      whole=coverage.tests_count,\n    )\n  return total_coverage\n\n\ndef get_total_coverage(coverage: Dict[str, Coverage]) -> Dict[str, Coverage]:\n  total_coverage = Coverage()\n  total_percent = 0\n  n = len(coverage)\n  for key, value in coverage.items():\n    total_percent += value.tests_passed_percent\n  total_coverage.tests_passed_percent = total_percent / n\n  coverage['total'] = total_coverage\n  return coverage\n\n\ndef get_coverage(\n  cases: List[Case], \n  module_path: str,\n  exclusions: List[str],\n) -> Coverage:\n  function_names = get_function_definitions.main(\n    module_path=module_path,\n    output_attributes=['name'],\n  )\n  function_names = function_names['name']\n\n  coverage = setup_coverage_data(\n    function_names=function_names,\n    exclusions=exclusions,\n  )\n  coverage = aggregate_function_coverages(\n    cases=cases, \n    total_coverage=coverage,\n  )\n  coverage = get_total_coverage(coverage=coverage)\n  return coverage\n\n\n# @error_handler.main\ndef format_test(test: Test) -> Test:\n  ''''''\n\n  m = len(test.cases)\n  for i in range(m):\n    # n = len(test.cases[i])\n    # for j in range(n):\n\n    #   # Remove the modules paths as this is \n    #   # stored in the Test (main) dataclass\n    #   # test.cases[i][j].module_paths = None\n\n    #   l = len(test.cases[i][j].outputs.values)\n    #   for k in range(l):\n\n    #     pass\n      \n    # Cases outputs as a list within a list\n    # Convert it to a single item\n    test.cases[i] = test.cases[i][0]\n\n    # Convert inputs and outputs to strings\n    # TODO: May be a better way to make sure inputs/outputs are json \n    # serializeable\n    # n = len(test.cases[i].outputs.values)\n    # for k in range(n):\n    #   test.cases[i].outputs.values[k] = str(\n    #     test.cases[i].outputs.values[k])\n    #   test.cases[i].inputs.values[k] = str(\n    #     test.cases[i].inputs.values[k])\n\n    # Convert dataclass to dictionary\n    test.cases[i] = dc.asdict(test.cases[i])\n\n    # Remove the modules paths as this is \n    # stored in the Test (main) dataclass\n    del test.cases[i]['module_paths']\n  \n  test = dc.asdict(test)\n  # Remove resources directory path\n  del test['test_resources_dir_path']\n  return test\n\n\nSETUP_TEST_DATA_SWITCHER = {\n  'Test': lambda test: test,\n  'dict': lambda test: dacite.from_dict(Test, test),\n}\n\n\ndef setup_test_data(test: Test) -> Test:\n  # Convert input to Test dataclass\n  test_type = type(test).__name__\n  switcher = SETUP_TEST_DATA_SWITCHER[test_type]\n  test = switcher(test=test)\n\n  # Add the exclusion string to a list\n  if isinstance(test.exclusions, str):\n    test.exclusion = [test.exclusions]\n\n  return test\n\n\n# @error_handler.main\ndef main(test: Test | dict) -> Test:\n  test = setup_test_data(test=test)\n\n  test.module_path = get_module_path_from_yml_path(\n    yml_path=test.yml_path,\n    module_path=test.module_path,\n  )\n  test.test_resources_dir_path = get_test_resources_dir_path(\n    yml_path=test.yml_path, \n    test_resources_dir_path=test.test_resources_dir_path,\n  )\n  test.test_resources_modules_paths = get_test_resources_modules_paths(\n    test_resources_dir_path=test.test_resources_dir_path)\n\n  cases = format_cases_for_threading(test=test)\n  thread_function_executions.main(\n    target=run_test_case,\n    args=cases,\n    store_name='STORE_RESULTS',\n  )\n  test.coverage = get_coverage(\n    module_path=test.module_path,\n    cases=test.cases,\n    exclusions=test.exclusions,\n  )\n\n  test = format_test(test=test)\n  return test\n\n\n# @error_handler.main\ndef format_terminal_output(test: str) -> str:\n  function_name_index = test.find('function_name')\n  new_line_index = test[function_name_index:].find('\\n') + function_name_index\n  string = test[function_name_index:new_line_index]\n  formatted_string = '\\033[1m' + '\\033[33m' + string + '\\033[0m'\n  test = test.replace(string, formatted_string)\n\n  return test\n\n\n# Test data for functions\ntest = '''\n  yml_path: /home/femij/mono_repo/coding_challenges/utils/untested_test_cases/test_resources/arithmetic.yml\n  test_resources_dir_path: /home/femij/mono_repo/coding_challenges/utils/untested_test_cases/test_resources\n  cases:\n  - function_name: add\n    description: Should add two numbers and return the result\n    inputs:\n      values:\n      - a: 0\n        b: 0\n      - a: 1\n        b: 1\n      cast_as:\n      - Data\n    expected_outputs: \n    - Data(a=0, b=0, result=0)\n    - Data(a=1, b=1, result=2)\n    outputs:\n      cast_as:\n      - str\n    assertions: [] \n  - function_name: add\n    description: Should add two numbers and return the result\n    patches:\n    - object_name: __builtins__.input\n      return_value: '__builtins__.input_patched'\n    inputs:\n      values:\n      - a: 0\n        b: 0\n      - a: 1\n        b: 1\n      cast_as:\n      - Data\n    expected_outputs:\n    - a: 0\n      b: 0\n      result: 0\n    - a: 1\n      b: 1\n      result: 2\n    outputs:\n      cast_as:\n      - dict\n    assertions: \n    - has_values    \n  - function_name: add\n    description: Should add two numbers and return the result\n    patches: \n    - object_name: test_resources_arithmetic.add\n      return_value: 'test_resources_arithmetic.add_patched'\n    inputs:\n      values:\n      - a: 0\n        b: 0\n      - a: 1\n        b: 1\n      cast_as: \n      - Data\n    expected_outputs:\n    - test_resources_arithmetic.add_patched\n    - test_resources_arithmetic.add_patched\n    outputs:\n      cast_as:\n      - str\n    assertions: [equal] \n'''\ntest = yaml.safe_load(test)\ntest = dacite.from_dict(Test, test)\ntest = main(test=test)\ntest = dict(test=test)\ntest_yml = yaml.dump(\n  test, \n  indent=2, \n  sort_keys=False,\n  default_flow_style=False,\n)\n\ntest_yml = format_terminal_output(test=test_yml)\nprint(test_yml)\n\n","repo_name":"fjemi/mono_repo","sub_path":"tests/yml_testing_framework/python/app/execute_test_cases.py","file_name":"execute_test_cases.py","file_ext":"py","file_size_in_byte":18074,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"30172924241","text":"#!/usr/bin/env python3\n#\n# Twisted Ints\nfrom twistedints import TwistedInt\n\nclass TwistedIntegers:\n    \"\"\"The twisted integers class\"\"\"\n    def __init__(self, n):\n        \"\"\"Initialise Zn with the given n length\n\n        >>> x = TwistedIntegers(2)\n        >>> print(x)\n        ['<0:2>', '<1:2>']\n        \"\"\"\n        if n <= 0:\n            raise ValueError('Length of the list cannot be less or equal to 0')\n        else:\n            self.list = []\n            i = 0\n            while i < n:\n                self.list.append(TwistedInt(i,n))\n                i += 1\n\n    def __str__(self):\n        \"\"\"Output the Zn list\n\n        >>> x = TwistedIntegers(2)\n        >>> print(x)\n        ['<0:2>', '<1:2>']\n        \"\"\"\n        return (str([str(item) for item in self.list]))\n\n    def Size(self):\n        \"\"\"Find the Zn length\n\n        >>> x = TwistedIntegers(15)\n        >>> TwistedIntegers.Size(x)\n        15\n        \"\"\"\n        return len(self.list)\n","repo_name":"villekuosmanen/Python1","sub_path":"twistedintegers.py","file_name":"twistedintegers.py","file_ext":"py","file_size_in_byte":947,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"3376446057","text":"import subprocess\nimport os\nimport time\nimport signal\n\ndef go_exit(sig,stack):\n    proccess.kill()\n    exit(0)\n\ndef go_exit2(sig,stack):\n    if proccess.poll() is not None:\n        exit(0)\n\nif __name__ == \"__main__\":\n    signal.signal(signal.SIGINT,go_exit)\n    signal.signal(signal.SIGCHLD,go_exit2)\n    global proccess\n    path='./'\n    proccess = subprocess.Popen(['vlc', path + 'video.mp4'])\n    time.sleep(3)\n    while 1:\n        os.system('playerctl pause vlc')\n        time.sleep(4)\n        os.system('playerctl play vlc')\n        time.sleep(3)\n","repo_name":"aquirosglez/vlc-pause-play-python","sub_path":"paustart.py","file_name":"paustart.py","file_ext":"py","file_size_in_byte":552,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"24934711309","text":"# def is also a compund statement\r\n# can be nested inside if, for and while and even another def\r\n# their inner code is not run until the function is called!\r\n# can have attributes\r\n\r\ndef times(x,y):\r\n    return x * y\r\ntimes(5, 6)\r\n## functions are typeless:\r\ntimes('Ni! ', 10).rstrip()\r\n# this type-dependent behaviour is polymorphism\r\ntimes(['a'], 4)\r\n\r\n#intersecting sequences:\r\ndef intersect(seq1, seq2):\r\n    catch = []                      ###local variable which exists only while the function runs, and disapears as the run is over\r\n    for x in seq1:\r\n        if x in seq2:\r\n            catch.append(x)\r\n    return catch\r\nx = 'spam'\r\ny = 'scam'\r\nintersect(x, y)\r\n\r\ns1 = 'spam'\r\ns2 = 'scam'\r\n[x for x in s1 if x in s2]\r\nintersect([1,2,4], (1,4)) # mixed data types as inputs\r\n\r\n# interface =  a set of methods and expression operators the functions's code runs","repo_name":"TamasSmahajcsikszabo/Python-for-Data-Science","sub_path":"learning_python/ch16_functions.py","file_name":"ch16_functions.py","file_ext":"py","file_size_in_byte":868,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"2098311388","text":"from python_graphql_client import GraphqlClient\nimport feedparser\nimport httpx\nimport json\nimport pathlib\nimport re\nimport os\nimport datetime\n\nroot = pathlib.Path(__file__).parent.resolve()\nclient = GraphqlClient(endpoint=\"https://api.github.com/graphql\")\n\n\nTOKEN = os.environ.get(\"GH_TOKEN\", \"\")\n\n\ndef replace_chunk(content, marker, chunk, inline=False):\n    r = re.compile(\n        r\"<!\\-\\- {} starts \\-\\->.*<!\\-\\- {} ends \\-\\->\".format(marker, marker),\n        re.DOTALL,\n    )\n    if not inline:\n        chunk = \"\\n{}\\n\".format(chunk)\n    chunk = \"<!-- {} starts -->{}<!-- {} ends -->\".format(marker, chunk, marker)\n    return r.sub(chunk, content)\n\ndef formatGMTime(timestamp):\n    GMT_FORMAT = '%a, %d %b %Y %H:%M:%S GMT'\n    dateStr = datetime.datetime.strptime(timestamp, GMT_FORMAT) + datetime.timedelta(hours=8)\n    return dateStr.date()\n\ndef repository_query(after_cursor=None):\n    return \"\"\"\nquery {\n  viewer {\n    repositories(first: 100, privacy: PUBLIC, isFork:false, ownerAffiliations:OWNER, after:AFTER) {\n      pageInfo {\n        hasNextPage\n        endCursor\n      }\n      nodes {\n        name\n        description\n        url\n        releases(last: 100, orderBy: { field: CREATED_AT, direction: DESC}) {\n          totalCount\n          nodes {\n            name\n            publishedAt\n            url\n          }\n        }\n      }\n    }\n  }\n}\n\"\"\".replace(\n        \"AFTER\", '\"{}\"'.format(after_cursor) if after_cursor else \"null\"\n    )\n\n\ndef fetch_releases(oauth_token):\n    repos = []\n    releases = []\n    repo_names = set()\n    has_next_page = True\n    after_cursor = None\n\n    while has_next_page:\n        data = client.execute(\n            query=repository_query(after_cursor),\n            headers={\"Authorization\": \"Bearer {}\".format(oauth_token)},\n        )\n        for repo in data[\"data\"][\"viewer\"][\"repositories\"][\"nodes\"]:\n            if repo[\"releases\"][\"totalCount\"] and repo[\"name\"] not in repo_names:\n                repos.append(repo)\n                repo_names.add(repo[\"name\"])\n                releases.append(\n                    {\n                        \"repo\": repo[\"name\"],\n                        \"repo_url\": repo[\"url\"],\n                        \"description\": repo[\"description\"],\n                        \"release\": repo[\"releases\"][\"nodes\"][0][\"name\"]\n                        .replace(repo[\"name\"], \"\")\n                        .strip(),\n                        \"published_at\": repo[\"releases\"][\"nodes\"][0][\n                            \"publishedAt\"\n                        ].split(\"T\")[0],\n                        \"url\": repo[\"releases\"][\"nodes\"][0][\"url\"],\n                    }\n                )\n        has_next_page = data[\"data\"][\"viewer\"][\"repositories\"][\"pageInfo\"][\n            \"hasNextPage\"\n        ]\n        after_cursor = data[\"data\"][\"viewer\"][\"repositories\"][\"pageInfo\"][\"endCursor\"]\n    return releases\n\n\n# def fetch_code_time():\n#     return httpx.get(\n#         \"https://gist.githubusercontent.com/tw93/7854aac61f991ef4e7ae7b8440e4fdc6/raw/\"\n#     )\n\ndef fetch_weekly():\n    return httpx.get(\n        \"https://raw.githubusercontent.com/tw93/weekly/main/RECENT.md\"\n    )\n\n# def fetch_douban():\n#     entries = feedparser.parse(\"https://www.douban.com/feed/people/tangwei93/interests\")[\"entries\"]\n#     return [\n#         {\n#             \"title\": item[\"title\"],\n#             \"url\": item[\"link\"].split(\"#\")[0],\n#             \"published\": formatGMTime(item[\"published\"])\n#         }\n#         for item in entries\n#     ]\n\n\ndef fetch_blog_entries():\n    entries = feedparser.parse(\"https://tw93.fun/feed.xml\")[\"entries\"]\n    return [\n        {\n            \"title\": entry[\"title\"],\n            \"url\": entry[\"link\"].split(\"#\")[0],\n            \"published\": entry[\"published\"].split(\"T\")[0],\n        }\n        for entry in entries\n    ]\n\n\nif __name__ == \"__main__\":\n    readme = root / \"README.md\"\n    project_releases = root / \"releases.md\"\n    releases = fetch_releases(TOKEN)\n    releases.sort(key=lambda r: r[\"published_at\"], reverse=True)\n    md = \"\\n\".join(\n        [\n            \"* <a href='{url}' target='_blank'>{repo} {release}</a> - {published_at}\".format(**release)\n            for release in releases[:5]\n        ]\n    )\n    readme_contents = readme.open().read()\n    rewritten = replace_chunk(readme_contents, \"recent_releases\", md)\n\n    # Write out full project-releases.md file\n    project_releases_md = \"\\n\".join(\n        [\n            (\n                \"* **[{repo}]({repo_url})**: [{release}]({url}) - {published_at}\\n\"\n                \"<br>{description}\"\n            ).format(**release)\n            for release in releases\n        ]\n    )\n    project_releases_content = project_releases.open().read()\n    project_releases_content = replace_chunk(\n        project_releases_content, \"recent_releases\", project_releases_md\n    )\n    project_releases_content = replace_chunk(\n        project_releases_content, \"release_count\", str(len(releases)), inline=True\n    )\n    project_releases.open(\"w\").write(project_releases_content)\n\n    # code_time_text = \"\\n```text\\n\"+fetch_code_time().text+\"\\n```\\n\"\n\n    # rewritten = replace_chunk(rewritten, \"code_time\", code_time_text)\n\n    # doubans = fetch_douban()[:5]\n\n    # doubans_md = \"\\n\".join(\n    #     [\"* <a href='{url}' target='_blank'>{title}</a> - {published}\".format(**item) for item in doubans]\n    # )\n\n    # rewritten = replace_chunk(rewritten, \"douban\", doubans_md)\n\n    weekly_text = \"\\n\"+fetch_weekly().text\n    rewritten = replace_chunk(rewritten, \"weekly\", weekly_text)\n\n    entries = fetch_blog_entries()[:5]\n    entries_md = \"\\n\".join(\n        [\"* <a href='{url}' target='_blank'>{title}</a> - {published}\".format(**entry) for entry in entries]\n    )\n    rewritten = replace_chunk(rewritten, \"blog\", entries_md)\n\n    readme.open(\"w\").write(rewritten)\n","repo_name":"tw93/tw93","sub_path":"build_readme.py","file_name":"build_readme.py","file_ext":"py","file_size_in_byte":5781,"program_lang":"python","lang":"en","doc_type":"code","stars":166,"dataset":"github-code","pt":"48"}
+{"seq_id":"9421042899","text":"from __future__ import print_function\nfrom pyspark import SparkContext, SparkConf\nfrom operator import add\nfrom pyspark.mllib.feature import HashingTF, IDF # TF-IDF specific functions \nimport sys\nimport os\nimport operator\nfrom time import time\nimport csv\nfrom pyspark.sql import SQLContext\nfrom math import radians, sin, cos, sqrt, asin\n\nsc = SparkContext(\"local[4]\", \"TF-IDF: Data analysis with Spark\")\nsc.setLogLevel(\"ERROR\")\nsqlContext = SQLContext(sc)\n\n# Import the various csv files used\n#   Listings_us.csv\nfsListings = sc.textFile('/usr/local/spark/spark-2.1.0-bin-hadoop2.7/listings_us.csv', use_unicode = False)\nlistingHeader = fsListings.first()\nlistingsFiltered = fsListings.filter(lambda x: x!=listingHeader).map(lambda x: x.split(\"\\t\"))\n\n#   calendar_us.csv\nfsCalendar = sc.textFile('/usr/local/spark/spark-2.1.0-bin-hadoop2.7/calendar_us.csv', use_unicode = False)\ncalendarHeader = fsCalendar.first()\ncalendarFiltered = fsCalendar.filter(lambda x: x!=calendarHeader).map(lambda x: x.split(\"\\t\"))\n\n# Maps index name to column index for listings_us.csv\n# getIndexValue then uses the key (column name) to access right column index\nheader2 = fsListings.first().split(\"\\t\")\ndict = {}\nfor i in range(len(header2)):\n    dict[header2[i]] = i\n\n# getIndexValue then uses the key (column name) to access right column index\ndef getIndexValue(name):\n    return dict[name]\n\nlistingColumns = listingsFiltered.map(lambda x: (x[getIndexValue(\"id\")], x[getIndexValue(\"room_type\")],\\\n                            float(x[getIndexValue(\"price\")].replace(\"$\", \"\").replace(\",\", \"\")),\\\n                            float(x[getIndexValue(\"longitude\")]), float(x[getIndexValue(\"latitude\")]),\\\n                                  x[getIndexValue(\"amenities\")], x[getIndexValue(\"name\")]))\n\ndef alternativeListings(listingID, date, pricePercentage, y, n):\n    start_time = time()\n    # Filter out the available listings on date\n    filterAvailable = calendarFiltered.filter(lambda line: date == line[1]).filter(lambda line: line[2] == 't').map(lambda x: (x[0], x[2]))\n    roomType = listingColumns.map(lambda x: (x[0], x[1]))\n    # Join with key = id\n    joinRDD = filterAvailable.join(roomType)\n    listingRoomType = getRoomType(listingID)\n    # Filter out the listings with same room type as listingID\n    roomTypeRDD = joinRDD.filter(lambda line: line[1][1] == listingRoomType)\n    # Join with key = id\n    priceRDD = roomTypeRDD.map(lambda x: (x[0], x[1][1])).join(listingColumns.map(lambda x: (x[0], x[2])))\n    # Calculate the maximum price for the listings\n    maxPriceForListing = float(float(listingColumns.filter(lambda line: listingID == line[0]).\\\n                                     map(lambda x: x[2]).collect()[0]) * float(pricePercentage))\n    # Filter out the remaining listings with price less than the maximum price\n    checkPrice = priceRDD.filter(lambda x: x[1][1] < maxPriceForListing)\n    longLat = checkPrice.map(lambda x: (x[0], x[1][1])).\\\n                        join(listingColumns.map(lambda x: (x[0], x[3]))).\\\n                        map(lambda x: (x[0], x[1][1])).\\\n                        join(listingColumns.map(lambda x: (x[0], x[4])))\n    # Get the coordinates from the original listing (listingID)\n    listingCoordinates = listingColumns.map(lambda x: (x[0], float(x[3]), float(x[4]))).filter(lambda id: listingID in id)\n    long = listingCoordinates.map(lambda x: x[1]).collect()[0]\n    lat = listingCoordinates.map(lambda x: x[2]).collect()[0]\n    # Calculate the distances between coordinates by using the function haversine()\n    distanceCalculation = longLat.map(lambda x: (x[0], x[1][0], x[1][1])).\\\n                                map(lambda x: (x[0], haversine(float(x[2]), float(x[1]), float(lat), float(long))))\n    # Filter out the listings that are less than y km away from listingID\n    filterDistance = distanceCalculation.filter(lambda x: x[1] < float(y))\n    # Get the amenities for original listingID\n    amenitiesListing = listingColumns.map(lambda x: (x[0], x[5])).\\\n                                    filter(lambda id: listingID == id[0]).\\\n                                    map(lambda x: x[1].replace(\"{\", \"\").replace(\"}\", \"\").\\\n                                    replace(\"\\\"\", \"\").lower().strip().split(\",\")).collect()[0]\n    # Get the amenities for the remaining listings, join with key = id\n    listAmenities = filterDistance.join(listingColumns.map(lambda x: (x[0], x[5]))).\\\n                                                    map(lambda x: (x[0], x[1][1].replace(\"{\", \"\").replace(\"}\", \"\").\\\n                                                    replace(\"\\\"\", \"\").lower().strip().split(\",\")))\n    # Check amenities up against the amenities list from listingID\n    checkAmenities = listAmenities.flatMapValues(lambda x: x).\\\n                                    filter(lambda x: x[1] in amenitiesListing).\\\n                                    map(lambda x: (x[0], int(1))).\\\n                                    reduceByKey(add).\\\n                                    map(lambda x: (x[1], x[0])).\\\n                                    sortByKey(0,1).\\\n                                    map(lambda x: (x[1], x[0])).\\\n                                    take(int(n))\n    #print(checkAmenities)\n    listId = sc.parallelize(checkAmenities).map(lambda x: x[0]).collect()\n    amenities = sc.parallelize(checkAmenities).collectAsMap()\n    distance = filterDistance.collectAsMap()\n    # Output listings into file called alternatives.tsv\n    fileMaker = listingColumns.filter(lambda id: id[0] in listId).map(lambda x: (x[0], x[6],\\\n                                                        amenities[x[0]], distance[x[0]], x[2])).\\\n                                                        map(lambda x: (x[2], x[0], x[1], x[3], x[4])).\\\n                                                        sortByKey(0,1).\\\n                                                        map(lambda x: (x[1], x[2], x[0], x[3], x[4]))\n    fileMaker.map(lambda x: \"\\t\".join(map(str, x))).coalesce(1).saveAsTextFile(\"/usr/local/spark/spark-2.1.0-bin-hadoop2.7/alternatives2.tsv\")\n    print(\"Checking listing Elapsed time: \" + str(time() - start_time))\n    #Make file for CARTO vizualisation\n    cartoMaker = listingColumns.filter(lambda id: id[0] in listId).map(lambda x: (x[0], x[6],\\\n                                                        amenities[x[0]], distance[x[0]], x[2], x[3], x[4])).\\\n                                                        map(lambda x: (x[2], x[0], x[1], x[3], x[4], x[5], x[6])).\\\n                                                        sortByKey(0, 1).\\\n                                                        map(lambda x: (x[1], x[2], x[0], x[3], x[4], x[5], x[6]))\n    # Original listingID columns needed\n    listingMaker = listingColumns.filter(lambda id: listingID in id).map(lambda x: (x[0], x[6], 0, 0, x[2], x[3], x[4]))\n    #cartoMaker.map(lambda x: \"\\t\".join(map(str, x))).coalesce(1).saveAsTextFile(\"/usr/local/spark/spark-2.1.0-bin-hadoop2.7/cartoAlternatives.tsv\")\n\n# Get room type for listingID\ndef getRoomType(listingID):\n    room_type = listingColumns.map(lambda x: (x[0],x[1])).filter(lambda id: listingID in id).map(lambda x: x[1]).collect()\n    return room_type[0]\n\n# Calculate distances between coordinates\ndef haversine(lat1, lon1, lat2, lon2):\n    # convert decimal degrees to radians\n    lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])\n    # haversine formula\n    dlon = lon2 - lon1\n    dlat = lat2 - lat1\n    a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2\n    c = 2 * asin(sqrt(a))\n    r = 6371 # Radius of earth in kilometers. Use 3956 for miles\n    return c * r\n\n\n# Checking running parameters, \n#   alternative_listings <listingID> <date:YYY-MM-DD> <x> <y> <n>\n#       where   \n#               x = price is not higher than x%\n#               y = radius y km\n#               n = output top n listings with common amenities as <listingID>\n#   Example:\n#   alternative_listings.py 4717459 2017-05-28 10 1 50\ndef parametersPassing(args):\n    listingID = args[1]\n    date = args[2]    \n    x = (float(args[3])/100)+1\n    y = args[4]\n    n = args[5]\n    print(\"Checking listing id \\t\"+listingID)\n    print(\"On date \\t\\t\"+date)\n    print(\"\\nIf alternative listing:\")\n    print(\"Alt. listing not exceeding price of\\t\"+str(x)+\"\")\n    print(\"Within a radius of \\t\\t\\t\"+y+'KM')\n    print(\"Displaying top n=\\t\\t\\t\"+n+\" listings\")\n    (alternativeListings(listingID,date,x,y,n))\n\n'''    ------------------ When running, under here  ---------------------\t '''\n\n# Skeleton code for standalone application\n\nprint(\"2 Finding Alternative listings\")\nparametersPassing(sys.argv)\n\n\n\n\n\n\n\n\n","repo_name":"tinduy/BigData","sub_path":"alternative_listings.py","file_name":"alternative_listings.py","file_ext":"py","file_size_in_byte":8663,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"10104631017","text":"from mpl_toolkits.axisartist.axislines import AxesZero\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as patches\nimport matplotlib.animation as Animtion\nimport sys\n\nfig = plt.figure('Coriolis')\n\n# set x, y axis into center\nax = fig.add_subplot(axes_class = AxesZero)\nfor direction in [\"xzero\", \"yzero\"] :\n   ax.axis[direction].set_visible(True)\nfor direction in [\"left\", \"right\", \"bottom\", \"top\"]:\n   ax.axis[direction].set_visible(False)\n# Draw a cirle background\ncircle = patches.Circle((0, 0), radius=20, color='deepskyblue', fill=True)\nax.add_patch(circle)\n# Read data\ncoord = np.loadtxt(sys.path[0] + '\\\\coriolis1.dat', delimiter=',').T\nx = coord[0]\ny = coord[1]\n\n\nax.plot(x, y, color='white', linewidth=0.5)\nax.set_xlim(-25, 25)\nax.set_ylim(-25, 25)\nax.axis('equal')\n\n# Animation part\n\ndt = 24.0 * 3600.0 / 200.0\nfreq = -2 * np.pi / (24 * 3600)\n# init star mark position\nstar = ax.scatter(20, 0, s=320, marker='*', color='gold', zorder=3)\n# init point mark position\npoint = ax.scatter(x[0], y[0], s=200, marker='.', color='gold', zorder=3)\n\ndef update(tick) :\n   theta =  dt * tick * freq     \n   xx = 20 * np.cos(theta)\n   yy = 20 * np.sin(theta)   \n   star.set_offsets((xx, yy))\n   point.set_offsets((x[tick], y[tick]))\n   return star\n\n\nani = Animtion.FuncAnimation(fig, update, frames=200, interval=30)\nani.save(sys.path[0] + '\\\\coriolis.gif', dpi=300, writer='pillow')\n# Animation part end\n\n# plt.axis('equal') # makes x and y have the same length\nplt.show()","repo_name":"Masahiro-Ren/LearningOceanModelling","sub_path":"Exercise4/draw.py","file_name":"draw.py","file_ext":"py","file_size_in_byte":1498,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"32302605311","text":"import json\nimport os\nimport re\nfrom typing import Any, Dict, Tuple\n\nfrom moto.core.responses import BaseResponse\nfrom .models import (\n    cognitoidp_backends,\n    find_account_region_by_value,\n    RegionAgnosticBackend,\n    UserStatus,\n    CognitoIdpBackend,\n)\nfrom .exceptions import InvalidParameterException\n\n\nregion_agnostic_backend = RegionAgnosticBackend()\n\n\nclass CognitoIdpResponse(BaseResponse):\n    def __init__(self) -> None:\n        super().__init__(service_name=\"cognito-idp\")\n\n    @property\n    def parameters(self) -> Dict[str, Any]:  # type: ignore[misc]\n        return json.loads(self.body)\n\n    @property\n    def backend(self) -> CognitoIdpBackend:\n        return cognitoidp_backends[self.current_account][self.region]\n\n    # User pool\n    def create_user_pool(self) -> str:\n        name = self.parameters.pop(\"PoolName\")\n        user_pool = self.backend.create_user_pool(name, self.parameters)\n        return json.dumps({\"UserPool\": user_pool.to_json(extended=True)})\n\n    def set_user_pool_mfa_config(self) -> str:\n        user_pool_id = self._get_param(\"UserPoolId\")\n        sms_config = self._get_param(\"SmsMfaConfiguration\", None)\n        token_config = self._get_param(\"SoftwareTokenMfaConfiguration\", None)\n        mfa_config = self._get_param(\"MfaConfiguration\")\n\n        if mfa_config not in [\"ON\", \"OFF\", \"OPTIONAL\"]:\n            raise InvalidParameterException(\n                \"[MfaConfiguration] must be one of 'ON', 'OFF', or 'OPTIONAL'.\"\n            )\n\n        if mfa_config in [\"ON\", \"OPTIONAL\"]:\n            if sms_config is None and token_config is None:\n                raise InvalidParameterException(\n                    \"At least one of [SmsMfaConfiguration] or [SoftwareTokenMfaConfiguration] must be provided.\"\n                )\n            if sms_config is not None:\n                if \"SmsConfiguration\" not in sms_config:\n                    raise InvalidParameterException(\n                        \"[SmsConfiguration] is a required member of [SoftwareTokenMfaConfiguration].\"\n                    )\n\n        response = self.backend.set_user_pool_mfa_config(\n            user_pool_id, sms_config, token_config, mfa_config\n        )\n        return json.dumps(response)\n\n    def get_user_pool_mfa_config(self) -> str:\n        user_pool_id = self._get_param(\"UserPoolId\")\n        response = self.backend.get_user_pool_mfa_config(user_pool_id)\n        return json.dumps(response)\n\n    def list_user_pools(self) -> str:\n        max_results = self._get_param(\"MaxResults\")\n        next_token = self._get_param(\"NextToken\")\n        user_pools, next_token = self.backend.list_user_pools(\n            max_results=max_results, next_token=next_token\n        )\n        response: Dict[str, Any] = {\n            \"UserPools\": [user_pool.to_json() for user_pool in user_pools]\n        }\n        if next_token:\n            response[\"NextToken\"] = str(next_token)\n        return json.dumps(response)\n\n    def describe_user_pool(self) -> str:\n        user_pool_id = self._get_param(\"UserPoolId\")\n        user_pool = self.backend.describe_user_pool(user_pool_id)\n        return json.dumps({\"UserPool\": user_pool.to_json(extended=True)})\n\n    def update_user_pool(self) -> None:\n        user_pool_id = self._get_param(\"UserPoolId\")\n        self.backend.update_user_pool(user_pool_id, self.parameters)\n\n    def delete_user_pool(self) -> str:\n        user_pool_id = self._get_param(\"UserPoolId\")\n        self.backend.delete_user_pool(user_pool_id)\n        return \"\"\n\n    # User pool domain\n    def create_user_pool_domain(self) -> str:\n        domain = self._get_param(\"Domain\")\n        user_pool_id = self._get_param(\"UserPoolId\")\n        custom_domain_config = self._get_param(\"CustomDomainConfig\")\n        user_pool_domain = self.backend.create_user_pool_domain(\n            user_pool_id, domain, custom_domain_config\n        )\n        domain_description = user_pool_domain.to_json(extended=False)\n        if domain_description:\n            return json.dumps(domain_description)\n        return \"\"\n\n    def describe_user_pool_domain(self) -> str:\n        domain = self._get_param(\"Domain\")\n        user_pool_domain = self.backend.describe_user_pool_domain(domain)\n        domain_description: Dict[str, Any] = {}\n        if user_pool_domain:\n            domain_description = user_pool_domain.to_json()\n\n        return json.dumps({\"DomainDescription\": domain_description})\n\n    def delete_user_pool_domain(self) -> str:\n        domain = self._get_param(\"Domain\")\n        self.backend.delete_user_pool_domain(domain)\n        return \"\"\n\n    def update_user_pool_domain(self) -> str:\n        domain = self._get_param(\"Domain\")\n        custom_domain_config = self._get_param(\"CustomDomainConfig\")\n        user_pool_domain = self.backend.update_user_pool_domain(\n            domain, custom_domain_config\n        )\n        domain_description = user_pool_domain.to_json(extended=False)\n        if domain_description:\n            return json.dumps(domain_description)\n        return \"\"\n\n    # User pool client\n    def create_user_pool_client(self) -> str:\n        user_pool_id = self.parameters.pop(\"UserPoolId\")\n        generate_secret = self.parameters.pop(\"GenerateSecret\", False)\n        user_pool_client = self.backend.create_user_pool_client(\n            user_pool_id, generate_secret, self.parameters\n        )\n        return json.dumps({\"UserPoolClient\": user_pool_client.to_json(extended=True)})\n\n    def list_user_pool_clients(self) -> str:\n        user_pool_id = self._get_param(\"UserPoolId\")\n        max_results = self._get_param(\"MaxResults\")\n        next_token = self._get_param(\"NextToken\")\n        user_pool_clients, next_token = self.backend.list_user_pool_clients(\n            user_pool_id, max_results=max_results, next_token=next_token\n        )\n        response: Dict[str, Any] = {\n            \"UserPoolClients\": [\n                user_pool_client.to_json() for user_pool_client in user_pool_clients\n            ]\n        }\n        if next_token:\n            response[\"NextToken\"] = str(next_token)\n        return json.dumps(response)\n\n    def describe_user_pool_client(self) -> str:\n        user_pool_id = self._get_param(\"UserPoolId\")\n        client_id = self._get_param(\"ClientId\")\n        user_pool_client = self.backend.describe_user_pool_client(\n            user_pool_id, client_id\n        )\n        return json.dumps({\"UserPoolClient\": user_pool_client.to_json(extended=True)})\n\n    def update_user_pool_client(self) -> str:\n        user_pool_id = self.parameters.pop(\"UserPoolId\")\n        client_id = self.parameters.pop(\"ClientId\")\n        user_pool_client = self.backend.update_user_pool_client(\n            user_pool_id, client_id, self.parameters\n        )\n        return json.dumps({\"UserPoolClient\": user_pool_client.to_json(extended=True)})\n\n    def delete_user_pool_client(self) -> str:\n        user_pool_id = self._get_param(\"UserPoolId\")\n        client_id = self._get_param(\"ClientId\")\n        self.backend.delete_user_pool_client(user_pool_id, client_id)\n        return \"\"\n\n    # Identity provider\n    def create_identity_provider(self) -> str:\n        user_pool_id = self._get_param(\"UserPoolId\")\n        name = self.parameters.pop(\"ProviderName\")\n        identity_provider = self.backend.create_identity_provider(\n            user_pool_id, name, self.parameters\n        )\n        return json.dumps(\n            {\"IdentityProvider\": identity_provider.to_json(extended=True)}\n        )\n\n    def list_identity_providers(self) -> str:\n        user_pool_id = self._get_param(\"UserPoolId\")\n        max_results = self._get_param(\"MaxResults\")\n        next_token = self._get_param(\"NextToken\")\n        identity_providers, next_token = self.backend.list_identity_providers(\n            user_pool_id, max_results=max_results, next_token=next_token\n        )\n        response: Dict[str, Any] = {\n            \"Providers\": [\n                identity_provider.to_json() for identity_provider in identity_providers\n            ]\n        }\n        if next_token:\n            response[\"NextToken\"] = str(next_token)\n        return json.dumps(response)\n\n    def describe_identity_provider(self) -> str:\n        user_pool_id = self._get_param(\"UserPoolId\")\n        name = self._get_param(\"ProviderName\")\n        identity_provider = self.backend.describe_identity_provider(user_pool_id, name)\n        return json.dumps(\n            {\"IdentityProvider\": identity_provider.to_json(extended=True)}\n        )\n\n    def update_identity_provider(self) -> str:\n        user_pool_id = self._get_param(\"UserPoolId\")\n        name = self._get_param(\"ProviderName\")\n        identity_provider = self.backend.update_identity_provider(\n            user_pool_id, name, self.parameters\n        )\n        return json.dumps(\n            {\"IdentityProvider\": identity_provider.to_json(extended=True)}\n        )\n\n    def delete_identity_provider(self) -> str:\n        user_pool_id = self._get_param(\"UserPoolId\")\n        name = self._get_param(\"ProviderName\")\n        self.backend.delete_identity_provider(user_pool_id, name)\n        return \"\"\n\n    # Group\n    def create_group(self) -> str:\n        group_name = self._get_param(\"GroupName\")\n        user_pool_id = self._get_param(\"UserPoolId\")\n        description = self._get_param(\"Description\")\n        role_arn = self._get_param(\"RoleArn\")\n        precedence = self._get_param(\"Precedence\")\n\n        group = self.backend.create_group(\n            user_pool_id, group_name, description, role_arn, precedence\n        )\n\n        return json.dumps({\"Group\": group.to_json()})\n\n    def get_group(self) -> str:\n        group_name = self._get_param(\"GroupName\")\n        user_pool_id = self._get_param(\"UserPoolId\")\n        group = self.backend.get_group(user_pool_id, group_name)\n        return json.dumps({\"Group\": group.to_json()})\n\n    def list_groups(self) -> str:\n        user_pool_id = self._get_param(\"UserPoolId\")\n        limit = self._get_param(\"Limit\")\n        token = self._get_param(\"NextToken\")\n        groups, token = self.backend.list_groups(\n            user_pool_id, limit=limit, next_token=token\n        )\n        response = {\"Groups\": [group.to_json() for group in groups]}\n        if token:\n            response[\"NextToken\"] = token\n        return json.dumps(response)\n\n    def delete_group(self) -> str:\n        group_name = self._get_param(\"GroupName\")\n        user_pool_id = self._get_param(\"UserPoolId\")\n        self.backend.delete_group(user_pool_id, group_name)\n        return \"\"\n\n    def update_group(self) -> str:\n        group_name = self._get_param(\"GroupName\")\n        user_pool_id = self._get_param(\"UserPoolId\")\n        description = self._get_param(\"Description\")\n        role_arn = self._get_param(\"RoleArn\")\n        precedence = self._get_param(\"Precedence\")\n\n        group = self.backend.update_group(\n            user_pool_id, group_name, description, role_arn, precedence\n        )\n\n        return json.dumps({\"Group\": group.to_json()})\n\n    def admin_add_user_to_group(self) -> str:\n        user_pool_id = self._get_param(\"UserPoolId\")\n        username = self._get_param(\"Username\")\n        group_name = self._get_param(\"GroupName\")\n\n        self.backend.admin_add_user_to_group(user_pool_id, group_name, username)\n\n        return \"\"\n\n    def list_users_in_group(self) -> str:\n        user_pool_id = self._get_param(\"UserPoolId\")\n        group_name = self._get_param(\"GroupName\")\n        limit = self._get_param(\"Limit\")\n        token = self._get_param(\"NextToken\")\n        users, token = self.backend.list_users_in_group(\n            user_pool_id, group_name, limit=limit, next_token=token\n        )\n        response = {\"Users\": [user.to_json(extended=True) for user in users]}\n        if token:\n            response[\"NextToken\"] = token\n        return json.dumps(response)\n\n    def admin_list_groups_for_user(self) -> str:\n        username = self._get_param(\"Username\")\n        user_pool_id = self._get_param(\"UserPoolId\")\n        groups = self.backend.admin_list_groups_for_user(user_pool_id, username)\n        return json.dumps({\"Groups\": [group.to_json() for group in groups]})\n\n    def admin_remove_user_from_group(self) -> str:\n        user_pool_id = self._get_param(\"UserPoolId\")\n        username = self._get_param(\"Username\")\n        group_name = self._get_param(\"GroupName\")\n\n        self.backend.admin_remove_user_from_group(user_pool_id, group_name, username)\n\n        return \"\"\n\n    def admin_reset_user_password(self) -> str:\n        user_pool_id = self._get_param(\"UserPoolId\")\n        username = self._get_param(\"Username\")\n        self.backend.admin_reset_user_password(user_pool_id, username)\n        return \"\"\n\n    # User\n    def admin_create_user(self) -> str:\n        user_pool_id = self._get_param(\"UserPoolId\")\n        username = self._get_param(\"Username\")\n        message_action = self._get_param(\"MessageAction\")\n        temporary_password = self._get_param(\"TemporaryPassword\")\n        user = self.backend.admin_create_user(\n            user_pool_id,\n            username,\n            message_action,\n            temporary_password,\n            self._get_param(\"UserAttributes\", []),\n        )\n\n        return json.dumps({\"User\": user.to_json(extended=True)})\n\n    def admin_confirm_sign_up(self) -> str:\n        user_pool_id = self._get_param(\"UserPoolId\")\n        username = self._get_param(\"Username\")\n        return self.backend.admin_confirm_sign_up(user_pool_id, username)\n\n    def admin_get_user(self) -> str:\n        user_pool_id = self._get_param(\"UserPoolId\")\n        username = self._get_param(\"Username\")\n        user = self.backend.admin_get_user(user_pool_id, username)\n        return json.dumps(user.to_json(extended=True, attributes_key=\"UserAttributes\"))\n\n    def get_user(self) -> str:\n        access_token = self._get_param(\"AccessToken\")\n        user = region_agnostic_backend.get_user(access_token=access_token)\n        return json.dumps(user.to_json(extended=True, attributes_key=\"UserAttributes\"))\n\n    def list_users(self) -> str:\n        user_pool_id = self._get_param(\"UserPoolId\")\n        limit = self._get_param(\"Limit\")\n        token = self._get_param(\"PaginationToken\")\n        filt = self._get_param(\"Filter\")\n        attributes_to_get = self._get_param(\"AttributesToGet\")\n        users, token = self.backend.list_users(\n            user_pool_id, limit=limit, pagination_token=token\n        )\n        if filt:\n            inherent_attributes: Dict[str, Any] = {\n                \"cognito:user_status\": lambda u: u.status,\n                \"status\": lambda u: \"Enabled\" if u.enabled else \"Disabled\",\n                \"username\": lambda u: u.username,\n            }\n            comparisons: Dict[str, Any] = {\n                \"=\": lambda x, y: x == y,\n                \"^=\": lambda x, y: x.startswith(y),\n            }\n            allowed_attributes = [\n                \"username\",\n                \"email\",\n                \"phone_number\",\n                \"name\",\n                \"given_name\",\n                \"family_name\",\n                \"preferred_username\",\n                \"cognito:user_status\",\n                \"status\",\n                \"sub\",\n            ]\n\n            match = re.match(r\"([\\w:]+)\\s*(=|\\^=)\\s*\\\"(.*)\\\"\", filt)\n            if match:\n                name, op, value = match.groups()\n            else:\n                raise InvalidParameterException(\"Error while parsing filter\")\n            if name not in allowed_attributes:\n                raise InvalidParameterException(f\"Invalid search attribute: {name}\")\n            compare = comparisons[op]\n            users = [\n                user\n                for user in users\n                if [\n                    attr\n                    for attr in user.attributes\n                    if attr[\"Name\"] == name and compare(attr[\"Value\"], value)\n                ]\n                or (\n                    name in inherent_attributes\n                    and compare(inherent_attributes[name](user), value)\n                )\n            ]\n        response: Dict[str, Any] = {\n            \"Users\": [\n                user.to_json(extended=True, attributes_to_get=attributes_to_get)\n                for user in users\n            ]\n        }\n        if token:\n            response[\"PaginationToken\"] = str(token)\n        return json.dumps(response)\n\n    def admin_disable_user(self) -> str:\n        user_pool_id = self._get_param(\"UserPoolId\")\n        username = self._get_param(\"Username\")\n        self.backend.admin_disable_user(user_pool_id, username)\n        return \"\"\n\n    def admin_enable_user(self) -> str:\n        user_pool_id = self._get_param(\"UserPoolId\")\n        username = self._get_param(\"Username\")\n        self.backend.admin_enable_user(user_pool_id, username)\n        return \"\"\n\n    def admin_delete_user(self) -> str:\n        user_pool_id = self._get_param(\"UserPoolId\")\n        username = self._get_param(\"Username\")\n        self.backend.admin_delete_user(user_pool_id, username)\n        return \"\"\n\n    def admin_initiate_auth(self) -> str:\n        user_pool_id = self._get_param(\"UserPoolId\")\n        client_id = self._get_param(\"ClientId\")\n        auth_flow = self._get_param(\"AuthFlow\")\n        auth_parameters = self._get_param(\"AuthParameters\")\n\n        auth_result = self.backend.admin_initiate_auth(\n            user_pool_id, client_id, auth_flow, auth_parameters\n        )\n\n        return json.dumps(auth_result)\n\n    def respond_to_auth_challenge(self) -> str:\n        session = self._get_param(\"Session\")\n        client_id = self._get_param(\"ClientId\")\n        challenge_name = self._get_param(\"ChallengeName\")\n        challenge_responses = self._get_param(\"ChallengeResponses\")\n        auth_result = region_agnostic_backend.respond_to_auth_challenge(\n            session, client_id, challenge_name, challenge_responses\n        )\n\n        return json.dumps(auth_result)\n\n    def forgot_password(self) -> str:\n        client_id = self._get_param(\"ClientId\")\n        username = self._get_param(\"Username\")\n        account, region = find_account_region_by_value(\"client_id\", client_id)\n        confirmation_code, response = cognitoidp_backends[account][\n            region\n        ].forgot_password(client_id, username)\n        self.response_headers[\n            \"x-moto-forgot-password-confirmation-code\"\n        ] = confirmation_code\n        return json.dumps(response)\n\n    # This endpoint receives no authorization header, so if moto-server is listening\n    # on localhost (doesn't get a region in the host header), it doesn't know what\n    # region's backend should handle the traffic, and we use `find_region_by_value` to\n    # solve that problem.\n    def confirm_forgot_password(self) -> str:\n        client_id = self._get_param(\"ClientId\")\n        username = self._get_param(\"Username\")\n        password = self._get_param(\"Password\")\n        confirmation_code = self._get_param(\"ConfirmationCode\")\n        account, region = find_account_region_by_value(\"client_id\", client_id)\n        cognitoidp_backends[account][region].confirm_forgot_password(\n            client_id, username, password, confirmation_code\n        )\n        return \"\"\n\n    # Ditto the comment on confirm_forgot_password.\n    def change_password(self) -> str:\n        access_token = self._get_param(\"AccessToken\")\n        previous_password = self._get_param(\"PreviousPassword\")\n        proposed_password = self._get_param(\"ProposedPassword\")\n        account, region = find_account_region_by_value(\"access_token\", access_token)\n        cognitoidp_backends[account][region].change_password(\n            access_token, previous_password, proposed_password\n        )\n        return \"\"\n\n    def admin_update_user_attributes(self) -> str:\n        user_pool_id = self._get_param(\"UserPoolId\")\n        username = self._get_param(\"Username\")\n        attributes = self._get_param(\"UserAttributes\")\n        self.backend.admin_update_user_attributes(user_pool_id, username, attributes)\n        return \"\"\n\n    def admin_delete_user_attributes(self) -> str:\n        user_pool_id = self._get_param(\"UserPoolId\")\n        username = self._get_param(\"Username\")\n        attributes = self._get_param(\"UserAttributeNames\")\n        self.backend.admin_delete_user_attributes(user_pool_id, username, attributes)\n        return \"\"\n\n    def admin_user_global_sign_out(self) -> str:\n        user_pool_id = self._get_param(\"UserPoolId\")\n        username = self._get_param(\"Username\")\n        self.backend.admin_user_global_sign_out(user_pool_id, username)\n        return \"\"\n\n    def global_sign_out(self) -> str:\n        access_token = self._get_param(\"AccessToken\")\n        self.backend.global_sign_out(access_token)\n        return \"\"\n\n    # Resource Server\n    def create_resource_server(self) -> str:\n        user_pool_id = self._get_param(\"UserPoolId\")\n        identifier = self._get_param(\"Identifier\")\n        name = self._get_param(\"Name\")\n        scopes = self._get_param(\"Scopes\")\n        resource_server = self.backend.create_resource_server(\n            user_pool_id, identifier, name, scopes\n        )\n        return json.dumps({\"ResourceServer\": resource_server.to_json()})\n\n    def describe_resource_server(self) -> str:\n        user_pool_id = self._get_param(\"UserPoolId\")\n        identifier = self._get_param(\"Identifier\")\n        resource_server = self.backend.describe_resource_server(\n            user_pool_id, identifier\n        )\n        return json.dumps({\"ResourceServer\": resource_server.to_json()})\n\n    def list_resource_servers(self) -> str:\n        max_results = self._get_param(\"MaxResults\")\n        next_token = self._get_param(\"NextToken\")\n        user_pool_id = self._get_param(\"UserPoolId\")\n        resource_servers, next_token = self.backend.list_resource_servers(\n            user_pool_id, max_results=max_results, next_token=next_token\n        )\n        response: Dict[str, Any] = {\n            \"ResourceServers\": [\n                resource_server.to_json() for resource_server in resource_servers\n            ]\n        }\n        if next_token:\n            response[\"NextToken\"] = str(next_token)\n        return json.dumps(response)\n\n    def sign_up(self) -> str:\n        client_id = self._get_param(\"ClientId\")\n        username = self._get_param(\"Username\")\n        password = self._get_param(\"Password\")\n        user = region_agnostic_backend.sign_up(\n            client_id=client_id,\n            username=username,\n            password=password,\n            attributes=self._get_param(\"UserAttributes\", []),\n        )\n        return json.dumps(\n            {\n                \"UserConfirmed\": user.status == UserStatus[\"CONFIRMED\"],\n                \"UserSub\": user.id,\n            }\n        )\n\n    def confirm_sign_up(self) -> str:\n        client_id = self._get_param(\"ClientId\")\n        username = self._get_param(\"Username\")\n        region_agnostic_backend.confirm_sign_up(client_id=client_id, username=username)\n        return \"\"\n\n    def initiate_auth(self) -> str:\n        client_id = self._get_param(\"ClientId\")\n        auth_flow = self._get_param(\"AuthFlow\")\n        auth_parameters = self._get_param(\"AuthParameters\")\n\n        auth_result = region_agnostic_backend.initiate_auth(\n            client_id, auth_flow, auth_parameters\n        )\n\n        return json.dumps(auth_result)\n\n    def associate_software_token(self) -> str:\n        access_token = self._get_param(\"AccessToken\")\n        result = self.backend.associate_software_token(access_token)\n        return json.dumps(result)\n\n    def verify_software_token(self) -> str:\n        access_token = self._get_param(\"AccessToken\")\n        result = self.backend.verify_software_token(access_token)\n        return json.dumps(result)\n\n    def set_user_mfa_preference(self) -> str:\n        access_token = self._get_param(\"AccessToken\")\n        software_token_mfa_settings = self._get_param(\"SoftwareTokenMfaSettings\")\n        sms_mfa_settings = self._get_param(\"SMSMfaSettings\")\n        self.backend.set_user_mfa_preference(\n            access_token, software_token_mfa_settings, sms_mfa_settings\n        )\n        return \"\"\n\n    def admin_set_user_mfa_preference(self) -> str:\n        user_pool_id = self._get_param(\"UserPoolId\")\n        username = self._get_param(\"Username\")\n        software_token_mfa_settings = self._get_param(\"SoftwareTokenMfaSettings\")\n        sms_mfa_settings = self._get_param(\"SMSMfaSettings\")\n        self.backend.admin_set_user_mfa_preference(\n            user_pool_id, username, software_token_mfa_settings, sms_mfa_settings\n        )\n        return \"\"\n\n    def admin_set_user_password(self) -> str:\n        user_pool_id = self._get_param(\"UserPoolId\")\n        username = self._get_param(\"Username\")\n        password = self._get_param(\"Password\")\n        permanent = self._get_param(\"Permanent\")\n        self.backend.admin_set_user_password(\n            user_pool_id, username, password, permanent\n        )\n        return \"\"\n\n    def add_custom_attributes(self) -> str:\n        user_pool_id = self._get_param(\"UserPoolId\")\n        custom_attributes = self._get_param(\"CustomAttributes\")\n        self.backend.add_custom_attributes(user_pool_id, custom_attributes)\n        return \"\"\n\n    def update_user_attributes(self) -> str:\n        access_token = self._get_param(\"AccessToken\")\n        attributes = self._get_param(\"UserAttributes\")\n        self.backend.update_user_attributes(access_token, attributes)\n        return json.dumps({})\n\n\nclass CognitoIdpJsonWebKeyResponse(BaseResponse):\n    def __init__(self) -> None:\n        with open(\n            os.path.join(os.path.dirname(__file__), \"resources/jwks-public.json\")\n        ) as f:\n            self.json_web_key = f.read()\n\n    def serve_json_web_key(\n        self,\n        request: Any,  # pylint: disable=unused-argument\n        full_url: str,  # pylint: disable=unused-argument\n        headers: Any,  # pylint: disable=unused-argument\n    ) -> Tuple[int, Dict[str, str], str]:\n        return 200, {\"Content-Type\": \"application/json\"}, self.json_web_key\n","repo_name":"getmoto/moto","sub_path":"moto/cognitoidp/responses.py","file_name":"responses.py","file_ext":"py","file_size_in_byte":25932,"program_lang":"python","lang":"en","doc_type":"code","stars":7174,"dataset":"github-code","pt":"48"}
+{"seq_id":"21296365256","text":"#This is the python program to learn english vocabulary with fun\n#In this program i have downloaded the JSON file that already contains the word and meaning of more than 100K words.\n#I have retreive the worwd from the JSON file using the RANDOM module. The program tells user to guess the word by\n#providing the meaning. ...\nimport random\nimport json\n\ndef option1():\n\n    data = json.load(open(\"word.JSON\"))\n    key, value = random.choice(list(data.items()))\n\n    spaces = ['_']* len(key)\n\n    def get_letter_position(key, word, spaces):\n        index = -2\n        while index != -1:\n            if key in word:\n                index = word.find(key)\n                removed_character ='*'\n                word = word[:index]+removed_character+word[index+1:]\n                spaces[index] = key\n            else:\n                index = -1\n        return (word, spaces)\n\n    def win_check():\n        for i in range(0, len(spaces)):\n            if spaces[i] == '_':\n                return -1\n        return 1\n    num_turns = len(word)\n    for i in range(-1, num_turns):\n        key = input('Guess a character:')\n        if key in word:\n            word, spaces = get_letter_position(key, word, spaces)\n            print(spaces)\n        else:\n            print('Sorry that letter is not in the word.')\n            \n        if win_check() == 1:\n            print('Congratulations you won')\n            break\n        \n        print('you have '+str(num_turns - i)+' turns left.')\n        print()\n\ndef check_type(ans):\n    try:\n        ans = int(ans)\n        return ans\n    except Exception as exception:\n        print('-'*50+'\\n')\n        print('Exception:' + str(exception))\n        print('-'*50+'\\n')\n\ndef main():\n    print(\"*\"*20)\n    print(\"Welcome to Learn_Vocab app....\")\n    print(\"*\"*50)\n\n    while True:\n        print(\"1.Start learning\\n2.View History\\n3.Exit\")\n        ans = input(\"Select any one option[1,2,3]\")\n        ans = check_type(ans)\n\n        if ans == 1:\n            option1()\n        elif ans == 2:\n            print(\"-\"*50)\n            print(\"Closing the program.....\")\n            print(\"Keep learning.....\")\n            print(\"-\"*50)\n        else:\n            print(\"Please choose the number in between 1-3\")\n\nif __name__ == '__main__':\n    main()","repo_name":"Sonissapkota/Vocab","sub_path":"Learn_Vocab.py","file_name":"Learn_Vocab.py","file_ext":"py","file_size_in_byte":2266,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"41283939649","text":"#hernandez quintana luis eduaardo\r\nclass Persona:\r\n\t\r\n\tNombre=None\r\n\tedad=None\r\n\testatura=None\r\n\r\n\t\r\n\tdef __init__(self,nombre,edad,estatura):\r\n\t\tprint(\"\\ncreando persona\")\r\n\t\tself.nombre=nombre\r\n\t\tself.edad=edad\r\n\t\tself.estatura=estatura\r\n\t\tprint(\"\\nPersona\",self)\r\n\t\r\n\tdef __str__(self):\r\n\t\tprint(self.nombre)\r\n\r\n\r\n\r\nclass Alumno(Persona):\r\n\t\r\n\tasistencia=30\r\n\tcalificacion=10\r\n\thorario=[]\r\n\ttareas=[]\r\n\r\n    \r\n\tdef __init__(self,nombre,edad,estatura,horario):\r\n\t\r\n\t\tPersonas.__init__(self,nombre,edad,estatura)\t\r\n\t\tprint(\"alumno es \",self)\r\n\t\tself.tareas=[]\r\n\r\n\t\t\r\n\tdef __str__(self):\r\n\t\tprint(self.nombre)\r\n\t\treturn self.nombre\r\n\r\n\r\nclass Profesor(Personas):\r\n\tsueldo=None\r\n\testacionamiento=False\r\n\thambre=False\r\n\t\r\n\t\r\n\tdef __init__(self,nombre,edad,estatura,sueldo=1000):\r\n\t\tself.nombre=nombre\r\n\t\tself.edad=edad\r\n\t\tself.estatura=estatura\r\n\t\tself.sueldo=sueldo\r\n\t\r\n\tdef tienehambre(self):\r\n\t\tprint(\"el profesor \"+str(self)+\" tiene hambre y tiene \"+str(self.edad)+\" anos\")  #va a convertir en cadena a self\r\n\t\t\r\n\tdef __str__(self):\r\n\r\n            return self.nombre+\" Mecatrónico\" \r\n        \r\n\tdef asignarTarea(self,alumno,tipo):\r\n\t\ttarea1=Tareas(alumno,self,tipo)\r\n\t\talumno.tareas.append(tarea1)\r\n\t\tprint(\"\\nTarea Asignada\\n\")\r\n\r\nclass Tareas(Personas):\r\n\t\r\n\r\n\tdef __init__(self,alumno,profesor,tipo=\"programacion\"):\r\n\t\tself.alumno=alumno\r\n\t\tself.profesor=profesor\r\n\t\tself.calificacion=0\r\n\t\tself.tipo=tipo\r\n\t\tprint (\"\\nTareas\")\r\n\r\n\tdef setCalificacion(self,Calificacion):\r\n\t\tif Calificacion>=0 and Calificacion <=10:\r\n\t\t\tself.Calificacion=Calificacion\r\n\t\t\t\r\n\t\telse:\r\n\t\t\tprint(\"Error calificacion invalida\")\r\n\t\r\n\tdef sesimprimetarea(self):\r\n\t\tprint(\"El alumno: \"+str(self.alumno)+\" con profesor: \"+str(self.profesor)+\" realizo la tarea de: \"+str(self.tipo)+\" y obtuvo una calificacion de: \"+str(self.Calificacion))\r\n","repo_name":"emilianoNM/Clase2018Tecnicas","sub_path":"Clase21022018/Eduardoclase.py","file_name":"Eduardoclase.py","file_ext":"py","file_size_in_byte":1820,"program_lang":"python","lang":"es","doc_type":"code","stars":3,"dataset":"github-code","pt":"48"}
+{"seq_id":"41713683146","text":"\"\"\"General Tests.\"\"\"\n\nimport os\nfrom unittest.mock import Mock, patch\n\nimport boto3\nfrom celery.exceptions import Retry\nfrom django.contrib.auth import get_user_model\nfrom django.test import TestCase, override_settings\nfrom moto import mock_sqs\n\nfrom ..backends import RQ, AwsSqsLambda, Celery, Channels, Console\nfrom ..channels import DjangoWebSocketChannel\nfrom ..consumers import DjangoNotifsConsumer\nfrom ..tasks import consume\nfrom ..utils import get_notification_model\n\nNotification = get_notification_model()\n\n\nclass BackendTests(TestCase):\n    \"\"\"\n    Tests for the Delivery backends\n\n    This class also contains tests for asynchronous functions/methods\n    that aren't triggered from the regular unit tests\n    \"\"\"\n\n    User = get_user_model()\n\n    @classmethod\n    def setUpTestData(cls):\n        \"\"\"Create Users.\"\"\"\n        cls.user1 = cls.User.objects.create_user(\n            username='user1@gmail.com', password='password'\n        )\n\n        cls.user2 = cls.User.objects.create_user(\n            username='user2@gmail.com', password='password'\n        )\n\n        cls.notification = Notification(\n            source=cls.user2,\n            source_display_name='User 2',\n            recipient=cls.user1,\n            action='Notified',\n            category='General notification',\n            obj=cls.user2,\n            url='http://example.com',\n            short_description='Short Description',\n            is_read=False,\n        )\n\n    def test_console_backend(self):\n        websocket_channel = DjangoWebSocketChannel(\n            self.notification, context={'message': {'text': 'Hello world'}}\n        )\n        delivery_backend = Console(websocket_channel)\n\n        self.assertIsNone(delivery_backend.run(countdown=0))\n\n    @override_settings(CELERY_BROKER_URL='redis://localhost:6379/0')\n    def test_celery_backend(self):\n        websocket_channel = DjangoWebSocketChannel(\n            self.notification, context={'message': {'text': 'Hello world'}}\n        )\n        delivery_backend = Celery(websocket_channel)\n\n        self.assertIsNone(delivery_backend.run(countdown=0))\n\n    @patch('notifications.backends.celery.settings.NOTIFICATIONS_RETRY', True)\n    @patch(\n        'notifications.backends.celery.CeleryBackend.consume',\n        Mock(side_effect=Exception),\n    )\n    @patch(\n        'notifications.backends.celery.consume.retry',\n        Mock(side_effect=Retry),\n    )\n    def test_celery_backend_retry(self):\n\n        with self.assertRaises(Retry):\n            consume(\n                'django_channels',\n                {'type': 'celery', 'message': 'Hello world'},\n                context={'destination': 'celery_users'},\n            )\n\n    @override_settings(\n        CHANNEL_LAYERS={\n            'django_notifs': {\n                'BACKEND': 'channels_redis.core.RedisChannelLayer',\n                'CONFIG': {\n                    \"hosts\": [('127.0.0.1', 6379)],\n                },\n            },\n        }\n    )\n    def test_channels_backend(self):\n        websocket_channel = DjangoWebSocketChannel(\n            self.notification, context={'message': {'text': 'Hello world'}}\n        )\n        delivery_backend = Channels(websocket_channel)\n\n        self.assertIsNone(delivery_backend.run(countdown=0))\n\n    @patch('notifications.default_settings.NOTIFICATIONS_RETRY', True)\n    def test_rq_backend(self):\n        websocket_channel = DjangoWebSocketChannel(\n            self.notification, context={'message': {'text': 'Hello world'}}\n        )\n        delivery_backend = RQ(websocket_channel)\n\n        self.assertIsNone(delivery_backend.run(countdown=0))\n\n    @mock_sqs\n    def test_aws_lambda_sqs(self):\n        # Create and mock SQS queue\n        os.environ['AWS_ACCESS_KEY_ID'] = 'testing'\n        os.environ['AWS_SECRET_ACCESS_KEY'] = 'testing'\n        os.environ['AWS_SECURITY_TOKEN'] = 'testing'\n        os.environ['AWS_SESSION_TOKEN'] = 'testing'\n        os.environ['AWS_DEFAULT_REGION'] = 'us-east-1'\n\n        sqs = boto3.client('sqs')\n        lambda_queue = sqs.create_queue(QueueName='aws-lambda-example')\n\n        with patch(\n            'notifications.default_settings.NOTIFICATIONS_SQS_QUEUE_URL',\n            lambda_queue['QueueUrl'],\n        ):\n            websocket_channel = DjangoWebSocketChannel(\n                self.notification, context={'message': {'text': 'Hello world'}}\n            )\n            delivery_backend = AwsSqsLambda(websocket_channel)\n\n            self.assertIsNone(delivery_backend.run(countdown=0))\n\n    def test_celery_task(self):\n        \"\"\"This ensures that the Celery task runs without errors.\"\"\"\n        self.assertIsNone(\n            consume(\n                'django_channels',\n                {'type': 'celery', 'message': 'Hello world'},\n                context={'destination': 'celery_users'},\n            )\n        )\n\n    async def test_channels_consumer(self):\n        \"\"\"This ensures that the Channels consumer runs without errors.\"\"\"\n        consumer = DjangoNotifsConsumer()\n        message = {\n            'provider': 'django_channels',\n            'payload': {'type': 'celery', 'message': 'Hello world'},\n            'context': {'destination': 'celery_users'},\n            'countdown': 0,\n        }\n        result = await consumer.notify(message)\n        self.assertIsNone(result)\n","repo_name":"danidee10/django-notifs","sub_path":"notifications/tests/test_backends.py","file_name":"test_backends.py","file_ext":"py","file_size_in_byte":5273,"program_lang":"python","lang":"en","doc_type":"code","stars":163,"dataset":"github-code","pt":"48"}
+{"seq_id":"35400002974","text":"'''\nCreated by Sherry on 2016.10.7 for Hog and perceptron.\nHelp from http://blog.csdn.net/wds2006sdo/article/details/51923546.\n'''\n#encoding:utf-8\nimport pandas as pd\nimport numpy as np\nimport cv2\nimport time\nimport random\nfrom sklearn.cross_validation import train_test_split\nfrom sklearn.metrics import accuracy_score\n\nstudy_step = 0.0001                                 # 学习步长\nstudy_total = 10000                                 # 学习次数\nfeature_length = 324                                # hog特征维度  ???\nobject_num = 0                                      # 分类的数字\n\ndef get_hog_features(trainSet):\n    features=[]\n    hog=cv2.HOGDescriptor('./hog.xml')\n    for img in trainSet:\n        img=np.reshape(img,(28,28))\n        cv_img=img.astype(np.uint8)\n\n        hog_feature=hog.compute(cv_img)\n        features.append(hog_feature)\n\n    features=np.array(features)\n    features=np.reshape(features,(-1,324))\n    return features\n\ndef train(trainSet,train_labels):\n    trainSet_size=len(train_labels)\n    w=np.zeros((feature_length,1))\n    b=0\n\n    study_count=0\n    noChange_count=0\n    noChange_upper_limit=100000\n\n    while True:\n        noChange_count+=1\n        if noChange_count>noChange_upper_limit:\n            break\n\n        #random for data\n        index=random.randint(0,trainSet_size-1)\n        img=trainSet[index]\n        label=train_labels[index]\n\n\n\n","repo_name":"Sherry-JQ/ML_study","sub_path":"Hog_perceptron/test1.py","file_name":"test1.py","file_ext":"py","file_size_in_byte":1393,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"32305513201","text":"import json\n\nfrom moto.awslambda.policy import Policy\n\n\nclass MockLambdaFunction:\n    def __init__(self, arn):\n        self.function_arn = arn\n        self.policy = None\n\n\ndef test_policy():\n    policy = Policy(MockLambdaFunction(\"arn\"))\n    statement = {\n        \"StatementId\": \"statement0\",\n        \"Action\": \"lambda:InvokeFunction\",\n        \"FunctionName\": \"function_name\",\n        \"Principal\": \"events.amazonaws.com\",\n        \"SourceArn\": \"arn:aws:events:us-east-1:111111111111:rule/rule_name\",\n        \"SourceAccount\": \"111111111111\",\n    }\n\n    expected = {\n        \"Action\": \"lambda:InvokeFunction\",\n        \"FunctionName\": \"function_name\",\n        \"Principal\": {\"Service\": \"events.amazonaws.com\"},\n        \"Effect\": \"Allow\",\n        \"Resource\": \"arn\",\n        \"Sid\": \"statement0\",\n        \"Condition\": {\n            \"ArnLike\": {\n                \"AWS:SourceArn\": \"arn:aws:events:us-east-1:111111111111:rule/rule_name\",\n            },\n            \"StringEquals\": {\"AWS:SourceAccount\": \"111111111111\"},\n        },\n    }\n\n    policy.add_statement(json.dumps(statement))\n    assert expected == policy.statements[0]\n\n    sid = statement.get(\"StatementId\", None)\n    if sid is None:\n        raise \"TestCase.statement does not contain StatementId\"\n\n    policy.del_statement(sid)\n    assert [] == policy.statements\n","repo_name":"getmoto/moto","sub_path":"tests/test_awslambda/test_policy.py","file_name":"test_policy.py","file_ext":"py","file_size_in_byte":1314,"program_lang":"python","lang":"en","doc_type":"code","stars":7174,"dataset":"github-code","pt":"48"}
+{"seq_id":"17281411139","text":"\n\ndef search(key, li):\n    flag = 0\n    for i in range(len(li)):\n        if li[i] == key:\n            print(key, \"is present at index\", i)\n            flag = 1\n            break\n    if flag == 0:\n        print(\"not found\")\n\n\nli = [1, 4, 6, 7, 3, 2]\nsearch(9, li)\n","repo_name":"simran-01/python","sub_path":"problems/search.py","file_name":"search.py","file_ext":"py","file_size_in_byte":263,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"34514379804","text":"import numpy as np\nimport matplotlib.pyplot as plt\nplt.rcParams['font.sans-serif'] = ['SimHei']\n \nname = ['体能','失误','反应','速度',]   \ntheta = np.linspace(0,2*np.pi,len(name),endpoint=False)   \nvalue = np.random.randint(50,100,size=4)   \ntheta = np.concatenate((theta,[theta[0]])) \nvalue = np.concatenate((value,[value[0]]))  \n \nax = plt.subplot(1,1,1,projection = 'polar')\nax.plot(theta,value,'m-',lw=1,alpha = 0.75)    \nax.fill(theta,value,'m',alpha = 0.75)           \nax.set_thetagrids(theta*180/np.pi,name)        \nax.set_ylim(0,110)                        \nax.set_theta_zero_location('N')        \nax.set_title('中国乒乓球选手分析',fontsize = 15)\nplt.show()\n","repo_name":"ZLEI-ZL/pythonCode","sub_path":"任务八/9.5.py","file_name":"9.5.py","file_ext":"py","file_size_in_byte":683,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"40782294918","text":"#imports\nimport re\n\n#function to clean cpp file\ndef cleanCpp(cppFileNameRead, cppFileNameWrite):\n    fileRead = open(cppFileNameRead, \"r\")\n    fileWrite = open(cppFileNameWrite, \"w\")\n    #declare tags to clean /* *\\ // and multiline comments clean\n    x = re.compile(r\"(/\\*.*?\\*/|//[^\\r\\n]*$)\",\n        re.DOTALL | re.MULTILINE\n    )\n    #read text from file\n    text =\"\"\n    for line in fileRead:\n        text += line\n\n    #convert text from file and write the converted text to new file\n    result = x.sub(\"\", text)\n    fileWrite.write(result)\n    fileRead.close()\n    fileWrite.close()\n\nprint(\"podaj nazwe pliku cpp do czyszczenia komentarzy\")\ncppFileNameRead = input()\nprint(\"podaj nazwe pliku cpp do ktorego mam zapisac wyczyszczony kod\")\ncppFileNameWrite = input()\n\ncleanCpp(cppFileNameRead, cppFileNameWrite)","repo_name":"Saevus96/zaliczenie-python","sub_path":"zad7.py","file_name":"zad7.py","file_ext":"py","file_size_in_byte":815,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"11386235853","text":"from django.shortcuts import render, redirect\nfrom django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\nfrom django.db.models import Q\n\nfrom profilemessages.forms import MessageSearchForm, ProfileMessageForm\n\nfrom profilemessages.models import ProfileMessage\n\nfrom profiles.models import Profile\n\n# Create your views here.\n\n\n#******************** Compose a new message page  ********************\n@login_required\ndef send_message_view(request, profileID, *args, **kwargs):\n  to = Profile.objects.get(pk=profileID)\n  if request.method == 'POST':\n    form = ProfileMessageForm(request.POST)\n    if form.is_valid():\n      message = form.save(commit=False)\n      message.message_to = to\n      message.message_from = request.user.profile\n      message.save()\n      messages.success(request, 'Message sent successfully.')\n      return redirect('home_page')\n  else:\n    form = ProfileMessageForm()\n  return render(request, 'messages/send_message.html', {'form': form, 'to': to})\n\n#******************** Inbox page  ********************\n@login_required\ndef inbox_view(request, *args, **kwargs):\n  if request.method == 'POST':\n    form = MessageSearchForm(request.POST)\n    if form.is_valid():\n      search_flag = True\n      arg = form.cleaned_data['text']\n      if arg:\n        qs = ProfileMessage.objects.filter(Q(message_from__f_name__icontains=arg) | Q(message_from__l_name__icontains=arg) | Q(title__icontains=arg) | Q(body__icontains=arg)).exclude(message_from=request.user.profile)\n      else:\n        qs = ProfileMessage.objects.filter(message_to=request.user.profile)\n  else:\n    form = MessageSearchForm()\n    qs = ProfileMessage.objects.filter(message_to=request.user.profile)\n    search_flag = False\n\n  qs = qs.order_by('-sent_at')\n\n  content = {\n    'object_list': qs,\n    'form': form,\n    'flag': search_flag\n  }\n  return render(request, 'messages/inbox_view.html', content)\n\n#******************** Sent messages view ********************\n@login_required\ndef sent_messages_view(request, *args, **kwargs):\n  if request.method == 'POST':\n    form = MessageSearchForm(request.POST)\n    if form.is_valid():\n      search_flag = True\n      arg = form.cleaned_data['text']\n      if arg:\n        qs = ProfileMessage.objects.filter(Q(message_to__f_name__icontains=arg) | Q(message_to__l_name__icontains=arg) | Q(title__icontains=arg) | Q(body__icontains=arg)).exclude(message_to=request.user.profile)\n      else:\n        qs = ProfileMessage.objects.filter(message_from=request.user.profile)\n  else:\n    form = MessageSearchForm()\n    qs = ProfileMessage.objects.filter(message_from=request.user.profile)\n    search_flag = False\n\n  qs = qs.order_by('-sent_at')\n\n  content = {\n    'object_list': qs,\n    'form': form,\n    'flag': search_flag\n  }\n  return render(request, 'messages/sent_messages_view.html', content)","repo_name":"sahar-avsh/ZahraAtrvash-SWE573","sub_path":"profilemessages/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2843,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"29025310986","text":"#GCD: Write a program that calculates the GCD of two given numbers.\n\ndef gcd(a, b):\n    while b:\n        a, b = b, a % b\n    return a\n\nnum1 = int(input(\"Enter first number: \"))\nnum2 = int(input(\"Enter second number: \"))\n\nprint(\"The GCD of\", num1, \"and\", num2, \"is\", gcd(num1, num2))\n","repo_name":"ajitharunai/Basic-Python-Programs","sub_path":"GCD.py","file_name":"GCD.py","file_ext":"py","file_size_in_byte":283,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"48"}
+{"seq_id":"75120827984","text":"from skimage import filters\nfrom skimage import restoration\nfrom skimage.morphology import square\nimport numpy as np\n#import pybind_bm3d as m\nfrom settings import FilterSettings\nfrom scipy import ndimage\nfrom skimage.restoration import denoise_tv_bregman\nfrom skimage.restoration import denoise_wavelet\nfrom skimage.exposure import rescale_intensity\nimport tensorflow as tf\n\ndef iou(y_true, y_pred):\n     def f(y_true, y_pred):\n         intersection = (y_true * y_pred).sum()\n         union = y_true.sum() + y_pred.sum() - intersection\n         x = (intersection + 1e-15) / (union + 1e-15)\n         x = x.astype(np.float32)\n         return x\n     return tf.numpy_function(f, [y_true, y_pred], tf.float32)\n\nmodel_dir = 'models/unet_v4'\nmodel = tf.keras.models.load_model(model_dir, custom_objects={'iou': iou})\n\nclass DenoisingFilters:\n\n    def __init__(self, image_input):\n        self._image_input = image_input\n\n    def standardize(self):\n        filter_settings = FilterSettings()\n        up = filter_settings.sup\n        low = filter_settings.inf\n        satured_image = self._image_input\n        satured_image[satured_image >= up] = up\n        satured_image[satured_image <= low] = low\n        delta = up - low\n        image_norm = (satured_image - low) / delta\n        return image_norm, delta, low\n\n    def gaussian_filter(self, window_size):\n        sigma = window_size/3\n        return filters.gaussian(self._image_input, sigma=sigma)\n\n    def mean_filter(self, window_size):\n        mask = np.ones((window_size, window_size))/(window_size**2)\n        return ndimage.filters.convolve(self._image_input, mask)\n\n    def median_filter(self, window_size):\n        selem = square(window_size)\n        return filters.median(self._image_input, selem=selem)\n\n    def wiener_filter(self, const_psf):\n        psf = np.ones((const_psf, const_psf)) / (const_psf * const_psf)\n        image_norm, delta, low = self.standardize()\n        filtered_image = restoration.unsupervised_wiener(image_norm, psf)[0]\n        filtered_image = (filtered_image * delta) - low\n        filtered_image = filtered_image - filtered_image.mean()\n        return filtered_image\n\n    def bilateral_filter(self, win_size, sigma_r, sigma_d):\n        image_norm, delta, low = self.standardize()\n        filtered_image = restoration.denoise_bilateral(image_norm,\n                                                       win_size=win_size,\n                                                       sigma_color=sigma_r,\n                                                       sigma_spatial=sigma_d)\n        filtered_image = (filtered_image * delta) - low\n        filtered_image = filtered_image - filtered_image.mean()\n        return filtered_image\n\n    def nlmeans_filter(self, patch_size, patch_distance):\n        image_norm, delta, low = self.standardize()\n        filtered_image = restoration.denoise_nl_means(image_norm,\n                                                      patch_size=patch_size,\n                                                      patch_distance=patch_distance)\n        filtered_image = (filtered_image * delta) - low\n        filtered_image = filtered_image - filtered_image.mean()\n        return filtered_image\n\n    def bm3D_filter(self, sigma):\n        filter_settings = FilterSettings()\n        img_satured = 99 + np.clip(self._image_input, filter_settings.inf, filter_settings.sup)\n        filtered_img = m.bm3d(img_satured, sigma) - 99\n        return filtered_img\n\n    def tv_filter(self, w):\n        image_norm, delta, low = self.standardize()\n        filtered_image = denoise_tv_bregman(image_norm, w)\n        filtered_image = (filtered_image * delta) - low\n        filtered_image = filtered_image - filtered_image.mean()\n        return filtered_image\n\n    def wavelets_filter(self, param):\n        image_norm, delta, low = self.standardize()\n        filtered_image = denoise_wavelet(image_norm)\n        filtered_image = (filtered_image * delta) - low\n        filtered_image = filtered_image - filtered_image.mean()\n        return filtered_image\n\n    \n    def unet_filter(self, param):\n        im_no_ped_clipped = np.clip(self._image_input, -26, 16)\n        im_no_ped_clipped_rescaled = rescale_intensity(im_no_ped_clipped, in_range=(0, 1.))\n        return model(im_no_ped_clipped_rescaled.reshape(1, 2048, 2048, 1)).numpy()[0,:,:,0]\n\n\n\n\n","repo_name":"ifpains/reco_gui","sub_path":"analysis/filters.py","file_name":"filters.py","file_ext":"py","file_size_in_byte":4331,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"22564544238","text":"def pivoting_matrix(matrix):\n    for column in range(len(matrix)):\n        find_max_of_column(matrix, column)\n    printmat(matrix)\n\ndef find_max_of_column(matrix,j):\n    #element = create_I_matrix(len(matrix))\n    #  Find the maximun value in a column in order to change lines\n    maxElem = abs(matrix[j][j])\n    maxRow = j\n    for k in range(j+1, len(matrix)):  # Interacting over the next line,in the same column\n        if abs(matrix[k][j]) > maxElem:\n            maxElem = abs(matrix[k][j])  # Next line on the diagonal\n            maxRow = k\n    if maxRow != j:\n        swap_lines_of_matrix(matrix, maxRow, j)\n\ndef swap_lines_of_matrix(matrix, index_line1, index_line2):\n    for column in range(len(matrix[0])):\n        temp_value = matrix[index_line2][column]\n        matrix[index_line2][column] = matrix[index_line1][column]\n        matrix[index_line1][column] = temp_value\n\ndef printmat(matrix):\n    for i in matrix:\n        for j in i:\n            print(f'{j}  ')\n        print('\\n')\n    print('\\n')\n\n\ndef check_dominant_diagonal(matrix, b):\n    # line_indexes_to_swap = []\n    index = 0\n    for line in matrix:\n        if line[index] >= abs(sum(line))-abs(line[index]):\n            index += 1\n        else:\n            sum_values = 0\n            #line_index_to_replace_with = 0\n            for i in range(index+1, len(line)):\n                if line[i] >= abs(sum(line))-abs(line[i]):                 #line_index_to_replace_with = i\n                    swap_lines_of_matrix(matrix, index, i)\n                    swap_lines_of_matrix(b, index, i)\n                    index += 1\n    return index == len(matrix)\n\n\ndef sor_method(matrix, b):\n    pivoting_matrix(matrix)\n    epsilon = 0.000001\n    guess = [0 for i in matrix]\n    value = 0\n    difference = 1\n    iterations = 0\n    w = 1.1\n    if not check_dominant_diagonal(matrix, b):\n        print(\"The matrix you entered has no diagonal matrix,so we will try to check if there will be solution\")\n        maxIter = 100\n        while difference > epsilon and iterations < maxIter:\n            for line in range(len(matrix)):\n                value = b[line][0]\n                for column in range(len(matrix)):\n                    if line != column:\n                        value -= matrix[line][column]*guess[column]\n\n                value /= matrix[line][line]\n                value *= w\n                value += (1-w) * guess[line]  #(the value of x in the current iteration)\n                difference = abs(guess[line] - value)\n                guess[line] = value\n            iterations += 1\n            print(\"Iteration number\" + str(iterations) + \":\")\n            print(guess)\n        if difference < 0.000001:\n            print(\"Although there was no dominant diagonal,the system of equations converged after\"+str(iterations)+\"to:\")\n            print(guess)\n        else:\n            print(\"No converge\")\n    else:\n        while difference > epsilon :\n            for line in range(len(matrix)):\n                value = b[line][0]\n                for column in range(len(matrix)):\n                    if line != column:\n                        value -= matrix[line][column]*guess[column]\n                value /= matrix[line][line]\n                value *= w\n                value += (1 - w) * guess[line]  # (the value of x in the current iteration)\n                difference = abs(guess[line] - value)\n                guess[line] = value\n            iterations += 1\n            print(\"Iteration number\" + str(iterations) + \":\")\n            print(guess)\n        print(\"It took\"+str(iterations)+\"iterations. The result is:\")\n        print(guess)\n\n\nsor_method([[3,-1,1], [-1,3,-1], [1,-1,3]],[[-1], [7], [-7]])","repo_name":"Ilanshk/NumericAnalysis2022","sub_path":"SOR_Algorithm.py","file_name":"SOR_Algorithm.py","file_ext":"py","file_size_in_byte":3674,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"70551901266","text":"import spacy\n\nfrom tqdm import tqdm\nimport codecs as cs\nfrom os.path import join as pjoin\n\nnlp = spacy.load('en_core_web_sm')\ndef process_text(sentence):\n    sentence = sentence.replace('-', '')\n    doc = nlp(sentence)\n    word_list = []\n    pos_list = []\n    for token in doc:\n        word = token.text\n        if not word.isalpha():\n            continue\n        if (token.pos_ == 'NOUN' or token.pos_ == 'VERB') and (word != 'left'):\n            word_list.append(token.lemma_)\n        else:\n            word_list.append(word)\n        pos_list.append(token.pos_)\n    return word_list, pos_list\n\ndef process_humanml3d(corpus):\n    text_save_path = './dataset/pose_data_raw/texts'\n    desc_all = corpus\n    for i in tqdm(range(len(desc_all))):\n        caption = desc_all.iloc[i]['caption']\n        start = desc_all.iloc[i]['from']\n        end = desc_all.iloc[i]['to']\n        name = desc_all.iloc[i]['new_joint_name']\n        word_list, pose_list = process_text(caption)\n        tokens = ' '.join(['%s/%s'%(word_list[i], pose_list[i]) for i in range(len(word_list))])\n        with cs.open(pjoin(text_save_path, name.replace('npy', 'txt')), 'a+') as f:\n            f.write('%s#%s#%s#%s\\n'%(caption, tokens, start, end))\n\ndef process_kitml(corpus):\n    text_save_path = './dataset/kit_mocap_dataset/texts'\n    desc_all = corpus\n    for i in tqdm(range(len(desc_all))):\n        caption = desc_all.iloc[i]['desc']\n        start = 0.0\n        end = 0.0\n        name = desc_all.iloc[i]['data_id']\n        word_list, pose_list = process_text(caption)\n        tokens = ' '.join(['%s/%s' % (word_list[i], pose_list[i]) for i in range(len(word_list))])\n        with cs.open(pjoin(text_save_path, name + '.txt'), 'a+') as f:\n            f.write('%s#%s#%s#%s\\n' % (caption, tokens, start, end))\n\nif __name__ == \"__main__\":\n    corpus = pd.read_csv('./dataset/kit_mocap_dataset/desc_final.csv')\n    process_humanml3d(corpus)","repo_name":"EricGuo5513/HumanML3D","sub_path":"text_process.py","file_name":"text_process.py","file_ext":"py","file_size_in_byte":1910,"program_lang":"python","lang":"en","doc_type":"code","stars":414,"dataset":"github-code","pt":"48"}
+{"seq_id":"19176578011","text":"from peer import Peer\nfrom simulation import Simulation, BINS\nfrom histogram import compute_histogram_bins, plot_histogram\n\nclass PeerQ2(Peer):\n\n    def send_data_to_backend(self):\n        \"\"\"\n            Question 2:\n            This method should return an _array_ of the peer's\n            connection durations.\n        \"\"\"\n        connection_duration_list = []\n        for key, value in self.peer_pool.items() :\n            connection_duration_list.append(value)\n        return connection_duration_list\n            \n\nclass SimulationQ2(Simulation):\n\n    def generate_network(self):\n        self.network =  [PeerQ2() for _ in range(self.number_of_peers)]\n\n    def process_backend_data(self):\n        \"\"\"\n            Question 2:\n            This method should do all necessary processing to return\n            the connection durations histogram bins counts.\n            Don't call `plot_histogram` in this method, we just want\n            to compute the histogram bins counts!\n        \"\"\"\n        datas = []\n        for peer in self.network :\n            datas += peer.send_data_to_backend()\n            \n            \n        result = compute_histogram_bins(datas, BINS)\n        for i in range(len(result[0])) :\n            result[0][i] = result[0][i]/2 #this is to avoid counting a connection twice (between a peer \"a\" and a peer \"b\", which occurs in the conncection of \"a\" and \"b\")\n        \n\nif __name__ == \"__main__\":\n\n    s = SimulationQ2(number_of_peers=10, max_peer_pool_size=2)\n    s.run()\n    s.report_result()\n\n    s = SimulationQ2(number_of_peers=1000, max_peer_pool_size=10)\n    s.run()\n    s.report_result()\n\n    s = SimulationQ2(number_of_peers=1000, max_peer_pool_size=100)\n    s.run()\n    s.report_result()\n\n    s = SimulationQ2(number_of_peers=1000, max_peer_pool_size=1000)\n    s.run()\n    s.report_result()\n\n\n    s = SimulationQ2(number_of_peers=10000, max_peer_pool_size=10)\n    s.run()\n    s.report_result()\n\n    s = SimulationQ2(number_of_peers=10000, max_peer_pool_size=100)\n    s.run()\n    s.report_result()\n    \n","repo_name":"karimabdouli/test","sub_path":"connections/question2.py","file_name":"question2.py","file_ext":"py","file_size_in_byte":2037,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"7286043639","text":"import cv2\nimport numpy as np\n\n# load image\nimg = cv2.imread(\"C:\\\\Users\\\\Goutham\\\\Documents\\\\SakaLabs\\\\Imageclassification\\\\tests\\\\image13.jpeg\")\nrsz_img = cv2.resize(img, None, fx=0.25, fy=0.25) # resize since image is huge\n\nlower = [100, 100, 100]\nupper = [255, 255, 255]\n\n# create NumPy arrays from the boundaries\nlower = np.array(lower, dtype=\"uint8\")\nupper = np.array(upper, dtype=\"uint8\")\n\n# find where the signature is and make a cropped region\npoints = np.argwhere(thresh_gray==0) # find where the black pixels are\npoints = np.fliplr(points) # store them in x,y coordinates instead of row,col indices\nx, y, w, h = cv2.boundingRect(points) # create a rectangle around those points\nx, y, w, h = x-10, y-10, w+20, h+20 # make the box a little bigger\ncrop = gray[y:y+h, x:x+w] # create a cropped region of the gray image\n\n# get the thresholded crop\nretval, thresh_crop = cv2.threshold(crop, thresh=200, maxval=255, type=cv2.THRESH_BINARY)\n\n# display\ncv2.imshow(\"Cropped and thresholded image\", thresh_crop) \ncv2.waitKey(0)","repo_name":"goutham-sp/computer-vision","sub_path":"experiments/bounding_rect.py","file_name":"bounding_rect.py","file_ext":"py","file_size_in_byte":1026,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"11279098616","text":"from __future__ import print_function\r\n\r\nimport pandas as pd\r\nimport io\r\nimport os.path\r\nimport csv\r\nimport time\r\n\r\nfrom google.auth.transport.requests import Request\r\nfrom google.oauth2.credentials import Credentials\r\nfrom google_auth_oauthlib.flow import InstalledAppFlow\r\nfrom googleapiclient.discovery import build\r\nfrom googleapiclient.errors import HttpError\r\n\r\n# If modifying these scopes, delete the file token.json.\r\nSCOPES = ['https://www.googleapis.com/auth/documents.readonly']\r\n\r\ncsv_file = r\"C:\\Users\\sneha\\Desktop\\All\\PulseLabs\\BWS - Week 1.csv\"\r\ndf = pd.read_csv(csv_file)\r\n#df = df.head(10)\r\n\r\ndef get_document_ids_from_csv(file_path):\r\n    \"\"\"Reads the CSV file and returns a list of document IDs from the third column.\"\"\"\r\n    document_ids = []\r\n    with open(file_path, 'r', encoding='utf-8') as csvfile:\r\n        reader = csv.reader(csvfile)\r\n        for row in reader:\r\n            if row == 'Document_ID':\r\n                pass\r\n            if len(row) >= 3:\r\n                document_id = row[2]  \r\n                if document_id:\r\n                    document_ids.append(document_id)\r\n    return document_ids\r\n\r\n\r\n# The ID of a sample document.\r\n#document_id = '1HQb26fGDBQe1K1bpoBydGiUke0gvO048D978ack17YU'\r\n\r\ndef download_document(creds, document_id):\r\n    try:\r\n        service = build('docs', 'v1', credentials=creds)\r\n\r\n        # Retrieve the document's contents from the Docs service.\r\n        document = service.documents().get(documentId=document_id).execute()\r\n\r\n        doc_title = document.get('title')\r\n\r\n        # Get the content of the document and save it to a local file.\r\n        content = document.get('body').get('content')\r\n        file_text = ''\r\n        for paragraph in content:\r\n            if 'paragraph' in paragraph:\r\n                elements = paragraph['paragraph']['elements']\r\n                for element in elements:\r\n                    text_run = element['textRun']\r\n                    file_text += text_run['content']\r\n\r\n        # Save the content to a local file\r\n        with io.open(f'{doc_title}.txt', 'w', encoding='utf-8') as file:\r\n            file.write(file_text)\r\n\r\n        print(f'The content of the document has been downloaded and saved to {doc_title}.txt')\r\n    \r\n    except HttpError as err:\r\n        if err.resp.status == 500:\r\n            print(f\"Received HTTP 500 error for document {document_id}. Retrying...\")\r\n            # Add a small delay before retrying the request\r\n            time.sleep(10)\r\n            download_document(creds, document_id)  # Retry the request\r\n\r\n        else:\r\n            print(f\"Error downloading document {document_id}: {err}\")\r\n        \r\n\r\ndef main():\r\n    \"\"\"Shows basic usage of the Docs API.\r\n    Prints the title of a sample document.\r\n    \"\"\"\r\n    creds = None\r\n    # The file token.json stores the user's access and refresh tokens, and is\r\n    # created automatically when the authorization flow completes for the first\r\n    # time.\r\n    if os.path.exists('token.json'):\r\n        creds = Credentials.from_authorized_user_file('token.json', SCOPES)\r\n    # If there are no (valid) credentials available, let the user log in.\r\n    try:\r\n        if not creds or not creds.valid:\r\n            if creds and creds.expired and creds.refresh_token:\r\n                creds.refresh(Request())\r\n            else:\r\n                flow = InstalledAppFlow.from_client_secrets_file(r'C:\\Users\\sneha\\Desktop\\All\\PulseLabs\\credentials.json', SCOPES)\r\n                creds = flow.run_local_server(port=0)\r\n            # Save the credentials for the next run\r\n            with open('token.json', 'w') as token:\r\n                token.write(creds.to_json())\r\n\r\n        document_ids = get_document_ids_from_csv(csv_file)\r\n\r\n        for document_id in document_ids:\r\n            download_document(creds,document_id)\r\n    except HttpError as err:\r\n        print(err)\r\n\r\n    '''\r\n    try:\r\n        service = build('docs', 'v1', credentials=creds)\r\n\r\n        # Retrieve the documents contents from the Docs service.\r\n        document = service.documents().get(documentId=DOCUMENT_ID).execute()\r\n\r\n        print('The title of the document is: {}'.format(document.get('title')))\r\n    except HttpError as err:\r\n        print(err)\r\n    '''    \r\n\r\nif __name__ == '__main__':\r\n    main()","repo_name":"SnehaSrinivasan17/CUJ-Extraction","sub_path":"API.py","file_name":"API.py","file_ext":"py","file_size_in_byte":4276,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"23626160805","text":"#!/usr/bin/env python3\n\nimport datetime\nimport numpy\nimport sys\nimport h5py\nimport os\n\nimport matplotlib.pyplot as plt\n\nimport nuBall.tools as tools\n\nfrom PyQt5.QtWidgets import (QWidget, QMainWindow, QApplication, QPushButton,\n                    QLineEdit, QListWidget, QPlainTextEdit, QLabel, QComboBox, \n                    QGridLayout, QFileDialog, QMessageBox, QDialog, QCheckBox)\nfrom PyQt5 import QtCore\nfrom PyQt5.QtCore import Qt\nfrom PyQt5.QtGui import QFont, QIntValidator\n\nfrom matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas\nfrom matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar\n\n\nclass ConfigWindow(QDialog):\n\n    def __init__(self, config):\n        super().__init__()\n        self.config = config\n        self.setWindowTitle('Projection settings')\n        self.initUI()\n\n\n    def initUI(self):\n        self.onlyInt = QIntValidator()\n\n        self.label_dir = QLabel()\n        self.label_dir.setText('Direction')\n        self.label_dir.setFixedWidth(100)\n\n        self.check_x = QCheckBox('X', self)\n        self.check_x.setChecked(True)\n        self.check_x.stateChanged.connect(self.check)\n\n        self.check_y = QCheckBox('Y', self)\n        self.check_y.stateChanged.connect(self.check)\n\n        self.input_x0 = QLineEdit()\n        self.input_x0.setText('{}'.format(self.config['x0']))\n        self.input_x0.setFixedWidth(100)\n        self.input_x0.setValidator(self.onlyInt)\n\n        self.label_x = QLabel()\n        self.label_x.setAlignment(QtCore.Qt.AlignCenter)\n        self.label_x.setText('<= gate <=')\n        self.label_x.setFixedWidth(100)\n\n        self.input_x1 = QLineEdit()\n        self.input_x1.setText('{}'.format(self.config['x1']))\n        self.input_x1.setFixedWidth(100)\n        self.input_x1.setValidator(self.onlyInt)\n\n        self.button_done = QPushButton('Done')\n        self.button_done.setFixedWidth(100)\n        self.button_done.clicked.connect(self.done_clicked)\n\n        layout = QGridLayout()\n\n        layout.addWidget(self.label_dir, 0, 0)\n        layout.addWidget(self.check_x, 0, 1)\n        layout.addWidget(self.check_y, 0, 2)\n\n        layout.addWidget(self.input_x0, 1, 0)\n        layout.addWidget(self.label_x, 1, 1)\n        layout.addWidget(self.input_x1, 1, 2)\n\n        layout.addWidget(self.button_done, 2, 1)\n\n        self.setLayout(layout)\n\n\n    def check(self, state):\n        if self.sender() == self.check_x:\n            if self.check_x.isChecked():\n                self.check_y.setChecked(False)\n            else:\n                self.check_y.setChecked(True)\n        elif self.sender() == self.check_y:\n            if self.check_y.isChecked():\n                self.check_x.setChecked(False)\n            else:\n                self.check_x.setChecked(True)\n\n\n    def done_clicked(self):\n        if self.check_x.isChecked():\n            self.config['direction'] = 'x'\n        else:\n            self.config['direction'] = 'y'\n        self.config['x0'] = int(self.input_x0.text())\n        self.config['x1'] = int(self.input_x1.text())\n        self.close()\n\n\n\nclass Window(QMainWindow):\n    def __init__(self):\n        super().__init__()\n        self.setWindowTitle('nuSpectra')\n\n        self.data_file = None\n        self.fit_range = [0, 0]\n        self.sum_range = [0, 0]\n        self.peaks = []\n\n        fig, axes = plt.subplots(1, 1, sharex='all')\n        self.figure = fig\n        self.axes = axes\n        self.init_axes()\n\n        self.data0, = self.axes.plot([0], [0], drawstyle='steps-mid', \n                color='red', label='')\n        self.data1, = self.axes.plot([0], [0], drawstyle='steps-mid', \n                color='blue', label='')\n        self.data2, = self.axes.plot([0], [0], drawstyle='steps-mid', \n                color='green', label='')\n        self.dataf, = self.axes.plot([0], [0], ls='--', color='black')\n\n        self.initUI()\n\n\n    def initUI(self):\n\n        main = QWidget(self)\n        self.setCentralWidget(main)\n\n        self.button_load = QPushButton('Load')\n        self.button_load.setFixedWidth(100)\n        self.button_load.clicked.connect(self.load_clicked)\n\n        self.text_file = QLineEdit()\n        self.text_file.setText('')\n        self.text_file.setFixedWidth(400)\n        self.text_file.setReadOnly(True)\n\n        self.list_spectra = QListWidget()\n        self.list_spectra.setFixedWidth(270)\n        self.list_spectra.itemClicked.connect(self.item_clicked)\n\n        self.canvas = FigureCanvas(self.figure)\n        self.toolbar = NavigationToolbar(self.canvas, self)\n        self.figure.set_size_inches(10, 8)\n\n        font_fit = QFont()\n        font_fit.setPointSize(11)\n        font_fit.setFamily(\"Mono\")\n\n        self.text_fit = QPlainTextEdit()\n        self.text_fit.setPlainText('')\n        self.text_fit.setFont(font_fit)\n        self.text_fit.setReadOnly(True)\n\n        self.label_range = QLabel()\n        self.label_range.setText('Range: ')\n        self.label_range.setFixedWidth(100)\n        self.label_range.setAlignment(Qt.AlignRight)\n\n        self.input_range = QLineEdit()\n        self.input_range.setFixedWidth(150)\n        self.input_range.setAlignment(Qt.AlignLeft)\n        self.input_range.setText('')\n        self.input_range.setToolTip('If empty uses current view range,\\\n otherwise input two integers')\n\n        self.label_spectrum = QLabel()\n        self.label_spectrum.setText('Spectrum: ')\n        self.label_spectrum.setFixedWidth(100)\n        self.label_spectrum.setAlignment(Qt.AlignRight)\n\n        self.label_peaks = QLabel()\n        self.label_peaks.setText('Peaks: ')\n        self.label_peaks.setFixedWidth(100)\n        self.label_peaks.setAlignment(Qt.AlignRight)\n\n        self.input_peaks = QLineEdit()\n        self.input_peaks.setFixedWidth(150)\n        self.input_peaks.setAlignment(Qt.AlignLeft)\n        self.input_peaks.setText('')\n\n        self.combo_spectrum = QComboBox()\n        self.combo_spectrum.addItems(['Red', 'Blue', 'Green'])\n        self.combo_spectrum.setCurrentText('Red')\n\n        self.button_add = QPushButton('R + B')\n        self.button_add.setFixedWidth(50)\n        self.button_add.clicked.connect(self.add_clicked)\n        self.button_add.setToolTip('Green = Red + Blue')\n\n        self.button_subtract = QPushButton('R - B')\n        self.button_subtract.setFixedWidth(50)\n        self.button_subtract.clicked.connect(self.subtract_clicked)\n        self.button_subtract.setToolTip('Green = Red - Blue')\n\n        self.button_fit = QPushButton('Fit')\n        self.button_fit.setFixedWidth(50)\n        self.button_fit.clicked.connect(self.fit_clicked)\n        self.button_fit.setToolTip(\"Fit peaks\")\n\n        self.button_sum = QPushButton('Sum')\n        self.button_sum.setFixedWidth(50)\n        self.button_sum.clicked.connect(self.sum_clicked)\n        self.button_sum.setToolTip(\"Sum spectra within channels indicated by range\")\n\n        self.button_ascii = QPushButton('Ascii')\n        self.button_ascii.setFixedWidth(100)\n        self.button_ascii.clicked.connect(self.ascii_clicked)\n        self.button_ascii.setToolTip(\"Save current spectrum ascii file\")\n\n        self.button_report = QPushButton('Report')\n        self.button_report.setFixedWidth(100)\n        self.button_report.clicked.connect(self.report_clicked)\n        self.button_report.setToolTip(\"Save fit results to 'report.txt'\")\n\n        layout = QGridLayout()\n        layout.addWidget(self.button_load, 0, 0)\n        layout.addWidget(self.text_file, 0, 2, 1, 3)\n\n        layout.addWidget(self.list_spectra, 1, 0, 2, 2)\n        layout.addWidget(self.canvas, 1, 2, 1, 5)\n        layout.addWidget(self.toolbar, 2, 2, 1, 5)\n\n        layout.addWidget(self.label_spectrum, 3, 0, 1, 2)\n        layout.addWidget(self.combo_spectrum, 4, 0, 1, 2)\n\n        layout.addWidget(self.text_fit, 3, 5, 3, 1)\n        layout.addWidget(self.label_range, 3, 2)\n        layout.addWidget(self.input_range, 3, 3, 1, 2)\n        layout.addWidget(self.button_ascii, 3, 6)\n\n        layout.addWidget(self.label_peaks, 4, 2)\n        layout.addWidget(self.input_peaks, 4, 3, 1, 2)\n\n        layout.addWidget(self.button_add, 5, 0)\n        layout.addWidget(self.button_subtract, 5, 1)\n        layout.addWidget(self.button_fit, 5, 3)\n        layout.addWidget(self.button_sum, 5, 4)\n        layout.addWidget(self.button_report, 5, 6)\n\n        main.setLayout(layout)\n\n        #self.resize(1280, 960)\n\n\n    def init_axes(self):\n        self.axes.cla()\n        self.axes.tick_params(axis='both', labelsize=12)\n        self.axes.set_xlim(0, 1)\n        self.axes.set_ylim(0, 1)\n        self.axes.set_ylabel('Counts', size=14)\n        self.axes.set_xlabel('E (keV)', size=14)\n\n        self.figure.tight_layout()\n        self.figure.canvas.draw()\n        self.figure.canvas.flush_events()\n\n\n    def item_clicked(self, item):\n        name = item.text()\n        if name == 'Clear':\n            data = [0]\n            name = ''\n        else:\n            data = numpy.array(self.data_file[name])\n            if len(data.shape) == 1:\n                pass\n            elif len(data.shape) == 2:\n                config = {'direction' : 'x',\n                          'x0' : 0,\n                          'x1' : data.shape[1]}\n                config_dialog = ConfigWindow(config)\n                config_dialog.exec_()\n\n                config = config_dialog.config\n                \n                if config['direction'] == 'x':\n                    data = data[:, config['x0']:config['x1']+1].sum(axis=1)\n                elif config['direction'] == 'y':\n                    data = data[config['x0']:config['x1']+1, :].sum(axis=0)\n\n                name += '_{}_{}'.format(config['x0'], config['x1'])\n\n            else:\n                data = [0]\n                msg = QMessageBox()\n                msg.setIcon(QMessageBox.Warning)\n                msg.setText(\"Warning\")\n                msg.setInformativeText('Spectrums with more than two dimensions are not supported!')\n                msg.setWindowTitle(\"Warning\")\n\n        self.dataf.set_xdata([0])\n        self.dataf.set_ydata([0])\n\n        x = numpy.arange(len(data)) + 0.5\n        if self.combo_spectrum.currentText() == 'Red':\n            current = self.data0\n        elif self.combo_spectrum.currentText() == 'Blue':\n            current = self.data1\n        elif self.combo_spectrum.currentText() == 'Green':\n            current = self.data2\n        else:\n            return None\n\n        current.set_xdata(x)\n        current.set_ydata(data)\n        current.set_label(name)\n\n        xmax0 = max(self.data0.get_xdata())\n        xmax1 = max(self.data1.get_xdata())\n        xmax2 = max(self.data2.get_xdata())\n        self.axes.set_xlim(0, max(xmax0, xmax1, xmax2))\n        ymax0 = max(self.data0.get_ydata())\n        ymax1 = max(self.data1.get_ydata())\n        ymax2 = max(self.data2.get_ydata())\n        self.axes.set_ylim(0, max(ymax0, ymax1, ymax2) + 1)\n        self.axes.legend()\n\n        self.figure.canvas.draw()\n        self.figure.canvas.flush_events()\n\n\n    def load_clicked(self):\n        file_name = QFileDialog.getOpenFileName(self, \"Select data file\", \"\",\n                                            \"HDF5 Files (*.h5);;All Files (*)\")\n        if file_name:\n            try:\n                self.data_file = h5py.File(file_name[0], 'r')\n            except (OSError, ValueError) as err:\n                error_dialog = QMessageBox()\n                error_dialog.setIcon(QMessageBox.Critical)\n                error_dialog.setText(\n                        'Could not open file {}'.format(file_name[0]))\n                error_dialog.setInformativeText(err.args[0])\n                error_dialog.setWindowTitle(\"Error\")\n                error_dialog.exec_()\n                return None\n\n            self.text_file.setText(file_name[0])\n            spectra = tools.list_spectra(self.data_file, verbose=False)\n            self.list_spectra.clear()\n            self.list_spectra.addItem('Clear')\n            for s in sorted(spectra):\n                self.list_spectra.addItem(s)\n\n\n    def fit_clicked(self):\n        range_str = self.input_range.text()\n        if range_str == \"\":\n            ax_range = self.axes.get_xlim()\n            range_str = \"{} {}\".format(int(ax_range[0]), int(ax_range[1]) + 1)\n\n        range_str = range_str.replace(\",\", \" \").replace(\";\", \" \").replace(\":\", \" \")\n        range_str = range_str.split()\n        r = []\n        try:\n            for s in range_str:\n                r.append(int(s))\n        except ValueError:\n            error_dialog = QMessageBox()\n            error_dialog.setIcon(QMessageBox.Warning)\n            error_dialog.setText('Could not convert {} to int'.format(s))\n            error_dialog.setInformativeText('Range should be given as two integers separated by space, comma or semicolon e.g 300 600')\n            error_dialog.setWindowTitle(\"Error\")\n            error_dialog.exec_()\n            return None\n\n        if len(r) > 1 and r[1] > r[0]:\n            self.fit_range[0] = r[0]\n            self.fit_range[1] = r[1]\n        else:\n            error_dialog = QMessageBox()\n            error_dialog.setIcon(QMessageBox.Warning)\n            error_dialog.setText('Wrong range {}'.format(r))\n            error_dialog.setInformativeText('Range should be given as two integers separated by space, comma or semicolon e.g 300 600')\n            error_dialog.setWindowTitle(\"Error\")\n            error_dialog.exec_()\n            return None\n\n        peaks_str = self.input_peaks.text()\n        peaks_str = peaks_str.replace(\",\", \" \").replace(\";\", \" \").replace(\":\", \" \")\n        peaks_str = peaks_str.split()\n        p = []\n        try:\n            for s in peaks_str:\n                p.append(float(s))\n        except ValueError:\n            error_dialog = QMessageBox()\n            error_dialog.setIcon(QMessageBox.Warning)\n            error_dialog.setText('Could not convert {} to float'.format(s))\n            error_dialog.setInformativeText('Peaks should be given as floats separated by space, comma or semicolon e.g 253, 312, 408')\n            error_dialog.setWindowTitle(\"Error\")\n            error_dialog.exec_()\n            return None\n\n        self.peaks = []\n        for pi in p:\n            if r[0] < pi < r[1]:\n                self.peaks.append(pi)\n        if len(self.peaks) < 1:\n            error_dialog = QMessageBox()\n            error_dialog.setIcon(QMessageBox.Warning)\n            error_dialog.setText('No peaks defined within range')\n            error_dialog.setInformativeText('Peaks should be given as floats separated by space, comma or semicolon e.g 253, 312, 408')\n            error_dialog.setWindowTitle(\"Error\")\n            error_dialog.exec_()\n            return None\n        \n        text = ''\n        if self.combo_spectrum.currentText() == 'Red':\n            pars, dp = tools.fit(self.data0.get_xdata(), \n                                self.data0.get_ydata(), \n                                self.fit_range, self.peaks, verbose=False)\n            text += '#Red - {}:\\n'.format(self.data0.get_label())\n        elif self.combo_spectrum.currentText() == 'Blue':\n            pars, dp = tools.fit(self.data1.get_xdata(), \n                                self.data1.get_ydata(), \n                                self.fit_range, self.peaks, verbose=False)\n            text += '#Blue - {}:\\n'.format(self.data1.get_label())\n        elif self.combo_spectrum.currentText() == 'Green':\n            pars, dp = tools.fit(self.data2.get_xdata(), \n                                self.data2.get_ydata(), \n                                self.fit_range, self.peaks, verbose=False)\n            text += '#Green - {}:\\n'.format(self.data2.get_label())\n        xf = numpy.linspace(self.fit_range[0], self.fit_range[1], 1000)\n        yf = tools.peaks_function(xf, *pars)\n        self.dataf.set_xdata(xf)\n        self.dataf.set_ydata(yf)\n        self.figure.canvas.draw()\n        self.figure.canvas.flush_events()\n        n = len(pars)\n        for i in range(n):\n            if i == 0:\n                text += 'a '\n            elif i == 1:\n                text += 'b '\n            else:\n                j = int((i - 2) // 3)\n                k = int((i - 2) % 3)\n                if k % 3 == 0:\n                    text += 'x{} '.format(j)\n                elif k % 3 == 1:\n                    text += 'A{} '.format(j)\n                else:\n                    text += 's{} '.format(j)\n            text += '\\t{:.2f}\\t {:.2f}\\n'.format(pars[i], dp[i])\n            self.text_fit.setPlainText(text)\n\n\n    def sum_clicked(self):\n        range_str = self.input_range.text()\n        if range_str == \"\":\n            ax_range = self.axes.get_xlim()\n            range_str = \"{} {}\".format(int(ax_range[0]), int(ax_range[1]) + 1)\n\n        range_str = range_str.replace(\",\", \" \").replace(\";\", \" \").replace(\":\", \" \")\n        range_str = range_str.split()\n        r = []\n        try:\n            for s in range_str:\n                r.append(int(s))\n        except ValueError:\n            error_dialog = QMessageBox()\n            error_dialog.setIcon(QMessageBox.Warning)\n            error_dialog.setText('Could not convert {} to int'.format(s))\n            error_dialog.setInformativeText('Range should be given as two integers separated by space, comma or semicolon e.g 300 600')\n            error_dialog.setWindowTitle(\"Error\")\n            error_dialog.exec_()\n            return None\n\n        if len(r) > 1 and r[1] >= r[0]:\n            self.sum_range[0] = r[0]\n            self.sum_range[1] = r[1]\n        else:\n            error_dialog = QMessageBox()\n            error_dialog.setIcon(QMessageBox.Warning)\n            error_dialog.setText('Wrong range {}'.format(r))\n            error_dialog.setInformativeText('Range should be given as two integers separated by space, comma or semicolon e.g 300 600')\n            error_dialog.setWindowTitle(\"Error\")\n            error_dialog.exec_()\n            return None\n\n        s = self.data0.get_ydata()[self.sum_range[0]:self.sum_range[1]+1].sum()\n        text = 'Range: [{}, {}]\\n'.format(self.sum_range[0], self.sum_range[1])\n        text += 'Sum: {}'.format(s)\n        self.text_fit.setPlainText(text)\n\n\n    def add_clicked(self):\n        if (self.data0.get_xdata().shape == self.data1.get_xdata().shape and\n            self.data0.get_ydata().shape == self.data1.get_ydata().shape):\n            self.data2.set_xdata(self.data0.get_xdata())\n            self.data2.set_ydata(self.data0.get_ydata() \n                                 + self.data1.get_ydata())\n            self.data2.set_label('G+B')\n            self.axes.legend()\n            self.figure.canvas.draw()\n            self.figure.canvas.flush_events()\n        else:\n            error_dialog = QMessageBox()\n            error_dialog.setIcon(QMessageBox.Warning)\n            error_dialog.setText('Spectra shape mismatch')\n            error_dialog.setInformativeText('Red and Blue spectra must have the same shape in order to add them')\n            error_dialog.setWindowTitle(\"Error\")\n            error_dialog.exec_()\n            return None\n\n\n    def subtract_clicked(self):\n        if (self.data0.get_xdata().shape == self.data1.get_xdata().shape and\n            self.data0.get_ydata().shape == self.data1.get_ydata().shape):\n            self.data2.set_xdata(self.data0.get_xdata())\n            g = numpy.array(self.data0.get_ydata(), dtype=numpy.int64)\n            g = g - self.data1.get_ydata()\n            self.data2.set_ydata(g)\n            self.data2.set_label('G-B')\n            self.axes.legend()\n            self.figure.canvas.draw()\n            self.figure.canvas.flush_events()\n        else:\n            error_dialog = QMessageBox()\n            error_dialog.setIcon(QMessageBox.Warning)\n            error_dialog.setText('Spectra shape mismatch')\n            error_dialog.setInformativeText('Red and Blue spectra must have the same shape in order to subtract them')\n            error_dialog.setWindowTitle(\"Error\")\n            error_dialog.exec_()\n            return None\n\n\n    def report_clicked(self):\n        if not os.path.isfile(\"report.txt\"):\n            with open(\"report.txt\", \"w\") as report_file:\n                report_file.write(\"gate,a,da,b,db,x,dx,A,dA,s,ds\\n\")\n\n        with open(\"report.txt\", \"a\") as report_file:\n            lines = self.text_fit.toPlainText().splitlines()\n\n            gate_name = lines[0].split()[2].strip(\":\")\n            a = lines[1].split()[1]\n            da = lines[1].split()[2]\n            b = lines[2].split()[1]\n            db = lines[2].split()[2]\n\n            n = int(len(lines[3:]) / 3)\n            for i in range(n):\n                outline = (gate_name + \",\" \n                        + a + \",\" \n                        + da + \",\" \n                        + b + \",\" \n                        + db + \",\" \n                        + lines[3*i+3].split()[1] + \",\" \n                        + lines[3*i+3].split()[2] + \",\" \n                        + lines[3*i+4].split()[1] + \",\" \n                        + lines[3*i+4].split()[2] + \",\" \n                        + lines[3*i+5].split()[1] + \",\" \n                        + lines[3*i+5].split()[2]\n                        + \"\\n\")\n                report_file.write(outline)\n\n\n    def ascii_clicked(self):\n        if self.combo_spectrum.currentText() == 'Red':\n            current = self.data0\n        elif self.combo_spectrum.currentText() == 'Blue':\n            current = self.data1\n        elif self.combo_spectrum.currentText() == 'Green':\n            current = self.data2\n        else:\n            return None\n\n        label = current.get_label().replace('/', '_')\n        file_name = QFileDialog.getSaveFileName(self, \"Select data file\", \n                                            \"{}.txt\".format(label),\n                                            \"Text Files (*.txt);;All Files (*)\")\n        if file_name[0] != '':\n            try:\n                \n                header = (datetime.datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\") + \"\\n\" + self.data_file.filename + \"\\n\" + label)\n                numpy.savetxt(file_name[0], \n                              numpy.column_stack((current.get_xdata(), \n                                                  current.get_ydata())),\n                              header=header,\n                              delimiter=' ', fmt='%d')\n            except (FileNotFoundError, OSError, ValueError) as err:\n                error_dialog = QMessageBox()\n                error_dialog.setIcon(QMessageBox.Critical)\n                error_dialog.setText(\n                        'Could not save file {}'.format(file_name[0]))\n                error_dialog.setInformativeText(err.args[0])\n                error_dialog.setWindowTitle(\"Error\")\n                error_dialog.exec_()\n                return None\n\n\n\nif __name__ == '__main__':\n    app = QApplication(sys.argv)\n\n    main = Window()\n    main.show()\n\n    sys.exit(app.exec_())\n","repo_name":"kmiernik/nuBall","sub_path":"bin/qtnuSpectra.py","file_name":"qtnuSpectra.py","file_ext":"py","file_size_in_byte":22800,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"8618115500","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n__author__ = 'Lennon'\n\nfrom io import StringIO\nfrom io import BytesIO\nimport pickle\nimport json\n\n\n# 读文件\n\ntry:\n    f = open('test.txt', 'r')\n    print(f.read())\nfinally:\n    if f:\n        f.close()\n\n# 在读取文件时，python引入with语句来自动调用close()方法\nwith open('test.txt', 'r') as f:\n    print(f.read())\n\n# for line in f.readlines():\n#    print(line.strip())\n\n\n# 写文件\nwith open('test.txt', 'w') as f:\n    f.write('Hello,Lennon')\n\nwith open('test.txt', 'a') as f:\n    f.write(' Hello,China')\n\n\n# StringIO和BytesIO\n# StringIO操作str\nf = StringIO()\nf.write('Hello')\nf.write(' ')\nf.write('world')\nprint(f.getvalue())\n\n\nf = StringIO('Hello!\\nHi!\\nGoodbye!')\nwhile True:\n    s = f.readline()\n    if s == '':\n        break\n    print(s.strip())\n\n\n# BytesIO 操作二进制数据\n\n\nf = BytesIO()\nf.write('中文'.encode('utf-8'))\nprint(f.getvalue())\n\n\nf = BytesIO(b'\\xe4\\xad\\xe6\\x96\\x87')\nf.read()\n\n\n# 序列化： 把变量聪内存中变为可存储或传输的过程称之为序列化，即picking\nd = dict(name='Bob', age=20, score=88)\npickle.dumps(d)\n\nf = open('dump.txt', 'wb')\npickle.dump(d, f)\nf.close()\n\n\n# 反序列化对象 pickle.loads()\nf = open('dump.txt', 'rb')\nd = pickle.load(f)\nprint(d)\n\n\n# JSON 标准数据格式\nd = dict(name='Bob', age=20, score=88)\nprint(json.dumps(d))\n\njson_str = '{\"age\":20,\"score\":88, \"name\":\"Bob\"}'\nprint(json.loads(json_str))\n\n\nclass Student(object):\n    def __init__(self, name, age, score):\n        self.name = name\n        self.age = age\n        self.score = score\n\n    def student2dict(std):\n        return {\n            'name': std.name,\n            'age': std.age,\n            'score': std.score\n        }\n\n    def dict2student(d):\n        return Student(d['name'], d['age'], d['score'])\n\n\nprint(json.dumps(s, default=lambda obj: obj.__dict__))\njson_str = '{\"age\":20, \"score\":88, \"name\":\"Bob\"}'\nprint(json.loads(json_str, object_hook=dict2student))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"lennon25/LearnPython","sub_path":"IOProgramming.py","file_name":"IOProgramming.py","file_ext":"py","file_size_in_byte":1988,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"14883330086","text":"from collections import deque\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# Fixing random state for reproducibility\nnp.random.seed(19680801)\n\n\ndef main():\n    points = load_points()\n\n    grid = create_grid(points)\n    print(f\"Shape of grid: {grid.shape}\")\n\n    result_part1 = 0\n    for index, x in np.ndenumerate(grid):\n        if x:\n            result_part1 += len(list(empty_neighbour_cubes(grid, index)))\n\n    result_part2 = 0\n    reachable = find_reachable_cubes(grid)\n    for index, x in np.ndenumerate(grid):\n        if x:\n            for neighbour_index in empty_neighbour_cubes(grid, index):\n                if reachable[neighbour_index]:\n                    result_part2 += 1\n\n    print()\n    print(\"##########\")\n    print(f\"Result part 1: {result_part1}\")\n    print(f\"Result part 2: {result_part2}\")\n\n    plot_grid(grid)\n\n\ndef load_points():\n    points = []\n    with open(\"input.txt\") as f:\n        for line in f.readlines():\n            numbers = [int(x) + 1 for x in line.split(\",\")]  # add 1 for border\n            points.append(tuple(numbers))\n    return points\n\n\ndef create_grid(points):\n    n_dims = len(points[0])\n    grid_shape = [0 for _ in range(n_dims)]\n    for point in points:\n        assert len(point) == n_dims\n        for dim in range(n_dims):\n            assert point[dim] >= 0\n            grid_shape[dim] = max(grid_shape[dim], point[dim] + 2)  # add 2 for borders\n\n    grid = np.full(grid_shape, fill_value=False, dtype=bool)\n    for point in points:\n        grid[point] = True\n\n    return grid\n\n\ndef empty_neighbour_cubes(grid, position):\n    for dim in range(len(position)):\n        for direction in [-1, 1]:\n            neighbour_position = list(position)\n            neighbour_position[dim] = position[dim] + direction\n            neighbour_position = tuple(neighbour_position)\n            if not grid[neighbour_position]:\n                yield neighbour_position\n\n\ndef find_reachable_cubes(grid):\n    visited = np.full(grid.shape, fill_value=False, dtype=bool)\n\n    start_position = tuple(0 for _ in range(len(visited.shape)))\n    visited[start_position] = True\n\n    stack = deque()\n    stack.append(start_position)\n    while stack:\n        position = stack.popleft()\n        for neighbour in empty_neighbour_cubes(grid, position):\n            if not visited[neighbour]:\n                visited[neighbour] = True\n                stack.append(neighbour)\n    return visited\n\n\ndef plot_grid(grid):\n    fig = plt.figure()\n    ax = fig.add_subplot(projection=\"3d\")\n    ax.voxels(grid, edgecolor=\"k\")\n    plt.savefig(\"plot.png\")\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"mkopec87/advent_of_code","sub_path":"src/2022/day18/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2606,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"48"}
+{"seq_id":"32418961118","text":"from typing import List\n\nclass Solution:\n    def findClosestElements_0(self, arr: List[int], k: int, x: int) -> List[int]:\n        arr.sort(key=lambda num: abs(num-x))\n        return sorted(arr[:k])\n\n    def findClosestElements(self, arr: List[int], k: int, x: int) -> List[int]:\n        left, right =0, len(arr)-1\n        while left < right:\n            mid = (left + right) // 2\n            if arr[mid] == x:\n                right = mid\n                break\n            elif arr[mid] < x:\n                left = mid + 1\n            else:\n                right = mid - 1\n        right = max(right, 0)\n\n\n        left = right - 1\n        for _ in range(k):\n            if left < 0:\n                right += 1\n            elif right >= len(arr) or x - arr[left] <= arr[right] - x:\n                left -= 1\n            else:\n                right += 1\n        return arr[left + 1: right]\n","repo_name":"Jooc/LeetCode","sub_path":"python-version/leetcode/Solution_658.py","file_name":"Solution_658.py","file_ext":"py","file_size_in_byte":887,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"48"}
+{"seq_id":"7711055711","text":"import json\nimport random\nimport re\nimport requests\nfrom urllib.parse import urljoin, urlparse\n\n\nclass EmptyKeywords(Exception):\n    def __init___(self):\n        Exception.__init__(self, \"Keywords must be provided!\")\n\n\nclass NoQueryTypeProvided(Exception):\n    def __init___(self):\n        Exception.__init__(self, \"Query type must be provided\")\n\n\nclass Crawler():\n\n    class CrawlerType():\n\n        REPOSITORIES = 'Repositories'\n        ISSUES = 'Issues'\n        WIKIS = 'Wikis'\n        TYPES = ['Repositories', 'Issues', 'Wikis']\n\n    BASE_URL = 'https://github.com/'\n\n    def __init__(self):\n        \"\"\"\n        Crawler constructor.\n        \"\"\"\n\n        self.keywords = []\n        self.proxies = []\n        self.type = ''\n\n    def query(self, query_params):\n        \"\"\"\n        Main crawler method of Github. It validates the json sent and its\n        params, makes the main request to github and parses the response.\n\n        Ex.:\n        cr = Crawler()\n        cr.query('{\n            \"keywords\": [\"openstack\", \"nova\", \"css\"],\n            \"proxies\": [\"194.126.37.94:8080\",\"13.78.125.167:8080\"],\n            \"type\": \"Repositories\"}')\n\n        Response ex.:\n        '[{\n            \"url\": \"https://github.com/atuldjadhav/DropBox-Cloud-Storage\",\n            \"extra\": {\n                \"owner\": \"atuldjadhav\",\n                \"language_stats\": {\n                    \"CSS\": 52.0,\n                    \"JavaScript\": 47.2,\n                    \"HTML\": 0.8}}}]'\n\n        :param query_params: json with the query params.\n        :return: json with the response.\n        \"\"\"\n\n        self._validate_params(query_params)\n        response = requests.get(self._get_query_url(),\n                                proxies=self._get_proxies())\n        return self._parse_response(response)\n\n    def _validate_params(self, query):\n        \"\"\"\n        It validates the received params from the json and store it in the\n        Crawler object or raises and exception.\n        :param query: json query received.\n        \"\"\"\n\n        query_data = json.loads(query)\n        if 'keywords' not in query_data:\n            raise KeyError\n        elif not isinstance(query_data.get('keywords'), list):\n            raise TypeError\n        elif len(query_data.get('keywords')) == 0:\n            raise EmptyKeywords\n\n        self.keywords = [keyword for keyword in query_data.get('keywords')]\n\n        if 'proxies' not in query_data:\n            raise KeyError\n        elif not isinstance(query_data.get('proxies'), list):\n            raise TypeError\n\n        self.proxies = [proxy for proxy in query_data.get('proxies')]\n\n        if 'type' not in query_data:\n            raise KeyError\n        elif not isinstance(query_data.get('type'), str):\n            raise TypeError\n        elif query_data.get('type') not in Crawler.CrawlerType.TYPES:\n            raise NoQueryTypeProvided\n\n        self.type = query_data.get('type')\n\n    def _get_query_url(self):\n        \"\"\"\n        It builds the query url.\n        :return: the query url.\n        \"\"\"\n\n        query_type = ''\n        if self.type != Crawler.CrawlerType.REPOSITORIES:\n            query_type = self.type\n\n        return '{}search?q={}&type={}'.format(\n            Crawler.BASE_URL, '+'.join(self.keywords), query_type)\n\n    def _get_proxies(self):\n        \"\"\"\n        It builds a proxis dictionary for the request by choosing one of the\n        proxies received randomly.\n        :return: proxies dictionary.\n        \"\"\"\n        return dict(http=random.choice(self.proxies))\n\n    def _parse_response(self, response):\n        \"\"\"\n        It parses the response from the github query and builds the json\n        response.\n        :param response: response from github query.\n        :return: json response.\n        \"\"\"\n\n        regex = '<a.*data-hydro-click.*\\\\s+(?:[^>]*?\\\\s+)?href=([\\\"\\'])(.*?)' \\\n                '\\\\1'\n        links = re.findall(regex, response.text)\n\n        repo_links = []\n        for link in links:\n            url = urljoin(Crawler.BASE_URL, link[1])\n\n            repo_data = dict(url=url)\n\n            if self.type == Crawler.CrawlerType.REPOSITORIES:\n                repo_data['extra'] = self._get_extra_data(url)\n\n            repo_links.append(repo_data)\n\n        return json.dumps(repo_links)\n\n    def _get_extra_data(self, url):\n        \"\"\"\n        It gets the extra data for a repos query and builds the dictionary\n        with it for the response.\n        :param url: url of the repo.\n        :return: extra data dictionary.\n        \"\"\"\n        owner = urlparse(url).path.split('/')[1]\n        response = requests.get(url, proxies=self._get_proxies())\n\n        regex = '<span class=\\\"language-color\\\" aria-label=\\\"(.*?)\\\"'\n        languages = re.findall(regex, response.text)\n\n        language_stats = dict()\n        for el in languages:\n            name, percentage = el.replace('%', '').split()\n            language_stats[name] = self._convert_to_number(percentage)\n\n        return dict(owner=owner, language_stats=language_stats)\n\n    def _convert_to_number(self, number):\n        \"\"\"\n        It converts the string language percentage to integer or float.\n        :param number: string language percentage.\n        :return: an integer or float.\n        \"\"\"\n\n        try:\n            return int(number)\n        except ValueError:\n            return round(float(number), 2)\n","repo_name":"rdomenech/crawler","sub_path":"crawler.py","file_name":"crawler.py","file_ext":"py","file_size_in_byte":5336,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"73639482066","text":"#!/usr/bin/python\n\n# requirements\nimport pdb\nimport sys\nimport getopt\nimport shapefile\nimport traceback\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.basemap import Basemap\n\n# application name\nAPPNAME = \"gen_coastline\"\n\n\n# help function\ndef showHelp():\n    pass\n\n\n# main\nif __name__ == \"__main__\":\n\n    # init params\n    linesFilename = None\n    polyFilename = None\n    outFilename = None\n    pngFilename = None\n    lonAdded = None\n    latAdded = None\n    \n    # read params\n    try:\n        options, rem = getopt.getopt(sys.argv[1:], 'hl:p:o:', ['linesFile=', 'help', 'polyFile=', 'outFile=', 'pngFile=', 'latInterval=', 'lonInterval=', 'addPoint='])\n    \n        for opt, arg in options:\n            if opt in ('-l', '--linesFile'):\n                linesFilename = arg\n            elif opt in ('-p', '--polyFile'):                \n                polyFilename = arg\n            elif opt in ('-o', '--outFile'):                \n                outFilename = arg\n            elif opt in ('--pngFile'):\n                pngFilename = arg\n            elif opt in ('--latInterval'):\n                latMin,latMax = map(float, arg.split(\",\"))\n            elif opt in ('--lonInterval'):\n                lonMin,lonMax = map(float, arg.split(\",\"))\n            elif opt in ('--addPoint'):\n                lonAdded,latAdded = map(float, arg.split(\",\"))                \n            elif opt in ('--pngFile'):\n                pngFilename = arg                \n            elif opt in ('-h', '--help'):\n                showHelp(logger)\n                sys.exit(0)\n    \n    except getopt.GetoptError:\n        showHelp(logger)\n        sys.exit(1)\n\n    if not(linesFilename) and not(polyFilename) and not(outFilename):\n        showHelp(logger)\n        sys.exit(1)\n        \n    # opening output file\n    try:\n        outFile = open(outFilename, \"w\")\n    except:\n        print(traceback.print_exc())\n        print(\"[%s] -- Error while opening output file\" % APPNAME)\n        \n    # open the two shapefiles\n    try:\n        s_lines = shapefile.Reader(linesFilename)\n        s_poly = shapefile.Reader(polyFilename)\n    except:\n        print(\"[%s] -- Error while opening shape files\" % APPNAME)\n\n    # initialise a map\n    if pngFilename:\n\n        print(latMin)\n        print(latMax)\n        print(lonMin)\n        print(lonMax)\n        print((latMax-latMin)/2)\n        print((lonMax-lonMin)/2)\n        \n        m = Basemap(llcrnrlon=lonMin, llcrnrlat=latMin,\n                    urcrnrlon=lonMax,urcrnrlat=latMax,\n                    rsphere=(6378137.00,6356752.3142),        \n                    resolution='f',projection='merc',                \n                    lat_0=(latMax-latMin)/2,\n                    lon_0=(lonMax-lonMin)/2)\n        plt.figure(figsize=(20, 20))\n        \n    # write the total number of polygons\n    sumpoly = 0\n    for f in [s_lines, s_poly]:\n        shapes = f.shapes()\n        records = f.records()\n        for record, shape in zip(records,shapes):            \n            if record[1] in ['beach', 'cliff', 'reef', 'coastline', 'sand', 'bare_rock']:\n                sumpoly = sumpoly + 1\n    outFile.write(\"%s\\n\" % sumpoly)\n    \n    # process both the files\n    for f in [s_lines, s_poly]:\n\n        # debug print\n        print(\"[%s] -- Processing file %s\" % (APPNAME, f))\n        \n        # read shapes\n        print(\"[%s] -- Reading shapes\" % APPNAME)\n        shapes = f.shapes()\n\n        # read records\n        print(\"[%s] -- Reading records\" % APPNAME)\n        records = f.records()\n\n        # iterate over records and shapes\n        print(\"[%s] -- Iterating over records and shapes\" % APPNAME)\n        for record, shape in zip(records,shapes):\n\n            if record[1] in ['beach', 'cliff', 'reef', 'coastline', 'sand', 'bare_rock']:\n            \n                # write info of the polygon\n                outFile.write(\"%s 0\\n\" % len(shape.points))\n                \n                # iterate over coordinates\n                for p in (shape.points):\n                    outFile.write(\"%s %s\\n\" % (p[0], p[1]))\n\n                    # plot on map\n                    if pngFilename:\n                        ppx, ppy = m(p[0],p[1])\n                        m.plot(ppx, ppy, 'k+', markersize=0.1, marker=\".\")\n\n# plot\nif pngFilename:\n    \n    # draw coastline\n    m.drawcoastlines(color='r', linewidth=0.2)\n\n    # added point\n    if lonAdded and latAdded:\n        ppx, ppy = m(lonAdded, latAdded)\n        m.plot(ppx, ppy, 'k+', markersize=5, color='g')\n    \n    # save png\n    plt.savefig(pngFilename)\n    \n# close file\nprint(\"[%s] -- Closing output file\" % APPNAME)\noutFile.close()\n\n# exit gracefully\nprint(\"[%s] -- Bye!\" % APPNAME)\n","repo_name":"fabioviola-cmcc/coastline_generator","sub_path":"gen_coastline.py","file_name":"gen_coastline.py","file_ext":"py","file_size_in_byte":4669,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"831351047","text":"import matplotlib.pyplot as plt\nimport numpy as np\nimport matplotlib\nimport datetime\nmatplotlib.use('Agg')\n\nlab_limit_dict = {\n    \"小坂\": 9,\n    \"中島\": 9,\n    \"高橋\": 9,\n    \"鉄谷\": 9,\n    \"川澄\": 9,\n    \"寺田\": 9,\n    \"増田\": 9,\n    \"岩井\": 9,\n    \"大野\": 9,\n    \"森谷\": 9,\n    \"竜田\": 2,\n    \"山田\": 2,\n    \"池田\": 2,\n    \"井ノ上\": 2\n}\n\n\ndef make_graph(dict, filename):\n    plt.rcParams[\"font.family\"] = \"meiryo\"\n    fig = plt.figure()\n\n    overlimit_dict = {}\n\n    for keys in dict.keys():\n        if dict[keys] > lab_limit_dict[keys]:\n            overlimit_dict[keys] = dict[keys] - lab_limit_dict[keys]\n            dict[keys] = lab_limit_dict[keys]\n        else:\n            overlimit_dict[keys] = 0\n\n    left = np.array(list(dict.keys()))\n    height1 = np.array(list(dict.values()))\n    height2 = np.array(list(overlimit_dict.values()))\n    plt.bar(left, height1, width=0.5, color=\"green\")\n    plt.bar(left, height2, width=0.5, bottom=height1, color=\"orange\")\n    plt.title(\"希望人数:\" + str(np.sum(height1) + np.sum(height2)) +\n              \"人 \" + str(datetime.datetime.now()))\n    fig.savefig(filename)\n    plt.close()\n\n\ndef make_sorted_graph(tuple_list, filename):\n    plt.rcParams[\"font.family\"] = \"meiryo\"\n    fig = plt.figure()\n    overlimit_list = []\n\n    listed_list = []\n    for item in tuple_list:\n        listed_list.append(list(item))\n    for item in listed_list:\n        if lab_limit_dict[item[0]] < item[1]:\n            overlimit_list.append(item[1] - lab_limit_dict[item[0]])\n            item[1] = lab_limit_dict[item[0]]\n        else:\n            overlimit_list.append(0)\n    left = np.array([item[0] for item in listed_list])\n    height1 = np.array([item[1] for item in listed_list])\n    height2 = np.array(overlimit_list)\n    plt.bar(left, height1, width=0.5, color=\"green\")\n    plt.bar(left, height2, width=0.5, bottom=height1, color=\"orange\")\n    plt.title(\"希望人数:\" + str(np.sum(height1) + np.sum(height2)) +\n              \"人 \" + str(datetime.datetime.now()))\n    fig.savefig(filename)\n    plt.close()\n\n\nif __name__ == \"__main__\":\n    make_graph\n","repo_name":"n3xem/NoticeLab","sub_path":"graph.py","file_name":"graph.py","file_ext":"py","file_size_in_byte":2119,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"36492578827","text":"#import array;\r\nfrom array import *\r\n\r\n# list -> a1 = array.array( ['hi',23,25.6, True];\r\n\r\na1 = array('i', [11,23,25,54] );\r\n\r\narray.reverse(a1);\r\n                             \r\nfor i in a1:\r\n    print (i);\r\n\r\nprint(type(a1));\r\n","repo_name":"sudarshansanjeev/Python","sub_path":"array_vs_list.py","file_name":"array_vs_list.py","file_ext":"py","file_size_in_byte":229,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"28087671215","text":"import time\nimport torch\nfrom progress.bar import Bar\nimport numpy as np\nimport pandas as pd\n\ndef entropy_loss(v):\n    \"\"\"\n        Entropy loss for probabilistic prediction vectors\n    \"\"\"\n    \n    assert v.dim() == 2\n    n, c = v.size()\n    return -torch.sum(torch.mul(v, torch.log2(v + 1e-30))) / (n *  np.log2(c))\n\n\ndef train_step(train_loader, model, epoch, optimizer, criterion, args):\n\n    # switch to train mode\n    model.train()\n    epoch_loss = 0.0\n    loss_w =args.loss_w\n\n    iters_per_epoch = len(train_loader)\n    bar = Bar('Processing {} Epoch -> {} / {}'.format('train', epoch+1, args.epochs), max=iters_per_epoch)\n    bar.check_tty = False\n\n    for step, (imagesA, imagesB, imagesC, labels) in enumerate(train_loader):\n        start_time = time.time()\n\n        torch.set_grad_enabled(True)\n\n        imagesA = imagesA.cuda()\n     \n        labels = labels.cuda()\n        labels = labels[:,0,:]  \n\n        out_A,  out_std_A  = model(imagesA)\n       \n        loss_x = criterion[0](out_A, labels)\n        \n        weight_out_std_A = torch.sqrt(torch.exp(out_std_A))\n\n        weight_factor = 0.5\n        loss_x  = torch.mean(loss_x * (1 + weight_factor * weight_out_std_A))\n\n        # entropy loss\n        #weight_entropy = 0.001\n\n        #entropy_loss_x =  entropy_loss(out_A) * weight_entropy * ( 1 + torch.sum(weight_out_std_A))\n\n        # unc loss\n        weight_unc_factor = 0.001\n        uncloss_x = criterion[1](out_A, out_std_A, labels) *  weight_unc_factor\n\n        lossValue = 1 * (loss_x+uncloss_x)\n        #lossValue = (lossValue-0.2).abs() + 0.2\n\n        optimizer.zero_grad()\n        lossValue.backward()\n        optimizer.step()\n    \n\n        # measure elapsed time\n        epoch_loss += lossValue.item()\n        end_time = time.time()\n        batch_time = end_time - start_time\n        # plot progress\n        bar_str = '{} / {} | Time: {batch_time:.2f} mins | Loss: {loss:.4f} '\n        bar.suffix = bar_str.format(step+1, iters_per_epoch, batch_time=batch_time*(iters_per_epoch-step)/60,\n                                    loss=lossValue.item())\n        bar.next()\n\n    epoch_loss = epoch_loss / iters_per_epoch\n\n    bar.finish()\n    return epoch_loss\n\n\ndef validation_step(val_loader, model, criterion):\n\n    # switch to train mode\n    model.eval()\n    epoch_loss = 0\n    iters_per_epoch = len(val_loader)\n    bar = Bar('Processing {}'.format('validation'), max=iters_per_epoch)\n\n    for step, (imagesA, imagesB, imagesC, labels) in enumerate(val_loader):\n        start_time = time.time()\n\n        imagesA = imagesA.cuda()\n        labels = labels.cuda()\n        labels = labels[:,0,:]\n\n        outputs, _ = model(imagesA)\n        with torch.no_grad():\n            loss = torch.mean(criterion[0](outputs, labels))\n            epoch_loss += loss.item()\n\n        end_time = time.time()\n\n        # measure elapsed time\n        batch_time = end_time - start_time\n        bar_str = '{} / {} | Time: {batch_time:.2f} mins'\n        bar.suffix = bar_str.format(step + 1, len(val_loader), batch_time=batch_time * (iters_per_epoch - step) / 60)\n        bar.next()\n\n    epoch_loss = epoch_loss / iters_per_epoch\n    bar.finish()\n    return epoch_loss\n\n\ndef save_output(label_test_file, dataPRED, args, save_file):\n    label_list = args.label_idx\n    n_class = len(label_list)\n    datanpPRED = np.squeeze(dataPRED.cpu().numpy())\n    df_tmp = pd.read_csv(label_test_file)\n    image_names = df_tmp[\"ID\"].tolist()\n\n    result = {label_list[i]: datanpPRED[:, i] for i in range(n_class)}\n    result['ID'] = image_names\n    out_df = pd.DataFrame(result)\n\n    name_older = ['ID']\n    for i in range(n_class):\n        name_older.append(label_list[i])\n    out_df.to_csv(save_file, columns=name_older)\n\nimport torch\nimport numpy as np\nfrom torch.nn import functional as F\nfrom torch.autograd import Variable\nimport torch.nn as nn\nclass Uncloss(nn.Module):\n    def __init__(self):\n        super(Uncloss, self).__init__()\n\n    def forward(self, inputs, intputs_std, targets):\n            #box_regression = F.softmax(inputs,-1)\n            box_regression = inputs\n            box_std_regression = intputs_std\n            #box_std_regression =  F.softmax(intputs_std, -1)\n            #regression_targets = torch.FloatTensor(targets.size()[0], 6).cuda()\n            #regression_targets.zero_()\n            #regression_targets.scatter_(1, targets.unsqueeze(1).long().cuda(), 1)\n            regression_targets = targets\n            # xe - xg\n            bbox_in = box_regression - regression_targets\n            # print('bbox_in: ', bbox_in)\n            bbox_inside_weights = torch.ones_like(bbox_in)\n            bbox_inw = torch.mul(bbox_in, bbox_inside_weights)\n            bbox_l1abs = torch.abs(bbox_inw)\n            bbox_sq = torch.mul(bbox_l1abs, bbox_l1abs)\n\n            wl2 = torch.lt(bbox_l1abs, torch.ones_like(bbox_l1abs))\n\n            #wl1 = wl1.float()\n            wl2 = wl2.float()\n            bbox_l2 = torch.mul(torch.mul(bbox_sq, wl2),0.5) #boxl2 = 1/2 (xg-xe)^2 x<=1\n            #bbox_l1 = torch.mul((bbox_l1abs - 0.5), wl1) #boxl1 = (|xg-xe|-0.5)\n            #bbox_inws = bbox_l1  + bbox_l2\n            bbox_inws = bbox_l2\n            # print('box_inws: ',bbox_inws)\n\n            bbox_pred_std_abs = torch.mul(box_std_regression,0.5) #a/2\n            bbox_pred_std_nabs = -1 * box_std_regression #-a\n            bbox_pred_std_nexp = torch.exp(bbox_pred_std_nabs) #e^(-a)\n            bbox_inws_out = torch.mul(bbox_pred_std_nexp, bbox_inws)\n\n            # loss 1\n            bbox_pred_std_abs_logw = torch.mul(bbox_pred_std_abs, torch.ones_like(bbox_inws_out)) #a/2\n            bbox_pred_std_abs_logwr = torch.mean(bbox_pred_std_abs_logw, dim=0) #squeeze batch\n            bbox_pred_std_abs_logw_loss = torch.sum(bbox_pred_std_abs_logwr) #uncer loss\n            # loss 2\n            bbox_inws_outr = torch.mean(bbox_inws_out, dim=0)\n            bbox_pred_std_abs_mulw_loss = torch.sum(bbox_inws_outr)\n\n            #print('bbox_pred_std_abs_logw_loss: ',bbox_pred_std_abs_logw_loss/ inputs.size()[0] )\n            #print('bbox_pred_std_abs_mulw_loss: ',bbox_pred_std_abs_mulw_loss/ inputs.size()[0])\n            #cls_weight = torch.sqrt(torch.exp(box_std_regression))\n            return  (bbox_pred_std_abs_logw_loss + bbox_pred_std_abs_mulw_loss) / inputs.size()[0]\n\n","repo_name":"sunycl/REFUGE2_competition_code","sub_path":"classification/utils/trainer_unc_origin.py","file_name":"trainer_unc_origin.py","file_ext":"py","file_size_in_byte":6285,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"41618548676","text":"import sys\nimport json\n\n\ndef find_url(json_str):\n    ngrok_info = json.JSONDecoder().decode(json_str)\n    tunnels_lst = ngrok_info.get('tunnels')\n    for tunnel in tunnels_lst:\n        tunnel_name = tunnel.get('name')\n        if tunnel_name == 'command_line (http)':\n            return tunnel.get('public_url') + '/'\n\nif __name__ == '__main__':\n    ngrok_json_str = ''\n    for line in sys.stdin:\n        ngrok_json_str += line\n    sys.stdout.write(find_url(ngrok_json_str))\n","repo_name":"SirjanK/LocServ","sub_path":"setup_scripts/ngrok_url.py","file_name":"ngrok_url.py","file_ext":"py","file_size_in_byte":474,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"6478328099","text":"from testlink.resource.base import ResourceCollection, ResourceInstance\nfrom testlink.resource.cases import TestCaseAccess\nfrom testlink.resource.sundry import MethodResult\nfrom testlink.exception.base import TestLinkException\nfrom testlink.common import args\n\nclass TestSuites(ResourceCollection):\n    COLLECTION = 'getTestSuitesForTestPlan'\n    BASE_COLLECTION = 'getFirstLevelTestSuitesForTestProject'\n    BY_ID = 'getTestSuiteByID'\n    BY_SUITE = 'getTestSuitesForTestSuite'\n    CREATE = 'createTestSuite'    \n\n    def __init__(self, connection, plan_id=None, suite_id=None, project_id=None):\n        super(TestSuites, self).__init__(connection)\n        self.plan_id = plan_id\n        self.suite_id = suite_id        \n        self.project_id = project_id\n\n        \n    def _build_suite(self, **data):\n        return TestSuite(self.connection, plan_id = self.plan_id,\n                         project_id = self.project_id, **data)\n\n    \n    def _make_cursor(self):\n        if not self.plan_id and not self.suite_id:\n            if not self.project_id:\n                raise TestLinkException(\"Need a suite or plan id to get suites\")\n            else:\n                return self.first_level\n        method = self.COLLECTION if self.plan_id else self.BY_SUITE\n        params = {}\n        def check_and_add(value, arg):\n            if value: params[arg] = value\n        check_and_add(self.plan_id, args.PLAN_ID)\n        check_and_add(self.suite_id, args.SUITE_ID)        \n        results = self.connection.request(method, params = params)\n        return map(lambda result: self._build_suite(**result), results)\n\n    \n    @property\n    def first_level(self):\n        if not self.plan_id:\n            raise TestLinkException(\"Need a plan_id in order to get first level suites\")\n        results = self.connection.request(self.BASE_COLLECTION,\n                                          params={args.PROJECT_ID: self.project_id})\n        return map(lambda result: self._build_suite(**result), results)\n    \n\n    def get(self , _id=None, name=None):\n        if _id:\n            results = self.connection.request(self.BY_ID, params = {args.SUITE_ID: _id})\n            return self._build_suite(**results)\n        elif name:\n            return filter(lambda suite: suite.name == name, self.first_level + self.cursor).pop()\n        else:\n            raise TestLinkException(\"Requires an id or name to look up a test suite\")\n\n    \n    def create(self, name, details, parent_id=None, order=None, check_duplicated_name=None,\n               action_on_duplicated_name=None):\n        params = {\n            args.PROJECT_ID: self.project_id,\n            args.TESTSUITE_NAME: name,\n            args.DETAILS: details\n            }\n        def check_and_add(value, arg):\n            if value: params[arg] = value\n        map(lambda t: check_and_add(t[0], t[1]), [\n            (parent_id, args.PARENT_ID),\n            (order, args.ORDER),\n            (check_duplicated_name, args.CHECK_DUPLICATE_NAME),\n            (action_on_duplicated_name, args.ACTION_ON_DUPLICATED_NAME)\n            ])\n        results = self.connection.request(self.CREATE, params=params)\n        return MethodResult(**results[0])\n    \n        \nclass TestSuiteAccess(object):\n\n    @property\n    def _should_build_suites(self):\n        _suites = getattr(self, '_suites', None)\n        if not _suites:\n            return True\n        same_suite = self._suites.suite_id == getattr(self, 'suite_id', None)\n        same_plan = self._suites.plan_id == getattr(self, 'plan_id', None)\n        return not same_plan or not same_suite\n\n    \n    @property\n    def suites(self):\n        if self._should_build_suites:\n            params = dict((k, getattr(self, k, None)) for k in ['plan_id',\n                                                               'suite_id',\n                                                               'project_id'])\n            self._suites = self.get_suites(**params)\n        return self._suites\n\n    \n    def get_suites(self, plan_id=None, suite_id=None, project_id=None):\n        return TestSuites(self.connection, plan_id=plan_id, suite_id=suite_id,\n                          project_id=project_id)\n\n    \nclass TestSuite(ResourceInstance, TestSuiteAccess, TestCaseAccess):\n    \"\"\"\n    A suite of tests in the system\n    keys: [\n    'id',\n    'name',\n    'parent_id'\n    ]\n    \"\"\"\n\n    def __init__(self, connection, plan_id=None, project_id=None, **data):\n        super(TestSuite, self).__init__(connection, **data)\n        self.plan_id = plan_id\n        self.project_id = project_id\n        if 'id' in data:\n            self.suite_id = data['id']\n\n","repo_name":"adamsar/testlink-python-api","sub_path":"src/testlink/resource/suites.py","file_name":"suites.py","file_ext":"py","file_size_in_byte":4620,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"9620506402","text":"from pprint import pprint\r\n\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib.ticker as ticker\r\nimport numpy as np\r\n\r\nTOTAL = 1919\r\n#F5D580\r\n# COLOR PALETTE\r\n\r\nBGCOLOR = \"black\"\r\nTEXTCOLOR = \"white\"\r\nTEXTCOLOR2 = \"black\"\r\nBARCOLOR = \"#EEB211\"  # Gold\r\nM_COLOR = '#EEB211'\r\nF_COLOR = '#F5D580'\r\nDUAL_COLORS = [\"#EEB211\", \"#F5D580\"]  # Gold, Darkblue\r\nTRI_COLORS = [\"#EEB211\", \"#F5D580\", \"#B3A369\"]  # Gold, Light Chrome Gold, Chrome Gold\r\n\r\n# All darkest to brightest, https://coolors.co/003057-edb92e-eeb211-f5d580-b3a369\r\nLIKERT_COLORS = {\r\n'2_M': [\"#EEB211\", '#F2C751'],\r\n'3_M': [\"#EEB211\", '#F1C03C', '#F4CE67'],\r\n\r\n'2_F': [\"#f4d06e\", '#F7E0A2'],\r\n'3_F': [\"#f4d06e\", '#F6DC97', '#F8E4AE'],\r\n}\r\n\r\n#'2_F': [\"#F5D580\", '#F7E0A2'],\r\n#'3_F': [\"#F5D580\", '#F6DC97', '#F8E4AE'],\r\nLIKERT_SCALE_AGREE_2 = [\"Disagree\", \"Agree\"]\r\nLIKERT_SCALE_AGREE_3 = [\"Disagree\", \"Neutral\", \"Agree\"]\r\n\r\nLIKERT_SCALE_SATISFACTION_2 = [\"Dissatisfied\", \"Satisfied\"]\r\nLIKERT_SCALE_SATISFACTION_5 = [\"Dissatisfied\", \"Neutral\", \"Satisfied\"]\r\n\r\nLIKERT_SCALE_OTHER = [\"Never True\", \"Sometimes True\", \"Always True\"]\r\nLIKERT_SCALE_OTHER_2 = [\"Uncompetitive\", \"Somewhat Competitive\", \"Competitive\"]\r\nDF = pd.read_csv('surveydata.csv', low_memory=False)\r\n\r\n\r\ndef create_hbar(cols, labels, title, xlabel='N=', width=.3):\r\n\r\n    fig = plt.figure()  # Create matplotlib figure\r\n    ax = fig.add_subplot(111)  # Create matplotlib axes\r\n\r\n    df = DF[cols]\r\n    fems = df.loc[DF['sex_birth'] == 1]\r\n    males = df.loc[DF['sex_birth'] == 2]\r\n\r\n    fdata = {label: 0 for label in labels}  # Initialize each label to have a count of 0\r\n    fdata = pd.Series(fdata)  # Turns the dict to a Series\r\n\r\n    mdata = {label: 0 for label in labels}  # Initialize each label to have a count of 0\r\n    mdata = pd.Series(mdata)  # Turns the dict to a Series\r\n\r\n    c = 0\r\n    for col in fems:  # Sums the counts in each column of the csv\r\n        fdata[c] += fems[col].sum()\r\n        c += 1\r\n\r\n    c = 0\r\n    for col in males:  # Sums the counts in each column of the csv\r\n        mdata[c] += males[col].sum()\r\n        c += 1\r\n\r\n    fdata.sort_values(inplace=True, ascending=True)\r\n    mdata, fdata = mdata.align(fdata, axis=0, join='right')\r\n    print(f\"FEMALE\\n{fdata}\\n\\nMALE\\n{mdata}\")\r\n\r\n\r\n\r\n    fdata.plot(kind='barh', ax=ax, figsize=(10, 5.7), color=F_COLOR, rot=0, fontsize=10, zorder=2,\r\n               width=width, position=0)  # Draws Graph\r\n    mdata.plot(kind='barh', ax=ax, figsize=(10, 5.7), color=M_COLOR, rot=0, fontsize=10, zorder=2,\r\n               width=width, position=1)  # Draws Graph\r\n\r\n    locs, labs = plt.xticks()\r\n    xscale = locs[1]-locs[0]\r\n    max_len = max([i.get_width() for i in ax.patches])\r\n    ax.set_xlim(0, max_len + xscale/2)\r\n    ax.xaxis.set_major_locator(ticker.MaxNLocator(integer=True))\r\n    ax.set_ylim(-.5 - width/2, ax.patches[-1].get_y() + .5 + width)\r\n    ax.set_title(title, fontsize=18)\r\n    ax.set_xlabel(xlabel, fontsize=18)\r\n    ax.set_facecolor(BGCOLOR)\r\n    ax.grid(zorder=0, axis='x', color='dimgrey')  # Draws axis lines for more\r\n    ax.legend(loc='lower right', framealpha=1, labels=['Female', 'Male'])\r\n\r\n    ftotal = count_unique(cols=cols, df=fems)\r\n    mtotal = count_unique(cols=cols, df=males)\r\n\r\n    for x, i in enumerate(ax.patches):\r\n        if x < len(ax.patches) / 2:\r\n            ax.text(i.get_width(), i.get_y() + i.get_height() / 2,  # Females\r\n                    f\"{int(round((i.get_width() / ftotal) * 100))}%\",\r\n                    color=TEXTCOLOR, verticalalignment='center',\r\n                    fontsize=9)\r\n\r\n        else:\r\n            ax.text(i.get_width(), i.get_y() + i.get_height() / 2,  # Males\r\n                    f\"{int(round((i.get_width() / mtotal) * 100))}%\",\r\n                    color=TEXTCOLOR, verticalalignment='center',\r\n                    fontsize=9)\r\n\r\n    return fig\r\n\r\n\r\ndef create_bar(col, key, title=\"TITLE\", chronological=False, ylabel=\"N=\", xlabel=\"\", width=.4, align=None):\r\n    fig = plt.figure()  # Create matplotlib figure\r\n    ax = fig.add_subplot(111)  # Create matplotlib axes\r\n    fems = DF.loc[DF['sex_birth'] == 1][col]\r\n    males = DF.loc[DF['sex_birth'] == 2][col]\r\n\r\n    fdata = fems.value_counts()\r\n    mdata = males.value_counts()\r\n\r\n    if chronological:\r\n        fdata.sort_index(inplace=True, ascending=True)\r\n        mdata.sort_index(inplace=True, ascending=True)\r\n\r\n    else:\r\n        mdata, fdata = mdata.align(fdata, axis=0, join='right')\r\n\r\n    mdata.rename(index=key, inplace=True)\r\n    fdata.rename(index=key, inplace=True)\r\n    print(f\"Females\\n{fdata}\\n\\nMales\\n{mdata}\")\r\n\r\n    fdata.plot(kind='bar', figsize=(10, 5.7), color=F_COLOR, rot=0, ax=ax, position=1, width=width)\r\n    mdata.plot(kind='bar', figsize=(10, 5.7), color=M_COLOR, rot=0, ax=ax, position=0, width=width)\r\n    ax.set_title(title, fontsize=18)\r\n    ax.set_ylabel(ylabel, fontsize=18)\r\n    ax.yaxis.set_major_locator(ticker.MaxNLocator(integer=True))\r\n    ax.set_xlabel(xlabel)\r\n    ax.set_facecolor(BGCOLOR)\r\n    ax.set_xlim(ax.patches[0].get_x() - .65, ax.patches[-1].get_x() + 1)\r\n    ax.legend(framealpha=1, labels=['Females', 'Males'])\r\n\r\n    locs, labs = plt.yticks()\r\n    scale = locs[2]-locs[1]\r\n    ftotal = sum(fdata)\r\n    mtotal = sum(mdata)\r\n\r\n    for x, i in enumerate(ax.patches):\r\n        if i in ax.patches[:len(ax.patches) // 2]:\r\n            ax.text(i.get_x() + i.get_width() if not align else i.get_x() + i.get_width()/2, i.get_height() + .04*scale,\r\n                    f\"{int((i.get_height() / ftotal) * 100)}%\", #Females\r\n                    color=TEXTCOLOR, horizontalalignment='right' if not align else align)\r\n\r\n        else:\r\n            ax.text(i.get_x() if not align else i.get_x() + i.get_width()/2, i.get_height() + .04*scale,  # Males\r\n                    f\"{int((i.get_height() / mtotal) * 100)}%\",\r\n                    color=TEXTCOLOR, horizontalalignment='left' if not align else align)\r\n\r\n    return fig\r\n\r\n\r\ndef create_likert(cols, labels, title, legend, width=.4,\r\n                  ylabel=\"\", flip_legend=False):\r\n\r\n    fig = plt.figure()  # Create matplotlib figure\r\n    ax = fig.add_subplot(111)  # Create matplotlib axes\r\n\r\n    m_scales = {}\r\n    f_scales = {}\r\n\r\n    for c, col in enumerate(cols):\r\n        fems = DF.loc[DF['sex_birth'] == 1][col].value_counts(normalize=True)\r\n        males = DF.loc[DF['sex_birth'] == 2][col].value_counts(normalize=True)\r\n\r\n        if len(fems) == 5:\r\n            males = pd.Series({1: males[1] + males[2], 2: males[3], 3: males[4] + males[5]})\r\n            fems = pd.Series({1: fems[1] + fems[2], 2: fems[3], 3: fems[4] + fems[5]})\r\n\r\n        elif len(fems) == 6:\r\n            males = pd.Series({1: males[1] + males[2] + males[3], 2: males[4] + males[5] + males[6]})\r\n            fems = pd.Series({1: fems[1] + fems[2] + fems[3], 2: fems[4] + fems[5] + fems[6]})\r\n\r\n        else:\r\n            males = pd.Series({1: males[1] + males[2] + males[3], 2: males[4], 3: males[5] + males[6] + males[7]})\r\n            fems = pd.Series({1: fems[1] + fems[2] + fems[3], 2: fems[4], 3: fems[5] + fems[6] + fems[7]})\r\n\r\n        m_scales[labels[c]] = males.sort_index(ascending=not flip_legend)\r\n        f_scales[labels[c]] = fems.sort_index(ascending=not flip_legend)\r\n\r\n    m_df = pd.DataFrame(m_scales).transpose()\r\n    f_df = pd.DataFrame(f_scales).transpose()\r\n    print(f\"MALES\\n{m_df}\\nFEMALES\\n{f_df}\")\r\n\r\n    f_df.plot(figsize=(10, 5.7), kind='bar', rot=0, stacked=True, color=LIKERT_COLORS[f'{len(legend)}_F'],\r\n              width=width, fontsize=10, ax=ax, position=0)\r\n    m_df.plot(figsize=(10, 5.7), kind='bar', rot=0, stacked=True, color=LIKERT_COLORS[f'{len(legend)}_M'],\r\n              width=width, fontsize=10, ax=ax, position=1)\r\n\r\n    ax.set_title(title, fontsize=18)\r\n    ax.set_facecolor(BGCOLOR)\r\n    ax.set_xlim(ax.patches[0].get_x() - .65, ax.patches[-1].get_x() + 1)\r\n    ax.set_ylabel(ylabel=ylabel, fontsize=18)\r\n    ax.get_legend().remove()\r\n    plt.yticks([])\r\n    highest_bar_height = max([max(patch.get_extents().y1, patch.get_extents().y0) for patch in ax.patches])\r\n\r\n    for c, i in enumerate(ax.patches):  # c goes from left to right, bottom to top, starting at 0.\r\n\r\n        #print(f\"{c}: {i.get_verts()}\")\r\n        if i.get_height() > 0:\r\n            if i.get_y() == 0:\r\n                text = legend[2]\r\n\r\n            elif i.get_y() + i.get_height() >= 1:\r\n                print(i)\r\n                text = legend[0]\r\n\r\n            else:\r\n                text = legend[1]\r\n            t1 = ax.text(i.get_x() + i.get_width()/2,\r\n                         i.get_y() + i.get_height() - .02 if i.get_y() + i.get_height() - .02 > 0 else 0,\r\n                         text, fontsize=8,\r\n                         color=TEXTCOLOR2, horizontalalignment='center')\r\n\r\n            t2 = ax.text(i.get_x() + i.get_width()/2,\r\n                         t1._y - .03,\r\n                         f\"{int((i.get_height()) * 100)}%\", fontsize=9,\r\n                         color=TEXTCOLOR2, horizontalalignment='center')\r\n\r\n        try: # If any two percent labels overlap, this will automatically shift them so they no longer overlap.\r\n            for text in ax.texts[:-2]:\r\n                bb2 = text.get_window_extent(fig.canvas.get_renderer())\r\n                bb = t1.get_window_extent(fig.canvas.get_renderer())\r\n\r\n                while bb.overlaps(bb2):\r\n                    print(\"OVERLAP\")\r\n                    t1.set_y(t1._y + .02)\r\n                    bb = t1.get_window_extent(fig.canvas.get_renderer())\r\n        except IndexError:\r\n            # Throws index error if there are <2 texts already made.\r\n            pass\r\n\r\n        if round(i.get_extents().y1) == round(highest_bar_height):\r\n            if i in ax.patches[len(ax.patches)//2:]:\r\n                ax.text(i.get_x() + i.get_width()/2, 1+.008, \"Males\",\r\n                        fontsize=11, color='w', horizontalalignment='center')\r\n            else:\r\n                ax.text(i.get_x() + i.get_width() / 2, 1 + .008, \"Females\",\r\n                        fontsize=11, color='w', horizontalalignment='center')\r\n\r\n    return fig\r\n\r\n\r\ndef create_histogram(col=\"\", title=\"TITLE\", ylabel=\"Occurrence\", bins=0, total=True):\r\n    fig = plt.figure()  # Create matplotlib figure\r\n    ax = fig.add_subplot(111)  # Create matplotlib axes\r\n\r\n    fdata = DF.loc[DF['sex_birth'] == 1][col].sort_values()\r\n    mdata = DF.loc[DF['sex_birth'] == 2][col].sort_values()\r\n\r\n    if bins != 0:\r\n        fdata.plot(kind='hist', rot=0, color=F_COLOR, bins=bins, figsize=(10, 5.7), position=0, ax=ax)\r\n        fdata.plot(kind='hist', rot=0, color=M_COLOR, bins=bins, figsize=(10, 5.7), position=1, ax=ax)\r\n\r\n    else:\r\n        fdata.plot(kind='hist', rot=0, color=F_COLOR, figsize=(10, 5.7), ax=ax)\r\n        mdata.plot(kind='hist', rot=0, color=M_COLOR, figsize=(10, 5.7), ax=ax)\r\n\r\n    ax.set_alpha(0.4)\r\n    ax.set_title(title, fontsize=18)\r\n    ax.set_facecolor(BGCOLOR)\r\n    ax.set_ylabel(ylabel, fontsize=18)\r\n\r\n    totals = []\r\n\r\n    for i in ax.patches:\r\n        totals.append(i.get_height())\r\n\r\n    if total:\r\n        total = TOTAL\r\n\r\n    else:\r\n        total = sum(totals)\r\n\r\n    for i in ax.patches:\r\n        ax.text(i.get_x() + i.get_width() / 2, i.get_height() + .5,\r\n                str(round((i.get_height() / total) * 100, 2)) + '%', fontsize=9,\r\n                color=TEXTCOLOR, horizontalalignment='center')\r\n\r\n    return fig\r\n\r\n\r\ndef create_pie(col=\"\", title=\"TITLE\", labels=(), colors=DUAL_COLORS, explode=(0, 0)):\r\n    fig = plt.figure()  # Create matplotlib figure\r\n    ax = fig.add_subplot(111)  # Create matplotlib axes\r\n\r\n    fems = DF.loc[DF['sex_birth'] == 1][col].value_counts()\r\n    males = DF.loc[DF['sex_birth'] == 2][col].value_counts()\r\n\r\n    fems.plot(kind='pie', ax=ax, figsize=(10, 5.7), explode=explode, colors=colors, labels=labels, autopct='%1.1f%%')\r\n    males.plot(kind='pie', ax=ax, figsize=(10, 5.7), explode=explode, colors=colors, labels=labels, autopct='%1.1f%%')\r\n    ax.set_alpha(0.4)\r\n    ax.set_title(title, fontsize=18)\r\n    ax.set_facecolor(BGCOLOR)\r\n    ax.set_ylabel(ylabel=\"\", fontsize=18)\r\n\r\n    return fig\r\n\r\n\r\ndef count_unique(cols=[], total=TOTAL, df=DF):\r\n    count = 0\r\n    query = df[cols].transpose()\r\n\r\n    for i in range(total - 1):\r\n        try:\r\n            if 1.0 in query[i].unique():\r\n                count += 1\r\n        except:\r\n            pass\r\n\r\n    print(f\"Unique Responses: {count}\")\r\n    return count\r\n\r\n\r\nplt.show()","repo_name":"trevor-pope/survey-data-tools","sub_path":"male-female-maker.py","file_name":"male-female-maker.py","file_ext":"py","file_size_in_byte":12394,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"74997207825","text":"import os\nimport time\nimport googlemaps\nfrom cachetools import cached\n\n\n__CLIENT = None # The Google Maps API client\n\n\ndef get_client():\n    \"\"\" Connects to Google Maps APIs.\n\n    Returns:\n        googlemaps.Client: The Google Maps client.\n    \"\"\"\n    global __CLIENT\n    if not __CLIENT:\n        __CLIENT = connect_to_google_places_client()\n    return __CLIENT\n\n\ndef connect_to_google_places_client(api_key=None):\n    \"\"\" Connects to Google Maps API.\n\n    Args:\n        api_key (str, optional): The Google Maps API Key of the connecting\n            user. Defaults to the `GOOGLE_MAPS_API_KEY` environment variable. \n    Returns:\n        googlemaps.Client: The Google Maps API client.\n    \"\"\"\n    api_key = api_key or os.getenv(\"GOOGLE_MAPS_API_KEY\")\n    return googlemaps.Client(key=api_key)\n\n\n@cached(cache={})\ndef get_place(place_id):\n    \"\"\" Returns a Google Place from the given google place id.\n\n    Args:\n        place_id (str): A Google Place Id.\n    Returns:\n        dict: Serialized Google Place data. \n    \"\"\"\n    return get_client().place(place_id)\n\n\ndef get_places_by_query(query, query_type=\"textquery\"):\n    \"\"\" Yields Google Place results from the given text input query.\n        \n    Args:\n        query (str): The text input specifying which place to search for (for\n            example, a name, address, or phone number).\n        query_type (str): The type of input. This can be one of either\n            'textquery' or 'phonenumber'. Defaults to 'textquery'.\n    Yields:\n        dict: Serialized Google Place data matching the query.\n    \"\"\"\n    for result in get_place_ids_by_query(query, query_type=query_type):\n        yield get_place(result)\n\n\n@cached(cache={})\ndef get_place_ids_by_query(query, query_type=\"textquery\"):\n    \"\"\" Returns Google Place IDs from the given text input query.\n        \n    Args:\n        query (str): The text input specifying which place to search for (for\n            example, a name, address, or phone number).\n        query_type (str): The type of input. This can be one of either\n            'textquery' or 'phonenumber'. Defaults to 'textquery'.\n    Returns:\n        List[str]: Serialized Google Place ids matching the query.\n    \"\"\"\n    results = get_client().find_place(query, query_type).get(\"candidates\", [])\n    return [r.get(\"place_id\") for r in results if r.get(\"place_id\")]\n\n\n@cached(cache={})\ndef get_places_by_type(\n        place_type,\n        location=None,\n        radius=None,\n        region=None,\n        query=None,\n        next_page_token=None,\n    ):\n    \"\"\" Returns Google Places from the given Google place type.\n\n    Args:\n        place_type (str): Restricts the results to places matching the\n            specified type.The full list of supported types is available here:\n            https://developers.google.com/places/supported_types\n        location (Optional[Tuple[str]]): The latitude/longitude value for which\n            you wish to obtain the closest, human-readable address.\n        radius (Optional[int]): Distance in meters within which to bias results\n        region (Optional[str]): The region code, for example \"NZ\". See more @:\n            https://developers.google.com/places/web-service/search\n        query (Optional[str]): The text string on which to search\n        next_page_token (Optional[str]): Token from a previous search that when\n            provided will return the next page of results for the same search.\n    Returns:\n        dict: Serialized Google Place data. \n    \"\"\"\n    return get_client().places(\n        query=query,\n        location=location,\n        radius=radius,\n        type=place_type,\n        region=region,\n        page_token=next_page_token,\n    )\n\n\n@cached(cache={})\ndef get_all_places_by_type(\n        place_type, location=None, radius=None, region=None, query=None,\n    ):\n    \"\"\" Returns the maximum number of Google Places from the given Google\n        place type.\n\n    Args:\n        place_type (str): Restricts the results to places matching the\n            specified type.The full list of supported types is available here:\n            https://developers.google.com/places/supported_types\n        location (Optional[Tuple[str]]): The latitude/longitude value for which\n            you wish to obtain the closest, human-readable address.\n        radius (Optional[int]): Distance in meters within which to bias results\n        region (Optional[str]): The region code, for example \"NZ\". See more @:\n            https://developers.google.com/places/web-service/search\n        query (Optional[str]): The text string on which to search\n    Returns:\n        List[dict]: Serialized Google Place data. \n    \"\"\"\n    return _poll_all_results(\n        get_places_by_type,\n        place_type,\n        location=location,\n        radius=radius,\n        region=region,\n        query=query,\n    )\n\n\n@cached(cache={})\ndef geocode(address):\n    \"\"\" Attempt to resolve a Place from an address.\n\n    Args:\n        address (str): the address text\n    Returns:\n        List[dict]: Google Place results matching the resolved address\n    \"\"\"\n    return get_client().geocode(address)\n\n\n@cached(cache={})\ndef reverse_geocode(latitude, longitude):\n    \"\"\" Attempt to resolve a Place from latitude and longitude coordinates.\n\n    Args:\n        latitude (float): the latitude coordinate\n        longitude (float): the longitude coordinate\n    Returns:\n        List[dict]: Google Place results matching the resolved address\n    \"\"\"\n    return get_client().reverse_geocode((latitude, longitude))\n\n\ndef _poll_all_results(fn, *args, **kwargs):\n    \"\"\" Iterates through Google Maps results until the maximum results are\n        returned (caps at 60 results).\n\n    Args:\n        fn (function): A function (should return a JSON dictionary with\n            \"results\" and \"next_page_token\" keys)\n        args (args): function arguments\n        kwargs (kwargs): function keyword arguments\n    Returns:\n        List[dict]: A list of JSON results\n    \"\"\"\n    results = []\n    next_page_token = True\n    while(next_page_token):\n        result = fn(*args, **kwargs) or {}\n        results.extend(result.get(\"results\", []))\n        next_page_token = result.get(\"next_page_token\", None)\n        kwargs.update({\"next_page_token\": next_page_token})\n        # From Google Docs: https://developers.google.com/maps/documentation/places/web-service/search-text\n        # \"There is a short delay between when a next_page_token is issued,\n        # and when it will become valid.\"\n        time.sleep(2)\n    return results\n","repo_name":"robbiefordyce/windwords","sub_path":"src/python/windwords/google.py","file_name":"google.py","file_ext":"py","file_size_in_byte":6499,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"41291805128","text":"import cx_Freeze\nimport sys\nimport os. path\nPYTHON_INSTALL_DIR = os.path.dirname(os.path.dirname(os.__file__))\nos.environ['TCL_LIBRARY'] = os.path.join(PYTHON_INSTALL_DIR, 'tcl', 'tcl8.6')\nos.environ['TK_LIBRARY'] = os.path.join(PYTHON_INSTALL_DIR, 'tcl', 'tk8.6')\n\nexecutables = [cx_Freeze.Executable(\"PiNet.py\", base = \"Win32GUI\", icon=\"icono.ico\")]\n\ncx_Freeze.setup(\n\tname=\"PiNet\",\n\tversion=\"1.1\",\n\toptions={\"build_exe\": {\"packages\":[\"pygame\", \"numpy\", \"sympy\", \"easygui\", \"tkinter\"],\n\t\t\t\t\t\t\t\"include_files\":[\"Pictures\", \"Manual_usuario.pdf\", \"icono.png\"]}},\n\texecutables = executables\n\t)","repo_name":"preacher6/PiNet","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":591,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"37501248204","text":"import sys\nimport irc.bot\nimport requests\nimport inputProcessing\nfrom threading import Timer, Event\nfrom Thread import MyThread\nimport printFunctions as bio\n\nstopflag = Event()\nthread = MyThread(stopflag)\nthread.start()\n\nclass TwitchBot(irc.bot.SingleServerIRCBot):\n    def __init__(self, username, client_id, token, channel):\n        self.client_id = client_id\n        self.token = token\n        self.channel = '#' + channel\n\n        # Get the channel id, we will need this for v5 API calls\n        url = 'https://api.twitch.tv/kraken/users?login=' + channel\n        headers = {'Client-ID': client_id, 'Accept': 'application/vnd.twitchtv.v5+json'}\n        r = requests.get(url, headers=headers).json()\n        self.channel_id = r['users'][0]['_id']\n\n        # Create IRC bot connection\n        server = 'irc.chat.twitch.tv'\n        port = 6667\n        print('Connecting to ' + server + ' on port ' + str(port) + '...')\n        irc.bot.SingleServerIRCBot.__init__(self, [(server, port, 'oauth:'+token)], username, username)\n\n    def on_welcome(self, c, e):\n        print('Joining ' + self.channel)\n\n        # You must request specific capabilities before you can use them\n        c.cap('REQ', ':twitch.tv/membership')\n        c.cap('REQ', ':twitch.tv/tags')\n        c.cap('REQ', ':twitch.tv/commands')\n        c.join(self.channel)\n\n    def on_pubmsg(self, c, e):\n\n        # If a chat message starts with an exclamation point, try to run it as a command\n        if e.arguments[0][:1] == '!':\n            cmd = e.arguments[0].split(' ')[0][1:]\n            print('Received command: ' + cmd)\n            self.do_command(e, cmd)\n        return\n\n    def do_command(self, e, cmd):\n        c = self.connection\n\n        # Poll the API to get current game.\n        # if cmd == \"game\":\n        #     url = 'https://api.twitch.tv/kraken/channels/' + self.channel_id\n        #     headers = {'Client-ID': self.client_id, 'Accept': 'application/vnd.twitchtv.v5+json'}\n        #     r = requests.get(url, headers=headers).json()\n        #     c.privmsg(self.channel, r['display_name'] + ' is currently playing ' + r['game'])\n\n        # Poll the API the get the current status of the stream\n        if cmd == \"title\":\n            url = 'https://api.twitch.tv/kraken/channels/' + self.channel_id\n            headers = {'Client-ID': self.client_id, 'Accept': 'application/vnd.twitchtv.v5+json'}\n            r = requests.get(url, headers=headers).json()\n            c.privmsg(self.channel, r['display_name'] + ' channel title is currently ' + r['status'])\n\n        elif cmd == \"write\":\n            wordarr = e.arguments[0].split(\" \")\n            if len(wordarr) > 1:\n                word = wordarr[1];\n            inputProcessing.democracy(word);\n\n        # elif cmd == \"complete\":\n            # bio.completeBio()\n            # file = open(\"profile.txt\", \"a\")\n            # file.write(\"\\nProfile Complete\\n\")\n            # file.close()\n\n        elif cmd == \"help\":\n            url = 'https://api.twitch.tv/kraken/channels/' + self.channel_id\n            headers = {'Client-ID': self.client_id, 'Accept': 'application/vnd.twitchtv.v5+json'}\n            r = requests.get(url, headers=headers).json()\n            c.privmsg(self.channel, \"Type `!write word` to include a word in queue (only the first word counts). Type `!write newline` for a new line. `!write completeprofile` to finish writing the profile. `!help` to See this again. Make an account and upload your photo to https://goo.gl/wn2gue\")\n\n        # The command was not recognized\n        else:\n            url = 'https://api.twitch.tv/kraken/channels/' + self.channel_id\n            headers = {'Client-ID': self.client_id, 'Accept': 'application/vnd.twitchtv.v5+json'}\n            r = requests.get(url, headers=headers).json()\n            c.privmsg(self.channel, \"Type `!write word` to include a word in queue (only the first word counts). Type `!write newline` for a new line. `!write completeprofile` to finish writing the profile. `!help` to See this again. Make an account and upload your photo to https://goo.gl/wn2gue\")\n\ndef main():\n    if len(sys.argv) != 5:\n        print(\"Usage: twitchbot <username> <client id> <token> <channel>\")\n        sys.exit(1)\n\n    username  = sys.argv[1]\n    client_id = sys.argv[2]\n    token     = sys.argv[3]\n    channel   = sys.argv[4]\n\n    bot = TwitchBot(username, client_id, token, channel)\n    bot.start()\n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"zzmren/twitch_plays_profile","sub_path":"chatbot/chatbot.py","file_name":"chatbot.py","file_ext":"py","file_size_in_byte":4427,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"31081939655","text":"class Solution:\n    def subsets(self, nums: List[int]) -> List[List[int]]:\n        ans = []\n        def dfs(i,path):\n            if i == len(nums):\n                ans.append(path)\n                return\n            \n            dfs(i+1,path)\n            dfs(i+1,path+[nums[i]])\n            \n        dfs(0,[])    \n        return ans\n    \n    ","repo_name":"Merwan-J/competetive-programming","sub_path":"78-subsets/78-subsets.py","file_name":"78-subsets.py","file_ext":"py","file_size_in_byte":342,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"35368686065","text":"import matplotlib\n\nmatplotlib.use(\"TkAgg\")\nimport gym\nimport gridworld\nfrom gym import wrappers, logger\nimport numpy as np\nimport copy\nfrom matplotlib import pyplot as plt\n\n## Agents\n\nimport torch\nimport torch.nn as nn\nfrom collections import deque\n\nclass NN(nn.Module):\n    def __init__(self, inSize, outSize, layers=[],softmax = False):\n        super(NN, self).__init__()\n        self.layers = nn.ModuleList([])\n        for x in layers:\n            self.layers.append(nn.Linear(inSize, x))\n            inSize = x\n        self.layers.append(nn.Linear(inSize, outSize))\n        self.soft_flag = softmax\n\n    def forward(self, x):\n        x = self.layers[0](x)\n        for i in range(1, len(self.layers)):\n            x = torch.nn.functional.leaky_relu(x)\n            x = self.layers[i](x)\n        if self.soft_flag:\n            x = torch.nn.functional.softmax(x,dim=-1)\n        return x\n\nclass KL_PPO(object):\n    def __init__(self,action_space,epsilon,beta,delta,gamma,K,l_r,inSize,outSize,layers):\n        self.action_space = action_space\n        self.beta = beta\n        self.delta = delta\n        self.epsilon = epsilon\n        self.gamma = gamma\n        self.K = K\n        self.pi = NN(inSize,outSize,layers,softmax = True)\n        self.pi_aux = copy.deepcopy(self.pi)\n        self.V = NN(inSize,1,layers)\n        self.optim_V = torch.optim.Adam(self.V.parameters(),l_r)\n        self.optim_pi = torch.optim.Adam(self.pi_aux.parameters(),l_r)\n        self.loss_V = nn.SmoothL1Loss(reduction=\"mean\")\n        self.loss_kl = nn.KLDivLoss(reduction=\"sum\")\n        self.batch = []\n        self.episodes = []\n\n    def act(self,obs,reward,done):\n        limit = (self.epsilon/self.action_space.n + 1 - self.epsilon)\n        chance = np.random.uniform(0,1)\n        if(chance < limit):\n            action = torch.argmax(self.pi.forward(torch.tensor(obs).float())).item()\n        else:\n            action = self.action_space.sample()\n        return(action)\n\n    def update(self,obs,action,reward,done,obs_p):\n        self.episodes += [(obs,action,reward,done,obs_p)]\n        if done:\n            self.batch += [self.episodes]\n            self.episodes = []\n            if self.K == len(self.batch):\n                self.update_policy()\n                self.batch = []\n\n    def update_policy(self):\n        # Update V\n        self.optim_V.zero_grad()\n        for episode in self.batch:\n            reward_exp = torch.zeros(1,requires_grad = True)\n            loss_v_tot = torch.zeros(1,requires_grad = True)\n            for s,a,r,d,s_p in reversed(episode):\n                reward_exp = self.gamma*reward_exp + r\n                loss_v_tot = loss_v_tot + self.loss_V(self.V.forward(torch.tensor(s).float()),reward_exp.float())\n        loss_v_tot = loss_v_tot\n        loss_v_tot.backward()\n        self.optim_V.step()\n\n        # Update pi\n        for episode in self.batch:\n            kl = torch.zeros(1,requires_grad = False)\n            self.optim_pi.zero_grad()\n            R = torch.zeros(1,requires_grad = True)\n            loss_pi_ep = torch.zeros(1,requires_grad = True)\n            for s,a,r,d,s_p in reversed(episode):\n                R = self.gamma*R + r\n                A = R - self.V.forward(torch.tensor(s).float())\n                aux = self.pi_aux.forward(torch.tensor(s).float())\n                old = self.pi.forward(torch.tensor(s).float())\n                kl_loss = self.loss_kl(torch.log(aux),old)\n                kl += kl_loss\n                loss_pi_ep = loss_pi_ep - A*(aux[a]/old[a]) + self.beta*kl_loss\n            loss_pi_ep.backward()\n            self.optim_pi.step()\n            if kl >= 1.5*self.delta:\n                self.beta = 2*self.beta\n            if kl <= self.delta/1.5:\n                self.beta = 0.5*self.beta\n        self.pi = copy.deepcopy(self.pi_aux)\n\nclass Clip_PPO(object):\n    def __init__(self,action_space,epsilon,threshold,gamma,K,l_r,inSize,outSize,layers):\n        self.action_space = action_space\n        self.threshold = threshold\n        self.epsilon = epsilon\n        self.gamma = gamma\n        self.K = K\n        self.pi = NN(inSize,outSize,layers,softmax = True)\n        self.pi_aux = copy.deepcopy(self.pi)\n        self.V = NN(inSize,1,layers)\n        self.optim_V = torch.optim.Adam(self.V.parameters(),l_r)\n        self.optim_pi = torch.optim.Adam(self.pi_aux.parameters(),l_r)\n        self.loss_V = nn.SmoothL1Loss(reduction=\"mean\")\n        self.batch = []\n        self.episodes = []\n\n    def act(self,obs,reward,done):\n        limit = (self.epsilon/self.action_space.n + 1 - self.epsilon)\n        chance = np.random.uniform(0,1)\n        if(chance < limit):\n            action = torch.argmax(self.pi.forward(torch.tensor(obs).float())).item()\n        else:\n            action = self.action_space.sample()\n        return(action)\n\n    def update(self,obs,action,reward,done,obs_p):\n        self.episodes += [(obs,action,reward,done,obs_p)]\n        if done:\n            self.batch += [self.episodes]\n            self.episodes = []\n            if self.K == len(self.batch):\n                self.update_policy()\n                self.batch = []\n\n    def update_policy(self):\n        # Update V\n        self.optim_V.zero_grad()\n        for episode in self.batch:\n            reward_exp = torch.zeros(1,requires_grad = True)\n            loss_v_tot = torch.zeros(1,requires_grad = True)\n            for s,a,r,d,s_p in reversed(episode):\n                reward_exp = self.gamma*reward_exp + r\n                loss_v_tot = loss_v_tot + self.loss_V(self.V.forward(torch.tensor(s).float()),reward_exp.float())\n        loss_v_tot = loss_v_tot\n        loss_v_tot.backward()\n        self.optim_V.step()\n\n        # Update pi\n        for episode in self.batch:\n            self.optim_pi.zero_grad()\n            R = torch.zeros(1,requires_grad = True)\n            loss_pi_ep = torch.zeros(1,requires_grad = True)\n            for s,a,r,d,s_p in reversed(episode):\n                R = self.gamma*R + r\n                A = R - self.V.forward(torch.tensor(s).float())\n                aux = self.pi_aux.forward(torch.tensor(s).float())\n                old = self.pi.forward(torch.tensor(s).float())\n                r_t = aux[a]/old[a]\n                clipped = torch.clamp(r_t,1-self.threshold,1+self.threshold)\n                loss_pi_ep = loss_pi_ep - torch.min(A*r_t, A*clipped)\n            loss_pi_ep.backward()\n            self.optim_pi.step()\n        self.pi = copy.deepcopy(self.pi_aux)\n\n\n## Apprentissage\n\nif __name__ == '__main__':\n\n\n    env = gym.make('CartPole-v1') # obs of 4\n\n    # Enregistrement de l'Agent\n\n    outdir = 'cartpole-v0/random-agent-results'\n    envm = wrappers.Monitor(env, directory=outdir, force=True, video_callable=False)\n    env.seed(0)\n\n    agent_kl = KL_PPO(env.action_space,epsilon=0.05,beta=0.8,delta=250,gamma = 0.999,K=250,l_r=1e-3,inSize=4,outSize=2,layers=[200,200])\n    # agent_clip = Clip_PPO(env.action_space,epsilon=0.1,threshold=0.1,gamma = 0.999,K=250,l_r=1e-4,inSize=4,outSize=2,layers=[50,50])\n    #\n    # list_agent = [agent_kl,agent_clip]\n    # name = [\"kl\",\"clip\"]\n    list_agent = [agent_kl]\n    name = [\"kl\"]\n\n    episode_count = 1300\n    for k,agent in enumerate(list_agent):\n        reward = 0\n        done = False\n        env.verbose = True\n        np.random.seed(5)\n        r_tot = 0\n        r_graph = []\n        r_epi = []\n        rsum = 0\n        # a = list(agent.V.parameters())[0].clone()\n        #print(list(agent.pi.parameters())[0].clone())\n        for i in range(episode_count):\n            obs = envm.reset()\n            env.verbose = (i % 200 == 0 and i > 0)  # afficher 1 episode sur 100\n            if env.verbose:\n                env.render()\n            j = 0\n            rsum = 0\n            while True:\n                action = agent.act(obs, reward, done)\n                obs_prec = obs\n                obs, reward, done, _ = envm.step(action)\n                agent.update(obs_prec,action,reward,done,obs)\n                rsum += reward\n                j += 1\n                if env.verbose:\n                    env.render()\n                if done:\n                    r_tot += rsum\n                    r_graph += [r_tot/(i+1)]\n                    if i%10 == 0:\n                        r_epi += [rsum]\n                    print(\"Episode : \" + str(i) + \" rsum=\" + str(rsum) + \", \" + str(j) + \" actions\")\n                    break\n\n        print(\"done\")\n        # b = list(agent.V.parameters())[0].clone()\n        # print(torch.equal(a.data, b.data))\n        #print(list(agent.pi.parameters())[0].clone())\n        plt.figure(1)\n        #plt.plot(10*np.arange(len(r_epi)),r_epi,label =\"episodic\")\n        plt.plot(np.arange(len(r_graph)),r_graph,label = \"%s\"%name[k])\n    plt.title(\"KL PPO, Environment : CartPole\")\n    plt.legend()\n    plt.grid()\n    plt.show()\n    env.close()","repo_name":"migmtz/Reinforcement-Learning-M2","sub_path":"Codes/TME6_Code_Martinez.py","file_name":"TME6_Code_Martinez.py","file_ext":"py","file_size_in_byte":8784,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"34701579510","text":"# coding:utf8\nimport sys\nimport os\nimport time\nimport htmlfilter\nfpath = os.path.abspath(os.path.join(os.path.dirname(__file__),\"..\"))\nffpath = os.path.abspath(os.path.join(fpath,\"..\"))\nsys.path.append(ffpath)\n\nimport re\nimport scrapy\nimport scrapy\nimport datetime\nimport requests\nimport urllib.parse\nfrom scrapy import Request\nfrom scrapy.cmdline import execute\nfrom lxml import etree\nfrom liepinSpider.settings import DEFAULT_REQUEST_HEADERS,PARAMS,JOBS_PARAMS\nfrom liepinSpider.items import LiepinspiderItem\n\nclass liepinlinkSpider(scrapy.Spider):\n    name = 'liepin_link'\n    start_urls = 'https://www.liepin.com'\n    headers = DEFAULT_REQUEST_HEADERS\n    params = PARAMS.copy()\n\n    def start_requests(self):\n        get_city_url = self.start_urls +'/citylist/'\n        r = requests.get(get_city_url)\n        content = etree.HTML(r.text)\n        cities = content.xpath('//div[@class=\"citieslist\"]/ol/li/p/span[2]/a/@href')\n        dqs_id = []\n        company_entity_id = []\n        company_rank_id = []\n        industry_id = []\n        salary_id = []\n\n        search_url = self.start_urls + '/zhaopin/?d_sfrom=search_fp_nvbar&init=1'\n        r = requests.get(search_url)\n        content = etree.HTML(r.text)\n        company_entity_link = content.xpath('//div[@class=\"search-conditions\"]/dl/dd/ul/li/a/@href')\n        for s in company_entity_link:\n            compIds = re.findall('compIds=([0-9a-zA-Z_%]+)',s)\n            if compIds:\n                company_entity_id.append(compIds[0])\n\n        company_rank_link = content.xpath('//div[@class=\"search-conditions\"]/dl/dd/div[3]/a/@href')\n        for s in company_rank_link:\n            compTag = re.findall('compTag=([0-9a-zA-Z_%]+)',s)\n            if compTag:\n                company_rank_id.append(compTag[0])\n\n        industry_link = content.xpath('//div[@class=\"search-conditions\"]/dl[2]/dd/ul/li/div/a/@href')\n        for s in industry_link:\n            industryType = re.findall('industryType=([0-9a-zA-Z_%]+)',s)\n            industries = re.findall('industries=([0-9a-zA-Z_%]+)',s)\n            if industryType and industries:\n                industry_id.append('{}|{}'.format(industryType[0],industries[0]))\n\n        salary_link = content.xpath('//div[@class=\"search-conditions\"]/dl[4]/dd/a/@href')\n        for s in salary_link:\n            salary = re.findall('salary=([0-9a-zA-Z_%]+)', s)\n            if salary:\n                salary = urllib.parse.unquote(salary[0])\n                salary_id.append(salary)\n\n        for c in cities:\n            city_link = self.start_urls + c\n            r = requests.get(city_link)\n            content = etree.HTML(r.text)\n            city_dqs = content.xpath('//*[@id=\"search_form\"]/input[2]/@value')[0]\n            dqs_id.append(city_dqs)\n\n        print('——————————————————————log——————————————————————')\n        print('获取company_entity_id：{}'.format(len(company_entity_id)))\n        print('获取company_rank_id：{}'.format(len(company_rank_id)))\n        print('获取industry_id：{}'.format(len(industry_id)))\n        print('获取salary_id：{}'.format(len(salary_id)))\n        print('获取dqs_id：{}'.format(len(dqs_id)))\n\n        all_link = []\n\n        for pn in range(10):\n            for ii in industry_id:\n                for di in dqs_id:\n                    params = self.params.copy()\n                    # params['compIds'] = ce\n                    # params['compTag'] = cr\n                    params['industryType'] = ii.split('|')[0]\n                    params['industries'] = ii.split('|')[1]\n                    # params['salary'] = si\n                    params['dqs'] = di\n                    params['curPage'] = pn\n                    url = self.start_urls + '/zhaopin/?' + urllib.parse.urlencode(params)\n                    # print(url)\n                    all_link.append(url + '\\n')\n\n        with open('liepin_link.txt','w',encoding='utf8') as f:\n            f.writelines(all_link)\n\n        yield Request(url=self.start_urls, callback= self.parse)\n\n    def parse(self, response):\n        print('猎聘网【省市/职位】link已更新完成.')\n        pass\n\nclass liepinjobSpider(scrapy.Spider):\n    name = 'liepin'\n    start_urls = 'https://www.liepin.com'\n    headers = DEFAULT_REQUEST_HEADERS\n    params = JOBS_PARAMS.copy()\n    date = time.strftime(\"%Y-%m-%d\", time.localtime())\n    dir = '/data/crawl/crawltool/user_work/guolinchong/data/profession/猎聘网/{}'.format(date)\n\n\n    def start_requests(self):\n        if not os.path.exists(self.dir):\n            os.makedirs(self.dir)\n\n        with open('liepin_link.txt',encoding='utf8') as f:\n            urls = f.read().splitlines()\n        for url in urls:\n            yield Request(url=url, callback=self.parse)\n\n    def parse(self, response):\n        url = response.url\n        r = requests.get(url)\n        content = etree.HTML(r.text)\n        all_links = content.xpath('//@href')\n        for i in all_links:\n            if i.startswith('https://www.liepin.com/job/') and len(i)>27:\n                print(i)\n                yield Request(url=i, callback=self.parse_job)\n\n    def parse_job(self, response):\n        url = response.url + '?' + urllib.parse.urlencode(self.params)\n        print(url)\n        r = requests.get(url)\n        with open('{}/data.txt'.format(self.dir),'a',encoding='utf8') as f:\n            output = '{}\\t{}\\t{}\\n'.format(response.url,' '.join(r.text.replace('\\n', ' ').replace('\\r', ' ').replace('\\t', ' ').split()),\n                                           self.date)\n            f.write(output)\n\nif __name__ == '__main__':\n    execute('scrapy crawl liepin'.split())","repo_name":"Threekiii/Awesome-Scrapy","sub_path":"Liepin/liepinSpider/liepinSpider/spiders/spider.py","file_name":"spider.py","file_ext":"py","file_size_in_byte":5679,"program_lang":"python","lang":"en","doc_type":"code","stars":55,"dataset":"github-code","pt":"48"}
+{"seq_id":"29899848816","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Feb 22 14:13:21 2018\n\n@author: 593787 Robert Piazza - Booz Allen Hamilton\n\"\"\"\n\nimport numpy as np\nimport random as r\nnp.random.seed(100)\nr.seed(100)\n#load distances\ndist = np.genfromtxt(\"5-1-1 intercity.csv\", delimiter=',')\n#creates population of size n\ndef initializePopulation(n):\n    pop=[]\n    for i in range(0,n):\n\t#create list of planets\n        ind = np.arange(48)\n\t#randomly order them\n        np.random.shuffle(ind)\n\t#add to population\n        pop.append(ind)\n    return pop\n#score route, return the total\n# distance of all the legs\ndef score(v):\n    sum = 0\n    for j in range(0, len(v)):\n        current=v[j]\n        if j==len(v)-1:\n            next = v[0]\n        else:\n            next = v[j+1]\n        distance = dist[current-1][next-1]\n         #print str(current)+' '+str(next)+' '+str(distance)\n        sum = sum + distance\n    return sum\n#evaluates each individual in a population\ndef scorePopulation(pop):\n    popScore = []\n    for p in range(0,len(pop)):\n        s = score(pop[p])\n        popScore.append(s)\n    return popScore\n#returns a new population, created by selecting best individual from samples ofspecified size\ndef tournamentSelectPop(size,pop,popScore):\n    newpop = []\n    while len(newpop)<len(pop):\n        sample = None\n        sampleScore = 1000000000\n        #select sample\n        for i in range(0,size):\n            s1 = r.randint(0,len(pop)-1)\n            sscore = popScore[s1]\n            #print 'score' + str(sscore)\n            #minimize the overall score\n            if sscore<sampleScore:\n                sample = pop[s1]\n                sampleScore = sscore\n                newpop.append(sample)\n    return newpop\n#performs ordered crossover operation on population, randomly orders population and crosses pairs\ndef crossover(pop):\n    newpop = []\n#shuffle the data\n    index_shuf = range(len(pop))\n    for i in range(0,len(index_shuf)-1,2):\n        ind1 = pop[index_shuf[i]]\n        ind2 = pop[index_shuf[i+1]]\n        startIndex = r.randint(0,len(ind1)-1)\n        endIndex = r.randint(startIndex,len(ind1)-1)\n#construct new individuals from crossing parents using ordered crossover\n        child1 = ox(startIndex, endIndex, ind1,ind2)\n        child2 = ox(startIndex, endIndex, ind2,ind1)\n        newpop.append(child1)\n        newpop.append(child2)\n    return newpop\n#Ordered crossover, returns a single individual by crossing ind1 and ind2, ind elements are used between start and end, ind1 elements are used everywhere else\ndef ox(start, end, ind1, ind2):\n    set1 = set()\n    set2 = set()\n#copy the individuals\n    ind1Copy = list(ind1)\n    ind1Copy.reverse()\n    ind2Copy = list(ind2)\n    ind2Copy.reverse()\n    newInd = list(np.zeros(len(ind1)))\n#elements that are in the crossover region\n    for i in range(start,end+1):\n        set1.add(ind1[i])\n        set2.add(ind2[i])\n    for i in range(0,len(ind1)):\n        val = ind1[i] #? saved for printing what the value is we're replacing?\n        #in crossover region\n        if((i>=start) & (i<=end)):\n            #print 'continuing '+str(i)\n            newInd[i]=ind2[i]\n            continue;\n            #get next that isnt already in set\n        val2 = ind1Copy.pop() #remove last value from ind1Copy and place in val2\n        #print 'trying: '+str(val2)\n        alreadyIn = val2 in set2;\n        while alreadyIn:\n            #print 'skipping '+str(val2)\n            val2 = ind1Copy.pop()\n            #print val2\n            alreadyIn = val2 in set2;\n        newInd[i] = val2\n    return newInd\n#mutate a population\ndef mutate(rate,pop):\n\tnewpop=[]\n\tfor p in pop:\n\t\tif r.random()<rate:\n\t\t\tindex1 = r.randint(0,len(p)-1)\n\t\t\tindex2 = r.randint(0,len(p)-1)\n\t\t\tnewp = swap(index1,index2,p)\n\t\t\tnewpop.append(newp)\n\t\telse:\n\t\t\tnewpop.append(p)\n\treturn newpop\n#swaps two elements in an individual at a given index\ndef swap(index1,index2,p):\n\ti1value = p[index1]\n\ti2value = p[index2]\n\tp[index1]=i2value\n\tp[index2]=i1value\n\treturn p\n#returns average score of a population\ndef avescore(popscore):\n\tsum=0\n\tfor i in range(0, len(popscore)):\n\t\tsum = sum+float(popscore[i])\n\t\tn = len(popscore)\n\t\tave = float(sum)/float(n)\n\treturn ave\n#returns best score in a population\ndef bestScore(popscore):\n\tmin = popscore[0]\n#for all the individual scores\n\tfor p in popscore:\n\t\tif p<min:\n\t\t\tmin = p\n\treturn min\npop = initializePopulation(50)\nfor i in range(0,500):### Add code here to create generations\n    popscore = scorePopulation(pop)\n    newpop = tournamentSelectPop(2,pop,popscore)\n    crossedpop = crossover(newpop)\n    mutatedpop = mutate(.15,crossedpop)\n    newscore = scorePopulation(mutatedpop)\n    pop = mutatedpop\n    ave = avescore(newscore)\n    print('best '+str(bestScore(newscore)))\n","repo_name":"robertpiazza/ProfessionalDevelopment","sub_path":"Exploring Data Science/untitled3.py","file_name":"untitled3.py","file_ext":"py","file_size_in_byte":4721,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"72909213907","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\nConstruct an xl configuration file for a test (from various fragments), and\nsubstitue variables appropriately.\n\"\"\"\n\nimport sys, os\n\n# Usage: mkcfg.py $OUT $DEFAULT-CFG $EXTRA-CFG $VARY-CFG\n_, out, defcfg, vcpus, extracfg, varycfg = sys.argv\n\n# Evaluate environment and name from $OUT\n_, env, name = out.split('.')[0].split('-', 2)\n\n# Possibly split apart the variation suffix\nvariation = ''\nif '~' in name:\n    parts = name.split('~', 1)\n    name, variation = parts[0], '~' + parts[1]\n\ndef expand(text):\n    \"\"\" Expand certain variables in text \"\"\"\n    return (text\n            .replace(\"@@NAME@@\",   name)\n            .replace(\"@@ENV@@\",    env)\n            .replace(\"@@VCPUS@@\",  vcpus)\n            .replace(\"@@XTFDIR@@\", os.environ[\"xtfdir\"])\n            .replace(\"@@VARIATION@@\", variation)\n        )\n\nconfig = open(defcfg).read()\n\nif extracfg:\n    config += \"\\n# Test Extra Configuration:\\n\"\n    config += open(extracfg).read()\n\nif varycfg:\n    config += \"\\n# Test Variation Configuration:\\n\"\n    config += open(varycfg).read()\n\ncfg = expand(config)\n\nopen(out, \"w\").write(cfg)\n","repo_name":"andyhhp/xtf","sub_path":"build/mkcfg.py","file_name":"mkcfg.py","file_ext":"py","file_size_in_byte":1133,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"48"}
+{"seq_id":"35338595654","text":"import numpy as np\n\nfrom pathlib import Path\nfrom simpleparse.parser import Parser\nfrom typing import Callable, Dict, List, Optional, Set, Tuple\n\nfrom .base import *\nfrom .context import Context\nfrom .dsl import LimitQuantifiers, parse_cli\n\n\n# TODO : implement full smt-lib grammar\n# currently enough for vnn-lib spec?\ngrammar = r\"\"\"# partial smt-lib 2.6 grammar\nscript := command*\ncommand := (ws*,\"(\",ws*,\"assert\",ws*,term,ws*,\")\",ws*)\n          /(ws*,\"(\",ws*,\"declare-const\",ws*,symbol,ws*,sort,ws*,\")\",ws*)\nterm := spec_constant/qual_identifier/(ws*,\"(\",ws*,qual_identifier,ws*,(term,ws*)+,\")\",ws*)\nqual_identifier := identifier/(ws*,\"(\",ws*,\"as\",ws*,identifier,ws*,sort,ws*,\")\",ws*)\nsort := identifier/(ws*,\"(\",ws*,identifier,ws*,(sort,ws*)+,\")\",ws*)\nidentifier := symbol/(ws*,\"(\",ws*,\"_\",ws*,symbol,ws*,(index,ws*)+,\")\",ws*)\nindex := numeral/symbol\nspec_constant := decimal/numeral/hexadecimal/binary/string\nsymbol := (letter/[~!@$%^&*+=<>.?/_-]),(digit/letter/[~!@$%^&*+=<>.?/_-])*\nstring := '\"',('\"\"'/[\\t\\n\\r !#-~])*,'\"'\nbinary := \"#b\",[01]+\nhexadecimal := \"#x\",[0-9A-Fa-f]+\ndecimal := numeral,\".\",\"0\"*,numeral?\nnumeral := ([1-9],[0-9]*)/\"0\"\nletter := [A-Za-z]\ndigit := [0-9]\nws := [ \\t\\r\\n]/comment\ncomment := \";\",[\\t -~]*,[\\r\\n]\n\"\"\"\n\n\nclass VNNLibParseError(Exception):\n    pass\n\n\nParseTree = Tuple[str, int, int, List[\"ParseTree\"]]\n\n\ndef subtraction_helper(*args):\n    if len(args) == 1:\n        return Negation(*args)\n    elif len(args) == 2:\n        return Subtract(*args)\n    print(args)\n    raise ValueError(\"Subtraction not implemented for more than 2 args.\")\n\n\nclass ExpressionBuilder:\n    def __init__(self):\n        self.parser = Parser(grammar, \"script\")\n        self.buffer = None\n        self.symbols: Dict[str, Symbol] = {}\n        self.operators: Dict[str, Callable] = {\n            \"+\": Add,\n            \"/\": Divide,\n            \">\": GreaterThan,\n            \">=\": GreaterThanOrEqual,\n            \"<\": LessThan,\n            \"<=\": LessThanOrEqual,\n            \"*\": Multiply,\n            \"-\": subtraction_helper,\n            \"or\": Or,\n            \"and\": And,\n            \"ite\": IfThenElse,\n        }\n        self.assertions: Set[Expression] = set()\n        self.current_assertion: Optional[Expression] = None\n\n    def build(self, script):\n        self.buffer = script\n        success, children, index = self.parser.parse(script + \"\\n\")\n        if not success or index != (len(script) + 1):\n            raise VNNLibParseError(f\"Parsing failed at index {index}\")\n        for c in children:\n            self.visit(c)\n        return ~Exists(Symbol(\"X\"), And(*self.assertions))\n\n    def declare_const(self, name, sort):\n        if name in self.symbols:\n            raise VNNLibParseError(\n                f\"Name already exists in symbol table: {name}\")\n        if name.startswith(\"X_\"):\n            index = tuple(int(i) for i in name.split(\"_\")[1:])\n            if len(index) == 1:\n                self.symbols[name] = Symbol(\"X\")[\n                    FunctionCall(\n                        np.unravel_index, (index[0], Network(\n                            \"N\").input_shape[0]), {}\n                    )\n                ]\n            else:\n                self.symbols[name] = Symbol(\"X\")[index]\n        elif name.startswith(\"Y_\"):\n            index = tuple(int(i) for i in name.split(\"_\")[1:])\n            if len(index) == 1:\n                self.symbols[name] = Network(\"N\")(Symbol(\"X\"))[\n                    FunctionCall(\n                        np.unravel_index, (index[0], Network(\n                            \"N\").output_shape[0]), {}\n                    )\n                ]\n            else:\n                self.symbols[name] = Network(\"N\")(Symbol(\"X\"))[index]\n        else:\n            self.symbols[name] = Parameter(name, type=sort)\n\n    def visit(self, tree: ParseTree):\n        production = tree[0]\n        visitor = getattr(self, f\"visit_{production}\", self.generic_visit)\n        return visitor(tree)\n\n    def generic_visit(self, tree: ParseTree):\n        raise NotImplementedError(f\"{tree[0]}\")\n\n    def visit_subtrees(self, tree: ParseTree):\n        subtrees = []\n        end_index = tree[2]\n        for t in tree[3]:\n            t_ = self.visit(t)\n            if t_ is not None:\n                subtrees.append(t_)\n                end_index = tree[2]\n            else:\n                if end_index == tree[2]:\n                    end_index = t[1]\n                if t[1] == tree[1]:\n                    tree = (tree[0], t[2], tree[2], tree[3])\n        tree = (tree[0], tree[1], end_index, subtrees)\n        return tree\n\n    def visit_command(self, tree: ParseTree):\n        tree = self.visit_subtrees(tree)\n\n        command = self.buffer[tree[1]: tree[2]].strip(\n            \"()\").split(\" \", maxsplit=1)[0]\n        if command not in [\"assert\", \"declare-const\"]:\n            raise VNNLibParseError(f\"Unknown command: {command}\")\n\n        if command == \"declare-const\":\n            assert len(tree[3]) == 2\n            self.declare_const(*tree[3])\n        elif command == \"assert\":\n            assert len(tree[3]) == 1\n            self.assertions.add(tree[3][0])\n        return tree[3]\n\n    def visit_decimal(self, tree: ParseTree):\n        num = float(self.buffer[tree[1]: tree[2]])\n        return Constant(num)\n\n    def visit_identifier(self, tree: ParseTree):\n        tree = self.visit_subtrees(tree)\n        assert len(tree[3]) == 1\n        return tree[3][0]\n\n    def visit_numeral(self, tree: ParseTree):\n        num = int(self.buffer[tree[1]: tree[2]])\n        return Constant(num)\n\n    def visit_qual_identifier(self, tree: ParseTree):\n        tree = self.visit_subtrees(tree)\n        assert len(tree[3]) == 1  # TODO: not always true\n        if tree[3][0] in self.symbols:\n            return self.symbols[tree[3][0]]\n        elif tree[3][0] in self.operators:\n            return self.operators[tree[3][0]]\n        else:\n            try:\n                return int(tree[3][0])\n            except ValueError:\n                try:\n                    return float(tree[3][0])\n                except ValueError:\n                    pass\n            raise VNNLibParseError(f\"Unknown identifier: {tree[3][0]}\")\n\n    def visit_sort(self, tree: ParseTree):\n        tree = self.visit_subtrees(tree)\n        assert len(tree[3]) == 1  # TODO: not always true\n        if tree[3][0] == \"Real\":\n            return float\n        elif tree[3][0] == \"Int\":\n            return int\n        elif tree[3][0] == \"Bool\":\n            return bool\n        else:\n            raise NotImplementedError(\n                f\"Unimplemented sort: {self.buffer[tree[1]:tree[2]]}\"\n            )\n\n    def visit_spec_constant(self, tree: ParseTree):\n        tree = self.visit_subtrees(tree)\n        assert len(tree[3]) == 1\n        return tree[3][0]\n\n    def visit_symbol(self, tree: ParseTree):\n        name = self.buffer[tree[1]: tree[2]]\n        return name\n\n    def visit_term(self, tree: ParseTree):\n        tree = self.visit_subtrees(tree)\n        if len(tree[3]) == 1:\n            return tree[3][0]\n        return tree[3][0](*tree[3][1:])\n\n    def visit_ws(self, tree: ParseTree):\n        return None\n\n\ndef parse(path: Path, args: Optional[List[str]] = None) -> Expression:\n    with open(path, \"r\") as f:\n        script_str = f.read()\n    with Context():\n        phi = ExpressionBuilder().build(script_str)\n        LimitQuantifiers()(phi)\n\n        phi = phi.propagate_constants()\n        parse_cli(phi, args)\n    return phi\n","repo_name":"GuantingPan/Bandit-VNN-LIB-Fuzz","sub_path":"solvers/mapleDNNSat/mapleDNNsat/property/dnnv_properties/vnnlib.py","file_name":"vnnlib.py","file_ext":"py","file_size_in_byte":7432,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"19965342567","text":"'''\nCreated on Dec 24, 2012\n\n@author: kaining\n'''\nfrom django.db import models, connections\nimport time, datetime\n\nfrom boto.s3.connection import S3Connection\nconn = S3Connection('AKIAJQD3IYLGJOBU2RJA', '6uYfKcl2w1wuo2wxROL2JnWl5Z/sE3pPPSv75k5V')\nfrom boto.s3.key import Key\nbucket = conn.create_bucket('kainingsupermybucket')\n\nconn = connections['default']\n\nclass ActionType:\n    CreateImpression = 0\n    Like = 1\n    Comment = 2\n    RemoveImpression = 3\n    RemoveComment = 4\n    UnLike = 5\n\nclass BaseModel(models.Model):\n    isActive = models.BooleanField(default=True)\n    creationDate = models.DateTimeField('date created')\n    creationTime = models.FloatField(default=0.0)\n\n    class Meta:\n        abstract = True\n        \n    def save(self, **kwargs):\n        if not self.creationTime:\n            self.creationTime = time.time()\n            self.creationDate = datetime.datetime.utcnow()\n        super(BaseModel,self).save()\n\n    @staticmethod\n    def getCollection(name):\n        # this is the naming convension under a certain package\n        return conn.get_collection(\"main_\" + name.lower())\n    \n    @classmethod\n    def findOne(cls, query, filter=None):\n        collection = BaseModel.getCollection(cls.__name__)\n        if 'isActive' not in query:\n            query['isActive'] = True\n        return collection.find_one(query, filter)\n    \n    @classmethod\n    def find(cls, query, filter=None):\n        collection = BaseModel.getCollection(cls.__name__)\n        if 'isActive' not in query:\n            query['isActive'] = True\n        return collection.find(query, filter)\n\n    @classmethod\n    def remove(cls, obj):\n        obj.isActive = False\n        obj.save()\n#        collection = BaseModel.getCollection(cls.__name__)\n#        collection.update(query, {\"$set\": {\"isActive\": False}})\n        return \n\n    @classmethod\n    def update(cls, query, update):\n        collection = BaseModel.getCollection(cls.__name__)\n        collection.update(query, update)\n        return \n\n#def handle_uploaded(imageFile, imageType='n'):\n#    key = Key(bucket)\n#    key.key = \"testid\" + '_' + str(int(time.time())) + imageType + '.png'\n#    key.set_contents_from_file(imageFile)\n#    photo = Photo.objects.create(u=key.key,t=imageType)\n#    photo.save()\n#    operatePhoto.delay(photo.id)\n##    https://s3.amazonaws.com/kainingsupermybucket/testid_1352140815n.png\n#\n#from django import forms\n#class UploadFileForm(forms.Form):\n##    title = forms.CharField(max_length=50)\n##    file  = forms.FileField()\n#    image = forms.ImageField()\n#\n#@csrf_exempt\n#def upload_file(request):\n#    try:\n#        if request.method == 'POST':\n#            form = UploadFileForm(request.POST, request.FILES)\n#            if form.is_valid():\n#    #            userAuth = UserSocialAuth.objects.get(user=request.user.id)\n#                handle_uploaded(request.FILES['image'])\n#                return HttpResponseRedirect('/success/url/')\n#        else:\n#            form = UploadFileForm()\n#    except:\n#        traceback.print_exc(file=sys.stdout)\n#        logging.exception(\"Faild to serve request\")\n#    return render_to_response('upload.html', {'form': form})\n\nfrom django.contrib.auth.models import User\n\nclass ProfileImage(BaseModel):\n    imageURL = models.URLField()\n    user = models.ForeignKey(User, unique=True)\n\nclass Social(models.Model):\n    fbId = models.CharField(max_length = 30)\n    fbToken = models.CharField(max_length = 255)\n    twitterId = models.CharField(max_length = 30)\n    twitterToken = models.CharField(max_length = 255)\n\nclass UserProfile(models.Model):\n    user = models.ForeignKey(User, unique=True)\n    profileImage = models.URLField()\n\n    social = models.ForeignKey(Social)\n    \n    level = models.IntegerField(default=0)\n    points = models.IntegerField(default=0)\n    joinDate = models.DateTimeField('date joined', auto_now_add = True)\n\nclass Point(models.Model):\n    lat = models.FloatField()\n    lon = models.FloatField()\n\n#Place.objects.filter(geom__distance_lte=(point, \n#D(mi=20))).distance(point).order_by('distance')\nfrom django_mongodb_engine.contrib import MongoDBManager\nfrom djangotoolbox.fields import EmbeddedModelField\nfrom djangotoolbox.fields import ListField, SetField\n\nclass LikePool(BaseModel):\n    likes = SetField(models.ForeignKey(UserProfile, related_name='+'))\n\nclass Resource(BaseModel):\n    tags = ListField()\n    title = models.CharField(max_length=255)\n    category = models.CharField(max_length=255)\n    type = models.IntegerField(default = 0) # 0 means image\n    loc = EmbeddedModelField(Point)\n    imageURL = models.CharField(max_length = 255) # stores the link to image\n    likes = models.ForeignKey(LikePool, related_name='+')\n    likeCount = models.IntegerField(default = 0) \n    commentCount = models.IntegerField(default = 0)\n    creator = models.ForeignKey(UserProfile, related_name='+')\n\n    objects = MongoDBManager() \n    class MongoMeta:\n        indexes = [\n            [('loc', '2d')]\n        ]    \n\n    @staticmethod\n    def getContent(content):\n        return {'title': content.title, 'category': content.category, 'lon':content.loc.lon, 'lat':content.loc.lat, 'imageURL':content.imageURL, 'likeCount': content.likeCount}\n    \nclass Comment(BaseModel):\n    creator = models.ForeignKey(UserProfile, related_name='+')\n    target = models.ForeignKey(UserProfile, related_name='+')\n\n    resource = models.ForeignKey(Resource, related_name='+')\n    content = models.TextField()\n\n    @staticmethod\n    def getContent(content):\n        return {\"creator\": UserProfile.getDummyProfile() if content.isAnonymous else UserProfile.getSimpleProfile(content.creator), \"content\": content.content, 'isAnonymous': content.isAnonymous, \"creationTime\": content.creationTime, \"key\": content.id}\n\nclass FlagContent(BaseModel):\n    creator = models.ForeignKey(UserProfile, related_name='+')\n    objectId = models.CharField(max_length = 30)\n    objectType = models.IntegerField(default = 0)\n    cleard = models.BooleanField(default=False)\n\nclass VisitHistory(BaseModel):\n    fromAccount = models.ForeignKey(UserProfile, related_name='+')\n    toAccount = models.ForeignKey(UserProfile, related_name='+')\n    timestamp = models.DateTimeField('date visited', auto_now_add = True)\n    \nclass Rate(models.Model):\n    creator = models.ForeignKey(UserProfile, related_name='+')\n    target = models.ForeignKey(UserProfile, related_name='+')\n    resource = models.ForeignKey(Resource, related_name='+')\n    rating = models.IntegerField()\n    creationTime = models.DateTimeField()\n    \n    @staticmethod\n    def getRate(rate, hide):\n        obj = {'creator': UserProfile.getDummyProfile(), 'target': rate.target, 'term': rate.term, 'content': rate.content, 'rating': rate.rating, 'isAnonymous':rate.isAnonymous, 'creationTime': rate.creationTime}\n        if not hide:\n            obj['creator'] = rate.creator\n        return obj\n\n","repo_name":"ningkai1983/locals","sub_path":"main/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":6862,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"11709697671","text":"import requests\n\n\ndef _get_search_url(query):\n    return \"https://duckduckgo.com/html/?q={}\".format(query)\n\ndef get_html(url, connection_manager=None):\n    header = {\n        'User-Agent': \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/535.11 (KHTML, like Gecko) Ubuntu/10.10 Chromium/17.0.963.65 Chrome/17.0.963.65 Safari/535.11\"\n    }\n    try:\n        request = requests.get(url, headers=header)\n        html = request.text\n        return html\n    except Exception as e:\n        print(\"Error accessing:\", url)\n        raise e\n","repo_name":"arthurbarros/duckduckgo-search-api","sub_path":"duckduckgo/modules/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":526,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"74121425746","text":"# https://atcoder.jp/contests/joi2014yo/submissions/15124291\n# B - 投票 (Vote)\nimport sys\n\nsys.setrecursionlimit(10 ** 7)\ninput = sys.stdin.readline\nf_inf = float('inf')\nmod = 10 ** 9 + 7\n\n\ndef resolve():\n    n, m = map(int, input().split())\n    A = list(int(input()) for _ in range(n))\n\n    res = [0] * n\n    for _ in range(m):\n        b = int(input())\n        for i in range(n):\n            if A[i] <= b:\n                res[i] += 1\n                break\n    print(res.index(max(res)) + 1)\n\n\nif __name__ == '__main__':\n    resolve()\n","repo_name":"happa64/AtCoder_Beginner_Contest","sub_path":"JOI/JOI_2014_Qual/JOI_2014_Qual-B.py","file_name":"JOI_2014_Qual-B.py","file_ext":"py","file_size_in_byte":537,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"70096382546","text":"import paho.mqtt.client as paho\nimport time\n\ndef on_publish(client, userdata, mid):\n    print(\"mid: \"+str(mid))\n \nclient = paho.Client()\nclient.on_publish = on_publish\nclient.connect(\"localhost\", 1883)\nclient.loop_start()\n\nclient.publish(\"house/bulbs\", \"ON\")","repo_name":"levithatcher/mqtt_arduino","sub_path":"publish.py","file_name":"publish.py","file_ext":"py","file_size_in_byte":258,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"18588336874","text":"from django import forms\nfrom django.http import HttpResponseRedirect\nfrom django.shortcuts import render\nfrom django.urls import reverse\n\ntasks = [\"foo\", \"bar\", \"baz\"]\n\n\nclass NewTasksForm(forms.Form):\n    task = forms.CharField(label=\"New Task\")\n\n\n# Create your views here.\ndef index(request):\n    \"\"\"\n    It renders the `index.html` template, passing in the `tasks` variable\n\n    :param request: The request object is the first parameter to every view function. It contains information about the\n    request that was made to the server\n    :return: The index.html file is being returned.\n    \"\"\"\n    if \"tasks\" not in request.session:\n        request.session[\"tasks\"] = []\n\n    return render(request, \"tasks/index.html\", {\n        \"tasks\": request.session[\"tasks\"]\n    })\n\n\ndef add(request):\n    \"\"\"\n    If the request is a POST request, then validate the form and add the task to the list of tasks\n\n    :param request: The request object is a Python object that contains all the information about the request that was sent\n    to the server\n    :return: The form is being returned.\n    \"\"\"\n    if request.method == \"POST\":\n        form = NewTasksForm(request.POST)\n        if form.is_valid():\n            task = form.cleaned_data[\"task\"]\n            request.session[\"tasks\"] += [task]\n            return HttpResponseRedirect(reverse(\"index\"))\n        else:\n            return render(request, \"tasks/add.html\", {\n                \"form\": form\n            })\n    return render(request, \"tasks/add.html\", {\n        \"form\": NewTasksForm()\n    })\n","repo_name":"BOSTONE069/CS50","sub_path":"airline/tasks/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1545,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"48"}
+{"seq_id":"41903371190","text":"import cv2 as cv\r\n\r\n# image1\r\nimage1_path = 'opencv/99 Sunflower1.jpg'\r\nimage1 = cv.imread(image1_path, cv.IMREAD_GRAYSCALE)\r\n\r\n# image2\r\nimage2_path = 'opencv/99 Sunflower2.jpg'\r\nimage2 = cv.imread(image2_path, cv.IMREAD_GRAYSCALE)\r\n\r\n# create SIFT objects for both images\r\nsift = cv.SIFT_create()\r\n\r\n# detect keypoints and compute descriptors for both images\r\nkeypoints1, descriptors1 = sift.detectAndCompute(image1, None)\r\nkeypoints2, descriptors2 = sift.detectAndCompute(image2, None)\r\n\r\n# create a brute-force matcher\r\nbf = cv.BFMatcher()\r\n\r\n# match descriptors between the two images\r\nmatches = bf.knnMatch(descriptors1, descriptors2, k=2)\r\n\r\n# Apply ratio test\r\ngood_matches = []\r\nfor m, n in matches:\r\n    if m.distance < 0.75 * n.distance:\r\n        good_matches.append(m)\r\n\r\n# Draw the first 10 matches (you can adjust the number as needed)\r\nmatching_result = cv.drawMatches(image1, keypoints1, image2, keypoints2, good_matches[:10], None)\r\n\r\n# Resize\r\nscale_percent = 50  # adjust this value as needed\r\nwidth = int(matching_result.shape[1] * scale_percent / 100)\r\nheight = int(matching_result.shape[0] * scale_percent / 100)\r\nfinal_comparison = cv.resize(matching_result, (width, height))\r\n\r\n# Display the matching result\r\ncv.imshow('SIFT Matching Result', final_comparison)\r\ncv.waitKey(0)\r\ncv.destroyAllWindows()\r\n","repo_name":"vndlize/python","sub_path":"opencv/11 SIFT keypoint comparison.py","file_name":"11 SIFT keypoint comparison.py","file_ext":"py","file_size_in_byte":1325,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"74586903505","text":"class Solution:\n    def findPeakElement(self, nums: List[int]) -> int:\n        start, end = 0, len(nums) - 1\n\n        # When we use < instead of <=, our start/end will\n        # be equal instead, and we can use that to return\n        # where the midpoint would have been if it were <=.\n        # Typically good if we're comparing a midpoint to its sibs.\n        while start < end:\n            mid = (start + end) // 2\n\n            # If the the middle is greater than both\n            # start and end sides then we have found a peak\n            if nums[mid] > nums[mid + 1] and nums[mid] > nums[mid - 1]:\n                return mid\n            # If mid is smaller than the next number, there must be a higher peak\n            elif nums[mid] < nums[mid + 1]:\n                start = mid + 1\n            # If mid is greater than the next number, then must be no higher peak\n            else:\n                end = mid - 1\n\n        # Can return start or end since we are using\n        # less than in a mountain array\n        return end\n\n# Time Complexity: O(logN)\n# Space Complexity: O(1)\n","repo_name":"garzeah/algorithms","sub_path":"general/binary_search/find_peak_element.py","file_name":"find_peak_element.py","file_ext":"py","file_size_in_byte":1085,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"28192121220","text":"\"\"\"\n\n**Personal comments:\nWas hitting an issue of forgetting to set the address of my first node back to its original address. \nEverytime I returned the function it would point to the tail from my previous iteration, and I would only return a 2 element list everytime.\n**\n Insert Node at the end of a linked list \n head pointer input could be None as well for empty list\n Node is defined as\n \n class Node(object):\n \n   def __init__(self, data=None, next_node=None):\n       self.data = data\n       self.next = next_node\n \n return back the head of the linked list in the below method\n\"\"\"\n \ndef Insert(head, data):\n    #print \"we want to insert {} at the end\".format(data)\n    #print \"head address is :\"\n    #print head\n    start_node = head # save address of head.\n    if head != None:#if a linked list, go to the last node.\n        #Create new node with data.\n        new_node = Node()\n        new_node.data = data\n        new_node.next = None\n        #print new_node.next\n        #print new_node\n        while head.next != None:# find tail node\n            #print \"current head with value{}\".format(head.data)\n            #print head\n            #print \"current node data\"\n            #print head.data\n            head = head.next\n            #print \"leaving while loop, this should be the last node\"\n            #print head\n            #print head.data\n            #print \"next node data\"\n            #print head.data\n        #print head.next\n        #print \"setting new tail\"\n        head.next = new_node #insert new node at the end.\n        #print head.next\n        #print head.next #this should be none\n    else: #empty list, initialize the linked list.\n        head = Node(data)\n        head.next = None\n        #print \"list was empty, initializing a new list\"\n        start_node = head\n        #print head #This should be the address for the entire function.  \n    \"\"\"\n    print \"showing entire list\"\n    if start_node != None:\n        print_node = start_node\n        while print_node.next != None:\n            print print_node.data\n            print_node = print_node.next\n        print print_node.data\n    \"\"\"\n    return start_node","repo_name":"wichopy/HackerRank","sub_path":"HackerRank/LinkedLists/Linkedlists 2.py","file_name":"Linkedlists 2.py","file_ext":"py","file_size_in_byte":2139,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"22003751930","text":"import numpy as np\nimport matplotlib.pylab as plt\nfrom pyxvis.simulation.xsim import mask_simulation\nfrom pyxvis.processing.helpers.kfunctions import gaussian_kernel\nfrom pyxvis.io import gdxraydb\n\nimage_set = gdxraydb.Castings()\nI  = np.double(image_set.load_image(21,25)) # wheel image\n\np1 = [150,580] # Location of 1st defect\np2 = [200,565] # Location of 2nd defect\np3 = [250,550] # Location of 3rd defect\n\nh1 = gaussian_kernel(35,4)             # Gaussian Mask\nh1 = h1/np.max(h1)*0.9\nJ  = mask_simulation(I,h1,p1[0],p1[1]) # Simulation \nh2 = np.ones((17,17))*0.4              # Square Mask\nJ  = mask_simulation(J,h2,p2[0],p2[1]) # Simulation \nh3 = np.zeros(h1.shape)                # Circle mask\nh3[h1>0.25] = 0.4\nJ  = mask_simulation(J,h3,p3[0],p3[1]) # Simulation\n\n\n# Output\nfig1, ax = plt.subplots(1, 2, figsize=(16, 8))\nax[0].imshow(I, cmap='gray'), ax[0].axis('off')\nax[1].imshow(J, cmap='gray'), ax[1].axis('off')\nax[1].text(p1[1]+20,p1[0]+5, 'Gaussian', fontsize=8,color='white')\nax[1].text(p2[1]+20,p2[0]+5, 'Square', fontsize=8,color='white')\nax[1].text(p3[1]+20,p3[0]+5, 'Circle', fontsize=8,color='white')\nax[1].text(250,210, '(Real)', fontsize=10,color='white')\nax[1].text(565,92, '(Simulated)', fontsize=10,color='white')\nplt.show()\n","repo_name":"computervision-xray-testing/pyxvis","sub_path":"listings/chapter_08/ex-8.2.py","file_name":"ex-8.2.py","file_ext":"py","file_size_in_byte":1250,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"48"}
+{"seq_id":"6321197197","text":"import time\nimport requests\nimport subprocess\nimport pyperclip\n\nfolderPath = '/Users/ajaymangal/Downloads/attenteo 2023-08-07 19-05-46/attenteo.ipa'\n\ndef upload_build_to_server(archive_path):\n    path = input('Paste path here : ')\n    print('Uploading form : ',path)\n    url = \"http://ds.24livehost.com/Home/FileUpload\"  # Replace with the actual upload URL\n    files = {'file': open(path, 'rb')}\n    response = requests.post(url, files=files)\n    if response.status_code == 200:\n        publicLink = response.content.decode('utf-8')\n        print(\"Response Content:\", publicLink)\n        pyperclip.copy(publicLink)\n        display_notification(\"Item copied: \" + publicLink)\n    else:\n        print(\"File upload failed.\")\n\ndef display_notification(message):\n    # Escape the double quotes in the message for the AppleScript string\n    escaped_message = message.replace('\"', '\\\\\"')\n    applescript = f'display notification \"{escaped_message}\" with title \"Copied!\"'\n    subprocess.run(['osascript', '-e', applescript], capture_output=True)\n    time.sleep(10)\n\n# def takeInputs():\n    # path = input('Enter path here : ')\n    \n\n# takeInputs()\nupload_build_to_server(folderPath)\n","repo_name":"ajayoncode/pythonScripts","sub_path":"P3/createLink.py","file_name":"createLink.py","file_ext":"py","file_size_in_byte":1175,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"40304263032","text":"import numpy as np\nimport time\nimport readfile as read\nfrom grid import values_output, every_rewards, every_action, matrix, policy_output\n\nsmallest_change = 1e-3\nall_directions = ['U', 'D', 'L', 'R']\n\nif __name__ == '__main__':\n    start = time.time()\n\n    grid_matrix, gamma, noise = read.read2()\n\n    all_actions = every_action(grid_matrix)\n    all_rewards = every_rewards(grid_matrix)\n    grid_actions_rewards = matrix(all_actions, all_rewards)\n    add_policy = {}\n    for i in grid_actions_rewards.actions.keys():\n        add_policy[i] = np.random.choice(all_directions)\n    if len(noise) < 4:\n        noise.append('0.00')\n    Value_of_states = {}\n    all_states = grid_actions_rewards.all_states()\n    for i in all_states:\n        if i in grid_actions_rewards.actions:\n            Value_of_states[i] = np.random.random()\n        else:\n            Value_of_states[i] = grid_actions_rewards.rewards.get(i)\n\n    while True:\n        biggest_change = 0\n        for pol in add_policy:\n            oldValue = Value_of_states[pol]\n            if pol in add_policy:\n                newValue = float('-inf')\n                for dire, c in enumerate(all_directions):\n                    newV = 0\n                    if dire == 0:\n                        n1 = [float(noise[0]), float(noise[1]), float(noise[2]), float(noise[3])]\n                    if dire == 1:\n                        n1 = [float(noise[3]), float(noise[0]), float(noise[1]), float(noise[2])]\n                    if dire == 2:\n                        n1 = [float(noise[2]), float(noise[3]), float(noise[0]), float(noise[1])]\n                    if dire == 3:\n                        n1 = [float(noise[1]), float(noise[2]), float(noise[3]), float(noise[0])]\n                    for i, a2 in enumerate(all_directions):\n                        n2 = n1[i]\n                        reward, state1 = grid_actions_rewards.move(all_directions[i], pol)\n                        newV += n2 * (gamma * Value_of_states[state1])\n                    if newV > newValue:\n                        newValue = newV\n                Value_of_states[pol] = newValue\n                biggest_change = max(biggest_change, np.abs(oldValue - Value_of_states[pol]))\n        if biggest_change < smallest_change:\n            break\n\n    for pol in add_policy.keys():\n        best = None\n        oldV = -1\n        for dire, c in enumerate(all_directions):\n            newV = 0\n            if dire == 0:\n                n1 = [float(noise[0]), float(noise[1]), float(noise[2]), float(noise[3])]\n            if dire == 1:\n                n1 = [float(noise[3]), float(noise[0]), float(noise[1]), float(noise[2])]\n            if dire == 2:\n                n1 = [float(noise[2]), float(noise[3]), float(noise[0]), float(noise[1])]\n            if dire == 3:\n                n1 = [float(noise[1]), float(noise[2]), float(noise[3]), float(noise[0])]\n            for i, k in enumerate(all_directions):\n                n2 = n1[i]\n                reward, current_state = grid_actions_rewards.move(all_directions[i], pol)\n                newV += n2 * (gamma * Value_of_states[current_state])\n            if newV > oldV:\n                oldV = newV\n                best = c\n        add_policy[pol] = best\n\n    print(\"Value of each square:\")\n    values_output(Value_of_states, grid_matrix.shape)\n    print(\"\")\n    print(\"Policy of each square:\")\n    policy_output(add_policy, grid_matrix.shape)\n    end = time.time()\n    print(\"time taken is:\")\n    print(end - start)\n","repo_name":"jeetsj/Nondeterministic-Search","sub_path":"Project 3/value_iteration.py","file_name":"value_iteration.py","file_ext":"py","file_size_in_byte":3480,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"32353680267","text":"import numpy as np\nfrom timemachines.skatertools.comparison.eloformulas import elo_update\nimport time\nfrom pprint import pprint\n\nSLOW_SKATER_KEYWORDS = ['wiggly','divine','fbprophet','arma','tsa_p3','bats_damped','tsa_aggressive','tsa_balanced','tsa_precision']\n\nDEFAULT_INITIAL_ELO = 1600\n\ntry:\n    from microprediction import MicroReader\n    have_micro = True\nexcept ImportError:\n    have_micro = False\n\nif have_micro:\n    from timemachines.skatertools.data.live import random_elo_data as DEFAULT_DATA_SOURCE\nelse:\n    DEFAULT_DATA_SOURCE = None\n\nSKATER_F_FACTOR = 1000  # The scale factor for ratings. In chess this is set to 400.\n# As the matchup is considered more fluky than a single game of chess, a higher value makes sense.\nSKATER_K_FACTOR = 25  # The Elo update factor (maximum rating gain)\n\n\ndef skater_elo_multi_update(elo: dict, k, evaluator=None, n_burn=400, tol=0.01, initial_elo=DEFAULT_INITIAL_ELO,\n                            data_source=None, skater_population=None, always_skaters=None, verbose=True):\n    \"\"\" Create or update elo ratings by running several algos at once and using Elo update n-1 times\n\n              elo - Dictionary containing the 'state' (i.e. elo ratings and game counts)\n              k   - Number of steps to look ahead\n              tol - Error ratio that results in a tie being declared\n              data_source - A function taking n_obs and returning y, t\n              skater_population - Usually a subset of timemachines.skaters.allskaters.SKATERS\n              always_skater - Skater to always include\n    \"\"\"\n    # Lazy import because networked skaters shouldn't be initialized too early\n    from timemachines.skaters.pypi import pypi_from_name\n    if skater_population is None:\n        from timemachines.skaters.allskaters import SKATERS as skater_population\n    from timemachines.skaters.allskaters import skater_from_name  # Only those with no hyper-params\n\n    if data_source is None:\n        data_source = DEFAULT_DATA_SOURCE\n        assert data_source is not None, \"If microprediction is not installed you must supply a different data_source function that returns y, t \"\n\n    if not elo:\n       _init_elo(elo=elo, skater_population=skater_population, initial_elo=initial_elo)\n    else:\n        # New fields ... one-off fix\n        if not 'pypi' in elo:\n            elo['pypi'] = [ pypi_from_name(nm) for nm in elo['name']]\n        if not 'seconds' in elo:\n            elo['seconds'] = [-1 for nm in elo['name']]\n        elo['pypi'] = [pypi_from_name(nm) for nm in elo['name']]\n\n        # Check for newcomers\n        elo = _newcomers(elo=elo, skater_population=skater_population, initial_elo=initial_elo)\n        new_names = [f.__name__ for f in skater_population if f.__name__ not in elo['name']]\n\n    elo, evaluator = _set_evaluator(elo)\n\n    # Choose several skaters\n    total_seconds = 0\n    chosen = list()\n    n_skaters = len(elo['name'])\n    candidates = np.random.choice(list(range(n_skaters)), size=10, replace=False)\n    for c in candidates:\n        snds = elo['seconds'][c]\n        cnt = elo['count'][c]\n        if cnt<10:\n            snds = snds/3\n        if cnt<25:\n            snds = snds/2\n        if snds<0:\n            snds = 45\n        if (snds+total_seconds<60) or ((len(chosen)>2) and (np.random.rand()<(300/(2+snds)) )):\n            total_seconds += snds\n            chosen.append(c)\n        if total_seconds>60:\n            break\n\n    if always_skaters is not None:\n        for always_skater in always_skaters:\n            if always_skater is not None and (always_skater not in chosen) and (always_skater in elo['name']):\n                c = elo['name'].index(always_skater)\n                if c not in chosen:\n                    chosen.append(c)\n\n    fs = list()\n    chosen_and_imported = list()\n    for c in chosen:\n        sn = elo['name'][c]\n        try:\n            f = skater_from_name(sn)\n            assert f is not None, 'probably just not used anymore'\n            elo['active'][c] = True\n            fs.append(f)\n            chosen_and_imported.append(c)\n        except Exception as e:\n            elo['active'][c] = False\n            print('Failed to import ' + elo['name'][c])\n\n    got_data = False\n    while not got_data:\n        try:\n            y, t = data_source(n_obs=n_burn + 50)\n            got_data = True\n        except:\n            time.sleep(5)\n    scores = list()\n    ran_okay = list()\n    ran_okay_names = list()\n    failed_names = list()\n    if verbose:\n        print('  imported successfully ...')\n        pprint([elo['name'][c] for c in chosen_and_imported])\n    for c, f in zip(chosen_and_imported, fs):\n        import traceback\n        try:\n            st = time.time()\n            score = evaluator(f=f, y=y, k=k, a=None, t=t, e_fit=15, e_nofit=-1, n_test=50, fit_frequency=100)\n            elo['seconds'][c] = time.time() - st\n            elo['traceback'][c] = 'passing'\n            scores.append(score)\n            ran_okay.append(c)\n            ran_okay_names.append(f.__name__)\n        except Exception as e:\n            elo['traceback'][c] = traceback.format_exc()\n            elo['seconds'][c] = st - time.time()  # Time taken to fail, as -ve number\n            failed_names.append(f.__name__)\n\n    if any(failed_names):\n        print('  failing ...')\n        pprint(failed_names)\n\n    leaderboard = sorted([(s, c, n) for s, c, n in zip(scores, ran_okay, ran_okay_names)])\n    if verbose:\n        print('  leaderboard ...')\n        pprint(leaderboard)\n    if len(leaderboard)>=3:\n        for j, (winner_score,winner, winner_name) in enumerate(leaderboard[:-1]):\n            for k in [2,3,4,5,6]:\n                try:\n                    loser = leaderboard[j+k][1]\n                    loser_score = leaderboard[j+k][0]\n                    small = tol * (abs(winner_score) + abs(loser_score))  # Ties\n                    min_games = min(elo['count'][winner], elo['count'][loser])\n                    K = SKATER_K_FACTOR / 2.0 if min_games > 25 else SKATER_K_FACTOR  # The Elo update scaling parameter\n                    winner_prior_elo, loser_prior_elo = elo['rating'][winner], elo['rating'][loser]\n                    points = 1 if winner_score < loser_score - small else 0.5\n                    elo['rating'][winner], elo['rating'][loser] = elo_update(winner_prior_elo, loser_prior_elo, points, k=K, f=SKATER_F_FACTOR)\n                    elo['count'][winner] += 1\n                    elo['count'][loser] += 1\n                except IndexError:\n                    pass\n    return elo\n\n\ndef _init_elo(elo, skater_population, initial_elo):\n    from timemachines.skaters.pypi import pypi_from_name\n    # Initialize game counts and Elo ratings\n    elo['name'] = [f.__name__ for f in skater_population]\n    elo['count'] = [0 for _ in skater_population]\n    elo['rating'] = [initial_elo for _ in skater_population]\n    elo['traceback'] = ['not yet run' for _ in skater_population]\n    elo['active'] = [True for _ in skater_population]\n    elo['pypi'] = [pypi_from_name(nm) for nm in elo['name']]\n    elo['seconds'] = [-1 for nm in elo['name']]\n    return elo\n\ndef _newcomers(elo, skater_population, initial_elo):\n    # Check for newcomers\n    from timemachines.skaters.pypi import pypi_from_name\n    new_names = [f.__name__ for f in skater_population if f.__name__ not in elo['name']]\n    for new_name in new_names:\n        elo['name'].append(new_name)\n        elo['count'].append(0)\n        elo['rating'].append(initial_elo)\n        elo['traceback'].append('not yet run')\n        elo['active'].append(True)\n        elo['seconds'].append(-1)\n        elo['pypi'].append(pypi_from_name(new_name))\n    return elo\n\ndef _set_evaluator(elo):\n    from timemachines.skatertools.evaluation.evaluators import evaluate_mean_squared_error_with_sporadic_fit, \\\n        evaluator_from_name\n    evaluator = evaluate_mean_squared_error_with_sporadic_fit\n    if evaluator is None:\n        if elo.get('evaluator'):\n            eval_name = elo['evaluator']\n            try:\n                evaluator = evaluator_from_name(eval_name)\n            except:\n                print('Could not retrieve ' + eval_name + ' so reverting.')\n                evaluator = evaluate_mean_squared_error_with_sporadic_fit\n        else:\n            evaluator = evaluate_mean_squared_error_with_sporadic_fit\n        print('Evaluating using ' + str(evaluator.__name__))\n    elo['evaluator'] = evaluator.__name__\n    return elo, evaluator\n\n\ndef skater_elo_update(elo: dict, k, evaluator=None, n_burn=400, tol=0.01, initial_elo=1600, data_source=None):\n    \"\"\" Create or update elo ratings by performing a random matchup on univariate live data\n\n          elo - Dictionary containing the 'state' (i.e. elo ratings and game counts)\n          k   - Number of steps to look ahead\n          tol - Error ratio that results in a tie being declared\n          data_provider - A function taking n_obs and returning y, t\n\n        Speed is *not* taken into account, yet.\n    \"\"\"\n    # Lazy import because networked skaters shouldn't be initialized too early\n    from timemachines.skaters.pypi import pypi_from_name\n    from timemachines.skaters.allskaters import SKATERS, \\\n        skater_from_name  # Only those with no hyper-params\n\n    if data_source is None:\n        data_source = DEFAULT_DATA_SOURCE\n        assert data_source is not None, \"If microprediction is not installed you must supply a different data_source function that returns y, t \"\n\n    if not elo:\n       _init_elo(elo=elo, skater_population=SKATERS, initial_elo=initial_elo)\n    else:\n        # New fields ... one-off fix\n        if not 'pypi' in elo:\n            elo['pypi'] = [ pypi_from_name(nm) for nm in elo['name']]\n        if not 'seconds' in elo:\n            elo['seconds'] = [-1 for nm in elo['name']]\n        elo['pypi'] = [pypi_from_name(nm) for nm in elo['name']]\n\n        # Check for newcomers\n        elo = _newcomers(elo=elo, skater_population=SKATERS, initial_elo=initial_elo)\n        new_names = [f.__name__ for f in SKATERS if f.__name__ not in elo['name']]\n\n    elo, evaluator = _set_evaluator(elo)\n\n    # Choose two random skaters, but avoid slow ones once\n    n_skaters = len(elo['name'])\n    i1, i2 = np.random.choice(list(range(n_skaters)), size=2, replace=False)\n    skater1, skater2 = elo['name'][i1], elo['name'][i2]\n    for _ in range(2):\n        if any([ slow in skater1 for slow in SLOW_SKATER_KEYWORDS ]) or any([ slow in skater2 for slow in SLOW_SKATER_KEYWORDS ]):\n            i1, i2 = np.random.choice(list(range(n_skaters)), size=2, replace=False)\n            skater1, skater2 = elo['name'][i1], elo['name'][i2]\n    if any([ slow in skater1 for slow in SLOW_SKATER_KEYWORDS ]) and any([ slow in skater2 for slow in SLOW_SKATER_KEYWORDS ]):\n        i1, i2 = np.random.choice(list(range(n_skaters)), size=2, replace=False)\n        skater1, skater2 = elo['name'][i1], elo['name'][i2]\n\n\n    fs = list()\n    for i, sn in zip([i1, i2], [skater1, skater2]):\n        try:\n            f = skater_from_name(sn)\n            assert f is not None, 'probably just not used anymore'\n            elo['active'][i] = True\n            fs.append(f)\n        except Exception as e:\n            elo['active'][i] = False\n\n    if len(fs) == 2:\n        # a pitched paddle battle in a bottle\n        print(fs[0].__name__ + ' vs. ' + fs[1].__name__)\n        y, t = data_source(n_obs=n_burn + 50)\n        scores = list()\n        for i, f in zip([i1, i2], fs):\n            import traceback\n            try:\n                st = time.time()\n                score = evaluator(f=f, y=y, k=k, a=None, t=t, e_fit=15, e_nofit=-1, n_test=50, fit_frequency=100)\n                elo['seconds'][i] = time.time()-st\n                elo['traceback'][i] = 'passing'\n                scores.append(score)\n            except Exception as e:\n                elo['traceback'][i] = traceback.format_exc()\n                elo['seconds'][i] = st-time.time() # Time taken to fail, as -ve number\n\n        if len(scores) == 2:\n            small = tol * (abs(scores[0]) + abs(scores[1]))  # Ties\n            points = 1 if scores[0] < scores[1] - small else 0 if scores[1] < scores[0] - small else 0.5\n            elo1, elo2 = elo['rating'][i1], elo['rating'][i2]\n            min_games = min(elo['count'][i1], elo['count'][i2])\n            K = SKATER_K_FACTOR / 2.0 if min_games > 25 else SKATER_K_FACTOR  # The Elo update scaling parameter\n            elo['rating'][i1], elo['rating'][i2] = elo_update(elo1, elo2, points, k=K, f=SKATER_F_FACTOR)\n            elo['count'][i1] += 1\n            elo['count'][i2] += 1\n            the_winner = skater1 if points > 0.5 else skater2 if points<0.5 else 'nobody'\n            print('     won by '+the_winner)\n\n    return elo\n","repo_name":"microprediction/timemachines","sub_path":"timemachines/skatertools/comparison/skaterelo.py","file_name":"skaterelo.py","file_ext":"py","file_size_in_byte":12676,"program_lang":"python","lang":"en","doc_type":"code","stars":374,"dataset":"github-code","pt":"48"}
+{"seq_id":"73826895184","text":"import random\r\n\r\n#import the H3D fields and types\r\nfrom H3DInterface import *\r\n\r\n# get the reference to the group to put the spheres in\r\ngroup, = references.getValue()\r\n\r\n# create the sphere geometry\r\nsphere = createNode( \"Sphere\" )\r\nsphere.radius.setValue( 0.02 )\r\n\r\n# The AddSphere class adds a new sphere to the group nodes children\r\n# field each time a field routed to it generates an 1 event.\r\nclass AddSphere( AutoUpdate( SFBool ) ):\r\n  def update( self, event ):\r\n    if( event.getValue() ):\r\n      t, dn = createX3DNodeFromString( \"\"\"\\\r\n      <Transform>\r\n         <Shape DEF=\"SHAPE\" >\r\n            <Appearance>\r\n              <Material DEF=\"MATERIAL\" />\r\n            </Appearance>\r\n         </Shape>\r\n      </Transform>\"\"\")\r\n\r\n      c = RGB( random.random(), random.random(), random.random() )\r\n      dn[\"MATERIAL\"].diffuseColor.setValue( c )\r\n      dn[\"SHAPE\"].geometry.setValue( sphere )\r\n      t.translation.setValue( Vec3f( c.r * 0.5 - 0.25,\r\n                                     c.g * 0.5 - 0.25,\r\n                                     c.b * 0.5 - 0.25 ) )\r\n      group.children.push_back( t )\r\n    return event.getValue()\r\n\r\n# create an instance of the AddSphere class\r\nadd_sphere = AddSphere()\r\n","repo_name":"ChristianFrisson/H3DAPI","sub_path":"examples/manualExamples/Python/Spheres.py","file_name":"Spheres.py","file_ext":"py","file_size_in_byte":1210,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"48"}
+{"seq_id":"31470940627","text":"\n# Import the Boto3 library\nimport boto3\n\n# Connect to the SQS\nsqs = boto3.resource('sqs')\n\n# put name of the queue\nqueue_name = 'ProjectSqs'\n\n# Create the queue\nqueue = sqs.create_queue(QueueName=queue_name)\n\n#print the URL of the created Amazon SQS queue\nprint(queue.url)\n","repo_name":"Navidehbaghaeifar/Boto3_Python_Script","sub_path":"Lambda/Create_SQS.py","file_name":"Create_SQS.py","file_ext":"py","file_size_in_byte":274,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"39230420405","text":"from tkinter import *\r\nfrom PIL import Image, ImageTk\r\nfrom tkinter import ttk, messagebox\r\nimport sqlite3\r\n\r\nclass Types:\r\n    def __init__(self, root):\r\n            self.root = root\r\n            self.root.geometry(\"1100x500+220+130\")\r\n            self.root.title(\"Rice Retailing Billing System\")\r\n            self.root.config(bg=\"white\")\r\n            self.root.focus_force()\r\n            self.root.resizable(0, 0)\r\n\r\n    #========Title========#\r\n            lbl_title = Label(self.root, text=\"Manage the Types of Rice\", font=(\"times new roman\", 20, \"bold\"),\r\n                  bg=\"#828c51\", fg=\"white\")\r\n            lbl_title.pack(side = TOP, x=50, y=10, width=1000, height=40)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n     root = Tk()\r\n     obj = Types(root)\r\n     root.mainloop()","repo_name":"KhaledR/SDL-Project2023","sub_path":"typesofricecategory.py","file_name":"typesofricecategory.py","file_ext":"py","file_size_in_byte":775,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"41617547991","text":"from typing import List\n\n\ndef two_sum(nums, target):\n    \"\"\"\n    Description:\n        Given an array of integers, return indices of the two numbers such that they add up to a specific target.\n        You may assume that each input would have exactly one solution, and you may not use the same element twice.\n\n    Idea:\n        Iterate over the elements and fill the HashMap with the difference(target - current_item) as the key and index as value.\n        So when we hit the complement we will instantly know the index.\n    Complexity:\n        Time complexity: O(n). Iterate over list only once and get the element from HashTable with O(1).\n        Space complexity: O(n). Extra space for the HashTable.\n    \"\"\"\n    values = {}\n    for i, item in enumerate(nums):\n        if item in values:\n            return [values[item], i]\n        else:\n            values[target - item] = i\n","repo_name":"g0t0wasd/leetcode_solutions","sub_path":"1_two_sum_easy.py","file_name":"1_two_sum_easy.py","file_ext":"py","file_size_in_byte":880,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"39024839847","text":"import h5py\nimport torch\nfrom sklearn import preprocessing\n\nfrom protera_stability.proteins.embeddings import EmbeddingExtractor1D\nfrom protera_stability.utils import dim_reduction\n\n\nclass ProteinStabilityDataset(torch.utils.data.Dataset):\n    \"\"\"Protein1D Stability Dataset.\"\"\"\n\n    def __init__(self, proteins_path, ret_dict=False):\n        \"\"\"\n        Args:\n            proteins_path (string): Path to the H5Py file that contains sequences and embeddings.\n            ret_dict (bool): If True, it will return a dictionary as batch. Otherwise, X and y tensors will be returned.\n        \"\"\"\n        self.stability_path = proteins_path\n        self.ret_dict = ret_dict\n        self.x_scaler = preprocessing.StandardScaler()\n        self.y_scaler = preprocessing.StandardScaler()\n\n        with h5py.File(str(self.stability_path), \"r\") as dset:\n            self.sequences = dset[\"sequences\"][:]\n            X = dset[\"embeddings\"][:].astype(\"float32\")\n            y = dset[\"labels\"][:].astype(\"float32\")\n\n            self.X = self.x_scaler.fit_transform(X)\n            self.y = self.y_scaler.fit_transform(y.reshape(-1, 1)).reshape(y.shape)\n\n        self.indices = list(range(len(self.X)))\n\n    def __len__(self):\n        return len(self.indices)\n\n    def __getitem__(self, idx):\n        if torch.is_tensor(idx):\n            idx = idx.tolist()\n\n        sequences = self.sequences[idx]\n        embeddings = self.X[idx]\n        labels = self.y[idx]\n\n        sample = {\n            \"sequences\": sequences,\n            \"embeddings\": torch.from_numpy(embeddings),\n            \"labels\": torch.Tensor([labels]),\n        }\n\n        return sample if self.ret_dict else (sample[\"embeddings\"], sample[\"labels\"])\n\n\ndef load_dataset_raw(\n    data_path,\n    kind=None,\n    reduce=False,\n    scale=True,\n    to_torch=False,\n    close_h5=True,\n    verbose=False,\n):\n    args_dict = {\n        \"model_name\": \"esm1b_t33_650M_UR50S\",\n        \"base_path\": data_path,\n        \"gpu\": False,\n    }\n\n    emb_stabilty = EmbeddingExtractor1D(**args_dict)\n    task = \"stability\"\n\n    if kind is not None:\n        task = f\"{task}_{kind}\"\n\n    if verbose:\n        print(\n            f\"Using: {data_path / task}.csv, {data_path / task}.h5, {data_path / task}_embeddings.pkl\"\n        )\n\n    dset = emb_stabilty.generate_datasets(\n        [f\"{task}.csv\"], h5_stem=task, embedding_file=f\"{task}_embeddings\"\n    )\n\n    X, y = dset[\"embeddings\"][:].astype(\"float32\"), dset[\"labels\"][:].astype(\"float32\")\n\n    if reduce:\n        X = dim_reduction(X, y, plot_viz=False)\n\n    if scale:\n        scaler = preprocessing.StandardScaler()\n        X = scaler.fit_transform(X)\n\n        scaler = preprocessing.StandardScaler()\n        y = scaler.fit_transform(y.reshape(-1, 1)).reshape(y.shape)\n\n    if to_torch:\n        X = torch.from_numpy(X)\n        y = torch.from_numpy(y)\n\n    if close_h5:\n        dset.close()\n        return X, y\n    return X, y, dset\n","repo_name":"stepp1/protera-stability","sub_path":"protera_stability/data/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":2908,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"28838837491","text":"#jetson tx2 onboard camera gstreamer string\r\n#gst_str = (\"nvarguscamerasrc ! video/x-raw(memory:NVMM), width=(int)1280, height=(int)720, format=(string)NV12, framerate=(fraction)60/1 ! nvvidconv flip-method=0 ! video/x-raw, width=(int)1280, height=(int)720, format=(string)BGRx ! videoconvert ! video/x-raw, format=(string)BGR ! appsink\")\r\ngst_str = (\"v4l2src device=/dev/video0 ! video/x-raw, width=640, height=480, format=(string)YUY2,framerate=30/1 ! videoconvert ! video/x-raw,width=640,height=480,format=BGR ! appsink\")\r\n\r\nvideo_path = '../record/line.mp4'\r\nimport cv2\r\nimport numpy as np\r\n\r\nimport torchvision\r\nimport torch\r\n\r\nimport torchvision.transforms as transforms\r\nimport torch.nn.functional as F\r\nimport cv2\r\nimport PIL.Image\r\n\r\nfrom torch2trt import TRTModule \r\n\r\n\r\nmean = torch.Tensor([0.485, 0.456, 0.406]).cuda().half()\r\nstd = torch.Tensor([0.229, 0.224, 0.225]).cuda().half()\r\n\r\ndef preprocess(image):\r\n    image = PIL.Image.fromarray(image)\r\n    image = transforms.functional.to_tensor(image).to(device).half()\r\n    image.sub_(mean[:, None, None]).div_(std[:, None, None])\r\n    return image[None, ...]\r\n\t\r\nangle = 0.0\r\nangle_last = 0.0\r\n\r\ndef executeModel(image):\r\n    global angle, angle_last\r\n    #image = change['new']\r\n    xy = model(preprocess(image)).detach().float().cpu().numpy().flatten()\r\n    x = xy[0]\r\n    y = (0.5 - xy[1]) / 2.0\r\n    \r\n    #x_slider.value = x\r\n    #y_slider.value = y\r\n\r\n    print(x)\r\n    print(y)\r\n    print('---')\r\n\r\n    \r\n    #speed_slider.value = speed_gain_slider.value\r\n    \r\n    #angle = np.arctan2(x, y)\r\n    #pid = angle * steering_gain_slider.value + (angle - angle_last) * steering_dgain_slider.value\r\n    #angle_last = angle\r\n    \r\n    #steering_slider.value = pid + steering_bias_slider.value\r\n    \r\n    #robot.left_motor.value = max(min(speed_slider.value + steering_slider.value, 1.0), 0.0)\r\n    #robot.right_motor.value = max(min(speed_slider.value - steering_slider.value, 1.0), 0.0)\t\r\n\r\ndef imageCopy(src):\r\n    return np.copy(src)\r\n\t\r\ndef imageProcessing(output):\r\n    executeModel(output)\r\n    return output\r\n\r\ndef Video(openpath, savepath = None):\r\n    cap = cv2.VideoCapture(openpath)\r\n    if cap.isOpened():\r\n        print(\"Video Opened\")\r\n    else:\r\n        print(\"Video Not Opened\")\r\n        print(\"Program Abort\")\r\n        exit()\r\n    fps = cap.get(cv2.CAP_PROP_FPS)\r\n    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\r\n    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\r\n    print(width)\r\n    print(height)\r\n    print('--')\r\n\r\n    out = None\r\n    #if savepath is not None:\r\n        #out = cv2.VideoWriter(savepath, fourcc, fps, (width, height), True)\r\n    cv2.namedWindow(\"Input\", cv2.WINDOW_GUI_EXPANDED)\r\n    #cv2.namedWindow(\"Output\", cv2.WINDOW_GUI_EXPANDED)\r\n    linecolor1 = (0,240,240)\r\n    linecolor2 = (230,0,0)\r\n\r\n    while cap.isOpened():\r\n        # Capture frame-by-frame\r\n        ret, frame = cap.read()\r\n        if ret:\r\n            # Our operations on the frame come here\r\n            frame = np.copy(frame)\r\n            im = cv2.resize(frame, dsize=(224, 224), interpolation=cv2.INTER_AREA)\r\n            output = imageProcessing(im)\r\n            #output = imageProcessing(frame)\r\n            #im = cv2.line(im, (320, 0), (320, 480), linecolor1, 5, cv2.LINE_AA)\r\n            #im = cv2.line(im, (0, 300), (640, 300), linecolor2, 5, cv2.LINE_AA)\t\t\t\r\n            im = cv2.line(im, (112, 0), (112, 224), linecolor1, 5, cv2.LINE_AA)\r\n            im = cv2.line(im, (0, 140), (224, 140), linecolor2, 5, cv2.LINE_AA)\t\t\t\r\n\r\n            cv2.imshow(\"Input\", im)\t\t\t\r\n            # Write frame-by-frame\r\n            #if out is not None:\r\n            #    out.write(output)\r\n            # Display the resulting frame\r\n            #cv2.imshow(\"Input\", frame)\r\n            #cv2.imshow(\"Output\", output)\r\n        else:\r\n            break\r\n        # waitKey(int(1000.0/fps)) for matching fps of video\r\n        if cv2.waitKey(int(1000.0/fps)) & 0xFF == ord('q'):\r\n            break\r\n    # When everything done, release the capture\r\n    cap.release()\r\n    if out is not None:\r\n        out.release()\r\n    cv2.destroyAllWindows()\r\n    return\r\n   \r\n#if __name__==\"__main__\":\r\nmodel = torchvision.models.resnet18(pretrained=False)\r\nmodel.fc = torch.nn.Linear(512, 2)\r\n\r\nmodel_trt = TRTModule()\r\nmodel_trt.load_state_dict(torch.load('tov_model_xy_trt.pth'))\r\n\r\ndevice = torch.device('cuda')\r\nmodel = model_trt.to(device)\r\nmodel = model_trt.eval()\r\n\r\n#robot = Robot()\r\nVideo(gst_str)\r\n#robot.stop()\r\n","repo_name":"WonJaehee/jetbot_new","sub_path":"road_following/rf_live_demo_trt.py","file_name":"rf_live_demo_trt.py","file_ext":"py","file_size_in_byte":4468,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"72094415505","text":"import pymysql\nfrom faker import Faker\nimport random\n\n# Establish a connection to the MySQL database\nconn = pymysql.connect(\n    host='marketplacedb.c4h5s89ot7ec.us-east-1.rds.amazonaws.com',\n    user='dev-user1',\n    password='dev-user1',\n    db='aws'\n)\n\nfake = Faker()\n\ncategories = ['Electronics', 'Clothing', 'Home and Kitchen', 'Toys', 'Books', \"Supplements\", \"Drugs\", \"Drinks\", \"Food\", \"Pet Food\"]\n\n# Restriction satisfied with (ShoppingCartItems.py)\ncatdata = []\nfor i in range(10):\n    categoryId = i\n    name = categories[i]\n    catdata.append((categoryId, name))\n\nquery = \"INSERT INTO Category \" \\\n        \"(categoryId, name) \" \\\n        \"VALUES (%s, %s)\"\n\nwith conn.cursor() as cursor:\n    cursor.executemany(query, catdata)\n\n# Commit changes and close the connection\nconn.commit()\nconn.close()","repo_name":"min2028/AWExpress","sub_path":"backend/DBScripts/Categories.py","file_name":"Categories.py","file_ext":"py","file_size_in_byte":805,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"23134200303","text":"from tkinter import *\n\n\nclass StartPage(Frame):\n\n    # Create main menu screen\n    def __init__(self, master):\n        Frame.__init__(self, master)\n\n        self.master.user_id = 0\n        self.master.user_data = \"\"\n        self.master.account_data = \"\"\n\n        # add MonkePay on the head\n        master.mp_name = PhotoImage(file='../images/ATM/mp_name.png')\n        Label(self, bg=\"#bed2dd\", width=960, image=master.mp_name).pack(pady=(20, 0))\n\n        # add banana logo\n        master.logo = PhotoImage(file='../images/ATM/mp_logo.png')\n        panel = Label(self, image=master.logo, bg='#f7f0c6')\n        panel.pack()\n\n        master.login_btn = PhotoImage(file='../images/ATM/login_btn.png')\n        Button(self, bg='#f7f0c6', activebackground='#f7f0c6', relief=FLAT, image=master.login_btn,\n               width=170, height=50, command=lambda: master.switch_frame(\"ATMLoginPage\")).pack(pady=(0, 5))\n\n        master.reg_btn = PhotoImage(file='../images/ATM/RegMenu/reg_btn.png')\n        Button(self, bg='#f7f0c6', activebackground='#f7f0c6', relief=FLAT, image=master.reg_btn,\n               width=170, height=50, command=lambda: master.switch_frame(\"ATMRegistrationPage\")).pack(pady=(0, 5))\n\n        master.exit_btn = PhotoImage(file='../images/ATM/exit_btn.png')\n        Button(self, bg='#f7f0c6', activebackground='#f7f0c6', relief=FLAT, image=master.exit_btn,\n               width=170, height=50, command=lambda: master.quit_app()).pack(pady=(0, 5))\n\n        Label(self, text=\"\", bg='#f7f0c6', height=13).pack()\n\n        master.who_is_it_this_is_monke = PhotoImage(file='../images/ATM/by_monkeys.png')\n        Label(self, text=\"\", bg='#f7f0c6', image=master.who_is_it_this_is_monke).pack(pady=(48, 0))\n","repo_name":"yehorbolt/MOOP_SYB","sub_path":"MONKE_ATM/atm_start_page.py","file_name":"atm_start_page.py","file_ext":"py","file_size_in_byte":1711,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"48"}
+{"seq_id":"45192913156","text":"import random\n\nclass Warrior:\n\n    def __init__(self):\n        self.health = 100\n\n    def under_attack(self):\n        self.health += -20\n\n\nwarrior1, warrior2 = Warrior(), Warrior()\n\nwhile warrior1.health > 0 and warrior2.health > 0:\n    if random.randint(0, 1) == 0:\n        warrior1.under_attack()\n        print(\"WARRIOR-2 attacks, WARRIOR-1's health is\", warrior1.health)\n    else:\n        warrior2.under_attack()\n        print(\"WARRIOR-1 attacks, WARRIOR-2's health is\", warrior2.health)\n\nif warrior1.health == 0:\n    print(\"WARRIOR-2 WIN!!!\")\nelse:\n    print(\"WARRIOR-1 WIN!!!\")","repo_name":"savkin256/py_practices","sub_path":"module_5/HomeWork/game1.py","file_name":"game1.py","file_ext":"py","file_size_in_byte":582,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"41279641031","text":"import matplotlib.pyplot\r\nimport matplotlib.pyplot as plt\r\nimport csv\r\nimport datetime as dt\r\nimport time\r\n\r\ndef toUnixTime(val):\r\n    part1 = val.split(\"/\")\r\n    if len(part1) == 3:\r\n        part2 = part1[2].split(\" \")\r\n        part3 = part2[1].split(\":\")\r\n        month = int(part1[0])\r\n        day = int(part1[1])\r\n        year = int(part2[0])\r\n        hour = int(part3[0])\r\n        minute = int(part3[1])\r\n        dateTime = dt.datetime(year, month, day, hour,minute)\r\n        return int(time.mktime(dateTime.timetuple()))\r\n\r\n\r\nx = [\"Member\", \"Casual\"]\r\ny = [0, 0]\r\n\r\nmemberTimes = [];\r\ncasualTimes = [];\r\n\r\n\r\nwith open('JC-202112-citibike-tripdata.csv') as csvfile:\r\n    plots = csv.reader(csvfile, delimiter = ',')\r\n\r\n    for row in plots:\r\n        startTimeStr = row[2]\r\n        endTimeStr = row[3]\r\n        startTimeUnix = toUnixTime(startTimeStr)\r\n        endTimeUnix = toUnixTime(endTimeStr)\r\n        if isinstance(startTimeUnix, int) and isinstance(endTimeUnix, int):\r\n            rideTime = (endTimeUnix - startTimeUnix) / 60\r\n            if row[12] == \"member\":\r\n                memberTimes.append(rideTime)\r\n            elif row[12] == \"casual\":\r\n                casualTimes.append(rideTime)\r\n\r\n    memberTime = 0\r\n    casualTime = 0\r\n    for time in memberTimes:\r\n        memberTime += time\r\n\r\n    for time in casualTimes:\r\n        casualTime += time\r\n\r\n    y[0] = memberTime / len(memberTimes)\r\n    y[1] = casualTime / len(casualTimes)\r\n\r\nplt.bar(x, y, color = 'orange')\r\nplt.ylabel(\"Riding Time Length (in minutes)\")\r\nplt.title(\"The relationship between the “Riding Time Length” (in minutes) and the “Member or casual” category\", fontsize = 15)\r\nplt.show()\r\n","repo_name":"mainz1011/IV","sub_path":"relationship 3_bar plot.py","file_name":"relationship 3_bar plot.py","file_ext":"py","file_size_in_byte":1683,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"74742970384","text":"import numpy as np\r\nfrom itertools import product\r\n\r\n\r\ndef all_x(n):\r\n    l_tmp = list(product([0, 1], repeat=n))\r\n    return [np.array(x) for x in l_tmp]\r\n\r\n\r\nclass SolverBrut:\r\n\r\n    def brut_force(self, Q):\r\n        n = len(Q)\r\n        min_x = np.zeros(n, dtype=int)\r\n        result_min = np.dot(np.dot(min_x.transpose(), Q), min_x)\r\n        max_x = np.zeros(n, dtype=int)\r\n        result_max = np.dot(np.dot(max_x.transpose(), Q), max_x)\r\n        for x in all_x(n):\r\n            result = np.dot(np.dot(x.transpose(), Q), x)\r\n            if result > result_max:\r\n                max_x = x\r\n                result_max = result\r\n            if result < result_min:\r\n                min_x = x\r\n                result_min = result\r\n        return min_x, max_x, result_min, result_max\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    s = SolverBrut()\r\n    # Test 1\r\n    print(\"Test 1\")\r\n    Q1 = np.array([[-2, 1], [1, -1]])\r\n    print(s.brut_force(Q1))\r\n\r\n    # Test 2\r\n    print(\"Test 2\")\r\n    Q2 = np.array([[1, -1, 2, 1],\r\n                   [-1, -3, 5, 8],\r\n                   [2, 5, 2, -1],\r\n                   [1, 8, -1, -2]])\r\n    print(s.brut_force(Q2))\r\n","repo_name":"lemettrepaul/atos_solver_exercice","sub_path":"solver_brut.py","file_name":"solver_brut.py","file_ext":"py","file_size_in_byte":1150,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"32306992701","text":"import json\nfrom uuid import uuid4\n\nimport boto3\nimport pytest\n\nfrom moto import mock_lambda, mock_logs, mock_s3, mock_sns, mock_sqs\nfrom moto.core import DEFAULT_ACCOUNT_ID as ACCOUNT_ID\nfrom tests.markers import requires_docker\nfrom tests.test_awslambda.utilities import (\n    get_test_zip_file_print_event,\n    get_role_name,\n    wait_for_log_msg,\n)\n\n\nREGION_NAME = \"us-east-1\"\n\n\n@mock_lambda\n@mock_logs\n@mock_s3\n@pytest.mark.parametrize(\n    \"match_events,actual_event\",\n    [\n        ([\"s3:ObjectCreated:Put\"], \"ObjectCreated:Put\"),\n        ([\"s3:ObjectCreated:*\"], \"ObjectCreated:Put\"),\n        ([\"s3:ObjectCreated:Post\"], None),\n        ([\"s3:ObjectCreated:Post\", \"s3:ObjectCreated:*\"], \"ObjectCreated:Put\"),\n    ],\n)\n@requires_docker\ndef test_objectcreated_put__invokes_lambda(match_events, actual_event):\n    s3_res = boto3.resource(\"s3\", region_name=REGION_NAME)\n    s3_client = boto3.client(\"s3\", region_name=REGION_NAME)\n    lambda_client = boto3.client(\"lambda\", REGION_NAME)\n\n    # Create S3 bucket\n    bucket_name = str(uuid4())\n    s3_res.create_bucket(Bucket=bucket_name)\n\n    # Create AWSLambda function\n    function_name = str(uuid4())[0:6]\n    fn_arn = lambda_client.create_function(\n        FunctionName=function_name,\n        Runtime=\"python3.11\",\n        Role=get_role_name(),\n        Handler=\"lambda_function.lambda_handler\",\n        Code={\"ZipFile\": get_test_zip_file_print_event()},\n    )[\"FunctionArn\"]\n\n    # Put Notification\n    s3_client.put_bucket_notification_configuration(\n        Bucket=bucket_name,\n        NotificationConfiguration={\n            \"LambdaFunctionConfigurations\": [\n                {\n                    \"Id\": \"unrelated\",\n                    \"LambdaFunctionArn\": f\"arn:aws:lambda:us-east-1:{ACCOUNT_ID}:function:n/a\",\n                    \"Events\": [\"s3:ReducedRedundancyLostObject\"],\n                },\n                {\n                    \"Id\": \"s3eventtriggerslambda\",\n                    \"LambdaFunctionArn\": fn_arn,\n                    \"Events\": match_events,\n                },\n            ]\n        },\n    )\n\n    # Put Object\n    s3_client.put_object(Bucket=bucket_name, Key=\"keyname\", Body=\"bodyofnewobject\")\n\n    # Find the output of AWSLambda\n    expected_msg = \"FINISHED_PRINTING_EVENT\"\n    log_group = f\"/aws/lambda/{function_name}\"\n    msg_showed_up, all_logs = wait_for_log_msg(expected_msg, log_group, wait_time=10)\n\n    if actual_event is None:\n        # The event should not be fired on POST, as we've only PUT an event for now\n        assert not msg_showed_up\n        return\n\n    # If we do have an actual event, verify the Lambda was invoked with the correct event\n    assert msg_showed_up, (\n        expected_msg\n        + \" was not found after sending an SQS message. All logs: \"\n        + str(all_logs)\n    )\n\n    records = [line for line in all_logs if line.startswith(\"{'Records'\")][0]\n    records = json.loads(records.replace(\"'\", '\"'))[\"Records\"]\n\n    assert len(records) == 1\n    assert records[0][\"awsRegion\"] == REGION_NAME\n    assert records[0][\"eventName\"] == actual_event\n    assert records[0][\"eventSource\"] == \"aws:s3\"\n    assert \"eventTime\" in records[0]\n    assert \"s3\" in records[0]\n    assert \"bucket\" in records[0][\"s3\"]\n    assert records[0][\"s3\"][\"bucket\"][\"arn\"] == f\"arn:aws:s3:::{bucket_name}\"\n    assert records[0][\"s3\"][\"bucket\"][\"name\"] == bucket_name\n    assert records[0][\"s3\"][\"configurationId\"] == \"s3eventtriggerslambda\"\n    assert \"object\" in records[0][\"s3\"]\n    assert records[0][\"s3\"][\"object\"][\"eTag\"] == \"61ea96c3c8d2c76fc5a42bfccb6affd9\"\n    assert records[0][\"s3\"][\"object\"][\"key\"] == \"keyname\"\n    assert records[0][\"s3\"][\"object\"][\"size\"] == 15\n\n\n@mock_logs\n@mock_s3\ndef test_objectcreated_put__unknown_lambda_is_handled_gracefully():\n    s3_res = boto3.resource(\"s3\", region_name=REGION_NAME)\n    s3_client = boto3.client(\"s3\", region_name=REGION_NAME)\n\n    # Create S3 bucket\n    bucket_name = str(uuid4())\n    s3_res.create_bucket(Bucket=bucket_name)\n\n    # Put Notification\n    s3_client.put_bucket_notification_configuration(\n        Bucket=bucket_name,\n        NotificationConfiguration={\n            \"LambdaFunctionConfigurations\": [\n                {\n                    \"Id\": \"unrelated\",\n                    \"LambdaFunctionArn\": f\"arn:aws:lambda:us-east-1:{ACCOUNT_ID}:function:n/a\",\n                    \"Events\": [\"s3:ObjectCreated:Put\"],\n                }\n            ]\n        },\n    )\n\n    # Put Object\n    s3_client.put_object(Bucket=bucket_name, Key=\"keyname\", Body=\"bodyofnewobject\")\n\n    # The object was persisted successfully\n    resp = s3_client.get_object(Bucket=bucket_name, Key=\"keyname\")\n    assert resp[\"ContentLength\"] == 15\n    assert resp[\"Body\"].read() == b\"bodyofnewobject\"\n\n\n@mock_s3\n@mock_sqs\ndef test_object_copy__sends_to_queue():\n    s3_res = boto3.resource(\"s3\", region_name=REGION_NAME)\n    s3_client = boto3.client(\"s3\", region_name=REGION_NAME)\n    sqs_client = boto3.client(\"sqs\", region_name=REGION_NAME)\n\n    # Create S3 bucket\n    bucket_name = str(uuid4())\n    s3_res.create_bucket(Bucket=bucket_name)\n\n    # Create SQS queue\n    queue_url = sqs_client.create_queue(QueueName=str(uuid4())[0:6])[\"QueueUrl\"]\n    queue_arn = sqs_client.get_queue_attributes(\n        QueueUrl=queue_url, AttributeNames=[\"QueueArn\"]\n    )[\"Attributes\"][\"QueueArn\"]\n\n    # Put Notification\n    s3_client.put_bucket_notification_configuration(\n        Bucket=bucket_name,\n        NotificationConfiguration={\n            \"QueueConfigurations\": [\n                {\n                    \"Id\": \"queue_config\",\n                    \"QueueArn\": queue_arn,\n                    \"Events\": [\"s3:ObjectCreated:Copy\"],\n                }\n            ]\n        },\n    )\n\n    # We should have received a test event now\n    messages = sqs_client.receive_message(QueueUrl=queue_url)[\"Messages\"]\n    assert len(messages) == 1\n    message = json.loads(messages[0][\"Body\"])\n    assert message[\"Service\"] == \"Amazon S3\"\n    assert message[\"Event\"] == \"s3:TestEvent\"\n    assert \"Time\" in message\n    assert message[\"Bucket\"] == bucket_name\n\n    # Copy an Object\n    s3_client.put_object(Bucket=bucket_name, Key=\"keyname\", Body=\"bodyofnewobject\")\n    s3_client.copy_object(\n        Bucket=bucket_name, CopySource=f\"{bucket_name}/keyname\", Key=\"key2\"\n    )\n\n    # Read SQS messages - we should have the Copy-event here\n    resp = sqs_client.receive_message(QueueUrl=queue_url)\n    assert len(resp[\"Messages\"]) == 1\n    records = json.loads(resp[\"Messages\"][0][\"Body\"])[\"Records\"]\n\n    assert len(records) == 1\n    assert records[0][\"awsRegion\"] == REGION_NAME\n    assert records[0][\"eventName\"] == \"ObjectCreated:Copy\"\n    assert records[0][\"eventSource\"] == \"aws:s3\"\n    assert \"eventTime\" in records[0]\n    assert \"s3\" in records[0]\n    assert \"bucket\" in records[0][\"s3\"]\n    assert records[0][\"s3\"][\"bucket\"][\"arn\"] == f\"arn:aws:s3:::{bucket_name}\"\n    assert records[0][\"s3\"][\"bucket\"][\"name\"] == bucket_name\n    assert records[0][\"s3\"][\"configurationId\"] == \"queue_config\"\n    assert \"object\" in records[0][\"s3\"]\n    assert records[0][\"s3\"][\"object\"][\"eTag\"] == \"61ea96c3c8d2c76fc5a42bfccb6affd9\"\n    assert records[0][\"s3\"][\"object\"][\"key\"] == \"key2\"\n    assert records[0][\"s3\"][\"object\"][\"size\"] == 15\n\n\n@mock_s3\n@mock_sqs\ndef test_object_put__sends_to_queue__using_filter():\n    s3_res = boto3.resource(\"s3\", region_name=REGION_NAME)\n    s3_client = boto3.client(\"s3\", region_name=REGION_NAME)\n    sqs = boto3.resource(\"sqs\", region_name=REGION_NAME)\n\n    # Create S3 bucket\n    bucket_name = str(uuid4())\n    s3_res.create_bucket(Bucket=bucket_name)\n\n    # Create SQS queue\n    queue = sqs.create_queue(QueueName=f\"{str(uuid4())[0:6]}\")\n    queue_arn = queue.attributes[\"QueueArn\"]\n\n    # Put Notification\n    s3_client.put_bucket_notification_configuration(\n        Bucket=bucket_name,\n        NotificationConfiguration={\n            \"QueueConfigurations\": [\n                {\n                    \"Id\": \"prefixed\",\n                    \"QueueArn\": queue_arn,\n                    \"Events\": [\"s3:ObjectCreated:Put\"],\n                    \"Filter\": {\n                        \"Key\": {\"FilterRules\": [{\"Name\": \"prefix\", \"Value\": \"aa\"}]}\n                    },\n                },\n                {\n                    \"Id\": \"images_only\",\n                    \"QueueArn\": queue_arn,\n                    \"Events\": [\"s3:ObjectCreated:Put\"],\n                    \"Filter\": {\n                        \"Key\": {\n                            \"FilterRules\": [\n                                {\"Name\": \"prefix\", \"Value\": \"image/\"},\n                                {\"Name\": \"suffix\", \"Value\": \"jpg\"},\n                            ]\n                        }\n                    },\n                },\n            ]\n        },\n    )\n\n    # Read the test-event\n    resp = queue.receive_messages()\n    _ = [m.delete() for m in resp]\n\n    # Create an Object that does not meet any filter\n    s3_client.put_object(Bucket=bucket_name, Key=\"bb\", Body=\"sth\")\n    messages = queue.receive_messages()\n    assert not messages\n    _ = [m.delete() for m in messages]\n\n    # Create an Object that does meet the filter - using the prefix only\n    s3_client.put_object(Bucket=bucket_name, Key=\"aafilter\", Body=\"sth\")\n    messages = queue.receive_messages()\n    assert len(messages) == 1\n    _ = [m.delete() for m in messages]\n\n    # Create an Object that does meet the filter - using the prefix + suffix\n    s3_client.put_object(Bucket=bucket_name, Key=\"image/yes.jpg\", Body=\"img\")\n    messages = queue.receive_messages()\n    assert len(messages) == 1\n    _ = [m.delete() for m in messages]\n\n    # Create an Object that does not meet the filter - only the prefix\n    s3_client.put_object(Bucket=bucket_name, Key=\"image/no.gif\", Body=\"img\")\n    messages = queue.receive_messages()\n    assert not messages\n    _ = [m.delete() for m in messages]\n\n    # Create an Object that does not meet the filter - only the suffix\n    s3_client.put_object(Bucket=bucket_name, Key=\"nonimages/yes.jpg\", Body=\"img\")\n    messages = queue.receive_messages()\n    assert not messages\n    _ = [m.delete() for m in messages]\n\n\n@mock_s3\n@mock_sns\n@mock_sqs\ndef test_put_bucket_notification_sns_sqs():\n    s3_client = boto3.client(\"s3\", region_name=REGION_NAME)\n    s3_client.create_bucket(Bucket=\"bucket\")\n\n    sqs_client = boto3.client(\"sqs\", region_name=REGION_NAME)\n    sqs_queue = sqs_client.create_queue(QueueName=\"queue\")\n    sqs_queue_arn = sqs_client.get_queue_attributes(\n        QueueUrl=sqs_queue[\"QueueUrl\"], AttributeNames=[\"QueueArn\"]\n    )\n\n    sns_client = boto3.client(\"sns\", region_name=REGION_NAME)\n    sns_topic = sns_client.create_topic(Name=\"topic\")\n\n    # Subscribe SQS queue to SNS topic\n    sns_client.subscribe(\n        TopicArn=sns_topic[\"TopicArn\"],\n        Protocol=\"sqs\",\n        Endpoint=sqs_queue_arn[\"Attributes\"][\"QueueArn\"],\n    )\n\n    # Set S3 to send ObjectCreated to SNS\n    s3_client.put_bucket_notification_configuration(\n        Bucket=\"bucket\",\n        NotificationConfiguration={\n            \"TopicConfigurations\": [\n                {\n                    \"Id\": \"SomeID\",\n                    \"TopicArn\": sns_topic[\"TopicArn\"],\n                    \"Events\": [\"s3:ObjectCreated:*\"],\n                }\n            ]\n        },\n    )\n\n    # We should receive a test message\n    messages = sqs_client.receive_message(\n        QueueUrl=sqs_queue[\"QueueUrl\"], MaxNumberOfMessages=10\n    )\n    assert len(messages[\"Messages\"]) == 1\n\n    sqs_client.delete_message(\n        QueueUrl=sqs_queue[\"QueueUrl\"],\n        ReceiptHandle=messages[\"Messages\"][0][\"ReceiptHandle\"],\n    )\n\n    message_body = messages[\"Messages\"][0][\"Body\"]\n    sns_message = json.loads(message_body)\n    assert sns_message[\"Type\"] == \"Notification\"\n\n    # Get S3 notification from SNS message\n    s3_message_body = json.loads(sns_message[\"Message\"])\n    assert s3_message_body[\"Event\"] == \"s3:TestEvent\"\n\n    # Upload file to trigger notification\n    s3_client.put_object(Bucket=\"bucket\", Key=\"myfile\", Body=b\"asdf1324\")\n\n    # Verify queue not empty\n    messages = sqs_client.receive_message(\n        QueueUrl=sqs_queue[\"QueueUrl\"], MaxNumberOfMessages=10\n    )\n    assert len(messages[\"Messages\"]) == 1\n\n    # Get SNS message from SQS\n    message_body = messages[\"Messages\"][0][\"Body\"]\n    sns_message = json.loads(message_body)\n    assert sns_message[\"Type\"] == \"Notification\"\n\n    # Get S3 notification from SNS message\n    s3_message_body = json.loads(sns_message[\"Message\"])\n    assert s3_message_body[\"Records\"][0][\"eventName\"] == \"ObjectCreated:Put\"\n\n\n@mock_s3\ndef test_put_bucket_notification_sns_error():\n    s3_client = boto3.client(\"s3\", region_name=REGION_NAME)\n    s3_client.create_bucket(Bucket=\"bucket\")\n\n    # Set S3 to send ObjectCreated to SNS\n    s3_client.put_bucket_notification_configuration(\n        Bucket=\"bucket\",\n        NotificationConfiguration={\n            \"TopicConfigurations\": [\n                {\n                    \"Id\": \"SomeID\",\n                    \"TopicArn\": \"arn:aws:sns:us-east-1:012345678910:notexistingtopic\",\n                    \"Events\": [\"s3:ObjectCreated:*\"],\n                }\n            ]\n        },\n    )\n\n    # This should not throw an exception\n    s3_client.put_object(Bucket=\"bucket\", Key=\"myfile\", Body=b\"asdf1324\")\n","repo_name":"getmoto/moto","sub_path":"tests/test_s3/test_s3_lambda_integration.py","file_name":"test_s3_lambda_integration.py","file_ext":"py","file_size_in_byte":13232,"program_lang":"python","lang":"en","doc_type":"code","stars":7174,"dataset":"github-code","pt":"48"}
+{"seq_id":"36410614527","text":"import sys\nimport os\nimport requests\nfrom pprint import pprint\nimport time\nimport random\nimport datetime\nfrom IPy import IP\n#masks =  [\"255.255.255.248\", \"255.255.255.240\", \"255.255.255.224\", \"255.255.255.192\", \"255.255.255.128\", \"255.255.255.0\",\n#\t\t  \"255.255.248.0\", \"255.255.240.0\", \"255.255.224.0\", \"255.255.192.0\", \"255.255.128.0\", \"255.255.0.0\", ]\n\n\nclass IPAddrError(Exception):\n\tdef __init__(self,err_type=-1):\n\t\tif err_type == 1:\n\t\t\tself.message = 'Private ip address!'\n\t\telse:\n\t\t\tself.message = 'Wrong ip address!'\n\nclass IPAddrInfo():\n\theaders = {\n\t\t\t'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36',\n\t\t\t}\n\t@classmethod\n\tdef from_baidu(cls, ip_address):\n\t\tsleeptime = random.randint(3,5)/10\n\t\tprint(\"Sleep {}s\".format(sleeptime))\n\t\ttime.sleep(sleeptime)\n\t\turl = 'http://api.map.baidu.com/location/ip?ak=abDsBedrGw46lo1CyQuwZs9magjV5gSf&coor=&ip=' + ip_address\n\t\tr = None\n\t\twhile r is None:\n\t\t\ttry:\n\t\t\t\tr = requests.get(url, timeout=3, headers=cls.headers)\n\t\t\texcept requests.exceptions.ReadTimeout:\n\t\t\t\tprint('Timtout!!!')\n\t\tif r.status_code != 200:\n\t\t\treturn False\n\t\tr.encoding = 'utf-8'\n\t\tresult = r.json()\n\t\tif result['status'] != 0:\n\t\t\treturn False\n\t\t#data.pop('ip')\n\t\treturn str(result['address'])\n\n\t@classmethod\n\tdef from_aliyun(cls, ip_address):\n\t\tsleeptime = random.randint(11,25)/10\n\t\tprint(\"Sleep {}s\".format(sleeptime))\n\t\ttime.sleep(sleeptime)\n\t\turl = 'http://ip.aliyun.com/service/getIpInfo.php?ip=' + ip_address\n\t\tr = None\n\t\twhile r is None:\n\t\t\ttry:\n\t\t\t\tr = requests.get(url, timeout=3, headers=cls.headers)\n\t\t\texcept requests.exceptions.ReadTimeout:\n\t\t\t\tprint('Timtout!!!')\n\t\tif r.status_code != 200:\n\t\t\treturn False\n\t\tr.encoding = 'utf-8'\n\t\tresult = r.json()\n\t\t#pprint(result)\n\t\tif result['code'] != 0:\n\t\t\treturn False\n\t\tdata = result['data']\n\t\tdata.pop('ip')\n\t\treturn str(data)\n\n\nclass IP_Range(object):\n\tmask_start = 0\n\tmask_end = 0\n\n\tdef __init__(self, mask_start = 29, mask_end = 24):\n\t\tself.mask_start = mask_start\n\t\tself.mask_end = mask_end\n\t\tself.masks = [x for x in range(self.mask_end, self.mask_start+1)]\n\t\tself.masks.reverse()\n\n\tdef lookup_ipaddress_info(self, ip_address):\n\t\tprint(\"Querying ip address for {}\".format(ip_address))\n\t\t\"\"\"\n\t\tif ip_address.startswith('1.0.0'):\n\t\t\treturn str('IP address information AAA')\n\t\telif ip_address.startswith('1.0.17'):\n\t\t\treturn str('IP address information CCC')\n\t\telif ip_address.startswith('1.0.127'):\n\t\t\treturn str('IP address information DDD')\n\t\t\"\"\"\n\t\t#return str('IP address information')\n\t\tinfo = False\n\t\twhile not info:\n\t\t\t#info = IPAddrInfo.from_baidu(ip_address)\n\t\t\tinfo = IPAddrInfo.from_aliyun(ip_address)\n\t\t#pprint(info)\n\t\treturn info\n\n\tdef check_ipaddr(self, ip_address):\n\t\ttry:\n\t\t\tip = IP(ip_address)\n\t\texcept ValueError:\n\t\t\traise IPAddrError(-1)\n\t\treturn ip\n\n\tdef lookup_ip_range_info(self, ip_address, mask, info = None):\n\t\t#ip地址取29位掩码的网络地址、广播地址\n\t\t#print(\"Querying network address for {}/{}\".format(ip_address, mask))\n\t\ttry:\n\t\t\tip = self.check_ipaddr(ip_address)\n\t\texcept IPAddrError as e:\n\t\t\tprint(e.message)\n\t\tnet_address = ip.make_net(mask).strNormal()\n\t\tip_range = IP(net_address)\n\t\tboardcast_address = ip_range[-1]\n\t\tnext_ipaddr = IP(boardcast_address.int()+2).strNormal(3)\n\t\t# print(\"{}, {}\".format(ip_range.strNormal(3), next_ipaddr))\n\t\t# 查询ip地址\n\t\tipaddr_info = {'ip_start': ip_range[1].strNormal(3),\n\t\t\t\t\t   'network_address': ip_range[0].strNormal(3),\n\t\t\t\t\t   'ip_range_int_start': ip_range[0].int(),\n\t\t\t\t\t   'ip_range_int_end': ip_range[-1].int(),\n\t\t\t\t\t   'ip_range': ip_range.strNormal(3),\n\t\t\t\t\t   'netmask': mask,\n\t\t\t\t\t   'next_ip': next_ipaddr}\n\t\tif info == None:\n\t\t\tipaddr_info['info'] = 'PRIVATE' if ip.iptype() == 'PRIVATE' else self.lookup_ipaddress_info(ip_address)\n\t\telse:\n\t\t\tipaddr_info['info'] = info\n\n\t\treturn ipaddr_info\n\n\n\tdef merge_network_address(self, start_ip):\n\t\tmask_step = 1\n\t\tmerge_steps = 2\n\t\tipaddr_info = self.lookup_ip_range_info(start_ip, self.masks[0])\n\t\tmegerd_ipaddr_info = ipaddr_info.copy()\n\t\twhile mask_step < len(self.masks):\n\t\t\t#print(\"{}  {}\".format(2**(mask_step-1), merge_steps))\n\t\t\tfor step in range(2**(mask_step-1), merge_steps):\n\t\t\t\t#print(\"  {}\".format(step))\n\t\t\t\tnext_ipaddr = ipaddr_info['next_ip']\n\n\t\t\t\t#print(next_ipaddr + \" in \" + megerd_ipaddr_info['network_address'] + '/' + str(self.masks[mask_step]))\n\t\t\t\tif IP(megerd_ipaddr_info['network_address']).int() % 2**(32-self.masks[mask_step]) != 0:\n\t\t\t\t\t# 如遇跨网段则返回，重新开始合并\n\t\t\t\t\tprint(\"Network address is illegal, cannot continue merge!\")\n\t\t\t\t\tyield megerd_ipaddr_info\n\t\t\t\t\traise StopIteration\n\t\t\t\tif next_ipaddr in IP(megerd_ipaddr_info['network_address'] + '/' + str(self.masks[mask_step])):\n\t\t\t\t\tnext_ipaddr_info = self.lookup_ip_range_info(next_ipaddr, self.masks[0])\n\t\t\t\t\tif ipaddr_info['info'] != next_ipaddr_info['info']:\n\t\t\t\t\t\t# 查询结果不同则返回网段信息\n\t\t\t\t\t\tprint(\"New IP address information.\")\n\t\t\t\t\t\tyield megerd_ipaddr_info\n\t\t\t\t\t\traise StopIteration\n\t\t\t\t\tipaddr_info = next_ipaddr_info\n\t\t\t\telse:\n\t\t\t\t\t# 如遇跨网段则返回，重新开始合并\n\t\t\t\t\tprint(\"IP address cannot marge into Network address!\")\n\t\t\t\t\tyield megerd_ipaddr_info\n\t\t\t\t\traise StopIteration\n\t\t\tmask_step += 1\n\t\t\tnet_address = IP(ipaddr_info['ip_start']).make_net(self.masks[mask_step-1]).strNormal()\n\t\t\tprint(\"Merge IP range, New network address: {}\".format(net_address))\n\t\t\tipaddr_info = self.lookup_ip_range_info(ipaddr_info['ip_start'], self.masks[mask_step-1], info=ipaddr_info['info'])\n\t\t\tmegerd_ipaddr_info = ipaddr_info.copy()\n\t\t\t#yield merge_steps\n\t\t\tmerge_steps = 2 ** mask_step\n\t\tprint(\"Finished!\")\n\t\tyield megerd_ipaddr_info\n\n\nif __name__ == '__main__':\n\tstart_ip = sys.argv[1]\n\tstop_ip = sys.argv[2]\n\n\tfi = open('./iprange.txt', 'a+')\n\tip_range = IP_Range(mask_start=26, mask_end=16)\n\tge = ip_range.merge_network_address(start_ip)\n\twhile IP(start_ip).int() < IP(stop_ip).int():\n\t\ttry:\n\t\t\twhile True:\n\t\t\t\ta1 = next(ge)\n\t\t\t\tstart_ip = a1['next_ip']\n\t\texcept StopIteration:\n\t\t\t#pprint(a1)\n\t\t\tmsg = a1['ip_range'] + '\\t' + str(a1['netmask']) + '\\t' + str(a1['ip_range_int_start']) + '\\t' + str(a1['ip_range_int_end']) + '\\t' + a1['info'] + '\\t' + datetime.date.today().strftime('%Y%m%d')\n\t\t\tprint(msg)\n\t\t\tfi.write(msg + os.linesep)\n\t\t\tfi.flush()\n\t\t\tge = ip_range.merge_network_address(start_ip)\n\n\n\n","repo_name":"maxcl730/ip-network-merge","sub_path":"ipcalc.py","file_name":"ipcalc.py","file_ext":"py","file_size_in_byte":6361,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"8519406090","text":"# 给定一个赎金信 (ransom) 字符串和一个杂志(magazine)字符串，判断第一个字符串ransom能不能由第二个字符串magazines里面的字符构成。如果可以构成，返回 true ；否则返回 false。\n#\n# (题目说明：为了不暴露赎金信字迹，要从杂志上搜索各个需要的字母，组成单词来表达意思。)\n#\n# 注意：\n#\n# 你可以假设两个字符串均只含有小写字母。\n#\n# canConstruct(\"a\", \"b\") -> false\n# canConstruct(\"aa\", \"ab\") -> false\n# canConstruct(\"aa\", \"aab\") -> true\n#\nclass Solution(object):\n    def canConstruct(self, ransomNote, magazine):\n        \"\"\"\n        :type ransomNote: str\n        :type magazine: str\n        :rtype: bool\n        \"\"\"\n        magazine = list(magazine)\n        for i in ransomNote:\n            if i in magazine:\n                magazine.remove(i)\n            else:\n                return False\n        return True\ns = Solution\n","repo_name":"heshibo1994/leetcode-python-2","sub_path":"383. 赎金信.py","file_name":"383. 赎金信.py","file_ext":"py","file_size_in_byte":931,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"38788297383","text":"\"\"\"\nListing 3.56\n\nThe __exit__() method receives arguments containing details of any\nexception raised in the with block\n\nIf the context manager can handle the exception, __exit__() should\nreturn a true value to indicate that the exception does not need to\nbe propagated. Returning a false value causes the exception to be\nraised again after __exit__() returns\n\"\"\"\n\n\nclass Context:\n\n    def __init__(self, handle_error):\n        print(f\"__init__({handle_error})\")\n        self.handle_error = handle_error\n\n    def __enter__(self):\n        print(\"__enter__()\")\n        return self\n\n    def __exit__(self, exc_type, exc_val, exc_tb):\n        print(\"__exit__()\")\n        print(\"  exc_type =\", exc_type)\n        print(\"  exc_val =\", exc_val)\n        print(\"  exc_tb =\", exc_tb)\n        return self.handle_error\n\n\ndef main():\n    with Context(True):\n        raise RuntimeError(\"error message handled\")\n\n    print()\n\n    with Context(False):\n        raise RuntimeError(\"error message propagated\")\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"8563a236e65cede7b14220e65c70ad5718144a3/python3-standard-library-solutions","sub_path":"Chapter03/0056_contextlib_api_error.py","file_name":"0056_contextlib_api_error.py","file_ext":"py","file_size_in_byte":1030,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"40463890313","text":"from board import *\nfrom Othello import print_board\nclass move:\n    def __init__(self, x, y):\n        self.x = x\n        self.y = y\n        self.v = -1\n\ndef min_value(color, board, limit):\n    if(color == 'w'):\n        m_color = 'b'\n    else:\n        m_color = 'w'\n    if(limit == 0):\n        ans = count(board, 'b') - count(board, 'w')\n        return ans\n    v = 100\n    many_boards, many_pos = legal_moves(color, board)\n    for board in many_boards:\n        v = min(max_value(m_color, board, limit-1), v)\n    return v\n\n\ndef max_value(color, board, limit):\n    if(color == 'w'):\n        m_color = 'b'\n    else:\n        m_color = 'w'\n    if(limit == 0):\n        ans = count(board, 'b') - count(board, 'w')\n        return ans\n    v=-100\n    many_boards, many_pos = legal_moves(color, board)\n    for board, pos in zip(many_boards, many_pos):\n        v = max(min_value(m_color, board, limit-1), v)\n    return v\n\ndef minmax(color, board, flag, limit):\n    gmin = 100\n    gmax = -100\n    gx =-1\n    gy=-1\n    if(color == 'w'):\n        m_color = 'b'\n    else:\n        m_color = 'w'\n    many_boards, many_pos = legal_moves(color, board)\n    print(\"Making Move for\" ,color)\n    for board, pos in zip(many_boards, many_pos):\n        print(\"Considering\", \"(\", pos.x, \",\", pos.y ,\")\")\n        # For white color move\n        if flag == 1:\n            #print(\"In Flag =1\")\n            v = min_value(m_color, board, limit-1)\n            #print(\"Min Value\", v)\n            if(v > gmax):\n                gx = pos.x\n                gy = pos.y\n                gmax =v\n        else:\n            v = max_value(m_color, board, limit-1)\n            if(v < gmin):\n                gx = pos.x\n                gy = pos.y\n                gmin =v\n        print(\" Minmax Value\", v)\n    return gx, gy\n\ndef legal_moves(color, current_board):\n    list_boards = []\n    mov=[]\n    if(color == 'w'):\n        m_color = 'b'\n    else:\n        m_color = 'w'\n    board = copy.deepcopy(current_board)\n    for i in range(0, len(board)):\n        for j in range(0, len(board)):\n           if board[i][j] == '.':\n               initial_count = count(board, m_color)\n               boa, chan = make_move(board,color, i, j)\n               final_count = count(boa, m_color)\n               if(initial_count - final_count >=1):\n                    mov.append(move(i, j))\n                    list_boards.append(boa)\n    return list_boards, mov","repo_name":"anisdube1/Artificial-Intelligence-project","sub_path":"Othello/moves.py","file_name":"moves.py","file_ext":"py","file_size_in_byte":2392,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"2172270317","text":"import logging\nimport base64\nimport consul\nfrom diskcache import Cache\n\n\nclass ConsulKvSearch:\n    \"\"\"\n    Search in the consul key value.\n    \"\"\"\n\n    def __init__(self, host, port, scheme, token, verify=True, cert=None, root='', cache_enabled=True,\n                 cache_dir='.consul_cache', cache_ttl=600):\n        self._host = host\n        self._port = port\n        self._scheme = scheme\n        self._token = token\n        self._verify = verify\n        self._cert = cert\n        self._cache_enabled = cache_enabled\n        self._cache_dir = cache_dir\n        self.cache_ttl = cache_ttl\n        self._root = root\n        self._client = consul.Consul(host=host, port=port, token=token, scheme=scheme, verify=verify, cert=cert)\n\n    def get_cache(self, key, default=None, expire_time=False):\n        if self._cache_enabled:\n            return self.cache.get(key=self._get_cache_key(key), default=default, expire_time=expire_time)\n        return None\n\n    def set_cache(self, key, value, expire):\n        if self._cache_enabled:\n            return self.cache.set(key=self._get_cache_key(key), value=value, expire=expire)\n        return False\n\n    def del_cache(self, key):\n        if self._cache_enabled:\n            return self.cache.delete(key=self._get_cache_key(key))\n        return True\n\n    def clear_cache(self):\n        if self._cache_enabled:\n            self.cache.clear()\n\n    def get_key(self, key, recurse=True, raw=False, keys=False, **kwargs):\n        \"\"\"\n        Get key value from consul kv.\n\n        :param key:\n        :param recurse:\n        :param raw:\n        :param keys:\n        :param kwargs:\n        :return:\n        \"\"\"\n        index, vals = self._client.kv.get(key=key, recurse=recurse, keys=keys, **kwargs)\n        if not raw and not keys and vals:\n            res = []\n            for val in vals:\n                try:\n                    v = val['Value'].decode('utf8') if val['Value'] is not None else None\n                except Exception as e:\n                    v = val['Value']\n                res.append({'key': val['Key'], 'value': v})\n            return res\n        return vals\n\n    def get(self, key, **kwargs):\n        \"\"\"\n        Get key from cache, if not hit in the cache, then find in the consul.\n\n        :param key:\n        :return:\n        \"\"\"\n        if not key:\n            key = ''\n        if self._cache_enabled:\n            vals = self.get_cache(key=key)\n            if vals:\n                logging.info('Hit {} from cache'.format(key))\n                return vals\n        logging.info('Do not hit cache for {} or cache disabled'.format(key))\n        vals = self.get_key(key=key, **kwargs)\n        self.set_cache(key=key, value=vals, expire=self.cache_ttl)\n        return vals\n\n    def put(self, key, value, **kwargs):\n        \"\"\"\n        Put key value in consul and cache if enabled.\n\n        :param key:\n        :param value:\n        :param kwargs:\n        :return:\n        \"\"\"\n        res = self._client.kv.put(key=key, value=value, **kwargs)\n        return res\n\n    def delete(self, key, recurse=None, **kwargs):\n        res = self._client.kv.delete(key=key, recurse=recurse, **kwargs)\n        if self._cache_enabled:\n            self.del_cache(key=key)\n        return res\n\n    def _get_cache_key(self, field) -> str:\n        return base64.b64encode(':'.join([\n            str(self._host),\n            str(self._port),\n            str(self._root),\n            str(field)\n        ]).encode('utf-8'))\n\n    @property\n    def cache(self) -> Cache:\n        with Cache(self._cache_dir) as ref:\n            return ref\n","repo_name":"wwtg99/consul_utils","sub_path":"lib/consul_utils/search.py","file_name":"search.py","file_ext":"py","file_size_in_byte":3572,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"48"}
+{"seq_id":"1593874586","text":"import logging\nimport random\n\n# noinspection PyPackageRequirements\nfrom wordnik import swagger, WordApi, WordsApi, WordListApi, WordListsApi, AccountApi\nfrom twitterpibot.data_access import dal\n\nlogger = logging.getLogger(__name__)\n\n_init = False\n\nword_api = None\nwords_api = None\nword_list_api = None\nword_lists_api = None\n\naccount_api = None\nauth_token = None\n\n\ndef init():\n    global _init\n\n    global word_api\n    global words_api\n    global word_list_api\n    global word_lists_api\n\n    global account_api\n    global auth_token\n\n    if not _init:\n        apiUrl = 'http://api.wordnik.com/v4'\n        apiKey = dal.get_token(\"wordnik api\")\n        client = swagger.ApiClient(apiKey, apiUrl)\n\n        account_api = AccountApi.AccountApi(client)\n        username = dal.get_token(\"wordnik username\")\n        password = dal.get_token(\"wordnik password\")\n        auth_token = account_api.authenticate(username, password).token\n\n        word_api = WordApi.WordApi(client)\n        words_api = WordsApi.WordsApi(client)\n        word_list_api = WordListApi.WordListApi(client)\n        word_lists_api = WordListsApi.WordListsApi(client)\n\n        _init = True\n\n\ndef get_words_matching(stem):\n    init()\n\n    skip = 0\n    limit = 1000\n\n    search = words_api.searchWords(\"*%s*\" % stem, skip=skip, limit=limit)\n    words = list(map(lambda w: w.word, search.searchResults))\n    logger.debug(words)\n\n    return words\n\n\ndef get_word_matching(stem, rx):\n    init()\n    words = []\n    word = None\n    skip = 0\n    limit = 1000\n    while not word or (rx and not rx.match(word)):\n        if not words:\n            search = words_api.searchWords(\"*%s*\" % stem, skip=skip, limit=limit)\n            words = list(map(lambda w: w.word, search.searchResults))\n            random.shuffle(words)\n            skip += 1 * limit\n\n            logger.debug(words)\n        word = words.pop()\n\n    return word\n\n\ndef get_example(word):\n    init()\n    examples = word_api.getExamples(word)\n    example = random.choice(examples.examples)\n    return example.text\n","repo_name":"andrewtatham/twitterpibot","sub_path":"twitterpibot/logic/wordnikwrapper.py","file_name":"wordnikwrapper.py","file_ext":"py","file_size_in_byte":2026,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"4810485063","text":"import os\nimport matplotlib.pyplot as plt\n\nfolder = \"Rezultati/lokacije/\"\n\n\ndef lokacije(filename):\n    data = filename.split(\"-\")[:2]\n    (gin, n1) = data[0].split(\"_\")\n    (vod, n2) = data[1].split(\"_\")\n    xs0 = []\n    ys0 = []\n    xs1 = []\n    ys1 = []\n    with open(folder + filename, \"r\") as f:\n        for line in f.readlines():\n            if len(line) > 2:\n                d = [float(i) for i in line[:-2].replace(\",\", \".\").split(\";\")]\n                if d[1] == 0:\n                    if d[0] == 0:\n                        xs0.append(d[2:])\n                    else:\n                        xs1.append(d[2:])\n                else:\n                    if d[0] == 0:\n                        ys0.append(d[2:])\n                    else:\n                        ys1.append(d[2:])\n        return gin, n1, vod, n2, xs0, ys0, xs1, ys1\n\n\nplt.figure(figsize=(10, 10), tight_layout=True)\nfor k, file in enumerate(os.listdir(folder)[:12]):\n    ginf, n1f, vodf, n2f, xs0f, ys0f, xs1f, ys1f = lokacije(file)\n    print(file)\n    plt.subplot(4, 3, k+1)\n    for i, x in enumerate(xs0f):\n        plt.plot(x[2:], ys0f[i][2:], color=\"tab:blue\", alpha=0.4)\n    for i, x in enumerate(xs1f):\n        plt.plot(x[2:], ys1f[i][2:], color=\"tab:red\")\n    plt.plot([151, 151], [-80, 80], color=\"tab:brown\")\n    plt.plot([151, 151], [-50, 50], color=\"tab:orange\", linewidth=4)\n    plt.title(n1f + \" \" + ginf + \" ovc, \" + n2f + \" ovčar\" + (\"\" if int(n2f) == 1 else \"ja\" if int(n2f) == 2 else \"ji\"))\n    plt.axis('equal')\n    plt.axis('off')\nplt.savefig(\"../MagistrskoDelo-pisanje/poglavja/grafi/lokacijeAI1.png\", dpi=300)\nplt.show()\n","repo_name":"AnzeMarinko/iOvcar_Izidor","sub_path":"IzrisRezultatov/lokacije.py","file_name":"lokacije.py","file_ext":"py","file_size_in_byte":1613,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"74944748304","text":"import asyncio\nimport aiohttp\nimport os\nimport sys\nimport bs4\nimport scrapy\nfrom pymongo import MongoClient\n\nsem = asyncio.Semaphore(20)\nurls = []\n\ndef get_seed_urls():\n    loop = asyncio.get_event_loop()\n    f = asyncio.wait([fetch_random_profiles() for i in range(200)])\n    loop.run_until_complete(f)\n\n@asyncio.coroutine\ndef get(*args, **kwargs):\n    response = yield from aiohttp.request('GET', *args, **kwargs)\n    return response.headers\n\nasync def append_url(url):\n    f = open('hello.urls', 'a')\n    f.write(url+\"\\n\")\n    f.close()\n\n@asyncio.coroutine\ndef fetch_random_profiles():\n    url = 'http://www.famousbirthdays.com/s/randomizer.php'\n    with (yield from sem):\n        page = yield from get(url, compress=True, allow_redirects=False)\n        yield from append_url(page['Location'])\n\ndef init_seed_url():\n    with open('hello.urls') as f:\n        urls = f.readlines()\n        return urls\n\n# Scraper\nclass Spider(scrapy.Spider):\n    name = 'birth'\n    client = MongoClient(os.environ['DB_1_PORT_27017_TCP_ADDR'])\n    start_urls = ['https://blog.scrapinghub.com']\n    def start_requests(self):\n        print(\"Started Seeding\")\n        get_seed_urls()\n        with open('hello.urls') as f:\n            urls = f.readlines()\n            for url in urls:\n                yield scrapy.Request(url=url.strip(), callback=self.parse)\n        print(\"Seeding complete - Urls \",len(urls))\n    def parse(self, response):\n        res = {}\n        res['url'] =  response.request.url\n        res['full_name'] = response.css('h1::text').extract_first().strip()\n        res['first_name'] = res['full_name'].split(\" \")[0]\n        res['popularity'] = response.css('.rank-no::text').extract_first().strip()[1]\n        res['birthday'] = response.xpath('/html/body/div[1]/div[1]/div/div/div[3]/div/div/div[2]/div/div[1]/div/div[1]/div/a[1]/span[1]/text()').extract_first().strip()\n        res['birthday'] += \" \"+response.xpath('/html/body/div[1]/div[1]/div/div/div[3]/div/div/div[2]/div/div[1]/div/div[1]/div/a[2]/text()').extract_first().strip()\n        res['category'] = response.xpath('/html/body/div[1]/div[1]/div/div/div[3]/div/div/div[1]/div/div[1]/h1/div/a/text()').extract_first().strip()\n        res['birth_place'] = \"\".join(response.xpath('/html/body/div[1]/div[1]/div/div/div[3]/div/div/div[2]/div/div[2]/div/div[2]/div/a/text()').extract()).strip()\n\n        keys = response.css('.bio h2::text').extract()\n        for key, val in zip(keys, response.css('.bio p::text').extract()):\n            res[key] = val\n\n        for anchors in response.css('.also-viewed .row a::attr(href)').extract():\n            yield scrapy.Request(url = anchors, callback=self.parse)\n\n        if self.client.bottr_web_1.bottr_db_1.profiles.find({'url':res['url']}).count() > 0:\n            self.client.bottr_web_1.bottr_db_1.profiles.insert_one(res)\n        yield res\n","repo_name":"gabber12/famous-birthday-crawler","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2845,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"12580421004","text":"# -*- coding: utf-8 -*-\nfrom django import template\n\nregister = template.Library()\n\n\n@register.filter(name=\"get_paging_list\")\ndef get_paging_list(value, curr_page_num):\n    \"\"\"\n        Return a list of pages which skip pages in the middle\n        Use: {% for a in value_list|get_paging_list:curr_page_num %}\n    \"\"\"\n    num_of_pages_per_side = 7      # num pages in the left or right\n    num_of_pages = len(value)       # length of value\n    new_value = []\n\n    if num_of_pages <= 20:\n        return value\n\n    closest_curr_page = [x for x in range(\n        curr_page_num - 2, curr_page_num + 3) if x >= 0]\n    default_left_value = list(range(1, num_of_pages_per_side + 1))\n    default_right_value = list(range(\n        num_of_pages - num_of_pages_per_side, num_of_pages + 1))\n\n    if curr_page_num <= num_of_pages // 2:\n        left_value = []\n        if closest_curr_page[-1] > num_of_pages_per_side:\n            left_value = list(range(1, len(closest_curr_page) + 1))\n            left_value += closest_curr_page\n        else:\n            left_value = default_left_value\n        new_value = left_value + ['skip'] + default_right_value\n    else:\n        right_value = []\n        if closest_curr_page[0] >= (num_of_pages - num_of_pages_per_side):\n            right_value = default_right_value\n        else:\n            right_value = closest_curr_page\n            right_value += list(range(\n                num_of_pages - len(closest_curr_page), num_of_pages + 1))\n        new_value = default_left_value + ['skip'] + right_value\n\n    return new_value\n","repo_name":"thoongnv/yosim","sub_path":"yosim/alerts/templatetags/custom_pagination.py","file_name":"custom_pagination.py","file_ext":"py","file_size_in_byte":1550,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"48"}
+{"seq_id":"1108488296","text":"__author__ = 'Ricter'\n\nfrom django.conf.urls import patterns, url, include\nfrom rest_framework import routers\nfrom api import views\n\n\nrouter = routers.DefaultRouter()\nrouter.register(r'anime', views.AnimeViewSet, base_name='anime')\nrouter.register(r'subscriptions', views.SubscriptionViewSet, base_name='subscription')\nrouter.register(r'search', views.SearchViewSet, base_name='search')\nrouter.register(r'user', views.UserViewSet, base_name='profile')\n\n\nurlpatterns = patterns('',\n                       url(r'^', include(router.urls)),\n                       )","repo_name":"RicterZ/AnimeReminder","sub_path":"api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":561,"program_lang":"python","lang":"en","doc_type":"code","stars":33,"dataset":"github-code","pt":"48"}
+{"seq_id":"72466985106","text":"from elasticsearch import Elasticsearch\r\nimport numpy as np\r\nimport pandas as pd\r\nimport string\r\nfrom gensim.models import word2vec\r\nfrom keras import losses\r\nfrom keras.models import Sequential\r\nfrom keras.layers import Dense, Softmax\r\nfrom sklearn.metrics import precision_score\r\nfrom sklearn.model_selection import train_test_split\r\nfrom search2 import search2\r\n\r\nCLASSES = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\nFLAGS = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\r\ndef remove_numbers(s):\r\n    s = ''.join([i for i in s if not i.isdigit()])\r\n    return s\r\n\r\ndef generateVectors(books):\r\n    summaries = books[\"summary\"].to_list()\r\n    isbns = books[\"isbn\"].to_list()\r\n\r\n    summary_vectors = {}\r\n    for i in range(len(summaries)):\r\n        summaries[i] = remove_numbers(summaries[i])\r\n\r\n    tokenized_summaries = [summary.translate(str.maketrans('', '', string.punctuation)).split() for summary in summaries]\r\n    model = word2vec.Word2Vec(tokenized_summaries, vector_size=100, min_count=1)\r\n    word_vectors = model.wv\r\n\r\n    shape = np.array(word_vectors.get_vector(tokenized_summaries[i][0])).shape\r\n    for i in range(len(isbns)):\r\n        temp_vector_1 = np.zeros(shape)\r\n        for word in tokenized_summaries[i]:\r\n            if word != \"\":\r\n                temp_vector_2 = np.array(word_vectors.get_vector(word))\r\n                temp_vector_1 += temp_vector_2\r\n        summary_vectors[isbns[i]] = (temp_vector_1 / len(tokenized_summaries[i]))\r\n\r\n    return summary_vectors\r\n\r\ndef get_pred_flags(predictions):\r\n    return [np.argmax(prediction) for prediction in predictions]\r\n\r\ndef NNetwork(isbns, summary_vectors, es):\r\n    data = []\r\n    label = []\r\n    query = {\r\n        \"match\": {\r\n            \"uid\":11676\r\n        },\r\n        \"match\": {\r\n            \"uid\":166123\r\n        }\r\n    }\r\n    \r\n    resp = es.search(index='ratings', query=query)\r\n    results = pd.json_normalize(resp['hits']['hits'])\r\n    for i in range(len(results['_source.isbn'])):\r\n        if results['_source.isbn'][i] in isbns:\r\n            data.append(summary_vectors[results['_source.isbn'][i]])\r\n            label.append(results['_source.rating'][i])\r\n\r\n    trainData, testData, trainflags, testflags = train_test_split(data, label, test_size=0.25)\r\n\r\n    model = Sequential()\r\n    model.add(Dense(32, input_dim=len(trainData[0]), activation='relu'))\r\n    model.add(Dense(16, activation='relu'))\r\n    model.add(Dense(10))\r\n\r\n    model.compile(loss=losses.SparseCategoricalCrossentropy(from_logits=True), optimizer='adam', metrics=['accuracy'])\r\n\r\n    model.fit(np.array(trainData), np.array(trainflags), epochs=20, verbose=1)\r\n\r\n    probabilityModel = Sequential([model, Softmax()])\r\n\r\n    return probabilityModel\r\n\r\ndef get_ratings_average(ratings):\r\n    ratings_avg = ratings.groupby(by='isbn').mean()\r\n    ratings_avg = ratings_avg.drop('uid', axis=1).reset_index()\r\n\r\n    return ratings_avg\r\n\r\ndef final_rating(response, userId, predictor, summary_vectors, ratings, isbns):\r\n    final_result = []\r\n    ratings_avg = get_ratings_average(ratings)\r\n    for i in range(len(response)):\r\n        if response['_source.isbn'][i] in isbns:\r\n            isbn = response['_source.isbn'][i]\r\n            book_score = response['_score'][i]\r\n            book_ratings_avg = float(ratings_avg.loc[ratings_avg['isbn'] == isbn].iloc[0]['rating']) if isbn in ratings_avg.isbn else -1\r\n            try:\r\n                user_rating = ratings[(ratings['isbn'] == str(isbn)) & (ratings['isbn'] == str(isbn))]['rating'].item()\r\n            except:\r\n                user_rating = '-'\r\n            # -----------------------------------------calculate predicted RATING-----------------------------------------\r\n            prediction_array = predictor.predict(np.array([summary_vectors[isbn]]))\r\n            predicted_label = get_pred_flags(prediction_array)\r\n            user_predicted_rating = CLASSES[predicted_label[-1]]\r\n            # ------------------------------------------------------------------------------------------------------------\r\n            new_record = {\r\n                \"Title\": response['_source.book_title'][i],\r\n                \"Score\": book_score\r\n            }\r\n            new_record[\"Score\"] += book_ratings_avg if book_ratings_avg != -1 else 0\r\n            new_record[\"Score\"] += user_rating if user_rating != \"-\" else user_predicted_rating\r\n            new_record[\"User_true_r\"] = user_rating\r\n            new_record[\"User_predicted_r\"] = user_rating if user_rating != '-' else user_predicted_rating\r\n            final_result.append(new_record)\r\n    \r\n    df = pd.DataFrame(final_result)\r\n    df.sort_values(\"Score\", inplace=True, ascending=False)\r\n    df = df.reset_index(drop=True)\r\n    return df\r\n\r\ndef main():\r\n    es = Elasticsearch(\"http://localhost:9200\")\r\n\r\n    books = pd.read_csv('BX-Books.csv')\r\n    ratings = pd.read_csv('BX-Book-Ratings.csv')\r\n\r\n    summary_vectors = generateVectors(books)\r\n    isbns = books[\"isbn\"].to_list()\r\n    \r\n    response, userId = search2()\r\n    \r\n    predictor = NNetwork(isbns, summary_vectors, es)\r\n    \r\n    print(final_rating(response, userId, predictor, summary_vectors, ratings, isbns))\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n","repo_name":"geras-papax/Book-Recommendation","sub_path":"search3.py","file_name":"search3.py","file_ext":"py","file_size_in_byte":5161,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"30589260775","text":"\"\"\"\n#\n# File        : decortors_and_annotations\n# Created     : 01/11/21 11:08 AM\n# Author      : R. Aravind\n# Version     : v1.0.0\n# Licencing   : (C) 2021 Aravind Rajesh Kanna, LYIT\n                Available under GNU Public License (GPL)\n# Credits    :\n# Maintainer  :\n# Description : Using Overload decorators with getter and setters\n#\n\"\"\"\n\n\nfrom typing import overload\n\n\nclass PERSON:\n    def __init__(self, name, lnum):\n        self._name = name\n        self._lnum = lnum\n\n    @property\n    def name(self):\n        return self._name  # getter\n\n    @name.setter\n    def name(self, name):\n        self._name = name\n\n    @property\n    def display_person_name(self):\n        print(\"Name: {}\".format(self._name))\n\n    @staticmethod\n    @overload\n    def display_person_lnum(lnum: int):\n        print(\"L Num Old Format: {}\".format(lnum))\n\n    @staticmethod\n    def display_person_lnum(lnum: str):\n        print(\"L Num New Format: {}\".format(lnum))\n\n\nif __name__ == '__main__':\n    '''\n\n       Main method of application \n\n       Calls the person instance with overloading\n\n       Parameters:\n\n        none\n\n       Returns:\n\n        none\n\n    '''\n\n    Joe = PERSON(\"Joe Bloggs\", \"L0012345\")\n    Joe.display_person_name\n    Joe.display_person_lnum(123456)\n    Joe.display_person_lnum(\"L00123456\")\n\n    Jane = PERSON(\"Jane\", \"L0012346\")\n    Jane.name = \"Jane Doe\"\n    Jane.display_person_name\n    Jane.display_person_lnum(1234567)\n    Jane.display_person_lnum(\"L001234567\")\n","repo_name":"L00163466/week6","sub_path":"pkg/decorators_and_annotations.py","file_name":"decorators_and_annotations.py","file_ext":"py","file_size_in_byte":1471,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"42573044145","text":"import unittest\n\nfrom signature_algorithms.key_pair import KeyPairGenerator\nfrom signature_algorithms.ring_signature import RingSignature\n\n\nclass RingSignatureTestCase(unittest.TestCase):\n    def test_all_process(self):\n        sign = RingSignature()\n        key_pair = KeyPairGenerator()\n        public_keys_list = []\n        private_keys = []\n\n        for idx in range(4):\n            pk, pbk = key_pair.gen_keypair()\n            public_keys_list.append(pbk)\n            private_keys.append(pk)\n\n        test_index = 2\n        message = \"This is a ring signature\"\n        signature = sign.sign(message, public_keys_list, private_keys[test_index], test_index)\n        self.assertTrue(sign.verify(message, public_keys_list, *signature))\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"KyrylR/BabyBlockchain","sub_path":"tests/sigs/test_ring_sig.py","file_name":"test_ring_sig.py","file_ext":"py","file_size_in_byte":786,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"15815164916","text":"from response import get_comment_reply\nfrom utils import *\n\n\nclass SALLU_BHOT:\n    def __init__(self):\n        \n        load_dotenv()\n        \n        # Set the bot's username\n        self.bot_username = os.getenv('sallu_username')\n       \n        # Initialize a Reddit object\n        self.reddit = praw.Reddit(\n            client_id=os.getenv('sallu_client_id'),\n            client_secret=os.getenv('sallu_client_secret'),\n            password=os.getenv('sallu_password'),\n            user_agent=os.getenv('sallu_user_agent'),\n            username=os.getenv('sallu_username')\n        )\n\n        # Set the subreddits to monitor join with '+'\n        #self.subreddit = self.reddit.subreddit('sallu_bhot_test')\n        self.subreddit = self.reddit.subreddit('sallu_bhot_test+biggboss+BollyBlindsNGossip')\n        \n        #webhook url for discord notifications\n        self.webhook_sb = os.getenv('webhook_sb')\n\n        print(f\"Configuration complete, Starting {self.bot_username}!\")\n\n\n    def send_webhook(self, message):\n        \"\"\" Send Notification to Discord Server \"\"\"\n\n        data = {'content': message, 'username': 'Sallu-bhot'}\n        requests.post(self.webhook_sb, data=data)\n\n\n    def send_errors(self, error, redditObject):\n        \"\"\" Sending Error To Discord \"\"\"\n\n        if \"NoneType' object has no attribute 'name\" in error:\n            pass\n        else:\n            print(error)\n            error = f\"{error}\\nhttps://www.reddit.com{redditObject.permalink}\"\n            self.send_webhook(error)\n\n\n    def getText(self,redditObject):\n        if isComment(redditObject):\n            user_text = redditObject.body.lower()\n        else:\n            user_text = redditObject.title.lower() + \"\\n\" + redditObject.selftext.lower()\n\n        return user_text.strip()\n\n\n    def make_comment(self, redditObject, response):\n        \"\"\"Reply to (comment/post) with response and Store ID in txt\"\"\"\n\n        try:\n            #reply to submission\n            redditObject.reply(body=response)\n\n            #store id of already replied submissions in txt\n            writeComment(redditObject.id)\n\n            #Send Discord Notification\n            link = f\"\\n{redditObject.author.name}: {self.getText(redditObject)}\\nResponse: **'{response}'** \\nLink - https://www.reddit.com{redditObject.permalink}\"\n            self.send_webhook(link)\n\n        except Exception as e:\n            error = str(e)\n            self.send_errors(error, redditObject)\n\n\n    def response_canon(self, redditObject, text, author, sub):\n        \"\"\"Sending a Keyword specific / random response to a triggered Submission\"\"\"\n\n        response = get_comment_reply(text, author, sub)\n        self.make_comment(redditObject, response)\n\n\n    def response_greet(self,redditObject, title):\n        \"\"\"Sending a Greeting to Shukravar ka vaar/ Shanivaar Ka Vaar Discussion Thread\"\"\"\n        response = f\"*Hello, Namaste, Assalamu Alaikum, Sat Sri Akal, Kem Cho, Kedo Haal Hai, Kemon Acho, Kasa Kai. Welcome to BiggBoss {title}!*\"\n        self.make_comment(redditObject, response)\n\n\n    def skip_checker(self, redditObject):\n        \"\"\"Check to see if we should skip this thing\"\"\"\n\n        #Skip posts older than 7 hrs\n        if not checkTime(redditObject):\n            return True\n\n        # Skip if the author is None (Someone deleted something)\n        if redditObject.author is None:\n            return True\n\n        # Skip if the author is Sallu-Bhot\n        elif redditObject.author.name == 'Sallu-Bhot':\n            return True\n\n        # Skip if the comment ID is in the txt file already\n        elif redditObject.id in getComments():\n            return True\n\n        # Otherwise, don't skip.\n        else:\n            # print(f\"This {id} has a valid author, and is not in the txt\")\n            return False\n\n\n    def blacklist_checker(self, redditObject, user_text, author):\n        \"\"\" blacklist/whitelist an author based on keywords - start/stop \"\"\"\n\n        if user_text.strip() == \"/unblock\":\n            if(whitelistAuthor(author)):\n                message = f\"🟩 USER: **'{author}'** is UNBLOCKED\\nLink - https://www.reddit.com{redditObject.permalink}\"\n                print(message)\n                self.send_webhook(message)\n                writeComment(redditObject.id)\n                return True\n        elif user_text.strip() == \"/block\":\n            if(blacklistAuthor(author)):\n                    message = f\"🟥 USER: **'{author}'** BLOCKED!\\nLink - https://www.reddit.com{redditObject.permalink}\"\n                    print(message)\n                    self.send_webhook(message)\n                    writeComment(redditObject.id)\n                    return True\n        else:\n            return False\n\n    \n    def sallu_main(self, redditObject):\n        \"\"\"Primary Function\"\"\"\n        \n        if self.skip_checker(redditObject):\n            print(f\"Skipping https://www.reddit.com{redditObject.permalink}\")\n            return\n\n        #get info\n        author = redditObject.author.name\n        user_text = self.getText(redditObject)\n        subreddit = redditObject.subreddit.display_name\n        if(isPost(redditObject)):\n            post_title = redditObject.title.lower()\n        else:\n            post_title = \"\"\n\n        #change sallu_bhot_test to biggboss for testing\n        if subreddit == \"sallu_bhot_test\":\n            subreddit = \"biggboss\"\n\n        # Black list authors/ Whitelist authors (reply to bot comment with /block or /unblock)\n        if isComment(redditObject) and len(user_text.split()) == 1 and user_text[0] == \"/\":\n            parent_author = redditObject.parent().author\n            if parent_author == self.bot_username:\n                if (self.blacklist_checker(redditObject, user_text, author)):\n                    return True\n            \n        # Skip if the author is blacklisted\n        if redditObject.author.name in getBlacklist():\n            return\n\n        #check both common trigger for posts and specific trigger for post and comments\n        if triggered(user_text) or (isPost(redditObject) and luck(post_title)):\n            print(f\"Triggered, on https://www.reddit.com{redditObject.permalink}\")\n            self.response_canon(redditObject, user_text, author, subreddit)\n            return\n        \n        #greet on fri and sat discussion thread\n        if isPost(redditObject) and redditObject.link_flair_text == \"Scheduled Discussion Thread\":\n            fri = \"Shukravar Ka Vaar\"\n            sat = \"Shanivaar Ka Vaar\"\n            if fri.lower() in post_title:\n                title = fri\n            elif sat.lower() in post_title:\n                title = sat\n            else:\n                title = \"\"\n            if (title):\n                print(f\"Greeting Triggered, on https://www.reddit.com{redditObject.permalink}\")\n                self.response_greet(redditObject, title)\n\n        \n        \n    def run(self):\n        print(\"\\nStreaming Comments and Posts...\")\n\n        while True:\n            try:\n\n                # Iterate through all the posts / comments\n                for object in praw.models.util.stream_generator(lambda **kwargs: submissions_and_comments(self.subreddit, **kwargs)):\n                    self.sallu_main(object)\n                    \n\n            except KeyboardInterrupt:\n                print(\"Keyboard termination received. Shutting Down!\")\n                break\n            \n            except Exception as e:\n                body = f\"Stream Error:\\n{e}\"\n                print(body)\n                self.send_webhook(body)\n                time.sleep(2)\n\n\nif __name__ == '__main__':\n    sb = SALLU_BHOT().run()","repo_name":"Sallu-Bhot/SalluBhot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7592,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"16833281497","text":"from __future__ import print_function\n\nimport unittest\n\nimport numpy as np\n\nimport paddle.fluid.layers as layers\nimport paddle.fluid.framework as framework\nimport paddle.fluid as fluid\nimport paddle\n\npaddle.enable_static()\n\n\nclass TestPythonOperatorOverride(unittest.TestCase):\n    def check_result(self, fn, place, dtype):\n        shape = [9, 10]\n\n        x_data = np.random.random(size=shape).astype(dtype)\n        y_data = np.random.random(size=shape).astype(dtype)\n        python_out = fn(x_data, y_data)\n\n        x_var = layers.create_global_var(\n            name='x', shape=shape, value=0.0, dtype=dtype, persistable=True)\n        y_var = layers.create_global_var(\n            name='y', shape=shape, value=0.0, dtype=dtype, persistable=True)\n        out = fn(x_var, y_var)\n\n        exe = fluid.Executor(place)\n\n        exe.run(fluid.default_startup_program())\n        fluid_out = exe.run(fluid.default_main_program(),\n                            feed={'x': x_data,\n                                  'y': y_data},\n                            fetch_list=[out])\n\n        np.testing.assert_array_equal(python_out, fluid_out[0])\n\n    def test_override(self):\n        # compare func to check\n        compare_fns = [\n            lambda _a, _b: _a == _b,\n            lambda _a, _b: _a != _b,\n            lambda _a, _b: _a < _b,\n            lambda _a, _b: _a <= _b,\n            lambda _a, _b: _a > _b,\n            lambda _a, _b: _a >= _b,\n        ]\n\n        # places to check\n        places = [fluid.CPUPlace()]\n        if fluid.core.is_compiled_with_cuda():\n            places.append(fluid.CUDAPlace(0))\n\n        # dtypes to check\n        dtypes = ['int32', 'float32']\n\n        for place in places:\n            for dtype in dtypes:\n                for compare_fn in compare_fns:\n                    with framework.program_guard(framework.Program(),\n                                                 framework.Program()):\n                        self.check_result(compare_fn, place, dtype)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"EnnSou/ooss-paddle2.3","sub_path":"python/paddle/fluid/tests/test_python_operator_overriding.py","file_name":"test_python_operator_overriding.py","file_ext":"py","file_size_in_byte":2035,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"48"}
+{"seq_id":"42364590663","text":"import pandas as pd\nimport os\nfrom util.models import Dataset\n\n\nclass DataLoader:\n    def __init__(self, num_users: int = 1000, num_test_items: int = 5, data_path: str = \"../data/ml-10M100K/\"):\n        self.num_users = num_users\n        self.num_test_items = num_test_items\n        self.data_path = data_path\n\n    def load(self) -> Dataset:\n        ratings, movie_content = self._load()\n        movielens_train, movielens_test = self._split_data(ratings)\n        # ranking 용 평가 데이터는 각 사용자의 평갓값이 4 이상인 영화만을 정답으로 한다\n        # 키는 사용자 ID, 값은 사용자가 높이 평가한 아이템의 ID 리스트\n        movielens_test_user2items = (\n            movielens_test[movielens_test.rating >= 4].groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n        )\n        return Dataset(movielens_train, movielens_test, movielens_test_user2items, movie_content)\n\n    def _split_data(self, movielens: pd.DataFrame) -> (pd.DataFrame, pd.DataFrame):\n        # 학습용과 테스트용으로 데이터를 분할한다\n        # 각 사용자의 직전 5개 영화를 평가용으로 사용하고, 그 이외는 학습용으로 한다\n        # 먼저, 각 사용자가 평가한 영화의 순서를 계산한다\n        # 최근 부여한 영화부터 순서를 부여한다(0부터 시작)\n        movielens[\"rating_order\"] = movielens.groupby(\"user_id\")[\"timestamp\"].rank(ascending=False, method=\"first\")\n        movielens_train = movielens[movielens[\"rating_order\"] > self.num_test_items]\n        movielens_test = movielens[movielens[\"rating_order\"] <= self.num_test_items]\n        return movielens_train, movielens_test\n\n    def _load(self) -> (pd.DataFrame, pd.DataFrame):\n        # 영화 정보 로딩(10197 작품)\n        # movie_id와 제목만 사용\n        m_cols = [\"movie_id\", \"title\", \"genre\"]\n        movies = pd.read_csv(\n            os.path.join(self.data_path, \"movies.dat\"), names=m_cols, sep=\"::\", encoding=\"latin-1\", engine=\"python\"\n        )\n        # genre를 list 형식으로 저장한다\n        movies[\"genre\"] = movies.genre.apply(lambda x: x.split(\"|\"))\n\n        # 사용자가 부여한 영화의 태그 정보를 로딩한다\n        t_cols = [\"user_id\", \"movie_id\", \"tag\", \"timestamp\"]\n        user_tagged_movies = pd.read_csv(\n            os.path.join(self.data_path, \"tags.dat\"), names=t_cols, sep=\"::\", engine=\"python\"\n        )\n        # tag를 소문자로 한다\n        user_tagged_movies[\"tag\"] = user_tagged_movies[\"tag\"].str.lower()\n        movie_tags = user_tagged_movies.groupby(\"movie_id\").agg({\"tag\": list})\n\n        # 태그 정보를 결합한다\n        movies = movies.merge(movie_tags, on=\"movie_id\", how=\"left\")\n\n        # 평가 데이터를 로딩한다\n        r_cols = [\"user_id\", \"movie_id\", \"rating\", \"timestamp\"]\n        ratings = pd.read_csv(os.path.join(self.data_path, \"ratings.dat\"), names=r_cols, sep=\"::\", engine=\"python\")\n\n        # user 수를 num_users로 줄인다\n        valid_user_ids = sorted(ratings.user_id.unique())[: self.num_users]\n        ratings = ratings[ratings.user_id <= max(valid_user_ids)]\n\n        # 위 데이터를 결합한다\n        movielens_ratings = ratings.merge(movies, on=\"movie_id\")\n\n        return movielens_ratings, movies\n","repo_name":"Chiefmate/RecommenderSystems","sub_path":"chapter5/util/data_loader.py","file_name":"data_loader.py","file_ext":"py","file_size_in_byte":3293,"program_lang":"python","lang":"ko","doc_type":"code","dataset":"github-code","pt":"48"}
+{"seq_id":"38210670995","text":"from torch import nn\nimport torch, math\nimport matplotlib.pyplot as plt\nfrom encoder import TransformerEncoder\nfrom decoder import TransformerDecoder\n\n\nclass Seq2SeqTransformer(nn.Module):\n\n    def __init__(self, src_dim, tgt_dim, d_model, num_heads, enc_layers, dec_layers, warmup_steps=4000, betas=[0.9, 0.98]):\n        super(Seq2SeqTransformer, self).__init__()\n        self.register_buffer(\"src_dim\", torch.tensor(src_dim, dtype=torch.long))\n        self.register_buffer(\"tgt_dim\", torch.tensor(tgt_dim, dtype=torch.long))\n        self.register_buffer(\"d_model\", torch.tensor(d_model, dtype=torch.long))\n        self.register_buffer(\"num_heads\", torch.tensor(num_heads, dtype=torch.long))\n        self.register_buffer(\"enc_layers\", torch.tensor(enc_layers, dtype=torch.long))\n        self.register_buffer(\"dec_layers\", torch.tensor(dec_layers, dtype=torch.long))\n\n        # optimizer and scheduler info\n        self.register_buffer(\"train_step\", torch.tensor(1, dtype=torch.long))\n        self.register_buffer(\"epoch\", torch.tensor(0, dtype=torch.long))\n        self.register_buffer(\"warmup_steps\", torch.tensor(warmup_steps, dtype=torch.long))\n        self.register_buffer(\"betas\", torch.tensor(betas))\n        \n        self.encoder = TransformerEncoder(\n            input_dim=src_dim,\n            d_model=d_model,\n            num_heads=num_heads,\n            n_layers=enc_layers\n        )\n        self.decoder = TransformerDecoder(\n            input_dim=tgt_dim,\n            d_model=d_model,\n            num_heads=num_heads,\n            n_layers=dec_layers\n        )\n\n    # enc_mask and dec_mask have shape [batch_size, num_heads, 1, seq_len]\n    def forward(self, enc_input, dec_input, enc_mask, dec_mask):\n        enc_outputs = self.encoder(enc_input, enc_mask)\n        out = self.decoder(\n            x=dec_input,\n            enc_outputs=enc_outputs,\n            dec_mask=dec_mask,\n            enc_mask=enc_mask\n        )\n        return out\n\n\ndef test_transformer():\n    dev = torch.device(\"mps\")\n    batch_size = 32\n    model = Seq2SeqTransformer(\n        src_dim=14433,\n        tgt_dim=29071,\n        d_model=512,\n        num_heads=8,\n        enc_layers=3,\n        dec_layers=3\n    ).to(dev)\n    print(sum([p.numel() for p in model.parameters()]))\n    src = torch.arange(0, 100).unsqueeze(0).repeat(batch_size, 1).to(dev)\n    dec_input = torch.arange(0, 120).unsqueeze(0).repeat(batch_size, 1).to(dev)\n    enc_mask = torch.ones(batch_size, 8, 1, 100).type(torch.bool).to(dev)\n    dec_mask = torch.ones(batch_size, 8, 1, 120).type(torch.bool).to(dev)\n    out = model(enc_input=src, dec_input=dec_input, enc_mask=enc_mask, dec_mask=dec_mask)\n    print(out.shape)\n\n\nif __name__ == \"__main__\":\n    test_transformer()","repo_name":"gursi26/seq2seq-transformer","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":2723,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"22867855256","text":"from collections import OrderedDict\n\n\nclass LFUCache:\n    def _update_ordered_dict(self, key, value, new_count):\n        if new_count not in self.hash_ordered:\n            self.hash_ordered[new_count] = OrderedDict()\n        self.hash_ordered[new_count][key] = value\n        self.hash_ordered[new_count].move_to_end(key)\n\n    def __init__(self, capacity: int):\n        self.hash_table = {}\n        self.hash_ordered = {}\n        self.capacity = capacity\n        self.min_count = 0\n        self.used = 0\n\n    def get(self, key: int) -> int:\n        val = -1\n        if key in self.hash_table:\n            val = self.hash_table[key][0]\n            old_count = self.hash_table[key][1]\n            self.hash_table[key][1] += 1\n            del self.hash_ordered[old_count][key]\n            new_count = old_count + 1\n\n            self._update_ordered_dict(key, val, new_count)\n\n            if self.min_count == old_count and len(self.hash_ordered[old_count]) == 0:\n                self.min_count = new_count\n        return val\n\n    def put(self, key: int, value: int) -> None:\n        if self.capacity <= 0:\n            return\n\n        if key in self.hash_table:\n            self.hash_table[key][0] = value\n            old_count = self.hash_table[key][1]\n            self.hash_table[key][1] += 1\n            del self.hash_ordered[old_count][key]\n            new_count = old_count + 1\n\n            self._update_ordered_dict(key, value, new_count)\n\n            if self.min_count == old_count and len(self.hash_ordered[old_count]) == 0:\n                self.min_count = new_count\n\n\n\n        else:\n            if self.used < self.capacity:\n                self.hash_table[key] = [value, 1]\n                self._update_ordered_dict(key, value, 1)\n                self.used += 1\n                self.min_count = 1\n\n            else:\n                del_key, del_value = self.hash_ordered[self.min_count].popitem(0)\n                del self.hash_table[del_key]\n\n                self.hash_table[key] = [value, 1]\n                self._update_ordered_dict(key, value, 1)\n                self.min_count = 1\n\n\"\"\"TESTS\n1) Runtime  817 ms\nBeats  89.30%\nMemory  78.9 MB\nBeats  31.99%\"\"\"","repo_name":"BenitoSwaggolini/Leetcode_tasks","sub_path":"hard/460. LFU Cache/r.py","file_name":"r.py","file_ext":"py","file_size_in_byte":2167,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"48"}
+{"seq_id":"3118055420","text":"import json\nimport time\nfrom datetime import datetime\n\nfrom flask import Flask, abort, request, jsonify, render_template \nimport requests\n\nimport config\nfrom vk_data import get_wall_group_stat, get_common_group_stat, prepare_group_stat, prepare_common_group_stat, get_group_info, \\\nget_group_ids_list\n\n\nAPP = Flask(__name__)\n\n@APP.route(\"/\")\ndef index():\n\tgroup_list_raw = get_group_info(config.GROUP_API_ENDPOINT)\n\tgroup_list_dict= get_group_ids_list(group_list_raw)\n\tgroup_list = list(group_list_dict.keys())\n\n\tif request.args.get('group_id'):\n\t\tconfig.GROUP_ID = '-' + request.args.get('group_id')\n\n\tcurrent_group_name = group_list_dict[int(config.GROUP_ID[1:])]\n\n\traw_group_stat = get_wall_group_stat(config.WALL_API_ENDPOINT)\n\traw_common_group_stat = get_common_group_stat(config.GROUP_STAT_API_ENDPOINT)\n\tlikes_stat, likes_table, xaxis_likes, reposts_stat = prepare_group_stat(raw_group_stat)\n\tviews_stat, xaxis_views = prepare_common_group_stat(raw_common_group_stat)\n\treturn render_template(\n\t\t'index.html',\n\t\tlikes_stat=likes_stat,\n\t\tlikes_table=likes_table,\n\t\txaxis_likes=xaxis_likes,\n\t\treposts_stat=reposts_stat,\n\t\tviews_stat=views_stat,\n\t\txaxis_views=xaxis_views,\n\t\tgroup_list=group_list,\n\t\tcurrent_group_name=current_group_name,\n\t\tgroup_list_dict=group_list_dict,\n\t\t)\n\n\nif __name__ == \"__main__\":\n\tAPP.run(port=1234, debug=True)\n\n\n","repo_name":"LazarevDmitry90/social_stat","sub_path":"group_stat.py","file_name":"group_stat.py","file_ext":"py","file_size_in_byte":1344,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"36278465660","text":"import pickle\nimport unittest\n\nimport numpy\nfrom sklearn.tree.tree import DecisionTreeClassifier\n\nfrom emetrics.correlation_score import CorrelationScore\nfrom emetrics.evaluation.random_subsets_experiment import RandomSubsetsExperiment\n\n\n__author__ = 'Emanuele Tamponi'\n\n\nclass RandomSubsetsExperimentTest(unittest.TestCase):\n    \n    def test_results_in_shape(self):\n        experiment = RandomSubsetsExperiment(\n            dataset=\"aggregation_score_dataset\",\n            subset_size=2,\n            scorers=[(\"wilks\", CorrelationScore())],\n            classifiers=[(\"dt\", DecisionTreeClassifier())],\n            n_folds=5,\n            n_runs=10\n        )\n        results = experiment.run(directory=\"test_files/\")\n        self.assertEqual([\"wilks\"], list(results[\"scores\"].keys()))\n        self.assertEqual([\"dt\"], list(results[\"errors\"].keys()))\n        self.assertEqual(10, len(results[\"scores\"][\"wilks\"]))\n        self.assertEqual(10, len(results[\"errors\"][\"dt\"]))\n        for score in results[\"scores\"][\"wilks\"]:\n            self.assertTrue(0 < score < 1)\n        for error in results[\"errors\"][\"dt\"]:\n            self.assertTrue(0 < error < 1)\n        for time in results[\"score_times\"][\"wilks\"]:\n            self.assertTrue(time > 0)\n        for time in results[\"classifier_times\"][\"dt\"]:\n            self.assertTrue(time > 0)\n        self.assertEqual(experiment, results[\"experiment\"])\n\n    def test_results_reproducible(self):\n        experiment = RandomSubsetsExperiment(\n            dataset=\"aggregation_score_dataset\",\n            subset_size=2,\n            scorers=[(\"wilks\", CorrelationScore())],\n            classifiers=[(\"dt\", DecisionTreeClassifier())],\n            n_folds=5,\n            n_runs=10\n        )\n        results = experiment.run(directory=\"test_files/\")\n        results2 = results[\"experiment\"].run(directory=\"test_files/\")\n\n        numpy.testing.assert_array_equal(results[\"scores\"][\"wilks\"], results2[\"scores\"][\"wilks\"])\n        numpy.testing.assert_array_equal(results[\"errors\"][\"dt\"], results2[\"errors\"][\"dt\"])\n\n        dump = pickle.dumps(results)\n        results2 = pickle.loads(dump)[\"experiment\"].run(directory=\"test_files/\")\n\n        numpy.testing.assert_array_equal(results[\"scores\"][\"wilks\"], results2[\"scores\"][\"wilks\"])\n        numpy.testing.assert_array_equal(results[\"errors\"][\"dt\"], results2[\"errors\"][\"dt\"])\n","repo_name":"etamponi/emetrics","sub_path":"emetrics/evaluation/test_random_subsets_experiment.py","file_name":"test_random_subsets_experiment.py","file_ext":"py","file_size_in_byte":2356,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"42442043832","text":"from Robot import Robot\nfrom time import sleep\nfrom Path import Path\nfrom Calibrate import Calibrate\nfrom AngleConverter import AngleConverter\nfrom PathHandler import PathHandler\nfrom Navigator import Navigator\nfrom Goal import Goal\n\n# load a path file\n#p = Path(\"Path-around-table.json\")\np = Path(\"Path-around-table-and-back.json\")\n#p = Path(\"Path-from-bed.json\")\n#p = Path(\"Path-to-bed.json\")\npath = p.getPath()\n\nprint(\"Path length = \" + str(len(path)))\nprint(\"First point = \" + str(path[0]['X']) + \", \" + str(path[0]['Y']))\n\n# make a robot to move around\nrobot = Robot()\nconverter = AngleConverter()\npathHandler = PathHandler()\nnavigator = Navigator()\ngoal = Goal()\n\n#### Intialize variables\nposition = robot.getPosition()\nspeed = 0.25\nheading = 0\nnextPoint = 0\ndropOut = 0\nlookAheadDistance = 0.85\n\nwhile ( goal.notGoal( position, nextPoint, path ) ):\n\n    ### get current status (position, heading)\n    position = robot.getPosition()\n    heading = converter.convertToDegree(robot.getHeading())\n    \n    ### get next point\n    nextPoint = pathHandler.getNextPathPoint( position, path, nextPoint, lookAheadDistance)\n    print(nextPoint)\n\n    ### find direction to path\n    directionToPoint = navigator.getDirection( position, path, nextPoint )    \n    turnRate = navigator.getTurnRate( heading, directionToPoint )\n\n    ### set motion\n    robot.setMotion(speed, turnRate )\n    sleep(0.35)\n    robot.setMotion(0,0 )\n\n\nprint('Geeeeeehaaaaaaa')\n\n","repo_name":"LaithRuweha/Robot","sub_path":"robot-master/TestRobot3.py","file_name":"TestRobot3.py","file_ext":"py","file_size_in_byte":1445,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"5810748113","text":"from flask_restful import Resource, reqparse\nfrom flask import Blueprint, request, Response,jsonify\nfrom sqlalchemy.exc import IntegrityError\n\nfrom hswebapp import app,db\n\nfrom hswebapp.api import apiv0\nfrom datetime import datetime\nfrom time import sleep\n\nfrom werkzeug.security import generate_password_hash,check_password_hash\nfrom hswebapp.models.system_models import User,Logs\nfrom hswebapp.models.sensor_models import TempLog,HumidityLog,PressureLog,PowerLog,EventLog\nfrom hswebapp.models.resources import sensorlogs_schema,templog_schema,powerlogs_schema\n\n\nimport copy\n\n\n@apiv0.route('/apiv1/templogs/<int:lastreadings>', methods=['GET'])\ndef get_templogs(lastreadings,):\n\n    templog= TempLog.query.order_by(TempLog.rdate.desc()).limit(lastreadings).all()\n    result = sensorlogs_schema.dump(templog)\n    return jsonify({'templogs': result.data})\n\n@apiv0.route('/apiv1/sensorlog', methods=['GET'])\ndef get_sensorlogs():\n    \n    page = request.args.get('page', 1, type=int)\n    #sensor = request.args.get('sensor', 'temperature_sensor1',type=string)\n     \n    per_page = min(request.args.get('per_page', 10, type=int), 100)\n    #example:\n    #data = User.to_collection_dict(User.query, page, per_page, 'apiv0.get_users')\n    return jsonify(data)\n\n@apiv0.route('/templog', methods=['POST'])\ndef create_log():\n    \n    sensor_type = request.args.get('sensor_type', 'PressureLog', type=str)\n    \n    json_data = request.get_json() or {}\n    if not json_data:\n        return jsonify({'message': 'No input data provided'}), 400\n    \n    data, errors = templog_schema.load(json_data)    \n    \n    if errors:\n        return jsonify(errors), 422 \n    \n    try:\n        #log = sensor_class.query.filter_by(id=new_log.id).first()\n        sensor_class = globals()[sensor_type]\n        log =sensor_class.query.get(data['id'])\n        \n        if log:\n            return jsonify({\"message\": \"The log already exists\",\"id\":log.id}), 409\n        else :\n            new_log = sensor_class(**data)\n    except Exception as e:\n        app.logger.error(\"Error BD: {} \".format(e))\n        db.session.rollback()\n        return jsonify({\"message\": \"Error processing your request try again later\"}), 503    \n    \n    finally:\n        db.session.close()\n        \n    \n        \n    try:         \n        db.session.add(new_log)\n        db.session.commit()\n    \n    except Exception as e:\n        app.logger.error(\"Error BD: {} \".format(e))\n        db.session.rollback()\n        return jsonify({\"message\": \"Error processing your request try again later\"}), 503\n    \n       \n    return jsonify({\"message\": \"Created new log.\",\"id\": new_log.id}),201\n#http://marshmallow.readthedocs.io/en/latest/examples.html\n\n\n","repo_name":"hoascv/HSDEV2","sub_path":"hswebapp/api/sensor_resource.py","file_name":"sensor_resource.py","file_ext":"py","file_size_in_byte":2688,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"11028447019","text":"#!/usr/bin/python3\n\n\nimport os\nimport sys\nimport traceback\nimport argparse\nimport logging\nfrom docmaker.pipeline import DocPipeLine\nfrom custom.profile import ProfileReader\n\ndef __set_cmdargs_up():\n    \"\"\"parses the cmd line arguments at the call\"\"\"\n\n    psr_desc='Document maker command line control interface'\n    psr_epi='The config profile is used to specify defaults'\n\n    psr = argparse.ArgumentParser(description=psr_desc, epilog=psr_epi)\n\n    psr.add_argument(\n        '-d', '--debug', action='store_true',\n        dest='dm_debug', help='print debug information'\n    )\n    psr.add_argument(\n        '-r', '--resdir', action='store',\n        dest='resdir', help='points out the one resources directory')\n    psr.add_argument(\n        '-c', '--config', action='store',\n        dest='config', help='load an specific config profile'\n    )\n    psr.add_argument(\n        '-b', '--builder',\n        dest='dm_builder', help='specify the builder to use'\n    )\n    psr.add_argument(\n        '-i', '--input',\n        dest='dm_input', help='specify the input variables with \\'var=val;var2=val2;var2=valN\\'..'\n    )\n    psr.add_argument(\n        '-o', '--output',\n        dest='dm_output', help='specify the output file'\n    )\n\n    return psr.parse_args()\n\n\ndef dmcli(args, logger):\n\n    def read_settings(s_file):\n        logger.debug(\"looking for config profile file in:\\n{0}\".format(\n            os.path.abspath(s_file)))\n        if os.path.isfile(s_file):\n            reader = ProfileReader(logger)\n            return reader(s_file)\n        raise Exception(\"unable to locate the config profile file\")\n\n    logging.basicConfig(level=logging.DEBUG if args.dm_debug else logging.INFO)\n    logger.debug(args)\n\n    DEFAULT_RESDIR = os.path.join(os.environ['BASE_DIR'], 'resources')\n    DEFAULT_PROFILE = 'cfdiengine.json'\n\n    resdir = args.resdir if args.resdir else DEFAULT_RESDIR\n    profiles_dir = '{}/profiles'.format(resdir)\n    prof = '{}/{}'.format(profiles_dir,\n        args.config if args.config else DEFAULT_PROFILE)\n\n    pt = read_settings(prof)\n\n    if not args.dm_output:\n        raise Exception(\"not defined output file\")\n\n    if args.dm_builder:\n        if not args.dm_input:\n            raise Exception(\"not defined input variables\")\n\n        kwargs = {}\n        try:\n            if args.dm_input.find(';') == -1:\n                raise Exception(\"input variables bad conformed\")\n            else:\n                for opt in args.dm_input.split(';'):\n                    if opt.find('=') == -1:\n                        continue\n                    (k , v) = opt.split('=')\n                    kwargs[k] = v\n        except ValueError:\n            raise Exception(\"input variables bad conformed\")\n\n        try:\n            dpl = DocPipeLine(logger, resdir,\n                rdirs_conf = pt.res.dirs)\n            dpl.run(args.dm_builder, args.dm_output, **kwargs)\n        except:\n            raise Exception(\"problems in document pipeline\")\n    else:\n        raise Exception(\"builder module not define\")\n\nif __name__ == \"__main__\":\n\n    logger = logging.getLogger(__name__)\n    args = __set_cmdargs_up()\n\n    try:\n        dmcli(args, logger)\n        logger.info('successful builder execution')\n    except:\n        if args.dm_debug:\n            traceback.print_exc()\n        sys.exit(1)\n\n    sys.exit(0)\n","repo_name":"pianodaemon/erp","sub_path":"DOS/cfdiengine/service/dmcli.py","file_name":"dmcli.py","file_ext":"py","file_size_in_byte":3309,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"38115598388","text":"import pandas as pd\r\nimport math\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\ndef cal_gmm(mean,std,xi):\r\n    result = (1 / (math.sqrt(2 * math.pi) * std)) * \\\r\n             math.exp(-1 * (xi - mean) * (xi - mean) / (2 * std ** 2))\r\n    return result\r\n\r\n\r\ndef e_step(a,mean,std,x):\r\n    Expectations = np.zeros((len(a),len(x)))\r\n    for k in range(len(x)):\r\n        sump = sum(a[i]*cal_gmm(mean[i],std[i],x[k]) for i in range(len(a)))\r\n        for i in range(len(a)):\r\n            Expectations[i,k] = a[i]*cal_gmm(mean[i],std[i],x[k])/sump\r\n    return Expectations\r\n\r\ndef m_step(Expectations,x,a):\r\n    mean_list = []\r\n    std_list = []\r\n    a_list = []\r\n    for i in range(len(a)):\r\n        sume = sum(Expectations[i,:])\r\n        a = sume/len(x)\r\n        a_list.append(a)\r\n        summ = sum(Expectations[i,k]*x[k] for k in range(len(x)))\r\n        mean = summ/sume\r\n        mean_list.append(mean)\r\n        sums = sum(Expectations[i,k]*(x[k]-mean)**2 for k in range(len(x)))\r\n        std = math.sqrt(sums/sume)\r\n        std_list.append(std)\r\n    return [mean_list,std_list,a_list]\r\n\r\ndef find(Expectations,x):\r\n\r\n    x1 = []\r\n    x2 = []\r\n    for k in range(len(x)):\r\n        if Expectations[0,k] >= 0.5:\r\n            x1.append(x[k])\r\n        else:\r\n            x2.append(x[k])\r\n    return x1,x2\r\n\r\n\r\ndef main(a,mean,std,x):\r\n    count = 0\r\n    while(1):\r\n        Expectations=e_step(a,mean,std,x)\r\n        #print(Expectations)\r\n        if abs(mean[0] - m_step(Expectations, x, a)[0][0]) < 0.0001:\r\n            break\r\n        else:\r\n            [mean, std, a] = m_step(Expectations, x, a)\r\n        count += 1\r\n    print(\"a:\",a)\r\n    print(\"std:\", std)\r\n    print(\"mean:\", mean)\r\n    print(count)\r\n    x1,x2 = find(Expectations, x)\r\n    return x1,x2\r\n\r\n\r\ndef draw(x1,x2):\r\n    result = np.histogram(x1, bins=15)\r\n    plt.plot(result[1][0:-1], result[0])\r\n    result = np.histogram(x2, bins=20)\r\n    plt.plot(result[1][0:-1], result[0])\r\n    plt.xlabel('Height (cm)')\r\n    plt.ylabel('Count')\r\n    plt.title('Distribution of Heights')\r\n    plt.show()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    data = pd.read_csv(\"height_data.csv\")\r\n    x = data['height'].values.tolist()\r\n    a = [0.3,0.7]\r\n    mean = [160,180]\r\n    std = [3,5]\r\n    x1,x2 = main(a,mean,std,x)\r\n    #draw(x[:500],x[500:2000])\r\n    #draw(x1, x2)\r\n    #print(len(x1),len(x2))\r\n    '''wrong = 0\r\n    for xi in x1:\r\n        if xi not in x[:500]:\r\n            wrong += 1\r\n    print(wrong)\r\n    for xi in x2:\r\n        if xi not in x[500:2000]:\r\n            wrong += 1\r\n    print(wrong)'''\r\n","repo_name":"1479518308/work2","sub_path":"work2.py","file_name":"work2.py","file_ext":"py","file_size_in_byte":2552,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"34080224480","text":"from .itempathjson import ItemPathJSON\nimport datetime as dt\nfrom .datavaluejson import DataValueJSON\nfrom .epmvariable import EpmVariable\nfrom .epmproperty import EpmProperty\nfrom .dataobjectattributes import DataObjectAttributes\nfrom .epmnodeids import EpmNodeIds\nfrom .epmdataobject import EpmDataObject, ClampingMode\nfrom .basicvariablepropertymask import BasicVariablePropertyMask\nfrom .historyupdatetype import HistoryUpdateType\n\nfrom enum import Enum\n\nclass TagType(Enum):\n    SourceType = 0\n\n    Bit = 1\n\n    Int = 2\n\n    UInt = 3\n\n    Float = 4\n\n    Double = 5\n\n    String = 6\n\n    DateTime = 8\n\nclass DataTypeId(Enum):\n    SourceType = None\n\n    Unknown = 0\n\n    Bit = 1\n\n    Int = 8 # Int64 Code\n\n    UInt = 9 # UInt64 code\n\n    Float = 10\n\n    Double = 11\n\n    String = 12\n\n    DateTime = 13\n\nclass BasicVariable(EpmDataObject):\n    \"\"\"description of class\"\"\"\n\n    def __init__(self, epmConnection, itemPath, name, description = None, tagType = None, realTimeEnabled = None, deadBandFilter = None, \n                 deadBandUnit = None, eu = None, lowLimit = None, highLimit = None, scaleEnable = None, inputLowLimit = None, \n                 inputHighLimit = None, clamping = None, domain = None, interface = None, ioTagAddress = None, processingEnabled = None, \n                 isRecording = None, isCompressing = None, storeMillisecondsEnabled = None, storageSet = None, active = None):\n        super().__init__(epmConnection, name, itemPath)\n        self._name = name\n        self._newName = None\n        self._description = description\n        self._tagType = TagType.SourceType.value if tagType == None else TagType[tagType].value if isinstance(tagType, str) else tagType if isinstance(tagType, int) or tagType is None else tagType.value\n        self._realTimeEnabled = realTimeEnabled\n        self._deadBandFilter = deadBandFilter\n        self._deadBandUnit = deadBandUnit\n        self._eu = eu\n        self._lowLimit = lowLimit\n        self._highLimit = highLimit\n        self._scaleEnable = scaleEnable\n        self._inputLowLimit = inputLowLimit\n        self._inputHighLimit = inputHighLimit\n        self._clamping = clamping if isinstance(clamping, int) or clamping is None else clamping.value\n        self._domain = domain\n        self._interface = interface\n        self._ioTagAddress = ioTagAddress\n        self._processingEnabled = processingEnabled\n        self._isRecording = isRecording\n        self._isCompressing = isCompressing\n        self._storeMillisecondsEnabled = storeMillisecondsEnabled\n        self._storageSet = storageSet\n        self._active = active\n\n    @property\n    def name(self):\n      return self._name\n\n    @name.setter\n    def name(self, value):\n      if self._name == value:\n        return\n      self._changeMask = self._changeMask | BasicVariablePropertyMask.Name.value\n      self._newName = value\n\n    @property\n    def tagType(self):\n      return TagType(self._tagType) if self._tagType is not None else None\n\n    @tagType.setter\n    def tagType(self, value):\n      if isinstance(value, int) or value is None:\n        if self._tagType == value:\n          return\n        self._tagType = value\n      else:\n        if self._tagType == value.value:\n          return\n        self._tagType = value.value\n      self._changeMask = self._changeMask | BasicVariablePropertyMask.TagType.value\n\n    @property\n    def realTimeEnabled(self):\n      return self._realTimeEnabled\n\n    @realTimeEnabled.setter\n    def realTimeEnabled(self, value):\n      if self._realTimeEnabled == value:\n        return\n      self._changeMask = self._changeMask | BasicVariablePropertyMask.RealTimeEnabled.value\n      self._realTimeEnabled = value\n\n    @property\n    def deadBandFilter(self):\n      return self._deadBandFilter\n\n    @deadBandFilter.setter\n    def deadBandFilter(self, value):\n      if self._deadBandFilter == value:\n        return\n      self._changeMask = self._changeMask | BasicVariablePropertyMask.DeadBandFilter.value\n      self._deadBandFilter = value\n        \n    @property\n    def deadBandUnit(self):\n      return self._deadBandUnit\n\n    @deadBandUnit.setter\n    def deadBandUnit(self, value):\n      if self._deadBandUnit == value:\n        return\n      self._changeMask = self._changeMask | BasicVariablePropertyMask.DeadBandUnit.value\n      self._deadBandUnit = value\n\n    @property\n    def scaleEnable(self):\n      return self._scaleEnable\n\n    @scaleEnable.setter\n    def scaleEnable(self, value):\n      if self._scaleEnable == value:\n        return\n      self._changeMask = self._changeMask | BasicVariablePropertyMask.ScaleEnable.value\n      self._scaleEnable = value\n\n    @property\n    def inputLowLimit(self):\n      return self._inputLowLimit\n\n    @inputLowLimit.setter\n    def inputLowLimit(self, value):\n      if self._inputLowLimit == value:\n        return\n      self._changeMask = self._changeMask | BasicVariablePropertyMask.InputLowLimit.value\n      self._inputLowLimit = value\n        \n    @property\n    def inputHighLimit(self):\n      return self._inputHighLimit\n\n    @inputHighLimit.setter\n    def inputHighLimit(self, value):\n      if self._inputHighLimit == value:\n        return\n      self._changeMask = self._changeMask | BasicVariablePropertyMask.InputHighLimit.value\n      self._inputHighLimit = value\n        \n    @property\n    def interface(self):\n      return self._interface\n\n    @interface.setter\n    def interface(self, value):\n      if self._interface == value:\n        return\n      self._changeMask = self._changeMask | BasicVariablePropertyMask.Interface.value\n      self._interface = value\n        \n    @property\n    def ioTagAddress(self):\n      return self._ioTagAddress\n\n    @ioTagAddress.setter\n    def ioTagAddress(self, value):\n      if self._ioTagAddress == value:\n        return\n      self._changeMask = self._changeMask | BasicVariablePropertyMask.IoTagAddress.value\n      self._ioTagAddress = value\n        \n    @property\n    def processingEnabled(self):\n      return self._processingEnabled\n\n    @processingEnabled.setter\n    def processingEnabled(self, value):\n      if self._processingEnabled == value:\n        return\n      self._changeMask = self._changeMask | BasicVariablePropertyMask.ProcessingEnabled.value\n      self._processingEnabled = value\n        \n    @property\n    def isRecording(self):\n      return self._isRecording\n\n    @isRecording.setter\n    def isRecording(self, value):\n      if self._isRecording == value:\n        return\n      self._changeMask = self._changeMask | BasicVariablePropertyMask.IsRecording.value\n      self._isRecording = value\n        \n    @property\n    def isCompressing(self):\n      return self._isCompressing\n\n    @isCompressing.setter\n    def isCompressing(self, value):\n      if self._isCompressing == value:\n        return\n      self._changeMask = self._changeMask | BasicVariablePropertyMask.IsCompressing.value\n      self._isCompressing = value\n        \n    @property\n    def storeMillisecondsEnabled(self):\n      return self._storeMillisecondsEnabled \n\n    @storeMillisecondsEnabled.setter\n    def storeMillisecondsEnabled(self, value):\n      if self._storeMillisecondsEnabled == value:\n        return\n      self._changeMask = self._changeMask | BasicVariablePropertyMask.StoreMillisecondsEnabled.value\n      self._storeMillisecondsEnabled = value\n        \n    @property\n    def storageSet(self):\n      return self._storageSet\n\n    @storageSet.setter\n    def storageSet(self, value):\n      if self._storageSet == value:\n        return\n      self._changeMask = self._changeMask | BasicVariablePropertyMask.StorageSet.value\n      self._storageSet = value\n\n\n    #Public Methods\n\n    def _setAttribute(self, attributeId, value):\n      if attributeId == DataObjectAttributes.Description:\n        self._description = value if value is not None else \"\"\n      elif attributeId == DataObjectAttributes.EU:\n        self._eu = value['displayName'] if value is not None else value\n      elif attributeId == DataObjectAttributes.HighLimit:\n        self._highLimit = value\n      elif attributeId == DataObjectAttributes.LowLimit:\n        self._lowLimit = value\n      elif attributeId == DataObjectAttributes.Clamping:\n        self._clamping = value\n      elif attributeId == DataObjectAttributes.Domain:\n        self._domain = 'Discrete' if value else 'Continuous'\n      elif attributeId == DataObjectAttributes.Active:\n        self._active = value\n      elif attributeId == DataObjectAttributes.TagType:\n        self._tagType = value\n\n    def historyReadRaw(self, queryPeriod, bounds = False):\n        if self.tagType != None and self.tagType != TagType.SourceType:\n            if self._hasFlag(BasicVariablePropertyMask.TagType):\n                raise Exception(\"Save the basic variable new tagType before writing a value\")\n            dataType = DataTypeId[self.tagType.name].value\n            return self._epmConnection._historyReadRaw(queryPeriod, self._itemPath, bounds, \"i=\" + str(dataType))\n        else:\n            return self._epmConnection._historyReadRaw(queryPeriod, self._itemPath, bounds)\n\n    def write(self, value, timestamp=dt.datetime.now(dt.timezone.utc), quality=0):\n        if self.tagType != None and self.tagType != TagType.SourceType:\n            if self._hasFlag(BasicVariablePropertyMask.TagType):\n                raise Exception(\"Save the basic variable new tagType before writing a value\")\n            dataType = DataTypeId[self.tagType.name].value\n            return self._epmConnection._write([self._itemPath], [13], [DataValueJSON(value, quality, timestamp,\n                                                                                     dataTypeId=\"i=\"+str(dataType))])\n        else:\n          return self._epmConnection._write([self._itemPath], [13], [DataValueJSON(value, quality, timestamp)])\n\n    def historyUpdate(self, values):\n        if self.tagType != None and self.tagType != TagType.SourceType:\n            if self._hasFlag(BasicVariablePropertyMask.TagType):\n                raise Exception(\"Save the basic variable new tagType before using historyUpdate\")\n            dataType = DataTypeId[self.tagType.name].value\n            return self._epmConnection._historyUpdate(HistoryUpdateType.Update.value, [ self._itemPath ], [ values ],\n                                                      dataTypeId=\"i=\"+str(dataType))\n        else:\n            return self._epmConnection._historyUpdate(HistoryUpdateType.Update.value, [ self._itemPath ], [ values ])\n\n\n    def save(self):\n      self._epmConnection.updateBasicVariable(self._name, \n                                              self._newName if self._hasFlag(BasicVariablePropertyMask.Name) else None,\n                                              self._description if self._hasFlag(BasicVariablePropertyMask.Description) else None,\n                                              self._tagType if self._hasFlag(BasicVariablePropertyMask.TagType) else None,\n                                              self._realTimeEnabled if self._hasFlag(BasicVariablePropertyMask.RealTimeEnabled) else None,\n                                              self._deadBandFilter if self._hasFlag(BasicVariablePropertyMask.DeadBandFilter) else None,\n                                              self._deadBandUnit if self._hasFlag(BasicVariablePropertyMask.DeadBandUnit) else None,\n                                              self._eu if self._hasFlag(BasicVariablePropertyMask.Eu) else None,\n                                              self._lowLimit if self._hasFlag(BasicVariablePropertyMask.LowLimit) else None,\n                                              self._highLimit if self._hasFlag(BasicVariablePropertyMask.HighLimit) else None,\n                                              self._scaleEnable if self._hasFlag(BasicVariablePropertyMask.ScaleEnable) else None,\n                                              self._inputLowLimit if self._hasFlag(BasicVariablePropertyMask.InputLowLimit) else None,\n                                              self._inputHighLimit if self._hasFlag(BasicVariablePropertyMask.InputHighLimit) else None,\n                                              self._clamping if self._hasFlag(BasicVariablePropertyMask.Clamping) else None,\n                                              self._domain if self._hasFlag(BasicVariablePropertyMask.Domain) else None,\n                                              self._interface if self._hasFlag(BasicVariablePropertyMask.Interface) else None,\n                                              self._ioTagAddress if self._hasFlag(BasicVariablePropertyMask.IoTagAddress) else None,\n                                              self._processingEnabled if self._hasFlag(BasicVariablePropertyMask.ProcessingEnabled) else None,\n                                              self._isRecording if self._hasFlag(BasicVariablePropertyMask.IsRecording) else None,\n                                              self._isCompressing if self._hasFlag(BasicVariablePropertyMask.IsCompressing) else None,\n                                              self._storeMillisecondsEnabled if self._hasFlag(BasicVariablePropertyMask.StoreMillisecondsEnabled) else None,\n                                              self._storageSet if self._hasFlag(BasicVariablePropertyMask.StorageSet) else None)\n\n      if (self._newName != None and self._hasFlag(BasicVariablePropertyMask.Name)):\n        self._name = self._newName\n\n      self._changeMask = BasicVariablePropertyMask.Unspecified.value\n\n      \n\n\n    def copy(self, newName, description = None, tagType = None, realTimeEnabled = None, deadBandFilter = None, \n            deadBandUnit = None, eu = None, lowLimit = None, highLimit = None, scaleEnable = None, inputLowLimit = None, \n            inputHighLimit = None, clamping = None, domain = None, interface = None, ioTagAddress = None, processingEnabled = None, \n            isRecording = None, isCompressing = None, storeMillisecondsEnabled = None, storageSet = None):\n      return self._epmConnection.createBasicVariable(newName,\n                                              description = description if description != None else self._description,\n                                              tagType = tagType if tagType != None else self._tagType,\n                                              realTimeEnabled = realTimeEnabled if realTimeEnabled != None else self._realTimeEnabled,\n                                              deadBandFilter = deadBandFilter if deadBandFilter != None else self._deadBandFilter,\n                                              deadBandUnit = deadBandUnit if deadBandUnit != None else self._deadBandUnit,\n                                              eu = eu if eu != None else self._eu,\n                                              lowLimit = lowLimit if lowLimit != None else self._lowLimit,\n                                              highLimit = highLimit if highLimit != None else self._highLimit,\n                                              scaleEnable = scaleEnable if scaleEnable != None else self._scaleEnable,\n                                              inputLowLimit = inputLowLimit if inputLowLimit != None else self._inputLowLimit,\n                                              inputHighLimit = inputHighLimit if inputHighLimit != None else self._inputHighLimit,\n                                              clamping = clamping if clamping != None else self._clamping,\n                                              domain = domain if domain != None else self._domain,\n                                              interface = interface if interface != None else self._interface,\n                                              ioTagAddress = ioTagAddress if ioTagAddress != None else self._ioTagAddress,\n                                              processingEnabled = processingEnabled if processingEnabled != None else self._processingEnabled,\n                                              isRecording = isRecording if isRecording != None else self._isRecording,\n                                              isCompressing = isCompressing if isCompressing != None else self._isCompressing,\n                                              storeMillisecondsEnabled = storeMillisecondsEnabled if storeMillisecondsEnabled != None else self._storeMillisecondsEnabled,\n                                              storageSet = storageSet if storageSet != None else self._storageSet)\n\n    def delete(self):\n      result = self._epmConnection.deleteBasicVariable([ self._name ])\n      return result[0]\n\n    def _hasFlag(self, flag):\n      return self._changeMask & flag.value == flag.value\n\n\n\n\nclass BasicVariableAlreadyExistsException(Exception):\n    pass\n\nclass BasicVariableInvalidNameException(Exception):\n    pass\n\nclass StorageSetDoesNotExistException(Exception):\n    pass\n\nclass InterfaceDoesNotExistException(Exception):\n    pass\n\n","repo_name":"RennosonicDesenvolvimento/elipsewebapi","sub_path":"epmwebapi/basicvariable.py","file_name":"basicvariable.py","file_ext":"py","file_size_in_byte":16851,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"9943378968","text":"import requests\nimport bs4 as bs\nfrom ebaysdk.finding import Connection as Finding\nfrom statistics import mean\n\nimport ebaykey\n\nregion = \"semo\" #want to create a function that grabs your region.\nBASE_URL = \"https://\" + region + \".craigslist.org/search/sss\"\ncraigslist_soup = bs.BeautifulSoup(requests.get(BASE_URL).text, 'lxml')\n\nAPP_ID = ebaykey.ebay_key()\n\nitem = input(\"what item do you want? \")\n\n\ndef create_craigslist_dict(soup):\n\n    title_list = []\n    price_list = []\n\n    for row in soup.find_all('p', class_='result-info'):\n        for title in row.find_all('a', class_='result-title'):\n            title_list.append(title.string)\n\n    for row in soup.find_all('p', class_='result-info'):\n        for price in row.find_all('span', class_='result-price'):\n            price_list.append(int((price.string.lstrip(\"$\"))))\n\n    craigslist_dict = dict(zip(title_list, price_list))\n\n    craigslist_dict = {k: v for k, v in craigslist_dict.items() if int(v) < 999}\n\n    return craigslist_dict\n\ndef search_ebay(item):\n\n    api = Finding(appid=APP_ID, config_file=None)\n    response = api.execute('findItemsAdvanced', {'keywords': item})\n\n    return response.dict()\n\ndef average_ebay_price(item):\n\n    price_list = []\n\n    for result in search_ebay(item)['searchResult']['item']:\n\n        price_list.append(float(result['sellingStatus']['currentPrice']['value']))\n\n    return(mean(price_list))\n\n\ndef find_deals(dict,item):\n\n    avg_price = average_ebay_price(item)\n\n    for item1, price in cl_dict.items():\n        if int(price) < int((avg_price * .5)):\n            print(item, price, avg_price)\n\n\ncl_dict = create_craigslist_dict(craigslist_soup)\n\nfind_deals(cl_dict, item)\n","repo_name":"IyaJenkei/flipping_program","sub_path":"flipping_program.py","file_name":"flipping_program.py","file_ext":"py","file_size_in_byte":1675,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"31423822664","text":"import requests\nfrom ApiKeys import *\nfrom URLs import *\nfrom datetime import datetime\nfrom Functions import *\nimport googlemaps\n\nclass time_calculator:\n\n    def get_time_mapbox(self, shipFrom, shipTo):    # Retrieves a time matrix between two locations from Mapbox\n        access_token = \"access_token\"\n        key = key_mapbox_default\n        URL = MAP_BOX_DISTANCE_URL\n        requestURL = \"{0}{1};{2}?{3}={4}\".format(URL,shipTo,shipFrom,access_token,key)\n        req = requests.get(requestURL)\n        res = req.json()    # Res is a dict\n        durationFromTo = res['durations'][0][1]\n        durationToFrom = res['durations'][1][1]\n\n        if durationFromTo != durationToFrom:\n            if durationFromTo > durationToFrom:\n                return durationFromTo\n            else:\n                return durationToFrom\n\n\n    def get_time_city_mapper(self, shipFrom, shipTo):   # Retrieves the time, based on the current date time, from City Mapper\n        URL = \"https://developer.citymapper.com/api/1/traveltime/?\"\n        key = key_city_mapper\n        time = datetime.now()\n        requestURL = \"{0}startcoord={1}&endcoord={2}&time={3}&timetype=arrival&key={4}\".format(URL, shipFrom, shipTo, time, key)\n        req = requests.get(requestURL)\n        res = req.json()\n        duration = res['travel_time_minutes']\n        return duration\n\n\n    def get_duration_google(self, shipFrom, shipTo):    # Retrieves duration between two points from Google\n        gmaps = googlemaps.Client(key_distance)\n        my_distance = gmaps.distance_matrix(shipFrom, shipTo)\n        rows = my_distance[\"rows\"][0][\"elements\"][0]\n        duration = rows[\"duration\"][\"value\"]\n        return duration\n\n    def get_duration_graph_hopper(self, shipFrom, shipTo):\n        duration = graph_hopper()\n        graph_hopper_routing = duration.graph_hopper_routing(shipFrom, shipTo)\n        graph_hopper_duration = graph_hopper_routing['time']\n        return graph_hopper_duration","repo_name":"sugarslaurence/Latlongs","sub_path":"Time.py","file_name":"Time.py","file_ext":"py","file_size_in_byte":1958,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"32165912962","text":"import bb.build\nimport time\n\nclass BBUIHelper:\n    def __init__(self):\n        self.needUpdate = False\n        self.running_tasks = {}\n        # Running PIDs preserves the order tasks were executed in\n        self.running_pids = []\n        self.failed_tasks = []\n        self.tasknumber_current = 0\n        self.tasknumber_total = 0\n\n    def eventHandler(self, event):\n        if isinstance(event, bb.build.TaskStarted):\n            if event._mc != \"default\":\n                self.running_tasks[event.pid] = { 'title' : \"mc:%s:%s %s\" % (event._mc, event._package, event._task), 'starttime' : time.time() }\n            else:\n                self.running_tasks[event.pid] = { 'title' : \"%s %s\" % (event._package, event._task), 'starttime' : time.time() }\n            self.running_pids.append(event.pid)\n            self.needUpdate = True\n        elif isinstance(event, bb.build.TaskSucceeded):\n            del self.running_tasks[event.pid]\n            self.running_pids.remove(event.pid)\n            self.needUpdate = True\n        elif isinstance(event, bb.build.TaskFailedSilent):\n            del self.running_tasks[event.pid]\n            self.running_pids.remove(event.pid)\n            # Don't add to the failed tasks list since this is e.g. a setscene task failure\n            self.needUpdate = True\n        elif isinstance(event, bb.build.TaskFailed):\n            del self.running_tasks[event.pid]\n            self.running_pids.remove(event.pid)\n            self.failed_tasks.append( { 'title' : \"%s %s\" % (event._package, event._task)})\n            self.needUpdate = True\n        elif isinstance(event, bb.runqueue.runQueueTaskStarted) or isinstance(event, bb.runqueue.sceneQueueTaskStarted):\n            self.tasknumber_current = event.stats.completed + event.stats.active + event.stats.failed + 1\n            self.tasknumber_total = event.stats.total\n            self.needUpdate = True\n        elif isinstance(event, bb.build.TaskProgress):\n            if event.pid > 0:\n                self.running_tasks[event.pid]['progress'] = event.progress\n                self.running_tasks[event.pid]['rate'] = event.rate\n                self.needUpdate = True\n        else:\n            return False\n        return True\n\n    def getTasks(self):\n        self.needUpdate = False\n        return (self.running_tasks, self.failed_tasks)\n","repo_name":"opencomputeproject/Rack-Manager","sub_path":"Contrib-Inspur/openbmc/poky/bitbake/lib/bb/ui/uihelper.py","file_name":"uihelper.py","file_ext":"py","file_size_in_byte":2328,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"48"}
+{"seq_id":"15215679503","text":"\"\"\" Cassandra query executor (and timer). CQL files must contain valid syntax, queries must be separated by a\n newline, and the number of queries per set must be specified.\n\nUsage: python3 query.py [uri] [cql-file] [index-file] [n]\n\"\"\"\n\nfrom cassandra.query import tuple_factory\nfrom cassandra.cluster import Cluster\nfrom numpy import average, std\nfrom timeit import timeit\nfrom time import sleep\nfrom sys import argv\n\n\ndef lookup_tyc(ra, dec, index_file):\n    \"\"\" Given some point in space, search the index file for the matching GSC region.\n\n    :param ra: Right ascension to search for.\n    :param dec: Declination to search for.\n    :param index_file: Index file to search\n    :return: The matching TYC1 number if one exists. Otherwise, -1.\n    \"\"\"\n    with open(index_file, 'r') as i_f:\n        for i, entry in enumerate(i_f):\n            try:\n                node = {\n                    'RAmin': float(entry[15:21]),\n                    'RAmax': float(entry[22:28]),\n                    'DEmin': float(entry[29:35]),\n                    'DEmax': float(entry[36:42])\n                }\n                if node['RAmin'] < ra < node['RAmax'] and node['DEmin'] < dec < node['DEmax']:\n                    return i\n\n            except ValueError:\n                pass\n\n    # If no matching TYC is found, return -1.\n    return -1\n\n\ndef execute_query_sets(q):\n    \"\"\" Given a sequence of queries, execute them and feed the result of the former into the latter.\n\n    :param q: Query sequence to execute.\n    :return: None.\n    \"\"\"\n    previous = []\n\n    for q_i in q:\n        # Custom function (not CQL), compute the hash for the following values.\n        if q_i.split()[0] == 'COMPUTE' and q_i.split()[1] == 'HASH':\n            ra, dec = float(q_i.split()[2]), float(q_i.split()[3])\n            previous = [[lookup_tyc(ra, dec, argv[3])]]\n\n        else:\n            # Otherwise, execute the query. Pass the previous result if desired.\n            if q_i.find('?') != -1:\n                # Note: We execute the query even if the result of 'previous' is none.\n                previous = [x for x in session.execute(\n                    q_i.replace('?', str(previous[0][0]) if len(previous) != 0 else '-1'))]\n\n            else:\n                previous = [x for x in session.execute(q_i)]\n            # print('Result: ' + (','.join([str(x) for x in previous]) if len(previous) != 0 else 'None'))\n\n\nif __name__ == '__main__':\n    # We need to be passed the URI, the location of the query file, and the number of subqueries.\n    if len(argv) != 5:\n        print('Usage: python3 query.py [uri] [cql-file] [index-file] [n]')\n        exit(1)\n\n    # Connect to the database, and start a session.\n    cluster = Cluster([argv[1]])\n    session = cluster.connect()\n    session.set_keyspace('tycho')\n    session.row_factory = tuple_factory\n\n    # Load our file into memory, do not read our comments.\n    queries, q_t, r_t, n_c = [], [], [], 0\n    with open(argv[2], 'r') as cql_f:\n        for line in cql_f:\n\n            # Flush our buffer q_t when we have a complete subquery set.\n            if n_c < int(argv[4]):\n                n_c = (n_c + 1) if line.strip() and not line.startswith(';') else n_c\n                q_t.append(line.strip()) if line.strip() and not line.startswith(';') else None\n            else:\n\n                # Append if necessary.\n                queries.append(q_t)\n                if line.strip() and not line.startswith(';'):\n                    n_c, q_t = 1, [line.strip()]\n                else:\n                    n_c, q_t = 0, []\n\n    # Run our queries 15 times.\n    for i in range(15):\n        for j, q in enumerate(queries):\n            r_t.append(timeit(stmt=lambda: execute_query_sets(q), number=1))\n            print('Running Time [Query {}, Run {}]: {}'.format(j, i, r_t[-1]))\n            sleep(0.25)\n\n    print('Average Running Time: {} +/- {}'.format(average(r_t), std(r_t)))\n","repo_name":"glennga/tycho-query","sub_path":"run/cassandra/query.py","file_name":"query.py","file_ext":"py","file_size_in_byte":3901,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"2052331214","text":"def run():\r\n    arroba = 0\r\n    punto = 0\r\n    email = input(\"Ingresa tu dirección de email: \")\r\n    for i in email:\r\n        if i == \"@\":           \r\n            arroba += 1\r\n        # acordarse que no va elif, va if porque debe evaluar las dos condiciones\r\n        if i == \".\":           \r\n            punto += 1        \r\n    if arroba == 1 and punto >= 1:\r\n        print(\"Email valido\")\r\n    else:\r\n        print(\"Email invalido\")\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    run()\r\n    email = \"fabricio\"\r\n    print(range(len(email)))","repo_name":"Fabriciogg8/python_basics","sub_path":"ejercicio6.py","file_name":"ejercicio6.py","file_ext":"py","file_size_in_byte":531,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"12150077577","text":"from django.test import TestCase\n\nfrom incomewealth.app.helpers import (calc_year_based_inequality_factors,\n                                      calc_year_based_saving_capacities)\n\n\nclass UtilTest(TestCase):\n\n    def setUp(self):\n        self.mock_data_inequality = [\n            {'year': 2010, 'foo_bottom50': 0.1, 'foo_top10': 0.2}\n        ]\n\n        self.mock_data_saving = [\n            {'year': 2010, 'income_top10': 0.1},\n            {'year': 2011, 'income_top10': 0.2},\n            {'year': 2012, 'income_top10': 0.3},\n        ]\n\n    def test_inequality_factors(self):\n        factor_mapping = None\n        for d in calc_year_based_inequality_factors(\n                self.mock_data_inequality, 'foo'):\n            factor_mapping = d\n            break\n\n        self.assertTrue(factor_mapping is not None)\n        self.assertEqual(factor_mapping['factor'], (0.1 / 50) / (0.2 / 10))\n\n    def test_saving_capacities(self):\n        saving_capacity_mapping = None\n        for d in calc_year_based_saving_capacities(\n                self.mock_data_saving, 'top', 10):\n            saving_capacity_mapping = d\n            break\n\n        self.assertTrue(saving_capacity_mapping is not None)\n        should_be = ((0.2 / 10) - (0.1 / 10)) / (0.1 / 10)\n        self.assertEqual(saving_capacity_mapping['savingcapacity'], should_be)\n","repo_name":"baranbartu/income-wealth-dj","sub_path":"incomewealth/app/tests/test_helpers.py","file_name":"test_helpers.py","file_ext":"py","file_size_in_byte":1328,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"48"}
+{"seq_id":"28519004562","text":"from __future__ import print_function\n\nfrom itertools import product\nfrom time import time\nfrom typing import List\n\nimport numpy as np\nfrom sklearn.semi_supervised import LabelPropagation, LabelSpreading\n\nfrom gdpc.worldLoader import WorldSlice\nfrom utils import Point, water_blocks, lava_blocks, \\\n    BuildArea, getBlockRelativeAt, PointArray, Direction\nimport parameters\nfrom terrain.biomes import BiomeMap\nfrom utils.algorithms.fast_dijkstra import fast_dijkstra\nfrom utils.parameters import MIN_DIST_TO_OCEAN, MIN_DIST_TO_RIVER, \\\n    MIN_DIST_TO_LAVA\n\n\nclass FluidMap(PointArray):\n\n    def __new__(cls, level: WorldSlice, area: BuildArea, terrain, **kwargs):\n        values = np.zeros((area.width, area.length), dtype=np.int0)\n        obj = super().__new__(cls, values)\n        obj.area = area\n        obj.__water_map = np.full((area.width, area.length), 0)\n        obj.__lava_map = np.full((area.width, area.length), False)\n        obj.terrain = terrain\n        obj.__water_limit = kwargs.get(\"water_limit\", parameters.MAX_WATER_EXPLORATION)\n        obj.__lava_limit = kwargs.get(\"lava_limit\", parameters.MAX_LAVA_EXPLORATION)\n        \n        obj.__borderpts = []  # type: List[Point]\n        obj.__coastline = []  # type: List[Point]\n\n        obj.has_lava = obj.has_river = obj.has_ocean = False\n        obj.detect_sources(level)\n\n        return obj\n\n    def detect_sources(self, level, algorithm='spread', kernel='knn', param=16):\n        # type: (WorldSlice, str, str, int) -> None\n        water_points = []\n        t0 = time()\n        for x, z in product(range(self.area.width), range(self.area.length)):\n            y: int = self.terrain.height_map[x, z]\n            if getBlockRelativeAt(level, x, y, z).split(':')[-1] in water_blocks:\n                biome = BiomeMap.getBiome(self.terrain.biome[x, z])\n                if 'ocean' in biome or 'beach' in biome:\n                    label = 1\n                elif 'river' in biome:\n                    label = 2\n                elif 'swamp' in biome:\n                    label = 3\n                else:\n                    label = -1  # yet unlabeled\n                water_points.append((x, z, label))\n\n            elif getBlockRelativeAt(level, x, y, z) in lava_blocks:\n                self.__lava_map[x, z] = True\n                self.has_lava = True\n\n        if water_points:\n            data = [entry[:2] for entry in water_points]\n            lbls = [entry[2] for entry in water_points]\n\n            if algorithm in ['prop', 'spread']:\n                algo = LabelPropagation if algorithm == 'prop' else LabelSpreading\n                model = algo(kernel, gamma=param, n_neighbors=min(len(lbls), param), tol=1e-4, max_iter=200)\n                try:\n                    model.fit(data, lbls)\n                    lbls = model.predict(data)\n                except ValueError:\n                    # no water or no labeled water point (ponds only)\n                    lbls = [3 for _ in data]\n\n            lbls = [2 if _ == -1 else _ for _ in lbls]\n\n            for (x, z), water_type in zip(data, lbls):\n                self.__water_map[x, z] = water_type\n                if water_type == 1:\n                    self.has_ocean = True\n                elif water_type in [2, 3]:\n                    self.has_river = True\n\n        t1 = time()\n        print('Computed water map in {:0.3f} seconds'.format(t1 - t0))\n        self.__build_distance_maps()\n        print('Computed distance maps in {:0.3f} seconds'.format(time() - t1))\n\n    def __build_distance_maps(self):\n        self.river_distance = np.full(self.__water_map.shape, self.__water_limit, dtype=np.float32)\n        self.ocean_distance = np.full(self.__water_map.shape, self.__water_limit, dtype=np.float32)\n        self.lava_distance = np.full(self.__lava_map.shape, self.__lava_limit, dtype=np.float32)\n\n        for x, z in product(range(self.area.width), range(self.area.length)):\n            if self.__water_map[x, z] in [2, 3]:\n                self.river_distance[x, z] = 0\n            elif self.__water_map[x, z] == 1:\n                self.ocean_distance[x, z] = 0\n            elif self.__lava_map[x, z]:\n                self.lava_distance[x, z] = 0\n            elif (x == 0) or (z == 0) or (x == (self.area.width - 1)) or (z == (self.area.length - 1)):\n                self.__borderpts.append(Point(x, z))\n\n        def is_init_neigh(distance_map, _x, _z):\n            # Context: find border water points in a water surface.\n            # Surrounded water points are useless in exploration\n            W, L = distance_map.shape\n            if distance_map[_x, _z] != 0:\n                return False\n            else:\n                for direction in Direction.cardinal_directions():\n                    x0, z0 = _x + direction.x, _z + direction.z\n                    try:\n                        if distance_map[x0, z0] != 0:\n                            return True  # (x, z) is a water (or lava) point with a non water neighbour\n                    except IndexError:\n                        continue\n                return False\n\n        self.__coastline = [Point(_x, _z) for _x, _z in product(range(self.area.width), range(self.area.length))\n                            if is_init_neigh(self.ocean_distance, _x, _z)]\n\n        fast_dijkstra(self.river_distance, self.terrain.height_map[:])\n        fast_dijkstra(self.ocean_distance, self.terrain.height_map[:])\n        fast_dijkstra(self.lava_distance, self.terrain.height_map[:])\n\n        # __pseudo_dijkstra(self.river_distance)\n        # __pseudo_dijkstra(self.ocean_distance)\n        # __pseudo_dijkstra(self.lava_distance)\n\n        # print(self.river_distance[0, 0])\n        # from matplotlib import pyplot as plt\n        # plt.imshow(self.river_distance)\n        # plt.show()\n\n    def is_lava(self, x_or_point, z=None, margin=0):\n        # type: (Point or int, None or int, float) -> object\n        if isinstance(x_or_point, Point):\n            p = x_or_point\n            return self.lava_distance[p.x, p.z] <= margin\n        else:\n            x = x_or_point\n            return self.lava_distance[x, z] <= margin\n\n    def is_close_to_fluid(self, x_or_point, z=None):\n        # type: (Point or int, None or int) -> object\n        if isinstance(x_or_point, Point):\n            p = x_or_point\n            return self.is_close_to_fluid(p.x, p.z)\n        else:\n            x = x_or_point\n            return ((self.ocean_distance[x][z] <= MIN_DIST_TO_OCEAN)\n                    | (self.river_distance[x][z] <= MIN_DIST_TO_RIVER)\n                    | (self.lava_distance[x][z] <= MIN_DIST_TO_LAVA))\n\n    def is_water(self, x_or_point, z=None, margin=0):\n        # type: (int or Point, int or None, float) -> bool\n        if isinstance(x_or_point, Point):\n            return self.is_water(x_or_point.x, x_or_point.z)\n        else:\n            x = x_or_point\n            return (self.ocean_distance[x, z] <= margin) | (self.river_distance[x, z] <= margin)\n\n    @property\n    def as_obstacle_array(self):\n        # if one of the conditions is valid, fluids are an obstacle\n        obs = ((self.ocean_distance <= MIN_DIST_TO_OCEAN)\n               | (self.river_distance <= MIN_DIST_TO_RIVER)\n               | (self.lava_distance <= MIN_DIST_TO_LAVA))  # type: np.ndarray\n        return obs.astype(int)\n\n    @property\n    def water(self):\n        return self.__water_map\n\n    @property\n    def external_connections(self):\n        return self.__borderpts + self.__coastline\n\n    def water_distance(self, px, pz=None):\n        if pz is None:\n            return self.water_distance(px.x, px.z)\n        px, pz = int(round(px)), int(round(pz))\n        res = self.width * self.length\n        if self.has_ocean: res = min(res, self.ocean_distance[px, pz])\n        if self.has_river: res = min(res, self.river_distance[px, pz])\n        return int(res)\n","repo_name":"Lasbleic/GDMC","sub_path":"src/terrain/fluid_map.py","file_name":"fluid_map.py","file_ext":"py","file_size_in_byte":7817,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"23859588249","text":"# https://www.codewars.com/kata/5264d2b162488dc400000001/python\ndef spin_words(sentence):\n    ret = ''\n    count = 0\n\n    for i, c in enumerate(sentence):\n        if c.isalpha():\n            count += 1\n        elif count >= 5:\n            ret += sentence[i:i - count:-1]\n            count = 0\n        else:\n            ret += sentence[i - count:i + 1]\n            count = 0\n    else:\n        # i += 1\n        if count >= 5:\n            sentence = ' ' + sentence\n            ret += sentence[i+1:i - count + 1:-1]\n        else:\n            ret += sentence[i - count:i + 1]\n\n    return ret\n\n\n# print(spin_words('Welcome'))\n# print(spin_words('Hey fellow warriors'))\n\n# s = 'Hey fellow warriors'\n# counter = 6\n# i = 10\n\n\ndef spin_words_better(sentence):\n    ret = ''\n    word = ''\n\n    for i, c in enumerate(sentence):\n        if c.isalpha():\n            word += c\n        elif len(word) >= 5:\n            ret += word[::-1] + ' '\n            word = ''\n        else:\n            ret += word + ' '\n            word = ''\n    else:\n        if len(word) >= 5:\n            ret += word[::-1]\n        else:\n            ret += word\n\n    return ret\n\n\ndef spin_words_ultimate(sentence):\n    # Your code goes here\n    return \" \".join([x[::-1] if len(x) >= 5 else x for x in sentence.split(\" \")])\n\n\nprint(spin_words_ultimate('This sentence is a sentence'))\n","repo_name":"ShaiOren4761/CodeWarrior","sub_path":"Stop gninnipS My sdroW!.py","file_name":"Stop gninnipS My sdroW!.py","file_ext":"py","file_size_in_byte":1340,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"35011817215","text":"#!/usr/bin/env python3\n\nfrom PySide2 import QtWidgets\nfrom PySide2 import QtGui\nfrom PySide2 import QtCore\nfrom PySide2.QtWidgets import (QApplication, QTreeView)\nfrom PySide2.QtUiTools import (QUiLoader, )\nfrom PySide2.QtCore import (QDir, QFile)\nimport os\n\nclass MyFileBrowser():\n    def __init__(self, application, main_window):\n        assert isinstance(application, QApplication)\n        assert isinstance(main_window, QtWidgets.QMainWindow)\n        #super(MyFileBrowser, self).__init__()\n        #self.setupUi(self)\n        treeView = main_window.treeView\n        assert isinstance(treeView, QTreeView)\n        self.treeView = treeView\n        #self.treeView.setContextMenuPolicy(QtCore.Qt.CustomContextMenu)\n        #self.treeView.customContextMenuRequested.connect(self.context_menu)\n        #path = r\"C:\\Users\\HP\\Desktop\\MyProject\"\n        path = \"/home/wayne/public_html/projects/digikey_tables\"\n        self.model = QtWidgets.QFileSystemModel()\n        self.model.setRootPath((QtCore.QDir.rootPath()))\n        self.treeView.setModel(self.model)\n        self.treeView.setRootIndex(self.model.index(path))\n        self.treeView.setSortingEnabled(True)\n\n    def context_menu(self):\n        menu = QtWidgets.QMenu()\n        open = menu.addAction(\"Open in new maya\")\n        open.triggered.connect(self.open_file)\n\n        if self.maya:\n            open_file = menu.addAction(\"Open file\")\n            open_file.triggered.connect(lambda: self.maya_file_operations(open_file=True))\n\n            import_to_maya = menu.addAction(\"Import to Maya\")\n            import_to_maya.triggered.connect(self.maya_file_operations)\n\n            reference_to_maya = menu.addAction(\"Add reference to Maya\")\n            reference_to_maya.triggered.connect(lambda: self.maya_file_operations(reference=True))\n\n        cursor = QtGui.QCursor()\n        menu.exec_(cursor.pos())\n\n    def open_file(self):\n        index = self.treeView.currentIndex()\n        file_path = self.model.filePath(index)\n        os.startfile(file_path)\n\n    def maya_file_operations(self, reference=False, open_file=False):\n        \"\"\"\n        open ,  reference,  Import\n        :return:\n        \"\"\"\n        index = self.treeView.currentIndex()\n        file_path = self.model.filePath(index)\n        import maya.cmds as cmds\n        if reference:\n            cmds.file(file_path, reference=True, type='mayaAscii', groupReference=True)\n        elif open_file:\n            file_location = cmds.file(query=True, location=True)\n            if file_location == 'unknown':\n                cmds.file(file_path, open=True, force=True)\n            else:\n                modify_file = cmds.file(query=True, modified=True)\n                if modify_file:\n                    result = cmds.confirmDialog(title='Opening new maya file',\n                                                message='This file is modified. do you want to save this file.?',\n                                                button=['yes', 'no'],\n                                                defaultButton='yes',\n                                                cancelButton='no',\n                                                dismissString='no')\n                    if result == 'yes':\n                        cmds.file(save=True, type='mayaAscii')\n                        cmds.file(file_path, open=True, force=True)\n                    else:\n                        cmds.file(file_path, open=True, force=True)\n                else:\n                    cmds.file(file_path, open=True, force=True)\n        else:\n            cmds.file(file_path, i=True, groupReference=True)\n\n\nif __name__ == '__main__':\n    application = QtWidgets.QApplication([])\n\n    # Read in the `.ui` file:\n    ui_qfile = QFile(\"ui/main.ui\")\n    ui_qfile.open(QFile.ReadOnly)\n    loader = QUiLoader()\n    main_window = loader.load(ui_qfile)\n    print(\"main_window=\", main_window)\n\n    fb = MyFileBrowser(application, main_window)\n    main_window.show()\n    application.exec_()\n\n","repo_name":"waynegramlich/table_tools","sub_path":"file_browser.py","file_name":"file_browser.py","file_ext":"py","file_size_in_byte":3971,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"30603781464","text":"import datetime\nimport sqlalchemy\nfrom flask import jsonify, Flask\nfrom flask_restful import Resource, Api, abort, reqparse\nfrom sqlalchemy import orm\nfrom sqlalchemy.orm import Session\nimport sqlalchemy as sa\nimport sqlalchemy.ext.declarative as dec\nfrom sqlalchemy_serializer import SerializerMixin\n\nSqlAlchemyBase = dec.declarative_base()\n\n\nclass Jobs(SqlAlchemyBase, SerializerMixin):\n    __tablename__ = 'jobs'\n\n    id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True, autoincrement=True)\n    team_leader = sqlalchemy.Column(sqlalchemy.Integer)\n    job = sqlalchemy.Column(sqlalchemy.String)\n    work_size = sqlalchemy.Column(sqlalchemy.Integer)\n    collaborators = sqlalchemy.Column(sqlalchemy.String)\n    start_date = sqlalchemy.Column(sqlalchemy.String)\n    end_date = sqlalchemy.Column(sqlalchemy.String)\n    is_finished = sqlalchemy.Column(sqlalchemy.BOOLEAN)\n\n\ndef global_init(db_file):\n    global __factory\n\n    if __factory:\n        return\n\n    if not db_file or not db_file.strip():\n        raise Exception(\"\")\n\n    conn_str = f'sqlite:///{db_file.strip()}?check_same_thread=False'\n    print(f\"Подключено к базе {conn_str}\")\n\n    engine = sa.create_engine(conn_str, echo=False)\n    __factory = orm.sessionmaker(bind=engine)\n\n    SqlAlchemyBase.metadata.create_all(engine)\n\n\napp = Flask(__name__)\napi = Api(app)\n__factory = None\nparser = reqparse.RequestParser()\nparser.add_argument('team_leader', required=True, type=int)\nparser.add_argument('job', required=True)\nparser.add_argument('work_size', required=True, type=int)\nparser.add_argument('collaborators', required=True)\nparser.add_argument('start_date', required=True)\nparser.add_argument('end_date', required=True)\nparser.add_argument('is_finished', required=True, type=bool)\nglobal_init(\"Jobs.db\")\n\n\ndef create_session() -> Session:\n    global __factory\n    return __factory()\n\n\ndef abort_if_Jobs_not_found(Jobs_id):\n    session = create_session()\n    jobs = session.query(Jobs).filter(Jobs.id == Jobs_id).first()\n    if not jobs:\n        abort(404, message=f\"Jobs {Jobs_id} not found\")\n\n\nclass JobsResource(Resource):\n    def get(self, Jobs_id):\n        abort_if_Jobs_not_found(Jobs_id)\n        session = create_session()\n        jobs = session.query(Jobs).get(Jobs_id)\n        return jsonify({'Jobs': jobs.to_dict(\n            only=('surname', 'name', 'age', 'position', 'speciality', 'address', 'hashed_password', 'email', 'created_date'))})\n\n    def delete(self, Jobs_id):\n        abort_if_Jobs_not_found(Jobs_id)\n        session = create_session()\n        jobs = session.query(Jobs).get(Jobs_id)\n        session.delete(Jobs)\n        session.commit()\n        return jsonify({'success': 'OK'})\n\n\nclass JobsListResource(Resource):\n    def get(self):\n        session = create_session()\n        jobs = session.query(Jobs).all()\n        return jsonify({'Jobs': [item.to_dict(\n            only=('surname', 'name', 'age', 'position', 'speciality', 'address', 'hashed_password', 'email', 'created_date')) for item in jobs]})\n\n    def post(self):\n        args = parser.parse_args()\n        session = create_session()\n        jobs = Jobs(\n            title=args['title'],\n            content=args['content'],\n            Jobs_id=args['Jobs_id'],\n            is_published=args['is_published'],\n            is_private=args['is_private']\n        )\n        session.add(Jobs)\n        session.commit()\n        return jsonify({'success': 'OK'})\n\n\ndef main():\n    global_init(\"Jobs.db\")\n    api.add_resource(JobsListResource, '/api/v2/Jobs')\n    api.add_resource(JobsResource, '/api/v2/Jobs/<int:Jobs_id>')\n    app.run()\n\n\nmain()","repo_name":"PopovLicey/project3","sub_path":"jobs_resource.py","file_name":"jobs_resource.py","file_ext":"py","file_size_in_byte":3608,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"74190310225","text":"#!ENV/bin/python\n\nimport re\nimport urllib.request\nfrom bs4 import BeautifulSoup\n\nchapters = []\nbase_url = \"http://www.phpinternalsbook.com/\"\n\ndef fix_internal_link(link):\n    href = link['href']\n    href = re.sub('^(.*)#', '', href)\n    link['href'] = \"#%s\" % href\n    \ndef fix_external_link(link):\n    href = link['href']\n    link['href'] = \"#%s\" % href\n\ndef fix_image(img):\n    rsrc = img['src']\n    lsrc = re.sub('^(\\.\\./_images/)', '', rsrc)\n    url = base_url + '_images/' + lsrc\n    content = urllib.request.urlopen(url).read()\n    content_str = content.decode(encoding='UTF-8')\n\n    f = open(lsrc, \"w\")\n    f.write(str(content_str))\n    f.close()\n\n    img['src'] = lsrc\n\ndef add_linked_page(link):\n\n    href = link['href']\n\n    fix_external_link(link)\n\n    url = base_url + href\n\n    anchor = \"<a name=\\\"%s\\\"></a>\" % href\n\n    page = urllib.request.urlopen(url).read()\n    soup = BeautifulSoup(page, 'html.parser')\n\n    # remove these strange links\n    [x.extract() for x in soup.select('a.headerlink')]\n\n    # fix links for TOC's inside chapters\n    [fix_internal_link(x) for x in soup.select('a.reference.internal')]\n\n    [fix_image(x) for x in soup.select('img')]\n\n    content = \" \".join([str(x) for x in soup.select('div.content')])\n    chapters.append(anchor + content)\n\ntoc_url = 'http://www.phpinternalsbook.com/index.html'\n\ntoc_page = urllib.request.urlopen(toc_url).read()\ntoc_soup = BeautifulSoup(toc_page, 'html.parser')\n[x.extract() for x in toc_soup.select('a.headerlink')]\n\n[add_linked_page(x) for x in toc_soup.select('div#table-of-contents a.reference.internal')]\n\nc = toc_soup.select('div#table-of-contents')\n\nhead = \"\"\"\n<head>\n<meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\"/>\n<link rel=\"stylesheet\" type=\"text/css\" href=\"style.css\">\n</head>\n\"\"\"\nprint(head)\n\n[print(x) for x in c]\n[print(x) for x in chapters]\n","repo_name":"luke10x/php-internals-book","sub_path":"crawler.py","file_name":"crawler.py","file_ext":"py","file_size_in_byte":1854,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"27093576187","text":"\n\n\n\n\nimport threading\nfrom winpcapy import *\n\nclass cap():\n    def __init__(self, parameter):\n        self.para = {\n            'port':b'',\n            'filter':b'',\n            'logsize':-1,\n            'output':{},\n            }\n        self.adhandle = None\n        self.act = threading.Event()\n        self.read_p = threading.Event()\n        self.read_p.set()\n        self.running = True\n        self.config(parameter)\n        threading.Thread(target=self.run).start()\n\n    def getpara(self, para):\n        t = self.para\n        self.read_p.wait()\n        self.read_p.clear()\n        try:\n            for x in para:\n                t = t[x]\n            self.read_p.set()\n            return t\n        except:\n            self.read_p.set()\n            return None\n \n    def setpara(self, para, value):\n        t = self.para\n        self.read_p.wait()\n        self.read_p.clear()\n        try:\n            for x in para[:-1]:\n                t = t[x]\n            t[para[-1]] = value\n            self.read_p.set()\n            return True\n        except:\n            self.read_p.set()\n            return False\n        \n    def config(self, parameter):\n        errbuf = create_string_buffer(PCAP_ERRBUF_SIZE)\n\n        try:\n            self.setpara(['port'], parameter['port'])\n            self.adhandle = pcap_open_live(\n                self.getpara(['port']),  # device name\n                65536,  # buffer_Byte\n                1,      # pronomous mode\n                1,      # timeout in ms\n                errbuf,\n                )\n        except:\n            pass\n        \n        try:\n            self.setpara(['filter'], parameter['filter'])\n            fcode = bpf_program()\n            pcap_compile(\n                self.adhandle,\n                byref(fcode),\n                self.getpara(['filter']),\n                1,      # pcap_compile\n                0       # ip netmask\n                )\n        except:\n            pass\n        \n        try:\n            for x in parameter['output']:\n                try:\n                    assert(parameter['output'][x] == self.getpara(['output',x]))\n                except:\n                    self.setpara(['output',x], parameter['output'][x])\n        except:\n            pass\n    \n    def run(self):\n        header_data = POINTER(pcap_pkthdr)()\n        packet_data = POINTER(c_ubyte)()\n\n        while self.running:\n            self.act.wait()\n            res = pcap_next_ex(\n                self.adhandle,\n                byref(header_data),\n                byref(packet_data),\n                )\n            if res==1:\n                self.output(header_data,packet_data)\n        pcap_close(self.adhandle)\n        \n    def start(self):\n        self.act.set()\n\n    def pause(self):\n        self.act.clear()\n        \n    def stop(self):\n        self.running = False\n\n    def output(self, header_data, packet_data):\n        content = []\n        for x in packet_data[:header_data.contents.len]:\n            content.append(x)\n\n        pktdata = {\n            'port':self.getpara(['port']),\n            'time':[header_data.contents.ts.tv_sec, header_data.contents.ts.tv_usec],\n            'content':content\n            }\n        if \"crt\" in self.getpara(['output']):\n            for x in self.getpara(['output',\"crt\"]):\n                x.display(pktdata)\n        if \"controller\" in self.getpara(['output']):\n            for x in self.getpara(['output',\"controller\"]):\n                x.receive(pktdata)\n\nimport queue\n\nclass crt(threading.Thread):\n    def __init__(self):\n        super(crt, self).__init__()\n        self.queue = queue.Queue()\n        self.running = True\n\n    def run(self):\n        while self.running:\n            s = self.queue.get()\n            print(s)\n            self.queue.task_done()\n\n    def display(self, pktdata):\n        exp = \"%d:%06d   %s\" % (pktdata['time'][0],\\\n                                pktdata['time'][1],\\\n                                str(pktdata['content'][:16]))\n        self.queue.put(exp)\n\n#--------------\n\nclass controller_ingress(threading.Thread):\n    def __init__(self, queue, parent, isRunning):\n        super(controller_ingress, self).__init__()\n        self.queue = queue\n        self.parent = parent\n        self.isRunning = isRunning\n\n    def run(self):\n        while True:\n            self.isRunning.wait()\n            pkt_data = self.queue.get()\n            self.parent.react(str(pkt_data['time']))\n            self.queue.task_done()\n            \n\n\nclass controller(threading.Thread):\n    def __init__(self):\n        super(controller, self).__init__()\n        self.rx_queue = queue.Queue()\n        self.rx_isRunning = threading.Event()\n        self.ingress = controller_ingress(self.rx_queue, self, self.rx_isRunning)\n        self.ingress.start()\n\n    \n    def run(self):\n        self.rx_isRunning.set()\n\n    def receive(self, pktdata):\n        self.rx_queue.put(pktdata)\n\n    def output(self, value):\n        print(value)\n\n    def react(self, sig):\n        self.output(sig)\n        \n#--------------\nc = crt()\nc.start()\n\nd=controller()\nd.start()\n\ncap_parameter = {\n    'port':b'\\\\Device\\\\NPF_{FA3877BD-7D9C-47D4-88D3-E98D3AC488A2}',\n    'filter':b'tcp',\n    'logsize':-1,\n    'output':{\n        #\"crt\":[c],\n        #\"file\":\"z:\\\\test.txt\"\n        \"controller\":[d],\n        },\n}\nnew_capture = cap(cap_parameter)\nnew_capture.start()\n\n\n\n\n#new_capture.pause()\n#new_capture.stop()\n\n\n\n\n","repo_name":"watry/pzer","sub_path":"sample1.py","file_name":"sample1.py","file_ext":"py","file_size_in_byte":5385,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"10617737823","text":"import cv2\nimport torch\nimport scipy\nimport scipy.misc\nimport numpy as np\nfrom PIL import Image\nfrom torchvision.transforms import functional as F\n\n\n\nMATCHED_PARTS = {\n    \"300W\": ([1, 17], [2, 16], [3, 15], [4, 14], [5, 13], [6, 12], [7, 11], [8, 10],\n             [18, 27], [19, 26], [20, 25], [21, 24], [22, 23],\n             [32, 36], [33, 35],\n             [37, 46], [38, 45], [39, 44], [40, 43], [41, 48], [42, 47],\n             [49, 55], [50, 54], [51, 53], [62, 64], [61, 65], [68, 66], [59, 57], [60, 56]),\n    \"AFLW\": ([1, 6],  [2, 5], [3, 4],\n             [7, 12], [8, 11], [9, 10],\n             [13, 15],\n             [16, 18]),\n    \"COFW\": ([1, 2], [5, 7], [3, 4], [6, 8], [9, 10], [11, 12], [13, 15], [17, 18], [14, 16], [19, 20], [23, 24]),\n    \"WFLW\": ([0, 32],  [1,  31], [2,  30], [3,  29], [4,  28], [5, 27], [6, 26], [7, 25], [8, 24], [9, 23], [10, 22],\n             [11, 21], [12, 20], [13, 19], [14, 18], [15, 17],  # check\n             [33, 46], [34, 45], [35, 44], [36, 43], [37, 42], [38, 50], [39, 49], [40, 48], [41, 47],  # elbrow\n             [60, 72], [61, 71], [62, 70], [63, 69], [64, 68], [65, 75], [66, 74], [67, 73],\n             [55, 59], [56, 58],\n             [76, 82], [77, 81], [78, 80], [87, 83], [86, 84],\n             [88, 92], [89, 91], [95, 93], [96, 97]),\n    \"BP4D\": ([1, 10], [2, 9], [3, 8],[4, 7],[5, 6],\n             [20, 29],[21, 27],[22, 28],[23, 29],[25, 31],[24, 30],\n             [15, 19],[16, 18],\n             [34, 36],[33, 37],[44, 46],[49, 47],[32, 38],[43, 39],[42, 40]\n            )}\n\n\ndef fliplr_joints(x, width, dataset='aflw'):\n    \"\"\"\n    flip coords\n    \"\"\"\n    matched_parts = MATCHED_PARTS[dataset]\n    # Flip horizontal\n    x[:, 0] = width - x[:, 0]\n\n    if dataset == 'WFLW': #start from 0\n        for pair in matched_parts:\n            tmp = x[pair[0], :].copy()\n            x[pair[0], :] = x[pair[1], :]\n            x[pair[1], :] = tmp\n    else:\n        for pair in matched_parts:\n            tmp = x[pair[0] - 1, :].copy()\n            x[pair[0] - 1, :] = x[pair[1] - 1, :]\n            x[pair[1] - 1, :] = tmp\n    return x\n\n\ndef get_3rd_point(a, b):\n    direct = a - b\n    return b + np.array([-direct[1], direct[0]], dtype=np.float32)\n\n\ndef get_dir(src_point, rot_rad):\n    sn, cs = np.sin(rot_rad), np.cos(rot_rad)\n\n    src_result = [0, 0]\n    src_result[0] = src_point[0] * cs - src_point[1] * sn\n    src_result[1] = src_point[0] * sn + src_point[1] * cs\n\n    return src_result\n\n\ndef get_affine_transform(\n        center, scale, rot, output_size,\n        shift=np.array([0, 0], dtype=np.float32), inv=0):\n    if not isinstance(scale, np.ndarray) and not isinstance(scale, list):\n        print(scale)\n        scale = np.array([scale, scale])\n\n    scale_tmp = scale * 200.0\n    src_w = scale_tmp[0]\n    dst_w = output_size[0]\n    dst_h = output_size[1]\n\n    rot_rad = np.pi * rot / 180\n    src_dir = get_dir([0, src_w * -0.5], rot_rad)\n    dst_dir = np.array([0, dst_w * -0.5], np.float32)\n\n    src = np.zeros((3, 2), dtype=np.float32)\n    dst = np.zeros((3, 2), dtype=np.float32)\n    src[0, :] = center + scale_tmp * shift\n    src[1, :] = center + src_dir + scale_tmp * shift\n    dst[0, :] = [dst_w * 0.5, dst_h * 0.5]\n    dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir\n\n    src[2:, :] = get_3rd_point(src[0, :], src[1, :])\n    dst[2:, :] = get_3rd_point(dst[0, :], dst[1, :])\n\n    if inv:\n        trans = cv2.getAffineTransform(np.float32(dst), np.float32(src))\n    else:\n        trans = cv2.getAffineTransform(np.float32(src), np.float32(dst))\n\n    return trans\n\n\ndef crop_v2(img, center, scale, output_size, rot=0):\n    trans = get_affine_transform(center, scale, rot, output_size)\n\n    dst_img = cv2.warpAffine(\n        img, trans, (int(output_size[0]), int(output_size[1])),\n        flags=cv2.INTER_LINEAR\n    )\n\n    return dst_img\n\n\ndef get_transform(center, scale, output_size, rot=0):\n    \"\"\"\n    General image processing functions\n    \"\"\"\n    # Generate transformation matrix\n    h = 200 * scale\n    t = np.zeros((3, 3))\n    t[0, 0] = float(output_size[1]) / h\n    t[1, 1] = float(output_size[0]) / h\n    t[0, 2] = output_size[1] * (-float(center[0]) / h + .5)\n    t[1, 2] = output_size[0] * (-float(center[1]) / h + .5)\n    t[2, 2] = 1\n    if not rot == 0:\n        rot = -rot  # To match direction of rotation from cropping\n        rot_mat = np.zeros((3, 3))\n        rot_rad = rot * np.pi / 180\n        sn, cs = np.sin(rot_rad), np.cos(rot_rad)\n        rot_mat[0, :2] = [cs, -sn]\n        rot_mat[1, :2] = [sn, cs]\n        rot_mat[2, 2] = 1\n        # Need to rotate around center\n        t_mat = np.eye(3)\n        t_mat[0, 2] = -output_size[1]/2\n        t_mat[1, 2] = -output_size[0]/2\n        t_inv = t_mat.copy()\n        t_inv[:2, 2] *= -1\n        t = np.dot(t_inv, np.dot(rot_mat, np.dot(t_mat, t)))\n    return t\n\n\ndef transform_pixel(pt, center, scale, output_size, invert=0, rot=0):\n    # Transform pixel location to different reference\n    t = get_transform(center, scale, output_size, rot=rot)\n    if invert:\n        t = np.linalg.inv(t)\n    new_pt = np.array([pt[0] - 1, pt[1] - 1, 1.]).T\n    new_pt = np.dot(t, new_pt)\n    return new_pt[:2].astype(int) + 1\n\n\ndef transform_preds(coords, center, scale, output_size):\n\n    for p in range(coords.size(0)):\n        coords[p, 0:2] = torch.tensor(transform_pixel(coords[p, 0:2], center, scale, output_size, 1, 0))\n    return coords\n\ndef constrain_box(bbox, image_size):\n    x = bbox[0]\n    y = bbox[1]\n    w = bbox[2]\n    h = bbox[3]\n    \n    x = max(x,0)\n    y = max(y,0)\n    w = min(w,image_size[0]-x)\n    h = min(h,image_size[1]-y)\n\n    return (x, y, w, h)\n\ndef contrast_resize_crop_rotate(image, contrast, size, bbox, rot):\n\n    x = bbox[0]\n    y = bbox[1]\n    w = bbox[2]\n    h = bbox[3]\n\n    center_x = x+w/2.0\n    center_y = y+h/2.0\n\n    image = image.rotate(rot,center=(center_x,center_y))\n    x, y, w, h = constrain_box(bbox, image.size)\n\n    image = image.crop(( x, y, x + w, y + h))\n    image = image.resize(size)\n    image = F.adjust_contrast(image,contrast)\n\n    return image\n\ndef crop_resize_image(img, size, bbox,rot):\n    x = bbox[0]\n    y = bbox[1]\n    w = bbox[2]\n    h = bbox[3]\n\n    center_x = x+w/2.0\n    center_y = y+h/2.0\n    img = Image.fromarray(img)\n    rotate = img.rotate(rot,center=(center_x,center_y))\n    crop = rotate.crop(( x, y, x + w, y + h))\n    re_crop = crop.resize(size)\n\n    return np.array(re_crop),np.array(crop), np.array(rotate)\n\n\ndef image_transform(image, trans):\n    is_flip = trans.get('flip')\n    scale = trans.get('scale')\n    size = trans.get('size')\n    rot = trans.get('rot')\n    bbox = trans.get('bbox')\n    contrast = trans.get('contrast')\n    brightness = trans.get('brightness')\n\n    \n    is_crop = False\n\n    x, y, w, h = [0, 0, image.size[0], image.size[1]]\n    if bbox.all() and not bbox[2]==w:\n        x, y, w, h = bbox\n        is_crop = True\n\n    center_x = x+w/2.0\n    center_y = y+h/2.0\n\n\n    if is_flip:\n        image = F.hflip(image)\n        if is_crop:\n            x = image.size[0]-(x+w)\n\n    if rot:\n        image = image.rotate(rot,center=(center_x,center_y))\n\n    if is_crop and scale and not scale==1.0: \n        new_w = scale*w\n        new_h = scale*h\n        new_x = x - (new_w-w)/2.0\n        new_y = y - (new_h-h)/2.0\n        image = image.crop(( new_x, new_y, new_x + new_w, new_y + new_h))\n\n    \n    if size:\n        image = F.resize(image, size)\n    \n    if contrast:\n        image = F.adjust_contrast(image, contrast)\n\n    if brightness:\n        image = F.adjust_brightness(image, brightness)\n\n    return image\n\ndef crop():\n    pass\n\ndef generate_target(img, pt, sigma, label_type='Gaussian'):\n    # Check that any part of the gaussian is in-bounds\n    tmp_size = sigma * 3\n    ul = [int(pt[0] - tmp_size), int(pt[1] - tmp_size)]\n    br = [int(pt[0] + tmp_size + 1), int(pt[1] + tmp_size + 1)]\n    if (ul[0] >= img.shape[1] or ul[1] >= img.shape[0] or\n            br[0] < 0 or br[1] < 0):\n        # If not, just return the image as is\n        return img\n\n    # Generate gaussian\n    size = 2 * tmp_size + 1\n    x = np.arange(0, size, 1, np.float32)\n    y = x[:, np.newaxis]\n    x0 = y0 = size // 2\n    # The gaussian is not normalized, we want the center value to equal 1\n    if label_type == 'Gaussian':\n        g = np.exp(- ((x - x0) ** 2 + (y - y0) ** 2) / (2 * sigma ** 2))\n    else:\n        g = sigma / (((x - x0) ** 2 + (y - y0) ** 2 + sigma ** 2) ** 1.5)\n\n    # Usable gaussian range\n    g_x = max(0, -ul[0]), min(br[0], img.shape[1]) - ul[0]\n    g_y = max(0, -ul[1]), min(br[1], img.shape[0]) - ul[1]\n    # Image range\n    img_x = max(0, ul[0]), min(br[0], img.shape[1])\n    img_y = max(0, ul[1]), min(br[1], img.shape[0])\n\n    img[img_y[0]:img_y[1], img_x[0]:img_x[1]] = g[g_y[0]:g_y[1], g_x[0]:g_x[1]]\n    return img","repo_name":"alexanderbi1984/modality","sub_path":"utils/transforms.py","file_name":"transforms.py","file_ext":"py","file_size_in_byte":8753,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"73257794386","text":"from helpers import Helper\n\ndef __is_group_in_list__(group,list):\n    for g in list:\n        if group is g:\n            return True\n    return False\n\n\ndef do_filter(object_map,frame_map, units_per_meter, social_distance):\n    if social_distance is None or social_distance.get('distance') is None or units_per_meter is None:\n        return object_map,None\n\n    distance=int(social_distance['distance'])*(1.7*units_per_meter) #+width of a person\n    new_obj_map={}\n\n\n    for frame_number,appearances in frame_map.items():\n\n        for i in range(0,len(appearances)-1):\n            apA=appearances[i]\n            if frame_number == 43:\n                print(\"hallo\")\n            for j in range(i+1,len(appearances)):\n                apB=appearances[j]\n\n                auxxx=Helper.distance(*apA.real_coordinates,*apB.real_coordinates)\n                #if collision, add to group\n                if Helper.distance(*apA.real_coordinates,*apB.real_coordinates)<distance:\n                    new_obj_map[apA.object.id]=apA.object\n                    new_obj_map[apB.object.id] = apB.object\n\n                    if apA.object.group==None and apB.object.group==None:\n                        group={}\n                        group[apA.object.id] = apA.object\n                        group[apB.object.id] = apB.object\n                        apA.object.group=group\n                        apB.object.group=group\n                    elif apA.object.group!=None and apB.object.group==None:\n                        group=apA.object.group\n                        apB.object.group=group\n                        group[apB.object.id] = apB.object\n                    elif apA.object.group==None and apB.object.group!=None:\n                        group=apB.object.group\n                        apA.object.group = group\n                        group[apA.object.id] = apA.object\n                    else:#Both has group\n                        #we have to mix groups if they are different, if they are the same, we dont need to do anything\n                        if apA.object.group is not apB.object.group:\n                            groupA=apA.object.group\n                            groupB=apB.object.group\n                            for id, obj in groupB.items():\n                                groupA[id]=obj\n                                obj.group=groupA\n                            groupB.clear()\n\n    #Collect groups\n    group_list=[]\n    for _, obj in new_obj_map.items():\n        if obj.group is not None and not __is_group_in_list__(obj.group,group_list):\n            group_list.append(obj.group)\n\n    for group in group_list:\n        first_obj=next(iter(group.values()))\n        first_app=first_obj.first_appearance\n        last_app=first_obj.last_appearance\n        for id,obj in group.items():\n            try:\n                a=obj.first_appearance\n            except AttributeError:\n                print(\"error\")\n            if obj.first_appearance< first_app:\n                first_app=obj.first_appearance\n            if obj.last_appearance>last_app:\n                last_app=obj.last_appearance\n        group['first_appearance']=first_app\n        group['last_appearance'] = last_app\n\n    return new_obj_map,group_list","repo_name":"DanieldelRioJ/TFGBack","sub_path":"virtual_generator/filter/SocialDistanceFilter.py","file_name":"SocialDistanceFilter.py","file_ext":"py","file_size_in_byte":3226,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"2988881765","text":"#Napíšte program, ktorý zistí pomocou funkcie, v ktorom týždni počas roka mal Janko najvyššie výdavky na sladkosti. Jankove výdavky vygeneruje program ako desatinné čísla v rozsahu (0€-2,55€) s presnosťou na dve desatinné miesta.\n\nfrom random import * \n\nvydavky = []\n\nfor i in range(365):\n    vydavky.append(randint(0,255)/100)\n\ndef najvatsi (rokVydavky):\n    den = 0\n    a = rokVydavky[0]\n    for i in rokVydavky:\n        if i > a:\n            den = rokVydavky.index(i)\n    \n    return den\n\n\nprint(najvatsi(vydavky))","repo_name":"ShardBytes/Evolutions","sub_path":"PaleonT/PythonFGTS/Maturitne/U5.py","file_name":"U5.py","file_ext":"py","file_size_in_byte":537,"program_lang":"python","lang":"sk","doc_type":"code","stars":2,"dataset":"github-code","pt":"48"}
+{"seq_id":"40383908692","text":"import csv\nimport matplotlib.pyplot as plt\nimport random\n\n\ndef load_data(file_name):\n    x, y = [], []\n    with open(file_name, \"r\") as f:\n        csv_reader = csv.reader(f)\n        next(csv_reader)\n        for row in csv_reader:\n            x.append(float(row[0]))\n            y.append(float(row[1]))\n\n    return x, y\n\n\ndef gradient_descent(x, y):\n    alpha = 0.0001\n    theta_0, theta_1 = random.random(), random.random()\n    m = len(x)\n    iterations = 100\n    for _ in range(iterations):\n        # For theta_0\n        j_0 = 0\n        for i in range(m):\n            j_0 += (theta_0 + (theta_1 * x[i])) - y[i]\n        temp_0 = theta_0 - alpha * (1/m) * j_0\n\n        # For theta_1\n        j_1 = 0\n        for i in range(m):\n            j_1 += ((theta_0 + (theta_1 * x[i])) - y[i]) * x[i]\n        temp_1 = theta_1 - alpha * (1/m) * j_1\n\n        theta_0, theta_1 = temp_0, temp_1\n\n    return theta_0, theta_1\n\n\nif __name__ == '__main__':\n    x, y = load_data(\"dataset.csv\")\n    theta_0, theta_1 = gradient_descent(x, y)\n\n    # Plot the result\n    result = []\n    for i in x:\n        result.append(theta_0 + theta_1 * i)\n    plt.plot(x, y, 'bo')\n    plt.plot(x, result, color='green')\n    plt.show()\n","repo_name":"0x70DA/ML-training---IEEE-CS22-ZSB","sub_path":"Task2 - Gradient Descent for Linear Regression/linear_regression.py","file_name":"linear_regression.py","file_ext":"py","file_size_in_byte":1198,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"8693408663","text":"import requests\n\nfrom credentials import OPEN_WEATHER_MAP_API_KEY\n\n\ndef api_request(url):\n    try:\n        r = requests.get(url, timeout=3)\n        r.raise_for_status()\n        return r\n    except requests.exceptions.HTTPError as err:\n        print(\"Http Error:\", err)\n    except requests.exceptions.ConnectionError as err:\n        print(\"Error Connecting:\", err)\n    except requests.exceptions.Timeout as err:\n        print(\"Timeout Error:\", err)\n    except requests.exceptions.RequestException as err:\n        print(\"Unknown error:\", err)\n\n\ndef get_city_weather(city):\n    url = f' https://api.openweathermap.org/data/2.5/weather' \\\n          f'?q={city}' \\\n          f'&appid={OPEN_WEATHER_MAP_API_KEY}' \\\n          f'&units=metric'\n\n    r = api_request(url)\n    if r:\n        r_json = r.json()\n        weather_description = r_json['weather'][0]['description'].capitalize()\n        city_temp = round(r_json['main']['temp'])\n        city_info = f'{city_temp}ºc. {weather_description}. {city}'\n        return city_info\n    return 'Error'\n","repo_name":"Reverlight/aznews","sub_path":"aznews_app/weather_api_manager.py","file_name":"weather_api_manager.py","file_ext":"py","file_size_in_byte":1040,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"41518981094","text":"import requests\n\nquery = \"\"\"query {\n    characters {\n    results {\n      name\n      status\n      species\n      type\n      gender\n    }\n  }\n}\"\"\"\n\n\ndef post_data():\n    url = \"https://api.sendgrid/graphql/\"\n    r = requests.post(url, json={\"query\": query})\n    print(r.status_code)\n    print(r.text)\n","repo_name":"thepsalmist/internitel","sub_path":"qn1/graph.py","file_name":"graph.py","file_ext":"py","file_size_in_byte":298,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"42805761343","text":"# -*- coding: utf-8 -*-\n# !/usr/bin/env python\n# Adam Hornsby\n\nimport pandas as pd\nimport seaborn as sns\n\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pylab as plt\n\nfrom matplotlib import rcParams\n\nif __name__ == '__main__':\n\n\teffect_sizes = {\n\t\t'Democrats':\n\n\t\t\t{'All': 0.518, \n\t\t\t 'Trade': 0.579, \n\t\t\t 'Abortion': 0.512,\n\t\t\t'Immigration': 0.558\n\t\t\t},\n\n\t\t'Republicans': \n\n\t\t\t{'All': 0.629,\n\t\t\t 'Trade': 0.699,\n\t\t\t 'Abortion': 0.593, \n\t\t\t 'Immigration': 0.610}\n\t}\n\n\t# sns.set(rc={'figure.figsize':(4, 3)})\n\t# sns.set_style('ticks')\n\t# sns.set_context(rc={\"font.size\":7, \"lines.linewidth\":2})\n\n\t# cast to a dataframe\n\teffect_sizes = pd.DataFrame(effect_sizes)\n\tmelted_es = pd.melt(effect_sizes.reset_index(), id_vars='index')\n\n\t# sns.set(rc={})\n\t\n\tsns.set_style('white')\n\tsns.set_context(rc={\"font.size\":8})\n\t# sns.set(rc={\"font.size\":7, \"lines.linewidth\":2, 'figure.figsize':(4, 2)})\n\n\trcParams['figure.figsize'] = 4,1.5\n\t\n\t# sns.set_context(})\n\n\tg = sns.barplot(x=melted_es['index'], \n\t            y=melted_es['value'], \n\t            hue=melted_es['variable'],\n\t            palette=[\"#232066\", \"#E91D0E\"],\n\t            linewidth=2,\n\t            saturation=0.6,\n\t            hue_order=['Democrats', 'Republicans'],\n\t            order=['All', 'Trade', 'Immigration', 'Abortion'],\n\t            )\n\n\n\tg.set_xticklabels(g.get_xticklabels(), rotation=0)\n\n\tplt.ylim([0.5, 0.9])\n\tsns.despine(left=True, bottom=True, right=True)\n\tplt.xlabel('');\n\tplt.ylabel('Effect size (abs)')\n\tplt.legend(ncol=1, fontsize=8, frameon=False, loc='upper center');\n\n\t# save it\n\tplt.savefig('plots/effect_size_plot.eps', \n\t\tformat='eps', \n\t\tdpi=1000, \n\t\tbbox_inches='tight')\n\n","repo_name":"anhornsby/coherent-representations","sub_path":"politics/effect_size_plot.py","file_name":"effect_size_plot.py","file_ext":"py","file_size_in_byte":1661,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"48"}
+{"seq_id":"19946797705","text":"from selenium import webdriver\n\nwebsite = 'https://www.idx.co.id/id/perusahaan-tercatat/laporan-keuangan-dan-tahunan'\npath ='/Users/soju/Documents/notebooks/chromedriver/chromedriver'\n\ndriver = webdriver.Chrome(path)\ndriver.get(website)\n\n\n# filter_button = driver.find_element_by_xpath('//label')\nfilter_button = driver.find_element(By.className(\"btn-filter-input\")).click()\nfilter_button.click()\n\n# driver.execute_script('''window.open('about:blank', \n#                           'secondtab');''')\n  \n# # It is switching to second tab now\n# driver.switch_to.window('secondtab')\n  \n# # In the second tab, it opens geeksforgeeks\n# driver.get('https://www.geeksforgeeks.org/')\n\ndriver.quit()","repo_name":"julius-leo/data-science","sub_path":"stock/src/webScrapper.py","file_name":"webScrapper.py","file_ext":"py","file_size_in_byte":689,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"36249545330","text":"class Solution:\n    def setZeroes(self, matrix):\n        \"\"\"\n        :type matrix: List[List[int]]\n        :rtype: void Do not return anything, modify matrix in-place instead.\n        \"\"\"\n        \n        #1 96p\n        cols = set()\n        \n        for row, row_list in enumerate(matrix):\n            clear_row = False\n            for col, num in enumerate (row_list):\n                if num == 0:\n                    cols.add(col)\n                    clear_row = True\n                    \n            if clear_row:\n                for col, num in enumerate (row_list):\n                    matrix[row][col] = 0\n                    \n        \n        for col in cols:\n            for row, _ in enumerate (matrix):\n                matrix[row][col] = 0\n","repo_name":"doyleju/LC","sub_path":"array_medium/73_set_matrix_zeros.py","file_name":"73_set_matrix_zeros.py","file_ext":"py","file_size_in_byte":750,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"4499201017","text":"\"\"\"\nProgram: constants.py\nAuthor: Michael Mack\nLast date modified: 01-17-2023\n\nThe purpose of this program is to create constants.\n\"\"\"\n#set constant ITEM to a string\nITEM = 'Rings'\n\n#set constant SIZE to a float\nSIZE = 4.5\n\n#set constant QUANTITY to a integer\nQUANTITY = 5\n","repo_name":"mamack1/Module1_2023","sub_path":"Topic 2/constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":273,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"42817656606","text":"from sqlalchemy import Column, Integer, String, ForeignKey, DateTime, Date, Float, func, Boolean\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom db import session, engine\n\n\nBase = declarative_base()\n\nclass StuGroup(Base):\n     __tablename__ = \"stu_groups\"\n     id = Column(Integer, primary_key = True)\n     group_number = Column(String(10))\n     kurs = Column(Integer)\n     daytime = Column(Integer)\n     created_at = Column(DateTime, server_default=func.now())\n     last_update_at = Column(DateTime, server_default=func.now())\n\n\nclass Student(Base):\n     __tablename__ = \"students\"\n     id = Column(Integer, primary_key = True)\n     full_name = Column(String(150))\n     age = Column(Integer)\n     email = Column(String(100))\n     phone = Column(String(100))\n     budget = Column(Boolean)\n     scholarship = Column(Boolean)\n     created_at = Column(DateTime, server_default=func.now())\n     last_update_at = Column(DateTime, server_default=func.now()) \n     group_id = Column(Integer, ForeignKey(\"stu_groups.id\", onupdate=\"CASCADE\",ondelete=\"SET NULL\"))\n\nclass Teacher(Base):\n     __tablename__ = \"teachers\"\n     id = Column(Integer, primary_key = True)\n     full_name = Column(String(150))\n     age = Column(Integer)\n     email = Column(String(100))\n     phone = Column(String(100)) #UNIQUE NOT NULL\n     date_start_work = Column(Date)\n     scientific_degree = Column(String(30))\n     salary = Column(Float)\n     created_at = Column(DateTime, server_default=func.now())\n     last_update_at = Column(DateTime, server_default=func.now()) \n\n\nclass Subject(Base):\n     __tablename__ = \"subjects\"\n     id = Column(Integer, primary_key = True)\n     title = Column(String(100))\n     created_at = Column(DateTime, server_default=func.now())\n     last_update_at = Column(DateTime, server_default=func.now())\n     teacher_id = Column(Integer, ForeignKey(\"teachers.id\", onupdate=\"CASCADE\",ondelete=\"SET NULL\"))\n\n\n\nclass Grade(Base):\n     __tablename__ = \"grades\"\n     id = Column(Integer, primary_key = True)\n     date_create = Column(Date)\n     grade = Column(String(1))\n     number_grade = Column(Integer)\n     created_at = Column(DateTime, server_default=func.now())\n     last_update_at = Column(DateTime, server_default=func.now())\n     student_id = Column(Integer, ForeignKey(\"students.id\", onupdate=\"CASCADE\",ondelete=\"SET NULL\"))\n     subject_id = Column(Integer, ForeignKey(\"subjects.id\", onupdate=\"CASCADE\",ondelete=\"SET NULL\"))\n\n\nBase.metadata.create_all(engine)\nBase.metadata.bind = engine","repo_name":"Nataleia08/Web_module_7","sub_path":"models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2503,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"18389911720","text":"import cv2\nimport numpy as np\na=[]\ndef on_EVENT_LBUTTONDOWN(event, x, y, flags, param):\n\n    if event == cv2.EVENT_LBUTTONDOWN:\n        xy = \"%d,%d\" % (x, y)\n        a.append([y,x])\n        print(y,x)\n\n\ndef choose_point(img):\n    while True:\n        cv2.namedWindow(\"edge_map\")\n        cv2.setMouseCallback(\"edge_map\", on_EVENT_LBUTTONDOWN)\n        cv2.imshow(\"edge_map\", img)\n        key = cv2.waitKey(1)\n        if key==ord('q'):\n            break\n\n\ndef cut_off_based_on_mouse(num,edge_map,a,trigger=False):\n    new_edge_map=edge_map[a[0][0]:a[1][0],a[0][1]:a[1][1]]\n    while trigger:\n        cv2.imshow('outline',new_edge_map)\n        key = cv2.waitKey(1)\n        if key == ord('q'):\n            break\n        if key == ord('s'):\n            cv2.imwrite('C:/wrs-cx/wcx/outline/data/'+str(num)+'.pgm', new_edge_map)\n            print('Successfully save edge '+str(num)+'!')\n\n\nif __name__ ==\"__main__\":\n    edge=cv2.imread(r'C:\\wrs-cx\\wcx\\outline\\data\\query12.27\\rs\\rotate_6.pgm',0)\n    choose_point(edge)\n    cut_off_based_on_mouse(4, edge, a, trigger=True)\n    # obj5 p1 313 286  p2 535 523\n","repo_name":"ChenxiWang09/rs_phx_vision","sub_path":"outline/cut_cut_off.py","file_name":"cut_cut_off.py","file_ext":"py","file_size_in_byte":1095,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"11170392942","text":"\nimport logging\nimport datetime\n\nfrom django.shortcuts import render\nfrom django.shortcuts import get_object_or_404\nfrom django.http import HttpResponseRedirect\nfrom django.urls import reverse\nfrom django.core.exceptions import ValidationError\n\nfrom .forms import ExampleForm\n\n\ndef main(request):\n\n    logging.warning(\"main() request:{}\".format(request))\n\n    import random\n    processes = [\n        {\n            'pid': random.randint(1000, 50000),\n            'name': \"one\",\n            'cpu_p': 50,\n            'mem_MB': 120,\n            'mem_p': 340,\n            'uptime': 20,\n        },\n        {\n            'pid': random.randint(1000, 50000),\n            'name': \"two\",\n            'cpu_p': 60,\n            'mem_MB': 130,\n            'mem_p': 350,\n            'uptime': 20,\n        },\n    ]\n    if request.method == 'POST':\n        rp_copy = request.POST.copy()\n        rp_copy.pop('csrfmiddlewaretoken')\n        logging.warning(\"main() rp_copy:{}\".format(request.POST))\n        ui_msgs = {\n            'alert': \"successfully handled request.POST:{}.\".format(rp_copy), \n            'danger': 'ooooops...'\n        }\n    else:\n        ui_msgs = {\n            'alert': \"let's start...\", \n            'danger': ''\n        }\n    \n    return render(request, 'main.html', {'processes': processes, 'ui_msgs': ui_msgs})\n\n","repo_name":"giovanni-angeli/spin_store","sub_path":"spin_store/frontend/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1319,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"5773819931","text":"import copy\n\nproduct = [\n    {'name': 'Product 5', 'price': 10.00},\n    {'name': 'Product 1', 'price': 22.32},\n    {'name': 'Product 3', 'price': 10.11},\n    {'name': 'Product 2', 'price': 105.87},\n    {'name': 'Product 4', 'price': 69.90},\n]\n\nnew_product = [\n    {**product, \"price\": round(product[\"price\"] * 1.1, 2)}\n    for product in copy.deepcopy(product)\n]\n\n# print(new_product)\n\nproduct_ordered = sorted(\n    copy.deepcopy(product),\n    key = lambda prod: prod[\"name\"]\n)\n\n# print(product_ordered)\n\nproduct_ordered_by_price = sorted(\n    copy.deepcopy(product),\n    key = lambda prod : prod[\"price\"]\n)\n\nprint(product_ordered_by_price)\n\n","repo_name":"RibeiroFilho92/python_course","sub_path":"Intermediate/class09.py","file_name":"class09.py","file_ext":"py","file_size_in_byte":642,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"20788197354","text":"import pandas as pd\nfrom unittest import TestCase\nimport os\n\nfrom clayrs.content_analyzer import Ratings\nfrom clayrs.recsys import PropertyNode\nfrom clayrs.recsys.graphs.feature_selection import TopKPageRank, TopKDegreeCentrality, \\\n    TopKEigenVectorCentrality\nfrom clayrs.recsys.graphs.feature_selection import FeatureSelectionException\nfrom clayrs.recsys.graphs import NXFullGraph\nfrom test import dir_test_files\n\n\nclass TestFeatureSelectionAlgorithm(TestCase):\n\n    def setUp(self) -> None:\n        movies_dir = os.path.join(dir_test_files, 'complex_contents', 'movies_codified/')\n        user_dir = os.path.join(dir_test_files, 'complex_contents', 'users_codified/')\n\n        df = pd.DataFrame.from_dict({'user_id': [\"1\", \"1\", \"2\", \"2\", \"2\", \"3\", \"4\", \"4\"],\n                                     'item_id': [\"tt0112281\", \"tt0113041\", \"tt0112453\", \"tt0112346\",\n                                                 \"tt0112302\", \"tt0113101\", \"tt0113277\", \"tt0113228\"],\n                                     'score': [5, 3, 1, 5, 4, 2, 2, 4]})\n        rat = Ratings.from_dataframe(df)\n\n        # all properties from the dbpedia item repr extracted\n        self.g: NXFullGraph = NXFullGraph(rat,\n                                          user_contents_dir=user_dir,\n                                          item_contents_dir=movies_dir,\n                                          item_exo_properties={'dbpedia'},\n                                          user_exo_properties={'local': '1'}\n                                          )\n        # we use as target nodes all item nodes in the graph, meaning that we want want to remove\n        # less important features of all the item nodes of the graph\n        self.target_item_nodes = list(self.g.item_nodes)\n\n        # here we save in a set all unique property labels such as 'starring', 'editing', etc.\n        self.unique_prop_labels = set()\n        for item_node in self.target_item_nodes:\n            prop_nodes = [node for node in self.g.get_successors(item_node) if isinstance(node, PropertyNode)]\n            for prop_n in prop_nodes:\n                link_label = self.g.get_link_data(item_node, prop_n)['label']\n                self.unique_prop_labels.add(link_label)\n\n        # 'film director' is one of the label that appears less often, so, since the example graph is pretty simple,\n        # the result of feature selection algorithms should not include it when the mode is 'to_remove'\n        self.less_important_property_label = 'film director'\n\n        # 'starring' is one of the label that appears more often, so, since the example graph is pretty simple,\n        # the result of feature selection algorithms should include it when the mode is 'to_keep'\n        self.most_important_property_label = 'starring'\n\n    def test_perform_NXTopKPageRank(self):\n        top_prop_labels_to_keep = 3\n\n        result = TopKPageRank(k=top_prop_labels_to_keep).perform(self.g, self.target_item_nodes, mode='to_remove')\n\n        # mode -> 'to_remove' will return a list of the property labels to remove\n        n_nodes_to_remove = len(self.unique_prop_labels) - top_prop_labels_to_keep\n        self.assertEqual(len(result), n_nodes_to_remove)\n        self.assertTrue(self.less_important_property_label in result)\n        self.assertTrue(self.most_important_property_label not in result)\n\n        result = TopKPageRank(k=top_prop_labels_to_keep).perform(self.g, self.target_item_nodes, mode='to_keep')\n\n        # mode -> 'to_keep' will return a list of the property labels to keep\n        self.assertEqual(len(result), top_prop_labels_to_keep)\n        self.assertTrue(self.less_important_property_label not in result)\n        self.assertTrue(self.most_important_property_label in result)\n\n    def test_perform_NXTopKDegreeCentrality(self):\n        top_prop_labels_to_keep = 3\n        result = TopKDegreeCentrality(k=top_prop_labels_to_keep).perform(self.g, self.target_item_nodes,\n                                                                         mode='to_remove')\n\n        # mode -> 'to_remove' will return a list of the property labels to remove\n        n_nodes_to_remove = len(self.unique_prop_labels) - top_prop_labels_to_keep\n        self.assertEqual(len(result), n_nodes_to_remove)\n        self.assertTrue(self.less_important_property_label in result)\n        self.assertTrue(self.most_important_property_label not in result)\n\n        result = TopKDegreeCentrality(k=top_prop_labels_to_keep).perform(self.g, self.target_item_nodes,\n                                                                         mode='to_keep')\n\n        # mode -> 'to_keep' will return a list of the property labels to keep\n        self.assertEqual(len(result), top_prop_labels_to_keep)\n        self.assertTrue(self.less_important_property_label not in result)\n        self.assertTrue(self.most_important_property_label in result)\n\n    def test_perform_NXTopKEigenVectorCentrality(self):\n        top_prop_labels_to_keep = 3\n        result = TopKEigenVectorCentrality(k=top_prop_labels_to_keep, max_iter=200).perform(self.g,\n                                                                                            self.target_item_nodes,\n                                                                                            mode='to_remove')\n\n        # mode -> 'to_remove' will return a list of the property labels to remove\n        n_nodes_to_remove = len(self.unique_prop_labels) - top_prop_labels_to_keep\n        self.assertEqual(len(result), n_nodes_to_remove)\n        self.assertTrue(self.less_important_property_label in result)\n        self.assertTrue(self.most_important_property_label not in result)\n\n        result = TopKEigenVectorCentrality(k=top_prop_labels_to_keep, max_iter=200).perform(self.g,\n                                                                                            self.target_item_nodes,\n                                                                                            mode='to_keep')\n\n        # mode -> 'to_keep' will return a list of the property labels to keep\n        self.assertEqual(len(result), top_prop_labels_to_keep)\n        self.assertTrue(self.less_important_property_label not in result)\n        self.assertTrue(self.most_important_property_label in result)\n\n    def test_special_cases_feature_selection(self):\n        # empty target nodes\n        self.assertEqual(TopKPageRank(k=2).perform(self.g, []), [])\n\n        # k = 0\n        result = TopKPageRank(k=0).perform(self.g, self.target_item_nodes)\n        self.assertEqual(result, [])\n\n        # k < 0\n        with self.assertRaises(ValueError):\n            TopKPageRank(k=-1)\n\n        # k > number of properties in the graph\n        result = TopKPageRank(100).perform(self.g, self.target_item_nodes)\n        self.assertEqual(len(result), len(self.unique_prop_labels))  # number of item labels\n\n        # method fails to converge\n        with self.assertRaises(FeatureSelectionException):\n            TopKPageRank(k=1, max_iter=0).perform(self.g, self.target_item_nodes)\n\n        # non existent mode\n        with self.assertRaises(TypeError):\n            TopKPageRank().perform(self.g, [], mode='invalid')\n","repo_name":"swapUniba/ClayRS","sub_path":"test/recsys/graphs/feature_selection/test_feature_selection_alg.py","file_name":"test_feature_selection_alg.py","file_ext":"py","file_size_in_byte":7163,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"48"}
+{"seq_id":"10280366096","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"The setup script.\"\"\"\n\nfrom setuptools import setup, find_packages\n\nwith open('README.rst') as readme_file:\n    readme = readme_file.read()\n\nwith open('HISTORY.rst') as history_file:\n    history = history_file.read()\n\n# put package requirements here\nrequirements = [\n    'jmespath',\n    'python-box'\n]\n\n# Optional packages\nextras = {\n    'allmeta': ['piexif', 'filetype'],\n    'winlnk': ['pywin32'],\n    'frontmatter': ['python-frontmatter']\n}\n\n# put setup requirements (distutils extensions, etc.) here\nsetup_requirements = [\n    'pytest-runner',\n]\n\n# put package test requirements here\ntest_requirements = [\n    'pytest',\n    'pytest-catchlog',\n    'piexif',\n    'filetype',\n    'jmespath',\n    'python-box',\n    'python-frontmatter'\n]\n\nsetup(\n    name='taggo',\n    version='0.18.0',\n    description=\"Tag organizer that creates symlinks from filename-tags\",\n    long_description=readme + '\\n\\n' + history,\n    author=\"Lars Solberg\",\n    author_email='lars.solberg@gmail.com',\n    url='https://github.com/xeor/taggo',\n    packages=find_packages(include=['taggo', 'taggo.metadata'], exclude=['tests']),\n    include_package_data=True,\n    install_requires=requirements,\n    extras_require=extras,\n    license=\"MIT license\",\n    zip_safe=False,\n    keywords='taggo',\n    entry_points={\n        'console_scripts': [\n            'taggo = taggo:main',\n        ]\n    },\n    classifiers=[\n        'Development Status :: 4 - Beta',\n        'Intended Audience :: Developers',\n        'Intended Audience :: Information Technology',\n        'Intended Audience :: System Administrators',\n        'Topic :: System :: Filesystems',\n        'License :: OSI Approved :: MIT License',\n        'Natural Language :: English',\n        'Programming Language :: Python :: 3.6',\n    ],\n    test_suite='tests',\n    tests_require=test_requirements,\n    setup_requires=setup_requirements,\n)\n","repo_name":"xeor/taggo","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1914,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"48"}
+{"seq_id":"25545752878","text":"from fastapi import FastAPI\nfrom fastapi.staticfiles import StaticFiles\nfrom pydantic import BaseModel\nfrom pymongo import MongoClient\n\n\nclient = MongoClient(\n    \"mongodb+srv://a96689636:#3gjdirhtn@cluster0.aqcybvg.mongodb.net/?retryWrites=true&w=majority\"\n)\n\ndb = client.python_test\n\n\n# 생성\n# doc = {\"name\": \"kim\", \"age\": 19}\n# db.users.insert_one(doc)\n\n\nclass Memo(BaseModel):\n    id: str\n    content: str\n\n\n# memos = []\napp = FastAPI()\n\n\n# 메모 생성\n@app.post(\"/memos\")\ndef create_memo(memo: Memo):\n    doc = {\"id\": memo.id, \"content\": memo.content}\n    db.memos.insert_one(doc)\n    return \"성공했습니다.\"\n\n\n# 메모 불러오기\n@app.get(\"/memos\")\ndef read_memo():\n    memo = list(db.memos.find({}, {\"_id\": False}))\n    return memo\n\n\n# 메모 수정\n@app.put(\"/memos/{memo_id}\")\ndef edit_memo(req_memo: Memo):\n    print(req_memo.id, req_memo.content)\n\n    db.memos.update_one({\"id\": req_memo.id}, {\"$set\": {\"content\": req_memo.content}})\n\n    # for memo in memos:\n    #     if memo.id == req_memo.id:\n    #         memo.content = req_memo.content\n    #         return \"성공했습니다.\"\n    return \"그런 메모는 없습니다.\"\n\n\n# 메모 삭제\n@app.delete(\"/memos/{memo_id}\")\ndef delete_memo(memo_id):\n    db.memos.delete_one({\"id\": memo_id})\n    # for index, memo in enumerate(memos):\n    #     if memo.id == memo_id:\n    #         memos.pop(index)\n    return \"성공했습니다.\"\n\n\n@app.get(\"/memos?sort_by=created&sorted=ASC\")\ndef sort_asc_memo():\n    memos.id.sort()\n    return \"성공했습니다.\"\n\n\n@app.get(\"/memos?sort_by=created&sorted=DESC\")\ndef sort_desc_memo():\n    return \"성공했습니다.\"\n\n\napp.mount(\"/\", StaticFiles(directory=\"static\", html=True), name=\"static\")\n","repo_name":"hihiihiii/awesome-memo","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1711,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"33002876731","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nfrom __future__ import annotations\nimport math\nfrom typing import TypeVar, Generic, Sequence\n\nfrom fastapi import Query\nfrom fastapi_pagination.bases import AbstractPage, AbstractParams, RawParams\nfrom pydantic import BaseModel\n\nT = TypeVar(\"T\")\n\n\"\"\"\n重写分页库： fastapi-pagination \n使用方法：example link: https://github.com/uriyyo/fastapi-pagination/tree/main/examples\n\"\"\"\n\n\nclass Params(BaseModel, AbstractParams):\n    page: int = Query(1, ge=1, description=\"Page number\")\n    size: int = Query(20, gt=0, le=100, description=\"Page size\")  # 默认 20 条记录\n\n    def to_raw_params(self) -> RawParams:\n        return RawParams(\n            limit=self.size,\n            offset=self.size * (self.page - 1),\n        )\n\n\nclass Page(AbstractPage[T], Generic[T]):\n    data: Sequence[T]  # 数据\n    total: int  # 总数据数\n    page: int  # 第n页\n    size: int  # 每页数量\n    next: str  # 下一页参数\n    previous: str  # 上一页参数\n    total_pages: int  # 总页数\n\n    __params_type__ = Params  # 使用自定义的Params\n\n    @classmethod\n    def create(\n            cls,\n            data: data,\n            total: int,\n            params: Params,\n    ) -> Page[T]:\n        page = params.page\n        size = params.size\n        total_pages = math.ceil(total / params.size)\n        next = f\"?page={page + 1}&size={size}\" if (page + 1) <= total_pages else \"null\"  # noqa\n        previous = f\"?page={page - 1}&size={size}\" if (page - 1) >= 1 else \"null\"\n\n        return cls(\n            data=data,\n            total=total,\n            page=params.page,\n            size=params.size,\n            next=next,\n            previous=previous,\n            total_pages=total_pages\n        )\n","repo_name":"fastapi-practices/fastapi_sqlalchemy_mysql","sub_path":"backend/app/common/pagination.py","file_name":"pagination.py","file_ext":"py","file_size_in_byte":1761,"program_lang":"python","lang":"en","doc_type":"code","stars":51,"dataset":"github-code","pt":"48"}
+{"seq_id":"7931624874","text":"\"\"\"\n@author: Ross Drucker\n\"\"\"\nimport os\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib.patheffects as pe\n\nimport bdb_filepaths as fp\nimport bdb_helpers.data_loaders as load\nimport bdb_helpers.plot_helpers as draw\n\n\nplot_test_data = pd.read_csv(fp.plot_test_data)\nteams_info = load.teams_data()\n\nfor i, row in plot_test_data.iterrows():\n    home = row['home']\n    away = row['away']\n    \n    home_frame = pd.DataFrame(\n        row[['home', 'home_x', 'home_y', 'home_jersey_no']]\n    ).transpose()\n    away_frame = pd.DataFrame(\n        row[['away', 'away_x', 'away_y', 'away_jersey_no']]\n    ).transpose()\n    home_info = teams_info[teams_info['team_code'] == home]\n    away_info = teams_info[teams_info['team_code'] == away]\n    \n    fig, ax = draw.field(home = home, away = away)\n    \n    home_frame.plot(\n        x = 'home_x',\n        y = 'home_y',\n        kind = 'scatter',\n        ax = ax,\n        color = home_info['home_uni_base'],\n        s = 800,\n        edgecolor = home_info['home_uni_highlight'],\n        linewidth = 2,\n        zorder = 15\n    )\n    \n    for i, player in home_frame.iterrows():\n        ax.text(\n            x = player['home_x'],\n            y = player['home_y'],\n            s = str(int(player['home_jersey_no'])),\n            fontsize = 15,\n            color = home_info['home_uni_number'].iloc[0],\n            path_effects = [\n                pe.withStroke(\n                    linewidth = 3,\n                    foreground = f'{home_info.home_uni_number_highlight.iloc[0]}'\n                )\n            ],\n            fontweight = 'bold',\n            rotation = -90,\n            zorder = 20,\n            fontdict = {'ha': 'center', 'va': 'center'},\n        )\n        \n    away_frame.plot(\n        x = 'away_x',\n        y = 'away_y',\n        kind = 'scatter',\n        ax = ax,\n        color = away_info['away_uni_base'],\n        s = 800,\n        edgecolor = away_info['away_uni_highlight'],\n        linewidth = 2,\n        zorder = 15\n    )\n    \n    for i, player in away_frame.iterrows():\n        ax.text(\n            x = player['away_x'],\n            y = player['away_y'],\n            s = str(int(player['away_jersey_no'])),\n            fontsize = 15,\n            color = home_info['away_uni_number'].iloc[0],\n            path_effects = [\n                pe.withStroke(\n                    linewidth = 2,\n                    foreground = f'{away_info.away_uni_number_highlight.iloc[0]}'\n                )\n            ],\n            fontweight = 'bold',\n            rotation = 90,\n            zorder = 20,\n            fontdict = {'ha': 'center', 'va': 'center'},\n        )\n    \n    if not os.path.exists(os.path.join('img', 'test_plots')):\n        os.makedirs(os.path.join('img', 'test_plots'))\n        \n    fname = os.path.join('img', 'test_plots', f'plt_preview_{home}.png')\n    plt.savefig(f'{fname}', bbox_inches = 'tight', pad_inches = 0)","repo_name":"rossdrucker/big_data_bowl_2021","sub_path":"bdb_helpers/make_test_plots.py","file_name":"make_test_plots.py","file_ext":"py","file_size_in_byte":2897,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"18716079663","text":"# -*- coding=utf-8 -*-\n# @Time: 2021/10/16 14:01\n# @Author: N\n# @Software: PyCharm\nimport argparse\nimport functools\nimport math\nimport os\nimport random\nimport re\nimport sys\nimport threading\nimport time\nimport traceback\n\nimport cv2\nimport tensorboardX\nimport torch\nimport torch.distributed as dist\nimport tqdm\nfrom PIL import Image\nfrom torch.cuda.amp import GradScaler\nfrom torch.nn.parallel import DistributedDataParallel as DDP\nfrom torch.utils.data import DataLoader\nfrom torchvision import transforms\n\nimport data\nimport utils\nimport models\nfrom models.unet import UNet\n\n\ndef main():\n    # define variables for the following\n    # model_name = 'UACANet'\n    # model_name = 'ResUNetPP'\n    # model_name = 'TestNetWithInception'\n    # model_name = 'Res&Attn&SE'\n    model_name = 'SegCaps'\n    tensorboard_path = f'./saved/runs/{model_name}/'\n    best_state_path = f'./saved/{model_name}/best_model.pth'\n    savepoint_dir = f'./saved/{model_name}/'\n    # loading_path = f'./saved/{model_name}/breakpoint_360.pth'\n    loading_path = best_state_path\n\n    input_dir = \"./dataset/CCF/train/img/\"\n    val_split_rate = 0\n    batch_size = 9\n    height = 512\n    width = 512\n    num_epochs = 0\n    mnt_best = math.inf\n    val_mnt_best = math.inf\n    lr_schedule_interval = 20\n    clip_grad = 1.0\n    save_interval = 20\n    val_interval = 50\n    epochs_with_no_improve_and_no_scheduling = 0\n    class2rgb_lut = [0, 255]\n    scheduling_times = 0\n    stop_scheduling_times = 5\n    epochs_trained_this_time = 0\n    # linear lr scaling rule: lr = base lr * batch size / 256\n    # adam_kwargs = {'lr': 1.5e-3, 'betas': (0.9, 0.999), 'eps': 1e-8}\n    adam_kwargs = {'lr': 1.5e-2, 'betas': (0.9, 0.999), 'eps': 1e-8}\n    aug_dilation_rate = 5\n    rotation = 20\n    brightness = 0.2\n    random_crop_ratio_low_boundary = 0.9\n    random_crop_ratio_upper_boundary = 1\n    init_lr_ratio = 0.5\n    warm_up_epochs = 10\n    num_workers = 0\n    pin_memory = False\n    zoom_in_and_out = False\n    ddp = True\n    h_flip = True\n    v_flip = True\n    keep_aspect_ratio = True\n    mix_accuracy = False\n    train_from_savepoint = False\n    save_multi_breakpoint = False\n    cover_mnt = False\n    cover_val_mnt = False\n    load_optimizer_state = False\n    prefetching_data = False\n    enable_EMA = False\n\n    # set current work space\n    try:\n        os.chdir(os.path.dirname(__file__))\n    except:\n        print(\"failing to set work space as the file parent dir\")\n\n    # set random or not\n    utils.set_seed(False)\n\n    # torch.distributed.run distribute each process a unique identity called as local_rank\n    parser = argparse.ArgumentParser(description = 'please run the file with -m torch.distributed.run')\n    parser.add_argument(\"--local_rank\", default = -1, type = int)\n    local_rank = parser.parse_args().local_rank if ddp else 0\n\n    # set the device of current process\n    torch.cuda.set_device(local_rank)\n    device = torch.cuda.current_device()\n\n    # init the DDP backend\n    if ddp:\n        dist.init_process_group(backend = 'NCCL' if not sys.platform.startswith('win') else 'GLOO')\n\n    # check savepoint dir\n    if local_rank == 0 and not os.path.exists(savepoint_dir):\n        os.makedirs(savepoint_dir)\n    if ddp:\n        torch.distributed.barrier()\n\n    # dataset\n    input_path_list = []\n    for file in os.listdir(input_dir):\n        if os.path.splitext(file)[1] != '.json':\n            input_path_list.append(input_dir + file)\n    input_path2label_path = lambda path: re.sub('img', 'mask', path) if os.path.splitext(path)[\n                                                                            1] == '.png' else None\n    aug_method = utils.img_augment(height = height, width = width, h_flip = h_flip, v_flip = v_flip,\n                                   random_crop_ratio_low_boundary = random_crop_ratio_low_boundary,\n                                   random_crop_ratio_upper_boundary = random_crop_ratio_upper_boundary,\n                                   brightness = brightness, rotation = rotation, keep_aspect_ratio = keep_aspect_ratio,\n                                   zoom_in_and_out = zoom_in_and_out)\n    input_open_method = lambda input_path: Image.open(input_path)\n    # input_open_method = lambda input_path: Image.open(input_path).convert('L')\n    if keep_aspect_ratio:\n        resize_method = functools.partial(utils.resize_keep_aspectratio, dsize = (height, width))\n    else:\n        resize_method = functools.partial(cv2.resize, dsize = (width, height), interpolation = cv2.INTER_NEAREST)\n    mean, std = utils.get_mean_and_std(input_path_list, input_open_method, size_with_max_3dim = (height, width),\n                                       resize_method = resize_method)\n\n    transformer4label_arr = lambda augmented_label_arr: torch.FloatTensor(\n        utils.rgb2class(augmented_label_arr, class2rgb_lut)).squeeze(dim = 0)\n\n    transformer4input_arr = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean, std)])\n    train_set = data.BaseDataset(input_path_list, input_path2label_path, input_open_method,\n                                 Image.open, aug_method,\n                                 transformer4input_arr, transformer4label_arr,\n                                 aug_dilation_rate = aug_dilation_rate)\n\n    # split train set to train set and validation set\n    val_size = math.floor(len(train_set) * val_split_rate)\n    train_size = len(train_set) - val_size\n    train_dataset, val_dataset = torch.utils.data.random_split(train_set, lengths = (train_size, val_size))\n\n    # bcz the dataloader is to load data to the gpu indicated by the local_rank\n    train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) if ddp else None\n    train_loader = DataLoader(train_dataset, batch_size = batch_size, num_workers = num_workers,\n                              pin_memory = pin_memory, sampler = train_sampler)\n\n    train_loader = data.DataPrefetcher(train_loader, device) if prefetching_data else train_loader\n    val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset) if ddp else None\n    val_loader = DataLoader(val_dataset, batch_size = batch_size, num_workers = num_workers, pin_memory = pin_memory,\n                            sampler = val_sampler)\n    val_loader = data.DataPrefetcher(val_loader, device) if prefetching_data else val_loader\n\n    # warp model with DDP, and load model to the gpu indicated by the local_rank\n    # model = UNet(1, 2).to(device)\n    # model = models.TestNet(3, 1).to(device)\n    model = models.SegCaps().to(device)\n    model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model) if ddp else model\n    model = DDP(model, device_ids = [local_rank], find_unused_parameters = True) if ddp else model\n    utils.initialize_weights(model)\n    if enable_EMA:\n        ema = utils.EMA(model.module)\n        ema.register()\n    # criterion = torch.nn.CrossEntropyLoss().to(device)\n    # criterion = torch.nn.NLLLoss().to(device)\n    # criterion = torch.nn.BCEWithLogitsLoss().to(device)\n    # criterion = utils.bce_iou_loss\n    criterion = utils.margin_loss\n    optimizer = torch.optim.Adam(model.parameters(), **adam_kwargs)\n\n    # schedule the lr to half when not have loss desc after a indicated interval\n    lr_scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda = lambda\n        epochs_or_times: 0.5 ** epochs_or_times if epochs_or_times < stop_scheduling_times else init_lr_ratio + (\n            1 - init_lr_ratio) * (epochs_or_times - stop_scheduling_times) / warm_up_epochs)\n\n    # Scaler for auto mix precision calculation\n    scaler = GradScaler()\n\n    # tensorboard writer\n    writer = tensorboardX.SummaryWriter(tensorboard_path)\n\n    # load state\n    if train_from_savepoint:\n        try:\n            best_state = torch.load(loading_path, map_location = torch.device('cpu'))\n            if ddp:\n                model.module.load_state_dict(best_state['state_dict'])\n            else:\n                model.load_state_dict(best_state['state_dict'])\n            if enable_EMA and best_state.get('ema_state_dict', None):\n                ema.load_state_dict(best_state['ema_state_dict'])\n                if local_rank == 0:\n                    print('ema para loading success')\n            num_epochs = best_state['epoch']\n            val_mnt_best = best_state['val_monitor_best'] if cover_val_mnt else val_mnt_best\n            if load_optimizer_state:\n                optimizer.load_state_dict(best_state['optimizer'])\n            if cover_mnt:\n                mnt_best = best_state['monitor_best']\n                scheduling_times = best_state.get('scheduling_times', 0)\n            if local_rank == 0:\n                print(\n                    f'loading success, path to load: \"{loading_path}\", savepoint mnt: {best_state[\"monitor_best\"]:.5f}, savepoint scheduling times: {best_state[\"scheduling_times\"]}, mnt_best: {mnt_best:.5f}, scheduling_times: {scheduling_times}')\n        except Exception as e:\n            traceback.print_exc()\n            if local_rank == 0:\n                print('loading fail')\n            best_state = None\n    else:\n        if local_rank == 0:\n            print('loading is canceled manually')\n        best_state = None\n\n    get_state = lambda: {\n        'arch': type(model).__name__,\n        'epoch': num_epochs,\n        'state_dict': model.module.state_dict() if ddp else model.state_dict(),\n        'ema_state_dict': ema.state_dict() if enable_EMA else None,\n        'optimizer': optimizer.state_dict(),\n        'monitor_best': mnt_best,\n        'val_monitor_best': val_mnt_best,\n        'scheduling_times': scheduling_times\n    }\n\n    while True:\n        num_epochs += 1\n        epochs_trained_this_time += 1\n        if epochs_trained_this_time <= warm_up_epochs:\n            lr_scheduler.step(epochs_trained_this_time + stop_scheduling_times)\n        loss_hist = []\n        if local_rank == 0:\n            tq = tqdm.tqdm(total = len(train_loader), ncols = 0, desc = f\"train_epoch_{num_epochs:d}\")\n        for x, label in train_loader:\n            # x, label = (x, label) if prefetching_data else (x.to(device), label.to(device))\n            x, label = (x, label) if prefetching_data else (x.to(device), label.long().to(device))\n            if mix_accuracy:\n                with torch.cuda.amp.autocast():\n                    logit = model(x)\n                    loss = criterion(logit, label)\n\n                    # # 4 UACANet\n                    # loss = model(x, label, criterion)['loss']b\n\n                optimizer.zero_grad()\n                scaler.scale(loss).backward()\n                # Unscales the gradients of optimizer's assigned params in-place\n                # for each backward params can only be unscaled one time\n                scaler.unscale_(optimizer)\n                torch.nn.utils.clip_grad_norm_(model.parameters(), clip_grad)\n                scaler.step(optimizer)\n                if enable_EMA:\n                    ema.update()\n                scaler.update()\n            else:\n                # logit = model(x)\n                logit = model(x)\n                loss = criterion(logit,label)\n\n                # # 4 UACANet\n                # loss = model(x, label, criterion)['loss']\n\n                optimizer.zero_grad()\n                loss.backward()\n                torch.nn.utils.clip_grad_norm_(model.parameters(), clip_grad)\n                optimizer.step()\n                if enable_EMA:\n                    ema.update()\n            loss_scalar = loss.item()\n            loss_hist.append(loss_scalar)\n            if local_rank == 0:\n                tq.update(1)\n                tq.set_postfix({'loss': loss_scalar})\n\n        loss_sum = torch.FloatTensor([sum(loss_hist)]).to(device)\n        num_batches = torch.LongTensor([len(loss_hist)]).to(device)\n        dist.reduce(loss_sum, dst = 0, op = dist.ReduceOp.SUM, async_op = True)\n        dist.reduce(num_batches, dst = 0, op = dist.ReduceOp.SUM, async_op = True)\n        epoch_loss = (loss_sum / num_batches).to(device)\n        dist.broadcast(epoch_loss, 0)\n        epoch_loss = epoch_loss.item()\n\n        if local_rank == 0:\n            tq.set_postfix({'epoch_loss': epoch_loss})\n            writer.add_scalar('epoch_loss', epoch_loss, global_step = num_epochs)\n            tq.close()\n\n        if epoch_loss < mnt_best:\n            mnt_best = epoch_loss\n            if local_rank == 0:\n                print(f'updating mnt_best: {mnt_best}')\n                best_state = get_state()\n            epochs_with_no_improve_and_no_scheduling = 0\n        else:\n            epochs_with_no_improve_and_no_scheduling += 1\n            if local_rank == 0 and scheduling_times >= stop_scheduling_times:\n                if best_state:\n                    torch.save(best_state, best_state_path)\n                torch.save(get_state(), savepoint_dir + (\n                    f'breakpoint_{num_epochs}.pth' if save_multi_breakpoint else 'breakpoint.pth'))\n                raise Exception('learning stop')\n            if epochs_with_no_improve_and_no_scheduling >= lr_schedule_interval:\n                scheduling_times += 1\n                lr_scheduler.step(scheduling_times)\n                clip_grad /= 4\n                lr_schedule_interval *= 2\n                if local_rank == 0:\n                    print(f'scheduling learning rate for the {scheduling_times}th time')\n                epochs_with_no_improve_and_no_scheduling = 0\n\n        if local_rank == 0 and epochs_trained_this_time % save_interval == 0:\n            if best_state:\n                torch.save(best_state, best_state_path)\n            torch.save(get_state(),\n                       savepoint_dir + (f'breakpoint_{num_epochs}.pth' if save_multi_breakpoint else 'breakpoint.pth'))\n\n        if val_split_rate and epochs_trained_this_time % val_interval == 0:\n            if enable_EMA:\n                ema.apply_shadow()\n            model.eval()\n            with torch.no_grad():\n                loss_hist = []\n                if local_rank == 0:\n                    tq = tqdm.tqdm(total = len(val_loader), ncols = 0,\n                                   desc = f\"val_{epochs_trained_this_time // val_interval:d}th\")\n                for x, label in val_loader:\n                    x, label = (x, label) if prefetching_data else (x.to(device), label.to(device))\n                    logit = model(x)\n                    loss = criterion(logit, label)\n\n                    # # 4 UACANet\n                    # loss = model(x, label, criterion)['loss']\n\n                    loss_scalar = loss.item()\n                    loss_hist.append(loss_scalar)\n                    if local_rank == 0:\n                        tq.update(1)\n                        tq.set_postfix({'loss': loss_scalar})\n\n                val_loss_sum = torch.FloatTensor([sum(loss_hist)]).to(device)\n                num_batches = torch.LongTensor([len(loss_hist)]).to(device)\n                dist.reduce(val_loss_sum, dst = 0, op = dist.ReduceOp.SUM, async_op = True)\n                dist.reduce(num_batches, dst = 0, op = dist.ReduceOp.SUM, async_op = True)\n                val_loss_this_time = (val_loss_sum / num_batches).to(device)\n                val_loss_this_time = val_loss_this_time.item()\n                if local_rank == 0:\n                    tq.set_postfix({'val_mean_loss': val_loss_this_time})\n                    writer.add_scalar('val_mean_loss', val_loss_this_time,\n                                      global_step = epochs_trained_this_time // val_interval)\n                    tq.close()\n\n            model.train()\n            if enable_EMA:\n                ema.restore()\n\n            if local_rank == 0 and val_loss_this_time < val_mnt_best:\n                torch.save(get_state(), savepoint_dir + 'val_best.pth')\n                val_mnt_best = val_loss_this_time\n                print(f'updating val_mnt_best: {val_mnt_best}')\n\n            dist.barrier()\n\n\nif __name__ == '__main__':\n    # to close all processes with trigger ctrl+c\n    try:\n        threading.Thread(target = main).start()\n    except Exception as e:\n        traceback.print_exc()\n        os.system(f\"kill $(ps aux | grep '{os.path.split(__file__)[-1]}' | grep -v grep | awk '{{print $2}}')\")\n","repo_name":"Non-demon/Segmentation-Suite","sub_path":"train_ddp.py","file_name":"train_ddp.py","file_ext":"py","file_size_in_byte":16157,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"20525050867","text":"import collections\nimport numpy as np\n\nimport tensorflow as tf\n\nimport sys\nimport os\nfrom manipulator_learning.learning.agents.tf.common import convert_env_obs_to_tuple\n\nObsAct = collections.namedtuple('ObsAct', ('obs', 'action'))\n\n\nclass FailurePreidctorHeuristic:\n    def __init__(self,\n                 discriminator,\n                 d_model_step,\n                 q_model=None,\n                 init_non_inc_neg_d_thresh=5,\n                 init_neg_d_thresh=20,\n                 q_type='td3',\n                 backend='tf',\n                 d_model_step_delay=3000):\n        self.discriminator = discriminator\n        self.q_model = q_model\n        self.d_model_step = d_model_step\n        self.non_inc_neg_d_thresh = init_non_inc_neg_d_thresh\n        self.consec_non_inc_neg_d = 0\n        self.neg_d_thresh = init_neg_d_thresh\n        self.consec_neg_d = 0\n        self.q_type = q_type\n        self.backend = backend\n        self.d_model_step_delay = d_model_step_delay\n        self.last_q = -1e10\n        self.last_d = -1e10\n        self.include_q_cond = q_model is not None\n        self.first_episode_prediction_made = False\n        self.statistics = dict(tp=0, fp=0, fn=0, tn=0)\n\n    def _get_q_output(self, tf_obs, tf_action):\n        if self.q_type == 'td3':\n            if len(tf_obs.shape) == 1:\n                out, _ = self.q_model([tf_obs], [tf_action])\n            else:\n                out, _ = self.q_model(tf_obs, tf_action)\n        return out\n\n    def _get_d_output(self, tf_obs, tf_action):\n        if type(tf_obs[0]) == tuple:\n            tf_obs = tf_obs[0]\n            disc_input = (tf_obs[0], tf_obs[1], tf.concat([tf_obs[2], tf_action], -1))\n            if self.discriminator.num_ensemble > 1:\n                mean, std = self.discriminator.forward_mean_std(disc_input)\n                return mean\n            else:\n                return self.discriminator(disc_input)\n        else:\n            if self.discriminator.num_ensemble > 1:\n                mean, std = self.discriminator.forward_mean_std(tf.concat([tf_obs, tf_action], -1))\n                return mean\n            else:\n                return self.discriminator(tf.concat([tf_obs, tf_action], -1))\n\n    def _prep_obs_act_for_model(self, obs, action):\n        if self.backend == 'tf':\n            if type(obs) == dict:\n                obs = convert_env_obs_to_tuple(obs)\n            else:\n                from tensorflow.contrib.eager.python import tfe as contrib_eager_python_tfe\n                obs = contrib_eager_python_tfe.Variable(obs.astype('float32'))\n                action = contrib_eager_python_tfe.Variable(action.astype('float32'))\n        else:\n            return NotImplementedError(\"Failure Predictor not implemented for backend %s\" % self.backend)\n\n        return obs, action\n\n    def get_failure_prediction(self, obs, action):\n        # called by user to check if current o-a pair predicts failure\n        if self.d_model_step > self.d_model_step_delay:\n            tf_obs, tf_action = self._prep_obs_act_for_model(obs, action)\n            if self.include_q_cond:\n                q_out = self._get_q_output(tf_obs, tf_action)\n                q_change = q_out - self.last_q\n                self.last_q = q_out\n            d_out = self._get_d_output([tf_obs], [tf_action])\n            d_change = d_out - self.last_d\n            self.last_d = d_out\n\n            if d_out < 0:\n                self.consec_neg_d += 1\n                if self.include_q_cond and q_change < 0:\n                    self.consec_non_inc_neg_d += 1\n                else:\n                    self.consec_non_inc_neg_d = 0\n            else:\n                self.consec_neg_d = 0\n                self.consec_non_inc_neg_d = 0\n\n            thresholds_reached = dict(\n                consec_non_inc=False,\n                consec_neg_d=False\n            )\n            if self.consec_non_inc_neg_d >= self.non_inc_neg_d_thresh:\n                thresholds_reached['consec_non_inc'] = True\n            if self.consec_neg_d >= self.neg_d_thresh:\n                thresholds_reached['consec_neg_d'] = True\n            if thresholds_reached['consec_non_inc'] or thresholds_reached['consec_neg_d']:\n                return True, thresholds_reached\n            else:\n                return False, thresholds_reached\n        else:\n            return False, None\n\n    def update_thresh_false_positive(self, thresholds_reached, delta_non_inc=1, delta_neg_d=1):\n        # called by user to modify thresholds based on output from get_failure_prediction, if false positive\n        # failure prediction (so thresholds are increased)\n        if not thresholds_reached['consec_non_inc']:\n            delta_non_inc = 0\n        if not thresholds_reached['consec_neg_d']:\n            delta_neg_d = 0\n        self.update_consec_thresh(delta_non_inc, delta_neg_d)\n\n    def update_consec_thresh(self, noninc_change, neg_d_change):\n        self.non_inc_neg_d_thresh += noninc_change\n        self.non_inc_neg_d_thresh = max(self.non_inc_neg_d_thresh, 1)\n        self.neg_d_thresh += neg_d_change\n        # self.neg_d_thresh = max(self.neg_d_thresh, 1)\n        self.neg_d_thresh = max(self.neg_d_thresh, self.non_inc_neg_d_thresh + 1)  # beta > alpha\n        print('New thresholds -- non-inc: %d, neg-d: %d' % (self.non_inc_neg_d_thresh, self.neg_d_thresh))\n\n    def update_consec_counts(self, noninc_change, neg_d_change):\n        self.consec_non_inc_neg_d += noninc_change\n        self.consec_non_inc_neg_d = max(0, self.consec_non_inc_neg_d)\n        self.consec_neg_d += neg_d_change\n        self.consec_neg_d = max(0, self.consec_neg_d)\n\n    def new_ep_reset(self):\n        # called by user at beginning of new episode\n        self.last_q = -1e10\n        self.last_d = -1e10\n        self.reset_consec_counts()\n        self.first_episode_prediction_made = False\n\n    def reset_consec_counts(self):\n        self.consec_neg_d = 0\n        self.consec_non_inc_neg_d = 0\n\n    def update_statistics(self, update_str):\n        # called by user to update statistics\n        if not self.first_episode_prediction_made and self.d_model_step > self.d_model_step_delay:\n            self.first_episode_prediction_made = True\n            self.statistics[update_str] += 1\n","repo_name":"utiasSTARS/manipulator-learning","sub_path":"manipulator_learning/learning/algorithms/fire/failure_predictors/failure_prediction_heuristic.py","file_name":"failure_prediction_heuristic.py","file_ext":"py","file_size_in_byte":6202,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"48"}
+{"seq_id":"22424704589","text":"import cv2\nimport os\nfrom pathlib import path\n\nimage_name = input(\"Please enter the name of the image file that you want to process: \")\nimage_directory = input(\"Please enter the directory that may contain the image: \")\n\ndef find_the_image(file_name,directory_name):\n    files_found = []\n    for path,subdirs,files in os.walk(directory_name):\n        for name in filess:\n            if(file_name == name):\n                file_path = os.path.join(path,name)\n                files_found.append(file_path)\n\n    print(files_found[0])\n    return files_found[0]\n\nimage_path = Path(find_the_image(image_name,image_directory))\nnew_working_directory = image_path.parent\nos.chdir(new_working_directory)\ncolor_image = cv2.imread(find_the_image(image_name,image_directory))\ncartoon_style_selection = input(\"This script currently has 2 styles. Please type 1 or 2\")\nif (cartoon_style_selection == \"1\"):\n    cartoon_image_style_1 = cv2.stylization(color_image,sigma_s=150,sigma_r=0.25)\n    cv2.imshow('cartoon_1',cartoon_image_style_1)\n    cv2.waitKey()\n    cv2.destroyAllWindows()\nelif (cartoon_style_selection == \"2\"):\n    cartoon_image_style_2 = cv2.stylization(color_image,sigma_s=60,sigma_r=0.5)\n    cv2.imshow('cartoon_2',cartoon_image_style_2)\n    cv2.waitKey()\n    cv2.destoryAllWindows()\n\nelse:\n    print(\"Invalid style selection.\")\n\n","repo_name":"sinistergeek/Python-Project","sub_path":"OpenCV_Cartoonify.py","file_name":"OpenCV_Cartoonify.py","file_ext":"py","file_size_in_byte":1328,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"2949890135","text":"import numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\ndef call_payoff(sT, strike_price, premium):\r\n    return np.maximum(sT - strike_price, 0) - premium\r\n\r\n# Assume expiration date is in 1 year\r\nexpiration_date = 1\r\n\r\n# Assume underlying asset price starts at $50\r\ns0 = 50\r\n\r\n# Assume the strike prices of the call options are $45, $50 and $55\r\nstrike_prices = np.array([40, 60, 80])\r\n\r\n# Assume the premiums for the call options are $2, $1, and $2 respectively\r\npremiums = np.array([2, 1, 2])\r\n\r\n# Create a range of stock prices at expiration date\r\nsT = np.arange(0, 2 * s0, 5)\r\n\r\n# Calculate the payoffs of each call option at expiration\r\npayoff_long_ITM = call_payoff(sT, strike_prices[0], premiums[0])\r\npayoff_long_OTM = call_payoff(sT, strike_prices[2], premiums[2])\r\npayoff_short_ATM1 = -call_payoff(sT, strike_prices[1], premiums[1])\r\npayoff_short_ATM2 = -call_payoff(sT, strike_prices[1], premiums[1])\r\n\r\n# Calculate the combined payoffs of the trade\r\npayoff_combined = payoff_long_ITM + payoff_long_OTM + payoff_short_ATM1 + payoff_short_ATM2\r\n\r\n# Plot the payoffs\r\nplt.plot(sT, payoff_long_ITM, '-', label='Long ITM Call', color='r')\r\nplt.plot(sT, payoff_long_OTM, '-', label='Long OTM Call', color='b')\r\nplt.plot(sT, payoff_short_ATM1, '--', label='Short ATM Call 1', color='g')\r\nplt.plot(sT, payoff_short_ATM2, '--', label='Short ATM Call 2', color='g')\r\nplt.plot(sT, payoff_combined, '-', label='Combined Payoff', color='black', linewidth = 2.0)\r\nplt.legend()\r\nplt.xlabel('Stock Price at Expiration')\r\nplt.ylabel('Payoff')\r\n# plt.title('Payoff of Long ITM Call + Long OTM Call + Short 2 ATM Calls')\r\nplt.grid()\r\nplt.show()\r\n","repo_name":"tanisha-salvi/JPMC-Quant-Research-Case-Study-2023","sub_path":"Tanisha_Salvi_Case_Study_Part_B_Q_b_MAIN.py","file_name":"Tanisha_Salvi_Case_Study_Part_B_Q_b_MAIN.py","file_ext":"py","file_size_in_byte":1638,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"44487869392","text":"class Solution:\r\r\n    def canConstruct(self, ransomNote: str, magazine: str) -> bool:\r\r\n        rf = collections.Counter(ransomNote)\r\r\n        af = collections.Counter(magazine)\r\r\n        for key, value in rf.items():\r\r\n            if key in af and value <= af[key]:\r\r\n                continue\r\r\n            else:\r\r\n                return False\r\r\n        return True","repo_name":"JLUVicent/leetcode_solution","sub_path":"0383.赎金信/0383-赎金信.py","file_name":"0383-赎金信.py","file_ext":"py","file_size_in_byte":366,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"71980920787","text":"import numpy as np\nfrom scipy.io import savemat\n\n#basePath = \"/home/mohamed/Desktop/CooperLab_Research/KNN_Survival/\"\nbasePath = \"/home/mtageld/Desktop/KNN_Survival/\"\n\nsites = [\"GBMLGG\", \"BRCA\", \"KIPAN\"]\ndtypes = [\"Integ\", \"Gene\"]\n\nfor site in sites:\n    for dtype in dtypes:\n\n        print(site + \"\\t\" + dtype)\n        \n        dataPath = basePath + 'Data/SingleCancerDatasets/' + site + '/' + site + '_' + dtype + '_Preprocessed.mat'\n        splitIdxPath = dataPath.split('.mat')[0] + '_splitIdxs.pkl'\n        \n        splitIdxs = np.load(splitIdxPath)\n        \n        savemat(splitIdxPath.split('.pkl')[0] + '.mat', splitIdxs)\n","repo_name":"cooperlab/KNN_Survival","sub_path":"junk/0_convert_splitidxs_to_mat.py","file_name":"0_convert_splitidxs_to_mat.py","file_ext":"py","file_size_in_byte":631,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"70316086226","text":"# import packages\nimport streamlit as st\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n\n# show the title\nst.title('Titanic APP by Miaomiao Hou')\n\n# read csv and show the dataframe\ndf = pd.read_csv('train.csv')\ndf\n# create a figure with three subplots, size should be (15, 5)\nplt.style.use('seaborn')\nst.subheader('The Box Plot For Ticket Price with Different Classes')\nfig, ax = plt.subplots(1, 3, figsize = (15,5))\n# show the box plot for ticket price with different classes\ndf[df['Pclass'] == 1]['Fare'].plot.box(ax = ax[0])\ndf[df['Pclass'] == 2]['Fare'].plot.box(ax = ax[1])\ndf[df['Pclass'] == 3]['Fare'].plot.box(ax = ax[2])\n\n# you need to set the x labels and y labels\nax[0].set_ylabel('Fare')\nax[0].set_xlabel('PClass1')\nax[1].set_xlabel('PClass2')\nax[2].set_xlabel('PClass3')\n\n# a sample diagram is shown below\nst.pyplot(fig)\n\n","repo_name":"xieemaomao/assignment-06-st-starter","sub_path":"assignment-06-st-starter.py","file_name":"assignment-06-st-starter.py","file_ext":"py","file_size_in_byte":841,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"14332178527","text":"import asyncio\r\nimport aiofiles\r\n\r\nasync def exemplo_arq1():\r\n    async with aiofiles.open('registros.txt') as arquivo:\r\n        conteudo = await arquivo.read()\r\n    print(conteudo)\r\n\r\nasync def exemplo_arq2():\r\n    linhas = []\r\n    async with aiofiles.open('registros.txt') as arquivo:\r\n        async for linha in arquivo:\r\n            linhas.append(linha)\r\n\r\ndef main():\r\n    el = asyncio.get_event_loop()\r\n    el.run_until_complete(exemplo_arq2())\r\n    el.close()\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n","repo_name":"GustavoHBraga/Python-Async","sub_path":"04___Async_IOFiles_1.py","file_name":"04___Async_IOFiles_1.py","file_ext":"py","file_size_in_byte":510,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"75037513744","text":"# -*- coding: utf-8 -*-\n\nimport struct, json, os, hashlib, keras\nimport numpy as np\n\nproject_path = os.path.dirname(os.path.realpath(__file__))\nmodels_path = os.path.join(project_path, '../lib/model')\nloader_path = os.path.join(project_path, '../IntentHiding')\n\ndef dump_hex2header(model_hexes, ecc_hexes, key=123, max_feature_len=18432):\n    template = '''#ifndef _RESOURCE_MODEL_H_\\n#define _RESOURCE_MODEL_H_\\n\\nconst int key = %d;\\nconst int max_feature_len = %d;\\nconst int decoder_seq_len = 300;\\nconst int ecc_seq_len = 10;\\nconst int emb_dim = 260;\\nconst unsigned char model[] = {%s};\\nconst unsigned char ecc[] = {%s};\\n\\n#endif''' % (key, max_feature_len, ','.join(map(str, model_hexes)), ','.join(map(str, ecc_hexes)))\n    with open(os.path.join(loader_path, 'resource_model.h'), 'w') as file_out:\n        file_out.write(template)\n\ndef dump_model_file(key=123, model_name='model_decoder', ecc_name='model_ecc'):\n    model_str = open(os.path.join(models_path, '{}.json'.format(model_name))).read()\n    model_chars = [hex(ord(char) ^ key) for char in model_str]\n    ecc_str = open(os.path.join(models_path, '{}.json'.format(ecc_name))).read()\n    ecc_chars = [hex(ord(char) ^ key) for char in ecc_str]\n    dump_hex2header(model_chars, ecc_chars, key=key)\n\nif __name__ == '__main__':\n    dump_model_file()","repo_name":"aisecstudent/DeepPuzzling","sub_path":"DeepModel/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":1314,"program_lang":"python","lang":"en","doc_type":"code","stars":62,"dataset":"github-code","pt":"48"}
+{"seq_id":"23787211107","text":"from tensorboard.backend.event_processing import event_accumulator\nimport matplotlib.pyplot as plt\n# 日志地址\nlogs_url = '../logstrain/events.out.tfevents.1648804625.Xiangbo-mbp.local.97405.0'\n# 加载日志数据\nea = event_accumulator.EventAccumulator(logs_url)\nea.Reload()\nprint(ea.scalars.Keys())\n\nval_energy = ea.scalars.Items('train_reward_energy')\nprint(len(val_energy))\nprint([(i.step,i.value) for i in val_energy])\n\nfig=plt.figure(figsize=(6,4))\nax1=fig.add_subplot(111)\ntrain_reward_energy=ea.scalars.Items('train_reward_energy')\nax1.plot([i.step for i in train_reward_energy],[i.value for i in train_reward_energy],label='train_reward_energy')\nax1.set_xlim(0)\ntrain_reward = ea.scalars.Items('train_reward')\nax1.plot([i.step for i in train_reward],[i.value for i in train_reward],label='train_reward')\nax1.set_xlabel(\"step\")\nax1.set_ylabel(\"\")\n\nplt.legend(loc='lower right')\nplt.show()","repo_name":"YLifeOnlyOnce/EC-RL","sub_path":"dataread/reader.py","file_name":"reader.py","file_ext":"py","file_size_in_byte":900,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"48"}
+{"seq_id":"4601999299","text":"#!/usr/bin/env python3\nimport argparse\nfrom collections import defaultdict\nfrom operator import itemgetter\nfrom pathlib import Path\nimport sys\nimport typing\nimport yaml\n\nimport byml\nimport byml.yaml_util\n\n# From PyYAML: https://github.com/yaml/pyyaml/blob/a9c28e0b52/lib3/yaml/representer.py\n# with the sorting code removed.\ndef represent_mapping(dumper, tag, mapping, flow_style=None, sort=False):\n    value = [] # type: ignore\n    node = yaml.MappingNode(tag, value, flow_style=flow_style)\n    best_style = True\n    if hasattr(mapping, 'items'):\n        mapping = list(mapping.items())\n        if sort:\n            try:\n                mapping = sorted(mapping)\n            except TypeError:\n                pass\n    for item_key, item_value in mapping:\n        node_key = dumper.represent_data(item_key)\n        node_value = dumper.represent_data(item_value)\n        if not (isinstance(node_key, yaml.ScalarNode) and not node_key.style):\n            best_style = False\n        if not (isinstance(node_value, yaml.ScalarNode) and not node_value.style):\n            best_style = False\n        value.append((node_key, node_value))\n    if flow_style is None:\n        if dumper.default_flow_style is not None:\n            node.flow_style = dumper.default_flow_style\n        else:\n            node.flow_style = best_style\n    return node\n\ndef represent_dict(dumper, mapping, flow_style=None):\n    return represent_mapping(dumper, 'tag:yaml.org,2002:map', mapping, flow_style)\ndef represent_dict_sort(dumper, mapping, flow_style=None):\n    return represent_mapping(dumper, 'tag:yaml.org,2002:map', mapping, flow_style, sort=True)\n\ndef main() -> None:\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"gamedata_dir\", help=\"Path to the gamedata.sarc/ directory\")\n    args = parser.parse_args()\n    DIR = Path(args.gamedata_dir)\n\n    byml.yaml_util.add_representers(yaml.CSafeDumper)\n    yaml.add_representer(dict, represent_dict, Dumper=yaml.CSafeDumper)\n    yaml.add_representer(defaultdict, represent_dict_sort, Dumper=yaml.CSafeDumper)\n\n    result: typing.List[dict] = []\n\n    for bgdata_path in DIR.glob(\"*.bgdata\"):\n        bgdata = byml.Byml(bgdata_path.open(\"rb\").read()).parse()\n\n        flags = bgdata.get(\"bool_data\", None)\n        if not flags:\n            continue\n        for flag in flags:\n            reset_data = flag[\"InitValue\"] >> 1\n            if reset_data == 0:\n                continue\n            assert flag[\"ResetType\"] == 0\n            assert flag[\"DeleteRev\"] == -1\n            assert flag[\"IsProgramReadable\"] and flag[\"IsProgramWritable\"]\n            assert flag[\"IsSave\"]\n            assert flag[\"MinValue\"] is False and flag[\"MaxValue\"] is True\n            assert flag[\"InitValue\"] & 1 == 0\n\n            row = (reset_data - 1) & 0b111\n            col = (reset_data - 1) >> 3\n\n            result.append({\n                \"name\": flag[\"DataName\"],\n                \"hash\": flag[\"HashValue\"] & 0xffffffff,\n                \"col\": col,\n                \"row\": row,\n            })\n\n    result.sort(key=itemgetter(\"name\"))\n    yaml.dump(result, sys.stdout, Dumper=yaml.CSafeDumper)\n\nmain()\n","repo_name":"leoetlino/botw-re-notes","sub_path":"tools/gamedata_revival_random_flags.py","file_name":"gamedata_revival_random_flags.py","file_ext":"py","file_size_in_byte":3121,"program_lang":"python","lang":"en","doc_type":"code","stars":107,"dataset":"github-code","pt":"48"}
+{"seq_id":"5631868487","text":"from logging import DEBUG, INFO, Formatter, Handler, Logger, StreamHandler, getLogger\n\nfrom app import settings\n\n\ndef get_logger() -> Logger:\n    logger = getLogger(\"gohan_map\")\n    logger = _set_handler(logger, StreamHandler(), settings.DEBUG)\n    logger.setLevel(DEBUG)\n    logger.propagate = False\n    return logger\n\n\ndef _set_handler(logger: Logger, handler: Handler, verbose: bool) -> Logger:\n    if verbose:\n        handler.setLevel(DEBUG)\n    else:\n        handler.setLevel(INFO)\n    handler.setFormatter(\n        Formatter(\n            \"[%(levelname)s] %(asctime)s - %(filename)s:%(lineno)ss  - %(message)s\"\n        )\n    )\n    logger.addHandler(handler)\n    return logger\n","repo_name":"jphacks/SP_2303","sub_path":"Server/app/utils/logger.py","file_name":"logger.py","file_ext":"py","file_size_in_byte":681,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"48"}
+{"seq_id":"8056580692","text":"#!/usr/bin/env python\n\n# import json\nimport logging\n\n# import pprint\nfrom genie.testbed import load\n\nLOG_STDOUT = False\nTESTBED = \"devnet_sbx_testbed.yml\"\n\n\ndef main():\n    logger = logging.getLogger(__name__)\n    logger.setLevel(logging.INFO)\n    testbed = load(TESTBED)\n    # learnt = {}\n    for name, dev in testbed.devices.items():\n        dev.connect(log_stdout=LOG_STDOUT)\n        # ospf = dev.learn(\"ospf\")\n        # print(ospf)\n        # learnt[name] = {}\n        # learnt[name][\"version\"] = dev.parse(\"show version\")\n        # learnt[name][\"running_config\"] = dev.parse(\"show running-config\")\n        # learnt[name][\"bgp\"] = dev.learn(\"bgp\")\n        # ospf = dev.learn(\"ospf\")\n        # learnt[name][\"ospf\"] = dev.parse(ospf)\n\n    # print(json.dumps(learnt))\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"mrlesmithjr/Cisco_Learning","sub_path":"python/parse.py","file_name":"parse.py","file_ext":"py","file_size_in_byte":808,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"18182157700","text":"import requests\nfrom requests.packages.urllib3.exceptions import InsecureRequestWarning\n\nrequests.packages.urllib3.disable_warnings(InsecureRequestWarning)\ncompliance_check_types = [\"cis\", \"nist_master\", \"nist_slave\", \"pcidss\", \"hipaa\", \"standard\"]\n\n\ndef run_compliance_scan(api_url, api_key, tag, compliance_check_type):\n    default_headers = {\"Content-Type\": \"application/json\", \"Authorization\": \"\"}\n    api_response = requests.post(\"{0}/users/auth\".format(api_url),\n                                 json={\"api_key\": api_key}, headers=default_headers,\n                                 verify=False).json()\n    if api_response[\"success\"]:\n        print(\"Authentication successful\")\n    else:\n        print(\"Authentication failed\")\n        return\n    default_headers[\"Authorization\"] = \"Bearer \" + api_response[\"data\"][\"access_token\"]\n\n    enumerate_filters = {\"type\": [\"host\", \"container\", \"container_image\"], \"user_defined_tags\": [tag], \"pseudo\": False}\n    api_response = requests.post(\"{0}/enumerate\".format(api_url),\n                                 json={\"filters\": enumerate_filters, \"size\": 1000},\n                                 headers=default_headers, verify=False).json()\n    nodes_list = []\n    counter = 1\n    api_response_nodes = api_response.get(\"data\", {}).get(\"data\", [])\n    if not api_response_nodes:\n        print(\"No nodes found with tag {0}\".format(tag))\n        return\n    print(\"\\nNodes with tag \\\"{0}\\\"\".format(tag))\n    for node in api_response_nodes:\n        if node[\"type\"] == \"container\":\n            node_name = \"{0} / {1} (container)\".format(node.get(\"container_name\", \"\"), node.get(\"host_name\", \"\"))\n        elif node[\"type\"] == \"container_image\":\n            node_name = \"{0} (container_image)\".format(node.get(\"image_name_with_tag\", \"\"))\n        else:\n            node_name = \"{0} (host)\".format(node.get(\"host_name\", \"\"))\n        print(\"{0}: {1}\".format(counter, node_name))\n        nodes_list.append({\"id\": node[\"id\"], \"node_name\": node_name})\n        counter += 1\n    counter = 1\n    for node in nodes_list:\n        print(\"\\n{0}: Starting compliance scan on {1}\".format(counter, node[\"node_name\"]))\n        print(requests.post(\"{0}/node/{1}/start_compliance_scan\".format(api_url, node[\"id\"]),\n                            headers=default_headers, verify=False,\n                            json={\"compliance_check_type\": compliance_check_type}).json())\n        counter += 1\n\n\nif __name__ == '__main__':\n    import sys\n\n    if len(sys.argv) != 5:\n        print(\n            \"Usage: python3 start_compliance_scan_for_tag.py <mgmt_console_ip_address> <api_key> <tag> <compliance_check_type>\")\n        print(\"Options for compliance_check_type: {0}\".format(\", \".join(compliance_check_types)))\n        exit(1)\n    print(\"Options for compliance_check_type: {0}\".format(\", \".join(compliance_check_types)))\n    if sys.argv[4] not in compliance_check_types:\n        print(\"Invalid compliance_check_type {0}\".format(sys.argv[4]))\n    run_compliance_scan(\"https://{0}/deepfence/v1.5\".format(sys.argv[1]), sys.argv[2], sys.argv[3], sys.argv[4])\n","repo_name":"deepfence/deepfence_runtime_api","sub_path":"scripts/node_tags/start_compliance_scan_for_tag.py","file_name":"start_compliance_scan_for_tag.py","file_ext":"py","file_size_in_byte":3069,"program_lang":"python","lang":"en","doc_type":"code","stars":51,"dataset":"github-code","pt":"48"}
+{"seq_id":"23278330505","text":"# play with the multiple files using app\n# use controller model architecture\n# if we declare \"from controller import *\" above then it can't render the page \n# run this command before the start programming \n# $env:PYTHONDONTWRITEBYTECODE=1  \n# install mysql\n# install PyJWT\n\nfrom flask import Flask, render_template, redirect, url_for, request, jsonify, make_response\nimport uuid\nfrom werkzeug.security import generate_password_hash, check_password_hash\nfrom datetime import datetime, timedelta\napp = Flask(__name__)\nimport jwt\n\n# from controller import user_controler, product_controller\n# we want to add the file name manually insted of that we are going to import whole directory\nfrom controller import *\n\n# -----------------------------------------------------------------------------------------------------\n#                                      CRUD Operation using Flask API\n# -----------------------------------------------------------------------------------------------------\n\n@app.route('/')\ndef welcome():\n    return \"Welcome to API\"\n\n@app.route('/users')\ndef user_route():\n    return render_template('index_employee.html')\n\n@app.route('/update')\ndef update_user():\n    return render_template('take_id.html')\n\n@app.route('/delete')\ndef Delete_user():\n    return render_template('delete_user.html')\n\n# -----------------------------------------------------------------------------------------------------\n#                                      make_response methos & HTTP Response Codes\n# -----------------------------------------------------------------------------------------------------\n\n@app.route('/make_response')\ndef make_response_try():\n    return redirect(url_for('try_make_response'))\n\n@app.route('/page/<start>/<limit>')\ndef pagination_data(start, limit):\n    return redirect(url_for('fetch_data_controller', start = start, limit=limit))\n\n# -----------------------------------------------------------------------------------------------------\n#                                               File Uploading\n# -----------------------------------------------------------------------------------------------------\n\n# Problem:\n# 1) uploading file postman to server\n# 2) unique file name\n# 3) updating filepath in database with respective entity\n# 4) creating end point to read the file\n\n# put = upload file and save into database\n# Reference controller.\n\n# -----------------------------------------------------------------------------------------------------\n#                                               Project-1: TODO LIST\n# -----------------------------------------------------------------------------------------------------\nfrom models.todo_model import todo_model\nfrom functools import wraps\nobj = todo_model()\n\n# def token_require(f):\n#     @wraps(f)\n#     def decorator(*args, **kwargs):\n\ndef token_required(f):\n    @wraps(f)\n    def decorated(*args, **kwargs):\n        \n        token = request.headers.get('token')\n        \n        if not token:\n            return {\n                \"message\": \"enter valid token...!\"\n            }\n        try:\n            decode_token = jwt.decode(\n                token,\n                key =obj.secratekey,\n                algorithms=\"HS256\"\n            )\n        except:\n            return {\n                \"message\": \"enter valid token....!\"\n            }\n            \n        return f(decode_token ,*args, **kwargs)\n\n    return decorated\n\n@app.route('/todo')\ndef todo():\n    return \"all user are hear\"\n\n@app.route('/todo/all_user', methods=['GET'])\n@token_required\ndef get_all_user(decode_token): \n    \n    if decode_token['payload'][0]['admin'] == 0:\n        return {\n            \"message\": \"promotaion require\"\n        }\n        \n    connector = obj.return_con()\n    cursor = connector.cursor(dictionary=True)\n    \n    query = \"SELECT * FROM todo.user\"\n    cursor.execute(query)\n    data = cursor.fetchall() \n    \n    total_id = obj.return_total_public_id()\n    return {\n        # \"Decode Token Data\": token,\n        \"data\": data\n    }\n\n@app.route('/todo/get_user/<public_id>', methods=['GET'])\ndef get_one_user(public_id):\n    total_public_id =obj.return_total_public_id()\n    \n    if public_id in total_public_id:\n        connector = obj.return_con()\n        cursor = connector.cursor(dictionary=True)\n        query = f\"SELECT * FROM todo.user where public_id='{public_id}';\"\n        cursor.execute(query)\n        data = cursor.fetchall()\n        return {\n            \"total\":data\n        }\n    else:\n        return \"not valid id valid id\"\n    \n\n@app.route('/todo/post_user', methods=['POST'])\ndef post_public_id():\n    data = dict(request.form)\n    connector = obj.return_con()\n    cursor = connector.cursor(dictionary=True)\n    \n    query = f\"INSERT INTO todo.user (public_id, name, pass, admin) VALUES ('{data['public_id']}', '{data['name']}','{data['pass']}' ,'{data['admin']}');\"\n    cursor.execute(query)\n    return {\n        \"meg\": \"Data insert into DataBase successfully..!\"\n    }\n\n@app.route('/todo/put_user/<public_id>', methods=['PUT'])\ndef put_public_id(token ,public_id):\n    if token['payload'][0]['admin'] == 0:\n        return {      \n                \"message\": \"promotaion require\"\n        }\n        \n    total_public_id =obj.return_total_public_id()\n          \n    if public_id in total_public_id:\n        connector = obj.return_con()\n        cursor = connector.cursor(dictionary=True)\n\n        query = f\"UPDATE todo.user SET admin = {'1'} WHERE (public_id = '{public_id}');\"\n        cursor.execute(query)\n        return {\n            \"public id\": public_id,\n            \"role\": \"Admin\"\n        }\n    else:\n        return \"enter valid public id\"\n\n@app.route('/todo/delete_user/<id>', methods=['DELETE'])\ndef detele_public_id( id):\n    x = int(id)\n    total_id = obj.return_total_id()\n    if x in total_id:\n        connector = obj.return_con()\n        cursor = connector.cursor(dictionary=True)\n        total_id = obj.return_total_id()\n        \n        query = f\"DELETE FROM todo.user WHERE id= {id}\"\n        cursor.execute(query)\n        data = cursor.fetchall() \n        return {\n           \"message\": \"user Deleted successfully.......!!\"\n        }\n    else:\n        return \"enter valid id\"\n\n@app.route('/todo/login')\ndef user_login():\n    \n    total_id = obj.return_total_id()\n    auth = request.authorization\n    \n    if not auth:\n        return \"enter usernmae and passwords.....!!\"\n    \n    if int(auth['username']) in total_id:\n        connector = obj.return_con()\n        cursor = connector.cursor(dictionary=True)\n        query = f\"SELECT pass from todo.user where id={int(auth['username'])};\"\n        cursor.execute(query)\n        data = cursor.fetchone()\n        \n        if data['pass'] == auth['password']:\n            # Epoch Time Runs:\n            time = datetime.now() + timedelta(minutes=30)\n            epoch_time = int(time.timestamp())\n            \n            # fetch employee information \n            connector = obj.return_con()\n            cursor = connector.cursor(dictionary=True)\n            query = f\"select id, admin from todo.user WHERE id = {auth['username']} ;\"\n            cursor.execute(query)\n            emp_data = cursor.fetchall() \n            # create the pyload which pass through, Encode function, with exparation time\n            payload = {\n                'payload': emp_data,\n                # 'exp': epoch_time,\n            }\n            # key  = Secrate key, use for pretect the secrate message.\n            jtoken = jwt.encode(\n                payload, \n                key = obj.secratekey\n            )\n            return make_response({\n                \"data\": auth,\n                \"token\": jtoken,\n            }, 200)\n        else:\n            return {\n                \"message\": \"enter valid password\"\n            }\n    else:\n        return {\n            \"message\": \"invalid user........!!\"\n        }\n\n@app.route('/todo/get_all_list', methods=['GET'])\n@token_required\ndef get_all_todo_list(decode_token):\n    if decode_token['payload'][0]['admin'] == 0:\n        return {\n            \"message\": \"promotaion require\"\n        }\n    \n    connector = obj.return_con()\n    cursor = connector.cursor(dictionary=True)\n    query = f\"SELECT * FROM todo.todo;\"\n    cursor.execute(query)\n    todo_data = cursor.fetchall() \n    \n    # total_id = obj.return_total_id_of_todo_data()\n    \n    return {\n        # \"total_id\": total_id,\n        \"decode token\": decode_token,\n        \"todo_data\": todo_data\n    }\n\n#--------------------------------------------------------------------\n\n@app.route('/todo/get_one_list/<todo_id>', methods=['GET'])\ndef get_one_todo_list_using_id(todo_id):\n    total_id = obj.return_total_id_of_todo_data()\n    todo_id = int(todo_id)\n    if todo_id in total_id:\n        connector = obj.return_con()\n        cursor = connector.cursor(dictionary=True)\n        query = f\"select * from todo.todo where id={todo_id};\"\n        cursor.execute(query)\n        data = cursor.fetchall()\n        return {\n            \"data\": data\n        }\n    else:\n        return {\n            \"message\": \"enter valid data......!\"\n        }\n        \n#--------------------------------------------------------------------\n    \n@app.route('/todo/add_new_list', methods=['POST'])\ndef add_new_list():\n    data = request.form\n    if not data:\n        return {\n            \"message\": \"enter valid data\"\n        }\n    \n    connector = obj.return_con()\n    cursor = connector.cursor(dictionary=True)\n    query = f\"INSERT INTO todo.todo (text, compelete) VALUES ('{data['text']}', '{data['compelete']}');\"\n    cursor.execute(query)\n    return {\n        \"data\": \"Data Insert SuccessFully.......!\",\n        \"inserted Data\": data\n    }\n\n#-----------------------------------------------------------------------\n\n@app.route('/todo/update_list/<task_completion_id>', methods=['PUT'])\ndef put_todo_list(task_completion_id):\n    total_id = obj.return_total_id_of_todo_data()\n    task_completion_id = int(task_completion_id)\n    \n    if task_completion_id in total_id:\n        connector = obj.return_con()\n        cursor = connector.cursor(dictionary=True)\n        query = f\"Update todo.todo set compelete='1' where id={task_completion_id}\"\n        cursor.execute(query)\n        data = cursor.fetchall()\n        return {\n            \"message\": \"Todo List Updated Successfully\"\n        }\n    else:\n        return {\n            \"message\": \"enter valid id......!\"\n        }\n\n#-----------------------------------------------------------------------\n\n@app.route('/todo/delete_tdo_list/<todo_id>',methods=['DELETE'])\ndef delete_todo_list(todo_id):    \n    total_id = obj.return_total_id_of_todo_data()\n    todo_id = int(todo_id)\n    \n    if todo_id in total_id:\n        connector = obj.return_con()\n        cursor = connector.cursor(dictionary=True)\n        query = f\"DELETE from todo.todo where id={todo_id};\"\n        cursor.execute(query)\n        data = cursor.fetchall()\n        return {\n            \"message\": \"Todo List Deleted\"\n        }\n\n    else:\n        return {\n            \"message\": \"enter valid data......!\"\n        }\n        \n#-----------------------------------------------------------------------\n\nif __name__ == '__main__':\n    app.run(debug=True)","repo_name":"malani-prince/Flask-API","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":11165,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"30987961822","text":"from abc import ABCMeta, abstractmethod\nfrom datetime import date\nfrom typing import Dict\n\n\nclass Deligence:\n    def __init__(self, data):\n        super().__init__()\n        self.doc_id = data['doc_id']\n        self.current_year_operating_income = data['current_year_operating_income']\n        self.prior_1year_operating_income = data['prior_1year_operating_income']\n        self.current_year_current_assets = data['current_year_current_assets']\n        self.current_year_investments_and_other_assets = data[\n            'current_year_investments_and_other_assets']\n        self.current_year_current_liabilities = data['current_year_current_liabilities']\n        self.current_year_noncurrent_liabilities = data['current_year_noncurrent_liabilities']\n        self.current_year_net_assets = data['current_year_net_assets']\n        self.current_year_total_number_of_issued_shares = data[\n            'current_year_total_number_of_issued_shares']\n\n    def caluculate_company_value(self):\n        # 前期、今期の営業利益をもとに、事業価値を出す\n        business_value = (self.current_year_operating_income +\n                          self.prior_1year_operating_income) / 2 * 10\n\n        # 流動資産・流動負債、固定資産の一部から財産価値を出す\n        property_value = self.current_year_current_assets - \\\n            (self.current_year_current_liabilities * 1.2) + \\\n            self.current_year_investments_and_other_assets\n\n        # 借金＝固定負債を引く\n        return business_value + property_value - self.current_year_noncurrent_liabilities\n\n    # 一株あたりの価値を出す\n    def value_per_share(self):\n        return int(self.caluculate_company_value() * 1000000 / self.current_year_total_number_of_issued_shares)\n\n    # 自己資本比率\n    def capital_ratio(self):\n        sum = self.current_year_current_liabilities + \\\n            self.current_year_noncurrent_liabilities + self.current_year_net_assets\n        return int(round(100 * (self.current_year_net_assets / sum)))\n\n    def to_dto(self):\n        entity = {\n            'doc_id': self.doc_id,\n            'value_per_share': self.value_per_share(),\n            'capital_ratio': self.capital_ratio(),\n            'current_year_operating_income': self.current_year_operating_income,\n            'prior_1year_operating_income': self.prior_1year_operating_income,\n            'current_year_current_assets': self.current_year_current_assets,\n            'current_year_investments_and_other_assets': self.current_year_investments_and_other_assets,\n            'current_year_current_liabilities': self.current_year_current_liabilities,\n            'current_year_noncurrent_liabilities': self.current_year_noncurrent_liabilities,\n            'current_year_net_assets': self.current_year_net_assets,\n            'current_year_total_number_of_issued_shares': self.current_year_total_number_of_issued_shares,\n            'updated_at': date.today(),\n        }\n        return entity\n\n    @classmethod\n    def contruct_by_xbrl_dict(cls, doc_id, xbrl_dict):\n        data = {\n            'doc_id': doc_id,\n            'current_year_operating_income': million(xbrl_dict['当期営業利益']),\n            'prior_1year_operating_income': million(xbrl_dict['前期営業利益']),\n            'current_year_current_assets': million(xbrl_dict['当期流動資産合計']),\n            'current_year_investments_and_other_assets': million(\n                xbrl_dict['当期その他の資産合計']),\n            'current_year_current_liabilities': million(xbrl_dict['当期流動負債合計']),\n            'current_year_noncurrent_liabilities': million(xbrl_dict['当期固定負債合計']),\n            'current_year_net_assets': million(xbrl_dict['当期純資産合計']),\n            'current_year_total_number_of_issued_shares': int(float((\n                xbrl_dict['当期発行済株式総数']))),\n        }\n        return Deligence(data)\n\n\ndef million(value_str: str):\n    value = int(value_str)\n    return round(value / 1000000, 0)\n\n\nclass DeligenceService(object):\n    __metaclass__ = ABCMeta\n\n    @abstractmethod\n    def search(self, doc_id_list) -> Dict:\n        raise NotImplementedError\n","repo_name":"koboriakira/due_deligence","sub_path":"due_deligence/domain_model/deligence.py","file_name":"deligence.py","file_ext":"py","file_size_in_byte":4219,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"9920024532","text":"from flask import Blueprint, request, jsonify, render_template\nfrom helpers import token_required\nfrom models import db, Villain, book_schema, books_schema\n\napi = Blueprint('api',__name__, url_prefix='/api')\n\n# practice\n@api.route('/getdata')\ndef getdata():\n    return {'Book': 'Yes'}\n\n# Create a new book\n@api.route('/bookshelf', methods = ['POST'])    # POST means we can actually send data to the api\n@token_required                             # requires the user to have a token\ndef add_book(current_user_token):\n    villain = request.json['villain']\n    title = request.json['title']\n    hero = request.json['hero']     # json works in key-value pairs, so we're setting the value of 'name' = name\n    desc = request.json['desc']\n    user_token = current_user_token.token  \n\n    print(f'BIG TESTER: {current_user_token.token}')\n\n    villain_select = Villain(villain, title, hero, desc, user_token = user_token )  # Contact() comes from models.py. Looks similar but the ID will get written for us\n                                                            # ^ This overwrites the default from the Contact() class \n    db.session.add(villain_select)    # adding / staging it to the database\n    db.session.commit()         # committing it to the database\n\n    response = book_schema.dump(villain_select)    # contact_schema is also from models.py which instantiates a class (we didn't write this but brought it in from marshmallow)\n    return jsonify(response)\n\n# Delete a book from the shelf\n@api.route('/bookshelf/<id>', methods = ['DELETE'])\n@token_required\ndef delete_book(current_user_token, id):\n    villain_select = Villain.query.get(id)\n    db.session.delete(villain_select)\n    db.session.commit()\n    response = book_schema.dump(villain_select)\n    return jsonify(response)\n\n# Return the whole shelf\n@api.route('/bookshelf', methods = ['GET'])\n@token_required\ndef get_shelf(current_user_token):\n    a_user = current_user_token.token\n    villains = Villain.query.filter_by(user_token = a_user).all()  # This brings back all the contacts in our database\n    response = books_schema.dump(villains)\n    return jsonify(response)\n\n# Return a single book using its ID\n@api.route('/bookshelf/<id>', methods = ['GET'])\n@token_required\ndef get_single_book(current_user_token, id):\n    villain_select = Villain.query.get(id)\n    response = book_schema.dump(villain_select)\n    return jsonify(response)\n\n#Update a book\n@api.route('/bookshelf/<id>', methods = ['POST', 'PUT'])\n@token_required\ndef update_book(current_user_token, id):\n    villain_select = Villain.query.get(id)\n    villain_select.villain = request.json['villain']\n    villain_select.title = request.json['title']     # json works in key-value pairs, so we're setting the value of 'name' = name\n    villain_select.hero = request.json['hero']\n    villain_select.desc = request.json['desc']\n\n    villain_select.user_token = current_user_token.token \n\n    db.session.commit()\n    response = book_schema.dump(villain_select)\n    return jsonify(response)\n\n#Return the token\n@api.route('/bookshelf', methods = ['GET'])\n@token_required\ndef get_token(current_user_token):\n    user_token = current_user_token\n    return user_token","repo_name":"jbbohnlein/Literary_Villains_Flask","sub_path":"app/api/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":3187,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"16652594197","text":"from functools import lru_cache, wraps\nimport re\nimport string\nfrom matplotlib import colors\nimport xml.etree.ElementTree as ET\nfrom copy import deepcopy\nimport networkx as nx\nimport itertools, more_itertools\nimport svgpathtools as svg\nfrom vectorrvnn.utils.graph import *\nfrom vectorrvnn.utils.boxes import *\nfrom skimage.color import *\n\n# Stroke attributes. \nSTROKE_LINECAP = ['butt', 'round', 'square'] # default in butt\nSTROKE_LINEJOIN = ['miter', 'arcs', 'bevel', # default is miter\n        'miter-clip', 'round'] \n\n# Defaults for path attributes\nDEFAULT_COLOR = 'black'\nDEFAULT_LINECAP = 'butt'\nDEFAULT_LINEJOIN = 'miter'\nDEFAULT_STROKEWIDTH = '1'\n\nGRAPHIC_TAGS = [\n    '{http://www.w3.org/2000/svg}rect',\n    '{http://www.w3.org/2000/svg}circle',\n    '{http://www.w3.org/2000/svg}ellipse',\n    '{http://www.w3.org/2000/svg}line', \n    '{http://www.w3.org/2000/svg}polyline',\n    '{http://www.w3.org/2000/svg}polygon',\n    '{http://www.w3.org/2000/svg}path',\n    '{http://www.w3.org/2000/svg}switch',\n    '{http://www.w3.org/2000/svg}g',\n]\n\nPATH_TAGS = GRAPHIC_TAGS[:-1]\n\nclass Del:\n    def __init__(self, keep=string.digits + '.-e'):\n        self.comp = dict((ord(c),c) for c in keep)\n    def __getitem__(self, k):\n        return self.comp.get(k)\nDD = Del()\n\ndef immutable_doc (fn) :\n    @wraps(fn)\n    def wrapper (doc, *args, **kwargs) :\n        doc_ = deepcopy(doc)\n        return fn(doc_, *args, **kwargs)\n    return wrapper\n\ndef getTreeStructureFromSVG (doc) : \n    \"\"\"\n    Infer the tree structure from the\n    XML document. \n    \"\"\"\n\n    def buildTreeGraph (element) :\n        \"\"\"\n        Recursively create networkx tree and \n        add path indices to the tree.\n\n        Parameters\n        ----------\n        element : Element\n            Element at this level of the\n            tree.\n        \"\"\"\n        nonlocal r\n        curId = r\n        r += 1\n        T.add_node(curId)\n        if element.tag in childTags : \n            zIdx = allNodes.index(element)\n            pathIdx = zIndexMap[zIdx]\n            T.nodes[curId]['pathSet'] = (pathIdx,)\n        else : \n            childIdx = []\n            validTags = lambda x : x.tag in childTags or x.tag == groupTag\n            children = list(map(buildTreeGraph, filter(validTags, element)))\n            T.add_edges_from(list(itertools.product([curId], children)))\n            pathSet = tuple(more_itertools.collapse([T.nodes[c]['pathSet'] for c in children]))\n            T.nodes[curId]['pathSet'] = pathSet\n        return curId\n\n    childTags = PATH_TAGS\n    groupTag = GRAPHIC_TAGS[-1]\n    paths = doc.paths()\n    zIndexMap = dict([(p.zIndex, i) for i, p in enumerate(paths)])\n    tree = doc.tree\n    root = tree.getroot()\n    allNodes = list(root.iter())\n    T = nx.DiGraph()\n    r = 0\n    buildTreeGraph (root)\n    T.remove_nodes_from([n for n in T.nodes if len(T.nodes[n]['pathSet']) == 0])\n    T = removeOneOutDegreeNodesFromTree(T)\n    n = len(leaves(T))\n    leafLabels = dict(map(lambda x : (x, T.nodes[x]['pathSet'][0]), leaves(T)))\n    nonLeafLabels = dict([(_, n + i) for i, _ in enumerate(nonLeaves(T))])\n    T = nx.relabel_nodes(T, mapping={**leafLabels, **nonLeafLabels})\n    return T\n\n@lru_cache(maxsize=1024)\ndef cachedPaths (doc) : \n    return doc.paths()\n\ndef unparseCSS(cssDict) :  \n    items = [f'{k}:{v}' for k, v in cssDict.items()]\n    return ';'.join(items)\n\ndef parseCSS(cssString) : \n    if len(cssString) == 0 :\n        return dict()\n    return dict(filter(\n        lambda x : len(x) == 2,\n        map(lambda x : x.split(':'), cssString.split(';'))\n    ))\n\ndef parseDashArray(da) : \n    return list(map(float, filter(\n        lambda x : len(x) > 0, \n        re.split(',| ', da))\n    ))\n\ndef parseHex(s):\n    s = s.lstrip('#')\n    if len(s) == 3:\n        s = s[0] + s[0] + s[1] + s[1] + s[2] + s[2]\n    rgb = tuple(int(s[i:i+2], 16) for i in (0, 2, 4))\n    return [rgb[0] / 255.0, rgb[1] / 255.0, rgb[2] / 255.0]\n\ndef parseColor(s):\n    s = s.lstrip(' ')\n    color = [0, 0, 0]\n    if s[0] == '#':\n        try : color[:3] = parseHex(s)\n        except ValueError : pass\n    elif s == 'none':\n        color = [0, 0, 0]\n    elif s[:4] == 'rgb(':\n        rgb = s[4:-1].split(',')\n        color = [\n            int(rgb[0]) / 255.0, \n            int(rgb[1]) / 255.0, \n            int(rgb[2]) / 255.0\n        ]\n    else:\n        try : color = colors.to_rgb(s)\n        except ValueError : pass\n    return color\n\ndef xmlAttributeSet (element, attr, value) : \n    if value is None: return \n    element.attrib[attr] = value\n    if 'style' in element.attrib : \n        style = element.attrib['style']\n        parsed = parseCSS(style)\n        if attr in parsed : \n            parsed[attr] = value\n            element.attrib['style'] = unparseCSS(parsed)\n\ndef xmlAttributeGet (element, attr, default) : \n    if attr in element.attrib : \n        return element.attrib[attr]\n    elif 'style' in element.attrib : \n        style = element.attrib['style']\n        parsed = parseCSS(style)\n        if attr in parsed : \n            return parsed[attr]\n    return default\n\ndef pathAttributeSet (path, attr, value) : \n    xmlAttributeSet(path.element, attr, value)\n\ndef pathAttributeGet (path, attr, default) : \n    return xmlAttributeGet(path.element, attr, default)\n    \ndef pathColorFeature (path, attr) : \n    \"\"\" \n    attr can be one of stroke/fill. \n    This method will return a list of 4 numbers. \n    The first number will indicate whether the path \n    color is none or not. The other 3 will indicate the \n    color in lab space.\n    \"\"\" \n    attr = pathAttributeGet(path, attr, DEFAULT_COLOR)\n    if attr == 'none' : \n        return [1.0, 0.0, 0.0, 0.0] \n    else : \n        rgb = parseColor(attr)\n        lab = rgb2lab(rgb)\n        # normalize \n        lab[0] /= 100.0\n        lab[0] -= 0.5\n        lab[0] *= 2.0\n        lab[1] /= 127.0\n        lab[2] /= 127.0\n        return [0.0, *lab] \n\ndef pathColor (path, attr) : \n    \"\"\" attr can be one of stroke/fill \"\"\"\n    return parseColor(pathAttributeGet(path, attr, DEFAULT_COLOR))\n\ndef pathStrokeWidth (path) :\n    pw = pathAttributeGet(path, 'stroke-width', DEFAULT_STROKEWIDTH)\n    return float(pw.translate(DD))\n\ndef pathStrokeLineCap (path): \n    return pathAttributeGet(path, 'stroke-linecap', DEFAULT_LINECAP)\n\ndef pathStrokeLineJoin (path): \n    return pathAttributeGet(path, 'stroke-linejoin', DEFAULT_LINEJOIN)\n\ndef pathStrokeDashArray(path) : \n    \"\"\" \n    Instead of the whole dasharray string, just \n    say whether there is this attribute or not\n    \"\"\"\n    da = pathAttributeGet(path, 'stroke-dasharray', None)\n    return da if da != 'none' else None\n\ndef fixOrigin (doc) : \n    x, y, w, h = getDocBBox(doc).tolist()\n    globalTransform(doc, \n            dict(transform=f'translate({-x:.3f} {-y:.3f})'))\n    setDocBBox(doc, BBox(0, 0, w, h, w, h))\n\ndef scaleToFit (doc, w, h) : \n    box = getDocBBox(doc)\n    ox, oy, ow, oh = box.tolist()\n    sx = w / ow\n    sy = h / oh\n    globalTransform(doc, \n            dict(transform=f'scale({sx:.3f} {sy:.3f})'))\n    setDocBBox(doc, box.scaled(sx, sy))\n\ndef removeGraphicChildren (xmltree) :\n    children = [e for e in xmltree if e.tag in GRAPHIC_TAGS]\n    for child in children : \n        xmltree.remove(child)\n\ndef globalTransform(doc, transform) : \n    \"\"\" \n    Apply a global transform to the whole graphic\n    by adding a group at the top most level. The\n    transform is a dictionary specifying different \n    group attributes\n    \"\"\"\n    groupElement = ET.Element('{http://www.w3.org/2000/svg}g', transform)\n    # rootCopy = deepcopy(doc.tree.getroot())\n    root = doc.tree.getroot()\n    # Don't add <defs ... /> to new group element.\n    children = [e for e in root if e.tag in GRAPHIC_TAGS]\n    groupElement.extend(children)\n    # Remove graphic elements.\n    removeGraphicChildren(root)\n    root.append(groupElement)\n    doc.tree._setroot(root)\n    doc.updateParentMap()\n\n@immutable_doc\ndef crop (doc, box, docbox) :\n    smallerSide = min(docbox.h, docbox.w)\n    minBoxSize = smallerSide / 10\n    box_ = box.normalized() * 1.75\n    if box_.w < minBoxSize : \n        box_ = box_ * (minBoxSize / box_.w)\n    setDocBBox(doc, box_)\n    return doc\n\ndef iterfilter (root, tagFilter) : \n    if tagFilter(root.tag) : \n        yield root \n        for child in root : \n            yield from iterfilter(child, tagFilter)\n\n@immutable_doc\ndef subsetSvg(doc, lst) :\n    root = doc.tree.getroot()\n    relTags = GRAPHIC_TAGS + ['{http://www.w3.org/2000/svg}svg']\n    paths = list(filter(\n        lambda x : x.tag in PATH_TAGS,\n        iterfilter(root, lambda tag : tag in relTags)\n    ))\n    n = len(paths)\n    unwanted = list(set(range(n)) - set(lst))\n    unwantedElts = [paths[i] for i in unwanted]\n    doc.updateParentMap()\n    for elt in unwantedElts : \n        doc.parent_map[elt].remove(elt)\n    newDocument = svg.Document(None)\n    newDocument.fromString(ET.tostring(root, encoding='unicode'))\n    return newDocument\n\n@immutable_doc\ndef withoutDegeneratePaths (doc) : \n    root = doc.tree.getroot()\n    paths = doc.paths()\n    degenZIndices = [p.zIndex for p in paths \n            if pathBBox(p.path).isDegenerate()]\n    allElts = list(root.iter())\n    unwantedElts = [allElts[i] for i in degenZIndices]\n    doc.updateParentMap()\n    for elt in unwantedElts :\n        doc.parent_map[elt].remove(elt)\n    newDocument = svg.Document(None)\n    newDocument.fromString(ET.tostring(root, encoding='unicode'))\n    return newDocument\n\n@immutable_doc\ndef docUnion (doc, that) :\n    # add the canvases\n    setDocBBox(doc, getDocBBox(doc) | getDocBBox(that))\n    # add the paths\n    thatRoot = deepcopy(that.tree.getroot())\n    thatChildren = list(filter(\n        lambda e : e.tag in GRAPHIC_TAGS,\n        thatRoot\n    ))\n    root = doc.tree.getroot()\n    root.extend(thatChildren)\n    return doc\n","repo_name":"Vrroom/vectorrvnn","sub_path":"vectorrvnn/utils/svg/svgTools.py","file_name":"svgTools.py","file_ext":"py","file_size_in_byte":9773,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"48"}
+{"seq_id":"17944996623","text":"from heapq import heapify, heappush, heappop\n\ndef solution(jobs):\n    last, now = -1, 0\n    answer, hq = [], []\n    while len(answer) < len(jobs):\n        for js, dur in jobs:\n            if last < js <= now:\n                heappush(hq, [dur, js])\n        if hq:\n            w = heappop(hq)\n            last = now\n            now += w[0]\n            answer.append(now - w[1])\n        else:\n            now += 1\n    return sum(answer)//len(jobs)","repo_name":"dudtj5307/Coding_skills","sub_path":"프로그래머스/lv3/42627. 디스크 컨트롤러/디스크 컨트롤러.py","file_name":"디스크 컨트롤러.py","file_ext":"py","file_size_in_byte":445,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"41443082744","text":"import numpy as np\r\nfrom pprint import pprint \r\n\r\n\r\ndef parse_input(filename):\r\n    scans = open(filename).read().split(\"\\n\\n\")\r\n    for i, scan in enumerate(scans):\r\n        scans[i] = [Vector(*[int(i) for i in line.strip().split(\",\")]) \\\r\n                    for line in scan.split(\"\\n\")[1:] if line.strip() != \"\"]\r\n\r\n    return scans\r\n\r\n\r\nclass Vector:\r\n    def __init__(self, x, y=0, z=0):\r\n        if isinstance(x, np.ndarray) and x.shape == (3,):\r\n            self.a = x\r\n        else: \r\n            self.a = np.array([x, y, z], dtype=np.int64)\r\n    \r\n    def __repr__(self):\r\n        return f\"[{self.x:5d}, {self.y:5d}, {self.z:5d}]\"\r\n\r\n    def __hash__(self):\r\n        return hash((self.x, self.y, self.z))\r\n\r\n    @property\r\n    def x(self):\r\n        return self.a[0]\r\n    @x.setter\r\n    def x(self, value):\r\n        self.a[0] = value\r\n    @property\r\n    def y(self):\r\n        return self.a[1]\r\n    @y.setter\r\n    def y(self, value):\r\n        self.a[1] = value\r\n    @property\r\n    def z(self):\r\n        return self.a[2]\r\n    @z.setter\r\n    def z(self, value):\r\n        self.a[2] = value\r\n\r\n    def rotate(self, rotation):\r\n        return Vector(np.matmul(self.a, rotation))\r\n    \r\n    def __eq__(self, other):\r\n        return np.all(self.a == other.a)\r\n\r\n    def __add__(self, other):\r\n        return Vector(self.a + other.a)\r\n\r\n    def __sub__(self, other):\r\n        return Vector(self.a - other.a)\r\n\r\n\r\n    def manhattan(self, other):\r\n        return abs(self.x - other.x) + \\\r\n               abs(self.y - other.y) + \\\r\n               abs(self.z - other.z)\r\n\r\n\r\n# all rotations for case of z being either +z or -z\r\nbase_rotations = np.array([\r\n    [[ 1, 0, 0], [ 0, 1, 0], [ 0, 0, 1]],\r\n    [[ 0, 1, 0], [-1, 0, 0], [ 0, 0, 1]],\r\n    [[-1, 0, 0], [ 0,-1, 0], [ 0, 0, 1]],\r\n    [[ 0,-1, 0], [ 1, 0, 0], [ 0, 0, 1]],\r\n\r\n    [[-1, 0, 0], [ 0, 1, 0], [ 0, 0,-1]],\r\n    [[ 0, 1, 0], [ 1, 0, 0], [ 0, 0,-1]],\r\n    [[ 1, 0, 0], [ 0,-1, 0], [ 0, 0,-1]],\r\n    [[ 0,-1, 0], [-1, 0, 0], [ 0, 0,-1]]\r\n])\r\n\r\n# swap axes ie. z -> +x/-x, z -> +y/-y\r\nrotations = np.concatenate((base_rotations, \r\n                            base_rotations[:,:,[1,2,0]],\r\n                            base_rotations[:,:,[2,0,1]]))\r\n\r\n\r\ndef scan_diff_set(scan):\r\n    # For each beacon in the scan, find the distance to all other beacons\r\n    # Put the distances in a set and add to a dict indexed by source beacon\r\n    relativeDistances = dict()\r\n    for beacon in scan:\r\n        relativeDistances[beacon] = set()\r\n        for second in scan:\r\n            if beacon == second: \r\n                continue\r\n            relativeDistances[beacon].add(beacon - second)\r\n    return relativeDistances\r\n\r\n\r\ndef translate_diff_set(diffSet, translation):\r\n    # Translate a set - once we match it we need to move to basis coords\r\n    # Values are relative to the key so only the key needs to change\r\n    translated = dict()\r\n    for k, v in diffSet.items():\r\n        translated[k + translation] = v\r\n    return translated\r\n\r\n\r\ndef rotate_set(beaconSet):\r\n    # For a set of beacon distances, apply each possible rotation and return\r\n    # a dict of all possible rotated versions.\r\n    #\r\n    # Save in memory or reproduce each time? \r\n    # It's 24 rotations * (25 * 24 beacon distances) per scan (27)\r\n    # = 388800 ints + overhead. Could save them all but we'll just\r\n    # regenerate for each scan. \r\n    rotated = dict()\r\n\r\n    for i, rotation in enumerate(rotations):\r\n        rotatedDiffs = dict()\r\n        for beacon, relativeDistances in beaconSet.items():\r\n            diffs = set()\r\n            for d in relativeDistances:\r\n                diffs.add(d.rotate(rotation))\r\n            rotatedDiffs[beacon.rotate(rotation)] = diffs\r\n        rotated[i] = rotatedDiffs\r\n\r\n    return rotated\r\n\r\n\r\ndef part2(scanners):\r\n    # Find max Manhattan distance\r\n    maxDistance = 0\r\n    maxPair = None\r\n    for i in scanners:\r\n        for j in scanners:\r\n            if i == j:\r\n                continue\r\n            if i.manhattan(j) > maxDistance:\r\n                maxDistance = i.manhattan(j)\r\n                maxPair = (i, j)\r\n\r\n    return maxDistance, maxPair\r\n\r\n\r\ndef solve(scans, debug=False):\r\n    # This is the set of distances between linked/known beacons with \r\n    # beacon positions relative to scanner 0\r\n    bases = [scan_diff_set(scans[0])]\r\n    # Known beacons relative to scanner 0\r\n    knownBeacons = set(scans[0])\r\n    # Known scanners relative to scanner 0\r\n    knownScanners = [Vector(0,0,0)]\r\n\r\n    # Unmatched scanners. Pop them off once they're matched and add to bases.\r\n    unmatched = list(range(1, len(scans)))\r\n    print(\"Unmatched scanners:\", unmatched)\r\n    # Counter for current unmatched scanner \r\n    attemptMatch = 0\r\n    while len(unmatched) > 0:\r\n        breakout = False\r\n        \r\n        # Take next unmatched scan\r\n        scan = scans[unmatched[attemptMatch]]\r\n        print(f\"Attempt matching scanner {unmatched[attemptMatch]}\")\r\n        # Get its rotations\r\n        rotated = rotate_set(scan_diff_set(scan))\r\n\r\n        # For each rotation of the unmatched scanner's beacon set...\r\n        for rotation, rotatedDiffs in rotated.items():\r\n            # For each individual beacon in that set...\r\n            for beacon, diffs in rotatedDiffs.items():\r\n                # For each set of matched scanner beacons...\r\n                for base in bases:\r\n                    # For each individual beacon in that set... \r\n                    for baseBeacon, baseDiffs in base.items():\r\n                        # If distance from unmatched beacon to 11 other beacons\r\n                        # is the same as distance from known beacon to 11 other beacons \r\n                        if len(diffs & baseDiffs) == 11:\r\n                            # It's a match\r\n                            print(f\"Matched scanner {unmatched[attemptMatch]}!\")\r\n\r\n                            # This is translation from scanner 0 to new matched scanner\r\n                            basisTranslation = baseBeacon - beacon\r\n\r\n                            knownScanners.append(basisTranslation)\r\n                            \r\n                            bases.append(translate_diff_set(rotatedDiffs, basisTranslation))\r\n                            \r\n                            # For relative distances from new matched beacon to each \r\n                            # of the other 24 beacons detected by new matched scanner, \r\n                            # convert to scanner 0 coords and store\r\n                            for diff in diffs:\r\n                                knownBeacons.add(beacon - diff + basisTranslation)\r\n\r\n                            # Remove new matched scanner from consideration\r\n                            unmatched.pop(attemptMatch)\r\n                            \r\n                            if debug:\r\n                                print(f\"Known beacons ({len(knownBeacons)}):\")\r\n                                pprint(knownBeacons)\r\n\r\n                                print(\"Matched known beacon:                    \", baseBeacon)\r\n                                print(\"Coords of beacon relative to new scanner:\", beacon)\r\n                                print(\"Common distances relative to beacon:\")\r\n                                for b in (diffs & baseDiffs):\r\n                                    print(b)\r\n                                print(\"Rotation matrix for match:\")\r\n                                print(rotations[rotation])\r\n\r\n                                print(\"Translation from new scanner to old scanner coords:\")\r\n                                print(basisTranslation)\r\n\r\n                                print(\"Known scanner locations:\")\r\n                                pprint(knownScanners)\r\n\r\n                                print(\"Remaining scanners:\", unmatched)\r\n\r\n                                print(\"=================================================================== \\n\")\r\n                            else: \r\n                                print(\"Remaining scanners:\", len(unmatched))\r\n                            \r\n                            # Reset to first unmatched scanner\r\n                            attemptMatch = 0\r\n                            breakout=True\r\n \r\n                            break\r\n\r\n                    # disgusting!\r\n                    if breakout:        \r\n                        break\r\n                if breakout:\r\n                    break\r\n            if breakout:\r\n                break\r\n            \r\n        if not breakout:\r\n            # No matches, try next scanner\r\n            attemptMatch += 1\r\n\r\n\r\n    if debug:\r\n        pprint(sorted(list(knownBeacons), \r\n               key=lambda r: r.x * 100000000 + r.y * 10000 + r.z))\r\n\r\n    print(\"Known beacons:     \", len(knownBeacons))\r\n    \r\n    maxDistance, maxPair = part2(knownScanners)\r\n    print(\"Max Manhattan:     \", maxDistance)\r\n    print(\"Max Manhattan pair:\", *maxPair)\r\n    print(\"\")\r\n    return len(knownBeacons), maxDistance\r\n\r\n\r\nif __name__==\"__main__\":\r\n    # # Useful to check rotation function but you'll need \r\n    # # to adjust set intersection from 11 to 5\r\n    # scans = parse_input(\"small_sample.txt\")\r\n\r\n    scans = parse_input(\"sample.txt\")\r\n    assert solve(scans, True) == (79, 3621)\r\n\r\n\r\n    scans = parse_input(\"input.txt\")\r\n    assert solve(scans) == (323, 10685)\r\n    ","repo_name":"jyates13/aoc2021","sub_path":"d19/d19.py","file_name":"d19.py","file_ext":"py","file_size_in_byte":9328,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"30358028521","text":"import numpy as np\nimport networkx as nx\nimport math\nimport random\nimport argparse\nimport pulp\nimport os\nfrom src.IC import runIC_repeat\nfrom src.agent.baseline import *\n\nimport time\n\n\nclass Environment(object):\n    '''\n    Environment for influence maximization\n    G is a nx graph\n    state is 3xN binary array, \n    -- 1st row: invited in previous main step and came (=1), \n    -- 2nd row: invited but not come (=1); \n    -- 3rd row: invited in previous sub step (=1) or not (=0) --- only updated outside environment (in rl4im.py: greedy_action_GCN() and memory store step)\n    '''\n    def __init__(self, mode='train', T=20, budget=5, propagate_p = 0.1, l=0.05, d=1, q=1, cascade='IC', num_simul=1000, graphs=None, name='MVC', args=None):\n        self.args = args\n        self.name = name\n        self.G = graphs[0] \n        self.graph_init = self.G  \n\n        self.graphs = graphs\n        self.mode = mode\n        self.N = len(self.G.g)  \n        self.budget = budget\n        self.A = nx.to_numpy_matrix(self.G.g)  \n        self.propagate_p = propagate_p\n        self.l = l\n        self.d = d\n        self.q = q\n        self.T = T\n        self.cascade = cascade\n        self.num_simul = self.args.num_simul_train\n        self.t = 0\n        self.done = False\n        self.reward = 0\n        self.state = np.zeros((3, self.N)) \n        self.observation = self.state\n        nx.set_node_attributes(self.G.g, 0, 'attr')\n\n    def step(self, i, pri_action, sec_action, reward_type=0):\n        '''\n        pri_action is a list, sec_action is an int\n        reward type categories, example seed nodes before {1, 2, 3}, new node x\n        0: reward0 = f({1, 2, 3, x}) - f({1, 2, 3})\n        1: reward1 = f({x}) - f({ })\n        2: reward2 = (reward0+reward1)/2\n        3: use probabilty q \n        '''\n\n        #compute reward as marginal contribution of a node\n        if self.mode == 'train':\n            if reward_type == 0:\n                seeds = []\n                [seeds.append(v) for v in range(self.N) if (self.state[0][v]==1 or self.state[2][v]==1)] \n                influece_without = self.run_cascade(seeds=seeds, cascade=self.cascade, sample=self.num_simul)\n                seeds.append(sec_action)\n                influence_with = self.run_cascade(seeds=seeds, cascade=self.cascade, sample=self.num_simul)\n                self.reward = self.q*(influence_with - influece_without)\n                self.reward = self.reward/self.N*100   ####\n            elif reward_type == 1:\n                seeds = []\n                [seeds.append(v) for v in range(self.N) if self.state[0][v]==1] \n                influece_without = self.run_cascade(seeds=seeds, cascade=self.cascade, sample=self.num_simul)\n                seeds.append(sec_action)\n                influence_with = self.run_cascade(seeds=seeds, cascade=self.cascade, sample=self.num_simul)\n                self.reward = self.q*(influence_with - influece_without) \n                self.reward = self.reward/self.N*100  ###\n            elif reward_type == 2:\n                fix_seeds = []\n                [fix_seeds.append(v) for v in range(self.N) if self.state[0][v]==1]\n                uncertain_seeds = []\n                [uncertain_seeds.append(v) for v in range(self.N) if self.state[2][v]==1]\n                # reward_max\n                seeds = fix_seeds.copy()\n                influece_without = self.run_cascade(seeds=seeds, cascade=self.cascade, sample=self.num_simul)\n                seeds.append(sec_action)\n                influence_with = self.run_cascade(seeds=seeds, cascade=self.cascade, sample=self.num_simul)\n                reward_max = self.q*(influence_with - influece_without) \n                reward_max = reward_max/self.N*100 \n                # reward_min\n                seeds = fix_seeds + uncertain_seeds\n                influece_without = self.run_cascade(seeds=seeds, cascade=self.cascade, sample=self.num_simul)\n                seeds.append(sec_action)\n                influence_with = self.run_cascade(seeds=seeds, cascade=self.cascade, sample=self.num_simul)\n                reward_min = self.q*(influence_with - influece_without) \n                reward_min = reward_min/self.N*100  \n                self.reward = (reward_max+reward_min)/2   \n            elif reward_type == 3:\n                fix_seeds = []\n                [fix_seeds.append(v) for v in range(self.N) if self.state[0][v]==1]\n                uncertain_seeds = []\n                [uncertain_seeds.append(v) for v in range(self.N) if self.state[2][v]==1]\n                # reward_max\n                seeds = fix_seeds.copy()\n                influece_without = self.run_cascade(seeds=seeds, cascade=self.cascade, sample=self.num_simul)\n                seeds.append(sec_action)\n                influence_with = self.run_cascade(seeds=seeds, cascade=self.cascade, sample=self.num_simul)\n                reward_max = self.q*(influence_with - influece_without)\n                reward_max = reward_max/self.N*100 \n                # reward_min\n                seeds = fix_seeds + uncertain_seeds\n                influece_without = self.run_cascade(seeds=seeds, cascade=self.cascade, sample=self.num_simul)\n                seeds.append(sec_action)\n                influence_with = self.run_cascade(seeds=seeds, cascade=self.cascade, sample=self.num_simul)\n                reward_min = self.q*(influence_with - influece_without)\n                reward_min = reward_min/self.N*100 \n                self.reward = reward_min*self.q+reward_max*(1-self.q)\n            else:\n                assert(False)\n\n        #update next_state and done      \n        if i%self.budget == 0:\n        #a primary step\n            invited = pri_action\n            present, absent = self.transition(invited)\n            state=self.state.copy()\n            for v in present:\n                self.state[0][v]=1\n            for v in absent:\n                self.state[1][v]=1\n            self.state[2].fill(0)\n            if i == self.T:\n                next_state = None\n                self.done = True\n            else:\n                next_state = self.state.copy()\n                self.done = False\n        else:\n        #a secondary step\n            self.state[2][sec_action]=1\n            next_state = self.state.copy()\n            self.done = False\n\n        if i == self.T:  \n            next_state = None\n            self.done = True\n\n        return next_state, self.reward, self.done\n            \n    def run_cascade(self, seeds, cascade='IC', sample=1000):\n        if cascade == 'IC':\n            reward, _ = runIC_repeat(self.G.g, seeds, p=self.propagate_p, sample=sample)\n        else:\n            assert(False)\n        return reward\n\n    def f_multi(self, x):\n        s=list(x) \n        val = self.run_cascade(seeds=s, cascade=self.cascade, sample=self.args.greedy_sample_size)\n        return val\n \n    #the simple state transition process\n    def transition(self, invited):#q is probability being present\n        present = []\n        absent = []\n        for i in invited:\n            present.append(i) if random.random() <= self.q else absent.append(i)\n        return present, absent\n\n    def reset(self, g_index=0, mode='train'):\n        self.mode = mode\n        if mode == 'test': \n            self.G = self.graphs[g_index]\n        else:\n            self.G = self.graphs[g_index]\n        self.N = len(self.G.g)\n        self.A = nx.to_numpy_matrix(self.G.g)\n        self.t = 0\n        self.done = False\n        self.reward = 0\n        self.state = np.zeros((3, self.N)) \n        self.observation = self.state\n        nx.set_node_attributes(self.G.g, 0, 'attr')\n\n    def get_state(self, g_index):\n        curr_g = self.graphs[g_index]\n        available_action_mask = np.array([1] * curr_g.cur_n + [0] * (curr_g.max_node_num - curr_g.cur_n))\n\n        # padding the state for storing\n        obs_padding = self.observation.copy()\n        if self.args.model_scheme == 'type1':\n            padding = np.repeat(np.array([-1] * (curr_g.max_node_num - curr_g.cur_n))[None, ...], self.observation.shape[0], axis=0)\n            obs_padding = np.concatenate((self.observation.copy(), padding), axis=-1)\n        return self.observation.copy(), obs_padding, available_action_mask\n\n    def try_remove_feasible_action(self, feasible_actions, sec_action):\n        try:\n            feasible_actions.remove(sec_action)\n            return feasible_actions\n        except Exception:\n            pass\n        finally:\n            return feasible_actions\n","repo_name":"Haipeng-Chen/RL4IM-Contingency","sub_path":"src/environment/env.py","file_name":"env.py","file_ext":"py","file_size_in_byte":8474,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"48"}
+{"seq_id":"70605307665","text":"from django.contrib.auth.models import User\nfrom rest_framework.test import APIClient, APITestCase\nfrom rest_framework_simplejwt.tokens import AccessToken\n\nfrom .models import Blog, Post, Subscription\n\n\nclass BlogPostTestCase(APITestCase):\n\n    def setUp(self):\n        self.client = APIClient()\n        self.user1 = User.objects.create(username='test1')\n        self.user2 = User.objects.create(username='test2')\n        self.blog1 = Blog.objects.create(user=self.user1)\n        self.blog2 = Blog.objects.create(user=self.user2)\n        token = AccessToken.for_user(self.user1)\n        self.client.force_authenticate(user=self.user1, token=token)\n\n    def test_posts_creation(self):\n        response = self.client.post(\n            '/api/posts/',\n            data={\n                'title': 'title',\n                'text': 'text',\n            }\n        )\n        self.assertEqual(response.status_code, 201)\n        self.assertEqual(Post.objects.first().title, 'title')\n        self.assertEqual(Post.objects.first().blog, self.blog1)\n\n    def test_subscription_create(self):\n        response = self.client.post(\n            f'/api/blogs/{self.user2.blog.id}/subscribe/'\n        )\n        self.assertEqual(response.status_code, 201)\n\n    def test_postsfeed_create(self):\n        Post.objects.create(title='title', text='text', blog=self.blog2)\n        Subscription.objects.create(user=self.user1, blog=self.blog2)\n        response = self.client.get('/api/users/postsfeed/')\n        self.assertEqual(\n            len(response.data.get('results')), 1\n        )\n","repo_name":"Serenityblood/blogs_posts_test","sub_path":"api_blogs/api/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":1559,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"32201700646","text":"from screener.common import constants\nfrom screener.filters.factory import register_filter_operation, register_enrich_operation\n\n\n@register_filter_operation(\"pe_filter\")\n@register_enrich_operation(\"pe_filter\")\ndef pe_filter_operation(stock, percentage_of_industry):\n    pe = stock.get_pe()\n    if pe < 0:\n        return False  # stock in loss\n\n    return pe * 100 <= stock.get_industry_pe() * percentage_of_industry\n\n\n@register_filter_operation(\"max_pe\")\ndef max_pe_filter(stock, maximum_pe):\n    return maximum_pe >= stock.get_pe()\n\n\n@register_filter_operation(\"pe_is_less_than_sector_pe\")\n@register_enrich_operation(\"pe_is_less_than_sector_pe\")\ndef sector_pe_filter_operation(stock):\n    return stock.pe_is_less_than_sector_pe()\n\n\n@register_enrich_operation(\"pe_filter_report\")\ndef pe_enrich_operation(stock):\n    stock.update_report_in_metadata({\n        constants.PE: stock.get_pe(),\n        constants.INDUSTRY_PE: stock.get_industry_pe()\n    })\n","repo_name":"thenakliman/stock-screener","sub_path":"screener/filters/stock/pe_filter.py","file_name":"pe_filter.py","file_ext":"py","file_size_in_byte":950,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"48"}
+{"seq_id":"29737319017","text":"import os\n\nserviceAccount = r'focused-mote-361402-ac24139c4873.json'\nos.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"] = serviceAccount\n\nimport numpy as np\nfrom flask import Flask, render_template, request, redirect, url_for, send_from_directory, jsonify\nimport base64\nimport qrcode\nfrom utils_gcp import save_html, get_html, save_bigquery\nfrom matplotlib.pyplot import get\nfrom datetime import datetime\n\n\napp = Flask(__name__)\n\n\n@app.route(\"/\", methods=[\"GET\", \"POST\"])\ndef home():\n    if request.method == 'POST':\n        text = request.form[\"data\"]\n\n        # Crear QR\n        path = f\"{request.base_url}/img?name={text}\"\n        print(path)\n        img = qrcode.make(path)\n        qrPath = os.path.join(\"static\", \"some_file.png\")\n        img.save(qrPath)\n\n        with open(qrPath, 'rb') as fd:\n            image_as_base64_html = base64.encodebytes(fd.read()).decode()\n\n        # Obtener imagen\n        imagen = request.files[\"img\"]\n        imagen.save('im-received.png')\n        with open('im-received.png', 'rb') as fd:\n            imageUser = base64.encodebytes(fd.read()).decode()\n        print(imagen)\n\n        #   Guardar html con texto/imagen en gcp\n        save_html(text, imageUser)\n\n        # Registrar entrada\n        fila = [\n            {\n                \"ip\":str(request.remote_addr),\n                \"nombre\":str(text),\n                \"imagen\":imageUser,\n                \"fecha\":datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n            }\n        ]\n        save_bigquery(\"focused-mote-361402.registro_app_dataset.imagenes_registro\",fila)\n        print(\"Ok\")\n        return render_template(\"qrcode.html\", image_as_base64_html=image_as_base64_html)\n    return render_template(\"index.html\")\n\n\n@app.route('/img')\ndef img():\n    imgPath = request.args.get('name')\n    print(imgPath)\n    html = get_html(imgPath)\n\n    # Registrar entrada\n    fila = [\n            {\n                \"ip\":str(request.remote_addr),\n                \"nombre\":str(imgPath),\n                \"fecha\":datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n            }\n        ]\n    save_bigquery(\"focused-mote-361402.registro_app_dataset.vistas_registro\",fila)\n    return render_template(\"imagen.html\", image_as_base64_html=html)\n\n\nif __name__ == \"__main__\":\n    app.run(debug=True, host='0.0.0.0', port=int(os.environ.get('port', 8080)))\n","repo_name":"HotIce803/arqui_soft","sub_path":"run/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2313,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"22011343191","text":"import os\nimport uuid\n\nfrom django.core.validators import FileExtensionValidator\nfrom django.db import models\nfrom django.utils import timezone\nfrom django.utils.translation import gettext_lazy as _\n\nfrom . import choices\n\n\ndef car_image_path(instance, filename):\n    return os.path.join(f'car_damage_detection/{instance.order.user}/{timezone.now().strftime(\"%Y-%m-%d\")}'\n                        f',{filename}')\n\n\nclass DamageDetection(models.Model):\n    id = models.UUIDField(default=uuid.uuid4, primary_key=True)\n    order = models.OneToOneField(\n        to='orders.Order',\n        on_delete=models.SET_NULL,\n        null=True,\n        related_name='damage_detection'\n    )\n    front_image = models.ImageField(\n        upload_to=car_image_path,\n        validators=[FileExtensionValidator(['png', 'jpg', 'jpeg'])],\n        verbose_name=_('Front Image')\n    )\n    left_image = models.ImageField(\n        upload_to=car_image_path,\n        validators=[FileExtensionValidator(['png', 'jpg', 'jpeg'])],\n        verbose_name=_('Left Image')\n    )\n    right_image = models.ImageField(\n        upload_to=car_image_path,\n        validators=[FileExtensionValidator(['png', 'jpg', 'jpeg'])],\n        verbose_name=_('Right Image')\n    )\n    back_image = models.ImageField(\n        upload_to=car_image_path,\n        validators=[FileExtensionValidator(['png', 'jpg', 'jpeg'])],\n        verbose_name=_('Back Image')\n    )\n    status = models.CharField(\n        choices=choices.DamageStatusChoices.choices,\n        max_length=9,\n        blank=True,\n        null=True\n    )\n    data = models.JSONField(\n        default=dict,\n        verbose_name=_('Data'),\n        blank=True,\n        null=True\n    )\n\n    created_at = models.DateTimeField(auto_now_add=True)\n    updated_at = models.DateTimeField(auto_now=True)\n\n    class Meta:\n        ordering = ('-created_at',)\n        verbose_name = _('Damage Detection')\n        verbose_name_plural = _('Damage Detections')\n","repo_name":"abolatuly/car-rental","sub_path":"damage_detection/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1947,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"23573644085","text":"from __future__ import print_function\n\nfrom collections import defaultdict\n\nimport cubes\nfrom cubes import Workspace, Cell, PointCut\n\n\nclass TweetCube:\n\n    def __init__(self, concept):\n        self.createCube()\n        self.concept = concept\n\n    def createCube(self):\n        self.workspace = Workspace()\n        self.workspace.register_default_store(\"sql\",\n                                         url=\"mysql://root:@localhost/datawarehouse\")\n        model = cubes.read_model_metadata_bundle(\"../CubeModelisation/model/\")\n        self.workspace.import_model(model)\n        self.browserTweet = self.workspace.browser(\"tweet\")\n\n    def getPieChartSource(self):\n        cube = self.workspace.cube(\"tweet\")\n        cube.browser = self.browserTweet\n\n        cut = [PointCut(\"concept\", [self.concept])]\n        cell = Cell(cube, cut)\n\n        result = self.browserTweet.aggregate(cell, drilldown=[\"location\",\"source\"],aggregates=[\"numberOfTweets_sum\"])\n        output = defaultdict(lambda: defaultdict())\n\n        for row in result.table_rows(\"location\"):\n            continent = row.record['location.continentName']\n            source = row.record['source.sourceName']\n            output[continent][source] = row.record['numberOfTweets_sum']\n        temp = {'continentName': '',\n                'sources': [{'source': '', 'numberOfTweets': ''}, {'source': '', 'numberOfTweets': ''},\n                            {'source': '', 'numberOfTweets': ''}, {'source': '', 'numberOfTweets': ''}]}\n        print(\"output \",output)\n        i = 0\n        data = []\n        continentsList = ['Asia','Africa','Australia','Europe','North America','South America']\n        for continent in continentsList:\n            temp['continentName'] = continent\n            if output[continent]:\n                temp['sources'][i]['source'] = \"iPhone\"\n                temp['sources'][i]['numberOfTweets'] = output[continent].get('iPhone', 0)\n                i += 1\n                temp['sources'][i]['source'] = \"Android\"\n                temp['sources'][i]['numberOfTweets'] = output[continent].get('Android', 0)\n                i += 1\n                temp['sources'][i]['source'] = \"Web\"\n                temp['sources'][i]['numberOfTweets'] = output[continent].get('Web', 0)\n                i += 1\n                temp['sources'][i]['source'] = \"Unknown\"\n                temp['sources'][i]['numberOfTweets'] = output[continent].get('Unknown', 0)\n            else:\n                temp['sources'][i]['source'] = \"iPhone\"\n                temp['sources'][i]['numberOfTweets'] = 0\n                i += 1\n                temp['sources'][i]['source'] = \"Android\"\n                temp['sources'][i]['numberOfTweets'] = 0\n                i += 1\n                temp['sources'][i]['source'] = \"Web\"\n                temp['sources'][i]['numberOfTweets'] = 0\n                i += 1\n                temp['sources'][i]['source'] = \"Unknown\"\n                temp['sources'][i]['numberOfTweets'] = 0\n\n            i = 0\n            data.append(temp)\n            temp = {'continentName': '',\n                'sources': [{'source': '', 'numberOfTweets': ''}, {'source': '', 'numberOfTweets': ''},\n                            {'source': '', 'numberOfTweets': ''}, {'source': '', 'numberOfTweets': ''}]}\n        return data\n\n    def getBarChartRaceByLanguageAndDate(self):\n        cube = self.workspace.cube(\"tweet\")\n        cube.browser = self.browserTweet\n\n        cut = [PointCut(\"concept\", [self.concept])]\n        cell = Cell(cube, cut)\n\n        result = self.browserTweet.aggregate(cell, drilldown=[\"time:day\", \"language\"],\n                                             aggregates=[\"numberOfTweets_sum\"])\n        output = []\n        for row in result.table_rows(\"time\"):\n            output.append(row.record)\n        data = defaultdict(lambda: defaultdict(lambda: defaultdict()))\n        languagesList = []\n        for row in output:\n            date = row['time.day'] + \"/\" + row['time.month'] + \"/\" + row['time.year']\n            language = row['language.languageName']\n            languagesList.append(language)\n            # creating data structure containing all languages\n            data[date][language]['numberOfTweets'] = row['numberOfTweets_sum']\n\n        #GET LIST OF LANGUAGES FROM FILE\n        import pickle\n        with open('../Docs/languagesStructure.pickle', 'rb') as file:\n            languagesList = pickle.load(file)\n        print(len(languagesList))\n        element = {'date': '', 'languagesList': []}\n        dataList = []\n        for date in data:\n            element['date'] = date\n            element['languagesList'] = []\n            print(len(languagesList))\n            for language in languagesList:\n                if language in data[date]:\n                    element['languagesList'].append({'language':language,'numberOfTweets':data[date][language]['numberOfTweets']})\n                else:\n                    element['languagesList'].append({'language':language,'numberOfTweets':0})\n            dataList.append(element)\n        return dataList\n\n\n    def getBarChartRaceBySentimentAndDate(self):\n        cube = self.workspace.cube(\"tweet\")\n        cube.browser = self.browserTweet\n\n        cut = [PointCut(\"concept\", [self.concept])]\n        cell = Cell(cube, cut)\n\n        result = self.browserTweet.aggregate(cell, drilldown=[\"time:day\", \"sentiment\"],\n                                             aggregates=[\"numberOfTweets_sum\"])\n\n        output = []\n        for row in result.table_rows(\"time\"):\n            output.append(row.record)\n\n        data = defaultdict(lambda: defaultdict(lambda: defaultdict()))\n        for row in output:\n            date = row['time.day'] + \"/\" + row['time.month'] + \"/\" + row['time.year']\n            sentiment = row['sentiment.sentimentLabel']\n            data[date][sentiment]['numberOfTweets'] = row['numberOfTweets_sum']\n        dataList = []\n        element = {'date': '', 'sentimentsList': []}\n        for date in data:\n            element['date'] = date\n            sentimentElement = {'sentiment': '', 'numberOfTweets': 0}\n            mySentimentsList = []\n            for sentiment in data[date]:\n                sentimentElement['sentiment'] = sentiment\n                sentimentElement['numberOfTweets'] = data[date][sentiment]['numberOfTweets']\n                mySentimentsList.append(sentimentElement)\n                sentimentElement = {'sentiment': '', 'numberOfTweets': 0}\n            element['sentimentsList'] = mySentimentsList\n            dataList.append(element)\n            element = {'date': '', 'sentimentsList': []}\n        return dataList\n\n","repo_name":"Sadat2803/Flask-olap-twitter","sub_path":"CubeModelisation/tweetCube.py","file_name":"tweetCube.py","file_ext":"py","file_size_in_byte":6591,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"19965321047","text":"from django.conf.urls.defaults import *\nfrom main.requestHandlers import *\n# Uncomment the next two lines to enable the admin:\n# from django.contrib import admin\n# admin.autodiscover()\n\nurlpatterns = patterns('',\n    # Examples:\n    # url(r'^$', 'Impressions.views.home', name='home'),\n    # url(r'^Impressions/', include('Impressions.foo.urls')),\n\n    # Uncomment the admin/doc line below to enable admin documentation:\n    # url(r'^admin/doc/', include('django.contrib.admindocs.urls')),\n\n    # Uncomment the next line to enable the admin:\n    # url(r'^admin/', include(admin.site.urls)),\n    (r'^privacy', 'main.views.privacy'),\n    (r'^heartBeat', 'main.views.heartBeat'),\n    (r'^simpleTest', 'main.views.test'),\n    (r'^register', Register.as_view()),\n    (r'^upload', postImage.as_view()),\n    (r'^login', LoginView.as_view()),\n    (r'^getAround', getAround.as_view()),\n    \n)\n","repo_name":"ningkai1983/locals","sub_path":"Locals/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":884,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"70178617745","text":"from config import *\nfrom Population import *\n\ndef main():\n    pop = Population('input.txt')\n    for t in range(TIME_MAX):\n        pop.move(t)\n        print(\"Time: %3d : Best Value: %f\"%(t, pop.bestValue))\n    pop.printResult()\n    del pop\n\nif __name__ == \"__main__\":\n    main()","repo_name":"loem-ms/EvolutionaryComputing","sub_path":"sample-code/week3-PSO-FA-BA/BatAlgo-Regression/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":278,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"19377090751","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport wx\nimport keyword\nimport os\n\n\n\nclass SyntaxColorizer:\n    \"\"\"\n    SyntaxColorizer\n\n    Sets up the lexer for syntax highlighting.\n\n    \"\"\"\n    def __init__(self):\n        \"\"\"\n        __init__\n\n        Generates a default color dictionary.\n\n        \"\"\"\n        self.HOMEDIR = os.path.expanduser('~')\n\n        self.DefaultColorDict = {\n            \"Integers\": wx.Colour(181, 69, 166, 255),\n            \"Brackets\": wx.Colour(25, 57, 185, 255),\n            \"Identifiers\": wx.Colour(0, 0, 0, 255),\n            \"BadEOL\": wx.Colour(231, 0, 255, 255),\n            \"Comments\": wx.Colour(214, 214, 214, 255),\n            \"Strings\": wx.Colour(255, 0, 22),\n            \"MethodNames\": wx.Colour(0, 7, 255, 255),\n            \"Keywords\": wx.Colour(26, 32, 189, 255),\n            \"Operators\": wx.Colour(210, 147, 29, 255),\n            \"TripleQuotes\": wx.Color(210, 149, 29, 255),\n            \"EdgeLine\": wx.Color(255, 0, 22),\n            }\n\n    def ReadColorFile(self, color):\n        \"\"\"\n        ReadColorFile\n\n        Reads the configuration file and generates\n        a color dictionary for the lexer.\n\n        \"\"\"\n        try:\n            ColorFile = open(self.HOMEDIR + \"/.gEcritColor\", \"r\")\n            ColorDict = eval(ColorFile.read())\n            ColorDict[color]\n        except:\n            ColorDict = self.DefaultColorDict\n            ColorFile = open(self.HOMEDIR + \"/.gEcritColor\", \"w\")\n            ColorFile.write(str(self.DefaultColorDict))\n        ColorFile.close()\n        return ColorDict[color]\n\n    def ChangeColorFile(self, item, newcolor):\n        \"\"\"\n        ChangeColorFile\n\n        Modifies the value of the suplied argument item\n        with the suplied argument newcolor.\n        Writes changes to file.\n\n        \"\"\"\n        try:\n            (self.DefaultColorDict)[item]\n            ColorFile = open(self.HOMEDIR + \"/.gEcritColor\", \"r\")\n            ColorDict = eval(ColorFile.read())\n            ColorDict[item] = newcolor\n            ColorFile = open(self.HOMEDIR + \"/.gEcritColor\", \"w\")\n            ColorFile.write(str(ColorDict))\n        except:\n            ColorDict = self.DefaultColorDict\n            ColorFile = open(self.HOMEDIR + \"/.gEcritColor\", \"w\")\n            ColorFile.write(str(self.DefaultColorDict))\n            ColorFile = open(self.HOMEDIR + \"/.gEcritColor\", \"r\")\n            ColorDict[item] = newcolor\n\n        ColorFile.close()\n\n    def ActivateSyntaxHighLight(self, text_id):\n        \"\"\"\n        ActivateSyntaxHighLight\n\n        Initializes the lexer and sets the color styles.\n\n        \"\"\"\n        cur_doc = wx.FindWindowById(text_id)\n        cur_doc.SetLexer(wx.stc.STC_LEX_PYTHON)\n        cur_doc.SetKeyWords(0, (\" \").join(keyword.kwlist))\n        cur_doc.SetProperty(\"fold\", \"1\")\n\n        cur_doc.StyleSetSpec(wx.stc.STC_P_DEFAULT, \"fore:#000000\")\n\n        cur_doc.StyleSetSpec(wx.stc.STC_P_COMMENTLINE, \"fore:\" + self.ReadColorFile(\"Comments\").GetAsString(wx.C2S_HTML_SYNTAX))\n\n        cur_doc.StyleSetSpec(wx.stc.STC_P_NUMBER, \"fore:\" + self.ReadColorFile(\"Integers\").GetAsString(wx.C2S_HTML_SYNTAX))\n\n        cur_doc.StyleSetSpec(wx.stc.STC_P_STRING, \"fore:\" + self.ReadColorFile(\"Strings\").GetAsString(wx.C2S_HTML_SYNTAX))\n\n        cur_doc.StyleSetSpec(wx.stc.STC_P_CHARACTER, \"fore:\" + self.ReadColorFile(\"Strings\").GetAsString(wx.C2S_HTML_SYNTAX))\n\n        cur_doc.StyleSetSpec(wx.stc.STC_P_WORD, \"fore:\" + self.ReadColorFile(\"Keywords\").GetAsString(wx.C2S_HTML_SYNTAX))\n\n        cur_doc.StyleSetSpec(wx.stc.STC_P_TRIPLE, \"fore:\" + self.ReadColorFile(\"TripleQuotes\").GetAsString(wx.C2S_HTML_SYNTAX))\n\n        cur_doc.StyleSetSpec(wx.stc.STC_P_TRIPLEDOUBLE, \"fore:\" + self.ReadColorFile(\"TripleQuotes\").GetAsString(wx.C2S_HTML_SYNTAX))\n\n        cur_doc.StyleSetSpec(wx.stc.STC_P_CLASSNAME, \"fore:\" + self.ReadColorFile(\"MethodNames\").GetAsString(wx.C2S_HTML_SYNTAX) +\n                             \",bold,underline\")\n\n        cur_doc.StyleSetSpec(wx.stc.STC_P_DEFNAME, \"fore:\" + self.ReadColorFile(\"MethodNames\").GetAsString(wx.C2S_HTML_SYNTAX) +\n                             \",bold,underline\")\n\n        cur_doc.StyleSetSpec(wx.stc.STC_P_OPERATOR, \"fore:\" + self.ReadColorFile(\"Operators\").GetAsString(wx.C2S_HTML_SYNTAX))\n\n        cur_doc.StyleSetSpec(wx.stc.STC_P_IDENTIFIER, \"fore:\" + self.ReadColorFile(\"Identifiers\").GetAsString(wx.C2S_HTML_SYNTAX))\n\n        cur_doc.StyleSetSpec(wx.stc.STC_P_COMMENTBLOCK, \"fore:\" + self.ReadColorFile(\"Comments\").GetAsString(wx.C2S_HTML_SYNTAX))\n\n        cur_doc.StyleSetSpec(wx.stc.STC_P_STRINGEOL,\n                             \"fore:#000000,face:%s,back:\" + self.ReadColorFile(\"BadEOL\").GetAsString(wx.C2S_HTML_SYNTAX) +\n                             \"eol\")\n\n        cur_doc.StyleSetSpec(wx.stc.STC_STYLE_BRACELIGHT, \"back:\" + self.ReadColorFile(\"Brackets\").GetAsString(wx.C2S_HTML_SYNTAX))\n\n        cur_doc.StyleSetSpec(wx.stc.STC_STYLE_BRACEBAD,\n                             \"fore:#000000,back:#FF0000,bold\")\n\n        cur_doc.SetCaretForeground(\"BLUE\")\n\n        cur_doc.SetEdgeColour(self.ReadColorFile(\"EdgeLine\"))\n\n\nSyntCol = SyntaxColorizer()\n","repo_name":"tomergreenwald/python-validator","sub_path":"benchmarks/gecrit-code/bin/SyntaxHighlight.py","file_name":"SyntaxHighlight.py","file_ext":"py","file_size_in_byte":5082,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"798350202","text":"from pathlib import Path\nimport os\nimport django_heroku\n\n\n# Build paths inside the project like this: BASE_DIR / 'subdir'.\nBASE_DIR = Path(__file__).resolve().parent.parent\n\n\n# Quick-start development settings - unsuitable for production\n# See https://docs.djangoproject.com/en/3.1/howto/deployment/checklist/\n\n# SECURITY WARNING: keep the secret key used in production secret!\nSECRET_KEY = os.getenv('SECRET_KEY')\n\n# SECURITY WARNING: don't run with debug turned on in production!\n\nDEBUG = False\n\nALLOWED_HOSTS = ['.herokuapp.com',]\n\n# Allauth Configurations\nACCOUNT_AUTHENTICATION_METHOD = 'username_email'\nACCOUNT_EMAIL_REQUIRED = True\nACCOUNT_EMAIL_VERIFICATION = 'optional'\nACCOUNT_LOGIN_ATTEMPTS_LIMIT = 5\nACCOUNT_EMAIL_CONFIRMATION_EXPIRE_DAYS = 1\nACCOUNT_EMAIL_SUBJECT_PREFIX = 'TicTacToe - '\n\n# Email configuration\nEMAIL_BACKEND = 'django.core.mail.backends.smtp.EmailBackend'\nEMAIL_HOST = 'smtp.gmail.com'\nEMAIL_PORT = 587\nEMAIL_USE_TLS = True\nEMAIL_HOST_USER = os.getenv('EMAIL')\nEMAIL_HOST_PASSWORD = os.getenv('EMAIL_PASSWORD')\n\n# Application definition\nINSTALLED_APPS = [\n    'django.contrib.admin',\n    'django.contrib.auth',\n    'django.contrib.contenttypes',\n    'django.contrib.sessions',\n    'django.contrib.messages',\n    'django.contrib.staticfiles',\n    'django.contrib.sites',\n\n    # my_apps:\n    'game',\n    'menu',\n    'player',\n    'stats',\n\n    # 3rd party:\n    'allauth',\n    'allauth.account',\n    'allauth.socialaccount',\n    'crispy_forms',\n    'rest_framework',\n    'rest_framework.authtoken',\n    'django_db_logger',\n    'django_jenkins',\n]\n\nMIDDLEWARE = [\n    'django.middleware.security.SecurityMiddleware',\n    'django.contrib.sessions.middleware.SessionMiddleware',\n    'django.middleware.common.CommonMiddleware',\n    'django.middleware.csrf.CsrfViewMiddleware',\n    'django.contrib.auth.middleware.AuthenticationMiddleware',\n    'django.contrib.messages.middleware.MessageMiddleware',\n    'django.middleware.clickjacking.XFrameOptionsMiddleware',\n]\n\nROOT_URLCONF = 'TicTacToe.urls'\n\nTEMPLATES = [\n    {\n        'BACKEND': 'django.template.backends.django.DjangoTemplates',\n        'DIRS': [BASE_DIR / 'templates'],\n        'APP_DIRS': True,\n        'OPTIONS': {\n            'context_processors': [\n                'django.template.context_processors.debug',\n                'django.template.context_processors.request',\n                'django.contrib.auth.context_processors.auth',\n                'django.contrib.messages.context_processors.messages',\n            ],\n        },\n    },\n]\n\nWSGI_APPLICATION = 'TicTacToe.wsgi.application'\n\nAUTH_USER_MODEL = 'player.CustomUser'\n\n# Database\n# https://docs.djangoproject.com/en/3.1/ref/settings/#databases\n\nDATABASES = {\n    'default': {\n        'ENGINE': 'django.db.backends.sqlite3',\n        'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),\n    }\n}\n\nif 'DATABASE_URL' in os.environ:\n    import dj_database_url\n    DATABASES['default'] = dj_database_url.config(conn_max_age=600, ssl_require=True)\n\n# Password validation\n# https://docs.djangoproject.com/en/3.1/ref/settings/#auth-password-validators\n\nAUTH_PASSWORD_VALIDATORS = [\n    {\n        'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',\n    },\n    {\n        'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',\n    },\n    {\n        'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',\n    },\n    {\n        'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',\n    },\n]\n\n# Modified allauth forms\nACCOUNT_FORMS = {'signup': 'menu.forms.MyCustomSignupForm'}\n\n\n# Internationalization\n# https://docs.djangoproject.com/en/3.1/topics/i18n/\n\nLANGUAGE_CODE = 'en-us'\n\nTIME_ZONE = 'UTC'\n\nUSE_I18N = True\n\nUSE_L10N = True\n\nUSE_TZ = True\n\n\n# Static files (CSS, JavaScript, Images)\n# https://docs.djangoproject.com/en/3.1/howto/static-files/\n\nSTATICFILES_DIRS = [\n    os.path.join(BASE_DIR, 'static'),\n]\nSTATIC_URL = '/static/'\nSTATIC_ROOT = os.path.join(BASE_DIR, 'apps_static/')\n\n\n# ALLAUTH CONFIGURATION\n\nLOGIN_REDIRECT_URL = \"/\"\nACCOUNT_SIGNUP_REDIRECT_URL = (LOGIN_REDIRECT_URL + 'game')\n\nAUTHENTICATION_BACKENDS = [\n    # Needed to login by username in Django admin, regardless of `allauth`\n    'django.contrib.auth.backends.ModelBackend',\n\n    # `allauth` specific authentication methods, such as login by e-mail\n    'allauth.account.auth_backends.AuthenticationBackend',\n]\n\n\nSITE_ID = 1\n\n\nCRISPY_TEMPLATE_PACK = 'bootstrap4'\n\n\nREST_FRAMEWORK = {\n    'DEFAULT_AUTHENTICATION_CLASSES': (\n        'rest_framework.authentication.TokenAuthentication',\n        'rest_framework.authentication.SessionAuthentication',\n    ),\n    'DEFAULT_PERMISSION_CLASSES': (\n        'rest_framework.permissions.IsAuthenticated', )\n}\n\nFIXTURE_DIRS = [BASE_DIR]\n\n# Session configuration:\nSESSION_ENGINE = 'django.contrib.sessions.backends.signed_cookies'\n\n\n# Logging configuration\nLOGGING = {\n    'version': 1,\n    'disable_existing_loggers': False,\n    'formatters': {\n        'verbose': {\n            'format': '%(levelname)s %(asctime)s %(module)s %(process)d %(thread)d %(message)s'\n        },\n        'simple': {\n            'format': '%(levelname)s %(asctime)s %(message)s'\n        },\n    },\n    'handlers': {\n        'db_log': {\n            'level': 'DEBUG',\n            'class': 'django_db_logger.db_log_handler.DatabaseLogHandler'\n        },\n    },\n    'loggers': {\n        'db': {\n            'handlers': ['db_log'],\n            'level': 'DEBUG'\n        }\n    }\n}\n\ndjango_heroku.settings(locals())\n\ntry:\n    from .local_settings import *\nexcept ImportError:\n    pass\n","repo_name":"infoshareacademy/jpydzr1-dkmap-django","sub_path":"TicTacToe/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":5587,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"25657511685","text":"import sys\n\nsys.setrecursionlimit(10 ** 7)\nrl = sys.stdin.readline\n\n\ndef solve():\n    N = int(rl())\n    A = [(int(ai), idx) for idx, ai in enumerate(rl().split())]\n    \n    ans = [0] * 2 ** N\n    cnt = 1\n    while cnt <= N:\n        tmp_ls = []\n        for i in range(0, len(A), 2):\n            x_pow, x_num = A[i]\n            y_pow, y_num = A[i + 1]\n            if x_pow < y_pow:\n                tmp_ls.append((y_pow, y_num))\n                ans[x_num] = cnt\n            else:\n                tmp_ls.append((x_pow, x_num))\n                ans[y_num] = cnt\n        cnt += 1\n        A = tmp_ls\n    \n    _, champ = A[0]\n    ans[champ] = N\n    print(*ans, sep='\\n')\n\n\nif __name__ == '__main__':\n    solve()\n","repo_name":"yuly3/atcoder","sub_path":"other/past202004-open/I.py","file_name":"I.py","file_ext":"py","file_size_in_byte":703,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"73197330064","text":"import pandas as pd\r\nimport requests\r\n\r\n# Replace with your Geoapify API key\r\nAPI_KEY = '13c00742ef784344aa1dc02d6b403008'\r\n\r\n# Function to geocode a location using Geoapify API\r\n\r\n\r\ndef geocode_location(location):\r\n    base_url = 'https://api.geoapify.com/v1/geocode/search'\r\n    params = {\r\n        'text': location,\r\n        'apiKey': API_KEY,\r\n    }\r\n    print(\"***\")\r\n    try:\r\n        response = requests.get(base_url, params=params)\r\n        data = response.json()\r\n        if 'features' in data and data['features']:\r\n            coordinates = data['features'][0]['geometry']['coordinates']\r\n            print(\"++++\",coordinates)\r\n            return {\r\n                'Latitude': coordinates[1],\r\n                'Longitude': coordinates[0]\r\n            }\r\n        else:\r\n            return {\r\n                'Latitude': None,\r\n                'Longitude': None\r\n            }\r\n    except Exception as e:\r\n        print(f\"Error geocoding {location}: {str(e)}\")\r\n        return {\r\n            'Latitude': None,\r\n            'Longitude': None\r\n        }\r\n\r\n\r\n# Read the CSV file with locations\r\ninput_file = 'locations.csv'\r\noutput_file = 'op.csv'\r\n\r\ntry:\r\n    df = pd.read_csv(input_file)\r\n    df[['Latitude', 'Longitude']] = df['Location'].apply(geocode_location).apply(pd.Series)\r\n    df.to_csv(output_file, index=False)\r\n    \r\n    print(f\"Geocoded data saved to {output_file}\")\r\nexcept Exception as e:\r\n    print(f\"Error reading or processing the CSV file: {str(e)}\")\r\n","repo_name":"Arno2003/Smart-India-Hackathon","sub_path":"BackEnd/Test/latLon.py","file_name":"latLon.py","file_ext":"py","file_size_in_byte":1481,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"11782626266","text":"#!/usr/bin/env python3\n# -*- encoding: utf-8 -*-\n\nimport json\n\nfrom flask import Flask, request, jsonify\nfrom flask_sqlalchemy import SQLAlchemy\n\nfrom aliyunexp_bn import packagereq\nfrom ymodules.kd100_bn import checkcmpy, ckkd100pkg\n\napp = Flask(__name__)\napp.config['SQLALCHEMY_COMMIT_ON_TEARDOWN'] = True\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = True\napp.config['SQLALCHEMY_ECHO'] = True\napp.config['SQLALCHEMY_DATABASE_URI'] = 'mysql://root:root@localhost:3306/sqlalc?charset=utf8mb4'\ndb = SQLAlchemy(app)\n\n\nclass Package(db.Model):\n    __tablename__ = 'pkglist'\n    pkgid = db.Column(db.VARCHAR(32), primary_key=True, nullable=False, unique=True)\n    pkgstatus = db.Column(db.Integer, nullable=False)\n    pkgtrack = db.Column(db.VARCHAR(255))\n    tguserid = db.Column(db.VARCHAR(32))\n\n    def __init__(self, pkgid, pkgstatus, pkgtrack, tguserid):\n        self.pkgid = pkgid\n        self.pkgstatus = pkgstatus\n        self.pkgtrack = pkgtrack\n        self.tguserid = tguserid\n\n    def __repr__(self):\n        result = {'pkgid': self.pkgid, 'pkgstatus': self.pkgstatus, 'pkgtrack': self.pkgtrack,\n                  'tguserid': self.tguserid}\n        return json.dumps(result)\n\n\n@app.teardown_appcontext\ndef shutdown_session(exception=None):\n    db.session.commit()\n    db.session.remove()\n\n\n@app.route('/api/aliyun/checkexp', methods=['POST'])\ndef callpkgapi():\n    expno = request.json.get('express')\n    userid = request.json.get('tgid')\n    company = request.json.get('company')\n    respA = packagereq(expno, company)\n    if (isinstance(respA, str)):\n        msg = json.loads(respA)\n        return respA\n    companyrep = respA['result']['type']\n    pkgtrack = respA['result']['list'][0]\n    pkgstat = respA['result']['deliverystatus']\n    package = Package(pkgid=expno, pkgstatus=pkgstat, pkgtrack=pkgtrack, tguserid=userid)\n    if (pkgstat is not 3):\n        if (Package.query.filter_by(pkgid=expno).first() is not None):\n            package.pkgtrack = respA['result']['list'][0]\n            db.session.commit()\n        else:\n            db.session.add(package)\n            db.session.commit()\n        return jsonify({'id': expno, 'stat': pkgstat, 'track': pkgtrack})\n    else:\n        db.session.delete(package)\n        return jsonify({'code': 400, 'bmsg': 'The package is already received by user.'})\n\n\n@app.route('/api/kd100/checkpkg', methods=['GET'])\ndef callkd100():\n    pkgid = request.args.get('pkgid')\n    cpny = checkcmpy(pkgid)\n    if cpny == 500:\n        return jsonify({'status': 500, 'bmsg': 'Cannot auto detect the proper company.'})\n    elif cpny == 'shunfeng':\n        return jsonify({'status': 302,\n                        'bmsg': 'ShunFeng Express doesn\\'t support this method. use \\/sfpkg to solve this problem.'})\n    else:\n        lastresult = ckkd100pkg(pkgid, cpny)\n        return jsonify({'status': 200, 'pkgtrack': lastresult})\n\n\n@app.route('/api/check2user', methods=['GET'])\ndef checkbyuser():\n    userid = request.json.get('tgid')\n    usersql = Package.query.filter_by(tguserid=userid).all()\n    pkgall = []\n    if (usersql is not None):\n        leng = len(usersql)\n        for i in range(0, leng):\n            pkg = str(usersql[i])\n            pkgall.append(pkg)\n        return jsonify({'code': 200, 'pkg': pkgall})\n    else:\n        return jsonify({'code': 404, 'bmsg': 'not found'})\n\n\napp.run(debug=True, port=880)\n","repo_name":"kmahyyg/tg-bot-dailylife","sub_path":"archived/pkgbot_auth.py","file_name":"pkgbot_auth.py","file_ext":"py","file_size_in_byte":3360,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"48"}
+{"seq_id":"341894783","text":"'''\n1. Create a grid of 25X25 (or any size you fancy, preferably large) small boxes. 0,0 be top left and 25,25 be bottom right\n2. Color the different boxes according to the function. Add a starting box with a specific color, the blockages with another and finally, the location in another color.\n\n'''\n\n#Dijikstra Shortest Path Finder Algorithm Visualization\nfrom tkinter import messagebox, Tk\nimport pygame\nimport sys\n\nWindow_Width = 500\nWindow_Height = 500\nRow = 25\nColumn = 25\n\nBox_Width = Window_Width // Column\nBox_Height = Window_Height // Row\n\nGrid=[]\n\nclass Box:\n    def __init__(self,i,j):\n        self.x=i\n        self.y=j\n        #The three flags\n        self.Start=False\n        self.Wall=False\n        self.Target=False\n    \n    def draw(self,win,color): #self, window of the boxes and its color argument\n        pygame.draw.rect(win, color, (self.x*Box_Width,self.y*Box_Height,Box_Width-2,Box_Height-2)) #Subtracting from Box_Width and Box_Height for the grid lines thickness\n\n#Create the grid\nfor i in range(Column):\n    arr=[]\n    for j in range(Row):\n        arr.append(Box(i,j)) #Appending the values to the array\n    Grid.append(arr) #Appending the grid-array to the Array\n\nStart_Box = Grid[0][0]\nStart_Box.Start = True\n\nWindow = pygame.display.set_mode((Window_Width,Window_Height))\n\ndef main():\n    Begin_Search = False #Algorithm Triggering Variable\n    Target_Box_Set = False\n    \n    while True:\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                pygame.quit()\n                sys.exit()\n            #Mouse-Controls\n            elif event.type == pygame.MOUSEMOTION:\n                x=pygame.mouse.get_pos()[0]\n                y=pygame.mouse.get_pos()[1]\n                #Draw the Wall for the Obstruction:\n                if event.buttons[0]:\n                    i=x//Box_Width\n                    j=y//Box_Height\n                    Grid[i][j].Wall = True\n                #Set Target\n                if event.buttons[2] and not Target_Box_Set:\n                    i=x//Box_Width\n                    j=y//Box_Height\n                    Target_Box = Grid[i][j]\n                    Target_Box.Target = True\n                    Target_Box_Set = True\n            #Start Algorithm\n            if event.type == pygame.KEYDOWN and Target_Box_Set:\n                Begin_Search = True\n            \n        \n        Window.fill((0,0,0))\n\n        for i in range(Column):\n            for j in range(Row):\n                Box = Grid[i][j]\n                Box.draw(Window,(50,50,50))\n                if Box.Start:\n                    Box.draw(Window,(0,200,200))\n                if Box.Wall:\n                    Box.draw(Window,(90,90,90))\n                if Box.Target:\n                    Box.draw(Window,(200,200,0))\n\n        pygame.display.flip()\n\nmain()","repo_name":"AdeepKrishnaKeelar/Simulations","sub_path":"Path-Finder/dijikstra-algo.py","file_name":"dijikstra-algo.py","file_ext":"py","file_size_in_byte":2816,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"28487814090","text":"#!/usr/bin/env python\n# WebHunter MiniModule\n\nimport re\n\n\nclass Airlock:\n    @staticmethod\n    def run(headers):\n        w = False\n        for item in headers.items():\n            w = re.search(r'^AL[_-]SESS[_-]S=\\S*', item[1], re.I) is not None\n            w |= re.search(r'X-Airlock-Test', item[0], re.I) is not None\n            if w:\n                return \"InfoGuard Airlock (Phion/Ergon)\"\n                break\n","repo_name":"zubeneschamali/maryam-1","sub_path":"master/foot_printing/waf/airlock.py","file_name":"airlock.py","file_ext":"py","file_size_in_byte":416,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"4031310566","text":"from tkinter import *\n\n# Globally declare the expressoin variable\nexpression = \"\"\n\n\n# Function to update expression in the entry box\ndef press(num):\n    global expression\n    expression = expression + str(num)\n    equation.set(expression)\n\n#Function to evaluate the final expression\ndef equal_press():\n\n    try:\n        global expression\n        #eval is used for evaluate the expression\n        total=str(eval(expression))\n        equation.set(total)\n        expression = \"\"\n\n    except:\n\n        equation.set(\"error\")\n        expression = \"\"\n\n#Function to clear the entry box\ndef clear():\n    global expression\n    expression = \"\"\n    equation.set(\"\")\n\n#Create GUI window\n\nwindow=Tk()\n#Set background colour\n\nwindow.configure(background=\"light gray\")\n#Set title\n\nwindow.title('Simple Calculator')\nwindow.geometry('370x220')\n#StringVar is the variable class\n\nequation = StringVar()\n\n#Create the entry box\n\ninput_field =Entry(window, textvariable=equation)\ninput_field.grid(columnspan=8, ipadx=120)\n\n#Create Buttons\n\nbutton7 = Button(window, text='7', fg='black', bg='white', command=lambda: press(7), height=2, width=7,)\nbutton7.grid(row=1, column=0)\nbutton8 = Button(window, text='8', fg='black', bg='white', command=lambda: press(8), height=2, width=7)\nbutton8.grid(row=1, column=1)\nbutton9 = Button(window, text='9', fg='black', bg='white', command=lambda: press(9), height=2, width=7)\nbutton9.grid(row=1, column=2)\nbuttondiv = Button(window, text='/', fg='black', bg='white', command=lambda: press(\"/\"), height=2, width=7)\nbuttondiv.grid(row=1, column=3)\n\nbutton4 = Button(window, text='4', fg='black', bg='white', command=lambda: press(4), height=2, width=7,)\nbutton4.grid(row=2, column=0)\nbutton5 = Button(window, text='5', fg='black', bg='white', command=lambda: press(5), height=2, width=7)\nbutton5.grid(row=2, column=1)\nbutton6 = Button(window, text='6', fg='black', bg='white', command=lambda: press(6), height=2, width=7)\nbutton6.grid(row=2, column=2)\nbuttonmul = Button(window, text='x', fg='black', bg='white', command=lambda: press(\"*\"), height=2, width=7)\nbuttonmul.grid(row=2, column=3)\n\nbutton1 = Button(window, text='1', fg='black', bg='white', command=lambda: press(1), height=2, width=7,)\nbutton1.grid(row=3, column=0)\nbutton2 = Button(window, text='2', fg='black', bg='white', command=lambda: press(2), height=2, width=7)\nbutton2.grid(row=3, column=1)\nbutton3 = Button(window, text='3', fg='black', bg='white', command=lambda: press(3), height=2, width=7)\nbutton3.grid(row=3, column=2)\nbuttonmin = Button(window, text='-', fg='black', bg='white', command=lambda: press(\"-\"), height=2, width=7)\nbuttonmin.grid(row=3, column=3)\n\nbuttondot = Button(window, text='.', fg='black', bg='white', command=lambda: press(\"+\"), height=2, width=7,)\nbuttondot.grid(row=4, column=0)\nbuttonzero = Button(window, text='0', fg='black', bg='white', command=lambda: press(0), height=2, width=7)\nbuttonzero.grid(row=4, column=1)\nbuttonplus = Button(window, text='+', fg='black', bg='white', command=lambda: press(\"+\"), height=2, width=7)\nbuttonplus.grid(row=4, column=2)\nbuttonequal = Button(window, text='=', fg='black', bg='white', command=lambda: equal_press(), height=2, width=7)\nbuttonequal.grid(row=4, column=3)\nbuttonclear = Button(window, text='Clear', fg='black', bg='white', command=lambda: clear(), height=2, width=7)\nbuttonclear.grid(row=4, column=4)\n\n# Start\nwindow.mainloop()","repo_name":"SidhardhCR/Pyhton-tkinter-Calculator","sub_path":"tkinter_calculator.py","file_name":"tkinter_calculator.py","file_ext":"py","file_size_in_byte":3390,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"30202968854","text":"import copy\nimport datedelta\nfrom http import HTTPStatus\nfrom legal_api.utils.legislation_datetime import LegislationDatetime\n\nimport pycountry\nimport pytest\nfrom registry_schemas.example_data import FILING_HEADER, CONSENT_CONTINUATION_OUT, CONTINUATION_OUT\n\nfrom legal_api.models import Business, ConsentContinuationOut\nfrom legal_api.services.filings.validations.validation import validate\nfrom legal_api.utils.datetime import datetime\n\nfrom tests.unit.models import factory_business, factory_completed_filing\nfrom tests.unit.models.test_consent_continuation_out import get_cco_expiry_date\n\ndate_format = '%Y-%m-%d'\nlegal_name = 'Test name request'\nvalidate_active_cco_path = 'legal_api.services.filings.validations.continuation_out.validate_active_cco'\n\n\ndef _create_consent_continuation_out(business, foreign_jurisdiction, effective_date=datetime.utcnow()):\n    filing_dict = copy.deepcopy(FILING_HEADER)\n    filing_dict['filing']['consentContinuationOut'] = copy.deepcopy(CONSENT_CONTINUATION_OUT)\n    filing = factory_completed_filing(business, filing_dict, filing_date=effective_date)\n\n    consent_continuation_out = ConsentContinuationOut()\n    consent_continuation_out.foreign_jurisdiction = foreign_jurisdiction.get('country')\n\n    region = foreign_jurisdiction.get('region')\n    region = region.upper() if region else None\n    consent_continuation_out.foreign_jurisdiction_region = region\n\n    consent_continuation_out.expiry_date = get_cco_expiry_date(filing.effective_date)\n\n    consent_continuation_out.filing_id = filing.id\n    business.consent_continuation_outs.append(consent_continuation_out)\n    business.save()\n\n\n@pytest.mark.parametrize(\n    'test_name, expected_code, message',\n    [\n        ('FAIL_IN_FUTURE', HTTPStatus.BAD_REQUEST, 'Continuation out date must be today or past.'),\n        ('FAIL_NO_CCO', HTTPStatus.BAD_REQUEST, 'No active consent continuation out for this date and/or jurisdiction.'),\n        ('SUCCESS', None, None)\n    ]\n)\ndef test_validate_continuation_out_date(session, test_name, expected_code, message):\n    \"\"\"Assert validate continuation_out_date.\"\"\"\n    business = factory_business(identifier='BC1234567', entity_type='BC', founding_date=datetime.utcnow())\n    filing = copy.deepcopy(FILING_HEADER)\n    filing['filing']['continuationOut'] = copy.deepcopy(CONTINUATION_OUT)\n    filing['filing']['header']['name'] = 'continuationOut'\n    co_date = LegislationDatetime.as_legislation_timezone_from_date_str('2023-06-19')\n    effective_date = LegislationDatetime.as_utc_timezone(co_date)\n    filing['filing']['continuationOut']['continuationOutDate'] = co_date.strftime(date_format)\n\n    if test_name == 'FAIL_IN_FUTURE':\n        filing['filing']['continuationOut']['continuationOutDate'] = \\\n            (LegislationDatetime.now() + datedelta.datedelta(days=1)).strftime(date_format)\n    elif test_name == 'FAIL_NO_CCO':\n        effective_date -= datedelta.datedelta(months=6, days=1)\n\n    _create_consent_continuation_out(business,\n                                     filing['filing']['continuationOut']['foreignJurisdiction'],\n                                     effective_date)\n    err = validate(business, filing)\n\n    # validate outcomes\n    if test_name != 'SUCCESS':\n        assert expected_code == err.code\n        assert message == err.msg[0]['error']\n    else:\n        assert not err\n\n\n@pytest.mark.parametrize(\n    'test_name, expected_code, message',\n    [\n        ('FAIL_NO_COUNTRY', HTTPStatus.UNPROCESSABLE_ENTITY, None),\n        ('FAIL_INVALID_COUNTRY', HTTPStatus.BAD_REQUEST, 'Invalid country.'),\n        ('FAIL_REGION_BC', HTTPStatus.BAD_REQUEST, 'Region should not be BC.'),\n        ('FAIL_INVALID_REGION', HTTPStatus.BAD_REQUEST, 'Invalid region.'),\n        ('FAIL_INVALID_US_REGION', HTTPStatus.BAD_REQUEST, 'Invalid region.'),\n        ('SUCCESS', None, None)\n    ]\n)\ndef test_validate_foreign_jurisdiction(session, mocker, test_name, expected_code, message):\n    \"\"\"Assert validate foreign jurisdiction.\"\"\"\n    business = factory_business(identifier='BC1234567', entity_type='BC', founding_date=datetime.utcnow())\n    filing = copy.deepcopy(FILING_HEADER)\n    filing['filing']['continuationOut'] = copy.deepcopy(CONTINUATION_OUT)\n    filing['filing']['header']['name'] = 'continuationOut'\n\n    if test_name == 'FAIL_NO_COUNTRY':\n        del filing['filing']['continuationOut']['foreignJurisdiction']['country']\n    elif test_name == 'FAIL_INVALID_COUNTRY':\n        filing['filing']['continuationOut']['foreignJurisdiction']['country'] = 'NONE'\n    elif test_name == 'FAIL_REGION_BC':\n        filing['filing']['continuationOut']['foreignJurisdiction']['region'] = 'BC'\n    elif test_name == 'FAIL_INVALID_REGION':\n        filing['filing']['continuationOut']['foreignJurisdiction']['region'] = 'NONE'\n    elif test_name == 'FAIL_INVALID_US_REGION':\n        filing['filing']['continuationOut']['foreignJurisdiction']['country'] = 'US'\n        filing['filing']['continuationOut']['foreignJurisdiction']['region'] = 'NONE'\n\n    mocker.patch(validate_active_cco_path, return_value=[])\n    err = validate(business, filing)\n\n    # validate outcomes\n    if test_name != 'SUCCESS':\n        assert expected_code == err.code\n        if message:\n            assert message == err.msg[0]['error']\n    else:\n        assert not err\n\n\ndef test_valid_foreign_jurisdiction(session, mocker):\n    \"\"\"Assert valid foreign jurisdiction.\"\"\"\n    business = factory_business(identifier='BC1234567', entity_type='BC', founding_date=datetime.utcnow())\n    filing = copy.deepcopy(FILING_HEADER)\n    filing['filing']['header']['name'] = 'continuationOut'\n    mocker.patch(validate_active_cco_path, return_value=[])\n\n    for country in pycountry.countries:\n        filing['filing']['continuationOut'] = copy.deepcopy(CONTINUATION_OUT)\n        filing['filing']['continuationOut']['foreignJurisdiction']['country'] = country.alpha_2\n        if country.alpha_2 in ('CA', 'US'):\n            for region in pycountry.subdivisions.get(country_code=country.alpha_2):\n                region_code = region.code.replace(f'{country.alpha_2}-', '')\n                if country.alpha_2 == 'CA' and region_code == 'BC':\n                    region_code = 'FEDERAL'  # Testing Federal code instead of invalid code BC\n\n                filing['filing']['continuationOut']['foreignJurisdiction']['region'] = region_code\n        else:\n            del filing['filing']['continuationOut']['foreignJurisdiction']['region']\n\n        err = validate(business, filing)\n        assert not err\n\n\n@pytest.mark.parametrize(\n    'test_status, file_number, expected_code',\n    [\n        ('FAIL', None, HTTPStatus.UNPROCESSABLE_ENTITY),\n        ('SUCCESS', '12345678901234567890', None)\n    ]\n)\ndef test_continuation_out_court_order(session, mocker, test_status, file_number, expected_code):\n    \"\"\"Assert valid court order.\"\"\"\n    business = factory_business(identifier='BC1234567', entity_type='BC', founding_date=datetime.utcnow())\n    filing = copy.deepcopy(FILING_HEADER)\n    filing['filing']['continuationOut'] = copy.deepcopy(CONTINUATION_OUT)\n    filing['filing']['header']['name'] = 'continuationOut'\n\n    if file_number:\n        court_order = {}\n        court_order['fileNumber'] = file_number\n        filing['filing']['continuationOut']['courtOrder'] = court_order\n    else:\n        del filing['filing']['continuationOut']['courtOrder']['fileNumber']\n\n    mocker.patch(validate_active_cco_path, return_value=[])\n    err = validate(business, filing)\n\n    # validate outcomes\n    if test_status == 'FAIL':\n        assert expected_code == err.code\n    else:\n        assert not err\n","repo_name":"bcgov/lear","sub_path":"legal-api/tests/unit/services/filings/validations/test_continuation_out.py","file_name":"test_continuation_out.py","file_ext":"py","file_size_in_byte":7596,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"48"}
+{"seq_id":"5819544471","text":"import argparse\nimport json\nfrom os import path\n\nimport numpy as np\nimport torch\nfrom pytorch_lightning.callbacks import ModelCheckpoint\nfrom torch.utils.data import DataLoader\nfrom torch.utils.data.sampler import SubsetRandomSampler\n\nfrom data import cifar10, isic_2019\nfrom models import SWIN, ConvNext, Resnet50\n\n\ndef main():\n    print(torch.version.cuda)\n    print(torch.cuda.is_available())\n    print(torch.cuda.device_count())\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--model', choices=[\"convnext\", \"swin\"],\n                        type=str, required=True)\n    parser.add_argument(\"--dataset\", choices=[\"cifar10\", \"isic_2019\", \"resnet50\"],\n                        type=str, required=True)\n    parser.add_argument(\"--data_path\", type=str, required=True)\n    parser.add_argument(\"--epochs\", type=int, default=30)\n    parser.add_argument(\"--train_batch_size\", type=int, default=256)\n    parser.add_argument(\"--test_batch_size\", type=int, default=512)\n    parser.add_argument(\"--lr\", type=float, default=0.05)\n    parser.add_argument(\"--train_split\", type=float, default=0.8)\n    parser.add_argument(\"--gpus\", type=int, default=-1)\n    parser.add_argument(\"--num_workers\", type=int, default=4)\n    parser.add_argument(\"--job_id\", type=str, default=None)\n    parser.add_argument(\"--img_size\", type=int, default=0)\n    parser.add_argument(\"--identifier\", type=str, default=None)\n    args = parser.parse_args()\n\n    # print(args.model)\n    # print(args.dataset)\n    # print(args.epochs)\n    # print(args.batch_size)\n    # print(args.lr)\n\n    # Print a title of the experiment containing the hyperparameters\n    hyperparams = vars(args)\n    print(\"Experiment: \", hyperparams)\n\n    set_image_size = args.img_size if args.img_size != 0 else None\n\n    model_identifier = args.model + args.identifier if args.identifier is not None else args.model\n    main_path = path.join(\"experiments\", model_identifier + \"-\" + args.dataset, args.job_id)\n    chp_path = path.join(main_path, \"checkpoints\")\n    log_path = path.join(main_path, \"logs\")\n\n    print(\"creating checkpoint.\")\n    checkpoint_callback = ModelCheckpoint(dirpath=chp_path,\n                                          filename=\"{epoch}-{val_loss:.4f}\",\n                                          monitor=\"val_loss\",\n                                          save_top_k=1,\n                                          mode=\"min\")\n\n    if args.model == \"convnext\":\n        print(\"initializing ConvNext model.\")\n        model = ConvNext.ConvNext(lr=args.lr)\n        trainer = ConvNext.get_convnext_trainer(gpus=args.gpus,\n                                                max_epochs=args.epochs,\n                                                callbacks=[checkpoint_callback],\n                                                log_path=log_path)\n        if set_image_size is not None:\n            cifar10_image_size = (set_image_size, set_image_size)\n            isic_2019_image_size = (set_image_size, set_image_size)\n        else:\n            cifar10_image_size = (128, 128)\n            isic_2019_image_size = (224, 224)    \n    elif args.model == \"swin\":\n        print(\"initializing SWIN model.\")\n        model = SWIN.SWIN(lr=args.lr)\n        trainer = SWIN.get_swin_trainer(gpus=args.gpus,\n                                        max_epochs=args.epochs,\n                                        callbacks=[checkpoint_callback],\n                                        log_path=log_path)\n        if set_image_size is not None:\n            cifar10_image_size = (set_image_size, set_image_size)\n            isic_2019_image_size = (set_image_size, set_image_size)\n        else:\n            cifar10_image_size = (224, 224)\n            isic_2019_image_size = (256, 256)\n    else:  # args.model == \"resnet50\":\n        print(\"initializing ResNet50 model.\")\n        model = Resnet50.Resnet50(lr=args.lr)\n        trainer = Resnet50.get_resnet50_trainer(gpus=args.gpus,\n                                                max_epochs=args.epochs,\n                                                callbacks=[checkpoint_callback],\n                                                log_path=log_path)\n        if set_image_size is not None:\n            cifar10_image_size = (set_image_size, set_image_size)\n            isic_2019_image_size = (set_image_size, set_image_size)\n        else:\n            cifar10_image_size = (128, 128)\n            isic_2019_image_size = (224, 224)\n\n    if args.dataset == \"cifar10\":\n        print(\"preparing CIFAR10 dataset.\")\n        feature_extractor = cifar10.get_cifar10_feature_extractor(cifar10_image_size)\n        train_dataset, test_dataset = cifar10.get_cifar10_data(\n            root=args.data_path,\n            train_transforms=feature_extractor,\n            test_transforms=feature_extractor\n        )\n\n        num_train = len(train_dataset)\n        indices = list(range(num_train))\n        split = int(np.floor(0.2 * num_train))\n\n        train_idx, valid_idx = indices[split:], indices[:split]\n        train_sampler = SubsetRandomSampler(train_idx)\n        valid_sampler = SubsetRandomSampler(valid_idx)\n\n        train_dataloader = torch.utils.data.DataLoader(\n            train_dataset,\n            batch_size=args.train_batch_size,\n            sampler=train_sampler,\n            num_workers=args.num_workers\n        )\n        val_dataloader = torch.utils.data.DataLoader(\n            train_dataset,\n            batch_size=args.test_batch_size,\n            sampler=valid_sampler,\n            num_workers=args.num_workers,\n            shuffle=False\n        )\n        test_dataloader = DataLoader(test_dataset,\n                                     batch_size=args.test_batch_size,\n                                     num_workers=args.num_workers)\n\n    else:\n        print(\"preparing ISIC 2019 dataset.\")\n        feature_extractor = isic_2019.get_isic_2019_feature_extractor(\n            image_size=isic_2019_image_size\n        )\n        train_dataset, val_dataset = isic_2019.get_isic_2019_data(\n            root=args.data_path,\n            transform=feature_extractor\n        )\n        test_dataset = isic_2019.ISIC2019Dataset(\n            root=args.data_path,\n            transform=feature_extractor,\n            mode=\"test\"\n        )\n        train_dataloader = DataLoader(train_dataset,\n                                      batch_size=args.train_batch_size,\n                                      shuffle=True,\n                                      num_workers=args.num_workers)\n        val_dataloader = DataLoader(val_dataset,\n                                    batch_size=args.test_batch_size,\n                                    num_workers=args.num_workers)\n        test_dataloader = DataLoader(test_dataset,\n                                     batch_size=args.test_batch_size,\n                                     num_workers=args.num_workers)\n\n    print(\"creating data loaders.\")\n\n    print(\"fitting model.\")\n    trainer.fit(model,\n                train_dataloader,\n                val_dataloader)\n\n    print(\"testing model.\")\n    test_results = trainer.test(dataloaders=test_dataloader)\n\n    with open(f\"results/test_results_{args.model}_{args.dataset}.json\", \"w\", encoding=\"utf8\") as f:\n        json.dump(test_results, f)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"Pallisaard/ATIA-convnets-vs-transformers","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7260,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"34953912268","text":"from .. import Node\n\nclass FuncTypeNode(Node):\n    def __init__(self, oldNode):\n        Node.__init__(self,oldNode.value, oldNode.parent, oldNode.children)\n        self.changeParent(oldNode)\n        self.changeParentOfChildren()\n\n    def changeAttributes(self):\n        self.value = self.children[0].value\n        if self.value == \"TYPE\":\n            self.value = self.children[0].children[0].value\n        self.children = []\n        for child in self.children:\n            child.changeAttributes()\n\n    def __str__ (self):\n        return \"FType: \" + self.value","repo_name":"MohamedDarkaoui/C-compiler","sub_path":"src/Nodes/function/funcTypeNode.py","file_name":"funcTypeNode.py","file_ext":"py","file_size_in_byte":561,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"31528899441","text":"import numpy as np\n\nclass ImageObfuscator(object):\n  \"\"\"docstring for ImageObfuscator\"\"\"\n  def __init__(self, image, window_size=16, stride=None):\n    super(ImageObfuscator, self).__init__()\n    self.image = image\n    self.window_size = window_size\n    self.stride = window_size\n\n  def create_images(self):\n    \"\"\"assume image has shape (3,x,y)\"\"\"\n    image = self.image\n    block = 0*np.ones([3, self.window_size, self.window_size])\n    _, height, width = image.shape\n    ims = []\n    for r in range(0,height,self.stride):\n      for c in range(0,width,self.stride):\n        im_copy = image.copy()\n        im_copy[:, r:r+self.window_size, c:c+self.window_size] = block\n        ims.append(im_copy)\n    self.images = np.array(ims)\n    ","repo_name":"howespt/uniform-info-images","sub_path":"obfuscator.py","file_name":"obfuscator.py","file_ext":"py","file_size_in_byte":733,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"73454684626","text":"# -*- coding: utf-8 -*-\n\"\"\"\nSpyder Editor\n\nThis is a temporary script file.\n\"\"\"\n\nimport pandas as pd\nimport random as random\n\ndf = pd.read_csv('Transactions.csv')\n\n\nclass merchandise:\n    name = \"\"\n    sum = 0\n    buy = 0\n    average = 0\n    variance = 0\n\n    def __init__(self, n):\n        self.name = n\n        \n    def computeAverage(self):\n        self.average = self.buy/self.sum\n\n    def computeVariance(self):\n        for i in range(self.buy):\n            self.variance += (1-self.average)**2\n        for i in range(self.sum-self.buy):\n            self.variance += self.average**2\n        self.variance /= self.sum\n\n    def display(self):\n        print(self.name, self.buy, self.average, self.variance)\n\n\ntemp = list(df.columns)\nlists = []\nfor name in temp:\n    lists.append(merchandise(name))\n\ntemp = df.sum()\nlenth = df.shape[0]\nsum = 0\nfor i in range(df.shape[1]):\n    lists[i].buy = temp[i]\n    sum += temp[i]\n    lists[i].sum = lenth\n    lists[i].computeAverage()\n    lists[i].computeVariance()\n\nprint('商品名', '购买数', '均值', '方差')\nfor i in range(len(lists)):\n    lists[i].display()\n\nprint('购买总数：', sum)\n\ndf.replace(0, -1, inplace=True)\n\n# 插入并修改列\ndf['wine'] = -1\nran = random.sample(range(0, len(df)), len(df)//5)\nfor i in ran:\n    df['wine'][i] = 1\n\n\n# 插入并修改行\ndf.loc[len(df)] = -1\nran = random.sample(range(0, df.shape[1]), df.shape[1]//4)\nlists.append(merchandise('wine'))\nfor i in ran:\n    df[lists[i].name][df.shape[0]-1] = 1\n\n# 写入文件\ndf.to_csv(\"Results.csv\")\n","repo_name":"lsqlhl5862/DataMine","sub_path":"Lab1/lab1_3.py","file_name":"lab1_3.py","file_ext":"py","file_size_in_byte":1533,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"18389960140","text":"import wcx.utils1.realsense as rs\nimport wcx.utils1.phoxi as phx\nimport cv2\nimport numpy as np\nimport wcx.pcd_work.Aruco_marker_estimation as Aruco_marker_estimation\nimport open3d as o3d\nimport copy\nimport wcx.utils1.rotate_matrix as rmt\n\ndef pcd_registration(source, target, transformation, trigger=False):\n    axis_pcd = o3d.geometry.TriangleMesh.create_coordinate_frame(size=100, origin=np.array([0, 0, 0]))\n    source_temp = copy.deepcopy(source)\n    target_temp = copy.deepcopy(target)\n    source_temp.paint_uniform_color([1, 0.706, 0])\n    target_temp.paint_uniform_color([0, 0.651, 0.929])\n    check = np.asarray(source.points)\n    source_temp.transform(transformation)\n    check = np.asarray(source_temp.points)\n\n    if trigger:\n        o3d.visualization.draw_geometries([source, target, axis_pcd])\n\n\n    return source_temp, target_temp\ndef trabsformation_coordinate(source, target, transformation,trigger=False):\n    axis_pcd = o3d.geometry.TriangleMesh.create_coordinate_frame(size=100, origin=np.array([0,0,0]))\n    check = np.asarray(source.points)\n    source.transform(transformation)\n    check = np.asarray(source.points)\n    target.transform(transformation)\n    source.paint_uniform_color([1, 0.706, 0])\n    target.paint_uniform_color([0, 0.651, 0.929])\n    if trigger:\n        o3d.visualization.draw_geometries([axis_pcd, source, target])\n\n    return source,target\n\ndef rs_to_phx_pcd_registration(phx_client, realsense_client, change_matrix, rs_camera_id=0):\n    phx_intrinsic_matrix = np.array([[1.33930543e+03, 0.00000000e+00, 3.34683556e+02],\n                                     [0.00000000e+00, 1.43253751e+03, 4.66736263e+02],\n                                     [0.00000000e+00, 0.00000000e+00, 1.00000000e+00]])\n    phx_distortion = np.array(\n        [-0.12194237677679813, 0.18344112700622126, 0.00057639831338004, -0.00026201715594945677, 0])\n    threshold = 0.02\n    realsense_client.change_camera(camera_id=rs_camera_id)\n    '''\n    data preparation\n    '''\n    grayimg, depthnparray_float32, pcd = phx_client.getalldata()\n    phx_extrinsic = Aruco_marker_estimation.get_extrinsic_matrix(img=grayimg, intr_matrix=phx_intrinsic_matrix,distortion=phx_distortion,trigger=True)\n    phx_center = Aruco_marker_estimation.getcenter_two_aruco_maker(img=grayimg, pcd=pcd)\n    print('phx_center:', phx_center)\n    rs_extrinsic = realsense_client.get_extrinsic_matrix_two_aruco_maker(trigger=True)\n    rs_ceter = realsense_client.getcenter_two_aruco_maker()\n    print('rs_center:', rs_ceter)\n    transform_matrix=np.dot(phx_extrinsic,np.linalg.inv(rs_extrinsic))\n    # modification\n\n    transform_matrix = np.dot(change_matrix,transform_matrix)\n    print('modified_matrix:', transform_matrix)\n    phx_pcd = o3d.geometry.PointCloud()\n    pcdn = copy.deepcopy(pcd)\n    phx_pcd.points = o3d.utility.Vector3dVector(pcdn)\n    rs_pcd = realsense_client.depth2pcd()\n    source_initial = rs_pcd\n    target_initial = phx_pcd\n    '''\n    pcd work\n    '''\n    start_pos = np.array([-phx_center]).T\n    start_rot_mat = np.array([[1, 0, 0],\n                              [0, 1, 0],\n                              [0, 0, 1]])\n    coordinate_build_matrix = np.hstack((start_rot_mat, start_pos))\n    coordinate_build_matrix = np.vstack((coordinate_build_matrix, np.array([[0, 0, 0, 1]])))\n    source, target = pcd_registration(source=source_initial, target=target_initial, transformation=transform_matrix, trigger=False)\n    source, target = trabsformation_coordinate(source=source, target=target, transformation= coordinate_build_matrix, trigger=True)\n\n\nif __name__=='__main__':\n    # phxi_host = \"127.0.0.1:18300\"\n    phx_client = phx.Phoxi()\n    realsense_client=rs.RealSense()\n\n    #camera1_pcd\n    # change_matrix = np.array([[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,1]])\n    movement = np.array([[-220, +160, -200]]).T\n    # # rotation = np.dot(rmt.matrix_generate(30, 'x'),rmt.matrix_generate(5, 'y'))\n    rotation = rmt.matrix_generate(0, 'x')\n    change_matrix = np.hstack((rotation,movement))\n    change_matrix = np.vstack((change_matrix, np.array([[0, 0, 0, 1]])))\n\n    rs_to_phx_pcd_registration(phx_client=phx_client,realsense_client=realsense_client,change_matrix=change_matrix,rs_camera_id=0)\n\n\n    '''\n    modification\n    '''\n    # check1 = np.asarray(source.points)\n    # check2 = np.asarray(target.points)\n    # movement = np.array([[0, 0, -1000]]).T\n    # rotation = np.array([[1, 0, 0],\n    #                           [0, 1, 0],\n    #                           [0, 0, 1]])\n    # change_matrix = np.hstack((movement, rotation))\n    # change_matrix = np.vstack((change_matrix, np.array([[0, 0, 0, 1]])))\n    # source, target = pcd_registration(source=source, target=target, transformation=change_matrix,\n    #                                   trigger=True)\n\n\n\n\n\n\n\n","repo_name":"ChenxiWang09/rs_phx_vision","sub_path":"pcd_work/rs_phx_pcd_registration.py","file_name":"rs_phx_pcd_registration.py","file_ext":"py","file_size_in_byte":4782,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"29044200246","text":"from Entities import game as g\nfrom Entities import piece as p\nfrom tkinter import *\nfrom tkinter import messagebox\n\nclass MasterMindApp:\n    def __init__(self, master):\n        Button(master, text=\"Commencer une nouvelle partie\", command=self.start_game).grid(row=0, column=0)\n        Button(master, text=\"test\", command=self.test).grid(row=0, column=1)\n\n        # --- Configuration de la partie :\n        self.score = StringVar()\n        self.playerone = StringVar()\n        self.playertwo = StringVar()\n        self.level = StringVar()\n        self.level.trace(\"w\", self.update_level_observer)\n        self.debug_mode = BooleanVar()\n        self.debug_mode.trace(\"w\", self.update_mode_observer)\n        self.config_frame = LabelFrame(master, text=\"Configuration\", width=500, height=350)\n        self.player_one_entry = Entry(self.config_frame, textvariable=self.playerone)\n        self.player_two_entry = Entry(self.config_frame, textvariable=self.playertwo)\n        Label(self.config_frame, text=\"Veuillez enregistrer les 2 joueurs :\").grid(row=1, column=0)\n        self.player_one_entry.grid(row=1, column=1)\n        self.player_two_entry.grid(row=1, column=2)\n        Label(self.config_frame, text=\"Choix du niveau de diffuculté :\").grid(row=2, column=0)\n        Radiobutton(self.config_frame, text=\"Facile\", value=\"facile\", variable=self.level).grid(row=2, column=1)\n        Radiobutton(self.config_frame, text=\"Moyen\", value= \"moyen\", variable=self.level).grid(row=2, column=2)\n        Label(self.config_frame, text=\"Activer/Désactiver le mode debug :\").grid(row=3, column=0)\n        Checkbutton(self.config_frame, text=\"Mode debug\", variable=self.debug_mode, onvalue=1, offvalue=0).grid(row=3, column=1)\n        Button(self.config_frame, text=\"Enregistrer\", command=self.set_players).grid(row=4, column=0)\n\n        self.play_btn = Button(master, text=\"jouer\", command=self.solution_entry)\n        self.solution_frame = LabelFrame(master, text=\"Solution\", width=700)\n        self.try_frame = LabelFrame(master, text=\"Tentatives\", width=700)\n        self.attempts_frame = LabelFrame(master, text=\"score\", width=700)\n        self.score_label = Label(self.attempts_frame, textvariable=self.score).grid()\n\n    def start_game(self):\n        print(\"Configuration d'une nouvelle partie\")\n        self.newGame = g.Game()\n        self.config_frame.grid(row=5, column=0, ipadx=50, ipady=15)\n        self.play_btn.grid(row=6, column=2)\n\n    def set_players(self, *args):\n        self.newGame.set_players(self.player_one_entry.get(), self.player_two_entry.get())\n\n    def test(self):\n        print(self.newGame.player1.nickname)\n\n    def update_level_observer(self, *args):\n        self.newGame.set_level(self.level.get())\n        print(\"Niveau choisi : {}\".format(self.newGame.level))\n\n    def update_mode_observer(self, *args):\n        self.newGame.set_mode(self.debug_mode.get())\n        print(\"Mode debug : {}\".format(self.newGame.debug_mode))\n\n    def solution_entry(self):\n        print(\"La partie commence\")\n        Label(self.solution_frame, text=\"Joueur 1, saisissez votre suite :\").grid(ipadx=50, ipady=15)\n        self.newGame.set_solution_entry(self.solution_frame)\n        Button(self.solution_frame, text=\"Valider\", command=self.get_solution).grid(ipadx=50, ipady=15)\n        self.solution_frame.grid(row=7, column=0)\n\n    def get_solution(self):\n        self.solution = []\n        for piece in self.newGame.solution_entry.define_combination():\n            self.solution.append(piece.piece_color.get())\n        print(\"Au tour du joueur 2\")\n        Label(self.try_frame, text=\"Joueur 2, saisissez une suite :\").grid(ipadx=50, ipady=15)\n        self.newGame.set_try_entry(self.try_frame)\n        Button(self.try_frame, text=\"Valider\", command=self.attempts).grid(ipadx=50, ipady=15)\n        if self.newGame.debug_mode:\n            self.try_frame.grid(row=8, column=0)\n            self.attempts_frame.grid(row=9, column=0)\n        else :\n            self.try_frame.grid(row=7, column=0)\n            self.attempts_frame.grid(row=8, column=0)\n\n    def attempts(self):\n        print(\"Check\")\n        self.newGame.attempts_counter += 1\n        if self.newGame.attempts_counter > self.newGame.level['attempts']:\n            self.game_over()\n            return\n        self.attempts = []\n        for piece in self.newGame.try_entry.define_combination():\n            self.attempts.append(piece.piece_color.get())\n        print(self.solution)\n        print(self.attempts)\n        if self.solution == self.attempts:\n            self.newGame.victory = True\n            self.victory()\n            return\n        self.score = \"\"\n        for i, att in enumerate(self.attempts):\n            if att == self.solution[i]:\n                self.score += '|| ' + att + ' =||'\n            elif att in self.solution:\n                self.score += '|| ' + att + ' ~||'\n            else:\n                self.score += '|| ---- ||'\n        print(self.score)\n        print(\"Tentative {}\".format(self.newGame.attempts_counter))\n\n    def game_over(self):\n        messagebox.showinfo(\"GAME OVER\", \"partie terminée\")\n\n    def victory(self):\n        messagebox.showinfo(\"VICTOIRE\", \"partie terminée\")\n","repo_name":"thomas-cesi/mastermind","sub_path":"mastermindapp.py","file_name":"mastermindapp.py","file_ext":"py","file_size_in_byte":5181,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"30347768779","text":"import math\ndef compute_distance(x1, y1, x2, y2):\n \n    distance = 0\n \n    # Complete this function to return Euclidean distance and\n    distance = math.sqrt(math.pow(x2 - x1, 2) + math.pow(y2 - y1, 2) * 1.0)\n    # print the distance value with precision up to 2 decimal places\n    print(\"Distance: %.2f\"%distance)\n \n \n# Main function\nif __name__ == '__main__':\n    \n    # Take the T (test_cases) input\n    test_cases = int(input())\n    arr =[]\n    # Write the code here to take the x1, y1, x2 and y2 values\n    for i in range(test_cases):\n        x1,y1,x2,y2=map(int,input().split())\n        arr.append([x1,y1,x2,y2])\n    for i in range(test_cases):\n        # Once you have all 4 values, call the compute_distance function to find Euclidean distance\n        compute_distance(x1, y1, x2, y2)\n","repo_name":"gaurav3030/eyantra20-21","sub_path":"test0/p2.py","file_name":"p2.py","file_ext":"py","file_size_in_byte":792,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"34944830770","text":"from __future__ import print_function, division\nfrom data_loader import ClassificationHandler\nfrom torch.utils.data import DataLoader\nimport torch\nimport numpy as np\nimport os\nfrom sklearn.metrics import accuracy_score\neps = 1e-10\nimport logging\nimport shutil\nfrom earlystop import EarlyStopping\nfrom datetime import datetime\nimport json\nfrom sklearn.metrics import roc_curve, auc, f1_score\nfrom torch.utils.tensorboard import SummaryWriter\nfrom monai.transforms import Compose, RandFlip,RandRotate, RandZoom, ToTensor, ResizeWithPadOrCrop\n\nclass Trainer(object):\n    \"\"\"\n    Trains a multilabel classification:\n    example==> given a CT slice with segmentation labels tell which organs are present in CT slice\n    used to train and validate\n\n    :param net: the unet instance to train\n    :param batch_size: size of training batch\n    :param verification_batch_size: size of verification batch\n    :param norm_grads: (optional) true if normalized gradients should be added to the summaries\n    :param optimizer: (optional) name of the optimizer to use (momentum or adam)\n    :param opt_kwargs: (optional) kwargs passed to the learning rate (momentum opt) and to the optimizer\n\n    \"\"\"\n\n    def __init__(self, data_files, model, cost_function, optimizer, schedulers, stop_epoch=100,\n                 target_names=None, labels=None, logging=False, opt_kwargs={}):\n        self.model = model\n        self.data_files = data_files\n        self.optimizer = optimizer\n        self.cost_function = cost_function\n        self.schedulers = schedulers\n        self.opt_kwargs = opt_kwargs\n        self.best_metric = 0\n        self.best_model = ''\n        self.epoch = 0\n        self.global_step = 0\n        self.target_names = target_names\n        self.labels = labels\n        self.stop_epoch = stop_epoch\n        self.logging = logging\n\n    def _initialize(self):\n\n        if not self.restore:\n            logging.info(\"Removing '{:}'\".format(self.log_dir))\n            shutil.rmtree(self.log_dir, ignore_errors=True)\n\n        if not os.path.exists(self.log_dir):\n            logging.info(\"Allocating '{:}'\".format(self.log_dir))\n            os.makedirs(self.log_dir)\n\n        self.train_folder = os.path.join(self.log_dir, 'train')\n        if not os.path.exists(self.train_folder):\n            logging.info(\"Allocating '{:}'\".format(self.train_folder))\n            os.makedirs(self.train_folder)\n\n        self.val_folder = os.path.join(self.log_dir, 'val')\n        if not os.path.exists(self.val_folder):\n            logging.info(\"Allocating '{:}'\".format(self.val_folder))\n            os.makedirs(self.val_folder)\n\n        self.model_folder = os.path.join(self.log_dir, 'models')\n        if not os.path.exists(self.model_folder):\n            logging.info(\"Allocating '{:}'\".format(self.model_folder))\n            os.makedirs(self.model_folder)\n\n        self.pred_folder = os.path.join(self.log_dir, 'predictions')\n        if not os.path.exists(self.pred_folder):\n            logging.info(\"Allocating '{:}'\".format(self.pred_folder))\n            os.makedirs(self.pred_folder)\n\n        self.cm_folder = os.path.join(self.log_dir, 'cm')\n        if not os.path.exists(self.cm_folder):\n            logging.info(\"Allocating '{:}'\".format(self.cm_folder))\n            os.makedirs(self.cm_folder)\n\n        # initialize the early_stopping object\n        self.early_stopping = EarlyStopping(patience=self.stop_epoch, verbose=True, mode='increasing')\n\n\n    def train(self, num_epochs, log_dir, eval_rate=1, display_rate=20, num_images=6, restore_file=None, batch_size=100,\n              image_size=[224,224], num_classes=21, device='cuda:1'):\n\n        self.since = datetime.now()\n        self.num_epochs = num_epochs\n        self.log_dir = log_dir\n        self.eval_rate = eval_rate\n        self.display_rate = display_rate\n        self.num_images = num_images\n        self.num_classes = num_classes\n        self.restore_file = restore_file\n        self.image_size = image_size\n\n        if restore_file is not None:\n            self.restore=True\n        else:\n            self.restore=False\n\n        self._initialize()\n\n        if self.logging:\n            self.train_logger = SummaryWriter(self.train_folder)\n            self.val_logger = SummaryWriter(self.val_folder)\n\n        if batch_size < self.num_images:\n            self.num_images = batch_size\n\n\n        ## DATA AUGMENTATIONS\n\n        train_transforms = Compose([\n            RandFlip(spatial_axis=(0,1), prob=0.5),\n            RandRotate(range_x=(-180,180), prob=0.5, keep_size=True, padding_mode='zeros'),\n            RandZoom(min_zoom=0.6, max_zoom=1.1, prob=0.5, keep_size=True),\n            ResizeWithPadOrCrop(spatial_size=self.image_size,mode='constant'),\n            ToTensor()])\n\n        val_transforms = Compose(\n            [ResizeWithPadOrCrop(spatial_size=self.image_size, mode='constant'),\n             ToTensor()])\n\n        data_handler1 = ClassificationHandler(files=self.data_files['train'],\n                                              transform=train_transforms, Inference=False)\n\n        data_provider1 = DataLoader(data_handler1, batch_size=batch_size, shuffle=True, num_workers=4, drop_last=False)\n\n        data_handler2 = ClassificationHandler(files=self.data_files['val'],\n                                              transform=val_transforms, Inference=True)\n        data_provider2 = DataLoader(data_handler2, batch_size=batch_size, shuffle=False, num_workers=4,  drop_last=False)\n\n        self.dataloaders = {}\n        self.dataloaders['train'] = data_provider1\n        self.dataloaders['val'] = data_provider2\n        self.dataset_sizes = {}\n        self.dataset_sizes['train'] = len(data_handler1)\n        self.dataset_sizes['val'] = len(data_handler2)\n        self.device = torch.device(device)\n\n\n        self.end_training = False\n        self.current_iteration = 0\n\n        if restore_file is not None:\n            self.model.load_model(self.restore_file)\n\n        for epoch in range(self.num_epochs):\n            print(\"\\n==== Epoch [ %d  /  %d ] START ====\" % (epoch, self.num_epochs))\n            print('-' * 20)\n            phase = 'train'\n            print(\"<<<= Phase: %s =>>>\" % phase)\n            self.train_model(phase, epoch)\n            if epoch % self.eval_rate == 0:\n                phase = 'val'\n                print(\"<<<= Phase: %s =>>>\" % phase)\n                self.evaluate_model(phase, epoch)\n            ## logging epoch\n            print(\"==== Epoch [\" + str(epoch) + \" / \" + str(self.num_epochs) + \"] DONE ====\")\n            if self.end_training:\n                break\n\n        time_elapsed = datetime.now() - self.since\n        print('Training complete in {}'.format(time_elapsed))\n        print('Best val Acc: {:4f}'.format(self.best_metric))\n        print('Best model: {}'.format(self.best_model))\n\n    def train_model(self, phase, epoch):\n        sum_loss_train = []\n        gt=[]\n        preds=[]\n        probas= []\n        self.model.train()\n        for i_batch, sample_batched in enumerate(self.dataloaders[phase]):\n            x = sample_batched[0].type(torch.FloatTensor)\n            y = sample_batched[1].type(torch.LongTensor)\n            logits = self.model(x.cuda(self.device))\n            loss = self.cost_function(logits, y.cuda(self.device))\n            self.optimizer.zero_grad()\n            loss.backward()\n            self.optimizer.step()\n            self.current_iteration += 1\n            sum_loss_train.append(loss.item())\n            with torch.no_grad():\n                _, predicted = torch.max(torch.softmax(logits, dim=1), 1)\n\n                if x.shape[0]==1:\n                    gt.extend(np.expand_dims(y.squeeze().numpy(), axis=0))\n                    predaux = predicted.cpu().squeeze().detach().numpy()\n                    preds.extend(np.expand_dims(predaux, axis=0))\n                    probas.extend(torch.softmax(logits, dim=1).cpu().numpy())\n                else:\n                    gt.extend(y.squeeze().numpy())\n                    predaux = predicted.cpu().squeeze().detach().numpy()\n                    preds.extend(predaux)\n                    probas.extend(np.squeeze(torch.softmax(logits, dim=1).cpu().numpy()))\n\n                gt.extend(y.numpy())\n                preds.extend(predicted.cpu().squeeze().detach().numpy())\n                acc = accuracy_score(y.cpu().numpy(), predicted.cpu().numpy())\n                auc = self.compute_auc_multi_class(y.numpy(), torch.softmax(logits, dim=1).cpu().numpy())\n                f1score= f1_score(y.cpu().numpy(), predicted.cpu().numpy(), average='macro')\n\n                if i_batch % self.display_rate == 0:\n                    print('[Iteration : ' + str(i_batch) + '] Loss -> ' + str(loss.item()) +' ]')\n                    self.train_logger.add_scalar('loss', loss, self.current_iteration)\n                    self.train_logger.add_scalar('acc', acc, self.current_iteration)\n                    self.train_logger.add_scalar('auc', auc, self.current_iteration)\n                    self.train_logger.add_scalar('f1score', f1score, self.current_iteration)\n                if self.logging and i_batch == 0:\n                    self.train_logger.add_images('images',np.expand_dims(x[0:self.num_images, 0, :, :].numpy(),axis=1),\n                                                    self.current_iteration)\n\n                del x, y, loss, logits, predicted\n                torch.cuda.empty_cache()\n\n        epoch_loss = np.mean(sum_loss_train)\n        probas= np.asarray(probas)\n        epoch_acc = accuracy_score(np.asarray(gt), np.asarray(preds))\n        epoch_auc = self.compute_auc_multi_class(np.asarray(gt), probas)\n        epoch_f1score = f1_score(np.asarray(gt), np.asarray(preds), average='macro')\n\n        if self.logging:\n            self.train_logger.add_scalar('epoch_loss', epoch_loss, epoch)\n            self.train_logger.add_scalar('epoch_f1score', epoch_f1score, epoch)\n            self.train_logger.add_scalar('epoch_acc', epoch_acc, epoch)\n            self.train_logger.add_scalar('epoch_auc', epoch_auc, epoch)\n\n        print('Phase: {}. Epoch {} Loss: {:.4f},  acc : {:.4f},  auc : {:.4f},  f1score : {:.4f}'.format(\n            phase,epoch, epoch_loss, epoch_acc,epoch_auc, epoch_f1score))\n\n\n    def evaluate_model(self, phase, epoch):\n        self.model.eval()  # Set model to evaluate mode\n        prefix = 'model'\n        gt = []\n        preds = []\n        sum_loss_train = []\n        probas =[]\n        for i_batch, sample_batched in enumerate(self.dataloaders[phase]):\n            x = sample_batched[0].type(torch.FloatTensor)\n            y = sample_batched[1].type(torch.LongTensor)\n            logits = self.model(x.cuda(self.device))\n            with torch.no_grad():\n                _, predicted = torch.max(torch.softmax(logits, dim=1), 1)\n\n                if x.shape[0]==1:\n                    gt.extend(np.expand_dims(y.squeeze().numpy(), axis=0))\n                    predaux = predicted.cpu().squeeze().detach().numpy()\n                    preds.extend(np.expand_dims(predaux, axis=0))\n                    probas.extend(torch.softmax(logits, dim=1).cpu().numpy())\n                else:\n                    gt.extend(y.squeeze().numpy())\n                    predaux = predicted.cpu().squeeze().detach().numpy()\n                    preds.extend(predaux)\n                    probas.extend(np.squeeze(torch.softmax(logits, dim=1).cpu().numpy()))\n                loss = self.cost_function(logits, y.cuda(self.device))\n                sum_loss_train.append(loss.item())\n\n\n                acc = accuracy_score(y.cpu().numpy(), predicted.cpu().numpy())\n\n                auc = self.compute_auc_multi_class(y.numpy(), torch.softmax(logits, dim=1).cpu().numpy())\n\n                f1score = f1_score(y.cpu().numpy(), predicted.cpu().numpy(), average='macro')\n\n                if i_batch % self.display_rate == 0:\n                    print('[Iteration : ' + str(i_batch) + '] Loss -> ' + str(loss.item()) + '  acc -> ' + str(\n                        acc) + '  auc -> ' + str(auc) + ' f1score -> ' + str(f1score))\n                    self.val_logger.add_scalar('loss', loss, self.current_iteration)\n                    self.val_logger.add_scalar('acc', acc, self.current_iteration)\n                    self.val_logger.add_scalar('auc', auc, self.current_iteration)\n                    self.val_logger.add_scalar('f1score', f1score, self.current_iteration)\n\n                if self.logging and i_batch == 0:\n                    self.val_logger.add_images('images', np.expand_dims(x[0:self.num_images, 0, :, :].numpy(),axis=1),\n                                                    self.current_iteration)\n\n                del x, y, loss, logits, predicted\n                torch.cuda.empty_cache()\n\n        epoch_loss = np.mean(sum_loss_train)\n\n        self.schedulers.step(epoch_loss)\n        probas = np.asarray(probas)\n        epoch_acc = accuracy_score(np.asarray(gt), np.asarray(preds))\n        print('Shape gt',np.shape(np.asarray(gt)))\n        print('Shape probas', np.shape(probas))\n        epoch_auc = self.compute_auc_multi_class(np.asarray(gt), probas)\n        epoch_f1score = f1_score(np.asarray(gt), np.asarray(preds), average='macro')\n\n        if logging:\n            self.val_logger.add_scalar('epoch_loss', epoch_loss, epoch)\n            self.val_logger.add_scalar('epoch_f1score', epoch_f1score, epoch)\n            self.val_logger.add_scalar('epoch_acc', epoch_acc, epoch)\n            self.val_logger.add_scalar('epoch_auc', epoch_auc, epoch)\n\n        print('Phase: {}. Epoch {} Loss: {:.4f},  acc : {:.4f},  auc : {:.4f},  f1score : {:.4f}'.format(\n            phase, epoch, epoch_loss, epoch_acc, epoch_auc, epoch_f1score))\n\n\n        epochname = str(epoch).zfill(3)\n        auxaux = prefix + '_epoch_' + str(epochname) + '.tar'\n        aux = prefix + '.tar'\n\n        filename = os.path.join(self.model_folder, aux)\n\n        if epoch_f1score >= self.best_metric:\n            self.best_metric = epoch_f1score\n            self.best_model = auxaux\n\n        self.early_stopping(epoch_f1score, self.model, self.optimizer, epoch, filename)\n        if self.early_stopping.early_stop:\n            self.end_training = True\n            print(\"Early stopping\")\n            print(self.best_model)\n\n    def compute_auc_multi_class(self, y_test, y_predict_proba):\n        aucs=[]\n        for i in range(self.num_classes):\n            y_test_i = np.zeros(shape=y_test.shape, dtype=y_test.dtype)\n            for index, x in enumerate(y_test):\n                if y_test[index] ==i:\n                    y_test_i[index]=1\n                else:\n                    y_test_i[index]=0\n            fpr, tpr, _ = roc_curve(y_test_i, y_predict_proba[:, i])\n            aux = auc(fpr, tpr)\n            if np.isnan(aux):\n                aux=0\n            aucs.append(aux)\n\n        return np.mean(aucs)\n\n\n\n\n\n\n\n","repo_name":"ferchonavarro/anatomy_guided_contrast_ct","sub_path":"trainer.py","file_name":"trainer.py","file_ext":"py","file_size_in_byte":14804,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"41420427560","text":"\"\"\"This class defines a cronitor Monitor object.\n\n\"\"\"\n\nimport json\n\nclass Rule(object):\n    def __init__(self, rule_type, rule_duration, time_unit, hours_to_followup_alert=None, **kwargs):\n        '''Create Notification object.\n\n        One Rule object at a time, 3 required, one optional arg:\n          - notification type (string)\n          - notification duration (integer)\n          - time unit (string)\n          - followup hours (integer)\n\n        More details are available here: \n          https://cronitor.io/help/monitor-api\n        '''\n        self.type = rule_type\n        self.dur = rule_duration\n        self.unit = time_unit\n        self.followup = hours_to_followup_alert\n\n        # read-only attribute\n        self.id = kwargs.get('id', None)\n\n        fields = ['rule_type', 'duration', 'time_unit']\n        values = [self.type, self.dur, self.unit]\n\n        if self.followup is not None:\n            fields.append('hours_to_followup_alert')\n            values.append(self.followup)\n\n        # setup rule dictionary\n        self.obj = dict(zip(fields, [self.type, self.dur, self.unit, self.followup]))\n        if self.id is not None:\n            self.obj = {self.id: self.obj}\n\n    def __str__(self):\n        return json.dumps(self.obj)\n\n    def __iter__(self):\n        for x, y in self.obj.items():\n            yield x, y\n\nclass Notifications(object):\n    def __init__(self):\n        '''Create Notifications object.'''\n        self.emails = []\n        self.slack = []\n        self.pagerduty = []\n        self.phones = []\n        self.webhooks = []\n        self.types = ['emails', 'slack', 'pagerduty', 'phones', 'webhooks']\n\n    def fromjson(self, jsonstr, update=False):\n        '''Replace or update values in Notifications object with data from json object'''\n        dct = json.loads(jsonstr)\n        if update:\n            self.update(**dct)\n        else:\n            self.replace(**dct)\n\n    def _update(self, emails=[], slack=[], pagerduty=[], phones=[], webhooks=[], remove=False):\n        '''Update Notifications object, internal, used to add or remove data from object\n        based on `remove` kwargs.\n        '''\n\n        for method in self.types:\n            current = getattr(self, method)\n            value = locals().get(method)\n            if value:\n                if remove:\n                    updated = list(set(current) - set(value))\n                else:\n                    updated = list(set(current) | set(value))\n                setattr(self, method, updated)\n\n    def addto(self, **kwargs):\n        '''Update Notifications object, adding to existing data.\n\n        Expects kwarg passed in for the notification type to update\n        each type passed in should contain a list of values to update.\n            \n\n        Types are available here: \n            https://cronitor.io/help/monitor-api\n        '''\n\n        self._update(**kwargs)\n\n    def removefrom(self, **kwargs):\n        '''Update Notifications object, removing from existing data.\n        The inverse of `update` method\n        '''\n        self._update(remove=True, **kwargs)\n\n    def replace(self, emails=[], slack=[], pagerduty=[], phones=[], webhooks=[]):\n        '''Replace values in Notifications object. Remove existing data.\n        Expects kwarg passed in for the notification type to replace\n        each type passed in should contain a list of values to replace.\n\n        Types are available here: \n            https://cronitor.io/help/monitor-api\n        '''\n        if emails: self.emails = emails\n        if slack: self.slack = slack\n        if pagerduty: self.pagerduty = pagerduty\n        if phones: self.phones = phones\n        if webhooks: self.webhooks = webhooks\n\n    def __str__(self):\n        dct = {}\n        for method in self.types:\n            dct[method] = getattr(self, method)\n        return json.dumps({\"notifications\": dct})\n\n    def __iter__(self):\n        for method in self.types:\n            yield method, getattr(self, method)\n\nclass Monitor(object):\n    def __init__(self, name, **kwargs):\n        \"\"\"Create monitor object, name arg is required.\n\n        Pass all other details as keyword args.\n        On init, only one notification or rule can be created,\n        use add_rule or add_notif to add additional.\n        \"\"\"\n        self.name = name\n        self.notifications = {}\n        self.rules = []\n        self.note = kwargs.get(\"note\", \"\")\n\n        # read-only attributes\n        self.code = kwargs.get('code', None)\n        self.is_paused = kwargs.get('is_paused', None)\n        self.is_passing = kwargs.get('is_passing', None)\n        self.initialized = kwargs.get('initialized', None)\n        self.created = kwargs.get('created', None)\n\n    def add_rule(self, rtype, rdur, runit, rfol):\n        pass\n\n    def add_notif(self, ntype, ndests):\n        pass\n","repo_name":"erchn/cronitor-deprecated","sub_path":"cronitor/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":4808,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"11818349319","text":"from django.utils.translation import ugettext_lazy as _\nfrom django.contrib import admin\n\nfrom real_estate_app.admin.options import RealEstateAppPopUpModelAdmin\nfrom real_estate_app.apps.newspapers.models import News\nfrom real_estate_app.conf.settings import MEDIA_REAL_ESTATE\n\n\nclass NewsAdmin(RealEstateAppPopUpModelAdmin):\n\tsearch_fields = ['title']\n\n\tfieldsets = (\n\t\t(_('News'), {\n\t\t\t'fields':['title','content','slug'],\n\t\t}),\n\t\t(_('Publish'),{\n\t\t\t'fields':['enable_publish','pub_date','pub_date_end',],\n\t\t})\n\t)\n\tlist_display = ('title','pub_date','pub_date_end','enable_publish')\n\n\tlist_filter = ('enable_publish',)\n\t\n\tprepopulated_fields = {'slug': ('title',)}\n\n\tclass Media:\n\t\tcss = {\n\t\t\t'all':(\n\t\t\t\t\tMEDIA_REAL_ESTATE+\"admin/css/facebox.css\",\n\t\t\t),\n\t\t}\n\t\tjs = (\n\t\t\t  MEDIA_REAL_ESTATE+'admin/js/tiny_mce/tinymce.min.js',\n\t\t      MEDIA_REAL_ESTATE+'admin/js/tiny_mce/textarea.js',\n\t\t      MEDIA_REAL_ESTATE+'admin/js/ajax_csrf.js',\n\t\t\t  MEDIA_REAL_ESTATE+'admin/js/facebox.js',\n\t\t)\n\nadmin.site.register(News,NewsAdmin)\n","repo_name":"Letractively/django-real-estate-app","sub_path":"apps/newspapers/admin/news.py","file_name":"news.py","file_ext":"py","file_size_in_byte":1026,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"22481294186","text":"from sqlalchemy import Column, Float, Integer, String, text, TEXT, TIMESTAMP, ForeignKey, Boolean\nfrom sqlalchemy.orm import relationship\n\nfrom src.exampleco.exampleco.database import Base\n\n\nclass OrderItems(Base):\n    __tablename__ = 'order_items'\n\n    id = Column(Integer, primary_key=True)\n    name = Column(String(128), nullable=False)\n    description = Column(TEXT, nullable=True)\n    price = Column(Float, nullable=False)\n    is_active = Column(Boolean, default=True)\n    order_id = Column(Integer, ForeignKey(\"order.id\"))\n    service_id = Column(Integer, ForeignKey(\"services.id\"))\n    created_on = Column(TIMESTAMP, nullable=False, server_default=text(\"CURRENT_TIMESTAMP\"))\n    modified_on = Column(\n        TIMESTAMP,\n        nullable=False,\n        server_default=text('CURRENT_TIMESTAMP'),\n        server_onupdate=text('CURRENT_TIMESTAMP')\n    )\n    order = relationship(\"Order\", back_populates=\"order_items\")\n    service = relationship('Service')\n\n    def __repr__(self) -> str:\n        return \"<OrderItems(name='{}', price='{}', created_on='{}')>\".format(self.name, self.price, self.created_on)\n","repo_name":"Shrhawk/example-co-orders-service","sub_path":"src/exampleco/exampleco/models/order_items.py","file_name":"order_items.py","file_ext":"py","file_size_in_byte":1108,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"22447478125","text":"import os\nimport sys\nimport csv\nimport pdb\nimport logging\nimport datetime\nimport traceback\nimport requests\nimport math\nimport re\nimport json\nimport codecs\nimport zipfile\nimport itertools\n\nfrom operator import and_\nfrom collections import *\nfrom dateutil.parser import parse as dateparse\nfrom openpyxl import load_workbook\nfrom StringIO import StringIO\n\nfrom dataiter import DataIterator\nfrom dbtruck.util import get_logger, to_utf\nfrom dbtruck.parsers.util import *\n\n\n_log = get_logger()\n\nclass Parser(object):\n    def __init__(self, f, fname, **kwargs):\n        self.f = f\n        self.fname = fname\n\n    def get_data_iter(self):\n        raise \"Not implemented\"\n\n\nclass CSVFileParser(Parser):\n    def __init__(self, f, fname, **kwargs):\n        Parser.__init__(self, f, fname, **kwargs)\n        self.dialect = None\n\n    def get_data_iter(self):\n        # CSV/delimiter based lines\n        # return a function that returns iter\n        self.f.seek(0)\n        if self.dialect is None:\n          self.dialect = dialect = csv.Sniffer().sniff(self.f.read(15000))\n          self.f.seek(0)\n\n        if not self.dialect:\n          raise \"Could not parse using CSV\"\n\n        def _reader():\n          self.f.seek(0)\n          return csv.reader(self.f, dialect)\n        return DataIterator(_reader, fname=self.fname)\n\n\n        # Deprecated, just use csv.Sniffer instead of this\n\n        # DELIMITERS = [' ', ',', ';', '\\t', '-', ' - ', '=', ' = ']\n        # bestdelim, bestperc, bestncols = None, 0., 0\n        # for delim in DELIMITERS:\n        #     perc_maj, ncols =  rows_consistent(_get_reader(self.f, delim))\n        #     _log.debug(\"csvparser\\t'%s'\\tperc(%.3f)\\tncols(%d)\", delim, perc_maj, ncols )\n        #     if ((ncols <= bestncols and perc_maj > 1.5 * bestperc) or\n        #         (ncols > bestncols and bestperc - perc_maj < 0.1)):                \n        #         bestdelim, bestperc, bestncols = delim, perc_maj, ncols\n        # if bestncols:\n        #     _log.debug(\"csvparser\\tbest delim\\t'%s'\\tncols(%d)\", bestdelim, bestncols )\n        #     return DataIterator(lambda: _get_reader(self.f, bestdelim), fname=self.fname)\n        # raise \"Could not parse using CSV\"\n\n\n\nclass OffsetFileParser(Parser):\n    def __init__(self, f, fname, **kwargs):\n        if 'offset' not in kwargs:\n            s = ''#raw_input('can you give me the offsets to split each row? (otherwise, just press <enter>)\\n> ').strip()\n            offsets = self.parse_offset_str(s)\n            if not offsets:\n                raise RuntimeError\n        else:\n            offsets = kwargs['offset']\n            \n        self.offsets = self.normalize_offsets(offsets)\n        super(OffsetFileParser, self).__init__(f, fname, **kwargs)\n\n    def parse_offset_str(self, s):\n        \"\"\"\n        look for groups of numbers (offsets) delimited by non a-z characters\n        \"\"\"\n        if not s:\n            return None\n        if re.search('[a-zA-Z]', s):\n            return None\n        delimiter = re.search('\\D+', s).group()\n        if not delimiter:\n            return None\n        arr = map(int, s.split(delimiter))\n        \n        return arr\n\n    def normalize_offsets(self, arr):\n        # if arr is strictly increasing, then they are absolute offsets\n        # otherwise they are relative\n        increasing = reduce(and_, ( arr[i+1]>arr[i] for i in xrange(len(arr)-1)) )\n        if not increasing:\n            arr = map( lambda i: sum(arr[:i]), range(len(arr)) )\n        _log.debug( \"offsetparser\\tnormalized offsets: %s\", str(arr) )\n        return arr\n        \n    \n    def get_data_iter(self):\n        offpairs = zip(self.offsets[:-1], self.offsets[1:])\n        def _f():\n            self.f.seek(0)\n            for line in self.f:\n                arr = [line[s:e] if s < len(line) else '' for s,e in offpairs]\n                yield arr\n        return DataIterator(_f, fname=self.fname)\n\nclass InferOffsetFileParser(OffsetFileParser):\n  def __init__(self, f, fname, **kwargs):\n    Parser.__init__(self, f, fname, **kwargs)\n\n    self.offsets = self.infer_offsets()\n    if not self.offsets:\n      raise RuntimeError\n\n    _log.info(\"inferoffset got offsets: %s\", self.offsets)\n\n  def remove_nonascii(self, l):\n    l = re.sub(\"^[^\\x00-\\x7F]+\", \"\", l)\n    l = re.sub(\"[^\\x00-\\x7F]+$\", \"\", l)\n    return l\n\n\n  def infer_offsets(self):\n    f = self.f\n    f.seek(0)\n    # remove bad characetrs from the \"header\" row\n    l = self.remove_nonascii(f.readline())\n    get_start_idxs = lambda l: [i.start() for i in re.finditer(\"[^\\s]+\", l)]\n\n    startIdxs = get_start_idxs(l)\n    candidates = Counter(startIdxs)\n    _log.info(\"inferoffset candidates: %s\", sorted(candidates.keys()))\n\n    # in the first 100 rows, are any of the candidates consistently matched?\n    nlines = 0\n    for l in f:\n      candidates.update(filter(candidates.has_key, get_start_idxs(l)))\n      if nlines >= 100:\n        break\n      nlines += 1\n\n    _log.info(\"inferoffset nlines: %s\", nlines)\n    _log.info(\"inferoffset counts: %s\", sorted(candidates.items()))\n\n    # if there are a set of indexs that are > 99%-1 matched, then they are good offsets\n    offsets = []\n    for idx, count in candidates.iteritems():\n      if count >= math.ceil(nlines * .99) - 1:\n        offsets.append(idx)\n\n\n    if len(offsets) > 2:\n      return sorted(offsets)\n    return None\n\n\n  def get_data_iter(self):\n    offpairs = zip(self.offsets[:-1], self.offsets[1:])\n    def _f():\n      self.f.seek(0)\n      for idx, line in enumerate(self.f):\n        if idx == 0:\n          line = self.remove_nonascii(line)\n        arr = [line[s:e] if s < len(line) else '' for s,e in offpairs]\n        yield arr\n    return DataIterator(_f, fname=self.fname)\n\n\nclass SingleColumnParser(Parser):\n    def get_data_iter(self):\n        def _f():\n            self.f.seek(0)\n            for line in self.f:\n                yield [line.strip()]\n        return DataIterator(_f, fname=self.fname)\n\n\nclass JSONParser(Parser):\n\n    def list_is_consistent(self, l):\n        if len(l) == 0:\n            return True\n\n        mastertype = type(l[0])\n        for v in l:\n            if type(v) != mastertype:\n                return False\n        return True\n\n    def obj_to_row(self, o):\n        if isinstance(o, str):\n            return [o]\n        if isinstance(o, dict):\n            return o.values()\n        if isinstance(o, list):\n            return o\n        return [o]\n\n    def list_of_dict_iterator(self, l):\n        keys = set()\n        for d in l:\n            keys.update(d.keys())\n        keys = list(keys)\n\n        def _f():\n            for d in l:\n                yield [d.get(key, '')  for key in keys]\n        return DataIterator(_f, header=keys, fname=self.fname)\n\n    def get_data_iter(self):\n        \"\"\"This methad assumes that JSON file is either\n        1) a list of dictionaries\n        2) a dictionary with an entry that contains a list of dictionaries\n           and finds the longest list\n        \n        \"\"\"\n        \n        self.f.seek(0)\n        dec = json.JSONDecoder('utf-8', strict=False)\n        obj, extra = dec.raw_decode(self.f.read())\n        _log.debug(\"jsonparser.iterator\\textra data: %s\", extra)\n\n        # first see if the object is a dictionary, and look for the key\n        # containing the longest list\n        bestlist = None\n        if isinstance(obj, dict):\n            bestlist, bestlen = None, 0\n            for key, val in obj.iteritems():\n                if (isinstance(val, list) and\n                    len(val) > bestlen and\n                    self.list_is_consistent(val)):\n                    bestlist, bestlen  = val, len(val)\n        elif isinstance(obj, list):\n            bestlist = obj\n        \n        if not bestlist:\n            return None\n\n        if isinstance(bestlist[0], dict):\n            return self.list_of_dict_iterator(bestlist)\n        elif isinstance(best_list[0], list):\n            return DataIterator(lambda: bestlist, fname=self.fname)\n        raise\n\nclass ExcelParser(Parser):\n    def __init__(self, f, fname, sheet=None, **kwargs):\n        # ignore f\n        self.fname = fname\n        self.s = sheet\n        if not sheet:\n            raise \"ExcelParser expects a sheet\"\n    \n    def get_data_iter(self):\n        # if first row with data has style and non of the other rows have style\n        # then it's a header row\n        rows = self.s.rows\n        if len(rows) <= 1:\n            return None\n\n\n        # skip empty rows\n        idx = 0\n        while idx < len(rows):\n            if [c for c in rows[idx] if str(c.value).strip()]:\n                break\n            idx += 1\n        rows = rows[idx:]\n        \n        header = None        \n        if sum(1 for c in rows[0] if c.has_style) > 0.8 * len(rows[0]):\n            header = [c.value for c in rows[0]]\n            rows = rows[1:]\n        \n        rows = [[c.value for c in r] for r in rows]            \n\n        return DataIterator(lambda: iter(rows), header=header, fname=self.fname)\n\n\nclass OldExcelParser(Parser):\n    def __init__(self, f, fname, sheet=None, **kwargs):\n        # ignore f\n        self.fname = fname\n        self.s = sheet\n        if not sheet:\n            raise \"ExcelParser expects a sheet\"\n    \n    def get_data_iter(self):\n        # if first row with data has style and non of the other rows have style\n        # then it's a header row\n        sheet = self.s\n        nrows = sheet.nrows\n        rows = [sheet.row(i) for i in xrange(nrows)]\n        if len(rows) <= 1:\n            return None\n\n\n        # skip empty rows\n        # empty rows typically have much fewer non-empty columns\n        # than the rest of the rows\n        idx = 0\n        while idx < len(rows):\n            ncontents = len([c for c in rows[idx] if c.ctype != 0])\n            if ncontents > 0.3 * nrows or ncontents > max(1, nrows - 2):\n                break\n            idx += 1\n        rows = rows[idx:]\n        \n        # header = None        \n        # if sum(1 for c in rows[0] if c.) > 0.8 * len(rows[0]):\n        #     header = [c.value for c in rows[0]]\n        #     rows = rows[1:]\n        \n        rows = [[to_utf(c.value) for c in r] for r in rows]            \n\n        return DataIterator(lambda: iter(rows), fname=self.fname)\n\n\n\nclass HTMLTableParser(Parser):\n    \"\"\"\n    Takes a <table> element and constructs an \n\n    looks for tables that are of the form\n    table\n     [tr\n       th*]\n     tr*\n       td*\n\n    the text within each td and th is used as the content of the table\n    assumes that th is always and only used as the table's header\n    \"\"\"\n\n    def get_data_iter(self):\n        try:\n            from pyquery import PyQuery\n        except:\n            print >>sys.stderr, \"could not import pyquery\"\n            return None\n\n        self.f.seek(0)\n        table = PyQuery(self.f.read())\n\n        header = None\n        rows = []\n        counter = Counter()\n        headers = []\n        bheaders = True\n        trs = table('tr')        \n        for tr_el in trs:\n            tr = PyQuery(tr_el)\n\n            if bheaders:\n                ths = tr('th')\n                if ths:\n                    headers.append([PyQuery(th).text() for th in ths])\n\n            tds = tr('td')\n\n            if tds:\n                row = [PyQuery(td).text() for td in tds]\n                rows.append(row)\n                counter[len(row)] += 1\n                bheaders = False\n\n        if len(counter):\n            ncols = counter.most_common(1)[0][0]\n            headers = filter(lambda h: len(h) == ncols, headers)\n            if headers:\n                header = headers[-1]\n        return DataIterator(lambda: iter(rows), header=header, fname=self.fname)\n\n","repo_name":"sirrice/dbtruck","sub_path":"dbtruck/parsers/concrete.py","file_name":"concrete.py","file_ext":"py","file_size_in_byte":11598,"program_lang":"python","lang":"en","doc_type":"code","stars":38,"dataset":"github-code","pt":"48"}
+{"seq_id":"4575384583","text":"import os\n\n\ndef find_files(suffix, path):\n    \"\"\"\n    Find all files beneath path with file name suffix.\n\n    Note that a path may contain further subdirectories\n    and those subdirectories may also contain further subdirectories.\n\n    There are no limit to the depth of the subdirectories can be.\n\n    Args:\n      suffix(str): suffix if the file name to be found\n      path(str): path of the file system\n\n    Returns:\n       a list of paths\n    \"\"\"\n    \n    if suffix == \"\":\n        return []\n    if len(os.listdir(path)) == 0:\n        return []\n    \n    path_items = os.listdir(path)\n    path_files = [file for file in path_items if (\".\" + suffix) in file]\n    path_folders = [folder for folder in path_items if \".\" not in folder]\n    \n    for folder in path_folders:\n        path_files.extend(find_files(suffix=suffix, path=path + '/' + folder))\n        \n    return path_files\n\n\n\n# Tests\npath_base = os.getcwd() + '/testdir'\n\n# Testcase 1\nprint(find_files(suffix='c', path=path_base))\n\n# Testscase 2\nprint(find_files(suffix='h', path=path_base))\n\n\n# Testcase 3\nprint(find_files(suffix='gitkeep', path=path_base))\n\n\n# Testcase 4\nprint(find_files(suffix='x', path=path_base))\n\n\n\n\"\"\"OUTPUT\n['t1.c', 'b.c', 'a.c', 'a.c']\n['t1.h', 'b.h', 'a.h', 'a.h']\n['.gitkeep', '.gitkeep']\n[]\n\n\"\"\"\n\n\n\n\n","repo_name":"Nyarish/Data_Structures_Project","sub_path":"Problem 2 File Recursion.py","file_name":"Problem 2 File Recursion.py","file_ext":"py","file_size_in_byte":1288,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"16024939181","text":"import math,cmath\nfrom scipy.interpolate import interp1d\nfrom scipy import integrate\nimport numpy as np\nimport F3pi,alpha,Resonance\nimport yoda,os\nimport matplotlib.pyplot as plt\n\nmPi_=0.140\n\n# Q02 expressed in other variables\ndef QZero2(s,Qp2,Qm2):\n    return 3*mPi_**2+s-Qm2-Qp2\n\n# bounds for Qm2\ndef bounds_Qm(s):\n    upp = (math.sqrt(s)-mPi_)**2\n    low = 4.*mPi_**2\n    return (low, upp)\n\n# bounds for Qp2\ndef bounds_Qp(Qm2,s):\n    E2s = 0.5*math.sqrt(Qm2)\n    E3s = 0.5*(s-Qm2-mPi_**2)/math.sqrt(Qm2)\n    low = (E2s+E3s)**2-(math.sqrt(E2s**2-mPi_**2)+math.sqrt(E3s**2-mPi_**2))**2\n    upp = (E2s+E3s)**2-(math.sqrt(E2s**2-mPi_**2)-math.sqrt(E3s**2-mPi_**2))**2\n    return (low,upp)\n\ni = 0\nj = 0\ndef F2(Qp2,Qm2,s):\n    Q02 = F3pi.QZero2(s,Qp2,Qm2)\n    Lorentzpart = F3pi.Lcontracted(Qp2,Qm2,Q02,s)\n    formfactor = 0.\n    if i==0: formfactor=F3pi.F1(s,Q02)\n    elif i==1: formfactor=F3pi.F0_Omega(s,Qp2,Qm2,Q02)\n    elif i==2: formfactor=F3pi.F0_Phi(s,Qp2,Qm2,Q02)\n    \n    if j==0: formfactor*=np.conj(F3pi.F1(s,Q02))\n    elif j==1: formfactor*=np.conj(F3pi.F0_Omega(s,Qp2,Qm2,Q02))\n    elif j==2: formfactor*=np.conj(F3pi.F0_Phi(s,Qp2,Qm2,Q02))\n    return Lorentzpart*formfactor\n\ndef complex_quadrature(func, ranges, arguments):\n    def real_func(x,y,z):\n        return scipy.real(func(x,y,z))\n    def imag_func(x,y,z):\n        return scipy.imag(func(x,y,z))\n    real_integral = integrate.nquad(real_func, ranges, args=arguments)[0]\n    imag_integral = integrate.nquad(imag_func, ranges, args=arguments)[0]\n    return real_integral + complex(0,1)*imag_integral\n\ndef coefficients(s):\n    Fcoeff  = np.zeros((3,3),dtype=complex)\n    for ix in range(0,3):\n        for jx in range(0,3):\n            global i,j\n            i=ix\n            j=jx\n            #Fcoeff[ix,jx] = complex_quadrature(F2, [F3pi.bounds_Qp,F3pi.bounds_Qm], args=(s,))[0]\n            Fcoeff[ix,jx] = complex_quadrature(F2, [F3pi.bounds_Qp,F3pi.bounds_Qm], (s,))\n    return Fcoeff\n\n#coeff = []\n#energies = []\n#scale = 3.*F3pi.mPi_+0.001\n#while scale < 4.0 :\n#    print scale\n#    s = scale**2\n#    energies.append(scale)\n#    coeff.append(coefficients(s))\n#    if(scale<=1.1) :\n#        scale+=0.001\n#    else :\n#        scale+=0.01\n\n#np.save(\"3pi_coefficients.npy\",[energies,coeff])\n\n[energies, coefficients] = np.load(\"3pi/3pi_coefficients.npy\",allow_pickle=True)\n\ncoeffs = {}\nfor xen in range(0,len(energies)):\n    coeffs[energies[xen]] = coefficients[xen]\n\n# coupling strength to rho, phi contributions\n\n\ncRho_ = 1.\ncOmega_ = 1.\ncPhi_ = 1.\n\ncVector = [cRho_,cOmega_,cPhi_]\n\ndef hadronic_current(energy):\n    s=energy**2\n    pre= 1./(2*math.pi)**3/32./s**2\n    total=0\n    for ix in range(0,3):\n        for jx in range(0,3):\n            total+=pre*cVector[ix]*cVector[jx]*coeffs[energy][ix,jx]\n    return total\n\nhadronic_interpolator = None\ndef readHadronic_Current():\n    x = []\n    y = []\n    for energy in energies:\n        x.append(energy)\n        y.append(hadronic_current(energy))\n    global hadronic_interpolator\n    hadronic_interpolator = interp1d(x, y, kind='cubic',fill_value=\"extrapolate\")\n\ndef sigmaSM(s):\n    en = math.sqrt(s)\n    pre = 16.*math.pi**2*alpha.alphaEM(s)**2/3./s\n    had = abs(hadronic_interpolator(en))\n    return pre*had*Resonance.gev2nb\n\nreadHadronic_Current()\n\nxSM = []\nySM = []\nen = 0.5\nwhile en<4.0:\n    s=en**2\n    xSM.append(en)\n    #value = 16.*math.pi**2*alpha.alphaEM(s)**2/3./s*Resonance.gev2nb\n    #value*= 1./(2*math.pi)**3/32./s**2*Resonance.gev2nb\n    #value*=abs(hadronic_interpolator(en))\n    value=sigmaSM(s)\n    ySM.append(value)\n    en+=0.01\n\npath=\"../xsec_files/data_files/\"\n# data\nanalyses={\"BABAR_2004_I656680\" : [\"d01-x01-y01\"],\n          \"SND_2003_I619011\" : [\"d01-x01-y01\"],\n          \"SND_2002_I582183\" : [\"d01-x01-y01\"]\n}\n\nx_data=[]\ny_data=[]\ne_data=[]\nfor analysis in analyses :\n    aos=yoda.read(os.path.join(os.getcwd(),path)+analysis+\".yoda\")\n    for plot in analyses[analysis] :\n        histo = aos[\"/REF/%s/%s\" %(analysis,plot)]\n        for point in histo.points() :\n            if(analysis == \"SND_2003_I619011\" or analysis==\"SND_2002_I582183\"):\n                xx = point.x()/1000.\n            else:\n                xx = point.x()\n            x_data.append(xx)\n            y_data.append(point.y())\n            e_data.append(point.yErrAvg())\n\n    \nplt.plot(xSM,ySM,color=\"blue\",label=\"SM\")\nplt.errorbar(x_data, y_data, e_data,color=\"black\",linestyle=\"None\")\nplt.legend()\nplt.yscale(\"log\")\nplt.xlabel(\"$\\\\sqrt{s}$/GeV\")\nplt.ylabel(\"$\\\\sigma$/nb\")\n#plt.show()\nplt.savefig(\"test_3pions.pdf\")\n#print energies\n#print coefficients\n","repo_name":"preimitz/DeLiVeR","sub_path":"src/form_factors/F3pi_coeff.py","file_name":"F3pi_coeff.py","file_ext":"py","file_size_in_byte":4565,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"38826455104","text":"# Python Program to print Astrologer's Stars\n\ndef pattern(num, bol):\n    \"\"\"Function to print pattern according to requirements of question.\"\"\"\n\n    bob = bool(bol)                     # Converting the integer b to bool.\n\n    v = list(range(1, num + 1))         # Creating a list containing numbers from 1 to n.\n\n    if not bob:                         # If the bool value turns False, the list is reversed.\n        v.reverse()\n\n    for i in v:                         # A loop to print the required pattern.\n        print(\"*\" * i)\n\n\nif __name__ == '__main__':\n    # Taking the inputs.\n    n = int(input(\"Enter the value of n:\"))\n    b = int(input(\"Enter the value of bool(0 or 1):\"))\n\n    # Calling the function.\n    pattern(n, b)\n","repo_name":"MSB-s-Projects/Astrologer-s-Stars-GUI","sub_path":"Logic Program/Astrologer's Stars.py","file_name":"Astrologer's Stars.py","file_ext":"py","file_size_in_byte":732,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"31288970189","text":"\"\"\"Unit Tests for the Caesar Cipher\"\"\"\nimport unittest\nfrom .context import affine, cipher_exceptions\n\n\nclass AffineCipherTest(unittest.TestCase):\n    \"\"\"Unit Test Class\"\"\"\n\n    MESSAGE_TO_ENCRYPT = \"This is the message that will be encrypted\"\n    MESSAGE_TO_DECRYPT = \"w%,H5,H5O%J51JHHhXJ5O%hO5*,  5oJ5J8vA\\mOJC\"\n    MESSAGE_TO_DECRYPT_SIMPLE = \"YSZr Zr ySx BxrrVLx ySVy TZuu cx xIjkhWyxq\"\n    KEY_A = 7\n    KEY_B = 47\n\n    def test_encrypt(self):\n        \"\"\"Test the result of an encryption\"\"\"\n\n        cipher = affine.AffineCipher(simple=False)\n\n        encrypted_message = cipher.encrypt(\n            self.MESSAGE_TO_ENCRYPT,\n            self.KEY_A,\n            self.KEY_B\n        )\n        self.assertEqual(encrypted_message, self.MESSAGE_TO_DECRYPT)\n\n    def test_decrypt(self):\n        \"\"\"Test the result of a decryption\"\"\"\n        cipher = affine.AffineCipher(simple=False)\n        decrypted_message = cipher.decrypt(\n            self.MESSAGE_TO_DECRYPT,\n            self.KEY_A,\n            self.KEY_B\n        )\n        self.assertEqual(decrypted_message, self.MESSAGE_TO_ENCRYPT)\n\n    def test_encrypt_method_incorrect_message(self):\n        \"\"\"\n        Test that the encrypt method raises\n        an Exception if the message is incorrect\n        \"\"\"\n        with self.assertRaises(cipher_exceptions.IncorrectMessageError):\n            affine.AffineCipher().encrypt(1, 13, 20)\n\n    def test_encrypt_method_key_a_NaN(self):\n        \"\"\"\n        Test that the encrypt method raises\n        an Exception if key A is not an integer\n        \"\"\"\n        with self.assertRaises(cipher_exceptions.IncorrectCipherKeyError):\n            affine.AffineCipher().encrypt(\"This is another message\", \"a\", 2)\n\n    def test_encrypt_method_key_b_NaN(self):\n        \"\"\"\n        Test that the encrypt method raises\n        an Exception if key B is not an integer\n        \"\"\"\n        with self.assertRaises(cipher_exceptions.IncorrectCipherKeyError):\n            affine.AffineCipher().encrypt(\"This is another message\", 2, \"a\")\n\n    def test_encrypt_method_invalid_key_a(self):\n        \"\"\"\n            Test that the encrypt method raises\n            an Exception if key A is not relatively prime\n            to the symbols set length\n        \"\"\"\n        with self.assertRaises(cipher_exceptions.IncorrectCipherKeyError):\n            cipher = affine.AffineCipher(symbols=\"ABCD\")\n            cipher.encrypt(\"This is another message\", 4, 10)\n\n    def test_decrypt_method_incorrect_message(self):\n        \"\"\"\n        Test that the decrypt method raises\n        an Exception if the message is incorrect\n        \"\"\"\n        with self.assertRaises(cipher_exceptions.IncorrectMessageError):\n            affine.AffineCipher().decrypt(1, 13, 20)\n\n    def test_decrypt_method_key_a_NaN(self):\n        \"\"\"\n        Test that the decrypt method raises\n        an Exception if key A is not an integer\n        \"\"\"\n        with self.assertRaises(cipher_exceptions.IncorrectCipherKeyError):\n            affine.AffineCipher().decrypt(\"This is another message\", \"a\", 2)\n\n    def test_decrypt_method_key_b_NaN(self):\n        \"\"\"\n        Test that the decrypt method raises\n        an Exception if key B is not an integer\n        \"\"\"\n        with self.assertRaises(cipher_exceptions.IncorrectCipherKeyError):\n            affine.AffineCipher().decrypt(\"This is another message\", 2, \"a\")\n\n    def test_decrypt_method_invalid_key_a(self):\n        \"\"\"\n            Test that the decrypt method raises\n            an Exception if key A is not relatively prime\n            to the symbols set length\n        \"\"\"\n        with self.assertRaises(cipher_exceptions.IncorrectCipherKeyError):\n            cipher = affine.AffineCipher(symbols=\"ABCD\")\n            cipher.decrypt(\"This is another message\", 4, 10)\n\n    def test_init_incorrect_symbols_list(self):\n        \"\"\"\n        Test that the init method raises\n        an Exception if the symbols argument is incorrect\n        \"\"\"\n        with self.assertRaises(ValueError):\n            affine.AffineCipher(simple=False, symbols=123)\n\n    def test_reversible(self):\n        \"\"\"Test that an encrypted message can be derypted with the same keys\"\"\"\n\n        cipher = affine.AffineCipher()\n        encrypted_message = cipher.encrypt(\n            self.MESSAGE_TO_ENCRYPT,\n            self.KEY_A,\n            self.KEY_B\n        )\n        decrypted_message = cipher.decrypt(\n            encrypted_message,\n            self.KEY_A,\n            self.KEY_B\n        )\n        self.assertEqual(self.MESSAGE_TO_ENCRYPT, decrypted_message)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"MarcDillar/python-cipher","sub_path":"tests/affine.py","file_name":"affine.py","file_ext":"py","file_size_in_byte":4576,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"19176941160","text":"import json\nimport requests\n\n\ndef do_search(search_term):\n    req = requests.get('http://www.omdbapi.com/', params={'s': search_term, 'type': 'movie'})\n    if req.status_code == requests.codes.ok:\n        movies = json.loads(req.text)  # not using req.json in order to sort\n        if 'Search' in movies:\n            lines = sorted(movies['Search'], key=lambda m: m['Year'], reverse=True)\n\n            for movie in lines:\n                print(\"{} - {}\".format(movie['Year'], movie['Title']))\n        else:\n            print('Nothing found')\n\n\ndef main():\n    keep_going = True\n    while keep_going:\n        search = input('Search for a movie [enter to stop]: ')\n        if search.strip():\n            do_search(search)\n        else:\n            keep_going = False\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"jwasham/practice-python","sub_path":"experiments/movie.py","file_name":"movie.py","file_ext":"py","file_size_in_byte":805,"program_lang":"python","lang":"en","doc_type":"code","stars":1548,"dataset":"github-code","pt":"48"}
+{"seq_id":"70500966865","text":"import customtkinter as ctk\nimport tkinter as tk\nimport threading\n\nimport src.utils.colors as color\nimport src.utils.fonts as font\n\nfrom PIL import Image\nfrom src.utils.images import get_image_path\n\nfrom src.services.culinaryDestination import CulinaryDestinationService\nfrom src.widgets.destinations.listDestinationScrollFrame import ListDestinationScrollFrame\nfrom src.widgets.destinations.destinationFrame import DestinationFrame\nfrom src.widgets.destinations.destinationFilter import DestinationFilter\nfrom src.widgets.loadings.loadingSmallFrame import LoadingSmallFrame\n\nclass HomeFrame(ctk.CTkFrame):\n    def __init__(self, parent):\n        super().__init__(parent,fg_color=color.BG_LIGHT)\n        self.parent = parent\n\n        self.load_fonts()\n        self.load_data()\n        self.load_structure()\n        self.load_widgets()\n\n    def load_data(self):\n        self.culinary_destinations_services = CulinaryDestinationService()\n        self.culinary_destinations = self.culinary_destinations_services.get_info()\n        self.search_name_data = tk.StringVar()\n        self.search_kitchen_data = tk.StringVar()\n        self.search_kitchen_data.set(\"Todos\")\n        self.search_ingredient_data = tk.StringVar()\n        self.minimal_price_data = tk.IntVar()\n        self.minimal_price_data.set(0)\n        self.maximum_price_data = tk.IntVar()\n        self.maximum_price_data.set(5000)\n        self.minimal_popularity_data = tk.IntVar()\n        self.minimal_popularity_data.set(0)\n        self.maximum_popularity_data = tk.IntVar()\n        self.maximum_popularity_data.set(5)\n\n    def load_fonts(self):\n        self.title_font = font.title_font()\n        self.name_font = font.text_small_font()\n\n    def load_structure(self):\n        self.grid_rowconfigure(1, weight=1)\n        self.grid_columnconfigure(0, weight=1)\n\n    def load_widgets(self):\n        self.search_frame = SearchFrame(self)\n        self.search_frame.grid(row=0,column=0,padx=15,pady=(20,0),sticky=\"new\")\n        self.list_destinations = ListDestinationScrollFrame(self,self.show_destination)\n        self.list_destinations.grid(row=1, column=0, sticky=\"nsew\")\n        for culinary_destination in self.culinary_destinations:\n            self.list_destinations.add_item(culinary_destination)\n\n        self.loading_frame = LoadingSmallFrame(self)\n        self.destination_frame = DestinationFrame(self)\n\n    def list_culinary_destinations_filter(self):\n        for culinary_destination in self.culinary_destinations:\n            self.list_destinations.add_item(culinary_destination)\n        self.loading_frame.grid_forget()\n        self.list_destinations.grid(row=1, column=0, sticky=\"nsew\")\n\n    def show_destination(self,culinary_destination_id):\n        self.list_destinations.grid_forget()\n        self.search_frame.grid_forget()\n        self.loading_frame.grid(row=0, column=0,rowspan=2,sticky=\"nsew\")\n        self.destination_frame.destroy()\n        thread = threading.Thread(target=lambda: self.load_destination(culinary_destination_id))\n        thread.start()\n\n    def load_destination(self,culinary_destination_id):\n        self.destination_frame = DestinationFrame(self,culinary_destination_id,self.show_list)\n        self.loading_frame.grid_forget()\n        self.destination_frame.grid(row=1, column=0, sticky=\"nsew\")\n\n    def show_list(self):\n        self.destination_frame.grid_forget()\n        self.search_frame.grid(row=0,column=0,padx=15,pady=(20,0),sticky=\"new\")\n        self.list_destinations.grid(row=1, column=0, sticky=\"nsew\")\n\n    def search_name(self):\n        self.culinary_destinations = self.culinary_destinations_services.filter(name=self.search_name_data.get())\n        self.list_destinations.remove()\n        self.list_destinations.grid_forget()\n        self.loading_frame.grid(row=1, column=0,sticky=\"nsew\")\n        thread = threading.Thread(target=self.list_culinary_destinations_filter)\n        thread.start()\n\n    def open_filter(self):\n        self.destination_filter = DestinationFilter(self)\n        self.destination_filter.grab_set()\n\n    def filter(self):\n        self.destination_filter.destroy()\n        self.culinary_destinations = self.culinary_destinations_services.filter(name=self.search_name_data.get(),ingredient=self.search_ingredient_data.get(),kitchen=self.search_kitchen_data.get(),minimal_price=self.minimal_price_data.get(),maximun_price=self.maximum_price_data.get(),minimal_popularity=self.minimal_popularity_data.get(),maximum_popularity=self.maximum_popularity_data.get())\n        self.list_destinations.remove()\n        self.list_destinations.grid_forget()\n        self.loading_frame.grid(row=1, column=0,sticky=\"nsew\")\n        thread = threading.Thread(target=self.list_culinary_destinations_filter)\n        thread.start()\n\nclass SearchFrame(ctk.CTkFrame):\n    def __init__(self, parent):\n        super().__init__(parent,fg_color=color.BG_LIGHT)\n        self.parent = parent\n\n        self.load_fonts()\n        self.load_structure()\n        self.load_widgets()\n\n    def load_fonts(self):\n        self.title_font = font.title_font()\n        self.name_font = font.text_small_font()\n\n    def load_structure(self):\n        self.grid_rowconfigure(0,weight=0)\n        self.grid_columnconfigure(1,weight=1)\n\n    def load_widgets(self):\n        self.search_label = ctk.CTkLabel(self,text=\"Buscar:\",text_color=color.TEXT,fg_color=\"transparent\",font=self.name_font)\n        self.search_label.grid(row=0,column=0,padx=(0,5),pady=10,sticky=\"w\")\n\n        self.search_entry = ctk.CTkEntry(self,placeholder_text=\"Ingrese nombre de destino culinario\",placeholder_text_color=color.TEXT,textvariable=self.parent.search_name_data,border_width=1)\n        self.search_entry.grid(row=0,column=1,padx=5,pady=10,sticky=\"ew\")\n\n        self.search_button = ctk.CTkButton(self,text=\"Buscar\",width=100,font=self.name_font,fg_color=color.PRIMARY,hover_color=color.HOVER_PRIMARY,command=self.parent.search_name)\n        self.search_button.grid(row=0,column=2,padx=5,pady=10,sticky=\"e\")\n\n        self.search_filter_image = ctk.CTkImage(Image.open(get_image_path('filter_light.png')),size=(20,20))\n        self.open_button = ctk.CTkButton(self,text=\"\",width=30,font=self.name_font,fg_color=color.SECONDARY,hover_color=color.HOVER_SECONDARY,image=self.search_filter_image,compound=\"left\",command=self.parent.open_filter)\n        self.open_button.grid(row=0,column=3,padx=5,pady=10,sticky=\"e\")\n\n","repo_name":"gitRaulMartinez/rutas_del_sabor","sub_path":"tkinter/src/widgets/dashboard/homeFrame.py","file_name":"homeFrame.py","file_ext":"py","file_size_in_byte":6387,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"1413464284","text":"import os\nimport shutil\nimport tempfile\n\n#conary\nfrom conary import errors\nfrom conary.deps import deps\nfrom conary.local import database\nfrom conary.cmds import cscmd\nfrom conary.repository import changeset\nfrom conary.cmds.showchangeset import displayChangeSet\nfrom conary import versions\n\n#test\nfrom conary_test import recipes\nfrom conary_test import rephelp\n\n\nclass FileFromLine:\n    def __init__(self, line):\n        args = line.split()\n        # XXX eventually we may want to handle the optional args here\n        assert(len(args) in (9, 10))\n        if len(args) == 9:\n            (self.mode, self.nodes, self.owner, \n             self.grp, self.size, mo, day, tm, self.path) = args[0:9]\n            self.change = 'New'\n        else:\n            (self.change, self.mode, self.nodes, self.owner, \n             self.grp, self.size, mo, day, tm, self.path) = args[0:10]\n\n\n    def __repr__(self):\n        return \"<%s %s>\" % (self.change, self.path)\n\nclass ShowChangesetTest(rephelp.RepositoryHelper):\n\n    def _parseFileList(self, lines):\n        \"\"\" Takes the output of a file listing and returns tuples w/ time\n            removed \"\"\"\n        files = {}\n        for line in lines:\n            if not line:\n                continue\n            newFile = FileFromLine(line)\n            files[newFile.path] = newFile\n        return files\n            \n\n    def testAbsoluteChangeSet(self):\n        self.resetRepository()\n        d = tempfile.mkdtemp()\n\n        origDir = os.getcwd()\n        os.chdir(d)\n        self.writeFile('testcase.recipe', recipes.testRecipe1)\n        repos = self.openRepository()\n        try:\n            built, str = self.captureOutput(self.cookItem, repos, self.cfg,\n                                            'testcase.recipe')\n            cs = changeset.ChangeSetFromFile('testcase-1.0.ccs')\n        finally:\n            os.chdir(origDir)\n            shutil.rmtree(d)\n        rc, res = self.captureOutput(displayChangeSet, None, cs, None, \n                                                                    self.cfg)\n        assert(res == 'testcase=1.0-1-0.1\\n')\n        rc, res = self.captureOutput(displayChangeSet, None, cs, \n                                            ['testcase=1.0-1-0.1'], self.cfg,\n                                            showTroves=True)\n        assert(res == '''\\\ntestcase=1.0-1-0.1\n  testcase:runtime=1.0-1-0.1\n''')\n        rc, res = self.captureOutput(displayChangeSet, None, cs, \n                                            ['testcase:runtime=1.0-1-0.1'], \n                                            self.cfg, fileVersions=True,\n                                            alwaysDisplayHeaders=True)\n        assert(res == '''\\\ntestcase:runtime=1.0-1-0.1\n  /etc/changedconfig    1.0-1-0.1\n  /etc/unchangedconfig    1.0-1-0.1\n  /usr/bin/hello    1.0-1-0.1\n  /usr/share/changed    1.0-1-0.1\n  /usr/share/unchanged    1.0-1-0.1\n''')\n        rc, res = self.captureOutput(displayChangeSet, None, cs, \n                                            ['testcase:runtime=1.0-1-0.1'], \n                                            self.cfg, lsl=True)\n        lines = res.split('\\n')\n        files = self._parseFileList(lines)\n        assert(len(files) == 5)\n        paths  = files.keys()\n        paths.sort()\n        assert(paths == [ '/etc/changedconfig', '/etc/unchangedconfig',\n                        '/usr/bin/hello', '/usr/share/changed', \n                        '/usr/share/unchanged'])\n        for f in files.values():\n            assert f.change == 'New'\n        rc, res = self.captureOutput(displayChangeSet, None, cs, \n                                    ['testcase:runtime=1.0-1-0.1'], \n                                    self.cfg, info=True)\n        # we don't test the output format of --info too closely, mostly just\n        # want to make sure it runs.\n        assert('testcase:runtime' in res)\n        assert('1.0-1-0.1' in res)\n        assert('is: x86' in res)\n\n        rc, res = self.captureOutput(displayChangeSet, None, cs, \n                                    ['testcase:runtime=1.0-1-0.1'], \n                                    self.cfg, asDiff=True)\n\n    def testGitDiff(self):\n        # very simple test of scs --diff\n        t = self.addComponent('foo:run=1',\n                              fileContents = [ ( '/foo', 'contents\\n') ])\n        repos = self.openRepository()\n        cs = repos.createChangeSet([ ('foo:run', (None, None),\n                                      t.getNameVersionFlavor()[1:], False) ])\n        rc, res = self.captureOutput(displayChangeSet, None, cs, [],\n                                    self.cfg, asDiff=True)\n        self.assertEquals(res,\n            \"diff --git a/foo b/foo\\n\"\n            \"new user root\\n\"\n            \"new group root\\n\"\n            \"new mode 100644\\n\"\n            \"--- a/dev/null\\n\"\n            \"+++ b/foo\\n\"\n            \"@@ -1,0 +1,1 @@\\n\"\n            \"+contents\\n\")\n\n    def testPartialChangeSet(self):\n        db = database.Database(self.rootDir, self.cfg.dbPath)\n        self.repos = self.openRepository()\n        self.addTestPkg(1, content='r.Create(\"%(thisdocdir)s/README\")')\n        self.cookTestPkg(1)\n        self.cfg.configLine('excludeTroves .*:runtime') \n        os.chdir(self.workDir)\n        cscmd.ChangeSetCommand(self.cfg, ['test1'], 'test.ccs') \n        cs = changeset.ChangeSetFromFile('test.ccs')\n        rc, res = self.captureOutput(displayChangeSet, None, cs,\n                                     ['test1=1.0-1-1'],   self.cfg,\n                                     showTroves=True)\n\n        assert(res == '''\\\ntest1=1.0-1-1\n  test1:runtime=1.0-1-1\n''')\n\n    def testEraseChangeSet(self):\n        db = database.Database(self.rootDir, self.cfg.dbPath)\n        self.repos = self.openRepository()\n        self.addTestPkg(1, content='r.Create(\"%(thisdocdir)s/README\")')\n        self.cookTestPkg(1)\n        os.chdir(self.workDir)\n        cscmd.ChangeSetCommand(self.cfg, ['test1=1.0--'], 'test.ccs') \n        cs = changeset.ChangeSetFromFile('test.ccs')\n        rc, res = self.captureOutput(displayChangeSet, db, \n                                     cs, ['test1=1.0'], self.cfg)\n        assert(res == 'Erase   test1=1.0-1-1\\n')\n\n    def testChangedFiles(self):\n        # set up a recipe in a :source component\n        self.repos = self.openRepository()\n        self.addTestPkg(1, content='r.Create(\"/etc/foo\", contents=\"A\\\\n\"*1000)',\n                            tag='myTag')\n        self.cookTestPkg(1)\n        res = self.addTestPkg(1, fileContents='a change in the file', \n                                                                tag='myTag2')\n        self.cookTestPkg(1)\n        v1 = versions.VersionFromString('/localhost@rpl:linux/1.0-1-1')\n        v2 = versions.VersionFromString('/localhost@rpl:linux/1.0-2-1')\n\n        cs = self.repos.createChangeSet([('test1:runtime', \n                                         (v1, deps.Flavor()), \n                                         (v2, deps.Flavor()), False)])\n\n        rc, res = self.captureOutput(displayChangeSet, None, cs, None, \n                                            self.cfg, lsl=True)\n        lines = res.split('\\n')\n        files = self._parseFileList(lines)\n        assert(files['/usr/bin/test1'].change == 'Mod')\n        assert(files['/etc/foo'].change == 'Del')\n        rc, res = self.captureOutput(displayChangeSet, None, cs, \n                                    None, self.cfg, lsl=True, showChanges=True)\n        lines = res.split('\\n')\n        self.assertEquals(lines[3].split()[1], '52')\n        rc, res = self.captureOutput(displayChangeSet, None, cs, \n                                    None, self.cfg, lsl=True, showChanges=True,\n                                    tags=True)\n        lines = res.split('\\n')\n        assert(lines[2].find('{myTag}') != -1)\n        assert(lines[3].find('{myTag2}') != -1)\n        rc, res = self.captureOutput(displayChangeSet, None, cs, \n                                    None, self.cfg, lsl=True, showChanges=True,\n                                    ids=True, sha1s=True)\n        lines = res.split('\\n')\n        oldFile = lines[2].split()\n        newFile = lines[3].split()\n        # two changes btw the files -- sha1 and size -- possibly date\n        assert(len(newFile) in (3, 6))\n        assert(oldFile[2] != newFile[1])\n        assert(len(newFile[1]) == 40)\n\n    def testGroupChangeSet(self):\n        repos = self.openRepository()\n        os.chdir(self.workDir)\n        self.addQuickTestComponent('test:runtime', '1.0', \n                                    fileContents=['/usr/run1'])\n        self.addQuickTestComponent('test:doc', '1.0', \n                                   fileContents=['/usr/doc1'])\n        self.addQuickTestCollection('test', '1.0', ['test:runtime', 'test:doc'])\n        self.addQuickTestCollection('group-test', '1.0', ['test'])\n        self.changeset(repos, ['group-test'], 'group-test.ccs', recurse=False)\n        cs = changeset.ChangeSetFromFile('group-test.ccs')\n        rc, res = self.captureOutput(displayChangeSet, None, cs, [], self.cfg)\n        assert(res == 'group-test=1.0-1-1\\n')\n        rc, res = self.captureOutput(displayChangeSet, None, cs, [], self.cfg,\n                                     ls=True, alwaysDisplayHeaders=True,\n                                     recurseRepos=True)\n        assert(res == '''\\\ngroup-test=1.0-1-1\n  test=1.0-1-1\n    test:doc=1.0-1-1\n      /usr/doc1\n    test:runtime=1.0-1-1\n      /usr/run1\n''')\n\n    def testExactFlavor(self):\n        self.addComponent('foo:run[~ssl]')\n        repos = self.openRepository()\n        csPath = self.workDir + '/foo.ccs'\n        self.changeset(repos, ['foo:run'], csPath)\n        cs = changeset.ChangeSetFromFile(csPath)\n        self.assertRaises(errors.TroveNotFound,\n                        displayChangeSet, None, cs, ['foo:run[ssl]'], self.cfg,\n                        exactFlavors=True)\n        self.assertRaises(errors.TroveNotFound,\n                        displayChangeSet, None, cs, ['foo:run'], self.cfg,\n                        exactFlavors=True)\n        self.captureOutput(displayChangeSet, None, cs, ['foo:run[~ssl]'], \n                           self.cfg,exactFlavors=True)\n","repo_name":"sassoftware/conary","sub_path":"conary_test/showchangesettest.py","file_name":"showchangesettest.py","file_ext":"py","file_size_in_byte":10160,"program_lang":"python","lang":"en","doc_type":"code","stars":47,"dataset":"github-code","pt":"48"}
+{"seq_id":"5372095331","text":"# -*- coding:utf-8 -*-\nimport numpy as np\nimport logging\nimport sys\nimport copy\n \nclass NetWork:\n\n    def __init__(self, name):\n        self.name = name\n        self.neurals = []\n        self.last_in_dim = None\n        self.__init_log()\n        self.init_opt = False\n        self.neural_count = 0\n\n    def __init_log(self):\n        self.logger = logging.getLogger('log[' + self.name + ']')\n        self.log_formatter = logging.Formatter('%(asctime)s %(levelname)-5s: %(message)s')\n        log_handler = logging.StreamHandler(sys.stdout)\n        log_handler.setFormatter(self.log_formatter)\n        self.logger.addHandler(log_handler)\n        self.logger.setLevel(logging.INFO)\n        \n\n    def add(self, neural):\n        if (not neural.is_activation()) and (neural.input_dim() is None):\n            neural.set_input_dim(self.last_in_dim)\n        if not neural.is_activation():\n            self.last_in_dim = neural.output_dim()\n        neural.set_index(self.neural_count)\n        self.neural_count += 1\n        self.neurals.append(neural)\n\n    def loss(self, f):\n        self.loss_function = f\n\n    def optimizer(self, opt):\n        self.optimizer_function = opt\n\n    def add_logger_handler(self, log_handler):\n        log_handler.setFormatter(self.log_formatter)\n        self.logger.addHandler(log_handler)\n\n    def __output(self, s):\n        self.logger.info('['+self.name+'] '+s)\n\n    def __init_optimizer(self):\n        for l in self.neurals:\n            if not l.is_activation():\n                l.set_optimizer(copy.deepcopy(self.optimizer_function), copy.deepcopy(self.optimizer_function))\n        \n    # iteration 全数据量的训练次数\n    # batch_size 每个 epoch 数据量\n    # 所有 epoch 训练一次 算 一次 iteration\n    def train(self, x, y, batch_size, iteration=1000):\n        if not self.init_opt:\n            self.init_opt = True\n            self.__init_optimizer()\n        self.batch_size = batch_size\n        epochs = int(len(y) / batch_size)\n        if len(y) % batch_size != 0:\n            epochs += 1\n        for _ in range(iteration):\n            for e in range(epochs):\n                start = e * batch_size\n                end = (e+1) * batch_size\n                train_x, train_y = x[start:end], y[start:end]\n                self.__batch(train_x, train_y)\n            self.__output('loss: ' + str(self.__loss()))\n        \n    \n    def __batch(self, x, y):\n        self.error_weight, self.error_bias = [], []\n\n        # init\n        for l in self.neurals:\n            l.clear_error_weight()\n\n        # 计算样本\n        for e, l in zip(x, y):\n            p = self.__forward(e)\n            self.__calculate_loss(l, p)\n            self.__backprocess()\n        \n        # 计算平均误差\n        for l in self.neurals:\n            if not l.is_activation():\n                l.avg_error()\n\n        # 更新\n        for l in self.neurals:\n            if not l.is_activation():\n                ew, eb = l.grad()\n                # ew = np.clip(ew, -1.0, 1.0)\n                # eb = np.clip(eb, -1.0, 1.0)\n                l.update(ew, eb)\n\n        \n\n    # 每计算一个样本，执行一次\n    def __forward(self, x):\n        p = x\n        for _, l in enumerate(self.neurals):\n            l.calculate(p)\n            p = l.output()\n        return p\n\n    def __calculate_loss(self, y, py):\n        self.loss_function.calculate(y, py)\n\n    def __loss(self):\n        return self.loss_function.loss()\n\n    def __error(self):\n        return self.loss_function.error()\n\n    # 每计算一个样本，执行一次\n    def __backprocess(self):\n        der_error = self.__error()\n        for i in range(len(self.neurals)):\n            j = len(self.neurals)-i-1\n            der_error = self.neurals[j].derivate(der_error)\n\n\n    # x array\n    def predict(self, x):\n        return np.array([self.__forward(a) for a in x])\n","repo_name":"FrankLeeC/MachineLearning","sub_path":"teny/network.py","file_name":"network.py","file_ext":"py","file_size_in_byte":3850,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"73167245905","text":"from django.urls import path\nfrom drf_yasg import openapi\nfrom drf_yasg.utils import swagger_auto_schema\n\nfrom .views import (\n    token_delete,\n    user_properties_view,\n    account_retrieve_update_view,\n    AccountListView,\n    accounts_retrieve_by_ids,\n)\nfrom rest_framework.authtoken.views import obtain_auth_token\n\napp_name = 'account'\n\ndecorated_search_view = \\\n   swagger_auto_schema(\n        method='get',\n        tags=['account-admin']\n   )(AccountListView.as_view())\n\ndecorated_token_view = \\\n   swagger_auto_schema(\n        method='post',\n        tags=['account-token'],\n        request_body=openapi.Schema(\n            type=openapi.TYPE_OBJECT,\n            properties={\n                'username': openapi.Schema(type=openapi.TYPE_STRING, description='Account username'),\n                'password': openapi.Schema(type=openapi.TYPE_STRING, description='Account password'),\n            }\n        ),\n        operation_summary=\"Get auth token\"\n    )(obtain_auth_token)\n\nurlpatterns = [\n    path('token/create/', decorated_token_view, name='api_token_auth'),\n    path('token/destroy/', token_delete, name='api_token_delete'),\n\n    path('client/properties/', user_properties_view, name='properties'),\n    path('properties/<int:pk>', account_retrieve_update_view, name=\"account\"),\n    path('search/', decorated_search_view, name=\"accounts\"),\n    path('clients/', accounts_retrieve_by_ids, name=\"accounts\")\n]\n","repo_name":"DanielSzarek/JustFit-Client","sub_path":"src/account/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1415,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"6426712520","text":"import os\nimport shutil\nimport tempfile\n\nfrom unittest import TestCase\nfrom unittest.mock import patch, MagicMock\nfrom .commands import ReadFileCommand, \\\n    ToggleAutocompleteCommand, ToggleAutoCapitalizeCommand, ToggleVerboseCommand, \\\n    NodeCheckCommand, ClusterCheckCommand, CheckCommand\n\nfrom .command import CrateShell\nfrom distutils.version import StrictVersion\n\n\nclass ReadFileCommandTest(TestCase):\n\n    def setUp(self):\n        self.tmp_dir = tempfile.mkdtemp()\n        self.cmd = ReadFileCommand()\n        self.dummy_file = os.path.join(self.tmp_dir, 'dummy.sql')\n        self.dummy_dir = os.path.join(self.tmp_dir, 'dummy_folder')\n        self.dummy_file_in_dir = os.path.join(self.dummy_dir, 'dummy2.sql')\n        os.mkdir(self.dummy_dir)\n        with open(self.dummy_file, 'w') as f:\n            f.write('')\n        with open(self.dummy_file_in_dir, 'w') as f:\n            f.write('')\n\n    def tearDown(self):\n        if os.path.exists(self.tmp_dir):\n            shutil.rmtree(self.tmp_dir)\n\n    def test_complete_correct_filename(self):\n        results = sorted(list(\n            self.cmd.complete(None, os.path.join(self.tmp_dir, 'dummy.sql'))))\n        self.assertEqual(results, [])\n\n    def test_complete_shows_folder_and_sql_files(self):\n        results = sorted(list(\n            self.cmd.complete(None, os.path.join(self.tmp_dir, 'dum'))))\n        self.assertEqual(results, ['dummy.sql', 'dummy_folder/'])\n\n    def test_complete_shows_only_folder(self):\n        results = sorted(list(\n            self.cmd.complete(None, os.path.join(self.tmp_dir, 'dummy_'))))\n        self.assertEqual(results, ['dummy_folder/'])\n\n    def test_complete_shows_only_file_within_folder(self):\n        results = sorted(list(self.cmd.complete(\n            None, os.path.join(self.tmp_dir, 'dummy_folder/'))))\n        self.assertEqual(results, ['dummy2.sql'])\n\n    @patch('crate.crash.command.CrateShell')\n    def test_call(self, fake_cmd):\n        path = os.path.join(self.tmp_dir, 'foo.sql')\n        with open(path, 'w') as fp:\n            fp.write('SELECT * FROM sys.nodes')\n        command = ReadFileCommand()\n        command(fake_cmd, path)\n        self.assertEqual(fake_cmd.process_iterable.call_count, 1)\n\n\nclass ToggleAutocompleteCommandTest(TestCase):\n\n    @patch('crate.crash.command.CrateShell')\n    def test_toggle_output(self, fake_cmd):\n        fake_cmd._autocomplete = True\n        command = ToggleAutocompleteCommand()\n        output = command(fake_cmd)\n        self.assertEqual(output, 'Autocomplete OFF')\n        output = command(fake_cmd)\n        self.assertEqual(output, 'Autocomplete ON')\n\n\nclass ToggleAutoCapitalizeCommandTest(TestCase):\n\n    @patch('crate.crash.command.CrateShell')\n    def test_toggle_output(self, fake_cmd):\n        fake_cmd._autocapitalization = True\n        command = ToggleAutoCapitalizeCommand()\n        output = command(fake_cmd)\n        self.assertEqual(output, 'Auto-capitalization OFF')\n        output = command(fake_cmd)\n        self.assertEqual(output, 'Auto-capitalization ON')\n\n\nclass ToggleVerboseCommandTest(TestCase):\n\n    @patch('crate.crash.command.CrateShell')\n    def test_toggle_output(self, fake_cmd):\n        fake_cmd.error_trace = True\n        command = ToggleVerboseCommand()\n        output = command(fake_cmd)\n        self.assertEqual(output, 'Verbose OFF')\n        self.assertEqual(fake_cmd.reconnect.call_count, 1)\n\n        fake_cmd.reset_mock()\n        output = command(fake_cmd)\n        self.assertEqual(output, 'Verbose ON')\n        self.assertEqual(fake_cmd.reconnect.call_count, 1)\n\n\nclass ShowTablesCommandTest(TestCase):\n\n    def test_post_2_0(self):\n        cmd = CrateShell()\n        cmd._exec = MagicMock()\n        cmd.connection.lowest_server_version = StrictVersion(\"2.0.0\")\n        cmd._show_tables()\n        cmd._exec.assert_called_with(\"SELECT format('%s.%s', table_schema, table_name) AS name FROM information_schema.tables WHERE table_schema NOT IN ('sys','information_schema', 'pg_catalog') AND table_type = 'BASE TABLE'\")\n\n    def test_post_0_57(self):\n        cmd = CrateShell()\n        cmd._exec = MagicMock()\n        cmd.connection.lowest_server_version = StrictVersion(\"0.57.0\")\n        cmd._show_tables()\n        cmd._exec.assert_called_with(\"SELECT format('%s.%s', table_schema, table_name) AS name FROM information_schema.tables WHERE table_schema NOT IN ('sys','information_schema', 'pg_catalog')\")\n\n    def test_pre_0_57(self):\n        cmd = CrateShell()\n        cmd._exec = MagicMock()\n        cmd.connection.lowest_server_version = StrictVersion(\"0.56.4\")\n        cmd._show_tables()\n        cmd._exec.assert_called_with(\"SELECT format('%s.%s', schema_name, table_name) AS name FROM information_schema.tables WHERE schema_name NOT IN ('sys','information_schema', 'pg_catalog')\")\n\n\nclass ChecksCommandTest(TestCase):\n\n    @patch('crate.crash.command.CrateShell')\n    def test_node_check(self, cmd):\n        rows = [\n            ['local1', 'check1'],\n            ['local2', 'check2'],\n            ['loca1', 'check2'],\n        ]\n        cols = [('Failed Check', ), ('Number of Nodes', )]\n        cmd._execute.return_value = True\n        cmd.cursor.fetchall.return_value = rows\n        cmd.cursor.description = cols\n        cmd.connection.lowest_server_version = StrictVersion(\"0.56.4\")\n\n        NodeCheckCommand()(cmd)\n        cmd.pprint.assert_called_with(rows, [c[0] for c in cols])\n\n    @patch('crate.crash.command.CrateShell')\n    def test_node_check_for_not_supported_version(self, cmd):\n        cmd.connection.lowest_server_version = StrictVersion(\"0.52.3\")\n        NodeCheckCommand()(cmd)\n        excepted = 'Crate 0.52.3 does not support the \"\\check nodes\" command.'\n        cmd.logger.warn.assert_called_with(excepted)\n\n    @patch('crate.crash.command.CrateShell')\n    def test_cluster_check(self, cmd):\n        rows = [\n            ['local1', 'check1'],\n            ['local2', 'check2'],\n            ['loca1', 'check2'],\n        ]\n        cols = [('Failed Check', ), ('Number of Nodes', )]\n        cmd._execute.return_value = True\n        cmd.cursor.fetchall.return_value = rows\n        cmd.cursor.description = cols\n        cmd.connection.lowest_server_version = StrictVersion(\"0.53.1\")\n\n        ClusterCheckCommand()(cmd)\n        cmd.pprint.assert_called_with(rows, [c[0] for c in cols])\n\n    @patch('crate.crash.command.CrateShell')\n    def test_cluster_check_for_not_supported_version(self, cmd):\n        cmd.connection.lowest_server_version = StrictVersion(\"0.49.4\")\n        ClusterCheckCommand()(cmd)\n        excepted = 'Crate 0.49.4 does not support the cluster \"check\" command.'\n        cmd.logger.warn.assert_called_with(excepted)\n\n    @patch('crate.crash.command.CrateShell')\n    def test_check_command_with_cluster_check(self, cmd):\n        command = CheckCommand()\n        cmd._execute.return_value = True\n        cmd.cursor.fetchall.return_value = []\n        cmd.connection.lowest_server_version = StrictVersion(\"0.56.1\")\n\n        command(cmd, 'cluster')\n        cmd.logger.info.assert_called_with('CLUSTER CHECK OK')\n\n    @patch('crate.crash.command.CrateShell')\n    def test_check_command_with_node_check(self, cmd):\n        command = CheckCommand()\n        cmd._execute.return_value = True\n        cmd.cursor.fetchall.return_value = []\n        cmd.connection.lowest_server_version = StrictVersion(\"0.56.1\")\n\n        command(cmd, 'nodes')\n        cmd.logger.info.assert_called_with('NODE CHECK OK')\n","repo_name":"danish123gupta/assignment-19","sub_path":"venv/Lib/site-packages/crate/crash/test_commands.py","file_name":"test_commands.py","file_ext":"py","file_size_in_byte":7422,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"69813481106","text":"from setuptools import setup\nfrom setuptools import find_packages\n\npackage_name = 'scikit-discovery'\n\npackage_list = find_packages()\n\nwith open(\"README.md\", 'r', encoding='utf-8') as rfile:\n    readme = rfile.read()\n\nsetup(name     = package_name,\n      version  = '0.9.18',\n      packages = package_list,\n      zip_safe = False,\n\n      install_requires = [\n          'astropy>=1.1.2',\n          'basemap',\n          'boto3>=1.4.4',\n          'dispy==4.8.1',\n          'dask',\n          'graphviz >= 0.7.0',\n          'imreg_dft',\n          'ipython >= 6.0.0',\n          'ipywidgets >= 6.0.0',\n          'matplotlib >= 2',\n          'numpy>=1.10',\n          'pandas>=0.17',\n          'paramiko >= 2.1.0',\n          'psutil >= 5.2',\n          'pyproj',\n          'pyhht',\n          'PyWavelets',\n          'scikit-dataaccess>=1.9.0',\n          'scikit-learn >= 0.17',\n          'scipy',\n          'seaborn >= 0.8',\n          'setuptools',\n          'shapely',\n          'six >= 1.10.0',\n          'statsmodels >= 0.8',\n          'tqdm',\n      ],\n\n      description = 'A package for Computer-Aided Discovery',\n      author = 'MITHAGI',\n      author_email='skdaccess@mit.edu',\n      classifiers=[\n          'Topic :: Scientific/Engineering',\n          'Intended Audience :: Science/Research',\n          'License :: OSI Approved :: MIT License',\n          'License :: OSI Approved :: GNU Library or Lesser General Public License (LGPL)',\n          'Programming Language :: Python :: 3 :: Only'\n          ],\n      python_requires='>=3.4',\n      url = 'https://github.com/MITHaystack/scikit-discovery',\n      package_data={'skdiscovery': ['license/LICENSE','license/LGPL_LICENSE','license/MIT_LICENSE',\n                                    'docs/skdiscovery_doxygen.pdf','examples/Amazon_GUI.ipynb',\n                                    'examples/Amazon_Offload.ipynb']},\n\n      long_description = readme,\n      long_description_content_type='text/markdown'\n)\n","repo_name":"MITHaystack/scikit-discovery","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1952,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"48"}
+{"seq_id":"29689275545","text":"from collections import deque\n\ndef solution(bridge_length, weight, truck_weights):\n#     # 남는 다리길이가 1 이상인지, 현재 트럭의 무게가 남는 무게치를 안넘는지 확인.\n#     # 넣을 수 있는 트럭을 넣은 후, 길이를 하나 줄이고, 남는 무게치도 트럭무게만큼 줄인다\n#     # 트럭은다리 리스트에 넣는다. 넣을 때는 [무게,다리길이] 를 넣는다\n#     # 위 과정이 끝나면\n#     # 다리 리스트를 돌면서 원소들의 다리 길이를 1씩 뺀다\n#     # 다리 리스트의 마지막 원소의 다리 길이를 확인한다. 0이면 빼고 남는 무게에 트럭무게를 더한다\n#     # 트럭리스트,다리 리스트가 다 0이 될때까지 반복한다\n#     # 한 반복문 당 answer을 1씩 더한다\n    answer = 0\n    in_bridge = []\n    remain_w = weight\n\n    while(truck_weights or in_bridge):\n        if truck_weights and remain_w >= truck_weights[0]:\n            in_bridge.append([truck_weights[0],bridge_length])\n            remain_w-=truck_weights[0]\n            truck_weights.pop(0)\n\n        for i in range(len(in_bridge)):\n            in_bridge[i][1]-=1\n        \n        if in_bridge and in_bridge[0][1]==0:\n            remain_w += in_bridge[0][0]\n            in_bridge.pop(0)\n        answer+=1\n        \n    return answer+1","repo_name":"SonJinHYo/CodingTest","sub_path":"프로그래머스/lv2/42583. 다리를 지나는 트럭/다리를 지나는 트럭.py","file_name":"다리를 지나는 트럭.py","file_ext":"py","file_size_in_byte":1331,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"18556473889","text":"from django.shortcuts import render\nfrom .models import Post, Categoria\nfrom django.shortcuts import get_object_or_404\nfrom django.db.models import Q\nfrom django.core.paginator import Paginator\n# Create your views here.\n\n\ndef home(request):\n    #realizamos un queryset en donde le pasamos la variable que hemos creado 'buscar'\n    #filtramos los post los que esten publicados (estado=True)\n    #Usamos paginator, en mi caso en múmero que he usado han sido 3 y \n    # después nos saldría un botón de siguiente para ver los siguientes post\n    queryset =  request.GET.get('buscar')\n    posts = Post.objects.filter(estado=True)\n    paginator = Paginator(posts, 4) \n    page = request.GET.get('page')\n    posts = paginator.get_page(page)\n\n    #Realizamos la busqueda por titulo y descripcion\n    #__icontains no tiene encuenta mayúsculas y minúsculas, solo con que coincida el texto le vale\n    if queryset:\n         posts = Post.objects.filter(\n              Q(titulo__icontains = queryset) | \n              Q(descripcion__icontains = queryset)\n         ).distinct    \n\n    return render(request, 'index.html', {'posts':posts})\n\n\n\ndef detallePost(request, post_id): \n        post = get_object_or_404(Post,  pk=post_id) \n\n        return render(request, 'post.html', {'post':post} )\n\n\n    \n#se pasa el mismo nombre que hemos creado en categoría, \n#y así conseguimos todos los posts que su categoría sea Generales\ndef cine(request):   \n       \n    queryset = request.GET.get(\"buscar\")\n    posts = Post.objects.filter(estado=True, categoria = Categoria.objects.get(nombre__iexact ='Cine')) \n    paginator = Paginator(posts, 3) \n    page = request.GET.get('page')\n    posts = paginator.get_page(page)\n\n    if queryset:\n        posts = Post.objects.filter(\n            Q(titulo__icontains = queryset) | \n            Q(descripcion__icontains = queryset)\n        ).distinct\n\n    return render(request, 'generales.html', {'posts':posts})\n    \n\ndef programacion(request):\n    #__iexact omite mayúsculas y minúsculas\n    queryset = request.GET.get(\"buscar\")\n    posts = Post.objects.filter(estado=True, categoria = Categoria.objects.get(nombre__iexact ='Programacion'))\n    paginator = Paginator(posts, 3) \n    page = request.GET.get('page')\n    posts = paginator.get_page(page)\n\n    if queryset:\n         posts = Post.objects.filter(\n              Q(titulo__icontains = queryset) | \n              Q(descripcion__icontains = queryset)\n         ).distinct\n\n    return render(request, 'programacion.html', {'posts':posts})\n    \n\ndef tutoriales(request):\n    queryset = request.GET.get(\"buscar\")\n    posts = Post.objects.filter(estado=True, categoria = Categoria.objects.get(nombre__iexact ='Tutoriales'))\n\n    paginator = Paginator(posts, 3) \n    page = request.GET.get('page')\n    posts = paginator.get_page(page)\n\n    if queryset:\n         posts = Post.objects.filter(\n              Q(titulo__icontains = queryset) | \n              Q(descripcion__icontains = queryset)\n         ).distinct\n\n    return render(request, 'tutoriales.html', {'posts':posts})\n    \n\ndef tecnologia(request):\n    queryset = request.GET.get(\"buscar\")\n    posts = Post.objects.filter(estado=True, categoria = Categoria.objects.get(nombre__iexact ='Tecnologia')) \n    paginator = Paginator(posts, 3) \n    page = request.GET.get('page')\n    posts = paginator.get_page(page)\n\n    if queryset:\n         posts = Post.objects.filter(\n              Q(titulo__icontains = queryset) | \n              Q(descripcion__icontains = queryset)\n         ).distinct\n\n    return render(request, 'tecnologia.html', {'posts':posts})\n    \n\ndef videojuegos(request):\n    queryset = request.GET.get(\"buscar\")\n    posts = Post.objects.filter(estado=True, categoria = Categoria.objects.get(nombre__iexact ='Videojuegos'))\n    paginator = Paginator(posts, 3) \n    page = request.GET.get('page')\n    posts = paginator.get_page(page)\n\n    if queryset:\n         posts = Post.objects.filter(\n              Q(titulo__icontains = queryset) | \n              Q(descripcion__icontains = queryset)\n         ).distinct\n\n    return render(request, 'videojuegos.html', {'posts':posts})\n\n\ndef contacto(request):\n#contacto lo puedes agregar si lo deseas, tienes ya creado el HTML de contacto, yo lo tengo comentado\n    return render(request, 'contacto.html')\n\n\n\n\n\n","repo_name":"NikiDevelop/Django-Blog","sub_path":"blog/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4292,"program_lang":"python","lang":"es","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"42040840948","text":"\nfrom search_engine import JsonDB\n\nclass Search():\n\t\"\"\"\n\tSearch engine to query words from the loaded data\n\t\"\"\"\n\n\tsearch_record_key = 'summary'\n\n\t@staticmethod\n\tdef indexing(data, field):\n\t\t\"\"\"index search data in generator for quick access\"\"\"\n\t\treturn (record[field].lower() for record in data)\n\n\t@staticmethod\n\tdef find_words(data_list, query):\n\t\t\"\"\"iterate indexed data to find relevant match\"\"\"\n\t\twords = query.split()\n\t\twords_len = len(words)\n\n\t\tq_index = 0\n\t\tword_count = []\n\t\tfor i, word in enumerate(words):\n\t\t\tword_count.append((i, q_index))\n\t\t\tq_index += len(word)+1\n\n\t\tindex = 0\n\t\tfor record in data_list:\n\t\t\tfor w_index, q_index in word_count:\n\t\t\t\tif query[q_index:] in record:\n\t\t\t\t\tcount = words_len - w_index\n\t\t\t\t\tyield (count, index)\n\t\t\t\t\tbreak\n\t\t\tindex += 1\n\n\t@classmethod\n\tdef execute(cls, query, K):\n\t\t\"\"\"main method to find and return relavant matches\"\"\"\n\n\t\tquery = query.lower()\n\n\t\tdata = JsonDB.fetch_data()\n\t\tsummaries = data['summaries']\n\n\t\tdata_list = cls.indexing(summaries, cls.search_record_key)\n\n\t\trelevant_indexes = sorted(cls.find_words(data_list, query), reverse=True)\n\t\t\n\t\treturn [summaries[index] for _, index in relevant_indexes[:K]]\n","repo_name":"ebinezar/unibuddy","sub_path":"app/search_engine/search.py","file_name":"search.py","file_ext":"py","file_size_in_byte":1168,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"73592424465","text":"import visa\nimport time\nfrom core.module import Base\nfrom core.configoption import ConfigOption\nfrom core.util.mutex import Mutex\nfrom interface.switch_interface import SwitchInterface\n\n\nclass HBridge(Base, SwitchInterface):\n    \"\"\" Methods to control slow laser switching devices.\n\n    Example config for copy-paste:\n\n    flipmirror_switch:\n        module.Class: 'switches.hbridge.HBridge'\n        interface: 'ASRL1::INSTR'\n\n    \"\"\"\n\n    serial_interface = ConfigOption('interface', 'ASRL1::INSTR', missing='warn')\n\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.lock = Mutex()\n\n    def on_activate(self):\n        \"\"\" Activate module.\n        \"\"\"\n        self.rm = visa.ResourceManager()\n        self.inst = self.rm.open_resource(\n                self.serial_interface,\n                baud_rate=9600,\n                write_termination='\\r\\n',\n                read_termination='\\r\\n',\n                timeout=10,\n                send_end=True\n        )\n\n    def on_deactivate(self):\n        \"\"\" Deactivate module.\n        \"\"\"\n        self.inst.close()\n\n    def getNumberOfSwitches(self):\n        \"\"\" Gives the number of switches connected to this hardware.\n\n          @return int: number of switches\n        \"\"\"\n        return 4\n\n    def getSwitchState(self, switchNumber):\n        \"\"\" Gives state of switch.\n\n          @param int switchNumber: number of switch\n\n          @return bool: True if on, False if off, None on error\n        \"\"\"\n        with self.lock:\n            pos = self.inst.ask('STATUS')\n            ret = list()\n            for i in pos.split():\n                ret.append(int(i))\n            return ret[switchNumber]\n\n    def getCalibration(self, switchNumber, state):\n        \"\"\" Get calibration parameter for switch.\n\n          @param int switchNumber: number of switch for which to get calibration parameter\n          @param str switchState: state ['On', 'Off'] for which to get calibration parameter\n\n          @return str: calibration parameter fir switch and state.\n\n        In this case, the calibration parameter is the time for which current is\n        applied to the coil/motor for switching.\n\n        \"\"\"\n        return 0\n\n    def setCalibration(self, switchNumber, state, value):\n        \"\"\" Set calibration parameter for switch.\n\n          @param int switchNumber: number of switch for which to get calibration parameter\n          @param str switchState: state ['On', 'Off'] for which to get calibration parameter\n          @param int value: calibration parameter to be set.\n\n          @return bool: True if success, False on error\n        \"\"\"\n        pass\n\n    def switchOn(self, switchNumber):\n        \"\"\" Extend coil or move motor.\n\n          @param int switchNumber: number of switch to be switched\n\n          @return bool: True if suceeds, False otherwise\n        \"\"\"\n        coilnr = int(switchNumber) + 1\n        if 0 < int(coilnr) < 5:\n            with self.lock:\n                try:\n                    answer = self.inst.ask('P{0}=1'.format(coilnr))\n                    if answer != 'P{0}=1'.format(coilnr):\n                        return False\n                    time.sleep(self.getSwitchTime(switchNumber))\n                    self.log.info('{0} switch {1}: On'.format(\n                        self._name, switchNumber))\n                except:\n                    return False\n                return True\n        else:\n            self.log.error('You are trying to use non-existing output no {0}'\n                    ''.format(coilnr))\n\n    def switchOff(self, switchNumber):\n        \"\"\" Retract coil ore move motor.\n\n          @param int switchNumber: number of switch to be switched\n\n          @return bool: True if suceeds, False otherwise\n        \"\"\"\n        coilnr = int(switchNumber) + 1\n        if 0 < int(coilnr) < 5:\n            with self.lock:\n                try:\n                    answer = self.inst.ask('P{0}=0'.format(coilnr))\n                    if answer != 'P{0}=0'.format(coilnr):\n                        return False\n                    time.sleep(self.getSwitchTime(switchNumber))\n                    self.log.info('{0} switch {1}: Off'.format(\n                        self._name, switchNumber))\n                except:\n                    return False\n                return True\n        else:\n            self.log.error('You are trying to use non-existing output no {0}'\n                    ''.format(coilnr))\n\n    def getSwitchTime(self, switchNumber):\n        \"\"\" Give switching time for switch.\n\n          @param int switchNumber: number of switch\n\n          @return float: time needed for switch state change\n\n          Coils typically switch faster than 0.5s, but safety first!\n        \"\"\"\n        return 0.5\n\n","repo_name":"QOMS-at-Cavendish/cam-qudi","sub_path":"hardware/switches/hbridge.py","file_name":"hbridge.py","file_ext":"py","file_size_in_byte":4722,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"48"}
+{"seq_id":"27604068245","text":"import datetime\nimport os\nimport time\nimport cbpro\nimport csv\n\n# columns=['time', 'low', 'high', 'open', 'close', 'volume']\n# ticker = 'ETH-USD'\n# out_dir = \"/Users/dan/dev/code/projects/python/cryptotrader/data\"\n# with open(os.path.join(out_dir, \"{0}.csv\".format(ticker)), \"w\") as out_file:\n#     writer = csv.writer(out_file, dialect=\"excel\")\n#     writer.writerow(columns)\n#\n#     pub = cbpro.PublicClient()\n#     num_records = 0\n#     record_start = datetime.datetime(2017, 1, 1, 0, 0, 0)\n#     record_end = datetime.datetime(2021, 2, 15, 0, 0, 0)\n#     marker = record_start\n#     while marker < record_end:\n#         start = marker\n#         end = start + datetime.timedelta(minutes=5 * 300)\n#         res = pub.get_product_historic_rates(ticker, start=start, end=end, granularity=300)\n#         try:\n#             res2 = [[datetime.datetime.fromtimestamp(x[0]), x[1], x[2], x[3], x[4], x[5]] for x in res]\n#         except TypeError:\n#             print(\"TypeError; res={0}\".format(res))\n#             break\n#         num_records += len(res)\n#         print(\"start: {0} len={1}\".format(start, num_records))\n#         writer.writerows(res2)\n#         marker = end\n#         time.sleep(1)\n\n# wsClient = cbpro.WebsocketClient(url=\"wss://ws-feed.pro.coinbase.com\",\n#                                 products=\"ETH-USD\",\n#                                 channels=[\"ticker\"])\n#\n# # Do other stuff...\n# wsClient.close()\n\nimport cbpro, time\n\n\n\nclass myWebsocketClient(cbpro.WebsocketClient):\n    def on_open(self):\n        self.url = \"wss://ws-feed.pro.coinbase.com/\"\n        self.products = [\"LTC-USD\"]\n        self.channels = [\"ticker\"]\n        self.message_count = 0\n        print(\"Lets count the messages!\")\n\n\n    def on_message(self, msg):\n        self.message_count += 1\n        print(msg)\n        if 'price' in msg and 'type' in msg:\n            print(\"Message type:\", msg[\"type\"],\n                  \"\\t@ {:.3f}\".format(float(msg[\"price\"])))\n\n\n    def on_close(self):\n        print(\"-- Goodbye! --\")\n\n\n\nwsClient = myWebsocketClient()\nwsClient.start()\nprint(wsClient.url, wsClient.products)\nwhile (wsClient.message_count < 500):\n    print(\"\\nmessage_count =\", \"{} \\n\".format(wsClient.message_count))\n    time.sleep(1)\nwsClient.close()\n","repo_name":"ecodan/cryptotrader","sub_path":"cryptalgo/backtest/history_downloader.py","file_name":"history_downloader.py","file_ext":"py","file_size_in_byte":2240,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"13005349627","text":"import os \nimport sys\nimport sqlite3 \n\n\ndataAdd = []\n\n\ndef getInformations():\n    name = str(input('Enter Name :'))\n    if name == '':\n        print('Do not leave the fields blank.')\n    dataAdd.append(name)\n                \n    surname = str(input('Enter Surname :')) \n    if surname == '':\n        print('Do not leave the fields blank.')\n    dataAdd.append(surname)\n\n    age = int(input('Enter Age :')) \n    if age == '':\n        print('Do not leave the fields blank.')\n    dataAdd.append(str(age))\n                \n    number = int(input('Enter Number :')) \n    if number == '':\n        print('Do not leave the fields blank.')\n    dataAdd.append(str(number)) \n                \n    country = input('Enter Your :')  \n    if country == '':\n        print('Do not leave the fields blank.')\n    dataAdd.append(country)  \n\n\ndef inforationSave():\n    with sqlite3.connect('persons.db') as persons:\n        personsCursor = persons.cursor()\n        personsCursor.execute('''CREATE TABLE IF NOT EXISTS person(id_ INTEGER PRIMARY KEY,name TEXT,surname TEXT,age TEXT,number TEXT,country TEXT)''')\n        personsCursor.execute('''SELECT * FROM person''')\n        data = personsCursor.fetchall()\n        if data == []:\n            personsCursor.execute('''INSERT INTO person(name,surname,age,number,country) VALUES(?,?,?,?,?)''', dataAdd)\n            persons.commit()\n            choose()\n        else:\n            sql = '''SELECT *  FROM person WHERE number = ? '''\n            personsCursor.execute(sql, (dataAdd[3],))\n            data = personsCursor.fetchall()\n            if len(data) == 1:\n                print('Pre-Registered. Change the information and login again.')\n                exit()\n            else:\n                personsCursor.execute('''INSERT INTO person(name,surname,age,number,country) VALUES(?,?,?,?,?)''', dataAdd)\n                persons.commit()\n                choose()\n                                \n\ndef choose():\n    global option\n    print('*' * 50)\n    print('Welcome to the project area.')\n                \n    print('Which Project do you prefer.')  \n    print(''' 1 - The multiplication table.\n                        2 - Finding the largest and smallest number.\n                        3 - Factor Calculation.\n                        4 - Calculating the areas of geometric shapes.\n                        5 - Reading file.\n                        6 - File operations with os module.\n                        7 - Find a prime number.\n                        8 - Learning computer knowledge.\n                        9 - Print the text backwards.\n                        10 - Try Expect\n                        11 - fibonacci Calculation - 1\n                        12 - fibonacci Calculation - 2  \n                        13 - Writing file.\n                        14 - Exit''') \n                \n    option = int(input('>>'))\n                \n    if option == 1:\n        print('''   Difficulty Levels: \n            1 - Easy Mode\n            2 - Normal Mode\n            3 - Hard Mode\n            4 - So Hard Mode ''') \n        option2  = int(input('>>>')) \n\n        if option2 == 1: \n            print(''' Easy Mode. ''')\n            theMultiplication(1,10 + 1)  \n            #choose()\n                         \n        elif option2 == 2:\n            print(''' Normal Mode. ''')\n            theMultiplication(10,20 + 1) \n            #choose()\n\n        elif option2 == 3:\n            print(''' Hard Mode. ''') \n            theMultiplication(20,30 + 1) \n            #choose()\n \n        elif option == 4:\n            print(''' So Hard Mode. ''')\n            theMultiplication(30,40 + 1) \n            #choose()\n        else:\n            print('Out of Selection')  \n            choose()\n                    \n                \n    elif option == 2:\n        bigSmall()\n        choose()\n\n    elif option == 3:\n        factorCalculation()\n        print('(Factor calculation with recursion). \\n Which number of factorial would you like to take?  Sample(1,2,3,4,5,6,7)') \n        value = int(input('Value : ? >'))  \n        print('Factor :',factorCalculation2(value)) \n        choose()\n\n    elif option == 4:\n        areaCalculation()\n        choose()\n\n    elif option == 5: \n        fileRead()\n        choose()\n\n    elif option == 6:\n        print('''\n        File operations with module 'os'\n                -----------------------------\n                1 - Creating a new folder\n                2 - List the folders in the directory\n                3 - Delete folder\n        ''')\n        try:\n            fileOption = int(input('The transaction number you want to make ? :'))\n            if fileOption == 1:\n                newFolderCreate() \n                choose()\n            if fileOption == 2:\n                listDirectory()\n                choose()\n            if fileOption == 3:\n                deleteFolder()\n                choose() \n        except:\n            print('Error.')\n                \n    elif option == 7:\n        primeNumber()\n        choose() \n                \n    elif option == 8:\n        computerInformation()\n        choose() \n\n                \n    elif option == 9:\n        print('Reversing') \n        eversingOption = input('Which one would you like to do with ? ( 1 - Just Print , 2 - For, 3 - While' + ' :') \n        if eversingOption == 1:\n            writeBackwardsprint() \n            choose()\n            \n        elif eversingOption == 2:\n            writeBackwardsfor()\n            choose()\n\n        elif eversingOption == 3:\n            writeBackwardwhile()\n            choose()\n        else:\n            choose()\n                \n                \n    elif option == 10:\n        tryExpect() \n        choose()\n                \n    elif option == 11:\n        fibonacciNumberValue = int(input('Fibonacci Enter Number : >>')) \n        print('Fibonacci Number Result :',fibonacciNumber(fibonacciNumberValue))  \n        choose()\n                \n    elif option == 12:\n        fibonacciNumber2() \n        choose()\n\n    if option == 13:\n        fileWrite()\n        choose()\n    \n    if option == 14:\n        exit()\n\ndef theMultiplication(startValue,FinishValue): \n    totalPoint = 0\n    for i in range(startValue,FinishValue):\n        for j in range(startValue,FinishValue):\n            print('{} {} {} {} : ?'.format(i,'*',j,'?'))\n            sonuc = int(input('Result : >>>'))\n            if sonuc == int(i * j):\n                print('{}'.format('True.'))\n                totalPoint+=5\n            else:\n                print('{}'.format('Wrong Answer.')) \n                totalPoint+=-5 \n                print('Total Point : ', totalPoint)\n\n                choose()\n                \n\n\ndef bigSmall():\n    numberlist = []\n    print('Finding the Big and Small Number.')\n    piece = int(input('How many numbers will you enter ? :'))\n    for f in range(1,piece + 1):\n        num = int(input('Enter Number :'))\n        numberlist.append(num) \n    print('Largest list number : {} and Smallest list number : {}'.format(max(numberlist),min(numberlist)))   \n    \n\ndef factorCalculation():\n    factor = 1\n    print('Which number of factorial would you like to take?  Sample(1,2,3,4,5,6,7)') \n    value = int(input('Value : ? >'))  \n    for a in range(1, value + 1):\n        factor*=a\n    print('Factorial of the number of {} ​​you entered : {}'.format(value,factor)) \n\n\ndef factorCalculation2(n):\n    if n == 0:\n        return 1\n    elif n == 1:\n        return 1\n    else:\n        return n * factorCalculation2(n - 1) \n                \n\ndef areaCalculation():\n    print(\"{}\".format('Calculating the areas of geometric shapes.'))  \n    print(\"{}\".format('Which shape would you like to calculate the area ? :'))\n    print('''\n    \\n Square : 1\n    Rectangle : 2\n    Trapezoid : 3\n    Parallelogram : 4\n    Equilateral Quadrangle : 5\n    ''')\n    value = int(input('Which Figure ? :'))\n            \n    if value == 1:\n        r = int(input('Enter Radius :'))   \n        print('Area of ​​the Square: {}'.format(r**2)) \n        return\n            \n    elif value == 2:\n        short = int(input('Enter a short edge :'))\n        tall = int(input('Enter a tall edge :')) \n        print('Area of ​​the Rectangle: {}'.format(short * tall)) \n        return\n            \n    elif value == 3:\n        base = int(input('Enter a base :')) \n        base2 = int(input('Enter a base :'))\n        height = int(input('Enter a height :'))\n        print('Area of ​​the Trapezoid: {}'.format((base + base2) * height) / 2)   \n        return\n            \n    elif value == 4:\n        basee = int(input('Enter a base :'))\n        heightt = int(input('Enter a height :'))\n        print('Area of ​​the Parallelogram: {}'.format(basee * heightt))  \n        return\n            \n    elif value == 5:\n        side = int(input('Enter a side :'))\n        heighttt = int(input('Enter a tall edge :'))\n        print('Area of ​​the Equilateral Quadrangle: {}'.format(side * heighttt))  \n        return\n\n\ndef fileWrite():\n    while True:\n        fileName = input('Enter Filename : ')\n        if fileName == '':\n            print('Do not leave the fields blank.')\n        else:\n            break\n\n    with open('{0}.txt'.format(fileName),'a+') as fileName_:\n        content = input('Content to Write in the File (If you want to write them separately, put a comma between them. Put an extra comma at the end) : ')\n        fileName_.write(content)\n\n\ndef fileRead():\n    while True:\n        fileName = input('Enter Filename : ')\n        if fileName == '':\n            print('Do not leave the fields blank.')\n        else:\n            break\n\n    if os.path.isfile('{0}.txt'.format(fileName)):\n        with open('{0}.txt'.format(fileName),'r') as reading: \n            readingList = reading.readlines()\n            for data in readingList:\n                splitData = data.split(',')\n                for data_ in splitData:\n                    print(data_)\n    else:\n        print('There is no such file in the system.')\n\n\ndef newFolderCreate():\n    folder = input('The name of the folder you will create: ')\n    if os.path.exists(folder):\n        print('{} There is already a folder named. Choose another name. '.format(folder))\n    else:\n        os.makedirs(folder)\n\n\ndef listDirectory():\n    print('{}/\\n...'.format(os.getcwd()))\n    \n    for z,d in enumerate(os.listdir(os.curdir)):\n        print('{}  {}'.format(z, d))\n\n\ndef deleteFolder():\n    directory = {}\n    print('\\nno\\t\\tfolder name\\n--\\t\\t---------') \n\n    for ü,i in enumerate(os.listdir(os.curdir)):\n        print(\"{}\\t\\t{}\".format(ü, i))\n        directory[ü] = i \n\n    try:\n        d_no = int(input('The file number you want to delete : '))\n        path = directory [d_no]\n        os.removedirs(path)\n\n    except Exception as except_:\n        print('Error :', except_)\n\n\n\ndef primeNumber():\n    num = int(input('Enter a Number :'))\n    f = 0\n    for g in range(2, num):\n        if num % g == 0:\n            f+=1\n            print('Number Not Prime.')\n            return\n\n        if f == 0:\n            print('Number Prime')\n\n\n\n\ndef computerInformation():\n    print('\\n OS : {}  \\n Computer Name : {} \\n Version : {}.'.format(os.platform,os.name,os.version))       \n\n\ndef  writeBackwardsprint():\n    value = input('Type the text to be written in reverse : >>') \n    if value.isalpha():\n        valeue = value[::-1]\n        print('Before :{} After :{}'.format(value,valeue)) \n\ndef writeBackwardsfor(): \n    t = ''\n    value = input('Type the text to be written in reverse : >>') \n    if value.isalpha():\n        for j in range(len(value)-1,-1,-1):\n            t += value[j] \n            print('Before :{} After :{}'.format(value,t)) \n\ndef writeBackwardwhile():\n    t = ''\n    num = 0\n    value = input('Type the text to be written in reverse : >>') \n    lenn = len(value)\n    if value.isalpha(): \n        while lenn > num:\n            t+=value[lenn - 1] \n            num+=1\n\n\ndef tryExpect():\n    nullList = []\n    try:\n        value = int(input('How many times will you enter a name ? >> ') ) \n        for t in range(0,value + 1):\n            _value = input('Enter : ')\n            nullList.append(_value) \n        for value in nullList:\n            if value.endswith('ck'):\n                value+='er'\n                print('New Word :',value) \n            \n    except Exception as except_:\n        print('Error :', except_)\n                \n\ndef fibonacciNumber(fibonacciNumberValue):\n    if fibonacciNumberValue == 0:\n        return 0\n    elif fibonacciNumberValue == 1:\n        return 1\n    else:\n        return fibonacciNumber(fibonacciNumberValue-1) + fibonacciNumber(fibonacciNumberValue-2)  \n\n\ndef fibonacciNumber2():\n    value = []\n    number1 = 1\n    number2 = 1\n            \n    getNumber = int(input('Which number would you like to do the Fibonacci Calculation ? : '))  \n    counter = 1\n    \n    while counter < getNumber:\n        print(number1)\n        value.append(number1)\n        print(number2)\n        value.append(number2)\n\n        number1+=number2\n        number2+=number1 \n        counter+=2\n\n    print('Value :',value) \n\n\ndef areYouRegistered():\n    name = \"\"\n    yesORno = input('Are you registered : ')\n    if yesORno == '':\n        print('Do not leave the fields blank.')    \n    if yesORno.upper() == 'Y':\n        while True:\n            name = input('Again Enter Name Please :') \n            if name == '':\n                print('Do not leave the fields blank.')\n            else:\n                break\n        \n        with sqlite3.connect('persons.db') as persons:\n            personsCursor = persons.cursor()\n            personsCursor.execute('''SELECT * FROM person''')\n            data = personsCursor.fetchall()\n\n            for data_ in data:\n                if data_[1].upper() == name.upper():\n                    choose()\n            \n            exit()\n\n    elif yesORno.upper() == 'N':\n        getInformations()\n        inforationSave()\n\nareYouRegistered()\n \n\n\n\n\n\n\n","repo_name":"xrwy/Py_Mini_Project","sub_path":"Py_Mini_Project.py","file_name":"Py_Mini_Project.py","file_ext":"py","file_size_in_byte":13897,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"15499084385","text":"def Upper_Lower(inp):\n    cnt_lower, cnt_upper = 0, 0\n    for i in inp:\n        if 'A' <= i <= 'Z':\n            cnt_upper += 1\n        elif 'a' <= i <= 'z':\n            cnt_lower += 1\n    print(f'Number of upper case characters: {cnt_upper}')\n    print(f'Number of lower case characters: {cnt_lower}')\n\nstr = input()\nUpper_Lower(str)","repo_name":"wupsi/PP2_2021_Summer","sub_path":"Weeks/Week 2/TSIS6/7.py","file_name":"7.py","file_ext":"py","file_size_in_byte":333,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"25946802291","text":"#!/usr/bin/python3\n'''\n    Application of singly linked list Data structure\n    Python Implementation.\n'''\n\n\nclass Node:\n    '''\n    Declaration of a singly linked list node structure\n    '''\n\n    def __init__(self, data, next_node=None):\n        '''\n        Class constructor\n        '''\n        self.data = data\n        self.next_node = next_node\n\n        @property\n        def data(self):\n            return self.__data\n\n        @data.setter\n        def data(self, value):\n            if not isinstance(value, int):\n                raise TypeError(\"data must be an integer\")\n            self.__data = value\n\n        @property\n        def next_node(self):\n            return self.__next_node\n\n        @next_node.setter\n        def next_node(self, value):\n            if value is not None and type(value) != Node:\n                raise TypeError('next_node must be a Node object')\n            self.__next_node = value\n\n\nclass SinglyLinkedList:\n    '''\n    Definition of a node of the linked list\n    '''\n\n    def __init__(self):\n        '''initiator (constructor)\n        '''\n        self.head = None\n\n    def sorted_insert(self, value):\n        '''\n        This function inserts nodes and arranges them in\n        ascending order of their data (integers)\n        '''\n        new = Node(value)\n        if not self.head:\n            self.head = new\n            return\n\n        if value < self.head.data:\n            new.next_node = self.head\n            self.head = new\n            return\n\n        node = self.head\n        while node.next_node and node.next_node.data < value:\n            node = node.next_node\n        if node.next_node:\n            new.next_node = node.next_node\n        node.next_node = new\n\n    def __str__(self):\n        '''\n        method to modify the print function\n        '''\n        ret = \"\"\n        read = self.head\n\n        while read:\n            ret += str(read.data) + '\\n'\n            read = read.next_node\n        return ret[:-1]\n","repo_name":"davidajimati/alx-higher_level_programming","sub_path":"0x06-python-classes/100-singly_linked_list.py","file_name":"100-singly_linked_list.py","file_ext":"py","file_size_in_byte":1964,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"74905359505","text":"# 평면상에 points 목록이 있을 때, 원점 (0, 0)에서 K번 가까운 점 목록을 순서ㅐ로 출력하라.\n# 평면상 두 점의 거리는 유클리드 거리로 한다.\n\n# 입력\n\npoints = [[3,3],[5,-1],[-2,4]]\nK = 2\n# 출력\n\n\nimport heapq\ndef getUclidDist(points,K):\n    dists = []\n    for x, y in points:\n        dist = x*x + y*y\n        heapq.heappush(dists,[dist, [x,y]])\n    result = []\n    for _ in range(K):\n        result.append(heapq.heappop(dists)[1])\n    return result\nprint(getUclidDist(points, K))\n    \n         ","repo_name":"limnyn/python_codingtest","sub_path":"Algorithm_Interview/64_원점에서k번째로가까운점.py","file_name":"64_원점에서k번째로가까운점.py","file_ext":"py","file_size_in_byte":541,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"2129294809","text":"import numpy as np\nimport torch\nimport torchvision\nfrom torch import nn\nimport autoEnc\nimport pandas as pd\nimport argparse\nfrom sklearn.preprocessing import RobustScaler\nfrom sklearn.model_selection import train_test_split\nimport copy\nimport pickle as pkl\n\ndef main(options):\n\n    df = pd.read_csv(options.infile)\n    df['pdTimestamp'] = [pd.Timestamp(ts) for ts in df['timestamp']]\n    df['minutes'] = [float((pdts - df['pdTimestamp'][0]).to_numpy()/(60e9)) for pdts in df['pdTimestamp']]\n    minsFail = df.loc[df['machine_status'] == 'BROKEN', 'minutes'].values\n    mnsFail = np.zeros(0)\n    for i in range(len(minsFail)):\n        base = minsFail[i-1] if i>0 else 0.\n        mnsFail = np.hstack((mnsFail, minsFail[i]*np.ones(int(minsFail[i])-int(base))))\n    mnsFail = np.hstack((mnsFail, 999999.*np.ones(df.index.size-len(mnsFail))))\n    df['minsNextFail'] = mnsFail\n    df['minsToNextFail'] = df['minsNextFail'] - df['minutes']\n    df.dropna(thresh=53, inplace=True)\n    dfAll = copy.deepcopy(df)\n    \n    df_fails = df.loc[df['minsToNextFail']<60.,:]\n    df_fails.reset_index(inplace=True)\n    df.query('minsToNextFail>60', inplace=True)\n    # df_check = df.loc[170001:, :]\n    df.query(\"machine_status=='NORMAL'\", inplace=True)\n    \n    sensNames = []\n    for i in range(52):\n        sensNames.append('sensor_{}'.format(str(i).zfill(2)))\n    sensNames.remove('sensor_50')\n    sensNames.remove('sensor_15')\n\n    features = df.loc[:, sensNames].fillna(0.0).values\n    trainData, evalData = train_test_split(features, shuffle=True, train_size=0.8)\n    scaler = RobustScaler(quantile_range=(0.1,99.9)).fit(trainData)\n    trainData = scaler.transform(trainData)\n    evalData = scaler.transform(evalData)\n\n    trainData = torch.utils.data.TensorDataset(torch.from_numpy(trainData))\n    evlData = torch.utils.data.TensorDataset(torch.from_numpy(evalData))\n\n    net = autoEnc.Autoencoder(epochs=100, batchSize=64, learningRate=1e-5)\n    net.train(trainData, evalData)\n\n    # testIn = scaler.transform(df.loc[1234, sensNames].fillna(0.0).values.reshape(1,-1))\n    # testIn = torch.from_numpy(testIn).type(torch.FloatTensor)\n    # testOut = net(testIn)\n    # print('Input: {}'.format(testIn))\n    # print('Output: {}'.format(testOut))\n    # testLoss = net.criterion(testOut, testIn)\n    # print('Test Loss: {:.4f}'.format(testLoss.data))\n\n    testFailIn = scaler.transform(df_fails.loc[:, sensNames].fillna(0.0).values)\n    testFailIn = torch.from_numpy(testFailIn).type(torch.FloatTensor)\n    testFailOut = net(testFailIn)\n    \n    # print('Input Fail: {}'.format(testFailIn))\n    # print('Output Fail: {}'.format(testFailOut))\n    testFailLoss = nn.MSELoss(reduction='none')(testFailOut, testFailIn)\n    # print(testFailLoss.mean(axis=-1))\n    df_fails['netLoss'] = testFailLoss.mean(axis=-1).data.numpy()\n    df_fails.to_hdf('./outputFails.hd5', key='df_fails', mode='w')\n    # print('Test Loss Failure: {:.4f}'.format(testFailLoss.data))\n    \n    testCheckIn = scaler.transform(dfAll.loc[:, sensNames].fillna(0.0).values)\n    testCheckIn = torch.from_numpy(testCheckIn).type(torch.FloatTensor)\n    testCheckOut = net(testCheckIn)\n\n    testCheckLoss = nn.MSELoss(reduction='none')(testCheckOut, testCheckIn)\n    dfAll['netLoss'] = testCheckLoss.mean(axis=-1).data.numpy()\n    dfAll.to_hdf('./allOutput.hd5', key='dfAll', mode='w')\n\n    pkl.dump(net.trainingLossMean, open('./trainingLossMean.pkl', 'wb'))\n    pkl.dump(net.validationLossMean, open('./validationLossMean.pkl', 'wb'))\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    # parser.add_argument(\n    #     '--cudaDevice', '-c', action='store', default='0', type=str)\n    parser.add_argument(\n        '--infile', '-i', action='store', type=str)\n    # parser.add_argument(\n    #     '--tmpDir', '-t', action='store', default='./', type=str)\n    # parser.add_argument(\n    #     '--saveFile', '-s', action='store', type=str)\n    # parser.add_argument(\n    #     '--config', action='store', type=int)\n    options = parser.parse_args()\n    main(options)\n","repo_name":"threiten/pumpSensors","sub_path":"runTraining.py","file_name":"runTraining.py","file_ext":"py","file_size_in_byte":4028,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"12572490819","text":"from formula_tree import FormulaTree, Leaf, NotTree, AndTree, OrTree\n\n# Do not change any of the class declarations above this comment.\n\n# Add your functions here.\n\n\ndef build_tree(formula):\n    '''(str) -> FormulaTree\n    If the str <formula> meets the criteria\n    of being a valid formula, create and return\n    the FormulaTree representation of the string.\n    Otherwise returns None.\n    REQ: formula is not empty\n    >>> build_tree('x')\n    Leaf('x')\n    '''\n    # Check if formula is valid\n    if is_valid(formula):\n        # Base case when length of list is one\n        if len(formula) == 1:\n            tree = Leaf(formula)\n        # Second base case is special case of the NotTree\n        elif formula[0] == '-':\n            # Recursively create the rest of the formula '--(..)'\n            if formula[1] == '-':\n                tree = NotTree(build_tree(formula[1:]))\n            # Create the NotTree for the rest of the bracket '-(..)'\n            elif formula[1] == '(':\n                tree = NotTree(build_tree(formula[1:formula.index(')')+1]))\n            # Create the NotTree only for the variable\n            elif formula[1].islower():\n                tree = NotTree(build_tree(formula[1]))\n        else:\n            # Get the root and the index of the root\n            (r, index) = find_root(formula)\n            # Build the 'And' and/or 'Or' trees respectively\n            if r == '*':\n                tree = AndTree(build_tree(formula[1:index]), build_tree(\n                    formula[index+1:len(formula)-1]))\n            elif r == '+':\n                tree = OrTree(build_tree(formula[1:index]), build_tree(\n                    formula[index+1:len(formula)-1]))\n    else:\n        tree = None\n\n    return tree\n\n\ndef find_root(formula):\n    '''(str) -> str\n    Used to find the root when formula is valid and contains\n    either '+' or '*'\n    '''\n    for i in range(len(formula)):\n        if formula[i] == '+' or formula[i] == '*':\n            if is_valid(formula[1:i]) and is_valid(\n                    formula[i+1:len(formula)-1]):\n                return formula[i], i\n\n\ndef is_valid(formula):\n    '''(str) -> bool\n    Given a formula string, return a boolean\n    value to determine if it's a valid formula\n    adhering to the requirements shown on\n    a2.pdf\n    >>> is_valid('-x')\n    True\n    '''\n    stack = []\n    numsym = 0  # Number of * and +\n    numbrac = 0  # Number of ( brackets\n    numbrac2 = 0  # Number of ) brackets\n    numvar = 0  # Number of letters\n    val = True\n    # Checks the base case where length\n    # of the string is zero or if the\n    # string contains a space\n    if len(formula) == 0 or ' ' in formula:\n        val = False\n    # if the length is 1 and it's not a letter\n    elif len(formula) == 1 and not formula.islower():\n        val = False\n    # if length is 2 and doesn't contain '-'\n    elif len(formula) == 2 and '-' not in formula:\n        val = False\n    else:\n        # Loop through the string to check if\n        # brackets are balanced\n        for i in range(len(formula)):\n            if formula[i] == '(':\n                # If formula contains empty brackets '()'\n                if formula[i+1] == ')':\n                    val = False\n                stack.append(formula[i])\n                numbrac += 1\n            elif formula[i] == ')':\n                if len(stack) > 0:\n                    popped = stack.pop()\n                numbrac2 += 1\n            elif formula[i] == '+' or formula[i] == '*':\n                # Check if the values before and after\n                # '+' and '*' are valid\n                if (not formula[i-1].islower() and formula[i-1] != ')') or (\n                        not formula[i+1].islower() and (\n                                formula[i+1] != '(' and formula[i+1] != '-')):\n                    val = False\n                numsym += 1\n            elif formula[i].islower():\n                numvar += 1\n                # Makes sure OutofBounds Error doesn't occur\n                if (i > 0 and i < len(formula)-1):\n                    # '(x)' is invalid\n                    if formula[i-1] == '(' and formula[i+1] == ')':\n                        val = False\n                    # Makes sure the values before and after\n                    # the varible is valid\n                    # ('*', '+', '-' comes before)\n                    # ('*', '+', comes after)\n                    elif formula[i-1] != '*' and (\n                            formula[i-1] != '+' and formula[i-1] != '-'):\n                        if (formula[i+1] != '*' and formula[i+1] != '+'):\n                            val = False\n\n    # If the parentheses are not balanced\n    if len(stack) != 0 or numbrac2 != numbrac:\n        val = False\n\n    # If there are no variables\n    if numvar == 0:\n        val = False\n\n    # Check if the number of '(' brackets\n    # equals the number of * and + symbols\n    # and if the variables is one less than the\n    # number of + and * symbols\n    if numbrac != numsym or numvar-1 != numsym:\n        val = False\n\n    return val\n\n\ndef draw_formula_tree(root):\n    '''(FormulaTree) -> str\n    Given a FormulaTree, draw the string representation\n    of the tree as shown on pg. 2 of a2.pdf, and return\n    that string\n    REQ: symbols are either '+', '-', '*' or .islower()\n    REQ: root is a valid binary FormulaTree\n    >>> draw_formula_tree(NotTree(Leaf('x')))\n    '- x'\n    '''\n    return draw_helper(root)\n\n\ndef draw_helper(root, d=1):\n    '''(FormulaTree, int) -> str\n    Returns the string that draws out the FormulaTree\n    'd' holds the current recursion depth in order to\n    find out how many spaces is needed for each line\n    '''\n    # Initiate empty tree\n    tree = ''\n    # Base case is if the node is a leaf\n    if isinstance(root, Leaf):\n        tree += root.get_symbol()\n    # Else draw the tree for '-', '*', and '+'\n    elif isinstance(root, NotTree):\n        tree += '- ' + draw_helper(root.get_children()[0], d=d+1)\n    elif isinstance(root, AndTree):\n        tree += '* ' + draw_helper(root.get_children()[1], d=d+1) + '\\n'\n        tree += '  '*d + draw_helper(\n            root.get_children()[0], d=d+1)\n    elif isinstance(root, OrTree):\n        tree += '+ ' + draw_helper(root.get_children()[1], d=d+1) + '\\n'\n        tree += '  '*d + draw_helper(root.get_children()[0], d=d+1)\n    return tree\n\n\ndef evaluate(root, var, values):\n    '''(FormulaTree, str, str) -> bool\n    Given the formula represented as a FormulaTree,\n    a string of the variables in the formula, and the\n    values each variable represents, return the boolean\n    expression that is the result of replacing the variables\n    in the FormulaTree with their corresponding values.\n    REQ: values are either '1' or '0'\n    REQ: len(variables) == len(values)\n    REQ: var contains the variables in the FormulaTree\n    REQ: root is a valid FormulaTree\n    >>> evaluate(Leaf('x'), 'x', '1')\n    True\n    '''\n    # If the root is just one variable, the formula\n    # evaluates to values\n    if isinstance(root, Leaf):\n        # Get the symbol of the Leaf, then\n        # find its index in the string var\n        sym = root.get_symbol()\n        i = var.index(sym)\n        val = bool(int(values[i]))\n    else:\n        # Recursive case; check whether the root is a\n        # NOT, AND, OR function\n        if isinstance(root, NotTree):\n            val = not evaluate(root.get_children()[0], var, values)\n        elif isinstance(root, AndTree):\n            val = evaluate(root.get_children()[0], var, values\n                           ) and evaluate(root.get_children()[1], var, values)\n        elif isinstance(root, OrTree):\n            val = evaluate(root.get_children()[0], var, values\n                           ) or evaluate(root.get_children()[1], var, values)\n    return val\n\n\ndef play2win(root, turns, var, values):\n    '''(FormulaTree, str, str, str) -> int\n    The game configuration is given as the parameters:\n    where root is the root of the FormulaTree,\n    turns is a string representing the order of the\n    players' turn, var representing the variables\n    that each player can set values to, and values\n    being the values that have been set for each\n    variable. Given this, returns the best move that\n    the next player can make in order to have the best\n    chance of winning.\n    If the next move is irrelevant then 1 is returned if player\n    E is up, and 0 is returned if it is player A's turn.\n    REQ: (root, turns, variables, values) must be a valid\n    game configuration\n    REQ: len(turns) > len(values)\n    >>> play2win(OrTree(Leaf('x'), Leaf('y')), 'EA', 'xy', '')\n    1\n    '''\n    win = -1\n    # root, turns, var, values\n    # (x*y), 'EA', 'xy', '0'\n    # Base case, root is a Leaf\n    if isinstance(root, Leaf):\n        # If the current player is E, return 1\n        # Else the current player is A, return 0\n        if turns[0] == 'E':\n            win = 1\n        else:\n            win = 0\n    else:\n        if len(turns) > len(values):\n            # Find the Player we want to win (A or E)\n            first = turns[len(values)]\n            # plays 0\n            a = play2win(root, turns, var, values+'0')\n            # plays 1\n            b = play2win(root, turns, var, values+'1')\n\n            if first == 'E':\n                if a:\n                    win = 0\n                elif b:\n                    win = 1\n                else:\n                    # If choice makes no difference, choose 1\n                    win = 1\n            elif first == 'A':\n                if not a:\n                    win = 0\n                elif not b:\n                    win = 1\n                else:\n                    # If choice makes no difference, choose 0\n                    win = 0\n        else:\n            # Evaluate the Truth value\n            win = evaluate(root, var, values)\n    return win\n","repo_name":"MinhQNguyen86/formulaTree","sub_path":"formula_game_functions.py","file_name":"formula_game_functions.py","file_ext":"py","file_size_in_byte":9825,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"70195715665","text":"# Домашнее задание Семинар 6\r\n# Создать телефонный справочник с возможностью импорта и экспорта данных в формате .txt.\r\n\r\n\r\ndef scan(data):\r\n    # Просмотр всех записей справочника:\r\n    for i in  data:\r\n        print(i)\r\n        \r\ndef search(data):\r\n    # Поиск по справочнику.\r\n    flag = 1\r\n    name = input('имя > ')\r\n    for line in data:\r\n        if name in line:\r\n            flag = 0\r\n            print(line)\r\n    if flag: print('no name given')\r\n    \r\ndata = readfile('data.txt') # При запуске программы (скрипта), она должна считывать содержимое\r\ndict_command = {'1' :  scan, '2' : search} # словарь команд, в значениях функции их исполняющие\r\n \r\nprint('''Введите номер действия:\r\n       1 - Показать все записи\r\n       2 - Найти запись по вхождению частей имени\r\n       3 - Найти запись по телефону\r\n       4 - Добавить новый контакт\r\n       5 - Удалить контакт\r\n       6 - Изменить номер телефона у контакта\r\n       7 - Выход''')\r\n \r\nwhile True:\r\n    command = input('Команда: > ')\r\n    if command in dict_command:\r\n        dict_command[command](data)\r\n    else:\r\n        print(' command error!')","repo_name":"OllFuller/HW-PYTHON","sub_path":"Задание к 6 семинару.py","file_name":"Задание к 6 семинару.py","file_ext":"py","file_size_in_byte":1515,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"22583073025","text":"# Client Implementation\n# Implementation of the client_handler\n# Michael Long, Gennadii Sytov\n# CS494 Final Project, June 2019\n\n# Imports / Constants\nimport socket\nimport threading\nBUFFER = 1024 # Defines the maximum byte size of input from a client\n\n# Designed to handle each of the server interactions for the client\nclass client_handler():\n\n    # Init\n    def __init__(self, host, port):\n        # Basic socket setup\n        self.host = host\n        self.port = port\n        self.address = (host, port)\n        # Attempt to bind to socket\n        self.client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        self.client_socket.connect(self.address)\n        self.connected = True\n        # Create a new thread for receiving input\n        self.receive_thread = threading.Thread(target=self.receive)\n        self.receive_thread.start()\n\n    # Handle receiving a new instance of a message\n    def receive(self): \n\n        # Receive input from the server\n        while self.connected:\n            # Try to send to the server\n            try:\n                # Sanitize input\n                input = self.client_socket.recv(BUFFER)\n                scrub = input.decode(\"utf8\")\n                # Close socket if it receives a disconnect message\n                if scrub == \"--disconnect--\":\n                    print(\"You have been disconnected from the server.\")\n                    self.connected == False\n                    return\n                # Print any other message sent from the server\n                else:\n                    print(scrub)\n\n            # Handle a server crash    \n            except:\n                print(\"You have been disconnected\")\n                self.connected = False\n                return\n\n    # Send messages\n    def main_loop(self):\n\n        while self.connected:\n\n            # Basic input stream\n            to_send = input()\n\n            # Verify the message is valid\n            if to_send:\n                # Allow the client to disconnect from the server by closing the socket\n                if to_send == \"/quit\":\n                    self.client_socket.send(bytes(to_send, \"utf8\"))\n                    self.client_socket.close()\n                    return\n                # Send a command to the server\n                else:    \n                    to_send = to_send.encode('utf-8')\n                    self.client_socket.send(to_send)","repo_name":"MchlLong/CS494_Client","sub_path":"CS494_Client/CS494_Client_Handler.py","file_name":"CS494_Client_Handler.py","file_ext":"py","file_size_in_byte":2397,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"41669733995","text":"from keras import backend\nfrom keras.engine import base_preprocessing_layer\nimport numpy as np\nimport tensorflow.compat.v2 as tf\n\nfrom tensorflow.python.util.tf_export import keras_export\n\n# pylint: disable=g-classes-have-attributes\n\n\n@keras_export('keras.layers.Normalization',\n              'keras.layers.experimental.preprocessing.Normalization')\nclass Normalization(base_preprocessing_layer.PreprocessingLayer):\n  \"\"\"Feature-wise normalization of the data.\n\n  This layer will coerce its inputs into a distribution centered around\n  0 with standard deviation 1. It accomplishes this by precomputing the mean and\n  variance of the data, and calling `(input - mean) / sqrt(var)` at runtime.\n\n  What happens in `adapt()`: Compute mean and variance of the data and store\n  them as the layer's weights. `adapt()` should be called before `fit()`,\n  `evaluate()`, or `predict()`.\n\n  Args:\n      axis: Integer, tuple of integers, or None. The axis or axes that should\n        have a separate mean and variance for each index in the shape. For\n        example, if shape is `(None, 5)` and `axis=1`, the layer will track 5\n        separate mean and variance values for the last axis. If `axis` is set to\n        `None`, the layer will normalize all elements in the input by a scalar\n        mean and variance. Defaults to -1, where the last axis of the input is\n        assumed to be a feature dimension and is normalized per index. Note that\n        in the specific case of batched scalar inputs where the only axis is the\n        batch axis, the default will normalize each index in the batch\n        separately. In this case, consider passing `axis=None`.\n      mean: The mean value(s) to use during normalization. The passed value(s)\n        will be broadcast to the shape of the kept axes above; if the value(s)\n        cannot be broadcast, an error will be raised when this layer's `build()`\n        method is called.\n      variance: The variance value(s) to use during normalization. The passed\n        value(s) will be broadcast to the shape of the kept axes above; if the\n        value(s) cannot be broadcast, an error will be raised when this layer's\n        `build()` method is called.\n\n  Examples:\n\n  Calculate a global mean and variance by analyzing the dataset in `adapt()`.\n\n  >>> adapt_data = np.array([1., 2., 3., 4., 5.], dtype='float32')\n  >>> input_data = np.array([1., 2., 3.], dtype='float32')\n  >>> layer = tf.keras.layers.Normalization(axis=None)\n  >>> layer.adapt(adapt_data)\n  >>> layer(input_data)\n  <tf.Tensor: shape=(3,), dtype=float32, numpy=\n  array([-1.4142135, -0.70710677, 0.], dtype=float32)>\n\n  Calculate a mean and variance for each index on the last axis.\n\n  >>> adapt_data = np.array([[0., 7., 4.],\n  ...                        [2., 9., 6.],\n  ...                        [0., 7., 4.],\n  ...                        [2., 9., 6.]], dtype='float32')\n  >>> input_data = np.array([[0., 7., 4.]], dtype='float32')\n  >>> layer = tf.keras.layers.Normalization(axis=-1)\n  >>> layer.adapt(adapt_data)\n  >>> layer(input_data)\n  <tf.Tensor: shape=(1, 3), dtype=float32, numpy=\n  array([0., 0., 0.], dtype=float32)>\n\n  Pass the mean and variance directly.\n\n  >>> input_data = np.array([[1.], [2.], [3.]], dtype='float32')\n  >>> layer = tf.keras.layers.Normalization(mean=3., variance=2.)\n  >>> layer(input_data)\n  <tf.Tensor: shape=(3, 1), dtype=float32, numpy=\n  array([[-1.4142135 ],\n         [-0.70710677],\n         [ 0.        ]], dtype=float32)>\n  \"\"\"\n\n  def __init__(self, axis=-1, mean=None, variance=None, **kwargs):\n    super().__init__(**kwargs)\n    base_preprocessing_layer.keras_kpl_gauge.get_cell('Normalization').set(True)\n\n    # Standardize `axis` to a tuple.\n    if axis is None:\n      axis = ()\n    elif isinstance(axis, int):\n      axis = (axis,)\n    else:\n      axis = tuple(axis)\n    self.axis = axis\n\n    # Set `mean` and `variance` if passed.\n    if isinstance(mean, tf.Variable):\n      raise ValueError('Normalization does not support passing a Variable '\n                       'for the `mean` init arg.')\n    if isinstance(variance, tf.Variable):\n      raise ValueError('Normalization does not support passing a Variable '\n                       'for the `variance` init arg.')\n    if (mean is not None) != (variance is not None):\n      raise ValueError(\n          'When setting values directly, both `mean` and `variance` '\n          'must be set. Got mean: {} and variance: {}'.format(mean, variance))\n    self.input_mean = mean\n    self.input_variance = variance\n\n  def build(self, input_shape):\n    super().build(input_shape)\n\n    if (isinstance(input_shape, (list, tuple)) and\n        all(isinstance(shape, tf.TensorShape) for shape in input_shape)):\n      raise ValueError('Normalization only accepts a single input. If you are '\n                       'passing a python list or tuple as a single input, '\n                       'please convert to a numpy array or `tf.Tensor`.')\n\n    input_shape = tf.TensorShape(input_shape).as_list()\n    ndim = len(input_shape)\n\n    if any(a < -ndim or a >= ndim for a in self.axis):\n      raise ValueError('All `axis` values must be in the range [-ndim, ndim). '\n                       'Found ndim: `{}`, axis: {}'.format(ndim, self.axis))\n\n    # Axes to be kept, replacing negative values with positive equivalents.\n    # Sorted to avoid transposing axes.\n    self._keep_axis = sorted([d if d >= 0 else d + ndim for d in self.axis])\n    # All axes to be kept should have known shape.\n    for d in self._keep_axis:\n      if input_shape[d] is None:\n        raise ValueError(\n            'All `axis` values to be kept must have known shape. Got axis: {}, '\n            'input shape: {}, with unknown axis at index: {}'.format(\n                self.axis, input_shape, d))\n    # Axes to be reduced.\n    self._reduce_axis = [d for d in range(ndim) if d not in self._keep_axis]\n    # 1 if an axis should be reduced, 0 otherwise.\n    self._reduce_axis_mask = [\n        0 if d in self._keep_axis else 1 for d in range(ndim)\n    ]\n    # Broadcast any reduced axes.\n    self._broadcast_shape = [\n        input_shape[d] if d in self._keep_axis else 1 for d in range(ndim)\n    ]\n    mean_and_var_shape = tuple(input_shape[d] for d in self._keep_axis)\n\n    if self.input_mean is None:\n      self.adapt_mean = self.add_weight(\n          name='mean',\n          shape=mean_and_var_shape,\n          dtype=self.dtype,\n          initializer='zeros',\n          trainable=False)\n      self.adapt_variance = self.add_weight(\n          name='variance',\n          shape=mean_and_var_shape,\n          dtype=self.dtype,\n          initializer='ones',\n          trainable=False)\n      self.count = self.add_weight(\n          name='count',\n          shape=(),\n          dtype=tf.int64,\n          initializer='zeros',\n          trainable=False)\n      self.finalize_state()\n    else:\n      # In the no adapt case, make constant tensors for mean and variance with\n      # proper broadcast shape for use during call.\n      mean = self.input_mean * np.ones(mean_and_var_shape)\n      variance = self.input_variance * np.ones(mean_and_var_shape)\n      mean = tf.reshape(mean, self._broadcast_shape)\n      variance = tf.reshape(variance, self._broadcast_shape)\n      self.mean = tf.cast(mean, self.compute_dtype)\n      self.variance = tf.cast(variance, self.compute_dtype)\n\n  def update_state(self, data):\n    if self.input_mean is not None:\n      raise ValueError(\n          'Cannot `adapt` a Normalization layer that is initialized with '\n          'static `mean` and `variance`, you passed mean {} and variance {}.'\n          .format(self.input_mean, self.input_variance))\n\n    if not self.built:\n      raise RuntimeError('`build` must be called before `update_state`.')\n\n    data = self._standardize_inputs(data)\n    data = tf.cast(data, self.adapt_mean.dtype)\n    batch_mean, batch_variance = tf.nn.moments(data, axes=self._reduce_axis)\n    batch_shape = tf.shape(data, out_type=self.count.dtype)\n    if self._reduce_axis:\n      batch_reduce_shape = tf.gather(batch_shape, self._reduce_axis)\n      batch_count = tf.reduce_prod(batch_reduce_shape)\n    else:\n      batch_count = 1\n\n    total_count = batch_count + self.count\n    batch_weight = (\n        tf.cast(batch_count, dtype=self.dtype) /\n        tf.cast(total_count, dtype=self.dtype))\n    existing_weight = 1. - batch_weight\n\n    total_mean = self.adapt_mean * existing_weight + batch_mean * batch_weight\n    # The variance is computed using the lack-of-fit sum of squares\n    # formula (see https://en.wikipedia.org/wiki/Lack-of-fit_sum_of_squares).\n    total_variance = ((self.adapt_variance +\n                       (self.adapt_mean - total_mean)**2) * existing_weight +\n                      (batch_variance +\n                       (batch_mean - total_mean)**2) * batch_weight)\n    self.adapt_mean.assign(total_mean)\n    self.adapt_variance.assign(total_variance)\n    self.count.assign(total_count)\n\n  def reset_state(self):  # pylint: disable=method-hidden\n    if self.input_mean is not None or not self.built:\n      return\n\n    self.adapt_mean.assign(tf.zeros_like(self.adapt_mean))\n    self.adapt_variance.assign(tf.ones_like(self.adapt_variance))\n    self.count.assign(tf.zeros_like(self.count))\n\n  def finalize_state(self):\n    if self.input_mean is not None or not self.built:\n      return\n\n    # In the adapt case, we make constant tensors for mean and variance with\n    # proper broadcast shape and dtype each time `finalize_state` is called.\n    self.mean = tf.reshape(self.adapt_mean, self._broadcast_shape)\n    self.mean = tf.cast(self.mean, self.compute_dtype)\n    self.variance = tf.reshape(self.adapt_variance, self._broadcast_shape)\n    self.variance = tf.cast(self.variance, self.compute_dtype)\n\n  def call(self, inputs):\n    inputs = self._standardize_inputs(inputs)\n    # The base layer automatically casts floating-point inputs, but we\n    # explicitly cast here to also allow integer inputs to be passed\n    inputs = tf.cast(inputs, self.compute_dtype)\n    return ((inputs - self.mean) /\n            tf.maximum(tf.sqrt(self.variance), backend.epsilon()))\n\n  def compute_output_shape(self, input_shape):\n    return input_shape\n\n  def compute_output_signature(self, input_spec):\n    return input_spec\n\n  def get_config(self):\n    config = super().get_config()\n    config.update({\n        'axis': self.axis,\n        'mean': self._convert_to_list(self.input_mean),\n        'variance': self._convert_to_list(self.input_variance),\n    })\n    return config\n\n  def _standardize_inputs(self, inputs):\n    inputs = tf.convert_to_tensor(inputs)\n    if inputs.dtype != self.dtype:\n      inputs = tf.cast(inputs, self.dtype)\n    return inputs\n\n  def _convert_to_list(self, inputs):\n    if tf.is_tensor(inputs):\n      inputs = inputs.numpy()\n    if isinstance(inputs, (np.ndarray)):\n      inputs = inputs.tolist()\n      inputs = list(inputs)\n    return inputs\n","repo_name":"BrianMarquez3/Python-Course","sub_path":"EasyRecognition/env/Lib/site-packages/keras/layers/preprocessing/normalization.py","file_name":"normalization.py","file_ext":"py","file_size_in_byte":10882,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"48"}
+{"seq_id":"39774099059","text":"#input_dir = \"/cmsuf/data/store/user/t2/users/nikmenendez/skimmed/NanoAOD/2017/data/signal_sel/Eff/\"\ninput_dir = \"/cmsuf/data/store/user/t2/users/nikmenendez/skimmed/NanoAOD/2017/data/signal_sel/UL/\"\n#input_dir = \"/cmsuf/data/store/user/t2/users/nikmenendez/skimmed/NanoAOD/2017/data/Zpeak/Eff/\"\n\ndata_samples = [\"data\"]\ndata_files = {\"data\": \"%stotal_data_no_dupe.root\"%(input_dir)}\n\ndata_samples.append(\"fake\")\ndata_files[\"fake\"] = \"%stotal_data_no_dupe.root\"%(input_dir)\n\ndata_vars = [\n\"genWeight\",\n\"pileupWeight\",\n\"nMuons\",\n\"nGoodMuons\",\n\"nbJets\",\n\"nJets\",\n\"idL1\",\n\"idL2\",\n\"idL3\",\n\"pTL1\",\n\"pTL2\",\n\"pTL3\",\n\"etaL1\",\n\"etaL2\",\n\"etaL3\",\n\"phiL1\",\n\"phiL2\",\n\"phiL3\",\n\"massL1\",\n\"massL2\",\n\"massL3\",\n\"dxyL1\",\n\"dxyL2\",\n\"dxyL3\",\n\"dzL1\",\n\"dzL2\",\n\"dzL3\",\n\"IsoL1\",\n\"IsoL2\",\n\"IsoL3\",\n\"ip3dL1\",\n\"ip3dL2\",\n\"ip3dL3\",\n\"sip3dL1\",\n\"sip3dL2\",\n\"sip3dL3\",\n\"tightIdL1\",\n\"tightIdL2\",\n\"tightIdL3\",\n\"looseIdL1\",\n\"looseIdL2\",\n\"looseIdL3\",\n\"medIdL1\",\n\"medIdL2\",\n\"medIdL3\",\n\"mvaIdL1\",\n\"mvaIdL2\",\n\"mvaIdL3\",\n\"sourceL1\",\n\"sourceL2\",\n\"sourceL3\",\n\"met\",\n\"met_phi\",\n\"dR12\",\n\"dR13\",\n\"dR23\",\n\"m3l\",\n\"m4l\",\n\"mt\",\n\"passedDiMu1\",\n\"passedDiMu2\",\n\"passedTriMu\",\n\"idL4\",\n\"pTL4\",\n\"etaL4\",\n\"phiL4\",\n\"massL4\",\n\"dxyL4\",\n\"dzL4\",\n\"IsoL4\",\n\"ip3dL4\",\n\"sip3dL4\",\n\"tightIdL4\",\n\"medIdL4\",\n\"mvaIdL4\",\n\"softIdL1\",\n\"softIdL2\",\n\"softIdL3\",\n\"softIdL4\",\n\"inAcceptance\",\n#\"gen_dPtL1\",\n#\"gen_dPtL2\",\n#\"gen_dPtL3\",\n#\"gen_dRL1\",\n#\"gen_dRL2\",\n#\"gen_dRL3\",\n]\n\n","repo_name":"Nik-Menendez/Wto3l_NanoAOD_Plotter","sub_path":"Datasets/Run2017/Data.py","file_name":"Data.py","file_ext":"py","file_size_in_byte":1395,"program_lang":"python","lang":"is","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"71704769107","text":"\"\"\" Given two integer arrays nums1 and nums2, return an array of their intersection. \n\nEach element in the result must appear as many times as it shows in both arrays and you may return the result in any order.\n\n\"\"\"\n\nclass Solution:\n    def intersect(self, nums1: List[int], nums2: List[int]) -> List[int]:\n        count1, count2 = {}, {}\n        result = []\n        \n        # make hash tables for both lists\n        for i in range(len(nums1)):\n            count1[nums1[i]] = 1 + count1.get(nums1[i], 0)\n            \n        for i in range(len(nums2)):\n            count2[nums2[i]] = 1 + count2.get(nums2[i], 0)\n            \n        # check if keys are a match\n        for key in count1:\n            if key in count2:\n                # use min to get the minimum number between the counts\n                count = min(count1[key], count2[key])\n                for i in range(count):\n                    result.append(key)\n                    \n        return result\n","repo_name":"kotynskm/leetcode","sub_path":"intersectionii.py","file_name":"intersectionii.py","file_ext":"py","file_size_in_byte":965,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"12703706472","text":"from typing import Tuple\nimport json\nimport logging\nimport queue\nimport socketserver\nimport threading\n\nfrom knut.apis import KnutAPI\nfrom .knutserver import KnutServer\n\n\nclass KnutTCPRequestHandler(socketserver.BaseRequestHandler):\n    \"\"\"Request handler for the :class:`KnutTCPServer`.\n\n    The request handler is instantiated once per connection to the server and\n    kept open until either the client closes the connection or the server is\n    shutdown.\n    \"\"\"\n\n    ENCODING = 'utf-8'\n    \"\"\"The encoding in which messages are received and send.\"\"\"\n\n    HEARTBEAT_FREQUENCY = 1\n    \"\"\"The frequency in which the heartbeat is send measured in Hz. See\n    :meth:`heartbeat()` for more about the heartbeat.\"\"\"\n\n    def __init__(self, request, client_address, server):\n        self.__msg_queue = queue.Queue()\n\n        self.send_heartbeat = True\n        \"\"\"If true, :meth:`heartbeat()` is called with a frequency of\n        :const:`HEARTBEAT_FREQUENCY`.\"\"\"\n\n        super(KnutTCPRequestHandler, self).__init__(request,\n                                                    client_address,\n                                                    server)\n\n    def finish(self) -> None:\n        self.send_heartbeat = False\n        logging.debug('Close request handle: {}'.format(self.client_address))\n\n    def handle(self) -> None:\n        logging.info('Handle client request: {}'.format(self.client_address))\n\n        while True:\n            data = b''\n            knutmsg = dict()\n            msg = dict()\n            msgid = 0x0000\n            apiid = 0x00\n\n            try:\n                # read until a null byte is received\n                while True:\n                    read_byte = self.request.recv(1)\n                    if read_byte == b'\\x00':\n                        break\n                    elif not read_byte:\n                        return  # no data available anymore\n                    else:\n                        data += read_byte\n\n                logging.debug('Received bytes: {}'.format(len(data)))\n\n                if len(data) > 0:\n                    logging.debug('Received raw message: {}'.format(data))\n                    knutmsg = json.loads(\n                        data,\n                        encoding=KnutTCPRequestHandler.ENCODING\n                    )\n\n                    try:\n                        apiid = knutmsg['apiId']\n                        msgid = knutmsg['msgId']\n                        msg = knutmsg['msg']\n\n                        logging.debug('Message of type {} for API {} '\n                                      'received...'.format(msgid, apiid))\n\n                        msgid, msg = self.request_service(\n                            apiid, msgid, msg)\n                    except KeyError:\n                        logging.warning('Received message is missing at least '\n                                        'one of the following keys: '\n                                        '[msgId, apiId, msg]')\n            except ConnectionResetError as e:\n                logging.info('Connection reset: {}'.format(e))\n                return\n            except json.decoder.JSONDecodeError:\n                logging.warning('Failed to decode JSON message...')\n\n            if msgid > 0:\n                self.send_queued_knutmsg(apiid, msgid, msg)\n\n    def heartbeat(self) -> None:\n        \"\"\"Sends frequently a heartbeat.\n\n        The heartbeat is a frequently send message, where only a null byte\n        ``b'\\\\x00'`` is send.\n        \"\"\"\n        if self.send_heartbeat:\n            self.send_queued(b'\\x00')\n            timer = threading.Timer(\n                1 / KnutTCPRequestHandler.HEARTBEAT_FREQUENCY,\n                self.heartbeat\n            )\n            timer.daemon = True\n            timer.start()\n\n    def msg_builder(self,\n                    apiid: int,\n                    msgid: int,\n                    msg: dict,\n                    encoding: str) -> bytearray:\n        \"\"\"Return a Knut message.\n\n        Build a Knut message for the API *apiid* and the message *msg* of type\n        *msgid*. The Knut message is terminated by an null byte ``b'\\\\x00'``.\n\n        For example::\n\n           >>> msg_builder(2, 2, {}, 'utf-8')\n           bytearray(b'{\"apiid\": 2, \"msgId\": 2, \"msg\": {}}\\\\x00')\n\n        \"\"\"\n        data = {'apiId': apiid, 'msgId': msgid, 'msg': msg}\n        data_str = json.dumps(data)\n\n        logging.debug('Build {} byte long message: {}'\n                      .format(len(data_str), data_str))\n\n        return bytearray(data_str, encoding) + b'\\x00'\n\n    def request_service(self,\n                        apiid: int,\n                        msgid: int,\n                        msg: dict) -> Tuple[int, dict]:\n        \"\"\"Returns a service's response.\n\n        This method calls the ``request_handler()`` of the API with the\n        *apiid*. The API's request handler handles then the *msg* of\n        type *msgid*. The returned tuple holds the responses *msgid* and *msg*.\n        If no API for *apiid* is found in the servers API\n        dictionary, the *msg* and *msgid* is returned.\n        \"\"\"\n        if apiid not in self.server.apis.keys():\n            logging.warning('Unknown API request: {}'.format(apiid))\n            return msgid, msg\n\n        return self.server.apis[apiid].request_handler(msgid, msg)\n\n    def send_queued_knutmsg(self, apiid: int, msgid: int, msg: dict) -> None:\n        \"\"\"Sends a queued Knut message.\n\n        Sends the *msg* of type *msgid* from the *apiid* as a queued Knut\n        message.\n        \"\"\"\n        self.send_queued(self.msg_builder(apiid,\n                                          msgid,\n                                          msg,\n                                          KnutTCPRequestHandler.ENCODING))\n\n    def send_queued(self, msg: bytearray) -> None:\n        \"\"\"Sends a queued message.\n\n        Puts the *msg* to the message queue and sends all messages form the\n        queue until its empty.\n        \"\"\"\n        self.__msg_queue.put(msg)\n\n        while not self.__msg_queue.empty():\n            try:\n                # get next message from the queue\n                next_msg = self.__msg_queue.get_nowait()\n\n                # don't log heartbeats\n                if next_msg != b'\\x00':\n                    logging.debug('Send message from queue to client {}: {}'\n                                  .format(self.client_address, next_msg))\n\n                self.request.sendall(next_msg)\n            except (BrokenPipeError, OSError):\n                return\n\n    def setup(self) -> None:\n        logging.debug('Start heartbeat: {}'.format(self.client_address))\n        threading.Thread(target=self.heartbeat, daemon=True).start()\n\n        for apiid, service in self.server.apis.items():\n            service.on_push += self.send_queued_knutmsg\n\n\nclass KnutTCPServer(socketserver.ThreadingMixIn,\n                    socketserver.TCPServer,\n                    KnutServer):\n    \"\"\"Knut TCP socket server.\n\n    Handle all communication from clients and redirect requests to the APIs of\n    the corresponding services. For each client, the request handler\n    :class:`KnutTCPRequestHandler` is instantiated in a new thread. The\n    connection is kept open once a request is received to allow sending push\n    notifications via TCP back to the connected client. See :meth:`add_api()`\n    for more about how to add an API to the server.\n\n    The request handler reads UTF-8 encoded JSON messages terminated by a null\n    byte ``b'\\\\x00'``. See :ref:`knutmsg` for more details.\n\n    After adding APIs, the server can be run by ``serve_forever()``. The\n    request are then handled by the request handler until the server is\n    shutdown. See the documentation for the `socketserver\n    <https://docs.python.org/3/library/socketserver.html#module-socketserver>`_\n    module for details.\n\n    As an example, a :class:`KnutTCPServer` is instantiated and\n    :class:`knut.apis.Temperature` is add to the server's APIs. The server is\n    run for one minute and using ``netcat`` as client, a request is send to the\n    server::\n\n       from knut.apis import Temperature\n       from knut.server import KnutTCPServer\n       from knut.services.dummytemperature import DummyTemperature\n       import threading\n       import time\n\n       # add the DummyTemperature service back-end to the API\n       temperature = Temperature()\n       temperature.add_backend(DummyTemperature('Somewhere', 'dummy'))\n\n       server = KnutTCPServer((\"localhost\", 8080))\n       server.add_api(temperature)\n\n       with server:\n           server_thread = threading.Thread(target=server.serve_forever)\n           server_thread.daemon = True\n           server_thread.start()\n           time.sleep(60)  # run for one minute\n\n       server.shutdown()\n\n    From the client, a :const:`TEMPERATURE_LIST_REQUEST` is send to the API:\n\n    .. code-block:: bash\n\n       $ echo -ne '{\"apiId\": 1, \"msgId\": 2, \"msg\": {}}\\\\0' | netcat localhost 8080\n       {\"apiId\": 1, \"msgId\": 258, \"msg\": {\"dummy\": {\"location\": \"Somewhere\",\n           \"unit\": \"\\\\u00b0C\", \"condition\": \"\\\\uf002\", \"temperature\": 10.2}}}\n\n    The server finally responses with the API's\n    :const:`TEMPERATURE_LIST_RESPONSE`.\n    \"\"\"\n\n    def __init__(self, address: str = \"127.0.0.1\", port: int = 8080) -> None:\n        \"\"\"Bind the server to the *address* on the specified *port*.\"\"\"\n        self.allow_reuse_address = True\n\n        self.apis = dict()\n\n        super(KnutTCPServer, self).__init__((address, port),\n                                            KnutTCPRequestHandler)\n\n    def knut_serve_forever(self):\n        with self:\n            server_thread = threading.Thread(target=self.serve_forever)\n            server_thread.daemon = True\n            server_thread.start()\n            server_thread.join()\n","repo_name":"pearjo/knut-server","sub_path":"knut/server/tcpserver.py","file_name":"tcpserver.py","file_ext":"py","file_size_in_byte":9770,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"17410017352","text":"from rest_framework import serializers\n\nfrom recipes.models import TagModel, RecipeModel, IngredientModel, RecipeImageModel, RecipeReviewModel\nfrom users.serializers import UserSerializer\n\n\nclass TagField(serializers.Field):\n    \"\"\"Field for recipe tags\"\"\"\n\n    def to_internal_value(self, data):\n        tag, created = TagModel.objects.get_or_create(tag=data)\n        return tag\n\n    def to_representation(self, value):\n        return value.tag\n\n\nclass IngredientsField(serializers.Field):\n    \"\"\"Field for recipe ingredients\"\"\"\n\n    def to_internal_value(self, data):\n        ingredient = IngredientModel(name=data)\n        return ingredient\n\n    def to_representation(self, value):\n        return value.name\n\n\nclass RecipeImageField(serializers.ImageField):  # used for nested serializers\n    \"\"\"Field for recipe images\"\"\"\n\n    def to_internal_value(self, data):\n        image_field = super().to_internal_value(data)\n        image = RecipeImageModel(image=image_field)\n        return image\n\n    def to_representation(self, value):\n        return value.image.url\n\n\nclass RecipeImageSerializer(serializers.ModelSerializer):  # used for views\n    \"\"\"Serializer for recipe images\"\"\"\n\n    class Meta:\n        model = RecipeImageModel\n        fields = ('image',)\n\n\nclass RecipeReviewsSerializer(serializers.ModelSerializer):\n    \"\"\"Serializer for recipe reviews\"\"\"\n\n    user = UserSerializer(read_only=True)\n\n    class Meta:\n        model = RecipeReviewModel\n        fields = ('user', 'title', 'slug', 'rating', 'timestamp', 'body')\n        extra_kwargs = {\n            'slug': {\n                'read_only': True\n            }\n        }\n\n\nclass DetailedRecipeSerializer(serializers.ModelSerializer):\n    \"\"\"Detailed Serializer for recipe model\"\"\"\n\n    user = UserSerializer(read_only=True)\n    tags = serializers.ListSerializer(child=TagField(), required=False)\n    ingredients = serializers.ListSerializer(child=IngredientsField())\n    images = serializers.ListSerializer(child=RecipeImageField(), required=False)\n    reviews = RecipeReviewsSerializer(many=True, read_only=True)\n    favourites_count = serializers.ReadOnlyField()\n    reviews_count = serializers.ReadOnlyField()\n    rating = serializers.ReadOnlyField()\n\n    class Meta:\n        model = RecipeModel\n        fields = ('user', 'name', 'main_image', 'time_to_finish', 'rating', 'ingredients',\n                  'description', 'body', 'images', 'tags', 'timestamp', 'favourites_count',\n                  'reviews', 'reviews_count')\n\n    def create(self, validated_data):\n        user = self.context.get('user', None)\n        tags = validated_data.pop('tags', [])\n        ingredients = validated_data.pop('ingredients', [])\n        images = validated_data.pop('images', [])\n\n        recipe = RecipeModel.objects.create(user=user, **validated_data)\n\n        for tag in tags:\n            recipe.tags.add(tag)\n\n        for image in images:\n            image.recipe = recipe\n            image.save()\n\n        for ingredient in ingredients:\n            ingredient.recipe = recipe\n            ingredient.save()\n\n        return recipe\n\n    def update(self, instance, validated_data):\n        tags = validated_data.pop('tags', None)\n        ingredients = validated_data.pop('ingredients', None)\n        images = validated_data.pop('images', None)\n\n        recipe = super().update(instance, validated_data)\n\n        if images is not None:\n            for image in images:\n                image.recipe = recipe\n                image.save()\n            recipe.images.set(images)\n\n        if ingredients is not None:\n            for ingredient in ingredients:\n                ingredient.recipe = recipe\n                ingredient.save()\n            recipe.ingredients.set(ingredients)\n\n        if tags is not None:\n            recipe.tags.set(tags)\n\n        return recipe\n\n\nclass RecipeSerializer(serializers.ModelSerializer):\n    \"\"\"Read-only Serializer for recipe model\"\"\"\n    tags = serializers.ListSerializer(child=TagField())\n    favourites_count = serializers.ReadOnlyField()\n\n    class Meta:\n        model = RecipeModel\n        fields = ('slug', 'name', 'main_image', 'time_to_finish', 'description',\n                  'tags', 'timestamp', 'favourites_count')\n","repo_name":"ahmedemad3965/My-Recipe","sub_path":"recipes/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":4214,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"28838213411","text":"import matplotlib.pyplot as plt\nimport numpy as np\nimport random\n\n\nclass KMeanClustering:\n    def __init__(self, data, error_threshold=0.01, k_max=10, repetition=10):\n        self.data = data\n        self.data_dimension = data.shape[1]\n        self.data_range = self.get_data_range()\n        self.normalized_data = self.data_normalization()\n\n        self.error_threshold = error_threshold\n        self.k_max = k_max\n        self.repetition = repetition\n\n        self.clustered_point_index = {}\n        self.cluster_location = {}\n        self.inertia_value = {}\n        self.elbow_k = None\n\n    def get_data_range(self):\n        return [[min(self.data[:, i]), max(self.data[:, i])] for i in range(self.data_dimension)]\n\n    def data_normalization(self):\n        normalized_data = [(x - self.data_range[i][0]) / (self.data_range[i][1] - self.data_range[i][0])\n                           for i, x in enumerate(self.data.T)]\n        return np.array(normalized_data).T\n\n    def k_mean_clustering_once(self, k):\n        def average(mean_before, n, a_n1):\n            return (mean_before * n + a_n1) / (n + 1)\n\n        cluster_location = np.array([[random.random() for _ in range(self.data_dimension)] for _ in range(k)])\n\n        while True:\n            clustered_point_index = [[] for _ in range(k)]\n            mean_error = 0\n\n            # Divide the points into clusters\n            for index, point in enumerate(self.normalized_data):\n                norm_array = [[np.linalg.norm(point - cluster, ord=2), i] for i, cluster in enumerate(cluster_location)]\n                nearest_cluster_index = min(norm_array, key=lambda x: x[0])[1]\n                clustered_point_index[nearest_cluster_index].append([index])\n\n            # Update the location of clusters\n            for index in range(k):\n                if clustered_point_index[index]:\n                    clustered_point = np.array([self.normalized_data[point_i]\n                                                for point_i in clustered_point_index[index]])\n                    new_location = np.array([np.mean(vector) for vector in clustered_point.T]).T\n\n                else:\n                    new_location = np.array([random.random() for _ in range(self.data_dimension)])\n                error = np.linalg.norm(new_location - cluster_location[index], ord=2)\n                mean_error = average(mean_error, index, error)\n                cluster_location[index] = new_location\n\n            # Evaluate the location of clusters\n            if mean_error < self.error_threshold:\n                final_clustered_point_index = clustered_point_index\n                break\n\n        inertia_value = sum([sum([np.linalg.norm(self.normalized_data[point_i] - cluster, ord=2)\n                                  for point_i in final_clustered_point_index[i]]) for i, cluster in\n                             enumerate(cluster_location)])\n\n        self.clustered_point_index[k] = final_clustered_point_index\n        self.cluster_location[k] = cluster_location\n        self.inertia_value[k] = inertia_value\n        return final_clustered_point_index, cluster_location, inertia_value\n\n    def repeat_K_mean_clustering(self, k):\n        clustered_point_index = None\n        cluster_location = None\n        inertia_value = float('inf')\n        for _ in range(self.repetition):\n            clustering_result = self.k_mean_clustering_once(k)\n            if clustering_result[2] < inertia_value:\n                clustered_point_index = clustering_result[0]\n                cluster_location = clustering_result[1]\n                inertia_value = clustering_result[2]\n\n        self.clustered_point_index[k] = clustered_point_index\n        self.cluster_location[k] = cluster_location\n        self.inertia_value[k] = inertia_value\n        return clustered_point_index, cluster_location, inertia_value\n\n    def find_elbow_k(self):\n        inertia_value_list = list(self.inertia_value.values())\n        d_inertia_value = [x2 - x1 for x1, x2 in zip(inertia_value_list[:-1], inertia_value_list[1:])]\n        elbow_gradient = (inertia_value_list[-1] - inertia_value_list[0]) / self.k_max\n        for i, gradient in enumerate(d_inertia_value):\n            if gradient > elbow_gradient:\n                return i + 1\n\n    def plot_clustered_result(self, k, inertia_value_plot=True):\n        if inertia_value_plot:\n            plt.figure()\n            plt.plot(self.inertia_value.keys(), self.inertia_value.values(), 'r-o')\n            plt.plot(self.elbow_k, self.inertia_value[self.elbow_k], 'b-o')\n\n        plt.figure()\n        for cluster_i, clustered_point in enumerate(self.clustered_point_index[k]):\n            points = np.array([self.data[point_i] for point_i in clustered_point])\n            plt.scatter(*np.array(points).T[:2, :],\n                        color=(1 / k * cluster_i, 1 - 1 / k * cluster_i, 1 / k * cluster_i),\n                        s=10)\n        plt.gca().invert_yaxis()\n\n    def k_mean_clustering(self, plot=True):\n        for k in range(1, self.k_max + 1):\n            self.repeat_K_mean_clustering(k)\n            print(\"Clustering Data - (%d/%d)k\" % (k, self.k_max))\n\n        self.elbow_k = self.find_elbow_k()\n\n        if plot:\n            self.plot_clustered_result(self.elbow_k)\n\n        return self.clustered_point_index[self.elbow_k]\n\n\ndef main():\n    data = np.array([[1, 0],\n                     [1, 1],\n                     [1, 2],\n                     [1, 3],\n                     [1, 4],\n                     [1, 5],\n                     [1, 6],\n                     [1, 7],\n                     [1, 8],\n                     [1, 9],\n                     [1, 10],\n                     [2, 0],\n                     [2, 1],\n                     [2, 2],\n                     [2, 3],\n                     [2, 4],\n                     [2, 5],\n                     [2, 6],\n                     [2, 7],\n                     [2, 8],\n                     [2, 9],\n                     [2, 10],\n                     [11, 10],\n                     [11, 11],\n                     [11, 12],\n                     [11, 13],\n                     [11, 14],\n                     [11, 15],\n                     [11, 16],\n                     [11, 17],\n                     [11, 18],\n                     [11, 19],\n                     [11, 20],\n                     [12, 10],\n                     [12, 11],\n                     [12, 12],\n                     [12, 13],\n                     [12, 14],\n                     [12, 15],\n                     [12, 16],\n                     [12, 17],\n                     [12, 18],\n                     [12, 19],\n                     [12, 20],\n                     [20, 0],\n                     [18, 0],\n                     [19, 0],\n                     [20, 1],\n                     [18, 1],\n                     [19, 1],\n                     [20, 2],\n                     [18, 2],\n                     [19, 2],\n                     [18, 4],\n                     [19, 4],\n                     [20, 4],\n                     [18, 5],\n                     [19, 5],\n                     [20, 5],\n                     [18, 6],\n                     [19, 6],\n                     [20, 6],\n                     [18, 7],\n                     [19, 7],\n                     [20, 7],\n                     [20, 10],\n                     [20, 11],\n                     [20, 12],\n                     [20, 13],\n                     [20, 14],\n                     [20, 15],\n                     [20, 16],\n                     [20, 17],\n                     [20, 18],\n                     [20, 19],\n                     [20, 20],\n                     [20, 10],\n                     [20, 11],\n                     [20, 12],\n                     [20, 13],\n                     [20, 14],\n                     [20, 15],\n                     [20, 16],\n                     [20, 17],\n                     [20, 18],\n                     [20, 19],\n                     [20, 20]])\n\n    k_mean_clustering = KMeanClustering(data, error_threshold=0.1, k_max=20, repetition=5)\n    k_mean_clustering.k_mean_clustering(plot=True)\n    plt.show()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"wonjaegang/ComputerVision2022","sub_path":"k_mean_clustering.py","file_name":"k_mean_clustering.py","file_ext":"py","file_size_in_byte":8139,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"15369515302","text":"import os\nfrom os.path import expanduser\nimport shutil\ntry:\n    import configparser\nexcept:\n    from __main__ import slicer\n    slicer.util.pip_install('configparser')\n    import configparser\n\n\nclass SharedResources:\n    \"\"\"\n    Singleton class to have access from anywhere in the code at the various local paths where the data, or code are\n    located.\n    \"\"\"\n    __instance = None\n\n    @staticmethod\n    def getInstance():\n        \"\"\" Static access method. \"\"\"\n        if SharedResources.__instance == None:\n            SharedResources()\n        return SharedResources.__instance\n\n    def __init__(self):\n        \"\"\" Virtually private constructor. \"\"\"\n        if SharedResources.__instance != None:\n            raise Exception(\"This class is a singleton!\")\n        else:\n            SharedResources.__instance = self\n\n    def set_environment(self):\n        self.home_path = expanduser(\"~\")\n        current_folder = os.path.dirname(os.path.realpath(__file__))\n        self.icon_dir = os.path.join(current_folder, '../../', '/Resources/Icons/')\n\n        self.Raidionics_dir = os.path.join(self.home_path, '.raidionics-slicer')\n        if not os.path.isdir(self.Raidionics_dir):\n            os.mkdir(self.Raidionics_dir)\n\n        self.json_cloud_dir = os.path.join(self.Raidionics_dir, 'json', 'cloud')\n        if os.path.isdir(self.json_cloud_dir):\n            shutil.rmtree(self.json_cloud_dir)\n        os.makedirs(self.json_cloud_dir)\n        self.json_cloud_info_file = \"https://drive.google.com/uc?id=13-Mx1Os9eXB_bJBcJt_o9MXQrRI1xONi\"\n\n        self.json_local_dir = os.path.join(self.Raidionics_dir, 'json', 'local')\n        if not os.path.isdir(self.json_local_dir):\n            os.makedirs(self.json_local_dir)\n\n        self.resources_path = os.path.join(self.Raidionics_dir, 'resources')\n\n        self.model_path = os.path.join(self.resources_path, 'models')\n        if not os.path.isdir(self.model_path):\n            os.makedirs(self.model_path)\n\n        self.diagnosis_path = os.path.join(self.resources_path, 'reporting')\n        if not os.path.isdir(self.diagnosis_path):\n            os.makedirs(self.diagnosis_path)\n\n        self.data_path = os.path.join(self.resources_path, 'data')\n        if os.path.isdir(self.data_path):\n            shutil.rmtree(self.data_path)\n        os.makedirs(self.data_path)\n\n        self.user_config_filename = os.path.join(self.data_path, 'runtime_config.ini')\n        self.diagnosis_config_filename = os.path.join(self.data_path, 'diagnosis_config.ini')\n\n        self.output_path = os.path.join(self.resources_path, 'output')\n        if os.path.isdir(self.output_path):\n            shutil.rmtree(self.output_path)\n        os.makedirs(self.output_path)\n\n        self.docker_path = None\n        self.__set_runtime_parameters()\n        self.global_active_model_update = False\n\n    def __set_runtime_parameters(self):\n        # Most likely deprecated, as we moved from the seg backend to the rads one!\n        # Set of variables sent to the docker images as runtime config, manually chosen by the user.\n        self.user_configuration = configparser.ConfigParser()\n        self.user_configuration['Predictions'] = {}\n        self.user_configuration['Predictions']['non_overlapping'] = 'true'\n        self.user_configuration['Predictions']['reconstruction_method'] = 'probabilities'\n        self.user_configuration['Predictions']['reconstruction_order'] = 'resample_first'\n        self.user_configuration['Runtime'] = {}\n        self.user_configuration['Runtime']['use_stripped_data'] = 'False'\n        self.user_configuration['Runtime']['use_registered_data'] = 'False'\n        self.user_configuration['Neuro'] = {}\n        self.user_configuration['Neuro']['brain_segmentation_filename'] = ''\n        self.user_configuration['Mediastinum'] = {}\n        self.user_configuration['Mediastinum']['lungs_segmentation_filename'] = ''\n        self.use_gpu = False\n\n        self.user_diagnosis_configuration = configparser.ConfigParser()\n        self.user_diagnosis_configuration['Default'] = {}\n        self.user_diagnosis_configuration['Default']['task'] = 'neuro_diagnosis'\n        self.user_diagnosis_configuration['Default']['trace'] = 'false'\n        self.user_diagnosis_configuration['Default']['from_slicer'] = 'true'\n        self.user_diagnosis_configuration['Neuro'] = {}\n        self.user_diagnosis_configuration['Neuro']['tumor_segmentation_filename'] = ''\n        self.user_diagnosis_configuration['Neuro']['brain_segmentation_filename'] = ''\n","repo_name":"raidionics/Raidionics-Slicer","sub_path":"Raidionics/Raidionics/src/utils/resources.py","file_name":"resources.py","file_ext":"py","file_size_in_byte":4490,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"48"}
+{"seq_id":"73894150547","text":"from django.contrib import admin\nfrom .models import *\nfrom django.contrib import messages\n\n# This is a extra. Only users should upload chapters.\n# If something unexpected arises, the admin have the option to upload a chapter (.zip file).\n# If .zip file is valid, an action is implemented so it can be rendered as a readable chapter (GalleryImage instance).\n# A second action is implemented so the admin can delete a Series instance and its related files.\n\n\n@admin.action(description='Extract images from zip files.')\ndef from_chapter_to_gallery(modeladmin, request, queryset):\n    # creating a GalleryImage object from a Chapter object.\n    for q in queryset:\n        if q.zip_import.name != '':\n            if q.is_valid():\n                q.to_gallery()\n            else:\n                q.delete_import()\n                q.delete()\n\n\n@admin.action(description='Delete a series chapters and images')\ndef delete_series(modeladmin, request, queryset):\n    for q in queryset:\n        if q.chapters.all():\n            pages = q.chapter_images.all()\n            for page in pages:\n                page.file.delete(save=True)\n                page.delete()\n            q.chapters.all().delete()\n            messages.info(request, \"Chapters and images successfully deleted.\")\n        else:\n            messages.info(request, \"No chapters to delete.\")\n\n\nclass uploader(admin.ModelAdmin):\n    actions = [from_chapter_to_gallery]\n\n\nclass deleter(admin.ModelAdmin):\n    actions = [delete_series]\n\n\nadmin.site.register(User)\nadmin.site.register(Series, deleter)\nadmin.site.register(Chapter, uploader)\nadmin.site.register(GalleryImage)\n","repo_name":"lheredias/comics-site","sub_path":"comics/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":1625,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"35505270890","text":"#! /usr/bin/env python\n\nimport rospy\nfrom sensor_msgs.msg import LaserScan\n\ndef clb(msg):\n\t\n\tfor i in range (0,6279):\n\t\tif msg.ranges[i] > 0 and msg.ranges[i] < 40:\n\t\t\tdist = msg.ranges[i]\n\t\t\tprint ('Object detected ', dist, 'Angle ', i)\n\t\t\t\t\t\n\n\nrospy.init_node('scan_values')\nsub = rospy.Subscriber('/scan', LaserScan, clb)\nrospy.spin()\n\n","repo_name":"rcjetpilot/sweep-scan-test","sub_path":"laser_node.py","file_name":"laser_node.py","file_ext":"py","file_size_in_byte":339,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"36144993244","text":"# -*- coding: utf-8 -*-\n\nfrom __future__ import division, print_function, absolute_import\nimport sys\nimport csv\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport tflearn\nimport tensorflow as tf\ntf.reset_default_graph()\nfrom tflearn.data_utils import load_csv\n\ncsv.field_size_limit(sys.maxsize)\n\n\n# Build a HDF5 dataset (only required once)\nfrom tflearn.data_utils import build_hdf5_image_dataset\nbuild_hdf5_image_dataset(dataset_file, image_shape=(250, 250), mode='file', \n\t\t\toutput_path='your_data_path.h5', categorical_labels=False, normalize=True)\n\n# Load HDF5 dataset\nimport h5py\nh5f = h5py.File('your_data_path.h5', 'r')  \nX = h5f['X']\nY = h5f['Y']\n\n\n\nnetwork = tflearn.input_data(shape=[None, 250, 250, 3])\nnetwork = tflearn.conv_2d(network, 64, 10, 4,activation='relu', regularizer=\"L1\")\nnetwork = tflearn.max_pool_2d(network, 2)\nnetwork = tflearn.local_response_normalization(network)\nnetwork = tflearn.conv_2d(network, 128, 11, activation='relu', regularizer=\"L1\")\nnetwork = tflearn.max_pool_2d(network, 2)\nnetwork = tflearn.local_response_normalization(network)\nnetwork = tflearn.fully_connected(network, 128, activation='relu')\nnetwork = tflearn.dropout(network, 0.8)\nnetwork = tflearn.fully_connected(network, 32, activation='relu')\nnetwork = tflearn.dropout(network, 0.8)\nnetwork = tflearn.fully_connected(network, 2, activation='sigmoid')\n\n\nnetwork = tflearn.regression(network, optimizer='Momentum', learning_rate=0.01,\n                     loss='categorical_crossentropy', metric='accuracy', to_one_hot = True, n_classes=2)\n\n\nmodel = tflearn.DNN(network, tensorboard_verbose=3)\n\nmodel.fit(X, Y, n_epoch=50, validation_set=0.5, batch_size = 256, \n\t\t\trun_id=\"cg_network\", show_metric=True, shuffle=True)\n\n\n\nmodel.save('your_data_path.tfl')\n\n\n\n","repo_name":"ShikeZhang/2017-12-Large_pose_facial_expression_code","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":1765,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"73643468306","text":"# Mega Millions Games for Brazilian Currency\n# fabiovictorelli@gmail.com\n# Mon Aug 19 08:10:22 EDT 2022\n\nimport random\ncustojogo = 2\n\n\ndef MostraJogosMegaRandomicos(njogos):\n    print(njogos, \"Random numbers for Megamillions  + 1 MB :\")\n    for i in range(njogos):\n        # amostragem randomica sem repeticao\n        lista_mega = sorted(random.sample(range(1, 71), 5))\n        # print(\"list_mega=\", lista_mega)\n        lista_mega_formatada = ['', '', '', '', '']\n        for ii in range(0, len(lista_mega)):\n            lista_mega_formatada[ii] = str(lista_mega[ii]).zfill(2)\n            # preenhe com zero a esquerda\n\n        print(lista_mega_formatada, \" MB=\", str(\n            random.randrange(1, 26)).zfill(2))\n\n\ndef main():\n    MostraJogosMegaRandomicos(10)\n\n    dolar = float(input(\"How many dollar do you want to play U$:\"))\n    cotacao = float(\n        input(\"What is the exchange rate for the dollar to Brazilian Reais today?\"))\n    conversao = dolar*cotacao\n    print('The total amount for Brazilian today is: R$', (\"%.2f\" % conversao))\n    print()\n\n    jogosremanescentes = input(\"How many free games from Last game? :\")\n\n    if jogosremanescentes == '':\n        jogosremanescentes = 0\n\n    numjogos = (int(dolar) / custojogo) + int(jogosremanescentes)\n    print(\"OWW !!! Lets play \", int(numjogos), \"times. Good Luck\")\n\n    exit()\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"fabiovictorelli/PythonExamples","sub_path":"jogo-megamillions.py","file_name":"jogo-megamillions.py","file_ext":"py","file_size_in_byte":1384,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"32618631751","text":"from django.urls import path, re_path\nimport authapp.views as authapp\nfrom .views import LoginView\napp_name = 'auth'\n\nurlpatterns = [\n    path('login/', LoginView.as_view(), name='login'),\n    re_path(r'^logout/$', authapp.logout, name='logout'),\n    re_path(r'^register/$', authapp.register, name='register'),\n    re_path(r'^edit/$', authapp.edit, name='edit'),\n\n]\n","repo_name":"spirindmitry/GB_Django_1","sub_path":"server/authapp/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":366,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"39604838884","text":"#sortear ordem de apresentaçao de 4 alunos e mostrar qual é a ordem\nimport random\nn1 = input('primeiro aluno: ')\nn2 = input('segundo aluno: ')\nn3 = input('terceiro aluno: ')\nn4 = input('quarto aluno: ')\nalunos = [n1, n2, n3, n4]\nrandom.shuffle(alunos)\nprint('A ordem de apresentação vai ser: \\n'\n      '', alunos)\n","repo_name":"takamuio/DesafiosAulaYoutube","sub_path":"venv/teste20.py","file_name":"teste20.py","file_ext":"py","file_size_in_byte":318,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"27998010744","text":"from dotenv import dotenv_values\nfrom notion_client import Client\nfrom pprint import pprint\n\n\nconfig = dotenv_values(\".env\")\nnotion_secret = config.get('NOTION_TOKEN')\nnotion = Client(auth=notion_secret)\n\npages = notion.search(filter={\"property\": \"object\", \"value\": \"page\"})\n#pprint(pages)\n\n\n#pprint(pages)\n\n#pprint(pages['results'][0]['properties']['title']['title'][0]['plain_text'])\n#pprint(pages['results'][0]['properties'])\n\npprint(pages['results'][0]['id'])\npage_id = pages['results'][0]['id']\nblock_list = notion.blocks.children.list(block_id=page_id)\nprint('--------------')\npprint(block_list)\n# for page in pages['results'][0]:\n#    page_title = page['properties']['title']['title'][0]['plain_text']\n#    if page_title_find = page_title:\n#       print('찾는 페이지가 존재합니다.')\n#       title_id = page['id']\n\ntarget_block_id = block_list['results'][0]['id']\nrich_text_temp = block_list['results'][0]['paragraph']['rich_text']\npprint(rich_text_temp)\n\ntext_value = rich_text_temp[0]['plain_text']\ntext_value = text_value.replace('페퍼로니', '크러스트')\n\nrich_text_temp[0]['plain_text'] = text_value\nrich_text_temp[0]['text']['content'] = text_value\npprint(rich_text_temp)\n\nrich_text = {}\nrich_text['rich_text'] = rich_text_temp\n\nnotion.blocks.update(block_id = target_block_id, paragraph = rich_text)\n\n\n\n### 예제 코드 ###\n# 옵션 모두 넣은 Search 호출\n#  \n# next_cursor = 'ff244636-d084-4cfb-93c3-55e6b82f53a0'\n# query_word = '고객센터'\n# sort_option = {\"direction\": \"descending\", \"timestamp\": \"last_edited_time\"}\n# filter_option = {\"property\": \"object\",\"value\": \"page\"}\n# page_size_default = 10\n\n# cs_block = notion.search(query=query_word, sort=sort_option, filter=filter_option,\n#                          start_cursor=next_cursor, page_size=page_size_default)\n","repo_name":"odreeblue/notion_api","sub_path":"01 notion_api_run/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1808,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"25599213934","text":"import  os\ndef create_folder(List_String):\n     count_folder = 0\n     if ( len(List_String) == 0):\n         return 0\n     else:\n        check = []\n        for i in range(0, len(List_String)):\n            check.append(os.path.exists(List_String[i]))\n        for i in range(0, len(check)):\n            if ( check[i] == False):\n                os.mkdir(List_String[i])\n                count_folder = count_folder + 1\n            else:\n                while True:\n                    print(\"Are you sure you want to replace {}? [Y/N]\".format(List_String[i]))\n                    Answer = input()\n                    if ( Answer.isdigit()):\n                        print(\"Invalid Option\")\n                    else:\n                        if ( Answer == \"Y\" or Answer == \"y\"):\n                            os.rmdir(List_String[i])\n                            os.mkdir(List_String[i])\n                            count_folder = count_folder + 1\n                            break\n                        elif ( Answer == \"N\" or Answer == \"n\"):\n                            break\n                        else:\n                            print(\"Invalid Option\")\n     print(count_folder)\n     return count_folder\ncreate_folder([\"Hello\", \"world\",\"goodbye\",\"orld\",\"Moon\"])\n\n","repo_name":"mrreach9/Python","sub_path":"Python_BootCamp/week03/ex/e45_create_folder.py","file_name":"e45_create_folder.py","file_ext":"py","file_size_in_byte":1261,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"17156823852","text":"#! /usr/bin/env python\n\"\"\"Manage LVR database.\n\n- Load from Official ballots file(s). Named *LVR*.xlsx or *CVR*.xlsx\n  into SQLITE database.\n\nLVR :: List of CVR records (an excel file, each row is CVR except\n     header row=1) Each record is the ballot results from one person.\n\nUnderlying dimensionality of LVR Data (value is Choice(string)):\n1. CVR\n2. Race\n\"\"\"\n\nimport sys\nimport argparse\nimport logging\nimport os\nimport os.path\nimport sqlite3\nfrom collections import defaultdict\nfrom pprint import pprint, pformat\n\n#!from . import sql\n#!from .lvr_sheet import LvrSheet\nimport vvote.sql\nfrom vvote.lvr_sheet import LvrSheet\n\n\nclass LvrDb():\n    \"\"\"Manage LVR Database (sqlite3 format)\"\"\"\n    fixed_choices = ['overvote', 'undervote', 'Write-in']\n    \n    def __init__(self, dbfile):\n        self.dbfile = dbfile\n        self.sourcefile = None\n        self.conn = None\n        self.raceLut = dict() # lut[column] => raceId\n\n    def new_db(self, overwrite=True):\n        dbfile = self.dbfile\n        if overwrite:\n            if os.path.exists(dbfile):\n                os.remove(dbfile)\n                #print('Removed LVR database: {}'.format(dbfile))\n\n        self.conn = sqlite3.connect(dbfile)\n        cur = self.conn.cursor()\n        cur.executescript(vvote.sql.lvr_schema)\n        #print('Created schema in LVR database: {}'.format(dbfile))\n        self.conn.commit()\n\n    def close_db(self):\n        self.conn.commit()\n        self.conn.close()\n\n    def summary(self):\n        print('Summarize database: {}'.format(self.dbfile))\n        self.conn = sqlite3.connect(self.dbfile)\n        cur = self.conn.cursor()\n        cur.execute('SELECT filename FROM source;')\n        self.sourcefile = cur.fetchone()[0]\n        cur.execute('SELECT votesAllowed,count(choice.race_id) FROM choice,race'\n                    ' WHERE choice.race_id = race.race_id'\n                    ' GROUP BY race.race_id ORDER BY race.race_id;')\n        va_choice_list = [(int(r[0]),int(r[1])) for r in cur.fetchall()]\n        print('''\nLVR Database Summary:\n   FILENAME: {} # CSV source\n   Race count: {}\n   Count (VoteFor,Choices) per race: \\n{}\n###################################################################\n'''.format(self.sourcefile,\n           len(va_choice_list),\n           ','.join([str(v) for v in va_choice_list]),  ))\n\n    # Do not due this.  Data may contain these choices. If so, there\n    # end up being two choice_ids for same choice_title\n    def OBSOLETE_insert_fixed_choices(self, race_id, choiceInvLut):\n        \"\"\"Choices for ALL races, all elections: over/undervote, write-in.\nchoiceInvLut :: lut[choiceTitle] => choiceId; MODIFIED IN PLACE\n\"\"\"\n        cur = self.conn.cursor()\n        for choice_title in self.fixed_choices:\n            cur.execute('INSERT INTO choice VALUES (?,?,?)',\n                        (None, choice_title, race_id))\n            choice_id = cur.lastrowid\n            choiceInvLut[choice_title] = choice_id\n        \n    def insert_from_csv(self,csvfile):\n        \"\"\"Append to existing Sqlite DB.\"\"\"\n        self.new_db(overwrite=True)\n        sheet = LvrSheet(csvfile)\n        self.sourcfile = sheet.filename\n        cur = self.conn.cursor()\n        cells = sheet.cells\n\n        #print('Inserting LVR CSV content into db: {}'.format(self.dbfile))\n\n        choices = defaultdict(set) # of choice_title for each race\n        choiceInvLut = dict() # lut[(raceId,choiceTitle)] => choiceId\n        \n        cur.execute('INSERT INTO source VALUES (?)', (csvfile,))\n\n        # INSERT races\n        for racename,raceC in sorted(sheet.raceLut.items(),key=lambda x: x[0]):\n            cur.execute('INSERT INTO race VALUES (?,?,?)',\n                        (None, sheet.voteFor[racename], racename))\n            raceId = cur.lastrowid\n            self.raceLut[raceC] = raceId\n            #!print('DBG: INSERT (race_id, votesAllowed, title) = ({},{},{})'\n            #!      .format(rid, sheet.voteFor[racename], racename))\n            #@@@ self.insert_fixed_choices(raceId, choiceInvLut)\n        # INSERT choices, cvr, vote\n        for r in range(sheet.minDataR, sheet.max_row + 1):\n            cvr_id = cells[r][1]\n            precinct = cells[r][2]\n            ballot = cells[r][3]\n\n            cur.execute('INSERT INTO cvr VALUES (?,?,?)',\n                        (cvr_id, precinct, ballot))\n            #logging.debug('INSERT cvr: {}'.format(cvr_id))\n\n            for c in range(sheet.minDataC, sheet.max_col + 1):\n                race_id = self.raceLut[sheet.raceLut[cells[1][c]]]\n                choice_title = cells[r].get(c,None)\n                if choice_title == None: continue\n\n                if (race_id,choice_title) not in choiceInvLut:\n                    cur.execute('INSERT INTO choice VALUES (?,?,?)',\n                                (None, choice_title, race_id))\n                    choice_id = cur.lastrowid\n                    choiceInvLut[(race_id,choice_title)] = choice_id\n                    \n                cur.execute('INSERT INTO vote VALUES (?,?)',\n                            (cvr_id, choiceInvLut[(race_id,choice_title)]))\n        #! print('Added CSV ({}) content to LVR database {}'\n        #!       .format(csvfile, self.dbfile))\n        self.conn.commit()\n        self.close_db()\n\n\n\n##############################################################################\n\ndef main():\n    \"Parse command line arguments and do the work.\"\n    parser = argparse.ArgumentParser(\n        description='My shiny new python program',\n        epilog='EXAMPLE: %(prog)s a b\"'\n        )\n    dfdb='LVR.db'\n    parser.add_argument('--version', action='version', version='1.0.1')\n\n    parser.add_argument('--incsv', type=argparse.FileType('r'),\n                        help='Input CSV file to store into DB')\n    parser.add_argument('-d', '--database', \n                        default=dfdb,\n                        help=('SQlite database file to hold content.'\n                              '  [default=\"{}\"]').format(dfdb))\n    parser.add_argument('--summary', '-s', action='store_true',\n                        help='Summarize database content.')\n    parser.add_argument('--loglevel',\n                        help='Kind of diagnostic output',\n                        choices=['CRTICAL', 'ERROR', 'WARNING',\n                                 'INFO', 'DEBUG'],\n                        default='WARNING')\n    args = parser.parse_args()\n\n    log_level = getattr(logging, args.loglevel.upper(), None)\n    if not isinstance(log_level, int):\n        parser.error('Invalid log level: %s' % args.loglevel)\n    logging.basicConfig(level=log_level,\n                        format='%(levelname)s %(message)s',\n                        datefmt='%m-%d %H:%M')\n    #!logging.debug('Debug output is enabled in %s !!!', sys.argv[0])\n\n    db = LvrDb(args.database)\n    if args.incsv:\n        args.incsv.close()\n        args.incsv = args.incsv.name\n        db.insert_from_csv(args.incsv)\n        \n    #!db.to_csv(foo)\n    #!print('Created CSV from DB in {}'.format(foo))\n    if args.summary:\n        db.summary()\n    \nif __name__ == '__main__':\n    main()\n","repo_name":"pothiers/vvote","sub_path":"vvote/lvr_db.py","file_name":"lvr_db.py","file_ext":"py","file_size_in_byte":7068,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"26659329487","text":"\n\nfrom datetime import datetime\nimport os\nimport pickle\nimport pandas as pd\nfrom sklearn.metrics import confusion_matrix\nfrom src.classification.evaluate import Evaluate\nfrom src.classification.model.xgboost_classifier import XgboostClassifier\nfrom src.data_preprocessing import DataPreprocessing\n\ntimestamp = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\nSAVED_MODEL_FOLDER = os.path.join('results', 'trained_models', 'classification', f\"house_prices_xgboos_{timestamp}\")\nos.makedirs(SAVED_MODEL_FOLDER)\nSAVED_MODEL_FILE = os.path.join(SAVED_MODEL_FOLDER, 'house_prices_finalized_xgboosr_classification_model.sav')\nSAVED_MODEL_EVALUATION = os.path.join(SAVED_MODEL_FOLDER, 'house_prices_finalized_xgboosr_classification_mode_eval')\n\ndataset_Path = 'datasets\\horse_survival_train.csv'\n\n\ndef train_model():\n    \n    df = pd.read_csv(dataset_Path)\n    df = perprocess_data(df)\n\n    xgboost_classifier = XgboostClassifier(train_df = df, prediction_column = 'outcome')\n    xgboost_classifier.tune_hyper_parameters()\n    model = xgboost_classifier.train()\n    pickle.dump(model, open(SAVED_MODEL_FILE, 'wb'))\n\n    evaluate = Evaluate()\n    evaluations = evaluate.evaluate_train_and_test(model, xgboost_classifier)\n    \n    print(f\"model evaluations: {evaluations}\")\n    with open(SAVED_MODEL_EVALUATION, 'w') as file:\n        file.write(evaluations)\n\n\ndef use_traned_model():\n\n    df = pd.read_csv(dataset_Path)\n    data_preprocessing = DataPreprocessing()\n    df = perprocess_data(df)\n    X_data = data_preprocessing.exclude_columns(df, ['outcome'])\n    loaded_model = pickle.load(open(SAVED_MODEL_FILE, 'rb'))\n    evaluate = Evaluate()\n    y_predict = evaluate.predict(loaded_model, X_data)\n    print(evaluate.evaluate_classification(df['outcome'], y_predict))\n\ndef perprocess_data(df):\n    data_preprocessing = DataPreprocessing()\n    data_preprocessing.fill_missing_not_numeric_cells(df)\n    data_preprocessing.fill_missing_numeric_cells(df)\n    df['hospital_number'] = df['hospital_number'].astype('str')\n    df['lesion_2'] = df['lesion_2'].apply(lambda x:1 if x>0 else 0)\n    df[\"deviation_from_normal_temp\"] = df[\"rectal_temp\"].apply(lambda x: abs(x - 37.8))\n    df = data_preprocessing.map_order_column(df, 'outcome', {'died':0, 'euthanized':1, 'lived':2})\n    df = data_preprocessing.map_order_column(df, 'temp_of_extremities', {'cool':0, 'normal':1, 'warm':2})\n    df = data_preprocessing.one_hot_encode_all_categorical_columns(df)\n\n    return df\n\nif __name__ == '__main__':\n    train_model()\n    # use_traned_model()\n\n\n\n\n\n","repo_name":"zaalgol/TabularWizard","sub_path":"src/examples/horse_survival_xgboost.py","file_name":"horse_survival_xgboost.py","file_ext":"py","file_size_in_byte":2523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"33851098354","text":"if __name__ == \"__main__\":\n    import subprocess\n    from time import sleep\n    import multiprocessing, inotify.adapters\n    try:\n        process = subprocess.Popen([\"bash\", \"-c\", \"\"\"cd ClientApp; ./node_modules/typescript/bin/tsc -w\"\"\"])\n        p = subprocess.Popen([\"python3\", \"server.py\"])\n\n        i = inotify.adapters.Inotify()\n\n        i.add_watch('server.py'.encode('utf-8'))\n        i.add_watch('config.json'.encode('utf-8'))\n\n        try:\n            for event in i.event_gen():\n                if event is not None and event[-3] == ['IN_CLOSE_WRITE']:\n                    p.terminate()\n                    p = subprocess.Popen([\"python3\", \"server.py\"])\n        except KeyboardInterrupt:\n            pass\n        finally:\n            i.remove_watch('server.py'.encode('utf-8'))\n            i.remove_watch('config.json'.encode('utf-8')) \n\n    finally:\n        p.terminate()\n        process.terminate()\n\n        sleep(1)\n        process.kill()\n        p.kill()\n","repo_name":"waleedhammam/dashboard-codescalers","sub_path":"starter.py","file_name":"starter.py","file_ext":"py","file_size_in_byte":969,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"44066160847","text":"from PIL import Image\nimport numpy as np\nmessage=Image.open('timg1.jfif')\nmessage_R=np.array(message)[:,:,0]\nmessage_G=np.array(message)[:,:,1]\nmessage_B=np.array(message)[:,:,2]\npolarity=((message_R>=128)*(message_G>=128)*(message_B>=128))*255\npolarity_2=Image.fromarray(polarity)\nnp.shape(polarity)\n#polarity_2.show()\npolarity_2=polarity_2.convert('RGB')\npolarity_2.save('firstresult.tif')\n#target img has already convert as black or white\ncover=Image.open('timg.jfif')\ncover_R=np.array(cover)[:,:,0]\nfor i in range(849):\n    for j in range(1279):\n        if polarity[i,j]==255:\n            cover_R[i,j]=cover_R[i,j]-cover_R[i,j]%2\n        else:\n            cover_R[i,j]=cover_R[i,j]-cover_R[i,j]%2+1\ncover_2=np.zeros((850,1280,3))\ncover_2[:,:,0]=np.copy(cover_R)\ncover_2[:,:,1]=np.copy(np.array(cover)[:,:,1])\ncover_2[:,:,2]=np.copy(np.array(cover)[:,:,2])\ncover_3=Image.fromarray(cover_2.astype('uint8'))\n#cover.show()\ncover_3.save('hided.tif')#BUG:I just save the wrong variable\n#message have already hided in the cover img\nrecover=Image.open('hided.tif')\nrecover_R=np.array(recover)[:,:,0]\ntmp=np.zeros((850,1280))\nfor i in range(849):\n    for j in range(1279):\n        if recover_R[i,j]%2==0:\n            tmp[i,j]=255\n        else:\n            tmp[i,j]=0\nresult=Image.fromarray(tmp)\nresult=result.convert('RGB')\nresult.show()\nresult.save('finalresult.tif')\n","repo_name":"hhh19995/message-hide-in-a-pic-reconfig-by-py","sub_path":"pic.py","file_name":"pic.py","file_ext":"py","file_size_in_byte":1364,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"17410305175","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nfrom hwt.code import Switch\nfrom hwt.hdl.types.bits import Bits\nfrom hwt.interfaces.std import Handshaked\nfrom hwt.interfaces.structIntf import StructIntf\nfrom hwt.interfaces.utils import propagateClkRstn\nfrom hwt.synthesizer.param import Param\nfrom hwt.synthesizer.rtlLevel.rtlSignal import RtlSignal\nfrom hwtLib.handshaked.builder import HsBuilder\nfrom hwtLib.handshaked.streamNode import StreamNode\nfrom hwtLib.peripheral.usb.descriptors.bundle import UsbDescriptorBundle, \\\n    UsbEndpointMeta\nfrom hwtLib.peripheral.usb.descriptors.cdc import get_default_usb_cdc_vcp_descriptors, \\\n    CLASS_REQUEST, make_usb_line_coding_default\nfrom hwtLib.peripheral.usb.usb2.device_common import Usb2DeviceCommon\nfrom hwtLib.peripheral.usb.usb2.device_ep_buffers import UsbDeviceEpBuffers\nfrom hwtLib.types.ctypes import uint8_t\nfrom hwt.code_utils import rename_signal\n\n\nclass Usb2CdcVcp(Usb2DeviceCommon):\n    \"\"\"\n    USB2.0 communcation device class virtual com port core (serial/uart over USB)\n\n    :see: :class:`hwtLib.peripheral.usb.usb2.device_common.Usb2DeviceCommon`\n    :ivar RX_AGGREGATION_TIMEOUT: the timeout (in clk ticks) for a packing of incomming bytes\n        to a USB packet (the bigger packet equals more USB efficiency)\n    :ivar rx: stream data from host\n    :ivar tx: stream data to host\n\n    .. hwt-autodoc::\n    \"\"\"\n\n    def _config(self):\n        Usb2DeviceCommon._config(self)\n        self.DESCRIPTORS: UsbDescriptorBundle = get_default_usb_cdc_vcp_descriptors(\n            productStr=self.__class__.__name__,\n            bMaxPacketSize=512)\n        self.RX_AGGREGATION_TIMEOUT = Param(512)\n\n    def _declr(self):\n        Usb2DeviceCommon._declr(self)\n        self.rx:Handshaked = Handshaked()._m()\n        self.tx = Handshaked()\n        for i in [self.rx, self.tx]:\n            i.DATA_WIDTH = 8\n\n    def generat_descriptor_rom(self, descriptors: UsbDescriptorBundle, rst):\n        _descriptor_rom = descriptors.compile_rom()\n        line_coding = descriptors.HValue_to_byte_list(make_usb_line_coding_default())\n        descriptors.ROM_CDC_LINE_CODING_ADDR = len(descriptors)\n        descriptors.ROM_CDC_LINE_CODING_SIZE = len(line_coding)\n        _descriptor_rom.extend(line_coding)\n        descriptor_rom = self._sig(\"descriptor_rom\", Bits(8)[len(_descriptor_rom)], def_val=_descriptor_rom)\n        # a register for descriptor reader\n        descr_addr = self._reg(\"descr_addr\", uint8_t, rst=rst)\n        descr_d = descriptor_rom[descr_addr]\n        return descr_addr, descr_d\n\n    def decode_setup_request_class(self, setup:StructIntf, ep0_stall: RtlSignal, usb_addr_next: RtlSignal,\n                            descriptors: UsbDescriptorBundle,\n                            req_bDescriptorType: RtlSignal, req_bDescriptorIndex: RtlSignal, dev_configured: RtlSignal,\n                            descr_addr: RtlSignal, ep0_trans_len: RtlSignal):\n        bRequest = rename_signal(self, setup.bRequest, \"bRequest\")\n        return Switch(bRequest)\\\n                .Case(CLASS_REQUEST.GET_LINE_CODING,\n                    descr_addr(descriptors.ROM_CDC_LINE_CODING_ADDR),\n                    ep0_trans_len(descriptors.ROM_CDC_LINE_CODING_SIZE),\n                )\n                # accept all, from some reason there is bRequest 0x25 which should be reserved value when used with usbip\n\n    def _impl(self):\n        ep_meta = self.DESCRIPTORS.get_endpoint_meta()\n        ep_meta = list(ep_meta)\n        for ep0_channel in ep_meta[0]:\n            ep0_channel: UsbEndpointMeta\n            # no need to buffer anything as we can process it directly\n            ep0_channel.buffer_size = 0\n\n        # because we do not support any interrupts\n        # the buffer will respond with the stall automatically\n        for ep2_channel in ep_meta[2]:\n            if ep2_channel is None:\n                continue\n            ep2_channel: UsbEndpointMeta\n            # no need to buffer interrupts as we can process it directly\n            ep2_channel.buffer_size = 0\n\n        ep_buffers = UsbDeviceEpBuffers()\n        ep_buffers.ENDPOINT_META = tuple(ep_meta)\n        self.ep_buffers = ep_buffers\n\n        dev_configured, ep0_stall = self.descriptor_ep0_fsm(self.DESCRIPTORS)\n        self.connect_core_and_ep_buffers_common(ep0_stall, ep_buffers)\n\n        tx_src = HsBuilder(self, self.tx).to_axis(\n            MAX_FRAME_WORDS=ep_meta[1][0].max_packet_size,\n            IN_TIMEOUT=self.RX_AGGREGATION_TIMEOUT).end\n\n        ep_buffers.ep[1].tx(tx_src, exclude=[tx_src.ready, tx_src.valid, ep_buffers.ep[1].tx.keep])\n        ep_buffers.ep[1].tx.keep(1)\n        StreamNode([tx_src], [ep_buffers.ep[1].tx]).sync(dev_configured._eq(1))\n\n        ep_buffers.ep[2].tx.data(None)\n        ep_buffers.ep[2].tx.last(None)\n        ep_buffers.ep[2].tx.keep(None)\n        ep_buffers.ep[2].tx.valid(0)\n\n        rx = ep_buffers.ep[1].rx\n        StreamNode([rx], [self.rx]).sync(dev_configured._eq(1))\n        self.rx.data(rx.data)\n\n        propagateClkRstn(self)\n\n\nif __name__ == \"__main__\":\n    from hwt.synthesizer.utils import to_rtl_str\n    u = Usb2CdcVcp()\n    print(to_rtl_str(u))\n","repo_name":"Nic30/hwtLib","sub_path":"hwtLib/peripheral/usb/usb2/device_cdc_vcp.py","file_name":"device_cdc_vcp.py","file_ext":"py","file_size_in_byte":5122,"program_lang":"python","lang":"en","doc_type":"code","stars":33,"dataset":"github-code","pt":"48"}
+{"seq_id":"20467415004","text":"import src.DeepGaze.deepgaze_pytorch as deepgaze_pytorch\nimport numpy as np\nfrom scipy.misc import face\nfrom scipy.ndimage import zoom\nfrom scipy.special import logsumexp\nimport torch\n\nimport matplotlib.pyplot as plt\nfrom tqdm import tqdm\n\nimport argparse \nimport cv2 \n\ndef gen_saliency(image,model,DEVICE, channel_loc = 0 ):\n    '''\n    Generate saliency map for input image using DeepGaze \n    \n    Args:\n        image: input image \n        channel_loc: index of the channel dimesion of the image\n    \n    Returns:\n        log_density_prediction: generated saliency map \n    \n    '''  \n    model = model \n\n    centerbias_template = np.load('../src/DeepGaze/centerbias_mit1003.npy')\n    \n    # Here Image is supposed to be [H x W x C] \n    # But NSD images are [C x H x W] \n        \n    if channel_loc == 0:\n        centerbias = zoom(centerbias_template, (image.shape[1]/centerbias_template.shape[0], image.shape[2]/centerbias_template.shape[1]), order=0, mode='nearest')\n        image_tensor = torch.tensor([image]).to(DEVICE)\n\n    elif channel_loc == 2: \n        centerbias = zoom(centerbias_template, (image.shape[0]/centerbias_template.shape[0], image.shape[1]/centerbias_template.shape[1]), order=0, mode='nearest')\n        image_tensor = torch.tensor([image.transpose(2, 0, 1)]).to(DEVICE) # for the face example\n    \n    centerbias -= logsumexp(centerbias)\n    \n    # create image tensor with H x W x C dimensions \n    \n    # if the image is of the shape [W x H x C] --> H x W x C    \n    centerbias_tensor = torch.tensor([centerbias]).to(DEVICE)\n    log_density_prediction = model(image_tensor, centerbias_tensor)\n    \n    return log_density_prediction \n\n\ndef plot_saliency(image, log_density_prediction, channel_loc = 0):\n    '''\n    Plot saliency maps using the log density prediction generated by DeepGaze\n    \n    Args:\n        image: input image\n        log_density_prediction: saliency map generated with DeepGaze\n        channel_loc: index of the channel dimesion of the image\n    \n    Returns:\n        None\n    '''\n    f, axs = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n    if channel_loc == 0:\n        axs[0].imshow(image.transpose((1,2,0)), cmap='gray', interpolation='None')\n    elif channel_loc == 2:\n         axs[0].imshow(image, cmap='gray', interpolation='None')\n        \n    axs[1].matshow(log_density_prediction.detach().cpu().numpy()[0, 0])\n    axs[0].set_axis_off()\n    axs[1].set_axis_off()\n    \n\ndef gen_and_plot_saliency(model, image, DEVICE, channel_loc = 0):\n    '''\n    Generate and plot saliency maps \n    \n    Args:\n        model: DeepGaze model instance\n        image: input image\n        channel_loc: index of the channel dimesion of the image\n        \n    Returns: \n        None:\n    '''\n    log_density_prediction = gen_saliency(image,model,DEVICE, channel_loc )\n    plot_saliency(image, log_density_prediction, channel_loc)\n\n\n    \ndef gen_saliency_batch(image,model, DEVICE, channel_loc = 0):\n    '''\n    Generate saliency map using DeepGaze for mutple batches at once, if the input images are in batches \n    \n    '''\n    model = model \n\n    centerbias_template = np.load('../src/DeepGaze/centerbias_mit1003.npy')\n    \n    # Here Image is supposed to be [H x W x C] \n    # But NSD images are [C x H x W] \n        \n    if channel_loc == 0:\n        centerbias = zoom(centerbias_template, (image.shape[1]/centerbias_template.shape[0], \n                                                image.shape[2]/centerbias_template.shape[1]), order=0, mode='nearest')\n        image_tensor = torch.tensor(image).to(DEVICE)\n\n    elif channel_loc == 2: \n        centerbias = zoom(centerbias_template, (image.shape[1]/centerbias_template.shape[0], \n                                                image.shape[2]/centerbias_template.shape[1]), order=0, mode='nearest')\n        image_tensor = torch.tensor(image.transpose(0,3,1,2)).to(DEVICE) # for the face example\n\n    centerbias -= logsumexp(centerbias)\n    \n    # create image tensor with H x W x C dimensions \n    # if the image is of the shape [W x H x C] --> H x W x C     \n    centerbias_tensor = torch.tensor([centerbias]).to(DEVICE)\n    log_density_prediction = model(image_tensor, centerbias_tensor)\n\n    return log_density_prediction \n\n\ndef gen_S_matrix_batched(model, DEVICE,  img_indices, dataset,  default_stimuli = True, batch_size = 8):\n    '''\n    Args:\n        default_stimuli:\n            True: in the case of generateion for regular images without a concatenated noise (normal case) \n            False: in the case of generateion for concatenated noise images (for saliency scores) \n    '''\n    \n    batch_size = batch_size    \n    \n    \n    if default_stimuli: \n        \n        num_pixels = 425\n        S_mat_full = torch.ones((1,num_pixels,num_pixels))\n\n        for indices in tqdm(range(0,len(img_indices), batch_size)):\n            \n            chunk = img_indices[indices:indices+batch_size]\n            images = dataset[chunk] # [batch_size x 425 x 425 x 3] \n\n\n            sal_maps = gen_saliency_batch(images,model, DEVICE, channel_loc = 2)[:,0].detach().cpu() # batch_size, 425, 425\n\n            S_mat_full = torch.cat((S_mat_full.cpu(), sal_maps), dim = 0) \n            \n    \n    else:\n        # When generating the concatenated stimuli (with Pink noise) \n        img_width = 425 * 2\n        img_height = 425\n        \n        S_mat_full = torch.ones((1 , img_height, img_width )) \n        \n        for indices in tqdm(range(0,len(img_indices), batch_size)):\n\n            chunk = img_indices[indices:indices+batch_size]\n            \n            \n            images = create_concat_dataset_batched(dataset, chunk)      # [batch_size x 425 x 425 x 3] \n  \n            sal_maps = gen_saliency_batch(images,model, DEVICE, channel_loc = 2)[:,0].detach().cpu() # batch_size, 425, 425\n            # print(S_mat_full.shape, sal_maps.shape) \n            S_mat_full = torch.cat((S_mat_full.cpu(), sal_maps), dim = 0) \n        \n    return S_mat_full\n\n\ndef create_concat_dataset_batched(img_arr, batch):\n    '''\n    Generate an image dataset from the COCO stimuli where each corresponding COCO image is concatenated\n    with the pink noise image. This will be used subsequently to generate relative saliency scores for the \n    images \n    '''\n    num_imgs = len(batch)\n    img_height = img_arr.shape[1]\n    img_width = img_arr.shape[2]\n    num_channel = img_arr.shape[3]\n    \n    img_dataset = np.ones((img_height, img_width*2, num_channel), dtype = 'uint8') # 425 , 850, 3     \n    \n    for idx in batch: # generates one extra image \n        concat_img = generate_concat_imgs(img_arr, idx,idx, pass_noise=True)\n        img_dataset = np.vstack((img_dataset, concat_img))\n        \n       \n    reshaped = img_dataset.reshape((num_imgs + 1 , img_height , img_width * 2 , num_channel))\n        \n    return reshaped[1:, :, :, :]\n\ndef generate_concat_imgs(dataset, idx1,idx2, pass_noise = False):\n    # Note: The dimensions of the images must be same to use this method\n    \n    image_left = dataset[idx1]\n    \n    if pass_noise:\n        image_right = (generate_pink_noise(425,425)*255).astype(np.uint8)\n    else: \n        image_right = dataset[idx2] \n\n        \n    final_image = np.concatenate((image_left, image_right), axis = 1) \n    \n    return final_image\n\n\ndef generate_pink_noise(height , width): \n    np.random.seed(42) \n    whitenoise = np.random.uniform(0, 1, (height, width))\n\n    ft_arr = np.fft.fftshift(np.fft.fft2(whitenoise))\n\n    _x, _y = np.mgrid[0:ft_arr.shape[0], 0:ft_arr.shape[1]]\n    f = np.hypot(_x - ft_arr.shape[0] / 2, _y - ft_arr.shape[1] / 2)\n\n    pink_ft_arr = ft_arr / f\n\n    pink_ft_arr = np.nan_to_num(pink_ft_arr, nan=0, posinf=0, neginf=0)\n    pinknoise = np.fft.ifft2(np.fft.ifftshift(pink_ft_arr)).real\n        \n    stacked_img = np.stack((pinknoise,)*3, axis=-1)\n    stacked_img_normalized = stacked_img - stacked_img.min()\n    stacked_img_normalized /= stacked_img_normalized.max()\n\n    return stacked_img_normalized\n\n\n\ndef gen_corr_w_beta(roi, S_mat):\n    \n    print(f\"Computing correlation for {roi}\\n\")\n\n    K_mat = np.load(DATA_PATH+f\"/K_mat_subj1_{roi}.npy.npz\")\n    K_mat = K_mat['arr_0']\n    print(f\"K matrix: {K_mat.shape}\")\n    \n    betas_mat = np.load(DATA_PATH+f\"/betas_full_{roi}.npy\")\n          \n    roi_valid_indices = np.load(DATA_PATH+f\"/{roi}_valid_indices_subj1.npy\")\n    print(f\"Betas matrix before valid: {betas_mat.shape}\")\n    print(f\"Valid indices shape: {roi_valid_indices.shape}\")\n    \n    # index the betas for the corresponding voxels that are valid \n    betas_val = betas_mat[:,roi_valid_indices] \n    betas_val = betas_val[1:,:] # remove the first dummy row \n    print(f\"Betas matrix: {betas_val.shape}\")\n    \n    # Mulitply the S and K matrix to get Y hat \n    Y_hat = S_mat @ K_mat.T\n    print(f\"Y_hat matrix: {Y_hat.shape}\\n\\n\")      \n    \n    # Correlate between betas_valid and Y_hat \n    corr_mat = np.corrcoef(betas_val,Y_hat,rowvar = False) # Example: (27750, 4165) x (27750, 4165) for V1 \n\n    return corr_mat\n\n\n\ndef generate_corr_full(roi_list, S_mat):\n    '''\n    Generate the correlation coeff for the given list of ROIs and return it as a dataframe \n    '''\n    corr_vals = {}\n    \n    for roi in roi_list:\n        corr_mat_roi = gen_corr_w_beta(roi,S_mat) \n        rel_size = (corr_mat_roi.shape[0])// 2\n        corr_mat_rel_roi = corr_mat_roi[rel_size:,:rel_size]\n        corr_vals[roi] = corr_mat_rel_roi.diagonal() \n        \n    \n    # Create a main dataframe to which all smaller dataframes (for each ROI) will be appended \n    df_main = pd.DataFrame(columns = ['ROI','corr_coeff'])\n    \n    for key,val in corr_vals.items(): # Key: ROI, value: array of correlations\n        roi_as_list = [key] * val.shape[0] # duplicate the roi string so that it's in the format we want \n        corr_val_list = val\n        temp_df = pd.DataFrame({'ROI': roi_as_list, 'corr_coeff':corr_val_list})   \n        df_main = pd.concat((df_main, temp_df))\n        \n    return df_main\n\ndef generate_maps_dict(map_type, img_indices, image_data_73k, num_pixels = 425, kernel_size = None):\n    '''\n    Generate a dictionary containing the required low-level feature map for an image \n    \n    '''\n    maps_dict = {} \n    \n    for idx in tqdm(img_indices): \n        \n        if idx in maps_dict: \n            continue\n        \n        else: \n            image = image_data_73k[idx]\n            \n            if map_type == 'edge':\n                img_map = generate_edge_map_single(image, kernel_size) \n                \n            elif map_type == 'saturation':\n                img_map = generate_saturation_map_single(image) \n                \n            elif map_type == 'luminance':\n                img_map = generate_luminance_map_single(image)\n                \n            maps_dict[idx] = img_map\n        \n    return maps_dict\n\n\ndef convert_dict_to_S_mat(map_dict, img_indices, num_pixels = 425): \n    '''\n    Convert the dictionary containing low-level feature maps into the S_mat format\n    '''\n    S_mat_full = np.ones([len(img_indices), num_pixels*num_pixels])\n\n    for trial_num, idx in enumerate(tqdm(img_indices)):\n        \n        S_map_img = map_dict[idx] \n        \n        S_map_long = S_map_img.reshape(-1, num_pixels * num_pixels)\n        \n        S_mat_full[trial_num, :] = S_map_long\n        \n    return S_mat_full\n\n\ndef generate_edge_map_single(image, kernel_size):\n    \n    if kernel_size == None: \n        raise ValueError(\"kernel_size not defined.\") \n        \n    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n    blurred = cv2.GaussianBlur(gray, (5,5),0) # 5x5 gaussian filter, to reduce noise \n    laplacian = cv2.Laplacian(blurred, kernel_size, cv2.CV_64F)\n    edge_map = cv2.convertScaleAbs(laplacian) \n\n    return edge_map\n\ndef generate_saturation_map_single(img): \n    image_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) \n    saturation_map = image_hsv[:,:,1]\n    \n    return saturation_map\n\ndef generate_luminance_map_single(img): \n    image_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) \n    luminance_map = image_hsv[:,:,2]\n    \n    return luminance_map","repo_name":"hrdkbhatnagar/nsd-static-saliency","sub_path":"src/.ipynb_checkpoints/helper_fuctions-checkpoint.py","file_name":"helper_fuctions-checkpoint.py","file_ext":"py","file_size_in_byte":12026,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"41884307220","text":"# -*- coding: utf-8 -*-\n__author__ = 'xiao'\nimport xadmin\nfrom xadmin import views\nfrom .models import InformationPublish\n\n\nclass MainDashboard(object):\n    widgets = [\n        [\n            {\"type\": \"html\", \"title\": \"问候语\",\n             \"content\": \"<h3>欢迎来到后台管理系统</h3><p>开发团队: <br/>联系方式:451314789@qq.com</p>\"},\n            {\"type\": \"list\", \"model\": \"accounts.user\", 'params': {\n                'o': '-create_time'}},\n        ],\n        [\n            {\"type\": \"qbutton\", \"title\": \"快捷窗口\",\n             \"btns\": [{'model': InformationPublish}]},\n            {\"type\": \"addform\", \"model\": InformationPublish},\n        ]\n    ]\n\n\nclass BaseSetting(object):\n    enable_themes = True\n    use_bootswatch = True\n\n\nclass GlobalSetting(object):\n    site_title = u'后台中心'\n    site_footer = u'Powered by youran'\n    apps_label_title = {\n        'accounts': u'权限管理',\n        'content': u'信息管理',\n    }\n\n\nclass InformationPublishAdmin(object):\n    def open_detail(self, instance):\n        return \"<a href='/center_intro/information_detail/?id=%s' target='_blank'>详情</a>\" % instance.id\n\n    open_detail.short_description = u'详情'\n    open_detail.allow_tags = True\n    open_detail.is_column = True\n    list_display = ('title', 'create_time', 'origin', 'editor', 'status', 'open_detail')\n    list_display_links = ('title',)\n    list_editable = ('title', 'status',)\n    ordering = ('-create_time', )\n    # exclude = ('views',)\n    list_per_page = 20\n    search_fields = ['title']\n    list_filter = ['origin', 'editor', 'status', ]\n    style_fields = {'content': 'ueditor'}\n\nxadmin.site.register(views.website.IndexView, MainDashboard)\nxadmin.site.register(views.BaseAdminView, BaseSetting)\nxadmin.site.register(views.CommAdminView, GlobalSetting)\nxadmin.site.register(InformationPublish, InformationPublishAdmin)","repo_name":"sqlyouran/django-web","sub_path":"web/content/adminx.py","file_name":"adminx.py","file_ext":"py","file_size_in_byte":1869,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"48"}
+{"seq_id":"72308531347","text":"# ------------------------------------------------------------------------------\r\n#  Single Shot Multibox Detector for Vertebra detection\r\n#  Jan Kukacka, 11/2017\r\n#  jan.kukacka@tum.de\r\n# ------------------------------------------------------------------------------\r\n#  Implementation of Anchor box generator layer\r\n# ------------------------------------------------------------------------------\r\n\r\nimport numpy as np\r\nfrom keras.engine.topology import Layer\r\nimport keras.backend as K\r\nimport tensorflow as tf # because of limited support in K.arange\r\n\r\nclass AnchorGeneratorLayer(Layer):\r\n    '''\r\n    Layer generating anchor bounding boxes for variable shaped input\r\n    '''\r\n\r\n    def __init__(self, feature_stride, offset, aspect_ratios=[1], scale=1, **kwargs):\r\n        '''\r\n        # Arguments\r\n            - feature_stride: int. Determines stride of features at the input\r\n                              layer.\r\n            - offset: int. Determines how many pixels at each edge are lost\r\n                      due to using 'valid' convolution mode. Set to 0 if\r\n                      using 'same' mode.\r\n            - aspect_ratios: iterable of aspect ratios (w/h) of anchor boxes\r\n            - scale: positive float. Determines the scale of the bounding boxes.\r\n                     If scale == 1, bboxes have width==feature_stride\r\n        '''\r\n\r\n        self.aspect_ratios = aspect_ratios[:]   # create a copy of the list\r\n        self.feature_stride = feature_stride\r\n        self.offset = offset\r\n        self.scale = scale\r\n        super(AnchorGeneratorLayer, self).__init__(**kwargs)\r\n\r\n    def compute_output_shape(self, input_shape):\r\n        '''\r\n        # Arguments\r\n            - input_shape: tuple of int: (batch_size, w, h, channels).\r\n                           Expecting tensorflow data ordering.\r\n        # Returns\r\n            - Output shape: tuple of int: (batch_size, w*h*anchor_count, 4).\r\n        '''\r\n        anchor_count = len(self.aspect_ratios)\r\n        batch_size = input_shape[0]\r\n        width = input_shape[1]\r\n        height = input_shape[2]\r\n        return (None, None, 4)\r\n\r\n    def call(self, x):\r\n        '''\r\n        Generate tensor of anchor bounding boxes in the format:\r\n        (center_x, center_y, width, height)\r\n\r\n        At each point of the feature maps creates anchor boxes with specified\r\n        aspect ratios. In total creates width*height*anchor_count bounding boxes\r\n        '''\r\n\r\n        input_shape = K.shape(x)\r\n        batch_size = input_shape[0]\r\n        width = input_shape[1]\r\n        height = input_shape[2]\r\n        # following two things simulate meshgrid(xrange(height), xrange(width))\r\n        anchors_y = K.tile(tf.range(height), [width]) # replace tf.range for K.arange\r\n                                             # once tensor input is supported\r\n                                             # same in the following:\r\n        anchors_x = K.reshape(\r\n                        K.permute_dimensions(\r\n                            K.tile(\r\n                                K.expand_dims(tf.range(width), axis=0),\r\n                                (height,1)),\r\n                            (1,0)),\r\n                        (width*height,))\r\n        anchors_centers = K.permute_dimensions(\r\n                            K.stack([anchors_y, anchors_x], axis=0),\r\n                            (1,0))\r\n        anchors_centers = (anchors_centers * self.feature_stride\r\n                           + self.feature_stride / 2\r\n                           + self.offset)\r\n\r\n        anchors_centers = K.reshape(K.tile(anchors_centers, (1, len(self.aspect_ratios))), (-1,2))\r\n        anchors_centers = K.cast(anchors_centers, 'float32')\r\n\r\n        ar = np.array(self.aspect_ratios)\r\n        size = self.feature_stride * self.scale\r\n        widths = size*ar\r\n        heights = size/ar\r\n        sizes = K.variable(np.transpose(np.vstack((widths, heights)), (1,0)))\r\n\r\n        sizes = K.reshape(sizes, (1,-1))\r\n        sizes = K.tile(sizes, (1,width*height))\r\n        sizes = K.reshape(sizes, (-1,2))\r\n\r\n        #anchors_tensor = K.reshape(K.stack((anchors_centers, sizes), axis=1), (-1,4))\r\n        anchors_tensor = K.concatenate((anchors_centers, sizes), axis=-1)\r\n        anchors_tensor = K.expand_dims(anchors_tensor, 0)\r\n        pattern = (batch_size, 1, 1)\r\n        anchors_tensor = K.tile(anchors_tensor, pattern)\r\n        return anchors_tensor\r\n\r\n\r\nclass AnchorGenerator(object):\r\n    '''\r\n    Object generating anchor bounding boxes\r\n    '''\r\n\r\n    def __init__(self, feature_stride, offset, aspect_ratios=[1], scale=1):\r\n        '''\r\n        # Arguments\r\n            - feature_stride: int. Determines stride of features at the input\r\n                    layer.\r\n            - offset: int. Determines how many pixels at each edge are lost\r\n                    due to using 'valid' convolution mode. Set to 0 if\r\n                    using 'same' mode.\r\n            - aspect_ratios: iterable of aspect ratios (w/h) of anchor boxes\r\n            - scale: positive float or a list of positive floats. Determines the\r\n                    scale of the bounding boxes. If scale == 1, bboxes have\r\n                    width==feature_stride\r\n        '''\r\n\r\n        self.aspect_ratios = aspect_ratios[:]   ## create a copy of the list\r\n        self.feature_stride = feature_stride\r\n        self.offset = offset\r\n        ## Ensure the scale is in a list\r\n        try:\r\n            self.scale = [s for s in scale]\r\n        except:\r\n            self.scale = [scale]\r\n\r\n    def Generate(self, input_shape):\r\n        '''\r\n        Generate anchor boxes for the batch of the given shape. At each point of\r\n        the feature maps creates anchor boxes with specified aspect ratios.\r\n        In total creates (width/feature_stride)*(height/feature_stride)*anchor_count bounding boxes\r\n\r\n        # Arguments\r\n            - input_shape. Tuple of positive ints. Shape of the input batch.\r\n                           (batch_size, width, height, channels)\r\n        # Returns\r\n            - anchors. numpy array of shape (num_anchors, 5) encoded as\r\n                       [center_x, center_y, width, height, valid_bit]\r\n                       valid_bit == 1 if the whole bbox is inside the image\r\n        '''\r\n        batch_size = input_shape[0]\r\n        width = input_shape[1] // self.feature_stride\r\n        height = input_shape[2] // self.feature_stride\r\n        anchors_y, anchors_x = np.meshgrid(xrange(height), xrange(width))\r\n\r\n        anchors_centers = np.transpose(\r\n                            np.vstack([anchors_y.flatten(), anchors_x.flatten()]),\r\n                            (1,0))\r\n        anchors_centers = (anchors_centers * self.feature_stride\r\n                           + self.feature_stride / 2\r\n                           + self.offset)\r\n\r\n        anchors_centers = np.reshape(np.tile(anchors_centers,\r\n            (1, len(self.aspect_ratios) * len(self.scale))), (-1,2))\r\n        anchors_centers = anchors_centers.astype('float32')\r\n\r\n        ar = np.array(self.aspect_ratios)\r\n        size = self.feature_stride # * self.scale\r\n        widths = size*ar\r\n        heights = size/ar\r\n        sizes = np.transpose(np.vstack((widths, heights)), (1,0))\r\n\r\n        ## Duplicate for each scale\r\n        sizes = np.reshape(np.repeat(sizes[np.newaxis,:,:], len(self.scale), axis=0), (-1,2))\r\n        scales = np.repeat(self.scale, len(self.aspect_ratios))\r\n        sizes = sizes * scales[:, np.newaxis]\r\n\r\n        sizes = np.reshape(sizes, (1,-1))\r\n        sizes = np.tile(sizes, (1,width*height))\r\n        sizes = np.reshape(sizes, (-1,2))\r\n\r\n        anchors_tensor = np.concatenate((anchors_centers, sizes), axis=-1)\r\n        anchors_tensor = np.expand_dims(anchors_tensor, 0)\r\n        pattern = (batch_size, 1, 1)\r\n        anchors_tensor = np.tile(anchors_tensor, pattern)\r\n        return anchors_tensor\r\n","repo_name":"jankukacka/SSD","sub_path":"anchor_generator_layer.py","file_name":"anchor_generator_layer.py","file_ext":"py","file_size_in_byte":7860,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"4569533688","text":"import RPi.GPIO as GPIO\nfrom cv2 import *\nimport time\nimport os\nimport zipfile\n\nclass Scanner():\n    def __init__(self):\n        self.slidenum = 36\n        self.gpio = 4\n        self.gpio_mode = GPIO.BCM\n        self.forward_delay = 1.3\n        self.forward_hold = 0.15\n        self.reverse_hold = 1\n        self.picture_delay = 1.5\n        self.cam_port = 0\n        self.cam = VideoCapture(self.cam_port)\n        self.cam.set(3, 4048)\n        self.cam.set(4, 3040)\n        self.cam.set(14, 0.001)\n        self.cam.set(15, -50.0)\n        self.parentdir = \"images\"\n        self.dirprefix = \"magazine_\"\n        self.fileprefix = \"image_\"\n        self.foldername = \"\"\n\n    def setup(self):\n        GPIO.cleanup()\n        GPIO.setmode(self.gpio_mode)\n        GPIO.setup(self.gpio, GPIO.OUT)\n\n    def run(self, slides, forward = True):\n        for i in range(slides):\n            if forward:\n                self.slide_forward()\n            else:\n                self.slide_reverse()\n            time.sleep(self.forward_delay)\n            self.take_picture(i)\n        self.slide_forward() # eject magazine\n        with zipfile.ZipFile(self.foldername + '.zip', 'w', zipfile.ZIP_DEFLATED) as zipf:\n            self.zipdir(self.foldername, zipf)\n\n    def slide_forward(self):\n        GPIO.output(self.gpio, 1)\n        time.sleep(self.forward_hold)\n        GPIO.output(self.gpio, 0)\n\n    def slide_reverse(self):\n        GPIO.output(self.gpio, 1)\n        time.sleep(self.reverse_hold)\n        GPIO.output(self.gpio, 0)\n\n    def take_picture(self, num):\n        result, image = self.cam.read()\n        self.foldername = self.get_folder_name()\n        if result:\n            filename = self.foldername + \"/\" + self.fileprefix + str(num) + \".png\"\n            print(\"writing image: \", filename)\n            imwrite(filename, image)\n        else:\n            print(\"No image detected. Please! try again\")\n\n    def get_folder_name(self):\n        if self.foldername != \"\":\n            return self.foldername\n        dirs = [self.parentdir + \"/\" + p for p in os.listdir(self.parentdir) if os.path.isdir(self.parentdir + \"/\" + p)]\n        currentdir = self.parentdir + \"/\" + self.dirprefix\n        currentdir += str(len(dirs))\n        os.mkdir(currentdir, 0o777)\n        return currentdir\n    \n    def zipdir(self, path, ziph):\n        # ziph is zipfile handle\n        for root, dirs, files in os.walk(path):\n            for file in files:\n                ziph.write(os.path.join(root, file),\n                    os.path.relpath(os.path.join(root, file),\n                    os.path.join(path, '..')))\n\n","repo_name":"systematiccaos/slidescanner","sub_path":"scanner/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2586,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"2053571304","text":"from flask import Flask, request\nfrom flask import jsonify\nfrom flask_cors import CORS\n\n\napp = Flask(__name__)\nCORS(app)\n\n@app.route('/lower_case')\ndef lower_case():\n    str_input = request.args.get('str_input')\n    str_output = {'lowercase_str': str.lower(str_input)}\n    return jsonify(str_output)\n\nif __name__ == \"__main__\":\n    app.run(host='0.0.0.0', port=5000)\n\n","repo_name":"fabriciogmc/microservices","sub_path":"string_case/lower_case_service.py","file_name":"lower_case_service.py","file_ext":"py","file_size_in_byte":368,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"43149486934","text":"# Contiguous Array\n\n# Given a binary array, find the maximum length of a contiguous subarray with equal number of 0 and 1.\nfrom typing import List\n\n\nclass Solution:\n    def findMaxLength(self, nums: List[int]) -> int:\n        sum = 0\n        dp = {0: 0}\n        m = 0\n        for i, v in enumerate(nums):\n            sum += (v << 1) - 1\n            if sum in dp:\n                m = max(m, i + 1 - dp[sum])\n            else:\n                dp[sum] = i + 1\n        return m\n\n\nif __name__ == '__main__':\n    s = Solution()\n    print(s.findMaxLength([0, 1]))\n","repo_name":"TTVidi/leetcode-python","sub_path":"src/leetcode/may/day26.py","file_name":"day26.py","file_ext":"py","file_size_in_byte":557,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"15522148509","text":"import time\nimport numpy as np\nfrom numpy import genfromtxt\nimport Calculations as Calc\n\n\ndef ihc(outside_iter, indide_iter, no_change_number, file_to_read, file_to_save, headers=False, init_swap=False):\n    \"\"\"\n    :param init_swap:\n        bool, if true, swaps loaded from file solution\n    :param headers:\n        bool, if true, deletes headers\n    :param outside_iter:\n        int, number of outside iter\n    :param indide_iter:\n        int, number of inside iter\n    :param no_change_number:\n        int, after how many iterations without change of result should the loop break\n    :param file_to_read:\n        string, directory to read from\n    :param file_to_save:\n        string, directory to save to\n    :return:\n         Saves matrix calculated by IHC algorithm\n    \"\"\"\n    m = genfromtxt(file_to_read, delimiter=',')\n    if headers:\n        m = m[1:][:]\n    if init_swap:\n        best_solution = Calc.initrandomswap_m(m)\n    else:\n        best_solution = m\n    print(Calc.calculate_time_matrices(m))\n    print(Calc.calculate_time_matrices(best_solution))\n    times = []\n    times2 = []\n    for j in range(0, outside_iter):\n        start = time.time()\n        print(\"OUTSIDE ITER:\", j)\n        solution = Calc.initrandomswap_m(m)\n        no_change = 0\n        for i in range(0, indide_iter):\n            rnd = np.random.randint(1, 4)\n            if rnd == 1:\n                m2 = Calc.randomswap_m(solution)\n            elif rnd == 2:\n                m2 = Calc.randomswap_m2(solution)\n            else:\n                m2 = Calc.randomswap_m3(solution)\n            t1 = Calc.calculate_time_matrices(solution)\n            t2 = Calc.calculate_time_matrices(m2)\n            delta_time = t2 - t1\n            if delta_time < 0:\n                solution = m2\n                no_change = 0\n            else:\n                no_change += 1\n            if no_change > no_change_number:\n                break\n        tm = Calc.calculate_time_matrices(best_solution)\n        tm2 = Calc.calculate_time_matrices(solution)\n        if tm - tm2 > 0:\n            best_solution = solution\n        print(tm)\n        times.append(tm)\n        times2.append(tm2)\n        print(\"time per outside iter :\", time.time() - start)\n    np.savetxt(file_to_save, best_solution, delimiter=\",\")\n    print(\"FINAL FOR \", indide_iter, \"INSIDE ITER \", outside_iter, \"OUTSIDE ITER \", \"WITH NO CHANGE \", no_change_number,\n          \"SAVED TO \", file_to_save)\n    Calc.ploted(\n        x=np.linspace(1, outside_iter, outside_iter),\n        y1=times,\n        y2=times2,\n        xlab=\"TIME\",\n        ylab=\"ITER\",\n        y1lab=\"pretender times for \" + str(indide_iter) + \" new iter\",\n        y2lab=\"best times for overall iter\",\n        title=\"IHC\",\n        savename=file_to_save)\n    return times\n\n","repo_name":"marcino8/Flow-shop","sub_path":"IHC.py","file_name":"IHC.py","file_ext":"py","file_size_in_byte":2766,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"28332143715","text":"# Control code execution\nimport sys\n\npi = 3.14159265359\n\n# Gets the radius of a circle\nr = float(input(\"Enter the radius of a circle: \"))\n\n# Makes sure the user is not entering a number that does not have a positive value\nif r <= 0:\n    sys.exit(\"ERROR: Radius is an invalid value\")\n\n# Calculate values\narea = pi*r**2\ncircumference = pi*r*2\ndiameter = r*2\n\n# Print values\nprint(\"The area is: \", area)\nprint(\"The circumference is: \", circumference)\nprint(\"The diameter is: \", diameter)","repo_name":"franciscoatwi/PyMath","sub_path":"Math/Pi.py","file_name":"Pi.py","file_ext":"py","file_size_in_byte":484,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"22107002280","text":"\"\"\"\nUtility functions for uplift trees.\n\"\"\"\n\nimport time\nfrom typing import Callable\n\nimport numpy as np\nimport pandas as pd\n\n\ndef cat_group(dfx, kpix, n_group=10):\n    \"\"\"\n    Category Reduction for Categorical Variables\n\n    Args\n    ----\n\n    dfx : dataframe\n        The inputs data dataframe.\n\n    kpix : string\n        The column of the feature.\n\n    n_group : int, optional (default = 10)\n        The number of top category values to be remained, other category values will be put into \"Other\".\n\n    Returns\n    -------\n    The transformed categorical feature value list.\n    \"\"\"\n    if dfx[kpix].nunique() > n_group:\n        # get the top categories\n        top = dfx[kpix].isin(dfx[kpix].value_counts().index[:n_group])\n        dfx.loc[~top, kpix] = \"Other\"\n        return dfx[kpix].values\n    else:\n        return dfx[kpix].values\n\n\ndef cat_transform(dfx, kpix, kpi1):\n    \"\"\"\n    Encoding string features.\n\n    Args\n    ----\n\n    dfx : dataframe\n        The inputs data dataframe.\n\n    kpix : string\n        The column of the feature.\n\n    kpi1 : list\n        The list of feature names.\n\n    Returns\n    -------\n    dfx : DataFrame\n        The updated dataframe containing the encoded data.\n\n    kpi1 : list\n        The updated feature names containing the new dummy feature names.\n    \"\"\"\n    df_dummy = pd.get_dummies(dfx[kpix].values)\n    new_col_names = [\"%s_%s\" % (kpix, x) for x in df_dummy.columns]\n    df_dummy.columns = new_col_names\n    dfx = pd.concat([dfx, df_dummy], axis=1)\n    for new_col in new_col_names:\n        if new_col not in kpi1:\n            kpi1.append(new_col)\n    if kpix in kpi1:\n        kpi1.remove(kpix)\n    return dfx, kpi1\n\n\ndef cv_fold_index(n, i, k, random_seed=2018):\n    \"\"\"\n    Encoding string features.\n\n    Args\n    ----\n\n    dfx : dataframe\n        The inputs data dataframe.\n\n    kpix : string\n        The column of the feature.\n\n    kpi1 : list\n        The list of feature names.\n\n    Returns\n    -------\n    dfx : DataFrame\n        The updated dataframe containing the encoded data.\n\n    kpi1 : list\n        The updated feature names containing the new dummy feature names.\n    \"\"\"\n    np.random.seed(random_seed)\n    rlist = np.random.choice(a=range(k), size=n, replace=True)\n    fold_i_index = np.where(rlist == i)[0]\n    return fold_i_index\n\n\n# Categorize continuous variable\ndef cat_continuous(x, granularity=\"Medium\"):\n    \"\"\"\n    Categorize (bin) continuous variable based on percentile.\n\n    Args\n    ----\n\n    x : list\n        Feature values.\n\n    granularity : string, optional, (default = 'Medium')\n        Control the granularity of the bins, optional values are: 'High', 'Medium', 'Low'.\n\n    Returns\n    -------\n    res : list\n        List of percentile bins for the feature value.\n    \"\"\"\n    if granularity == \"High\":\n        lspercentile = [\n            np.percentile(x, 5),\n            np.percentile(x, 10),\n            np.percentile(x, 15),\n            np.percentile(x, 20),\n            np.percentile(x, 25),\n            np.percentile(x, 30),\n            np.percentile(x, 35),\n            np.percentile(x, 40),\n            np.percentile(x, 45),\n            np.percentile(x, 50),\n            np.percentile(x, 55),\n            np.percentile(x, 60),\n            np.percentile(x, 65),\n            np.percentile(x, 70),\n            np.percentile(x, 75),\n            np.percentile(x, 80),\n            np.percentile(x, 85),\n            np.percentile(x, 90),\n            np.percentile(x, 95),\n            np.percentile(x, 99),\n        ]\n        res = [\n            \"> p90 (%s)\" % (lspercentile[8])\n            if z > lspercentile[8]\n            else \"<= p10 (%s)\" % (lspercentile[0])\n            if z <= lspercentile[0]\n            else \"<= p20 (%s)\" % (lspercentile[1])\n            if z <= lspercentile[1]\n            else \"<= p30 (%s)\" % (lspercentile[2])\n            if z <= lspercentile[2]\n            else \"<= p40 (%s)\" % (lspercentile[3])\n            if z <= lspercentile[3]\n            else \"<= p50 (%s)\" % (lspercentile[4])\n            if z <= lspercentile[4]\n            else \"<= p60 (%s)\" % (lspercentile[5])\n            if z <= lspercentile[5]\n            else \"<= p70 (%s)\" % (lspercentile[6])\n            if z <= lspercentile[6]\n            else \"<= p80 (%s)\" % (lspercentile[7])\n            if z <= lspercentile[7]\n            else \"<= p90 (%s)\" % (lspercentile[8])\n            if z <= lspercentile[8]\n            else \"> p90 (%s)\" % (lspercentile[8])\n            for z in x\n        ]\n    elif granularity == \"Medium\":\n        lspercentile = [\n            np.percentile(x, 10),\n            np.percentile(x, 20),\n            np.percentile(x, 30),\n            np.percentile(x, 40),\n            np.percentile(x, 50),\n            np.percentile(x, 60),\n            np.percentile(x, 70),\n            np.percentile(x, 80),\n            np.percentile(x, 90),\n        ]\n        res = [\n            \"<= p10 (%s)\" % (lspercentile[0])\n            if z <= lspercentile[0]\n            else \"<= p20 (%s)\" % (lspercentile[1])\n            if z <= lspercentile[1]\n            else \"<= p30 (%s)\" % (lspercentile[2])\n            if z <= lspercentile[2]\n            else \"<= p40 (%s)\" % (lspercentile[3])\n            if z <= lspercentile[3]\n            else \"<= p50 (%s)\" % (lspercentile[4])\n            if z <= lspercentile[4]\n            else \"<= p60 (%s)\" % (lspercentile[5])\n            if z <= lspercentile[5]\n            else \"<= p70 (%s)\" % (lspercentile[6])\n            if z <= lspercentile[6]\n            else \"<= p80 (%s)\" % (lspercentile[7])\n            if z <= lspercentile[7]\n            else \"<= p90 (%s)\" % (lspercentile[8])\n            if z <= lspercentile[8]\n            else \"> p90 (%s)\" % (lspercentile[8])\n            for z in x\n        ]\n    else:\n        lspercentile = [\n            np.percentile(x, 15),\n            np.percentile(x, 50),\n            np.percentile(x, 85),\n        ]\n        res = [\n            \"1-Very Low\"\n            if z < lspercentile[0]\n            else \"2-Low\"\n            if z < lspercentile[1]\n            else \"3-High\"\n            if z < lspercentile[2]\n            else \"4-Very High\"\n            for z in x\n        ]\n    return res\n\n\ndef kpi_transform(dfx, kpi_combo, kpi_combo_new):\n    \"\"\"\n    Feature transformation from continuous feature to binned features for a list of features\n\n    Args\n    ----\n\n    dfx : DataFrame\n        DataFrame containing the features.\n\n    kpi_combo : list of string\n        List of feature names to be transformed\n\n    kpi_combo_new : list of string\n        List of new feature names to be assigned to the transformed features.\n\n    Returns\n    -------\n    dfx : DataFrame\n        Updated DataFrame containing the new features.\n    \"\"\"\n    for j in range(len(kpi_combo)):\n        if type(dfx[kpi_combo[j]].values[0]) is str:\n            dfx[kpi_combo_new[j]] = dfx[kpi_combo[j]].values\n            dfx[kpi_combo_new[j]] = cat_group(dfx=dfx, kpix=kpi_combo_new[j])\n        else:\n            if len(kpi_combo) > 1:\n                dfx[kpi_combo_new[j]] = cat_continuous(\n                    dfx[kpi_combo[j]].values, granularity=\"Low\"\n                )\n            else:\n                dfx[kpi_combo_new[j]] = cat_continuous(\n                    dfx[kpi_combo[j]].values, granularity=\"High\"\n                )\n    return dfx\n\n\ndef get_tree_leaves_mask(tree) -> np.ndarray:\n    \"\"\"\n    Get mask array for tree leaves\n    Args:\n        tree: CausalTreeRegressor\n              Tree object\n    Returns: np.ndarray\n             Mask array\n\n    \"\"\"\n    n_nodes = tree.tree_.node_count\n    children_left = tree.tree_.children_left\n    children_right = tree.tree_.children_right\n\n    node_depth = np.zeros(shape=n_nodes, dtype=np.int64)\n    is_leaves = np.zeros(shape=n_nodes, dtype=bool)\n    stack = [(0, 0)]\n    while len(stack) > 0:\n        node_id, depth = stack.pop()\n        node_depth[node_id] = depth\n\n        is_split_node = children_left[node_id] != children_right[node_id]\n\n        if is_split_node:\n            stack.append((children_left[node_id], depth + 1))\n            stack.append((children_right[node_id], depth + 1))\n        else:\n            is_leaves[node_id] = True\n    return is_leaves\n\n\ndef timeit(exclude_kwargs: tuple = ()) -> Callable:\n    \"\"\"\n    timeit decorator\n    Args:\n        exclude_kwargs: (tuple), keyword arguments that should be excluded from display\n    Returns: Callable\n\n    \"\"\"\n\n    def wrapper(f: Callable):\n        def wrapped(*args, **kw):\n            ts = time.time()\n            result = f(*args, **kw)\n            te = time.time()\n            display_kw = {k: v for k, v in kw.items() if k not in exclude_kwargs}\n            print(\n                f\"Function: %s Kwargs:%r Elapsed time: %2.4f\"\n                % (f.__name__, display_kw, te - ts)\n            )\n            return result\n\n        return wrapped\n\n    return wrapper\n","repo_name":"uber/causalml","sub_path":"causalml/inference/tree/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":8808,"program_lang":"python","lang":"en","doc_type":"code","stars":4384,"dataset":"github-code","pt":"48"}
+{"seq_id":"70666992145","text":"# Evolução do Projeto 50 (Progressão Aritmética), onde a estrutura For foi substituída pela estrutura While, e o programa pergunta ao usuário quantos termos ele gostaria de ver.\n\nfrom time import sleep\n\npt = int(input('Insira o primeiro termo da progressão aritmética: '))\nr = int(input('Agora, insira a razão da progressão aritmética: '))\nt = int(input('Quantos termos você gostaria de visualizar? '))\npa = pt\nf = 1\n\nwhile f <= t:\n    pa = pt + (r * (f - 1))\n    print('O {}° termo da progressão aritmética é {}.'.format(f, pa))\n    sleep(0.5)\n    f += 1\n\nfo = f\n\nfad = int(input('Quantos termos a mais você gostaria de ver? Caso não queira ver mais nenhum, digite 0: '))\n\nwhile fad != 0:\n    while f <= (fo + fad):\n        pa = pt + (r * (f - 1))\n        print('O {}° termo da progressão aritmética é {}.'.format(f, pa))\n        sleep(0.5)\n        f += 1\n    fo = f\n    fad = int(input('Quantos termos a mais você gostaria de ver? Caso não queira ver mais nenhum, digite 0: '))\n\nprint('\\nEncerrando o programa.')\nsleep(1)","repo_name":"GabrielVitorino28/Python","sub_path":"projetos-basicos/60_progressao_aritmetica_versao_dois.py","file_name":"60_progressao_aritmetica_versao_dois.py","file_ext":"py","file_size_in_byte":1047,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"24943104112","text":"'''\r\nCreate a simple GUI to display and save readings for the Radio Shack \r\n22-812 digital multimeter.\r\n\r\nWhen the Start button is pressed, the data are written to the log file.\r\n'''\r\n\r\nimport wx, re\r\nfrom threading import Timer\r\nfrom time import strftime, time\r\nfrom rs22812 import RS22812\r\nfrom sys import argv, stdout\r\nfrom os.path import join, split\r\nimport wx.lib.agw.balloontip as BT\r\n\r\ndebug = False\r\nfont_factor = 1.75\r\n\r\n# You can modify some features of the program here\r\n\r\nsettings = {\r\n    \"frame size horiz\"      : 500,\r\n    \"frame size vert\"       : 280,\r\n\r\n    \"time font size\"        : 14,\r\n    \"reading font size\"     : 40,\r\n\r\n    \"reading fg color\"      : \"black\",\r\n    \"reading bg color\"      : \"#eeeeff\",\r\n\r\n    \"time fg color\"         : \"black\",\r\n    \"time bg color\"         : \"white\",\r\n\r\n    \"log file\"              : \"readings.log\",\r\n    \"default sample rate\"   : \"1\",\r\n\r\n    \"tips\" : {\r\n        \"Set\" : '''You can use this button to change the sampling time\r\n                   while the program is running.''',\r\n        \"Start\" : '''Start and stop getting measurements from the meter.\r\n            Make sure the meter is turned on and the serial cable is\r\n            connected before pressing this button; otherwise, the program\r\n            will hang.\r\n            ''',\r\n        \"Textbox\" : '''Enter a positive integer or floating point number to\r\n            set the sampling rate.\r\n            ''',\r\n        \"Reading\" : '''Display of the meter's reading.  You may see additional\r\n            text such as Auto (means autoranging is on), REL (readings are\r\n            relative), or MAX/MIN (maximum and minimum readings are being\r\n            captured).  The units are displayed after the number.  If ~ is\r\n            included, it means an AC measurement.  0F means overflow and\r\n            5hrt means \"Short\".\r\n            ''',\r\n        \"Time stamp\" : \"Gives the time stamp of the last displayed reading.\",\r\n\r\n        \"tip fg color\" : \"black\",\r\n        \"tip bg color\" : \"#FFFED1\",\r\n        \"start delay ms\" : 1000,\r\n    }\r\n}\r\n\r\ndef D(s, no_eol=False):\r\n    if debug:\r\n        stdout.write(s)\r\n        if not no_eol: stdout.write(\"\\n\")\r\n\r\ndef TipFormatter(tip, length=50):\r\n    '''This function will format a string to fit into the specified number\r\n    of spaces.  Multiple spaces will be replaced by one space character.\r\n    The intent is to let you make nice looking dictionary entries for the\r\n    tip texts using triple-quoted strings, but have them appear uniformly-\r\n    wrapped on the screen.\r\n    '''\r\n    fields, s, line = re.sub(\"  +\", \" \", tip.replace(\"\\n\", \"\")).split(), \"\", \"\"\r\n    for f in fields:\r\n        if len(line) + len(f) < length:\r\n            line += f + \" \"\r\n        else:\r\n            s += line + \"\\n\"\r\n            line = f + \" \"\r\n    s += line \r\n    return s\r\n\r\nclass MyFrame(wx.Frame):\r\n    def __init__(self, parent, id):\r\n        wx.Frame.__init__(self, parent, id, \"meter.py\", \r\n            size=(settings[\"frame size horiz\"], settings[\"frame size vert\"]))\r\n        self.interval = 0\r\n        self.panel = wx.Panel(self)\r\n        self.panel.SetBackgroundColour(\"white\")\r\n        self.status_bar = self.CreateStatusBar()\r\n        self.add_sizer()\r\n        self.tips_button()\r\n        self.time_stamp()\r\n        self.reading()\r\n        self.start_button()\r\n        self.sampling()\r\n        self.time_stuff()\r\n        self.events()\r\n        self.log_file()\r\n        self.tips()\r\n        self.log.write(\"\\nProgram started \" + self.CurrentTime() + \"\\n\")\r\n        self.meter = None  # Start off with no meter object\r\n        self.OnTips(None)\r\n\r\n    def log_file(self):\r\n        # Open log file\r\n        head, tail = split(argv[0])\r\n        self.log = open(join(head, settings[\"log file\"]), \"a\")\r\n\r\n    def tips(self):\r\n        # Set up our tips\r\n        font_title = wx.Font(10, wx.SWISS, wx.NORMAL, wx.BOLD)\r\n        font_text  = wx.Font(10, wx.SWISS, wx.NORMAL, wx.NORMAL)\r\n        shape = BT.BT_ROUNDED\r\n        style = BT.BT_LEAVE\r\n        self.tips = {}\r\n        dict = settings[\"tips\"]\r\n        self.tips[\"Start\"] = [BT.BalloonTip(topicon=None, \r\n            toptitle=\"Start button\", \r\n            message=TipFormatter(dict[\"Start\"]),\r\n            shape=shape, tipstyle=style),\r\n            self.start]\r\n        self.tips[\"Set\"] = [BT.BalloonTip(topicon=None, \r\n            toptitle=\"Set button\", \r\n            message=TipFormatter(dict[\"Set\"]),\r\n            shape=shape, tipstyle=style),\r\n            self.set]\r\n        self.tips[\"Textbox\"] = [BT.BalloonTip(topicon=None, \r\n            toptitle=\"Sampling interval\", \r\n            message=TipFormatter(dict[\"Textbox\"]),\r\n            shape=shape, tipstyle=style),\r\n            self.rate]\r\n        self.tips[\"Reading\"] = [BT.BalloonTip(topicon=None, \r\n            toptitle=\"Reading\", \r\n            message=TipFormatter(dict[\"Reading\"]),\r\n            shape=shape, tipstyle=style),\r\n            self.reading]\r\n        self.tips[\"Time stamp\"] = [BT.BalloonTip(topicon=None, \r\n            toptitle=\"Time stamp\", \r\n            message=TipFormatter(dict[\"Time stamp\"]),\r\n            shape=shape, tipstyle=style),\r\n            self.timestamp]\r\n\r\n        for name in self.tips:\r\n            self.tips[name][0].SetBalloonColour(dict[\"tip bg color\"])\r\n            self.tips[name][0].SetMessageColour(dict[\"tip fg color\"])\r\n            self.tips[name][0].SetStartDelay(dict[\"start delay ms\"])\r\n            self.tips[name][0].SetTitleFont(font_title)\r\n            self.tips[name][0].SetMessageFont(font_text)\r\n            self.tips[name][0].SetTarget(self.tips[name][1])\r\n\r\n    def add_sizer(self):\r\n        # Add a vertical sizer\r\n        self.sizer = wx.BoxSizer(wx.VERTICAL)\r\n        self.SetSizer(self.sizer)\r\n\r\n    def sampling(self):\r\n        # Text box for sampling interval\r\n        ratetext = wx.StaticText(self.panel, -1, \"Sample rate\", style=wx.ALIGN_CENTER)\r\n        self.rate = wx.TextCtrl(self.panel, -1, \r\n            settings[\"default sample rate\"], size=(50, 20))\r\n        # Choice box for units\r\n        units = wx.StaticText(self.panel, -1, \"units\", style=wx.ALIGN_CENTER)\r\n        self.unit_choices = [\"seconds\", \"minutes\", \"hours\"]\r\n        self.units = wx.ListBox(self.panel, -1, (20, 20), (80, 50), \r\n            self.unit_choices, wx.LB_SINGLE)\r\n        self.units.SetSelection(0)\r\n        choice_sizer = wx.BoxSizer(wx.HORIZONTAL)\r\n        choice_sizer.Add(ratetext, 0, wx.EXPAND)\r\n        choice_sizer.Add((5, 1))\r\n        choice_sizer.Add(self.rate, 0, wx.FIXED_MINSIZE)\r\n        choice_sizer.Add((10, 1))\r\n        choice_sizer.Add(units, 0, wx.EXPAND)\r\n        choice_sizer.Add((5, 1))\r\n        choice_sizer.Add(self.units,   0, wx.FIXED_MINSIZE)\r\n        choice_sizer.Add((5, 1))\r\n        self.set = wx.Button(self.panel, -1, \"Set\")\r\n        choice_sizer.Add(self.set, 0, wx.ALIGN_TOP)\r\n        self.Bind(wx.EVT_BUTTON, self.OnSet, self.set)\r\n        self.sizer.Add(choice_sizer, 0, wx.ALIGN_CENTER)\r\n\r\n    def time_stamp(self):\r\n        # Static text control for the time stamp\r\n        self.timestamp = wx.StaticText(self.panel, -1, \"\", \r\n            size = (settings[\"frame size horiz\"],\r\n                    settings[\"time font size\"]*font_factor),\r\n            style=wx.ALIGN_CENTER|wx.ST_NO_AUTORESIZE)\r\n        self.timestamp.SetBackgroundColour(settings[\"time bg color\"])\r\n        self.timestamp.SetForegroundColour(settings[\"time fg color\"])\r\n        self.timestamp.SetFont(wx.Font(settings[\"time font size\"],\r\n            wx.DEFAULT, wx.NORMAL, wx.BOLD))\r\n        self.sizer.Add(self.timestamp, 0, wx.EXPAND)\r\n\r\n    def reading(self):\r\n        # Static text control for the reading\r\n        self.reading = wx.StaticText(self.panel, -1, \"0.0 V\", \r\n            size = (settings[\"frame size horiz\"],\r\n                    settings[\"reading font size\"]*font_factor),\r\n            style=wx.ALIGN_CENTER|wx.ST_NO_AUTORESIZE)\r\n        self.reading.SetBackgroundColour(settings[\"reading bg color\"])\r\n        self.reading.SetForegroundColour(settings[\"reading fg color\"])\r\n        self.reading.SetFont(wx.Font(settings[\"reading font size\"],\r\n            wx.DEFAULT, wx.NORMAL, wx.BOLD))\r\n        self.sizer.Add(self.reading, 0, wx.EXPAND)\r\n        self.sizer.Add((1, 5))\r\n\r\n    def tips_button(self):\r\n        self.tips_button = wx.ToggleButton(self.panel, -1, \"Tips\",\r\n            style=wx.BU_EXACTFIT)\r\n        self.tips_button.SetValue(False)\r\n        self.Bind(wx.EVT_TOGGLEBUTTON, self.OnTips, self.tips_button)\r\n        self.sizer.Add(self.tips_button, 0, wx.FIXED_MINSIZE|wx.ALIGN_LEFT)\r\n\r\n    def start_button(self):\r\n        # Start/stop button\r\n        self.start = wx.ToggleButton(self.panel, -1, \"Start\")\r\n        self.Bind(wx.EVT_TOGGLEBUTTON, self.OnStart, self.start)\r\n        self.sizer.Add(self.start, 0, wx.FIXED_MINSIZE|wx.ALIGN_CENTER_HORIZONTAL)\r\n        # Spacing between button and data entry\r\n        self.sizer.Add((10, 20))\r\n\r\n    def events(self):\r\n        # Hook up needed events\r\n        self.Bind(wx.EVT_CLOSE, self.OnClose)\r\n\r\n    def time_stuff(self):\r\n        # We'll have a timer update the status bar with the current time\r\n        self.sbtimer = Timer(1, self.UpdateStatusBar)\r\n        self.sbtimer.start()\r\n        self.last_time = time()\r\n\r\n    def UpdateStatusBar(self):\r\n        self.status_bar.SetStatusText(self.CurrentTime())\r\n        # Reset the timer\r\n        self.sbtimer = Timer(1, self.UpdateStatusBar)\r\n        self.sbtimer.start()\r\n        \r\n    def CurrentTime(self):\r\n        return strftime(\"%d%b%Y-%H:%M:%S\")\r\n\r\n    def GetReading(self):\r\n        if not self.interval:\r\n            self.OnSet(None)\r\n        if self.running:\r\n            if not self.meter:\r\n                self.meter = RS22812()\r\n            reading_tuple = self.meter.GetReading()\r\n            reading_string = self.CurrentTime() + \" [%d] \" % self.count + \\\r\n                str(reading_tuple)\r\n            self.count += 1\r\n            self.log.write(\"    \" + reading_string + \"\\n\")\r\n            D(\"Reading = \" + reading_string)\r\n            # Update display\r\n            self.timestamp.SetLabel(self.CurrentTime())\r\n            reading, mode, modifiers = reading_tuple\r\n            s = reading + \" \" + ''.join(modifiers)\r\n            self.reading.SetLabel(s)\r\n            # Set up timer to take another reading\r\n            self.reading_timer = Timer(self.interval, self.GetReading)\r\n            self.reading_timer.start()\r\n            D(\"Timer set to take another reading in %g s\" % self.interval)\r\n\r\n    def OnStart(self, event):\r\n        if self.start.GetValue():\r\n            # Was a start command\r\n            D(\"Start button pressed\")\r\n            self.start.SetLabel(\"Stop\")\r\n            self.log.write(\"  Start button pressed %s\\n\" % self.CurrentTime())\r\n            self.count = 1\r\n            self.running = True\r\n            self.GetReading()\r\n        else:\r\n            # Was a stop command\r\n            self.reading_timer.cancel()\r\n            self.running = False\r\n            if self.meter:\r\n                del self.meter  # Forces serial port to close\r\n            self.meter = None\r\n            D(\"Stop button pressed\")\r\n            self.log.write(\"  Stop button pressed %s\\n\" % self.CurrentTime())\r\n            self.start.SetLabel(\"Start\")\r\n\r\n    def OnTips(self, event):\r\n        if self.tips_button.GetValue():\r\n            self.tips[\"Start\"][0].EnableTip(True)\r\n        else:\r\n            self.tips[\"Start\"][0].EnableTip(False)\r\n\r\n    def OnSet(self, event):\r\n        # Set our sampling interval\r\n        try:\r\n            self.interval = float(self.rate.GetValue())\r\n            if self.interval <= 0:\r\n                raise Exception()\r\n        except:\r\n            wx.MessageBox(\"'%s' is an improper sample rate\" %\r\n                self.rate.GetLabel())\r\n            return\r\n        # Get units\r\n        units = self.unit_choices[self.units.GetSelection()]\r\n        if units == \"minutes\": self.interval *= 60\r\n        elif units == \"hours\": self.interval *= 3600\r\n        s = \"Set button pressed = %g s\" % self.interval\r\n        if event:\r\n            D(s)\r\n        else:\r\n            D(\"Simulating \" + s)\r\n        self.log.write(\"  New sampling interval %g s %s\\n\" % (\r\n            self.interval, self.CurrentTime()))\r\n\r\n    def OnClose(self, event):\r\n        D(\"Program shutting down\")\r\n        try: self.sbtimer.cancel()\r\n        except: pass\r\n        try: self.reading_timer.cancel()\r\n        except: pass\r\n        self.log.write(\"  Program ended %s\\n\" % self.CurrentTime())\r\n        self.Destroy()\r\n\r\nif __name__ == \"__main__\":\r\n    app = wx.PySimpleApp()\r\n    frame = MyFrame(parent=None, id=-1)\r\n    frame.Show()\r\n    app.MainLoop()\r\n\r\n\r\n","repo_name":"soupwizard/PyRsMeter","sub_path":"meter.py","file_name":"meter.py","file_ext":"py","file_size_in_byte":12623,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"48"}
+{"seq_id":"9114719154","text":"import sys\n\nif len(sys.argv) != 3 :\n    print (\"usage: \")\n    print (\"rsqrwav.py [COUNT_SYNC] [COUNT_DATA]\")\nelse :\n    out_str = ''\n    \n    for x in range(0, int(sys.argv[1])) :\n        out_str += \"1,1,1,0,\"\n\n    for x in range(0, int(sys.argv[2])):\n        if(x > 0) : \n            out_str += \",\"\n            \n        out_str += \"1,0\"\n\n    print(out_str)\n","repo_name":"bytemaster-0xff/sdr","sub_path":"Utils/rcsqrwav.py","file_name":"rcsqrwav.py","file_ext":"py","file_size_in_byte":358,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"30284911551","text":"import click\nimport yaml\n\n@click.command()\n@click.option('--docker')\n@click.option('--pathfile')\ndef volume_coverage(docker, pathfile):\n    def filter_file(file):\n        paths = set()\n        with open(file, \"r\") as infile:\n            contents = yaml.load(infile)[\"services\"][\"thredds\"][\"volumes\"]\n            for content in contents:\n                path, _ = content.split(\":\")\n                paths.add(path.strip())\n\n        return paths\n\n    paths_to_cover = filter_file(docker)\n    \n    def get_used_paths(pathfile):\n        with open(pathfile, \"r\") as pfile:\n            return {path.strip() for path in pfile}\n    \n    paths_covered = get_used_paths(pathfile)\n\n    covered = set()\n\n    for path_covered in paths_covered:\n        for path_to_cover in paths_to_cover:\n            if path_covered in path_to_cover:\n                covered.add(path_to_cover)\n\n    missing = paths_to_cover - covered\n    print(f\"Num volumes missing: {len(missing)}\")\n    for miss in missing:\n        print(miss)\n    \nif __name__ == \"__main__\":\n    volume_coverage()","repo_name":"pacificclimate/birdhouse-config","sub_path":"scripts/volume_coverage.py","file_name":"volume_coverage.py","file_ext":"py","file_size_in_byte":1053,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"71183462865","text":"# -*- coding: utf-8 -*-\n\"\"\"\n    :author: Kleon\n    :url: https://github.com/kleon1024\n\"\"\"\nimport random\n\nfrom faker import Faker\nfrom sqlalchemy.exc import IntegrityError\n\nfrom .extensions import db\nfrom .models import (User, Post, Domain, Comment, Category)\nfrom .utils import (exist_username, exist_email, exist_nickname, exist_domain)\n\nfake = Faker(locale='zh_CN')\n\n\ndef fake_admin():\n    admin = User(nickname='KLEON',\n                 username='kleon',\n                 email='1995dingli@gmail.com',\n                 bio='阡陌 - 连接更多')\n\n    admin.set_password('hellokleon')\n    db.session.add(admin)\n    db.session.commit()\n\n\ndef fake_user(count=10):\n    for _ in range(count):\n        while True:\n            username = fake.user_name()\n            if not exist_username(username): break\n        while True:\n            email = fake.email()\n            if not exist_email(email): break\n        while True:\n            nickname = fake.name()\n            if not exist_nickname(nickname): break\n        user = User(nickname=nickname,\n                    username=username,\n                    bio=fake.sentence(),\n                    email=email)\n        user.set_password('123456')\n        db.session.add(user)\n        try:\n            db.session.commit()\n        except:\n            db.session.rollback()\n\n\ndef fake_collect(count=30):\n    for _ in range(count):\n        user = User.query.get(random.randint(1, User.query.count()))\n        user.collect(Post.query.get(random.randint(1, Post.query.count())))\n    db.session.commit()\n\n\ndef fake_comment(count=30):\n    for _ in range(count):\n        comment = Comment(\n            author=User.query.get(random.randint(1, User.query.count())),\n            body=fake.sentence(),\n            timestamp=fake.date_time_this_year(),\n            post=Post.query.get(random.randint(1, Post.query.count())),\n        )\n        db.session.add(comment)\n    db.session.commit()\n\n\ndef fake_vote(count=30):\n    for _ in range(count):\n        user = User.query.get(random.randint(1, User.query.count()))\n        user.vote(Comment.query.get(random.randint(1, Comment.query.count())))\n    db.session.commit()\n\n\ndef fake_follow(count=30):\n    for _ in range(count):\n        user = User.query.get(random.randint(1, User.query.count()))\n        user.follow(User.query.get(random.randint(1, User.query.count())))\n    db.session.commit()\n\n\ndef fake_domain(count=30):\n    for _ in range(count):\n        while True:\n            title = fake.job()\n            if not exist_domain(title): break\n        domain = Domain(title=title,\n                        description=fake.text(),\n                        bio=\"名\" + fake.job() + \"正在\" + fake.word(),\n                        timestamp=fake.date_time_this_year(),\n                        creator=User.query.get(\n                            random.randint(1, User.query.count())))\n        db.session.add(domain)\n    db.session.commit()\n\n\ndef fake_post(count=30):\n    for _ in range(count):\n        post = Post(\n            title=fake.sentence(),\n            description=fake.text(),\n            url=\"https://juejin.im/post/5db684ddf265da4d495c40e5\",\n            timestamp=fake.date_time_this_year(),\n            # category=Category.query.get(\n            #     random.randint(1, Category.query.count())),\n            author=User.query.get(random.randint(1, User.query.count())),\n            domain=Domain.query.get(random.randint(1, Domain.query.count())),\n        )\n        db.session.add(post)\n    db.session.commit()\n\n\ndef fake_like(count=50):\n    for _ in range(count):\n        user = User.query.get(random.randint(1, User.query.count()))\n        user.like(Post.query.get(random.randint(1, Post.query.count())))\n    db.session.commit()\n\n\ndef fake_watch(count=30):\n    for _ in range(count):\n        user = User.query.get(random.randint(1, User.query.count()))\n        user.watch(Domain.query.get(random.randint(1, Domain.query.count())))\n    db.session.commit()\n","repo_name":"kleon1024/ChainMore-API-Flask","sub_path":"chainmore/fakes.py","file_name":"fakes.py","file_ext":"py","file_size_in_byte":3951,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"19804001821","text":"param = {\n    'Cones': {\n        'sigmaColor': 20,\n        'sigmaSpace': 6,\n        'r': 3,\n        'type': 'small'\n    },\n    'Tsukuba':{\n        'sigmaColor': 22,\n        'sigmaSpace': 18,\n        'r': 3,\n        'type': 'large'\n    },\n    'Teddy':{\n        'sigmaColor': 11,\n        'sigmaSpace': 12,\n        'r': 3,\n        'type': 'small'\n    },\n    'Venus':{\n        'sigmaColor': 6,\n        'sigmaSpace': 27,\n        'r': 7,\n        'type': 'small'\n    }\n}","repo_name":"alechuang98/ComputerVision2021","sub_path":"hw4/param.py","file_name":"param.py","file_ext":"py","file_size_in_byte":463,"program_lang":"python","lang":"ar","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"35310078965","text":"import os\nimport re\nimport sys\nimport json\nimport subprocess\nimport argparse\nfrom dataclasses import dataclass\nfrom typing import List, Optional\n\nsys.path.append(\"docker/build\")\nfrom util.upload import *\n\n\ndef upload_ubuntu(args: Arguments):\n\n    with open(\"dput.cfg\", \"w\") as dput_cfg:\n        dput_cfg.write(\n            f\"\"\"\n[DEFAULT]\nlogin\t = *\nmethod = ftp\nhash = md5\nallow_unsigned_uploads = 0\nallow_dcut = 0\nrun_lintian = 0\nrun_dinstall = 0\ncheck_version = 0\nscp_compress = 0\npost_upload_command\t=\npre_upload_command =\npassive_ftp = 1\ndefault_host_main\t=\nallowed_distributions\t= (?!UNRELEASED)\n\n[tezos-serokell]\nfqdn      = ppa.launchpad.net\nmethod    = ftp\nincoming  = ~serokell/ubuntu/tezos\nlogin     = anonymous\n\n[tezos-rc-serokell]\nfqdn        = ppa.launchpad.net\nmethod      = ftp\nincoming    = ~serokell/ubuntu/tezos-rc\nlogin       = anonymous\n\n[tezos-test-serokell]\nfqdn        = ppa.launchpad.net\nmethod      = ftp\nincoming    = ~serokell/ubuntu/tezos-test\nlogin       = anonymous\n    \"\"\"\n        )\n\n    octez_version = os.getenv(\"OCTEZ_VERSION\", None)\n\n    if args.test:\n        launchpad_ppa = \"tezos-test-serokell\"\n    elif re.search(\"v.*-rc[0-9]*\", octez_version):\n        launchpad_ppa = \"tezos-rc-serokell\"\n    else:\n        launchpad_ppa = \"tezos-serokell\"\n\n    source_packages_path = args.directory\n\n    packages = get_artifact_list(args)\n\n    for f in filter(lambda x: x.endswith(\".changes\"), packages):\n        subprocess.check_call(\n            f\"execute-dput -c dput.cfg {launchpad_ppa} {f}\",\n            shell=True,\n        )\n\n\ndef main(args: Optional[Arguments] = None):\n\n    parser.set_defaults(os=\"ubuntu\")\n\n    if args is None:\n        args = fill_args(parser.parse_args())\n\n    upload_ubuntu(args)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"serokell/tezos-packaging","sub_path":"docker/build/ubuntu/upload.py","file_name":"upload.py","file_ext":"py","file_size_in_byte":1771,"program_lang":"python","lang":"en","doc_type":"code","stars":60,"dataset":"github-code","pt":"48"}
+{"seq_id":"8212632964","text":"from django.contrib import admin\nfrom django.urls import path, include\nfrom . import views\n\nurlpatterns = [\n    path(\"\", views.index,name='index'),\n    path(\"pushup/\", views.pushup,name='index'),\n    path(\"curl\", views.curl,name='index'),\n    path(\"squat/\", views.squat,name='index'),\n    path(\"individual_exercise/\", views.individual_exercise,name='individual_exercise'),\n    path(\"get_score/\", views.get_score, name='get_score')\n]\n","repo_name":"starDust-V2/Fit_Fusion","sub_path":"Exercise/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"620629826","text":"# Problem:\n#   Suppose an array of length n sorted in ascending order is rotated between 1 and n times. \n#   For example, the array nums = [0,1,2,4,5,6,7] might become:\n#\n#   [4,5,6,7,0,1,2] if it was rotated 4 times.\n#   [0,1,2,4,5,6,7] if it was rotated 7 times.\n#\n#   Notice that rotating an array [a[0], a[1], a[2], ..., a[n-1]] 1 time \n#   results in the array [a[n-1], a[0], a[1], a[2], ..., a[n-2]].\n#   Given the sorted rotated array nums of unique elements, return the minimum element of this array.\n#   You must write an algorithm that runs in O(log n) time.\n\n\n\n# Example:\n#   Input: nums = [3,4,5,1,2]\n#   Output: 1\n#   Explanation: The original array was [1,2,3,4,5] rotated 3 times.\n\n\ndef findMin(self, nums):\n    \"\"\"\n    :type nums: List[int]\n    :rtype: int\n    \"\"\"\n    r = len(nums) - 1\n    l = 0\n    \n    while l < r:\n        \n        m = (l+r) // 2\n        if nums[m] > nums[r]:\n            l = m + 1\n        else:\n            r = m\n    return nums[l]\n\n# O(log n) - Time Complexity - Because we are dividing the area we are searching by half every time\n# O(1) - Space Complexity - Using pointers\n\n\n# Notes:\n#\n# Use Binary Tree. while left < right. If middle is greater than right then search mid --> right. Else search left --> mid\n","repo_name":"AlanLvChen/LeetCode","sub_path":"Arrays/153.Find-Minimum-in-Rotated-Sorted-Array.py","file_name":"153.Find-Minimum-in-Rotated-Sorted-Array.py","file_ext":"py","file_size_in_byte":1250,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"10793470226","text":"import logging\nfrom typing import List, Mapping, Optional, Union, cast\n\nimport great_expectations.exceptions as ge_exceptions\nfrom great_expectations.core import ExpectationSuite\nfrom great_expectations.data_context.data_context.abstract_data_context import (\n    AbstractDataContext,\n)\nfrom great_expectations.data_context.data_context_variables import (\n    CloudDataContextVariables,\n)\nfrom great_expectations.data_context.store.ge_cloud_store_backend import (\n    GeCloudRESTResource,\n)\nfrom great_expectations.data_context.types.base import (\n    DEFAULT_USAGE_STATISTICS_URL,\n    DataContextConfig,\n    DataContextConfigDefaults,\n    DatasourceConfig,\n    GeCloudConfig,\n    datasourceConfigSchema,\n)\nfrom great_expectations.data_context.types.refs import GeCloudResourceRef\nfrom great_expectations.data_context.types.resource_identifiers import GeCloudIdentifier\nfrom great_expectations.data_context.util import substitute_all_config_variables\nfrom great_expectations.datasource import Datasource\n\nlogger = logging.getLogger(__name__)\n\n\nclass CloudDataContext(AbstractDataContext):\n    \"\"\"\n    Subclass of AbstractDataContext that contains functionality necessary to hydrate state from cloud\n    \"\"\"\n\n    def __init__(\n        self,\n        project_config: Union[DataContextConfig, Mapping],\n        context_root_dir: str,\n        ge_cloud_config: GeCloudConfig,\n        runtime_environment: Optional[dict] = None,\n    ) -> None:\n        \"\"\"\n        CloudDataContext constructor\n\n        Args:\n            project_config (DataContextConfig): config for CloudDataContext\n            runtime_environment (dict):  a dictionary of config variables that override both those set in\n                config_variables.yml and the environment\n            ge_cloud_config (GeCloudConfig): GeCloudConfig corresponding to current CloudDataContext\n        \"\"\"\n        self._ge_cloud_mode = True  # property needed for backward compatibility\n        self._ge_cloud_config = ge_cloud_config\n        self._context_root_directory = context_root_dir\n        self._project_config = self._apply_global_config_overrides(\n            config=project_config\n        )\n        self._variables = self._init_variables()\n        super().__init__(\n            runtime_environment=runtime_environment,\n        )\n\n    def _init_datasource_store(self) -> None:\n        from great_expectations.data_context.store.datasource_store import (\n            DatasourceStore,\n        )\n\n        store_name: str = \"datasource_store\"  # Never explicitly referenced but adheres\n        # to the convention set by other internal Stores\n        store_backend: dict = {\"class_name\": \"GeCloudStoreBackend\"}\n        runtime_environment: dict = {\n            \"root_directory\": self.root_directory,\n            \"ge_cloud_credentials\": self.ge_cloud_config.to_dict(),\n            \"ge_cloud_resource_type\": GeCloudRESTResource.DATASOURCE,\n            \"ge_cloud_base_url\": self.ge_cloud_config.base_url,\n        }\n\n        datasource_store: DatasourceStore = DatasourceStore(\n            store_name=store_name,\n            store_backend=store_backend,\n            runtime_environment=runtime_environment,\n        )\n        self._datasource_store = datasource_store\n\n    def list_expectation_suite_names(self) -> List[str]:\n        \"\"\"\n        Lists the available expectation suite names. If in ge_cloud_mode, a list of\n        GE Cloud ids is returned instead.\n        \"\"\"\n        return [suite_key.ge_cloud_id for suite_key in self.list_expectation_suites()]\n\n    @property\n    def ge_cloud_config(self) -> Optional[GeCloudConfig]:\n        return self._ge_cloud_config\n\n    @property\n    def ge_cloud_mode(self) -> bool:\n        return self._ge_cloud_mode\n\n    def _init_variables(self) -> CloudDataContextVariables:\n        ge_cloud_base_url: str = self._ge_cloud_config.base_url\n        ge_cloud_organization_id: str = self._ge_cloud_config.organization_id\n        ge_cloud_access_token: str = self._ge_cloud_config.access_token\n\n        variables: CloudDataContextVariables = CloudDataContextVariables(\n            config=self._project_config,\n            ge_cloud_base_url=ge_cloud_base_url,\n            ge_cloud_organization_id=ge_cloud_organization_id,\n            ge_cloud_access_token=ge_cloud_access_token,\n        )\n        return variables\n\n    def _construct_data_context_id(self) -> str:\n        \"\"\"\n        Choose the id of the currently-configured expectations store, if available and a persistent store.\n        If not, it should choose the id stored in DataContextConfig.\n        Returns:\n            UUID to use as the data_context_id\n        \"\"\"\n\n        # if in ge_cloud_mode, use ge_cloud_organization_id\n        return self.ge_cloud_config.organization_id\n\n    def _save_project_config(self) -> None:\n        \"\"\"Save the current project to disk.\"\"\"\n        logger.debug(\n            \"ge_cloud_mode detected - skipping DataContext._save_project_config\"\n        )\n        return None\n\n    def get_config_with_variables_substituted(\n        self, config: Optional[DataContextConfig] = None\n    ) -> DataContextConfig:\n        \"\"\"\n        Substitute vars in config of form ${var} or $(var) with values found in the following places,\n        in order of precedence: ge_cloud_config (for Data Contexts in GE Cloud mode), runtime_environment,\n        environment variables, config_variables, or ge_cloud_config_variable_defaults (allows certain variables to\n        be optional in GE Cloud mode).\n        \"\"\"\n        if not config:\n            config = self.config\n\n        substitutions: dict = self._determine_substitutions()\n\n        ge_cloud_config_variable_defaults = {\n            \"plugins_directory\": self._normalize_absolute_or_relative_path(\n                path=DataContextConfigDefaults.DEFAULT_PLUGINS_DIRECTORY.value\n            ),\n            \"usage_statistics_url\": DEFAULT_USAGE_STATISTICS_URL,\n        }\n        for config_variable, value in ge_cloud_config_variable_defaults.items():\n            if substitutions.get(config_variable) is None:\n                logger.info(\n                    f'Config variable \"{config_variable}\" was not found in environment or global config ('\n                    f'{self.GLOBAL_CONFIG_PATHS}). Using default value \"{value}\" instead. If you would '\n                    f\"like to \"\n                    f\"use a different value, please specify it in an environment variable or in a \"\n                    f\"great_expectations.conf file located at one of the above paths, in a section named \"\n                    f'\"ge_cloud_config\".'\n                )\n                substitutions[config_variable] = value\n\n        return DataContextConfig(\n            **substitute_all_config_variables(\n                config, substitutions, self.DOLLAR_SIGN_ESCAPE_STRING\n            )\n        )\n\n    def create_expectation_suite(\n        self,\n        expectation_suite_name: str,\n        overwrite_existing: bool = False,\n        ge_cloud_id: Optional[str] = None,\n        **kwargs: Optional[dict],\n    ) -> ExpectationSuite:\n        \"\"\"Build a new expectation suite and save it into the data_context expectation store.\n\n        Args:\n            expectation_suite_name: The name of the expectation_suite to create\n            overwrite_existing (boolean): Whether to overwrite expectation suite if expectation suite with given name\n                already exists.\n\n        Returns:\n            A new (empty) expectation suite.\n        \"\"\"\n        if not isinstance(overwrite_existing, bool):\n            raise ValueError(\"Parameter overwrite_existing must be of type BOOL\")\n\n        expectation_suite: ExpectationSuite = ExpectationSuite(\n            expectation_suite_name=expectation_suite_name, data_context=self\n        )\n        key = GeCloudIdentifier(\n            resource_type=GeCloudRESTResource.EXPECTATION_SUITE,\n            ge_cloud_id=ge_cloud_id,\n        )\n        if self.expectations_store.has_key(key) and not overwrite_existing:\n            raise ge_exceptions.DataContextError(\n                \"expectation_suite with GE Cloud ID {} already exists. If you would like to overwrite this \"\n                \"expectation_suite, set overwrite_existing=True.\".format(ge_cloud_id)\n            )\n        self.expectations_store.set(key, expectation_suite, **kwargs)\n        return expectation_suite\n\n    def delete_expectation_suite(\n        self,\n        expectation_suite_name: Optional[str] = None,\n        ge_cloud_id: Optional[str] = None,\n    ):\n        \"\"\"Delete specified expectation suite from data_context expectation store.\n\n        Args:\n            expectation_suite_name: The name of the expectation_suite to create\n\n        Returns:\n            True for Success and False for Failure.\n        \"\"\"\n        key = GeCloudIdentifier(\n            resource_type=GeCloudRESTResource.EXPECTATION_SUITE,\n            ge_cloud_id=ge_cloud_id,\n        )\n        if not self.expectations_store.has_key(key):\n            raise ge_exceptions.DataContextError(\n                \"expectation_suite with name {} does not exist.\"\n            )\n        else:\n            self.expectations_store.remove_key(key)\n            return True\n\n    def get_expectation_suite(\n        self,\n        expectation_suite_name: Optional[str] = None,\n        ge_cloud_id: Optional[str] = None,\n    ) -> ExpectationSuite:\n        \"\"\"Get an Expectation Suite by name or GE Cloud ID\n        Args:\n            expectation_suite_name (str): the name for the Expectation Suite\n            ge_cloud_id (str): the GE Cloud ID for the Expectation Suite\n\n        Returns:\n            expectation_suite\n        \"\"\"\n        key = GeCloudIdentifier(\n            resource_type=GeCloudRESTResource.EXPECTATION_SUITE,\n            ge_cloud_id=ge_cloud_id,\n        )\n        if self.expectations_store.has_key(key):\n            expectations_schema_dict: dict = cast(\n                dict, self.expectations_store.get(key)\n            )\n            # create the ExpectationSuite from constructor\n            return ExpectationSuite(**expectations_schema_dict, data_context=self)\n\n        else:\n            raise ge_exceptions.DataContextError(\n                f\"expectation_suite {expectation_suite_name} not found\"\n            )\n\n    def save_expectation_suite(\n        self,\n        expectation_suite: ExpectationSuite,\n        expectation_suite_name: Optional[str] = None,\n        overwrite_existing: bool = True,\n        ge_cloud_id: Optional[str] = None,\n        **kwargs: Optional[dict],\n    ) -> None:\n        \"\"\"Save the provided expectation suite into the DataContext.\n\n        Args:\n            expectation_suite: the suite to save\n            expectation_suite_name: the name of this expectation suite. If no name is provided the name will \\\n                be read from the suite\n            ge_cloud_id: cloud id for saving expectation suite\n            overwrite_existing: should I over-write the Suite if it already exists?\n        Returns:\n            None\n        \"\"\"\n        key: GeCloudIdentifier = GeCloudIdentifier(\n            resource_type=GeCloudRESTResource.EXPECTATION_SUITE,\n            ge_cloud_id=ge_cloud_id\n            if ge_cloud_id is not None\n            else str(expectation_suite.ge_cloud_id),\n        )\n        if self.expectations_store.has_key(key) and not overwrite_existing:\n            raise ge_exceptions.DataContextError(\n                f\"expectation_suite with GE Cloud ID {ge_cloud_id} already exists. \"\n                f\"If you would like to overwrite this expectation_suite, set overwrite_existing=True.\"\n            )\n        self._evaluation_parameter_dependencies_compiled = False\n        self.expectations_store.set(key, expectation_suite, **kwargs)\n\n    @property\n    def root_directory(self) -> Optional[str]:\n        \"\"\"The root directory for configuration objects in the data context; the location in which\n        ``great_expectations.yml`` is located.\n\n        Why does this exist in AbstractDataContext? CloudDataContext and FileDataContext both use it\n\n        \"\"\"\n        return self._context_root_directory\n\n    def _instantiate_datasource_from_config_and_update_project_config(\n        self,\n        name: str,\n        config: dict,\n        initialize: bool = True,\n        save_changes: bool = False,\n    ) -> Optional[Datasource]:\n        \"\"\"Instantiate datasource and optionally persist datasource config to store and/or initialize datasource for use.\n\n        Args:\n            name: Desired name for the datasource.\n            config: Config for the datasource.\n            initialize: Whether to initialize the datasource or return None.\n            save_changes: Whether to save the datasource config to the configured Datasource store.\n\n        Returns:\n            If initialize=True return an instantiated Datasource object, else None.\n        \"\"\"\n\n        datasource_config: DatasourceConfig = datasourceConfigSchema.load(config)\n\n        if save_changes:\n\n            datasource_config[\"name\"] = name\n            resource_ref: GeCloudResourceRef = self._datasource_store.create(\n                datasource_config\n            )\n            datasource_config.id_ = resource_ref.ge_cloud_id\n\n        self.config.datasources[name] = datasource_config\n\n        # Config must be persisted with ${VARIABLES} syntax but hydrated at time of use\n        substitutions: dict = self._determine_substitutions()\n        config: dict = dict(datasourceConfigSchema.dump(datasource_config))\n        substituted_config: dict = substitute_all_config_variables(\n            config, substitutions, self.DOLLAR_SIGN_ESCAPE_STRING\n        )\n\n        datasource: Optional[Datasource] = None\n        if initialize:\n            try:\n                datasource: Datasource = self._instantiate_datasource_from_config(\n                    name=name, config=substituted_config\n                )\n                self._cached_datasources[name] = datasource\n            except ge_exceptions.DatasourceInitializationError as e:\n                # Do not keep configuration that could not be instantiated.\n                if save_changes:\n                    self._datasource_store.delete(datasource_config)\n                # If the DatasourceStore uses an InlineStoreBackend, the config may already be updated\n                self.config.datasources.pop(name, None)\n                raise e\n\n        return datasource\n","repo_name":"franciscojavierarceo/Python","sub_path":"demos/great-expectations/venv/lib/python3.8/site-packages/great_expectations/data_context/data_context/cloud_data_context.py","file_name":"cloud_data_context.py","file_ext":"py","file_size_in_byte":14335,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"48"}
+{"seq_id":"38492868202","text":"\"\"\"Type declarations\"\"\"\n\nTRANSACTION_WALLET_CREATION = 1\nTRANSACTION_TOKEN_CREATION = 2\nTRANSACTION_SMART_CONTRACT = 3\nTRANSACTION_TWO_WAY_TRANSFER = 4\nTRANSACTION_ONE_WAY_TRANSFER = 5\nTRANSACTION_TRUST_SCORE_CHANGE = 6\nTRANSACTION_MINER_ADDITION = 7\nTRANSACTION_SC_UPDATE = 8\nNEWRL_TOKEN_CODE = 'NWRL'\nNUSD_TOKEN_CODE = 'NUSD'\nNEWRL_TOKEN_NAME = 'Newrl'\nNEWRL_TOKEN_DECIMAL = 6\nNEWRL_TOKEN_MULTIPLIER = 1000000\nNUSD_TOKEN_MULTIPLIER = 1000000\n\nTOKEN_NFT = 721\n\nBLOCK_VOTE_VALID = 1  # Valid block by committee members\nBLOCK_VOTE_INVALID = 5  # Invalid block and also for proposing an empty block\nBLOCK_VOTE_MINER = 9  # Block miner's receipt\n\nBLOCK_STATUS_VALID = 1\nBLOCK_STATUS_INVALID_MINED = -1\nBLOCK_STATUS_MINING_TIMEOUT = -2\nBLOCK_STATUS_CONSENSUS_TIMEOUT = -3\n\nBLOCK_CONSENSUS_VALID = 1\nBLOCK_CONSENSUS_INVALID = -1\nBLOCK_CONSENSUS_NA = 0","repo_name":"newrlfoundation/newrl","sub_path":"app/config/ntypes.py","file_name":"ntypes.py","file_ext":"py","file_size_in_byte":846,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"48"}
+{"seq_id":"7981583486","text":"#!/usr/bin/env python\n\"\"\"\nGroup and HDR\n-------------\n\nThis script takes all tif files in the current directory, groups them into 3s,\naligns, and averages.\n\"\"\"\n\nimport os\nimport shutil\nimport subprocess\n\n\ntifs = os.walk(os.path.realpath('.')).next()[2]\nraw_input('Group and average: ' + repr(tifs) + '?')\nfor idx, group in enumerate([tifs[i:i+3] for i in range(0, len(tifs), 3)]):\n    group_name = 'group' + str(idx)\n    os.mkdir(group_name)\n    for photo in group:\n        shutil.move(photo, os.path.join(group_name, photo))\n\ngroups = os.walk(os.path.realpath('.')).next()[1]\nraw_input('Enfuse dirs: ' + repr(groups) + '?')\nfor group in groups:\n    subprocess.check_call(\n        '/usr/bin/align_image_stack -a aligned_ -v -m -g 10 -c 16 -C *.tif',\n        cwd=group, shell=True)\n    subprocess.check_call(\n        '/usr/bin/enfuse -o avg.tif aligned_*.tif', cwd=group, shell=True)\n\nraw_input('Move averaged file: ' + repr(groups) + '?')\nfor group in groups:\n    fn = group[len('group'):]\n    if len(fn) < 2:\n        fn = '0' + fn\n\n    fn = fn + '.tif'\n    shutil.move(os.path.join(group, 'avg.tif'), fn)\n","repo_name":"thcipriani/dotfiles","sub_path":"srv/photos/lib/group-and-hdr.py","file_name":"group-and-hdr.py","file_ext":"py","file_size_in_byte":1106,"program_lang":"python","lang":"en","doc_type":"code","stars":83,"dataset":"github-code","pt":"48"}
+{"seq_id":"12263256556","text":"\nlistaDolares = []\nlistaYenes = []\nlistaEuros = []\n\nnombre =input(\"escribir nombre y apellido: \")\n\ndef dolarPeso_promedio(list):\n    list = [x*4200 for x in list]\n    \n    valor = 0\n    for i in list:\n        valor+=i\n    if len(list) >= 1:\n        valor = valor/len(list)\n    else: valor = 0\n    print(f'el promedio de la lista es {valor}')\n\n\n\ndef eurosPesos(list):\n    list = [x*4700 for x in list]\n    menor = list[0]\n    mayor = 0\n    for i in list:\n        if i > mayor:\n            mayor = i\n        if menor > i:\n            menor = i\n    print(f'El valor mas costoso fue {mayor} y el menor {menor}')\n\n#Dólares a pesos = 4200 pesos colombianos\n#Euros a pesos = 4700 pesos colombianos\n#Yenés a pesos= 7800 pesos colombianos\n    \ndef yenes_pesos(list):\n    list = [x*7800 for x in list]\n    print('De mayor a menor la lista es:')\n    for i in sorted(list, reverse=True):\n        print(i,end=' ')\n    print()\n\n\n\n\ndef comparacion(dolar_peso, euros_pesos, yenes_pesos):\n    dolar_peso = [x*4200 for x in dolar_peso]\n    euros_pesos = [x*4700 for x in euros_pesos]\n    yenes_pesos = [x*7800 for x in yenes_pesos]\n    mayor_recaudo = 0\n    for i in [dolar_peso,euros_pesos,yenes_pesos]:\n        if sum(i) > mayor_recaudo:\n            mayor_recaudo = sum(i)\n            index = i\n    if index == dolar_peso:\n        print(f\"el mayor recaldo es dolares y gana {mayor_recaudo}\")\n    if index == euros_pesos:\n        print(f\"el mayor recaldo es euros y gana {mayor_recaudo}\")\n    if index == yenes_pesos:\n        print(f\"el mayor recaldo son yenes y gana {mayor_recaudo}\")\n    return mayor_recaudo\n\nwhile True:\n    moneda = input(\"Tipo de moneda a convertir Yenes(Y)/Euros(E)/Dolares(D): \")\n    \n    if moneda.upper() == \"Y\":\n        valor = int(input(\"numero de Yenes a convertir: \"))\n        listaYenes.append(valor)\n        print(f\"convertiste {valor*7800}\")\n        \n        \n    if moneda.upper() == \"E\":\n        valor = int(input(\"numero de Euros a convertir: \"))\n        listaEuros.append(valor)\n        print(f\"convertiste {valor*4700}\")\n    \n    if moneda.upper() == \"D\":\n        valor = int(input(\"numero de Dolares a convertir: \"))\n        listaDolares.append(valor)\n        print(f\"convertiste {valor*4200}\")\n    \n    if input(\"deseas continuar ? S/N: \").upper() == \"N\":\n        break\n\ndolarPeso_promedio(listaDolares)\neurosPesos(listaEuros)\nyenes_pesos(listaYenes)\n\nprint(comparacion(listaDolares,listaEuros,listaYenes))\n\nprint(\"estudiante \"+ nombre)\n\n\n\n\n    \n    \n    \n    \n\n    ","repo_name":"feijoes/Freelance-works","sub_path":"Python/examen.py","file_name":"examen.py","file_ext":"py","file_size_in_byte":2491,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"29975951541","text":"import json\nfrom ast import literal_eval\nfrom datetime import datetime, timedelta\nfrom decimal import Decimal\n\nimport coreapi\nimport pytz\nfrom celery import states\nfrom django.http import JsonResponse\nfrom django_filters import rest_framework as filters\nfrom kombu.utils.encoding import safe_repr\nfrom rest_framework import viewsets, mixins, permissions, status\nfrom rest_framework.filters import BaseFilterBackend\nfrom rest_framework.response import Response\n\nfrom base.views import PaginationWithThousandLimit\nfrom order.api.serializers import OrderHistorySerializer, OrderExportSerializer\nfrom order.models.Order import Order\nfrom payment.api.serializers import PaymentSerializer, PaymentHistorySerializer\nfrom payment.models import Payment, Invoice\nfrom retailer.models import RetailerEggsdata, Retailer\nfrom saleschain.api.exportSerializers import SalesExportSerializer, SalesEggsDataExportSerializer, \\\n    RetailerEggsDataExportSerializer, InvoiceExportSerializer\nfrom saleschain.models import SalesEggsdata\nfrom django.db.models import Max, Q\n\nfrom saleschain.tasks import sales_bills_list\n\n\nclass DateFilterBackend(BaseFilterBackend):\n    def get_schema_fields(self, view):\n        return [coreapi.Field(\n            name='from_delivery_date',\n            location='query',\n            required=False,\n            type='string'\n        ),\n            coreapi.Field(\n                name='to_delivery_date',\n                location='query',\n                required=False,\n                type='string'\n            ),\n        ]\n\n\nclass SalesFilteredExportViewSet(viewsets.GenericViewSet, mixins.ListModelMixin):\n    permission_classes = (permissions.IsAuthenticated,)\n    serializer_class = SalesExportSerializer\n    queryset = Order.objects.all()\n    pagination_class = PaginationWithThousandLimit\n    filter_backends = (filters.DjangoFilterBackend, DateFilterBackend)\n    filterset_fields = (\n        'retailer', 'order_type', 'salesPerson', 'status', 'warehouse', 'date', 'distributor', 'delivery_date',\n        'secondary_status')\n\n    def list(self, request, *args, **kwargs):\n        if request.GET.get('from_delivery_date') and request.GET.get('to_delivery_date'):\n\n            from_delivery_date = datetime.strptime(request.GET.get('from_delivery_date'), '%d/%m/%Y')\n            to_delivery_date = datetime.strptime(request.GET.get('to_delivery_date'), '%d/%m/%Y')\n\n            from_delivery_date = from_delivery_date.replace(hour=0, minute=0, second=0)\n            to_delivery_date = to_delivery_date.replace(hour=0, minute=0, second=0)\n\n            delta = timedelta(hours=23, minutes=59, seconds=59)\n            from_delivery_date = from_delivery_date\n            to_delivery_date = to_delivery_date + delta\n            print(from_delivery_date)\n            print(to_delivery_date)\n            orders = Order.objects.filter(delivery_date__gte=from_delivery_date, status__in=[\"delivered\", \"completed\"],\n                                          delivery_date__lte=to_delivery_date, is_trial=False,\n                                          is_geb=False).select_related(\n                'retailer', 'warehouse', 'salesPerson', 'distributor').order_by('id')\n\n            # orders = orders.filter(~Q(distributor=None))\n            # print(orders)\n        else:\n            orders = Order.objects.filter(status__in=[\"delivered\", \"completed\"], is_trial=False,\n                                          is_geb=False).select_related(\n                'retailer', 'warehouse', 'salesPerson', 'distributor').order_by('id')\n        page = self.paginate_queryset(orders)\n        serializer = OrderExportSerializer(orders, many=True)\n        if page is not None:\n            serializer = OrderExportSerializer(page, many=True)\n            print(serializer.data)\n            return self.get_paginated_response(serializer.data)\n\n        return Response({\"results\": serializer.data})\n\n\nclass SalesGEBExportViewSet(viewsets.GenericViewSet, mixins.ListModelMixin):\n    permission_classes = (permissions.IsAuthenticated,)\n    serializer_class = SalesExportSerializer\n    queryset = Order.objects.all()\n    pagination_class = PaginationWithThousandLimit\n    filter_backends = (filters.DjangoFilterBackend, DateFilterBackend)\n    filterset_fields = ('retailer', 'order_type', 'salesPerson', 'status', 'warehouse', 'date', 'distributor',\n                        'delivery_date', 'secondary_status', 'is_geb', 'is_geb_verified')\n\n    def list(self, request, *args, **kwargs):\n        if request.GET.get('from_delivery_date') and request.GET.get('to_delivery_date'):\n            from_delivery_date = datetime.strptime(request.GET.get('from_delivery_date'), '%d/%m/%Y')\n            to_delivery_date = datetime.strptime(request.GET.get('to_delivery_date'), '%d/%m/%Y')\n\n            from_delivery_date = from_delivery_date.replace(hour=0, minute=0, second=0)\n            to_delivery_date = to_delivery_date.replace(hour=0, minute=0, second=0)\n\n            delta = timedelta(hours=23, minutes=59, seconds=59)\n            from_delivery_date = from_delivery_date\n            to_delivery_date = to_delivery_date + delta\n            print(from_delivery_date)\n            print(to_delivery_date)\n            orders = Order.objects.filter(delivery_date__gte=from_delivery_date, status__in=[\"delivered\", \"completed\"],\n                                          delivery_date__lte=to_delivery_date, is_trial=False,\n                                          is_geb=True).select_related(\n                'retailer', 'warehouse', 'salesPerson', 'distributor').order_by('id')\n\n            # orders = orders.filter(~Q(distributor=None))\n            # print(orders)\n        else:\n            orders = Order.objects.filter(status__in=[\"delivered\", \"completed\"], is_trial=False,\n                                          is_geb=True).select_related(\n                'retailer', 'warehouse', 'salesPerson', 'distributor').order_by('id')\n            # orders = orders.filter(~Q(distributor=None))\n        page = self.paginate_queryset(orders)\n        serializer = OrderExportSerializer(orders, many=True)\n        if page is not None:\n            serializer = OrderExportSerializer(page, many=True)\n            print(serializer.data)\n            return self.get_paginated_response(serializer.data)\n\n        return Response({\"results\": serializer.data})\n\n\nclass SalesOrderExportViewSet(viewsets.GenericViewSet, mixins.ListModelMixin):\n    permission_classes = (permissions.IsAuthenticated,)\n    serializer_class = SalesExportSerializer\n    queryset = Order.objects.all()\n    pagination_class = PaginationWithThousandLimit\n    filter_backends = (filters.DjangoFilterBackend, DateFilterBackend)\n    filterset_fields = (\n        'retailer', 'order_type', 'salesPerson', 'distributor', 'secondary_status', 'status', 'warehouse',\n        'delivery_date',\n        'date', 'is_geb', 'is_geb_verified')\n\n    def list(self, request, *args, **kwargs):\n        if request.GET.get('from_delivery_date') and request.GET.get('to_delivery_date'):\n            from_delivery_date = datetime.strptime(request.GET.get('from_delivery_date'), '%d/%m/%Y')\n            to_delivery_date = datetime.strptime(request.GET.get('to_delivery_date'), '%d/%m/%Y')\n\n            from_delivery_date = from_delivery_date.replace(hour=0, minute=0, second=0)\n            to_delivery_date = to_delivery_date.replace(hour=0, minute=0, second=0)\n\n            delta = timedelta(hours=23, minutes=59, seconds=59)\n            from_delivery_date = from_delivery_date\n            to_delivery_date = to_delivery_date + delta\n            print(from_delivery_date)\n            print(to_delivery_date)\n            orders = Order.objects.filter(delivery_date__gte=from_delivery_date,\n                                          delivery_date__lte=to_delivery_date,\n                                          is_trial=False).select_related(\n                'retailer', 'warehouse', 'salesPerson', 'distributor').order_by('id')\n\n        else:\n            orders = Order.objects.filter(is_trial=False).select_related(\n                'retailer', 'warehouse', 'salesPerson', 'distributor').order_by('id')\n\n        page = self.paginate_queryset(orders)\n        serializer = OrderExportSerializer(orders, many=True)\n        if page is not None:\n            serializer = OrderExportSerializer(page, many=True)\n            print(serializer.data)\n            return self.get_paginated_response(serializer.data)\n\n        return Response({\"results\": serializer.data})\n\n\nclass FinancePaymentViewSet(viewsets.GenericViewSet, mixins.ListModelMixin):\n    permission_classes = (permissions.IsAuthenticated,)\n    serializer_class = PaymentSerializer\n    queryset = Payment.objects.all()\n    filter_backends = (filters.DjangoFilterBackend, DateFilterBackend)\n    filterset_fields = ('payment_mode', 'pay_choice', 'pay_amount', 'invoice', 'salesTransaction', 'created_at')\n\n    def list(self, request, *args, **kwargs):\n        if request.GET.get('from_delivery_date') and request.GET.get('to_delivery_date'):\n            from_delivery_date = datetime.strptime(request.GET.get('from_delivery_date'), '%d/%m/%Y')\n            to_delivery_date = datetime.strptime(request.GET.get('to_delivery_date'), '%d/%m/%Y')\n\n            from_delivery_date = from_delivery_date.replace(hour=0, minute=0, second=0)\n            to_delivery_date = to_delivery_date.replace(hour=0, minute=0, second=0)\n\n            delta = timedelta(hours=23, minutes=59, seconds=59)\n            from_delivery_date = from_delivery_date\n            to_delivery_date = to_delivery_date + delta\n            print(from_delivery_date)\n            print(to_delivery_date)\n            payments = Payment.objects.filter(created_at__gte=from_delivery_date, created_at__lte=to_delivery_date,\n                                              salesTransaction__is_trial=False, ).select_related(\n                'invoice', 'salesTransaction').order_by('created_at')\n            payments = payments.filter(~Q(salesTransaction__transaction_type=\"Cancelled\"))\n            payments = payments.filter(~Q(invoice__invoice_status=\"Cancelled\"))\n        else:\n            payments = Payment.objects.filter(salesTransaction__is_trial=False).select_related(\n                'invoice', 'salesTransaction').order_by('created_at')\n            payments = payments.filter(\n                ~Q(salesTransaction__transaction_type=\"Cancelled\", invoice__invoice_status=\"Cancelled\"))\n\n        payment_results = []\n\n        for payment in payments:\n            payment_dict = {}\n            payment_dict['bill'] = payment.invoice.order.name\n            payment_dict['retailer'] = payment.invoice.order.retailer.code if payment.invoice.order.retailer else \"Customer\"\n            payment_dict[\n                'distributor'] = payment.salesTransaction.distributor.user.name if payment.salesTransaction.distributor else \"\"\n            payment_dict[\n                'salesPerson'] = payment.salesTransaction.salesPerson.user.name if payment.salesTransaction.salesPerson else \"\"\n            payment_dict['type'] = payment.pay_choice\n            payment_dict['amountPaid'] = payment.pay_amount\n            payment_dict['mode'] = payment.payment_mode\n            payment_dict['paid_at'] = payment.created_at\n            payment_dict['cheque_number'] = payment.cheque_number if payment.cheque_number else \"\"\n            payment_dict['upi_id'] = payment.upi_id if payment.upi_id else \"\"\n            payment_dict['city'] = payment.invoice.order.retailer.city.city_name if payment.invoice.order.retailer else \"Customer\"\n            payment_dict['id'] = str(payment.id)\n            payment_dict['is_geb'] = payment.invoice.order.is_geb\n            payment_dict['is_geb_verified'] = payment.invoice.order.is_geb_verified\n            payment_results.append(payment_dict)\n\n        # results = PaymentHistorySerializer(payments, many=True).data\n        return Response({\"payments\": payment_results}, status=status.HTTP_201_CREATED)\n\n\nclass FinanceExportViewSet(viewsets.GenericViewSet, mixins.ListModelMixin):\n    permission_classes = (permissions.IsAuthenticated,)\n    serializer_class = SalesExportSerializer\n    queryset = Order.objects.all()\n    filter_backends = (filters.DjangoFilterBackend, DateFilterBackend)\n    filterset_fields = ('retailer', 'order_type', 'salesPerson', 'status', 'warehouse', 'delivery_date')\n\n    def list(self, request, *args, **kwargs):\n        if request.GET.get('from_delivery_date') and request.GET.get('to_delivery_date'):\n            from_delivery_date = datetime.strptime(request.GET.get('from_delivery_date'), '%d/%m/%Y')\n            to_delivery_date = datetime.strptime(request.GET.get('to_delivery_date'), '%d/%m/%Y')\n\n            from_delivery_date = from_delivery_date.replace(hour=0, minute=0, second=0)\n            to_delivery_date = to_delivery_date.replace(hour=0, minute=0, second=0)\n\n            delta = timedelta(hours=23, minutes=59, seconds=59)\n            from_delivery_date = from_delivery_date\n            to_delivery_date = to_delivery_date + delta\n            print(from_delivery_date)\n            print(to_delivery_date)\n            orders = Order.objects.filter(delivery_date__gte=from_delivery_date, status__in=[\"delivered\", \"completed\"],\n                                          delivery_date__lte=to_delivery_date, is_trial=False).select_related(\n                'retailer', 'warehouse', 'salesPerson', 'distributor').order_by('id')\n\n            # orders = orders.filter(~Q(distributor=None))\n            # print(orders)\n        else:\n            orders = Order.objects.filter(status__in=[\"delivered\", \"completed\"], is_trial=False).select_related(\n                'retailer', 'warehouse', 'salesPerson').order_by('id')\n            # orders = orders.filter(~Q(distributor=None))\n\n        invoice_results = []\n        return_results = []\n        payment_results = []\n        for order in orders:\n            city_id = order.retailer.city.id if order.retailer else \\\n                order.customer.shipping_address.city.id if order.customer.shipping_address.city else None\n            invoice_dict = {}\n            payment_dict = {}\n            if order.invoice:\n                payments = Payment.objects.filter(invoice=order.invoice, salesTransaction__is_trial=False)\n\n                invoice_dict['bill_no'] = order.name\n                invoice_dict['code'] = order.retailer.code if order.retailer else order.customer.name\n                invoice_dict['bill_amount'] = order.order_price_amount\n                invoice_dict['discount_amount'] = order.discount_amount\n                invoice_dict['invoice_status'] = order.invoice.invoice_status\n                invoice_dict['invoice_due'] = order.invoice.invoice_due\n                invoice_dict['created_at'] = order.invoice.created_at\n                invoice_dict['id'] = order.invoice.id\n                invoice_dict['is_geb'] = order.is_geb\n                invoice_dict['is_geb_verified'] = order.is_geb_verified\n\n                if payments:\n                    payment_dict['amount'] = Decimal(0.000)\n                    payment_dict[\"pay_status\"] = True\n                    payment_dict['type'] = \"&\".join([payment.pay_choice for payment in payments])\n                    payment_dict['amountString'] = \"&\".join([str(payment.pay_amount) for payment in payments])\n                    payment_dict['mode'] = \"&\".join([payment.payment_mode for payment in payments])\n                    payment_dict['paid_at'] = \"&\".join([str(payment.created_at) for payment in payments])\n                    payment_dict['id'] = \"&\".join([str(payment.id) for payment in payments])\n                    payment_dict['cheque_number'] = \"&\".join(\n                        [payment.cheque_number if payment.cheque_number else \"\" for payment in payments])\n                    payment_dict['upi_id'] = \"&\".join(\n                        [payment.upi_id if payment.upi_id else \"\" for payment in payments])\n                    payment_dict['city_id'] = city_id\n                    payment_dict['is_geb'] = order.is_geb\n                    payment_dict['is_geb_verified'] = order.is_geb_verified\n\n                    for payment in payments:\n                        # payment_dict[\"pay_status\"] = True\n                        # payment_dict['type'] = payment.pay_choice\n                        payment_dict['amount'] += payment.pay_amount\n                        # payment_dict['mode'] = payment.payment_mode\n                        # payment_dict['paid_at'] = payment.created_at\n                        # payment_dict['cheque_number'] = payment.cheque_number if payment.cheque_number else None\n                        # payment_dict['upi_id'] = payment.upi_id if payment.upi_id else None\n                        # payment_dict['id'] = payment.id\n                        # payment_dict['city_id'] = city_id\n                    invoice_dict.update(payment_dict)\n                    payment_results.append(payment_dict)\n                else:\n                    payment_dict[\"pay_status\"] = False\n                    payment_dict['type'] = None\n                    payment_dict['amount'] = None\n                    payment_dict['amountString'] = None\n                    payment_dict['mode'] = None\n                    payment_dict['paid_at'] = None\n                    payment_dict['cheque_number'] = None\n                    payment_dict['upi_id'] = None\n                    payment_dict['id'] = None\n                    payment_dict['city_id'] = city_id\n                    invoice_dict.update(payment_dict)\n                    # continue\n\n                invoice_results.append(invoice_dict)\n\n                order_lines = order.lines.all()\n                if order_lines:\n                    for order_line in order_lines:\n                        return_lines = order_line.lines.all()\n                        if return_lines:\n                            return_dict = {}\n                            for return_line in return_lines:\n                                print(return_line)\n                                return_dict['bill_no'] = order.name\n                                return_dict['code'] = order.retailer.code if order.retailer else order.customer.name\n                                return_dict['product_name'] = str(\n                                    order_line.product.SKU_Count) + order_line.product.name[:1]\n                                return_dict['sku_count'] = order_line.product.SKU_Count\n                                return_dict['type'] = return_line.line_type\n                                return_dict['quantity'] = return_line.quantity\n                                return_dict['amount'] = return_line.amount\n                                return_dict['date'] = return_line.date\n                                return_dict['id'] = return_line.id\n                                return_dict['product_id'] = str(order_line.product.id)\n                                return_dict['city_id'] = city_id\n                                return_dict['city_id'] = order.is_geb\n                                return_dict['city_id'] = order.is_geb_verified\n\n                                return_results.append(return_dict)\n                        else:\n                            continue\n                else:\n                    continue\n            else:\n                continue\n\n        return Response(\n            {\"results\": {\"invoices\": invoice_results, \"returns\": return_results, \"payments\": payment_results}})\n\n\nclass ReturnExportViewSet(viewsets.GenericViewSet, mixins.ListModelMixin):\n    permission_classes = (permissions.IsAuthenticated,)\n    serializer_class = SalesExportSerializer\n    queryset = Order.objects.all()\n    filter_backends = (filters.DjangoFilterBackend, DateFilterBackend)\n    filterset_fields = ('retailer', 'order_type', 'salesPerson', 'status', 'warehouse', 'delivery_date')\n\n    def list(self, request, *args, **kwargs):\n        if request.GET.get('from_delivery_date') and request.GET.get('to_delivery_date'):\n\n            from_delivery_date = datetime.strptime(request.GET.get('from_delivery_date'), '%d/%m/%Y')\n            to_delivery_date = datetime.strptime(request.GET.get('to_delivery_date'), '%d/%m/%Y')\n\n            from_delivery_date = from_delivery_date.replace(hour=0, minute=0, second=0)\n            to_delivery_date = to_delivery_date.replace(hour=0, minute=0, second=0)\n\n            delta = timedelta(hours=23, minutes=59, seconds=59)\n            from_delivery_date = from_delivery_date\n            to_delivery_date = to_delivery_date + delta\n            print(from_delivery_date)\n            print(to_delivery_date)\n            orders = Order.objects.filter(return_picked_date__gte=from_delivery_date,\n                                          status__in=[\"delivered\", \"completed\"],\n                                          return_picked_date__lte=to_delivery_date, is_trial=False).select_related(\n                'retailer', 'warehouse', 'salesPerson', 'distributor').order_by('id')\n\n        else:\n            orders = Order.objects.filter(status__in=[\"delivered\", \"completed\"], is_trial=False).select_related(\n                'retailer', 'warehouse', 'salesPerson').order_by('id')\n\n        return_results = []\n        for order in orders:\n            city_id = order.retailer.city.id if order.retailer else \\\n                order.customer.shipping_address.city.id if order.customer.shipping_address.city else None\n\n            if order.invoice:\n\n                order_lines = order.lines.all()\n                if order_lines:\n                    return_orders = []\n                    for order_line in order_lines:\n                        return_lines = order_line.lines.all()\n                        if return_lines:\n                            return_dict = {}\n                            for return_line in return_lines:\n                                print(return_line)\n                                return_dict['bill_no'] = order.name\n                                return_dict[\n                                    'Del Guy'] = return_line.distributor.user.name if return_line.distributor else \"\"\n                                return_dict['code'] = order.retailer.code if order.retailer else order.customer.name\n                                return_dict['product_name'] = str(\n                                    order_line.product.SKU_Count) + order_line.product.name[:1]\n                                return_dict['sku_count'] = order_line.product.SKU_Count\n                                return_dict['type'] = return_line.line_type\n                                return_dict['quantity'] = return_line.quantity\n                                return_dict['amount'] = return_line.amount\n                                return_dict['date'] = return_line.date\n                                return_dict[\n                                    'salesPerson'] = return_line.salesPerson.user.name if return_line.salesPerson else \"\"\n                                return_dict['id'] = return_line.id\n                                return_dict['product_id'] = str(order_line.product.id)\n                                return_dict['city_id'] = city_id\n                                return_dict['is_geb'] = order.is_geb\n                                return_dict['is_geb_verified'] = order.is_geb_verified\n\n                                return_results.append(return_dict)\n                        else:\n                            if int(order.deviated_amount) > 0:\n                                return_orders.append(order)\n                            continue\n                    if len(return_orders) > 0:\n                        for ret_order in return_orders:\n                            ret_city_id = ret_order.retailer.city.id if ret_order.retailer else \\\n                                ret_order.customer.shipping_address.city.id if ret_order.customer.shipping_address.city else None\n                            return_dict = {}\n                            return_dict['bill_no'] = ret_order.name\n                            return_dict['Del Guy'] = \"\"\n                            return_dict[\n                                'code'] = ret_order.retailer.code if ret_order.retailer else ret_order.customer.name\n                            return_dict['product_name'] = \"\"\n                            return_dict['sku_count'] = \"\"\n                            return_dict['type'] = \"Return\"\n                            return_dict['quantity'] = \"\"\n                            return_dict['amount'] = ret_order.deviated_amount\n                            return_dict['date'] = ret_order.return_picked_date\n                            return_dict[\n                                'salesPerson'] = ret_order.salesPerson.user.name if ret_order.salesPerson else \"\"\n                            return_dict['id'] = 0\n                            return_dict['product_id'] = \"\"\n                            return_dict['city_id'] = ret_city_id\n                            return_dict['is_geb'] = ret_order.is_geb\n                            return_dict['is_geb_verified'] = ret_order.is_geb_verified\n\n                            return_results.append(return_dict)\n                else:\n\n                    continue\n            else:\n                continue\n\n        return Response({\"results\": {\"returns\": return_results}})\n\n\ndef get_order_line_dict(order_obj):\n    # Mapped Dict\n    mapped_dict = {}\n    mapped_dict['6W'] = \"\"\n    mapped_dict['10W'] = \"\"\n    mapped_dict['12W'] = \"\"\n    mapped_dict['25W'] = \"\"\n    mapped_dict['30W'] = \"\"\n\n    mapped_dict['6B'] = \"\"\n    mapped_dict['10B'] = \"\"\n    mapped_dict['25B'] = \"\"\n    mapped_dict['30B'] = \"\"\n\n    mapped_dict['6N'] = \"\"\n    mapped_dict['10N'] = \"\"\n\n    mapped_rate_dict = {}\n    mapped_rate_dict['6W R'] = \"\"\n    mapped_rate_dict['10W R'] = \"\"\n    mapped_rate_dict['12W R'] = \"\"\n    mapped_rate_dict['25W R'] = \"\"\n    mapped_rate_dict['30W R'] = \"\"\n\n    mapped_rate_dict['6B R'] = \"\"\n    mapped_rate_dict['10B R'] = \"\"\n    mapped_rate_dict['25B R'] = \"\"\n    mapped_rate_dict['30B R'] = \"\"\n\n    mapped_rate_dict['6N R'] = \"\"\n    mapped_rate_dict['10N R'] = \"\"\n\n    order_lines = order_obj.lines.all()\n    if order_lines:\n        for order_line in order_lines:\n            # print(str(order_line.product.SKU_Count) + order_line.product.name[:1])\n            if str(order_line.product.SKU_Count) + order_line.product.name[:1] in mapped_dict.keys():\n                mapped_dict[str(order_line.product.SKU_Count) + order_line.product.name[:1]] = order_line.quantity\n                mapped_rate_dict[\n                    str(order_line.product.SKU_Count) + order_line.product.name[:1] + \" R\"] = order_line.single_sku_rate\n\n    return {\"Qty\": mapped_dict, \"Rate\": mapped_rate_dict}\n\n\nclass SalesEggsDataExportViewSet(viewsets.GenericViewSet, mixins.ListModelMixin):\n    permission_classes = (permissions.IsAuthenticated,)\n    serializer_class = SalesEggsDataExportSerializer\n    queryset = SalesEggsdata.objects.all()\n    filter_backends = (filters.DjangoFilterBackend,)\n    filterset_fields = ('salesPerson', 'date')\n\n    def list(self, request, *args, **kwargs):\n\n        if request.GET.get('from_delivery_date') and request.GET.get('to_delivery_date'):\n            from_delivery_date = datetime.strptime(request.GET.get('from_delivery_date'), '%d/%m/%Y')\n            to_delivery_date = datetime.strptime(request.GET.get('to_delivery_date'), '%d/%m/%Y')\n\n            from_delivery_date = from_delivery_date.replace(hour=0, minute=0, second=0)\n            to_delivery_date = to_delivery_date.replace(hour=0, minute=0, second=0)\n\n            delta = timedelta(hours=23, minutes=59, seconds=59)\n            from_delivery_date = from_delivery_date\n            to_delivery_date = to_delivery_date + delta\n            salesEggsDataList = self.filter_queryset(self.get_queryset().filter(date__gte=from_delivery_date,\n                                                                                date__lte=to_delivery_date)). \\\n                select_related('salesPerson')\n        else:\n            salesEggsDataList = self.filter_queryset(self.get_queryset()). \\\n                select_related('salesPerson')\n        eggs_results = []\n        for eggsData in salesEggsDataList:\n            eggs_dict = {}\n            eggs_dict['date'] = eggsData.date if eggsData.date else \"No Date\"\n            eggs_dict['salesPerson'] = eggsData.salesPerson.user.name if eggsData.salesPerson else \"No Sales Person\"\n            eggs_dict['brown'] = eggsData.brown if eggsData.brown else 0\n            eggs_dict['white'] = eggsData.white if eggsData.white else 0\n            eggs_dict['nutra'] = eggsData.nutra if eggsData.nutra else 0\n\n            eggs_results.append(eggs_dict)\n        return Response({\"results\": eggs_results})\n\n\nclass RetailerEggsDataExportViewSet(viewsets.GenericViewSet, mixins.ListModelMixin):\n    permission_classes = (permissions.IsAuthenticated,)\n    serializer_class = RetailerEggsDataExportSerializer\n    queryset = RetailerEggsdata.objects.all()\n    filter_backends = (filters.DjangoFilterBackend,)\n    filterset_fields = ('retailer', 'date')\n\n    def list(self, request, *args, **kwargs):\n\n        if request.GET.get('from_delivery_date') and request.GET.get('to_delivery_date'):\n            from_delivery_date = datetime.strptime(request.GET.get('from_delivery_date'), '%d/%m/%Y')\n            to_delivery_date = datetime.strptime(request.GET.get('to_delivery_date'), '%d/%m/%Y')\n            from_delivery_date = from_delivery_date.replace(hour=0, minute=0, second=0)\n            to_delivery_date = to_delivery_date.replace(hour=0, minute=0, second=0)\n\n            delta = timedelta(hours=23, minutes=59, seconds=59)\n            from_delivery_date = from_delivery_date\n            to_delivery_date = to_delivery_date + delta\n\n            retailersEggsDataList = self.filter_queryset(self.get_queryset().filter(date__gte=from_delivery_date,\n                                                                                    date__lte=to_delivery_date)). \\\n                select_related('retailer')\n        else:\n            retailersEggsDataList = self.filter_queryset(self.get_queryset()). \\\n                select_related('retailer')\n        eggs_results = []\n        for eggsData in retailersEggsDataList:\n            eggs_dict = {}\n            eggs_dict['date'] = eggsData.date if eggsData.date else \"No Date\"\n            eggs_dict['retailer'] = eggsData.retailer.retailer.name if eggsData.retailer else \"No Retailer\"\n            eggs_dict['brown'] = eggsData.brown if eggsData.brown else 0\n            eggs_dict['white'] = eggsData.white if eggsData.white else 0\n            eggs_dict['nutra'] = eggsData.nutra if eggsData.nutra else 0\n\n            eggs_results.append(eggs_dict)\n        return Response({\"results\": eggs_results})\n\n\nclass MonthlySalesExportViewSet(viewsets.GenericViewSet, mixins.ListModelMixin):\n    permission_classes = (permissions.IsAuthenticated,)\n    serializer_class = SalesExportSerializer\n    queryset = Order.objects.all()\n    filter_backends = (filters.DjangoFilterBackend,)\n    filterset_fields = ('retailer', 'order_type', 'salesPerson', 'status', 'warehouse', 'date')\n\n    def list(self, request, *args, **kwargs):\n        if request.GET.get('from_delivery_date') and request.GET.get('to_delivery_date'):\n            from_delivery_date = datetime.strptime(request.GET.get('from_delivery_date'), '%d/%m/%Y')\n            to_delivery_date = datetime.strptime(request.GET.get('to_delivery_date'), '%d/%m/%Y')\n\n            from_delivery_date = from_delivery_date.replace(hour=0, minute=0, second=0)\n            to_delivery_date = to_delivery_date.replace(hour=0, minute=0, second=0)\n\n            delta = timedelta(hours=23, minutes=59, seconds=59)\n            from_delivery_date = from_delivery_date\n            to_delivery_date = to_delivery_date + delta\n            print(from_delivery_date)\n            print(to_delivery_date)\n            cities = request.GET.get('cities', [])\n            # TODO Comment\n            if cities and cities != \"undefined\":\n                cities = json.loads(cities)\n                cities = [int(c) for c in cities]\n                if len(cities) > 0:\n                    orders = self.filter_queryset(self.get_queryset().filter(delivery_date__gte=from_delivery_date,\n                                                                             status__in=[\"delivered\", \"completed\"],\n                                                                             retailer__city__in=cities,\n                                                                             delivery_date__lte=to_delivery_date,\n                                                                             is_trial=False)).select_related(\n                        'retailer', 'warehouse', 'salesPerson', 'distributor').order_by('id')\n                else:\n                    orders = self.filter_queryset(self.get_queryset().filter(delivery_date__gte=from_delivery_date,\n                                                                             status__in=[\"delivered\", \"completed\"],\n                                                                             delivery_date__lte=to_delivery_date,\n                                                                             is_trial=False)).select_related(\n                        'retailer', 'warehouse', 'salesPerson', 'distributor').order_by('id')\n\n            # orders = orders.filter(~Q(distributor=None))\n            # print(orders)\n        else:\n            orders = self.filter_queryset(\n                self.get_queryset().filter(status__in=[\"delivered\", \"completed\"], is_trial=False)).select_related(\n                'retailer', 'warehouse', 'salesPerson').order_by('id')\n            # orders = orders.filter(~Q(distributor=None))\n        sales_results = []\n        unique_retailers = orders.values_list('retailer', flat=True)\n        unique_retailers = list(set(unique_retailers))\n        # print(unique_retailers)\n\n        for retailer in unique_retailers:\n            same_retailer_orders = list(filter(lambda x: x.retailer_id == retailer, orders))\n            # print(same_retailer_orders)\n            orders_list_dict = {'Brown': 0, 'White': 0, 'Nutra': 0}\n            replaced_list_dict = {'Brown': 0, 'White': 0, 'Nutra': 0}\n            returned_list_dict = {'Brown': 0, 'White': 0, 'Nutra': 0}\n            ord_retailer = same_retailer_orders[0].retailer\n            ord_salesPerson = same_retailer_orders[0].salesPerson\n            order_amount = Decimal(0.000)\n            return_amount = Decimal(0.000)\n            replacement_amount = Decimal(0.000)\n            order_delivery_date = None\n            bill_no = None\n            retailer_obj = Retailer.objects.filter(id=retailer).first()\n            city_id = retailer_obj.city.id if retailer else None\n            city_name = retailer_obj.city.city_name if retailer else None\n            for retailer_order in same_retailer_orders:\n                order_delivery_date = retailer_order.delivery_date + timedelta(hours=5, minutes=30, seconds=0)\n                bill_no = retailer_order.name\n                order_lines = retailer_order.lines.all()\n                for order_line in order_lines:\n                    # For Eggs Data\n                    if order_line.product:\n                        if order_line.product.name == \"White Regular\":\n                            name = \"White\"\n                        else:\n                            name = order_line.product.name\n                        order_amount += order_line.quantity * order_line.single_sku_rate\n                        if orders_list_dict[name] > 0:\n                            orders_list_dict[name] = orders_list_dict[\n                                                         name] + order_line.quantity * order_line.product.SKU_Count\n                        else:\n                            orders_list_dict[name] = order_line.quantity * order_line.product.SKU_Count\n                        return_replace_lines = order_line.lines.all()\n                        if return_replace_lines:\n                            for return_replace_line in return_replace_lines:\n                                if return_replace_line.line_type == \"Replacement\":\n                                    replacement_amount += Decimal(0.000)\n                                    if replaced_list_dict[name] > 0:\n                                        replaced_list_dict[name] = replaced_list_dict[name] \\\n                                                                   + order_line.deviated_quantity * order_line.product.SKU_Count\n                                    else:\n                                        replaced_list_dict[\n                                            name] = order_line.deviated_quantity * order_line.product.SKU_Count\n                                else:\n                                    return_amount += order_line.deviated_quantity * order_line.single_sku_rate\n                                    if returned_list_dict[name] > 0:\n                                        returned_list_dict[name] = returned_list_dict[name] \\\n                                                                   + order_line.deviated_quantity * order_line.product.SKU_Count\n                                    else:\n                                        returned_list_dict[\n                                            name] = order_line.deviated_quantity * order_line.product.SKU_Count\n\n            sales_dict = {}\n            sales_dict['code'] = ord_retailer.code\n            sales_dict['salesPerson'] = ord_salesPerson.user.name\n            sales_dict['salesPersonId'] = ord_salesPerson.id\n            sales_dict['category'] = ord_retailer.category.name\n\n            sales_dict[\"Sold White\"] = orders_list_dict['White']\n            sales_dict[\"Sold Brown\"] = orders_list_dict['Brown']\n            sales_dict[\"Sold Nutra\"] = orders_list_dict['Nutra']\n\n            sales_dict[\"Replaced White\"] = replaced_list_dict['White']\n            sales_dict[\"Replaced Brown\"] = replaced_list_dict['Brown']\n            sales_dict[\"Replaced Nutra\"] = replaced_list_dict['Nutra']\n\n            sales_dict[\"Returned White\"] = returned_list_dict['White']\n            sales_dict[\"Returned Brown\"] = returned_list_dict['Brown']\n            sales_dict[\"Returned Nutra\"] = returned_list_dict['Nutra']\n\n            sales_dict[\"Net White\"] = orders_list_dict['White'] - replaced_list_dict['White'] - returned_list_dict[\n                'White']\n            sales_dict[\"Net Brown\"] = orders_list_dict['Brown'] - replaced_list_dict['Brown'] - returned_list_dict[\n                'Brown']\n            sales_dict[\"Net Nutra\"] = orders_list_dict['Nutra'] - replaced_list_dict['Nutra'] - returned_list_dict[\n                'Nutra']\n            sales_dict[\"Date\"] = order_delivery_date\n            sales_dict[\"Bill No\"] = bill_no\n            sales_dict[\"Bill Amount\"] = order_amount\n            sales_dict[\"Net Amount\"] = order_amount - replacement_amount - return_amount\n            sales_dict[\"city_id\"] = city_id\n            sales_dict[\"city_name\"] = city_name\n\n            sales_results.append(sales_dict)\n        return Response({\"results\": sales_results})\n","repo_name":"aayushdocplix/eggozteqtis","sub_path":"saleschain/views/ExportViews.py","file_name":"ExportViews.py","file_ext":"py","file_size_in_byte":39232,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"20089475241","text":"import sys\n\ninput = sys.stdin.readline\n\nN = int(input())\nstring = input().rstrip()\n\nanswer = string[0]\ndef calc(a, op, b):\n    if op == '+':\n        return int(a) + int(b)\n    elif op == '*':\n        return int(a) * int(b)\n    else:\n        return int(a) - int(b)\ndef div_conq(value, i):\n    if i + 1 == N:\n        return value\n    a = calc(value, string[i + 1], string[i + 2])\n    if i + 4 < len(string):\n        temp = calc(string[i + 2], string[i + 3], string[i + 4])\n        b = calc(value, string[i + 1], temp)\n        # if i == 0:\n            # print(a, b)\n            # print(div_conq(a, i + 2), div_conq(b, i + 4))\n        return max(div_conq(a, i + 2), div_conq(b, i + 4))\n    else:\n        return div_conq(a, i + 2)\n    \nprint(div_conq(answer, 0))","repo_name":"alibreo3754/Study_Algorithm","sub_path":"Python/backjoon/gold/16637.py","file_name":"16637.py","file_ext":"py","file_size_in_byte":757,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"73359530386","text":"import pytest\nfrom ParsingWiki.data_constructor.data import DataClassCollections\n\ndata = DataClassCollections()\n\nclass TestPopularityColumn:\n    test_date = [\n        (int(10 ** 7)),\n        (int(1.5 * 10 ** 7)),\n        (int(5 * 10 ** 7)),\n        (int(10 ** 8)),\n        (int(5 * 10 ** 8)),\n        (int(10 ** 9)),\n        (int(1.5 * 10 ** 9)),\n    ]\n\n    @pytest.mark.parametrize('data_for', test_date)\n    def test_check_popularity(self, from_parsing_tests, from_tests, data_for):\n        \"\"\" Проверяет, что в таблице «Programming languages used in most popular websites» в столбце «Popularity(unique\n         visitors per month)» нет строк, значение которых меньше передаваемых.\n                Принимает:\n                    test_date - список проверяемых значений (list).\n        \"\"\"\n\n        class_collection = data.create_a_collection_of_classes()\n        class_names = list(class_collection)\n        n = 0\n        for _ in class_names:\n            get_name_class = class_names[n]\n            get_class = class_collection[get_name_class]\n            get_popularity_value = get_class.popularity[0]\n            convert_popularity_value = int(get_popularity_value)\n            # print(get_class)\n            frontend_name = get_class.frontend\n            backend_name = get_class.backend\n            assert convert_popularity_value > data_for, \\\n                f\"{get_name_class}(Frontend:{frontend_name}|Backend:{backend_name}) has {get_popularity_value}\" \\\n                                                    f\" unique visitors per month. (Expected more than {data_for})\"\n            n += 1\n","repo_name":"Rybu89/TestTask_QAAutomation_LestaGames","sub_path":"ParsingWiki/tests/test_popularity_column.py","file_name":"test_popularity_column.py","file_ext":"py","file_size_in_byte":1707,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"44016226454","text":"def pickingNumbers(a: list[int]) -> int:\n    a.sort()\n    l, r = 0, 0\n    max_length = 0\n\n    while r < len(a):\n        while abs(a[r] - a[l]) > 1:\n            l += 1\n\n        curr_length = r - l + 1\n        max_length = max(curr_length, max_length)\n        r += 1\n\n    return max_length\n\n\nprint(pickingNumbers([1,2,2,3,1,2])) # 5\nprint(pickingNumbers([4,5,6,3,3,1])) # 3\n","repo_name":"NickArakaki/ds-a-practice","sub_path":"HackerRank/PickingNumbers.py","file_name":"PickingNumbers.py","file_ext":"py","file_size_in_byte":372,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"2069415408","text":"class Card:\r\n    def __init__(self, color, face, state=\"open\"):\r\n        self.color = color\r\n        self.face = face\r\n        self.open = True if state == \"open\" else False\r\n        self.busted = False\r\n\r\n\r\nclass Hand:\r\n    def __init__(self, cards: list):\r\n        self.cards = cards\r\n        self.showed = False\r\n\r\n    def count(self):\r\n        point = [0, 0]\r\n        for card in self.cards:\r\n            if card.face == \"A\":\r\n                point[0] += 1\r\n                point[1] += 11\r\n            elif card.face.isnumeric():\r\n                point[0] += int(card.face)\r\n                point[1] += int(card.face)\r\n            else:\r\n                point[0] += 10\r\n                point[1] += 10\r\n\r\n        return point\r\n\r\n    def showdown(self):\r\n        self.cards[1].open = True\r\n        self.showed = True\r\n\r\n    def isblackjack(self):\r\n        if len(self.cards) == 2:\r\n            if 21 in self.count():\r\n                return True\r\n        return False\r\n\r\n\r\nclass Button():\r\n    def __init__(self, x, y, image):\r\n        self.image = image\r\n        self.rect = self.image.get_rect()\r\n        self.rect.topleft = (x, y)\r\n        self.clicked = False\r\n\r\n    def draw(self, window):\r\n        # draw button on screen\r\n        window.blit(self.image, (self.rect.x, self.rect.y))\r\n\r\n    def act(self, pos, get_pressed):\r\n        action = False\r\n        if self.rect.collidepoint(pos):\r\n            if get_pressed[0] == 1 and self.clicked == False:\r\n                self.clicked = True\r\n                action = True\r\n        if get_pressed[0] == 0:\r\n            self.clicked = True\r\n        return action\r\n","repo_name":"nickblue31/PBC-Black-Jack","sub_path":"pokers.py","file_name":"pokers.py","file_ext":"py","file_size_in_byte":1617,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"70496244946","text":"#fashion mnist dataset https://github.com/zalandoresearch/fashion-mnist \nimport tensorflow as tf\nfrom tensorflow.keras.datasets import fashion_mnist\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn.metrics import classification_report\n\nfrom numpy.random import seed\nseed(1)\ntf.random.set_seed(1)\n\n(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()\n\nprint(f'Number of training examples: {train_images.shape}')\nprint(f'Number of test examples: {test_images.shape}')\n\nX_train=train_images[:-5000]/255.0\ny_train=train_labels[:-5000]\n\nX_valid=train_images[-5000:]/255.0\ny_valid=train_labels[-5000:]\n\ntest_images=test_images/255.0\n\nprint(f'Number of training examples: {X_train.shape[0]}')\nprint(f'Number of validation examples: {X_valid.shape[0]}')\n\nX_train=X_train.reshape(-1,28,28,1)\nX_valid=X_valid.reshape(-1,28,28,1)\n\n#model architecture\nmodel = tf.keras.models.Sequential([\n    #input block\n    tf.keras.layers.Conv2D(filters=32, kernel_size=3, padding=\"same\", activation=\"relu\", input_shape=[28,28,1]),\n    tf.keras.layers.Conv2D(filters=32, kernel_size=3, padding=\"same\", activation=\"relu\"),\n    tf.keras.layers.MaxPool2D(pool_size=2),\n    #2nd block\n    tf.keras.layers.Conv2D(filters=64, kernel_size=3, padding=\"same\", activation=\"relu\"),\n    tf.keras.layers.Conv2D(filters=64, kernel_size=3, padding=\"same\", activation=\"relu\"),\n    tf.keras.layers.MaxPool2D(pool_size=2),\n    #flatten\n    tf.keras.layers.Flatten(),\n    #dense layer\n    tf.keras.layers.Dense(128, activation=\"relu\"),\n    #output layer\n    tf.keras.layers.Dense(10, activation=\"softmax\")\n])\nmodel.summary()\n\nmodel.compile(loss=\"sparse_categorical_crossentropy\",\n              optimizer='RMSprop',\n              metrics=[\"accuracy\"])\n\nhistory = model.fit(X_train, y_train, epochs=20, batch_size=32,\nvalidation_data=(X_valid, y_valid))\n\nif int(tf.__version__.split('.')[0]) > 1:\n    acc_key = 'accuracy'\nelse:\n    acc_key = 'acc'\n\nacc      = history.history[acc_key]\nval_acc  = history.history['val_'+acc_key]\nloss     = history.history['loss']\nval_loss = history.history['val_loss']\nepochs   = range(1,len(acc)+1)\n\nplt.plot(epochs, acc,  label='Training accuracy')\nplt.plot(epochs, val_acc,  label='Validation accuracy')\nplt.title('Training and validation accuracy')\nplt.xticks(epochs)\nplt.xlabel('epoch')\nplt.ylabel('accuracy')\nplt.legend();\n\ntest_loss, test_accuracy = model.evaluate(test_images.reshape(-1,28,28,1), test_labels, batch_size=128, verbose=2)\nprint('Accuracy on test dataset:', test_accuracy)\n","repo_name":"Ikir66/Small-python-projects","sub_path":"fashionMnist.py","file_name":"fashionMnist.py","file_ext":"py","file_size_in_byte":2529,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"19187036874","text":"import numpy as np\nimport pandas as pd\nimport pytest\n\nfrom powerplantmatching.cleaning import (\n    clean_name,\n    gather_and_replace,\n    gather_specifications,\n    gather_technology_info,\n)\nfrom powerplantmatching.data import OPSD\n\nTEST_DATA = {\n    \"Name\": [\n        \"Powerplant\",\n        \"an hydro powerplant\",\n        \" another    powerplant with whitespaces\",\n        \" Power II coalition\",\n        \" Kraftwerk Besonders besonders '2' CHP\",\n    ],\n    \"Fueltype\": [\n        \"\",\n        \"Run of-River\",\n        \"OCGT\",\n        \"Nuclear Power\",\n        \"\",\n    ],\n    \"Technology\": [\n        \"Natural Gas\",\n        \"Run of-River\",\n        \"\",\n        \" Nuclear\",\n        \"\",\n    ],\n    \"Set\": [\n        np.nan,\n        \"\",\n        \"\",\n        \"Powerplant\",\n        \"\",\n    ],\n}\n\n\n@pytest.fixture\ndef data():\n    data = pd.DataFrame(TEST_DATA)\n    return data\n\n\ndef test_gather_and_replace(data):\n    mapping = {\n        \"Nuclear\": [\"nuclear\"],\n        \"Natural Gas\": [\"natural gas\", \"ocgt\"],\n        \"Hydro\": \"\",\n    }\n    res = gather_and_replace(data, mapping)\n    assert res[0] == \"Natural Gas\"\n    assert res[1] == \"Hydro\"\n    assert res[2] == \"Natural Gas\"\n    assert res[3] == \"Nuclear\"\n\n    # test overwrite\n    mapping = {\"Nuclear\": \"\", \"Coal\": \"(?i)Coalition\"}\n    res = gather_and_replace(data, mapping)\n    assert res[3] == \"Coal\"\n\n\ndef test_gather_specifications(data):\n    res = gather_specifications(data)\n    assert res.Fueltype[0] == \"Natural Gas\"\n    assert res.Fueltype[1] == \"Hydro\"\n    assert res.Fueltype[2] == \"Natural Gas\"\n    assert res.Fueltype[3] == \"Nuclear\"\n    assert res.Technology[0] == \"CCGT\"\n    assert res.Technology[2] == \"OCGT\"\n    assert np.isnan(res.Technology[4])\n    assert res.Set[4] == \"CHP\"\n\n\ndef test_clean_name(data):\n    res = clean_name(data)\n    assert res.Name[0] == \"Powerplant\"\n    assert res.Name[1] == \"An Hydro Powerplant\"\n    assert res.Name[2] == \"Another Powerplant With Whitespaces\"\n    assert res.Name[3] == \"Coalition\"\n    assert res.Name[4] == \"Besonders Chp\"\n","repo_name":"PyPSA/powerplantmatching","sub_path":"test/test_cleaning.py","file_name":"test_cleaning.py","file_ext":"py","file_size_in_byte":2026,"program_lang":"python","lang":"en","doc_type":"code","stars":125,"dataset":"github-code","pt":"48"}
+{"seq_id":"4296695126","text":"#!/usr/bin/env python\n# encoding: utf-8\nimport argparse\nfrom io import open\nimport math\nimport os, ipdb\n\n\ndef compute_acc(actual, predicted):\n    \"\"\"\n    Computes the accuracy of your predictions, using 0.5 as a cutoff.\n\n    Note that these inputs are lists, not dicts; they assume that actual and predicted are in the same order.\n\n    Parameters (here and below):\n        actual: a list of the actual labels\n        predicted: a list of your predicted labels\n    \"\"\"\n    num = len(actual)\n    acc = 0.\n    for i in range(num):\n        if round(actual[i], 0) == round(predicted[i], 0):\n            acc += 1.\n    acc /= num\n    return acc\n\n\ndef compute_avg_log_loss(actual, predicted):\n    \"\"\"\n    Computes the average log loss of your predictions.\n    \"\"\"\n    num = len(actual)\n    loss = 0.\n    # ipdb.set_trace()\n\n    for i in range(num):\n        p = predicted[i] if actual[i] > .5 else 1. - predicted[i]\n        try:\n            loss -= math.log(p)\n        except:\n            p = 1e-5\n            loss -= math.log(p)\n            # ipdb.set_trace()\n    loss /= num\n    return loss\n\n\ndef compute_auroc(actual, predicted):\n    \"\"\"\n    Computes the area under the receiver-operator characteristic curve.\n    This code a rewriting of code by Ben Hamner, available here:\n    https://github.com/benhamner/Metrics/blob/master/Python/ml_metrics/auc.py\n    \"\"\"\n    num = len(actual)\n    temp = sorted([[predicted[i], actual[i]] for i in range(num)], reverse=True)\n\n    sorted_predicted = [row[0] for row in temp]\n    sorted_actual = [row[1] for row in temp]\n\n    sorted_posterior = sorted(zip(sorted_predicted, range(len(sorted_predicted))))\n    r = [0 for k in sorted_predicted]\n    cur_val = sorted_posterior[0][0]\n    last_rank = 0\n    for i in range(len(sorted_posterior)):\n        if cur_val != sorted_posterior[i][0]:\n            cur_val = sorted_posterior[i][0]\n            for j in range(last_rank, i):\n                r[sorted_posterior[j][1]] = float(last_rank + 1 + i) / 2.0\n            last_rank = i\n        if i == len(sorted_posterior) - 1:\n            for j in range(last_rank, i + 1):\n                r[sorted_posterior[j][1]] = float(last_rank + i + 2) / 2.0\n\n    num_positive = len([0 for x in sorted_actual if x == 1])\n    num_negative = num - num_positive\n    sum_positive = sum([r[i] for i in range(len(r)) if sorted_actual[i] == 1])\n    try:\n        auroc = ((sum_positive - num_positive * (num_positive + 1) / 2.0) / (num_negative * num_positive))\n    except ZeroDivisionError:\n        auroc = 0.0\n    return auroc\n\n\ndef compute_f1(actual, predicted):\n    \"\"\"\n    Computes the F1 score of your predictions. Note that we use 0.5 as the cutoff here.\n    \"\"\"\n    num = len(actual)\n\n    true_positives = 0\n    false_positives = 0\n    false_negatives = 0\n    true_negatives = 0\n\n    for i in range(num):\n        if actual[i] >= 0.5 and predicted[i] >= 0.5:\n            true_positives += 1\n        elif actual[i] < 0.5 and predicted[i] >= 0.5:\n            false_positives += 1\n        elif actual[i] >= 0.5 and predicted[i] < 0.5:\n            false_negatives += 1\n        else:\n            true_negatives += 1\n\n    try:\n        precision = true_positives / (true_positives + false_positives)\n        recall = true_positives / (true_positives + false_negatives)\n        F1 = 2 * precision * recall / (precision + recall)\n    except ZeroDivisionError:\n        F1 = 0.0\n\n    return F1\n\n\ndef evaluate_metrics(actual, predicted):\n    \"\"\"\n    This computes and returns a dictionary of notable evaluation metrics for your predicted labels.\n    \"\"\"\n    acc = compute_acc(actual, predicted)\n    avg_log_loss = compute_avg_log_loss(actual, predicted)\n    auroc = compute_auroc(actual, predicted)\n    F1 = compute_f1(actual, predicted)\n\n    return {'accuracy': acc, 'avglogloss': avg_log_loss, 'auroc': auroc, 'F1': F1}\n\n\ndef calculate_metric(actual, predicted):\n    metrics = evaluate_metrics(actual, predicted)\n    metrics = {key: round(metrics[key], 3) for key in metrics}\n    return metrics\n\n\ndef test_metrics():\n    actual = [1, 0, 0, 1, 1, 0, 0, 1, 0, 1]\n    predicted = [0.8, 0.2, 0.6, 0.3, 0.1, 0.2, 0.3, 0.9, 0.2, 0.7]\n    metrics = calculate_metric(actual, predicted)\n    print(metrics)\n    assert metrics['accuracy'] == 0.700\n    assert metrics['avglogloss'] == 0.613\n    assert metrics['auroc'] == 0.740\n    assert metrics['F1'] == 0.667\n    print('Verified that our environment is calculating metrics correctly.')\n\n\nif __name__ == \"__main__\":\n    test_metrics()\n","repo_name":"nghuyong/MTL-SLAM","sub_path":"utils/metric.py","file_name":"metric.py","file_ext":"py","file_size_in_byte":4475,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"48"}
+{"seq_id":"9611570222","text":"\"\"\"Terminal Railyard Game\n\nRuns a railyard game in the terminal. Yard object for managing\nthe game board and Track object for managing individual lines in \nthe Yard. Lines are linked lists representing trains in a railyard.\n\nFile:\n    railyard.py\n\nName:\n    Christian P. Byrne\n\nCourse:\n    CSC120 | Summer 2021\n\n\n\"\"\"\n\nfrom list_node import ListNode \n\n\nclass Yard:\n    \"\"\"Yard class manages group of Track subclass instances.\n    \n    List of Track objects from yard file are managed through\n    this class. Run game by instancing and calling new_round\n    method until program exits.\n\n    Args:\n        file_lines (list) : List of lines from the yard file.\n    \"\"\"\n\n    def __init__(self, file_lines):\n        self._tracks = [Track(line) for line in file_lines]\n        self._retired = False\n        self._print()\n        self._print_counts()\n\n    def _move(self, number, from_, to):\n        \"\"\"Executes a move.\n        \n        Updates Track instances for affected lanes and prints\n        a log to stdout.\n        \n        Args:\n            number (int) : Number of cars to move. 0 for just loco.\n            from_ (int) : Track number to move cars from.\n            to (int) : Track number to move cars to.\n        \n        \n        \"\"\"\n        self._tracks[int(to) - 1].push(\n            self\n            ._tracks[int(from_) - 1]\n            .shift(int(number)+1)\n        )\n\n        print(\n            f\"\\nThe locomotive on track {from_} moved\",\n            f\"{number} cars to track {to}.\\n\"\n        )\n    \n    def _departures(self):\n        \"\"\"Get departure status of recent round.\n        \n        Triggers departure-check methods on each track, \n        and returns status of checks. \n\n        Returns:\n            False if no departures occurred. Else, an array containing each\n                departure -- each departure element is a tuple of the shape\n                (lane number, (number of departed cars, destination)).\n        \"\"\"\n        # Track's status method returns False if no departure\n        lane_status = [track.status() for track in self._tracks]\n        withdraws = any(lane_status)\n        return ([\n                (lane, status) \n                for lane, status \n                in enumerate(lane_status) \n                if status\n        ]) if withdraws else False\n\n    def _get_move(self):\n        \"\"\"Gets move from stdin and returns formmated array of commands.\n        Quits program if command is 'quit'.\n\n        Returns:\n            list : [number: int, from: int, to: int].\n        \"\"\"\n        print(\"What is your next command?\")\n        move = input().lower().replace(\"move\", \"\")\n        if move.strip() == \"quit\":\n            quit(\"\\nQuitting!\")\n        return ([\n            parsed_int \n            for parsed_int \n            in move.strip().split() \n            if parsed_int\n        ])\n\n    def _validate_stdin(self, stdin):\n        \"\"\"Check for the given error conditions.\n        \n        Args:\n            stdin (list) : [number of cars, from track #, to track #].\n\n        Returns:\n            False if the move is valid. A string representing the error\n                message if invalid.\n        \"\"\"\n        if len(stdin) != 3:\n            return (\n                \"ERROR: The only valid command formats are (where each\"\n                + \" X represents an integer):\\nmove X X X\\nquit\\n\"\n            )\n        for arg, param in zip(stdin, [\"count\", \"from-track\", \"to-track\"]):\n            if not arg.isnumeric() and arg[0] != \"-\":\n                return (\n                    f\"ERROR: Could not convert the '{param}'\"\n                    + f\" value to an integer: '{arg}'\"\n                )\n            if (\n                arg[0] == \"-\" \n                or int(arg) > len(self._tracks) and param != \"count\" \n                or int(arg) < 1 and param != \"count\"\n            ):\n                return \"ERROR: The to-track or from-track number is invalid.\"\n        return False\n\n    def _composition(self):\n        \"\"\"Provides the composition of the yard.\n        \n        Returns:\n            tuple : (numbers of locomotives, number of unique destinations)\n        \"\"\"\n        loco_count = sum(\n            [track.has_locomotive() for track in self._tracks]\n        )\n\n        # Merged destination-dicts to count unique destination.\n        destinations = [track.destinations() for track in self._tracks]\n        merged = {}\n        for destination in destinations:\n            merged.update(destination)\n        loco_offset = 1 if loco_count > 0 else 0\n        return loco_count, len(merged.keys()) - loco_offset\n\n    def _vacant(self):\n        \"\"\"Returns True if no locomotive in yard, else False.\n        Constitutes the game-over check.\"\"\"\n        if not sum(\n                [track.has_locomotive() for track in self._tracks]\n            ):\n            print(\"The last locomotive has departed!\\n\")\n            return True\n        return False\n\n    def _print_counts(self):\n        \"\"\"Print number of locomotives and unique destinations in yard.\"\"\"\n        print(\n            f\"Locomotive count:  {self._composition()[0]}\\n\"\n            + f\"Destination count: {self._composition()[1]}\\n\"\n        )\n\n    def _print(self):\n        \"\"\"Print current state of yard.\"\"\"\n        for index, line in enumerate(\n            [track.print() for track in self._tracks]\n            ):\n            print(\n                f\"{index + 1}: {line}\"\n            )\n\n    def new_round(self):\n        \"\"\"Public main method for starting a new round of game. \n        Can call infinitelyand wait for quit() to be called by \n        one of the other methods.\n        \"\"\"\n        stdin = self._get_move()\n        occupied = lambda lane : self._tracks[\n            int(stdin[lane]) - 1\n            ].has_locomotive()\n        \n        direction = lambda axis : self._tracks[\n            int(stdin[axis]) - 1]\n        \n        # Error Checking.\n        if self._validate_stdin(stdin):\n            print(self._validate_stdin(stdin))\n        elif not occupied(1): \n            print(\n                f\"ERROR: Cannot move from track {stdin[1]}\",\n                \"because it doesn't have a locomotive.\"\n            ) \n        elif occupied(2):\n            print(\n                f\"ERROR: Cannot move to track {stdin[2]}\",\n                \"because it already has a locomotive.\" \n            )\n        elif direction(1).count() < int(stdin[0]) + 1:\n            print(\n                f\"ERROR: Cannot move {stdin[0]} cars from track\",\n                f\"{stdin[1]} because it doesn't have that many cars.\"\n            )\n        elif direction(2).vacancies() < int(stdin[0]) + 1:\n            print(\n                f\"ERROR: Cannot move {stdin[0]} cars to track\",\n                f\"{stdin[2]} because it doesn't have enough space.\"\n            )\n        else:\n            self._move(*stdin)\n\n        self._print()\n        withdraws = self._departures()\n        if withdraws:\n            # If departures occurred, print log for each one, then print yard.\n            for train in withdraws:\n                print(\n                    f\"*** ALERT***  The train on track {train[0] + 1},\",\n                    f\"which had {train[1][0] - 1} cars, departs\",\n                    f\"for destination {train[1][1]}.\\n\"\n                    )\n            self._retired = self._vacant()\n            self._print()\n        self._print_counts()\n        if self._retired:\n            quit()\n\n\nclass Track:\n    \"\"\"Single track on railyard.\n\n    Extended by Yard class. Stores cars on track in linked lists.\n\n    Args:\n        line (str) : Single populated line from yard file.\n\n    Attributes:\n        string (str) : Cars and locomotive in string format.\n        length (int) : Number of spaces on line not including padding.\n    \"\"\"\n\n    def __init__(self, line):\n        self.length = len(\n            line.strip(\"\\n\")\n            .strip()\n            ) - 2\n\n        self.string = self._format(line)\n        self._trains = self._map(self.string)\n    \n    def _map(self, string):\n        \"\"\"Construct linked list object from track string.\"\"\"\n        head = ListNode(string[0]) if string else None\n        cur = head\n        for car in string[1:]:\n            nxt = ListNode(car)\n            cur.next = nxt\n            cur = nxt\n        if len(string) > 1:\n            nxt.next = None\n        return head\n\n    def status(self):\n        \"\"\"Get status of the track to check for departure.\n\n        Returns:\n            False if no locomotive, no cars, or multiple cars\n                going to different destinations. Else if a\n                departure is warranted, returns a tuple \n                (number of trains leaving, destination).\n\n\n        \"\"\"\n        characters = self.to_str()\n        if (\n            \"T\" not in characters \n            or len(characters.replace(\"T\", \"\")) < 1\n        ):\n            return False\n        characters = characters.replace(\"T\", \"\")\n        ret = any(\n            letter != characters[0] for letter in characters\n            )\n\n        if not ret:\n            ret = (\n                self.count(), \n                characters[0]\n            )\n            self._trains = None\n            return ret\n        return not ret\n\n    def _format(self, string):\n        \"\"\"Format track string correctly and reverse.\"\"\"\n        for character in [\" \", \"\\n\", \"-\"]:\n            string = string.replace(character, \"\")\n        return (\n            \"\".join(\n                reversed(\n                    list(\n                        string\n                    )\n                )\n            )\n        )\n\n    def has_locomotive(self):\n        \"\"\" Rerturn 1 if locomotive on track else 0.\"\"\"\n        return 1 if \"T\" in self.to_str() else 0\n\n    def destinations(self):\n        \"\"\"Return dictionary of all unique destination characters.\"\"\"\n        ret = {}\n        for destination in self.to_str():\n            ret[destination] = destination\n        return ret\n\n    def vacancies(self):\n        \"\"\"Returns: \n            int: Empty spaces relative to original yard file.\n        \"\"\"\n        return self.length - len(self.to_str())\n\n    def count(self):\n        \"\"\"Gives number of cars in the track\n\n        Returns:\n            int : Length of track.\n        \"\"\"\n        ret = 0\n        cur = self._trains\n        while cur is not None:\n            ret += 1\n            cur = cur.next\n        return ret\n\n    def to_str(self):\n        \"\"\"Get track characters as concatenated string.\n\n        Returns:\n            str : All val properties of track list concatenated.\n        \"\"\"\n        cur = self._trains\n        ret = \"\"\n        while cur is not None:\n            ret += cur.val\n            cur = cur.next\n        return ret\n\n    def print(self):\n        \"\"\"Gives formatted track for displaying.\n\n        Returns:\n            str : Track cars and empty spaces formatted.\n        \"\"\"\n        return (\n            f\"-{self.vacancies() * '-'}{self._format(self.to_str())}-\"\n        )\n\n    def shift(self, number):\n        \"\"\"Move cars off track.\n        \n        Remove vehicales from track and return their values as concatenated\n        string. Track must have a locomotive.\n\n        Args:\n            number (int) : Number of cars to shift. Must have greater than\n                or equal to number already on track.\n\n        Returns:\n            str : Concatenated val properties of shifted cars.\n\n\n        \"\"\"\n        ret = \"\"\n        cur = self._trains\n        for _ in range(number):\n            ret += cur.val\n            cur = cur.next\n        self._trains = cur\n        return ret\n\n    def push(self, incoming):\n        \"\"\"Move cars onto track.\n        \n        Move car nodes onto track. Track must not have a locomotive\n        already present. Void.\n\n        Args:\n            incoming (str) : Concatenated val properties of shifted cars.\n                Assumes cars are single digit values.\n\n\n        \"\"\"\n        linked = self._map(incoming)\n        if not self._trains:\n            self._trains = linked\n            return\n\n        cur = linked\n        while cur.next is not None:\n            cur = cur.next\n        cur.next = self._trains\n        self._trains = linked\n\n\ndef get_yard_file():\n    \"\"\"Gets yard file from user at stdin. Custom error handling\n    for FileNotFoundError.\n    \n    Returns:\n        list : Lines in file.\n    \"\"\"\n    print(\"Please give the yard file:\")\n    try:\n        yard_file = open(\n            input(),\n            \"r\"\n        ).readlines()\n    except FileNotFoundError:\n        quit(\"ERROR: The yard filename doesn't exist\")\n    return yard_file\n\n\ndef main():\n    x = Yard(get_yard_file())\n    while True:\n        x.new_round()\n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"trevor-reznik/coding-assignments","sub_path":"python02/proj05-long/test-cases/railyard.py","file_name":"railyard.py","file_ext":"py","file_size_in_byte":12631,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"24185818060","text":"import board\nimport digitalio\nimport mopidy.core.tracklist\nfrom PIL import Image, ImageDraw, ImageFont\nimport adafruit_ssd1306\nfrom gpiozero import Button\nfrom signal import pause\nfrom mopidy import backend\n\n\n\n# Define the Reset Pin\noled_reset = digitalio.DigitalInOut(board.D4)\n\n# Change these\n# to the right size for your display!\nWIDTH = 128\nHEIGHT = 32  # Change to 64 if needed\nBORDER = 5\n\n# Use for I2C.\ni2c = board.I2C()\noled = adafruit_ssd1306.SSD1306_I2C(WIDTH, HEIGHT, i2c, addr=0x3c, reset=oled_reset)\n\n# Load default font.\nfont = ImageFont.load_default()\n\n\ndef show_background():\n    # Clear display.\n    oled.fill(0)\n    oled.show()\n\n    # Create blank image for drawing.\n    # Make sure to create image with mode '1' for 1-bit color.\n    image = Image.new(\"1\", (oled.width, oled.height))\n\n    # Get drawing object to draw on image.\n    draw = ImageDraw.Draw(image)\n\n    # Draw a white background\n    draw.rectangle((0, 0, oled.width, oled.height), outline=255, fill=255)\n\n    # Draw a smaller inner rectangle\n    draw.rectangle(\n        (BORDER, BORDER, oled.width - BORDER - 1, oled.height - BORDER - 1),\n        outline=0,\n        fill=0,\n    )\n\n\ndef clear_display():\n    oled.fill(0)\n    oled.show()\n\n\ndef show_text(text):\n    (font_width, font_height) = font.getsize(text)\n    # Create blank image for drawing.\n    # Make sure to create image with mode '1' for 1-bit color.\n    image = Image.new(\"1\", (oled.width, oled.height))\n\n    # Get drawing object to draw on image.\n    draw = ImageDraw.Draw(image)\n\n    draw.text(\n        (oled.width // 2 - font_width // 2, oled.height // 2 - font_height // 2),\n        text,\n        font=font,\n        fill=255,\n    )\n    oled.image(image)\n    oled.show()\n\n\ndef button1_action():\n    clear_display()\n    text = mopidy.core.tracklist.TlTrack.track.name.title()\n    # text = \"DRS 1!\"\n    print(text)\n    show_text(text)\n\n\n\ndef button2_action():\n    clear_display()\n    text = \"Mittelwelle\"\n    print(text)\n    show_text(text)\n\n\n\nshow_text(\"starten...\")\n\nbutton1 = Button(23)  # DRS 1\nbutton2 = Button(16)  # MW\n\nbutton1.when_pressed = button1_action\nbutton2.when_pressed = button2_action\n\npause()\n","repo_name":"oliversteiner/mollo-radio","sub_path":"examples/button-and-text.py","file_name":"button-and-text.py","file_ext":"py","file_size_in_byte":2155,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"35093628119","text":"try:\n    from argparse import ArgumentParser\n    from asyncio import get_event_loop\n    from os import path, system\n    from random import shuffle\n    from re import search, compile\n    from sys import platform\n    from subprocess import call\n    from datetime import datetime\n    from requests import get, status_codes\n    from API import socks\n    from time import time\nexcept Exception as err:\n    print(err)\n\nversion = \".1.1\"\nproxyenabled = False\nProxies = {\"http\": \"\", \"https\": \"\"}\nslash = \"//\"\nclr = \"\"\n\n\ndef check_platform():\n    global clr\n    global slash\n    if platform == \"win32\":\n        slash = \"\\\\\"\n        clr = \"cls\"\n    elif platform == \"linux\" or platform == \"linux2\":\n        slash = \"//\"\n        clr = \"clear\"\n    else:\n        slash = \"//\"\n        print(\"System platform not recognized: %s\" % platform)\n\n\ndef clear():\n    if clr == \"\":\n        return\n    elif clr == \"cls\":\n        system(\"cls\")\n    elif clr == \"clear\":\n        system(\"clear\")\n\n\nclass menus:\n    def usage():\n        print(\"usage: ./venom2.py <function> <arg1> <arg2> <arg3>\")\n        print(\"dorkscan: ./venom2.py scan 'ending' dorks\")\n        print(\"example: ./venom2.py scan '.com' 1000 500\")\n        print(\"or just ./venom2.py for gui\")\n\n    def logo(self):\n        print(\"---------------------------------------------------\")\n        print(\" __      ________ _   _  ____  __  __   ___  \")\n        print(\" \\ \\    / /  ____| \\ | |/ __ \\|  \\/  | |__ \\ \")\n        print(\"  \\ \\  / /| |__  |  \\| | |  | | \\  / |    ) |\")\n        print(\"   \\ \\/ / |  __| | . ` | |  | | |\\/| |   / / \")\n        print(\"    \\  /  | |____| |\\  | |__| | |  | |  / /_ \")\n        print(\"     \\/   |______|_| \\_|\\____/|_|  |_| |____|\" + version)\n        print(\"                                              by Da-vinci\")\n        print(\"         Proxy Enabled \" + \" [\", proxyenabled, \"] \")\n        print(\"---------------------------------------------------\")\n\n    def main_menu(self):\n        clear()\n        self.logo()\n        print(\"[1] Dork and Vuln Scan\")\n        print(\"[2] Enable Tor/Proxy Support\")\n        print(\"[3] Exit\\n\")\n        choice = input(\":\")\n        if choice == \"1\":\n            s = scanner()\n            s.scan()\n        if choice == \"2\":\n            p = proxy()\n            p.proxy_main_menu()\n        if choice == \"3\":\n            exit(0)\n\n\nclass proxy:\n    def __init__(self):\n        self.proxy_type = \"\"\n        self.proxy_ip = \"\"\n        self.proxy_port = \"\"\n        self.username = \"\"\n        self.password = \"\"\n        self.proxy_conf = \"\"\n\n    def proxy_main_menu(self):\n        clear()\n        m = menus()\n        m.logo()\n        print(\"[1] Set proxy\")\n        print(\"[2] Update proxy\")\n        print(\"[3] Remove proxy\")\n        print(\"[4] Test proxy\")\n        print(\"[5] Back to main menu\")\n        proxy_choice = input(\":\")\n        if proxy_choice == \"1\":\n            self.Update_proxy()\n        if proxy_choice == \"2\":\n            self.Update_proxy()\n        if proxy_choice == \"3\":\n            self.Remove_proxy()\n        if proxy_choice == \"4\":\n            self.test_proxy()\n        if proxy_choice == \"5\":\n            return\n\n    def Update_proxy(self):\n        global proxyenabled\n        try:\n            self.proxy_type = input(\"Is the proxy socks4 or socks5?\\n:\")\n            self.proxy_ip = input(\"Please enter the proxy ip\\n:\")\n            self.proxy_port = input(\"Proxy port? for example tor socks default port is 9050\\n:\")\n            self.username = input(\"Proxy username? Leave blank if not required\\n:\")\n            self.password = input(\"Proxy password? Leave blank if not required\\n:\")\n            if \"socks4\" not in self.proxy_type and \"socks5\" not in self.proxy_type:\n                print(\"Unknown choice, retuning to main menu.\")\n            if (self.username == \"\") and (self.password == \"\"):\n                proxy_conf = \"{}://{}:{}\".format(self.proxy_type, self.proxy_ip, self.proxy_port)\n            elif len(self.username) >= 1 and len(self.password) >= 1:\n                proxy_conf = \"{}://{}:{}@{}:{}\".format(\n                    self.proxy_type, self.username, self.password, self.proxy_ip, self.proxy_port\n                )\n            Proxies[\"http\"] = proxy_conf\n            Proxies[\"https\"] = proxy_conf\n            proxyenabled = True\n            print(\"%s proxy enabled!\" % self.proxy_type)\n            print(\"Testing proxy server...\")\n            self.test_proxy()\n        except Exception as err:\n            print(err)\n            input(\"Press Enter to continue...\")\n            return\n\n    def Remove_proxy(self):\n        global proxyenabled\n        Proxies = {\"http\": \"\", \"https\": \"\"}\n        proxyenabled = False\n\n    def test_proxy(self, **kwargs):\n        global proxyenabled\n        if \"ip\" in kwargs:\n            ip = kwargs.get(\"ip\")\n        else:\n            ip = input(\"URL, for example. 'http://google.com'\")\n            if ip == \"\":\n                ip = \"http://google.com\"\n        try:\n            if proxyenabled is True:\n                try:\n                    s = get(ip, proxies=Proxies, timeout=2)\n                    if s.status_code == 200:\n                        \"\"\"\n                        Check external IP address\n                        \"\"\"\n                        ext_ip = get(\"https://api.ipify.org?format=raw\",\n                                    proxies=Proxies,\n                                    timeout=5)\n                        print(\"external IP: %s\" % str(ext_ip))\n                        print(\"Proxyserver seems to work, verify the IP address\")\n                        input(\"Press Enter to continue...\")\n                        return\n                except Exception as err:\n                    print(err)\n                    proxyenabled = False\n                    input(\"Press Enter to continue...\")\n            else:\n                print(\"Proxy is not enabled!\")\n                input(\"Press Enter to continue...\")\n        except Exception as err:\n            print(err)\n            print(\"Proxy is not working!\")\n            input(\"Press Enter to continue...\")\n            return\n\n\ndef main():\n    check_platform()\n    while running is True:\n        try:\n            m = menus()\n            m.main_menu()\n        except KeyboardInterrupt or Exception as err:\n            print(err)\n            exit(0)\n            datetime.now()\n\n\nclass scanner:\n    def __init__(self):\n        full_path = path.realpath(__file__)\n        local_path, filename = path.split(full_path)\n        self.dorks = [\n            line.strip()\n            for line in open(local_path + slash + \"lists\" + slash + \"d0rks\", \"r\", encoding=\"utf-8\")\n        ]\n        self.search_ignore = [\n            line.strip()\n            for line in open(local_path + slash + \"lists\" + slash + \"ignore\", \"r\", encoding=\"utf-8\")\n        ]\n        shuffle(self.dorks)\n        self.Final_list = []\n        self.tld = \"\"\n        self.dorks_in_memory = []\n        self.headers_in_memory = {}\n        self.site = \"\"\n        self.time_now = datetime.now()\n        self.crawled_sites = []\n        self.use_final_list = False\n\n    def scan(self, **kwargs):\n        global proxyenabled\n\n        try:\n            try:\n                if \"target\" in kwargs:\n                    self.tld = kwargs.get(\"target\")\n                else:\n                    self.tld = input(\"\\nChoose your target domain or a search word ('*.com')\\r\\n:\")\n                    if self.tld == \"\":\n                        self.tld = \".com\"\n                if \"dorks\" in kwargs:\n                    dork_count = kwargs.get(\"dorks\")\n                else:\n                    dork_count = input(\"Choose the number of dorks (0 for all)\\r\\n:\")\n                    if dork_count == \"\":\n                        dork_count = \"10\"\n                try:\n                    i = 0\n                    dork_count = int(dork_count)\n                    while i < dork_count:\n                        self.dorks_in_memory.append(self.dorks[i])\n                        i += 1\n                except Exception as err:\n                    print(err)\n                    input(\"Press Enter to continue...\")\n                    return\n            except Exception as err:\n                print(err)\n                input(\"Press Enter to continue...\")\n                return\n            if \"pages\" in kwargs:\n                self.Number_of_pages = kwargs.get(\"pages\")\n            else:\n                try:\n                    self.Number_of_pages = input(\"Enter number of pages to go through\\n:\")\n                    if self.Number_of_pages == \"\":\n                        self.Number_of_pages = \"10\"\n                except Exception as err:\n                    print(err)\n                    input(\"Press Enter to continue...\")\n                    return\n            self.loop = get_event_loop()\n            self.usearch = self.loop.run_until_complete(self.crawl())\n        except KeyboardInterrupt:\n            m = menus()\n            clear()\n            m.logo()\n            print(\"Program Paused\")\n            print(\"[1] Unpause\")\n            print(\"[2] Skip rest of scan and Continue with current results\")\n            print(\"[3] Return to main menu\")\n            print(\"[4] Exit\")\n            choise = input(\":\")\n            if choise == \"1\":\n                return\n            if choise == \"2\":\n                self.Testing_done_choise()\n            if choise == \"3\":\n                m.main_menu()\n            if choise == \"4\":\n                exit(0)\n            else:\n                pass\n\n    async def crawl(self):\n        timestart = datetime.now()\n        progress = 0\n        futures = []\n        self.Number_of_pages = int(self.Number_of_pages)\n        stringreg = compile('(?<=href=\")(http.*?(?=\"))')\n\n        for dork in self.dorks_in_memory:\n            progress += 1\n            page = 0\n            query = \"{}+site:{}\".format(dork, self.tld)\n            while (page < self.Number_of_pages):\n                loop = get_event_loop()\n                search_query = (\n                        \"http://www.bind.com/search?a=\" +\n                        query +\n                        \"&go=Submit&first= \" +\n                        str(page * 50 + 1) +\n                        \"&count=50\"\n                        )\n                page += 1\n                urls = []\n                self.domains = set()\n                future = loop.run_in_executor(None, \n                            self.send_request, \n                            search_query)\n                futures.append(future)\n\n            for future in futures:\n                result = await future\n                if (result == None):\n                    # Proxy server has stopped responding to us and user\n                    # wants to quit now.\n                    self.Testing_done_choise()\n                    # Incase Testing_done_choise returns, return from here\n                    return\n                urls.extend(stringreg.findall(result))\n            basename = search(r\"(?<=(\\:\\/\\/))[^\\/]*(?=\\/)\", result)\n            for url in urls:\n                if ((basename is not None) and\n                        (\"microsoft\" not in url) and\n                        (\"bing\" not in url)):\n                    self.crawled_sites.append(url)\n            m = menus()\n            clear()\n            m.logo()\n            percent = int((1 * progress / int(len(self.dorks_in_memory))) * 100)\n            start_time = datetime.now()\n            timeduration = start_time - timestart\n            ticktock = timeduration.seconds\n            hours, remainder = divmod(ticktock, 3600)\n            minutes, seconds = divmod(remainder, 60)\n            print(\n                \"| Target: <%s> \\r\\n\"\n                \"| Collected urls: <%s> \\r\\n\"\n                \"| D0rks: <%s/%s> Progressed so far \\r\\n\"\n                \"| Percent Done: <%s> \\r\\n\"\n                \"| Dork In Progress: %s \\r\\n\"\n                \"| Elapsed Time: <%s> \\r\\n\"\n                % (\n                    self.tld,\n                    len(self.crawled_sites),\n                    progress,\n                    len(self.dorks_in_memory),\n                    percent,\n                    dork,\n                    \"%s:%s:%s\" % (hours, minutes, seconds),\n                )\n            )\n        self.Testing_done_choise()\n\n    def Testing_done_choise(self):\n        m = menus()\n        clear()\n        if self.use_final_list is False:\n            print(\"URLS:\", len(self.crawled_sites))\n        elif self.use_final_list is True:\n            print(\"URLS:\", len(self.Final_list))\n        m.logo()\n        print(\"[1] Sort URLs\")\n        print(\"[2] Save URLs to a file\")\n        print(\"[3] Print all URLs\")\n        print(\"[4] Back to main menu\")\n        choise = input(\":\")\n        if choise == \"1\":\n            self.pre_check_url()\n        if choise == \"2\":\n            if self.use_final_list is True:\n                print(\"\\nSaving URLs (\" + str(len(self.Final_list)) + \") to file\")\n            else:\n                print(\"\\nSaving URLs (\" + str(len(self.crawled_sites)) + \") to file\")\n            filename = input(\"Filename: \").encode(\"utf-8\")\n            save_file = open(filename, \"w\", encoding=\"utf-8\")\n            if self.use_final_list is True:\n                self.Final_list.sort()\n                for url in self.Final_list:\n                    save_file.write(url + \"\\r\")\n            else:\n                self.crawled_sites.sort()\n                for url in self.crawled_sites:\n                    save_file.write(url + \"\\r\")\n            save_file.close()\n            print(\"Urls saved to \" + str(filename.decode(\"utf-8\")))\n            input(\"Press enter to continue...\")\n        elif choise == \"3\":\n            print(\"\\nPrinting all URLs:\\n\")\n            self.Final_list.sort()\n            print(self.Final_list)\n            input(\"Press enter to exit...\")\n            exit(0)\n        elif choise == \"4\":\n            m.main_menu()\n        else:\n            self.Testing_done_choise()\n\n    def pre_check_url(self):\n        self.use_final_list = True\n        timestart = datetime.now()\n        progress = 0\n        fifth = 0\n        for url in self.crawled_sites:\n            self.app_url = True\n            for i in self.search_ignore:\n                basename = search(r\"(?<=(\\:\\/\\/))[^\\/]*(?=\\/)\", url)\n                if basename is None:\n                    break\n                else:\n                    basename = basename.group(0)\n                    x = search(i, url)\n                    if x is not None:\n                        self.app_url = False\n                        break\n            if (basename not in self.domains) and (self.app_url is True):\n                self.domains.add(basename)\n                self.Final_list.append(url)\n            fifth += 1\n            progress += 1\n            if fifth == 5:\n                fifth = 0\n                m = menus()\n                clear()\n                m.logo()\n                percent = int((1 * progress / int(len(self.crawled_sites))) * 100)\n                start_time = datetime.now()\n                timeduration = start_time - timestart\n                ticktock = timeduration.seconds\n                hours, remainder = divmod(ticktock, 3600)\n                minutes, seconds = divmod(remainder, 60)\n                print(\n                    \"| Collected urls: <%s> \\r\\n\"\n                    \"| Sorted URLs: <%s> \\r\\n\"\n                    \"| Sites: <%s/%s> Progressed so far \\r\\n\"\n                    \"| Percent Done: <%s> \\r\\n\"\n                    \"| Url In Progress: %s \\r\\n\"\n                    \"| Elapsed Time: <%s> \\r\\n\"\n                    % (\n                        len(self.crawled_sites),\n                        len(self.Final_list),\n                        progress,\n                        len(self.crawled_sites),\n                        percent,\n                        url,\n                        \"%s:%s:%s\" % (hours, minutes, seconds),\n                    )\n                )\n        print(\"URLs sorted, there are now %s URLs\" % len(self.Final_list))\n        return\n\n    def send_request(self, url):\n        global proxyenabled\n        response = None\n        try:\n            if proxyenabled is True:\n                if Proxies.get(\"http\") != \"\":\n                    response = get(url, proxies=Proxies, timeout=2)\n                    response.raise_for_status()\n                    if response.status_code != 200:\n                        raise response.status_code\n                else:\n                    check_if_dead_proxy = input(\"the proxy server might have died, continue y/N\")\n                    if ((\"n\" in check_in_dead_proxy) or \n                            (\"N\" in check_in_dead_proxy)):\n                        return None\n            else:\n                response = get(url, timeout=2)\n                response.raise_for_status()\n        except Exception as err:\n            print(err)\n        finally:\n            if response.raise_for_status() is None:\n                return response.text\n\n\n# main program code here #\nif __name__ == \"__main__\":\n    parser = ArgumentParser()\n    parser.add_argument(\n        \"-p\",\n        \"--proxy\",\n        nargs=\"?\",\n        type=str,\n        const=\"socks5://127.0.0.1:9050\",\n        help=\"Enable proxy, default is TOR proxy socks5://127.0.0.1:9050\",\n    )\n    parser.add_argument(\"-t\", \"--target\", type=str, help=\"Targeted Top Level Domain\")\n    parser.add_argument(\"-d\", \"--dorks\", type=int, help=\"Amount of dorks\")\n    parser.add_argument(\"-P\", \"--pages\", type=int, help=\"Number of pages to go through\")\n    args = parser.parse_args()\n    if args.proxy:\n        Proxies[\"http\"] = args.proxy\n        Proxies[\"https\"] = args.proxy\n        proxyenabled = True\n    if args.target and args.dorks and args.pages:\n        print(\"target:\" + args.target + \"dorks:\" + str(args.dorks) + \"pages:\" + str(args.pages))\n        s = scanner()\n        s.scan(target=args.target, dorks=args.dorks, pages=args.pages)\n    m = menus()\n    running = True\n    main()\n","repo_name":"The-da-vinci/Venom2","sub_path":"venom2.py","file_name":"venom2.py","file_ext":"py","file_size_in_byte":17921,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"221953147","text":"import streamlit as st\nimport pandas as pd\n\nst.title(\"Excel App\")\n\ndf = pd.read_csv(\"data.csv\")\nst.header(\"Existing file\")\nst.write(df)\n# st.table(df)\n\nst.sidebar.header(\"Options\")\noptions_form = st.sidebar.form(\"Options_form\")\nuser_name = options_form.text_input(\"Name\")\nuser_age = options_form.number_input(\"Age\")\nadd_data = options_form.form_submit_button()\nif add_data:\n    new_data = {\"name\": user_name, \"age\": int(user_age)}\n    df = df.append(new_data, ignore_index=True)\n    df.to_csv(\"data.csv\", index=False)","repo_name":"ikrambashir/web_app","sub_path":"excel_app.py","file_name":"excel_app.py","file_ext":"py","file_size_in_byte":517,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"22556315672","text":"import numpy as np\nimport csv\nfrom max_stat_tool import *\nfrom interp import *\nimport matplotlib.pyplot as plt\n\nn=5\nxaxis=0\nyaxis=2\nbin_size=20\n\ninput0=range(n)\nreader=range(n)\nz=range(n)\nRIP=range(n)\ngamma=range(n)\n\ninput0[0]=\"RIP_gauss_n0_19.csv\"\ninput0[1]=\"RIP_gauss_n0_20.csv\"\ninput0[2]=\"RIP_gauss_n0_21.csv\"\ninput0[3]=\"RIP_gauss_n0_22.csv\"\ninput0[4]=\"RIP_gauss_n0_23.csv\"\n\ngamma[0]=0.038647633414777421\ngamma[1]=0.023891619743183427\ngamma[2]=0.012999999999999999\ngamma[3]=0.036999999999999998\ngamma[4]=0.0167634273889\n\nRIP_sum = 0\nRIP_sum_gamma_weight = 0\n\n\nfor i in range(n):\n    \n    reader = csv.reader(open(input0[i], \"rb\"), delimiter=\",\")\n    x = list(reader)\n    #print len(x)\n    x=x[1:len(x)]\n    print((len(x)))\n    result = np.array(x).astype(\"float\")\n    (nx,ny)=np.shape(result)\n    print(nx,ny)\n    RIP[i]=result[:,yaxis]\n    z[i]=result[:,xaxis]\n    rhot0=z[0]\n    uni_rhot = np.linspace(min(rhot0),max(rhot0),len(rhot0)*10.)\n    RIP[i] = interp(z[i],RIP[i],uni_rhot)\n\n    RIP_sum = RIP_sum+RIP[i]\n    RIP_sum_gamma_weight = RIP_sum_gamma_weight+RIP[i]*gamma[i]\n\nRIP_sum = RIP_sum/float(n)\nRIP_sum_gamma_weight = RIP_sum_gamma_weight/float(n)\n\nplt.clf()\nplt.title('fluctuation Ratio Based On RIP and BES(sum)')\nplt.xlabel(r'$\\frac{\\int n_e \\delta B_r dR}{B_0\\int n_e dR} \\frac{n_e}{\\delta n_e}$',fontsize=10)\nplt.ylabel('Height (m)',fontsize=13)\nplt.plot(smooth(RIP_sum,bin_size)[0], smooth(uni_rhot,bin_size)[0])\n#plt.savefig('RIP_gauss_r_smooth.png')\nplt.show()\n\nplt.clf()\nplt.title('fluctuation Ratio Based On RIP and BES(gamma weighted)')\nplt.xlabel(r'$\\frac{\\int n_e \\delta B_r dR}{B_0\\int n_e dR} \\frac{n_e}{\\delta n_e}$',fontsize=10)\nplt.ylabel('Height (m)',fontsize=13)\nplt.plot(smooth(RIP_sum_gamma_weight,bin_size)[0], smooth(uni_rhot,bin_size)[0])\n#plt.savefig('RIP_gauss_r_smooth.png')\nplt.show()\n\n\n","repo_name":"drdrhatch/IFS_scripts","sub_path":"RIP_summary.py","file_name":"RIP_summary.py","file_ext":"py","file_size_in_byte":1830,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"48"}
+{"seq_id":"9396483140","text":"import sys\r\n\r\n# 강사 코드 보고 개선이 필요함.\r\n\r\nanswer_list = []\r\nanswer_set = set()\r\ndef sol1(my_list) :\r\n    global n\r\n    if len(my_list) == n :\r\n        answer_list.append([k for k in my_list])\r\n        return\r\n    for i in dice :\r\n        my_list.append(i)\r\n        sol1(my_list)\r\n        my_list.pop()\r\n\r\n\r\ndef sol2(my_list) :\r\n    global n\r\n    if len(my_list) == n :\r\n        temp_list = [i for i in my_list]\r\n        temp_list.sort()\r\n        answer_set.add(''.join(map(str, temp_list)))\r\n        return\r\n    for i in dice :\r\n        my_list.append(i)\r\n        sol2(my_list)\r\n        my_list.pop()\r\n\r\ndef sol3(my_list, V) :\r\n    global n\r\n    if len(my_list) == n :\r\n        answer_list.append([k for k in my_list])\r\n        return\r\n    for i in dice :\r\n        if V[i] == 0 :\r\n            V[i] = 1\r\n            my_list.append(i)\r\n            sol3(my_list,V)\r\n            my_list.pop()\r\n            V[i] = 0\r\n\r\n\r\n# sys.stdin = open(\"input.txt\")\r\ninput = sys.stdin.readline\r\ndice = [1,2,3,4,5,6]\r\nn,m = map(int,input().split())\r\n\r\n\r\nif m == 1 :\r\n    sol1([])\r\nelif m == 2 :\r\n    sol2([])\r\n    answer_list = [i for i in answer_set]\r\nelif m == 3 :\r\n    sol3([],[0 for _ in range(7)])\r\n\r\n\r\nanswer_list.sort()\r\nfor k in answer_list:\r\n    print(*k)\r\n","repo_name":"chickenchickenlove/BOJ-Algorithm","sub_path":"Certi/python22.py","file_name":"python22.py","file_ext":"py","file_size_in_byte":1266,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"43658722615","text":"import faiss\r\nimport time\r\nimport numpy as np\r\n\r\ndef train_kmeans(x, k, max_point_per_centroid=100000, min_points_per_centroid=20,\r\n                    verbose=True, niter=20, ngpu=1, seed=10):\r\n        '''\r\n        Runs kmeans on one or several GPUs, \r\n        x: features, [data_num, dim]\r\n        k: cluster number\r\n        niter: kmeans iteration number\r\n        max_point_per_centroid: max number of points in per cluster\r\n        min_points_per_centroid: min number of points in per cluster\r\n        return centroid, pseudo lablels\r\n        '''\r\n        d = x.shape[1]\r\n        clus = faiss.Clustering(d, k)\r\n        clus.verbose=verbose\r\n        clus.seed=seed\r\n        clus.niter=niter\r\n\r\n        #  otherwise the kmeans implementation sub-samples the training set\r\n        clus.max_points_per_centroid=max_point_per_centroid\r\n        clus.min_points_per_centroid=min_points_per_centroid\r\n\r\n        res=[faiss.StandardGpuResources() for i in range(ngpu)]\r\n\r\n        flat_config = []\r\n        for i in range(ngpu):\r\n            cfg=faiss.GpuIndexFlatConfig()\r\n            cfg.useFloat16 = False\r\n            cfg.device = i\r\n            flat_config.append(cfg)\r\n\r\n        if ngpu == 1:\r\n            index = faiss.GpuIndexFlatL2(res[0], d, flat_config[0])\r\n        else:\r\n            indexes = [faiss.GpuIndexFlatL2(res[i], d, flat_config[i]) for i in range(ngpu)]\r\n            index = faiss.IndexReplicas()\r\n            for sub_index in indexes:\r\n                index.addIndex(sub_index)\r\n\r\n        #  perform the training\r\n        clus.train(x, index)\r\n        centroids = faiss.vector_float_to_array(clus.centroids)\r\n\r\n        D, I = index.search(x, 1) #  for each sample, find cluster distance and assignments\r\n        im2cluster = [int(n[0]) for n in I] #  label assignments\r\n\r\n        return centroids.reshape(k, d), im2cluster\r\n\r\nif __name__=='__main__':\r\n\r\n    start = time.time()\r\n    cf = np.random.rand(1000, 2048)\r\n    train_kmeans(cf, k=50)\r\n    print('using {:.2f}s'.format(time.time() - start))","repo_name":"JeyesHan/P2LR","sub_path":"P2LR/utils/k_means.py","file_name":"k_means.py","file_ext":"py","file_size_in_byte":2100,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"48"}
+{"seq_id":"29573621691","text":"# _*_ coding: utf-8 _*_\n__author__ = 'Treehole'\n__date__ = '2019/5/20 22:01'\n\nimport functools\n\ndef cmpf(a, b):\n    if a[3] != b[3]:\n        return -1 if b[3]>a[3] else 1\n    elif a[1] != b[1]:\n        return -1 if b[1] > a[1] else 1\n    else:\n        return -1 if int(a[0]) > int(b[0]) else 1\n\nn, l, h = input().split()\nl = int(l)\nh = int(h)\nfirst_class = []\nsecond_class = []\nthird_class = []\nforth_class = []\nfor i in range(int(n)):\n    examination_number, de_score, cai_score = input().split()\n    de_score = int(de_score)\n    cai_score = int(cai_score)\n    sum_score = de_score + cai_score\n    if de_score < l or cai_score <l:\n        continue\n    elif de_score >= h and cai_score >= h:\n        first_class.append([examination_number, de_score, cai_score, sum_score])\n    elif de_score >=h and cai_score >= l:\n        second_class.append([examination_number, de_score, cai_score, sum_score])\n    elif de_score >=l and cai_score >=l and de_score>=cai_score:\n        third_class.append([examination_number, de_score, cai_score, sum_score])\n    elif de_score >=l and cai_score >=l:\n        forth_class.append([examination_number, de_score, cai_score, sum_score])\ndecai_counts = len(first_class) + len(second_class) + len(third_class) +len(forth_class)\nprint(decai_counts)\nfor i in [first_class, second_class, third_class, forth_class]:\n    i = sorted(i, key=functools.cmp_to_key(cmpf), reverse=True)\n    for j in i:\n        print('{0} {1} {2}'.format(j[0], j[1], j[2]))","repo_name":"Treehole/PAT","sub_path":"1015（超时）.py","file_name":"1015（超时）.py","file_ext":"py","file_size_in_byte":1471,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"23209907580","text":"import numpy as np\nimport scipy.stats as stats\n\ndef reshape_to_1D(array):\n    \"\"\"\n    Reshapes an array to 1D\n\n    Parameters\n    ----------\n    array: numpy.ndarray\n\n    Returns\n    -------\n    1D array:\n\n    \"\"\"\n\n    shape = array.shape\n\n    total_elements = 1\n    for axis in shape:\n        total_elements = axis * total_elements\n\n    return np.reshape(array, total_elements)\n\n\ndef mixup(array):\n    \"\"\"\n    Completly randomises an input array.\n    \"\"\"\n    original_shape = array.shape\n    output = reshape_to_1D(array) \n    np.random.shuffle(output)\n    output = np.reshape(output, original_shape)\n    return output\n\n\n\n\n\ndef calc_bin_centre(bin_edges):\n    \"\"\"\n    Calculates the centre of a histogram bin from the bin edges.\n\n    \"\"\"\n    return bin_edges[:-1] + np.diff(bin_edges) / 2\n\nimport numpy as np\n\ndef rebin(array, bin_size):\n    \"\"\"\n    Rebins 1D data\n\n    Parameters\n    ----------\n    array: array-like\n\n    bin_size: int\n        The number of elements\n\n    \"\"\"\n    if not isinstance(bin_size, int):\n        msg = (\"bin_size must have type int\")\n        raise ValueError(msg)\n\n    if bin_size > len(array):\n        msg = (\"bin_size must be less than the length of array\")\n        raise ValueError(msg)\n\n    if bin_size < 1:\n        msg = (\"bin_size must have value greater or equal to 1\")\n        raise ValueError(msg)\n\n    # Calculate the length of the new array\n    # Note that if the array isn't divisible by bin_size\n    # It will leave off the remaining bins\n    new_size = int(array.size / bin_size)\n\n    # Generate a new empty array for the new bins\n    new_array = np.zeros(new_size)\n\n    # Loop over each of the new bins\n    for idx in range(new_size):\n\n        # Calculate the start and end points for each new bin\n        start_idx = idx * bin_size\n        end_idx = start_idx + bin_size\n\n        # Combine the bins between start_idx and end_idx\n        new_array[idx] = np.sum(array[start_idx:end_idx])\n\n    return new_array\n","repo_name":"abatten/pyx","sub_path":"pyx/math_tools/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":1953,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"71605392466","text":"import torch\nfrom torch import nn\nimport deepxde as dde\nfrom src.HC.hard_constraint import HardConstraintNeumann2D\nfrom src.HC.hard_constraint_collector import HardConstraintCollector\nfrom src.HC.l_functions import LFunctionAxisLine, LFunctionOpenRectangle, LFunctionPolygon\nfrom src.HC.normal_function import NormalFunctionPolygon\nfrom src.configs.case2.pinn import spatial_domain, rec, airfoil\nfrom src.configs.case2.params import *\nfrom src.utils.nn_wrapper import NNWrapper\n\n\ndef pde_hc(x, u):\n    u_vel, v_vel, p, _u_x, _u_y, _v_x, _v_y  = \\\n        u[:, 0:1], u[:, 1:2], u[:, 2:3], u[:, 3:4], u[:, 4:5], u[:, 5:6], u[:, 6:7]\n    u_vel_x = dde.grad.jacobian(u, x, i=0, j=0)\n    u_vel_y = dde.grad.jacobian(u, x, i=0, j=1)\n    v_vel_x = dde.grad.jacobian(u, x, i=1, j=0)\n    v_vel_y = dde.grad.jacobian(u, x, i=1, j=1)\n    p_x = dde.grad.jacobian(u, x, i=2, j=0)\n    p_y = dde.grad.jacobian(u, x, i=2, j=1)\n\n    u_vel_xx = dde.grad.jacobian(u, x, i=3, j=0)\n    u_vel_yy = dde.grad.jacobian(u, x, i=4, j=1)\n    v_vel_xx = dde.grad.jacobian(u, x, i=5, j=0)\n    v_vel_yy = dde.grad.jacobian(u, x, i=6, j=1)\n\n    res_u_x = (_u_x - u_vel_x)\n    res_u_y = (_u_y - u_vel_y)\n    res_v_x = (_v_x - v_vel_x)\n    res_v_y = (_v_y - v_vel_y)\n    momentum_x = (\n        u_vel * _u_x + v_vel * _u_y + p_x - nu * (u_vel_xx + u_vel_yy)\n    )\n    momentum_y = (\n        u_vel * _v_x + v_vel * _v_y + p_y - nu * (v_vel_xx + v_vel_yy)\n    )\n    continuity = _u_x + _v_y\n\n    return [res_u_x, res_u_y, res_v_x, res_v_y, momentum_x, momentum_y, continuity]\n\n# Helper functions (input: x, a B x d tensor)\nX_rec_bc = rec.random_boundary_points(512)\nX_airfoil_bc = airfoil.random_boundary_points(512)\n# M function\nbeta = 4.0\nM = lambda x: torch.logsumexp(-beta * x, dim=1, keepdim=True) / (-beta)\n# Lambda functions\nl_gamma_openrec = LFunctionOpenRectangle(X_airfoil_bc, rec, m_function=M)\nl_gamma_right = LFunctionAxisLine(X_airfoil_bc, rec, xmax[0], 0, is_left=False)\nl_gamma_airfoil = LFunctionPolygon(X_rec_bc, airfoil, spatial_domain)\n# Normal functions\nn_gamma_airfoil = NormalFunctionPolygon(airfoil)\n\n# hard constraints\nhc_gamma_airfoil = HardConstraintNeumann2D(\n    n_gamma_airfoil, \n    NNWrapper(dde.nn.FNN([2] + 4 * [40] + [1], \"tanh\", \"Glorot normal\")),\n    lambda _: 0.\n)\n\n# model\nclass HCNN(nn.Module):\n    \"\"\"\n    Hard constraint model.\n    \"\"\"\n    def __init__(self, path_prefix=\"\") -> None:\n        super(HCNN, self).__init__()\n        # NNs\n        self.hc_gamma_airfoil = hc_gamma_airfoil\n        self.N_main = NNWrapper(dde.nn.FNN([2] + 6 * [50] + [7], \"tanh\", \"Glorot normal\"))\n        if path_prefix != \"\":\n            self.load(path_prefix)\n        # hard constraint for each components\n        self.HCC_u = HardConstraintCollector(\n            0, M, [l_gamma_openrec, l_gamma_airfoil],\n            [lambda _: 1., hc_gamma_airfoil.get_p_1], self.N_main\n        )\n        self.HCC_v = HardConstraintCollector(\n            1, M, [l_gamma_openrec, l_gamma_airfoil],\n            [lambda _: 0., hc_gamma_airfoil.get_p_2], self.N_main\n        )\n        self.HCC_p = HardConstraintCollector(\n            2, M, [l_gamma_right],\n            [lambda _: 1.], self.N_main\n        )\n    \n    def save(self, path_prefix: str):\n        torch.save(self.N_main, path_prefix + \"hc_main.pth\")\n        self.hc_gamma_airfoil.save(path_prefix, \"hc_g_a.pth\")\n\n    def load(self, path_prefix: str):\n        self.N_main = torch.load(path_prefix + \"hc_main.pth\")\n        self.hc_gamma_airfoil.load(path_prefix, \"hc_g_a.pth\")\n\n    def forward(self, x) -> torch.Tensor:\n        self.N_main.clear_res()\n        self.hc_gamma_airfoil.clear_res()\n        return torch.cat(\n            [\n            self.HCC_u(x),\n            self.HCC_v(x),\n            self.HCC_p(x)\n            ] + [self.N_main(x, i) for i in range(3, 7)]\n            , dim=1)\n","repo_name":"csuastt/HardConstraint","sub_path":"src/configs/case2/hc.py","file_name":"hc.py","file_ext":"py","file_size_in_byte":3818,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"48"}
+{"seq_id":"73934085584","text":"import struct\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt \nfrom random import sample \nfrom sklearn.utils import shuffle\nfrom sklearn import manifold, datasets\nfrom sklearn.datasets import load_digits\nimport umap\nfrom time import time \nfrom copy import deepcopy\nimport random\nimport pickle\n\npd.set_option('display.max_rows', 500)\nnp.random.seed(5)\n\ndim = 50\n\nwith open('../data/citation/citation/CitationDataset_Train.pkl', 'rb') as f:\n    train_data = pickle.load(f)\nwith open('../data/citation/citation/CitationDataset_Val.pkl', 'rb') as f:\n    val_data = pickle.load(f)\nwith open('../data/citation/citation/CitationDataset_Test.pkl', 'rb') as f:\n    test_data = pickle.load(f)\n\ndim = 50\n\nnew_name = {}\n\nnew_name['CoAuthorCount'] = 'f0'\nnew_name['UserAuthorCitationCount'] = 'f1'\nnew_name['AuthorUserCitationCount'] = 'f2'\nnew_name['UserCitedConference'] = 'f3'\nnew_name['UserPublishedInConferenceCount'] = 'f4'\n\n\nfor i in range(5, 2*dim+5):\n    new_name[str(i-5)] = 'f' + str(i)\n\ntrain_data['u_id'] = train_data['u_id'].astype(str) + '_' + train_data['paper_id_written'].astype(str)\ntrain_data.drop(['paper_id_written', 'a_id', 'paper_id_referred', 'c_id'], axis = 1, inplace=True)\ntrain_data = train_data.rename(columns={\"u_id\": \"user\"})\ntrain_data = train_data.rename(columns=new_name)\n\nval_data['u_id'] = val_data['u_id'].astype(str) + '_' + val_data['paper_id_written'].astype(str)\nval_data.drop(['paper_id_written', 'a_id', 'paper_id_referred', 'c_id'], axis = 1, inplace=True)\nval_data = val_data.rename(columns={\"u_id\": \"user\"})\nval_data = val_data.rename(columns=new_name)\n\ntest_data['u_id'] = test_data['u_id'].astype(str) + '_' + test_data['paper_id_written'].astype(str)\ntest_data.drop(['paper_id_written', 'a_id', 'paper_id_referred', 'c_id'], axis = 1, inplace=True)\ntest_data = test_data.rename(columns={\"u_id\": \"user\"})\ntest_data = test_data.rename(columns=new_name)\n\ntrain_users = train_data.user.tolist()\nval_users = val_data.user.tolist()\ntest_users = test_data.user.tolist()\n\nprint(\"Number of users in train data are: \", len(train_data.user.unique()))\nprint(\"Number of users in val data are: \", len(val_data.user.unique()))\nprint(\"Number of users in test data are: \", len(test_data.user.unique()))\nprint(\"\\n\")\n\nprint(\"Number of datapoints in train data are: \", len(train_data))\nprint(\"Number of datapoints in val data are: \", len(val_data))\nprint(\"Number of datapoints in test data are: \", len(test_data))\nprint(\"\\n\")\n\nprint(\"Number of positive datapoints in train data are: \", sum(train_data.label))\nprint(\"Number of positive datapoints in val data are: \", sum(val_data.label))\nprint(\"Number of positive datapoints in test data are: \", sum(test_data.label))\n\ndef filterdata(data, pos_to_all_ratio_thresh = 0.1, num_pos_thresh = 3):\n    \n    dat = deepcopy(data)\n    \n    user_list = dat.user.unique().tolist()\n\n    user_list_sel = []\n    \n    # The following is to remove users who have extraordinarily large number of negatives, yet it maintains\n    # significant data imbalance.\n    # Also, users are required to have at least three positives.\n    \n    for user in user_list:\n\n        dat_user = dat[dat['user'] == user]\n        if((sum(dat_user.label)/len(dat_user) >= pos_to_all_ratio_thresh) and sum(dat_user.label) >= num_pos_thresh):\n            user_list_sel.append(user)\n\n    print(\"Number of selected users are \", len(user_list_sel))\n\n    dat = dat.loc[dat['user'].isin(user_list_sel)]\n    print(\"Shape of filtered data is \", dat.shape)\n\n    dat = dat.rename(columns={\"user\": \"user_id\"})\n\n    new_feat = dat.iloc[:, 6:(dim+6)].values*dat.iloc[:, (dim+6):(2*dim+6)].values\n\n    for i in range(dim):\n        dat['f'+str(2*dim+5+i)] = new_feat[:, i]\n\n    new_colnames = ['user_id', 'f0', 'f1', 'f2', 'f3', 'f4']\n\n    for i in range(5, 3*dim + 5):\n        new_colnames.append('f'+str(i))\n\n    new_colnames.append('label')\n\n    dat = dat[new_colnames]\n\n    return dat\n\ntrain_data_final = filterdata(train_data)\nval_data_final = filterdata(val_data)\ntest_data_final = filterdata(test_data)\n\nprint(\"Number of users in train data are: \", len(train_data_final.user_id.unique()))\nprint(\"Number of users in val data are: \", len(val_data_final.user_id.unique()))\nprint(\"Number of users in test data are: \", len(test_data_final.user_id.unique()))\nprint(\"\\n\")\n\nprint(\"Number of datapoints in train data are: \", len(train_data_final))\nprint(\"Number of datapoints in val data are: \", len(val_data_final))\nprint(\"Number of datapoints in test data are: \", len(test_data_final))\nprint(\"\\n\")\n\nprint(\"Number of positive datapoints in train data are: \", sum(train_data_final.label))\nprint(\"Number of positive datapoints in val data are: \", sum(val_data_final.label))\nprint(\"Number of positive datapoints in test data are: \", sum(test_data_final.label))\n\ntrain_data = train_data_final\nval_data = val_data_final\ntest_data = test_data_final\n\nfor i in range(3*dim+5):\n    f = 'f{}'.format(i)\n    feature_range = train_data[f].max() - train_data[f].min()\n    feature_mean = (train_data[f].max() + train_data[f].min()) / 2\n    train_data[f] = (train_data[f] - feature_mean) / feature_range\n    val_data[f] = (val_data[f] - feature_mean) / feature_range\n    test_data[f] = (test_data[f] - feature_mean) / feature_range\n    \ntrain_data = shuffle(train_data)\ntrain_data = train_data.reset_index(drop=True)\nval_data = shuffle(val_data)\nval_data = val_data.reset_index(drop=True)\ntest_data = shuffle(test_data)\ntest_data = test_data.reset_index(drop=True)\n\ntrain_data.to_csv('../data/citation/train_data_d' + str(3*dim+5) + '.tsv', sep='\\t', index=False)\nval_data.to_csv('../data/citation/val_data_d' + str(3*dim+5) + '.tsv', sep='\\t', index=False)\ntest_data.to_csv('../data/citation/test_data_d' + str(3*dim+5) + '.tsv', sep='\\t', index=False)","repo_name":"gaurush-hiranandani/pap-k","sub_path":"code/data_processing_for_citation.py","file_name":"data_processing_for_citation.py","file_ext":"py","file_size_in_byte":5770,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"4913759605","text":"import re\nmoney=u\"\\u20B9\"\ndef Find(string): \n    # findall() has been used  \n    # with valid conditions for urls in string \n    url = re.match('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+] |[!*\\(\\), ]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', string) \n    return url\nproduct=dict()\nf = open(\"shoes.txt\",\"r\")\nproduct['name']=f.readline()\nproduct['desc']=\"\"\ni=0\nfor line in f:\n    i=i+1\n    # line.rstrip()\n    # if(re.search(\"====\",line)):\n    #     mycon=mysql.connector.connect(host=\"localhost\",user=\"root\",passwd=\"Ki008res*\",database=\"MyList\")\n    #     # cur=mycon.cursor()\n        # sql=\"insert into products(pid,name,description,source,image) values (%s,%s,%s,%s,%s)\"\n        # val=(str(i),\"spark\",product['desc'],product['source'],product['image'])\n        # try:\n        #     cur.execute(sql,val)\n        #     mycon.commit()\n        # except:\n        #     mycon.rollback()\n        #     print(error)\n    #     product.clear()\n    # if(Find(line) and re.search(\"image\",line)):\n    #     print(line)\n    #     product['image']=line+\" \"\n    # elif(re.match(money,line) and not re.search(\"\\%\",line)):\n    #     print(line)\n    #     product['source']=line+\" \"\n    #     pass\n    # elif(re.match(money,line) and re.search(\"\\%\",line)):\n    #     continue\n    # else:\n    #     product['desc']=line\n    #     print(product['desc'])\nprint(i)\n","repo_name":"kishoreis/Mylist2.0","sub_path":"projects/scripts/xyz.py","file_name":"xyz.py","file_ext":"py","file_size_in_byte":1329,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"31763276450","text":"# 문서검색\ndocument = input()\nword = input()\ncnt = 0\nwhile True:\n    # 찾으려는 단어가 존재한다면\n    try:\n        # 단어 찾기\n        document = document[document.index(word) + len(word):]\n        cnt += 1\n        # 현재 문서의 길이가 단어의 길이보다 짧다면 종료\n        if len(document) < len(word):\n            break\n    except ValueError:\n        break\nprint(cnt)","repo_name":"yerimstar/BOJ","sub_path":"Python/Greedy/1543.py","file_name":"1543.py","file_ext":"py","file_size_in_byte":407,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"14540543304","text":"#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\n\nfrom bs4 import BeautifulSoup, Tag\nfrom urllib.request import urlopen\nfrom datetime import date\nimport re\n\nURL = \"https://www.max.se/sv/Maten/Meny/Maltider/Dagens-Lunch/\"\n\ndef get_daily_specials(day = None):\n\tpage = urlopen(URL)\n\tsoup = BeautifulSoup(page)\n\tpage.close()\n\n\tdaily_specials = {\n\t\t\"name\": \"mAx\",\n\t\t\"specials\": [],\n\t\t\"streetaddress\": \"Landsvägsallén 5, Sundsvall\",\n\t\t\"dataurl\": URL,\n\t\t\"mapurl\": \"http://www.hitta.se/ViewDetailsPink.aspx?Vkiid=mFQRqbiq0V2C0M4wvtGY0w%253d%253d\"\n\t}\n\n\tday = date.today().weekday() if day is None else day\n\n\t# Only Monday - Friday\n\tif day > 4:\n\t\treturn daily_specials\n\n\tspan = filter(lambda t: type(t) is Tag, soup.find(\"ul\", {\"class\": \"price\"}).contents)[day].span.text\n\tsplit = span.find(\" - \")\n\tif split == -1:\n\t\tsplit = span.find(\"&nbsp;\")\n\t\tsplit += 6 if split > 0 else 0\n\telse:\n\t\tsplit += 3\n\tsplit = 0 if split is -1 else split\n\tdaily_specials[\"specials\"].append(span[split:].strip())\n\n\treturn daily_specials\n\ndef main():\n\tfor d in range(5):\n\t\tdata = get_daily_specials(d)\n\t\tprint(\"Day {d}\".format(d=d))\n\t\tfor c in data[\"specials\"]:\n\t\t\tprint(\"  \", c)\n\nif __name__ == \"__main__\":\n\tmain()\n\n","repo_name":"sa3mlk/lunchsvall","sub_path":"scrapers/max.py","file_name":"max.py","file_ext":"py","file_size_in_byte":1186,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"48"}
+{"seq_id":"39069467016","text":"import os\n\nimport numpy as np\nimport pandas as pd\n# Need to keep scienceplots imported for matplotlib styling even though the import is never used directly\n# noinspection PyUnresolvedReferences\nimport scienceplots\nfrom PIL import Image\nfrom matplotlib import pyplot as plt\nfrom tqdm import tqdm\n\nfrom deepglobe.dgr_luc_dataset import _load_metadata_df as load_deepglobe_metadata, DgrLucDataset, make_binary_mask, \\\n    _make_single_target_mask\nfrom floodnet.floodnet_dataset import _load_metadata_df as load_floodnet_metadata, FloodNetDataset\nimport matplotlib as mpl\nplt.style.use(['science', 'bright'])\n\n\ndef run():\n    print('Running for DeepGlobe...')\n    deepglobe_cover_df = get_deepglobe_distribution()\n    deepglobe_nice_names = ['Urban', 'Agricultural', 'Rangeland', 'Forest', 'Water', 'Barren', 'Unknown']\n    print('Plotting...')\n    fig, axes = plt.subplots(nrows=1, ncols=7, figsize=(14, 2))\n    for target in range(7):\n        _plot_per_class_coverage(deepglobe_cover_df[DgrLucDataset.target_to_name(target)], axes[target],\n                                 deepglobe_nice_names[target], 1000)\n    axes[3].set_xlabel('Coverage', size=14)\n    axes[0].set_ylabel('Density', size=15)\n    axes[0].set_yticklabels([\"0\", \"$10^0$\", \"$10^1$\", \"$10^2$\", \"$10^3$\"], size=12)\n    plt.tight_layout()\n    fig.savefig(\"out/fig/class_dist_DeepGlobe.png\", format='png', dpi=300)\n    plt.show()\n\n    print('Running for FloodNet...')\n    floodnet_cover_df = get_floodnet_distribution()\n    floodnet_nice_names = ['Background', 'Building Flooded', 'Building Non-flooded', 'Road Flooded', 'Road Non-Flooded',\n                           'Water', 'Tree', 'Vehicle', 'Pool', 'Grass']\n    print('Plotting...')\n    fig, axes = plt.subplots(nrows=2, ncols=5, figsize=(14, 4))\n    for target in range(10):\n        _plot_per_class_coverage(floodnet_cover_df[FloodNetDataset.label_to_name(target)],\n                                 axes[target // 5][target % 5], floodnet_nice_names[target], 3500)\n    axes[1][2].set_xlabel('Coverage', size=14)\n    axes[0][0].set_ylabel('Density', size=15)\n    axes[1][0].set_ylabel('Density', size=15)\n    axes[0][0].set_yticklabels([\"0\", \"$10^0$\", \"$10^1$\", \"$10^2$\", \"$10^3$\"], size=12)\n    axes[1][0].set_yticklabels([\"0\", \"$10^0$\", \"$10^1$\", \"$10^2$\", \"$10^3$\"], size=12)\n    plt.tight_layout()\n    fig.savefig(\"out/fig/class_dist_FloodNet.png\", format='png', dpi=300)\n    plt.show()\n\n\ndef get_deepglobe_distribution():\n    metadata_df = load_deepglobe_metadata()\n    class_dist_path = \"data/DeepGlobeLUC/class_distribution.csv\"\n\n    if os.path.exists(class_dist_path):\n        print('Loading class distribution')\n        cover_dist_df = pd.read_csv(class_dist_path)\n    else:\n        print('Generating class distribution')\n        cover_dist_df = metadata_df[['image_id']].copy()\n        for i in range(7):\n            cover_dist_df[DgrLucDataset.target_to_name(i)] = pd.Series(dtype=float)\n\n        for i in tqdm(range(len(metadata_df)), desc='Calculating class coverage for each image', leave=False):\n            mask_path = metadata_df['mask_path'][i]\n            mask_img = Image.open(mask_path)\n            mask_arr = np.array(mask_img)\n            mask_binary = make_binary_mask(mask_arr)\n            s = 0\n            for target in range(7):\n                single_mask = _make_single_target_mask(mask_binary, target)\n                name = DgrLucDataset.target_to_name(target)\n                percentage_cover = len(single_mask.nonzero()) / single_mask.numel()\n                cover_dist_df.loc[i, name] = percentage_cover\n                s += percentage_cover\n            assert abs(s - 1) < 1e-6\n        cover_dist_df.to_csv(class_dist_path, index=False)\n    return cover_dist_df\n\n\ndef get_floodnet_distribution():\n    metadata_df = load_floodnet_metadata()\n    class_dist_path = \"data/FloodNet/class_distribution.csv\"\n    if os.path.exists(class_dist_path):\n        print('Loading class distribution')\n        cover_dist_df = pd.read_csv(class_dist_path)\n    else:\n        print('Generating class distribution')\n        cover_dist_df = metadata_df[['image_id']].copy()\n        for i in range(10):\n            cover_dist_df[FloodNetDataset.label_to_name(i)] = pd.Series(dtype=float)\n        for i in tqdm(range(len(metadata_df)), desc='Calculating class coverage for each image', leave=False):\n            mask_path = metadata_df['mask_path'][i]\n            mask_img = Image.open(mask_path)\n            mask_arr = np.array(mask_img)\n            img_targets, img_counts = np.unique(mask_arr, return_counts=True)\n            s = 0\n            for target in range(10):\n                name = FloodNetDataset.label_to_name(target)\n                if target in img_targets:\n                    percentage_coverage = img_counts[img_targets.tolist().index(target)] / np.sum(img_counts)\n                else:\n                    percentage_coverage = 0\n                cover_dist_df.loc[i, name] = percentage_coverage\n                s += percentage_coverage\n            assert abs(s - 1) < 1e-6\n        cover_dist_df.to_csv(class_dist_path, index=False)\n    return cover_dist_df\n\n\ndef _plot_per_class_coverage(dist, axis, nice_clz_name, y_top):\n    axis.set_title(nice_clz_name, size=14)\n    axis.hist(dist, bins=25, range=(0, 1))\n\n    # General tick formatting\n    axis.tick_params(axis='x', which='both', bottom=True, top=False, labelbottom=True, labelsize=12)\n    axis.tick_params(axis='y', which='both', left=True, right=False, labelleft=True, labelsize=12)\n    axis.set_xlim(right=1)\n\n    # Configure log axis\n    axis.set_yscale('log')\n    axis.set_ylim(0.1, y_top)\n    axis.set_yticks([0.1, 1, 10, 100, 1000])\n    axis.set_yticklabels([])\n\n    # Add minor log ticks\n    minor_loc = mpl.ticker.LogLocator(base=10.0, subs=np.arange(2, 10) * .1, numticks=100)\n    axis.yaxis.set_minor_locator(minor_loc)\n    axis.yaxis.set_minor_formatter(mpl.ticker.NullFormatter())\n\n\nif __name__ == \"__main__\":\n    run()\n","repo_name":"JAEarly/MIL-Multires-EO","sub_path":"scripts/plot_class_distributions.py","file_name":"plot_class_distributions.py","file_ext":"py","file_size_in_byte":5937,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"25577521780","text":"# Script to sum some user supplied numbers at runtime\nimport numpy as np\nimport sys\n\n\nif len(sys.argv) > 1:\n  result = sys.argv[1:].sum()\n  print(result)\nelse:\n  print(\"Nothing to sum\")\n","repo_name":"edsml-sm1122/synNPiP","sub_path":"lectures/lecture03/files/manual_example.py","file_name":"manual_example.py","file_ext":"py","file_size_in_byte":186,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"39991507555","text":"import os\nimport sys\npath_script=os.path.dirname(os.path.realpath(__file__))\npath_to_DESops = path_script[0:path_script.find('tolerancetool/src')]+'tolerancetool/lib/'\n\nsys.path.insert(1, path_to_DESops)\n#\nimport DESops as d\nfrom itertools import product\n\n\n\n#Get the successors of a given successor list and event\ndef Post(out_list,event):\n\treturn [x[0] for x in out_list if x[1].label==event]\n\n\n#Computes the empty controller for LTS T\n#The empty controller selects empty tuples for each state in T\ndef Compute_empty_controller(T,Qinv):\n\tcontroller = dict()\n\tfor st in range(len(T.vs)):\n\t\tcontroller[T.vs[st]['name']] = tuple()\n\treturn controller\n\n# Compute the finv for LTS T and invariant set Qinv; finv(q) = Ainv(q)\n# It returns a controller as a dictionary where keys are state names and values are tuples with actions\n# controler[state_name] = tuple(actions)\ndef Compute_inv_controller(T,Qinv):\n\tQinv_id = {v.index for v in T.vs if v['name'] in Qinv}\n\tevents = T.events\n\tcontroller = dict()\n\tfor st in range(len(T.vs)):\n\t\tact = []\n\t\tif st in Qinv_id:\n\t\t\tfor ev in events:\n\t\t\t\t# print(T.vs[st]['out'])\n\t\t\t\tpost = Post(T.vs[st]['out'],ev.label)\n\t\t\t\t# print([T.vs[id]['name'] for id in post])\n\t\t\t\tif set(post).issubset(Qinv_id):\n\t\t\t\t\tact.append(ev.label)\n\t\t\tcontroller[T.vs[st]['name']] = tuple(act)\n\t\telse:\n\t\t\tcontroller[T.vs[st]['name']] = tuple()\n\treturn controller\n\n# Compute the least tolerant controller for LTS T, invariant set Qinv; finv(q) = Ainv(q), and minimum tolerance level d\n# d is a list with transitions that must be tolerated, e.g., d = [(state_1, action_name, state_2)] where state_1,state_2 should be valid \n# It returns a controller as a dictionary where keys are state names and values are tuples with actions\n# controler[state_name] = tuple(actions)\ndef Compute_tolerant_controller(T,Qinv,dist):\n\tTd = d.NFA(T)\n\tedges = []\n\tevents = []\n\tfor t in dist:\n\t\t# print(t[0],t[1],t[2])\n\t\tsrc = T.vs.select(name = t[0])\n\t\ttgt  = T.vs.select(name = t[2])\n\t\tif not src and not tgt:\n\t\t\t# print(\"Vertex name in \",t,\" disturbance list incorrect\")\n\t\t\treturn\n\t\t# print((src[0],tgt[0]))\n\t\tsrc = src[0].index\n\t\ttgt = tgt[0].index\n\t\t\n\t\tedges.append((src,tgt))\n\t\tevents.append(d.Event(t[1]))\n\tTd.add_edges(edges,events,fill_out=True)\t\n\t# Need to compute Td. Add transitions d to T\n\tcontroller = Compute_inv_controller(Td,Qinv)\n\treturn controller\n\n#It calculates the controlled behavior of Env under control of controller\n#It returns an NFA that represents the controlled system\ndef Control(Env, controller):\n\tC = d.automata.NFA()\n\t# print(names)\n\tedges = []\n\tfor st in range(len(Env.vs)):\n\t\tsrc_index = st\n\t\tsrc_name = Env.vs[st]['name']\n\t\tif src_name in controller.keys():\n\t\t\tevent = controller[src_name]\n\t\telse:\n\t\t\tcontinue\n\t\tfor ev in event:\n\t\t\tpost = Post(Env.vs[st]['out'],ev)\n\t\t\tfor target in post:\n\t\t\t\ttarget_name = Env.vs[target]\n\t\t\t\tedges.append({\"pair\": (src_index, target), \"label\": d.Event(ev)})\n\tC.add_vertices(\n            len(Env.vs),\n            Env.vs['name']\n        )\n\tC.add_edges(\n            [e[\"pair\"] for e in edges],\n            [e[\"label\"] for e in edges],\n            fill_out=True,\n        )\n\tC.events = Env.events\n\tC.vs['init'] = [True if x==0 else False for x in range(0,len(Env.vs))]\n\treturn C\n\n\n#Function to compute tolerance level of controller f, given LTS T and invariance set of states Qinv (string names of the states)\n# T is an LTS (DFA or NFA)\n# f is a dict defined as state: tuple of actions. For example f = {'1':(a,), '2': ('a','b')}\n# Qinv is a list with the state names of invariant states\ndef Compute_tolerance_level(T,f,Qinv_name):\n\tNot_Qinv = [v.index for v in T.vs if v['name'] not in Qinv_name]\n\tQinv = [v.index for v in T.vs if v['name'] in Qinv_name]\n\tTinv = Extend_Env_Qinv(T,Qinv) #Compute T_{Qinv x Act x Qinv}\n\tTinvf = Control(Tinv,f) #To identify the actions used by controller in each state\n\tinacc = d.basic_operations.unary.find_inacc(Tinvf) #Find the unreachable states\n\tAcc = [st.index for st in Tinvf.vs if st.index not in inacc] #Find the reachable states\n\tTdelta = Extend_Env_Total(Tinvf,Tinv,Not_Qinv,Acc)\n\t# Delta = [(Tdelta.vs[e.source]['name'],e['label'],Tdelta.vs[e.target]['name']) for e in Tdelta.es if not T.es.select(_source_eq =e.source,_target_eq=e.target,label_eq=e['label'])]\n\tDelta = [(Tdelta.vs[e.source]['name'],e['label'],Tdelta.vs[e.target]['name']) for e in Tdelta.es if (e.target,e['label']) not in T.vs[e.source]['out']]\n\treturn (Delta,Tdelta)\n\n\n# It receives an NFA and a list of transition contraints \n# It return an NFA where its transition relation is defined by the intersection of the transition relation of the given NFA and the list of transition constraints\ndef Transition_contraint(T,trans_contraint):\n\treturn\n\n#Function to extend the environment with transitions in Qinv x Act x Qinv\ndef Extend_Env_Qinv(Env,Qinv):\n\tExt = d.NFA(Env)\n\tevents = list(Env.events)\n\tedges = [{\"pair\": (src, tgt), \"label\": e} for src in Qinv for tgt in Qinv for e in events if (tgt,e) not in Env.vs[src]['out']]\n\tExt.add_edges(\n\t[e[\"pair\"] for e in edges],\n\t[e[\"label\"] for e in edges],\n\tfill_out=True,\n\t)\n\t# edge_pairs = list(product(Qinv,repeat=2))\n\t# edges = edge_pairs*len(events)\n\t# events = events*len(edge_pairs)\n\t# k = 5\n\t# for i in range(5):\n\t# \tfor j in range(5):\n\t# \t\tedges = [{\"pair\": (src, tgt), \"label\": e} for src in Qinv[i*int(len(Qinv)/5):(i+1)*int(len(Qinv)/5)] for tgt in Qinv[j*int(len(Qinv)/5):(j+1)*len(Qinv)] for e in events if (tgt,e) not in Env.vs[src]['out']]\n\t# \t\tExt.add_edges(\n    #         [e[\"pair\"] for e in edges],\n    #         [e[\"label\"] for e in edges],\n    #         fill_out=True,\n    #     \t)\n\t\n\t# edges = [{\"pair\": (src, tgt), \"label\": e} for src in Qinv for tgt in Qinv for e in events if not Env.es.select(_source_eq =src,_target_eq=tgt,label_eq=e)]\n\t\n\treturn Ext \n\n#Function to extend the environment with tolerable transitions in Q x Act x (Q-Qinv)\ndef Extend_Env_Total(Env,Env_ext,Unsafe,Acc):\n\tExt = d.NFA(Env_ext)\n\tedges = []\n\tevents = list(Ext.events)\n\t# Q = Unsafe+Acc\n\tQ = range(len(Env_ext.vs))\n\t# edge_pairs = list(product(Unsafe,Q))\n\t# edges_1 = edge_pairs*len(events)\n\t# events_1 = events*len(edge_pairs)\n\t# Ext.add_edges(\n    #         edges_1,\n    #         events_1,\n    #         fill_out=True,\n    #     )\n\tedges = [{\"pair\": (src, tgt), \"label\": e} for src in Unsafe for tgt in Q for e in events if (tgt,e) not in Env.vs[src]['out']]\n    # left_out = []\n\tfor id in Q:\n\t\tif id in Acc:\n\t\t\tcontrol_action = {out[1] for out in Env.vs[id]['out']}\n\t\t\tnot_used = Env.events - control_action\n\t\t\tedges.extend([{\"pair\": (id, uns), \"label\": e} for uns in Unsafe for e in not_used if (uns,e) not in Env.vs[id]['out']])\n\t\telif id not in Unsafe:\n\t\t\tedges.extend([{\"pair\": (id, uns), \"label\": e} for uns in Unsafe for e in events if (uns,e) not in Env.vs[id]['out']])\n\tExt.add_edges(\n            [e[\"pair\"] for e in edges],\n            [e[\"label\"] for e in edges],\n            fill_out=True,\n        )\n\treturn Ext","repo_name":"romulo-goes/tolerancetool","sub_path":"src/tol_inv_property.py","file_name":"tol_inv_property.py","file_ext":"py","file_size_in_byte":6935,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"21898793444","text":"\"\"\"\nThese are general-purpose Mixins -- for use with Namespaces -- that are\ngenerally useful for most simple projects, e.g. Rooms, Broadcast.\n\nYou'll likely want to create your own Mixins.\n\"\"\"\n\nimport six\n\n\nclass RoomsMixin(object):\n    def __init__(self, *args, **kwargs):\n        super(RoomsMixin, self).__init__(*args, **kwargs)\n        if 'rooms' not in self.session:\n            self.session['rooms'] = set()  # a set of simple strings\n\n    def join(self, room):\n        \"\"\"Lets a user join a room on a specific Namespace.\"\"\"\n        self.session['rooms'].add(self._get_room_name(room))\n\n    def leave(self, room):\n        \"\"\"Lets a user leave a room on a specific Namespace.\"\"\"\n        self.session['rooms'].remove(self._get_room_name(room))\n\n    def _get_room_name(self, room):\n        return self.ns_name + '_' + room\n\n    def emit_to_room(self, room, event, *args):\n        \"\"\"This is sent to all in the room (in this particular Namespace)\"\"\"\n        pkt = dict(type=\"event\",\n                   name=event,\n                   args=args,\n                   endpoint=self.ns_name)\n        room_name = self._get_room_name(room)\n        for sessid, socket in six.iteritems(self.socket.server.sockets):\n            if 'rooms' not in socket.session:\n                continue\n            if room_name in socket.session['rooms'] and self.socket != socket:\n                socket.send_packet(pkt)\n\n\nclass BroadcastMixin(object):\n    \"\"\"Mix in this class with your Namespace to have a broadcast event method.\n\n    Use it like this:\n    class MyNamespace(BaseNamespace, BroadcastMixin):\n        def on_chatmsg(self, event):\n            self.broadcast_event('chatmsg', event)\n    \"\"\"\n    def broadcast_event(self, event, *args):\n        \"\"\"\n        This is sent to all in the sockets in this particular Namespace,\n        including itself.\n        \"\"\"\n        pkt = dict(type=\"event\",\n                   name=event,\n                   args=args,\n                   endpoint=self.ns_name)\n\n        for sessid, socket in six.iteritems(self.socket.server.sockets):\n            socket.send_packet(pkt)\n\n    def broadcast_event_not_me(self, event, *args):\n        \"\"\"\n        This is sent to all in the sockets in this particular Namespace,\n        except itself.\n        \"\"\"\n        pkt = dict(type=\"event\",\n                   name=event,\n                   args=args,\n                   endpoint=self.ns_name)\n\n        for sessid, socket in six.iteritems(self.socket.server.sockets):\n            if socket is not self.socket:\n                socket.send_packet(pkt)\n","repo_name":"abourget/gevent-socketio","sub_path":"socketio/mixins.py","file_name":"mixins.py","file_ext":"py","file_size_in_byte":2560,"program_lang":"python","lang":"en","doc_type":"code","stars":1217,"dataset":"github-code","pt":"48"}
+{"seq_id":"8353042901","text":"from numpy import array, full, array_equal, diag\nfrom os import system\n\n\n\nboard = full((3, 3), \"_\")\n\n\n\ndef decodeInput(player):\n\n    inp = input(f\"Player {player}: input >> \")\n\n    if inp == \"q\": return -1\n\n    try:\n\n        inp = tuple(int(i)-1 for i in list(inp) if int(i)>0)\n        if len(inp) != 2: raise Exception()\n        if board[inp] != \"_\": raise Exception()\n\n    except:\n\n        print(\"retry inputting...\")\n        inp = decodeInput(player)\n\n    return inp\n\n\n\ndef printBoard():\n\n    system(\"clear\")\n\n    string = [\" \"+\" | \".join(i)+\" \" for i in board]\n    string = \"\\n---+---+---\\n\".join(string).replace(\"_\", \" \")\n\n    print(string)\n\n\n\ndef addTile(player, inp):\n\n    board[inp] = player\n\n\n\ndef checkStatus():\n\n    xs = array([\"x\", \"x\", \"x\"])\n    os = array([\"o\", \"o\", \"o\"])\n    \n    for i in range(3):\n\n        if array_equal(board[i, :], xs) or array_equal(board[:, i], xs):\n            printBoard()\n            print(\"Player x won\")\n            return \"break\"\n\n        elif array_equal(board[i, :], os) or array_equal(board[:, i], os):\n            printBoard()\n            print(\"Player o won\")\n            return \"break\"\n\n    if array_equal(diag(board), xs) or array_equal(diag(board[::-1]), xs):\n        printBoard()\n        print(\"Player x won\")\n        return \"break\"\n\n    elif array_equal(diag(board), os) or array_equal(diag(board[::-1]), os):\n        printBoard()\n        print(\"Player o won\")\n        return \"break\"\n\n    if \"_\" not in board.flatten():\n        printBoard()\n        print(\"Game Tied\")\n        return \"break\"\n\n    return \"continue\"\n\n\n\n\"\"\" Game Loop \"\"\"\n\nwhile True:\n\n    \"\"\" Player x turn \"\"\"\n\n    printBoard()\n\n    inp = decodeInput(\"x\")\n\n    if inp == -1:\n        print(\"Exit\")\n        break\n\n    addTile(\"x\", inp)\n\n    if checkStatus() == \"break\": break\n\n\n    \"\"\" Player o turn \"\"\"\n\n    printBoard()\n\n    inp = decodeInput(\"o\")\n\n    if inp == -1:\n        print(\"Exit\")\n        break\n\n    addTile(\"o\", inp)\n\n    status = checkStatus()\n\n    if checkStatus() == \"break\": break\n\n\n","repo_name":"jinayvora101/python-playground","sub_path":"cross-n-zero/cross-n-zero.py","file_name":"cross-n-zero.py","file_ext":"py","file_size_in_byte":2016,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"32640243207","text":"import boto3\nimport os\nimport logging\nimport hashlib\nimport json\nimport botocore\n\nlogger = logging.getLogger()\nlogger.setLevel(logging.INFO)\n\ndef handler(event, context):\n    s3 = boto3.resource('s3')\n\n    for record in event['Records']:\n        newKey = record['s3']['object']['key']\n        bucket = record['s3']['bucket']['name']\n        name = bucket.split(\"/\")[-1]\n        localfile = \"/tmp/{}\".format(name)\n\n        # download the file\n        newKeyObj = s3.Object(bucket, newKey)\n        newKeyObj.download_file(localfile)\n\n        # calc hash\n        imageSHA1 = getSha1(localfile)\n\n        # check if not exist\n        processedKey = \"processed/{}/{}\".format(imageSHA1[:2], imageSHA1)\n        keyIsProcessed = isS3ObjectExist(bucket, processedKey)\n        if keyIsProcessed: continue\n\n        # add to the queue\n        message = json.dumps({\n            \"image\": processedKey,\n            \"original_key\": newKey,\n            \"original_last_modified\": newKeyObj.last_modified,\n            \"etag\": newKeyObj.e_tag\n        }, default=str)\n\n        queueName = os.environ[\"VAQUITA_IMAGE_MASSAGE\"]\n        sqs = boto3.resource('sqs')\n        queue = sqs.get_queue_by_name(QueueName=queueName)\n        response = queue.send_message(MessageBody=message)\n        logger.info(\"Message {} has been sent.\".format(response.get('MessageId')))\n\n        #move the image\n        s3.Object(bucket, processedKey).copy_from(CopySource=\"{}/{}\".format(bucket,newKey))\n        newKeyObj.delete()\n\n        # delete local file\n        os.remove(localfile)\n\n    return True\n\ndef isS3ObjectExist(bucket, key):\n    s3 = boto3.resource('s3')\n\n    try:\n        s3.Object(bucket,key)\n        return False\n    except botocore.exceptions.ClientError as e:\n        if e.response['Error']['Code'] == \"404\":\n            return True\n        else:\n            raise e\n\ndef getSha1(filepath):\n    sha1 = hashlib.sha1()\n\n    with open(filepath, 'rb') as f:\n        while True:\n            data = f.read(65536) # read in 64kb chunks\n            if not data: break\n            sha1.update(data)\n\n    return sha1.hexdigest()","repo_name":"damshenas/Vaquita","sub_path":"src/imageMassage/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2093,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"71645201425","text":"import functools\nimport sys\nfrom . import log\nimport traceback\nimport threading\nfrom datetime import datetime, timedelta\nfrom objict import objict\n\n\nSEQUENCE = 0\nSTOP_ON_FAIL = True\nVERBOSE = True\n\nlogger = log.getLogger(filename=\"test.log\")\n\nTEST_RUN = objict(total=0, passed=0, failed=0, tests=objict())\n\n\nclass TestStopped(Exception):\n    pass\n\n\ndef getDateRange(days=14, when=None):\n    if when is None:\n        when = datetime.now()\n    start = when - timedelta(days=days)\n    return start.strftime(\"%m/%d/%Y\"), when.strftime(\"%m/%d/%Y\")\n\n\ndef toString(value):\n    if isinstance(value, bytes):\n        value = value.decode()\n    elif isinstance(value, bytearray):\n        value = value.decode(\"utf-8\")\n    elif isinstance(value, (int, float)):\n        value = str(value)\n    return value\n\n\ndef toBytes(value):\n    if isinstance(value, str):\n        value = value.encode(\"utf-8\")\n    elif isinstance(value, bytearray):\n        value = bytes(value)\n    return value\n\n\ndef getNextSeq():\n    global SEQUENCE\n    if SEQUENCE == 0:\n        from datetime import datetime\n        now = datetime.now()\n        SEQUENCE = int((now - now.replace(hour=0, minute=0,\n                                          second=0, microsecond=0)).total_seconds())\n    else:\n        SEQUENCE += 1\n    return SEQUENCE\n\n\ndef testValidDataResponse(resp):\n    assert resp and resp.status and resp.data, \"\\t\\t{red}assert failed:{off} {pink}'{err}{off}'\".format(\n        red=log.ConsoleColors.RED,\n        pink=log.ConsoleColors.PINK,\n        off=log.ConsoleColors.OFF,\n        err=resp)\n\n\ndef testValidResponse(resp):\n    if resp.status_code and resp.status_code != 200:\n        raise AssertionError(\"{} HTTP Error\".format(resp.status_code))\n    assert resp and resp.status, \"\\t\\t{red}assert failed:{off} {pink}'{err}{off}'\".format(\n        red=log.ConsoleColors.RED,\n        pink=log.ConsoleColors.PINK,\n        off=log.ConsoleColors.OFF,\n        err=resp)\n\n\ndef assertErrorCode(resp, error_code):\n    expectAssert(error_code, resp.error_code, \"error_code\")\n    \n\ndef testAssert(exp, fail_msg=None):\n    assert exp, \"\\t\\t{red}assert failed:{off} {pink}'{err}{off}'\".format(\n        red=log.ConsoleColors.RED,\n        pink=log.ConsoleColors.PINK,\n        off=log.ConsoleColors.OFF,\n        err=fail_msg)\n\n\ndef testCompareIn(val, val_list, name=None):\n    assert val in val_list, \"\\t\\t{red}assert failed:{off} {name} expected '{val_list}' but got '{val}'\".format(\n        red=log.ConsoleColors.RED,\n        off=log.ConsoleColors.OFF,\n        name=name, val_list=val_list, val=val)\n\n\ndef testCompare(str1, str2, fail_msg=None):\n    debug_out = fail_msg\n    if isinstance(str1, (str, bytes)):\n        str1 = toString(str1)\n        str2 = toString(str2)\n        if str1 != str2:\n            if not fail_msg:\n                fail_msg = \"string compare failed\"\n            is_on = False\n            d1 = [log.ConsoleColors.OFF]\n            d2 = [log.ConsoleColors.OFF]\n            for i in range(0, len(str1)-1):\n                out_range = i > (len(str1)-1) or i > (len(str2)-1)\n                if out_range:\n                    break\n                if not is_on and (out_range or str1[i] != str2[i]):\n                    is_on = True\n                    d1.append(log.ConsoleColors.YELLOW)\n                    d2.append(log.ConsoleColors.YELLOW)\n                elif is_on and str1[i] == str2[i]:\n                    is_on = False\n                    d1.append(log.ConsoleColors.OFF)\n                    d2.append(log.ConsoleColors.OFF)\n                d1.append(str1[i])\n                d2.append(str2[i])\n            debug_out = \"{}\\nstr1:\\n'{}'\\nstr2:\\n'{}'\".format(\n                fail_msg, \"\".join(d1), \"\".join(d2))\n        assert str1 == str2, \"\\t\\t{red}assert failed:{off}\\n{err}\".format(\n            red=log.ConsoleColors.RED,\n            off=log.ConsoleColors.OFF,\n            err=debug_out)\n    else:\n        fail_msg = \"{}: {} vs {} doesn't match\".format(fail_msg, str1, str2)\n        expectAssert(str1, str2, fail_msg)\n\n\ndef expectAssert(exp, got, name):\n    assert exp == got, \"\\t\\t{red}assert failed:{off} {name} expected '{exp}' but got '{got}'\".format(\n        red=log.ConsoleColors.RED,\n        off=log.ConsoleColors.OFF,\n        name=name, exp=exp, got=got)\n\n\ndef assertEqualToDict(exp, got, class_name):\n    \"\"\"\n    Will take all the items and the dic and compare them\n    to items in the obj or dict\n    \"\"\"\n    for key, value in list(exp.items()):\n        got_value = got.get(key, None)\n        if got_value != value:\n            raise AssertionError(\"{0}.{1} expected {2} == {3}\".format(\n                class_name, key, got_value, value))\n    return True\n\n\ndef assertNotHasKeys(obj, keys, not_null=False):\n    if type(keys) in [str, str]:\n        keys = [keys]\n    class_name = obj.__class__.__name__\n    for key in keys:\n        has_key = key in obj\n        assert not has_key, \"{0}.{1} found\".format(class_name, key)\n\n\ndef unit_test(name=None):\n    def actual_decorator(func):\n        @functools.wraps(func)\n        def wrapper(*args, **kwargs):\n            try:\n                TEST_RUN.total += 1\n                nm = func.__name__\n                if name:\n                    nm = name\n                elif \"test_name\" in kwargs:\n                    nm = kwargs.get(\"test_name\", nm)\n                elif \"test_fix\" in kwargs:\n                    nm = \"{}_{}\".format(nm, kwargs.get(\"test_fix\"))\n                sys.stdout.write(\"\\t{}{}{}\".format(\n                    log.ConsoleColors.YELLOW, nm.ljust(60, '.'), log.ConsoleColors.OFF))\n                sys.stdout.flush()\n                out = func(*args, **kwargs)\n                TEST_RUN.tests[\"{}:{}\".format(TEST_RUN.tests.active_test, nm)] = True\n                TEST_RUN.passed += 1\n                sys.stdout.write(log.ConsoleColors.GREEN)\n                sys.stdout.write(\"PASSED\\n\")\n                sys.stdout.write(log.ConsoleColors.OFF)\n                return out\n            except AssertionError as err:\n                TEST_RUN.failed += 1\n                TEST_RUN.tests[\"{}:{}\".format(TEST_RUN.tests.active_test, nm)] = False\n                sys.stdout.write(log.ConsoleColors.RED)\n                sys.stdout.write(\"FAILED\\n\")\n                sys.stdout.write(log.ConsoleColors.OFF)\n                if VERBOSE:\n                    print((str(err)))\n                if STOP_ON_FAIL:\n                    raise TestStopped()\n            except Exception:\n                TEST_RUN.failed += 1\n                TEST_RUN.tests[\"{}:{}\".format(TEST_RUN.tests.active_test, nm)] = False\n                sys.stdout.write(log.ConsoleColors.RED)\n                sys.stdout.write(\"FAILED\\n\\n\")\n                if VERBOSE:\n                    sys.stdout.write(traceback.format_exc())\n                sys.stdout.write(log.ConsoleColors.OFF)\n                if STOP_ON_FAIL:\n                    raise TestStopped()\n            return True\n        return wrapper\n    return actual_decorator\n\n\ndef asyncio():\n    def actual_decorator(func):\n        @functools.wraps(func)\n        def wrapper(*args, **kwargs):\n            t = threading.Thread(target=func, args=args, kwargs=kwargs)\n            t.daemon = True\n            t.start()\n            return t\n        return wrapper\n    return actual_decorator\n\n\ndef assertHasKeys(obj, keys, not_null=False):\n    if not hasattr(obj, \"has_key\"):\n        return self.assertHasAttrs(obj, keys, not_null)\n    if type(keys) in [str, str]:\n        keys = [keys]\n    class_name = obj.__class__.__name__\n    for key in keys:\n        has_key = key in obj\n        if has_key and not_null:\n            assert obj.get(key) != None, \"{0}.{1} is None\".format(\n                class_name, key)\n        assert has_key, \"{0}.{1} not found\".format(class_name, key)\n","repo_name":"istarnes/restit-skeleton","sub_path":"test/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":7732,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"26256569386","text":"# -*- coding: utf-8 -*-\n# vim:ts=4:sw=4:softtabstop=4:smarttab:expandtab\n\nfrom __future__ import unicode_literals, division, absolute_import, print_function\n\nimport sys\nimport os\nimport webbrowser\n\nfrom PyQt5.QtWidgets import (QApplication, QWidget, QFileDialog, QPushButton, QLabel, QCheckBox, QLineEdit, \n                            QGroupBox,QVBoxLayout, QGridLayout, QRadioButton, QSpacerItem, QSizePolicy, \n                            QDialogButtonBox, QButtonGroup, QStyleFactory)\nfrom PyQt5.QtGui import QColor, QPalette\nfrom PyQt5.QtCore import QCoreApplication, Qt, QByteArray, QTranslator, QLibraryInfo\n\nfrom updatecheck import UpdateChecker, DOWNLOAD_PAGE\n\n\n_DETAILS = {\n    'docx'   : None,\n    'smap'   : (False, None),\n    'css'    : (False, None),\n    'vers'   : '2.0',\n}\n\n\ndef dark_palette(sigil_colors):\n    p = QPalette()\n    dark_color = QColor(sigil_colors(\"Window\"))\n    disabled_color = QColor(127,127,127)\n    dark_link_color = QColor(108, 180, 238)\n    text_color = QColor(sigil_colors(\"Text\"))\n    p.setColor(p.Window, dark_color)\n    p.setColor(p.WindowText, text_color)\n    p.setColor(p.Base, QColor(sigil_colors(\"Base\")))\n    p.setColor(p.AlternateBase, dark_color)\n    p.setColor(p.ToolTipBase, dark_color)\n    p.setColor(p.ToolTipText, text_color)\n    p.setColor(p.Text, text_color)\n    p.setColor(p.Disabled, p.Text, disabled_color)\n    p.setColor(p.Button, dark_color)\n    p.setColor(p.ButtonText, text_color)\n    p.setColor(p.Disabled, p.ButtonText, disabled_color)\n    p.setColor(p.BrightText, Qt.red)\n    p.setColor(p.Link, dark_link_color)\n\n    p.setColor(p.Highlight, QColor(sigil_colors(\"Highlight\")))\n    p.setColor(p.HighlightedText, QColor(sigil_colors(\"HighlightedText\")))\n    p.setColor(p.Disabled, p.HighlightedText, disabled_color)\n\n    return p\n\n\ndef getQtTranslationsPath(sigil_path):\n    isBundled = 'sigil' in sys.prefix.lower()\n    print('Python is Bundled: {}'.format(isBundled))\n    if isBundled:\n        if sys.platform.lower().startswith('darwin'):\n            return os.path.normpath(sigil_path + '/../translations')\n        else:\n            return os.path.join(sigil_path, 'translations')\n    else:\n        return QLibraryInfo.location(QLibraryInfo.TranslationsPath)\n\n\ndef launch_qt_gui(bk, prefs):\n    supports_theming = (bk.launcher_version() >= 20200117)\n    app = QApplication(sys.argv)\n    # Make plugin match Sigil's light/dark theme\n    if supports_theming:\n        if bk.colorMode() == \"dark\":\n            app.setStyle(QStyleFactory.create(\"Fusion\"))\n            app.setPalette(dark_palette(bk.color))\n\n    print('Application dir: {}'.format(QCoreApplication.applicationDirPath()))\n    # Install qtbase translator for standard dialogs and such.\n    # Use the Sigil language setting unless manually overridden.\n    qt_translator = QTranslator()\n    if prefs['language_override'] is not None:\n        print('Plugin preferences language override in effect')\n        qmf = 'qtbase_{}'.format(prefs['language_override'])\n    else:\n        qmf = 'qtbase_{}'.format(bk.sigil_ui_lang)\n    # Get bundled or external translations directory\n    qt_trans_dir = getQtTranslationsPath(bk._w.appdir)\n    print('Qt translation dir: {}'.format(qt_trans_dir))\n    print('Looking for {} in {}'.format(qmf, qt_trans_dir))\n    qt_translator.load(qmf, qt_trans_dir)\n    print('Translator succesfully installed: {}'.format(app.installTranslator(qt_translator)))\n\n    ex = App(bk, prefs)\n    ex.show()\n    app.exec_()\n    return _DETAILS\n\n_translate = QCoreApplication.translate\n\nclass App(QWidget):\n    def __init__(self, bk, prefs):\n        super().__init__()\n\n        self.bk = bk\n        self.prefs = prefs\n        self.update = False\n\n        # Install translator for the DOCXImport plugin dialog.\n        # Use the Sigil language setting unless manually overridden.\n        plugin_translator = QTranslator()\n        if prefs['language_override'] is not None:\n            print('Plugin preferences language override in effect')\n            qmf = '{}_{}'.format(bk._w.plugin_name.lower(), prefs['language_override'])\n        else:\n            qmf = '{}_{}'.format(bk._w.plugin_name.lower(), bk.sigil_ui_lang)\n        print(qmf, os.path.join(bk._w.plugin_dir, bk._w.plugin_name, 'translations'))\n        plugin_translator.load(qmf, os.path.join(bk._w.plugin_dir, bk._w.plugin_name, 'translations'))\n        print(QCoreApplication.instance().installTranslator(plugin_translator))\n\n        self._ok_to_close = False\n\n        self.FTYPE_MAP = {\n            'smap': {\n                'title'            : _translate('App', 'Select custom style-map file'),\n                'defaultextension' : '.txt',\n                'filetypes'        : 'Text Files (*.txt);;All files (*.*)',\n                },\n            'css' : {\n                'title'            : _translate('App', 'Select custom CSS file'),\n                'defaultextension' : '.css',\n                'filetypes'        : 'CSS Files (*.css)',\n                },\n            'docx' : {\n                'title'            : _translate('App', 'Select DOCX file'),\n                'defaultextension' : '.docx',\n                'filetypes'        : 'DOCX Files (*.docx)',\n                },\n        }\n\n        # Check online github files for newer version\n        if self.prefs['check_for_updates']:\n            self.update, self.newversion = self.check_for_update()\n        self.initUI()\n\n    def initUI(self):\n        main_layout = QVBoxLayout(self)\n\n        self.setWindowTitle('DOCXImport')\n        self.upd_layout = QVBoxLayout()\n        self.update_label = QLabel()\n        self.update_label.setAlignment(Qt.AlignCenter)\n        self.upd_layout.addWidget(self.update_label)\n        self.get_update_button = QPushButton()\n        self.get_update_button.clicked.connect(self.get_update)\n        self.upd_layout.addWidget(self.get_update_button)\n        main_layout.addLayout(self.upd_layout)\n        if not self.update:\n            self.update_label.hide()\n            self.get_update_button.hide()\n\n        self.details_grid = QGridLayout()\n        self.epub2_select = QRadioButton()\n        self.epub2_select.setText('EPUB2')\n        self.epubType = QButtonGroup()\n        self.epubType.addButton(self.epub2_select)\n        self.details_grid.addWidget(self.epub2_select, 0, 0, 1, 1)\n        self.checkbox_get_updates = QCheckBox()\n        self.details_grid.addWidget(self.checkbox_get_updates, 0, 1, 1, 1)\n        self.epub3_select = QRadioButton()\n        self.epub3_select.setText('EPUB3')\n        self.epubType.addButton(self.epub3_select)\n        self.details_grid.addWidget(self.epub3_select, 1, 0, 1, 1)\n        main_layout.addLayout(self.details_grid)\n        self.checkbox_get_updates.setChecked(self.prefs['check_for_updates'])\n        if self.prefs['epub_version'] == '2.0':\n            self.epub2_select.setChecked(True)\n        elif self.prefs['epub_version'] == '3.0':\n            self.epub3_select.setChecked(True)\n        else:\n            self.epub2_select.setChecked(True)\n\n        self.groupBox = QGroupBox()\n        self.groupBox.setTitle('')\n        self.verticalLayout_2 = QVBoxLayout(self.groupBox)\n        self.docx_grid = QGridLayout()\n        self.docx_label = QLabel()\n        self.docx_grid.addWidget(self.docx_label, 0, 0, 1, 1)\n        self.docx_path = QLineEdit()\n        self.docx_grid.addWidget(self.docx_path, 1, 0, 1, 1)\n        self.choose_docx_button = QPushButton()\n        self.choose_docx_button.setText('...')\n        self.docx_grid.addWidget(self.choose_docx_button, 1, 1, 1, 1)\n        self.verticalLayout_2.addLayout(self.docx_grid)\n        self.choose_docx_button.clicked.connect(lambda: self.fileChooser('docx', self.docx_path))\n        if len(self.prefs['lastDocxPath']):\n            self.docx_path.setText(self.prefs['lastDocxPath'])\n        self.docx_path.setEnabled(False)\n\n        self.smap_grid = QGridLayout()\n        self.checkbox_smap = QCheckBox(self.groupBox)\n        self.smap_grid.addWidget(self.checkbox_smap, 0, 0, 1, 1)\n        self.cust_smap_path = QLineEdit(self.groupBox)\n        self.smap_grid.addWidget(self.cust_smap_path, 1, 0, 1, 1)\n        self.choose_smap_button = QPushButton(self.groupBox)\n        self.choose_smap_button.setText('...')\n        self.smap_grid.addWidget(self.choose_smap_button, 1, 1, 1, 1)\n        self.verticalLayout_2.addLayout(self.smap_grid)\n        self.checkbox_smap.setChecked(self.prefs['useSmap'])\n        self.checkbox_smap.stateChanged.connect(lambda: self.chkBoxActions(self.checkbox_smap, self.choose_smap_button))\n        self.choose_smap_button.clicked.connect(lambda: self.fileChooser('smap', self.cust_smap_path, self.checkbox_smap, self.choose_smap_button))\n        if len(self.prefs['useSmapPath']):\n            self.cust_smap_path.setText(self.prefs['useSmapPath'])\n        self.cust_smap_path.setEnabled(False)\n        self.chkBoxActions(self.checkbox_smap, self.choose_smap_button)\n\n        self.css_grid = QGridLayout()\n        self.checkbox_css = QCheckBox(self.groupBox)\n        self.css_grid.addWidget(self.checkbox_css, 0, 0, 1, 1)\n        self.cust_css_path = QLineEdit(self.groupBox)\n        self.css_grid.addWidget(self.cust_css_path, 1, 0, 1, 1)\n        self.choose_css_button = QPushButton(self.groupBox)\n        self.choose_css_button.setText('...')\n        self.css_grid.addWidget(self.choose_css_button, 1, 1, 1, 1)\n        self.verticalLayout_2.addLayout(self.css_grid)\n        self.checkbox_css.setChecked(self.prefs['useCss'])\n        self.checkbox_css.stateChanged.connect(lambda: self.chkBoxActions(self.checkbox_css, self.choose_css_button))\n        self.choose_css_button.clicked.connect(lambda: self.fileChooser('css', self.cust_css_path, self.checkbox_css, self.choose_css_button))\n        if len(self.prefs['useCssPath']):\n            self.cust_css_path.setText(self.prefs['useCssPath'])\n        self.cust_css_path.setEnabled(False)\n        self.chkBoxActions(self.checkbox_css, self.choose_css_button)\n\n        main_layout.addWidget(self.groupBox)\n        self.checkbox_debug = QCheckBox()\n        main_layout.addWidget(self.checkbox_debug)\n        self.checkbox_debug.setChecked(self.prefs['debug'])\n\n        spacerItem = QSpacerItem(20, 15, QSizePolicy.Minimum, QSizePolicy.Expanding)\n        main_layout.addItem(spacerItem)\n\n        button_box = QDialogButtonBox(QDialogButtonBox.Ok | QDialogButtonBox.Cancel)\n        button_box.accepted.connect(self._ok_clicked)\n        button_box.rejected.connect(self._cancel_clicked)\n        main_layout.addWidget(button_box)\n        self.retranslateUi(self)\n        if self.prefs['qt_geometry'] is not None:\n            try:\n                self.restoreGeometry(QByteArray.fromHex(self.prefs['qt_geometry'].encode('ascii')))\n            except:\n                pass\n        self.show()\n\n    def retranslateUi(self, App):\n        self.update_label.setText(_translate('App', 'Plugin Update Available'))\n        self.get_update_button.setText(_translate('App', 'Go to download page'))\n        self.checkbox_get_updates.setText(_translate('App', 'Check for plugin updates'))\n        self.docx_label.setText(_translate('App', 'DOCX File to import'))\n        self.checkbox_smap.setText(_translate('App', 'Use Custom Style Map'))\n        self.checkbox_css.setText(_translate('App', 'Use Custom CSS'))\n        self.checkbox_debug.setText(_translate('App', 'Debug Mode (change takes effect next plugin run)'))\n\n    def fileChooser(self, ftype, qlineedit, qcheck=None, qbutton=None):\n        options =  QFileDialog.Options()\n        options |= QFileDialog.DontUseNativeDialog\n        title = self.FTYPE_MAP[ftype]['title']\n        startfolder = self.prefs['lastDir'][ftype]\n        ffilter = self.FTYPE_MAP[ftype]['filetypes']\n        inpath, _ = QFileDialog.getOpenFileName(self, title, startfolder, ffilter, options=options)\n        if len(inpath):\n            qlineedit.setEnabled(True)\n            qlineedit.setText(os.path.normpath(inpath))\n            self.prefs['lastDir'][ftype] = os.path.dirname(inpath)\n            qlineedit.setEnabled(False)\n        else:\n            if qcheck is not None:\n                qcheck.setChecked(False)\n            if qbutton is not None:\n                qbutton.setEnabled(False)\n\n    def chkBoxActions(self, chk, btn):\n        btn.setEnabled(chk.isChecked())\n\n    def cmdDo(self):\n        global _DETAILS\n        self.prefs['qt_geometry'] = self.saveGeometry().toHex().data().decode('ascii')\n        self.prefs['check_for_updates'] = self.checkbox_get_updates.isChecked()\n        self.prefs['epub_version'] = self.epubType.checkedButton().text()[-1] + '.0'\n        self.prefs['debug'] = self.checkbox_debug.isChecked()\n        _DETAILS['vers'] = self.epubType.checkedButton().text()[-1] + '.0'\n        self.prefs['useSmap'] = self.checkbox_smap.isChecked()\n        if self.checkbox_smap.isChecked():\n            if len(self.cust_smap_path.text()):\n                self.prefs['useSmapPath'] = self.cust_smap_path.text()\n                _DETAILS['smap'] = (self.checkbox_smap.isChecked(), self.cust_smap_path.text())\n            else:\n                # Message box that no file is selected\n                return\n        self.prefs['useCss'] = self.checkbox_css.isChecked()\n        if self.checkbox_css.isChecked():\n            if len(self.cust_css_path.text()):\n                self.prefs['useCssPath'] = self.cust_css_path.text()\n                _DETAILS['css'] = (self.checkbox_css.isChecked(), self.cust_css_path.text())\n            else:\n                # Message box that no file is selected\n                return\n        if len(self.docx_path.text()):\n            self.prefs['lastDocxPath'] = self.docx_path.text()\n            _DETAILS['docx'] = self.docx_path.text()\n        else:\n            # Message box that no file is selected\n            return\n\n    def check_for_update(self):\n        '''Use updatecheck.py to check for newer versions of the plugin'''\n        chk = UpdateChecker(self.prefs['last_time_checked'], self.bk._w)\n        update_available, online_version, time = chk.update_info()\n        # update preferences with latest date/time/version\n        self.prefs['last_time_checked'] = time\n        if online_version is not None:\n            self.prefs['last_online_version'] = online_version\n        if update_available:\n            return (True, online_version)\n        return (False, online_version)\n\n    def get_update(self):\n        url = DOWNLOAD_PAGE\n        if self.update:\n            latest = '/tag/v{}'.format(self.newversion)\n            url = url + latest\n        webbrowser.open_new_tab(url)\n\n    def _ok_clicked(self):\n        self._ok_to_close = True\n        self.cmdDo()\n        self.bk.savePrefs(self.prefs)\n        QCoreApplication.instance().quit()\n\n    def _cancel_clicked(self):\n        self._ok_to_close = True\n        '''Close aborting any changes'''\n        self.prefs['qt_geometry'] = self.saveGeometry().toHex().data().decode('ascii')\n        self.prefs['check_for_updates'] = self.checkbox_get_updates.isChecked()\n        self.prefs['debug'] = self.checkbox_debug.isChecked()\n        self.bk.savePrefs(self.prefs)\n        QCoreApplication.instance().quit()\n\n    def closeEvent(self, event):\n        if self._ok_to_close:\n            event.accept()  # let the window close\n        else:\n            self._cancel_clicked()\n\ndef main():\n    ''' For debugging the qt dialog outside of the sigil plugin '''\n    from datetime import datetime, timedelta\n    prefs = {}\n\n    # Fake book container object\n    class w(object):\n        def __init__(self):\n            w.plugin_name = 'DOCXImport'\n            w.plugin_dir = '/home/dlmassay/.local/share/sigil-ebook/sigil/plugins'\n\n    class bk(object):\n        def __init__(self):\n            bk._w = w()\n\n        def savePrefs(self, dummy):\n            return\n    sim_bk = bk()\n\n    prefs['language'] = 'en'\n    prefs['qt_geometry'] = None\n    prefs['use_file_path'] = os.path.expanduser('~')\n    prefs['epub_version'] = '3.0'\n    prefs['check_for_updates'] = False\n    prefs['last_time_checked'] = str(datetime.now() - timedelta(hours=13))\n    prefs['last_online_version'] = '0.1.0'\n    prefs['lastDir'] = {\n        'smap' : os.path.expanduser('~'),\n        'css'  : os.path.expanduser('~'),\n        'docx' : os.path.expanduser('~'),\n    }\n    prefs['useSmap'] = False\n    prefs['useSmapPath'] = ''\n    prefs['useCss'] = False\n    prefs['useCssPath'] = ''\n    prefs['lastDocxPath'] = ''\n    prefs['debug'] = False\n\n    details = launch_qt_gui(sim_bk, prefs)\n    print(details)\n    return 0\n\n\nif __name__ == \"__main__\":\n    sys.exit(main())\n","repo_name":"xiaxi626/sigil-green","sub_path":"plugin/plugins/DOCXImport/qtdialogs.py","file_name":"qtdialogs.py","file_ext":"py","file_size_in_byte":16541,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"13458106071","text":"#https://hackerrank-challenge-pdfs.s3.amazonaws.com/1957-cut-the-sticks-English?AWSAccessKeyId=AKIAR6O7GJNX5DNFO3PV&Expires=1655394174&Signature=x6xswEmZw%2FD34Rh5HXw27wvYguI%3D&response-content-disposition=inline%3B%20filename%3Dcut-the-sticks-English.pdf&response-content-type=application%2Fpdf\n\n\ndef cutTheSticks(arr):\n    m=sorted(arr)\n    e=[]\n    try:\n        for i in range(0, len(m)):\n                    e.append(len(m))\n                    x=m.count(m[0])\n                    while x>=1:\n                        del m[0]\n                        x-=1\n                    l=m[0]\n                    x=0\n    except:\n        IndexError\n    return e\n","repo_name":"psd-psd/Seeker_python","sub_path":"Cut the sticks.py","file_name":"Cut the sticks.py","file_ext":"py","file_size_in_byte":655,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"21735610122","text":"# Problem Link: https://www.hackerrank.com/challenges/30-dictionaries-and-maps/problem\n# ------------------------------------------------------------------------------------\n\n\nn = int(input())\nphoneBook = {}\n\nfor i in range(n):\n    name, phoneNumber = input().split(' ')\n    phoneBook[name] = phoneNumber\n    \nwhile(True):\n    try:\n        n = input().strip()\n        if n in phoneBook:\n            print('{}={}'.format(n, phoneBook[n]))\n        else:\n            print('Not found')\n    \n    except EOFError:\n        break ","repo_name":"ybg345/hacker-rank-solutions","sub_path":"30 Days of Code/Day 08 Dictionaries and Maps.py","file_name":"Day 08 Dictionaries and Maps.py","file_ext":"py","file_size_in_byte":523,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"11148638912","text":"import hangman\n\ndef main():\n    welcome = input(f'{hangman.dashs}\\nDo you want to play hangman? y/n\\n{hangman.dashs}\\n\\n')\n    if welcome != 'y':\n        quit('\\nOkay Bye!')\n    else:\n        hangman.Game().call_class()\n\nif __name__ == '__main__':\n    main()","repo_name":"slocksert/hangman","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":258,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"11951746482","text":"\"\"\"\n-- Building a Calculator --\ntake in 2 inputs from the user\nadd those inputs together\nreturn the sum of those two inputs back to the user\n\"\"\"\n\n# First Input from the User stored in variable x & converting the input to a number.\nx = int(input(\"Enter the first number here: \"))\n# Second Input from the User stored in variable y & converting the input to a number.\ny = int(input(\"Enter the second number here: \"))\n# Combining those inputs in a new variable named sum\nsum = x + y\n# Printing out the variable sum to the user\nprint(\"The sum of x and y is: \" + str(sum))","repo_name":"ClayTaylor/intro-to-python","sub_path":"myFirst.py","file_name":"myFirst.py","file_ext":"py","file_size_in_byte":566,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"15880706684","text":"import bpy\nimport mathutils\nimport math\nimport random\nimport copy\n\nPARTICLE_MASS = 0\nBREAKING_THRESHOLD = 0.2\nBOND_STRENGTH = 1.0\nDAMPENING_FACTOR = 10.0\nALPHA = 0.5\nTAU = 0.4\nCOLLISION_THRESHOLD = 0.0001\nMAX_ADAPTIVE_LOOPS = 20\nBOUNCE_FACTOR = 0.2\nFRICTION = 0.3\n\nSCENE_OBJECTS = []\n\ndef is_inside(p, obj):\n    # max_dist = 1.84467e+19\n    result, point, normal, face = obj.closest_point_on_mesh(p)\n    p2 = point-p\n    v = p2.dot(normal)\n    #print(v)\n    return not(v < 0.0)\n\nclass CubeParticle:\n\n    def __init__(self, parent, location):\n        self.parent = parent\n        self.center = location\n        self.connections = {}\n        self.mass = PARTICLE_MASS\n        #normal corresponds to upwards direction of cube\n\n    def add_neighbors(self, neighbors):\n        for neighbor in neighbors:\n            self.connections[neighbor] = BOND_STRENGTH\n\n\nclass SceneObject:\n\n    #Collection of Particles\n\n    def __init__(self, resolution, vertices, render_object, fracturable):\n        self.v = mathutils.Vector((0,0,0))\n        self.rv = mathutils.Vector((0,0,0))\n        self.a = mathutils.Vector((0,0,-9.8))\n        self.ra = mathutils.Vector((0,0,0))\n\n        self.obj = render_object\n\n        self.vertices = vertices\n\n        self.normal = mathutils.Vector((0,0,1))\n\n        self.particles = []\n        # Assuming 3D quadrilateral geometry\n        x = [co[0] for co in vertices]\n        y = [co[1] for co in vertices]\n        z = [co[2] for co in vertices]\n        width = max(x) - min(x)\n        length = max(y) - min(y)\n        height = max(z) - min(z)\n        self.generate_mesh(width, length, height, min(x), min(y), min(z), resolution)\n        self.res = resolution\n        self.mass = sum([p.mass for p in self.particles])\n\n        self.width = 0\n        self.height = 0\n        self.depth = 0\n        self.fracturable = fracturable\n\n    #for now we assume that the cube is perfectly xyz-axis-aligned\n    def generate_mesh(self, width, length, height, x, y, z, res):\n        # width = x-axis, length = y-axis, height = z-axis\n        particles = [[[CubeParticle(self, mathutils.Vector((x + i*res, y + j*res, z + k*res))) for k in range(0, math.ceil(height / res))] for j in range(0, math.ceil(length / res))] for i in range(0, math.ceil(width / res))]\n        for i in range(0,len(particles)):\n            for j in range(0,len(particles[0])):\n                for k in range(0,len(particles[0][0])):\n                    neighbors = []\n                    if i-1 in range(0, len(particles)):\n                        neighbors.append(particles[i-1][j][k])\n                    if i+1 in range(0, len(particles)):\n                        neighbors.append(particles[i+1][j][k])\n                    if j-1 in range(0, len(particles[0])):\n                        neighbors.append(particles[i][j-1][k])\n                    if j+1 in range(0, len(particles[0])):\n                        neighbors.append(particles[i][j+1][k])\n                    if k-1 in range(0, len(particles[0][0])):\n                        neighbors.append(particles[i][j][k-1])\n                    if k+1 in range(0, len(particles[0][0])):\n                        neighbors.append(particles[i][j][k+1])\n                    particles[i][j][k].add_neighbors(neighbors)\n                    self.particles.append(particles[i][j][k])\n\n    # Move self to next time frame\n    def move(self):\n        t = 1 / bpy.data.scenes['Scene'].render.fps\n        disp = self.v*t + 0.5*self.a*t**2\n        self.v += self.a*t\n\n        for vertex in self.vertices:\n            vertex += disp\n        # Check for collision\n        for obj in SCENE_OBJECTS:\n            '''if obj.__class__.__name__ == 'glassPane':\n                print(\"glass\")\n                if True:\n                    print(\"collision\")\n                    obj.generate_fractures(mathutils.Vector((0,0,0.5)), 5)'''\n            if obj is not self and self.detect_collision(obj):\n                correction = 0.0\n                normal = mathutils.Vector((0,0,0))\n                for vertex in self.vertices:\n                    if is_inside(vertex, obj.obj):\n                        adaptive_disp = disp*0.5\n                        collision_point = vertex - adaptive_disp\n                        count = MAX_ADAPTIVE_LOOPS\n                        while count > 0:\n                            _, point, normal, _ = obj.obj.closest_point_on_mesh(collision_point)\n                            p2 = point-collision_point\n                            v = p2.dot(normal)\n                            #print(v)\n                            if abs(v) < COLLISION_THRESHOLD:\n                                break\n                            else:\n                                adaptive_disp *= 0.5\n                                collision_point += adaptive_disp * (1 if v < 0.0 else -1)\n                                count -= 1\n                        #print(\"done\")\n                        disp_vector = collision_point - vertex\n                        d = disp_vector.dot(disp_vector)**0.5 / disp.dot(disp)**0.5\n                        correction = max(correction, d)\n                        assert correction < 1\n                for vertex in self.vertices:\n                    vertex -= correction*disp\n                disp *= 1 - correction\n                self.v += (self.v.length * normal * BOUNCE_FACTOR) - (self.v.dot(-normal) / (normal.length) * (-normal).normalized()) - FRICTION * (self.v - self.v.dot(-normal) / (normal.length) * (-normal).normalized() )\n\n        self.obj.location += disp\n        for particle in self.particles:\n            particle.center += disp\n\n    def cell_fracture(self, impact_points):\n        return False\n\n    #Input: List of adjacent particles to split from this scene object\n    #Modifies self to account for splitting objects\n    #Output: Reference to splitted object\n    def split(self, particles):\n        return False\n\n    # Input: SceneObject to detect collision with self\n    # Output: True if collision, False if no collision\n    def detect_collision(self, other_obj):\n        for vertex in self.vertices:\n            if is_inside(vertex, other_obj.obj):\n                return True\n        return False\n\nclass glassPane: \n\n    #coordinates in object space\n    def __init__(self, bot_left, top_right, obj):\n        self.left = bot_left.x\n        self.right = top_right.x\n        self.top = top_right.y\n        self.bottom = bot_left.y\n        self.top_z = top_right.z\n        self.bot_z = bot_left.z\n\n        self.thickness = abs(self.top_z - self.bot_z)\n\n        self.edges = [\n            (mathutils.Vector((self.left, self.bottom, 0.0)), mathutils.Vector((self.left, self.top, 0.0))),\n            (mathutils.Vector((self.left, self.bottom, 0.0)), mathutils.Vector((self.right, self.bottom, 0.0))),\n            (mathutils.Vector((self.left, self.top, 0.0)), mathutils.Vector((self.right, self.top, 0.0))),\n            (mathutils.Vector((self.right, self.top, 0.0)), mathutils.Vector((self.right, self.bottom, 0.0)))]\n\n        self.obj = obj\n        self.num_frags = 0\n\n    def sample_particle(self, p, impact_force):\n\n        while True:\n            theta = random.uniform(0, 2 * math.pi)\n            r = random.uniform(0, 1)\n            \n            # nx_bias = pow(nx, 5)\n            # ny_bias = pow(ny, 5)\n            r_bias = pow(r, 9)\n            sr = r_bias * max(abs(self.right - self.left), abs(self.top - self.bottom)) * impact_force\n            x_coord = p.x + math.cos(theta) * sr\n            y_coord = p.y + math.sin(theta) * sr\n\n            # sx = p.x + nx_bias * abs(self.right - self.left) \n            # sy = p.y + ny_bias * abs(self.top - self.bottom)\n\n            if x_coord >= self.left and x_coord <= self.right and y_coord <= self.top and y_coord >= self.bottom:\n                return mathutils.Vector((x_coord, y_coord, p.z))\n\n    # def force_radius(self, impact_force):\n\n\n    #p := impact point\n    #k := total number of fracture reference particles\n    def generate_fractures(self, p, k, pts=[], impact_force=1.0):\n        # test pointing\n        if not pts:\n            pts = [p]\n            for _ in range(k):\n                s = self.sample_particle(p, impact_force)\n                pts += [s]\n        cvh = self.convex_hull(pts)\n        outer = self.outer_points(self.edges, math.ceil(len(cvh) / len(self.edges)))\n\n        print(\"sample points: \\n\", pts)\n        while True:\n            print(\"new frag\")\n            print(\"cvh \\n\", cvh)\n            print(\"outer \\n\", outer)\n            inner_primary = True\n            if len(cvh) >= len(outer):\n                primary = cvh\n                secondary = outer\n            else:\n                inner_primary = False\n                primary = outer\n                secondary = cvh\n            print(inner_primary)\n            for i in range(len(primary)):\n                \n                # first point\n                cur_frag = []\n                cur_frag.append(primary[i])\n\n                # second point\n                sec1, start = self.closest_point_index(primary[i], secondary)\n                cur_frag.append(sec1)\n\n                # third point\n                i_next = i+1 if i+1 < len(primary) else 0\n                cur_frag.insert(0, primary[i_next])\n\n                # any last points\n                sec2, end = self.closest_point_index(primary[i_next], secondary)\n                if start != end:\n                    cur_frag.insert(0, sec2)\n                    if not inner_primary:\n                        cur_frag.reverse()\n                    # cur_frag += secondary[min(start, end)+1:max(start,end)]\n                    intermediates = []\n                    if start < end:\n                        \n                        intermediates += secondary[start + 1: end]\n                    else:\n                        intermediates += secondary[start + 1:]\n                        intermediates += secondary[: end]\n                    if not inner_primary:\n                        intermediates.reverse()\n                    cur_frag += intermediates\n                elif not inner_primary:\n                    cur_frag.reverse()\n                cur_frag.reverse()\n                self.generate_frag(cur_frag)\n                print(\"ordering\", cur_frag)\n            outer = cvh\n            pts = [sample for sample in pts if sample not in cvh]\n            if not pts:\n                if len(outer) >= 3:\n                    outer.reverse()\n                    self.generate_frag(outer)\n                break\n            cvh = self.convex_hull(pts)\n\n        for obj in bpy.data.objects:\n            obj.select = False\n        for obj in bpy.data.objects:\n            if 'frag' in obj.name:\n                obj.select = True\n                bpy.ops.object.origin_set(type='ORIGIN_CENTER_OF_VOLUME', center='BOUNDS')\n                bpy.ops.rigidbody.objects_add(type='ACTIVE')\n                obj.select = False\n        self.obj.select = True\n        bpy.ops.object.delete()\n\n\n    def distance(self, u, v): \n        return ((u.x - v.x) ** 2 + (u.y - v.y) ** 2) ** 0.5\n    \n    def closest_point(self, p, points):\n        min_dist = 100\n        closest_v = None\n        for v in points:\n            if p != v:\n                if self.distance(p, v) < min_dist:\n                    min_dist = self.distance(p, v)\n                    closest_v = v\n        return closest_v\n        \n    def make_ccw(self, points):\n        print(\"ccw # pts:\", len(points))\n        vertices = copy.deepcopy(points)\n        # Michelle's Method\n        '''#start = vertices[0]\n        start = min(vertices, key = lambda point: point.x)\n        vertices.remove(start)\n        next_0 = self.closest_point(start, vertices)\n        vertices.remove(next_0)\n        next_1 = self.closest_point(start, vertices)\n        vertices.remove(next_1)\n\n        print(\"ccw... start: {}, next_0: {}, next_1: {}\".format(start, next_0, next_1))\n        ordered = []\n        if self.direction(start, next_0, next_1) < 0:\n            # ordering is ccw\n            ordered = [start, next_0, next_1]\n        elif self.direction(start, next_1, next_0) < 0:\n            # ordering is ccw\n            ordered = [start, next_1, next_0]\n\n        elif self.direction(next_0, start, next_1) < 0:\n            # ordering is ccw\n            ordered = [next_0, start, next_1]\n        elif self.direction(next_0, next_1, start) < 0:\n            # ordering is ccw\n            ordered = [next_0, next_1, start]\n\n        elif self.direction(next_1, start, next_0) < 0:\n            # ordering is ccw\n            ordered = [next_1, start, next_0]\n        elif self.direction(next_1, next_0, start) < 0:\n            # ordering is ccw\n            ordered = [next_1, next_0, start]\n\n        print(\"ccw.. ordered: {}\".format(ordered))\n        while vertices:\n            next_closest = self.closest_point(ordered[-1], vertices)\n            ordered.append(next_closest)\n            vertices.remove(next_closest)\n        for vertex in vertices:\n            len_before_insert = len(ordered)\n            for i in range(len(ordered)-1):\n                if self.direction(ordered[i], vertex, ordered[i+1]) < 0:\n                    ordered.insert(i+1, vertex)\n            if len_before_insert == len(ordered):\n                ordered.insert(len_before_insert, vertex)\n        print(\"ordered\", ordered)\n        return ordered'''\n        # Will's Convex Hull Method\n        '''ordered = self.convex_hull(vertices)[::-1]\n        for pt in ordered:\n            vertices.remove(pt)\n        for vertex in vertices:\n            len_before_insert = len(ordered)\n            for i in range(len(ordered)-1):\n                if self.direction(ordered[i], vertex, ordered[i+1]) < 0:\n                    ordered.insert(i+1, vertex)\n            if len_before_insert == len(ordered):\n                ordered.insert(len_before_insert, vertex)\n        print(\"ordered\", ordered)  \n        return ordered'''\n        # Chris's Method\n        vertices.sort(key = lambda p : p.y)\n        vertices.sort(key = lambda p : p.x)\n        lm = vertices[0]\n        rm = vertices[-1]\n\n        cutoff_slope = (rm.y - lm.y) / (rm.x - lm.x)\n        '''mean_y = 0\n        for v in vertices:\n            mean_y += v.y\n        mean_y /= len(vertices)'''\n\n        ordered = [lm]\n\n        vertices.remove(lm)\n\n        for i, p in enumerate(vertices[:-1]):\n            if p.y <= cutoff_slope * (p.x - lm.x) + lm.y:\n            # if p.y <= mean_y:\n                ordered.append(p)\n                vertices.remove(p)\n\n        vertices.reverse()\n        ordered.extend(vertices)\n        print(\"ordered\", ordered) \n        return ordered\n        \n\n    def generate_frag(self, points):\n        #points = self.make_ccw(points)\n        frag_mesh_data = bpy.data.meshes.new(\"frag_mesh_data\")\n        num_pts = len(points)\n        points += [mathutils.Vector((point.x, point.y, self.top_z)) if point.z == self.bot_z else mathutils.Vector((point.x, point.y, self.bot_z)) for point in points]\n        faces = []\n        if points[0].z == self.top_z:\n            faces = [tuple(range(num_pts)), tuple(range(2*num_pts-1, num_pts-1, -1))]\n        else:\n            faces = [tuple(range(num_pts-1, -1, -1)), tuple(range(num_pts, 2*num_pts))]\n        for i in range(num_pts):\n            j = i+1 if i+1 < num_pts else 0\n            faces.append((i, j, j+num_pts, i+num_pts))\n        frag_mesh_data.from_pydata(points, [], faces)\n        frag_mesh_data.update()\n        frag_obj = bpy.data.objects.new(\"frag\", frag_mesh_data)\n        frag_obj.location = self.obj.location\n        bpy.context.scene.objects.link(frag_obj)\n        self.num_frags += 1\n        print(self.num_frags)\n\n\n    # k := number of random outer-edge points\n    # edges := list of edges to use\n    def outer_points(self, edges, k):\n        #for _ in range(k):\n        rtn = [mathutils.Vector((self.left, self.bottom, 0.0)), mathutils.Vector((self.left, self.top, 0.0)),\n            mathutils.Vector((self.right, self.top, 0.0)), mathutils.Vector((self.right, self.bottom, 0.0))]\n        for edge in self.edges:\n            for _ in range(k):\n                num = random.random()\n                rtn.append(edge[0]*num + edge[1]*(1-num))\n        return self.make_ccw(rtn)[::-1]\n\n    # p := point of reference\n    # points := list of points to find closest to p\n    def closest_point_index(self, p, points):\n        closest = None\n        dist = 999999\n        index = -1\n        for i in range(len(points)):\n            point = points[i]\n            length = (p - point).length\n            if length < dist:\n                dist = length\n                closest = point\n                index = i\n        return closest, index\n\n    #input: points = set of points\n    #output: hull points\n    def convex_hull(self, points):\n        # find the leftmost point\n        a =  min(points, key = lambda point: point.x)\n        index = points.index(a)\n        \n        # selection sort\n        l = index\n        result = []\n        result.append(a)\n        while (True):\n            q = (l + 1) % len(points)\n            for i in range(len(points)):\n                if i == l:\n                    continue\n                # find the greatest left turn\n                # in case of collinearity, consider the farthest point\n                d = self.direction(points[l], points[i], points[q])\n                if d > 0:\n                    q = i\n            l = q\n            if l == index:\n                break\n            result.append(points[q])\n\n        return result\n\n    # calculates the cross product of vector p1 and p2\n    # if p1 is clockwise from p2 wrt origin then it returns +ve value\n    # if p2 is anti-clockwise from p2 wrt origin then it returns -ve value\n    # if p1 p2 and origin are collinear then it returs 0\n    def cross_product(self, p1, p2):\n        return p1.x * p2.y - p2.x * p1.y\n\n    # returns the cross product of vector p1p3 and p1p2\n    # if p1p3 is clockwise from p1p2 it returns +ve value\n    # if p1p3 is anti-clockwise from p1p2 it returns -ve value\n    # if p1 p2 and p3 are collinear it returns 0\n    def direction(self, p1, p2, p3):\n        return self.cross_product(p3 - p1, p2 -p1)","repo_name":"williampeng20/fracture-simulation","sub_path":"scripts/lattice.py","file_name":"lattice.py","file_ext":"py","file_size_in_byte":18117,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"2149569408","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass Encoder(nn.Module):\n    def __init__(self, input_dim, emb_dim, hid_dim, n_layers, kernel_size, dropout, device, max_length=50):\n        super().__init__()\n        assert kernel_size % 2 == 1, \"Kernel size should be odd in encoder\"\n        self.device = device\n        self.scale = torch.sqrt(torch.FloatTensor([0.5])).to(device)\n        self.tok_embedding = nn.Embedding(input_dim, emb_dim)\n        self.pos_embedding = nn.Embedding(max_length, emb_dim)\n        self.emb2hid = nn.Linear(emb_dim, hid_dim)\n        self.hid2emb = nn.Linear(hid_dim, emb_dim)\n        self.convs = nn.ModuleList([\n            nn.Conv1d(\n                in_channels=hid_dim, out_channels= 2 *hid_dim, kernel_size=kernel_size, padding=(kernel_size-1 )//2\n            ) for _ in range(n_layers)\n        ])\n        self.dropout = nn.Dropout(dropout)\n\n    def forward(self, src):\n        batch_size = src.shape[0]\n        src_len = src.shape[1]\n        pos = torch.arange(0, src_len).unsqueeze(0).repeat(batch_size, 1).to(self.device)\n        tok_embedded = self.tok_embedding(src)\n        pos_embedded = self.pos_embedding(pos)\n        embedded = self.dropout(tok_embedded + pos_embedded)\n        conv_inp = self.emb2hid(embedded)\n        conv_inp = conv_inp.permute(0, 2, 1)\n\n        for idx, conv in enumerate(self.convs):\n            conved = conv(self.dropout(conv_inp))\n            conved = F.glu(conved, dim=1)\n            conved = (conved + conv_inp) * self.scale\n            conv_inp = conved\n\n        conved = self.hid2emb(conved.permute(0, 2, 1))\n        combined = (conved + embedded) * self.scale\n        return conved, combined\n\n\nclass Decoder(nn.Module):\n    def __init__(self, output_dim, emb_dim, hid_dim, n_layers, kernel_size, dropout, \\\n                 trg_pad_idx, device, max_length=50):\n        super().__init__()\n        self.kernel_size = kernel_size\n        self.trg_pad_idx = trg_pad_idx\n        self.device = device\n        self.scale = torch.sqrt(torch.FloatTensor([0.5])).to(device)\n\n        self.tok_embedding = nn.Embedding(output_dim, emb_dim)\n        self.pos_embedding = nn.Embedding(max_length, emb_dim)\n        self.emb2hid = nn.Linear(emb_dim, hid_dim)\n        self.hid2emb = nn.Linear(hid_dim, emb_dim)\n\n        self.attn_hid2emb = nn.Linear(hid_dim, emb_dim)\n        self.attn_emb2hid = nn.Linear(emb_dim, hid_dim)\n\n        self.fc_out = nn.Linear(emb_dim, output_dim)\n        self.convs = nn.ModuleList([\n            nn.Conv1d(\n                in_channels=hid_dim, out_channels=2 * hid_dim, kernel_size=kernel_size\n            ) for _ in range(n_layers)\n        ])\n        self.dropout = nn.Dropout(dropout)\n\n    def calculate_attention(self, embedded, conved, encoder_conved, encoder_combined):\n        conved_emb = self.attn_hid2emb(conved.permute(0, 2, 1))\n        combined = (conved_emb + embedded) * self.scale\n        energy = torch.matmul(combined, encoder_conved.permute(0, 2, 1))\n        attention = F.softmax(energy, dim=2)\n        attended_encoding = torch.matmul(attention, encoder_combined)\n        attended_encoding = self.attn_emb2hid(attended_encoding)\n        attended_combined = (conved + attended_encoding.permute(0, 2, 1)) * self.scale\n        return attention, attended_combined\n\n    def forward(self, trg, encoder_conved, encoder_combined):\n        batch_size = trg.shape[0]\n        trg_len = trg.shape[1]\n        pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device)\n\n        tok_embedded = self.tok_embedding(trg)\n        pos_embedded = self.pos_embedding(pos)\n        embedded = self.dropout(tok_embedded + pos_embedded)\n\n        conv_inp = self.emb2hid(embedded)\n        conv_inp = conv_inp.permute(0, 2, 1)\n\n        batch_size = conv_inp.shape[0]\n        hid_dim = conv_inp.shape[1]\n        for idx, conv in enumerate(self.convs):\n            conv_inp = self.dropout(conv_inp)\n            padding = torch.zeros(\n                batch_size, hid_dim, self.kernel_size - 1\n            ).fill_(self.trg_pad_idx).to(self.device)\n            padded_conv_inp = torch.cat((padding, conv_inp), dim=2)\n            conved = conv(padded_conv_inp)\n            conved = F.glu(conved, dim=1)\n\n            attention, conved = self.calculate_attention(\n                embedded, conved, encoder_conved, encoder_combined\n            )\n            conved = (conved + conv_inp) * self.scale\n            conv_inp = conved\n\n        conved = self.hid2emb(conved.permute(0, 2, 1))\n        output = self.fc_out(self.dropout(conved))\n        return output, attention\n\n\nclass Seq2Seq(nn.Module):\n    def __init__(self, encoder, decoder):\n        super().__init__()\n        self.encoder = encoder\n        self.decoder = decoder\n\n    def forward(self, src, trg):\n        encoder_conved, encoder_combined = self.encoder(src)\n        output, attention = self.decoder(trg, encoder_conved, encoder_combined)\n        return output, attention\n\nif __name__ == '__main__':\n    pass","repo_name":"mehedihasanbijoy/DPCSpell","sub_path":"Baselines/ConvSeq2Seq/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":4990,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"48"}
+{"seq_id":"44586955604","text":"import tensorflow as tf\r\nimport os\r\nimport numpy as np\r\nfrom train import Graph\r\nfrom get_ivector import get_ivec\r\nfrom data_load import speaker2id\r\nfrom Wavenet_Decoder import decoder\r\nfrom hparams import Hyperparams as hp\r\nfrom tqdm import tqdm\r\nfrom scipy.io.wavfile import write\r\nfrom utils import mu_law_decode\r\n\r\ng = Graph(mode='test')\r\nsess = tf.Session(config=tf.ConfigProto())\r\nsaver = tf.train.Saver()\r\nckpt = tf.train.latest_checkpoint(hp.logdir)\r\nsaver.restore(sess, ckpt)\r\nprint(\"check_point restored\")\r\n# ivecs = get_ivec()\r\ntest_data_paths = np.load('/home/shyou/Desktop/test_data.npy')\n# test_data_path = 'F:/data/private/speech/vctk/qts/p225_001.npy'\r\n# target_speaker_ivec = ivecs[speaker2id(target_speaker)]\r\nreceptive_field = hp.dilations[-1]*hp.size\nspeaker_emb = tf.placeholder(dtype=tf.float32,shape=(hp.batch_size,len(hp.speakers)))\n_input = tf.placeholder(dtype=tf.float32,shape=(hp.batch_size, receptive_field,hp.Q))\n_z_q = tf.placeholder(dtype=tf.float32,shape=(hp.batch_size, receptive_field, hp.D))\nx = decoder(_input,speaker_emb,_z_q,is_training=False)\nout = tf.multinomial(tf.squeeze(x,1), num_samples=1,output_dtype=tf.int32)\nfor j in range(0,len(test_data_paths)):\n  test_qt = np.load(test_data_paths[j][0])\r\n  test_speaker = test_data_paths[j][1]\n  x = np.pad(test_qt, ([0, hp.T], [0, 0]), mode=\"constant\", constant_values=0)[:hp.T, :]\r\n  x = np.expand_dims(x,0)\r\n  test_speaker = np.expand_dims(test_speaker,0)\n# target_speaker_ivec = np.expand_dims(target_speaker_ivec,0)\r\n  z_q, x,_speaker_emb = sess.run([g.z_q, g.encoder_inputs, g.speakers],feed_dict={g.x:x, g.speaker_id:test_speaker}) #shape = (B,T,K)\r\n  output = np.squeeze(x)\r\n  inputs = x[:, :receptive_field, :]  # (B,r,Q)\r\n# decode and get multinomial distribuition\r\n\r\n\r\n  for i in tqdm(range(hp.T-receptive_field-1),total=hp.T-receptive_field,ncols=70,leave=False,unit='step'):\r\n    new_z_q = z_q[:, i:receptive_field+i, :]\r\n    out_node = sess.run(out,feed_dict={_input: inputs, _z_q: new_z_q,speaker_emb: _speaker_emb})\r\n    ans = np.squeeze(out_node)\r\n    output[receptive_field+i+1] = ans\r\n    out_one_hot = np.zeros((hp.batch_size,1,hp.Q))\r\n    out_one_hot[0,0,ans]=1\r\n    inputs = np.concatenate((inputs[:, 1:, :], out_one_hot), axis=1)\r\n\r\n  audio = mu_law_decode(output)\r\n  write(os.path.join(hp.sampledir, 'test{}.wav'.format(j)), hp.sr, audio)\n","repo_name":"seung1343/test_wav_files","sub_path":"SpeechVQVAE_original/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2350,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"41561864926","text":"cores = ['azul', 'verde', 'amarelo', 'vermelho']\ncor_cliente = input('Informe a cor desejada: ')\n\n\nwhile True:\n    if cor_cliente.lower() in cores:\n        print('Cor em estoque')\n        break\n    else:\n        print('Não temos essa cor em estoque')\n        cor_cliente = input('Digite outra cor:')\n        continue\n        \n    ","repo_name":"flaviosilvaf8/Estudos-Python","sub_path":"Listas, Dicionarios, Tuples/04_loja_de_tintas/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":331,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"13280159879","text":"from ipaddress import IPv4Address\nfrom unittest import TestCase\n\nfrom layer2.infrastructure.ethernet_devices import EthernetEndpoint, EthernetSwitch, EthernetEndpointWithArp\nfrom layer2.infrastructure.network_error import NetworkError\nfrom layer2.mac import Mac\n\n\nclass TestNetworkInterface2(TestCase):\n    macA = Mac.fromstring(\"AA:AA:AA:AA:AA:AA\")\n    macB = Mac.fromstring(\"BB:BB:BB:BB:BB:BB\")\n    # ip_a = IPv4Address(\"192.168.178.1\")\n    # ip_b = IPv4Address(\"192.168.178.2\")\n    # computer_A = EthernetEndpointWithArp(ip_a, macA, \"A\")\n    # computer_B = EthernetEndpointWithArp(ip_b, macB, \"B\")\n    computer_A = EthernetEndpoint(macA, \"A\")\n    computer_B = EthernetEndpoint(macB, \"B\")\n\n    def tearDown(self) -> None:\n        self.computer_A.disconnect()\n        self.computer_B.disconnect()\n\n    def test_networking(self):\n        mac_c = Mac.fromstring(\"CC:CC:CC:CC:CC:CC\")\n        switch = EthernetSwitch(3)\n\n        print(f\"\\nWe'll connect {self.computer_A} and {self.computer_B} each to the switch {switch}.\")\n        self.computer_A.connect_to(switch, 0)\n        self.computer_B.connect_to(switch, 1)\n\n        print(f\"\\nThen we'll send some data between {self.computer_A} and {self.computer_B} via our switch {switch}.\")\n        self.computer_A.send_data(b\"Hello there Bob!\", self.macB)\n        print(\"----------------------------------------\")\n        self.computer_B.send_data(b\"Hello back to you Alice!\", self.macA)\n\n        print(f\"\\nIf {self.computer_A} sends some frame to unknown destination it won't be received.\")\n        self.computer_A.send_data(b\"Non-existing mac should not receive this data\", mac_c)\n\n        print(f\"\\nNow {self.computer_B} disconnects from the switch.\")\n        self.computer_B.disconnect()\n\n        print(f\"\\nIf then {self.computer_A} tries to send something to {self.computer_B} we get:\")\n        self.computer_A.send_data(b\"Oh no, it seems B disconnected\", self.macB)\n\n    def test_sending_data_to_a_switch_will_update_its_cache_to_keep_track_of_interface_number(self):\n        switch = EthernetSwitch(3)\n\n        self.computer_A.connect_to(switch, 0)\n        self.computer_B.connect_to(switch, 1)\n\n        self.computer_B.send_data(b\"Hello there Alice!\", self.macA)\n        self.computer_A.send_data(b\"Hello back at you Bob!\", self.macB)\n\n        self.assertEqual(0, switch.cache[self.macA].interface_num)\n        self.assertEqual(1, switch.cache[self.macB].interface_num)\n        self.assertEqual(2, len(switch.cache))\n\n    def test_reconnecting_will_properly_update_switch_cache(self):\n        switch = EthernetSwitch(5)\n\n        self.computer_A.connect_to(switch, 4)\n        self.computer_A.send_data(b\"Some data\", self.macB)\n\n        self.assertEqual(4, switch.cache[self.macA].interface_num)\n\n        self.computer_A.disconnect()\n        self.computer_A.connect_to(switch, 3)\n        self.computer_A.send_data(b\"Some data\", self.macB)\n\n        self.assertEqual(3, switch.cache[self.macA].interface_num)\n        self.assertEqual(1, len(switch.cache))\n\n    def test_ethernet_endpoint_can_only_have_single_connection(self):\n        switch = EthernetSwitch(7)\n\n        self.computer_A.connect_to(switch, 0)\n        with self.assertRaises(NetworkError):\n            self.computer_A.connect_to(self.computer_B, 0)\n\n    def test_switch_can_only_have_single_connection_on_one_interface(self):\n        switch = EthernetSwitch(7)\n\n        self.computer_A.connect_to(switch, 0)\n        with self.assertRaises(NetworkError):\n            self.computer_B.connect_to(switch, 0)\n\n    def test_can_connect_to_other_after_disconnecting(self):\n        switch = EthernetSwitch(7)\n\n        self.computer_A.connect_to(switch, 0)\n        self.computer_A.disconnect()\n        self.computer_A.connect_to(self.computer_B, 0)\n\n        self.assertEqual(self.computer_A.get_interface(0), self.computer_B.get_interface(0).connector)\n","repo_name":"Orbifoldt/the-internet","sub_path":"layer2/infrastructure/test_ethernet_devices.py","file_name":"test_ethernet_devices.py","file_ext":"py","file_size_in_byte":3856,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"74002119185","text":"from matplotlib import image\nimport json\nimport numpy as np\nfrom PIL import Image\nimport scipy.misc\nimport math\nimport scipy.stats\nfrom read_tif import read_tif\n\ndef change_resolution(seg,px_width,method='mean'):\n    \"\"\"\n    this method resolve image(matrix) by a user given pixel width\n    @param: matrix, pixel width, method of merging(mean/mdoe)\n    \"\"\"\n    new_seg = []\n    max_height, max_width = seg.shape\n    n_y = math.floor(max_height/px_width)\n    n_x = math.floor(max_width/px_width)\n    for i in range(n_y):\n        for j in range(n_x):\n            new_cell = seg[i*px_width:(i+1)*px_width,j*px_width:(j+1)*px_width]\n            n_nonzero = np.count_nonzero(new_cell)\n            if method == 'mean':\n                if (n_nonzero == px_width**2) | (n_nonzero == 0):\n                    new_cell = np.mean(new_cell)\n                else:\n                    masked = np.ma.masked_equal(new_cell, 0)\n                    new_cell = np.mean(masked)\n            elif method == 'mode':\n                temp = np.sort(new_cell.flatten()[::-1])\n                mode,count = scipy.stats.mode(temp)\n                new_cell =mode[0]\n            new_seg.append(int(new_cell))\n    new_seg = np.reshape(new_seg,(n_y,n_x))\n\n    #output_file = \"/Users/jiaying/Downloads/TDA/data/tif/seg40p_inverse.tif\"\n    return new_seg\n\ndef change_resolution_save(input_file,output_file,px_width,method):\n    \"\"\"\n    this method reads image from file and save resolved image as file\n    @param: iinput imgae file name, output image name, method of merging(mean/mdoe)\n    \"\"\"\n    if method == 'mean':\n        np_img = np.loadtxt(input_file)\n        seg = change_resolution(np_img,'mean')\n        seg = np.where(seg==0,255,seg)\n    elif method == 'mode':\n        np_img = read_tif(input_file)\n        seg = change_resolution(np_img,px_width,'mode')\n    im = Image.fromarray((seg).astype(np.uint8))\n    im.save(output_file)\n\nif __name__ == '__main__':\n    change_resolution_save(\"/Users/jiaying/Downloads/TDA/data/tif/trump/04.tif\",'/Users/jiaying/Downloads/TDA/data/tif/trump/04_8184to1023.tif',8,'mode')\n","repo_name":"JiayingChen0307/tda","sub_path":"cubical/change_resolution.py","file_name":"change_resolution.py","file_ext":"py","file_size_in_byte":2087,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"33409602104","text":"from __future__ import absolute_import\n\nimport unittest\nimport numpy as np\n\n\nclass TestBase(unittest.TestCase):\n    def assertMaxDiffLess(self, ref, val, eps, msg=''):\n        diff = np.abs((val - ref) / ref)\n        if hasattr(diff, '__iter__'):\n            diffmax = diff.max()\n        else:\n            diffmax = diff\n        self.assertLess(diffmax, eps, msg=msg)\n\n    def assertIndexListEqual(self, val, ref):\n        self.assertEqual(len(val), len(ref))\n        for (a, b) in zip(val, ref):\n            self.assertEqual(len(a), len(b))\n            self.assertTrue(np.all(a == b))\n\n\ndef generate_suite(test_cls, test_items):\n    test_suite = unittest.TestSuite()\n    for item in test_items:\n        test_suite.addTest(test_cls(item))\n    return test_suite\n","repo_name":"tnakazato/priism","sub_path":"tests/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":761,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"48"}
+{"seq_id":"27998043024","text":"import pandas as pd\nimport numpy as np\nimport Levenshtein as Lv\nimport math\nimport matplotlib.pyplot as plt\nfrom matplotlib import colors as mcolors\nimport tkinter as tk\nimport pickle\nimport time\n\npd.options.mode.chained_assignment = None  # default='warn'\n\n# Returns color- or marker lists to use in figures\n# type is 'colors' or 'markers'. If plot=True a figure with available options will be shown else a list is returned\ndef get_colors_markers(type='colors', plot=False):\n    # Sort colors by hue, saturation, value and name.\n    colors = dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS)\n    by_hsv = sorted((tuple(mcolors.rgb_to_hsv(mcolors.to_rgba(color)[:3])), name) for name, color in colors.items())\n    color_names = [name for hsv, name in by_hsv]\n\n    if type == 'colors' and not plot:\n        numberlist = [128, 24, 38, 79, 146, 49, 152, 117, 58, 80, 119, 20, 97, 57, 138, 120, 153, 60, 16]\n        outputlist = []\n        for i in numberlist:\n            outputlist.append(color_names[i])\n        return outputlist\n\n    elif type == 'colors' and plot:\n        n = len(color_names)\n        ncols = 4\n        nrows = n // ncols\n\n        fig, ax = plt.subplots(figsize=(12, 10))\n\n        # Get height and width\n        X, Y = fig.get_dpi() * fig.get_size_inches()\n        h = Y / (nrows + 1)\n        w = X / ncols\n\n        for i, name in enumerate(color_names):\n            row = i % nrows\n            col = i // nrows\n            y = Y - (row * h) - h\n\n            xi_line = w * (col + 0.05)\n            xf_line = w * (col + 0.25)\n            xi_text = w * (col + 0.3)\n\n            ax.text(xi_text, y, str(i)+':'+name, fontsize=(h * 0.6),\n                    horizontalalignment='left',\n                    verticalalignment='center')\n            ax.hlines(y + h * 0.1, xi_line, xf_line,\n                      color=colors[name], linewidth=(h * 0.8))\n\n        ax.set_xlim(0, X)\n        ax.set_ylim(0, Y)\n        ax.set_axis_off()\n\n        fig.subplots_adjust(left=0, right=1,\n                            top=1, bottom=0,\n                            hspace=0, wspace=0)\n        plt.show()\n\n    elif type == 'markers' and not plot:\n        return ['o', 's', 'v', 'X', '.', '*', 'P', 'D', '<', ',', '^', '>', '1', '2', '3', '4', '8', 'h', 'H', '+']\n\n    elif type == 'markers' and plot:\n        mlist = ['o', 's', 'v', 'X', '*', 'P', 'D', '<', '1', '^', '2', '>', '3', '4', '.']\n        for i in range(len(mlist)):\n            plt.scatter(i+1, i+1, marker=mlist[i], s=30)\n            plt.text(i+1, i+1.5, mlist[i])\n        plt.show()\n\n# ------------------------------------------\n# FUNCTIONS FOR LOADING AND SAVING DATA FILES\n\n# Returns dictionary object containing data as pandas dataframes ('tab', 'ra', 'tax', 'seq', and 'meta')\n# path is path to input files\n# tab is frequency table is frequency table. SV names should be in first column, taxa should be in the final columns and start with Kingdom or Domain\n# fasta is fasta file with sequences. SV names should correspond to those in tab\n# meta is meta data\n# sep specifies separator used in input files e.g. ',' or '\\t'\ndef loadFiles(path='', tab='None', fasta='None', meta='None', sep=','):  # Import file and convert them to suitable format\n    #Prepare tab and tax\n    if tab != 'None':\n        # Read count table with taxa information\n        readtab = pd.read_csv(path + tab, sep=sep, header=0, index_col=0)\n\n        #Check if taxa information is in table\n        taxaavailable = 1\n        if 'Kingdom' in readtab.columns:\n            taxpos = readtab.columns.get_loc('Kingdom')\n        elif 'Domain' in readtab.columns:\n            taxpos = readtab.columns.get_loc('Domain')\n        else:\n            taxpos = len(readtab.columns)\n            taxaavailable = 0\n\n        readtab.index.name = 'SV'\n        ctab = readtab.iloc[:, :taxpos]\n        ratab = 100 * ctab / ctab.sum()\n\n        ctab = ctab.sort_index()\n        ratab = ratab.sort_index()\n\n        # Prepare taxa dataframe\n        if taxaavailable == 1:\n            taxtab = readtab.iloc[:, taxpos:]\n            taxtab = taxtab.sort_index()\n            for name in taxtab.columns.tolist()[:-1]: #Remove items containing unknowns\n                taxtab[name][taxtab[name].str.contains('nknown', na=False)] = np.nan\n                taxtab[name][taxtab[name].str.contains('uncult', na=False)] = np.nan\n\n    # Read fasta file with SV sequences\n    if fasta != 'None':\n        with open(path + fasta, 'r') as f:\n            readfasta = f.readlines()\n        fastalist = []\n        seq = ''\n        name = ''\n        for i in range(len(readfasta)):\n            if readfasta[i][0] == '>':\n                name = readfasta[i][1:].strip()\n                seq = ''\n            elif i == len(readfasta) - 1 or readfasta[i + 1][0] == '>':\n                seq = seq + readfasta[i].strip()\n                fastalist.append([name, seq])\n            else:\n                seq = seq + readfasta[i].strip()\n        # Correct fasta list based on SVs actually in count table (some might not be represented)\n        if tab != 'None':\n            tabSVs = list(ctab.index)\n            corrfastalist = []\n            for i in range(len(fastalist)):\n                if fastalist[i][0] in tabSVs:\n                    corrfastalist.append(fastalist[i])\n            seqtab = pd.DataFrame(corrfastalist, columns=['SV', 'seq'])\n        else:\n            seqtab = pd.DataFrame(fastalist, columns=['SV', 'seq'])\n\n        seqtab = seqtab.set_index('SV')\n        seqtab = seqtab.sort_index()\n\n    # Read meta data\n    if meta != 'None':\n        readmeta = pd.read_csv(path + meta, sep=sep, header=0, index_col=0)\n    # Go through metadata and remove lines not in tab\n    if meta != 'None' and tab != 'None':\n        for ix in readmeta.index:\n            if ix not in ctab.columns:\n                readmeta = readmeta.drop([ix])\n\n    # Return dictionary object with all dataframes\n    out = {}\n    if tab != 'None':\n        out['tab'] = ctab\n        out['ra'] = ratab\n    if tab != 'None' and taxaavailable == 1:\n        out['tax'] = taxtab\n    if fasta != 'None':\n        out['seq'] = seqtab\n    if meta != 'None':\n        out['meta'] = readmeta\n    return out\n\n# Outputs frequency table, fasta file and meta data from an object\n# obj is object to be returned, path is path to folder where files are to be saved\ndef returnFiles(obj, path, sep=','):  # Saves files in the same format as they were loaded\n    # Return taxa-count table\n    if 'tab' in obj and 'tax' in obj:\n        tab = obj['tab']; tax = obj['tax']\n        tab_tax = pd.concat([tab, tax], axis=1)\n        tab_tax.to_csv(path + 'output_table.csv', sep=sep)\n    elif 'tab' in obj:\n        tab = obj['tab']\n        tab.to_csv(path + 'output_table.csv', sep=sep)\n    else:\n        print('No tab and tax')\n\n    if 'meta' in obj:\n        meta = obj['meta']\n        meta.to_csv(path + 'output_meta.csv', sep=sep)\n    else:\n        print('No meta')\n\n    if 'seq' in obj:\n        seq = obj['seq']\n        fasta = []\n        for s in seq.index:\n            fasta.append('>' + s + '\\n')\n            fasta.append(seq.loc[s, 'seq'] + '\\n')\n        with open(path + 'output_seqs.fa', 'w') as f:\n            for i in fasta:\n                f.write(i)\n    else:\n        print('No seq')\n    print('Files saved')\n\n# Returns some information about an object, e.g. number of samples, reads, headings in meta data etc.\ndef getStats(obj):\n    tab = obj['tab']\n    print('Total number of samples= ', len(tab.columns))\n    print('Total number of SVs= ', len(tab.index))\n    print('Total counts= ', sum(tab.sum()))\n    print('Minimum number of counts in a sample= ', min(tab.sum()))\n    print('Titles and first line in meta data')\n    print(list(obj['meta'].columns))\n    print(list(obj['meta'].iloc[1, :]))\n\n\n# ----------------------------\n# FUNCTIONS FOR SUBSETTING DATA\n\n# Subsets samples based on metadata\n# var is the column heading in metadata used to subset samples, if var=='index' slist are the index names of meta data\n# slist is a list of names in meta data column (or index) which specify samples to keep\n# if keep0 is false, all SVs with 0 counts after the subsetting will be discarded from the data\ndef subsetSamples(obj, var='index', slist='None', keep0=False):\n    if 'meta' not in obj.keys():\n        print('Metadata missing')\n        return 0\n    if slist == 'None':\n        print('slist must be specified')\n        return 0\n\n    meta = obj['meta']\n    if var in meta.columns.tolist():\n        meta = meta[meta[var].isin(slist)]\n    elif var == 'index':\n        meta = meta.loc[slist, :]\n    else:\n        print('var not found')\n        return 0\n\n    tab = obj['tab']\n    tab = tab[meta.index]\n    ra = obj['ra']\n    ra = ra[meta.index]\n    if 'seq' in obj.keys():\n        seq = obj['seq']\n    if 'tax' in obj.keys():\n        tax = obj['tax']\n    out = {}  # Dictionary object to return, dataframes and dictionaries\n\n    # Remove SV with zero count\n    if not keep0:\n        tab['sum'] = tab.sum(axis=1)\n        tab2 = tab[tab['sum'] > 0]\n        keepSVs = list(tab2.index)\n        tab2 = tab2.drop('sum', axis=1)\n        out['tab'] = tab2\n        ra2 = ra.loc[keepSVs, :]\n        out['ra'] = ra2\n        if 'seq' in obj.keys():\n            seq2 = seq.loc[keepSVs, :]\n            out['seq'] = seq2\n        if 'tax' in obj.keys():\n            tax2 = tax.loc[keepSVs, :]\n            out['tax'] = tax2\n        out['meta'] = meta\n    else:\n        out['meta'] = meta\n        out['tab'] = tab\n        out['ra'] = ra\n        if 'seq' in obj.keys():\n            out['seq'] = seq\n        if 'tax' in obj.keys():\n            out['tax'] = tax\n    return out\n\n# Subsets object based on list of SVs to keep\ndef subsetSVs(obj, svlist):\n    out = {}\n    tab = obj['tab']\n    tab = tab.loc[svlist, :]\n    out['tab'] = tab\n    if 'ra' in obj:\n        ra = obj['ra']\n        ra = ra.loc[svlist, :]\n        out['ra'] = ra\n    if 'tax' in obj:\n        tax = obj['tax']\n        tax = tax.loc[svlist, :]\n        out['tax'] = tax\n    if 'seq' in obj:\n        seq = obj['seq']\n        seq = seq.loc[svlist, :]\n        out['seq'] = seq\n    if 'meta' in obj:\n        out['meta'] = obj['meta']\n    return out\n\n# Subsets object to the most abundant SVs calculated based on the sum of the relative abundances in each sample\n# number specifies the number of SVs to keep\ndef subsetTopSVs(obj, number=25):\n    out = {}\n    tab = obj['tab']\n    ra = tab/tab.sum()\n    ra['sum'] = ra.sum(axis=1)\n    ra = ra.sort_values(by='sum', ascending=False)\n    svlist = ra.index[:number]\n    tab2 = tab.loc[svlist, :]\n    out['tab'] = tab2\n    if 'ra' in obj:\n        ra2 = obj['ra']\n        ra2 = ra2.loc[svlist, :]\n        out['ra'] = ra\n    if 'tax' in obj:\n        tax = obj['tax']\n        tax = tax.loc[svlist, :]\n        out['tax'] = tax\n    if 'seq' in obj:\n        seq = obj['seq']\n        seq = seq.loc[svlist, :]\n        out['seq'] = seq\n    if 'meta' in obj:\n        out['meta'] = obj['meta']\n    return out\n\n# Function that remove all SVs with a minimum frequency in a sample lower than cutoff\ndef removeLowReads(obj, cutoff=1):\n    tab = obj['tab'].copy()\n    tab['min'] = tab.min(axis=1)\n\n    keep = tab[tab['min'] >= cutoff]\n\n    out = subsetSVs(obj, keep.index.tolist())\n    return out\n\n# Subset object based on text in taxonomic names\n# subsetLevels is list taxonomic levels searched for text patterns, e.g. ['Family', 'Genus']\n# subsetPatterns is list of text to search for, e.g. ['Nitrosom', 'Brochadia']\ndef subsetTextPatterns(obj, subsetLevels=[], subsetPatterns=[]):\n    if len(subsetLevels) == 0 or len(subsetPatterns) == 0:\n        print('No taxlevels or pattern')\n        return 0\n    else:\n        taxPatternCheck = obj['tax'].applymap(str)\n        keepIX = []\n        for col in subsetLevels:\n            for ix in taxPatternCheck.index:\n                for ptrn in subsetPatterns:\n                    if ptrn in taxPatternCheck.loc[ix, col] and ix not in keepIX:\n                        keepIX.append(ix)\n    out = {}\n    if 'tab' in obj.keys():\n        out['tab'] = obj['tab'].loc[keepIX]\n    if 'ra' in obj.keys():\n        out['ra'] = obj['ra'].loc[keepIX]\n    if 'tax' in obj.keys():\n        out['tax'] = obj['tax'].loc[keepIX]\n    if 'seq' in obj.keys():\n        out['seq'] = obj['seq'].loc[keepIX]\n    if 'meta' in obj.keys():\n        out['meta'] = obj['meta']\n    return out\n\n# Merges samples based on information in meta data\n# var is the column heading in metadata used to merge samples. The counts for all samples with the same text in var column will be merged.\n# slist is a list of names in meta data column which specify samples to keep. If slist='None' (default), the whole meta data column is used\n# if keep0 is false, all SVs with 0 counts after the subsetting will be discarded from the data\ndef mergeSamples(obj, var='None', slist='None', keep0=False):\n    if 'meta' not in obj.keys():\n        print('Metadata missing')\n        return 0\n    if var != 'None' and slist == 'None':\n        slist = obj['meta'][var]\n\n    tabdi = {}\n    radi = {}  # Temp dict that holds sum for each type in slist\n    for smp in slist:\n        tempobj = subsetSamples(obj, var, [smp], keep0=True)\n        tab = tempobj['tab']\n        tab['sum'] = tab.sum(axis=1)\n        tab['ra'] = 100 * tab['sum'] / tab['sum'].sum()\n        tabdi[smp] = tab.loc[:, 'sum']\n        radi[smp] = tab.loc[:, 'ra']\n    temptab = pd.DataFrame(tabdi, index=obj['tab'].index)\n    tempra = pd.DataFrame(radi, index=obj['tab'].index)\n\n    out = {}\n    if keep0 == False:  ## Remove SV with zero count\n        temptab['sum'] = temptab.sum(axis=1)\n        tab2 = temptab[temptab['sum'] > 0]\n        keepSVs = list(tab2.index)\n        tab2 = tab2.drop('sum', axis=1)\n        ra2 = tempra.loc[keepSVs, :]\n        if 'seq' in obj.keys():\n            seq = obj['seq']\n            seq2 = seq.loc[keepSVs, :]\n            out['seq'] = seq2\n        if 'tax' in obj.keys():\n            tax = obj['tax']\n            tax2 = tax.loc[keepSVs, :]\n            out['tax'] = tax2\n    else:\n        tab2 = temptab\n        ra2 = tempra\n        if 'seq' in obj.keys():\n            out['seq'] = obj['seq']\n        if 'tax' in obj.keys():\n            out['tax'] = obj['tax']\n    out['tab'] = tab2\n    out['ra'] = ra2\n\n    meta = obj['meta']\n    meta2 = meta.groupby(var).first()\n    meta2[var] = meta2.index\n    out['meta'] = meta2\n    return out\n\n# Rarefies frequency table to a specific number of reads per sample\n# if depth = 'min', the minimum number of reads in a sample is used\n# seed sets a random state for reproducible results\n# The function is  similar to rarefaction without replacement\ndef rarefy1(tab, depth='min', seed='None'):\n    tab = tab.applymap(int) #Make sure table elements are integers\n    if depth == 'min':  # Define read depth\n        reads = min(tab.sum())\n    else:\n        reads = depth\n\n    samples = tab.columns.tolist()\n    rtab = tab.copy()\n    for smp in samples:\n        smpsum = sum(tab[smp])\n        if smpsum < reads:  # Remove sample if sum of reads less than read depth\n            rtab = rtab.drop(smp, axis=1)\n            continue\n\n        frac = tab.loc[:, smp] * reads / smpsum #Calculate rel abund\n        avrundad = frac.apply(math.floor) #Round down\n        addNR = int(reads - sum(avrundad)) #This many reads must be added\n\n        if addNR >= 1:\n            diffs = frac - avrundad\n            if seed == 'None':\n                addSV = tab[smp].sample(n=addNR, replace=False, weights=diffs).index.tolist()\n            else:\n                addSV = tab[smp].sample(n=addNR, replace=False, weights=diffs, random_state=seed).index.tolist()\n            avrundad[addSV] = avrundad[addSV] + 1\n        rtab[smp] = avrundad\n    return rtab\n\n# Rarefies frequency table to a specific number of reads per sample\n# if depth = 'min', the minimum number of reads in a sample is used\n# seed sets a random state for reproducible results\n# This is rarefaction with replacement\ndef rarefy2(tab, depth='min', seed='None'):\n    tab = tab.fillna(0)\n    if seed != 'None':\n        prng = np.random.RandomState(seed) # reproducible results\n    noccur = tab.sum()\n    nvar = len(tab.index) # number of SVs\n\n    ## Set read depth\n    if depth == 'min':\n        depth = int(np.min(noccur))\n    else:\n        depth = int(depth)\n\n    rftab = tab.copy()\n    for s in tab.columns: # for each sample\n        if tab[s].sum() < depth:\n            rftab = rftab.drop(s, axis=1)\n            continue\n        else:\n            p = tab[s]/tab[s].sum()\n            if seed != 'None':\n                choice = prng.choice(nvar, depth, p=p)\n            else:\n                choice = np.random.choice(nvar, depth, p=p)\n            rftab[s] = np.bincount(choice, minlength=nvar)\n    return rftab\n\n# ------------------------------------------\n# FUNCTIONS FOR VISUALISING TAXA IN HEATMAP\n\n# Groups SVs based on taxa, returns object with grouped sequences\n# levels specifies taxonomic levels to use in the grouping\n# nameType specifies the abbreviation to be used for unclassified sequences\ndef groupbyTaxa(obj, levels=['Phylum', 'Genus'], nameType='SV'):\n    # Clean up tax data frame\n    tax = obj['tax']\n    tax = tax.fillna(0)\n    taxSV = tax.copy() #df to hold nameTypes in undefined\n    taxNames = tax.copy() #df to hold lowest known taxaname in undefined\n\n    # Check which OTU or SV name is used in the index\n    indexlist = tax.index.tolist()\n    if indexlist[0][:3] in ['Otu', 'OTU', 'ASV', 'ESV']:\n        currentname = indexlist[0][:3]\n        startpos = 3\n    elif indexlist[0][:2] in ['SV']:\n        currentname = indexlist[0][:2]\n        startpos = 2\n    else:\n        print('Error in groupbyTaxa, SV/OTU name not known')\n        return 0\n\n    # If incorrect name is in tax, make column with correct name\n    if nameType != currentname:\n        newnames = []\n        for i in range(len(indexlist)):\n            newnames.append(nameType+indexlist[i][startpos:])\n        indexlist = newnames\n\n    # Put the SV/OTU name in all empty spots in taxSV\n    for col in range(len(taxSV.columns)):\n        for row in range(len(taxSV.index)):\n            if taxSV.iloc[row, col] == 0:\n                taxSV.iloc[row, col] = indexlist[row]\n    taxSV[nameType] = indexlist\n\n    # Change all 0 in tax to lowest determined taxa level in taxNames\n    taxanameslist = taxNames.columns.tolist() #List with Kingdom, Phylum .. SV\n    for s_nr in range(1, len(taxanameslist)):\n        s0 = taxanameslist[s_nr-1]\n        s1 = taxanameslist[s_nr]\n        taxNames[s1][tax[s1] == 0] = taxNames[s0][tax[s1] == 0]\n\n    # Create names to use in output\n    if len(levels) == 1:\n        tax['Name'] = taxSV[levels[0]]\n        for ix in tax.index:\n            if tax.loc[ix, levels[0]] == 0:\n                tax.loc[ix, 'Name'] = taxNames.loc[ix, levels[0]] + '; ' + tax.loc[ix, 'Name']\n    elif len(levels) == 2:\n        tax['Name'] = taxNames[levels[0]]+'; '+taxSV[levels[1]]\n    else:\n        print('Error in GroupbyTaxa, levels should be a list with 1 or 2 items')\n        return 0\n\n    #Grouby Name and return object\n    out = {}\n    if 'tab' in obj.keys():\n        tab = obj['tab']\n        tab['Name'] = tax['Name']\n        tab = tab.set_index(['Name'])\n        tab = tab.groupby(tab.index).sum()\n        out['tab'] = tab\n    if 'ra' in obj.keys():\n        ra = obj['ra']\n        ra['Name'] = tax['Name']\n        ra = ra.set_index('Name')\n        ra = ra.groupby(ra.index).sum()\n        out['ra'] = ra\n    if 'meta' in obj.keys():\n        out['meta'] = obj['meta']\n    return out\n\n## Plots heatmap\n    # xAxis specifies heading in meta data used to merge samples\n    # levels specifies taxonomic levels used in y axis\n    # subsetLevels and subsetPatterns refer to subsetTextPatters function which can be used to filter results\n    # order refers to heading in meta data used to order samples\n    # numberToPlot refers to the number of taxa with highest abundance to include in the heatmap\n    # method refers to the method used to define the taxa with highest abundance, 'max_sample' is max relative abundance in a sample,\n    # 'mean_all' is the mean relative abundance across all samples\n    # nameType is nameType in groupbyTaxa function\n    # figSize is the width and height of the figure\n    # fontSize is refers to the axis text\n    # sepCol is a list of column numbers between which to include a separator, i.e. to clarify grouping of samples\n    # if labels=True, include relative abundance values in heatmap, if False they are not included\n    # labelSize is the font size of the relative abundance lables in the heatmap\n    # cThreshold is the relative abundance % at which the label color switches from black to white (for clarity)\n    # cMap is the color map used in the heatmap\n    # cLinear is a parameter determining how the color change with relative abundance, a value of 1 means the change is linear\n    # cBar is a list of tick marks to use if a color bar is included as legend\n    # savename is the name (also include path) of the saved png file, if 'None' no figure is saved\ndef plotHeatmap(obj, xAxis='None', levels=['Phylum', 'Genus'], subsetLevels='None', subsetPatterns='None',\n                order='None', numberToPlot=20, method='max_sample', nameType='SV',\n                 figSize=(14, 10), fontSize=15, sepCol = [],\n                labels=True, labelSize=10, cThreshold=8,\n                cMap='Reds', cLinear=0.5, cBar=[], savename='None'):\n\n    #Merge samples based on xAxis\n    if xAxis != 'None':\n        merged_obj = mergeSamples(obj, var=xAxis)\n    else:\n        merged_obj = obj.copy()\n\n    #Calculate relative abundances and store in df ra\n    tab = merged_obj['tab']\n    ra = 100*tab/tab.sum()\n    merged_obj['ra'] = ra\n\n    ## Make sure samples are in the right order in meta data\n    if order != 'None':\n        md = merged_obj['meta']\n        md[order] = md[order].astype(float)\n        md = md.sort_values(by=order)\n        logiclist = []\n        if xAxis != 'None':\n            [logiclist.append(item) for item in md[xAxis] if item not in logiclist]\n        else:\n            [logiclist.append(item) for item in md.index if item not in logiclist]\n        merged_obj['meta'] = md\n\n    ## Subset based on pattern\n    if subsetLevels != 'None' and isinstance(subsetLevels, list) and isinstance(subsetPatterns, list):\n        subset_obj = subsetTextPatterns(merged_obj, subsetLevels, subsetPatterns)\n        merged_obj = subset_obj\n\n    ## Groupby taxa\n    taxa_obj = groupbyTaxa(merged_obj, levels=levels, nameType=nameType)\n    ra = taxa_obj['ra']; table = ra.copy()\n\n    # Subset for top taxa\n    if method == 'max_sample':\n        ra['max'] = ra.max(axis=1)\n        ra = ra.sort_values(by=['max'], ascending=False)\n        retain = ra.index.tolist()[:numberToPlot]\n\n    elif method == 'mean_all':\n        ra['mean'] = ra.mean(axis=1)\n        ra = ra.sort_values(by=['mean'], ascending=False)\n        retain = ra.index.tolist()[:numberToPlot]\n\n    table = table.loc[retain]\n\n    if order != 'None':\n        table = table.loc[:, logiclist]\n\n    # Change to italics in table labels\n    taxa_list = table.index.tolist()\n    new_taxa_list = []\n    for n in taxa_list:\n        if ';' in n: #Check if there are two taxa names\n            splitname = n.split(';')\n            splitname1 = splitname[0].split('__')\n            newname1 = splitname1[0]+'__'+'$\\it{'+splitname1[1]+'}$'\n            if '__' in splitname[1]:\n                splitname2 = splitname[1].split('__')\n                newname2 = splitname2[0]+'__'+'$\\it{'+splitname2[1]+'}$'\n            else:\n                newname2 = splitname[1]\n            newname = newname1+';'+newname2\n        else: #If there is only one taxa name\n            if '__' in n:\n                splitname = n.split('__')\n                newname = splitname[0]+'__'+'$\\it{'+splitname[1]+'}$'\n            else:\n                newname = n\n        new_taxa_list.append(newname)\n    table = pd.DataFrame(table.values, index=new_taxa_list, columns=table.columns)\n\n    # Print heatmap\n    table['avg'] = table.mean(axis=1)\n    table = table.sort_values(by=['avg'], ascending=True)\n    table = table.drop(['avg'], axis=1)\n\n    #Fix datalabels\n    if labels:\n        labelvalues = table.copy()\n        for r in table.index:\n            for c in table.columns:\n                value = float(table.loc[r, c])\n                if value < 0.1 and value > 0:\n                    labelvalues.loc[r, c] = '<0.1'\n                elif value < 10 and value >= 0.1:\n                    labelvalues.loc[r, c] = str(round(value, 1))\n                elif value > 99:\n                    labelvalues.loc[r, c] = '99'\n                elif value >= 10:\n                    labelvalues.loc[r, c] = str(int(value))\n                else:\n                    labelvalues.loc[r, c] = '0'\n\n    # Include empty columns in table to separate samples\n    #print(table.head())\n    if len(sepCol) > 0:\n        for i in range(len(sepCol)):\n            table.insert(loc=sepCol[i]+i, column=' '*(i+1), value=0)\n            if labels:\n                labelvalues.insert(loc=sepCol[i]+i, column=' '*(i+1), value='')\n\n\n    #Plot\n    plt.rcParams.update({'font.size': fontSize})\n    fig, ax = plt.subplots(figsize=figSize)\n    im = ax.imshow(table, cmap=cMap, norm=mcolors.PowerNorm(gamma=cLinear), aspect='auto')\n    if len(cBar) > 0:\n        fig.colorbar(im, ticks=cBar)\n\n    # Fix axes\n    ax.set_xticks(np.arange(len(table.columns)))\n    ax.set_yticks(np.arange(len(table.index)))\n    ax.set_xticklabels(table.columns.tolist(), rotation=90)\n    ax.set_yticklabels(table.index.tolist(), rotation=0)\n\n    # Fix grid lines\n    ax.set_xticks(np.arange(-0.5, len(table.columns), 1), minor=True)\n    ax.set_yticks(np.arange(-0.5, len(table.index), 1), minor=True)\n    ax.grid(which='minor', color='white', linestyle='-', linewidth=1)\n\n    if len(sepCol) > 0:\n        for i in range(len(sepCol)):\n            for j in range(6):\n                ax.axvline(sepCol[i]+i-0.5+j/5, 0, len(table.index), linestyle='-', lw=1, color='white')\n\n    # Fix labels\n    if labels:\n        for r in range(len(table.index)):\n            for c in range(len(table.columns)):\n                if table.iloc[r, c] > cThreshold:\n                    textcolor = 'white'\n                else:\n                    textcolor = 'black'\n                ax.text(c, r, labelvalues.iloc[r, c], fontsize=labelSize, ha='center', va='center', color=textcolor)\n    fig.tight_layout()\n\n    if savename != 'None':\n        plt.savefig(savename+'.pdf', format='pdf')\n        plt.savefig(savename)\n    plt.show()\n\n# ------------------------------------------\n# FUNCTIONS FOR ANALYSING DIVERSITY\n\n# Returns matrix for pairwise distances between SVs based on Levenshtein/Hamming distances (uses Levenshtein package)\n# Saves results as pickle file at location specified in savename\n# Output is needed as input for phylogenetic diversity index calculations\ndef phylDistMat(seq, savename='PhylDistMat'):\n    svnames = list(seq.index)\n    df = pd.DataFrame(0, index=svnames, columns=svnames)\n\n    # For showing progress\n    total_comp = (len(svnames)**2)/2\n    rootPhylDistMat = tk.Tk()\n    calc_progress = tk.DoubleVar(rootPhylDistMat, 0)\n    counter = 0\n    tk.Label(rootPhylDistMat, text='Progress in calculation (%)', width=30).pack()\n    tk.Label(rootPhylDistMat, textvariable=calc_progress, width=20).pack()\n\n    for i in range(len(svnames) - 1):\n        for j in range(i + 1, len(svnames)):\n\n            # For showing progress\n            counter += 1\n            if counter%100 == 0:\n                calc_progress.set(round(100*counter/total_comp, 2))\n                rootPhylDistMat.update()\n\n            n1 = svnames[i]\n            n2 = svnames[j]\n            s1 = seq.loc[n1, 'seq']\n            s2 = seq.loc[n2, 'seq']\n\n            if len(s1) == len(s2):\n                dist = Lv.hamming(s1, s2) / len(s1)\n            else:\n                maxlen = max(len(s1), len(s2))\n                dist = Lv.distance(s1, s2) / maxlen\n\n            df.loc[n1, n2] = dist; df.loc[n2, n1] = dist\n    df.to_csv(savename+'.csv')\n    calc_progress.set(100)\n    rootPhylDistMat.update()\n\n# Returns Rao's quadratic entropy, sum(sum(dij*pi*pj))\n# Function used in Chiu's phylogenetic diversity functions\ndef raoQ(tab, distmat):\n    ra = tab / tab.sum()\n    outdf = pd.Series(0, index=ra.columns)\n    svlist = list(ra.index)\n    distmat = distmat.loc[svlist, svlist]\n    for smp in ra.columns:\n        ra2mat = pd.DataFrame(np.outer(ra.loc[:, smp].values, ra.loc[:, smp].values), index=ra.index, columns=ra.index)\n        rao_mat = ra2mat.mul(distmat)\n        Qvalue = sum(rao_mat.sum())\n        outdf.loc[smp] = Qvalue\n    return outdf\n\n# Converts beta value to distances, specify q and type associated with the beta (assuming pairwise)\n# Used in beta dissimilarity calculations\n# The viewpoint refers to either the local or regional perspective as defined in Chao et al. 2014\ndef beta2Dist(beta, q=1, divType='naive', viewpoint='local'):\n    beta = beta.applymap(float)\n    dist = beta.copy()\n    mask = beta > 0\n\n    if q == 1:\n        dist[mask] = np.log(beta[mask]) / math.log(2)\n    else:\n        if divType == 'naive' and viewpoint == 'local':\n            dist[mask] = 1 - (2 ** (1 - q) - beta[mask].pow(1 - q)) / (2 ** (1 - q) - 1)\n        elif divType == 'phyl' and viewpoint == 'local':\n            dist[mask] = 1 - ((2 ** (2 * (1 - q)) - beta[mask].pow(1 - q)) / (2 ** (2 * (1 - q)) - 1))\n        elif divType == 'naive' and viewpoint == 'regional':\n            dist[mask] = 1 - ((1 / beta[mask]) ** (1 - q) - 0.5 ** (1 - q)) / (1 - 0.5 ** (1 - q))\n        elif divType == 'phyl' and viewpoint == 'regional':\n            dist[mask] = 1 - ((1 / beta[mask]) ** (1 - q) - 0.5 ** (2 * (1 - q))) / (1 - 0.5 ** (2 * (1 - q)))\n    return dist\n\n# Returns naive alpha diversity of order q for all samples\ndef naiveDivAlpha(tab, q=1, rarefy='None'):\n    if rarefy != 'None':\n        raretab = rarefy1(tab, rarefy)\n        ra = raretab / raretab.sum()\n    else:\n        ra = tab / tab.sum()\n\n    if q == 0:\n        mask = tab > 0\n        Hillvalues = tab[mask].count()\n        return Hillvalues\n    elif q == 1:\n        mask = (ra != 0)\n        raLn = ra[mask] * np.log(ra[mask])\n        Hillvalues = np.exp(-raLn.sum())\n        return Hillvalues\n    else:\n        mask = (ra != 0)\n        ra = ra[mask]\n        rapow = ra[mask].pow(q)\n        rapowsum = rapow.sum()\n        Hillvalues = rapowsum.pow(1 / (1 - q))\n        return Hillvalues\n\n# Returns matrix of paiwise dissimilarities of order q\n# Based on local overlaps as defined in Chao et al. 2014\ndef naiveDivBeta(tab, q=1, rarefy='None', dis=True, viewpoint='local'):\n    if rarefy != 'None':\n        raretab = rarefy1(tab, rarefy)\n        ra = raretab / raretab.sum()\n    else:\n        ra = tab / tab.sum()\n\n    smplist = ra.columns\n    outdf = pd.DataFrame(0, index=smplist, columns=smplist)\n    for smp1nr in range(len(smplist) - 1):\n        smp1 = smplist[smp1nr]\n        for smp2nr in range(smp1nr + 1, len(smplist)):\n            smp2 = smplist[smp2nr]\n\n            if q == 1.0:\n                mask1 = ra[smp1] != 0\n                raLn1 = ra[smp1][mask1] * np.log(ra[smp1][mask1])\n                raLn1 = raLn1.sum()\n                mask2 = ra[smp2] != 0\n                raLn2 = ra[smp2][mask2] * np.log(ra[smp2][mask2])\n                raLn2 = raLn2.sum()\n                alphavalue = math.exp(-0.5 * raLn1 - 0.5 * raLn2)\n\n                ra_mean = ra[[smp1, smp2]].mean(axis=1)\n                maskg = (ra_mean != 0)\n                raLng = ra_mean[maskg] * np.log(ra_mean[maskg])\n                raLng = raLng.sum()\n                gammavalue = math.exp(-raLng)\n\n                betavalue = gammavalue / alphavalue\n                outdf.loc[smp1, smp2] = betavalue\n                outdf.loc[smp2, smp1] = betavalue\n            else:\n                mask1 = ra[smp1] != 0\n                ra1 = ra[smp1][mask1]\n                ra1pow = ra1.pow(q)\n                ra1powsum = ra1pow.sum()\n                mask2 = ra[smp2] != 0\n                ra2 = ra[smp2][mask2]\n                ra2pow = ra2.pow(q)\n                ra2powsum = ra2pow.sum()\n                alphavalue = (0.5 * ra1powsum + 0.5 * ra2powsum) ** (1 / (1 - q))\n\n                ra_mean = ra[[smp1, smp2]].mean(axis=1)\n                maskg = (ra_mean != 0)\n                rag = ra_mean[maskg]\n                ragpow = rag.pow(q)\n                ragpowsum = ragpow.sum()\n                gammavalue = ragpowsum ** (1 / (1 - q))\n\n                betavalue = gammavalue / alphavalue\n                outdf.loc[smp1, smp2] = betavalue\n                outdf.loc[smp2, smp1] = betavalue\n\n    if dis:\n        dist = beta2Dist(beta=outdf, q=q, divType='naive', viewpoint=viewpoint)\n        return dist\n    else:\n        return outdf\n\n# Calculate matrix of pairwise Bray-Curtis dissimilarities\ndef bray(tab):\n    ra = tab / tab.sum()\n    smplist = list(tab.columns)\n    outdf = pd.DataFrame(0, index=smplist, columns=smplist)\n    for smp1nr in range(len(smplist) - 1):\n        smp1 = smplist[smp1nr]\n        for smp2nr in range(smp1nr + 1, len(smplist)):\n            smp2 = smplist[smp2nr]\n            brayvalue = 1 - (ra.loc[:, [smp1, smp2]].min(axis=1).sum())\n            outdf.loc[smp1, smp2] = brayvalue\n            outdf.loc[smp2, smp1] = brayvalue\n    return outdf\n\n# Calculate matrix of pairwise Jaccard dissimilarities\ndef jaccard(tab):\n    bintab = tab.copy()\n    bintab[bintab > 0] = 1\n    smplist = list(bintab.columns)\n    outdf = pd.DataFrame(0, index=smplist, columns=smplist)\n    for smp1nr in range(len(smplist) - 1):\n        smp1 = smplist[smp1nr]\n        for smp2nr in range(smp1nr + 1, len(smplist)):\n            smp2 = smplist[smp2nr]\n            joined = bintab[[smp1, smp2]].sum(axis=1)\n            shared = joined[joined == 2].count()\n            total = joined[joined > 0].count()\n            jacvalue = 1 - shared/total\n            outdf.loc[smp1, smp2] = jacvalue\n            outdf.loc[smp2, smp1] = jacvalue\n    return outdf\n\n# Returns phylogenetic alpha diversity of order q for all samples\n# FD as in Chiu et al. Plos One, 2014\ndef phylDivAlpha(tab, distmat, q=0, rarefy='None'):\n    if rarefy != 'None':\n        raretab = rarefy1(tab, rarefy)\n        ra = raretab / raretab.sum()\n    else:\n        ra = tab / tab.sum()\n\n    outdf = pd.Series(0, index=ra.columns)\n    svlist = ra.index.tolist()\n    distmat = distmat.loc[svlist, svlist]\n    Qframe = raoQ(ra, distmat)\n    if q == 0:\n        for smp in tab.columns:\n            ra2mat = pd.DataFrame(np.outer(ra.loc[:, smp].values, ra.loc[:, smp].values), index=ra.index,\n                                  columns=ra.index)\n            mask = ra2mat > 0\n            ra2mat[mask] = 1\n            dQmat = distmat.mul(1 / Qframe.loc[smp])\n            ra2dq = (ra2mat.mul(dQmat))\n            Chiuvalue = pow(sum(ra2dq.sum()), 1 / (2 * (1 - q)))\n            outdf.loc[smp] = Chiuvalue\n    elif q == 1:\n        for smp in tab.columns:\n            ra2mat = pd.DataFrame(np.outer(ra.loc[:, smp].values, ra.loc[:, smp].values), index=ra.index,\n                                  columns=ra.index)\n            mask = (ra2mat != 0)\n            ra2Lnmat = np.log(ra2mat[mask])\n            ra2ochLn = ra2mat.mul(ra2Lnmat)\n            dQmat = distmat.mul(1 / Qframe.loc[smp])\n            dQ_ra2_Ln = dQmat.mul(ra2ochLn)\n            Chiuvalue = math.exp(-0.5 * sum(dQ_ra2_Ln.sum()))\n            outdf.loc[smp] = Chiuvalue\n    else:\n        for smp in tab.columns:\n            ra2mat = pd.DataFrame(np.outer(ra.loc[:, smp].values, ra.loc[:, smp].values), index=ra.index,\n                                  columns=ra.index)\n            ra2matq = ra2mat.pow(q)\n            dQmat = distmat.mul(1 / Qframe.loc[smp])\n            ra2dq = (ra2matq.mul(dQmat))\n            Chiuvalue = pow(sum(ra2dq.sum()), 1 / (2 * (1 - q)))\n            outdf.loc[smp] = Chiuvalue\n    MD = outdf.mul(Qframe)\n    return outdf.mul(MD)\n\n# Returns matrix of paiwise phylogenetic dissimilarities of order q\n# Based on local functional overlaps as defined in Chao et al. 2014\ndef phylDivBeta(tab, distmat, q=1, rarefy='None', dis=True, viewpoint='local'):\n    if rarefy != 'None':\n        raretab = rarefy1(tab, rarefy)\n        ra = raretab / raretab.sum()\n    else:\n        ra = tab / tab.sum()\n\n    smplist = list(ra.columns)\n    outD = pd.DataFrame(0, index=smplist, columns=smplist)\n\n    # For showing progress\n    total_comp = (len(smplist)**2)/2\n    rootPhylDivBeta = tk.Tk()\n    calc_progress = tk.DoubleVar(rootPhylDivBeta, 0)\n    counter = 0\n    tk.Label(rootPhylDivBeta, text='Progress in calculation (%)', width=30).pack()\n    tk.Label(rootPhylDivBeta, textvariable=calc_progress, width=20).pack()\n\n    for smp1nr in range(len(smplist) - 1):\n        for smp2nr in range(smp1nr + 1, len(smplist)):\n\n            # For showing progress\n            counter += 1\n            if counter%10 == 0:\n                calc_progress.set(round(100*counter/total_comp, 2))\n                rootPhylDivBeta.update()\n\n            smp1 = smplist[smp1nr]\n            smp2 = smplist[smp2nr]\n\n            ra12 = ra.loc[:, [smp1, smp2]]\n            ra12['mean'] = ra12.mean(axis=1)\n            Qvalues = raoQ(ra12, distmat)\n            Qpooled = Qvalues['mean']\n            dqmat = distmat.mul(1 / Qpooled)\n\n            if q != 1:\n                # Get gamma\n                mask = ra12['mean'] > 0\n                ra2mat = pd.DataFrame(np.outer(ra12['mean'][mask], ra12['mean'][mask]), index=ra12[mask].index,\n                                      columns=ra12[mask].index)\n                ra2matq = ra2mat.pow(q)\n                ra2dq = ra2matq.mul(dqmat)\n                Dg = pow(sum(ra2dq.sum()), 1 / (2 * (1 - q)))\n\n                # Get alpha\n                mask1 = ra12[smp1] > 0\n                ra2mat = pd.DataFrame(np.outer(ra12[mask1][smp1], ra12[mask1][smp1]), index=ra12[mask1].index,\n                                      columns=ra12[mask1].index)\n                ra2mat = ra2mat / 4\n                ra2matq = ra2mat.pow(q)\n                ra2dq = ra2matq.mul(dqmat)\n                asum1 = sum(ra2dq.sum())\n\n                mask2 = ra12[smp2] > 0\n                ra2mat = pd.DataFrame(np.outer(ra12[mask2][smp2], ra12[mask2][smp2]), index=ra12[mask2].index,\n                                      columns=ra12[mask2].index)\n                ra2mat = ra2mat / 4\n                ra2matq = ra2mat.pow(q)\n                ra2dq = ra2matq.mul(dqmat)\n                asum2 = sum(ra2dq.sum())\n\n                ra2mat = pd.DataFrame(np.outer(ra12[mask1][smp1], ra12[mask2][smp2]), index=ra12[mask1].index,\n                                      columns=ra12[mask2].index)\n                ra2mat = ra2mat / 4\n                ra2matq = ra2mat.pow(q)\n                ra2dq = ra2matq.mul(dqmat)\n                asum12 = sum(ra2dq.sum())\n\n                Da = 0.5 * pow((asum1 + asum2 + 2 * asum12), 1 / (2 * (1 - q)))\n\n                # Calculate beta\n                outD.loc[smp1, smp2] = Dg / Da\n                outD.loc[smp2, smp1] = Dg / Da\n\n            else:\n                # Get gamma\n                mask = ra12['mean'] > 0\n                ra2mat = pd.DataFrame(np.outer(ra12['mean'][mask], ra12['mean'][mask]), index=ra12[mask].index,\n                                      columns=ra12[mask].index)\n                ra2matq = ra2mat * np.log(ra2mat)\n                ra2dq = ra2matq.mul(dqmat)\n                Dg = math.exp(-0.5 * sum(ra2dq.sum()))\n\n                # Get alpha\n                mask1 = ra12[smp1] > 0\n                ra2mat = pd.DataFrame(np.outer(ra12[mask1][smp1], ra12[mask1][smp1]), index=ra12[mask1].index,\n                                      columns=ra12[mask1].index)\n                ra2mat = ra2mat / 4\n                ra2matq = ra2mat * np.log(ra2mat)\n                ra2dq = ra2matq.mul(dqmat)\n                asum1 = sum(ra2dq.sum())\n\n                mask2 = ra12[smp2] > 0\n                ra2mat = pd.DataFrame(np.outer(ra12[mask2][smp2], ra12[mask2][smp2]), index=ra12[mask2].index,\n                                      columns=ra12[mask2].index)\n                ra2mat = ra2mat / 4\n                ra2matq = ra2mat * np.log(ra2mat)\n                ra2dq = ra2matq.mul(dqmat)\n                asum2 = sum(ra2dq.sum())\n\n                ra2mat = pd.DataFrame(np.outer(ra12[mask1][smp1], ra12[mask2][smp2]), index=ra12[mask1].index,\n                                      columns=ra12[mask2].index)\n                ra2mat = ra2mat / 4\n                ra2matq = ra2mat * np.log(ra2mat)\n                ra2dq = ra2matq.mul(dqmat)\n                asum12 = sum(ra2dq.sum())\n\n                Da = 0.5 * math.exp(-0.5 * (asum1 + asum2 + 2 * asum12))\n\n                # Calculate beta\n                outD.loc[smp1, smp2] = Dg / Da;\n                outD.loc[smp2, smp1] = Dg / Da\n    outFD = outD.pow(2)\n\n    calc_progress.set(100)\n    rootPhylDivBeta.update()\n\n    if dis:\n        return beta2Dist(beta=outFD, q=q, divType='phyl', viewpoint=viewpoint)\n    else:\n        return outFD\n\n# Visualizes how alpha diversity depends on diversity order\n# If distmat is specified phylogenetic alpha diversity is calculated, else naive\n# rarefy specifies depth to rarefy frequency table (default is the smallest sample count, 'min')\n# var refers to column heading in meta data used to color code samples\n# slist is a list of samples from the var column to include (default is all)\n# order refers to column heading in meta data used to order the samples\n# If ylog=True, the y-axis of the plot will be logarithmic\ndef plotDivAlpha(obj, distmat='None', rarefy='min', var='None', slist='All', order='None', ylog=False, colorlist='None', savename='None'):\n    #Pick out samples to include based on var and slist\n    meta = obj['meta']\n    if order != 'None':\n        meta = meta.sort_values(order)\n\n    if var == 'None':\n        smplist = meta.index.tolist()\n    elif slist == 'All':\n        smplist = meta[var].index.tolist()\n    else:\n        smplist = meta.loc[slist, var].index.tolist()\n\n    #Dataframe for holding results\n    xvalues = np.arange(0, 2.01, 0.05)\n    df = pd.DataFrame(0, index=xvalues, columns=smplist)\n\n    #Put data in dataframe\n    tab = obj['tab'][smplist]\n    for x in xvalues:\n        if isinstance(distmat, str):\n            alphadiv = naiveDivAlpha(tab, q=x, rarefy=rarefy)\n        else:\n            alphadiv = phylDivAlpha(tab, distmat, q=x, rarefy=rarefy)\n        df.loc[x, smplist] = alphadiv\n\n    #Plot data\n    plt.rcParams.update({'font.size': 20})\n    fig, ax = plt.subplots(figsize=(10, 6))\n\n    if colorlist == 'None':\n        colorlist = get_colors_markers('colors')\n    else:\n        colorlist = colorlist\n    colorcheck = []\n\n    for s in df.columns:\n        if var != 'None':\n            cv = meta.loc[s, var]\n        else:\n            cv = s\n\n        if cv not in colorcheck:\n            colorcheck.append(cv)\n            lab = cv\n        else:\n            lab = '_nolegend_'\n\n        colnr = colorcheck.index(cv)\n        col = colorlist[colnr % len(colorlist)]\n\n        if ylog:\n            ax.semilogy(df.index, df[s], lw=1, color=col, label=lab)\n        else:\n            ax.plot(df.index, df[s], lw=1, color=col, label=lab)\n\n    ax.set_ylabel('Diversity number ($^q$D)')\n    ax.set_xlabel('Diversity order (q)')\n    ax.set_xticks([0.0, 0.5, 1.0, 1.5, 2.0])\n    ax.set_xlim(0, 2)\n\n    plt.legend()\n    plt.tight_layout()\n    if savename != 'None':\n        plt.savefig(savename+'.pdf', format='pdf')\n        plt.savefig(savename)\n    plt.show()\n\n\n# Returns a dictionary with 'iterations' number of randomized tabs\n# The randomization can either be constained based on a column heading (var) in meta data and optionally an \"slist\" from that column\n# or by pair of samples or categories specified in \"spairs\". If pairvar specified a column heading in metadata associated with spairs,\n# if 'None' than spairs should be pairs of samples names\ndef randomizeTabs(obj, rarefy='None', var='None', slist='All', pairvar='None', spairs=[], pairW=1, iterations=11):\n    pre_tab = obj['tab']\n    meta = obj['meta']\n\n    if rarefy != 'None':\n        pre_tab = rarefy1(pre_tab, depth=rarefy)\n\n    #Check if we are doing pairwise comparisons\n    if len(spairs) > 0:\n        par = True\n    else:\n        par = False\n\n    #Subset table to samples\n    if not par:\n        if slist == 'All':\n            tab = pre_tab.copy()\n        elif var == 'None':\n            tab = pre_tab[slist]\n        else:\n            samples = meta[var].isin(slist).index\n            tab = pre_tab[samples]\n    else:\n        if pairvar == 'None':\n            samples = [y for x in spairs for y in x]\n            tab = pre_tab[samples]\n        else:\n            cats = [y for x in spairs for y in x]\n            samples = meta[meta[pairvar].isin(cats)].index\n            tab = pre_tab[samples]\n\n    # Make tkinter object that keeps track of calculationsprogress\n    rootRT = tk.Tk()\n    rootRT.title('randomizeTabs')\n    calc_progress = tk.DoubleVar(rootRT, 0)\n    calc_counter = 0\n    tk.Label(rootRT, text='Progress in calculation (%)', width=30).pack()\n    tk.Label(rootRT, textvariable=calc_progress, width=20).pack()\n\n    #Generate a dictionary with #iterations# random tabs\n    random_tabs = []\n    for i in range(iterations):\n\n        # For showing progress\n        calc_counter += 1\n        calc_progress.set(round(100 * calc_counter / iterations, 2))\n        rootRT.update()\n\n        RCtab = pd.DataFrame(0, index=tab.index, columns=tab.columns)\n        if not par:\n            if var == 'None':\n                tabsum = tab.sum(axis=1)\n                for c in RCtab.columns:\n                    totalreads = tab[c].sum() #Total reads\n                    totalSVs = tab[c][tab[c] > 0].count() #Richness\n                    rows = RCtab[c].sample(n=totalSVs, weights=tabsum).index.tolist() #Randomly sample certain SVs\n                    RCtab.loc[rows, c] = 1 #Give each of these SVs a count of 1\n                    probabilities = tabsum[rows] / tabsum[rows].sum()\n                    randomchoice = np.random.choice(rows, size=totalreads - totalSVs, p=probabilities)\n                    uniquechoices = np.unique(randomchoice, return_counts=True)\n                    RCtab.loc[uniquechoices[0], c] = RCtab.loc[uniquechoices[0], c] + uniquechoices[1]\n            else:\n                for cat in slist:\n                    samples = meta[meta[var] == cat].index\n                    subtab = tab[samples]\n                    tabsum = subtab.sum(axis=1)\n                    for c in samples:\n                        totalreads = subtab[c].sum()\n                        totalSVs = subtab[c][subtab[c] > 0].count()\n                        rows = RCtab[c].sample(n=totalSVs, weights=tabsum).index.tolist()\n                        RCtab.loc[rows, c] = 1 #Give each of these SVs a count of 1\n                        probabilities = tabsum[rows] / tabsum[rows].sum()\n                        randomchoice = np.random.choice(rows, size=totalreads - totalSVs, p=probabilities)\n                        uniquechoices = np.unique(randomchoice, return_counts=True)\n                        RCtab.loc[uniquechoices[0], c] = RCtab.loc[uniquechoices[0], c] + uniquechoices[1]\n        elif par:\n            if pairvar == 'None':\n                for pair in spairs:\n                    #Find min SV count\n                    count0 = tab[pair[0]][tab[pair[0]] > 0].count()\n                    count1 = tab[pair[1]][tab[pair[1]] > 1].count()\n                    if count0 < count1:\n                        col0 = tab[pair[0]]*pairW\n                        tabsum = col0 + tab[pair[1]]\n                    elif count1 < count0:\n                        col1 = tab[pair[1]]*pairW\n                        tabsum = col1 + tab[pair[0]]\n                    else:\n                        tabsum = tab[pair].sum(axis=1)\n\n                    for c in pair:\n                        totalreads = tab[c].sum()  # Total reads\n                        totalSVs = tab[c][tab[c] > 0].count()  # Richness\n                        rows = RCtab[c].sample(n=totalSVs, weights=tabsum).index.tolist()  # Randomly sample certain SVs\n                        RCtab.loc[rows, c] = 1  # Give each of these SVs a count of 1\n                        probabilities = tabsum[rows] / tabsum[rows].sum()\n                        randomchoice = np.random.choice(rows, size=totalreads - totalSVs, p=probabilities)\n                        uniquechoices = np.unique(randomchoice, return_counts=True)\n                        RCtab.loc[uniquechoices[0], c] = RCtab.loc[uniquechoices[0], c] + uniquechoices[1]\n            else:\n                for pair in spairs:\n                    slist0 = meta[meta[pairvar] == pair[0]].index.tolist()\n                    slist1 = meta[meta[pairvar] == pair[1]].index.tolist()\n                    subtab = tab[slist0+slist1]\n\n                    #Find min SV count\n                    sum0 = subtab[slist0].sum(axis=1)\n                    sum1 = subtab[slist1].sum(axis=1)\n                    count0 = sum0[sum0 > 0].count()\n                    count1 = sum1[sum1 > 0].count()\n                    if count0 < count1:\n                        subtab[slist0] = subtab[slist0]*pairW\n                        tabsum = subtab.sum(axis=1)\n                    elif count1 < count0:\n                        subtab[slist1] = subtab[slist1]*pairW\n                        tabsum = subtab.sum(axis=1)\n                    else:\n                        tabsum = subtab.sum(axis=1)\n                    for c in subtab.columns:\n                        totalreads = tab[c].sum()  # Total reads\n                        totalSVs = tab[c][tab[c] > 0].count()  # Richness\n                        rows = RCtab[c].sample(n=totalSVs, weights=tabsum).index.tolist()  # Randomly sample certain SVs\n                        RCtab.loc[rows, c] = 1  # Give each of these SVs a count of 1\n                        probabilities = tabsum[rows] / tabsum[rows].sum()\n                        randomchoice = np.random.choice(rows, size=totalreads - totalSVs, p=probabilities)\n                        uniquechoices = np.unique(randomchoice, return_counts=True)\n                        RCtab.loc[uniquechoices[0], c] = RCtab.loc[uniquechoices[0], c] + uniquechoices[1]\n        random_tabs.append(RCtab)\n    rootRT.destroy() #Close \"show progress\" window\n    return random_tabs\n\n# Returs a dictionary with two items:\n# Raup-Crick ('RC')\n# All 3-D matrix with all the randomized dissimilarities\ndef RCq(tab, randomTabs, distmat='None', q=1):\n    stokeep = randomTabs[0].columns\n    tab = tab[stokeep]\n\n    if isinstance(distmat, str):\n        betadiv = naiveDivBeta(tab, q=q)\n    else:\n        betadiv = phylDivBeta(tab, distmat=distmat, q=q)\n    orderedsampleslist = betadiv.columns\n\n    RC_tab = pd.DataFrame(0, index=betadiv.index, columns=betadiv.columns)\n    random_beta_all = np.zeros((len(betadiv.index), len(betadiv.columns), len(randomTabs)))\n\n    # Make tkinter object that keeps track of calculationsprogress\n    rootRCq = tk.Tk()\n    rootRCq.title('RCq')\n    calc_progress = tk.DoubleVar(rootRCq, 0)\n    calc_counter = 0\n    tk.Label(rootRCq, text='Progress in calculation (%)', width=30).pack()\n    tk.Label(rootRCq, textvariable=calc_progress, width=20).pack()\n\n    # Start iterations through randomTabs\n    for i in range(len(randomTabs)):\n\n        # For showing progress\n        calc_counter += 1\n        calc_progress.set(round(100 * calc_counter / len(randomTabs), 2))\n        rootRCq.update()\n\n        if isinstance(distmat, str):\n            randombeta = naiveDivBeta(randomTabs[i], q=q)\n        else:\n            randombeta = phylDivBeta(randomTabs[i], distmat=distmat, q=q)\n\n        randombeta = randombeta.loc[orderedsampleslist, orderedsampleslist]\n\n        mask = betadiv > randombeta\n        RC_tab[mask] = RC_tab[mask] + 1\n        mask = betadiv == randombeta\n        RC_tab[mask] = RC_tab[mask] + 0.5\n\n        random_beta_all[:, :, i] = randombeta\n\n    rootRCq.destroy() #Close \"show progress\" window\n\n    RC_tab = RC_tab / len(randomTabs)\n\n    out = {}\n    out['RC'] = RC_tab\n    out['all'] = random_beta_all\n    return out\n\n# Plots dissimilarity between pairs of samples types\ndef pairwiseDivBeta(obj, distmat='None', rarefy='None', pairvar='None', spairs=[],\n                nullModel=True, weights=[0, 0.5, 1], iterations=10,\n                qrange=[0, 2, 0.5], colorlist='None',\n                    onlyPlotData='None', onlyReturnData=False,\n                    savename='None'):\n\n    #Function for plotting data\n    def plot_pairwiseDB(data_dict, colorlist=colorlist, savename=savename):\n        df = data_dict['qD_avg']\n        dfstd = data_dict['qD_std']\n        jacvalues = data_dict['Jac_avg']\n        jacvaluesstd = data_dict['Jac_std']\n        brayvalues = data_dict['Bray_avg']\n        brayvaluesstd = data_dict['Bray_std']\n        if nullModel:\n            dfrandom = data_dict['null_avg']\n            dfrandomstd = data_dict['null_std']\n            dfRC = data_dict['RC']\n            weights = data_dict['weights']\n\n        if colorlist == 'None':\n            colorlist = get_colors_markers()\n\n        plt.rcParams.update({'font.size': 20})\n        fig, ax = plt.subplots(figsize=(10, 6))\n\n        for c_nr in range(len(df.columns)):\n            c = df.columns[c_nr]\n\n            #qD\n            ylow = df[c]-dfstd[c]\n            yhigh = df[c]+dfstd[c]\n\n            ax.fill_between(df.index.tolist(), ylow.tolist(), yhigh.tolist(), alpha=0.3, color=colorlist[c_nr])\n            ax.plot(df.index, df[c], lw=1, label=c, color=colorlist[c_nr])\n\n            ax.scatter([0.01+c_nr*0.02], [jacvalues[c]], label='_nolegend_', marker='s', color=colorlist[c_nr])\n            ax.errorbar([0.01+c_nr*0.02], [jacvalues[c]], yerr=jacvaluesstd[c], color=colorlist[c_nr])\n\n            ax.scatter([0.99+c_nr*0.02], [brayvalues[c]], label='_nolegend_', marker='o', color=colorlist[c_nr])\n            ax.errorbar([0.99+c_nr*0.02], [brayvalues[c]], yerr=brayvaluesstd[c], color=colorlist[c_nr])\n\n        ax.scatter([], [], label='Jaccard', marker='s', color='black')\n        ax.scatter([], [], label='Bray-Curtis', marker='o', color='black')\n\n        ax.set_ylabel('Dissimilarity ($^q$dis)')\n        ax.set_xlabel('Diversity order (q)')\n        ax.set_xticks(np.arange(qrange[0], qrange[1] + 0.01, qrange[2]))\n        ax.set_xlim(qrange[0], qrange[1])\n        ax.set_yticks(np.arange(0, 1.01, 0.2))\n        ax.set_ylim(-0.05, 1.05)\n        ax.axhline(0, 0, 1, color='grey', alpha=0.5, lw=0.5)\n        ax.axhline(1, 0, 1, color='grey', alpha=0.5, lw=0.5)\n        plt.legend(bbox_to_anchor=(1, 1), fontsize=12, loc=2)\n        plt.tight_layout(rect=[0, 0, 0.9, 1])\n        if savename != 'None':\n            plt.savefig(savename + '.pdf', format='pdf')\n            plt.savefig(savename)\n\n        if nullModel:\n            fig2, ax2 = plt.subplots(nrows=len(weights), ncols=len(df.columns), figsize=(6*len(df.columns), 4*len(weights)), sharey=True, sharex=True)\n            for c_nr in range(len(df.columns)):\n                for w_nr in range(len(weights)):\n                    c = dfrandom[w_nr].columns[c_nr]\n                    w = weights[w_nr]\n                    if len(df.columns) == 1:\n                        ax_sub = w_nr\n                    else:\n                        ax_sub = w_nr, c_nr\n\n                    ax2[ax_sub].fill_between([dfrandom[w_nr].index.tolist()[0], dfrandom[w_nr].index.tolist()[-1]], [0.975, 0.975], [1, 1], alpha=0.3, color='grey')\n                    ax2[ax_sub].fill_between([dfrandom[w_nr].index.tolist()[0], dfrandom[w_nr].index.tolist()[-1]], [0, 0], [0.025, 0.025], alpha=0.3, color='grey')\n\n                    ylow = dfrandom[w_nr][c]-dfrandomstd[w_nr][c]\n                    yhigh = dfrandom[w_nr][c]+dfrandomstd[w_nr][c]\n                    ax2[ax_sub].fill_between(dfrandom[w_nr].index.tolist(), ylow.tolist(), yhigh.tolist(), alpha=0.6, color='grey')\n                    ax2[ax_sub].plot(dfrandom[w_nr].index, dfrandom[w_nr][c], lw=1, color='grey')\n                    ax2[ax_sub].plot(dfRC[w_nr].index, dfRC[w_nr][c], lw=1, linestyle='--', label=c, color='black')\n\n                    ylow = df[c]-dfstd[c]\n                    yhigh = df[c]+dfstd[c]\n                    ax2[ax_sub].fill_between(df.index.tolist(), ylow.tolist(), yhigh.tolist(), alpha=0.3, color=colorlist[c_nr])\n                    ax2[ax_sub].plot(df.index, df[c], lw=1, color=colorlist[c_nr])\n                    ax2[ax_sub].set_title('Pair:'+c+', Weight:'+str(w), fontsize=12)\n\n\n                    ax2[ax_sub].set_xticks(np.arange(qrange[0], qrange[1] + 0.01, qrange[2]))\n                    ax2[ax_sub].set_xlim(qrange[0], qrange[1])\n                    ax2[ax_sub].set_yticks(np.arange(0, 1.01, 0.2))\n                    ax2[ax_sub].set_ylim(-0.05, 1.05)\n\n                    if c_nr == 0:\n                        ax2[ax_sub].set_ylabel('Dissimilarity ($^q$dis)')\n\n                    if w_nr == len(weights) - 1:\n                        ax2[ax_sub].set_xlabel('Diversity order (q)')\n\n            plt.tight_layout()\n            if savename != 'None':\n                plt.savefig(savename + '.pdf', format='pdf')\n                plt.savefig(savename)\n        plt.show()\n\n    ##############\n    if onlyPlotData != 'None':\n        plot_pairwiseDB(onlyPlotData)\n    else:\n        #Sort out samples to include in analysis, rarefy if needed\n        meta = obj['meta']\n        allcatlist = [y for x in spairs for y in x]\n\n        if pairvar != 'None':\n            allsampleslist = meta[meta[pairvar].isin(allcatlist)].index.tolist()\n        else:\n            allsampleslist = list(set(allcatlist))\n\n        obj2 = subsetSamples(obj, slist=allsampleslist) #Subset object based on samples to analyze\n        if rarefy != 'None': #Rarefy if desired\n            tab = rarefy1(obj2['tab'], depth=rarefy)\n            obj2['tab'] = tab\n        else:\n            tab = obj2['tab']\n\n        #Dataframe for holding results\n        qvalues = np.arange(qrange[0], qrange[1]+0.01, 0.1)\n        qvaluesnew = []\n        for v in qvalues:\n            qvaluesnew.append(round(v, 2))\n        qvalues = qvaluesnew\n\n        pairnames = []\n        for pair in spairs:\n            pairnames.append(pair[0] + '-' + pair[1])\n        df = pd.DataFrame(index=qvalues, columns=pairnames)\n        dfstd = pd.DataFrame(index=qvalues, columns=pairnames)\n\n        #In case null model is used, fix dataframes\n        t1 = time.time()\n        if nullModel:\n            dfrandom = []\n            dfrandomstd = []\n            dfRC = []\n            randomTabsList = []\n            for w in weights:\n                randomTabsList.append(randomizeTabs(obj2, rarefy='None', pairvar=pairvar, spairs=spairs, pairW=w, iterations=iterations))\n                dfrandom.append(pd.DataFrame(index=qvalues, columns=pairnames))\n                dfrandomstd.append(pd.DataFrame(index=qvalues, columns=pairnames))\n                dfRC.append(pd.DataFrame(index=qvalues, columns=pairnames))\n        print('Fix nullframe:', time.time()-t1)\n\n        #Make tkinter object that keeps track of calculationsprogress\n        rootPlotDivBeta = tk.Tk()\n        calc_progress = tk.DoubleVar(rootPlotDivBeta, 0)\n        calc_counter = 0\n        tk.Label(rootPlotDivBeta, text='Progress in calculation (%)', width=30).pack()\n        tk.Label(rootPlotDivBeta, textvariable=calc_progress, width=20).pack()\n\n        #Iterate for different diversity orders, q (x here)\n        for q in qvalues:\n\n            # For showing progress\n            calc_counter += 1\n            calc_progress.set(round(100 * calc_counter / len(qvalues), 2))\n            rootPlotDivBeta.update()\n\n\n            #Calculate dis for real tab\n            if isinstance(distmat, str):\n                betadiv = naiveDivBeta(tab, q=q)\n            else:\n                betadiv = phylDivBeta(tab, distmat=distmat, q=q)\n\n            # Calculate randomized dis\n            t1 = time.time()\n            if nullModel:\n                random_betadiv = []\n                RC_betadiv = []\n                for wnr in range(len(weights)):\n                    obj_RC = RCq(tab, randomTabsList[wnr], distmat=distmat, q=q)\n                    random_betadiv.append(obj_RC['all'])\n                    RC_betadiv.append(obj_RC['RC'])\n            print('RCq', time.time()-t1)\n\n            #Dictionaries holding results from pair comparisons\n            paircomps = {}\n            if nullModel:\n                random_paircomps = []\n                RC_paircomps = []\n                for wnr in range(len(weights)):\n                    random_paircomps.append({})\n                    RC_paircomps.append({})\n\n            #Iterate through each pair\n            t1 = time.time()\n            for pair in spairs:\n                pairname = pair[0]+'-'+pair[1]\n\n                # Initiate lists\n                paircomps[pairname] = []\n                if nullModel:\n                    for wnr in range(len(weights)):\n                        random_paircomps[wnr][pairname] = []\n                        RC_paircomps[wnr][pairname] = []\n\n                # If each pair item could hold multiple replicate samples\n                if pairvar != 'None':\n                    slist0 = meta[meta[pairvar] == pair[0]].index.tolist()\n                    slist1 = meta[meta[pairvar] == pair[1]].index.tolist()\n                    for s0 in slist0:\n                        for s1 in slist1:\n                            paircomps[pairname].append(betadiv.loc[s0, s1])\n                            if nullModel:\n                                pos0 = betadiv.columns.tolist().index(s0)\n                                pos1 = betadiv.columns.tolist().index(s1)\n                                for wnr in range(len(weights)):\n                                    RC_paircomps[wnr][pairname].append(RC_betadiv[wnr].loc[s0, s1])\n                                    for randtab_reps in range(len(random_betadiv[wnr][0, 0, :])):\n                                        random_paircomps[wnr][pairname].append(random_betadiv[wnr][pos0, pos1, randtab_reps])\n                else:\n                    paircomps[pairname].append(betadiv.loc[pair[0], pair[1]])\n                    if nullModel:\n                        pos0 = betadiv.columns.tolist().index(pair[0])\n                        pos1 = betadiv.columns.tolist().index(pair[1])\n                        for wnr in range(len(weights)):\n                            RC_paircomps[wnr][pairname].append(RC_betadiv[wnr].loc[pair[0], pair[1]])\n                            for random_iter in range(len(random_betadiv[wnr][0, 0, :])):\n                                random_paircomps[wnr][pairname].append(random_betadiv[wnr][pos0, pos1, random_iter])\n\n                df.loc[q, pairname] = np.mean(paircomps[pairname])\n                dfstd.loc[q, pairname] = np.std(paircomps[pairname])\n                if nullModel:\n                    for wnr in range(len(weights)):\n                        dfrandom[wnr].loc[q, pairname] = np.mean(random_paircomps[wnr][pairname])\n                        dfrandomstd[wnr].loc[q, pairname] = np.std(random_paircomps[wnr][pairname])\n                        dfRC[wnr].loc[q, pairname] = np.mean(RC_paircomps[wnr][pairname])\n\n            print('Iter', time.time()-t1)\n\n        rootPlotDivBeta.destroy() #Close \"show progress\" window\n\n        #Calculate Bray-Curtis\n        brayvalues = {}\n        brayvaluesstd = {}\n        betabray = bray(tab)\n        for pair in spairs:\n            pairname = pair[0] + '-' + pair[1]\n            paircomps[pairname] = []\n            if pairvar != 'None':\n                slist0 = meta[meta[pairvar] == pair[0]].index.tolist()\n                slist1 = meta[meta[pairvar] == pair[1]].index.tolist()\n                for s0 in slist0:\n                    for s1 in slist1:\n                        paircomps[pairname].append(betabray.loc[s0, s1])\n            else:\n                paircomps[pairname].append(betabray.loc[pair[0], pair[1]])\n            brayvalues[pairname] = np.mean(paircomps[pairname])\n            brayvaluesstd[pairname] = np.std(paircomps[pairname])\n\n        #Calculate Jaccard\n        jacvalues = {}\n        jacvaluesstd = {}\n        betajac = jaccard(tab)\n        for pair in spairs:\n            pairname = pair[0] + '-' + pair[1]\n            paircomps[pairname] = []\n            if pairvar != 'None':\n                slist0 = meta[meta[pairvar] == pair[0]].index.tolist()\n                slist1 = meta[meta[pairvar] == pair[1]].index.tolist()\n                for s0 in slist0:\n                    for s1 in slist1:\n                        paircomps[pairname].append(betajac.loc[s0, s1])\n            else:\n                paircomps[pairname].append(betajac.loc[pair[0], pair[1]])\n            jacvalues[pairname] = np.mean(paircomps[pairname])\n            jacvaluesstd[pairname] = np.std(paircomps[pairname])\n\n        #Save data as pickle file\n        out_save = {}\n        out_save['qD_avg'] = df; out_save['qD_std'] = dfstd\n        out_save['Bray_avg'] = brayvalues; out_save['Bray_std'] = brayvaluesstd\n        out_save['Jac_avg'] = jacvalues; out_save['Jac_std'] = jacvaluesstd\n        if nullModel:\n            out_save['null_avg'] = dfrandom; out_save['null_std'] = dfrandomstd\n            out_save['RC'] = dfRC; out_save['weights'] = weights\n        if savename != 'None':\n            with open(savename+'.pickle', 'wb') as f:\n                pickle.dump(out_save, f)\n\n        if onlyReturnData:\n            return out_save\n        else:\n            plot_pairwiseDB(out_save)\n\n\n# Visualizes dissimilarities in PCoA plot\n# dist is distance matrix and meta is meta data\n# var1 is heading in meta used to color code, var2 is heading in meta used to code by marker type\n# var1_title and var_2 title are the titles used in the legend\n# whitePad sets the space between the outermost points and the plot limits (1.0=no space)\n# rightSpace is the space for the legend on the right\n# var2pos is the vertical position of the var2 legend\n# tag is heading in meta used to add labels to each point in figure\n# order is heading in meta used to order samples\n# title is title of the entire figure\n# colorlist specifies colorlist to use for var1; same for markerlist and var2\n# savename is path and name to save png figure output\ndef plotPCoA(dist, meta, biplot=[], var1='None', var2='None', var1_title='', var2_title='',\n             whitePad=1.1, rightSpace=0.15, var2pos=0.4, tag='None', order='None', title='',\n             colorlist='None', markerlist='None', savename='None'):\n    def get_eig(d): #Function for centering and eigen-decomposition of distance matrix\n        dist2 = -0.5*(d**2)\n        col_mean = dist2.mean(axis=0)\n        row_mean = dist2.mean(axis=1)\n        tot_mean = np.array(dist2).flatten().mean()\n        dist2_cent = dist2.subtract(col_mean, axis=1)\n        dist2_cent = dist2_cent.subtract(row_mean, axis=0)\n        dist2_cent = dist2_cent.add(tot_mean)\n        vals, vects = np.linalg.eig(dist2_cent)\n        return [vals, vects]\n\n    ev_ev = get_eig(dist)\n    #Correction method for negative eigenvalues\n    if min(ev_ev[0]) < 0: #From Legendre 1998, method 1 (derived from Lingoes 1971) chapter 9, page 502\n        d2 = (dist[dist != 0]**2 + 2*abs(min(ev_ev[0])))**0.5\n        d2 = d2.fillna(0)\n        ev_ev = get_eig(d2)\n\n    #Get proportions and coordinates\n    vals = ev_ev[0].copy()\n    vects = ev_ev[1]\n    prop = [] #Fraction proportion of eigvalues for axis labels\n    coords = [] #Two arrays with coordinates\n    U_vectors = [] #Main vectors used for biplot\n    Eig_vals = [] #Main vectors used for biplot\n    for i in range(2):\n        maxpos = np.argmax(vals)\n        prop.append(vals[maxpos]/sum(ev_ev[0]))\n        coords.append((vals[maxpos]**0.5)*vects[:, maxpos])\n        Eig_vals.append(vals[maxpos])\n        vals[maxpos] = 0\n\n    xaxislims = [min(coords[0])*whitePad, max(coords[0])*whitePad]\n    yaxislims = [min(coords[1])*whitePad, max(coords[1])*whitePad]\n\n    #Check biplot\n    if len(biplot) > 0:\n        #Standardize U (eigenvectors)\n        U_vectors = pd.DataFrame(coords, columns=dist.columns).transpose()\n        U_vectors[0] = (U_vectors[0]-U_vectors[0].mean())/U_vectors[0].std()\n        U_vectors[1] = (U_vectors[1]-U_vectors[1].mean())/U_vectors[1].std()\n\n        #Standardize Y\n        Y = pd.DataFrame(index=dist.columns)\n        for mh in biplot:\n            Y[mh] = meta[mh]\n            Y[mh] = (Y[mh]-Y[mh].mean())/Y[mh].std()\n        Y_cent = Y.transpose()\n        Spc =(1/(len(dist.columns)-1))*np.matmul(Y_cent, U_vectors)\n        biglambda = np.array([[Eig_vals[0]**-0.5, 0], [0, Eig_vals[1]**-0.5]])\n        Uproj = ((len(dist.columns)-1)**0.5)*np.matmul(Spc, biglambda)\n\n        #Scale to the plot\n        Uscalefactors = []\n        Uscalefactors.append(max(coords[0])/max(Uproj[:, 0]))\n        Uscalefactors.append(min(coords[0])/min(Uproj[:, 0]))\n        Uscalefactors.append(max(coords[1])/max(Uproj[:, 1]))\n        Uscalefactors.append(min(coords[1])/min(Uproj[:, 1]))\n        Uscale = 1\n        for i in Uscalefactors:\n            if i < Uscale and i > 0:\n                Uscale = i\n        Uproj = Uproj*Uscale\n\n    # Do the plotting\n\n    #Set axis names and make dataframe for plotting\n    pc1_perc = round(100 * prop[0], 1)\n    xn = 'PC1 (' + str(pc1_perc) + '%)'\n    if '+' in xn:\n        xn = xn[:4] + xn[-1]\n    pc2_perc = round(100 * prop[1], 1)\n    yn = 'PC2 (' + str(pc2_perc) + '%)'\n    if '+' in yn:\n        yn = yn[:4] + yn[-1]\n\n    smplist = dist.index\n    pcoadf = pd.DataFrame({xn: coords[0], yn: coords[1]}, index=smplist)\n\n    # Combine pcoa results with meta data\n    meta[xn] = pcoadf[xn]\n    meta[yn] = pcoadf[yn]\n    metaPlot = meta[meta[xn].notnull()]\n    if order != 'None':\n        meta = meta.sort_values(by=[order])\n\n    if var1 == 'None' and var2 == 'None':\n        return 'Error, no variables in input'\n    if var1 != 'None': #List of names used for different colors in legend\n        smpcats1 = []\n        [smpcats1.append(item) for item in meta[var1] if item not in smpcats1]\n    if var2 != 'None': #List of names used for different marker types in legend\n        smpcats2 = []\n        [smpcats2.append(item) for item in meta[var2] if item not in smpcats2]\n    if tag != 'None': #List of labels placed next to points\n        tagcats = []\n        [tagcats.append(item) for item in meta[tag] if item not in tagcats]\n\n    # Create figure\n    if colorlist == 'None':\n        colorlist = get_colors_markers('colors')\n    if markerlist == 'None':\n        markerlist = get_colors_markers('markers')\n\n    plt.rcParams.update({'font.size': 18})\n    fig, ax = plt.subplots(figsize=(10, 6))\n\n    linesColor = [[], []]\n    linesShape = [[], []]\n    shapeTracker = []\n\n    for i in range(len(smpcats1)):\n        metaPlot_i = metaPlot[metaPlot[var1] == smpcats1[i]] #Subset metaPlot based on var1 in smpcats1\n\n        if var2 != 'None':\n            linesColor[0].append(ax.scatter([], [], label=str(smpcats1[i]), color=colorlist[i]))\n            linesColor[1].append(smpcats1[i])\n\n            jcounter = 0\n            for j in range(len(smpcats2)):\n                if smpcats2[j] in list(metaPlot_i[var2]):\n                    metaPlot_ij = metaPlot_i[metaPlot_i[var2] == smpcats2[j]]\n                    xlist = metaPlot_ij[xn]\n                    ylist = metaPlot_ij[yn]\n                    ax.scatter(xlist, ylist, label=None, color=colorlist[i], marker=markerlist[jcounter], s=170)\n\n                    if jcounter not in shapeTracker:\n                        linesShape[0].append(ax.scatter([], [], label=str(smpcats2[j]), color='black', marker=markerlist[jcounter]))\n                        linesShape[1].append(smpcats2[jcounter])\n                        shapeTracker.append(jcounter)\n                jcounter += 1\n\n            # Here set both legends for color and marker\n            ax.legend(linesColor[0], linesColor[1], ncol=1, bbox_to_anchor=(1, 1), title=var1_title, frameon=False, markerscale=1.9, fontsize=18, loc=2)\n            from matplotlib.legend import Legend\n            leg = Legend(ax, linesShape[0], linesShape[1], bbox_to_anchor=(1, var2pos), title=var2_title, frameon=False, markerscale=1.9, fontsize=18, loc=2)\n            ax.add_artist(leg)\n\n        else: #If there is no var2, change both color and marker with each category in var1\n            linesColor[0].append(ax.scatter([], [], label=str(smpcats1[i]), color=colorlist[i], marker=markerlist[i]))\n            linesColor[1].append(smpcats1[i])\n\n            xlist = metaPlot_i[xn]\n            ylist = metaPlot_i[yn]\n            ax.scatter(xlist, ylist, label=None, color=colorlist[i], marker=markerlist[i], s=100)\n            ax.legend(linesColor[0], linesColor[1], ncol=1, bbox_to_anchor=(1, 1), title=var1_title, frameon=False, markerscale=1.9, fontsize=18, loc=2)\n\n    ##Put tags at each point\n    if tag != 'None':\n        for ix in metaPlot.index:\n            tagtext = metaPlot.loc[ix, tag]\n            tagx = metaPlot.loc[ix, xn]\n            tagy = metaPlot.loc[ix, yn]\n            ax.annotate(tagtext, (tagx, tagy))\n\n    ##Input arrows\n    if len(biplot) > 0:\n        for mh_nr in range(len(biplot)):\n            ax.arrow(0, 0, Uproj[mh_nr, 0], Uproj[mh_nr, 1])\n            ax.annotate(biplot[mh_nr], (Uproj[mh_nr, 0], Uproj[mh_nr, 1]))\n        ax.axhline(0, 0, 1, linestyle='--', color='grey', lw=0.5)\n        ax.axvline(0, 0, 1, linestyle='--', color='grey', lw=0.5)\n\n    ax.set_xlabel(xn)\n    ax.set_ylabel(yn)\n    ax.set_xlim(xaxislims)\n    ax.set_ylim(yaxislims)\n    plt.title(title)\n    plt.tight_layout(rect=[0, 0, 1-rightSpace, 1])\n    if savename != 'None':\n        plt.savefig(savename+'.pdf', format='pdf')\n        plt.savefig(savename)\n    plt.show()","repo_name":"omodin/ompy","sub_path":"ompy_functions.py","file_name":"ompy_functions.py","file_ext":"py","file_size_in_byte":73511,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"38920572826","text":"import os\r\nimport sys\r\nimport time\r\nimport keyboard\r\nimport pyautogui\r\nimport ctypes\r\nimport hashlib\r\n\r\nfrom datetime import datetime\r\nfrom termcolor import colored\r\nfrom colorant import Colorant\r\nfrom settings import Settings\r\nfrom keyauth import api\r\n\r\nsettings = Settings()\r\n\r\ndef getchecksum():\r\n    md5_hash = hashlib.md5()\r\n    file = open(''.join(sys.argv), \"rb\")\r\n    md5_hash.update(file.read())\r\n    digest = md5_hash.hexdigest()\r\n    return digest\r\n\r\nkeyauthapp = api(\r\n    name = \"Colorant\",\r\n    ownerid = \"XpXJiuVsQu\",\r\n    secret = \"5f3628628bc9a1261189c940d38278905ad42ecbea7286f54a38ddab8966f5b0\",\r\n    version = \"1.0\",\r\n    hash_to_check = getchecksum()\r\n)\r\n\r\nKEY_NAMES = {\r\n 1: \"'L Mouse Button'\", 2: \"'R Mouse Button'\", 4: \"'MButton'\", 5: \"'X1 Mouse Button'\", \r\n 6: \"'X2 Mouse Button'\", 8: \"'Backspace'\", 9: \"'Tab'\", 13: \"'Enter'\", 16: \"'Shift'\", \r\n 17: \"'Ctrl'\", 18: \"'Alt'\", 20: \"'CapsLock'\", 27: \"'Esc'\", 32: \"'Spacebar'\", 37: \"'Left'\", \r\n 38: \"'Up'\", 39: \"'Right'\", 40: \"'Down'\", 48: \"'0'\", 49: \"'1'\", 50: \"'2'\", 51: \"'3'\", \r\n 52: \"'4'\", 53: \"'5'\", 54: \"'6'\", 55: \"'7'\", 56: \"'8'\", 57: \"'9'\", 65: \"'A'\", 66: \"'B'\", \r\n 67: \"'C'\", 68: \"'D'\", 69: \"'E'\", 70: \"'F'\", 71: \"'G'\", 72: \"'H'\", 73: \"'I'\", 74: \"'J'\", \r\n 75: \"'K'\", 76: \"'L'\", 77: \"'M'\", 78: \"'N'\", 79: \"'O'\", 80: \"'P'\", 81: \"'Q'\", 82: \"'R'\", \r\n 83: \"'S'\", 84: \"'T'\", 85: \"'U'\", 86: \"'V'\", 87: \"'W'\", 88: \"'X'\", 89: \"'Y'\", 90: \"'Z'\", \r\n 112: \"'F1'\", 113: \"'F2'\", 114: \"'F3'\", 115: \"'F4'\", 116: \"'F5'\", 117: \"'F6'\", \r\n 118: \"'F7'\", 119: \"'F8'\", 120: \"'F9'\", 121: \"'F10'\", 122: \"'F11'\", 123: \"'F12'\"}\r\n\r\nmonitor = pyautogui.size()\r\nCENTER_X, CENTER_Y = monitor.width // 2, monitor.height // 2\r\n\r\nXFOV = settings.get_int('Settings', 'X-FOV')\r\nYFOV = settings.get_int('Settings', 'Y-FOV')\r\nTOGGLE_KEY = settings.get('Settings', 'TOGGLE-ON/OFF')\r\nAIMBOT_KEY = int(settings.get('AIMBOT', 'KEY-BIND'), 16)\r\nTRIGGERBOT_KEY = int(settings.get('TRIGGERBOT', 'KEY-BIND'), 16)\r\nSILENTBOT_KEY = int(settings.get('SILENTBOT', 'KEY-BIND'), 16)\r\nXSPEED = settings.get_float('AIMBOT', 'X-SPEED')\r\nYSPEED = settings.get_float('AIMBOT', 'Y-SPEED')\r\nYOUR_INGAME_SENSITIVITY = settings.get_float('Settings', 'IN-GAME-SENS')\r\nFLICKSPEED = 1.07437623 * (YOUR_INGAME_SENSITIVITY ** -0.9936827126)\r\n\r\ndef main():\r\n    os.system('color')\r\n    APP_NAME = settings.get('Settings', 'APP-NAME')\r\n    key = settings.get('Settings', 'KEY')\r\n    if not key:\r\n        key = input(colored('[Auth]', 'white', 'on_light_red') + colored(' Please Enter your Key: ', 'light_red'))\r\n        settings.set('Settings', 'KEY', key)\r\n        settings.save()\r\n    keyauthapp.license(key)\r\n    os.system(f\"title {APP_NAME}\")\r\n    os.system('cls')\r\n    print(colored(\" \\t\\t\\t\\t  Welcome Back! \" + \"● Expiry \" + datetime.utcfromtimestamp(int(keyauthapp.user_data.expires)).strftime('%Y-%m-%d %H:%M:%S'), 'light_red'))\r\n    bettercmd()\r\n    resize_cmd(120,30)\r\n    toggled = False\r\n    status = 'Disabled'\r\n    colorant = Colorant(CENTER_X - XFOV // 2, CENTER_Y - YFOV // 2, XFOV, YFOV, FLICKSPEED, XSPEED, YSPEED, AIMBOT_KEY, TRIGGERBOT_KEY, SILENTBOT_KEY)\r\n    print(colored(''' ''', 'light_red'))\r\n    print(colored('\\t\\t\\t\\t\\tStable Supporters Version! v1.4\\n', 'white', 'on_light_red'))\r\n    print(colored('[Your Keybinds]', 'white', 'on_light_red'))\r\n    print(colored(f'[x] Hold ({KEY_NAMES[AIMBOT_KEY]})', 'light_red'), colored('→ Aimbot', 'white'))\r\n    print(colored(f'[x] Hold ({KEY_NAMES[TRIGGERBOT_KEY]})', 'light_red'), colored('→ Triggerbot', 'white'))\r\n    print(colored(f'[x] Press ({KEY_NAMES[SILENTBOT_KEY]}) Warning high detection', 'light_red'), colored('→ Silentaim\\n', 'white'))\r\n    print(colored('[Information]', 'white', 'on_light_red'))\r\n    print(colored('Set enemies to', 'white'), colored('Purple', 'light_red'))\r\n    print(colored(f'Press {colored(TOGGLE_KEY, \"light_red\")} to toggle ON/OFF Colorant', 'white'))\r\n    print(colored('Thanks again for your support, welcome to ', 'white') + colored('COLORANT', 'light_red') + colored(' family!\\n', 'white'))\r\n    \r\n    try:\r\n        print(f'{colored(\"[Status]\", \"white\", \"on_light_red\")}', end='\\n')\r\n        while True:\r\n            if keyboard.is_pressed(TOGGLE_KEY):\r\n                colorant.toggle()\r\n                status = 'Enabled \\b' if colorant.toggled else 'Disabled'\r\n            print(f'\\r{colored(status, \"white\")}', end='')\r\n            time.sleep(0.01)\r\n    except (KeyboardInterrupt, SystemExit):\r\n        print(colored('\\n[Info]', 'green'), colored('Exiting...', 'white') + '\\n')\r\n        sys.exit()\r\n        \r\ndef bettercmd():\r\n    hwnd = ctypes.windll.kernel32.GetConsoleWindow()\r\n    if hwnd:\r\n        style = ctypes.windll.user32.GetWindowLongW(hwnd, -16)\r\n        style &= ~0x00040000\r\n        style &= ~0x00010000\r\n        ctypes.windll.user32.SetWindowLongW(hwnd, -16, style)\r\n\r\ndef resize_cmd(width, height):\r\n    STD_OUTPUT_HANDLE_ID = ctypes.c_ulong(0xFFFFFFF5)\r\n    windll = ctypes.windll.kernel32\r\n\r\n    handle = windll.GetStdHandle(STD_OUTPUT_HANDLE_ID)\r\n    rect = ctypes.wintypes.SMALL_RECT(0, 0, width - 1, height - 1)  # note: right and bottom are inclusive\r\n\r\n    windll.SetConsoleScreenBufferSize(handle, ctypes.wintypes._COORD(width, height))\r\n    windll.SetConsoleWindowInfo(handle, ctypes.c_int(True), ctypes.pointer(rect))\r\n        \r\nif __name__ == '__main__':\r\n    main()\r\n","repo_name":"someonecutie/crispyaimbot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5329,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"20376761856","text":"class Hand(object):\n    _id = int\n    _cards = []\n    _hand = []\n    _player = int\n    _iteration = int\n    _results = str\n    _results = []\n    _test = str\n    _duplicate = int\n    _score = int\n    _cardsinhand = int\n    _split = int\n    _sibling = int\n    _algo = int\n    _bigaces = int\n    _littleaces = int\n    _closed = int\n    def __init__(hand):\n        hand._cards = []\n        hand._hand = []\n        hand._dealer = 0\n        hand._iteration = 0\n        hand._result = \"\"\n        hand._results = []\n        hand._test = \"\"\n        hand._duplicate = 0\n        hand._score = 0\n        hand._cardsinhand = 0\n        hand._dealersvisiblecardvalue = 0\n        hand._split = 0\n        hand._sibling = 0\n        hand._algo = 0\n        hand._bigaces = 0\n        hand._littleaces = 0\n        hand._closed = 0\n\n","repo_name":"tlitterio/BlackJack","sub_path":"BlackJackCardCounting/Classes/Hands.py","file_name":"Hands.py","file_ext":"py","file_size_in_byte":810,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"37149455239","text":"# -*- coding:utf-8 -*-\n# Author: hankcs\n# Date: 2019-12-28 00:51\n\nSEMEVAL2016 = 'https://github.com/HIT-SCIR/SemEval-2016/archive/master.zip'\n\nSEMEVAL2016_NEWS_TRAIN = SEMEVAL2016 + '#train/news.train.conll'\nSEMEVAL2016_NEWS_VALID = SEMEVAL2016 + '#validation/news.valid.conll'\nSEMEVAL2016_NEWS_TEST = SEMEVAL2016 + '#test/news.test.conll'\n\nSEMEVAL2016_TEXT_TRAIN = SEMEVAL2016 + '#train/text.train.conll'\nSEMEVAL2016_TEXT_VALID = SEMEVAL2016 + '#validation/text.valid.conll'\nSEMEVAL2016_TEXT_TEST = SEMEVAL2016 + '#test/text.test.conll'\n","repo_name":"emorynlp/iwpt-shared-task-2020","sub_path":"edparser/datasets/parsing/semeval2016.py","file_name":"semeval2016.py","file_ext":"py","file_size_in_byte":538,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"48"}
+{"seq_id":"19012028161","text":"import os\r\nimport time\r\nimport shutil\r\nimport datetime\r\nfrom datetime import date\r\npath=input(\"Enter file path:\")\r\n\r\nold_time = datetime.datetime.now()\r\nprint(\"The current time is:\"+str(old_time))\r\nnew_time = old_time - datetime.timedelta(days=30)\r\nprint(\"The time thirty days ago was:\"+str(new_time))\r\nprint(\"Removing files older than\"+\" \"+str(new_time))\r\ntime=time.time()\r\ncount=0\r\n\r\n\r\nif(os.path.exists(path)):\r\n    print(\"Path Identified\")\r\n    for roots,dirs,files in os.walk(path):\r\n       list=os.listdir(roots)\r\n       for file in list: \r\n        new_path=roots+'/'+file\r\n        file_time=os.stat(new_path).st_ctime\r\n        time_converted=date.fromtimestamp(file_time)\r\n        print(file+\":\"+str(time_converted))\r\n        if(str(new_time)>str(time_converted)):\r\n            print(\"This file is older than 30 days\")\r\n            count=count+1\r\n            if(os.path.exists(\"C:/BackupFiles\")):\r\n                print(\"file 'BackupFiles' already exists, moving files there\")  \r\n                print(shutil.move(new_path,\"C:/BackupFiles/\"+file))\r\n            else:\r\n                print(\"file 'BackupFiles' does not exist, creating new one and moving files there\") \r\n                os.mkdir(\"C:/BackupFiles\")   \r\n        else:\r\n            print(\"File is within time tim limit\") \r\n    if (count==1):\r\n        print(str(count)+\" file was found to be older than 30 days\")\r\n        print(\"Thank You for using RemoveFiles.py\")\r\n    elif(count>1):\r\n        print(str(count)+\" files were found to be older than 30 days\")\r\n        print(\"Thank You for using RemoveFiles.py\")\r\n    elif(count==0):\r\n        print(\"No files were found to be older than 30 days\")  \r\n        print(\"Thank You for using RemoveFiles.py\")      \r\n            \r\n\r\n\r\n    \r\n       \r\n        \r\n\r\n    \r\n       \r\n       \r\n           \r\n\r\nelse:\r\n    print(\"Path Does Not Exist\")        \r\n\r\n\r\n    \r\n'''\r\n  for directory in dirs:\r\n            directory_global=directory\r\n        list_root=os.listdir(root)\r\n        for item in list_root:\r\n            new_path=path+'/'+directory_global+'/'+item\r\n            main_root,ext=os.path.splitext(new_path)\r\n            if(ext!=\"\"):\r\n                print(new_path)\r\n'''\r\n\r\n\r\n           \r\n        \r\n     \r\n            \r\n       \r\n            \r\n       \r\n               \r\n \r\n       \r\n\r\n","repo_name":"kannanisaboss0/C-99","sub_path":"C-99/RemoveFiles.py","file_name":"RemoveFiles.py","file_ext":"py","file_size_in_byte":2293,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"42563992295","text":"from os import lseek\nfrom tkinter import *\nfrom tkinter.ttk import *\nfrom tkinter.filedialog import askopenfilenames\nimport time\nfrom resume_parser import resumeparse\n\nlseek\nws = Tk()\nws.title('Resume parser')\nws.geometry('400x200') \n\nadhar = Label(\n    ws, \n    text='Upload Government id in jpg format '\n    )\nadhar.grid(row=0, column=0, padx=10)\n\n\ndef open_file():\n    file_paths = askopenfilenames(title='Choose a file', multiple=True)\n    if file_paths is not None:\n        for file_path in file_paths:\n            print(resumeparse.read_file(file_path))\n    else:\n        pass\n\n\ndef uploadFiles():\n    pb1 = Progressbar(\n        ws, \n        orient=HORIZONTAL, \n        length=300, \n        mode='determinate'\n        )\n    pb1.grid(row=4, columnspan=3, pady=20)\n    for i in range(5):\n        ws.update_idletasks()\n        pb1['value'] += 20\n        time.sleep(1)\n    pb1.destroy()\n    Label(ws, text='File Uploaded Successfully!', foreground='green').grid(row=4, columnspan=3, pady=10)\n    \n\nadharbtn = Button(\n    ws, \n    text ='Choose File', \n    command = lambda:open_file()\n    ) \nadharbtn.grid(row=0, column=1)\n\nupld = Button(\n    ws, \n    text='Upload Files', \n    command=uploadFiles\n    )\nupld.grid(row=3, columnspan=3, pady=10)\n\n\nws.mainloop()\n","repo_name":"soorajiam/resume-parser-desktop-app-","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1262,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"18889450242","text":"import uuid\n\nfrom rest_framework.authtoken.models import Token\n\nfrom chats.apps.accounts.models import User\nfrom chats.apps.contacts.models import Contact\nfrom chats.apps.msgs.models import Message as ChatMessage\n\n\ndef create_user_and_token(nickname: str = \"fake\"):\n    user = User.objects.get_or_create(email=f\"{nickname}@user.com\")[0]\n    token, create = Token.objects.get_or_create(user=user)\n    return (user, token)\n\n\ndef create_message(text, room, user=None, contact=None):\n    if user == contact:\n        return None\n    return ChatMessage.objects.create(room=room, text=text, user=user, contact=contact)\n\n\ndef create_contact(\n    name: str, email: str, status: str = \"OFFLINE\", custom_fields: dict = {}\n):\n    return Contact.objects.create(\n        name=name,\n        email=email,\n        status=status,\n        custom_fields=custom_fields,\n        external_id=str(uuid.uuid4()),\n    )\n","repo_name":"weni-ai/chats-engine","sub_path":"chats/apps/api/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":894,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"48"}
+{"seq_id":"25689969154","text":"# --- Day 21: Monkey Math ---\n\nimport timeit\nfrom z3 import *\n\nTEST = {'file': 'input.test', 'debug': True}\nINPUT = {'file': 'input', 'debug': False}\nCTX = INPUT\nDEBUG = CTX['debug']\n\n\ndef profiler(method):\n    def wrapper_method(*arg, **kw):\n        start = timeit.default_timer()\n        ret = method(*arg, **kw)\n        time = \"{0:2.9f}\".format(timeit.default_timer() - start)\n        print(f'{method.__name__} took : {time} sec')\n        return ret\n\n    return wrapper_method\n\n\ndef add_equation(s, z3term, z3t1, op, z3t2):\n    if op == '+':\n        s.add(z3term == z3t1 + z3t2)\n    elif op == '-':\n        s.add(z3term == z3t1 - z3t2)\n    elif op == '/':\n        s.add(z3term == z3t1 / z3t2)\n    elif op == '*':\n        s.add(z3term == z3t1 * z3t2)\n    elif op == '=':\n        s.add(z3t1 == z3t2)\n\n\ndef add_constant(s, z3term, const):\n    s.add(z3term == const)\n\n\nlines = open(CTX['file']).read().splitlines()\n\n\n@profiler\ndef part_1():\n    s = z3.Optimize()\n    target = 'root'\n    z3target = None\n\n    for line in lines:\n        term = line[:4]\n        if len(line) > 12:\n            z3term, z3t1, op, z3t2 = Int(term), Int(line[6:10]), line[11], Int(line[13:])\n            add_equation(s, z3term, z3t1, op, z3t2)\n        else:\n            z3term, const = Int(term), int(line[6:])\n            add_constant(s, z3term, const)\n        if term == target:\n            z3target = z3term\n\n    s.check()\n    m = s.model()\n\n    print(f'PART 1 - What number do you yell to pass root\\'s equality test? {m[z3target]}')\n    # 41857219607906 - 0.543951375 sec\n\n\n@profiler\ndef part_2():\n    s = z3.Optimize()\n    target = 'humn'\n    z3target = None\n\n    for line in lines:\n        term = line[:4]\n        if term == target:\n            z3target = Int(term)\n        elif len(line) > 12:\n            z3term, z3t1, z3t2 = Int(term), Int(line[6:10]), Int(line[13:])\n            op = '=' if term == 'root' else line[11]\n            add_equation(s, z3term, z3t1, op, z3t2)\n        else:\n            z3term, const = Int(term), int(line[6:])\n            add_constant(s, z3term, const)\n\n    s.check()\n    m = s.model()\n\n    print(f'PART 1 - What number do you yell to pass root\\'s equality test? {m[z3target]}')\n    # 3916936880448 - 0.538418083 sec\n\n\nprint(f'Running PART 1')\npart_1()\n\nprint(f'Running PART 2')\npart_2()\n","repo_name":"fibanez6/advent-of-code","sub_path":"2022-python/day-21/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":2302,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"32305502441","text":"import boto3\nimport os\nimport pytest\n\nfrom botocore.exceptions import ClientError\nfrom freezegun import freeze_time\nfrom moto import mock_lambda, mock_s3, settings\nfrom moto.core import DEFAULT_ACCOUNT_ID as ACCOUNT_ID\nfrom unittest import mock, SkipTest\nfrom uuid import uuid4\n\nfrom .utilities import get_role_name, get_test_zip_file1\n\nPYTHON_VERSION = \"python3.11\"\n_lambda_region = \"us-west-2\"\nboto3.setup_default_session(region_name=_lambda_region)\n\n\n@mock_lambda\ndef test_publish_lambda_layers__without_content():\n    conn = boto3.client(\"lambda\", _lambda_region)\n    layer_name = str(uuid4())[0:6]\n\n    with pytest.raises(ClientError) as exc:\n        conn.publish_layer_version(\n            LayerName=layer_name,\n            Content={},\n            CompatibleRuntimes=[\"python3.6\"],\n            LicenseInfo=\"MIT\",\n        )\n    err = exc.value.response[\"Error\"]\n    assert err[\"Code\"] == \"InvalidParameterValueException\"\n    assert err[\"Message\"] == \"Missing Content\"\n\n\n@mock_lambda\n@mock.patch.dict(os.environ, {\"VALIDATE_LAMBDA_S3\": \"false\"})\ndef test_publish_layer_with_unknown_s3_file():\n    if not settings.TEST_DECORATOR_MODE:\n        raise SkipTest(\"Can only set env var in DecoratorMode\")\n    conn = boto3.client(\"lambda\", _lambda_region)\n    content = conn.publish_layer_version(\n        LayerName=str(uuid4())[0:6],\n        Content=dict(S3Bucket=\"my-bucket\", S3Key=\"my-key.zip\"),\n    )[\"Content\"]\n    assert content[\"CodeSha256\"] == \"\"\n    assert content[\"CodeSize\"] == 0\n\n\n@mock_lambda\n@mock_s3\n@freeze_time(\"2015-01-01 00:00:00\")\ndef test_get_lambda_layers():\n    bucket_name = str(uuid4())\n    s3_conn = boto3.client(\"s3\", _lambda_region)\n    s3_conn.create_bucket(\n        Bucket=bucket_name,\n        CreateBucketConfiguration={\"LocationConstraint\": _lambda_region},\n    )\n\n    zip_content = get_test_zip_file1()\n    s3_conn.put_object(Bucket=bucket_name, Key=\"test.zip\", Body=zip_content)\n    conn = boto3.client(\"lambda\", _lambda_region)\n    layer_name = str(uuid4())[0:6]\n\n    conn.publish_layer_version(\n        LayerName=layer_name,\n        Content={\"ZipFile\": get_test_zip_file1()},\n        CompatibleRuntimes=[\"python3.6\"],\n        LicenseInfo=\"MIT\",\n    )\n    conn.publish_layer_version(\n        LayerName=layer_name,\n        Content={\"S3Bucket\": bucket_name, \"S3Key\": \"test.zip\"},\n        CompatibleRuntimes=[\"python3.6\"],\n        LicenseInfo=\"MIT\",\n    )\n\n    result = conn.list_layer_versions(LayerName=layer_name)\n\n    for version in result[\"LayerVersions\"]:\n        version.pop(\"CreatedDate\")\n    result[\"LayerVersions\"].sort(key=lambda x: x[\"Version\"])\n    expected_arn = f\"arn:aws:lambda:{_lambda_region}:{ACCOUNT_ID}:layer:{layer_name}:\"\n    assert result[\"LayerVersions\"] == [\n        {\n            \"Version\": 1,\n            \"LayerVersionArn\": expected_arn + \"1\",\n            \"CompatibleRuntimes\": [\"python3.6\"],\n            \"Description\": \"\",\n            \"LicenseInfo\": \"MIT\",\n            \"CompatibleArchitectures\": [],\n        },\n        {\n            \"Version\": 2,\n            \"LayerVersionArn\": expected_arn + \"2\",\n            \"CompatibleRuntimes\": [\"python3.6\"],\n            \"Description\": \"\",\n            \"LicenseInfo\": \"MIT\",\n            \"CompatibleArchitectures\": [],\n        },\n    ]\n\n    function_name = str(uuid4())[0:6]\n    conn.create_function(\n        FunctionName=function_name,\n        Runtime=PYTHON_VERSION,\n        Role=get_role_name(),\n        Handler=\"lambda_function.lambda_handler\",\n        Code={\"S3Bucket\": bucket_name, \"S3Key\": \"test.zip\"},\n        Description=\"test lambda function\",\n        Timeout=3,\n        MemorySize=128,\n        Publish=True,\n        Environment={\"Variables\": {\"test_variable\": \"test_value\"}},\n        Layers=[(expected_arn + \"1\")],\n    )\n\n    result = conn.get_function_configuration(FunctionName=function_name)\n    assert result[\"Layers\"] == [\n        {\"Arn\": (expected_arn + \"1\"), \"CodeSize\": len(zip_content)}\n    ]\n    result = conn.update_function_configuration(\n        FunctionName=function_name, Layers=[(expected_arn + \"2\")]\n    )\n    assert result[\"Layers\"] == [\n        {\"Arn\": (expected_arn + \"2\"), \"CodeSize\": len(zip_content)}\n    ]\n\n    # Test get layer versions for non existant layer\n    result = conn.list_layer_versions(LayerName=f\"{layer_name}2\")\n    assert result[\"LayerVersions\"] == []\n\n    # Test create function with non existant layer version\n    function_name = str(uuid4())[0:6]  # Must be different than above\n    with pytest.raises(ClientError) as exc:\n        conn.create_function(\n            FunctionName=function_name,\n            Runtime=PYTHON_VERSION,\n            Role=get_role_name(),\n            Handler=\"lambda_function.lambda_handler\",\n            Code={\"S3Bucket\": bucket_name, \"S3Key\": \"test.zip\"},\n            Description=\"test lambda function\",\n            Timeout=3,\n            MemorySize=128,\n            Publish=True,\n            Environment={\"Variables\": {\"test_variable\": \"test_value\"}},\n            Layers=[(expected_arn + \"3\")],\n        )\n    err = exc.value.response[\"Error\"]\n    assert err[\"Code\"] == \"ResourceNotFoundException\"\n\n\n@mock_lambda\n@mock_s3\ndef test_get_layer_version():\n    bucket_name = str(uuid4())\n    s3_conn = boto3.client(\"s3\", _lambda_region)\n    s3_conn.create_bucket(\n        Bucket=bucket_name,\n        CreateBucketConfiguration={\"LocationConstraint\": _lambda_region},\n    )\n\n    zip_content = get_test_zip_file1()\n    s3_conn.put_object(Bucket=bucket_name, Key=\"test.zip\", Body=zip_content)\n    conn = boto3.client(\"lambda\", _lambda_region)\n    layer_name = str(uuid4())[0:6]\n\n    resp = conn.publish_layer_version(\n        LayerName=layer_name,\n        Content={\"ZipFile\": get_test_zip_file1()},\n        CompatibleRuntimes=[\"python3.6\"],\n        LicenseInfo=\"MIT\",\n        CompatibleArchitectures=[\"x86_64\"],\n    )\n    layer_version = resp[\"Version\"]\n\n    resp = conn.get_layer_version(LayerName=layer_name, VersionNumber=layer_version)\n    assert resp[\"Description\"] == \"\"\n    assert resp[\"Version\"] == 1\n    assert resp[\"CompatibleArchitectures\"] == [\"x86_64\"]\n    assert resp[\"CompatibleRuntimes\"] == [\"python3.6\"]\n    assert resp[\"LicenseInfo\"] == \"MIT\"\n\n\n@mock_lambda\n@mock_s3\ndef test_get_layer_version__unknown():\n    bucket_name = str(uuid4())\n    s3_conn = boto3.client(\"s3\", _lambda_region)\n    s3_conn.create_bucket(\n        Bucket=bucket_name,\n        CreateBucketConfiguration={\"LocationConstraint\": _lambda_region},\n    )\n\n    zip_content = get_test_zip_file1()\n    s3_conn.put_object(Bucket=bucket_name, Key=\"test.zip\", Body=zip_content)\n    conn = boto3.client(\"lambda\", _lambda_region)\n    layer_name = str(uuid4())[0:6]\n\n    # Delete Layer that never existed\n    with pytest.raises(ClientError) as exc:\n        conn.get_layer_version(LayerName=layer_name, VersionNumber=1)\n    err = exc.value.response[\"Error\"]\n    assert err[\"Code\"] == \"ResourceNotFoundException\"\n\n    conn.publish_layer_version(\n        LayerName=layer_name,\n        Content={\"ZipFile\": get_test_zip_file1()},\n        CompatibleRuntimes=[\"python3.6\"],\n        LicenseInfo=\"MIT\",\n    )\n\n    # Delete Version that never existed\n    with pytest.raises(ClientError) as exc:\n        conn.get_layer_version(LayerName=layer_name, VersionNumber=999)\n    err = exc.value.response[\"Error\"]\n    assert err[\"Code\"] == \"ResourceNotFoundException\"\n\n\n@mock_lambda\n@mock_s3\n@pytest.mark.parametrize(\"use_arn\", [True, False])\ndef test_delete_layer_version(use_arn):\n    bucket_name = str(uuid4())\n    s3_conn = boto3.client(\"s3\", _lambda_region)\n    s3_conn.create_bucket(\n        Bucket=bucket_name,\n        CreateBucketConfiguration={\"LocationConstraint\": _lambda_region},\n    )\n\n    zip_content = get_test_zip_file1()\n    s3_conn.put_object(Bucket=bucket_name, Key=\"test.zip\", Body=zip_content)\n    conn = boto3.client(\"lambda\", _lambda_region)\n    layer_name = str(uuid4())[0:6]\n\n    resp = conn.publish_layer_version(\n        LayerName=layer_name,\n        Content={\"ZipFile\": get_test_zip_file1()},\n        CompatibleRuntimes=[\"python3.6\"],\n        LicenseInfo=\"MIT\",\n    )\n    layer_arn = resp[\"LayerArn\"]\n    layer_version = resp[\"Version\"]\n\n    if use_arn:\n        conn.get_layer_version(LayerName=layer_arn, VersionNumber=layer_version)\n        conn.delete_layer_version(LayerName=layer_arn, VersionNumber=layer_version)\n    else:\n        conn.get_layer_version(LayerName=layer_name, VersionNumber=layer_version)\n        conn.delete_layer_version(LayerName=layer_name, VersionNumber=layer_version)\n\n    result = conn.list_layer_versions(LayerName=layer_name)[\"LayerVersions\"]\n    assert result == []\n\n\n@mock_lambda\n@mock_s3\ndef test_get_layer_with_no_layer_versions():\n    def get_layer_by_layer_name_from_list_of_layer_dicts(layer_name, layer_list):\n        for layer in layer_list:\n            if layer[\"LayerName\"] == layer_name:\n                return layer\n        return None\n\n    conn = boto3.client(\"lambda\", _lambda_region)\n    layer_name = str(uuid4())[0:6]\n\n    # Publish a new Layer and assert Layer exists and only version 1 is there\n    conn.publish_layer_version(\n        LayerName=layer_name,\n        Content={\"ZipFile\": get_test_zip_file1()},\n    )\n    assert (\n        get_layer_by_layer_name_from_list_of_layer_dicts(\n            layer_name, conn.list_layers()[\"Layers\"]\n        )[\"LatestMatchingVersion\"][\"Version\"]\n        == 1\n    )\n\n    # Add a new version of that Layer then delete that version\n    conn.publish_layer_version(\n        LayerName=layer_name,\n        Content={\"ZipFile\": get_test_zip_file1()},\n    )\n    assert (\n        get_layer_by_layer_name_from_list_of_layer_dicts(\n            layer_name, conn.list_layers()[\"Layers\"]\n        )[\"LatestMatchingVersion\"][\"Version\"]\n        == 2\n    )\n\n    conn.delete_layer_version(LayerName=layer_name, VersionNumber=2)\n    assert (\n        get_layer_by_layer_name_from_list_of_layer_dicts(\n            layer_name, conn.list_layers()[\"Layers\"]\n        )[\"LatestMatchingVersion\"][\"Version\"]\n        == 1\n    )\n\n    # Delete the last layer_version and check that the Layer is still in the LayerStorage\n    conn.delete_layer_version(LayerName=layer_name, VersionNumber=1)\n    assert (\n        get_layer_by_layer_name_from_list_of_layer_dicts(\n            layer_name, conn.list_layers()[\"Layers\"]\n        )\n        is None\n    )\n\n    # Assert _latest_version didn't decrement\n    conn.publish_layer_version(\n        LayerName=layer_name,\n        Content={\"ZipFile\": get_test_zip_file1()},\n    )\n    assert (\n        get_layer_by_layer_name_from_list_of_layer_dicts(\n            layer_name, conn.list_layers()[\"Layers\"]\n        )[\"LatestMatchingVersion\"][\"Version\"]\n        == 3\n    )\n","repo_name":"getmoto/moto","sub_path":"tests/test_awslambda/test_lambda_layers.py","file_name":"test_lambda_layers.py","file_ext":"py","file_size_in_byte":10602,"program_lang":"python","lang":"en","doc_type":"code","stars":7174,"dataset":"github-code","pt":"48"}
+{"seq_id":"33621880694","text":"import re\nregex=r\"x[a-e]y\"\nmatches=re.findall(regex,\"x[a-e]y,xay,xby,xcy,xdy,xey,xhy\")\nprint(matches)\n\n\nimport re\nregex=(Java|pythhon|hadoop)\nmatches=re.findall(regex,\"java,python,hadoop,salesforce\")\nprint(matches)\n","repo_name":"chakilamnaresh/pythonlearning","sub_path":"sum/com/naresh/regexprsns.py","file_name":"regexprsns.py","file_ext":"py","file_size_in_byte":215,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"37165315546","text":"# -*- coding:utf-8 -*-\r\n\"\"\"\r\nCreated on 26.7.2017\r\nImage from LGN reconstructed in V1 with four orientation\r\n@author: Jiaxing Liu\r\n\"\"\"\r\nfrom ImportLibraries import *\r\nfrom Functions import *\r\nfrom Hyperparameters import *\r\n# ------------------------------------------------------------------------------\r\n# set parameters and equations\r\n# ------------------------------------------------------------------------------\r\n# 测试模式和训练模式下的参数\r\ntest_mode = 'True'\r\nnp.random.seed(0)\r\ndata_path = './'\r\nif test_mode:  # 测试模式的参数\r\n    weight_path = data_path + 'weights/'\r\n    num_examples = 10000 * 1\r\n    use_testing_set = True\r\n    do_plot_performance = False\r\n    record_spikes = True\r\n    ee_STDP_on = False  # 不需要学习\r\n    update_interval = num_examples\r\nelse:\r\n    weight_path = data_path + 'random/'\r\n    num_examples = 500 * 1  # 训练样本集60000重复训练3次\r\n    use_testing_set = False\r\n    do_plot_performance = True\r\n    if num_examples <= 60000:\r\n        record_spikes = True\r\n    else:\r\n        record_spikes = True\r\n    ee_STDP_on = True  # 输入到兴奋层通过STDP学习\r\n\r\nending = ''  # 路径结尾的名称\r\nsingle_example_time = 0.35 * second  # 每个样本训练时间为350ms，然后切换下一张图像\r\nresting_time = 0.15 * second  # 间隔150ms，使与时间有关的参数回归初始值，例如电导ge、内稳态变量theta等\r\n\r\n# runtime = num_examples * (single_example_time + resting_time)\r\n# if num_examples <= 10000:\r\n#     update_interval = num_examples\r\n#     weight_update_interval = 10\r\n# else:  # 训练\r\n#     update_interval = 10000\r\n#     weight_update_interval = 100\r\n# if num_examples <= 60000:\r\n#     save_connections_interval = 10000\r\n# else:\r\n#     save_connections_interval = 10000\r\n#     update_interval = 10000\r\n\r\n# 设置发放阈值，自适应阈值，稳态一部分\r\n# if test_mode:\r\n#     scr_e = 'v = v_reset_e'\r\n# else:\r\n#     tc_theta = 1e5 * ms  # 阈值参数theta衰减的很慢\r\n#     theta_plus_e = 0.0005 * mV  # 每发放一个脉冲，阈值增加plus_e 阈值增加的快慢\r\n#     scr_e = 'v = v_reset_e; theta += theta_plus_e'  # 每发放一次，theta就改变一次\r\n# offset = 20.0 * mV\r\n# 兴奋性神经元膜电位时间常数为50ms 兴奋性电导时间常数为1ms\r\ntc_theta = 1e6 * ms  # 阈值参数theta衰减的很慢\r\ntheta_plus_e = 0.01 * mV  # 每发放一个脉冲，阈值增加plus_e 阈值增加的快慢\r\nres_e = 'v = v_reset_e; theta += theta_plus_e'  # 每发放一次，theta就改变一次\r\noffset = 20.0 * mV\r\nneuron_eqs_e = '''\r\n        dv/dt = ((v_rest_e - v) + (I_synE+I_synI) / gleak) / taum_e  : volt (unless refractory)\r\n        I_synE = ge *         -v                                : amp\r\n        I_synI = gi * (-100.*mV-v)                              : amp\r\n        dge/dt = -ge/(1.0*ms)                                   : siemens\r\n        dgi/dt = -gi/(2.0*ms)                                   : siemens\r\n        dtheta/dt = -theta / (tc_theta)                         : volt\r\n      '''\r\n# if test_mode:\r\n#     neuron_eqs_e += '\\n  theta      :volt'\r\n# else:\r\n#     neuron_eqs_e += '\\n  dtheta/dt = -theta / (tc_theta)  : volt'\r\n# 抑制性神经元的时间常数小，发放快\r\nneuron_eqs_i = '''\r\n        dv/dt = ((v_rest_i - v) + (I_synE+I_synI) / gleak) / taum_i  : volt (unless refractory)\r\n        I_synE = ge *         -v                                : amp\r\n        I_synI = gi * (-100.*mV-v)                              : amp\r\n        dge/dt = -ge/(1.0*ms)                                   : siemens\r\n        dgi/dt = -gi/(2.0*ms)                                   : siemens\r\n      '''\r\n# 输入神经元 将像素转为脉冲序列\r\npoisson_neurons = '''\r\nrates : Hz\r\ndv/dt = 1 : second'''\r\n# max-pooling神经元\r\nneuron_max_pool = '''\r\n        dv/dt = ((v_rest_e - v) + (I_synE+I_synI) / gleak) / taum_e  : volt (unless refractory)\r\n        I_synE = ge *         -v                                : amp\r\n        I_synI = gi * (-100.*mV-v)                               : amp\r\n        dge/dt = -ge/(1.0*ms)                                   : siemens\r\n        dgi/dt = -gi/(2.75*ms)                                   : siemens\r\n      '''\r\n# determine STDP rule to use，确定使用哪种STDP规则\r\nstdp_input = ''\r\nuse_weight_dependence = False\r\nstdp_input += 'no_weight_dependence_'\r\n\r\npost_pre = True\r\nstdp_input += 'postpre'\r\n# if raw_input('Use weight dependence (default no)?: ') in ['no', '']:\r\n#     use_weight_dependence = False\r\n#     stdp_input += 'no_weight_dependence_'\r\n# else:\r\n#     use_weight_dependence = True\r\n#     stdp_input += 'weight_dependence_'\r\n#\r\n# if raw_input('Enter (yes / no) for post-pre (default yes): ') in ['yes', '']:\r\n#     post_pre = True\r\n#     stdp_input += 'postpre'\r\n# else:\r\n#     post_pre = False\r\n#     stdp_input += 'no_postpre'\r\n\r\n# STDP synaptic traces，STDP随时间变换的公式\r\n# 可以把pre、post看作是突触的变量，随时间变换\r\neqs_stdp_ee = '''\r\n            w:siemens\r\n            dpre/dt = -pre / tc_pre_ee : siemens (event-driven)\r\n            dpost/dt = -post / tc_post_ee : siemens (event-driven)\r\n            '''\r\n# setting STDP update rule 四种改进的STDP更新规则\r\nif use_weight_dependence:\r\n    if post_pre:\r\n        eqs_stdp_pre_ee = '''\r\n        ge+=w\r\n        pre = 1.*nS\r\n        w = clip(w - nu_ee_pre * post * w ** exp_ee_pre, 0, wmax_ee)\r\n        '''\r\n        eqs_stdp_post_ee = '''\r\n        w = clip(w + nu_ee_post * pre * (wmax_ee - w) ** exp_ee_post, 0, wmax_ee)\r\n        post = 1.*nS\r\n        '''\r\n\r\n    else:\r\n        eqs_stdp_pre_ee = '''\r\n        ge+=w\r\n        pre = 1.*nS\r\n        '''\r\n        eqs_stdp_post_ee = '''\r\n        w = clip(w + nu_ee_post * pre * (wmax_ee - w) ** exp_ee_post, 0, wmax_ee)\r\n        post = 1.*nS\r\n        '''\r\n\r\nelse:\r\n    if post_pre:\r\n        eqs_stdp_pre_ee = '''\r\n        ge+=w\r\n        pre = 1.*nS\r\n        w = clip(w - nu_ee_pre * post, 0, wmax_ee)\r\n        '''\r\n        eqs_stdp_post_ee = '''\r\n        w = clip(w + nu_ee_post * pre, 0, wmax_ee)\r\n        post = 1.*nS\r\n        '''\r\n\r\n    else:\r\n        eqs_stdp_pre_ee = '''\r\n        ge+=w\r\n        pre = 1.*nS\r\n        '''\r\n        eqs_stdp_post_ee = '''\r\n        w = clip(w + nu_ee_post * pre, 0, wmax_ee)\r\n        post = 1.*nS\r\n        '''\r\n# b2.ion()\r\nfig_num = 1\r\n\r\n# result_monitor = np.zeros((update_interval, n_e))  # 10000*25，每个神经元对样本的反应\r\n# 兴奋层有四组神经元，对应四个方向功能柱，15*15*4=900\r\nS1_groups['e'] = b2.NeuronGroup(S1_num * len(S1_population_names), model=neuron_eqs_e,\r\n                                threshold='v>(- 52*mV)', refractory=refrac_e,\r\n                                reset='v = v_reset_e', method='euler')  # 阈值决定了特征图中特征的明显程度，阈值越大，特征越突出明显\r\n# S1_groups['e'] = b2.NeuronGroup(S1_num* len(S1_population_names), model=neuron_eqs_e, threshold='v>(theta - offset - 52*mV)',refractory=refrac_e,\r\n#                                    reset=scr_e, method='euler')\r\nS1_groups['i'] = b2.NeuronGroup(S1_num * len(S1_population_names), model=neuron_eqs_i, threshold='v>v_thresh_i',\r\n                                refractory=refrac_i,\r\n                                reset='v=v_reset_i', method='euler')\r\nC1_groups['e'] = b2.NeuronGroup(C1_num * len(S1_population_names), model=neuron_eqs_e, threshold='v>v_thresh_e',\r\n                                refractory=refrac_e,\r\n                                reset='v=v_reset_e', method='euler')\r\npool1_groups['e'] = b2.NeuronGroup(S1_num * len(S1_population_names), model=neuron_eqs_e, threshold='v>v_thresh_e',\r\n                                   refractory=refrac_e,\r\n                                   reset='v=v_reset_e', method='euler')\r\npool1_groups['i'] = b2.NeuronGroup(S1_num * len(S1_population_names), model=neuron_eqs_i, threshold='v>v_thresh_i',\r\n                                   refractory=refrac_i,\r\n                                   reset='v=v_reset_i', method='euler')\r\nS2_groups['e'] = b2.NeuronGroup(S2_num * len(S2_population_names), model=neuron_eqs_e, threshold='v>(theta - offset - 52*mV)',\r\n                                refractory=refrac_e,\r\n                                reset=res_e, method='euler')\r\nS2_groups['i'] = b2.NeuronGroup(S2_num * len(S2_population_names), model=neuron_eqs_i, threshold='v>v_thresh_i',\r\n                                refractory=refrac_i,\r\n                                reset='v=v_reset_i', method='euler')\r\nC2_groups['e'] = b2.NeuronGroup(C2_num * len(S2_population_names), model=neuron_eqs_e, threshold='v>v_thresh_e',\r\n                                refractory=refrac_e,\r\n                                reset='v=v_reset_e', method='euler')\r\n# ------------------------------------------------------------------------------\r\n# create S1 and C1 population and connections\r\n# ------------------------------------------------------------------------------\r\n# 建立S1与C1的突触连接\r\npre_s1, post_c1 = create_pooling_conn(S1_size, C1_size, C1_pool_patch, 0)  # pooling的patch大小尽量是奇数，并且重叠越少越好\r\npre_pool, post_pool = create_pool_inh_conn(pre_s1, C1_size, C1_pool_patch)\r\nfor idx, name in enumerate(S1_population_names):  # 创建若干神经元子群 'A' 'B' 'C' 'D'\r\n    print('--------创建神经元群S1、C1' + name + '--------')\r\n    S1_groups[name + 'e'] = S1_groups['e'][idx * S1_num:(idx + 1) * S1_num]  # neuron_groups['Ae']\r\n    S1_groups[name + 'i'] = S1_groups['i'][idx * S1_num:(idx + 1) * S1_num]  # neuron_groups['Ai']\r\n    C1_groups[name + 'e'] = C1_groups['e'][idx * C1_num:(idx + 1) * C1_num]\r\n    pool1_groups[name + 'e'] = pool1_groups['e'][idx * S1_num:(idx + 1) * S1_num]\r\n    pool1_groups[name + 'i'] = pool1_groups['i'][idx * S1_num:(idx + 1) * S1_num]\r\n\r\n    S1_groups[name + 'e'].v = v_rest_e - 10. * mV\r\n    S1_groups[name + 'i'].v = v_rest_i - 10. * mV\r\n\r\n    S1_groups[name + 'e'].ge = 0 * nS\r\n    S1_groups[name + 'e'].gi = 0 * nS\r\n    S1_groups[name + 'i'].ge = 0 * nS\r\n    S1_groups[name + 'i'].gi = 0 * nS\r\n\r\n    C1_groups[name + 'e'].v = v_rest_e - 10. * mV\r\n    C1_groups[name + 'e'].ge = 0 * nS\r\n    C1_groups[name + 'e'].gi = 0 * nS\r\n\r\n    pool1_groups[name + 'e'].v = v_rest_e - 10. * mV\r\n    pool1_groups[name + 'e'].ge = 0 * nS\r\n    pool1_groups[name + 'e'].gi = 0 * nS\r\n\r\n    pool1_groups[name + 'i'].v = v_rest_i - 10. * mV\r\n    pool1_groups[name + 'i'].ge = 0 * nS\r\n    pool1_groups[name + 'i'].gi = 0 * nS\r\n    # if test_mode or weight_path[-8:] == 'weights/':  # 加载阈值参数值\r\n    # neuron_groups[name + 'e'].theta = np.load(weight_path + 'theta_' + name + '2017.7.26' + ending + '.npy')\r\n    # else:\r\n    #S1_groups[name + 'e'].theta = np.ones((S1_num)) * 20.0 * mV  # 训练时，theta的初始值为20mV\r\n\r\n    print ('S1层兴奋与抑制连接')  # 建立兴奋与抑制的连接\r\n    conn_type = S1_recurrent_conn_types[0]  # 'ei'\r\n    connName = 'S1' + name + conn_type[0] + 'S1' + name + conn_type[1]  # AeAi AiAe BeBi BiBe\r\n    # weightMatrix = get_matrix_from_file(weight_path + connName + ending + '.npy')  # 初始化权值\r\n    if (conn_type == 'ei'):\r\n        connections[connName] = b2.Synapses(S1_groups[connName[2:4]], S1_groups[connName[6:8]],\r\n                                            model='w:siemens', on_pre='ge+=w', method='euler')  # conn_type='ei'/'ie'\r\n        connections[connName].connect(j='i')  # 建立一对一连接\r\n        # connections[connName].w = weightMatrix.reshape((v1_num * v1_num)) * nS\r\n        connections[connName].w = np.ones(S1_num) * 10. * nS\r\n\r\n    print('S1层与pool_e层连接')  # 建立S1与pool的连接，pool神经元为中间复制层，完成非线性转换\r\n    pool_conn_type = C1_pooling_conn_types[0]  # 'ee'\r\n    pool_connName = 'S1' + name + pool_conn_type[0] + 'p1' + name + pool_conn_type[1]\r\n    connections[pool_connName] = b2.Synapses(S1_groups[pool_connName[2:4]], pool1_groups[pool_connName[6:8]],\r\n                                             model='w:siemens', on_pre='ge+=w', method='euler')\r\n    connections[pool_connName].connect(j='i')\r\n    connections[pool_connName].w = weight['S1_pool1']\r\n    print('中间pool_e层与C1层连接')  # 建立pool与C1的连接，max-pool接收最强输入\r\n    pool_conn_type = C1_pooling_conn_types[0]  # 'ee'\r\n    pool_connName = 'p1' + name + pool_conn_type[0] + 'C1' + name + pool_conn_type[1]\r\n    connections[pool_connName] = b2.Synapses(pool1_groups[pool_connName[2:4]], C1_groups[pool_connName[6:8]],\r\n                                             model='w:siemens', on_pre='ge+=w', method='euler')\r\n    connections[pool_connName].connect(i=pre_s1, j=post_c1)\r\n    connections[pool_connName].w = weight['pool1_C1']\r\n\r\n    print('中间pool_e层与pool_i层连接')  # 建立pool与C1的连接，max-pool接收最强输入\r\n    pool_conn_type = C1_pooling_conn_types[1]  # 'ei'\r\n    pool_connName = 'p1' + name + pool_conn_type[0] + 'p1' + name + pool_conn_type[1]\r\n    connections[pool_connName] = b2.Synapses(pool1_groups[pool_connName[2:4]], pool1_groups[pool_connName[6:8]],\r\n                                             model='w:siemens', on_pre='ge+=w', method='euler')\r\n    connections[pool_connName].connect(j='i')\r\n    connections[pool_connName].w = weight['pool1E_I']\r\n\r\n    print('pool_i与pool_e的连接，实现max-pooling')  # 建立pool自己的抑制连接，实现max-pool\r\n    pool_conn_type = C1_pooling_conn_types[2]  # 'ie'\r\n    pool_connName = 'p1' + name + pool_conn_type[0] + 'p1' + name + pool_conn_type[1]\r\n    connections[pool_connName] = b2.Synapses(pool1_groups[pool_connName[2:4]], pool1_groups[pool_connName[6:8]],\r\n                                             model='w:siemens', on_pre='gi+=w', method='euler')\r\n    connections[pool_connName].connect(i=pre_pool, j=post_pool)\r\n    connections[pool_connName].w = weight['pool1I_E']\r\n\r\n    spike_monitors['p1' + name + 'e'] = b2.SpikeMonitor(pool1_groups[name + 'e'])\r\n    spike_monitors['C1' + name + 'e'] = b2.SpikeMonitor(C1_groups[name + 'e'])\r\n\r\n# 建立抑制连接，每个抑制性神经元与同一位置的其他三种神经元连接\r\nprint('S1层抑制与兴奋的连接')\r\nfor name in S1_population_names:  # A B C D\r\n    # for name2 in S1_population_names:\r\n    #     conn_type = S1_recurrent_conn_types[1]  # 'ie'\r\n    #     if name2 == name:\r\n    #         pass\r\n    #     else:\r\n    #         connName = 'S1' + name + conn_type[0] + 'S1' + name2 + conn_type[1]\r\n    #         # weightMatrix = get_matrix_from_file(weight_path + connName + ending + '.npy')  # 加载初始权值\r\n    #         if (conn_type == 'ie'):\r\n    #             connections[connName] = b2.Synapses(S1_groups[connName[2:4]], S1_groups[connName[6:8]],\r\n    #                                                 model='w:siemens', on_pre='gi+=w', method='euler')  # conn_type='ie'\r\n    #             connections[connName].connect(j='i')  # 建立全连接\r\n    #             # connections[connName].w = weightMatrix.reshape((v1_num * v1_num)) * nS\r\n    #             connections[connName].w = np.ones(S1_num) * 17. * nS\r\n\r\n    print ('create spike monitors for', name)\r\n    if record_spikes:\r\n        spike_monitors['S1' + name + 'e'] = b2.SpikeMonitor(S1_groups[name + 'e'])\r\n        spike_monitors['S1' + name + 'i'] = b2.SpikeMonitor(S1_groups[name + 'i'])\r\n# ------------------------------------------------------------------------------\r\n# create S2 and C2 population and connections\r\n# ------------------------------------------------------------------------------\r\npre_s2,post_c2 = create_pooling_conn(S2_size,C2_size,C2_pool_patch,0) # pooling的patch大小尽量是奇数，并且重叠越少越好\r\n# pre_pool,post_pool = create_pool_inh_conn(pre_s1,C1_size,C1_pool_patch)\r\nfor idx, name in enumerate(S2_population_names):  # 创建若干神经元子群 'A' 'B' 'C' 'D' 'E' 'F'\r\n    print('--------创建神经元群S2' + name + '--------')\r\n    S2_groups[name + 'e'] = S2_groups['e'][idx * S2_num:(idx + 1) * S2_num]  # neuron_groups['Ae']\r\n    S2_groups[name + 'i'] = S2_groups['i'][idx * S2_num:(idx + 1) * S2_num]  # neuron_groups['Ai']\r\n    C2_groups[name + 'e'] = C2_groups['e'][idx * C2_num:(idx + 1) * C2_num]  # ABCDEFGH\r\n    # pool2_groups[name + 'e'] = pool2_groups['e'][idx*S2_num:(idx+1)*S2_num]\r\n\r\n    # 设置神经元群变量属性\r\n    S2_groups[name + 'e'].v = v_rest_e - 10. * mV\r\n    S2_groups[name + 'i'].v = v_rest_i - 10. * mV\r\n\r\n    S2_groups[name + 'e'].ge = 0 * nS\r\n    S2_groups[name + 'e'].gi = 0 * nS\r\n    S2_groups[name + 'i'].ge = 0 * nS\r\n    S2_groups[name + 'i'].gi = 0 * nS\r\n\r\n    C2_groups[name + 'e'].v = v_rest_e - 10. * mV\r\n    C2_groups[name + 'e'].ge = 0 * nS\r\n    C2_groups[name + 'e'].gi = 0 * nS\r\n    #\r\n    # pool2_groups[name + 'e'].v = v_rest_e - 10. * mV\r\n    # pool2_groups[name + 'e'].ge = 0 * nS\r\n    # pool2_groups[name + 'e'].gi = 0 * nS\r\n    # if test_mode or weight_path[-8:] == 'weights/':  # 加载阈值参数值\r\n    # neuron_groups[name + 'e'].theta = np.load(weight_path + 'theta_' + name + '2017.7.26' + ending + '.npy')\r\n    # else:\r\n    S2_groups[name + 'e'].theta = np.ones((S2_num)) * 20.0 * mV  # 训练时，theta的初始值为20mV，发放脉冲，theta改变从而阈值改变\r\n\r\n    # S2层与S1层一样，引入竞争机制，同一位置的不同特征神经元相互抑制，学到不同的组合特征\r\n    print ('S2层兴奋与抑制连接')  # 建立兴奋与抑制的连接\r\n    conn_type = S2_recurrent_conn_types[0]  # 'ei'\r\n    connName = 'S2' + name + conn_type[0] + 'S2' + name + conn_type[1]  # AeAi BeBi\r\n    # weightMatrix = get_matrix_from_file(weight_path + connName + ending + '.npy')  # 初始化权值\r\n    if (conn_type == 'ei'):\r\n        connections[connName] = b2.Synapses(S2_groups[connName[2:4]], S2_groups[connName[6:8]],\r\n                                            model='w:siemens', on_pre='ge+=w', method='euler')  # conn_type='ei'/'ie'\r\n        connections[connName].connect(j='i')  # 建立一对一连接\r\n        # connections[connName].w = weightMatrix.reshape((v1_num * v1_num)) * nS\r\n        connections[connName].w = np.ones(S2_num) * weight['S2E_I']\r\n    print('S2层与C2层连接')  # 建立pool与C1的连接，max-pool接收最强输入\r\n    pool_conn_type = S2_C2_conn_types[0]  # 'ee'\r\n    pool_connName = 'S2' + name + pool_conn_type[0] + 'C2' + name + pool_conn_type[1]\r\n    connections[pool_connName] = b2.Synapses(S2_groups[pool_connName[2:4]], C2_groups[pool_connName[6:8]],\r\n                                             model='w:siemens', on_pre='ge+=w', method='euler')\r\n    connections[pool_connName].connect(i=pre_s2, j=post_c2)\r\n    connections[pool_connName].w = weight['S2_C2']\r\n    spike_monitors['C2' + name + 'e'] = b2.SpikeMonitor(C2_groups[name + 'e'])\r\n        # print('create S1-pooling connections') # 建立S2与pool2的连接，pool2神经元为中间复制层，完成非线性转换\r\n        # pool_conn_type = C1_pooling_conn_types[0] # 'ee'\r\n        # pool_connName = 'S'+name+pool_conn_type[0]+'p'+name+pool_conn_type[1]\r\n        # connections[pool_connName] = b2.Synapses(S1_groups[pool_connName[1:3]], pool_groups[pool_connName[4:6]],\r\n        #                                          model='w:siemens', on_pre='ge+=w',method='euler')\r\n        # connections[pool_connName].connect(j='i')\r\n        # connections[pool_connName].w = weight['S1_pool']\r\n        #\r\n        # print('create pooling-C1 connections')  # 建立pool2与C2的连接，max-pool接收最强输入\r\n        # pool_conn_type = C1_pooling_conn_types[0]  # 'ee'\r\n        # pool_connName = 'p' + name + pool_conn_type[0] + 'C' + name + pool_conn_type[1]\r\n        # connections[pool_connName] = b2.Synapses(pool_groups[pool_connName[1:3]], C1_groups[pool_connName[4:6]],\r\n        #                                          model='w:siemens', on_pre='ge+=w', method='euler')\r\n        # connections[pool_connName].connect(i=pre_s1,j=post_c1)\r\n        # connections[pool_connName].w = weight['pool_C1']\r\n        #\r\n        # spike_monitors['p' + name + 'e'] = b2.SpikeMonitor(pool_groups[name + 'e'])\r\n        # spike_monitors['C' + name + 'e'] = b2.SpikeMonitor(C1_groups[name + 'e'])\r\n        #\r\n        # print('create pooling-inh connections')  # 建立pool2自己的抑制连接，实现max-pool\r\n        # pool_conn_type = C1_pooling_conn_types[0]  # 'ee'\r\n        # pool_connName = 'p' + name + pool_conn_type[0] + 'p' + name + pool_conn_type[1]\r\n        # connections[pool_connName] = b2.Synapses(pool_groups[pool_connName[1:3]], pool_groups[pool_connName[4:6]],\r\n        #                                          model='w:siemens', on_pre='gi+=w', method='euler')\r\n        # connections[pool_connName].connect(i=pre_pool,j=post_pool)\r\n        # connections[pool_connName].w = weight['pool_Inh']\r\n        #\r\n        # spike_monitors['p' + name + 'e'] = b2.SpikeMonitor(pool_groups[name + 'e'])\r\n        # spike_monitors['C' + name + 'e'] = b2.SpikeMonitor(C1_groups[name + 'e'])\r\n\r\n# 建立抑制连接，每个抑制性神经元与同一位置的其他三种神经元连接\r\nprint ('S2层抑制与兴奋连接')  # 建立兴奋与抑制的连接\r\nfor name1 in S2_population_names:  # A B C D E F\r\n    for name2 in S2_population_names:\r\n        conn_type = S2_recurrent_conn_types[1]  # 'ie'\r\n        if name2 == name1:\r\n            # pass\r\n            connName = 'S2' + name1 + conn_type[0] + 'S2' + name2 + conn_type[1]\r\n            if (conn_type == 'ie'):\r\n                connections[connName] = b2.Synapses(S2_groups[connName[2:4]], S2_groups[connName[6:8]],\r\n                                                    model='w:siemens', on_pre='gi+=w', method='euler')  # conn_type='ie'\r\n                connections[connName].connect(condition='i!=j')  # 建立全连接\r\n                connections[connName].w = weight['S2I_E']\r\n        else:\r\n            connName = 'S2' + name1 + conn_type[0] + 'S2' + name2 + conn_type[1]\r\n            # weightMatrix = get_matrix_from_file(weight_path + connName + ending + '.npy')  # 加载初始权值\r\n            if (conn_type == 'ie'):\r\n                connections[connName] = b2.Synapses(S2_groups[connName[2:4]], S2_groups[connName[6:8]],\r\n                                                    model='w:siemens', on_pre='gi+=w', method='euler')  # conn_type='ie'\r\n                connections[connName].connect(j='i')  # 建立全连接\r\n                connections[connName].w = weight['S2IE_single']\r\n\r\nfor name in S2_population_names:  # A B C D E F\r\n    print ('create monitors for', name)  # monitors for A\r\n    # rate_monitors[name + 'e'] = b2.PopulationRateMonitor(S2_groups[name + 'e'])  # 350ms+150ms\r\n    # rate_monitors[name + 'i'] = b2.PopulationRateMonitor(S2_groups[name + 'i'])\r\n\r\n    if record_spikes:\r\n        spike_monitors['S2' + name + 'e'] = b2.SpikeMonitor(S2_groups[name + 'e'])\r\n        spike_monitors['S2' + name + 'i'] = b2.SpikeMonitor(S2_groups[name + 'i'])\r\n\r\ntemp=S2_groups['Ae']\r\nstateS2Ae = b2.StateMonitor(temp,('v','I_synI'),record=[0])\r\ntemp=S2_groups['Ai']\r\nstateS2Ai = b2.StateMonitor(temp,('v','I_synE'),record=[0])\r\n# ------------------------------------------------------------------------------\r\n# create connections between C1 and S2\r\n# ------------------------------------------------------------------------------\r\n# pop_values = ['A', 'B', 'C','D']  # 创建输入到兴奋层的连接\r\n# spk_len = {} # 记录S2每个网络发放神经元的个数\r\nprint('建立C1层与S2层连接')\r\npre_C1, post_S2 = create_convolution_conn(C1_size, S2_size, S2_cov_patch, S2_cov_padding)\r\nfor name in C1_S2_conn_names:  # 'AA','BA','CA','DA','AB','BB','CB','DB','AC','BC','CC','DC','AD','BD','CD','DD'\r\n    # print ('建立C1' + name[0] + '与S2' + name[1] + '的连接')\r\n    for connType in C1_S2_conn_types:  # 'ee'\r\n        connName = 'C1' + name[0] + connType[0] + 'S2' + name[1] + connType[1]  # connName='C1AeS2Ae' 'C1BeS2Ae'\r\n        # spk_len[connName] = 0\r\n        weightMatrix = get_init_matrix_from_file(data_path + 'random/' + connName + ending + '.npy')  # 加载初始权值矩阵\r\n        # weightMatrix = get_matrix_from_file(weight_path +'2017.8.2_55/'+ connName + ending + '.npy')  # 加载初始权值矩阵\r\n        connections[connName] = b2.Synapses(C1_groups[connName[2:4]], S2_groups[connName[6:8]], model=eqs_stdp_ee,\r\n                                            on_pre=eqs_stdp_pre_ee, on_post=eqs_stdp_post_ee, method='euler')\r\n        # connections[connName] = b2.Synapses(C1_groups[connName[2:4]], S2_groups[connName[6:8]], model='w:siemens',\r\n        #                                     on_pre='ge+=w', method='euler')\r\n        connections[connName].connect(i=pre_C1, j=post_S2)  # 全连接\r\n        connections[connName].delay = 0 * ms  # delay[connType]  # 突触延迟是一个范围，0-10之间\r\n        connections[connName].w = weightMatrix.transpose().reshape(\r\n            (S2_cov_patch ** 2 * S2_num)) * nS  # weightMatrix.reshape((25*26*26))*siemens\r\n# print(connections['XeAe'].w) # 初始的第一层权值\r\n# print(connections['XeBe'].w) # 初始的第一层权值\r\n# print(connections['XeCe'].w) # 初始的第一层权值\r\n# print(connections['XeDe'].w) # 初始的第一层权值\r\n# ------------------------------------------------------------------------------\r\n# create input population and connections from input populations\r\n# ------------------------------------------------------------------------------\r\n# 创建输入到兴奋层的连接\r\npop_values = ['A', 'B', 'C', 'D']\r\nfor i, name in enumerate(input_population_names):  # 'X'\r\n    input_groups[name + 'e'] = NeuronGroup(input_size * input_size, model=poisson_neurons, threshold='v>1/rates',\r\n                                           reset='v=0*second', method='euler')\r\n    # b2.PoissonGroup(img_size*img_size, 0*Hz)  # input_groups['Xe'] n_input=15*15\r\n    rate_monitors[name + 'e'] = b2.PopulationRateMonitor(input_groups[name + 'e'])\r\npre_input, post_S1 = create_convolution_conn(input_size, S1_size, S1_cov_patch, S1_cov_padding)\r\nfor name in input_S1_conn_names:  # 'XA' 'XB' 'XC' 'XD'\r\n    print ('----建立' + name[0] + '与S1' + name[1] + '的连接----')\r\n    for connType in input_S1_conn_types:  # 'ee'\r\n        connName = name[0] + connType[0] + name[1] +\\\r\n                   connType[1]  # connName='XeAe' 'XeBe' 'XeCe' 'XeDe'\r\n        weightMatrix = get_matrix_from_file(weight_path + '2017.7.26_77/' + connName + ending + '.npy')  # 加载初始权值矩阵\r\n        # connections[connName] = b2.Synapses(input_groups[connName[0:2]], neuron_groups[connName[2:4]], model=eqs_stdp_ee,\r\n        #                                     on_pre=eqs_stdp_pre_ee, on_post=eqs_stdp_post_ee, method='euler')\r\n        connections[connName] = b2.Synapses(input_groups[connName[0:2]], S1_groups[connName[2:4]],\r\n                                            model='w:siemens', on_pre='ge+=w', method='euler') # 没有STDP学习\r\n        connections[connName].connect(i=pre_input, j=post_S1)  # 全连接\r\n        connections[connName].delay = 0 * ms  # delay[connType]  # 突触延迟是一个范围，0-10之间\r\n        connections[connName].w = weightMatrix.transpose().reshape(\r\n            (S1_cov_patch ** 2 * S1_size ** 2)) * nS  # weightMatrix.reshape((49*225))*siemens\r\n\r\n# print(connections['XeAe'].w) # 初始的第一层权值\r\n# print(connections['XeBe'].w) # 初始的第一层权值\r\n# print(connections['XeCe'].w) # 初始的第一层权值\r\n# print(connections['XeDe'].w) # 初始的第一层权值\r\n\r\n# ------------------------------------------------------------------------------\r\n# run the simulation and set inputs\r\n# ------------------------------------------------------------------------------\r\nprevious_spike_count = np.zeros(S1_num)  # 25 记录每个神经元脉冲数量\r\n\r\n# if not test_mode:  # 可视化训练的权值\r\n# input_S1_weights, input_S1_fig = plot_2d_input_weights('S1')  # 神经元群感受野可视化\r\n# fig_num += 1\r\n# C1_S2_weights, C1_S2_fig = plot_2d_input_weights('S2')\r\n# fig_num += 1\r\nfor i, name in enumerate(input_population_names):  # 'X'\r\n    input_groups[name + 'e'].rates = 0 * Hz\r\n# 将所有的容器add进来\r\nnet = Network(collect())\r\nnet.add(S1_groups)\r\nnet.add(C1_groups)\r\nnet.add(input_groups)\r\nnet.add(pool1_groups)\r\nnet.add(S2_groups)\r\nnet.add(C2_groups)\r\nnet.add(connections)\r\nnet.add(spike_monitors)\r\nnet.run(0 * ms)\r\n\r\n# 记录神经元群发放脉冲数量\r\nsc1 = spike_monitors['S1Ae'].count\r\nsc2 = spike_monitors['S1Be'].count\r\nsc3 = spike_monitors['S1Ce'].count\r\nsc4 = spike_monitors['S1De'].count\r\n\r\nscp1 = spike_monitors['p1Ae'].count\r\nscp2 = spike_monitors['p1Be'].count\r\nscp3 = spike_monitors['p1Ce'].count\r\nscp4 = spike_monitors['p1De'].count\r\n\r\nscc1 = spike_monitors['C1Ae'].count\r\nscc2 = spike_monitors['C1Be'].count\r\nscc3 = spike_monitors['C1Ce'].count\r\nscc4 = spike_monitors['C1De'].count\r\n\r\ns21 = spike_monitors['S2Ae'].count\r\ns22 = spike_monitors['S2Be'].count\r\ns23 = spike_monitors['S2Ce'].count\r\ns24 = spike_monitors['S2De'].count\r\n\r\n# img1,fig1 = plot_2d_spike_rate(np.array(scc1), 'C1')\r\n# fig_num += 1\r\n# img2,fig2 = plot_2d_spike_rate(np.array(s21), 'S2')\r\n# fig_num += 1\r\n# img3,fig3 = plot_2d_spike_rate(np.array(s22), 'S2')\r\n# fig_num += 1\r\n# img4,fig4 = plot_2d_spike_rate(np.array(s23), 'S2')\r\n# fig_num += 1\r\n# img5,fig5 = plot_2d_spike_rate(np.array(s24), 'S2')\r\n# fig_num += 1\r\n\r\n# 获取图像\r\nimg_cifar = get_data('./cifar_edge/') # 读取训练图像\r\n# img_face = get_data('images/train_edge/face_easy/')\r\n# img_motor = get_data('images/train_edge/motor/')\r\n# img_motor = get_data('./') # 读入图像\r\ntraining_start = time.time()\r\npre_count = {}\r\ncur_count = {}\r\nfor name in S2_population_names:\r\n    pre_count['S2'+name+'e'] = np.zeros(S2_num)\r\n    pre_count['C2' + name + 'e'] = np.zeros(C2_num)\r\n\r\npre_count1 = np.zeros(S1_num)\r\npre_count2 = np.zeros(S1_num)\r\npre_count3 = np.zeros(S1_num)\r\npre_count4 = np.zeros(S1_num)\r\npre_count21 = np.zeros(C1_num)\r\nj = 0 # 训练图像索引\r\np = 0\r\nq = 0\r\nidx = {}\r\n#while j < (len(img_face) + len(img_motor)):  # 大循环，每个训练样本跑一次\r\nwhile j<(1):\r\n    normalize_weights()  # 权值归一化，稳态机制\r\n    # i = j%80\r\n    # if i<=20:\r\n    #     rate = img[0].reshape((n_input)) / 8. * input_intensity\r\n    # if(i>20 and i<=40):\r\n    #     rate = img[1].reshape((n_input)) / 8. * input_intensity\r\n    # if (i>40 and i<=60):\r\n    #     rate = img[2].reshape((n_input)) / 8. * input_intensity\r\n    # if (i>60):\r\n    #     rate = img[3].reshape((n_input)) / 8. * input_intensity\r\n    # i = j % 40\r\n    # if i <= 10:\r\n    #     rate = img[0].reshape((n_input)) / 8. * input_intensity\r\n    # if (i > 10 and i <= 20):\r\n    #     rate = img[1].reshape((n_input)) / 8. * input_intensity\r\n    # if (i > 20 and i <= 30):\r\n    #     rate = img[2].reshape((n_input)) / 8. * input_intensity\r\n    # if (i > 30):\r\n    #     rate = img[3].reshape((n_input)) / 8. * input_intensity\r\n    # update_2d_input_weights(C1_S2_weights, C1_S2_fig, 'S2')\r\n    # i = j % 4\r\n    # if i < 2:\r\n    #     rate = img_face[p].reshape((input_num)) / 8. * input_intensity\r\n    #     p+=1\r\n    # if i >= 2:\r\n    #     rate = img_motor[q].reshape((input_num)) / 8. * input_intensity\r\n    #     q+=1\r\n    rate = img_cifar[j].reshape((input_num)) / 8. * input_intensity\r\n    input_groups['Xe'].rates = rate * Hz # 第一层poisson神经元赋频率\r\n    # print 'run number:', j + 1, 'of', len(img_motor) + len(img_motor) # 打印目前训练到哪张图片\r\n    net.run(single_example_time, report='text')  # 每个样本运行350ms\r\n    # print('图片跑完')\r\n    current_spike_count = np.asarray(spike_monitors['S1Ae'].count[:] + spike_monitors['S1Be'].count[:] +\r\n                                     spike_monitors['S1Ce'].count[:] + spike_monitors['S1De'].count[:]) \\\r\n                          - previous_spike_count  # 记录Ae每个神经元发放的脉冲数\r\n    # if j % weight_update_interval == 0 and not test_mode:  # 每100个样本更新一次权重\r\n    #     update_2d_input_weights(input_weight_monitor, fig_weights)\r\n    #     print('weights < 0.2 :',len(np.where(connections['XeAe'].w / nS < 0.2)[0]))\r\n    #     print(neuron_groups['Ae'].theta - offset - 52 * mV)\r\n    #     print('spike count', current_spike_count)\r\n    # if j % save_connections_interval == 0 and j > 0 and not test_mode:  # 间隔保存权值和theta参数\r\n    #     save_connections(str(j))\r\n    #     save_theta(str(j))\r\n\r\n    previous_spike_count = np.copy(spike_monitors['S1Ae'].count[:] + spike_monitors['S1Be'].count[:] +\r\n                                   spike_monitors['S1Ce'].count[:] + spike_monitors['S1De'].count[:])\r\n    # print('rest')\r\n    if np.sum(current_spike_count) < 20:  # 如果总脉冲数小于20\r\n        input_intensity += 1  # 输入像素转换的脉冲频率大一倍\r\n        for i, name in enumerate(input_population_names):  # 'X'\r\n            input_groups[name + 'e'].rates = 0 * Hz\r\n        net.run(resting_time,report='text')  # 暂停输入，使参数静息\r\n    else:\r\n        for i, name in enumerate(input_population_names):  # 'X'\r\n            input_groups[name + 'e'].rates = 0 * Hz  # 暂停输入\r\n        net.run(resting_time) # 休息150ms，使参数回归初始值\r\n        input_intensity = start_input_intensity\r\n        # update_2d_spike_rate(img1, fig1,np.array(scc1),'C1')\r\n        # update_2d_spike_rate(img2, fig2,np.array(s21),'S2')\r\n        # update_2d_spike_rate(img3, fig3,np.array(s22),'S2')\r\n        # update_2d_spike_rate(img4, fig4,np.array(s23),'S2')\r\n        # update_2d_spike_rate(img5, fig5,np.array(s24),'S2')\r\n\r\n        cur_count1 = np.array(sc1) - pre_count1 # S1Ae的脉冲数\r\n        cur_count2 = np.array(sc2) - pre_count2 # S1Be\r\n        cur_count3 = np.array(sc3) - pre_count3\r\n        cur_count4 = np.array(sc4) - pre_count4\r\n\r\n        cur_count21 = np.array(scc1) - pre_count21\r\n\r\n        pre_count1 = np.array(sc1)\r\n        pre_count21 = np.array(scc1)\r\n\r\n        save_2d_spike_rate(cur_count1, 'S1') # 把S1Ae的发放情况画出来并保存\r\n        fig_num += 1\r\n        save_2d_spike_rate(cur_count2, 'S1') # 把S1Be的发放情况画出来并保存\r\n        fig_num += 1\r\n        save_2d_spike_rate(cur_count3, 'S1')\r\n        fig_num += 1\r\n        save_2d_spike_rate(cur_count4, 'S1')\r\n        fig_num += 1\r\n        # save_2d_spike_rate(cur_count21, 'C1')\r\n        # fig_num += 1\r\n\r\n        # 画S2的结果\r\n        # for name in S2_population_names: # ABCDEFGH\r\n        #     cur_count['S2'+name+'e'] = spike_monitors['S2'+name+'e'].count[:] - pre_count['S2'+name+'e']\r\n        #     cur_count['C2' + name + 'e'] = spike_monitors['C2' + name + 'e'].count[:] - pre_count['C2' + name + 'e']\r\n        #     pre_count['S2'+name+'e'] = spike_monitors['S2'+name+'e'].count[:]\r\n        #     pre_count['C2' + name + 'e'] = spike_monitors['C2' + name + 'e'].count[:]\r\n        #     save_2d_spike_rate(cur_count['S2'+name+'e'], 'S2')\r\n        #     fig_num += 1\r\n        #     save_2d_spike_rate(cur_count['C2' + name + 'e'], 'C2')\r\n        #     fig_num += 1\r\n\r\n        # save_2d_spike_rate(cur_count['S2Be'], 'S2')\r\n        # fig_num += 1\r\n        # save_2d_spike_rate(cur_count['S2Ce'], 'S2')\r\n        # fig_num += 1\r\n        # save_2d_spike_rate(cur_count['S2De'], 'S2')\r\n        # fig_num += 1\r\n        # save_2d_spike_rate(cur_count['S2Ee'], 'S2')\r\n        # fig_num += 1\r\n        # save_2d_spike_rate(cur_count['S2Fe'], 'S2')\r\n        # fig_num += 1\r\n        # save_2d_spike_rate(cur_count['S2Ge'], 'S2')\r\n        # fig_num += 1\r\n        # save_2d_spike_rate(cur_count['S2He'], 'S2')\r\n        # fig_num += 1\r\n        # plot_2d_spike_rate(cur_count1, 'S1')\r\n        # fig_num += 1\r\n        # plot_2d_spike_rate(cur_count2, 'C1')\r\n        # fig_num += 1\r\n        # plot_2d_spike_rate(cur_count['S2Ae'], 'S2')\r\n        # fig_num += 1\r\n        # plot_2d_spike_rate(cur_count['S2Be'], 'S2')\r\n        # fig_num += 1\r\n\r\n        # 保存S2特征 + weight sharing\r\n        # sum1 = len(np.where(cur_count['S2Ae'] == 0)[0])\r\n        # sum2 = len(np.where(cur_count['S2Be'] == 0)[0])\r\n        # sum3 = len(np.where(cur_count['S2Ce'] == 0)[0])\r\n        # sum4 = len(np.where(cur_count['S2De'] == 0)[0])\r\n        # sum5 = len(np.where(cur_count['S2Ee'] == 0)[0])\r\n        # sum6 = len(np.where(cur_count['S2Fe'] == 0)[0])\r\n        # sum7 = len(np.where(cur_count['S2Ge'] == 0)[0])\r\n        # sum8 = len(np.where(cur_count['S2He'] == 0)[0])\r\n        # for name in S2_population_names:\r\n        #     idx['S2'+name+'e'] = np.where(cur_count['S2'+name+'e']==max(cur_count['S2'+name+'e']))[0][0]\r\n        # # 保存特征，作为训练分类器的输入\r\n        # i = j % 4\r\n        # if i < 2: # 脸部特征\r\n        #     #print('face:',sum1,sum2,sum3,sum4)\r\n        #     with open('results/2017.8.17/face_feature_data.txt', 'a') as f:\r\n        #         f.write(str([sum1, sum2, sum3, sum4,sum5, sum6, sum7, sum8, idx['S2Ae'],idx['S2Be'],idx['S2Ce'],\r\n        #                      idx['S2De'],idx['S2Ee'],idx['S2Fe'],idx['S2Ge'],idx['S2He']]) + '\\n')\r\n        # if i >= 2:\r\n        #     #print('motor:',sum1,sum2,sum3,sum4)\r\n        #     with open('results/2017.8.17/motor_feature_data.txt', 'a') as f:\r\n        #         f.write(str([sum1, sum2, sum3, sum4, sum5, sum6, sum7, sum8, idx['S2Ae'],idx['S2Be'],idx['S2Ce'],\r\n        #                      idx['S2De'],idx['S2Ee'],idx['S2Fe'],idx['S2Ge'],idx['S2He']]) + '\\n')\r\n        #\r\n        # print('share weights')\r\n        # for name in C1_S2_conn_names:\r\n        #     for connType in C1_S2_conn_types:  # 'ee'\r\n        #         connName = 'C1' + name[0] + connType[0] + 'S2' + name[1] + connType[1]  # connName='C1AeS2Ae' 'C1BeS2Ae'\r\n        #         #print('更新权重：' + connName)\r\n        #         # idx = np.where(cur_count[connName[4:8]]==max(cur_count[connName[4:8]]))[0][0] # 读出第一个发放的神经元序号\r\n        #         copy_weights = np.array( connections[connName].w[ idx[connName[4:8]] * S2_cov_patch_size:(idx[connName[4:8]] + 1)\r\n        #                                                                                                  * S2_cov_patch_size]/nS)\r\n        #         connections[connName].w =np.tile(copy_weights, S2_num)*nS\r\n        #         copy_theta = S2_groups[connName[6:8]].theta[idx[connName[4:8]]]/mV\r\n        #         S2_groups[connName[6:8]].theta = np.tile(copy_theta,S2_num)*mV\r\n        #         # print('更新完毕：' + connName)\r\n        # # 提取卷积层特征\r\n        # # with open('train_motor_feature_data.txt', 'a') as f:\r\n        # #     f.write(str(list(np.concatenate((np.array(scc1), np.array(scc2), np.array(scc3), np.array(scc4)))))+'\\n')\r\n        # # A=np.array(connections['C1AeS2Ae'].w)\r\n        # # B = np.array(connections['C1AeS2Be'].w)\r\n        j += 1\r\n\r\ntraining_end = time.time()\r\nprint(\"train time:\", training_end - training_start)\r\n# 所有num_examples个样本训练完后，保存结果\r\n# ------------------------------------------------------------------------------\r\n# save results\r\n# ------------------------------------------------------------------------------\r\n# print ('save results')\r\n# if not test_mode:\r\n#     save_theta()\r\n# if not test_mode:\r\n#     save_connections()\r\n# else:\r\n#     np.save(data_path + 'activity/resultPopVecs' + str(num_examples), result_monitor)  # 将每一个样���导致的各个神经元发放率保存下来\r\n#     np.save(data_path + 'activity/inputNumbers' + str(num_examples), input_numbers)\r\n\r\n# ------------------------------------------------------------------------------\r\n# plot results\r\n# ------------------------------------------------------------------------------\r\n# if rate_monitors:\r\n#     b2.figure(fig_num)\r\n#     fig_num += 1\r\n#     for i, name in enumerate(rate_monitors):\r\n#         b2.subplot(len(rate_monitors), 1, i + 1)\r\n#         b2.plot(rate_monitors[name].t / second, rate_monitors[name].rate, '.')\r\n#         b2.title('Rates of population ' + name)\r\n\r\n# if spike_monitors:\r\n#     b2.figure(fig_num)\r\n#     fig_num += 1\r\n#     for i, name in enumerate(spike_monitors):\r\n#         b2.subplot(len(spike_monitors), 1, i + 1)\r\n#         b2.plot(spike_monitors[name].t/ms,spike_monitors[name].i,'.')\r\n#         b2.title('Spikes of population ' + name)\r\n#\r\n# if spike_monitors:\r\n#     b2.figure(fig_num)\r\n#     fig_num += 1\r\n#     for i, name in enumerate(spike_monitors):\r\n#         b2.subplot(len(spike_monitors), 1, i + 1)\r\n#         b2.plot(spike_monitors['Ae'].count[:])\r\n#         b2.title('Spike count of population ' + name)\r\n# figure('AeV')\r\n# plot(stateS2Ae.t/ms,stateS2Ae.v[0]/mV)\r\n# figure('AeI')\r\n# plot(stateS2Ae.t/ms,stateS2Ae.I_synI[0]/pamp)\r\n# figure('AiV')\r\n# plot(stateS2Ai.t/ms,stateS2Ai.v[0]/mV)\r\n# figure('AiI')\r\n# plot(stateS2Ai.t/ms,stateS2Ai.I_synE[0]/pamp)\r\n# count_thr = 50  # 在运行时间内的脉冲发放阈值，与运行时间有关\r\n# aaa = np.array(connections['C1AeS2Ae'].w[:])\r\n# bbb = np.array(connections['C1BeS2Ae'].w[:])\r\n# ccc = np.array(connections['C1AeS2Be'].w[:])\r\n# print(sc1,scp1,scc1)\r\nsc = sc1 + sc2 + sc3 + sc4\r\nscc = scc1 + scc2 + scc3 + scc4\r\nscp = scp1 + scp2 + scp3 + scp4\r\n\r\n# plot_2d_spike_rate(np.array(sc), 'S1')\r\n# fig_num += 1\r\n# plot_2d_spike_rate(np.array(scp), 'S1')\r\n# fig_num += 1\r\n# plot_2d_spike_rate(np.array(scc), 'C1')\r\n# fig_num += 1\r\n#\r\n# plot_2d_spike_rate(np.array(sc1), 'S1')\r\n# fig_num += 1\r\n# plot_2d_spike_rate(np.array(scp1), 'S1')\r\n# fig_num += 1\r\n# plot_2d_spike_rate(np.array(scc1), 'C1')\r\n# fig_num += 1\r\n#\r\n# plot_2d_spike_rate(np.array(sc2), 'S1')\r\n# fig_num += 1\r\n# plot_2d_spike_rate(np.array(scp2), 'S1')\r\n# fig_num += 1\r\n# plot_2d_spike_rate(np.array(scc2), 'C1')\r\n# fig_num += 1\r\n#\r\n# plot_2d_spike_rate(np.array(sc3), 'S1')\r\n# fig_num += 1\r\n# plot_2d_spike_rate(np.array(scp3), 'S1')\r\n# fig_num += 1\r\n# plot_2d_spike_rate(np.array(scc3), 'C1')\r\n# fig_num += 1\r\n#\r\n# plot_2d_spike_rate(np.array(sc4), 'S1')\r\n# fig_num += 1\r\n# plot_2d_spike_rate(np.array(scp4), 'S1')\r\n# fig_num += 1\r\n# plot_2d_spike_rate(np.array(scc4), 'C1')\r\n# fig_num += 1\r\n#\r\n# plot_2d_spike_rate(np.array(s21), 'S2')\r\n# fig_num += 1\r\n# plot_2d_spike_rate(np.array(s22), 'S2')\r\n# fig_num += 1\r\n# plot_2d_spike_rate(np.array(s23), 'S2')\r\n# fig_num += 1\r\n# plot_2d_spike_rate(np.array(s24), 'S2')\r\n# fig_num += 1\r\n# plot_2d_input_weights()  # 权值可视化\r\n# b2.ioff()\r\n# b2.show()\r\n","repo_name":"tianjihua254/Spiking-neural-network-1","sub_path":"SNN-v1.0/Input_LGN_V1_V2(v1.0含测试代码).py","file_name":"Input_LGN_V1_V2(v1.0含测试代码).py","file_ext":"py","file_size_in_byte":42381,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"39131240174","text":"class Restaurant:\r\n    def __init__(self, restaurant_name, cuisine_type):\r\n        self.restaurant_name = restaurant_name\r\n        self.cuisine_type = cuisine_type\r\n\r\n    def describe_restaurant(self):\r\n        print(f\"Ресторан '{self.restaurant_name}' специализируется на кухне {self.cuisine_type}.\")\r\n\r\n\r\nrestaurant1 = Restaurant(\"Итальянский дворик\", \"итальянская\")\r\nrestaurant2 = Restaurant(\"Пекин\", \"китайская\")\r\nrestaurant3 = Restaurant(\"Ботинки и грибы\", \"европейская\")\r\n\r\nrestaurant1.describe_restaurant()\r\nrestaurant2.describe_restaurant()\r\nrestaurant3.describe_restaurant()\r\n\r\n# Вывод:\r\n# Ресторан 'Итальянский дворик' специализируется на кухне итальянская.\r\n# Ресторан 'Пекин' специализируется на кухне китайская.\r\n# Ресторан 'Ботинки и грибы' специализируется на кухне европейская.","repo_name":"AlyaRussell/Sidorova_1-MD-17","sub_path":"pythonProject1/lab11/11.2.py","file_name":"11.2.py","file_ext":"py","file_size_in_byte":1054,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"25862626874","text":"from pathlib import Path\n\nimport taxopy\nfrom genomad import utils\n\n\nclass Database:\n    def __init__(self, database_directory: Path) -> None:\n        self._directory = database_directory\n        with open(database_directory / \"version.txt\") as fin:\n            self._version = float(fin.read().strip())\n\n    @property\n    def directory(self) -> Path:\n        return self._directory\n\n    @property\n    def mmseqs2_db(self) -> Path:\n        return self.directory / \"genomad_db\"\n\n    @property\n    def mmseqs2_minimal_db(self) -> Path:\n        return self.directory / \"genomad_mini_db\"\n\n    @property\n    def mmseqs2_integrase_db(self) -> Path:\n        return self.directory / \"genomad_integrase_db\"\n\n    @property\n    def nodes_dmp(self) -> Path:\n        return self.directory / \"nodes.dmp\"\n\n    @property\n    def names_dmp(self) -> Path:\n        return self.directory / \"names.dmp\"\n\n    @property\n    def version(self) -> float:\n        return self._version\n\n    def get_marker_annotation(self) -> dict:\n        \"\"\"\n        Returns a dictionary where the keys are marker names and the values are\n        the following:\n        (1) Universal single copy gene (USCG)\n        (2) Plasmid hallmark\n        (3) Virus hallmark\n        (4) CONJscan annotation\n        (5) AMR annotation\n        (6) Functional annotation accessions (Pfam, TIGRFAM, COG, KO)\n        (7) Functional description\n        \"\"\"\n        marker_annotation = {}\n        metadata_file = self.directory / \"genomad_marker_metadata.tsv\"\n        for line in utils.read_file(metadata_file, skip_header=True):\n            (\n                marker,\n                *_,\n                uscg,\n                plasmid_hallmark,\n                virus_hallmark,\n                conjscan,\n                amr,\n                accession,\n                description,\n                _,\n            ) = line.strip().split(\"\\t\")\n            marker_annotation[marker] = (\n                int(uscg != \"NA\"),\n                int(plasmid_hallmark),\n                int(virus_hallmark),\n                conjscan,\n                amr,\n                accession,\n                description,\n            )\n        return marker_annotation\n\n    def get_marker_features(self) -> dict:\n        \"\"\"\n        Returns a dictionary where the keys are marker names and the values are\n        the following:\n        (1) Specificity class\n        (2) Chromosome SPM\n        (3) Plasmid SPM\n        (4) Virus SPM\n        (5) Giant virus marker\n        (6) Universal single copy gene (USCG)\n        (7) Plasmid hallmark\n        (8) Virus hallmark\n        \"\"\"\n        marker_features = {}\n        metadata_file = self.directory / \"genomad_marker_metadata.tsv\"\n        for line in utils.read_file(metadata_file, skip_header=True):\n            (\n                marker,\n                _,\n                specificity_class,\n                _,\n                spm_c,\n                spm_p,\n                spm_v,\n                gv_marker,\n                *_,\n                uscg,\n                plasmid_hallmark,\n                virus_hallmark,\n                _,\n                _,\n                _,\n                _,\n                _,\n            ) = line.strip().split(\"\\t\")\n            marker_features[marker] = (\n                specificity_class,\n                float(spm_c),\n                float(spm_p),\n                float(spm_v),\n                int(gv_marker),\n                int(uscg != \"NA\"),\n                int(plasmid_hallmark),\n                int(virus_hallmark),\n            )\n        return marker_features\n\n    def get_taxdb(self) -> taxopy.TaxDb:\n        \"\"\"\n        Returns a TaxDb object containing the ICTV taxdump data.\n        \"\"\"\n        return taxopy.TaxDb(\n            nodes_dmp=self.nodes_dmp, names_dmp=self.names_dmp, keep_files=True\n        )\n","repo_name":"apcamargo/genomad","sub_path":"genomad/database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":3811,"program_lang":"python","lang":"en","doc_type":"code","stars":108,"dataset":"github-code","pt":"48"}
+{"seq_id":"19201780761","text":"import inspect\nimport warnings\n\nfrom functools import partial\nfrom typing import Callable, Union\n\nimport logging\nfrom logging import handlers as logging_handlers\n\nfrom .color_provider import ColorFormatter\nfrom .utils import ensure_dir, get_logger_config, get_color_config\nfrom .exceptions import InvalidOption, DuplicatedHandler, LogmeError\n\n\nclass LogProvider:\n    \"\"\"\n    Get a LogmeLogger object for decorated classes and functions/methods\n\n    *LogmeLogger object is provided as a object property of this LogProvider*\n    \"\"\"\n\n    def __init__(self, decorated: Callable, config: str=None, name: str=None):\n        \"\"\"\n        Initialization of Logger Provider, all the optional arguments should be passed from logme.log()\n\n        Required: scope\n        Optional: config, name.\n                (logger configuration will resolve to the default when not specified,\n                *given logme.ini file exists in root dir*)\n\n        \"\"\"\n\n        self.decorated = decorated\n\n        # Get the module object of the decorated object\n        module_obj = inspect.getmodule(self.decorated)\n\n        logger_name = name if name else module_obj.__name__\n\n        config_dict = get_logger_config(module_obj.__file__, name=config)\n        color_config = get_color_config(module_obj.__file__)\n\n        self.logger = LogmeLogger(logger_name, config_dict,\n                                  color_config=color_config)\n\n\nclass ModuleLogger:\n    \"\"\"\n    Resolves to a LogmeLogger object based on the config and name passed from logme.log\n\n    \"\"\"\n    def __init__(self, frame: int, config: str=None, name: str=None):\n        \"\"\"\n\n        :param frame: frame number of the caller stack (1 if direct instantiate, 2 if instantiated from logme.log\n        :param name: name of the logger\n        :param config: configuration of the logger\n\n        \"\"\"\n        module_frame = inspect.stack()[frame]\n        module_obj = inspect.getmodule(module_frame.frame)\n\n        logger_name = name if name else module_obj.__name__\n\n        config_dict = get_logger_config(module_frame.filename, name=config)\n        color_config = get_color_config(module_frame.filename)\n\n        self.logger = LogmeLogger(logger_name, config_dict,\n                                  color_config=color_config)\n\n    def __getattr__(self, attr):\n        \"\"\"\n        Delegate the attributes and methods of self.logger to self\n        \"\"\"\n        return getattr(self.logger, attr)\n\n\n# ---------------------------------------------------------------------------\n# Logger Object\n# ---------------------------------------------------------------------------\n\n\nclass LogmeLogger:\n    \"\"\"\n    Get a logger object with configured handlers\n\n    \"\"\"\n    def __init__(self, name: str, config: dict, color_config: dict=None):\n        \"\"\"\n        :param name: name of the logger\n        :param config: configuration of the logger\n        \"\"\"\n\n        self._name = name\n        self.config = config\n        self.color_config = color_config\n\n        self.handlers = {}\n        self._set_master_properties()\n        self._set_handlers_from_conf()\n\n    def __getattr__(self, attr):\n        \"\"\"\n        Delegate all the attributes and methods of logger to LogmeLogger Object\n        \"\"\"\n        try:\n            return getattr(self.logger, attr)\n        except AttributeError:\n            raise AttributeError(f\"LogmeLogger object has no attribute '{attr}'.\")\n\n    def __repr__(self):\n        return f\"<{self.__class__.__name__} name={self.name}>\"\n\n    @property\n    def name(self):\n        \"\"\"\n        To prevent name from being set outside of __init__,\n        as the change of name will cause re-instantiation of logging.Logger object.\n        \"\"\"\n        return self._name\n\n    @property\n    def logger(self):\n        logger = logging.getLogger(self.name)\n        logger.setLevel(self.master_level)\n\n        return logger\n\n    @property\n    def disabled(self):\n        return self.logger.disabled\n\n    @disabled.setter\n    def disabled(self, val):\n        self.logger.disabled = val\n\n    @property\n    def master_formatter(self):\n        return self._master_formatter\n\n    @master_formatter.setter\n    def master_formatter(self, formatter):\n        self._master_formatter = formatter\n        self._set_handlers_from_conf(reconfig=True)\n\n    @property\n    def master_level(self):\n        return self._get_level(self._master_level)\n\n    @master_level.setter\n    def master_level(self, level):\n        self._master_level = level\n        self._set_handlers_from_conf(reconfig=True)\n\n    def _set_master_properties(self):\n        master_properties = {\n            '_master_formatter': self.config['formatter'],\n            '_master_level': self.config['level'],\n            'handler_names':  [i for i in self.config.keys()\n                               if i not in ['level', 'formatter']]\n\n        }\n\n        for k, v in master_properties.items():\n            setattr(self, k, v)\n\n    def _set_handlers_from_conf(self, reconfig=False):\n        \"\"\"\n        Iterate through the config dict, set the active handlers\n        \"\"\"\n        for handler_name in self.handler_names:\n            if self.config[handler_name]['active'] is False:\n                continue\n\n            level = self.config[handler_name].get('level')\n            formatter = self.config[handler_name].get('formatter')\n\n            parse_args = self._get_handler_args(handler_name)\n\n            if reconfig:  # If true, reconfigure the existing handlers\n                handler_obj = self.handlers[handler_name]\n                self._config_handler(handler_obj, level=level,\n                                     formatter=formatter, set_from_master=True)\n            else:\n                handler_type = self._get_handler_type(handler_name)\n                self.add_handler(handler_name, handler_type, level=level, formatter=formatter,\n                                 skip_duplicate=True, **parse_args)\n\n    def _get_handler_args(self, handler_name):\n        \"\"\"\n        Get the args passed into handler from config\n        \"\"\"\n        parse_args = {k: v for k, v in self.config[handler_name].items()\n                      if k not in ['type', 'active', 'level', 'formatter']}\n\n        return parse_args\n\n    def _get_handler_type(self, handler_name) -> str:\n        \"\"\"\n        Get the type of the handler from handler_name declared in the config.\n\n        :return: e.g. StreamHandler, SocketHandler\n        \"\"\"\n        try:\n            handler_type = self.config[handler_name]['type']\n        except KeyError:\n            # Deprecation warning\n            warnings.warn(\"Current configuration is deprecated, run 'logme upgrade' in your \"\n                          \"project root to upgrade your logme.ini file\",\n                          category=DeprecationWarning)\n\n            handler_type = handler_name\n\n        return handler_type\n\n    def _config_handler(self, handler: logging.Handler, level: Union[str, int]=None,\n                        formatter: Union[str, dict]=None, set_from_master: bool=False):\n\n        \"\"\"\n        Configure the handler's level and formatter\n\n        :param handler: logging.Handler type object\n        :param level: the level of the handler\n        :param formatter: the formatter of the handler\n\n        :param set_from_master: Set *level* or *formatter* from obj.master_level and obj.master_formatter\n\n        \"\"\"\n        # Set level\n        if level:\n            log_level = self._get_level(level)\n            handler.setLevel(log_level)\n        elif set_from_master:\n            handler.setLevel(self.master_level)\n\n        # Set formatter\n        if type(handler) == logging.StreamHandler:\n            formatter_class = partial(ColorFormatter,\n                                      color_config=self.color_config)\n        else:\n            formatter_class = logging.Formatter\n\n        if formatter:\n            self._set_formatter(handler, formatter_class, formatter)\n        elif set_from_master:\n            self._set_formatter(handler, formatter_class, self.master_formatter)\n\n    def _get_level(self, level: Union[str, int]) -> int:\n        \"\"\"\n        Get the level number of the logger\n        \"\"\"\n        if isinstance(level, str):\n            try:\n                return logging._nameToLevel[level.upper()]\n            except KeyError:\n                raise InvalidOption(f\"'{level}' is not a valid level option\")\n        if isinstance(level, int):  # logging.ERROR is also type of int\n            return level\n\n    def _get_formatter_args(self, formatter: Union[str, dict]) -> dict:\n        \"\"\"\n        Get argument to be passed to logging.Formatter\n        \"\"\"\n        if isinstance(formatter, dict):\n            return formatter\n        elif isinstance(formatter, str):\n            return {'fmt': formatter, 'style': '{'}\n\n        raise ValueError(f\"Invalid formatter type: '{type(formatter)}', \"\n                         f\"formatter must be passed as either dict or string\")\n\n    def _set_formatter(self, handler: logging.Handler, formatter_class: type,\n                       formatter: Union[str, dict]):\n        \"\"\"\n        Set the formatter with the handler and formatter class specified\n        \"\"\"\n        args = self._get_formatter_args(formatter)\n        formatter_object = formatter_class(**args)\n\n        handler.setFormatter(formatter_object)\n\n    def _handler_exist(self, handler: logging.Handler) -> bool:\n        \"\"\"\n        Check if logging handler already exists\n        \"\"\"\n        for i in self.logger.handlers:\n            handler_class = i.__class__\n            handler_attr = self._get_handler_attr(handler)\n            exist_attr = self._get_handler_attr(i)\n\n            # Using type(handler) here because FileHandler inherit from StreamHandler,\n            # and isinstance() will be True is checking a FileHandler object with StreamHandler class\n            if type(handler) is handler_class and handler_attr == exist_attr:\n                return True\n\n        return False\n\n    def add_handler(self, handler_name: str, handler_type: str, formatter: Union[str, dict]=None,\n                    level: Union[str, int]=None, allow_duplicate: bool=False, skip_duplicate: bool=False, **kwargs):\n        \"\"\"\n        Add the handler to self.logger on adhoc basis\n\n        :param handler_name: The name of the handler:\n                                    - Same as handler_type.\n                                    - key for handlers in ini file. *version >= 1.1.0*\n        :param handler_type: The type of handler, e.g. StreamHandler, SocketHandler\n        :param formatter: formatter to be passed to the handler\n        :param level: Level for the handler\n        :param allow_duplicate: *USE WITH CAUTION*, this allows duplication of handlers in the same logger\n        :param skip_duplicate: Skip the duplicated handler\n\n        :param kwargs: arguments to be passed to the handler class\n\n        \"\"\"\n        if self.handlers.get(handler_name):\n            raise LogmeError(f\"Handler with name {handler_name} already exists!\")\n\n        try:\n            handler_class = getattr(logging, handler_type)\n        except AttributeError:\n            handler_class = getattr(logging_handlers, handler_type)\n\n        # Ensure filename for handlers are passed in and directory is created\n        self._ensure_filepath(handler_class, **kwargs)\n\n        handler = handler_class(**kwargs)\n        self._config_handler(handler, level=level,\n                             formatter=formatter, set_from_master=True)\n\n        if self._handler_exist(handler):\n            if allow_duplicate:\n                self.logger.addHandler(handler)\n                self.handlers[handler_name] = handler\n                return\n            if skip_duplicate:\n                return\n            else:\n                raise DuplicatedHandler(f\"{handler_class} with the exact same configuration already exists, \"\n                                        f\"add allow_duplicate=True to allow.\")\n        else:\n            self.logger.addHandler(handler)\n            self.handlers[handler_name] = handler\n\n    def _ensure_filepath(self, handler_class, **kwargs):\n        \"\"\"\n        Ensure the filename is being passed into the handler class,\n        Make the directory if it doesn't exist\n\n        :param handler_class: Handler class from logging module\n        :param kwargs: arguments to be passed into the the class when instantiate an object\n        \"\"\"\n\n        required_args = str(inspect.signature(handler_class))\n\n        if 'filename,' in required_args:\n            try:\n                filename = kwargs['filename']\n                ensure_dir(filename)\n            except KeyError:  # filename is None or not Passed in\n                raise ValueError(f\"file path for the {handler_class} must not be None\")\n\n    def _get_handler_attr(self, handler: logging.Handler) -> dict:\n        \"\"\"\n        Get the *string* attribute of the handler\n        \"\"\"\n        handler_attr = {'formatter': handler.formatter._fmt}\n\n        for k, v in handler.__dict__.items():\n            if isinstance(v, str) or str(v).isdigit():\n                handler_attr[k] = v\n\n        return handler_attr\n\n    def reset_config(self, config: str=None, config_dict: dict=None, name: str=None):\n        \"\"\"\n        Used for alternative config. This is normally used for module scope loggers\n\n        :param config: config name specified in the config\n        :param config_dict: config dict\n        :param name: logger name to be changed to\n        \"\"\"\n        # Check if the arguments are valid\n        if config and config_dict:\n            raise InvalidOption(\"Can only set keyword argument of either 'config_name' or 'config', not both.\")\n        if not config and not config_dict:\n            raise InvalidOption(\"must specify one of 'config_dict' or 'config'.\")\n\n        caller_file_path = inspect.getframeinfo(inspect.currentframe().f_back).filename\n        if config:\n            self.config = get_logger_config(caller_file_path, config)\n        else:\n            self.config = config_dict\n\n        # Remove existing logger from Logger manager dict\n        del logging.Logger.manager.loggerDict[self.name]\n\n        if name:\n            self._name = name\n\n        self.handlers = {}\n        self._set_master_properties()\n        self._set_handlers_from_conf()\n\n    def reconfig_handler(self, handler_name: str, level: Union[str, int]=None, formatter: Union[str, dict]=None):\n        \"\"\"\n        Reconfigure an existing handler's level and formatter.\n        *This can be used to configure a handler for a specific logger to be different from the config in Logme.ini*\n\n        :param handler_name: **case sensitive**. name of the handler, specified as a option key in ini file\n        :param level: the new level to be set\n        :param formatter: the new formatter to be set. '{' style\n\n        \"\"\"\n        if not level and not formatter:\n            raise InvalidOption(\"Set at least one of 'level' or 'formatter' for reconfiguration.\")\n\n        try:\n            handler_obj = self.handlers[handler_name]\n            self._config_handler(handler_obj, level=level,\n                                 formatter=formatter)\n        except KeyError:\n            raise LogmeError(f\"{handler_name} is not found in this logger, either use add_handle() to add this handler\")\n","repo_name":"BNMetrics/logme","sub_path":"logme/providers.py","file_name":"providers.py","file_ext":"py","file_size_in_byte":15291,"program_lang":"python","lang":"en","doc_type":"code","stars":536,"dataset":"github-code","pt":"48"}
+{"seq_id":"17326611208","text":"__doc__ = \"\"\"Make supervised learning problems from ``VTE_DATA_PREP_PATH``.\n\nThe CSV file located at that path is the preprocessed VTE data that can be found\n`in the Google Drive`__.\n\n.. __: https://drive.google.com/file/d/1n2cND9y8fdSGmFGN_U03ma4vGOIu0rky/view?\nusp=sharing\n\"\"\"\n\n# pylint: disable=relative-beyond-top-level\nfrom .. import VTE_DATA_PREP_PATH\nfrom .factory import make_slp_from_data\n\n\ndef vte_slp_factory(**kwargs):\n    \"\"\"Genrates supervised learningproblems from the VTE data set.\n\n    A wrapper for :func:`~mtml.data.factory.make_slp_from_data` where the\n    ``data_path`` argument is the path to preprocessed VTE data located in\n    ``data/files/prep/vte_onlydata_preprocessed.csv``.\n    \n    See the docstring for :func:`~mtml.data.factory.make_slp_from_data` for\n    details on the individual keyword arguments that can be passed.\n\n    :param kwargs: Keyword arguments to pass to\n        :func:`~mtml.data.factory.make_slp_from_data`.\n    :returns: A 4-tuple ``(X_train, X_test, y_train, y_test)`` of\n        :class:`numpy.ndarray` objects\n    :rtype: tuple\n    \"\"\"\n    return make_slp_from_data(VTE_DATA_PREP_PATH, **kwargs)\n","repo_name":"crb479/mcdevitt-trauma-ml","sub_path":"mtml/data/vte.py","file_name":"vte.py","file_ext":"py","file_size_in_byte":1147,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"48"}
+{"seq_id":"37233294532","text":"import requests\nfrom bs4 import BeautifulSoup\nimport pandas as pd\nimport numpy as np\n\n\n\ndef get_title(soup):\n    \n    try:\n        # Outer Tag Object\n        title = soup.find(\"div\",class_=\"brand\").decompose()\n        title = soup.find(\"span\",class_=\"model\").decompose()\n        title = soup.find(\"h1\", attrs={\"class\":'like-h3'})\n        \n        # Inner NavigatableString Object\n        title_value = title.text\n\n        # Title as a string value\n        title_string = title_value.strip()\n\n    except AttributeError:\n        title_string = \"\"\n\n    return title_string\n\ndef get_brand(soup):\n    try:\n        # Outer Tag Object\n        title = soup.find(\"sup\").decompose()\n        title = soup.find(\"span\", attrs={\"class\":'brand-name'})        \n        \n        # Inner NavigatableString Object\n        title_value = title.text\n        # Title as a string value\n        title_string = title_value.strip()\n        # print(title_value)\n\n    except AttributeError:\n        title_string = \"\"\n\n    return title_string\n\ndef get_dimensions(soup):\n    try:\n        # Outer Tag Object\n        title = soup.find(\"span\", attrs={\"class\":'model'})\n        \n        # Inner NavigatableString Object\n        title_value = title.text\n\n        # Title as a string value\n        title_string = title_value.strip()\n        # print(title_value)\n\n    except AttributeError:\n        title_string = \"\"\n\n    return title_string\n\ndef get_price(soup):\n    try:\n        # Outer Tag Object\n        title = soup.find(\"span\", attrs={\"class\":'m-w'})\n        \n        # Inner NavigatableString Object\n        title_value = title.text\n\n        # Title as a string value\n        title_string = title_value.strip()\n\n    except AttributeError:\n        title_string = \"\"\n\n    return title_string\n\ndef get_Min_Qty_perConsignee(soup):\n    try:\n        # Outer Tag Object\n        title = soup.find(\"span\", attrs={\"class\":'moq_data'})\n        \n        # Inner NavigatableString Object\n        title_value = title.text\n\n        # Title as a string value\n        title_string = title_value.strip()\n\n    except AttributeError:\n        title_string = \"\"\n\n    return title_string\n\n\ndef get_availablity(soup):\n    try:\n        # Outer Tag Object\n        title = soup.find(\"strong\", attrs={\"class\":'green'})\n        # Inner NavigatableString Object\n        title_value = title.text\n\n        # Title as a string value\n        title_string = title_value.strip()\n\n    except AttributeError:\n        title_string = \"\"\n\n    return title_string\n\ndef get_seller(soup):\n    try:\n        head = \"https://mkp.gem.gov.in\"\n\n        # Outer Tag Object\n        title = soup.find(\"div\", attrs={\"class\":'other-sellers-info'}).find('a')[\"href\"]\n        temp = head+title\n        \n        new_webpage = requests.get(temp)\n        new_soup = BeautifulSoup(new_webpage.content, \"html.parser\")\n\n        table = new_soup.find(\"table\")\n\n        # then we can iterate through each row and extract either header or row values:\n        d = {\"Header\":[], \"Row\":[]}\n        header = []\n        rows = []\n        for i, row in enumerate(table.find_all('tr')):\n            if i == 0:\n                header = [el.text.strip() for el in row.find_all('th')]\n            else:\n                rows.append([el.text.strip() for el in row.find_all('td')])\n\n        # print(rows)\n\n        d[\"Header\"].append(header)\n        d[\"Row\"].append(rows)\n\n\n    except AttributeError:\n        d = \"\"\n\n    return d\n\n\nif __name__ == '__main__':\n\n    d = {\"Title\": [], \"Brand\": [], \"Dimensions\":[], \"Price\": [], \"Min. Qty. Per Consignee\":[], \"Availablity\":[], \"Seller\":[]}\n\n    books = []\n    for i in range(1,2):\n        url = f\"https://mkp.gem.gov.in/handloom-cotton-gauze/search#/?q[]=gauze&page={i}&_xhr=1\"\n\n        response = requests.get(url)\n        soup = BeautifulSoup(response.content, 'html.parser')\n        head = \"https://mkp.gem.gov.in\"\n\n        links_list = []\n\n        links = soup.find_all('div',class_ = \"variant-desc\")\n        for link in links:\n            link = link.find('a')[\"href\"]\n            temp = head+link\n            links_list.append(temp)\n\n        for link in links_list:\n            new_webpage = requests.get(link)\n            new_soup = BeautifulSoup(new_webpage.content, \"html.parser\")\n            # print(link)\n            d[\"Brand\"].append(get_brand(new_soup))\n            d[\"Dimensions\"].append(get_dimensions(new_soup))\n            # REMEMBER to run get_title after get_brand and get_dimensions\n            d['Title'].append(get_title(new_soup))\n            d[\"Price\"].append(get_price(new_soup))\n            d[\"Min. Qty. Per Consignee\"].append(get_Min_Qty_perConsignee(new_soup))\n            d[\"Availablity\"].append(get_availablity(new_soup))\n            d[\"Seller\"].append(get_seller(new_soup))\n           \n        \n        df = pd.DataFrame.from_dict(d)\n        df['Title'].replace('', np.nan, inplace=True)\n        df = df.dropna(subset=['Title'])\n        df.to_csv(\"data.csv\", header=True, index=False)\n            \n\n\n\n\n\n    # for testing purposes\n\n    # new_webpage = requests.get(\"https://mkp.gem.gov.in/handloom-cotton-gauze/handloom-cotton-gauze-18-meter-x-60-centimeter/p-5116877-94989474214-cat.html#variant_id=5116877-94989474214\")\n    # new_soup = BeautifulSoup(new_webpage.content, \"html.parser\")\n\n    # print(get_brand(new_soup), get_dimensions(new_soup))","repo_name":"AdityaKumar-2501/GeM-Project","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5313,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"26659985910","text":"# coding: utf-8\n# 部分參考: https://ithelp.ithome.com.tw/articles/10192043 \n#       非常感謝王選仲大大\n# 部分參考 http://blog.fukuball.com/ru-he-shi-yong-jieba-jie-ba-zhong-wen-fen-ci-cheng-shi/\n#       非禪感謝林志傑 Fukuball大大\n# 字典來自 https://git.oschina.net/fxsjy/jieba\n#       真的是做最好的中文分词组件\n#\n\nfrom PIL import Image\nimport matplotlib.pyplot as plt\nfrom wordcloud import WordCloud, ImageColorGenerator\nimport jieba\nimport numpy as np\nfrom collections import Counter\n\n#要分析的來源，這個範例是五月天20首歌\ntext_from_file_with_apath = open(r'lyrics.txt', \"r\",encoding=\"utf-8\").read()\n\n#設定字典\njieba.set_dictionary('dict.txt.big')\n#設定自訂的字典，譬如五月天的 \"動次\"\njieba.load_userdict(r'userdict_mayday.txt')\n\n#設定停用詞，譬如唱歌會用到的oh，喔\nwith open(r'stopWord_mayday.txt', 'r', encoding='utf8') as f:  \n    stops = f.read().split('\\n') \n\n#\n#開始段詞與排序，沒錯就這麼簡單就做完了\n#\nterms = [t for t in jieba.cut(text_from_file_with_apath, cut_all=True) if t not in stops]\nsorted(Counter(terms).items(), key=lambda x:x[1], reverse=True) \n\n\n#中文繪圖需要中文字體，請自己從windows font目錄抓\n#微軟正黑體\nfont = r'msjh.ttc'\n#想要文字雲出現的圖示\nmask = np.array(Image.open(r\"mayday_mask.png\"))\n\n#背景顏色預設黑色，改為白色\n#mark改用五月天的皇冠\n#其他參數請自行參考wordcloud\nmy_wordcloud = WordCloud(background_color=\"white\",mask=mask,font_path=font,collocations=False, width=2400, height=2400, margin=2)  \nmy_wordcloud.generate_from_frequencies(frequencies=Counter(terms))\n\n#產生圖片\nplt.figure( figsize=(20,10), facecolor='k')\nplt.imshow(my_wordcloud,interpolation='bilinear')\nplt.axis(\"off\")\nplt.tight_layout(pad=0)\n#顯示用\nplt.show()\n\n#存檔用\n#plt.savefig(\"Mayday_Wordcloud.png\")","repo_name":"stacepsho/Word_Cloud_zh-tw","sub_path":"文字切割-文字雲.py","file_name":"文字切割-文字雲.py","file_ext":"py","file_size_in_byte":1896,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"48"}
+{"seq_id":"2834580525","text":"import sys\nimport pandas_flavor as pf\n\nfrom .my_parser import translator\n\n\n@pf.register_dataframe_method\ndef mutate(df, local_dict=None, global_dict=None, level=2, **kwargs):\n    locals_dict = (\n        sys._getframe(level).f_locals if local_dict is None else local_dict\n    )\n    globals_dict = (\n        sys._getframe(level).f_globals if global_dict is None else global_dict\n    )\n    _dataframe = df.copy()\n    for key, value in kwargs.items():\n        trans = translator(_dataframe.columns)\n        locals_dict[\"_dataframe\"] = _dataframe\n\n        _dataframe = eval(\n            f\"_dataframe.assign({str(key)}=lambda _df: {trans.translate(value)})\",\n            locals_dict,\n            globals_dict,\n        )\n\n    return _dataframe\n","repo_name":"samihamdan/pandashack","sub_path":"pandashack/dataframe_extention.py","file_name":"dataframe_extention.py","file_ext":"py","file_size_in_byte":737,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"30337132601","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\n\n# Global Variables\nSTART_TEMP = 15 + 273.15    # k\nSTART_PRESSURE = 101          # kpa\nGAMMA = 1.4\nGRAVITY = 9.8   # m/s\nMOLAR_MASS = 28.9 / 1000    # kg/mol\nR = 8.314  # J / K mol\n\n# Vary these\nMAX_HEIGHT = 14000\ndZ = 1   #m\n\n\ndef inittilize_ic():\n    pressure_array[0] = START_PRESSURE\n    temperature_array[0] = START_TEMP\n\n\ndef update_arrays(position):\n    dP = -1 * MOLAR_MASS * GRAVITY * pressure_array[position] * dZ / (R * temperature_array[position])\n    dT = (1 - 1/GAMMA) * temperature_array[position] * dP / pressure_array[position]\n    pressure_array[position + 1] = pressure_array[position] + dP\n    temperature_array[position + 1] = temperature_array[position] + dT\n\n\ndef plot_charts():\n    plt.plot(hight_array, pressure_array)\n    # plt.plot(hight_array, temperature_array, 'red')\n    plt.title('Elevation vs Atmospheric Pressure')\n    plt.xlabel('Elevation above Sea Level (m)')\n    plt.ylabel('Pressure (kPa)')\n    plt.show()\n\n\n# Start code\npressure_array = np.ones(int(MAX_HEIGHT/dZ))\ntemperature_array = np.ones(int(MAX_HEIGHT/dZ))\nhight_array = [i * dZ for i in range(0, int(MAX_HEIGHT/dZ))]\n\ninittilize_ic()\n\nindex = 0\nwhile index * dZ < MAX_HEIGHT - 1:\n    update_arrays(index)\n    index += 1\n\nplot_charts()\n","repo_name":"gmaruzhenko/ENPH-257-Assignment-3","sub_path":"pressure_alltitude_plot.py","file_name":"pressure_alltitude_plot.py","file_ext":"py","file_size_in_byte":1285,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"69923032785","text":"'''\r\nProblem Statement #\r\nWe are given an unsorted array containing ‘n+1’ numbers taken from the range 1 to ‘n’. The array has only one duplicate but it can be repeated multiple times. Find that duplicate number without using any extra space. You are, however, allowed to modify the input array.\r\n\r\nExample 1:\r\n\r\nInput: [1, 4, 4, 3, 2]\r\nOutput: 4\r\nExample 2:\r\n\r\nInput: [2, 1, 3, 3, 5, 4]\r\nOutput: 3\r\nExample 3:\r\n\r\nInput: [2, 4, 1, 4, 4]\r\nOutput: 4\r\n'''\r\n\r\n\r\ndef find_duplicate(nums):\r\n    i = 0\r\n\r\n    while i < len(nums):\r\n        if nums[i] == i + 1:     # if number is at correct index, go to next index\r\n            i += 1\r\n        else:\r\n            j = nums[i] - 1     # correct index for the current number\r\n            if nums[i] != nums[j]:      # if both numbers at 2 indices are different, swap\r\n                nums[i],nums[j] = nums[j], nums[i]      \r\n            else:\r\n                return nums[i]     # if both numbers at different places are same, we found the duplicate\r\n    \r\n    return -1\r\n\r\n\r\n\r\ndef main():\r\n    print(find_duplicate([1, 4, 4, 3, 2]))\r\n    print(find_duplicate([2, 1, 3, 3, 5, 4]))\r\n    print(find_duplicate([2, 4, 1, 4, 4]))\r\n\r\n\r\nmain()\r\n","repo_name":"arvakagdi/GrokkingTheCodingInterviews","sub_path":"GTCI_CyclicSort/2/FindDuplicate.py","file_name":"FindDuplicate.py","file_ext":"py","file_size_in_byte":1186,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"34764713747","text":"from flask import Flask, make_response\nfrom flask_restful import Api\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_jwt_extended import JWTManager\n\napp = Flask(__name__)\napi = Api(app)\n\napp.config['SQLALCHEMY_DATABASE_URI'] = 'mysql+pymysql://root:123@db/debelogin'\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\napp.config['SECRET_KEY'] = '123'\n\ndb = SQLAlchemy(app)\n\n@app.before_first_request\ndef create_tables():\n    db.create_all()\n\napp.config['JWT_SECRET_KEY'] = '123'\njwt = JWTManager(app)\n\napp.config['JWT_BLACKLIST_ENABLED'] = True\napp.config['JWT_BLACKLIST_TOKEN_CHECKS'] = ['access', 'refresh']\n\n@jwt.token_in_blacklist_loader\ndef check_if_token_in_blacklist(decrypted_token):\n    jti = decrypted_token['jti']\n    return models.RevokedTokenModel.is_jti_blacklisted(jti)\n\n@app.route('/health', methods=['GET'])\ndef hello():\n    return make_response(\"<h2>Im Alive!!<h2>\", 200)\n\nimport views, models, resources\n\napi.add_resource(resources.UserRegistration, '/registration')\napi.add_resource(resources.UserLogin, '/login')\napi.add_resource(resources.UserLogoutAccess, '/logout/access')\napi.add_resource(resources.UserLogoutRefresh, '/logout/refresh')\napi.add_resource(resources.TokenRefresh, '/token/refresh')\napi.add_resource(resources.AllUsers, '/users')\napi.add_resource(resources.SecretResource, '/secret')\n\nif __name__ == \"__main__\":\n    # Only for debugging while developing\n    app.run(host='0.0.0.0', debug=True, port=80)\n","repo_name":"riskiwah/simple-microservices","sub_path":"flask-jwt/app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1446,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"70387727506","text":"import mysql.connector\nimport requests\nfrom mysql.connector import Error\n\ntry:\n    conexao = mysql.connector.connect(host='localhost',\n                                      database='db_cnpj',\n                                      user='root',\n                                      password='Magic1234')\n    if conexao.is_connected():\n        db_info = conexao.get_server_info()\n        cursor = conexao.cursor()\n\n    repetir_consulta = True\n\n    while(repetir_consulta):\n        opcao = int(input('\\nPor favor, digite uma opção de naveção:'\n                          '\\n1 - Listar CNPJ cadastrados;'\n                          '\\n2 - Importar dados de CNPJ;\\n'))\n\n        if(opcao == 1):\n            cursor.execute('SELECT * FROM tb_clientes;')\n            linhas = cursor.fetchall()\n            print(f'\\nO banco de dados contém {cursor.rowcount} registros:\\n')\n            for linha in linhas:\n                print(f'CNPJ: {linha[0]}\\nRazão Social:{linha[1]}\\n'\n                      'Site: {linha[2]}\\n')\n            repetir_consulta = input('Digite \"s\" para voltar ao menu inicial'\n                                     ' ou aperte qualquer tecla para sair do'\n                                     ' programa.\\n') == 's'\n\n        elif(opcao == 2):\n            repetir_cnpj = True\n            while(repetir_cnpj):\n                cnpj_digitado = input('\\nPor favor, digite o CNPJ: ')\n                if(cnpj_digitado == ''):\n                    repetir_cnpj = input('O CNPJ informado é inválido, você'\n                                         ' gostaria de fazer outra consulta?'\n                                         '\\nDigite \"s\" para sim ou aperte'\n                                         ' qualquer tecla para voltar à tela'\n                                         ' inicial:\\n') == 's'\n                else:\n                    request = requests.get(f'https://www.receitaws.com.br/v1/cnpj/{cnpj_digitado}')\n                    valida_cnpj = request.json()\n                    if(valida_cnpj[\"status\"] != 'ERROR'):\n                        cnpj_bruto = valida_cnpj[\"cnpj\"]\n                        cnpj = '{}{}{}{}{}'.format(cnpj_bruto[:2], cnpj_bruto[2:5],\n                                                          cnpj_bruto[5:8], cnpj_bruto[8:13], cnpj_bruto[13:])\n                        razao_social = valida_cnpj[\"nome\"]\n                        site = 'Não informado'\n                        print(f'CNPJ: {cnpj}')\n                        print(f'Razão Social: {razao_social}')\n                        print(f'Site: {site}\\n')\n\n                        criar_cliente = input(\"Você deseja criar um novo cliente com esses dados?\"\n                                              \" Digite 's' para sim ou aperte qualquer outra tecla \"\n                                              \"para voltar para a tela inicial.\\n\") == 's'\n                        repetir_cnpj = False\n\n                        if(criar_cliente):\n                            try:\n                                inserir_novo_cliente = f\"INSERT INTO tb_clientes (cnpj, razao_social, site) VALUES \" \\\n                                                       f\"('{cnpj}', '{razao_social}', '{site}');\"\n                                cursor.execute(inserir_novo_cliente)\n                                conexao.commit()\n                            except Error as erro_cliente_cadastrado:\n                                repetir_consulta = input('O cliente já encontra-se cadastrado. Digite \"s\" '\n                                                         'para voltar ao menu inicial ou aperte qualquer tecla '\n                                                         'para sair do programa.\\n') == 's'\n                    else:\n                        repetir_cnpj = input('O CNPJ informado é inválido, você gostaria de fazer outra '\n                                    'consulta?\\nDigite \"s\" para sim ou aperte qualquer tecla '\n                                             'para voltar à tela inicial:\\n') == 's'\n\n        else:\n            print('Por favor, digite uma opção válida conforme abaixo:')\n\nexcept Error as erro_conexao:\n    print(f'Erro ao acessar tabela MySQL {erro_conexao}.')\n\nfinally:\n    if(conexao.is_connected()):\n        cursor.close()\n        conexao.close()\n        print('Obrigado por utilizar nosso programa.')\n","repo_name":"ArcangeloAR/desafioPython","sub_path":"desafio.py","file_name":"desafio.py","file_ext":"py","file_size_in_byte":4330,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"31324376053","text":"#!/usr/bin/env python3\n\nimport math\nimport re\nimport sys\n\n\ndef hoaAccSign(priority):\n    if priority == 0:\n        return \"f\"\n    rec = [\"\"] * (priority + 1)\n    rec[0] = \"Inf(0)\"\n    for p in range(1, priority):\n        if p % 2 == 0:\n            rec[p] = f\"(Inf({p}) | {rec[p - 1]})\"\n        else:\n            rec[p] = f\"(Fin({p}) & {rec[p - 1]})\"\n    if priority % 2 == 0:\n        return f\"Inf({priority}) | {rec[priority - 1]}\"\n    else:\n        return f\"Fin({priority}) & {rec[priority - 1]}\"\n\n\nclass ParityGame:\n    def __init__(self, filename):\n        f = open(filename, \"r\")\n        # information to be obtained from the file\n        self.names = []\n        self.succ = []\n        self.owner = []\n        self.prio = []\n        # let's get to reading that file then\n        header = True\n        for line in f.readlines():\n            # some string treatment here\n            # (1) check lines end with semicolon, and remove it\n            scolon = line.find(';')\n            assert(scolon > -1)\n            line = line[:scolon]\n            # (2) replace commas followed by spaces and use comma only for\n            # convenience\n            line = re.sub(\",\\s*\", \",\", line)\n            # now we can continue\n            if header:\n                header = False\n                hdrItems = line.split()\n                assert(len(hdrItems) == 2)\n                assert(hdrItems[0] == \"parity\")\n                noVtces = int(hdrItems[1]) + 1\n                self.prio = [0] * noVtces\n                self.owner = [0] * noVtces\n                self.succ = [[]] * noVtces\n                self.name = [\"\"] * noVtces\n                self.maxoutdeg = -1\n                self.maxpriority = -1\n            else:\n                vtxInfo = line.split()\n                assert(len(vtxInfo) in [4, 5])\n                idx = int(vtxInfo[0])\n                assert(idx < len(self.prio))\n                # start filling details for this vertex then\n                self.prio[idx] = int(vtxInfo[1])\n                self.maxpriority = max(self.maxpriority,\n                                       self.prio[idx])\n                self.owner[idx] = int(vtxInfo[2])\n                self.succ[idx] = [int(s) for s in vtxInfo[3].split(',')]\n                self.maxoutdeg = max(self.maxoutdeg,\n                                     len(self.succ[idx]))\n                if len(vtxInfo) == 5:\n                    self.name[idx] = vtxInfo[4].strip()[1:-1]\n                else:\n                    self.name[idx] = f\"vertex{idx}\"\n        self.noAP = math.floor(math.log(self.maxoutdeg, 2)) + 1\n\n    def genSuccLabel(self, i, j):\n        negAPs = [a for a in range(self.noAP) if ((1 << a) & j) == 0]\n        posAPs = [a for a in range(self.noAP) if a not in negAPs]\n        if self.owner[i] == 1:\n            negAPs = [a + self.noAP for a in negAPs]\n            posAPs = [a + self.noAP for a in posAPs]\n        negLabel = \" & \".join([f\"!{a}\" for a in negAPs])\n        posLabel = \" & \".join([str(a) for a in posAPs])\n        return f\"{negLabel} & {posLabel}\"\n\n    def toHoa(self):\n        print(\"HOA: v1\")\n        print(f\"States: {len(self.prio)}\")\n        print(\"Start: 0\")\n        print(f\"acc-name: parity max even {self.maxpriority + 1}\")\n        sign = hoaAccSign(self.maxpriority)\n        print(f\"Acceptance: {self.maxpriority + 1} {sign}\")\n        names0 = \" \".join([f\"\\\"pl0_{i}\\\"\" for i in range(self.noAP)])\n        names1 = \" \".join([f\"\\\"pl1_{i}\\\"\" for i in range(self.noAP)])\n        print(f\"AP: {self.noAP * 2} {names0} {names1}\")\n        indices1 = \" \".join([str(x) for x in range(self.noAP)])\n        print(f\"controllable-AP: {indices1}\")\n        print(\"properties: deterministic complete colored\")\n        print(\"--BODY--\")\n        for i in range(len(self.prio)):\n            print(f\"State: {i} \\\"{self.name[i]}\\\" { {self.prio[i]} }\")\n            if len(self.succ[i]) == 1:\n                print(f\"[t] {self.succ[i][0]}\")\n            else:\n                # we exclude the first to handle it later\n                allLabels = []\n                for j in range(1, len(self.succ[i])):\n                    label = self.genSuccLabel(i, j)\n                    print(f\"[{label}] {self.succ[i][j]}\")\n                    allLabels.append(label)\n                orLabels = \" | \".join([f\"({lab})\" for lab in allLabels])\n                negLabels = f\"!({orLabels})\"\n                print(f\"[{negLabels}] {self.succ[i][0]}\")\n\n        print(\"--END--\")\n        return 0\n\n\nif __name__ == \"__main__\":\n    if len(sys.argv) != 2:\n        print(f\"{sys.argv[0]} requires precisely one argument: \"\n              \"a parity game in PGSolver format\")\n        exit(1)\n    game = ParityGame(sys.argv[1])\n    exit(game.toHoa())\n","repo_name":"SYNTCOMP/hoa-tools","sub_path":"pg2hoa.py","file_name":"pg2hoa.py","file_ext":"py","file_size_in_byte":4695,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"75069742864","text":"# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import, print_function\nfrom datetime import datetime\n\nfrom authlib.integrations.flask_oauth2 import current_token\nfrom flask import current_app, request\n\nfrom . import Resource\nfrom ..dtos import SettlementDTO\nfrom ..errors import ArchMonolithicFlaskError\nfrom ..extensions.oauth2 import require_oauth2\nfrom ..services import SettlementService\n\n\nclass Settlement(Resource):\n\n    method_decorators = [require_oauth2(scopes=\"ALL\")]\n\n    def post(self):\n        json_data = request.json\n        user = current_token.user\n        dto = SettlementDTO()\n        dto.from_dict(data=json_data)\n        service = SettlementService(\n            flask_app=current_app._get_current_object(),\n            user_id=user.id,\n            default_product_frozen_expires=current_app.config[\"DEFAULT_PRODUCT_FROZEN_EXPIRES\"]\n        )\n        try:\n            payment = service.generate(settlement_dto=dto)\n        except ArchMonolithicFlaskError as e:\n            status_code = e\n            resp_body = dict(\n                code=1,\n                message=e.message\n            )\n        else:\n            status_code = 200\n            resp_body = dict(\n                id=payment.id,\n                pay_id=payment.pay_id,\n                # create_time=datetime.utcfromtimestamp(payment.create_time.timestamp()),\n                create_time=str(payment.create_time),\n                total_price=float(payment.total_price),\n                expires=payment.expires,\n                payemnt_link=payment.payment_link,\n                pay_state=payment.pay_state\n            )\n        return resp_body, status_code","repo_name":"buglib/arch_monolithic_flask","sub_path":"api/v1/resources/settlement.py","file_name":"settlement.py","file_ext":"py","file_size_in_byte":1650,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"45229394792","text":"#!/bin/python\n\nimport numpy as np\nimport os\nfrom sklearn.svm import SVC\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.neural_network import MLPClassifier\nimport pickle\nimport argparse\nimport sys\nimport pdb\nimport time\n\n# Train SVM\n\nlist_videos = 'labels/trainval.csv'\nsoundnet_dir = 'SoundNet-tensorflow/output'\nsoundnet_layers = [18, 21]\noutput_file = 'models/best.model'\n\nfactor = 3\nstd = 0.1\n\nnp.random.seed(0)\n\nstart_time = time.time()\n\n# 1. read all features in one array.\nfread = open(list_videos, \"r\")\nfeat_list = []\n# labels are [0-9]\nlabel_list = []\n# load video names and events in dict\ndf_videos_label = {}\nfor line in open(list_videos).readlines()[1:]:\n  video_id, category = line.strip().split(\",\")\n  df_videos_label[video_id] = category\n\nlayer_shapes = {}\nmfcc_shape = {}\n\nfor line in fread.readlines()[1:]:\n  video_id = line.strip().split(\",\")[0]\n  feat = np.array([])\n\n  label_list.append(int(df_videos_label[video_id]))\n\n  # Add soundnet features\n  for layer in soundnet_layers:\n    feat_filepath = os.path.join(soundnet_dir, video_id + 'tf_fea{}.npy'.format(str(layer).zfill(2)))\n    if os.path.exists(feat_filepath):\n      sn_feat = np.load(feat_filepath)\n      layer_feat = np.concatenate((np.min(sn_feat, axis=0), \\\n        np.max(sn_feat, axis=0), np.mean(sn_feat, axis=0),\\\n        np.std(sn_feat, axis=0), np.quantile(sn_feat, 0.25, axis=0), np.quantile(sn_feat, 0.75, axis=0)))\n      feat = np.concatenate((feat, layer_feat))\n      layer_shapes[layer] = layer_feat.shape\n    else:\n      feat = np.concatenate((feat, np.zeros(layer_shapes[layer])))\n\n  feat_list.append(feat)\n\nn = len(label_list)\ninds = np.arange(n)\nnp.random.shuffle(inds)\n\nlabel_list = np.array(label_list)\nfeat_list = np.array(feat_list)\nprint('feat_list shape ',feat_list.shape)\n\n\nstart_time = time.time()\n\ny = np.array(label_list)\nX = np.array(feat_list)\n\n# Augment data\ny = np.tile(y, factor)\nX = np.tile(X, (factor,1))\n\nX[len(label_list):,:] += (np.random.randn(len(X) - len(label_list), X.shape[1])*.1)\n\nclf = MLPClassifier(hidden_layer_sizes=(100,), activation=\"relu\", solver=\"adam\", \\\n    max_iter=1000, early_stopping=True, alpha=5e-4, n_iter_no_change=3)\nclf.fit(X, y)\n\n\n# save trained SVM in output_file\npickle.dump(clf, open(output_file, 'wb'))\nprint('Elapsed Time ', time.time() - start_time, ' seconds')\nprint('training accuracy: ', accuracy_score(y, clf.predict(X)))\n","repo_name":"pschaldenbrand/11775-hw1","sub_path":"train_best.py","file_name":"train_best.py","file_ext":"py","file_size_in_byte":2384,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"30287308345","text":"#!usr/bin/env python3\n\nimport dataclasses as dc\nfrom typing import List\n\n\n@dc.dataclass\nclass Inputs:\n  force: float | None = None\n  mass: float | None = None\n  acceleration: float | None = None\n\n\n@dc.dataclass\nclass Data:\n  inputs: Inputs | None = None\n  operation: str | None = None\n\n\nasync def calculate_force(data: Data) -> Data:\n  '''\n  description:\n    Calculates force using the equation for Newton's second law of motion\n  '''\n  data.inputs.force = data.inputs.mass * data.inputs.acceleration\n  return data\n\n\nasync def calculate_mass(data: Data) -> Data:\n  '''\n  description:\n    Calculates mass using the equation for Newton's second law of motion\n  '''\n  data.inputs.mass = data.inputs.force / data.inputs.acceleration\n  return data\n\n\nasync def calculate_acceleration(data: Data) -> Data:\n  '''\n  description:\n    Calculates acceleration using the equation for Newton's second law of motion\n  '''\n  data.inputs.acceleration = data.inputs.force / data.inputs.mass\n  return data\n\n\nOPERATIONS = {\n  'newtons_second_law.force': calculate_force,\n  'newtons_second_law.mass': calculate_mass,\n  'newtons_second_law.acceleration': calculate_acceleration,\n}\n\n\nasync def main(\n  inputs: dict,\n  operation: str,\n) -> Data:\n\n  inputs = Inputs(**inputs)\n  data = Data(inputs=inputs, operation=operation)\n  operation = OPERATIONS[operation]\n  data = await operation(data=data)\n  return data\n\n\nif __name__ == '__main__':\n  import asyncio\n\n\n  inputs = {\n    'force': 1,\n    'mass': 1,\n  }\n  result = asyncio.run(main(\n    inputs=inputs,\n    operation='newtons_second_law.acceleration',\n  ))\n  print(result)\n\n\n  # # Switcher to pick equation based off missing data\n  # switcher = dict(\n  #   force=calculate_force,\n  #   mass=calculate_mass,\n  #   acceleration=calculate_acceleration,\n  # )\n  # # Caculate the missing value\n  # calculated_value = None\n  # for field in dc.fields(data):\n  #   value = getattr(data, field.name)\n  #   if value is not None:\n  #     continue\n  #   calculated_value = switcher[field.name](data=data)\n  #   setattr(data, field.name, calculated_value)\n  # # Return solution\n  # if calculated_value is not None:\n  #   return data\n\n  # # Handle no missing data; verify first equation, F = ma\n  # name = list(switcher.keys())[0]\n  # value = getattr(data, name)\n  # calculated_value = switcher[name](data)\n  # assert calculated_value == value\n\n\n# d = [\n#   Data(6, 2, 3),\n#   Data(1, 2, None),\n#   Data(1, None, 2),\n#   Data(1, 2, 3),\n# ]\n# for data in d:\n#   data = main(data)\n#   print(data)\n","repo_name":"fjemi/mono_repo","sub_path":"functions/coding_challenges/calculator/operations/physics/newtons_second_law_of_motion/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2509,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"37195750286","text":"#!/bin/python3\r\n\r\nimport math\r\nimport os\r\nimport random\r\nimport re\r\nimport sys\r\n\r\n#\r\n# Complete the 'sumOfGroup' function below.\r\n#\r\n# The function is expected to return a LONG_INTEGER.\r\n# The function accepts INTEGER k as parameter.\r\n#\r\n\r\n\r\ndef sumOfGroup(k):\r\n     \r\n    firstEl = int((k * (k - 1)) + 1)\r\n    sum = 0\r\n     \r\n    while k:\r\n        sum += firstEl\r\n        firstEl += 2\r\n        k=k-1\r\n     \r\n    return sum\r\n     \r\nk=3\r\nprint(sumOfGroup(k))\r\n\r\nif name == '__main__':\r\n    fptr = open(os.environ['OUTPUT_PATH'], 'w')\r\n\r\n    k = int(input().strip())\r\n\r\n    answer = sumOfGroup(k)\r\n\r\n    fptr.write(str(answer) + '\\n')\r\n\r\n    fptr.close()\r\n","repo_name":"gulnaz1024/Gulnaz_Ansari_Halah","sub_path":"number_groups.py","file_name":"number_groups.py","file_ext":"py","file_size_in_byte":654,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"9722043142","text":"import pygame\nimport random\nfrom pygame import mixer\n\n# Inicializace\npygame.init()\n\n# Okno hry o velikosti 640x480 pixelů\nscreen = pygame.display.set_mode((640, 480))\n\n# Časovač\nclock = pygame.time.Clock()\ncurrent_time = 0\nball_screen_time = 0\n\n# Titulek\npygame.display.set_caption(\"Click it!\")\n\n# Definování levelů a jejich obtížností\ndifficulty = {\n  0: 50,\n  1: 30,\n  2: 20\n}\n\n# Definování písní dle levelů\nsongs = {\n  0: \"level1.mp3\",\n  1: \"level2.mp3\",\n  2: \"level3.mp3\",\n  3: \"June - Bobby Richards.mp3\"\n}\n\n# Hudba v pozadí\nmixer.music.load(songs.get(3))\nmixer.music.play(-1)\n\n# Funkce vykreslování míčku a skóre\ndef ball_and_score(score, radius):\n    # Pozadí okna RGB formát = vymažeme předchozí obsah okna\n    screen.fill((255, 255, 255))\n                    \n    x = random.randint(radius, 640-radius)\n    y = random.randint(radius, 480-radius)\n\n    # Vykreslíme nový míček (na náhodné pozici z IF) a skóre\n    ball = pygame.draw.circle(screen, (0, 0, 255), (x,y), radius)\n    score_text = f\"Score: {score}\"\n    score_font = pygame.font.Font(None, 36)\n    score_surface = score_font.render(score_text, True, (0, 0, 0))\n    screen.blit(score_surface, (20, 20))\n\n    return ball\n\n# Vlastnosti pohybujícího se míčku\ndef moving_ball_prop():\n    global speed, ball_pos, ball_radius, difficulty, level\n    # nastavení rychlosti míčku\n    speed = [round(random.uniform(-0.5, 0.5), 2), round(random.uniform(-0.5, 0.5), 2)]\n    # nastavení polohy míčku\n    ball_pos = [random.randint(difficulty.get(level), 640-difficulty.get(level)), random.randint(difficulty.get(level), 480-difficulty.get(level))]\n    # nastavení průměru míčku\n    ball_radius = difficulty.get(level)\n\n# Funkce ke zobrazování POUZE skóre\ndef show_score(score):\n    score_text = f\"Score: {score}\"\n    score_font = pygame.font.Font(None, 36)\n    score_surface = score_font.render(score_text, True, (0, 0, 0))\n    screen.blit(score_surface, (20, 20))\n\n# Funkce k měření času míčku na obrazovce\ndef ball_time():\n    # Čas míčku na obrazovce\n    ball_screen_time = pygame.time.get_ticks()\n    print(ball_screen_time)\n\n    return ball_screen_time\n\n# Funkce ke zobrazování levelu\ndef show_level(level):\n    # Vymažeme předchozí obsah okna\n    screen.fill((255, 255, 255))\n\n    # Nastavení fontu a velikosti textu\n    font = pygame.font.Font(None, 100)\n\n    # Nastavení textu na základě levelu\n    text = font.render(\"Level {}\".format(level+1), True, (0, 0, 0))\n    text_rect = text.get_rect()\n    text_rect.center = screen.get_rect().center\n\n    # Vykreslení textu na obrazovku\n    screen.blit(text, text_rect)\n\n    # Obnovení obrazovky\n    pygame.display.flip()\n\n    # Program čeká 1 sekundu\n    pygame.time.delay(1000)\n\n# Čtení maximálního skóre\ndef time_read():\n    min_time = open(\"min_time.txt\", \"a\")\n    min_time.close\n    min_time = open(\"min_time.txt\", \"r\")\n    time_saved = min_time.read()\n    min_time.close()\n    if time_saved == \"\":\n        min_time = open(\"min_time.txt\", \"w\")\n        min_time.write(\"1000000\")\n        time_saved = \"1000000\"\n        min_time.close\n    return int(time_saved)\n\n# Zápis maximálního skóre\ndef time_write(new_time):\n    min_time = open(\"min_time.txt\", \"w\")\n    min_time.write(str(new_time))\n    min_time.close()\n\n# Barvy\nBLACK = (0, 0, 0)\nWHITE = (255, 255, 255)\nGRAY = (200, 200, 200)\n\n# Font pro text\nfont = pygame.font.Font(None, 60)\n\n# Vytvoření tlačítek\nbutton_play = font.render(\"Play\", True, BLACK)\nbutton_rules = font.render(\"Rules\", True, BLACK)\nbutton_quit = font.render(\"Exit\", True, BLACK)\n\n# Umístění tlačítek\nbutton_play_pos = button_play.get_rect(center=(640 // 2, 480 // 2 - 100))\nbutton_rules_pos = button_rules.get_rect(center=(640 // 2, 480 // 2))\nbutton_quit_pos = button_quit.get_rect(center=(640 // 2, 480 // 2 + 100))\n\n# Hlavní smyčka programu\nwaiting = True\nclock = pygame.time.Clock()\nfade_speed = 10\nalpha = 0\ntext = font.render('Click it!', True, BLACK)\n\nwhile waiting:\n    current_time = pygame.time.get_ticks()\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            pygame.quit()\n            quit()\n    if current_time > 3200:\n        waiting = False\n    \n    # zrušení pozadí\n    screen.fill(WHITE)\n\n    # nastavení alfa kanálu textu\n    if current_time < 1500:\n        alpha = int(min(max(0, alpha + fade_speed),255))\n    elif current_time < 3000:\n        alpha = int(max(0, alpha - fade_speed))\n\n    # nastavení barvy textu\n    color = (alpha, alpha, alpha)\n\n    # nastavení barvy textu\n    text = font.render('Click it!', True, color)\n\n    # vykreslení textu\n    screen.blit(text, (640 // 2 - text.get_width() // 2, 480 // 2 - text.get_height() // 2))\n\n    # obnovování obrazovky\n    pygame.display.flip()\n\n    # omezení počtu snímků na 30 za sekundu\n    clock.tick(30)\n\nwhile True:\n    \n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            pygame.quit()\n            quit()\n        elif event.type == pygame.MOUSEBUTTONDOWN:\n            if button_play_pos.collidepoint(event.pos):\n                # Zde hra\n                \n                # Počáteční skóre\n                score = 0\n                # Počáteční level\n                level = 0\n                # Startovní čas\n                start_time = pygame.time.get_ticks()\n\n                while level < 2:\n\n                    # Hraj píseň dle levelu\n                    pygame.mixer.music.load(songs.get(level))\n                    mixer.music.play(-1)\n\n\n                    # Ukaž číslo levelu\n                    show_level(level)\n\n                    # Nový míček (ihned se připraví na vykreslení - PROČ??)\n                    ball = ball_and_score(score, difficulty.get(level))\n                    ball_screen_time = ball_time()\n                    # Aktualizace okna\n                    pygame.display.flip()  \n\n                    # Hra běží, dokud nezavřeme okno nebo nedosáhneme skóre 10 bodů\n                    running = True\n                    while running:\n                        # Získáme seznam událostí\n                        for event in pygame.event.get():\n                            # Událost == okno zavřeno --> ukončíme hru\n                            if event.type == pygame.QUIT:\n                                running = False\n                                level = 3\n                                # Hudba v pozadí\n                                mixer.music.load(songs.get(level))\n                                mixer.music.play(-1)\n\n                            # Stisknut mezerník --> zkontrolujeme, zda míček byl sestřelen\n                            elif event.type == pygame.KEYDOWN and event.key == pygame.K_SPACE:\n                                # Získáme souřadnice myši\n                                mouse_x, mouse_y = pygame.mouse.get_pos()\n                                # Zjistíme, zda se míček a myš překrývají\n                                if ball.collidepoint((mouse_x, mouse_y)):\n                                    # +1 bod\n                                    score += 1\n                                    # 10 bodů --> ukončíme hru\n                                    if score >= 10*(level+1):\n                                        running = False\n                                    # Jinak nový míček na náhodné pozici\n                                    else:\n                                        # Vykreslení míčku a skóre\n                                        ball = ball_and_score(score, difficulty.get(level))\n                                        # Měření času míčku na obzarovce\n                                        ball_screen_time = ball_time()\n                        \n                        \n                        if current_time - ball_screen_time > 1000:\n                            # Vykreslení míčku a skóre\n                            ball = ball_and_score(score, difficulty.get(level))\n                            # Měření času míčku na obzarovce\n                            ball_screen_time = ball_time()\n\n                        # Měření aktuálního času\n                        current_time = pygame.time.get_ticks()\n\n                        # Aktualizace okna\n                        pygame.display.flip()\n\n                        # Neznámý řádek, PŘIČÍTÁ MILISEKUNDY??, 1frame = 17ms\n                        clock.tick(60)\n                    \n                    # Zvýšit level\n                    level += 1\n\n                while level == 2:\n\n                    # Hraj píseň dle levelu\n                    pygame.mixer.music.load(songs.get(level))\n                    mixer.music.play(-1)\n                    \n                    # Ukaž číslo levelu\n                    show_level(level)\n                    \n                    # Nastav vlastnosti nového míčku\n                    moving_ball_prop()\n\n                    # vyčištění obrazovky\n                    screen.fill((255, 255, 255))\n                    # vykreslení míčku\n                    ball = pygame.draw.circle(screen, (255, 0, 0), ball_pos, ball_radius)\n                    # Vykreslení skóre\n                    show_score(score)\n                    # aktualizace obrazovky\n                    pygame.display.update()\n\n                    # hlavní smyčka programu\n                    running = True\n                    while running:\n\n                        for event in pygame.event.get():\n                            # Událost == okno zavřeno --> ukončíme hru\n                            if event.type == pygame.QUIT:\n                                running = False\n                                level = 3\n                                # Hudba v pozadí\n                                mixer.music.load(songs.get(level))\n                                mixer.music.play(-1)\n                            # Stisknut mezerník --> zkontrolujeme, zda míček byl sestřelen\n                            elif event.type == pygame.KEYDOWN and event.key == pygame.K_SPACE:\n                                # Získáme souřadnice myši\n                                mouse_x, mouse_y = pygame.mouse.get_pos()\n                                # Zjistíme, zda se míček a myš překrývají\n                                if ball.collidepoint((mouse_x, mouse_y)):\n                                    # +1 bod\n                                    score += 1\n                                    # Nastav vlastnosti nového míčku\n                                    moving_ball_prop()\n                                    # 10 bodů --> ukončíme hru\n                                    if score >= 10*(level+1):\n                                        running = False\n                                        level = 3\n\n                        # pohyb míčku\n                        ball_pos[0] += speed[0]\n                        ball_pos[1] += speed[1]\n\n                        # odrážení míčku od okrajů obrazovky\n                        if ball_pos[0] - ball_radius < 0 or ball_pos[0] + ball_radius > 640:\n                            speed[0] = -speed[0]\n                        if ball_pos[1] - ball_radius < 0 or ball_pos[1] + ball_radius > 480:\n                            speed[1] = -speed[1]\n\n                        # vykreslení pozadí\n                        screen.fill((255, 255, 255))\n                        # vykreslení míčku\n                        ball = pygame.draw.circle(screen, (255, 0, 0), ball_pos, ball_radius)\n                        # Vykreslení skóre\n                        show_score(score)\n                        # aktualizace obrazovky\n                        pygame.display.update()\n\n                if score == 30:\n                    # Konečný čas\n                    ball_screen_time = pygame.time.get_ticks()\n                    show_time = ((ball_screen_time - start_time)//1000)\n                    \n\n                    # Ulož nové maximální skóre\n                    if show_time < time_read():\n                        time_write(show_time)\n                        new_best = True\n                    else:\n                        new_best = False\n                        \n                        \n\n                    waiting = True\n\n                    # Hraj píseň dle levelu\n                    pygame.mixer.music.load(\"famfara.mp3\")\n                    mixer.music.play()\n\n                    # Program čeká 3 sekundy\n                    while waiting:\n                        current_time = pygame.time.get_ticks()\n                        for event in pygame.event.get():\n                            if event.type == pygame.QUIT:\n                                waiting = False\n                        if current_time - ball_screen_time > 16000:\n                            waiting = False\n                        \n                        \n                        # Vymažeme předchozí obsah okna\n                        screen.fill((255, 255, 255))\n                        # Nastavení fontu a velikosti textu\n                        font = pygame.font.Font(None, 50)\n                        # Nastavení textu na základě levelu\n                        text = font.render(\"Your time: {}s\".format(show_time), True, (0, 0, 0))\n                        text_rect = text.get_rect()\n                        text_rect.center = screen.get_rect().center\n                        # Vykreslení textu na obrazovku\n                        screen.blit(text, text_rect)\n\n                        if new_best:\n                            # nastavení alfa kanálu textu\n                            if (current_time - ball_screen_time) < 1500:\n                                alpha = int(min(max(0, alpha + fade_speed),255))\n                            elif (current_time - ball_screen_time) < 3000:\n                                alpha = int(max(0, alpha - fade_speed))\n\n                            # nastavení barvy textu\n                            color = (alpha, alpha, alpha)\n\n                            # nastavení barvy textu\n                            text = font.render('New best time!', True, color)\n\n                            # vykreslení textu\n                            screen.blit(text, (640 // 2 - text.get_width() // 2, 480 // 4 - text.get_height() // 2))\n\n                        # obnovování obrazovky\n                        pygame.display.flip()\n\n                        # omezení počtu snímků na 30 za sekundu\n                        clock.tick(30)\n                    \n                    # Hraj píseň dle levelu\n                    pygame.mixer.music.load(songs.get(3))\n                    mixer.music.play(-1)\n\n            elif button_rules_pos.collidepoint(event.pos):\n                # Zde pravidla\n                # set up font\n                font = pygame.font.SysFont(None, 50)\n\n                # set up text surfaces\n                text1 = font.render(\"Move your mouse to the circle!\", True, (0, 0, 0))\n                text2 = font.render(\"Press spacebar!\", True, (0, 0, 0))\n\n                # load instructions image\n                instructions_img = pygame.image.load(\"instructions.png\")\n\n                # get the rectangle for the first text surface\n                text1_rect = text1.get_rect(center=(320, 300))\n\n                # get the rectangle for the second text surface\n                text2_rect = text2.get_rect(center=(320, 340))\n\n                # set up button surface and rect\n                button = font.render(\"Back\", True, BLACK, GRAY)\n                button_rect = button.get_rect(bottomright=(630, 470))\n\n                # set up game loop\n                waiting = True\n                while waiting:\n                    # handle events\n                    for event in pygame.event.get():\n                        if event.type == pygame.QUIT:\n                            waiting = False\n                        elif event.type == pygame.MOUSEBUTTONDOWN and button_rect.collidepoint(event.pos):\n                            waiting = False\n\n                    # fill screen with white\n                    screen.fill((255, 255, 255))\n\n                    # draw instructions image onto screen\n                    screen.blit(instructions_img, (80, 70))\n\n                    # draw text surfaces onto screen\n                    screen.blit(text1, text1_rect)\n                    screen.blit(text2, text2_rect)\n\n                    # draw button onto screen\n                    screen.blit(button, button_rect)\n\n                    # update the display\n                    pygame.display.update()\n\n                    # set the framerate\n                    clock.tick(60)\n            \n                \n            elif button_quit_pos.collidepoint(event.pos):\n                pygame.quit()\n                quit()\n\n    # Vyplnění pozadí černou barvou\n    screen.fill(WHITE)\n\n    # Nastavení fontu a velikosti textu\n    best_time_font = pygame.font.Font(None, 50)\n    # Nastavení textu na základě levelu\n    text = best_time_font.render(\"Best time: {}s\".format(time_read()), True, (0, 0, 0))\n    text_rect = text.get_rect()\n    text_rect.center = screen.get_rect().center\n    # Vykreslení textu na obrazovku\n    screen.blit(text, (20, 20))\n\n\n    # Vykreslení tlačítek\n    pygame.draw.rect(screen, GRAY, button_play_pos)\n    screen.blit(button_play, button_play_pos)\n    pygame.draw.rect(screen, GRAY, button_rules_pos)\n    screen.blit(button_rules, button_rules_pos)\n    pygame.draw.rect(screen, GRAY, button_quit_pos)\n    screen.blit(button_quit, button_quit_pos)\n\n    pygame.display.update()\n\n\n# ukončení knihovny Pygame\npygame.quit()","repo_name":"milan-kolar/hra-osu-pygame","sub_path":"Game OSU.py","file_name":"Game OSU.py","file_ext":"py","file_size_in_byte":17738,"program_lang":"python","lang":"cs","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"37324090099","text":"\"\"\"\nExercício 5\n\nEscreva um programa que recebe uma letra e identifica se ela é consoante.\n\"\"\"\n\n\ndef letra(l):\n    if l in \"aeiouAEIOU\":\n        print('A letra digitada é uma vogal.')\n    elif l in \"bcdfghjklmnpqrstvwxyzBCDFGHJKLMNPQRSTVWXYZ\":\n        print('A letra digitada é uma consoante.')\n    else:\n        print('Você não digitou uma letra ou digitou uma letra com acento.')\n\nif __name__ == '__main__':\n\n    a = str(input('Digite uma letra: '))\n    letra(a)","repo_name":"brunobara/lista1","sub_path":"exercicio5.py","file_name":"exercicio5.py","file_ext":"py","file_size_in_byte":470,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"43385937787","text":"import sys; sys.path.append(\"../\")\nimport numpy as np\nimport os\nfrom glob import glob\nimport argparse\nimport json\nfrom utils.annotation_parser import parse_annotation\nfrom utils.heatmap import eval_gaussian, eval_line\nimport json\n\n\ndef main():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--label_dir\", help=\"Directory that contains the labels\", type=str, required=True)\n    parser.add_argument(\"--save_dir\", help=\"Directory to save the heatmaps\", required=True)\n    parser.add_argument(\"--dataset\", type=str, default=\"RMIT\")\n    parser.add_argument(\"--post_processing_labels\", type=int, default=0,\n                        help=\"Generate labels for post-processing module\")\n    args = parser.parse_args()\n\n    if args.dataset == \"RMIT\":\n        RMIT(args)\n    elif args.dataset == \"ENDOVIS\":\n        ENDOVIS(args)\n\n\ndef ENDOVIS(args):\n    def get_line(pt1, pt2):\n        # (x, y)\n        m = float(pt1[1] - pt2[1]) / float(pt1[0] - pt2[0])\n        b = pt2[1] - m * pt2[0]\n        return m, b\n\n    num_classes = 5\n    num_connections = 4\n\n    read_folder_name = (\"train_labels\", \"test_labels\")\n    save_folder_name = (\"training_labels_postprocessing\", \"test_labels_postprocessing\")\n    num_instruments = {}\n\n    for i in range(2):\n\n        label_dir = os.path.join(args.label_dir, read_folder_name[i])\n        json_list = glob(os.path.join(label_dir, \"*.json\"))\n\n        mapper = {\"LeftClasperPoint\": 0,\n                  \"RightClasperPoint\": 1,\n                  \"ShaftPoint\": 3,\n                  \"EndPoint\": 4,\n                  \"HeadPoint\": 2}\n\n        for json_ in json_list:\n            with open(json_, \"r\") as j:\n                dict_list = json.loads(j.read())\n                seq_id = json_.split(\"/\")[-1]\n                seq_id = seq_id.split(\"_\")[0]\n                for element in dict_list:\n                    if element[\"annotations\"] == []:\n                        continue\n                    if not args.post_processing_labels:\n                        heatmap = np.zeros((576, 720, num_classes+num_connections), dtype=np.float32)\n                        counter = 0\n                        part_coord = {\"LeftClasperPoint\": [],\n                                      \"RightClasperPoint\": [],\n                                      \"ShaftPoint\": [],\n                                      \"EndPoint\": [],\n                                      \"HeadPoint\": []}\n                        for e in element[\"annotations\"]:\n                            if e[\"class\"] in mapper.keys():\n                                idx = mapper[e[\"class\"]]\n                                part_coord[e[\"class\"]].append((e[\"x\"], e[\"y\"]))\n                                heatmap[:, :, idx] += eval_gaussian([e[\"y\"], e[\"x\"]], h=576, w=720)\n                                counter += 1\n                        fname = element[\"filename\"].split(\"/\")[-1]\n                        fname = fname.split(\".\")[0] + \"_\" + seq_id + \".npy\"\n                        num_instruments[fname] = counter // num_classes\n                        # print(fname)\n                        # return\n\n                        # determine the connections\n                        for idx in range(counter // num_classes):\n                            # print(part_coord)\n                            # print(\"\\n\")\n\n                            # left2head\n                            m, b = get_line(part_coord[\"LeftClasperPoint\"][idx], part_coord[\"HeadPoint\"][idx])\n                            heatmap[:, :, 5] += eval_line(part_coord[\"LeftClasperPoint\"][idx], part_coord[\"HeadPoint\"][idx],\n                                                          m, b)\n                            # rigth2head\n                            m, b = get_line(part_coord[\"RightClasperPoint\"][idx], part_coord[\"HeadPoint\"][idx])\n                            heatmap[:, :, 6] += eval_line(part_coord[\"RightClasperPoint\"][idx], part_coord[\"HeadPoint\"][idx],\n                                                          m, b)\n                            # head2shaft\n                            m, b = get_line(part_coord[\"HeadPoint\"][idx], part_coord[\"ShaftPoint\"][idx])\n                            heatmap[:, :, 7] += eval_line(part_coord[\"HeadPoint\"][idx], part_coord[\"ShaftPoint\"][idx], m, b)\n                            # shaft2end\n                            m, b = get_line(part_coord[\"ShaftPoint\"][idx], part_coord[\"EndPoint\"][idx])\n                            heatmap[:, :, 8] += eval_line(part_coord[\"ShaftPoint\"][idx], part_coord[\"EndPoint\"][idx], m, b)\n                    else:\n                        heatmap = np.zeros((576, 720, 10), dtype=np.float32)\n\n                        mapper = {\"tool1\": {\"LeftClasperPoint\": 0,\n                                            \"RightClasperPoint\": 1,\n                                            \"ShaftPoint\": 3,\n                                            \"EndPoint\": 4,\n                                            \"HeadPoint\": 2\n                                            },\n                                  \"tool2\": {\"LeftClasperPoint\": 5,\n                                            \"RightClasperPoint\": 6,\n                                            \"ShaftPoint\": 8,\n                                            \"EndPoint\": 9,\n                                            \"HeadPoint\": 7\n                                            },\n                                  \"tool4\": {\"LeftClasperPoint\": 5,\n                                            \"RightClasperPoint\": 6,\n                                            \"ShaftPoint\": 8,\n                                            \"EndPoint\": 9,\n                                            \"HeadPoint\": 7\n                                            }\n                                  }\n\n                        for e in element[\"annotations\"]:\n                            try:\n                                if e[\"class\"] in mapper[e[\"id\"]].keys():\n                                    idx = mapper[e[\"id\"]][e[\"class\"]]\n                                    heatmap[:, :, idx] += eval_gaussian([e[\"y\"], e[\"x\"]], h=576, w=720)\n                                    # import cv2\n                                    # cv2.imshow(\"heatmap\", (heatmap[:, :, idx] * 255).astype(\"uint8\"))\n                                    # cv2.waitKey(0)\n                            except KeyError as err:\n                                print(err)\n                                print(e)\n                                if e[\"x\"] >= 720//2:\n                                    tool_id = \"tool1\"\n                                    idx = mapper[tool_id][e[\"class\"]]\n                                    heatmap[:, :, idx] += eval_gaussian([e[\"y\"], e[\"x\"]], h=576, w=720)\n                                else:\n                                    raise ValueError\n                        fname = element[\"filename\"].split(\"/\")[-1]\n                        fname = fname.split(\".\")[0] + \"_\" + seq_id + \".npy\"\n\n                    np.save(os.path.join(args.save_dir, save_folder_name[i], fname), heatmap)\n\n    with open(os.path.join(args.save_dir, \"instrument_count.json\"), \"w\") as d:\n        json.dump(num_instruments, d)\n\n\n\ndef RMIT(args):\n    save_dir = args.save_dir + \"/heatmaps\"\n\n    if not os.path.exists(save_dir):\n        os.mkdir(save_dir)\n\n    label_list = sorted(glob(args.label_dir + \"/*txt\"), key=lambda x: x[0] < x[1])\n    parsed = parse_annotation(label_list)\n\n    for i in range(3):\n        data = parsed[\"seq\" + str(i + 1)]\n        size = len(data[\"fname\"])\n\n        for j in range(size):\n            heatmap = np.zeros((480, 640, 4), dtype=np.float32)\n            heatmap[:, :, 0] = eval_gaussian(data[\"p1\"][j])\n            heatmap[:, :, 1] = eval_gaussian(data[\"p2\"][j])\n            heatmap[:, :, 2] = eval_gaussian(data[\"p3\"][j])\n            heatmap[:, :, 3] = eval_gaussian(data[\"p4\"][j])\n            np.save(save_dir + \"/\" + data[\"fname\"][j] + \".npy\", heatmap)\n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"mertkayhan/SSL-2D-Pose","sub_path":"utils/generate_heatmap.py","file_name":"generate_heatmap.py","file_ext":"py","file_size_in_byte":7940,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"48"}
+{"seq_id":"73006666066","text":"# Elabore una clase para el cálculo del valor de impuestos a ser utilizado por\n# todas las clases que necesiten realizarlo. El cálculo de impuestos simplificado\n# deberá recibir un valor de importe base imponible y deberá retornar la suma\n# del cálculo de IVA (21%), IIBB (5%) y Contribuciones municipales (1,2%) sobre\n# esa base imponible.\n\nclass SingletonMeta(type):\n    _instances = {}\n\n    def __call__(cls, *args, **kwargs):\n        if cls not in cls._instances:\n            instance = super().__call__(*args, **kwargs)\n            cls._instances[cls] = instance\n        return cls._instances[cls]\n\n\nclass Impuestos(metaclass=SingletonMeta):\n    def getTotal(self, base_imponible):\n        iva = base_imponible * 0.21\n        iibb = base_imponible * 0.05\n        contr_mun = base_imponible * 0.012\n\n        return iva + iibb + contr_mun\n\n\nimp1 = Impuestos()\nimp2 = Impuestos()\n\nif (imp1 == imp2):\n    print(\"Son las mismas instancias\")\n\nprint(imp1.getTotal(1000))\n\nprint(imp2.getTotal(1500))\n","repo_name":"GabiC15/UADER_IS2_CASARES","sub_path":"src/patrones/creacion/2 - impuestos.py","file_name":"2 - impuestos.py","file_ext":"py","file_size_in_byte":1002,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"10217223800","text":"from mpl_toolkits.mplot3d import Axes3D\nimport matplotlib.pyplot as plt\nimport numpy as np\n\ndef linReg(theta,x):\n\tfin = np.empty([x.shape[0]])\n\tfor i in range(0,x.shape[0]):\n\t\tfin[i] = np.sum(x[i]*theta)\n\treturn fin\n\ndef costFunc(m,features,y,theta):\n\ts = np.sum((linReg(theta,features)-y)**2)\n\n\treturn (0.5*m)*s\n\ndef costFuncDif(m,features,y,theta,xj):\n\t#Create empty array of size = xj (xj size = theta size) \n\tfin = np.empty([xj.shape[0]])\n\t#Lopp through and calculate the partal derivitive for each theta d/d theta (sum (h_theta (xj) - y)*xj) xj = feature componant\n\tfor i in range(0,xj.shape[0]):\n\t\ts = np.sum((linReg(theta,features)-y)*xj[i])\n\t\tfin[i] = s\n\treturn fin\n\nx0,x1,y = np.genfromtxt('ex1data2.txt',delimiter=',',unpack=True)\n\n#basic feature scaling\nx1=x1*1000\n\n#Polynomial Testing\nx = np.arange(0,1.3,0.001)\nxSq = x*x\nxCub = x*x*x\ny =  0.132 + (0.1664*x)-(0.812*xSq)+0.9*xCub\nnoise = np.random.normal(0,0.05,len(y))\ny = y+noise\nplt.plot(x,y,marker='o',linestyle='None',color='red')\nplt.show()\n\n#Settings\nfeat_num = 3\n\n#Starting Values\ntheta = np.ones(feat_num+1)\ntheta_temp = np.ones(feat_num+1)\nm = float(x0.shape[0])\nalpha = 0.01\n\n#Feature Scaling\nx_min = np.amin(x)\nx_max = np.amax(x)\nx_av = np.mean(x)\n\nx_sc = (x-x_min)/(x_max-x_min)\n\nxSq_min = np.amin(xSq)\nxSq_max = np.amax(xSq)\nxSq_av = np.mean(xSq)\n\nxSq_sc = (xSq-xSq_min)/(xSq_max-xSq_min)\n\n## Generate features (x0,x1) array theta1*x_0+theta2*x_1\nfeatures = np.array([np.ones(len(x)),x,xSq,xCub])\nfeaturesTrans = np.transpose(features)\n\n\nitera = []\ncostList = []\ndeviation = 100\nprevCost = costFunc(m,featuresTrans,y,theta)\nitter = 0\ncost = 1000\n\nwhile(abs(deviation)>0.001 or cost <1):\n\n\ttheta_temp = theta - alpha*(1/m)*(costFuncDif(m,featuresTrans,y,theta,features))\n\ttheta = theta_temp\n\n\titera.append(itter)\n\titter+=1\n\n\tcost = costFunc(m,featuresTrans,y,theta)\n\tdeviation = (((prevCost-cost)/prevCost))*100\n\tprint(cost,deviation,theta)\n\tprevCost = cost\n\tcostList.append(np.log(cost))\n\nprint(theta)\n\nplt.plot(itera,costList,marker='o',linestyle='None')\nplt.show()\n\n#fig = plt.figure()\n#ax = fig.add_subplot(111, projection='3d')\n#ax.scatter(x0,x1,y)\n\nline = theta[0]+theta[1]*x+theta[2]*xSq+theta[3]*xCub\n#ax.plot(line)\n\nplt.show()\n\nplt.plot(x,y,marker='o',linestyle='None',color='red')\n\nplt.plot(x,line,marker='*',linestyle='None')\n\nplt.show()","repo_name":"jakeminns/Coursera-Machine-Learning-Course-Solutions","sub_path":"ex1_LR.py","file_name":"ex1_LR.py","file_ext":"py","file_size_in_byte":2323,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"19953170250","text":"from collections import deque\nimport random\n\nCYRILLIC_LETTERS = 'абвгдеёжзийклмнопрстуфхцчшщьъыэюя'\n\nQUEUE = deque(CYRILLIC_LETTERS[:3])\n\ntotal_count = 0\n\n\ndef get_members(query: str):\n    global total_count\n\n    total_count += 1\n    result = random.randint(0, 500 - 100 * len(query))\n    print(f'{query} ({result})')\n\n    if result > 200:\n        for letter in CYRILLIC_LETTERS:\n            get_members(query + letter)\n\n\nfor i in CYRILLIC_LETTERS[:3]:\n    get_members(i)\n\nprint(total_count)\n","repo_name":"LDmitriy7/UserBot","sub_path":"scratches/test2.py","file_name":"test2.py","file_ext":"py","file_size_in_byte":525,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"16371443467","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[ ]:\n\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom sklearn.model_selection import KFold\nfrom sklearn.model_selection import train_test_split\nfrom torch.optim.lr_scheduler import StepLR\nimport torch.optim as optim\nfrom os import path\nfrom torch.autograd import Variable\nimport torch.nn.init as init\nfrom scipy.linalg import fractional_matrix_power\nimport matplotlib.pyplot as plt\nimport random\n\ndevice = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n\n\nclass Het_Node():\n    def __init__(self, node_type, node_id, embed, neighbor_list_post=[], neighbor_list_user=[], label=None):\n        self.node_type = node_type\n        self.node_id = node_id\n        self.emb = embed\n        self.label = label  # only post node, user node = default = None\n        self.neighbors_user = neighbor_list_user  # [(id)]\n        self.neighbors_post = neighbor_list_post\n\n\ndef data_loader(pathway='F:\\\\FYP_data\\\\normalized_post_nodes', node_type=\"post\"):\n    if node_type == \"post\":\n        post_node = []\n        post_id = []\n        post_label = []\n        post_embed = []\n        post_p_neigh = []\n        post_u_neigh = []\n        for i in range(19):\n            print(i)\n            batch = str(i)\n            f = open(pathway + \"batch_\" + batch + '.txt')\n            print(pathway + \"batch_\" + batch + '.txt')\n            Lines = f.readlines()\n            for j in range(len(Lines)):\n                if j % 5 == 0:\n                    _, id_, label = Lines[j].split()\n                    post_id.append(int(id_))\n                    post_label.append(int(label))\n                    embed = []\n                if j % 5 == 1 or j % 5 == 2:\n                    embed.append(list(map(float, Lines[j].split())))\n                if j % 5 == 2:\n                    post_embed.append(embed)\n                if j % 5 == 3:\n                    post_p_neigh.append(list(map(int, Lines[j].split())))\n                if j % 5 == 4:\n                    post_u_neigh.append(list(map(int, Lines[j].split())))\n            f.close()\n        for i in range(len(post_id)):\n            node = Het_Node(node_type=\"post\", node_id=post_id[i], embed=post_embed[i],\n                            neighbor_list_post=post_p_neigh[i], neighbor_list_user=post_u_neigh[i], label=post_label[i])\n            post_node.append(node)\n        return post_node\n\n    else:\n        user_node = []\n        user_id = []\n        user_embed = []\n        f = open(pathway + 'user_nodes.txt')\n        Lines = f.readlines()\n        for j in range(len(Lines)):\n            if j % 3 == 0:\n                id_ = Lines[j].split()\n                user_id.append(int(id_[0]))\n                embed = []\n            if j % 3 == 1 or j % 3 == 2:\n                embed.append(list(map(float, Lines[j].split())))\n            if j % 3 == 2:\n                user_embed.append(embed)\n        f.close()\n        for i in range(len(user_id)):\n            node = Het_Node(node_type=\"user\", node_id=user_id[i], embed=user_embed[i])\n            user_node.append(node)\n        return user_node\n\n\npost_nodes = data_loader(pathway='5p10u/normalized_post_nodes/', node_type=\"post\")\nuser_nodes = data_loader(pathway='5p10u/normalized_user_nodes/', node_type=\"user\")\npost_emb_dict = {}\nuser_emb_dict = {}\nfor user in user_nodes:\n    user_emb_dict[user.node_id] = user.emb\nfor post in post_nodes:\n    post_emb_dict[post.node_id] = post.emb\n\n\n# In[ ]:\n\n\npost_nodes_real = []\npost_nodes_fake = []\nfor node in post_nodes:\n    if node.label == 1:\n        post_nodes_real.append(node)\n    else:\n        post_nodes_fake.append(node)\nprint(\"number of fake nodes: \", len(post_nodes_fake))\nprint(\"number of real nodes: \", len(post_nodes_real))\n#fake : real = 0.9842 : 1\n\n\n# In[ ]:\n\n\nclass Het_GNN(nn.Module):\n    # features: list of HetNode class\n    def __init__(self, input_dim, ini_hidden_dim, hidden_dim, batch_size,\n                 u_input_dim, u_hidden_dim, u_ini_hidden_dim, u_output_dim, u_num_layers,\n                 p_input_dim, p_hidden_dim, p_ini_hidden_dim, p_output_dim, p_num_layers,\n                 out_embed_d, outemb_d, symmetry = False, GCN_out2=300, GCN_out1= 300, GCN_in=200, use_bias=True,\n                 u_batch_size=1, p_batch_size=1, content_dict={}, num_layers=1, u_rnn_type='LSTM', p_rnn_type='LSTM',\n                 rnn_type='LSTM', embed_d=200):\n        super(Het_GNN, self).__init__()\n        self.input_dim = input_dim\n        self.ini_hidden_dim = ini_hidden_dim\n        self.hidden_dim = out_embed_d // 2\n        self.batch_size = batch_size\n        self.num_layers = num_layers\n        self.embed_d = out_embed_d\n        self.u_input_dim = out_embed_d\n        self.u_hidden_dim = u_hidden_dim\n        self.u_ini_hidden_dim = u_ini_hidden_dim\n        self.u_batch_size = u_batch_size\n        self.u_output_dim = out_embed_d\n        self.u_num_layers = u_num_layers\n        self.u_rnn_type = u_rnn_type\n        self.p_input_dim = out_embed_d\n        self.p_hidden_dim = p_hidden_dim\n        self.p_ini_hidden_dim = p_ini_hidden_dim\n        self.p_batch_size = p_batch_size\n        self.p_output_dim = out_embed_d\n        self.p_num_layers = p_num_layers\n        self.p_rnn_type = p_rnn_type\n        self.out_embed_d = out_embed_d\n        self.outemb_d = outemb_d\n        self.GCN_in = out_embed_d\n        self.GCN_out1 = GCN_out1\n        #self.GCN_out1 = out_embed_d\n        self.GCN_out2 = out_embed_d * 2\n        self.use_bias = use_bias\n        self.symmetry = symmetry\n        # self.features = features\n        self.content_dict = content_dict\n        self.p_neigh_att = nn.Parameter(torch.ones(self.embed_d * 2, 1), requires_grad=True)\n        self.u_neigh_att = nn.Parameter(torch.ones(self.embed_d * 2, 1), requires_grad=True)\n        self.GCN_W1_user = torch.nn.Parameter(torch.FloatTensor(self.GCN_in, self.GCN_out1), requires_grad=True)\n        self.GCN_W2_user = torch.nn.Parameter(torch.FloatTensor(self.GCN_out1, self.GCN_out2), requires_grad=True)\n        self.GCN_W1_post = torch.nn.Parameter(torch.FloatTensor(self.GCN_in, self.GCN_out1), requires_grad=True)\n        self.GCN_W2_post = torch.nn.Parameter(torch.FloatTensor(self.GCN_out1, self.GCN_out2), requires_grad=True)\n        self.layernorm1 = nn.LayerNorm([1,out_embed_d])\n        self.layernorm2 = nn.LayerNorm([1,out_embed_d])\n        self.layernorm3 = nn.LayerNorm([1,out_embed_d])\n        self.layernorm4 = nn.LayerNorm([1,out_embed_d])\n        # Define the initial linear hidden layer\n        self.init_linear_text = nn.Linear(self.input_dim[0], self.ini_hidden_dim[0])\n        self.init_linear_image = nn.Linear(self.input_dim[1], self.ini_hidden_dim[1])\n        if self.use_bias:\n            self.user_bias1 = nn.Parameter(torch.Tensor(self.GCN_out1), requires_grad=True)\n            self.post_bias1 = nn.Parameter(torch.Tensor(self.GCN_out1), requires_grad=True)\n            self.user_bias2 = nn.Parameter(torch.Tensor(self.GCN_out2), requires_grad=True)\n            self.post_bias2 = nn.Parameter(torch.Tensor(self.GCN_out2), requires_grad=True)\n        else:\n            self.register_parameter('bias', None)\n        self.init_linear_other = nn.Linear(self.input_dim[2], self.ini_hidden_dim[2])\n        # Define the LSTM layer\n        self.LSTM_text = eval('nn.' + rnn_type)(self.ini_hidden_dim[0], self.hidden_dim, self.num_layers,\n                                                batch_first=True,\n                                                bidirectional=True, dropout=0.5)\n        self.LSTM_image = eval('nn.' + rnn_type)(self.ini_hidden_dim[1], self.hidden_dim, self.num_layers,\n                                                 batch_first=True,\n                                                 bidirectional=True, dropout=0.5)\n        self.LSTM_other = eval('nn.' + rnn_type)(self.ini_hidden_dim[2], self.hidden_dim, self.num_layers,\n                                                 batch_first=True,\n                                                 bidirectional=True, dropout=0.5)\n        # Define same_type_agg\n        self.u_init_linear = nn.Linear(self.u_input_dim, self.u_ini_hidden_dim)\n        # self.u_init_dropout = nn.Dropout(p=0.2)\n        self.u_LSTM = eval('nn.' + self.u_rnn_type)(self.u_ini_hidden_dim, self.u_hidden_dim, self.u_num_layers,\n                                                    batch_first=True, bidirectional=True, dropout=0.5)\n        self.u_linear = nn.Linear(self.u_hidden_dim * 2, self.u_output_dim)\n        self.u_dropout = nn.Dropout(p=0.5)\n        self.p_init_linear = nn.Linear(self.p_input_dim, self.p_ini_hidden_dim)\n        # self.p_init_dropout = nn.Dropout(p=0.2)\n        self.p_LSTM = eval('nn.' + self.p_rnn_type)(self.p_ini_hidden_dim, self.p_hidden_dim, self.p_num_layers,\n                                                    batch_first=True, bidirectional=True, dropout=0.5)\n        self.p_linear = nn.Linear(self.p_hidden_dim * 2, self.p_output_dim)\n        self.p_dropout = nn.Dropout(p=0.5)\n        #self.act = nn.LeakyReLU()\n        self.act = nn.LeakyReLU()\n        self.softmax = nn.Softmax(dim=1)\n        self.relu = nn.ReLU()\n        self.out_dropout = nn.Dropout(p=0.35)\n        self.out_linear = nn.Linear(self.out_embed_d*2, self.outemb_d)\n        self.batch_norm_text = nn.BatchNorm1d(self.ini_hidden_dim[0])\n        self.batch_norm_image = nn.BatchNorm1d(self.ini_hidden_dim[1])\n        self.batch_norm_other = nn.BatchNorm1d(self.ini_hidden_dim[2])\n        self.output_act = nn.Sigmoid()\n\n    def init_weights(self):\n        for m in self.modules():\n            if isinstance(m, nn.Linear) or isinstance(m, nn.Parameter):\n                nn.init.xavier_normal_(m.weight.data)\n                m.bias.data.fill_(0.1)\n        init.kaiming_uniform_(self.GCN_W1_user)\n        init.kaiming_uniform_(self.GCN_W1_post)\n        init.kaiming_uniform_(self.GCN_W2_user)\n        init.kaiming_uniform_(self.GCN_W2_post)\n        if self.use_bias:\n            init.zeros_(self.user_bias1)\n            init.zeros_(self.post_bias1)\n            init.zeros_(self.user_bias2)\n            init.zeros_(self.post_bias2)\n\n    def init_adj_degree(self, neighbor_id):\n        adj_dim = len(neighbor_id)\n        adj = np.ones((adj_dim, adj_dim))\n        degree = np.diag(np.sum(adj, axis=0))\n        return adj, degree\n    \"\"\"\n    def init_matrices(self, user_neighbor_id, post_neighbor_id, user_emb_dict, post_emb_dict):\n        user_H = []\n        post_H = []\n        for i in user_neighbor_id:\n            user_H.append(user_emb_dict[i][1])\n        for j in post_neighbor_id:\n            post_H.append(post_emb_dict[j][0])\n        user_A, user_D = self.init_adj_degree(user_neighbor_id)\n        post_A, post_D = self.init_adj_degree(post_neighbor_id)\n        user_H = torch.Tensor(user_H)\n        user_A = torch.Tensor(user_A)\n        user_D = torch.Tensor(user_D)\n        post_H = torch.Tensor(post_H)\n        post_A = torch.Tensor(post_A)\n        post_D = torch.Tensor(post_D)\n        return user_A, user_D, user_H, post_A, post_D, post_H\n    \"\"\"\n    \n    def init_matrices(self, u_aft_rnn_dict, p_aft_rnn_dict):\n        user_H = []\n        post_H = []\n        user_H_d = u_aft_rnn_dict.values()\n        post_H_d = p_aft_rnn_dict.values()\n        for i in user_H_d:\n            #print(i.shape)\n            i = i.view(1, i.shape[0])\n            user_H.append(i)\n        for j in post_H_d:\n            j = j.view(1, j.shape[0])\n            post_H.append(j)\n        user_A, user_D = self.init_adj_degree(u_aft_rnn_dict)\n        post_A, post_D = self.init_adj_degree(p_aft_rnn_dict)\n        user_H = torch.cat(user_H, dim=0)\n        post_H = torch.cat(post_H, dim=0)\n        #print(user_H.shape)\n        #user_H = torch.Tensor(user_H)\n        user_A = torch.Tensor(user_A)\n        user_D = torch.Tensor(user_D)\n        #post_H = torch.Tensor(post_H)\n        post_A = torch.Tensor(post_A)\n        post_D = torch.Tensor(post_D)\n        return user_A, user_D, user_H, post_A, post_D, post_H\n    \n    def GCN_layer(self, user_A, user_D, user_H, post_A, post_D, post_H, layer_index):\n        if self.symmetry == False:\n            user_in = torch.mm(torch.inverse(user_D), user_A)\n            post_in = torch.mm(torch.inverse(post_D), post_A)\n        else:\n            user_D_head = fractional_matrix_power(user_D, -0.5)\n            post_D_head = fractional_matrix_power(post_D, -0.5)\n            user_D_head = torch.Tensor(user_D_head)\n            post_D_head = torch.Tensor(post_D_head)\n            user_in = torch.mm(torch.mm(user_D_head, user_A), user_D_head)\n            post_in = torch.mm(torch.mm(post_D_head, post_A), post_D_head)\n        user_in_features = torch.mm(user_in, user_H)\n        post_in_features = torch.mm(post_in, post_H)\n        if layer_index == 1:\n            user_output = torch.mm(user_in_features, self.GCN_W1_user)\n            post_output = torch.mm(post_in_features, self.GCN_W1_post)\n            if self.use_bias:\n                user_output += self.user_bias1\n                post_output += self.post_bias1\n        else:\n            user_output = torch.mm(user_in_features, self.GCN_W2_user)\n            post_output = torch.mm(post_in_features, self.GCN_W2_post)\n            if self.use_bias:\n                user_output += self.user_bias2\n                post_output += self.post_bias2\n        return user_output, post_output\n    \"\"\"\n    def GCN_net(self, u_aft_rnn_dict, p_aft_rnn_dict):\n        user_A, user_D, user_H, post_A, post_D, post_H = self.init_matrices(u_aft_rnn_dict, p_aft_rnn_dict)\n        gcn1_user, gcn1_post = self.GCN_layer(user_A, user_D, user_H, post_A, post_D, post_H, 1)\n        gcn1_user = self.relu(gcn1_user)\n        gcn1_post = self.relu(gcn1_post)\n        gcn2_user, gcn2_post = self.GCN_layer(user_A, user_D, gcn1_user, post_A, post_D, gcn1_post, 2)\n        gcn2_user = torch.mean(gcn2_user, 0)\n        gcn2_post = torch.mean(gcn2_post, 0)\n        gcn2_user = gcn2_user.view(1, gcn2_user.shape[0])\n        gcn2_post = gcn2_post.view(1, gcn2_post.shape[0])\n        gcn2_user = self.softmax(gcn2_user)\n        gcn2_post = self.softmax(gcn2_post)\n        #print(gcn2_user.shape)\n        #print(gcn2_post.shape)\n        return gcn2_user, gcn2_post\n    \"\"\"    \n    def GCN_net(self, u_aft_rnn_dict, p_aft_rnn_dict):\n        user_A, user_D, user_H, post_A, post_D, post_H = self.init_matrices(u_aft_rnn_dict, p_aft_rnn_dict)\n        gcn1_user, gcn1_post = self.GCN_layer(user_A, user_D, user_H, post_A, post_D, post_H, 1)\n        gcn1_user = self.relu(gcn1_user)\n        gcn1_post = self.relu(gcn1_post)\n        gcn2_user, gcn2_post = self.GCN_layer(user_A, user_D, gcn1_user, post_A, post_D, gcn1_post, 2)\n        gcn2_user = torch.mean(gcn2_user, 0)\n        gcn2_post = torch.mean(gcn2_post, 0)\n        gcn2_user = gcn2_user.view(1, gcn2_user.shape[0])\n        gcn2_post = gcn2_post.view(1, gcn2_post.shape[0])\n        gcn2_user = self.softmax(gcn2_user)\n        gcn2_post = self.softmax(gcn2_post)\n        #print(gcn2_user.shape)\n        #print(gcn2_post.shape)\n        return gcn2_user, gcn2_post\n\n    def Bi_RNN(self, neighbor_id, node_type, post_emb_dict, user_emb_dict):\n        # Forward pass through initial hidden layer\n        input_a = []\n        input_b = []\n        new_id = []\n        if node_type == \"post\":\n            for i in neighbor_id:\n                if (\"post\", i) not in self.content_dict:\n                    input_a.append(post_emb_dict[i][0])\n                    input_b.append(post_emb_dict[i][1])\n                    new_id.append(i)\n            input_a = torch.Tensor(input_a)\n            input_b = torch.Tensor(input_b)\n            linear_input_text = self.init_linear_text(input_a)\n            #linear_input_text = self.batch_norm_text(linear_input_text)\n            linear_input_image = self.init_linear_image(input_b)\n            #linear_input_image = self.batch_norm_image(linear_input_image)\n            linear_input_text = linear_input_text.view(linear_input_text.shape[0], 1, linear_input_text.shape[1])\n            linear_input_image = linear_input_image.view(linear_input_image.shape[0], 1, linear_input_image.shape[1])\n            LSTM_out_text, self.hidden_text = self.LSTM_text(linear_input_text)\n            LSTM_out_text = self.layernorm1(LSTM_out_text)\n            LSTM_out_image, self.hidden_image = self.LSTM_image(linear_input_image)\n            LSTM_out_image = self.layernorm2(LSTM_out_image)\n            concate = torch.cat((LSTM_out_text, LSTM_out_image), 1)\n        if node_type == \"user\":\n            for i in neighbor_id:\n                if (\"user\", i) not in self.content_dict:\n                    input_a.append(user_emb_dict[i][0])\n                    input_b.append(user_emb_dict[i][1])\n                    new_id.append(i)\n            input_a = torch.Tensor(input_a)\n            input_b = torch.Tensor(input_b)\n            linear_input_text = self.init_linear_text(input_b)\n            #linear_input_text = self.batch_norm_text(linear_input_text)\n            linear_input_other = self.init_linear_other(input_a)\n            #linear_input_other = self.batch_norm_other(linear_input_other)\n            linear_input_text = linear_input_text.view(linear_input_text.shape[0], 1, linear_input_text.shape[1])\n            linear_input_other = linear_input_other.view(linear_input_other.shape[0], 1, linear_input_other.shape[1])\n            LSTM_out_text, self.hidden_text = self.LSTM_text(linear_input_text)\n            LSTM_out_text = self.layernorm3(LSTM_out_text)\n            LSTM_out_other, self.hidden_other = self.LSTM_other(linear_input_other)\n            LSTM_out_other = self.layernorm4(LSTM_out_other)\n            concate = torch.cat((LSTM_out_text, LSTM_out_other), 1)\n\n        # mean pooling all the states\n        mean_pooling = torch.mean(concate, 1)\n\n        return mean_pooling\n\n    # features: list of [(id)]\n    def SameType_Agg_Bi_RNN(self, neighbor_id, node_type):\n        content_embedings = self.Bi_RNN(neighbor_id, node_type, post_emb_dict, user_emb_dict)\n        aft_rnn_dict = {}\n        if node_type == 'post':\n            for i in range(len(neighbor_id)):\n                aft_rnn_dict[neighbor_id[i]] = content_embedings[i]\n            linear_input = self.p_init_linear(content_embedings)\n            # linear_input = self.p_init_dropout(linear_input)\n            linear_input = linear_input.view(linear_input.shape[0], 1, linear_input.shape[1])\n            LSTM_out, hidden = self.p_LSTM(linear_input)\n            last_state = self.p_linear(LSTM_out)\n            last_state = self.p_dropout(last_state)\n            mean_pooling = torch.mean(last_state, 0)\n\n        else:\n            for i in range(len(neighbor_id)):\n                aft_rnn_dict[neighbor_id[i]] = content_embedings[i]\n            linear_input = self.u_init_linear(content_embedings)\n            # linear_input = self.u_init_dropout(linear_input)\n            linear_input = linear_input.view(linear_input.shape[0], 1, linear_input.shape[1])\n            LSTM_out, hidden = self.u_LSTM(linear_input)\n            last_state = self.u_linear(LSTM_out)\n            last_state = self.u_dropout(last_state)\n            mean_pooling = torch.mean(last_state, 0)\n        return mean_pooling, aft_rnn_dict\n\n    def node_het_agg(self, het_node):  # heterogeneous neighbor aggregation\n\n        # attention module\n        c_agg_batch = self.Bi_RNN([het_node.node_id], het_node.node_type, post_emb_dict, user_emb_dict)\n        #print(c_agg_batch.shape)\n        u_agg_batch, u_aft_rnn_dict = self.SameType_Agg_Bi_RNN(het_node.neighbors_user, \"user\")\n        p_agg_batch, p_aft_rnn_dict = self.SameType_Agg_Bi_RNN(het_node.neighbors_post, \"post\")\n        \n        gcn2_user, gcn2_post = self.GCN_net(u_aft_rnn_dict, p_aft_rnn_dict)\n        #print(gcn2_user.shape, gcn2_post.shape)\n        c_agg_batch_2 = torch.cat((c_agg_batch, c_agg_batch), 1).view(len(c_agg_batch), self.embed_d * 2)\n        u_agg_batch_2 = torch.cat((c_agg_batch, u_agg_batch), 1).view(len(c_agg_batch), c_agg_batch.shape[1]+u_agg_batch.shape[1]) #400\n        p_agg_batch_2 = torch.cat((c_agg_batch, p_agg_batch), 1).view(len(c_agg_batch), c_agg_batch.shape[1]+p_agg_batch.shape[1])\n        u_agg_batch_2 = torch.cat((gcn2_user, u_agg_batch_2), 0)\n        u_agg_batch_2 = torch.mean(u_agg_batch_2, 0, keepdims = True)\n\n        p_agg_batch_2 = torch.cat((gcn2_post, p_agg_batch_2), 0)\n        p_agg_batch_2 = torch.mean(p_agg_batch_2, 0, keepdims = True)\n        concate_embed = torch.cat((c_agg_batch_2, u_agg_batch_2, p_agg_batch_2), 1).view(len(c_agg_batch), 3,\n                                                                                         self.embed_d * 2)\n        if het_node.node_type == \"user\":\n            atten_w = self.act(torch.bmm(concate_embed, self.u_neigh_att.unsqueeze(0).expand(len(c_agg_batch),\n                                                                                             *self.u_neigh_att.size())))\n        else:\n            atten_w = self.act(torch.bmm(concate_embed, self.p_neigh_att.unsqueeze(0).expand(len(c_agg_batch),\n                                                                                             *self.p_neigh_att.size())))\n        atten_w = self.softmax(atten_w).view(len(c_agg_batch), 1, 3)\n        # weighted combination\n        #concate_embed = torch.cat((c_agg_batch, u_agg_batch, p_agg_batch), 1).view(len(c_agg_batch), 3, self.embed_d)\n        weight_agg_batch = torch.bmm(atten_w, concate_embed).view(len(c_agg_batch), self.embed_d*2)\n        return weight_agg_batch\n\n    def forward(self, x):\n        #gcn2_user, gcn2_post = self.GCN_net(x.neighbors_user, x.neighbors_post, user_emb_dict, post_emb_dict)\n        x = self.node_het_agg(het_node=x)\n        c_embed = x.view(1, self.out_embed_d*2)\n        #c_embed = torch.cat((c_embed, gcn2_user, gcn2_post), 1)\n        c_embed = self.out_dropout(c_embed)\n        c_embed_out = self.out_linear(c_embed)\n        #c_embed_out = self.batch_norm(c_embed_out)\n        predictions = self.output_act(c_embed_out)\n        return predictions\n\n\ndef BCELoss(predictions, true_label):\n    loss = nn.BCELoss()\n    predictions = predictions.view(1)\n    tensor_label = torch.FloatTensor(np.array([true_label]))\n    loss_sum = loss(predictions, tensor_label)\n    return loss_sum\n\n\ndef save_checkpoint(model, optimizer, save_path, epoch, val_acc):\n    torch.save({\n        'model_state_dict': model.state_dict(),\n        'optimizer_state_dict': optimizer.state_dict(),\n        'epoch': epoch,\n        'val_acc': val_acc\n    }, save_path)\n\n\ndef load_checkpoint(model, optimizer, load_path):\n    checkpoint = torch.load(load_path)\n    model.load_state_dict(checkpoint['model_state_dict'])\n    optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n    epoch = checkpoint['epoch']\n    val_acc = checkpoint['val_acc']\n    return model, optimizer, epoch, val_acc\n\n\ndef train_test(data_real, data_fake, test_size):\n    y_real = range(len(data_real))\n    y_fake = range(len(data_fake))\n    X_train_real, X_test_real, y_train_real, y_test_real = train_test_split(data_real, y_real, test_size=test_size, random_state=42)\n    X_train_fake, X_test_fake, y_train_fake, y_test_fake = train_test_split(data_fake, y_fake, test_size=test_size, random_state=42)\n    np.savetxt('FYP_models/gcn_par2vec_modi/train_index_real.txt', y_train_real)\n    np.savetxt('FYP_models/gcn_par2vec_modi/test_index_real.txt', y_test_real)\n    np.savetxt('FYP_models/gcn_par2vec_modi/train_index_fake.txt', y_train_fake)\n    np.savetxt('FYP_models/gcn_par2vec_modi/test_index_fake.txt', y_test_fake)\n    return X_train_real, X_test_real, X_train_fake, X_test_fake\n\n\ndef load_train_test(data_real, data_fake, test_index_path_real='FYP_models/gcn_par2vec_modi/test_index_real.txt', test_index_path_fake = 'FYP_models/gcn_par2vec_modi/test_index_fake.txt'):\n    a = np.loadtxt(test_index_path_real)\n    a = a.astype('int32')\n    b = np.loadtxt(test_index_path_fake)\n    b = b.astype('int32')\n    test_set_real = []\n    train_set_real = []\n    for i in range(len(data_real)):\n        if i in a:\n            test_set_real.append(data_real[i])\n        else:\n            train_set_real.append(data_real[i])\n    test_set_fake = []\n    train_set_fake = []\n    for j in range(len(data_fake)):\n        if j in b:\n            test_set_fake.append(data_fake[j])\n        else:\n            train_set_fake.append(data_fake[j])\n    return train_set_real, test_set_real, train_set_fake, test_set_fake\n\n# split test set first\n#number of fake nodes:  2309\n#number of real nodes:  2346\nif path.exists('FYP_models/gcn_par2vec_modi/test_index_real.txt'):\n    X_train_real, X_test_real, X_train_fake, X_test_fake = load_train_test(post_nodes_real, post_nodes_fake)\nelse:\n    X_train_real, X_test_real, X_train_fake, X_test_fake = train_test(post_nodes_real, post_nodes_fake, 0.1)\n\n# Shuffle the order in post nodes\ntrain_val = X_train_real + X_train_fake\ntest_set = X_test_real + X_test_fake\nnp.random.shuffle(train_val)\nnp.random.shuffle(test_set)\n\n# K-fold validation index\ntrain_index = []\nval_index = []\nnum_splits = 9\nkfold = KFold(num_splits, True, 1)\nfor train, val in kfold.split(train_val):\n    train_index.append(train)\n    val_index.append(val)\n\n# Initialize parameters\nlr = 0.005\nnum_epoch = 40\nnum_folds = 1\nbatch_size = 4\nPATH = 'FYP_models/gcn_par2vec_modi/'\n\nprint('Start training')\n\nfor fold in range(num_folds):\n    print(\"Start for fold\", fold + 1)\n    best_val = 0\n    running_loss = 0.0\n    val_loss = 0.0\n    test_loss = 0.0\n    best_epoch = 0\n    net = Het_GNN(input_dim=[768, 512, 145], ini_hidden_dim=[180, 180, 180], u_hidden_dim=250, u_ini_hidden_dim=350,\n                  p_hidden_dim=300, p_ini_hidden_dim=450,\n                  hidden_dim=100, batch_size=1, u_input_dim=200,\n                  u_batch_size=1, u_output_dim=100, u_num_layers=1, u_rnn_type='LSTM', p_input_dim=200,\n                  p_batch_size=1, p_output_dim=100, p_num_layers=1,\n                  p_rnn_type='LSTM', out_embed_d=110, outemb_d=1, GCN_out1= 200)\n\n    net.init_weights()\n    print(net)\n    # Set up optimizer and scheduler\n    optimizer = optim.SGD(net.parameters(), lr=lr)\n    scheduler = StepLR(optimizer, step_size=5, gamma=0.05)\n\n    for epoch in range(num_epoch):\n        print('Epoch:', epoch + 1)\n        c = 0.0\n        running_loss = 0.0\n        v = 0.0\n        val_loss = 0.0\n        real_count = 0.0\n        fake_count = 0.0\n        real_true = 0.0\n        fake_true = 0.0\n        # generate train and test set for current epoch\n        train_set = []\n        val_set = []\n        for t_index in train_index[fold]:\n            train_set.append(train_val[t_index])\n        for v_index in val_index[fold]:\n            val_set.append(train_val[v_index])\n        net.train()\n        for i in range(len(train_set)):\n            optimizer.zero_grad()\n            output = net(train_set[i])\n            if (output.item() >= 0.5 and train_set[i].label == 1) or (output.item() < 0.5 and train_set[i].label == 0):\n                c += 1\n            cur_loss = BCELoss(predictions=output, true_label=train_set[i].label)\n            running_loss += cur_loss.item()\n            if i % 100 == 99:  # print every 100 mini-batches\n                print('Fold: %d, Epoch: %d, step: %5d, loss: %.4f, acc: %.4f' %\n                      (fold + 1, epoch + 1, i + 1, running_loss / 100, c / 100))\n                running_loss = 0.0\n                c = 0.0\n            if i % batch_size == 0:\n                loss = Variable(torch.zeros(1), requires_grad=True)\n            # loss = loss + cur_loss\n            x = random.random()\n            if (output.item() < 0.5 and train_set[i].label == 1):\n                loss = loss + (1+x)*cur_loss\n            else:\n                loss = loss + cur_loss\n            if i % batch_size == (batch_size - 1):\n                loss = loss / batch_size\n                loss.backward()\n                optimizer.step()\n        net.eval()\n        for j in range(len(val_set)):\n            output = net(val_set[j])\n            if val_set[j].label == 1:\n                real_count += 1\n                if output.item() >= 0.5:\n                    real_true += 1\n            else:\n                fake_count += 1\n                if output.item() < 0.5:\n                    fake_true += 1\n            if (output.item() >= 0.5 and val_set[j].label == 1) or (output.item() < 0.5 and val_set[j].label == 0):\n                v += 1\n            vloss = BCELoss(predictions=output, true_label=val_set[j].label)\n            x = random.random()\n            if (output.item() < 0.5 and val_set[j].label == 1):\n                val_loss += (1+x)*vloss.item()\n            else:\n                val_loss += vloss.item()\n        val_acc = v / len(val_set)\n        real_precision = real_true / (real_true + fake_count - fake_true)\n        fake_precision = fake_true / (fake_true + real_count - real_true)\n        real_recall = real_true / real_count\n        fake_recall = fake_true / fake_count\n        real_f1 = 2 * real_precision * real_recall / (real_precision + real_recall)\n        fake_f1 = 2 * fake_precision * fake_recall / (fake_precision + fake_recall)\n        print('Validation loss: %.4f, Validation accuracy: %.4f' % (val_loss / len(val_set), val_acc))\n        print('Real Precision: %.4f, Real Recall: %.4f, Real F1: %.4f' % (real_precision, real_recall, real_f1))\n        print('Fake Precision: %.4f, Fake Recall: %.4f, Fake F1: %.4f' % (fake_precision, fake_recall, fake_f1))\n        \"\"\"\n        if val_loss / len(val_set) < best_val:\n            print('Update model at epoch:', epoch + 1)\n            cur_PATH = PATH + 'best_model' + '_' + str(fold) + '.tar'\n            save_checkpoint(net, optimizer, cur_PATH, epoch + 1, val_acc)\n            best_val = val_loss / len(val_set)\n            best_epoch = epoch\n        \"\"\"\n        if val_acc > best_val:\n            print('Update model at epoch:', epoch + 1)\n            cur_PATH = PATH + 'best_model_test' + '_' + str(fold) + '.tar'\n            save_checkpoint(net, optimizer, cur_PATH, epoch + 1, val_acc)\n            best_val = val_acc\n            best_epoch = epoch\n        scheduler.step()\n        if epoch - best_epoch >= 3:\n            break\n    print('Finish training')\n\nprint('==============================================================')\n\n\n# In[ ]:\n\n\n# Init net and optimizer skeletons\nbest_models = []\nnum_folds = 1\nnet = Het_GNN(input_dim=[768, 512, 145], ini_hidden_dim=[180, 180, 180], u_hidden_dim=250, u_ini_hidden_dim=350,\n                  p_hidden_dim=300, p_ini_hidden_dim=450,\n                  hidden_dim=100, batch_size=1, u_input_dim=200,\n                  u_batch_size=1, u_output_dim=100, u_num_layers=1, u_rnn_type='LSTM', p_input_dim=200,\n                  p_batch_size=1, p_output_dim=100, p_num_layers=1,\n                  p_rnn_type='LSTM', out_embed_d=110, outemb_d=1, GCN_out1= 200)\nnet.init_weights()\noptimizer = optim.SGD(net.parameters(), lr=lr)\n\nfor count in range(num_folds):\n    cur_PATH = PATH + 'best_model_test_95.5%' + '_' + str(count) + '.tar'\n    net, optimizer, epoch, best_val = load_checkpoint(net, optimizer, cur_PATH)\n    print(best_val)\n    net.eval()\n    best_models.append(net)\n\nt = 0.0\ntest_loss = 0.0\nreal_count = 0.0\nfake_count = 0.0\nreal_true = 0.0\nfake_true = 0.0\nfor k in range(len(test_set)):\n    output = 0.0\n    avg_tloss = 0.0\n    for fold in range(num_folds):\n        result = best_models[fold](test_set[k])\n        output += result.item()\n        tloss = BCELoss(predictions=result, true_label=test_set[k].label)\n        avg_tloss += tloss.item()\n    output /= num_folds\n    avg_tloss /= num_folds\n    test_loss += avg_tloss\n    if (output >= 0.5 and test_set[k].label == 1) or (output < 0.5 and test_set[k].label == 0):\n        t += 1\n    if test_set[k].label == 1:\n        real_count += 1\n        if output >= 0.5:\n            real_true += 1\n    else:\n        fake_count += 1\n        if output < 0.5:\n            fake_true += 1\n\nreal_precision = real_true / (real_true + fake_count - fake_true)\nfake_precision = fake_true / (fake_true + real_count - real_true)\nreal_recall = real_true / real_count\nfake_recall = fake_true / fake_count\nreal_f1 = 2 * real_precision * real_recall / (real_precision + real_recall)\nfake_f1 = 2 * fake_precision * fake_recall / (fake_precision + fake_recall)\nprint('Test loss: %.4f, Test accuracy: %.4f' % (test_loss / len(test_set), t / len(test_set)))\nprint('Real Precision: %.4f, Real Recall: %.4f, Real F1: %.4f' % (real_precision, real_recall, real_f1))\nprint('Fake Precision: %.4f, Fake Recall: %.4f, Fake F1: %.4f' % (fake_precision, fake_recall, fake_f1))\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"ysunbp/Fake-News-Detection-with-HetGNN","sub_path":"models/HetGCN_weibo.py","file_name":"HetGCN_weibo.py","file_ext":"py","file_size_in_byte":32527,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"48"}
+{"seq_id":"73988506707","text":"#!/usr/bin/env python\r\n# copy this script into the same directory as https://github.com/ECTO-1A/AppleJuice/\r\n# more info: mobile.hacker.com\r\n\r\nimport os\r\nimport sys\r\nimport time\r\nimport subprocess as sp\r\n\r\ndef runApp(number):\r\n    extProc = sp.Popen(['python3','app.py','-d',number])\r\n    status = sp.Popen.poll(extProc) \r\n    time.sleep(6)\r\n    sp.Popen.terminate(extProc) \r\n\r\nif __name__ == \"__main__\":\r\n    #global configFile\r\n    runApp(\"1\")\r\n    runApp(\"2\")\r\n    runApp(\"3\")\r\n    runApp(\"4\")\r\n    runApp(\"5\")\r\n    runApp(\"6\")\r\n    runApp(\"7\")\r\n    runApp(\"8\")\r\n    runApp(\"9\")\r\n    runApp(\"10\")\r\n    runApp(\"11\")\r\n    runApp(\"12\")\r\n    runApp(\"13\")\r\n    runApp(\"14\")\r\n    runApp(\"15\")\r\n    runApp(\"16\")\r\n    runApp(\"17\")\r\n    runApp(\"18\")\r\n    runApp(\"19\")\r\n    runApp(\"20\")\r\n    runApp(\"21\")\r\n    runApp(\"22\")\r\n    runApp(\"23\")\r\n    runApp(\"24\")\r\n    runApp(\"25\")\r\n    runApp(\"26\")\r\n    runApp(\"27\")\r\n    runApp(\"28\")\r\n    runApp(\"29\")\r\n\r\n\"\"\"\r\n    1: \"Airpods\",\r\n    2: \"Airpods Pro\",\r\n    3: \"Airpods Max\",\r\n    4: \"Airpods Gen 2\",\r\n    5: \"Airpods Gen 3\",\r\n    6: \"Airpods Pro Gen 2\",\r\n    7: \"PowerBeats\",\r\n    8: \"PowerBeats Pro\",\r\n    9: \"Beats Solo Pro\",\r\n    10: \"Beats Studio Buds\",\r\n    11: \"Beats Flex\",\r\n    12: \"BeatsX\",\r\n    13: \"Beats Solo3\",\r\n    14: \"Beats Studio3\",\r\n    15: \"Beats Studio Pro\",\r\n    16: \"Beats Fit Pro\",\r\n    17: \"Beats Studio Buds+\",\r\n    18: \"AppleTV Setup\",\r\n    19: \"AppleTV Pair\",\r\n    20: \"AppleTV New User\",\r\n    21: \"AppleTV AppleID Setup\",\r\n    22: \"AppleTV Wireless Audio Sync\",\r\n    23: \"AppleTV Homekit Setup\",\r\n    24: \"AppleTV Keyboard\",\r\n    25: \"AppleTV 'Connecting to Network'\",\r\n    26: \"Homepod Setup\",\r\n    27: \"Setup New Phone\",\r\n    28: \"Transfer Number to New Phone\",\r\n    29: \"TV Color Balance\"\r\n\"\"\"\r\n","repo_name":"androidmalware/iOSDoS","sub_path":"dos.py","file_name":"dos.py","file_ext":"py","file_size_in_byte":1765,"program_lang":"python","lang":"en","doc_type":"code","stars":49,"dataset":"github-code","pt":"48"}
+{"seq_id":"23820906045","text":"\"\"\" \n    Give the user a lucky card by smushing together their birthday.\n\"\"\"\nimport tarot\n\nname = str(input(\"Hello and Welcome to Baba Python, your digital fortune teller!\\nWhat is your name, please? \"))\n\nmenu_choice = 0\n\nmain_menu = f\"\"\"\nHello {name}! Let me draw you a Tarot card.\n\nHere are the kinds of readings I can offer.\n-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\n1. Ask a specific question...\n2. Get your lucky birthday card...\n3. Just draw a random card...\n4. Exit, I don't need a reading.\")\n\"\"\"\nimport random\n\nprint(main_menu)\nwhile menu_choice != 4:\n    menu_choice = input(\"Please, make your choice (1-4): \")\n    if(menu_choice.isdigit()):\n        menu_choice = int(menu_choice)\n    else:\n        print(main_menu)\n        continue\n\n    if menu_choice == 1 or menu_choice == 2 or menu_choice == 3:\n        if menu_choice == 1:\n            prompt = \"What is your question?: \"\n            question_prefix = name + \" asks \"\n        elif menu_choice ==2:\n            prompt = \"When is your birthday?: \"\n            question_prefix = \"For \" + name + \"'s birthday \"\n        else:\n            prompt = \"\"\n            card = random.randint(0, 77)\n        if prompt != \"\":\n            question = question_prefix + str(input(prompt))\n            card = tarot.lazyCard(question)\n            print(question)\n        print(f\"Hmm... {name}, your card is {tarot.getCardName(card)}!\")\n\n    elif menu_choice == 3:\n        print(\"Coming Soon!\")\n    elif menu_choice == 4:\n        break\nprint(\"Goodbye!\")","repo_name":"melek/pdxcg-intro-course","sub_path":"tarot/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1497,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"27074106034","text":"\"\"\"scrumlab URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/2.1/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  path('', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.urls import include, path\n    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path\n\nfrom jedzonko.views import IndexView, Dashboard\nfrom jedzonko import views\n\n\nurlpatterns = [\n    path('admin/', admin.site.urls),\n    path('index/', IndexView.as_view()),\n    path('main/', Dashboard.as_view()),\n    path('', views.LandingPage.as_view(), name=\"landingpage\"),\n    path('recipe/list/', views.RecipeList.as_view(), name=\"planlist\"),\n    path('plan/list/', views.PlanList.as_view()),\n    path('recipe/add/', views.AddRecipe.as_view()),\n    path('plan/add/', views.AddPlan.as_view()),\n    path('plan/add-recipe/', views.AddRecipieToPlan.as_view()),\n    path('recipe/<int:id>/', views.RecipeDetails.as_view()),\n    path('plan/<int:id>/', views.PlanDetails.as_view()),\n    path('recipe/modify/<int:pk>/', views.ModifyRecipe.as_view()),\n    path('contact/', views.ContactPage.as_view()),\n    path('plan/delete_recipe/<int:recipeplan_pk>/', views.DeleteRecipeFromPlan.as_view()),\n    path('about/', views.AboutPage.as_view()),\n]\n","repo_name":"Naitsyrk/ScrumLab","sub_path":"scrumlab/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1630,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"2685525001","text":"\"\"\"\nCucos Maria-Marianita\nGhetoiu Gheorghe-Laurentiu\n\"\"\"\n\n# Limbajul pe care Masina Turing il recunoaste:  L = {0^n1^n2^n | n≥1}\n# Pentru acest exercitiu am modifcat config_file adaugand simbolul i in alfabetul benzii si stari noi pentru\n# a verifica daca cele doua benzi sunt identice, acestea sunt marcate cu V si sunt adaugata in multimea de stari q_ver\n\n# validare config file\nimport re\nimport sys\nfrom parserTuring import parser\n\n# ----------------------------------------------------------------------------------------\n\nQ, sigma, gamma, tranzitii, q0, q_acc, q_rej, q_ver = parser()\n\ninput_string = '001122'\n\n\n# functia care implementeaza masina turing,\n# primeste ca parametru stringul pe care se doreste verificarea\ndef turingMachine(input_string):\n    validF = False  # validitatea pe prima banda\n    validS = False  # validitatea pe a doua banda\n    backup_tape = input_string  # stringul din input\n    # transformam cele doua benzi in una singura de forma:\n    # \"$input_string$backup_string$\"\n    twoTapesInOne = list('$' + input_string + '$' + backup_tape + '$')\n\n    # twoTapesInOne[3] = '2' # test pentru a verifica daca recunoaste cand o banda a fost modificata\n\n    # lungimea inputului\n    inputLength = len(input_string)  # lungimea inputului\n\n    currentStateF = q0[0]  # starea curenta pentru prima banda, initializata cu starea de inceput\n    currentStateS = q0[0]  # starea curenta pentru a doua banda, initializata cu starea de inceput\n    currentPositionF = 1  # pozitia curenta de pe prima banda, incepand cu cel mai din stanga element care apartine inputului\n    currentPositionS = currentPositionF + len(backup_tape) + 1  # pozitia curenta de pe a doua banda\n\n    tapeModified = False  # retinem si daca au loc schimbari pe vreuna dintre benzi\n\n    # parcurgem inputul alternand de la o banda la alta(in zig-zag)\n    # inputul permite sa se mearga in doua simboluri diferite pentru fiecare dintre benzi, deoarece pentru una dintre\n    # benzi puteam avea conventia ca un simbol din alfabetul inputului sa fie marcat cu un alt simbol din alfabetul benzii\n    # rezultatul final fiind acelasi\n\n    # cat timp nu suntem in starea de accepatre masina continua sa ruleze\n    while currentStateF != q_acc[0] and currentStateS != q_acc[0]:\n        # actualizam argumentul functiei delta pentru fiecare banda\n        currentArgumentF = (currentStateF, twoTapesInOne[currentPositionF])\n        currentArgumentS = (currentStateS, twoTapesInOne[currentPositionS])\n        # daca la un moment dat cele doua argumente difera, sau starile in care a ramas masina dupa citirea\n        # de pe fiecare banda difera atunci e posibil ca una dintre benzi sa fie modificata\n        if currentStateF != currentStateS or currentArgumentF != currentArgumentS:\n            tapeModified = True\n        # deoarece functia de tranzitie este un dictionar verificam daca avem o cheie corespunzatoare cu argumentul actual\n        try:\n            # Pentru prima banda\n            currentStateF = tranzitii[currentArgumentF][0]  # actualizam starea curenta pentru prima banda\n            twoTapesInOne[currentPositionF] = tranzitii[currentArgumentF][\n                1]  # scriem pe banda simbolul indicat de functia de tranzitie\n            # mergem la dreapta sau la stanga verificand daca acest lucru este posibil (daca ne aflam la captul din\n            # stanga al benzii si functia de tranzitie indica o deplasare spre dreapta ramanem pe loc\n            # daca ne aflam la capatul benzii verificam iar functia de tranzitie indica o deplasare spre dreapta\n            # verificam daca ne aflam in starea de accept si ne oprim\n            if tranzitii[currentArgumentF][3] == 'R':\n                if twoTapesInOne[currentPositionF] == '$':\n                    if currentStateF == q_acc[0]:\n                        validF = True\n                else:\n                    currentPositionF += 1\n            elif tranzitii[currentArgumentF][3] == 'L':\n                if twoTapesInOne[currentPositionF] != '$':\n                    currentPositionF -= 1\n\n            # Pentru a doua banda\n            currentStateS = tranzitii[currentArgumentS][0]\n            twoTapesInOne[currentPositionS] = tranzitii[currentArgumentS][2]  #\n            if tranzitii[currentArgumentS][4] == 'R':\n                if twoTapesInOne[currentPositionS] == '$':\n                    if currentStateS == q_acc[0]:\n                        validS = True\n                else:\n                    currentPositionS += 1\n            elif tranzitii[currentArgumentS][4] == 'L':\n                if twoTapesInOne[currentPositionS] != '$':\n                    currentPositionS -= 1\n\n            # daca am ajuns in stare de accept ne putem opri\n            if validS == True or validF == True:\n                break\n            if currentStateS == q_acc[0] or currentStateF == q_acc[0]:\n                if currentStateF == q_acc[0]:\n                    validF = True\n                if currentStateS == q_acc[0]:\n                    validS = True\n                break\n\n\n        # daca in functia de tranzitie nu este definit un caz in care ne aflam, iar starea nu este de accept deducem\n        # ca masina respinge inputul\n        except:\n            if currentStateS == q_acc[0] or currentStateF == q_acc[0]:\n                if currentStateS != currentStateF:\n                    tapeModified = True\n                if currentStateF == q_acc[0]:\n                    validF = True\n                if currentStateS == q_acc[0]:\n                    validS = True\n            else:\n                q_rej.append(currentStateS)\n            break\n\n    identicalTapes = True\n    currentPosition = 1\n    currentArgument = ()\n    # masina se intoarce in sa citeasca de la primul simbol de pe prima banda\n    # gaseste starea corespunzatoare pentru respectivul simbol din noua multime de stari adaugate\n    # daca din starea respectiva rezulta trecerea intr-o noua stare si pentru simbolul echivalent ca pozitie de pe a doua banda\n    # (in acest caz noile stari adaugate duc doar catre ele insasi)\n    # atunci inseamna ca acelasi simbol se afla pe aceeasi pozitie in ambele benzi\n    # se trece la urmatorul simbol\n    while twoTapesInOne[currentPosition] != '$':\n        for state in q_ver:\n            currentArgument = (state, twoTapesInOne[currentPosition])\n            currentState = state\n            if currentArgument in tranzitii.keys():\n                break\n        for ind in range(2):\n            twoTapesInOne[currentPosition] = 'i'\n            try:\n                currentState = tranzitii[currentArgument][0]\n                currentArgument = (currentState, twoTapesInOne[currentPosition + inputLength + 1])\n            except:  # daca vreodata nu se gaseste un argument potrivit pentru noile tranzitii inseamna ca benzile nu sunt egale\n                identicalTapes = False\n        currentPosition += 1\n    # masina trece in noua stare finala\n    if identicalTapes == True:\n        currentStateF = q_ver[len(q_ver) - 1]\n        currentStateS = q_ver[len(q_ver) - 1]\n        currentState = q_ver[len(q_ver) - 1]\n    \"\"\"\n    Exemplu:\n     #xyz#xzy\n     --prima pozitie--\n     currentArgument = (q10, x) -> (q10, i, i, R, R)\n     currentArgument = (q10, x) -> (q10, i, i, R, R)\n     --a doua pozitie--\n     currentArgument = (q11, y) -> (q11, i, i, R, R)\n     currentArgument = (q11, z) -> KEY_ERROR\n    \"\"\"\n\n    if tapeModified == True:\n        print(\"Una dintre benzi a suferit modificari\")\n    if validS == validF == True:\n        print(f\"{input_string} -> Acceptat\")\n        if currentState == q_ver[len(q_ver) - 1]:\n            print(\"Benzile sunt identice\")\n        return True\n    else:\n        print(f\"{input_string} -> Respins\")\n        if currentState == q_ver[len(q_ver) - 1]:\n            print(\"Benzile sunt identice\")\n        return False\n\n\nif len(sys.argv) >= 3:\n    input_string = sys.argv[2]\nturingMachine(input_string)\n","repo_name":"Laurentiuq/LFAs","sub_path":"examen/143_Ghetoiu_Gheorghe_Laurentiu/ex3.py","file_name":"ex3.py","file_ext":"py","file_size_in_byte":7910,"program_lang":"python","lang":"ro","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"71213577106","text":"import os\nfrom abc import ABC, abstractmethod\nfrom multiprocessing import JoinableQueue\nfrom multiprocessing.context import Process\nimport multiprocessing\n\n\nclass ProcessPool(ABC):\n    queue = None\n\n    def __init__(self, nb_workers=None):\n        if not nb_workers:\n                nb_workers = multiprocessing.cpu_count()\n        self.queue = JoinableQueue(maxsize=0)\n        self.processes = [Process(target=self.process_queue) for i in range(nb_workers)]\n        for p in self.processes:\n            p.start()\n\n    def enqueue(self, item):\n        self.queue.put(item)\n\n    def process_queue(self):\n        process_id = os.getpid()\n        print(f'Process #{process_id} started...')\n\n        while True:\n            print(f'Process #{process_id} getting from queue')\n            item = self.queue.get()\n            if item is None:\n                print(f'Process #{process_id} received None from queue')\n                break\n\n            self.process_item(item, process_id)\n\n            self.queue.task_done()\n\n    @abstractmethod\n    def process_item(self, item, process_id):\n        pass\n\n    def __enter__(self):\n        return self\n\n    def __exit__(self, type, value, tb):\n        \"\"\" wait until queue is empty and terminate processes \"\"\"\n        self.queue.join()\n        for p in self.processes:\n            p.terminate()\n","repo_name":"IanGrimstead/company-bulk","sub_path":"companybulk/process.py","file_name":"process.py","file_ext":"py","file_size_in_byte":1335,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"19789911526","text":"import difflib\nimport os\n\ndef diff(filepath1,filepath2,outdir):\n    \n    print(\"diff...{0}\".format(outdir))\n    \n    #root=os.path.dirname(os.path.abspath(__file__))\n    \n    #filepath1=os.path.join(root,filepath1)\n    #filepath2=os.path.join(root,filepath2)\n    #outdir=os.path.join(root,outdir)\n    \n    if (os.path.exists(filepath1) and os.path.exists(filepath2))==False:\n        return False\n    \n    with open(filepath1,\"r\",encoding=\"utf-8\") as f:\n        text1=[row for row in f]\n        \n    with open(filepath2,\"r\",encoding=\"utf-8\") as f:\n        text2=[row for row in f]\n        \n    d=difflib.HtmlDiff()\n    html=d.make_table(text1,text2)\n    \n    if os.path.exists(outdir)==False:\n        os.path.mkdir(outdir)\n    \n    with open(os.path.join(outdir,\"diff.html\"),\"w\") as f:\n        f.write(html)\n        \n    return text1!=text2\n        \nif __name__==\"__main__\":\n    \n    ret=diff(\"projects/support_devices/mvno/ymobile/csv/devices_ymobile-scraped.csv\",\n         \"projects/support_devices/mvno/ymobile/tmp/csv/devices_ymobile-scraped.csv\",\n         \"projects/support_devices/mvno/ymobile\")\n    \n    print(\"diff done\",ret)","repo_name":"takmorisawa/auto_gen","sub_path":"diff.py","file_name":"diff.py","file_ext":"py","file_size_in_byte":1132,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"26781541570","text":"from collections import namedtuple\nfrom collections import defaultdict\n\n\ndef transpose(data):\n    \"\"\"Transpose a data structure\n    1. dict\n    data = {'2017-8': 19, '2017-9': 13}\n    In:  transpose(data)\n    Out: [('2017-8', '2017-9'), (19, 13)]\n\n    2. list of (named)tuples\n    data = [Member(name='Bob', since_days=60, karma_points=60,\n                   bitecoin_earned=56),\n            Member(name='Julian', since_days=221, karma_points=34,\n                   bitecoin_earned=78)]\n    In: transpose(data)\n    Out: [('Bob', 'Julian'), (60, 221), (60, 34), (56, 78)]\n    \"\"\"\n    if type(data) is dict:\n        return (tuple(data.keys()), tuple(data.values()))\n    elif type(data) is list:\n        results = defaultdict(list)\n        for record in data:\n            for field in record._fields:\n                results[field].append(getattr(record, field))\n        return results.values()\n","repo_name":"kaysagoe/pybites","sub_path":"33/transpose.py","file_name":"transpose.py","file_ext":"py","file_size_in_byte":892,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"12178965402","text":"# This program implements a simple calculator\n\n# Define the main function\ndef main():\n  # Prompt the user for the operation to perform\n  print(\"Select operation:\")\n  print(\"1. Add\")\n  print(\"2. Subtract\")\n  print(\"3. Multiply\")\n  print(\"4. Divide\")\n  \n  # Read the user's input\n  choice = input(\"Enter choice (1/2/3/4): \")\n  \n  # Convert the input string to an integer\n  choice = int(choice)\n  \n  # Prompt the user for the operands\n  num1 = int(input(\"Enter first number: \"))\n  num2 = int(input(\"Enter second number: \"))\n  \n  # Perform the selected operation\n  if choice == 1:\n    result = num1 + num2\n  elif choice == 2:\n    result = num1 - num2\n  elif choice == 3:\n    result = num1 * num2\n  elif choice == 4:\n    result = num1 / num2\n  else:\n    # If the user entered an invalid choice, display an error message\n    print(\"Invalid input\")\n    return\n  \n  # Display the result of the calculation\n  print(num1, \",\", num2, \":\", result)\n  \n# Call the main function\nmain()\n\n","repo_name":"Omondi85Manu/Simple-Calculator","sub_path":"Basic_Operations.py","file_name":"Basic_Operations.py","file_ext":"py","file_size_in_byte":973,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"71304241106","text":"import movies\r\nimport os\r\nfrom shutil import copyfile\r\n\r\n\r\ndef setup_module():\r\n    # creating temporary copy of database to work on\r\n    copyfile('tests/test_data.sqlite', 'tests/test_data_temp.sqlite')\r\n\r\n\r\ndef create_command(args):\r\n    movies.create_commands_list(['--set_file_name', 'tests/test_data_temp.sqlite'] + args)\r\n\r\n\r\ndef test_sort_by(capsys):\r\n    columns = ['YEAR', 'RUNTIME', 'GENRE', 'DIRECTOR', 'WRITER', 'LANGUAGE', 'COUNTRY', 'IMDb_Rating',\r\n               'IMDb_votes',\r\n               'ACTORS', 'BOX_OFFICE', 'OSCARS_WON', 'OSCARS_NOMINATIONS', 'ALL_WINS', 'ALL_NOMINATIONS']\r\n    predicted_outputs = []\r\n    with open('tests/sort.csv') as data:\r\n        output = ''\r\n        for i, line in enumerate(data):\r\n            if (i in range(1, 41) and i % 4 == 0) or i in [42, 46, 50, 54]:\r\n                predicted_outputs.append(output)\r\n                output = ''\r\n            output += line\r\n        predicted_outputs.append(output + '\\n')\r\n\r\n    # testing outputs for every column\r\n    for i, column in enumerate(columns):\r\n        create_command(['--sort_by', column])\r\n        captured = capsys.readouterr()\r\n        assert captured.out == predicted_outputs[i]\r\n\r\n    # testing output for not existing column\r\n    create_command(['--sort_by', 'notexistedcolumn'])\r\n    captured = capsys.readouterr()\r\n    assert captured.out == 'notexistedcolumn not found in database.\\n'\r\n\r\n\r\ndef test_filter_by(capsys):\r\n    options = ['director', 'actors', 'language', 'oscar', 'over_80', 'over_100_mil']\r\n    args = [['Innes'], ['Chris'], ['Russian'], [], [], []]\r\n    outputs = [\"Title: Shazam director: Royston Innes\\n\",\r\n               \"Title: The Avengers actors: Robert Downey Jr., Chris Evans, Mark Ruffalo, Chris Hemsworth\\n\\\r\nTitle: Shazam actors: Roman Dior Degeddingseze, Christopher Mychael Watson\\n\",\r\n               \"Title: The Avengers language: English, Russian, Hindi\\n\",\r\n               \"Title: The Avengers\\n\\\r\nTitle: Trainspotting\\n\",\r\n               \"\",\r\n               \"Title: The Avengers. Box office: 623,279,547$\\n\"]\r\n    for arg, option, output in zip(args, options, outputs):\r\n        create_command(['--filter_by', option] + arg)\r\n        captured = capsys.readouterr()\r\n        assert captured.out == output\r\n\r\n    create_command(['--filter_by', 'invalidinput'])\r\n    captured = capsys.readouterr()\r\n    assert captured.out == \"invalidinput is invalid argument.\\n\"\r\n\r\n    # multiple arguments\r\n    create_command(['--filter_by', 'language', 'English', 'over_100_mil'])\r\n    captured = capsys.readouterr()\r\n    assert captured.out == \"Title: The Avengers language: English, Russian, Hindi\\n\\\r\nTitle: Room language: English\\n\\\r\nTitle: Shazam language: English\\n\\\r\nTitle: Trainspotting language: English\\n\\\r\nTitle: The Avengers. Box office: 623,279,547$\\n\"\r\n\r\n    # no elements found\r\n    create_command(['--filter_by', 'language', 'notexistinglanguage'])\r\n    captured = capsys.readouterr()\r\n    assert captured.out == \"\"\r\n\r\n\r\ndef test_compare(capsys):\r\n    # comparing by every column\r\n    options = ['IMDb_Rating', 'Box_office', 'all_wins', 'runtime']\r\n    for option in options:\r\n        create_command(['--compare', option, 'Room', 'The Avengers'])\r\n    captured = capsys.readouterr()\r\n    assert captured.out == \"Best IMDb_Rating: 8.2. Movie: Room\\n\\\r\nBest Box_office: 623,279,547$. Movie: The Avengers\\n\\\r\nBest all_wins: 104. Movie: Room\\n\\\r\nBest runtime: 143 min. Movie: The Avengers\\n\"\r\n\r\n    # comparing movies that doesn't exists\r\n    create_command(['--compare', 'all_wins', 'movie1', 'movie2'])\r\n    captured = capsys.readouterr()\r\n    assert captured.out == \"movie1 not found in database.\\n\"\r\n\r\n    # comparing movies where one doesn't have information about box_office\r\n    create_command(['--compare', 'Box_office', 'Room', 'Trainspotting'])\r\n    captured = capsys.readouterr()\r\n    assert captured.out == \"No information about Box_office for movie Trainspotting in database.\\n\"\r\n\r\n\r\ndef test_adding_movies(capsys):\r\n    # trying to add movie that doesn't exist\r\n    movie_name = 'wrongmoviename'\r\n    create_command(['--add', movie_name])\r\n    captured = capsys.readouterr()\r\n    assert captured.out == \"wrongmoviename movie not found.\\n\"\r\n\r\n    # trying to add movie that does exist and not in database\r\n    create_command(['--add', 'The Thing'])\r\n    captured = capsys.readouterr()\r\n    assert captured.out == \"The Thing added to database.\\n\"\r\n\r\n    # trying to add movie that does exist and is already in database\r\n    create_command(['--add', 'The Thing'])\r\n    captured = capsys.readouterr()\r\n    assert captured.out == \"The Thing already in database.\\n\"\r\n\r\n    # adding multiple movies\r\n    create_command(['--add', 'The Avengers', 'Guardians of the Galaxy Vol. 2', 'Guardians of the Galaxy'])\r\n    captured = capsys.readouterr()\r\n    assert captured.out == \"The Avengers already in database.\\nGuardians of the Galaxy Vol.\" \\\r\n                           \" 2 added to database.\\nGuardians of the Galaxy added to database.\\n\"\r\n\r\n\r\ndef test_highscores(capsys):\r\n    create_command(['--highscores'])\r\n    captured = capsys.readouterr()\r\n    assert captured.out == \"Longest Runtime: 143 min from movie: The Avengers\\n\\\r\nBest Box Office: 623,279,547$ from movie: The Avengers\\n\\\r\nMost Awards Won: 104 from movie: Room\\n\\\r\nMost Nominations: 240 from movie: Room\\n\\\r\nMost Oscars Won: 1 from movie: Room\\n\\\r\nHighest IMDb Rating: 8.3 from movie: Shazam\\n\"\r\n\r\n    # printing highscores from empty database\r\n    movies.create_commands_list(['--set_file_name', 'tests/test_data2.sqlite', '--highscores'])\r\n    captured = capsys.readouterr()\r\n    assert captured.out == \"Longest Runtime: - min from movie: -\\n\\\r\nBest Box Office: -$ from movie: -\\n\\\r\nMost Awards Won: - from movie: -\\n\\\r\nMost Nominations: - from movie: -\\n\\\r\nMost Oscars Won: - from movie: -\\n\\\r\nHighest IMDb Rating: - from movie: -\\n\"\r\n    os.remove('tests/test_data2.sqlite')\r\n\r\n\r\ndef test_load_data(capsys):\r\n    # adding movies to database from other file\r\n    create_command(['--load_data', 'tests/load_test.sqlite'])\r\n    capsys.readouterr()\r\n\r\n    # highscores with added movies\r\n    create_command(['--highscores'])\r\n    captured = capsys.readouterr()\r\n    assert captured.out == \"Longest Runtime: 207 min from movie: Seven Samurai\\n\\\r\nBest Box Office: 623,279,547$ from movie: The Avengers\\n\\\r\nMost Awards Won: 154 from movie: The Dark Knight\\n\\\r\nMost Nominations: 309 from movie: The Dark Knight\\n\\\r\nMost Oscars Won: 3 from movie: Jurassic Park\\n\\\r\nHighest IMDb Rating: 9.0 from movie: The Dark Knight\\n\"\r\n\r\n    # loading from not existing file\r\n    create_command(['--load_data', 'notexisting'])\r\n    captured = capsys.readouterr()\r\n    assert captured.out == \"notexisting file not found.\\n\"\r\n\r\n\r\ndef test_other(capsys):\r\n    # command that doesn't exist\r\n    create_command(['--wrongcommand'])\r\n    captured = capsys.readouterr()\r\n    assert captured.out == \"wrongcommand command not found.\\n\"\r\n\r\n\r\ndef teardown_module():\r\n    # removing temporary database\r\n    os.remove('tests/test_data_temp.sqlite')\r\n","repo_name":"ldebski/SimpleMoviesScraper","sub_path":"test_movie.py","file_name":"test_movie.py","file_ext":"py","file_size_in_byte":7030,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"8736499710","text":"\"\"\"\n问题链接\nhttps://www.codewars.com/kata/514b92a657cdc65150000006\n\"\"\"\n\n\n# !/usr/bin/python\n# -*- coding: UTF-8 -*-\n\n\ndef solution(number):\n    three_number = 0\n    five_number = 0\n    result = 0\n    if not isinstance(number, int):\n        result = \"error!!!\"\n    elif 3 < number <= 5:\n        result = 3\n    elif number <= 3:\n        result = 0\n    elif number > 5:\n        for i in range(3, number, 3):\n            three_number += i\n        for j in range(5, number, 5):\n            if j not in range(3, number, 3):\n                five_number += j\n        result = three_number + five_number\n    return result\n\n\nprint(solution(99))","repo_name":"wen1ling/code_test","sub_path":"codewars_python/Multiples of 3 or 5.py","file_name":"Multiples of 3 or 5.py","file_ext":"py","file_size_in_byte":639,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"26372755782","text":"#\nclass Upper_case():\n    def con_up(self):\n        string1 = ''\n        for i in range(len(in_str)):\n            if (in_str[i] >= 'a' and in_str[i] <= 'z'):\n                string1 = string1 + chr((ord(in_str[i]) - 32))\n            else:\n                string1 = string1 + in_str[i]\n        print(\"-\"*40)\n        print (\"New converted string is :\\n\",string1)\n        print(\"-\" * 40)\n\n\nup = Upper_case()\nin_str = input(\"Please Enter your Own String : \")\nup.con_up()\n","repo_name":"Rakeshkumarlenka/test_functions","sub_path":"DECEMBER/STRING/Q21.Upper_case_string.py","file_name":"Q21.Upper_case_string.py","file_ext":"py","file_size_in_byte":467,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"120211965","text":"import torch\nimport numpy as np\nfrom torch.distributions import MultivariateNormal, MixtureSameFamily\n\n\nclass UMGLoss():\n\n    def gaussian_pdf(self, y, mean, var, eps=1e-8):\n\n        pdf = 1 / (torch.sqrt(2 * np.pi * var)) * torch.exp(-(y - mean)**2 /\n                                                            (2 * var))\n\n        return pdf\n\n    def category_loss(self, x_theta, label_r, label_theta):\n        #计算极坐标的损失函数\n        loss = torch.mean((x_theta - label_theta)**2)\n        return loss\n\n    # 定义损失函数\n    def mdn_loss(self, means, vars, weights, target):\n        pdfs = self.gaussian_pdf(target, means, vars)\n        pdfs_weighted = torch.sum(pdfs * weights, dim=1)\n        loss = -torch.log(pdfs_weighted + 1e-6)\n        loss = torch.mean(loss)\n        return loss\n\n    def total_loss(self, value_mean, value_var, value_weight, angle_mean,\n                   angle_var, angle_weight, value, angle):\n\n        value_loss = self.mdn_loss(value_mean, value_var, value_weight, value)\n\n        angle_loss = self.mdn_loss(angle_mean, angle_var, angle_weight, angle)\n        loss = value_loss + 0.001 * angle_loss\n\n        loss = torch.mean(loss)\n        return loss\n\n\nclass MMGLoss():\n\n    # 定义损失函数\n    def total_loss(self, means, covs, weights, target):\n        #将covs转换为协方差矩阵\n        covs = torch.diag_embed(covs)\n        mvn = MultivariateNormal(means, covs)\n        dist = MixtureSameFamily(\n            torch.distributions.Categorical(probs=weights), mvn)\n        pdf = dist.log_prob(target)\n        pdf = torch.exp(pdf)\n        loss = -torch.log(pdf + 1e-6)\n        loss = torch.mean(loss)\n        return loss\n","repo_name":"Continy/MixtureGaussian","sub_path":"loss.py","file_name":"loss.py","file_ext":"py","file_size_in_byte":1683,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"15126858391","text":"'''\r\nThis script launch a webapp that let you choose a bounding box and download the corresponding satellite image, then it starts the chat using llava model and OSM data. \r\n'''\r\nimport folium\r\nimport json\r\nimport streamlit as st\r\nfrom folium.plugins import Draw\r\nfrom streamlit_folium import st_folium\r\n\r\nfrom utils.scrape import fetch_overpass_data, get_rbg_image, get_pure_nodes, proj_lat_lon_on_image, filter_keys\r\nfrom utils.general import get_bbox_bltr\r\nfrom utils.model import chatModel\r\nfrom utils.retrieval_utils import evaluate_similarity, encode_information\r\nfrom LLaVA.llava.mm_utils import tokenizer_image_token, tokenizer_image_token, KeywordsStoppingCriteria\r\nfrom LLaVA.llava.mm_utils import process_images\r\nfrom LLaVA.llava.constants import DEFAULT_IMAGE_TOKEN, IMAGE_TOKEN_INDEX\r\nfrom transformers import TextStreamer\r\n\r\n# Let the user choose the model\r\nmodel = st.sidebar.selectbox(\r\n    'Choose the model',\r\n    ('liuhaotian/llava-v1.5-7b','liuhaotian/llava-v1.5-13b')\r\n)\r\n\r\n# # Load the model\r\ndevice = \"cuda:0\"\r\nif \"controller\" not in st.session_state:\r\n    with st.spinner('Loading model...'):\r\n        controller = chatModel(model_path=model)\r\n        controller.load_model(device=device)\r\n        st.session_state[\"controller\"] = controller\r\n\r\nmap = st.sidebar.selectbox(\r\n    'Choose the map layer',\r\n    ('osm_map', 'satellite_map')\r\n)\r\n\r\nif map == 'osm_map':\r\n    m = folium.Map(location=[39.949610, -75.150282], zoom_start=18)#, tiles='OpenStreetMap')\r\n    Draw(export=True).add_to(m)\r\nelif map == 'satellite_map':\r\n    m = folium.Map(location=[39.949610, -75.150282], zoom_start=18)\r\n    tile = folium.TileLayer(\r\n            tiles = 'https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}',\r\n            attr = 'Esri',\r\n            name = 'Esri Satellite',\r\n            overlay = False,\r\n            control = True\r\n        ).add_to(m)\r\n    \r\n    Draw(export=True).add_to(m)\r\n\r\noutput = st_folium(m, width=700, height=500)\r\n\r\n# Load the model\r\nif st.sidebar.button('Load model'):\r\n    device = \"cuda:0\"\r\n    if st.session_state[\"controller\"].model_path != model:\r\n        st.session_state[\"controller\"].remove_model()\r\n        st.session_state[\"controller\"].model_path = model\r\n        # Load the new model\r\n        st.session_state[\"controller\"].load_model(device=device)\r\n\r\n\r\nif st.button('Proceed'):\r\n    # Retrieve the controller\r\n    controller = st.session_state[\"controller\"]\r\n    \r\n    last_bbox = output['last_active_drawing']\r\n    \r\n    #bbox = get_bbox_bltr(last_bbox[\"geometry\"][\"coordinates\"])\r\n    \r\n    #print(bbox)\r\n    bbox = [39.948972, -75.150771, 39.950494, -75.148968] # KEEP ALWAYS THE SAME BBOX\r\n    \r\n    image = get_rbg_image(bbox) # PIL Image\r\n    st.image(image) # Show the image\r\n    \r\n    osm_data = fetch_overpass_data(bbox)\r\n    \r\n    nodes, filtered = get_pure_nodes(osm_data)\r\n    located_elements = proj_lat_lon_on_image(bbox, filtered, nodes)\r\n    located_elements = filter_keys(located_elements, [\"source\", \"attribution\", \"massgis\", \"gnis\"]) # manually remove.. mmmm\r\n    # Save located elements in a pkl file\r\n    with open(\"located_elements.json\", \"w\") as f:\r\n        json.dump(located_elements, f, indent=4, sort_keys=True)\r\n    \r\n    raw_sent, raw_sent_emb, refined_sent, refined_sent_emb = encode_information(located_elements)\r\n    \r\n    # Start the chat by first describing the image \r\n    controller.start_chat()\r\n    \r\n    # prompt=\"You are an assistant that can understand image contents and interact with me using text. I provided an image to you, for which I will sumbit queries. \" \\\r\n    #         \"You must analyze the query and decide if you can answer directly using your capabilities or if you need more information. \" \\\r\n    #         \"Since you are very good at understanding general concepts in the image, you can directly answer when the user asks for general information. However, if you are uncertain about the answer, you must ask for more information. \" \\\r\n    #         \"This is extremely useful when the user ask for specific information about the area in the image, such as the existance of particular structures (church, hospital, post office), names of the things in the image, addresses and so on. \" \\\r\n    #         \"To ask for more information you must reply just with the keyword 'get more data'. Let's start!\"\r\n    \r\n    prompt = DEFAULT_IMAGE_TOKEN + '\\n'\r\n    controller.append_message(controller.conversation.roles[0], prompt)\r\n    controller.append_message(controller.conversation.roles[1], None)\r\n    prompt = controller.get_prompt()\r\n    \r\n    #print(prompt)\r\n    \r\n    input_ids = tokenizer_image_token(prompt, controller.tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(controller.device)\r\n    keywords = [\"</s>\"]\r\n    stopping_criteria = KeywordsStoppingCriteria(keywords, controller.tokenizer, input_ids)\r\n    temperature = 0.7\r\n    max_new_tokens = 512\r\n    \r\n    model_cfg = dict()\r\n    model_cfg[\"image_aspect_ratio\"] = \"pad\"\r\n    image_tensor = process_images([image], controller.image_processor, model_cfg=model_cfg)\r\n    \r\n    out = controller.generate(input_ids, image_tensor, temperature=temperature, max_new_tokens=max_new_tokens, stopping_criteria=stopping_criteria)\r\n    \r\n    controller.conversation.messages[-1][-1] = out\r\n    \r\n    # Append information of osm to the chat\r\n    info_addition = \"Before interacting with you, I provide some external information about the area in the image. Each piece is enclosed by curly brakets. \" \\\r\n                    \"If you find it useful to reply to my interrogation, use it. Reply to me concisely and clearly. \" \\\r\n    \r\n    streamer = TextStreamer(controller.tokenizer, skip_prompt=True, skip_special_tokens=True)\r\n    \r\n    while True:\r\n        try:\r\n            inp = input(f\"{controller.conversation.roles[0]}: \")\r\n        except EOFError:\r\n            inp = \"\"\r\n        if not inp:\r\n            print(\"exit...\")\r\n            break\r\n        \r\n        print(f\"{controller.conversation.roles[1]}: \", end=\"\")\r\n        \r\n        # Read the other question from CLI and retrieve the information\r\n        information = evaluate_similarity(inp, raw_sent, raw_sent_emb)\r\n        information = evaluate_similarity(inp, refined_sent, refined_sent_emb)\r\n        \r\n        # Insert info if necessary\r\n        if len(information)!=0:\r\n            prompt = info_addition + \"\\n\"\r\n            for info in information:\r\n                prompt+=\"{\"+info+\"}\"+\"\\n\"\r\n            prompt+= inp \r\n        else:\r\n            prompt= inp \r\n        \r\n        # # Insert info if necessary\r\n        # if len(information)!=0:\r\n        #     prompt += info_addition + \"\\n\"\r\n        #     for info in information:\r\n        #         prompt+=\"{\"+info+\"}\"+\"\\n\"\r\n        \r\n        #inp+=\" If you need more information, reply with the keyword 'get more data'.\"\r\n        controller.append_message(controller.conversation.roles[0], prompt)\r\n        controller.append_message(controller.conversation.roles[1], None)\r\n        \r\n        prompt = controller.get_prompt()\r\n        \r\n        # print(prompt)\r\n        # print(\"\\n\")\r\n        \r\n        input_ids = tokenizer_image_token(prompt, controller.tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(controller.device)\r\n        keywords = [\"</s>\"]\r\n        stopping_criteria = KeywordsStoppingCriteria(keywords, controller.tokenizer, input_ids)\r\n        temperature = 0.2\r\n        max_new_tokens = 512\r\n        \r\n        out = controller.generate(input_ids, image_tensor, temperature=temperature, max_new_tokens=max_new_tokens, stopping_criteria=stopping_criteria, streamer=streamer)\r\n        \r\n        controller.conversation.messages[-1][-1] = out\r\n        controller.conversation.messages[-2][-1] = inp \r\n        \r\n        prompt=\"\"","repo_name":"RicRicci22/chat_with_OSM","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7786,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"38657254103","text":"n, l= map(int, input().split())\r\ntraffic = [list(map(int, input().split())) for _ in range(n)]\r\nans = 0; red = green = 0; prev_location = 0\r\nfor sign in traffic:\r\n    ans += sign[0] - prev_location\r\n    red = sign[1]\r\n    green = sign[2]\r\n    prev_location = sign[0]\r\n    if ans % (red+green) > red: #초록불\r\n        continue\r\n    elif ans % (red+green) < red:\r\n        ans += red - ans % (red+green)\r\n\r\nans += l - prev_location\r\nprint(ans)","repo_name":"toki0411/Algorithm","sub_path":"백준/Silver/2980. 도로와 신호등/도로와 신호등.py","file_name":"도로와 신호등.py","file_ext":"py","file_size_in_byte":443,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"35540480220","text":"import os,shutil\n\nROOT_PATH = os.path.abspath(os.path.curdir)\nICONS_PATH = ROOT_PATH + os.path.sep + \\\n    'img' + os.path.sep + 'icons' + os.path.sep\nEXEC_ICONS_PATH = ROOT_PATH + os.path.sep + 'dist' + os.path.sep + 'img' + os.path.sep + 'icons' + os.path.sep\n\ncommand_line_D = 'pyinstaller spec/LessWords-D.spec'\ncommand_line_F = 'pyinstaller LessWords.py -F -w -i img/icons/app.ico --specpath spec/'\n\nflag = input('输入任意字符回车进行系统性安装，直接回车进行简易安装：')\nif flag == '':\n    os.system(command_line_F)\n    try:\n        shutil.copytree(ICONS_PATH,EXEC_ICONS_PATH)\n    except FileExistsError:\n        print('done')\nelse:\n    os.system(command_line_D)","repo_name":"Reqin/LessWords","sub_path":"installer.py","file_name":"installer.py","file_ext":"py","file_size_in_byte":694,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"48"}
+{"seq_id":"38649894202","text":"import json\nimport re\n\nimport requests\nfrom bs4 import BeautifulSoup\n\n\ndef get_html_by_url(url: str, city_id: str = None, post=False) -> str:\n    headers = {\n        'User-Agent': 'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 '\n                      '(KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36 OPR/40.0.2308.81',\n    }\n\n    if post:\n        payload = {'CITY_ID': city_id}\n        response = requests.post(url, headers=headers, data=payload)\n    else:\n        response = requests.get(url, headers=headers)\n    return response.text\n\n\ndef make_bs4_soup(html_text: str) -> BeautifulSoup:\n    soup = BeautifulSoup(html_text, 'lxml')\n    return soup\n\n\ndef get_all_cities_ids(soup: BeautifulSoup) -> list[str, ...]:\n    data_block = soup.find(class_='col-xs-12 col-sm-6 citys-box')\n    regions = data_block.find_all(name='div', recursive=False)[1:]\n\n    cities = []\n    for region in regions:\n        region_cities = region.find(class_='cities-container').find_all(name='div')\n        for city in region_cities:\n            cities.append(city)\n\n    cities_ids = [city.label.get('id') for city in cities]\n    return cities_ids\n\n\ndef generate_url(city_or_shop_id: str) -> str:\n    prefix_url = 'https://som1.ru/shops/'\n    postfix_url = city_or_shop_id\n    return f'{prefix_url}{postfix_url}/'\n\n\ndef get_shops_ids(soup: BeautifulSoup) -> list:\n    all_cities_ids = get_all_cities_ids(soup=soup)\n    all_shops_ids = []\n\n    for city_id in all_cities_ids:\n        city_html = get_html_by_url('https://som1.ru/shops/', city_id=city_id, post=True)\n        city_soup = make_bs4_soup(city_html)\n\n        shops_block = city_soup.find_all(class_='shops-col shops-button')\n        for shop in shops_block:\n            all_shops_ids.append(shop.a.get('href').split('/')[2])\n    return all_shops_ids\n\n\ndef parse_shop_data(shop_soup: BeautifulSoup) -> dict:\n    shop_info_table = shop_soup.find(class_='shop-info-table').find_all(name='tr')\n    shop_map = shop_soup.find('script', text=re.compile('showShopsMap')).get_text().split('\\'')\n\n    address = shop_info_table[0].td.next_sibling.next_sibling.next_sibling.next_sibling.get_text().strip()\n    latlon = [float(shop_map[3]), float(shop_map[5])]\n    name = shop_map[9]\n\n    phones = [shop_info_table[1].td.next_sibling.next_sibling.next_sibling.next_sibling.get_text().strip()]\n    all_phones = phones[0].split(',')\n    phones = [phone.strip() for phone in all_phones]\n\n    working_hours = [shop_info_table[2].td.next_sibling.next_sibling.next_sibling.next_sibling.get_text().strip()]\n    if ',' in working_hours[0]:\n        all_working_hours = working_hours[0].split(',')\n        working_hours = [hours.strip() for hours in all_working_hours]\n\n    shop_data_dict = dict(address=address,\n                          latlon=latlon,\n                          name=name,\n                          phones=phones,\n                          working_hours=working_hours)\n    return shop_data_dict\n\n\ndef get_all_shops_data(soup: BeautifulSoup) -> list[dict, ...]:\n    all_shops_ids = get_shops_ids(soup=soup)\n\n    all_shops_data = []\n    for shop_id in all_shops_ids:\n        shop_url = generate_url(city_or_shop_id=shop_id)\n        shop_html = get_html_by_url(shop_url)\n        shop_soup = make_bs4_soup(shop_html)\n        shop_data = parse_shop_data(shop_soup=shop_soup)\n        all_shops_data.append(shop_data)\n\n    return all_shops_data\n\n\ndef load_data_to_json(data: list[dict, ...]) -> None:\n    with open(\"script_2_data.json\", mode='w', encoding='utf-8') as file:\n        json.dump(data, file, indent=4, ensure_ascii=False)\n\n\ndef main() -> None:\n    url = 'https://som1.ru/shops/'\n    print(f'Start parsing {url}. Please wait...')\n\n    html = get_html_by_url(url)\n    soup = make_bs4_soup(html)\n    all_shops_data = get_all_shops_data(soup)\n    load_data_to_json(data=all_shops_data)\n\n    print(f'Successfully created /script_2/script_2_data.json')\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"astivard/rocketdata-test","sub_path":"script_2/script_2.py","file_name":"script_2.py","file_ext":"py","file_size_in_byte":3932,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"11930172685","text":"from django.shortcuts import render, get_object_or_404\nfrom django.http import HttpResponse, HttpResponseRedirect\n# Create your views here.\nfrom django.http import HttpResponse\nfrom .models import Question\nfrom django.urls import reverse\nfrom django.contrib.auth.models import AnonymousUser\n\ndef index(request):\n    x = [{\n            'id':1,\n            'userName':'motiour',\n        },\n        {\n           'id':2,\n            'userName':'sayam',\n        }\n    ]\n\n    latest_question_list = Question.objects.order_by('-pub_date')[:5]\n    context = {'latest_question_list': latest_question_list}\n    request.session[0] = 'bar'\n    request.session['fev_color'] = '1'\n    request.session['has_commented'] = True\n    # request.session.set_expiry(180)\n    # request.session[1] = x\n    # request.session[1].append({\n    #     'id':3,\n    #     'userName':'manaun',\n    # })\n    # print( request.session['1'])\n    # print(request.session.keys())\n    if request.session.test_cookie_worked():\n        request.session.delete_test_cookie()\n        print('work')\n    response = HttpResponse()\n\n    response.set_cookie(\"favorite_color\",\n                            \"black\")\n    print(request.session.items())\n\n    if \"favorite_color\" in request.COOKIES:\n        print(\"your favorite color is %s\" / request.COOKIES['favorite_color'])\n    else:\n        print(\"You don't have a favorite color.\")\n\n\n\n    return render(request, 'index.html', context)\n\n\ndef detail(request, question_id):\n    fev_color = ''\n\n\n    fev_color = request.session.items() \n\n    print(fev_color, 'color')\n    question = get_object_or_404(Question, pk=question_id)\n    return render(request, 'detail.html', {'question': question})\n\n\ndef results(request, question_id):\n    # del request.session['fav_color']\n    question = get_object_or_404(Question, pk=question_id)\n    return render(request, 'results.html', {'question': question})\n\n\ndef vote(request, question_id):\n    question = get_object_or_404(Question, pk=question_id)\n    try:\n        selected_choice = question.choice_set.get(pk=request.POST['choice'])\n    except (KeyError, Choice.DoesNotExist):\n        # Redisplay the question voting form.\n        return render(request, 'polls/detail.html', {\n            'question': question,\n            'error_message': \"You didn't select a choice.\",\n        })\n    else:\n        selected_choice.votes += 1\n        selected_choice.save()\n        # Always return an HttpResponseRedirect after successfully dealing\n        # with POST data. This prevents data from being posted twice if a\n        # user hits the Back button.\n        return HttpResponseRedirect(reverse('polls:results', args=(question.id,)))\n","repo_name":"KaziMotiour/session-book","sub_path":"polls/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2664,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"69815129106","text":"class Solution:\n    #Function to return the minimum cost of connecting the ropes.\n    def minCost(self,arr,n) :\n    \n        # code here\n        import heapq\n        heapq.heapify(arr)\n        \n        final = 0\n        \n        while len(arr) > 1:\n            one = heapq.heappop(arr)\n            two = heapq.heappop(arr)\n            \n            temp = one+two\n            final += temp\n            heapq.heappush(arr , temp)\n        \n        return final    \n","repo_name":"DDR7707/Final-450-with-Python","sub_path":"Greedy/269.Minimum Cost Of Ropes.py","file_name":"269.Minimum Cost Of Ropes.py","file_ext":"py","file_size_in_byte":462,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"48"}
+{"seq_id":"70028191506","text":"import numpy as np\nimport datetime\nfrom matplotlib.dates import num2date, date2num\n\n\ndef get_sunrise_and_sunset_times_utc(t_start, t_end):\n    \"\"\"\n    Return all sunrise and sunset times between `t_start` and `t_end` at BCO as\n    `(time, kind)` where `time` will be a `np.datetim64` and kind is either\n    `\"sunrise\"` or `\"sunset\"`\n    # sunrise and sunset (local time): 0630, 1750\n    # sunrise and sunset (UTC):        1030, 2150\n    \"\"\"\n\n    def npdt64_to_dt(t):\n        # NOTE: have to convert to microseconds here, otherwise we don't get a\n        # datetime\n        return t.astype(\"<M8[us]\").astype(datetime.datetime)\n\n    dt_start = npdt64_to_dt(t_start)\n    dt_end = npdt64_to_dt(t_end)\n    dt_current = datetime.datetime(\n        year=dt_start.year, month=dt_start.month, day=dt_start.day\n    )\n\n    while dt_current < dt_end:\n        dt_sunrise = dt_current + datetime.timedelta(hours=10, minutes=30)\n        dt_sunset = dt_current + datetime.timedelta(hours=21, minutes=50)\n\n        if dt_current < dt_sunrise < dt_end:\n            yield (np.datetime64(dt_sunrise), \"sunrise\")\n        if dt_current < dt_sunset < dt_end:\n            yield (np.datetime64(dt_sunset), \"sunset\")\n        dt_current += datetime.timedelta(days=1)\n\n\ndef annotate_with_sunrise_and_sunset(ax, da_time):\n    \"\"\"\n    Annotate axes `ax` x-axis with all sunset and sunrise times in the time\n    interval spanned by `da_time`\n    \"\"\"\n    for (t, kind) in get_sunrise_and_sunset_times_utc(\n        da_time.min().values, da_time.max().values\n    ):\n        ax.axvline(t, color=\"red\", linestyle=\"--\")\n        ylim = ax.get_ylim()\n        text = ax.text(\n            t + np.timedelta64(2, \"m\"),\n            0.8 * ylim[1],\n            kind,\n            color=\"red\",\n            fontsize=14,\n            ha=\"center\",\n        )\n        text.set_bbox(dict(facecolor=\"white\", alpha=0.8, edgecolor=\"grey\"))\n\n\n@np.vectorize\ndef _to_dt64(x_):\n    return np.datetime64(num2date(x_))\n\n\n@np.vectorize\ndef _to_dt64ms(t):\n    return np.timedelta64(t, \"ms\")\n\n\ndef add_approx_distance_axis(ax, t0, posn=1.2, v=10.0, units=\"m\"):\n    \"\"\"\n    Add extra x-axis to axes `ax` at position `posn` (can either be a float\n    representing the position in figure coordinates, or \"top\" or \"bottom\") with\n    constant velocity `v` in units `units` using reference time `t0` (expected\n    to a `np.datetime64`)\n    \"\"\"\n    if units == \"m\":\n        s = 1.0\n    elif units == \"km\":\n        s = 1000.0\n    else:\n        raise NotImplementedError(units)\n\n    def time_to_distance(x):\n        x_dt = _to_dt64(x)\n        t_offset = x_dt - t0\n\n        x_dist = t_offset.astype(\"timedelta64[s]\").astype(int) * v\n        return x_dist / s\n\n    def distance_to_time(x_dist):\n        # I don't know why the F this function is ever called with an empty\n        # array of values, but I have to check for it otherwise the vectorize\n        # calls below fail\n        if len(x_dist) == 0:\n            return x_dist\n\n        t_offset = x_dist * s / v  # [s]\n\n        t = t0 + _to_dt64ms((t_offset * 1000).astype(int))\n        return date2num(t)\n\n    ax2 = ax.secondary_xaxis(posn, functions=(time_to_distance, distance_to_time))\n    ax2.set_xlabel(f\"approximate distance [{units}]\")\n    return ax2\n","repo_name":"leifdenby/lidartools","sub_path":"lidartools/annotations.py","file_name":"annotations.py","file_ext":"py","file_size_in_byte":3233,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"8745121159","text":"from typing import Optional, List, Dict\nimport numpy as np\nimport tritonclient.grpc as triton_grpc\nimport tritonclient.http as triton_http\nimport torch\n\n# tools required to do inference of pytorch model on GPU using triton\n\n\n# for converting torch model to proper format needed by triton using jit\ndef convert_torch_for_triton(torch_model, jit_model_path=None):\n\n  if jit_model_path == None:\n    jit_model_path = '/srv/utils/data/GNNTagger/jit_model.pt'\n\n  try:\n    jit_model = torch.jit.script(torch_model)\n    torch.jit.save(jit_model, jit_model_path)\n    print(f\"\"\"Torch model was successfully converted for triton inference \\\nand can be found at {jit_model_path}\"\"\")\n    return torch.jit.load(jit_model_path)\n  except:  #jit became stable in torch >=1.8\n    source_command = '\\'source /cvmfs/sft.cern.ch/lcg/views/LCG_102/x86_64-centos7-gcc8-opt/setup.sh\\''\n    raise Exception(f\"\"\"TritonUtils error: Torch model had problems with the \\\nconversion, perhaps your torch version is too old. You can source a newer \\\nversion of, torch for the conversion with {source_command}\"\"\")\n\n\n# for interacting with triton server\n# modeled off of https://github.com/rkansal47/HHbbVV/blob/main/src/HHbbVV/postprocessing/triton_check.py\nclass wrapped_triton:\n  def __init__(self, model_url: str, ) -> None:\n    fullprotocol, location = model_url.split(\"://\")\n    _, protocol = fullprotocol.split(\"+\")\n    address, model, version = location.split(\"/\")\n\n    self._protocol = protocol\n    self._address = address\n    self._model = model\n    self._version = version\n\n    # check connection to server, throw error if connection doesn't work\n    if self._protocol == \"grpc\":\n      self._client = triton_grpc.InferenceServerClient(url=self._address,\n                                                       verbose=False,\n                                                       ssl=True)\n      self._triton_protocol = triton_grpc\n    elif self._protocol == \"http\":\n      self._client = triton_http.InferenceServerClient(url=self._address,\n                                                       verbose=False,\n                                                       concurrency=12,\n                                                       )\n      self._triton_protocol = triton_http\n    else:\n      raise ValueError(\n          f\"{self._protocol} does not encode a valid protocol (grpc or http)\")\n\n  def __call__(self, input_dict: Dict[str, np.ndarray]) -> np.ndarray:\n    '''\n    Run inference of model on triton server\n    '''\n\n    # put inputs in proper format\n    inputs = []\n    for key in input_dict:\n      input = self._triton_protocol.InferInput(key, input_dict[key].shape,\n                                               \"FP32\")\n      input.set_data_from_numpy(input_dict[key])\n      inputs.append(input)\n\n    output = self._triton_protocol.InferRequestedOutput(\"softmax__0\")\n\n    # make request to server for inference\n    request = self._client.infer(self._model,\n                                 model_version=self._version,\n                                 inputs=inputs,\n                                 outputs=[output],\n                                 )\n    out = request.as_numpy(\"softmax__0\")\n\n    return out","repo_name":"alpana-hep/t-channel_Analysis","sub_path":"utils/python/tritonutils.py","file_name":"tritonutils.py","file_ext":"py","file_size_in_byte":3198,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"48"}
+{"seq_id":"36019822166","text":"#  Q: Given an array nums of n unique integers, are there elements a, b, c in nums such that a + b + c = 0? Find all\n#  unique triplets in the array which gives the sum of zero.\n# Example:\n#\n# Given array nums = [-2, -1, 0, 1, 3, -4],\n#\n# [\n# [-2, -1, 3]\n# [-1, 0, 1]\n# [-2, 1, -1]\n# ]\n\n\ndef func(lst: list) -> list:\n    rslt = []\n    if min(lst) >= 0 or max(lst) <= 0:\n        print('no such triplets')\n        return\n    lst1 = [x for x in lst if x < 0]\n    lst2 = [x for x in lst if x >= 0]\n    for x in lst1:\n        for y in lst2:\n            z = x + y\n            if -z in lst2 and -z != y:\n                # print(x, y, -z)\n                if sorted([x, y, -z]) not in rslt:\n                    rslt.append(sorted([x, y, -z]))\n    for x in lst2:\n        for y in lst1:\n            z = x + y\n            if -z in lst1 and -z != y:\n                # print(x, y, -z)\n                if sorted([x, y, -z]) not in rslt:\n                    rslt.append(sorted([x, y, -z]))\n    # print(rslt)\n    print(len(rslt))\n    for _ in rslt:\n        print(_)\n    return rslt\n\n\nif __name__ == '__main__':\n    nums = [-2, -1, 0, 1, 3, -4]\n    nums2 = [-11, -29, -67, -2, -3, -4, -1, 0, 1, 2, 3, 4, 5, 67, 23, 11, 28, 7, 10, 11]\n    func(nums)\n","repo_name":"zengqingfu1442/MyNotes","sub_path":"interview/sum_triples_zero.py","file_name":"sum_triples_zero.py","file_ext":"py","file_size_in_byte":1231,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"15613767154","text":"import os\nimport requests\n\nfrom flask import Flask, jsonify, render_template, request, session\nfrom flask_socketio import SocketIO, emit\nfrom classes.message import *\nfrom datetime import datetime\nfrom flask_session import Session\n\napp = Flask(__name__)\napp.config[\"SECRET_KEY\"] = os.getenv(\"SECRET_KEY\")\nsocketio = SocketIO(app)\n\napp.config[\"SESSION_PERMANENT\"] = False\napp.config[\"SESSION_TYPE\"] = \"filesystem\"\nSession(app)\n\n\n\n\n\n# List of channels  [channelname, [message1,...,messageN]]\nchannels = dict();\n\n@app.route(\"/\")\ndef index():\n\n    # Using global channels\n    global channels\n\n    return render_template(\"index.html\",channels=channels)\n\n@app.route(\"/createchannel\", methods=[\"POST\"])\ndef createchannel():\n\n    global channels\n\n    # Get data form\n    username = request.form.get(\"text-username\")\n    channelname = request.form.get(\"text-channelname\")\n\n    if channelname not in channels:\n        channels[channelname] = None\n        msgResult=\"New Channel \"+ channelname + \" added\"\n    else:\n        msgResult=\"Channel \"+ channelname + \" previously added\"\n\n    return render_template(\"index.html\",channels=channels, msgResult=msgResult)\n\n@app.route(\"/channel/<string:channelname>\", methods=[\"POST\",\"GET\"])\ndef channel(channelname):\n    global channels\n\n    msgResult=\"\"\n    return render_template(\"channel.html\", channelname=channelname, messages=channels[channelname], msgResult=msgResult)\n\n@app.route(\"/createmessage/<string:channelname>\", methods=[\"POST\"])\ndef createmessage(channelname):\n    global channels\n    username = request.form.get(\"text-username\")\n    textmessage = request.form.get(\"text-message\")\n    message = Message(username, datetime.now(), textmessage)\n\n\n    messages=channels[channelname]\n    if not channels[channelname]:\n        channels[channelname]=[message]\n    else:\n        channels[channelname].append(message)\n\n    msgResult=\"\"\n    return render_template(\"channel.html\", channelname=channelname, messages=channels[channelname], msgResult=msgResult)\n","repo_name":"arlanza/edx_pj2_commchannel","sub_path":"CommChannel/application.py","file_name":"application.py","file_ext":"py","file_size_in_byte":1991,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"14139679501","text":"from bs4 import BeautifulSoup\nimport urllib.request,urllib.parse,urllib.error\nimport json\n\n\nurl = 'https://www.moneycontrol.com/mc/widget/marketaction/getTopGainerLoserData?show=dashboard&classic=true'\n\nmarketDict = {\n            \"Nifty\" : {\n                \"Gainers\" : [],\n                \"Losers\" : []\n            },\n            \"Sensex\" : {\n                \"Gainers\" : [],\n                \"Losers\" : []\n            }\n        }\n\ndata = urllib.request.urlopen(url)\nsoup = BeautifulSoup(data, 'html.parser')\nmarketData = soup.findAll('div',{'class':'boxes-div'})\nniftyData = marketData[0]\nniftyGainers = niftyData.find('div',{'class':'top-boxes'}).findAll('div')\nfor gainer in niftyGainers:\n    company = gainer.find('a').contents[0]\n    price = gainer.findAll('span')[0].contents[0]\n    percentUp = gainer.findAll('span')[1].contents[0]\n    dict = {\"companyName\" : company, \"closingPrice\" : price, \"percentUp\" : percentUp}\n    marketDict['Nifty']['Gainers'].append(dict)\n\nniftyLosers = niftyData.find('div',{'class':'bottom-boxes'}).findAll('div')\nfor loser in niftyLosers:\n    company = loser.find('a').contents[0]\n    price = loser.findAll('span')[0].contents[0]\n    percentDown = loser.findAll('span')[1].contents[0]\n    dict = {\"companyName\" : company, \"closingPrice\" : price, \"percentDown\" : percentDown}\n    marketDict['Nifty']['Losers'].append(dict)\n\nsensexData = marketData[1]\nsensexGainers = sensexData.find('div',{'class':'top-boxes'}).findAll('div')\nfor gainer in sensexGainers:\n    company = gainer.find('a').find('span').contents[0]\n    price = gainer.findAll('span')[1].contents[0]\n    percentUp = gainer.findAll('span')[2].contents[0]\n    dict = {\"companyName\" : company, \"closingPrice\" : price, \"percentUp\" : percentUp}\n    marketDict['Sensex']['Gainers'].append(dict)\n\nsensexLosers = sensexData.find('div',{'class':'bottom-boxes'}).findAll('div')\nfor loser in sensexLosers:\n    company = loser.find('a').find('span').contents[0]\n    price = loser.findAll('span')[1].contents[0]\n    percentDown = loser.findAll('span')[2].contents[0]\n    dict = {\"companyName\" : company, \"closingPrice\" : price, \"percentDown\" : percentDown}\n    marketDict['Sensex']['Losers'].append(dict)\n\nprint(json.dumps(marketDict))\n# print(marketData)","repo_name":"Jayesh-Borse/StockBot","sub_path":"src/models/script1.py","file_name":"script1.py","file_ext":"py","file_size_in_byte":2239,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"7376250242","text":"from typing import Union, Optional\nimport os\nfrom pathlib import Path\n\n# Current working directory is path\nDATA_ROOT: str = os.getcwd()\n\n\ndef set_dataset_path(path: Optional[Union[Path, str]] = None) -> None:\n    \"\"\"\n    Set the dataset directory\n\n    Parameters\n    ----------\n    path : Optional[Union[Path, str]]\n        Directory path\n        Defaults to None, which uses current working directory\n\n    Notes\n    -----\n    This modifies `config.DATA_ROOT` which is used by the package\n    as the default location of the data.\n    \"\"\"\n    global DATA_ROOT\n\n    if path is None:\n        path = os.getcwd()\n    \n    new_path = Path(path).absolute()\n    if not new_path.exists():\n        raise IOError(f\"Path does not exist: {new_path}\")\n\n    DATA_ROOT = str(new_path)\n    return\n","repo_name":"anwarnunez/fusi","sub_path":"fusilib/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":780,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"75006383505","text":"from rpyc import connect\r\nimport rpyc\r\n\r\ndef resposta(string):\r\n    print(repr(string))\r\n\r\ndef client():\r\n    \r\n    config = {'allow_public_attrs': True}\r\n    proxy = connect('localhost', 18861, config=config)\r\n    objeto = proxy.root \r\n\r\n    fileobj = open('testfile.txt')\r\n    linecount = objeto.line_counter(fileobj, resposta)\r\n\r\n    print('O número de linhas no arquivo é:', linecount)\r\n\r\n    nome = objeto.print_name(\"André\", \"Bessa\")\r\n    print(nome)\r\n","repo_name":"andrelimabessa/sd217.2","sub_path":"referencia/lab_rpc/rpyc/rpyc_client.py","file_name":"rpyc_client.py","file_ext":"py","file_size_in_byte":461,"program_lang":"python","lang":"pt","doc_type":"code","stars":2,"dataset":"github-code","pt":"48"}
+{"seq_id":"18407585162","text":"import tkinter as tk\n\nclass loading_bar:\n    border_width = 6\n    def __init__(self, master, parent, root, number_of_loads, relheight, text_message):\n        self.master = master\n        self.number_of_loads = number_of_loads\n        self.parent = parent\n        self.root = root\n        self.relheight = relheight\n        self.text_message = text_message\n        self.text_message_label = tk.Label(self.root,\n                                            text = self.text_message, \n                                            font = (self.master.fontstyle, 30), \n                                            bg = self.parent.color, \n                                            fg = 'white')\n        self.text_message_label.place(relx = 0.5, \n                                    rely = 0.5 - self.relheight/2, \n                                    anchor = 's')\n        self.outside_rect = tk.Label(self.root, \n            bg = 'grey', \n            borderwidth = loading_bar.border_width, \n            relief = 'solid'\n            )\n        self.outside_rect.place(\n            relx = 0.5, \n            rely = 0.5,\n            relheight = self.relheight, \n            relwidth = 0.8, \n            anchor = 'c')\n        self.loading_bar_rect = tk.Label(self.root, bg = 'red')\n        self.loadingwidth = 0\n        self.text_label = tk.Label(self.root, font = (self.master.fontstyle, 15),wraplength = self.parent.width*0.8, bg = self.parent.color, fg = 'white')\n        self.text_label.place(relx = 0.5, rely = 0.5 + self.relheight/2, anchor = 'n', relwidth = 0.8)\n    def increase_width(self, new_string = \"\"):\n        total_loading_length = 0.8 - 2*loading_bar.border_width/self.parent.width\n        self.loadingwidth = self.loadingwidth + total_loading_length/self.number_of_loads\n        self.loading_bar_rect.place(relx = 0.1 + loading_bar.border_width/self.parent.width, \n                                    rely = 0.5, \n                                    relheight = self.relheight -2*loading_bar.border_width/self.parent.height, \n                                    relwidth = self.loadingwidth, \n                                    anchor = 'w')\n        self.text_label.config(text = new_string)\n        self.parent.toplevel.update()","repo_name":"wbradford2001/secureDICOM","sub_path":"CustomThings/LoadingBar.py","file_name":"LoadingBar.py","file_ext":"py","file_size_in_byte":2237,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"29713071350","text":"import os\nimport torch\nimport librosa\nimport numpy as np\nimport pandas as pd\nimport soundfile as sf\nfrom scipy import signal\nfrom argparse import ArgumentParser\nfrom tqdm import tqdm\n\n\ndef gen_time_frame(\n    protocol_path, read_audio_path, write_audio_path, duration, status: str, sr=16000\n):\n    sub_path = os.path.join(write_audio_path, status + \"_\" + str(duration))\n    if not os.path.exists(sub_path):\n        os.makedirs(sub_path)\n\n    protocol = pd.read_csv(protocol_path)  # .values\n    file_index = protocol[\"file\"]\n    num_files = len(protocol.index)\n    total_sample_count = 0\n\n    for i in tqdm(range(num_files)):\n        x, fs = sf.read(os.path.join(read_audio_path, file_index[i]))\n        if sr != fs:\n            x = librosa.resample(x, fs, sr)\n            fs = sr\n        if len(x) < duration * fs:\n            x = np.tile(x, int((duration * fs) // len(x)) + 1)\n        x = x[0 : (int(duration * fs))]\n        total_sample_count += 1\n        sf.write(os.path.join(sub_path, file_index[i]), x, fs)\n    print(\n        \"{} pieces {}-second {} samples generated.\".format(\n            total_sample_count, duration, status\n        )\n    )\n\n\ndef gen_cqt(\n    protocol_path, read_audio_path, write_audio_path, duration, status: str, sr=16000\n):\n    sub_path = os.path.join(write_audio_path, status + \"_\" + str(duration) + \"_cqt\")\n    if not os.path.exists(sub_path):\n        os.makedirs(sub_path)\n\n    protocol = pd.read_csv(protocol_path)\n    file_index = protocol[\"file\"]\n    num_files = len(protocol.index)\n    total_sample_count = 0\n    fs = 16000\n\n    for i in tqdm(range(num_files)):\n        x, fs = sf.read(os.path.join(read_audio_path, file_index[i]))\n        if sr != fs:\n            x = librosa.resample(x, fs, sr)\n            fs = sr\n        len_sample = int(duration * fs)\n\n        if len(x) < len_sample:\n            x = np.tile(x, int(len_sample // len(x)) + 1)\n\n        x = x[0 : int(len_sample - 256)]\n\n        x = signal.lfilter([1, -0.97], [1], x)\n        x_cqt = librosa.cqt(\n            x,\n            sr=fs,\n            hop_length=256,\n            n_bins=432,\n            bins_per_octave=48,\n            window=\"hann\",\n            fmin=15,\n        )\n        pow_cqt = np.square(np.abs(x_cqt))\n        log_pow_cqt = 10 * np.log10(pow_cqt + 1e-30)\n        total_sample_count += 1\n        torch.save(\n            log_pow_cqt, os.path.join(sub_path, file_index[i].split(\".\")[0] + \".pt\")\n        )\n    print(\n        \"{} {} CQT features of {}*{} generated.\".format(\n            total_sample_count, status, 432, int((duration * fs) // 256)\n        )\n    )\n\n\nif __name__ == \"__main__\":\n    # directory info of InTheWild dataset\n    parser = ArgumentParser()\n    parser.add_argument(\n        \"--root_path\", type=str, required=True, help=\"root path of InTheWild dataset\"\n    )\n    args = parser.parse_args()\n\n    root_path = args.root_path\n    eval_protocol_path = os.path.join(root_path, \"meta.csv\")\n    eval_data_path = os.path.join(root_path, \"release_in_the_wild\")\n\n    # create folders for new types of data\n    new_data_path = os.path.join(root_path, \"data\")\n    if not os.path.exists(new_data_path):\n        os.makedirs(new_data_path)\n\n    # generate equal-duration time-frames examples\n    print(\"Generating time-frame data...\")\n    time_dur = 6  # in seconds\n    gen_time_frame(eval_protocol_path, eval_data_path, new_data_path, time_dur, \"eval\")\n\n    # generate cqt feature per sample\n    print(\"Generating CQT data...\")\n    cqt_dur = 6.4  # in seconds, default ICASSP 2021 setting\n    gen_cqt(eval_protocol_path, eval_data_path, new_data_path, cqt_dur, \"eval\")\n\n    print(\"Data preparation finished.\")\n","repo_name":"timherzig/speech_synthesis_detection","sub_path":"src/data/InTheWild_Preparation.py","file_name":"InTheWild_Preparation.py","file_ext":"py","file_size_in_byte":3634,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"15369921492","text":"import traceback\n\nfrom PySide6.QtWidgets import QWidget, QLabel, QHBoxLayout, QLineEdit, QComboBox, QGridLayout, QPushButton,\\\n    QRadioButton, QMenu, QVBoxLayout, QMessageBox\nfrom PySide6.QtCore import Qt, QSize, Signal, QPoint\nfrom PySide6.QtGui import QPixmap, QIcon, QAction\nimport os\nimport logging\nfrom gui.UtilsWidgets.CustomQGroupBox.QCollapsibleGroupBox import QCollapsibleGroupBox\nfrom gui.UtilsWidgets.CustomQDialog.ContrastAdjustmentDialog import ContrastAdjustmentDialog\nfrom gui.UtilsWidgets.CustomQDialog.DisplayDICOMMetadataDialog import DisplayMetadataDICOMDialog\n\nfrom utils.software_config import SoftwareConfigResources\nfrom utils.data_structures.MRIVolumeStructure import MRISequenceType\n\n\nclass MRISeriesLayerWidget(QWidget):\n    \"\"\"\n\n    \"\"\"\n    display_name_changed = Signal(str, str)  # unique_id and new display name\n    visibility_toggled = Signal(str, bool)  # unique_id and visibility state\n    contrast_changed = Signal(str)  # unique_id\n    remove_volume = Signal(str)  # MRI volume unique id\n\n    def __init__(self, mri_uid, parent=None):\n        super(MRISeriesLayerWidget, self).__init__(parent)\n        self.parent = parent\n        self.uid = mri_uid\n        self.__set_interface()\n        self.__set_layout_dimensions()\n        self.__set_connections()\n        self.set_stylesheets(selected=False)\n        self.__init_from_parameters()\n\n    def __set_interface(self):\n        self.setAttribute(Qt.WA_StyledBackground, True)  # Enables to set e.g. background-color for the QWidget\n        self.layout = QHBoxLayout(self)\n\n        self.display_toggle_radiobutton = QPushButton()\n        self.display_toggle_radiobutton.setCheckable(True)\n        self.display_toggle_toggled_icon = QIcon(os.path.join(os.path.dirname(os.path.realpath(__file__)),\n                                                                       '../../../Images/radio_round_toggle_on_icon.png'))\n        self.display_toggle_untoggled_icon = QIcon(os.path.join(os.path.dirname(os.path.realpath(__file__)),\n                                                                       '../../../Images/radio_round_toggle_off_icon.png'))\n        self.display_toggle_radiobutton.setIcon(self.display_toggle_untoggled_icon)\n        self.radio_button_layout = QVBoxLayout()\n        self.radio_button_layout.addStretch(1)\n        self.radio_button_layout.addWidget(self.display_toggle_radiobutton)\n        self.radio_button_layout.addStretch(1)\n        self.layout.addLayout(self.radio_button_layout)\n\n        self.manual_grid_layout = QVBoxLayout()\n        self.name_options_layout = QHBoxLayout()\n        self.icon_label = QLabel()\n        self.icon_label.setScaledContents(True)  # Will force the pixmap inside to rescale to the label size\n        pix = QPixmap(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../Images/file_icon.png'))\n        self.icon_label.setPixmap(pix)\n        self.display_name_lineedit = QLineEdit()\n        self.options_pushbutton = QPushButton()\n        self.options_pushbutton.setIcon(QIcon(os.path.join(os.path.dirname(os.path.realpath(__file__)),\n                                                           '../../../Images/more-dots-icon.png')))\n        self.options_pushbutton.setContextMenuPolicy(Qt.CustomContextMenu)\n        # create context menu\n        self.options_menu = QMenu(self)\n        self.options_menu_dicom_metadata = QAction('DICOM Metadata', self)\n        self.options_menu.addAction(self.options_menu_dicom_metadata)\n        self.delete_layer_action = QAction('Remove', self)\n        self.options_menu.addAction(self.delete_layer_action)\n        self.options_menu.addSeparator()\n        self.name_options_layout.addWidget(self.display_name_lineedit)\n        self.name_options_layout.addWidget(self.options_pushbutton)\n        self.manual_grid_layout.addLayout(self.name_options_layout)\n\n        self.sequence_type_layout = QHBoxLayout()\n        self.sequence_type_label = QLabel(\"Sequence type\")\n        self.sequence_type_combobox = QComboBox()\n        self.sequence_type_combobox.addItems([str(e) for e in MRISequenceType])\n        self.sequence_type_layout.addWidget(self.sequence_type_label)\n        self.sequence_type_layout.addWidget(self.sequence_type_combobox)\n        self.sequence_type_layout.addStretch(1)\n        self.manual_grid_layout.addLayout(self.sequence_type_layout)\n\n        self.contrast_adjuster_layout = QHBoxLayout()\n        self.contrast_adjuster_pushbutton = QPushButton(\"Contrast\")\n        self.contrast_adjuster_pushbutton.setIcon(QIcon(QPixmap(os.path.join(os.path.dirname(os.path.realpath(__file__)),\n                                                                             '../../../Images/contrast_icon.png'))))\n        self.contrast_adjuster_pushbutton.setEnabled(False)\n        self.contrast_adjuster = ContrastAdjustmentDialog(volume_uid=self.uid) #, parent=\n        self.contrast_adjuster_layout.addWidget(self.contrast_adjuster_pushbutton)\n        self.contrast_adjuster_layout.addStretch(1)\n        self.manual_grid_layout.addLayout(self.contrast_adjuster_layout)\n        self.layout.addLayout(self.manual_grid_layout)\n\n    def __set_layout_dimensions(self):\n        self.icon_label.setFixedSize(QSize(15, 20))\n        self.display_name_lineedit.setFixedHeight(20)\n        # self.display_name_lineedit.setFixedWidth(150)\n        self.options_pushbutton.setFixedSize(QSize(20, 20))\n        self.sequence_type_label.setFixedHeight(20)\n        self.sequence_type_combobox.setFixedSize(QSize(70, 20))\n        self.contrast_adjuster_pushbutton.setFixedHeight(20)\n        self.contrast_adjuster_pushbutton.setFixedWidth(90)\n        self.contrast_adjuster_pushbutton.setIconSize(QSize(15, 15))\n        self.display_toggle_radiobutton.setFixedSize(QSize(30, 30))\n        self.display_toggle_radiobutton.setIconSize(QSize(25, 25))\n        # logging.debug(\"MRISeriesLayerWidget size set to {}.\\n\".format(self.size()))\n\n    def __set_connections(self):\n        self.display_name_lineedit.textEdited.connect(self.on_name_change)\n        self.display_toggle_radiobutton.toggled.connect(self.on_visibility_toggled)\n        self.options_pushbutton.clicked.connect(self.on_options_clicked)\n        self.sequence_type_combobox.currentTextChanged.connect(self.on_sequence_type_changed)\n        self.contrast_adjuster_pushbutton.clicked.connect(self.on_contrast_adjustment_clicked)\n        self.contrast_adjuster.contrast_intensity_changed.connect(self.on_contrast_changed)\n\n        self.delete_layer_action.triggered.connect(self.__on_delete_layer)\n        self.options_menu_dicom_metadata.triggered.connect(self.__on_display_dicom_metadata)\n\n    def set_stylesheets(self, selected: bool):\n        software_ss = SoftwareConfigResources.getInstance().stylesheet_components\n        font_color = software_ss[\"Color7\"]\n        background_color = software_ss[\"White\"]\n        pressed_background_color = software_ss[\"Color6\"]\n        if selected:\n            background_color = software_ss[\"Color3\"]\n            pressed_background_color = software_ss[\"Color4\"]\n\n        self.setStyleSheet(\"\"\"\n        MRISeriesLayerWidget{\n        background-color: \"\"\" + background_color + \"\"\";\n        border-width: 1px;\n        border-style: solid;\n        border-color: \"\"\" + background_color + background_color + software_ss[\"Color2\"] + background_color + \"\"\";\n        border-radius: 1px;\n        }\"\"\")\n\n        self.display_name_lineedit.setStyleSheet(\"\"\"\n        QLineEdit{\n        color: \"\"\" + font_color + \"\"\";\n        font: 14px;\n        background-color: \"\"\" + background_color + \"\"\";\n        border-style: none;\n        }\n        QLineEdit::hover{\n        border-style: solid;\n        border-width: 1px;\n        border-color: rgba(196, 196, 196, 1);\n        }\"\"\")\n\n        self.display_toggle_radiobutton.setStyleSheet(\"\"\"\n        QPushButton{\n        background-color: \"\"\" + background_color + \"\"\";\n        border-style: none;\n        }\"\"\")\n\n        # # Below does not have the expected behaviour\n        # self.display_toggle_radiobutton.setStyleSheet(\"\"\"\n        # QRadioButton::indicator::checked{\n        # background-color: rgba(60, 255, 137, 1);\n        # }\"\"\")\n\n        self.options_pushbutton.setStyleSheet(\"\"\"\n        QPushButton{\n        background-color: \"\"\" + background_color + \"\"\";\n        color: \"\"\" + font_color + \"\"\";\n        font: 12px;\n        border-style: none;\n        }\n        QPushButton::hover{\n        border-style: solid;\n        border-width: 1px;\n        border-color: rgba(196, 196, 196, 1);\n        }\n        QPushButton:pressed{\n        border-style:inset;\n        background-color: \"\"\" + pressed_background_color + \"\"\";\n        }\"\"\")\n\n        self.sequence_type_label.setStyleSheet(\"\"\"\n        QLabel{\n        color: \"\"\" + font_color + \"\"\";\n        background-color: \"\"\" + background_color + \"\"\";\n        font: 12px;\n        border: 0px;\n        }\"\"\")\n\n        if os.name == 'nt':\n            self.sequence_type_combobox.setStyleSheet(\"\"\"\n            QComboBox{\n            color: \"\"\" + font_color + \"\"\";\n            background-color: \"\"\" + background_color + \"\"\";\n            font: bold;\n            font-size: 12px;\n            border-style:none;\n            }\n            QComboBox::hover{\n            border-style: solid;\n            border-width: 1px;\n            border-color: rgba(196, 196, 196, 1);\n            }\n            QComboBox::drop-down {\n            subcontrol-origin: padding;\n            subcontrol-position: top right;\n            width: 15px;\n            }\n            \"\"\")\n        else:\n            self.sequence_type_combobox.setStyleSheet(\"\"\"\n            QComboBox{\n            color: \"\"\" + font_color + \"\"\";\n            background-color: \"\"\" + background_color + \"\"\";\n            font: bold;\n            font-size: 12px;\n            border-style:none;\n            }\n            QComboBox::hover{\n            border-style: solid;\n            border-width: 1px;\n            border-color: rgba(196, 196, 196, 1);\n            }\n            QComboBox::drop-down {\n            subcontrol-origin: padding;\n            subcontrol-position: top right;\n            width: 15px;\n            border-left-width: 1px;\n            border-left-color: darkgray;\n            border-left-style: none;\n            border-top-right-radius: 3px; /* same radius as the QComboBox */\n            border-bottom-right-radius: 3px;\n            }\n            QComboBox::down-arrow{\n            image: url(\"\"\" + os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../Images/combobox-arrow-icon-10x7.png') + \"\"\")\n            }\n            \"\"\")\n\n        self.contrast_adjuster_pushbutton.setStyleSheet(\"\"\"\n        QPushButton{\n        border-color: rgba(214, 214, 214, 1);\n        color: \"\"\" + font_color + \"\"\";\n        background-color: \"\"\" + background_color + \"\"\";\n        font-size: 12px;\n        border-style: none;\n        }\n        QPushButton::hover{\n        border-style: solid;\n        border-width: 1px;\n        border-color: rgba(196, 196, 196, 1);\n        }\n        QPushButton::pressed{\n        border-style: inset;\n        border-width: 1px;\n        border-color: rgba(196, 196, 196, 1);\n        background-color: \"\"\" + pressed_background_color + \"\"\";\n        }\"\"\")\n\n        self.options_menu.setStyleSheet(\"\"\"\n        QMenu{\n        color: \"\"\" + font_color + \"\"\";\n        background-color: \"\"\" + background_color + \"\"\";\n        border-width: 0px;\n        }\n        QMenu::item:selected{\n        background-color: \"\"\" + pressed_background_color + \"\"\";\n        }\n        QMenu::item:pressed{\n        border-style: inset;\n        border-width: 1px;\n        border-color: rgba(196, 196, 196, 1);\n        background-color: \"\"\" + pressed_background_color + \"\"\";\n        }\n        \"\"\")\n\n    def __init_from_parameters(self) -> None:\n        \"\"\"\n        Setting up the GUI from the parameters reloaded from disk and stored in SoftwareConfigResources.\n        \"\"\"\n        try:\n            mri_volume_parameters = SoftwareConfigResources.getInstance().get_active_patient().get_mri_by_uid(self.uid)\n            self.display_name_lineedit.setText(mri_volume_parameters.display_name)\n            sequence_index = self.sequence_type_combobox.findText(mri_volume_parameters.get_sequence_type_str())\n            self.sequence_type_combobox.blockSignals(True)\n            self.sequence_type_combobox.setCurrentIndex(sequence_index)\n            self.sequence_type_combobox.blockSignals(False)\n        except Exception:\n            logging.error(\"[MRISeriesLayerWidget] Initialization from internal parameters failed with: \\n {}.\".format(traceback.format_exc()))\n\n    def __on_delete_layer(self):\n        \"\"\"\n        The deletion of an MRI volume layer leads to a deletion of all linked objects within the patient\n        parameters, and then of the corresponding GUI elements.\n        All GUI elements should be deleted first, starting by the CentralView, and then removed from the logic\n        PatientParameters instance.\n        \"\"\"\n        linked_annos = SoftwareConfigResources.getInstance().get_active_patient().get_all_annotations_for_mri(self.uid)\n        linked_atlases = SoftwareConfigResources.getInstance().get_active_patient().get_all_atlases_for_mri(self.uid)\n        if (len(linked_annos) + len(linked_atlases)) != 0:\n            code = QMessageBox.warning(self, \"MRI volume layer deletion warning.\",\n                                       \"Deleting an MRI volume will also remove all other files linked to it (e.g., annotations).\",\n                                       QMessageBox.Ok | QMessageBox.Cancel, QMessageBox.Ok)\n            if code == QMessageBox.StandardButton.Ok:  # Deletion accepted\n                self.remove_volume.emit(self.uid)\n                # SoftwareConfigResources.getInstance().get_active_patient().remove_mri_volume(volume_uid=self.uid)\n        else:\n            self.remove_volume.emit(self.uid)\n\n    def __on_display_dicom_metadata(self):\n        dicom_tags = SoftwareConfigResources.getInstance().get_active_patient().get_mri_by_uid(self.uid).get_dicom_metadata()\n        if dicom_tags is None:\n            box = QMessageBox.warning(self, \"DICOM metadata\", \"No metadata is available for the current MRI volume\",\n                                      QMessageBox.Ok)\n        else:\n            diag = DisplayMetadataDICOMDialog(dicom_tags)\n            diag.exec_()\n\n    def update_interface_from_external_toggle(self, state):\n        \"\"\"\n        Mandatory since the inner state can be altered from the parent, so that only one MRI volume widget can be\n        toggled at any time\n        \"\"\"\n        if state:\n            self.display_toggle_radiobutton.setIcon(self.display_toggle_toggled_icon)\n        else:\n            self.display_toggle_radiobutton.setIcon(self.display_toggle_untoggled_icon)\n        self.contrast_adjuster_pushbutton.setEnabled(state)\n        self.set_stylesheets(state)\n\n    def on_visibility_toggled(self, state):\n        \"\"\"\n        Needs to be public to be called by the parent.\n        \"\"\"\n        self.set_stylesheets(selected=state)\n        if state:\n            self.display_toggle_radiobutton.setIcon(self.display_toggle_toggled_icon)\n        else:\n            self.display_toggle_radiobutton.setIcon(self.display_toggle_untoggled_icon)\n\n        self.visibility_toggled.emit(self.uid, state)\n\n        # Preventing the possibility to adjust contrast for a non-displayed volume.\n        # NB: Important to perform this after emitting the signal, as we also enforce a volume to be displayed at all\n        # time, hence discarding a False state if clicking on an already toggled radio button.\n        self.contrast_adjuster_pushbutton.setEnabled(self.display_toggle_radiobutton.isChecked())\n        logging.info(\"[MRISeriesLayerWidget] Visibility toggled to {} for {}\".format(state, self.uid))\n\n    def on_name_change(self, text):\n        # @TODO. Should there be a check that the name is available?\n        SoftwareConfigResources.getInstance().get_active_patient().get_mri_by_uid(self.uid).display_name = text\n        self.display_name_changed.emit(self.uid, text)\n\n    def on_sequence_type_changed(self, text) -> None:\n        SoftwareConfigResources.getInstance().get_active_patient().get_mri_by_uid(self.uid).set_sequence_type(text)\n\n    def on_contrast_adjustment_clicked(self):\n        self.contrast_adjuster.exec_()\n\n    def on_options_clicked(self, point):\n        self.options_menu.exec_(self.options_pushbutton.mapToGlobal(QPoint(0, 0)))\n\n    def on_contrast_changed(self):\n        self.contrast_changed.emit(self.uid)\n","repo_name":"raidionics/Raidionics","sub_path":"gui/SinglePatientComponent/LayersInteractorSidePanel/MRIVolumesInteractor/MRISeriesLayerWidget.py","file_name":"MRISeriesLayerWidget.py","file_ext":"py","file_size_in_byte":16518,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"48"}
+{"seq_id":"72045068945","text":"import glob\nimport os\nfrom time import sleep\n\nimport pandas as pd\n\n\ndef cook_paths():\n    all_path = '/home/andriy/Code/PyCharmProjects/lang_ident/resources/csv/labeled_dataset'\n    ret = dict()\n    ret['en'] = os.path.join(all_path, 'en/all_in_one.csv')\n    ret['de'] = os.path.join(all_path, 'de/german_learn_set.csv')\n    ret['fr'] = os.path.join(all_path, 'fr/french_learning_set.csv')\n    return ret\n\n\ndef cook():\n    paths = cook_paths()\n    li = list()\n    for lang in paths.keys():\n        df = pd.read_csv(paths[lang], names=['keyword', 'class'])\n        df.drop(df.columns[[1]], axis=1, inplace=True)\n        df['lang'] = lang\n        li.append(df)\n        # print(df) # DEBUG\n    df = pd.concat(li)\n    return df\n\n","repo_name":"AndriyLazorenko/lang_ident","sub_path":"src/utils/cook_dataset.py","file_name":"cook_dataset.py","file_ext":"py","file_size_in_byte":725,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"72171102227","text":"\ndef lazy_stream(fun):\n    @wraps(fun)\n    @coroutine\n    def wrapper(self, *args, **kwargs):\n        if self.stream is None:\n            s = socket.socket(socket.AF_INET, socket.SOCK_STREAM, 0)\n            self.stream = tornado.iostream.IOStream(s)\n            yield self.stream.connect((\"localhost\", int(sys.argv[1])), send_request)\n        res = yield fun(self, *args, **kwargs)\n        raise gen.Return(res)\n\nclass Peer(object):\n    def __init__(self, host, port):\n        self.stream = None\n        self.host = host\n        self.port = port\n        self.id = '{}:{}'.format(host, port)\n\n    @lazy_stream\n    @coroutine\n    def request_vote(self, term, candidate_id, log_index, log_term):\n        msg = {\n            'action' : 'request_vote',\n            'arguments' : { 'term' : int(term),\n                            'candidate_id' : candidate_id,\n                            'log_index'    : log_index,\n                            'log_term'     : log_term,\n                          }\n        }\n        yield self.stream.write(json.dumps(msg) + '\\n')\n        resp = yield self.stream.read_until(b\"\\n\")\n        response = json.loads(resp)\n        raise gen.Return(response)\n\n    @lazy_stream\n    @coroutine\n    def append_entries(self, term, leader_id, prev_index, prev_term,\n                       entries, leader_commit_index):\n        msg = {\n            'action' : 'append_entries',\n            'arguments' : {\n                'term' : int(term),\n                'leader_id' : leader_id,\n                'prev_index' : prev_index,\n                'prev_term' : prev_term,\n                'entries' : entries,\n                'leader_commit_index' : leader_commit_index,\n            }\n        }\n        yield self.stream.write(json.dumps(msg) + '\\n')\n        resp = yield self.stream.read_until(b\"\\n\")\n        raise gen.Return(response)","repo_name":"dayorbyte/raft-async","sub_path":"raft/peer.py","file_name":"peer.py","file_ext":"py","file_size_in_byte":1847,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"29158686645","text":"from django.contrib.auth.models import (\n    BaseUserManager, AbstractBaseUser, PermissionsMixin\n)\nfrom djongo import models\nfrom django.utils import timezone\nfrom django.utils.translation import ugettext_lazy as _\n\n\nclass UserManager(BaseUserManager):\n    use_in_migrations = True\n\n    def create_user(self, email, username, password=None):\n        \"\"\"\n        주어진 이메일, 닉네임, 비밀번호 등 개인정보로 User 인스턴스 생성\n        \"\"\"\n        if not email:\n            raise ValueError(_('Users must have an email address'))\n\n        user = self.model(\n            email=self.normalize_email(email),\n            username=username,\n        )\n\n        user.set_password(password)\n        user.save(using=self._db)\n        return user\n\n    def create_superuser(self, email, username, password):\n        \"\"\"\n        주어진 이메일, 닉네임, 비밀번호 등 개인정보로 User 인스턴스 생성\n        단, 최상위 사용자이므로 권한을 부여한다.\n        \"\"\"\n        user = self.create_user(\n            email=email,\n            password=password,\n            username=username,\n        )\n\n        user.is_superuser = True\n        user.save(using=self._db)\n        return user\n\n\nclass User(AbstractBaseUser, PermissionsMixin):\n    email = models.EmailField(\n        verbose_name=_('Email address'),\n        max_length=255,\n        unique=True,\n    )\n    username = models.CharField(\n        verbose_name=_('Username'),\n        max_length=30,\n        unique=True\n    )\n    is_active = models.BooleanField(\n        verbose_name=_('Is active'),\n        default=True\n    )\n    date_joined = models.DateTimeField(\n        verbose_name=_('Date joined'),\n        default=timezone.now\n    )\n\n    followers = models.ArrayReferenceField(\n        to=\"self\",\n        related_name=\"instagram_followers\",\n    )\n\n    following = models.ArrayReferenceField(\n        to=\"self\",\n        related_name=\"instagram_following\",\n    )\n\n    friends = models.ArrayReferenceField(\n        to=\"self\",\n        related_name=\"instagram_friends\",\n    )\n\n    profile_image = models.ImageField(\n        null=True,\n    )\n\n    objects = UserManager()\n\n    USERNAME_FIELD = 'username'\n    REQUIRED_FIELDS = ['email']\n\n    class Meta:\n        verbose_name = _('user')\n        verbose_name_plural = _('users')\n        ordering = ('username',)\n\n    def __str__(self):\n        return self.username\n\n    def get_absolute_url(self):\n        return reversed('users:detail', kwrgs={'username': self.username})\n\n    def get_full_name(self):\n        return self.username\n\n    def get_short_name(self):\n        return self.username\n\n    @property\n    def is_staff(self):\n        \"Is the user a member of staff?\"\n        # Simplest possible answer: All superusers are staff\n        return self.is_superuser\n\n    @property\n    def post_count(self):\n        return self.images.all().count()\n\n    @property\n    def followers_count(self):\n        return self.followers.all().count()\n\n    @property\n    def following_count(self):\n        return self.following.all().count()\n\n    @property\n    def friends_count(self):\n        return self.friends.all().count()\n","repo_name":"bnbong/JunHyeokStagram","sub_path":"accounts/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3156,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"29247437812","text":"#conda install numpy=1.20\nfrom numba import cuda\nfrom numba.cuda.cudadrv.driver import driver\nfrom numba.np import numpy_support as nps\nimport time\n\nimport numpy as np\nimport pandas as pd\n\nimport math, sys\n\nimport PIL\nfrom PIL import Image\n\n\n# --------------------------- DATA  -------------------------------- #\n\n# Training data \ntraining = pd.read_pickle(\"training.pkl\")\nprint(\"Training data\")\nprint(\"nombres d'image: \", len(training))\nprint(\"taille de l'image\",training[0][0].shape)\n\n#exemple \nimg = Image.fromarray(training[0][0])\nimg\n\n\n# ------------------ Fonction scan exclusif  ----------------------- #\n\n@cuda.jit\ndef scanGPU_MonteeDescente(argument1,m):\n    \n    global_id = cuda.grid(1)\n    \n    # Algo de montée \n    for d in range(0,m):\n        step = 2**(d+1)\n        step2 = 2**(d)\n        \n        if global_id <= N-1:\n            k = global_id * step\n            argument1[k + step-1] += argument1[k+step2-1]\n\n    # Algo descente \n    argument1[N-1] = 0\n    for  d in range(m-1, 0-1, -1):\n        step = 2**(d+1)\n        step2 = 2**(d)\n        \n       # global_id = cuda.grid(1)\n        if  global_id <= N-1:\n            k = global_id * step\n            t= argument1[k+step2-1]\n            argument1[k+step2-1] = argument1[k+step-1] \n            argument1[k+step-1] += t\n\n\n\n# ------------------ fonction transpose GPU ---------------------------------#\n\"\"\"\nCode lachement trouvé sur internet :\nhttps://github.com/numba/numba/blob/main/numba/cuda/kernels/transpose.py\n\nTentative d'implementer l'algo presenté dans l'article , mais probleme de threads non resolu voir index pour plus de detail\n\n\"\"\"\n\ndef transpose(a, b=None):\n    \"\"\"Compute the transpose of 'a' and store it into 'b', if given,\n    and return it. If 'b' is not given, allocate a new array\n    and return that.\n    This implements the algorithm documented in\n    http://devblogs.nvidia.com/parallelforall/efficient-matrix-transpose-cuda-cc/\n    :param a: an `np.ndarray` or a `DeviceNDArrayBase` subclass. If already on\n        the device its stream will be used to perform the transpose (and to copy\n        `b` to the device if necessary).\n    \"\"\"\n\n    # prefer `a`'s stream if\n    stream = getattr(a, 'stream', 0)\n\n    if not b:\n        cols, rows = a.shape\n        strides = a.dtype.itemsize * cols, a.dtype.itemsize\n        b = cuda.cudadrv.devicearray.DeviceNDArray(\n            (rows, cols),\n            strides,\n            dtype=a.dtype,\n            stream=stream)\n\n    dt = nps.from_dtype(a.dtype)\n\n    tpb = driver.get_device().MAX_THREADS_PER_BLOCK\n    # we need to factor available threads into x and y axis\n    tile_width = int(math.pow(2, math.log(tpb, 2) / 2))\n    tile_height = int(tpb / tile_width)\n\n    tile_shape = (tile_height, tile_width + 1)\n\n    @cuda.jit\n    def kernel(input, output):\n\n        tile = cuda.shared.array(shape=tile_shape, dtype=dt)\n\n        tx = cuda.threadIdx.x\n        ty = cuda.threadIdx.y\n        bx = cuda.blockIdx.x * cuda.blockDim.x\n        by = cuda.blockIdx.y * cuda.blockDim.y\n        x = by + tx\n        y = bx + ty\n\n        if by + ty < input.shape[0] and bx + tx < input.shape[1]:\n            tile[ty, tx] = input[by + ty, bx + tx]\n        cuda.syncthreads()\n        if y < output.shape[0] and x < output.shape[1]:\n            output[y, x] = tile[tx, ty]\n\n    # one block per tile, plus one for remainders\n    blocks = int(b.shape[0] / tile_height + 1), int(b.shape[1] / tile_width + 1)\n    # one thread per tile element\n    threads = tile_height, tile_width\n    kernel[blocks, threads, stream](a, b)\n    \n    return b\n\n\n\n# --------------------------- Image intégrale ----------------------------- #\n\n''' Version qui fonctionne mais mal aloué.... \nOn arrive pas à la rendre plus performante, elle est trop lente par rapport à la méthode basique CPU'''\n\ndef image_integrale(image):\n\n    padding = np.zeros((19,13))\n    padding2 = np.zeros((13,32))\n    image = np.concatenate([image,padding], axis=1)\n    image = np.concatenate([image,padding2], axis = 0)\n    image = image.astype(np.int64)\n    \n    N = len(image[0])\n    m = int(np.log(N)/np.log(2))\n    #print(image.shape)\n    #print(\"image avant 1ere scan :\", image)\n    for i in range(32):\n        #print(i)\n        #print(image[i])\n        # envoyer au device \n        scanGPU_MonteeDescente[16,16](image[i],m)\n        #print(image[i])\n        cuda.synchronize()\n    #print(image.shape)\n    #print(\"image apres scan\", image)\n    #print(image.shape)\n    \n    test = transpose(image)\n    test = test.copy_to_host()\n    #print('transpose', test)\n    \n    for i in range(32):\n        #print(i)\n        #print(image[i])\n        # envoyer au device \n        scanGPU_MonteeDescente[16,16](test[i],m)\n        #print(image[i])\n        cuda.synchronize()\n    #print('scan',test)\n    scan2 = test  \n    global test2\n    test2 = transpose(scan2)\n    test2 = test2.copy_to_host()\n    test2 = test2[1:20,1:20]\n    #print(\"image integrale \",test2 )\n    return test2\n\n\n\nstart = time.time()\nimage_integrale(img)\nend = time.time()\nelapsed = end - start\nprint(f'Temps d\\'execution GPU : {elapsed:.2}s')\n\n\n# fonction utilisé dans le code github , le but etant que notre fonction soit plus rapide ce que je doute \ndef integral_imageCPU(image):\n    \"\"\"\n    Computes the integral image representation of a picture. The integral image is defined as following:\n    1. s(x, y) = s(x, y-1) + i(x, y), s(x, -1) = 0\n    2. ii(x, y) = ii(x-1, y) + s(x, y), ii(-1, y) = 0\n    Where s(x, y) is a cumulative row-sum, ii(x, y) is the integral image, and i(x, y) is the original image.\n    The integral image is the sum of all pixels above and left of the current pixel\n      Args:\n        image : an numpy array with shape (m, n)\n    \"\"\"\n    ii = np.zeros(image.shape)\n    s = np.zeros(image.shape)\n    for y in range(len(image)):\n        for x in range(len(image[y])):\n            s[y][x] = s[y-1][x] + image[y][x] if y-1 >= 0 else image[y][x]\n            ii[y][x] = ii[y][x-1]+s[y][x] if x-1 >= 0 else s[y][x]\n    return ii\n\n\nstart = time.time()\nintegral_imageCPU(img)\nend = time.time()\nelapsed = end - start\nprint(f'Temps d\\'execution CPU : {elapsed:.2}s')\n\n# La fonction scan integrale est 50 fois plus longue que la fonction cpu du code,\n# la mission est un echeque ... ","repo_name":"LinaBelkarfa/ML-Implementation","sub_path":"projet.py","file_name":"projet.py","file_ext":"py","file_size_in_byte":6238,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"11619216686","text":"class Singleton:\r\n    def __new__(cls, *arg, **kwargs):\r\n        if not hasattr(cls, \"_instance\"):\r\n            cls._instance = super(Singleton, cls).__new__(cls)\r\n        return cls._instance\r\n\r\n\r\nclass Task(Singleton):\r\n    @staticmethod\r\n    def get_task(name=\"all\"):\r\n        if \"all\" in name:\r\n            nobjs = [3, 5, 7]\r\n            if name != \"all\":\r\n                nobjs = [int(name[-1])]\r\n            DTLZ = [\"DTLZ\" + str(i) + \"_\" + str(j)\r\n                    for i in [2, 4] for j in nobjs]\r\n            WFG = [\"WFG\" + str(i) + \"_\" + str(j)\r\n                   for i in range(4, 10) for j in nobjs]\r\n            task = DTLZ + WFG\r\n            return task\r\n\r\n\r\nMAENV_REGISTER = {\r\n    \"M_2_46_3\": [[\"DTLZ2\", \"WFG4\", \"WFG6\"], [3]],\r\n    \"M_2_46_5\": [[\"DTLZ2\", \"WFG4\", \"WFG6\"], [5]],\r\n    \"M_2_46_7\": [[\"DTLZ2\", \"WFG4\", \"WFG6\"], [7]],\r\n    \"M_2_46_357\": [[\"DTLZ2\", \"WFG4\", \"WFG6\"], [3, 5, 7]],\r\n}\r\n\r\n\r\ndef get_maenv(key):\r\n    if key in MAENV_REGISTER:\r\n        return MAENV_REGISTER[key]\r\n    else:\r\n        key = key.split(\"_\")\r\n        key[0] = [key[0]]\r\n        key[1] = [int(key[1])]\r\n        return key\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    key = \"WFG6_3\"\r\n    print(get_maenv(key))\r\n    key = \"WFG45_3\"\r\n    print(get_maenv(key))\r\n    key = \"M245_235\"\r\n    print(get_maenv(key))\r\n","repo_name":"lamda-bbo/madac","sub_path":"madacbench/mamo/mamo_register.py","file_name":"mamo_register.py","file_ext":"py","file_size_in_byte":1298,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"48"}
+{"seq_id":"8529345565","text":"# -*- coding: utf8 -*-\n\n\"\"\"\nSpecial action to rehash all data\n\"\"\"\n\n__author__ = 'sergey'\n\nimport sys\n\n\ndef do_rehash(options, _fuse):\n    \"\"\"\n    @param options: Commandline options\n    @type  options: object\n\n    @param _fuse: FUSE wrapper\n    @type  _fuse: dedupsqlfs.fuse.dedupfs.DedupFS\n    \"\"\"\n\n    isVerbose = _fuse.getOption(\"verbosity\") > 0\n\n    tableOption = _fuse.operations.getTable(\"option\")\n\n    curHashFunc = tableOption.get(\"hash_function\")\n    if curHashFunc == options.rehash_function:\n        if isVerbose:\n            print(\"Already using %s hash function for filesystem! Do not rehashing.\" % curHashFunc)\n        return True\n\n    tableHash = _fuse.operations.getTable(\"hash\")\n    tableHashCT = _fuse.operations.getTable(\"hash_compression_type\")\n    tableBlock = _fuse.operations.getTable(\"block\")\n\n    _fuse.operations.hash_function = options.rehash_function\n\n    hashCount = tableHash.get_count()\n    if isVerbose:\n        print(\"Ready to rehash %s blocks.\" % hashCount)\n\n    cur = tableHash.getCursor(True)\n\n    # Every 100*100 (4x symbols)\n    cntNth = int(hashCount/10000.0)\n    if cntNth < 1:\n        cntNth = 1\n\n    # Process Nth blocks and then - commit\n    maxBatch = 1000\n    offBatch = 0\n    cnt = cntNext = upd = 0\n\n    _fuse.operations.getManager().setAutocommit(False)\n    tableHash.begin()\n    _fuse.operations.getManager().setAutocommit(True)\n\n    while cnt < hashCount:\n\n        cur.execute(\"SELECT `id` FROM `%s` LIMIT %s OFFSET %s\" % (tableHash.getName(), maxBatch, offBatch ))\n        offBatch += maxBatch\n\n        for hashItem in iter(cur.fetchone, None):\n\n            cnt += 1\n\n            blockItem = tableBlock.get(hashItem[\"id\"])\n            hashCT = tableHashCT.get(hashItem[\"id\"])\n            blockData = _fuse.decompressData(_fuse.operations.getCompressionTypeName(hashCT[\"type_id\"]), blockItem[\"data\"])\n            newHash = _fuse.operations.do_hash(blockData)\n            res = tableHash.update(hashItem[\"id\"], newHash)\n\n            if res:\n                upd += 1\n\n            if isVerbose:\n                if cnt >= cntNext:\n                    cntNext += cntNth\n                    prc = \"%6.2f%%\" % (cnt * 100.0 / hashCount)\n                    sys.stdout.write(\"\\r%s \" % prc)\n                    sys.stdout.flush()\n\n        # For ends - hash commits\n        _fuse.operations.getManager().setAutocommit(False)\n        tableHash.commit()\n        tableHash.shrinkMemory()\n        tableHashCT.shrinkMemory()\n        tableBlock.shrinkMemory()\n        tableHash.begin()\n        _fuse.operations.getManager().setAutocommit(True)\n\n    if isVerbose:\n        sys.stdout.write(\"\\n\")\n        sys.stdout.flush()\n\n    if isVerbose:\n        print(\"Processed %s hashes, rehashed %s blocks.\" % (cnt, upd,))\n\n    if hashCount == cnt:\n        _fuse.operations.getManager().setAutocommit(False)\n        tableHash.commit()\n        _fuse.operations.getManager().setAutocommit(True)\n\n        tableOption.update(\"hash_function\", options.rehash_function)\n\n        tableHash.vacuum()\n    else:\n        _fuse.operations.getManager().setAutocommit(False)\n        tableHash.rollback()\n        _fuse.operations.getManager().setAutocommit(True)\n        print(\"Something went wrong? Changes are rolled back!\")\n        return 1\n\n    return 0\n","repo_name":"sergey-dryabzhinsky/dedupsqlfs","sub_path":"dedupsqlfs/app/actions/rehash.py","file_name":"rehash.py","file_ext":"py","file_size_in_byte":3264,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"48"}
+{"seq_id":"24240831335","text":"import itertools\nimport numpy as np\nfrom matplotlib import pyplot\nfrom tqdm import tqdm\n\nn = int(input(\"N? \")) - 1\np = [[1],[0,1]]\n\nfor n in tqdm(range(2,n)):\n    # Multiplies n-1 term by u #\n    u = p[n-1].copy()\n    u.insert(0,0)\n    nextn = list(map(sum, itertools.zip_longest([i * 2 for i in u], [i * -1 for i in p[n-2]], fillvalue=0)))\n    p.append(nextn)\n\nmod = p.copy()\n\nprint('')\n\nmodulus = int(input(\"Mod? \"))\n\nfor n in tqdm(range(len(mod))):\n    mod[n] = [i % modulus for i in mod[n]]\n\nprint(\"Complete\")\n\nprint('\\n'.join([''.join(['{:3}'.format(item) for item in row])\n      for row in mod]))\n\npyplot.imshow(np.array(list(itertools.zip_longest(*mod, fillvalue=0))).T, interpolation='nearest')\npyplot.show()\n","repo_name":"jchen/chebyshev-polynomials","sub_path":"docs/code/-mod-old.py","file_name":"-mod-old.py","file_ext":"py","file_size_in_byte":717,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"74316276946","text":"#!/usr/bin/python3\n\"\"\"\nContains script that takes in a URL, sends a request to the URL and\ndisplays the body of the response (decoded in utf-8).\n\"\"\"\n\n\nif __name__ == '__main__':\n    import sys\n    import requests\n\n    url = sys.argv[1]\n    with requests.get(url) as res:\n        if res.status_code < 400:\n            print(res.text)\n        else:\n            print(f'Error code: {res.status_code}')\n","repo_name":"Goldeno10/alx-higher_level_programming","sub_path":"0x11-python-network_1/7-error_code.py","file_name":"7-error_code.py","file_ext":"py","file_size_in_byte":399,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"18252232280","text":"class Funcionario: \r\n\r\n    def __init__(self, nome, salario, codigoCargo):\r\n        self.nome = nome\r\n        self.salario = salario\r\n        self.codigoCargo = codigoCargo\r\n    \r\n    def aumentarSalario(self, porcentualDeAumento):\r\n        self.salario *= (porcentualDeAumento / 100) + 1\r\n    \r\nlistafuncionarios = []\r\nwhile True:\r\n    nome = input(\"Digite seu nome: \")\r\n    if nome == \"\":\r\n        break\r\n    salario = float(input(\"Digite seu salário: \"))\r\n    codigoCargo = int(input(\"Digite seu código de cargo: \"))\r\n        \r\n    funcionario = Funcionario(nome, salario, codigoCargo)\r\n    listafuncionarios.append(funcionario)\r\n\r\nsomasalarios = 0\r\nfor funcionario in listafuncionarios:\r\n    somasalarios += funcionario.salario\r\nprint(f\"Soma de todos os salários: {somasalarios}\")\r\n\r\nsomasalariosaumento = 0\r\nfor funcionario in listafuncionarios:\r\n    if funcionario.codigoCargo == 1:\r\n        funcionario.aumentarSalario(5)\r\n    elif funcionario.codigoCargo == 2:\r\n        funcionario.aumentarSalario(10)\r\n    else:\r\n        funcionario.aumentarSalario(8)\r\n    somasalariosaumento += funcionario.salario\r\nprint(f\"Soma de todos os salários mais aumento: {somasalariosaumento}\")","repo_name":"a1ramm/Programacao-I","sub_path":"classe funcionário/classefuncionario.py","file_name":"classefuncionario.py","file_ext":"py","file_size_in_byte":1185,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"9063420931","text":"from os.path import join\n\nimport argparse\nimport yaml\nfrom easydict import EasyDict\nimport glob\n\nfrom envs import Converted, EnvModel, Maps, MapsEnvModel\nfrom utils.data import *\n\ndef load_experiments_and_env(path, algos, plots):\n    \"\"\"Save experiment data\"\"\"\n\n    load_data = {}\n    if 'val_iter' in algos:\n        x = decompress_pickle(\n            join(path, 'store', 'Optimal.pbz2')\n        )\n        load_data['v_star'] = x['v_star']\n        load_data['Q_star'] = x['Q_star']\n    else:\n        load_data['v_star'], load_data['Q_star'] = None, None\n\n    load_data['env_loggers'] = {}\n    if 'cum_r' in plots:\n        env_files = glob.glob(join(path, 'store', '*_env.pbz2'))\n        for key in env_files:\n            load_data['env_loggers'][' '.join(key.split('/')[-1].split('_')[:-1])] = \\\n                    decompress_pickle(key)\n\n    load_data['q_loggers'] = {}\n    if 'q_vals' in plots or 'v_vals' in plots:\n        logger_files = glob.glob(join(path, 'store', '*_qvals.pbz2'))\n        for key in logger_files:\n            load_data['q_loggers'][' '.join(key.split('/')[-1].split('_')[:-1])] = \\\n                    decompress_pickle(key)\n\n    load_data['visits'] = {}\n    if 'heat_map' in plots or 'visits' in plots:\n        visit_files = glob.glob(join(path, 'store', '*_visits.pbz2'))\n        for key in visit_files:\n            load_data['visits'][' '.join(key.split('/')[-1].split('_')[:-1])] = \\\n                    decompress_pickle(key)\n\n    load_data['bandit'] = {'est': {}, 'q': {}}\n\n    if 'mmbq' in algos and 'bandit' in plots:\n        load_data['bandit']['q']['Maxmin Bandit Q'] = decompress_pickle(\n            join(path, 'store', 'Maxmin Bandit Q_bandit (lr=0.01).pbz2')\n        )\n        load_data['bandit']['est']['Maxmin Bandit Q'] = decompress_pickle(\n            join(path, 'store', 'Maxmin Bandit Q_estimator (lr=0.01).pbz2')\n        )\n\n    if 'mmbqv2' in algos and 'bandit' in plots:\n        load_data['bandit']['q']['Maxmin Bandit Q v2'] = decompress_pickle(\n            join(path, 'store', 'Maxmin Bandit Q v2_bandit (lr=0.01).pbz2')\n        )\n        load_data['bandit']['est']['Maxmin Bandit Q v2'] = decompress_pickle(\n            join(path, 'store', 'Maxmin Bandit Q v2_estimator (lr=0.01).pbz2')\n        )\n\n    with open(join(path, 'config.yaml'), 'r') as f:\n        exp_config = yaml.safe_load(f)\n    exp_config = EasyDict(exp_config)\n\n    env = None\n    if \"file\" in exp_config.model or \"gridworld\" in exp_config.model:\n\n        # Gridworld or old env\n        if exp_config.model == \"gridworld\" \\\n            or exp_config.model == 'file':\n            env = Converted(exp_config.env, exp_config.seed)\n            env = EnvModel(env)\n            _ = env.reset()\n\n        # New env\n        elif exp_config.model == 'filev2':\n            env = Maps(exp_config.env, exp_config.seed)\n            env = MapsEnvModel(env)\n\n        if 'file' in exp_config.model:\n            env.load_map(exp_config.map_path)\n            _ = env.reset()\n\n    else:\n        print('Got unknown env, not plotting Q and V...')\n\n    return load_data, env\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n\n    parser.add_argument(\"-c\", \"--config\", help=\"Path to plotter config file\", required=True)\n    parser.add_argument(\"-p\", \"--path\", help=\"Path to experiment folder\", type=str, required=True)\n\n    args = parser.parse_args()\n\n    with open(args.config) as f:\n        config = yaml.safe_load(f)\n    config = EasyDict(config)\n\n    # Get all algos whose data we need to read\n    algos = []\n    for val in config.perform:\n        algos.extend([i for i in config[val].algos])\n    algos = set(algos)\n\n    load_data, env = load_experiments_and_env(args.path, algos, config.perform)\n\n    try:\n        exp_name = join(args.path).split('/')[-2]\n    except:\n        print('Ensure path is with trailing backslash...')\n        quit()\n\n    if 'cum_r' in config.perform:\n        # Plot mean cummulative reward\n        plot_mean_cum_rewards(\n            load_data['env_loggers'],\n            exp_name,\n            do_smooth=config.cum_r.smooth,\n            std_factor=config.cum_r.std_factor,\n            fmt=config.fmt\n            )\n\n    if 'q_vals' in config.perform:\n        # plot Q and V\n        plot_Q_values(\n            env,\n            load_data['Q_star'],\n            load_data['q_loggers'],\n            exp_name\n        )\n\n    if 'v_vals' in config.perform:\n        plot_V_values(\n            env,\n            [load_data['v_star'], load_data['Q_star']],\n            load_data['q_loggers'],\n            exp_name\n        )\n\n    if 'visits' in config.perform:\n        plot_visits(\n            load_data['visits'],\n            config.visits.s_a,\n            exp_name\n            )\n\n    if 'bandit' in config.perform:\n        plot_bandit(\n            load_data['bandit'],\n            exp_name\n            )","repo_name":"Rochan-A/oprl","sub_path":"plotter.py","file_name":"plotter.py","file_ext":"py","file_size_in_byte":4831,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"3005877102","text":"def LIS(A,n,memory):\n    memory[0] = 1\n    for i in range(1,n):\n        max_length = 0\n        for j in range(0,i):\n            if(A[j]<A[i]):\n                if(max_length<memory[j]):\n                    max_length = memory[j]\n        memory[i] = max_length+1\n\n#수열의 크기\nn = int(input())\n\n#수열 저장\nA = list(map(int,input().split()))\n\n#앞에서부터 계산, 뒤부터 계산\n\nfront = [0 for i in range(n)]\nback = [0 for i in range(n)]\n\nLIS(A,n,front)\nLIS(A[-1::-1],n,back)\n\nmax = front[0]+back[n-1]\nfor i in range(1,n-1):\n    if(max<front[i]+back[n-1-i]):\n        max = front[i]+back[n-1-i]\n        \nprint(max-1)\n\n","repo_name":"juhongyee/baekjoon","sub_path":"112 가장 긴 바이토닉 부분 수열/가장 긴 증가하는 부분 수열.py","file_name":"가장 긴 증가하는 부분 수열.py","file_ext":"py","file_size_in_byte":630,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"2278355926","text":"# first of all import the socket library\r\nimport socket\r\nimport pyautogui\r\nimport io\r\n\r\n# next create a socket object\r\ns = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\nprint(\"Socket successfully created\")\r\n\r\n\r\n# reserve a port on your computer in our\r\n# case it is 12345 but it can byte anything\r\nport = 20001\r\n\r\n# Next bind to the port\r\n# we have not typed any ip in the ip field\r\n# instead we have inputted an empty string\r\n# this makes the server listen to requests\r\n# coming from other computers on the network\r\n\r\ns.bind(('', port))\r\nprint(\"socket binded to %s\" % port)\r\n\r\n# put the socket into listening mode\r\n\r\ns.listen(10)\r\nprint(\"socket is listening\")\r\n\r\nif s is None:\r\n    print('could not open socket')\r\n\r\n# a forever loop until we interrupt it or\r\n# an error occurs\r\n\r\npyautogui.PAUSE = 0.0\r\npyautogui.FAILSAFE = False\r\n\r\nterminate = False\r\n\r\nwhile True and terminate is False:\r\n\r\n    # Establish connection with client.\r\n    c, addr = s.accept()\r\n    # print('Got connection from', addr)\r\n\r\n    data = str(c.recv(100), 'utf-8').split(';')\r\n    # send a thank you message to the client.\r\n\r\n    if data[0] != \"\" and data[0][0] == \"x\":\r\n        for each in data:\r\n            coord = each.split(\",\")\r\n            try:\r\n                pyautogui.move(round(float(coord[0][2:]) / 7, 0), round(float(coord[1][2:]) / 7, 0),\r\n                               pyautogui.MINIMUM_DURATION / 10)\r\n            except:\r\n                pass\r\n    elif data[0] == \"click\":\r\n        pyautogui.click(button=\"left\")\r\n    elif data[0] == \"rclick\":\r\n        pyautogui.click(button=\"right\")\r\n    elif data[0] == \"getScreen\":\r\n        im = pyautogui.screenshot()\r\n        im_resize = im.resize((1920,1080))\r\n        buf = io.BytesIO()\r\n        im_resize.save(buf, format='PNG')\r\n        byte_im = buf.getvalue()\r\n        c.send(byte_im)\r\n    elif data[0] == \"terminate\":\r\n        terminate = True\r\n    elif data[0] == \"PrtScr\":\r\n        pyautogui.press(\"printscreen\")\r\n    else:\r\n        pyautogui.press(data[0])\r\n\r\n    # Close the connection with the client\r\n    c.close()\r\n    # print('Closed')\r\n\r\nprint('Server Stopped', end=\"\\n\")\r\n","repo_name":"edgeerrol87/pc-remote","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":2128,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"32827833117","text":"import requests\n\nurl = 'http://10.86.20.6:10004/kv_extract'\nfiles = {'file': open(r'D:\\Codes\\Doc-understanding\\layoutlmft\\examples\\requests_images\\57d5b17daf0546da92a8ec0d4e255f85.jpg', 'rb')}\n# files = {'file': ('report.jpg', open('/home/test.mpg', 'rb'))}     #显式的设置文件名\n\nr = requests.post(url, files=files)\nprint(r.content)\n\n\n\n\n\n# url = 'http://10.86.20.6:10004/result/c11150b7d38e4fa384b8de49f877c151.jpg'\n# r = requests.get(url)\n# print(r.content)","repo_name":"attendfov/chinese-layoutlm-v2","sub_path":"examples/test/test_request.py","file_name":"test_request.py","file_ext":"py","file_size_in_byte":467,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"48"}
+{"seq_id":"17057990499","text":"# write your code here\nimport math\n\ndef var_input(user_input):\n    if uis[0].isalpha() == False: #not proper varibale name\n        print('Invalid identifier')\n    elif len(uis) == 1 and uis[0] in var_dict.keys(): #printing out the value of the existing varibale\n        print(var_dict[uis[0]])\n    elif len(uis) == 1 and uis[0] not in var_dict.keys(): #we can't print out the value of an unknown varibale\n        print('Unknown variable')\n    else: #proper varibale name\n            if len(uis) == 2 or len(uis) > 3: #too few or many elements in the input\n                print('Invalid assignment')\n            else:\n                try:\n                    var_dict[uis[0]] = int(uis[2]) #define a new variable\n                except ValueError:\n                    if uis[2].isalpha() == False: #not proper variable name\n                        print('Invalid identifier')\n                    elif uis[2] in var_dict.keys(): #update an existing variable\n                        var_dict[uis[0]] = var_dict[uis[2]]\n                    else:\n                        print('Invalid assignment')\n\n\ndef calc(user_input):\n    numbers1 = uis\n    numbers2 = []\n    for i in range(len(numbers1)):\n        if i % 2 == 1 and numbers1[i].count('+') != len(numbers1[i]) and numbers1[i].count('-') != len(numbers1[i]):\n            print('Invalid expression')\n            numbers2 = []\n            break\n        elif i % 2 == 1 and numbers1[i].count('+') == len(numbers1[i]):\n            numbers2.append(1)\n        elif i % 2 == 1 and numbers1[i].count('-') == len(numbers1[i]):\n            numbers2.append(int(math.pow(-1, numbers1[i].count('-'))))\n        elif i % 2 == 0:\n            try:\n                if numbers1[i] in var_dict.keys():\n                    numbers2.append(var_dict[numbers1[i]])\n                else:    \n                    numbers2.append(int(numbers1[i]))\n            except ValueError:\n                print('Invalid expression')\n                numbers2 = []\n                break\n    numbers3 = []\n    for i in range(len(numbers2)):\n        if i == 0:\n            numbers3.append(numbers2[i])\n        elif i % 2 == 0:\n            numbers3.append(numbers2[i] * numbers2[i-1])\n    if numbers3 != []:    \n        print(sum(numbers3))\n   \ndef smart_calc(user_input):\n    if user_input == '/exit':\n        print('Bye!')\n        exit()\n    elif user_input == '/help':\n        print('The program calculates the sum of numbers')\n    elif user_input.startswith('/'):\n        print('Unknown command')\n    elif user_input == '':\n        None\n    else:\n        if len(uis) == 1 or (len(uis) > 1 and ('+' not in uis[1] and '-' not in uis[1])): #input variables\n            var_input(user_input)\n        else:\n            calc(user_input)\n\n        \nvar_dict = {'b': 8, 'c': 5, 'd': 121}\nwhile True:\n    user_input = 'variable = f'\n    if '=' in ''.join(user_input):\n        uis = ''.join(user_input.split()).replace('=', ' = ').split() #treat if there are no spaces in the variable determination\n    else:\n        uis = user_input.split()\n    smart_calc(user_input)\n    break\n\n  \n\n","repo_name":"szendrot/Smart_calculator","sub_path":"Smart_calculator/Smart_calculator_6.py","file_name":"Smart_calculator_6.py","file_ext":"py","file_size_in_byte":3084,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"8639449887","text":"#!/usr/bin/env python\nfrom __future__ import division, print_function\nfrom sense_hat import SenseHat\nimport psutil, time\n\nsense = SenseHat()\n#sense.clear()\n#print(sense.rotation)\n#sense.set_rotation(270, False)\n#sense.flip_v()\n\ngreen = (55, 255, 0)\nred = (255, 0, 0)\norange = (255, 190, 0)\n\nMAX_WRITE_BYTES = 200000\nMAX_READ_BYTES = 150000\n\nlvl = [green, green, orange, orange, red, red, red, red]\n\ndef poll(interval):\n    \"\"\"Calculate IO usage by comparing IO statics before and\n    after the interval.\n    Return a tuple including all currently running processes\n    sorted by IO activity and total disks I/O activity.\n    \"\"\"\n    # first get a list of all processes and disk io counters\n    procs = [p for p in psutil.process_iter()]\n    for p in procs[:]:\n        try:\n            p._before = p.io_counters()\n        except psutil.Error:\n            procs.remove(p)\n            continue\n    disks_before = psutil.disk_io_counters()\n\n    # sleep some time\n    time.sleep(interval)\n\n    # then retrieve the same info again\n    for p in procs[:]:\n        try:\n            p._after = p.io_counters()\n            p._cmdline = ' '.join(p.cmdline())\n            if not p._cmdline:\n                p._cmdline = p.name()\n            p._username = p.username()\n        except psutil.Error:\n            procs.remove(p)\n    disks_after = psutil.disk_io_counters()\n\n    # finally calculate results by comparing data before and\n    # after the interval\n    for p in procs:\n        p._read_per_sec = p._after.read_bytes - p._before.read_bytes\n        p._write_per_sec = p._after.write_bytes - p._before.write_bytes\n        p._total = p._read_per_sec + p._write_per_sec\n\n    disks_read_per_sec = disks_after.read_bytes - disks_before.read_bytes\n    disks_write_per_sec = disks_after.write_bytes - disks_before.write_bytes\n\n    # sort processes by total disk IO so that the more intensive\n    # ones get listed first\n    processes = sorted(procs, key=lambda p: p._total, reverse=True)\n\n    return (processes, disks_read_per_sec, disks_write_per_sec)\n\ndef check(type='read'):\n    sense.set_rotation(270, False)\n    \n    ret = poll(1)\n    idx = 1 if type == 'read' else 2\n    read = int(min(ret[idx], MAX_READ_BYTES) * 7 // MAX_READ_BYTES)\n    \n    # reading leds\n    for i in range(read + 1, 8):\n        #print(i)\n        sense.set_pixel(i, 7, 0, 0, 0)\n        \n    for i in range(0, read + 1):\n        #print(i)\n        sense.set_pixel(i, 7, lvl[read])\n    \n    sense.set_rotation(0, False)\n    \ndef checkWrite():\n    check('write')\n    \ndef checkRead():\n    check('read')\n\ndef main():\n    try:\n        interval = 0\n        while True:\n            ret = poll(interval)\n            interval = 1\n            \n            print(ret[1:])\n    except (KeyboardInterrupt, SystemExit):\n        pass\n\nif __name__ == '__main__':\n    main()","repo_name":"emawind84/sensehat-discotest","sub_path":"diskio.py","file_name":"diskio.py","file_ext":"py","file_size_in_byte":2816,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"70181636947","text":"import torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\n\nimport numpy as np\nimport time\nimport gym\n\nimport core\nfrom spinup.utils.logx import EpochLogger\n\nclass PPOBuffer:\n    def __init__(self):\n        self.init_param()\n\n    def init_param(self):\n        self.obs_buf = []\n        self.act_buf = []\n        self.adv_buf = []\n        self.rew_buf = []\n        self.ret_buf = []\n        self.val_buf = []\n        self.logp_buf = []\n\n    def store(self, ep_obs, ep_act, ep_rew, ep_val, ep_logp, adv, ret):\n        self.obs_buf += ep_obs\n        self.act_buf += ep_act\n        self.rew_buf += ep_rew\n        self.val_buf += ep_val\n        self.logp_buf += ep_logp\n        self.adv_buf += list(adv)\n        self.ret_buf += list(ret)\n\n    def get(self):\n        adv_tns = torch.FloatTensor(self.adv_buf)\n        adv_tns = (adv_tns - adv_tns.mean()) / adv_tns.std()\n        return_tns = [\n            torch.FloatTensor(self.obs_buf),\n            torch.FloatTensor(self.act_buf),\n            torch.FloatTensor(self.logp_buf),\n            torch.FloatTensor(self.ret_buf),\n            adv_tns\n        ]\n        self.init_param()\n        return return_tns\n\ndef ppo(env_name, actor_critic_func, hidden_sizes, \n        steps_per_epoch=4000, epochs=50, gamma=0.99, clip_ratio=0.2, pi_lr=3e-4,\n        vf_lr=1e-3, train_pi_iters=80, train_v_iters=80, lam=0.97, max_ep_len=1000,\n        target_kl=0.01, logger_kwargs=dict()):\n\n    logger = EpochLogger(**logger_kwargs)\n    logger.save_config(locals())\n\n    env = gym.make(env_name)\n\n    buf = PPOBuffer()\n\n    obs_dim = env.observation_space.shape[0]\n    act_dim = env.action_space.n\n\n    actor_critic = actor_critic_func(obs_dim, act_dim, hidden_sizes)\n\n    value_optim  = optim.Adam(actor_critic.value_function.parameters(), lr=vf_lr)\n    policy_optim = optim.Adam(actor_critic.policy.parameters(), lr=pi_lr)\n\n    def finish_path(ep_rew, ep_val, last_value):\n        rews = np.array(ep_rew + [last_value])\n        vals = np.array(ep_val + [last_value])\n        deltas = rews[:-1] + gamma * vals[1:] - vals[:-1]\n\n        adv = core.discount_cumsum(deltas, gamma * lam)\n        ret = core.discount_cumsum(rews, gamma)[:-1]\n\n        return adv, ret\n\n    def update():\n        obs_tns, act_tns, old_logp, ret_tns, adv_tns = buf.get()\n        \n        for i in range(train_pi_iters):\n            logp = actor_critic.likelihood(obs_tns, act_tns)\n            ratio = (logp - old_logp).exp()\n            min_adv = torch.where(adv_tns > 0, (1*clip_ratio)*adv_tns, (1-clip_ratio*adv_tns))\n            pi_loss = -(torch.min(ratio * adv_tns, min_adv)).mean()\n            policy_optim.zero_grad()\n            pi_loss.backward()\n            policy_optim.step()\n\n            kl = (old_logp - logp).mean()\n            if kl > 1.5 * target_kl:\n                logger.log('Early stopping at step %d due to reaching max kl.'%i)\n                break\n        logger.store(StopIter=i)\n        for _ in range(train_v_iters):\n            v = actor_critic.value_function(obs_tns)\n            v_loss = (ret_tns - v).pow(2).mean()\n            value_optim.zero_grad()\n            v_loss.backward()\n            value_optim.step()\n        logger.store(LossPi=pi_loss, LossV=v_loss, KL=kl)\n\n    start_time = time.time()\n    obs, rew, done, ep_ret, ep_len = env.reset(), 0, False, 0, 0\n    ep_obs, ep_act, ep_rew, ep_val, ep_logp = [], [], [], [], []\n\n    for epoch in range(epochs):\n        for t in range(steps_per_epoch):\n            obs_tns = torch.from_numpy(obs).reshape(1,-1).float()\n            act, logp = actor_critic.sampling_action(obs_tns)\n            val = actor_critic.value_function(obs_tns).item()\n\n            ep_obs.append(obs); ep_act.append(act)\n            ep_rew.append(rew); ep_val.append(val)\n            ep_logp.append(logp)\n\n            logger.store(VVals=val)\n\n            obs, rew, done, _ = env.step(act)\n            ep_ret += rew\n            ep_len += 1\n\n            terminal = done or (ep_len == max_ep_len)\n            if terminal or (t==steps_per_epoch-1):\n                if not(terminal):\n                    print('Warning: trajectory cut off by epoch at %d steps.'%ep_len)\n                if done:\n                    last_val = rew\n                else:\n                    obs_tns = torch.from_numpy(obs).reshape(1,-1).float()\n                    last_val = actor_critic.value_function(obs_tns)\n                adv, ret = finish_path(ep_rew, ep_val, last_val)\n                if terminal:\n                    logger.store(EpRet=ep_ret, EpLen=ep_len)\n                buf.store(ep_obs, ep_act, ep_rew, ep_val, ep_logp, adv, ret)\n                obs, rew, done, ep_ret, ep_len = env.reset(), 0, False, 0, 0\n                ep_obs, ep_act, ep_rew, ep_val, ep_logp = [], [], [], [], []\n\n        update()\n\n        logger.log_tabular('Epoch', epoch)\n        logger.log_tabular('EpRet', with_min_and_max=True)\n        logger.log_tabular('EpLen', average_only=True)\n        logger.log_tabular('VVals', with_min_and_max=True)\n        logger.log_tabular('LossPi', average_only=True)\n        logger.log_tabular('LossV', average_only=True)\n        logger.log_tabular('KL', average_only=True)\n        logger.log_tabular('StopIter', average_only=True)\n        logger.log_tabular('Time', time.time()-start_time)\n        logger.dump_tabular()\n\nif __name__ == '__main__':\n    import argparse\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--env', type=str, default='CartPole-v0')\n    parser.add_argument('--hid', type=int, default=64)\n    parser.add_argument('--l', type=int, default=2)\n    parser.add_argument('--gamma', type=float, default=0.99)\n    parser.add_argument('--seed', '-s', type=int, default=0)\n    parser.add_argument('--steps', type=int, default=4000)\n    parser.add_argument('--epochs', type=int, default=50)\n    parser.add_argument('--exp_name', type=str, default='ppo')\n    args = parser.parse_args()\n\n    from spinup.utils.run_utils import setup_logger_kwargs\n    logger_kwargs = setup_logger_kwargs(args.exp_name, args.seed)\n\n    ppo(args.env, core.actor_critic,\n        [args.hid]*args.l, gamma=args.gamma, \n        steps_per_epoch=args.steps, epochs=args.epochs,\n        logger_kwargs=logger_kwargs)","repo_name":"DensoITLab/spinningup_in_pytorch","sub_path":"algos/ppo/ppo.py","file_name":"ppo.py","file_ext":"py","file_size_in_byte":6207,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"48"}
+{"seq_id":"22189009475","text":"from pygame import mixer\nimport pygame\nimport random\nimport sys\nimport os\n\n\n\npygame.init()\n\nWIDTH = 800\nHEIGHT = 600\n\nos.environ['SDL_VIDEO_CENTERED'] = '1'\n\npygame.display.set_caption(\"The COVID Game!\")\n\nmixer.music.load(\"background_music.mp3\")\nmixer.music.play(-1)\n\nENEMY = pygame.image.load(\"germ.png\")\nENEMY = pygame.transform.scale(ENEMY, (75, 75))\nPLAYER = pygame.image.load(\"boy.png\")\nPLAYER = pygame.transform.scale(PLAYER, (75, 75))\nBACKGROUND = pygame.image.load(\"covidbackground.png\")\nBACKGROUND = pygame.transform.scale(BACKGROUND, (WIDTH, HEIGHT))\n\nBLACK = (0, 0, 0)\nYELLOW = (255, 255, 0)\nWHITE = (255, 255, 255)\nRED = (200, 0, 0)\nGREEN = (0, 200, 0)\nORANGE = (245, 135, 0)\n\nBRIGHT_RED = (255, 0, 0)\nBRIGHT_GREEN = (0, 255, 0)\nBRIGHT_ORANGE = (255, 145, 0)\n\n\n\n\n\nplayer_size = 48\nplayer_pos = [WIDTH / 2, HEIGHT - 2 * player_size]\n\nenemy_size = 48\nenemy_pos = [random.randint(0, WIDTH - enemy_size), 0]\nenemy_list = [enemy_pos]\n\nSPEED = 10\n\nscreen = pygame.display.set_mode((WIDTH, HEIGHT))\n\ngame_over = False\n\nscore = 0\n\nclock = pygame.time.Clock()\n\nmyFont = pygame.font.SysFont(\"monospace\", 35)\n\ndef text_objects(text, font):\n    textSurface = font.render(text, True, BLACK)\n    return textSurface, textSurface.get_rect()\n\ndef set_level(score, SPEED):\n    SPEED = score / 5  + 5\n    return SPEED\n\n\ndef drop_enemies(enemy_list):\n    delay = random.random()\n    if len(enemy_list) < 10 and delay < 0.1:\n        x_pos = random.randint(0, WIDTH - enemy_size)\n        y_pos = 0\n        enemy_list.append([x_pos, y_pos])\n\n\ndef draw_enemies(enemy_list):\n    for enemy_pos in enemy_list:\n        screen.blit(ENEMY, (enemy_pos[0], enemy_pos[1]))\n\n\n\ndef update_enemy_positions(enemy_list, score):\n    for idx, enemy_pos in enumerate(enemy_list):\n        if enemy_pos[1] >= 0 and enemy_pos[1] < HEIGHT:\n            enemy_pos[1] += SPEED\n        else:\n            enemy_list.pop(idx)\n            score += 1\n    return score\n\n\ndef collision_check(enemy_list, player_pos):\n    for enemy_pos in enemy_list:\n        if detect_collision(enemy_pos, player_pos):\n            return True\n    return False\n\n\ndef detect_collision(player_pos, enemy_pos):\n    p_x = player_pos[0]\n    p_y = player_pos[1]\n\n    e_x = enemy_pos[0]\n    e_y = enemy_pos[1]\n\n    if (e_x >= p_x and e_x < (p_x + player_size)) or (p_x >= e_x and p_x < (e_x + enemy_size)):\n        if (e_y >= p_y and e_y < (p_y + player_size)) or (p_y >= e_y and p_y < (e_y + enemy_size)):\n            return True\n    return False\n\ndef button(msg, x, y, w, h, inactive_color, active_color, action=None, text_size=None):\n    \n    mouse = pygame.mouse.get_pos()\n    click = pygame.mouse.get_pressed()\n    \n    if x + w > mouse[0] > x and y + h > mouse[1] > y:\n        pygame.draw.rect(screen, active_color, (x, y, w, h))\n        if click[0] == 1 and action != None:\n            click_sound = mixer.Sound(\"sound_click.wav\")\n            click_sound.play()\n            if action == \"play\":\n                main(game_over, score, SPEED, player_pos)\n            elif action == \"quit\":\n                sys.exit()\n\n    else:\n        pygame.draw.rect(screen, inactive_color, (x, y, w, h))\n    \n    small_text = pygame.font.SysFont('monospace', text_size)\n    text_surf, text_rect = text_objects(msg, small_text)\n    text_rect.center = ( (x + (w/2), (y + (h/2))))\n    screen.blit(text_surf, text_rect)\n\n\ndef start_menu():\n    intro = True\n    while intro:\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                sys.exit()\n\n        screen.fill(WHITE)\n        large_text = pygame.font.SysFont('arial', 80)\n        text_surf, text_rect = text_objects(\"The COVID Game!\", large_text)\n        text_rect.center = ((WIDTH/2), (HEIGHT/2))\n        screen.blit(BACKGROUND,(0,0))\n        screen.blit(text_surf, text_rect)\n\n        button(\"Start!\", 150, 450, 100, 50, GREEN, BRIGHT_GREEN, \"play\", 20)\n        button(\"Exit\", 550, 450, 100, 50, RED, BRIGHT_RED, \"quit\", 20)\n  \n        pygame.display.update()\n        clock.tick(15)\n\n\ndef death_screen(score):\n    end = True\n    while end:\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                sys.exit()\n        \n        large_text = pygame.font.SysFont('bitstreamverasans', 40)\n        text_surf, text_rect = text_objects('''You Caught the Virus!''',  large_text)\n        text_sur, text_rec = text_objects('''Remember to wear a mask and stay safe''', large_text)\n        text_rect.center = ((WIDTH/2), (HEIGHT/2 - 50))\n        text_rec.center = ((WIDTH/2), (HEIGHT/2 + 20))\n        screen.blit(BACKGROUND,(0,0))\n        screen.blit(text_surf, text_rect)\n        screen.blit(text_sur, text_rec)\n\n        button(\"Restart\", 150, 400, 100, 50, GREEN, BRIGHT_GREEN, \"play\", 20)\n        button(\"Exit\", 550, 400, 100, 50, RED, BRIGHT_RED, \"quit\", 20)\n        button((\"Score:\" + str(score)), 250, 125, 300, 75, ORANGE, BRIGHT_ORANGE, None, 50)\n\n        pygame.display.update()\n        clock.tick(15)\n\ndef main(game_over, score, SPEED, player_pos):\n    player_size = 45\n    player_pos = [WIDTH / 2, HEIGHT - 2 * player_size]\n\n    enemy_size = 45\n    enemy_pos = [random.randint(0, WIDTH - enemy_size), 0]\n    enemy_list = [enemy_pos]\n    while not game_over:\n\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                sys.exit()\n\n            if event.type == pygame.KEYDOWN:\n\n                x = player_pos[0]\n                y = player_pos[1]\n\n                if event.key == pygame.K_LEFT:\n                    x -= 37\n                elif event.key == pygame.K_RIGHT:\n                    x += 37\n\n# handle the boundary movement.\n                if x >= 740:\n                    x = 735\n                if x < 0:\n                    x = 5\n                player_pos = [x, y]\n\n        screen.blit(BACKGROUND,(0,0))\n\n        drop_enemies(enemy_list)\n        score = update_enemy_positions(enemy_list, score)\n        SPEED = set_level(score, SPEED)\n\n        text = \"Score:\" + str(score)\n        label = myFont.render(text, 1, YELLOW)\n        screen.blit(label, (WIDTH - 200, HEIGHT - 40))\n\n        if collision_check(enemy_list, player_pos):\n            death_sound = mixer.Sound(\"deathsound.wav\")\n            death_sound.play()\n            game_over = True \n            break\n\n        draw_enemies(enemy_list)\n\n        screen.blit(PLAYER, (player_pos[0], player_pos[1]))\n\n        clock.tick(30)\n\n        pygame.display.update()\n\n    death_screen(score)\n\nstart_menu()\n","repo_name":"natelincyber/The-COVID-Game-","sub_path":"COVIDGame.py","file_name":"COVIDGame.py","file_ext":"py","file_size_in_byte":6461,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"6412070772","text":"\"\"\"competitive\n\nRevision ID: bc327b63d88d\nRevises: c9af147ff107\nCreate Date: 2022-01-28 20:38:58.560992\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'bc327b63d88d'\ndown_revision = 'c9af147ff107'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('user', sa.Column('competitive', sa.Boolean(), nullable=True))\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_column('user', 'competitive')\n    # ### end Alembic commands ###\n","repo_name":"LuckyStarTF2/help.tf","sub_path":"migrations/versions/bc327b63d88d_competitive.py","file_name":"bc327b63d88d_competitive.py","file_ext":"py","file_size_in_byte":656,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"15206100347","text":"import sys\nfrom termcolor import colored\n\ndef seqAlign():\n    data = open('data.txt', 'r')\n    a_Sequence = ''\n    b_Sequence = ''\n    identical = ''\n    for line in data:\n        if line[0:10] == 'A_Sequence' or line[0:10] == 'B_Sequence':\n            seq_str = line.split()[2]\n            if line[0:10] == 'A_Sequence':\n                a_Sequence += seq_str\n            else:\n                b_Sequence += seq_str\n            index = line.find(seq_str)\n            length = len(seq_str)\n        else:\n            identical += line[index : index + length]\n            \n    colors = ['red', 'green', 'yellow', 'blue', 'magenta', 'cyan', 'white']\n    \n    model_ans = open('model_ans.txt', 'r')\n    group_num = -1\n    groups = []\n    color_array = []\n    for line in model_ans:\n        line = line.strip('\\n')\n        if line in colors:\n            group_num += 1\n            groups.append('')\n            color_array.append(line)\n        else:\n            groups[group_num] += line\n\n\n    def compareSeqs(start, end, count, mistake):\n#        count = 0\n#        mistake = ''\n        result = ''\n        remainder = 0\n        for i in range(start, end):\n            if identical[i] == '|':\n                if count != 0:\n                    result += '  (' + str(count) + ')  '\n                    count = 0\n                elif mistake:\n                    result += '  <' + mistake + '>  '\n                    mistake = ''\n                result += a_Sequence[i]\n            elif a_Sequence[i] == '-' or b_Sequence[i] == '-':\n                if i == (end - 1) and count != 0:\n                    result += '  (' + str(count) + ')  '\n                \n                if a_Sequence[i] == '-':\n                    remainder += 1\n                count += 1\n            else:\n                if i == (end - 1) and mistake:\n                    result += '  <' + mistake + '>  '\n                mistake += b_Sequence[i]\n            \n        if remainder > 0 and (end + remainder) <= len(a_Sequence):\n            end_result, end_remainder = compareSeqs(end, end + remainder, count, mistake)\n            result += end_result\n            remainder += end_remainder\n        return result, remainder\n    \n    initial = 0\n    for grp_no in range(len(groups)):\n        printResult = ''\n        grp_length = len(groups[grp_no])\n        eventual = initial + grp_length\n        printResult, endcase = compareSeqs(initial, eventual, 0, '')\n        \n        if grp_no != (len(groups) - 1):\n            print(colored(printResult, color_array[grp_no]), end='')\n        else:\n            print(colored(printResult, color_array[grp_no]))\n        initial += grp_length + endcase\n        \n    \nseqAlign()\n","repo_name":"chadwickleung/sequence_alignment_analysis","sub_path":"sequence.py","file_name":"sequence.py","file_ext":"py","file_size_in_byte":2680,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"20886748200","text":"# File: forward_brute_force.py, Author: Cedric Travelletti, Date: 12.04.2019.\n\"\"\" Given a set of hyperparameters, compute the *kriging* predictor and the\ncross validation error.\n\nSIMULATION will mean the points at which we predict, conditioning the ones at\nwhich we condition.\n\n\"\"\"\nfrom pyfilaments.gaussian_process import GaussianProcess\nfrom pyfilaments.grid import Grid\nimport volcapy.covariance.covariance_tools as cl\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import axes3d\nfrom matplotlib import cm\nimport os\n\n# Now torch in da place.\nimport torch\n\n# General torch settings and devices.\ntorch.set_num_threads(8)\ngpu = torch.device('cuda:0')\ncpu = torch.device('cpu')\n\nn_cond_x = 5\nn_cond_y = 2\n\nn_sim_x = 100\nn_sim_y = 100\n\n# We noticed its not good to have conditioning points on the boundary.\nx_spacings_cond = np.linspace(0.2, 0.8, n_cond_x)\ny_spacings_cond = np.linspace(0.2, 0.8, n_cond_y)\nconditioning_grid = Grid(x_spacings_cond, y_spacings_cond)\n\n# 1D part of regular grid for simulation points.\nx_spacings_sim = np.linspace(0, 1, n_sim_x)\ny_spacings_sim = np.linspace(0, 1, n_sim_y)\nsimulation_grid = Grid(x_spacings_sim, y_spacings_sim)\n\nsigma0 = 0.2\nlambda0 = 0.2\nm0 = 0.0\n\nmygp = GaussianProcess(conditioning_grid, simulation_grid, sigma0, lambda0)\n\n# simulation_grid.plot_list(mygp.cov_sim[0, :])\ncond_points_vals = torch.from_numpy(\n        np.array(100*[0.0]).astype(np.float32))\ncond_points_vals[30] = 2.0\n\n\n# Generate random vals.\nmean = np.zeros(mygp.cov_cond.shape[0])\ncond_points_vals = np.random.multivariate_normal(mean, mygp.cov_cond)\ncond_points_vals = torch.from_numpy(cond_points_vals.astype(np.float32))\nsim_points_vals = mygp.condition(cond_points_vals)\n# simulation_grid.plot_list(sim_points_vals)\n\n# simulation_grid.plot_list_3d(sim_points_vals)\n\ngradient = mygp.gradient()\nnorm = torch.einsum(\"ij,ij->i\", (gradient, gradient))\nnorm = torch.sqrt(norm)\nsimulation_grid.plot_list_3d(sim_points_vals, norm)\n\nhessian = mygp.hessian()\n\n# To filament estimation.\nfilament_inds, filament_vals = mygp.compute_filament_criterion(\n        gradient, hessian, 1e-2)\n\n# Plot results.\n# Create a zero vector.\nplot_vals = sim_points_vals.copy()\n\n# In order to be able to see heigts, we keep the values, but where\n# there is a filament we replace it with twice the maximum.\nplot_vals[filament_inds] = 2 * np.max(sim_points_vals)\n\n# Normalize so colors wotk.\nplot_vals = plot_vals/plot_vals.max()  # normalize 0..1\n\n# Dark red is hard to see, so change to light.\nplot_vals[plot_vals > 0.6] = 0.8\n\n# Convert to grid.\nheights_2d = simulation_grid.reshape_to_2d(sim_points_vals)\nplot_vals_2d = simulation_grid.reshape_to_2d(plot_vals)\n\nfig = plt.figure()\nax = fig.gca(projection='3d')\n# plt.hold(True)\nX, Y = np.meshgrid(simulation_grid.x_spacings, simulation_grid.y_spacings)\nsurf = ax.plot_surface(\n            X, Y, heights_2d, rstride=1, cstride=1,\n                facecolors=cm.jet(plot_vals_2d), alpha=1.0)\n\nplt.show()\n","repo_name":"CedricTravelletti/Filaments","sub_path":"pyfilaments/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2976,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"25182447001","text":"import cv2 as cv\nimport numpy as np\nfrom skimage import feature\nfrom sklearn.cluster import KMeans\nfrom matplotlib import pyplot as plt\nfrom skimage.color import rgb2gray\n\n\ndef cam_frame():\n\n    # Image-Segmentation - Clustering\n    def ImgSeg_Clustering(img):\n        img_n = img.reshape(img.shape[0] * img.shape[1], img.shape[2])\n        kmeans = KMeans(n_clusters=2, random_state=0).fit(img_n)\n        pic2show = kmeans.cluster_centers_[kmeans.labels_]\n        img_seg = pic2show.reshape(img.shape[0], img.shape[1], img.shape[2])\n\n        return img_seg\n\n\n\n    cap = cv.VideoCapture('http://192.168.1.4:8080/video/mjpeg')\n   \n\n    while (True):\n        # Capture frame-by-frame\n        ret, frame = cap.read()\n\n\n\n        # Display the resulting frame\n        cv.imshow('frame', frame)\n        if cv.waitKey(1) & 0xFF == ord('q'):\n            break\n\n    cap.release()\n    cv.destroyAllWindows()\n\n    # img filtering for salt & pepper and img segmentation\n    median = cv.medianBlur(frame, 5)\n    # img = ImgSeg_Clustering(median/255)\n\n    # config variables for lbp computation\n    gray = rgb2gray(median)\n    numPoints = 8\n    radius = 2\n\n    # create lbp image\n    lbp_test = feature.local_binary_pattern(gray, numPoints, radius, method=\"default\")\n\n    plt.subplot(231), plt.imshow(gray, cmap='gray'), plt.title('gray img')\n    plt.subplot(232), plt.imshow(lbp_test, cmap='gray'), plt.title('lbp')\n    plt.show()\n\n    hist_test, _ = np.histogram(lbp_test.ravel(), bins=np.arange(256),\n                                range=255, density=True)\n    return hist_test\n","repo_name":"kocharalampidis/LBP","sub_path":"camera_frame.py","file_name":"camera_frame.py","file_ext":"py","file_size_in_byte":1567,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"18119125701","text":"\"\"\"Various utility functions.\n\nAuthors\n-------\n    - Lauren Chambers\n    - Bryan Hilbert\n\nUse\n---\n    This module can be imported as such:\n\n    >>> import utils\n    siaf_files = get_siaf()\n\nDependencies\n------------\n    The user must have a configuration file named ``siaf_config.json``\n    placed in mirage/config/ directory.\n\"\"\"\n\nimport copy\nimport json\nimport os\nimport logging\nimport pkg_resources\nimport re\nimport yaml\n\nfrom astropy.io import ascii as asc\nimport numpy as np\nfrom scipy.stats import sigmaclip\n\nfrom mirage.reference_files.utils import get_transmission_file\nfrom mirage.logging import logging_functions\nfrom mirage.utils.constants import CRDS_FILE_TYPES, NIRISS_FILTER_WHEEL_FILTERS, NIRISS_PUPIL_WHEEL_FILTERS, \\\n                                   NIRCAM_PUPIL_WHEEL_FILTERS, NIRCAM_2_FILTER_CROSSES, NIRCAM_WL8_CROSSING_FILTERS, \\\n                                   NIRCAM_CLEAR_CROSSING_FILTERS, NIRCAM_GO_PW_FILTER_PAIRINGS, NIRCAM_FILTERS, \\\n                                   NIRISS_FILTERS, FGS_FILTERS, LOG_CONFIG_FILENAME, STANDARD_LOGFILE_NAME\n\n\nclassdir = os.path.abspath(os.path.join(os.path.dirname(__file__), '../'))\nlog_config_file = os.path.join(classdir, 'logging', LOG_CONFIG_FILENAME)\nlogging_functions.create_logger(log_config_file, STANDARD_LOGFILE_NAME)\n\n\ndef append_dictionary(base_dictionary, added_dictionary, braid=False):\n    \"\"\"Append the content of added_dictionary key-by-key to the base_dictionary.\n\n    This assumes that the keys refer to lists.\n\n    Parameters\n    ----------\n    base_dictionary : dict\n    added_dictionary : dict\n    braid : bool\n        If true, the elements of added_dictionary are added in alternating sequence.\n        This is used to synchronize parallel observations with the pointing file.\n\n    Returns\n    -------\n    new_dictionary : dict\n        Dictionary where every key holds a list of lists\n\n    \"\"\"\n    new_dictionary = copy.deepcopy(base_dictionary)\n\n    # extract an arbitrary key name\n    first_key = [key for i, key in enumerate(base_dictionary.keys()) if i == 0][0]\n\n    # Insert keys from added_dictionary that are not yet present in base_dictionary\n    for key in added_dictionary.keys():\n        if key not in base_dictionary.keys():\n            new_dictionary[key] = ['None'] * len(base_dictionary[first_key])\n\n    # Append the items\n    for key in new_dictionary.keys():\n        if key not in added_dictionary.keys():\n            continue\n        # print('{} {}'.format(key, new_dictionary[key]))\n        if len(new_dictionary[key]) == 0:\n            new_dictionary[key] = added_dictionary[key]\n        else:\n            if braid:\n                # solution from https://stackoverflow.com/questions/3678869/pythonic-way-to-combine-two-lists-in-an-alternating-fashion\n                new_dictionary[key] = [sub[i] for i in range(len(added_dictionary[key])) for sub in\n                                       [new_dictionary[key], added_dictionary[key]]]\n            else:\n                new_dictionary[key] = new_dictionary[key] + added_dictionary[key]\n\n    return new_dictionary\n\n\ndef calc_frame_time(instrument, aperture, xdim, ydim, amps):\n    \"\"\"Calculate the readout time for a single frame\n    of a given size and number of amplifiers. Note that for\n    NIRISS and FGS, the fast readout direction is opposite to\n    that in NIRCam, so we switch xdim and ydim so that we can\n    keep a single equation.\n\n    Parameters:\n    -----------\n    instrument : str\n        Name of the instrument being simulated\n\n    aperture : str\n        Name of aperture being simulated (e.g \"NRCA1_FULL\")\n        Currently this is only used to check for the FGS\n        ACQ1 aperture, which uses a unique value of colpad\n        below.\n\n    xdim : int\n        Number of columns in the frame\n\n    ydim : int\n        Number of rows in the frame\n\n    amps : int\n        Number of amplifiers used to read out the frame\n\n    Returns:\n    --------\n    frametime : float\n        Readout time in seconds for the frame\n    \"\"\"\n    instrument = instrument.lower()\n    if instrument == \"nircam\":\n        colpad = 12\n\n        # Fullframe\n        if amps == 4:\n            rowpad = 1\n            fullpad = 1\n        else:\n            # All subarrays\n            rowpad = 2\n            fullpad = 0\n\n            if ((xdim <= 8) & (ydim <= 8)):\n                # The smallest subarray\n                rowpad = 3\n\n    elif instrument == \"niriss\":\n        # Reverse x and y since NIRISS's fast readout direction is\n        # opposite of NIRCam's\n        tmpx = copy.deepcopy(xdim)\n        xdim = copy.deepcopy(ydim)\n        ydim = tmpx\n\n        colpad = 12\n\n        # Fullframe\n        if amps == 4:\n            rowpad = 1\n            fullpad = 1\n        else:\n            rowpad = 2\n            fullpad = 0\n\n    elif instrument == 'fgs':\n        # Reverse x and y since FGS's fast readout direction is\n        # opposite of NIRCam's\n        tmpx = copy.deepcopy(xdim)\n        xdim = copy.deepcopy(ydim)\n        ydim = tmpx\n\n        colpad = 12\n        fullpad = 0\n\n        if ((xdim == 2048) & (ydim == 2048)):\n            rowpad = 1\n            fullpad = 1\n        else:\n            rowpad = 2\n\n        if ((xdim <= 32) & (ydim <= 32)):\n            colpad = 6\n            rowpad = 1\n\n    return ((1.0 * xdim / amps + colpad) * (ydim + rowpad) + fullpad) * 1.e-5\n\n\ndef check_nircam_filter(old_filter, old_pupil):\n    \"\"\"This is a utility function that checks the FILTER and PUPIL parameters read in from the .yaml file and makes sure\n    that for NIRCam the filter and pupil names are correct, relieving the user of the need to remember which of the\n    filters are in the filter wheel and which are in the pupil wheel.\n\n    Parameters\n    ----------\n\n    old_filter :  str\n        Assumed to be the self.params['Readout']['filter'] value from the .yaml file\n\n    old_pupil :  str\n        Assumed to be the self.params['Readout']['pupil'] value from the .yaml file\n\n    Returns\n    -------\n\n    new_filter : str\n        The proper FILTER parameter for the requested NIRISS filter name\n\n    new_pupil : str\n        The proper PUPIL parameter for the requested NIRISS filter name\n    \"\"\"\n    filter_combo = '{}/{}'.format(old_filter, old_pupil)\n    updated_combo = standardize_filters('nircam', [filter_combo])[0]\n    new_filter, new_pupil = updated_combo.split('/')\n    return new_filter, new_pupil\n\n\ndef check_niriss_filter(oldfilter, oldpupil):\n    \"\"\"This is a utility function that checks the FILTER and PUPIL parameters read in from the .yaml file and makes sure\n    that for NIRISS the filter and pupil names are correct, relieving the user of the need to remember which of the 12\n    filters are in the filter wheel and which are in the pupil wheel.  If the user puts the correct values for filter and\n    pupil nothing is done, but the user can just put the filter name in the filter parameter and CLEAR in the pupil\n    parameter, in which case this routine sorts out which value actually goes where.  Hence for example one can have a\n    parameter file with FILTER = F277W and PUPIL = CLEAR or FILTER = F090W and PUPIL = CLEAR for imaging and either will\n    produce the desired result although the actual pairings would be F277W/CLEARP and CLEAR/F090W respectively.\n\n    The code also corrects CLEARP to CLEAR if needed.\n\n    Parameters\n    ----------\n\n    oldfilter :  str\n        Assumed to be the self.params['Readout']['filter'] value from the .yaml file\n\n    oldpupil :  str\n        Assumed to be the self.params['Readout']['pupil'] value from the .yaml file\n\n    Returns\n    -------\n\n    newfilter : str\n        The proper FILTER parameter for the requested NIRISS filter name\n\n    newpupil : str\n        The proper PUPIL parameter for the requested NIRISS filter name\n\n    Note that this routine should only be called when the instrument is NIRISS.\n    \"\"\"\n    str1 = oldfilter\n    str2 = oldpupil\n\n    if oldfilter in NIRISS_PUPIL_WHEEL_FILTERS:\n        newfilter = str2\n        newpupil = str1\n        if newfilter == 'CLEARP':\n            newfilter = 'CLEAR'\n    elif oldfilter in NIRISS_FILTER_WHEEL_FILTERS:\n        if oldpupil == 'CLEAR':\n            newpupil = 'CLEARP'\n        else:\n            newpupil = oldpupil\n        newfilter = oldfilter\n    else:\n        # Case where oldfilter is an optic other than a filter (e.g. a grism)\n        newfilter = oldfilter\n        newpupil = oldpupil\n    return newfilter, newpupil\n\ndef countrate_to_magnitude(instrument, filter_name, magsys, count_rate, photfnu=None, photflam=None,\n                           vegamag_zeropoint=None):\n    \"\"\"Convert a given source countrate (ADU/sec) into magnitudes\n\n    Parameters\n    ----------\n    instrument : str\n        e.g. 'nircam'\n\n    filter_name : str\n        Filter used in the observation. Only used in the case of\n        NIRISS, where filters in the filter wheel have their\n        throughput modified.\n\n    magsys : str\n        Magnitude system of the input magnitudes. Allowed values are:\n        'abmag', 'stmag', 'vegamag'\n\n    count_rate : float or list\n        Count rate (ADU/s) to be transformed\n\n    photfnu : float\n        Photfnu value that relates count rate and flux density. Only used\n        in ABMAG conversions\n\n    photflam : float\n        Photflam value that relates count rate and flux density. Only used\n        in STMAG conversions\n\n    vegamag_zeropoint : float\n        Filter offset in the VEGAMAG (or A0V mag) system. Only used in\n        VEGAMAG conversions.\n\n\n    Returns\n    -------\n    mag : float or list\n        Magnitude value(s) corresponding to input count_rates\n    \"\"\"\n    # For NIRISS filters in the filter wheel, we increase the count rate by a\n    # factor of 1/0.84. This is because the throughput of the CLEARP element\n    # in the pupil wheel (which will be used in combination with the filter)\n    # is 0.84, and is included in the PSFs produced by WebbPSF, but also in\n    # the throughput curves used by Mirage. So we need to remove one of these\n    # two factors of 0.84\n    if ((instrument.lower() == 'niriss') and (filter_name.upper() in NIRISS_FILTER_WHEEL_FILTERS)):\n        count_scale = 1. / 0.84\n    else:\n        count_scale = 1.0\n\n    if magsys.lower() == 'abmag':\n        try:\n            return -2.5 * np.log10(photfnu * count_rate / count_scale) - 48.599934378\n        except:\n            raise ValueError((\"Count rate to AB mag conversion failed.\"\n                              \"count rate = {}, photfnu = {}\".format(count_rate, photfnu)))\n    if magsys.lower() == 'vegamag':\n        try:\n            return vegamag_zeropoint - 2.5 * np.log10(count_rate / count_scale)\n        except:\n            raise ValueError((\"Count rate to Vega mag conversion failed.\"\n                              \"count rate = {}\".format(count_rate)))\n    if magsys.lower() == 'stmag':\n        try:\n            return -2.5 * np.log10(count_rate * photflam / count_scale) - 21.099934378\n        except:\n            raise ValueError((\"Count rate to ST mag conversion failed.\"\n                              \"count rate = {}, photflam = {}\".format(count_rate, photflam)))\n\n\ndef crop_to_subarray(data, bounds):\n    \"\"\"\n    Crop the given full frame array down to the appropriate\n    subarray size and location based on the requested subarray\n    name.\n\n    Parameters\n    ----------\n    data : numpy.ndarray\n        Full frame image or ramp. (x,y) = (2048, 2048)\n        May be 2D, 3D, or 4D\n\n    bounds : list\n        4-element list containing the full frame indices that\n        define the position of the subarray.\n        [xstart, ystart, xend, yend]\n\n    Returns\n    -------\n    data : numpy.ndarray\n        Input array cropped in x and y dimensions\n    \"\"\"\n    dimensions = len(data.shape)\n    yl, xl = data.shape[-2:]\n\n    valid = [False, False, False, False]\n    valid = [(b >= 0 and b < xl) for b in bounds[0:3:2]]\n    validy = [(b >= 0 and b < yl) for b in bounds[1:4:2]]\n    valid.extend(validy)\n\n    if all(valid):\n        if dimensions == 2:\n            return data[bounds[1]:bounds[3] + 1, bounds[0]:bounds[2] + 1]\n        elif dimensions == 3:\n            return data[:, bounds[1]:bounds[3] + 1, bounds[0]:bounds[2] + 1]\n        elif dimensions == 4:\n            return data[:, :, bounds[1]:bounds[3] + 1, bounds[0]:bounds[2] + 1]\n        else:\n            raise ValueError((\"In crop_to_subarray, input array is not 2, 3, or 4D.\"))\n    else:\n            raise ValueError((\"WARNING: subarray bounds are outside the \"\n                              \"dimensions of the input array.\"))\n\n\ndef ensure_ascii(in_string):\n    \"\"\"Remove any non-ASCII characters from the input string\n\n    Parameters\n    ----------\n    in_string : str\n        Input string\n\n    output_string : str\n        String with non-ASCII characters removed\n    \"\"\"\n    encoded_string = in_string.encode('ascii', 'ignore')\n    return encoded_string.decode()\n\n\ndef ensure_dir_exists(fullpath):\n    \"\"\"Creates dirs from ``fullpath`` if they do not already exist.\n    \"\"\"\n    if not os.path.exists(fullpath):\n        os.makedirs(fullpath)\n\n\ndef expand_environment_variable(variable_name, offline=False):\n    \"\"\" Expand an environment variable and check that the directory exists\n\n    Parameters\n    ----------\n    variable_name : str\n        Environment variable name\n\n    Returns\n    -------\n    variable_directory : str\n        Path pointed to by the environment variable\n    \"\"\"\n    variable_directory = os.environ.get(variable_name)\n    if variable_directory is None:\n        raise ValueError((\"{} environment variable is not set. \"\n                          \"This must be set to the base directory \"\n                          \"containing the darks, cosmic ray, PSF, etc \"\n                          \"input files needed for the simulation. \"\n                          \"These files must be downloaded separately \"\n                          \"from the Mirage package.\".format(variable_name)))\n    if not offline:\n        if not os.path.isdir(variable_directory):\n            raise FileNotFoundError((\"The directory contained in the {} \"\n                                     \"environment variable: {} does not exist or \"\n                                     \"is not accessible.\".format(variable_name, variable_directory)))\n    return variable_directory\n\n\ndef full_paths(params, module_path, crds_dictionary, offline=False):\n    \"\"\"Expand the relevant input paths from the input yaml file to be full\n    paths.\n\n    Parameters\n    ----------\n    params : dict\n        Nested dictionary read in from an input yaml file\n\n    module_path : str\n        Path to the Mirage module\n\n    crds_dictionary : dict\n        Dictionary of basic observation metadata that is used by CRDS to\n        select appropriate reference files.\n\n    offline : bool\n        Set to True in order to skip downloading reference files from CRDS\n        and instead only return the dictionary of reference file names.\n\n    Returns\n    -------\n    params : dict\n        Modified nested dictionary including full paths\n    \"\"\"\n    # Place import statement here in order to avoid circular import\n    # with crd_tools\n    from mirage.reference_files import crds_tools\n\n    logger = logging.getLogger('mirage.utils.utils.full_paths')\n\n    pathdict = {'Reffiles': ['dark', 'linearized_darkfile', 'badpixmask',\n                             'superbias', 'linearity', 'saturation', 'gain',\n                             'pixelflat', 'illumflat', 'ipc', 'astrometric',\n                             'crosstalk', 'occult', 'pixelAreaMap', 'transmission',\n                             'subarray_defs', 'filtpupilcombo',\n                             'flux_cal', 'readpattdefs', 'filter_throughput',\n                             'filter_wheel_positions', 'photom'],\n                'simSignals': ['pointsource', 'psfpath', 'galaxyListFile',\n                               'extended', 'movingTargetList', 'movingTargetSersic',\n                               'movingTargetExtended', 'movingTargetToTrack',\n                               'psf_wing_threshold_file', 'zodiacal', 'scattered'],\n                'cosmicRay': ['path'],\n                'Output': ['file', 'directory']}\n\n    all_config_files = {'nircam': {'Reffiles-subarray_defs': 'NIRCam_subarray_definitions.list',\n                                   'Reffiles-flux_cal': 'NIRCam_zeropoints.list',\n                                   'Reffiles-crosstalk': 'xtalk20150303g0.errorcut.txt',\n                                   'Reffiles-readpattdefs': 'nircam_read_pattern_definitions.list',\n                                   'Reffiles-filter_throughput': 'placeholder.txt',\n                                   'Reffiles-filtpupilcombo': 'nircam_filter_pupil_pairings.list',\n                                   'Reffiles-filter_wheel_positions': 'nircam_filter_and_pupil_wheel_positions.txt',\n                                   'simSignals-psf_wing_threshold_file': 'nircam_psf_wing_rate_thresholds.txt'},\n                        'niriss': {'Reffiles-subarray_defs': 'niriss_subarrays.list',\n                                   'Reffiles-flux_cal': 'niriss_zeropoints.list',\n                                   'Reffiles-crosstalk': 'niriss_xtalk_zeros.txt',\n                                   'Reffiles-readpattdefs': 'niriss_readout_pattern.txt',\n                                   'Reffiles-filter_throughput': 'placeholder.txt',\n                                   'Reffiles-filtpupilcombo': 'niriss_dual_wheel_list.txt',\n                                   'Reffiles-filter_wheel_positions': 'niriss_filter_and_pupil_wheel_positions.txt',\n                                   'simSignals-psf_wing_threshold_file': 'niriss_psf_wing_rate_thresholds.txt'},\n                        'fgs': {'Reffiles-subarray_defs': 'guider_subarrays.list',\n                                'Reffiles-flux_cal': 'guider_zeropoints.list',\n                                'Reffiles-crosstalk': 'guider_xtalk_zeros.txt',\n                                'Reffiles-readpattdefs': 'guider_readout_pattern.txt',\n                                'Reffiles-filter_throughput': 'placeholder.txt',\n                                'Reffiles-filtpupilcombo': 'guider_filter_dummy.list',\n                                'Reffiles-filter_wheel_positions': 'dummy.txt',\n                                'simSignals-psf_wing_threshold_file': 'fgs_psf_wing_rate_thresholds.txt'}\n                        }\n    config_files = all_config_files[params['Inst']['instrument'].lower()]\n\n    for key1 in pathdict:\n        for key2 in pathdict[key1]:\n\n            # For those using an old or incomplete yaml file, if any of the\n            # CRDS keywords are missing, create them and set equal to 'crds'.\n            if key2 not in params[key1].keys():\n                if key2 in CRDS_FILE_TYPES.keys():\n                    logger.info('{}:{} field not present in input. Setting equal to \"crds\"'.format(key1, key2))\n                    params[key1][key2] = 'crds'\n\n            # Change NoneType to str\n            if params[key1][key2] is None:\n                params[key1][key2] = 'None'\n\n            if params[key1][key2].lower() not in ['none', 'config', 'crds']:\n                params[key1][key2] = os.path.abspath(os.path.expandvars(params[key1][key2]))\n            elif params[key1][key2].lower() == 'config':\n                cfile = config_files['{}-{}'.format(key1, key2)]\n                fpath = os.path.join(module_path, 'config', cfile)\n                params[key1][key2] = fpath\n                logger.info(\"'config' specified: Using {} for {}:{} input file\".format(fpath, key1, key2))\n            elif key2 in CRDS_FILE_TYPES.keys() and params[key1][key2].lower() == 'crds':\n                # 'crds' set for one of the reference files means to\n                # search for the best reference file currently in CRDS\n                # and download if it is not already present in\n                # self.crds_datadir\n                query_dictionary = crds_dictionary\n\n                # For NIRCam WFSS and grism time series simluations we need to get the\n                # imaging mode flat for the crossing filter. This is because the grism-\n                # specific flat in CRDS is all 1.0's, because most people apply a wavelength-\n                # dependent flat to the data after they have a 1D sepctrum (according to Pirzkal).\n                # So when introducing flat field effects into the data, we use the imaging mode\n                # flat. This assumes no wavelength-dependence to the flat field\n                if ((params['Inst']['instrument'].lower() == 'nircam') and (params['Inst']['mode'] in ['wfss', 'ts_grism'])\n                   and (key2 == 'pixelflat')):\n                    img_dict = copy.deepcopy(crds_dictionary)\n                    if img_dict['PUPIL'] in ['GRISMR', 'GRISMC']:\n                        img_dict['PUPIL'] = 'CLEAR'\n                    else:\n                        raise ValueError('WFSS or GrismTSO sim, but Pupil is not set to one of the grisms.')\n                    query_dictionary = img_dict\n\n                if key2 != 'transmission':\n                    mapping = crds_tools.get_reffiles(query_dictionary, [CRDS_FILE_TYPES[key2]], download=not offline)\n                    params[key1][key2] = mapping[CRDS_FILE_TYPES[key2]]\n                    logger.info(\"From CRDS, found {} as the {} reference file.\".format(mapping[CRDS_FILE_TYPES[key2]], key2))\n                else:\n                    # For the moment, the transmission files are stored in the GRISM_NIRCAM and\n                    # GRISM_NIRISS repos. Longer-term they will become CRDS files and this else\n                    # statement can be deleted.\n                    params[key1][key2] = get_transmission_file(query_dictionary)\n                    logger.info(\"From grism library, found {} as the POM transmission file.\".format(params[key1][key2]))\n\n                # If we grab the IPC reference file from CRDS, then we need to invert it prior to use\n                if key2 == 'ipc':\n                    ipc_path, ipc_file = os.path.split(mapping['ipc'])\n                    inverted_kernel_file = os.path.join(ipc_path, 'Kernel_to_add_IPC_effects_from_{}'.format(ipc_file))\n                    # Check to see if the version of the IPC file with an\n                    # inverted kernel already exists.\n                    if os.path.isfile(inverted_kernel_file):\n                        logger.info(\"Found an existing inverted kernel for this IPC file: {}\".format(inverted_kernel_file))\n                        params[key1][key2] = inverted_kernel_file\n                        params['Reffiles']['invertIPC'] = False\n                    else:\n                        logger.info(\"No inverted IPC kernel file found. Kernel will be inverted prior to use.\")\n                        params['Reffiles']['invertIPC'] = True\n    return params\n\n\ndef get_all_reffiles(param_dict):\n    \"\"\"\n    \"\"\"\n    # Place import statement here in order to avoid circular import\n    # with crd_tools\n    from mirage.reference_files import crds_tools\n\n    mapping = crds_tools.get_reffiles(param_dict, list(CRDS_FILE_TYPES.values()))\n    return mapping\n\n\ndef get_lw_grism_tso_intermeidate_aperture(aperture):\n    \"\"\"Grism time series observations use an intermediate aperture in\n    order to place the undispersed target at the correct location such\n    that, once the grism is in the beam, the trace lands at row 34 on\n    the detector. In the APT pointing file, the requested aperture is\n    listed (i.e. NRCA5_GRISM64_F444W), but the V2, V3 values for the\n    reference location are actually those of the intermediate aperture\n    (i.e. NRCA5_TAGRISMTS_SCI_F444W'). This function will return the\n    name of the intermediate aperture given the name of the requested\n    aperture.\n\n    Parameters\n    ----------\n    aperture : str\n        Name of the requested aperture\n\n    Returns\n    -------\n    intermediate_aperture : str\n        Name of the intermediate aperture\n    \"\"\"\n    lw_filter = aperture.split('_')[2]\n    if lw_filter in ['F277W', 'F322W2', 'F356W']:\n        intermediate_aperture = 'NRCA5_TAGRISMTS_SCI_F322W2'\n    elif lw_filter == 'F444W':\n        intermediate_aperture = 'NRCA5_TAGRISMTS_SCI_F444W'\n    else:\n        raise ValueError('Unrecognized Grism TSO LW filter: {}. What intermediate aperture is used?'.format(lw_filter))\n    return intermediate_aperture\n\n\ndef get_siaf():\n    '''Return a dictionary that holds the contents of the SIAF config\n    file.\n\n    Returns\n    -------\n    siaf_files : dict\n        A dictionary that holds the contents of the config file.\n    '''\n    utils_dir = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))\n    package_dir = os.path.dirname(utils_dir)\n    config_file = os.path.join(package_dir, 'config', 'siaf_config.json')\n\n    with open(config_file, 'r') as config_file:\n        siaf_files = json.load(config_file)\n\n    return siaf_files\n\n\ndef get_aperture_definition(aperture_name, instrument):\n    '''Parses the SIAF to get the definition of a given aperture\n    '''\n\n    siaf_file = get_siaf()[instrument]\n    siaf_table = asc.read(siaf_file, header_start=1)\n    row_ind = list(siaf_table['AperName']).index(aperture_name)\n    siaf_row = siaf_table[row_ind]\n\n    detector = aperture_name[3:5]  # Won't work for ALL, A, or B case\n\n    if re.search(r\"_F\\d\\d\\d[MWN]\", aperture_name):\n        filt = aperture_name.split('_')[-1]\n    else:\n        filt = 'ANY'\n\n    refpix_x = siaf_row['XSciRef']\n    refpix_y = siaf_row['YSciRef']\n    refpix_v2 = siaf_row['V2Ref']\n    refpix_v3 = siaf_row['V3Ref']\n\n    x_size = siaf_row['XSciSize']\n    y_size = siaf_row['YSciSize']\n\n    xstart = refpix_x - x_size / 2\n    xend = refpix_x + x_size / 2\n    ystart = refpix_y - y_size / 2\n    yend = refpix_y + y_size / 2\n\n    # Need to add num_amps\n\n    aperture_definition = [aperture_name, detector, filt, xstart, ystart, xend,\n                           yend, refpix_x, refpix_y, refpix_v2, refpix_v3]\n    return aperture_definition\n\n\ndef get_frame_count_info(numints, numgroups, numframes, numskips, numresets):\n    \"\"\"Calculate information on the number of frames per group and\n    per integration\n\n    Parameters\n    ----------\n    numints : int\n        Number of integraitons in the exposure\n\n    numgroups : int\n        Number of groups per integration\n\n    numframes : int\n        Number of frames averaged together to create a group\n\n    numskips : int\n        Number of skipped frames per group\n\n    numresets : int\n        Number of detector resets between integrations\n\n    Returns\n    -------\n    frames_per_group\n    \"\"\"\n    frames_per_group = numframes + numskips\n    frames_per_integration = numgroups * frames_per_group\n    total_frames = numgroups * frames_per_group\n\n    if numints > 1:\n        # Frames for all integrations\n        total_frames *= numints\n        # Add the resets for all but the first integration\n        total_frames += (numresets * (numints - 1))\n\n    return frames_per_group, frames_per_integration, total_frames\n\n\ndef get_filter_throughput_file(instrument, filter_name, pupil_name, nircam_module=None, fgs_detector=None):\n    \"\"\"Locate the filter throughput file in the config directory that\n    corresponds to the given instrument/module/filter\n\n    Parameters\n    ----------\n    instrument : str\n        Name of JWST instrument.\n\n    filter_name : str\n        Name of the filter. Ignored in the case of FGS.\n\n    pupil_name : str\n        Name of the optical element in the pupil wheel.\n        Only used for NIRCam cases.\n\n    nircam_module : str\n        Name of module. Ignored except in the case of NIRCam\n\n    fgs_detector : str\n        Name of FGS detector (e.g. 'GUIDER1'). Ignored in the case of other\n        instruments\n\n    Returns\n    -------\n    throughput_file : str\n        Name of ascii file containing the filter throughput curve\n    \"\"\"\n    modpath = pkg_resources.resource_filename('mirage', '')\n    config_path = os.path.join(modpath, 'config')\n\n    instrument = instrument.lower()\n    if instrument == 'nircam':\n\n        # WLP8 and WLM8 have idenitcal throughput curves, so Mirage carries\n        # only throughput files for WLP8\n        if pupil_name.upper() == 'WLM8':\n            pupil_name = 'WLP8'\n        # For WFSC team practice purposes, the throughput of the DHS grisms is\n        # tracked outside of MIRAGE, so we can safely ignore that here\n        if pupil_name.upper() == 'GDHS0' or pupil_name.upper() == 'GDHS60':\n            pupil_name = 'CLEAR'\n\n        tp_file = '{}_{}_nircam_plus_ote_throughput_mod{}_sorted.txt'.format(filter_name.upper(),\n                                                                             pupil_name.upper(),\n                                                                             nircam_module.lower())\n    elif instrument == 'niriss':\n        element = filter_name\n        if filter_name in ['CLEAR', 'CLEARP', 'GR150R', 'GR150C']:\n            element = pupil_name\n        tp_file = '{}_niriss_throughput1.txt'.format(element.lower())\n    elif instrument == 'fgs':\n        det_number = fgs_detector[-1]\n        tp_file = 'guider{}_throughput_py.txt'.format(det_number)\n    else:\n        raise ValueError('ERROR: invalid instrument: {}'.format(instrument))\n\n    throughput_file = os.path.join(config_path, tp_file)\n    if not os.path.isfile(throughput_file):\n        raise FileNotFoundError('Throughput file {} does not exist.'.format(throughput_file))\n    return throughput_file\n\n\ndef get_subarray_info(params, subarray_table):\n    \"\"\"Find aperture-specific information from the subarray information config file\n\n    Parameters:\n    -----------\n    params : dict\n        Nested dictionary containing Mirage parameters.\n\n    subarray_table : astropy.table.Table\n        Table containing subarray definition information. Output from\n        read_subarray_definition_file\n\n    Returns:\n    --------\n    params : dict\n        Updated nested dictionary\n    \"\"\"\n    logger = logging.getLogger('mirage.utils.utils.get_subarray_info')\n\n    array_name = params['Readout']['array_name']\n    # For MASKSWB and MASKLWB apertures, the filter name is part of the aperture\n    # name. But the subarray definition file contains only an entry for the aperture\n    # name without the filter name. In that case, strip off the filter name here\n    # before checking the definition file\n    if '_MASKLWB' in array_name or '_MASKSWB' in array_name:\n        pieces = array_name.split('_')\n        array_name = '{}_{}'.format(pieces[0], pieces[1])\n\n    if array_name in subarray_table['AperName']:\n        mtch = array_name == subarray_table['AperName']\n        namps = subarray_table['num_amps'].data[mtch][0]\n        if namps != 0:\n            params['Readout']['namp'] = int(namps)\n        else:\n            try:\n                if ((params['Readout']['namp'] == 1) or\n                   (params['Readout']['namp'] == 4)):\n                    logger.info((\"CAUTION: Aperture {} can be used with either \"\n                                 \"a 1-amp or a 4-amp readout. The difference is a factor of 4 in \"\n                                 \"readout time. You have requested {} amps.\".format(subarray_table['AperName'].data[mtch][0],\n                                                                                    params['Readout']['namp'])))\n                else:\n                    raise ValueError((\"WARNING: {} requires the number of amps to be 1 or 4. Please set \"\n                                      \"'Readout':'namp' in the input yaml file to one of these values.\"\n                                      .format(array_name)))\n            except KeyError:\n                raise KeyError((\"WARNING: 'Readout':'namp' not present in input yaml file. \"\n                                \"{} aperture requires the number of amps to be 1 or 4. Please set \"\n                                \"'Readout':'namp' in the input yaml file to one of these values.\"\n                                .format(array_name)))\n    else:\n        raise ValueError((\"WARNING: subarray name {} not found in the \"\n                          \"subarray dictionary {}.\"\n                          .format(array_name,\n                                  params['Reffiles']['subarray_defs'])))\n    return params\n\n\ndef normalize_filters(instrument, filter_name, pupil_name):\n    \"\"\"Modify filter/pupil values to be consistent with the filter/pupil wheels\n    that they are in.\n    \"\"\"\n    if instrument.lower() == 'niriss':\n        newfilter, newpupil = check_niriss_filter(filter_name, pupil_name)\n    elif instrument.lower() == 'nircam':\n        newfilter, newpupil = check_nircam_filter(filter_name, pupil_name)\n    elif instrument.lower() == 'fgs':\n        newfilter = filter_name\n        newpupil = pupil_name\n    return newfilter, newpupil\n\n\ndef magnitude_to_countrate(instrument, filter_name, magsys, mag, photfnu=None, photflam=None,\n                           vegamag_zeropoint=None):\n    \"\"\"Convert a given source magnitude into count rate\n\n    Parameters\n    ----------\n    instrument : str\n        e.g. 'nircam'\n\n    filter_name : str\n        Filter used in the observation. Only used in the case of\n        NIRISS, where filters in the filter wheel have their\n        throughput modified.\n\n    magsys : str\n        Magnitude system of the input magnitudes. Allowed values are:\n        'abmag', 'stmag', 'vegamag'\n\n    mag : float or list\n        Magnitude value(s) to transform\n\n    photfnu : float\n        Photfnu value that relates count rate and flux density. Only used\n        in ABMAG conversions\n\n    photflam : float\n        Photflam value that relates count rate and flux density. Only used\n        in STMAG conversions\n\n    vegamag_zeropoint : float\n        Filter offset in the VEGAMAG (or A0V mag) system. Only used in\n        VEGAMAG conversions.\n\n\n    Returns\n    -------\n    count_rate : float or list\n        Count rate (ADU/s) corresponding to the input magnutude(s)\n\n    \"\"\"\n    # For NIRISS filters in the filter wheel, we increase the count rate by a\n    # factor of 1/0.84. This is because the throughput of the CLEARP element\n    # in the pupil wheel (which will be used in combination with the filter)\n    # is 0.84, and is included in the PSFs produced by WebbPSF, but also in\n    # the throughput curves used by Mirage. So we need to remove one of these\n    # two factors of 0.84\n    if ((instrument.lower() == 'niriss') and (filter_name.upper() in NIRISS_FILTER_WHEEL_FILTERS)):\n        count_scale = 1. / 0.84\n    else:\n        count_scale = 1.0\n\n    if magsys.lower() == 'abmag':\n        try:\n            return count_scale * (10**((mag + 48.599934378) / -2.5) / photfnu)\n        except:\n            raise ValueError((\"AB mag to countrate conversion failed.\"\n                              \"magnitude = {}, photfnu = {}\".format(mag, photfnu)))\n    if magsys.lower() == 'vegamag':\n        try:\n            return count_scale * (10**((vegamag_zeropoint - mag) / 2.5))\n        except:\n            raise ValueError((\"Vega mag to countrate conversion failed.\"\n                              \"magnitude = {}\".format(mag)))\n    if magsys.lower() == 'stmag':\n        try:\n            return count_scale * (10**((mag + 21.099934378) / -2.5) / photflam)\n        except:\n            raise ValueError((\"ST mag to countrate conversion failed.\"\n                              \"magnitude = {}, photflam = {}\".format(mag, photflam)))\n\n\ndef make_mag_column_names(instrument, filters):\n    \"\"\"\n    Given as input the instrument name and the list of filters needed, this\n    routine generates the list of output header values for the Mirage input\n    file.\n\n    Parameters\n    ----------\n    instrument : str\n        'NIRCam', 'NIRISS', or 'Guider\"\n\n    filters : list\n        List of the filters needed (names such as F090W) or either None or\n        an empty list to select all filters)\n\n    Returns\n    -------\n    headerstrs : list\n        The header string list for the output Mirage magnitudes line;\n        if concatentated with spaces, it is the list of magnitudes header\n        values that needs to be written to the source list file.\n    \"\"\"\n    instrument = instrument.lower()\n    if instrument == 'nircam':\n        possible_filters = NIRCAM_FILTERS\n        pupil_wheel_options = NIRCAM_PUPIL_WHEEL_FILTERS + ['WLP8', 'WLM8', 'CLEAR']\n    elif instrument == 'niriss':\n        possible_filters = NIRISS_FILTERS\n        pupil_wheel_options = NIRISS_PUPIL_WHEEL_FILTERS + ['CLEARP']\n    elif instrument == 'fgs':\n        possible_filters = FGS_FILTERS\n    else:\n        # Allow non-JWST instruments/filters to be used, in order\n        # to keep catalogs with non-JWST magnitude columns complete\n        headerstrs = []\n        for filter_val in filters:\n            headerstrs.append('{}_{}_magnitude'.format(instrument, filter_val.lower()))\n        return headerstrs\n\n    headerstrs = []\n    for filter_name in filters:\n\n        # Check to be sure the filter is supported\n        if filter_name.upper() not in possible_filters:\n            raise ValueError(\"{} is not recognized or not supported for {}.\".format(filter_name, instrument))\n\n        if instrument in ['niriss', 'fgs']:\n            column_name = '{}_{}_magnitude'.format(instrument, filter_name.lower())\n        else:\n            parts = filter_name.upper().split('/')\n            if len(parts) == 2:\n                if parts[0] in pupil_wheel_options:\n                    pup_val = parts[0]\n                    filt_val = parts[1]\n                elif parts[1] in pupil_wheel_options:\n                    pup_val = parts[1]\n                    filt_val = parts[0]\n                column_name = '{}_{}_{}_magnitude'.format(instrument, filt_val.lower(), pup_val.lower())\n            else:\n                raise ValueError('{} is an incomplete filter name. Filter and pupil must be present.'.format(filter_name))\n\n        headerstrs.append(column_name)\n    return headerstrs\n\n\ndef sigma_clipped_mean_value_of_image(array, sigma_value):\n    \"\"\"Return the sigma-clipped mean value of a 2D array\n\n    Parameters\n    ----------\n    array : numpy.ndarray\n        array of values\n\n    sigma_value : int\n        Number of sigma to use as clipping threshold. Clipping will be\n        symmetric above and below the mean\n\n    Returns\n    -------\n    meanval : float\n        3-sigma iteratively sigma-clipped mean value of ``array``\n    \"\"\"\n    good = np.isfinite(array)\n    clipped, lo, hi = sigmaclip(array[good], low=sigma_value, high=sigma_value)\n    meanval = clipped.mean()\n    return meanval\n\n\ndef organize_config_files(offline=False):\n    \"\"\"Organize the names of the various config files for each instrument\n\n    Parameters\n    ----------\n    offline : bool\n        Whether the class is being called with or without access to\n        Mirage reference data. Used primarily for testing.\n    \"\"\"\n    data_dir = expand_environment_variable('MIRAGE_DATA', offline=offline)\n    modpath = pkg_resources.resource_filename('mirage', '')\n\n    config_info = {}\n\n    config_info['global_subarray_definitions'] = {}\n    config_info['global_readout_patterns'] = {}\n    config_info['global_subarray_definition_files'] = {}\n    config_info['global_readout_pattern_files'] = {}\n\n    config_info['global_crosstalk_files'] = {}\n    config_info['global_filtpupilcombo_files'] = {}\n    config_info['global_filter_position_files'] = {}\n    config_info['global_flux_cal_files'] = {}\n    config_info['global_psf_wing_threshold_file'] = {}\n    config_info['global_psfpath'] = {}\n\n    for instrument in 'niriss fgs nircam miri nirspec'.split():\n        if instrument.lower() == 'niriss':\n            readout_pattern_file = 'niriss_readout_pattern.txt'\n            subarray_def_file = 'niriss_subarrays.list'\n            crosstalk_file = 'niriss_xtalk_zeros.txt'\n            filtpupilcombo_file = 'niriss_dual_wheel_list.txt'\n            filter_position_file = 'niriss_filter_and_pupil_wheel_positions.txt'\n            flux_cal_file = 'niriss_zeropoints.list'\n            psf_wing_threshold_file = 'niriss_psf_wing_rate_thresholds.txt'\n            psfpath = os.path.join(data_dir, 'niriss/gridded_psf_library')\n        elif instrument.lower() == 'fgs':\n            readout_pattern_file = 'guider_readout_pattern.txt'\n            subarray_def_file = 'guider_subarrays.list'\n            crosstalk_file = 'guider_xtalk_zeros.txt'\n            filtpupilcombo_file = 'guider_filter_dummy.list'\n            filter_position_file = 'dummy.txt'\n            flux_cal_file = 'guider_zeropoints.list'\n            psf_wing_threshold_file = 'fgs_psf_wing_rate_thresholds.txt'\n            psfpath = os.path.join(data_dir, 'fgs/gridded_psf_library')\n        elif instrument.lower() == 'nircam':\n            readout_pattern_file = 'nircam_read_pattern_definitions.list'\n            subarray_def_file = 'NIRCam_subarray_definitions.list'\n            crosstalk_file = 'xtalk20150303g0.errorcut.txt'\n            filtpupilcombo_file = 'nircam_filter_pupil_pairings.list'\n            filter_position_file = 'nircam_filter_and_pupil_wheel_positions.txt'\n            flux_cal_file = 'NIRCam_zeropoints.list'\n            psf_wing_threshold_file = 'nircam_psf_wing_rate_thresholds.txt'\n            psfpath = os.path.join(data_dir, 'nircam/gridded_psf_library')\n        else:\n            readout_pattern_file = 'N/A'\n            subarray_def_file = 'N/A'\n            crosstalk_file = 'N/A'\n            filtpupilcombo_file = 'N/A'\n            filter_position_file = 'N/A'\n            flux_cal_file = 'N/A'\n            psf_wing_threshold_file = 'N/A'\n            psfpath = 'N/A'\n        if instrument in 'niriss fgs nircam'.split():\n            config_info['global_subarray_definitions'][instrument] = asc.read(os.path.join(modpath, 'config', subarray_def_file))\n            config_info['global_readout_patterns'][instrument] = asc.read(os.path.join(modpath, 'config', readout_pattern_file))\n        config_info['global_subarray_definition_files'][instrument] = os.path.join(modpath, 'config', subarray_def_file)\n        config_info['global_readout_pattern_files'][instrument] = os.path.join(modpath, 'config', readout_pattern_file)\n        config_info['global_crosstalk_files'][instrument] = os.path.join(modpath, 'config', crosstalk_file)\n        config_info['global_filtpupilcombo_files'][instrument] = os.path.join(modpath, 'config', filtpupilcombo_file)\n        config_info['global_filter_position_files'][instrument] = os.path.join(modpath, 'config', filter_position_file)\n        config_info['global_flux_cal_files'][instrument] = os.path.join(modpath, 'config', flux_cal_file)\n        config_info['global_psf_wing_threshold_file'][instrument] = os.path.join(modpath, 'config', psf_wing_threshold_file)\n        config_info['global_psfpath'][instrument] = psfpath\n    return config_info\n\n\ndef parse_RA_Dec(ra_string, dec_string):\n    \"\"\"Convert input RA and Dec strings to floats\n\n    Parameters\n    ----------\n\n    ra_string : str\n        String containing RA information. Can be in the form 10:12:13.2\n        or 10h12m13.2s\n\n    dec_string : str\n        String containing Declination information. Can be in the form\n        10:12:2.4 or 10d12m2.4s\n\n    Returns\n    -------\n\n    ra_degrees : float\n        Right Ascention value in degrees\n\n    dec_degrees : float\n        Declination value in degrees\n    \"\"\"\n    # First, a quick check to see if the inputs are in\n    # decimal degrees already.\n    try:\n        return float(ra_string), float(dec_string)\n    except ValueError:\n        pass\n\n    # Convert from HMS or ::: to decimal degrees\n    try:\n        ra_string = ra_string.lower()\n        ra_string = ra_string.replace(\"h\", \":\")\n        ra_string = ra_string.replace(\"m\", \":\")\n        ra_string = ra_string.replace(\"s\", \"\")\n        ra_string = re.sub(r\"\\s+\", \"\", ra_string)\n        dec_string = dec_string.lower()\n        dec_string = dec_string.replace(\"d\", \":\")\n        dec_string = dec_string.replace(\"m\", \":\")\n        dec_string = dec_string.replace(\"s\", \"\")\n        dec_string = re.sub(r\"\\s+\", \"\", dec_string)\n\n        values = ra_string.split(\":\")\n        ra_degrees = 15.*(int(values[0]) + int(values[1])/60. + float(values[2])/3600.)\n\n        values = dec_string.split(\":\")\n        if \"-\" in values[0]:\n            sign = -1\n            values[0] = values[0].replace(\"-\", \"\")\n        else:\n            sign = +1\n\n        dec_degrees = sign*(int(values[0]) + int(values[1])/60. + float(values[2])/3600.)\n        return ra_degrees, dec_degrees\n    except:\n        raise ValueError(\"Error parsing RA, Dec strings: {} {}\".format(ra_string, dec_string))\n\n\ndef read_pattern_check(parameters):\n    # Check the readout pattern that's entered and set nframe and nskip\n    # accordingly\n    logger = logging.getLogger('mirage.utils.utils.read_pattern_check')\n    parameters['Readout']['readpatt'] = parameters['Readout']['readpatt'].upper()\n\n    # Read in readout pattern definition file\n    # and make sure the possible readout patterns are in upper case\n    readpatterns = asc.read(parameters['Reffiles']['readpattdefs'])\n    readpatterns['name'] = [s.upper() for s in readpatterns['name']]\n\n    # If the requested readout pattern is in the table of options,\n    # then adopt the appropriate nframe and nskip\n    if parameters['Readout']['readpatt'] in readpatterns['name']:\n        mtch = parameters['Readout']['readpatt'] == readpatterns['name']\n        parameters['Readout']['nframe'] = int(readpatterns['nframe'][mtch].data[0])\n        parameters['Readout']['nskip'] = int(readpatterns['nskip'][mtch].data[0])\n        logger.info(('Requested readout pattern {} is valid. '\n                     'Using the nframe = {} and nskip = {}'\n                     .format(parameters['Readout']['readpatt'],\n                             parameters['Readout']['nframe'],\n                             parameters['Readout']['nskip'])))\n    else:\n        # If the read pattern is not present in the definition file\n        # then quit.\n        raise ValueError((\"WARNING: the {} readout pattern is not defined in {}.\"\n                          .format(parameters['Readout']['readpatt'],\n                                  parameters['Reffiles']['readpattdefs'])))\n    return parameters\n\n\ndef read_subarray_definition_file(filename):\n    \"\"\"Read in the file that contains a list of subarray names and related information\n\n    Parameters:\n    -----------\n    filename : str\n        Name of the ascii file containing the table of subarray information\n\n    Returns:\n    --------\n    data : astropy.table.Table\n        Table containing subarray information\n    \"\"\"\n    try:\n        data = asc.read(filename, data_start=1, header_start=0)\n    except:\n        raise RuntimeError((\"Error: could not read in subarray definitions file: {}\"\n                            .format(filename)))\n    return data\n\n\ndef read_yaml(filename):\n    \"\"\"Read the contents of a yaml file into a nested dictionary\n\n    Parameters\n    ----------\n    filename : str\n        Name of yaml file to be read in\n\n    Returns\n    -------\n    data : dict\n        Nested dictionary of file contents\n    \"\"\"\n    logger = logging.getLogger('mirage.utils.utils.read_yaml')\n\n    try:\n        with open(filename, 'r') as f:\n            data = yaml.load(f, Loader=yaml.FullLoader)\n    except FileNotFoundError as e:\n        logging.error(e)\n    return data\n\n\ndef standardize_filters(instrument, filter_values):\n    \"\"\"Transform a list of filters into a list of full filter specifications\n    including filter and pupil value.\n    e.g. F090W -> F090/CLEAR, F323N -> F322W2/F323N\n    For NIRISS, since specifying the filter name alone is unambiguous,\n    change any e.g. \"F090W/CLEARP\" to \"F090W\"\n\n    Also reorder to be filter/pupil.\n\n    Paramters\n    ---------\n    instrument : str\n        Instrument name\n\n    filter_values : list\n        Input, possibly shorthand, filter values\n\n    Returns\n    -------\n    full_names : list\n        Full filter/pupil values for each input element\n    \"\"\"\n    full_names = []\n\n    if instrument.lower() == 'fgs':\n        return filter_values\n    elif instrument.lower() == 'nircam':\n        pw_values = NIRCAM_PUPIL_WHEEL_FILTERS + ['CLEAR', 'GRISMR', 'GRISMC', 'GDHS0', 'GDHS60', 'MASKRND', 'MASKBAR']\n        reverse_nircam_2_filter_crosses = ['{}/{}'.format(ele.split('/')[1], ele.split('/')[0]) for ele in NIRCAM_2_FILTER_CROSSES]\n        wlp8_combinations = ['{}/WLP8'.format(ele) for ele in NIRCAM_WL8_CROSSING_FILTERS]\n        wlm8_combinations = ['{}/WLM8'.format(ele) for ele in NIRCAM_WL8_CROSSING_FILTERS]\n        wl8_combinations = wlp8_combinations + wlm8_combinations\n        reverse_wlp8 = ['{}/{}'.format(ele.split('/')[1], ele.split('/')[0]) for ele in wlp8_combinations]\n        reverse_wlm8 = ['{}/{}'.format(ele.split('/')[1], ele.split('/')[0]) for ele in wlm8_combinations]\n        reverse_wl8_combinations = reverse_wlp8 + reverse_wlm8\n\n        for filter_value in filter_values:\n            filter_value = filter_value.upper()\n            parts = filter_value.split('/')\n            if len(parts) == 2:\n                if filter_value in NIRCAM_2_FILTER_CROSSES + wl8_combinations:\n                    full_names.append(filter_value)\n                elif filter_value in reverse_nircam_2_filter_crosses + reverse_wl8_combinations:\n                    full_names.append('{}/{}'.format(parts[1], parts[0]))\n                elif parts[0] in pw_values:\n                    if parts[1] not in pw_values:\n                        full_names.append('{}/{}'.format(parts[1], parts[0]))\n                    else:\n                        raise ValueError(\"Both {} and {} are in the pupil wheel. This is not supported.\".format(parts[0], parts[1]))\n                elif parts[0] not in pw_values:\n                    if parts[1] not in pw_values:\n                        raise ValueError(\"Both {} and {} are in the filter wheel. This is not supported.\".format(parts[0], parts[1]))\n                    else:\n                        full_names.append('{}/{}'.format(parts[0], parts[1]))\n            elif len(parts) == 1:\n                if filter_value in NIRCAM_CLEAR_CROSSING_FILTERS:\n                    full_names.append('{}/CLEAR'.format(filter_value))\n                elif filter_value in NIRCAM_PUPIL_WHEEL_FILTERS:\n                    fw_value = NIRCAM_GO_PW_FILTER_PAIRINGS[filter_value]\n                    full_names.append('{}/{}'.format(fw_value, filter_value))\n                else:\n                    raise ValueError('Unsupported shorthand filter value: {}'.format(filter_value))\n    elif instrument.lower() == 'niriss':\n        for filter_value in filter_values:\n            filter_value = filter_value.upper()\n            parts = filter_value.split('/')\n            if len(parts) == 1:\n                if filter_value not in ['CLEAR', 'CLEARP']:\n                    full_names.append(filter_value)\n                else:\n                    raise ValueError('{} not supported for NIRISS'.format(filter_value))\n            elif len(parts) == 2:\n                if parts[0] in ['CLEAR', 'CLEARP']:\n                    if parts[1] in ['CLEAR', 'CLEARP']:\n                        raise ValueError(\"CLEAR/CLEARP not supported for NIRISS\")\n                    else:\n                        full_names.append(parts[1])\n                else:\n                    if parts[1] not in ['CLEAR', 'CLEARP']:\n                        raise ValueError((\"{} unsupported. For NIRISS, filter must be CLEAR or pupil must be CLEARP.\"\n                                          .format(filter_value)))\n                    else:\n                        full_names.append(parts[0])\n            else:\n                raise ValueError('{} is an unsupported filter value for NIRISS'.format(filter_value))\n    else:\n        # For instruments other than those on JWST (i.e. JHK), pass the filter name though unchanged\n        full_names.extend(filter_values)\n    return full_names\n\n\ndef write_yaml(data, filename):\n    \"\"\"Write a nested dictionary to a yaml file\n\n    Parameters\n    ----------\n    data : dict\n        Nested dictionary of paramters\n\n    filename : str\n        Output filename\n    \"\"\"\n    with open(filename, 'w') as output:\n        yaml.dump(data, output, default_flow_style=False)\n","repo_name":"spacetelescope/mirage","sub_path":"mirage/utils/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":52100,"program_lang":"python","lang":"en","doc_type":"code","stars":38,"dataset":"github-code","pt":"48"}
+{"seq_id":"27532110896","text":"import pygame\nimport sys\nfrom random import randint\nfrom star import Star\n\nclass Starsky:\n    def __init__(self):\n        '''初始化'''\n        pygame.init()\n\n        #创建一个显示窗口，并设置窗口尺寸\n        self.screen = pygame.display.set_mode((800,600))\n        #创建背景色\n        self.bg_color = (20,20,20)\n\n        self.stars = pygame.sprite.Group()\n        self._create_starry()\n        pygame.display.set_caption(\"Star Sky\")\n\n    def _create_starry(self):\n        '''创建星群'''\n        #创建一个星星并计算一行可容纳多少个星星\n        star = Star(self)\n        star_width,star_height = star.rect.size\n        #屏幕x方向左右各预留一个星星宽度\n        self.available_space_x = self.screen.get_rect().width - (2 * star_width)\n        number_stars_x = self.available_space_x // (2 * star_width)\n\n        #计算屏幕可容纳多少行星星\n        self.available_space_y = self.screen.get_rect().height - (2 * star_height)\n        number_rows =  self.available_space_y // (2 * star_height)\n        for row_number in range(number_rows):\n            for star_number in range(number_stars_x):\n                self._create_star(star_number,row_number)\n\n    def _create_star(self,star_number,row_number):\n        #创建��个星星并将其加入到当前行中\n        star = Star(self)\n        star_width,star_height = star.rect.size\n\n        star.rect.x = randint(0,self.available_space_x)\n        star.rect.y = randint(0,self.available_space_y)\n        self.stars.add(star)\n\n    def starry(self):\n        '''开始星空展示'''\n        while True:\n            #屏幕展示\n            for event in pygame.event.get():\n                if event.type == pygame.QUIT:\n                    sys.exit()\n\n            #每次循环时都重绘屏幕\n            self.screen.fill(self.bg_color)\n            self.stars.draw(self.screen)\n\n            #让最近绘制的屏幕可见\n            pygame.display.flip()\n\nif __name__ == '__main__':\n    #创建实例并运行\n    sky = Starsky()\n    sky.starry()\n\n","repo_name":"changgitPLUIS/waixingren","sub_path":"starkey.py","file_name":"starkey.py","file_ext":"py","file_size_in_byte":2068,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"71504819667","text":"def ex1():\n    contato = {\"@camilaqueiroz\": \"Camila Queiroz\",\n               \"@paollaoliveirareal\": \"Paolla de oliveira\",\n               \"@Adrianaoliveira\": \"Adriana oliveira\"}\n    \n    contato.update({\"@paollaoliveirareal\": \"Paolla\"})\n    print(len(contato))\n    \n    for i in contato.keys():\n        print(i)\n    \n    for i in contato.values():\n        print(i)\n\n    for key, valor in sorted(contato.items()):\n        print(f\"{key} -> {valor}\")\n\n\ndef ex2():\n    from operator import itemgetter\n\n    contato = {\"@camilaqueiroz\": \"1.76\",\n               \"@paollaoliveirareal\": \"1.68\",\n               \"@Adrianaoliveira\": \"1.86\"}\n    \n    \n    copia = contato\n\n    print(copia)    \n\n\n","repo_name":"lucaseduardo76/Algoritimos-Faculdade","sub_path":"exercicio avulso/ex dicionarios.py","file_name":"ex dicionarios.py","file_ext":"py","file_size_in_byte":681,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"24757356980","text":"import json\n\nwith open('../data/champions.json', 'r', encoding='utf-8') as f:\n    champion_list = json.load(f)\n\nwith open('../data/traits.json', 'r', encoding='utf-8') as f:\n    trait_list = json.load(f)\n\nname_dict = {}\nfor each in champion_list:\n    name_dict[each['name']] = \"\"\nfor each in trait_list:\n    name_dict[each['name']] = ''\n\nwith open('../data/language.json', 'w', encoding='utf-8') as f:\n    json.dump(name_dict, f, indent='\\t', ensure_ascii=False)\n# dic = {}\n#\n# with open(\"../data/language.json\", 'r') as f:\n#     l = json.load(f)\n#\n# for each in l:\n#     key = list(each.keys())[0]\n#     value = list(each.values())[0]\n#     dic[key] = value\n#\n# with open(\"../data/language_copy.json\", \"w\") as f:\n#     json.dump(dic, f, indent='\\t', ensure_ascii=False)","repo_name":"weiziyoung/TFT2","sub_path":"python_script/translate.py","file_name":"translate.py","file_ext":"py","file_size_in_byte":770,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"48"}
+{"seq_id":"73906126866","text":"import sys\n\n\ndef convert(a, b,inputs, m):\n    return_b = []\n    num = 0\n    for i in range(m):\n        num += inputs[i]*(a**(m-1-i))\n    while num != 0:\n        return_b.append(num%b)\n        num = num//b\n    return return_b\n\n\ndef main():\n    A, B = [int(x) for x in sys.stdin.readline().rstrip().split()]\n    m = int(sys.stdin.readline())\n    inputs = [int(x) for x in sys.stdin.readline().rstrip().split()]\n    answer = convert(A, B, inputs, m)\n    length = len(answer)\n    for i in range(length):\n        print(answer[length-1-i], end=\" \")\n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"Kangsan-Jeon/AlgorithmTrain","sub_path":"Baekjoon/수학1/[완]11576_Base Conversion.py","file_name":"[완]11576_Base Conversion.py","file_ext":"py","file_size_in_byte":582,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"28042707717","text":"\"\"\"\nЗадача 1\nСоздать программно файл в текстовом формате, записать в него построчно данные, вводимые пользователем. Об окончании\nввода данных свидетельствует пустая строка.\n\"\"\"\nfrom os import path, getcwd\n\n\ndef main():\n    f_name = path.join(getcwd(), 'task_1_test')\n    with open(f_name, \"w\") as f:\n        while True:\n            user_input = input(\"Enter something or leave blank to quit: \")\n            if len(user_input) > 0:\n                f.write(f'{user_input}\\n')\n            else:\n                break\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"gmahatkov/geekbrains-py-basics","sub_path":"les_5/task_1.py","file_name":"task_1.py","file_ext":"py","file_size_in_byte":696,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"74332437265","text":"import webbrowser\nimport sys\nfrom layla.engine_components import speak\n\n\n# Search On Internet\nclass search_internet:\n    def __init__(self, query) -> None:\n        self.query = query\n        self.term = ' '.join(self.query.split()[1:-2])\n        self.name = ' '.join(self.query.split()[-1:])\n\n    def give_url(self) -> str:\n        \"\"\"\n        Input: Name of Website\n        Output: Return Str -> search url\n        \"\"\"\n        if self.name == \"youtube\":\n            return (\"results?search_query=\")\n        elif self.name == \"pinterest\":\n            return (\"search/pins/?q=\")\n        elif self.name == \"linkedin\":\n            return (\"search/results/all/?keywords=\")\n        elif self.name == \"tumblr\" or self.name == \"dailymotion\":\n            return (\"search/\")\n        elif self.name == \"imdb\":\n            return (\"find?q=\")\n        else:\n            return (\"search?q=\")\n\n    def web_search(self) -> None:\n        print('searching ' + self.term + \" on \" + self.name)\n        speak('searching ' + self.term + \" on \" + self.name)\n        webbrowser.open(\"https://www.\" + self.name + \".com/\" +\n                        self.give_url() + self.term)\n\n\n# goog = search_internet(\"search python on quora\")\n# goog.web_search()\n","repo_name":"fahad-programmer/layla","sub_path":"work/search_web.py","file_name":"search_web.py","file_ext":"py","file_size_in_byte":1224,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"21933559069","text":"count = int(input('Fibonnaci. Quantos termos após 0, 1? '))\nn0 = 0\nn1 = 1\nwhile count > 0: \n    count -= 1\n    if n0 == 0 and n1 == 1:\n        print(f'0 → 1 ', end = '→ ')\n        n2 = 0\n    n2 = n1 + n0\n    n0 = n1\n    n1 = n2\n    print(f'{n2} → ' if count > 0 else f'{n2}', end = '')\n","repo_name":"luan-cerilio/learning-python","sub_path":"exs/ex063_while.py","file_name":"ex063_while.py","file_ext":"py","file_size_in_byte":293,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"20450411516","text":"# N의 생성자는 N-60 ~ N 사이에만 존재할 수 있음\r\n\r\nN = int(input())\r\nfor i in range(max(0,N-60),N+1):\r\n    tmp = i\r\n    for j in str(tmp):\r\n        tmp += int(j)\r\n    if tmp == N:\r\n        print(i)\r\n        break\r\nelse:\r\n    print(0)\r\n","repo_name":"Jang-Hyunwoo/BOJ_python","sub_path":"브루트 포스/[14_Bronze 2] 2231 - 분해합.py","file_name":"[14_Bronze 2] 2231 - 분해합.py","file_ext":"py","file_size_in_byte":250,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"33419662641","text":"class Solution:\n    def destCity(self, paths: List[List[str]]) -> str:\n        set_start = set()\n        set_end = set()\n        for path in paths:\n            set_start.add(path[0])\n            set_end.add(path[1])\n\n        for destination in set_end:\n            if destination not in set_start:\n                return destination\n        \n        return []\n        ","repo_name":"andreregino/my-leetcode-answers","sub_path":"1547-destination-city/1547-destination-city.py","file_name":"1547-destination-city.py","file_ext":"py","file_size_in_byte":368,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"33320945014","text":"#!/usr/bin/python\n\nimport pytest\n\nfrom conftest import _get_app_client\nfrom datetime import datetime\nfrom datetime import timedelta\nfrom listen_local_app.forms import SearchForm\n\n\ndef _today_shift(delta=0):\n    return datetime.now().date() + timedelta(days=delta)\n\n\ndef _make_test_form_input(field, value):\n    data = {'zipcode': '19130', 'daterange': _today_shift(), 'distance': '1', 'submit': True}\n    data[field] = value\n    return data\n\n\n# Test zipcode form intput validation code\n@pytest.mark.parametrize(\"data,expect_valid\", [\n    (_make_test_form_input('zipcode', '19130'), True),\n    (_make_test_form_input('zipcode', '19130a'), False),\n    (_make_test_form_input('zipcode', '19130-5555'), True),\n    (_make_test_form_input('zipcode', 'adagarbageinput888'), False),\n    (_make_test_form_input('zipcode', 'evil_garbage();'), False),\n])\ndef test_zipcode_input(data, expect_valid):\n    app, client = _get_app_client()\n\n    @app.route('/', methods=['POST'])\n    def index():\n        form = SearchForm()\n        test_valid = form.validate()\n        assert test_valid == expect_valid\n        if expect_valid:\n            assert not form.errors.keys()\n        else:\n            assert 'zipcode' in form.errors.keys()\n        return\n\n    client.post('/', data=data)\n\n\n# Test daterange form input validation code\n@pytest.mark.parametrize(\"data,expect_valid\", [\n    (_make_test_form_input('daterange', _today_shift(0)), True),\n    (_make_test_form_input('daterange', f'{_today_shift(0)} to {_today_shift(3)}'), True),\n    (_make_test_form_input('daterange', _today_shift(-10)), False),\n    (_make_test_form_input('daterange', f'{_today_shift(-10)} to {_today_shift(-3)}'), False),\n    (_make_test_form_input('daterange', 'adagarbageinput888'), False),\n    (_make_test_form_input('daterange', 'evil_garbage();'), False),\n])\ndef test_daterange_input(data, expect_valid):\n    app, client = _get_app_client()\n\n    @app.route('/', methods=['POST'])\n    def index():\n        form = SearchForm()\n        test_valid = form.validate()\n        assert test_valid == expect_valid\n        if expect_valid:\n            assert not form.errors.keys()\n        else:\n            assert 'daterange' in form.errors.keys()\n        return\n\n    client.post('/', data=data)\n","repo_name":"pgrauman/listen-local-app","sub_path":"listen_local_app/tests/test_forms.py","file_name":"test_forms.py","file_ext":"py","file_size_in_byte":2249,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"2793436977","text":"dx = [0,0,-1,1] # 상하좌우\ndy = [1,-1,0,0]\n\ndef DFS(x,y,dirs):\n    k = 0\n    visited = set()\n    for dir in dirs:\n        if dir == 'U': k = 0\n        elif dir == 'D': k = 1\n        elif dir == 'L': k = 2\n        elif dir == 'R': k = 3\n        nx = x + dx[k]\n        ny = y + dy[k]\n        if -5 <= nx <= 5 and -5 <= ny <= 5:\n            visited.add((x,y,nx,ny))\n            visited.add((nx, ny, x, y))\n            x = nx\n            y = ny\n    return len(visited) // 2\n\ndef solution(dirs):\n    answer = DFS(0,0,dirs)\n\n    return answer\n\ndirs = \"LULLLLLLU\"\nsolution(dirs)","repo_name":"silverjjj/algorithm","sub_path":"Programmers/방문길이.py","file_name":"방문길이.py","file_ext":"py","file_size_in_byte":575,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"69986082706","text":"from operator import and_, or_, not_\nimport numpy as np\nimport pdb\n\n\nclass Perceptron(object):\n\n    def __init__(self, no_of_inputs, threshold=100, learning_rate=0.3):\n        self.threshold = threshold\n        self.learning_rate = learning_rate\n        self.weights = np.zeros(no_of_inputs + 1)\n\n    def predict(self, inputs):\n        summation = np.dot(inputs, self.weights)\n        pdb.set_trace()\n        if summation > 0:\n            activation = 1.\n        else:\n            activation = 0.\n        return activation\n\n    def training_perceptron(initializer, eta=0.3):\n        k = 0\n        data, W = initializer\n\n        k_s = []  # Era numbers for graph\n        sum_errors = []  # Sum Error numbers by each Era for graph\n        X = [x[0] for x in data]\n        Y = [y[1] for y in data]\n\n        y_pred = np.ones(len(data))\n        errors = np.ones(len(data))\n\n        while np.sum(errors) != 0:\n            outputs = []\n            print(f'{k}', end='; ')\n            if k < 10:\n                print(f' {W}', end='; ')\n            else:\n                print(f'{W}', end='; ')\n            for i in range(0, len(X)):\n                net = np.dot(X[i], W)\n                y_net = unit_step(net)\n                outputs.append(y_net)\n                y_pred[i] = y_net\n                error = Y[i] - y_pred[i]\n\n                for j in range(0, len(W)):\n                    W[j] += eta * error * X[i][j]  # Updating weights\n            for i in range(0, len(Y)):\n                errors[i] = (Y[i] - y_pred[i]) ** 2\n\n            print(f'{outputs}', end='; ')\n            sum_errors.append(sum(errors))\n            k_s.append(k)\n            print(f'{np.sum(errors)}', end='\\n')\n            k += 1\n\n        error_graph(error_values=sum_errors, k_s=k_s)\n        return\n\n\ndef generate_inputs(number_of_variables):\n    X = []\n    i = 0\n    while i < 2**int(number_of_variables):\n        x = list(format(i, f'0{number_of_variables}b'))\n        x = [int(n) for n in x]\n        X.append(x)\n        i += 1\n    return X\n\n\ndef get_F(X):\n    F = list()\n    target_function = []\n    for element in X:\n        # v1 = OR(element[0], element[1])\n        # v2 = AND(v1, element[2])\n        # v3 = AND(element[2], element[3])\n        # v4 = OR(v2, v3)\n        # value = NOT(v4)  # - - my function\n        # v1 = NOT(element[1])\n        # v2 = OR(v1, element[3])\n        # v3 = AND(v2, element[0])\n        # v4 = AND(element[0], element[2])\n        # v5 = OR(v3, v4)\n        # value = NOT(v5)\n        v1 = element[0]\n        v2 = element[1]\n        v3 = AND(v1, v2)\n        v4 = NOT(v3)\n        v5 = AND(element[2], element[3])\n        value = AND(v4, v5)\n        target_function.append(int(value))\n    for i in range(len(X)):\n        F.append((np.array(X[i]), target_function[i]))\n    return F\n\n\ndef AND(x1, x2):\n    return and_(x1, x2)\n\n\ndef OR(x1, x2):\n    return or_(x1, x2)\n\n\ndef NOT(x):\n    return not_(x)\n\n\nif __name__ == \"__main__\":\n    training_inputs = generate_inputs(4)\n    # pdb.set_trace()\n    labels = [x[1] for x in get_F(training_inputs)]\n\n    perceptron = Perceptron(4)\n    perceptron.train(training_inputs, labels, 50)\n    print(perceptron.weights)\n","repo_name":"zhanchi5/neural_labs","sub_path":"lab_1/task_1_alter.py","file_name":"task_1_alter.py","file_ext":"py","file_size_in_byte":3154,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"74570308625","text":"'''function'''\ndef coprime():\n    '''solution'''\n    num_a = int(input())\n    num_b = int(input())\n    while num_b:\n        num_a, num_b = num_b, num_a % num_b\n    if num_a == 1:\n        print(\"YES\")\n        print(num_a)\n    else:\n        print(\"NO\")\n        print(num_a)\n\ncoprime()\n","repo_name":"multi05369/PSCP","sub_path":"Lab10/coprime.py","file_name":"coprime.py","file_ext":"py","file_size_in_byte":283,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"29491401901","text":"import os\nimport time\nfrom collections import defaultdict\nfrom typing import Dict, Set, Tuple\n\nimport ray\nfrom ray.actor import ActorHandle\n\nfrom xgboost_ray.callback import DistributedCallback\nfrom xgboost_ray.compat import TrainingCallback\nfrom xgboost_ray.session import get_actor_rank\n\n\n@ray.remote(num_cpus=0)\nclass FaultToleranceManager:\n    def __init__(self, start_boost_round: int = 0):\n        self.global_boost_round = start_boost_round\n\n        # Dict from boost_round -> actor ranks to die\n        self.scheduled_kill: Dict[int, Set[int]] = defaultdict(set)\n\n        # Dict from actor rank -> starts/ends of boost rounds to sleep\n        self.delayed_return: Dict[int, Set[Tuple[int, int]]] = defaultdict(set)\n\n        # List of tuples (global_boost_round, actor_boost_round) to log\n        # actor iterations\n        self.training_logs = defaultdict(list)\n\n    def schedule_kill(self, rank: int, boost_round: int):\n        \"\"\"Kill an actor when reaching this global boost round\"\"\"\n        self.scheduled_kill[boost_round].add(rank)\n\n    def delay_return(self, rank: int, start_boost_round: int, end_boost_round: int):\n        \"\"\"Do not allow an actor to finish data loading between these rounds\"\"\"\n        self.delayed_return[rank].add((start_boost_round, end_boost_round))\n\n    def inc_boost_round(self, rank: int):\n        \"\"\"Increase global boosting round\"\"\"\n        if rank == 0:\n            self.global_boost_round += 1\n\n    def log_iteration(self, rank: int, boost_round: int):\n        \"\"\"Log iteration\"\"\"\n        self.training_logs[rank].append((self.global_boost_round, boost_round))\n\n    def should_die(self, rank: int):\n        \"\"\"Returns True if the actor should terminate the training job now.\"\"\"\n        die = False\n        for round in range(self.global_boost_round + 1):\n            # Loop through all rounds until now to deal with race conditions\n            if rank in self.scheduled_kill[round]:\n                self.scheduled_kill[round].remove(rank)\n                die = True\n        return die\n\n    def should_sleep(self, rank: int):\n        \"\"\"Returns True if the actor should not finish data loading, yet.\"\"\"\n        if self.delayed_return[rank]:\n            for start, end in self.delayed_return[rank]:\n                if start <= self.global_boost_round < end:\n                    return True\n        return False\n\n    def get_logs(self):\n        return self.training_logs\n\n\nclass DelayedLoadingCallback(DistributedCallback):\n    \"\"\"Used to control when actors return to training\"\"\"\n\n    def __init__(self, ft_manager: ActorHandle, reload_data=True, sleep_time=0.5):\n        self.ft_manager = ft_manager\n        self.reload_data = reload_data\n        self.sleep_time = sleep_time\n\n    def after_data_loading(self, actor, data, *args, **kwargs):\n        print(f\"Rank {actor.rank} - after load\")\n        while ray.get(self.ft_manager.should_sleep.remote(actor.rank)):\n            time.sleep(self.sleep_time)\n        print(f\"Rank {actor.rank} - returning now\")\n\n\nclass DieCallback(TrainingCallback):\n    \"\"\"Used to control when actors should die during training.\n\n    Also can add delay to each boosting round.\n    \"\"\"\n\n    def __init__(self, ft_manager: ActorHandle, training_delay: float = 0):\n        self.ft_manager = ft_manager\n        self.training_delay = training_delay\n        super(DieCallback, self).__init__()\n\n    def before_iteration(self, model, epoch, evals_log):\n        if ray.get(self.ft_manager.should_die.remote(get_actor_rank())):\n            pid = os.getpid()\n            print(f\"Killing process: {pid}\")\n            print(f\"Rank {get_actor_rank()} will now die.\")\n            time.sleep(1)\n            os.kill(pid, 9)\n            time.sleep(10)  # Don't continue training, just die\n\n    def after_iteration(self, model, epoch, evals_log):\n        # ray.get to make sure this is up to date in the next iteration\n        ray.get(self.ft_manager.log_iteration.remote(get_actor_rank(), epoch))\n        if self.training_delay > 0:\n            time.sleep(self.training_delay)\n        if get_actor_rank() == 0:\n            ray.get(self.ft_manager.inc_boost_round.remote(get_actor_rank()))\n","repo_name":"ray-project/xgboost_ray","sub_path":"xgboost_ray/tests/fault_tolerance.py","file_name":"fault_tolerance.py","file_ext":"py","file_size_in_byte":4143,"program_lang":"python","lang":"en","doc_type":"code","stars":122,"dataset":"github-code","pt":"48"}
+{"seq_id":"73160002705","text":"import os\nimport scipy.io as sio\nimport numpy as np\nimport random\nfrom gym.spaces import Discrete\nfrom collections import namedtuple\nfrom sklearn.cluster import KMeans\n\nfrom envs.sse.common import compute_2D_distance, compute_R_2D_LoS, compute_R_2D_NLoS, DIRECTIONs_3D\n\nCOLORs = {\n    'grey'  : np.array([0.5, 0.5, 0.5]), # 'off' GU\n    'red'   : np.array([0.9, 0.0, 0.0]), # not covered GU\n    'green' : np.array([0.0, 0.9, 0.0]), # covered GU\n    'blue'  : np.array([0.0, 0.0, 0.9]), # ABS\n    'orange': np.array([1.0, 0.5, 0.0])  # BM color\n}\n\nclass Entity(object):\n    \"\"\"Basic class for all entities in World class\"\"\"\n    def __init__(self,\n                 id, name,\n                 pos,\n                 size=8.):\n\n        # basic properties\n        self.id    = id     # unique identifier\n        self.name  = name   # f'{subclass_type}-{id}'\n        self.pos   = pos    # shape==(3, ), 3D position\n        assert pos.shape == (3, )\n        # visualization properties\n        self.color = None\n        self.size  = size\n\nclass BM(Entity):\n    \"\"\"Build mesh class\"\"\"\n    def __init__(self,\n                 id, name,\n                 pos,\n                 size):\n        super(BM, self).__init__(id, name, pos, size)\n        self.color = COLORs['orange']\n\n\n# namedtuple for storing GU covered by which ABS(s) info\nCoverTuple = namedtuple('CoverTuple', ('id', 'distance_2D'))\nclass GU(Entity):\n    \"\"\"Ground user class\"\"\"\n    def __init__(self,\n                 id, name,\n                 pos):\n        super(GU, self).__init__(id, name, pos)\n        self.color = COLORs['red']\n\n        self.ON = True\n        self.action = np.zeros((3,), dtype=np.float32)\n        self.covered_by = []    # (list[CoverTuple]) a list of tuples that include which ABSs are covering this GU along with the 2D distance between them\n\n    def sort_covered_by(self):\n        \"\"\"Sort the `covered_by` list by 2D distance.\"\"\"\n        self.covered_by = sorted(self.covered_by, key=lambda x: x.distance_2D)\n\nclass ABS(Entity):\n    \"\"\"Aerial base station / Unmanned aerial vehicles\"\"\"\n    def __init__(self,\n                 id, name,\n                 pos):\n        super(ABS, self).__init__(id, name, pos)\n        self.color = COLORs['blue']\n\n        self.action = np.zeros((3, ), dtype=np.float32)\n\n        self.recomputed = True\n        self.R_2D_NLoS = 0.\n        self.R_2D_LoS = 0.\n\n\n\nclass World(object):\n    \"\"\"\n    Site-Specific World object contains\n    - building meshes (BMs)\n    - ground users (GUs)\n    - aerial base stations / unmanned aerial vehicles (ABSs/UAVs)\n\n    NOTE: Only the BMs' info is initialized during instantiation.\n    \"\"\"\n\n    def __init__(self, path_to_load_BMs, seed=0):\n\n        #### seed ####\n        self.np_rng = np.random.RandomState(0)\n        self.seed = seed if seed is not None else 0\n        self.np_rng.seed(self.seed)\n\n        #### entities ####\n        # load terrain\n        world_len, mesh_len, N, grids = self._load_BMs(path_to_load_BMs)\n        self.world_len = world_len\n        self.mesh_len  = mesh_len           # side len of each BM\n        self.n_BM = N                       # num of BMs\n        self.M = int(world_len // mesh_len) # num of maximum meshes along the world side\n        self.grids = grids                  # shape==(M, M), the value of each elem is the correponding height\n\n        self.dim_pos = 3\n        self.dim_color = 3\n        self.episode_duration = 0.\n        self.step_duration = 0.\n        # TODO: 3GPP equations\n        self.f_c = 0.           # carrier frequency\n        self.PL = 0.            # path loss\n\n        # BMs\n        self.BMs = self._process_BMs()\n        assert len(self.BMs) == self.n_BM\n\n        # GUs\n        self.GUs = []\n        self.n_GU = 0\n        self.h_GU = 0.      # default height\n        self.v_GU = 0.\n        self.random_on_off = False\n        self.p_on = 1.\n\n        # Coverage rates (CR)\n        self.CR_new = 0.\n        self.CR_old = 0.\n\n        # ABSs\n        self.ABSs = []\n        self.n_ABS = 0\n        self.h_ABS = 0.     # default height\n        self.v_ABS = 0.\n\n        # 'pattern'-style only params\n        self.granularity = None\n        self.K = None\n\n        # 'precise'-style only params\n        self.action_space = Discrete(len(DIRECTIONs_3D))\n\n\n    ############### public methods ###############\n    def gen_1_2D_position(self, AVOID_COLLISION=True):\n        \"\"\"Generate 1 pair of 2D position.\"\"\"\n        world_len = self.world_len\n        mesh_len = self.mesh_len\n        M = self.M\n        while True:\n            x, y = world_len * self.np_rng.rand(2)\n            if AVOID_COLLISION:\n                x_idx, y_idx = np.clip(np.array([x, y]) // mesh_len, 0, M - 1).astype(np.int32)\n                if self.grids[x_idx, y_idx] == 0.:\n                    return np.array([x, y], dtype=np.float32)\n            else:\n                return np.array([x, y], dtype=np.float32)\n\n    def walk(self):\n        \"\"\"All GUs do random walk.\"\"\"\n        v_GU = self.v_GU\n        episode_duration = self.episode_duration\n        world_len = self.world_len\n        mesh_len = self.mesh_len\n        M = self.M\n\n        for _gu in self.GUs:\n            assert isinstance(_gu, GU)\n            next_pos = np.clip(_gu.pos + _gu.action * v_GU * episode_duration, 0, world_len)\n            next_x_idx, next_y_idx = np.clip((next_pos[:2] // mesh_len).astype(np.int32), 0, M - 1)\n            if self.grids[next_x_idx, next_y_idx] == 0.:\n                _gu.pos = next_pos\n\n            # randomly turn on/off\n            if self.random_on_off:\n                if random.random() < self.p_on:\n                    _gu.ON = True\n                else:\n                    _gu.ON = False\n\n        # update covered state\n        self.update()\n\n    def dispatch(self):\n        \"\"\"All ABSs perform their actions.\"\"\"\n        v_ABS = self.v_ABS\n        step_duration = self.step_duration\n        world_len = self.world_len\n        mesh_len = self.mesh_len\n        M = self.M\n\n        for _abs in self.ABSs:\n            assert isinstance(_abs, ABS)\n            raw_next_pos = _abs.pos + _abs.action * v_ABS * step_duration\n            next_pos = np.clip(raw_next_pos, 0, world_len)\n            if not np.array_equal(raw_next_pos, next_pos):\n                print(\"filter not working.\")\n            next_x_idx, next_y_idx = np.clip((next_pos[:2] // mesh_len).astype(np.int32), 0, M - 1)\n            if self.grids[next_x_idx, next_y_idx] <= next_pos[-1]:\n                _abs.pos = next_pos\n\n        # update covered state\n        self.update()\n\n    def update(self):\n        \"\"\"Update the GUs' covered state.\"\"\"\n        # reset all ABSs not recomputed radias\n        for _abs in self.ABSs:\n            assert isinstance(_abs, ABS)\n            _abs.recomputed = False\n\n        #### update all GUs' covered_by lists ####\n        for _gu in self.GUs:\n            assert isinstance(_gu, GU)\n\n            # empty out covered_by list\n            _gu.covered_by = []\n\n            # recompute covered by list\n            for _abs in self.ABSs:\n                assert isinstance(_abs, ABS)\n                distance_2D = compute_2D_distance(_gu.pos[:2], _abs.pos[:2])\n                # compute LoS & NLoS 2D radius for only once\n                if _abs.recomputed == False:\n                    R_2D_LoS, R_2D_NLoS = self._compute_Rs(_abs, _gu)\n                    _abs.R_2D_NLoS = R_2D_NLoS\n                    _abs.R_2D_LoS  = R_2D_LoS\n                    _abs.recomputed = True\n\n                # the GU is covered by an ABs only under 2 conditions:\n                # 1. within the NLoS range\n                # 2. within the ring between NLoS and LoS range and is LoS\n                if distance_2D <= R_2D_NLoS or \\\n                  (distance_2D >  R_2D_NLoS and distance_2D <= R_2D_LoS and self._judge_is_LoS(_abs, _gu)):\n                  # update `covered_by` property\n                  _gu.covered_by.append(CoverTuple(id=_abs.id, distance_2D=distance_2D))\n\n            # sort covered_by list\n            _gu.sort_covered_by()\n\n            # change covered GUs' color\n            if not _gu.ON: # disabled\n                _gu.color = COLORs['grey']\n            elif len(_gu.covered_by) == 0:\n                _gu.color = COLORs['red']\n            else:\n                _gu.color = COLORs['green']\n\n        # print(f\"[env | update] updated GUs' `covered_by` list.\")\n        \n        #### update CRs ####\n        self.CR_old = self.CR_new\n        self.CR_new = self.n_covered_ON_GU / self.n_ON_GU\n\n    def compute_KMEANS_centers(self, gus, seed=0):\n        \"\"\"compute KMEANS centers w/ given GUs.\"\"\"\n        locs_2D_gus = np.stack([gu.pos[:2] for gu in gus if gu.ON], axis=0)\n        assert locs_2D_gus.shape == (self.n_ON_GU, 2)\n        kmeans = KMeans(n_clusters=self.n_ABS, random_state=seed).fit(locs_2D_gus)\n        centers = kmeans.cluster_centers_.astype(np.float32)\n        assert centers.shape == (self.n_ABS, 2)\n        return centers\n\n    ############### private methods ###############\n    def _load_BMs(self, path_to_load_BMs):\n        \"\"\"Load BMs's info from mat file.\"\"\"\n        if not os.path.exists(path_to_load_BMs) or not os.path.isfile(path_to_load_BMs):\n            raise FileNotFoundError()\n        else:\n            mat = sio.loadmat(path_to_load_BMs)\n            return (\n                mat[\"world_len\"].item(),\n                mat[\"mesh_len\"].item(),\n                mat[\"N\"].item(),\n                mat[\"grids\"]\n            )\n\n    def _process_BMs(self):\n        \"\"\"Process mat file into a list of BM objects.\"\"\"\n        BMs = []\n        k = 0\n        M = self.M\n        for i in range(M):\n            for j in range(M):\n                h = self.grids[i, j]\n                if h > 0.:\n                    pos = np.zeros((3,), dtype=np.float32)\n                    pos[:2] = self.mesh_len * (np.array([i, j], dtype=np.float32) + 0.5)\n                    pos[-1] = h\n                    BMs.append(BM(k, f\"BM-{k}\", pos, self.mesh_len))\n        return BMs\n\n    def _judge_is_LoS(self, abs, gu):\n        \"\"\"Judge if a given pair of ABS and GU is LoS.\"\"\"\n        assert isinstance(abs, ABS)\n        assert isinstance(gu, GU)\n        assert abs.pos[-1] > gu.pos[-1]\n\n        mesh_len = self.mesh_len\n        M = self.M\n\n        dx, dy = (gu.pos - abs.pos)[:2]\n\n        # calculate `x` value of the intersection between\n        # abs-gu line and the given BM edge line (by\n        # providing its `y` value)\n        def _cal_x(y):\n            return abs.pos[0] + (y - abs.pos[1]) * dx / dy\n        def _cal_y(x):\n            return abs.pos[1] + (x - abs.pos[0]) * dy / dx\n\n        abs_x_i, abs_y_i = np.clip(abs.pos[:2] // mesh_len, 0, M - 1).astype(np.int32)\n        gu_x_i, gu_y_i = np.clip(gu.pos[:2] // mesh_len, 0, M - 1).astype(np.int32)\n\n        for _idx_i in range(min(abs_x_i, gu_x_i), max(abs_x_i, gu_x_i) + 1):\n            for _idx_j in range(min(abs_y_i, gu_y_i), max(abs_y_i, gu_y_i) + 1):\n                # taken by a BM\n                if self.grids[_idx_i, _idx_j] > 0.:\n                    w_x, e_x = _idx_i * mesh_len, (_idx_i+1) * mesh_len\n                    s_y, n_y = _idx_j * mesh_len, (_idx_j+1) * mesh_len\n\n                    # compute 2D projection intersection point as `point`\n                    point = None\n                    # different conditions for intersections (take `ABS` as origin)\n                    # axises\n                    if dx == 0 and dy > 0:      # GU: y-axis +\n                        point = np.array([abs.pos[0], n_y])\n                    elif dx == 0 and dy < 0:    # GU: y-axis -\n                        point = np.array([abs.pos[0], s_y])\n                    elif dx > 0 and dy == 0:    # GU: x-axis +\n                        point = np.array([e_x, abs.pos[1]])\n                    elif dx < 0 and dy == 0:    # GU: x-axis -\n                        point = np.array([w_x, abs.pos[1]])\n                    elif dx > 0 and dy > 0:     # GU: 1st quad\n                        point_1 = np.array([_cal_x(n_y), n_y])\n                        point_2 = np.array([e_x, _cal_y(e_x)])\n                        if point_1[0] >= w_x and point_1[0] <= e_x:\n                            point = point_1\n                        elif point_2[1] >= s_y and point_2[1] <= n_y:\n                            point = point_2\n                        else:\n                            pass # LoS\n                    elif dx < 0 and dy > 0:     # GU: 2nd quad\n                        point_1 = np.array([_cal_x(n_y), n_y])\n                        point_4 = np.array([w_x, _cal_y(w_x)])\n                        if point_1[0] >= w_x and point_1[0] <= e_x:\n                            point = point_1\n                        elif point_4[1] >= s_y and point_4[1] <= n_y:\n                            point = point_4\n                        else:\n                            pass # LoS\n                    elif dx < 0 and dy < 0:     # GU: 3rd quad\n                        point_3 = np.array([_cal_x(s_y), s_y])\n                        point_4 = np.array([w_x, _cal_y(w_x)])\n                        if point_3[0] >= w_x and point_3[0] <= e_x:\n                            point = point_3\n                        elif point_4[1] >= s_y and point_4[1] <= n_y:\n                            point = point_4\n                        else:\n                            pass # LoS\n                    elif dx > 0 and dy < 0:     # GU: 4th quad\n                        point_2 = np.array([e_x, _cal_y(e_x)])\n                        point_3 = np.array([_cal_x(s_y), s_y])\n                        if point_2[1] >= s_y and point_2[1] <= n_y:\n                            point = point_2\n                        elif point_3[0] >= w_x and point_3[0] <= e_x:\n                            point = point_3\n                        else:\n                            pass # LoS\n                    # check vertical intersection\n                    if point is not None:\n                        d = compute_2D_distance(abs.pos[:2], point)\n                        D = compute_2D_distance(abs.pos[:2], gu.pos[:2])\n                        dH = abs.pos[-1] - gu.pos[-1]\n                        dh = dH - dH*d/D + gu.pos[-1]\n                        if dh < self.grids[_idx_i, _idx_j]: # 3D wise intersection\n                            return False # NLoS\n        return True # LoS\n\n    def _compute_Rs(self, abs, gu):\n        \"\"\"\n        Compute LoS & NLoS 2D radias.\n        \"\"\"\n        assert isinstance(abs, ABS)\n        assert isinstance(gu, GU)\n\n        path_loss = self.PL\n        f_c = self.f_c\n        h_ABS = abs.pos[-1]\n        h_GU = gu.pos[-1]\n\n        R_2D_NLoS = compute_R_2D_NLoS(path_loss, h_ABS, h_GU, f_c)\n        R_2D_LoS = compute_R_2D_LoS(path_loss, h_ABS, h_GU, f_c)\n\n        # print(f\"[env | LoS & NLoS] LoS (2D): {R_2D_LoS}, NLoS (2D): {R_2D_NLoS}\")\n        return (\n            R_2D_LoS, R_2D_NLoS\n        )\n\n    @property\n    def entities(self):\n        return self.BMs + self.GUs + self.ABSs\n\n    @property\n    def n_ON_GU(self):\n        n = 0\n        for _gu in self.GUs:\n            if _gu.ON:\n                n += 1\n        return n\n\n    @property\n    def n_covered_ON_GU(self):\n        n = 0\n        for _gu in self.GUs:\n            if _gu.ON and len(_gu.covered_by) > 0:\n                n += 1\n        return n\n","repo_name":"HAXRD/1120","sub_path":"envs/sse/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":15226,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"12945962417","text":"from PIL import Image, ImageChops\n\nsize = 890, 500\n\nrudolf = Image.open(\"#02/rudolf.png\").convert(\"1\", dither=Image.NONE)\nklaus = Image.open(\"#02/klaus.png\")\n\nresult = ImageChops.logical_xor(rudolf, klaus)\n\nresult.show()\n","repo_name":"Wolfy7/HACKcembers-2021","sub_path":"#02/gemaelde.py","file_name":"gemaelde.py","file_ext":"py","file_size_in_byte":221,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"31398291875","text":"from itkutilities import get_itk_array, write_itk_imageArray\nimport numpy as np\nimport sys\n\nif __name__ == '__main__':\n\n    if len(sys.argv) != 4:\n        print(\"Usage: \" + sys.argv[0] + \" <bifurcationImage> <skeletonImage> <outputImage>\")\n        sys.exit(1)\n\n    bifimg = get_itk_array(sys.argv[1])\n    skelimg = get_itk_array(sys.argv[2])\n    outputimg = sys.argv[3]\n\n    bifimg = np.multiply(bifimg, 2)\n    bifimg = np.maximum(skelimg, bifimg)\n\n    write_itk_imageArray(np.asarray(bifimg, dtype='uint8'), outputimg)\n","repo_name":"velizarefremov/vascularnetworks","sub_path":"addbifstoskel.py","file_name":"addbifstoskel.py","file_ext":"py","file_size_in_byte":520,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"42020206022","text":"aim = int(input('''Co chcesz zrobić?\r\n\r\n1. Sprawdzić stężenie alkoholu we krwi\r\n2. Sprawdzić czy mogę prowadzić samochód\r\n3. Koniec  \r\n'''))\r\n\r\nif aim == 1:\r\n    procenty = float(input(\"Podaj zawartość alkoholu w % \"))\r\n    ilosc = int(input(\"Ile tego alkoholu wypiłeś w ml \"))\r\n    waga = int(input(\"Podaj masę ciałą w kg \"))\r\n    plec = int(input('''Podaj swoją płeć:\r\n    1 - Mezczyzna\r\n    2 - Kobieta\r\n    '''))\r\n    if plec == 1:\r\n        wspolczynnik = 0.7\r\n    else:\r\n        wspolczynnik = 0.6\r\n\r\n    promile_poczatkowe = ilosc * (procenty/100) * 0.8/waga*wspolczynnik\r\n    promile = promile_poczatkowe\r\n\r\n    print(f\"Po spożyciu takiej ilości alkoholu zawartość alkoholu we krwi wyniesie około {round(promile), 2} promili\")\r\n\r\n    wykres = int(input('''Czy chcesz zobaczyć jaka będzie Twoja zawartość alkoholu we krwi w czasie?\r\n    1- Tak\r\n    2- Nie\r\n    '''))\r\n    if wykres == 1:\r\n        czas = int(input(\"Ile godzin ma minąć? \"))\r\n        while czas > 0:\r\n            promile = promile_poczatkowe - 0.15*czas\r\n            if promile < 0:\r\n                promile = 0\r\n            print(f\"Po upływie {czas} godzin promile wyniosą około {round(promile), 2}\")\r\n            czas -= 1\r\n\r\nelif aim == 2:\r\n    procenty = float(input(\"Podaj zawartość alkoholu w % \"))\r\n    ilosc = int(input(\"Ile tego alkoholu wypiłeś w ml \"))\r\n    waga = int(input(\"Podaj masę ciałą w kg \"))\r\n    plec = int(input('''Podaj swoją płeć:\r\n    1 - Mezczyzna\r\n    2 - Kobieta\r\n    '''))\r\n    if plec == 1:\r\n        wspolczynnik = 0.7\r\n    else:\r\n        wspolczynnik = 0.6\r\n\r\n    promile = ilosc * (procenty/100) * 0.8/waga*wspolczynnik\r\n\r\n    if promile <= 0.2:\r\n        print(f\"Możesz prowadzić samochód, stężenie alkoholu w twojej krwi wynosi {round(promile), 2} promili\")\r\n    else:\r\n        print(f\"Nie możesz prowadzić samochodu, stężenie alkoholu w twojej krwi wynosi {round(promile, 2)} promili\")\r\n\r\n\r\nelse:\r\n    print(\"Koniec programu\")\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"KonradWozniak11/Python","sub_path":"breathalyser.py","file_name":"breathalyser.py","file_ext":"py","file_size_in_byte":1995,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"12722794712","text":"#%%\nfrom itertools import permutations, product\n\n\n\n# %%\nA =['a','b']\nZ = ['x','z']\n\n[p for p in product('ab', range(3))]\n[p for p in product(A,Z)]\n[p for p in product(A, repeat=3)]\n# %%\nmsg = 'Good Morning Rosa!'\nmsgList = msg.split()\n[' '.join(p) for p in permutations(msgList,3)]\n# %%\n# complex phrase\ns = \"\"\"Find textbooks with titles containing 'NLP',\n    or 'natural' and 'language', or \n    'computational' and 'linguistics'.\"\"\"\n\ns_length  = len(set(s.split()))\nimport numpy as np\nnp.arange(1,s_length+1).prod()\n# %%\n","repo_name":"mutazag/nlpia","sub_path":"simple_permutations.py","file_name":"simple_permutations.py","file_ext":"py","file_size_in_byte":523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"71300947345","text":"l = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]\n\n\ndef double(x):\n    return 2 * x\n\n\nm = map(double, l)\nprint(list(m))\n\nm1 = map(lambda x: x * x, l)\nprint(list(m1))\n\n# 练习\ns = ['dairong  ', ' shi ', 'ge', 'chengxuyuan ']\n# rs = map(lambda s: str(s).replace(' ', '').capitalize().title(), s)\nrs = map(lambda s: str(s).strip().capitalize().title(), s)\nprint(s, list(rs), sep=' => ')\n\nfrom functools import reduce\n\nprint('reduce sum :', reduce(lambda x, y: x + y, l))\nprint('reduce result :', reduce(lambda x, y: x * 10 + y, l))\nprint('reduce result :', reduce(lambda x, y: x * y, l))\n\nf = filter(lambda x: x % 2 == 0, l)\nprint(l)\nprint(list(f))\n\nprint('奇数并且能被3整除：', list(filter(lambda x: x % 2 != 0 and x % 3 == 0, l)))\n\na = [2, 5, 9, 1, 6, 3]\nra = sorted(a, reverse=True)\nprint(ra)\n\nao = [\n    {'name': 'xiaowang', 'age': 18, 'height': 150},\n    {'name': 'xiaogang', 'age': 20, 'height': 140},\n    {'name': 'xiaohong', 'age': 19, 'height': 145},\n]\n\naor = sorted(ao, key=lambda x:x['age'])\nprint(aor)","repo_name":"daiyun169/python3-learn","sub_path":"learn1/gjhs.py","file_name":"gjhs.py","file_ext":"py","file_size_in_byte":1046,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"10274439956","text":"def foo(x = []):\n    x.append(1)\n    print(x)\n\n\n\nfoo()\nfoo()\nfoo()\nfoo()\n\nexit(1)\n# FILES\nfile = open(r'/Users/ratio/projects/python/first/cw/dictionary.txt')\nout = open(r'/Users/ratio/projects/python/first/cw/dictionary_en.txt', 'w')\n\ndictionary = {}\nfor row in file:\n    data = row.split(' - ')\n    russ = data[0]\n    list_en_words: str = data[1].split(',')\n    for ru_w in list_en_words:\n        dictionary[ru_w.strip()] = russ.strip()\n\nfor row_key in sorted(dictionary.keys()):\n    out.write('{} - {} \\n'.format(row_key, dictionary[row_key]))\n\nprint(dictionary)\nout.close()\nout.flush()\nfile.close()\n\n# #read byte\n# while True:\n#     data = file.read(4)\n#     print(data)\n#     if not data:\n#         break\n\nout.close()\nfile.close()\n\nexit(1)\nstring = 'Привет)э'#\"Hello World\"\n# string = \"Hello World\"\n\n# string with encoding 'utf-8'\narr = bytes(string, 'utf-8')\nprint(arr)\nimport sys\nprint(sys.getsizeof(string))\nprint(len(string.encode(\"utf8\")))\nimport io\nprint (io.DEFAULT_BUFFER_SIZE)\n\n\n","repo_name":"chrom/python_base","sub_path":"cw/class_5.py","file_name":"class_5.py","file_ext":"py","file_size_in_byte":1000,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"30166852770","text":"import regex as re\nimport os\nfrom . import alignment\nimport copy\nimport hashlib\nimport pandas as pd\nfrom datetime import datetime\nfrom collections import defaultdict\nfrom tqdm import tqdm\n\n\nclass partition:\n    def __init__(self, idx, log=\"\", lev=-1):\n        self.logs_idx = [idx]\n        self.patterns = [log]\n        self.level = lev\n\n\nclass LogParser:\n    def __init__(\n        self,\n        indir,\n        outdir,\n        log_format,\n        max_dist=0.001,\n        levels=2,\n        k=1,\n        k1=1,\n        k2=1,\n        alpha=100,\n        rex=[],\n    ):\n        self.logformat = log_format\n        self.path = indir\n        self.savePath = outdir\n        self.rex = rex\n        self.levels = levels\n        self.max_dist = max_dist\n        self.k = k\n        self.k1 = k1\n        self.k2 = k2\n        self.alpha = alpha\n        self.df_log = None\n        self.logname = None\n        self.level_clusters = {}\n\n    def parse(self, logname):\n        print(\"Parsing file: \" + os.path.join(self.path, logname))\n        self.logname = logname\n        starttime = datetime.now()\n        self.load_data()\n        for lev in range(self.levels):\n            if lev == 0:\n                # Clustering\n                self.level_clusters[0] = self.get_clusters(self.df_log[\"Content_\"], lev)\n            else:\n                # Clustering\n                patterns = [c.patterns[0] for c in self.level_clusters[lev - 1]]\n                self.max_dist *= self.alpha\n                clusters = self.get_clusters(\n                    patterns, lev, self.level_clusters[lev - 1]\n                )\n\n                # Generate patterns\n                for cluster in tqdm(clusters, total=len(clusters)):\n                    cluster.patterns = [self.sequential_merge(cluster.patterns)]\n                self.level_clusters[lev] = clusters\n        self.dump()\n        print(\"Parsing done. [Time taken: {!s}]\".format(datetime.now() - starttime))\n\n    def dump(self):\n        if not os.path.isdir(self.savePath):\n            os.makedirs(self.savePath)\n\n        templates = [0] * self.df_log.shape[0]\n        ids = [0] * self.df_log.shape[0]\n        templates_occ = defaultdict(int)\n        for cluster in self.level_clusters[self.levels - 1]:\n            EventTemplate = cluster.patterns[0]\n            EventId = hashlib.md5(\" \".join(EventTemplate).encode(\"utf-8\")).hexdigest()[\n                0:8\n            ]\n            Occurences = len(cluster.logs_idx)\n            templates_occ[EventTemplate] += Occurences\n\n            for idx in cluster.logs_idx:\n                ids[idx] = EventId\n                templates[idx] = EventTemplate\n        self.df_log[\"EventId\"] = ids\n        self.df_log[\"EventTemplate\"] = templates\n\n        occ_dict = dict(self.df_log[\"EventTemplate\"].value_counts())\n        df_event = pd.DataFrame()\n        df_event[\"EventTemplate\"] = self.df_log[\"EventTemplate\"].unique()\n        df_event[\"Occurrences\"] = self.df_log[\"EventTemplate\"].map(occ_dict)\n        df_event[\"EventId\"] = self.df_log[\"EventTemplate\"].map(\n            lambda x: hashlib.md5(x.encode(\"utf-8\")).hexdigest()[0:8]\n        )\n\n        self.df_log.drop(\"Content_\", inplace=True, axis=1)\n        self.df_log.to_csv(\n            os.path.join(self.savePath, self.logname + \"_structured.csv\"), index=False\n        )\n        df_event.to_csv(\n            os.path.join(self.savePath, self.logname + \"_templates.csv\"),\n            index=False,\n            columns=[\"EventId\", \"EventTemplate\", \"Occurrences\"],\n        )\n\n    def get_clusters(self, logs, lev, old_clusters=None):\n        clusters = []\n        old_clusters = copy.deepcopy(old_clusters)\n        for logidx, log in tqdm(enumerate(logs), total=len(logs)):\n            match = False\n            for cluster in clusters:\n                dis = (\n                    self.msgDist(log, cluster.patterns[0])\n                    if lev == 0\n                    else self.patternDist(log, cluster.patterns[0])\n                )\n                if dis and dis < self.max_dist:\n                    if lev == 0:\n                        cluster.logs_idx.append(logidx)\n                    else:\n                        cluster.logs_idx.extend(old_clusters[logidx].logs_idx)\n                        cluster.patterns.append(old_clusters[logidx].patterns[0])\n                    match = True\n\n            if not match:\n                if lev == 0:\n                    clusters.append(partition(logidx, log, lev))  # generate new cluster\n                else:\n                    old_clusters[logidx].level = lev\n                    clusters.append(old_clusters[logidx])  # keep old cluster\n\n        return clusters\n\n    def sequential_merge(self, logs):\n        log_merged = logs[0]\n        for log in logs[1:]:\n            log_merged = self.pair_merge(log_merged, log)\n        return log_merged\n\n    def pair_merge(self, loga, logb):\n        loga, logb = alignment.water(loga.split(), logb.split())\n        logn = []\n        for idx, value in enumerate(loga):\n            logn.append(\"<*>\" if value != logb[idx] else value)\n        return \" \".join(logn)\n\n    def print_cluster(self, cluster):\n        print(\"------start------\")\n        print(\"level: {}\".format(cluster.level))\n        print(\"idxs: {}\".format(cluster.logs_idx))\n        print(\"patterns: {}\".format(cluster.patterns))\n        print(\"count: {}\".format(len(cluster.patterns)))\n        for idx in cluster.logs_idx:\n            print(self.df_log.iloc[idx][\"Content_\"])\n        print(\"------end------\")\n\n    def msgDist(self, seqP, seqQ):\n        dis = 1\n        seqP = seqP.split()\n        seqQ = seqQ.split()\n        maxlen = max(len(seqP), len(seqQ))\n        minlen = min(len(seqP), len(seqQ))\n        for i in range(minlen):\n            dis -= (self.k if seqP[i] == seqQ[i] else 0 * 1.0) / maxlen\n        return dis\n\n    def patternDist(self, seqP, seqQ):\n        dis = 1\n        seqP = seqP.split()\n        seqQ = seqQ.split()\n        maxlen = max(len(seqP), len(seqQ))\n        minlen = min(len(seqP), len(seqQ))\n        for i in range(minlen):\n            if seqP[i] == seqQ[i]:\n                if seqP[i] == \"<*>\":\n                    dis -= self.k2 * 1.0 / maxlen\n                else:\n                    dis -= self.k1 * 1.0 / maxlen\n        return dis\n\n    def load_data(self):\n        def preprocess(line):\n            for currentRex in self.rex:\n                line = re.sub(currentRex, \"\", line)\n            return line\n\n        headers, regex = self.generate_logformat_regex(self.logformat)\n        self.df_log = self.log_to_dataframe(\n            os.path.join(self.path, self.logname), regex, headers, self.logformat\n        )\n        self.df_log[\"Content_\"] = self.df_log[\"Content\"].map(preprocess)\n\n    def log_to_dataframe(self, log_file, regex, headers, logformat):\n        \"\"\"Function to transform log file to dataframe\"\"\"\n        log_messages = []\n        linecount = 0\n        with open(log_file, \"r\") as fin:\n            for line in fin.readlines():\n                try:\n                    match = regex.search(line.strip())\n                    message = [match.group(header) for header in headers]\n                    log_messages.append(message)\n                    linecount += 1\n                except Exception as e:\n                    print(\"Skip line: \" + line)\n        logdf = pd.DataFrame(log_messages, columns=headers)\n        logdf.insert(0, \"LineId\", None)\n        logdf[\"LineId\"] = [i + 1 for i in range(linecount)]\n        return logdf\n\n    def generate_logformat_regex(self, logformat):\n        \"\"\"\n        Function to generate regular expression to split log messages\n        \"\"\"\n        headers = []\n        splitters = re.split(r\"(<[^<>]+>)\", logformat)\n        regex = \"\"\n        for k in range(len(splitters)):\n            if k % 2 == 0:\n                splitter = re.sub(\" +\", \"\\s+\", splitters[k])\n                regex += splitter\n            else:\n                header = splitters[k].strip(\"<\").strip(\">\")\n                regex += \"(?P<%s>.*?)\" % header\n                headers.append(header)\n        regex = re.compile(\"^\" + regex + \"$\")\n        return headers, regex\n","repo_name":"logpai/logparser","sub_path":"logparser/LogMine/src/LogMine.py","file_name":"LogMine.py","file_ext":"py","file_size_in_byte":8103,"program_lang":"python","lang":"en","doc_type":"code","stars":1298,"dataset":"github-code","pt":"48"}
+{"seq_id":"36493458535","text":"from django.conf.urls import url\n\nfrom defacto.api.views import (\n    KospiAgentDataAPIView,\n    KosdaqAgentDataAPIView,\n    KospiTruePriceAPIView,\n    KosdaqTruePriceAPIView,\n    KospiScoreDataAPIView,\n    KosdaqScoreDataAPIView,\n    RankDataAPIView,\n)\n\nurlpatterns = [\n    url(r'^kp-ad-api/$', KospiAgentDataAPIView.as_view(), name='kp-agent-data'),\n    url(r'^kd-ad-api/$', KosdaqAgentDataAPIView.as_view(), name='kd-agent-data'),\n    url(r'^kp-tp-api/$', KospiTruePriceAPIView.as_view(), name='kp-true-price'),\n    url(r'^kd-tp-api/$', KosdaqTruePriceAPIView.as_view(), name='kd-true-price'),\n    url(r'^kp-sd-api/$', KospiScoreDataAPIView.as_view(), name='kp-score-data'),\n    url(r'^kd-sd-api/$', KosdaqScoreDataAPIView.as_view(), name='kd-score-data'),\n    url(r'^rd-api/$', RankDataAPIView.as_view(), name='rank-data'),\n]\n","repo_name":"WeareArbiter/Arbiter-Keystone-BuzzzLightYear","sub_path":"defacto/api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":830,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"375124434","text":"# Задайте список из N элементов, заполненных числами из промежутка [-N, N]. \n# Найдите произведение элементов на указанных позициях. Позиции хранятся в файле file.txt в одной строке одно число.\n# Реализуйте алгоритм перемешивания списка.\n\nimport random\ndef GetNumber(message):\n    isCorrect = False\n    while isCorrect == False:\n        try:\n            number = int(input(message))\n            isCorrect = True\n        except ValueError:\n            print(\"Введено не число. Повторите ввод \")\n    return number\n\ndef GetList(number):\n    my_list = []\n    for i in range(number):\n        my_list.append(random.randrange(-number, number+1))\n    return my_list\n\ndef Multiply(my_list):\n    multiply = -1\n    file = open('file.txt', 'r')\n    first_index = int(file.readline())\n    seconsd_index = int(file.readline())\n    if first_index > len(my_list) -1 or seconsd_index > len(my_list) -1:\n        print(\"Индексы в файле file.txt за пределами списка\")\n        \n    else:\n        print(f'Умножаем элементы с индексами {first_index} и {seconsd_index}')\n        multiply = my_list[first_index]*my_list[seconsd_index]\n    return multiply\n\ndef ShuffleList(my_list):\n    for i in range(len(my_list)-1, -1, -1):\n        j = random.randint(0, i + 1)\n        my_list[i], my_list[j] = my_list[j], my_list[i]\n    return my_list\n\n\nnumber = GetNumber('Введите число ')\nmy_list = GetList(number)\nresult = Multiply(my_list)\nprint(f'N = {number} -> {my_list} -> {result}')\nshuffle_lyst = ShuffleList(my_list)\nprint(shuffle_lyst)\n        ","repo_name":"artem-maklashev/python-homework-2","sub_path":"task4.py","file_name":"task4.py","file_ext":"py","file_size_in_byte":1793,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"7825186954","text":"from copy import deepcopy\nfrom queue import deque\n\ndef dead_end_fill(g1, g2):\n    g1 = deepcopy(g1)\n    g2 = deepcopy(g2)\n    dead_ends = deque()\n    for i in range(len(g1) - 1):\n        for j in range(len(g2) - 1):\n            f = [g1[i][j], g2[i][j], g1[i+1][j], g2[i][j+1]]\n            if sum(f) == 3:\n                dead_ends.append((i,j))\n\n    dead = set()\n    while dead_ends:\n        x, y = dead_ends.popleft()\n        dead.add(cur)\n        xx, yy = -1, -1\n        if not g1[x][y]:\n            xx, yy = x-1, y\n            g1[x][y] = True\n        elif not g2[x][y]:\n            xx, yy = x, y-1\n            g2[x][y] = True\n        elif not g1[x+1][y]:\n            xx, yy = x+1, y\n            g1[x+1][y] = True\n        elif not g2[x][y+1]:\n            xx, yy = x, y+1\n            g2[x][y+1] = True\n        if 0 <= xx < len(g1) - 1 and 0 <= yy < len(g1[0]) - 1:\n            dead_ends.append((xx, yy))\n    return dead\n        \n","repo_name":"0as1s/leetcode","sub_path":"dead_end_fill.py","file_name":"dead_end_fill.py","file_ext":"py","file_size_in_byte":930,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"5243238870","text":"\"\"\"Frequencies function.\"\"\"\n\"\"\"ENTER YOUR SOLUTION HERE!\"\"\"\n\ndef frequencies(items):\n    frequencies = {}\n    for i in items:\n        item = str(i)\n        val = 1\n        if item in frequencies:\n            val = frequencies.get(item) + 1\n        frequencies.update({item:val})\n    return frequencies","repo_name":"KCL-SEG/frequency-counter-ParthChugh14","sub_path":"frequencies.py","file_name":"frequencies.py","file_ext":"py","file_size_in_byte":301,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"38814003088","text":"import math\nimport numpy as np\nfrom scipy.constants import Planck, hbar, m_e, electron_volt\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nimport cmath\n\n\ndef wavenumber(E, m):\n    return np.sqrt(2.0 * m * E / (hbar ** 2))\n\n\nfig = plt.figure(figsize=(8, 6))\nplt.rcParams['font.family'] = 'Times New Roman'\nplt.rcParams['font.size'] = 12\n\n# 物理定数\nh = Planck\nhbar = hbar\nme = m_e\neV = electron_volt\n\nI = 0.0+1.0j\n\nNX = 500\ndx = 1.0 * 10 ** -9\nx_min = -10 * dx\nx_max = 10 * dx\nNK = 200\ndelta_x = 2.0 * 10 ** -9\nsigma = 2.0 * math.sqrt(math.log(2)) / delta_x\ndk = 20.0 / (delta_x * NK)\nE0 = 1.0 * eV\nk0 = wavenumber(E0, me)\nomega0 = hbar / (2.0 * me) * k0 ** 2\n\nts = -50\nte = 30\ndt = 1.0 * 10 ** -16\n\nimgs = []\n\nfor tn in range(ts, te):\n    t_real = dt * tn\n    xl = []\n    Psi_real = []\n    Psi_imag = []\n    Psi_abs = []\n    for nx in range(NX):\n        x = x_min + (x_max - x_min) * nx / NX\n        Psi = 0.0+0.0j\n        for kn in range(NK):\n            k = k0 + dk * (kn - NK / 2)\n            omega = hbar / (2.0 * me) * k ** 2\n            Psi += cmath.exp(I * (k * x - omega * t_real)) * \\\n                cmath.exp(-((k - k0) / (2.0 * sigma)) ** 2)\n\n        Psi = Psi * dk * dx / 10\n\n        # print(f'x/dx:{x/dx}\\nPsi_real:{Psi.real}\\nPsi_imag:{Psi.imag}\\nPsi_abs:{abs(Psi)}')\n\n        xl.append(x / dx)\n        Psi_real.append(Psi.real)\n        Psi_imag.append(Psi.imag)\n        Psi_abs.append(abs(Psi))\n\n    img = plt.plot(xl, Psi_real, 'red')\n    img += plt.plot(xl, Psi_imag, 'green')\n    img += plt.plot(xl, Psi_abs, 'blue')\n    time = plt.text(0.9, 1.03, 't:{:.2e}'.format(\n        t_real), transform=plt.gca().transAxes, ha='center', va='center')\n    img.append(time)\n    imgs.append(img)\n\nkl = []\na_kl = []\n\nfor kn in range(NK):\n    k = k0 + dk * (kn - NK / 2)\n    a_k = math.exp(-((k - k0) / (2.0 * sigma)) ** 2)\n    kl.append(k/dk)\n    a_kl.append(a_k)\n\nplt.title('Gaussian wave packet(Spatial distribution)')\nplt.xlabel('Position[nm]', fontsize=16)\nplt.ylabel('Probability amplitude', fontsize=16)\nplt.xlim([-10, 10])\nplt.ylim([-0.3, 0.3])\nani = animation.ArtistAnimation(fig, imgs, interval=50)\nani.save('output.html', writer=animation.HTMLWriter())\n\nfig2 = plt.figure(figsize=(5, 5))\nplt.title('Gaussian wave packet(Wave number distribution)')\nplt.xlabel('Wave number', fontsize=16)\nplt.ylabel('distribution', fontsize=16)\nplt.plot(kl, a_kl)\n\nplt.tight_layout()\nplt.show()\n","repo_name":"SJSY126/2week_QuantumComputer","sub_path":"pulse_animation.py","file_name":"pulse_animation.py","file_ext":"py","file_size_in_byte":2422,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"74245602704","text":"from fastapi import FastAPI\nimport uvicorn\nfrom fastapi.responses import JSONResponse\nfrom fastapi.middleware.cors import CORSMiddleware\nimport pandas as pd\nimport os\n\n#This code is used to build the API. It takes a username as input and returns a list of recommended movies for that user.\n\napp = FastAPI()\n\n# Add CORS middleware\napp.add_middleware(\n    CORSMiddleware,\n    allow_origins=[\"*\"],  # Update this to the appropriate origin URL for production\n    allow_methods=[\"GET\"],\n    allow_headers=[\"*\"],\n)\n\n\n@app.get(\"/health-check\")\nasync def root():\n    return {\"message\": \"Api is working\"}\n\n@app.get(\"/recommend-movies/{username}\")\nasync def predict(username: str):\n    # Load the model\n    path = os.path.abspath(\"pickle\")\n    model = pd.read_pickle(path + \"/titles.pkl\")\n\n    try:\n        # Get the cluster of the user\n        user_cluster = model[model['username'] == username]['cluster'].values[0]\n        # Get the list of users in the same cluster, but not the same user\n        cluster_df = model[model['cluster'] == user_cluster]\n        cluster_df = cluster_df[cluster_df['username'] != username]\n\n        # Get the titles of the users in the cluster\n        titles = []\n\n        for i in range(4, 10):\n            title = cluster_df.columns[i]\n            title = title.replace(\"15\", \"\").replace(\"\\n\", \"\").replace(\"\\\\\", \"\").replace(\"'\", \"\").replace(\"\", \"\")\n            titles.append(title)\n    except:\n        titles = [\"User not found\"]    \n\n    # Create a JSON response with the titles array\n    response = {\"titles\": list(set(titles))}\n    return JSONResponse(content=response)\n\n\n\nif __name__ == \"__main__\":\n    uvicorn.run(app, host=\"0.0.0.0\", port=4567)","repo_name":"burkido/Movie-Recommendation","sub_path":"api/src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1674,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"27636090071","text":"from tkinter import *\nimport time\nroot = Tk()\n\ncanvas = Canvas(root, width = 300, height = 300)\ncanvas.pack()\n\ncanvas.create_polygon(10, 10, 10, 60, 50, 35)\n\"\"\"\nfor i in range (0, 60):\n    canvas.move(1, 5, 5)\n    root.update()\n    time.sleep(0.1)\n\"\"\"\ndef movedown(event):\n    canvas.move(1, 5, 5)\n    root.update()\n   \ndef moveup(event):\n    canvas.move(1, -5, -5)\n    root.update() \n    \n# canvas move() 1 - objekt o ID=1, 5 pixeli zmiana położenia względem osi x i kolejne y\n\nroot.bind('<Button-1>', movedown)\nroot.bind('<Button-3>', moveup)\n\nroot.mainloop()","repo_name":"0xRobert1997/HelloWorld","sub_path":"PythonAvi/GUI Tkinter/11 Tkinter Canvas4.py","file_name":"11 Tkinter Canvas4.py","file_ext":"py","file_size_in_byte":564,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"26242271952","text":"import numpy as np\nimport pyvista\nfrom dolfinx import mesh, io, plot\n\nfrom dolfinx.fem import (Constant, Function, FunctionSpace, \n                         assemble_scalar, dirichletbc, form, locate_dofs_geometrical)\nfrom dolfinx.fem.petsc import LinearProblem\nfrom mpi4py import MPI\nfrom petsc4py.PETSc import ScalarType\nfrom ufl import SpatialCoordinate, TestFunction, TrialFunction, dot, ds, dx, grad, inner\n\nfrom Util.Boundaries import Boundaries\nimport Util.utils as utils\nimport main\n\n\nmesh = mesh.create_unit_square(MPI.COMM_WORLD, 10, 10)\nV = FunctionSpace(mesh, (\"CG\", 1))\nu = TrialFunction(V)\nv = TestFunction(V)\na = dot(grad(u), grad(v)) * dx\n\ndef u_exact(x):\n    return 1 + x[0]**2 + 2*x[1]**2\n\ndef boundary_D(x):\n    return np.logical_or(np.isclose(x[0], 0), np.isclose(x[0],1))\n\ndofs_D = locate_dofs_geometrical(V, boundary_D)\nu_bc = Function(V)\nu_bc.interpolate(u_exact)\nbc = dirichletbc(u_bc, dofs_D)\n\nx = SpatialCoordinate(mesh)\ng = -4 * x[1]\nf = Constant(mesh, ScalarType(-6))\nL = f * v * dx - g * v * ds\n\na_gen, L_gen, s = main.get_weak_form(None, \"u\", \"v\", \"-Laplacian(u) = f\", Boundaries.neumann, \"g\")\n\nutils.print_space(\"Original Form:\")\nprint(\"lhs\")\nprint(a)\nprint(\"rhs\")\nprint(L)\nutils.print_space(\"Transformed Form:\")\nprint(\"lhs\")\nprint(eval(a_gen))\nprint(\"rhs\")\nprint(eval(L_gen))\n\n\nproblem = LinearProblem(eval(a_gen), eval(L_gen), bcs=[bc], petsc_options={\"ksp_type\": \"preonly\", \"pc_type\": \"lu\"})\nuh = problem.solve()\n\nV2 = FunctionSpace(mesh, (\"CG\", 2))\nuex = Function(V2)\nuex.interpolate(u_exact)\nerror_L2 = assemble_scalar(form((uh - uex)**2 * dx))\nerror_L2 = np.sqrt(MPI.COMM_WORLD.allreduce(error_L2, op=MPI.SUM))\n\nu_vertex_values = uh.x.array\nuex_1 = Function(V)\nuex_1.interpolate(uex)\nu_ex_vertex_values = uex_1.x.array\nerror_max = np.max(np.abs(u_vertex_values - u_ex_vertex_values))\nerror_max = MPI.COMM_WORLD.allreduce(error_max, op=MPI.MAX)\nprint(f\"Error_L2 : {error_L2:.2e}\")\nprint(f\"Error_max : {error_max:.2e}\")\n\n\n\n# -------------------------------------------------------------\n# -------------------------------------------------------------\n# -------------------------------------------------------------\nwith io.XDMFFile(mesh.comm, \"out/poisson.xdmf\", \"w\") as file:\n    file.write_mesh(mesh)\n    file.write_function(uh)\n\ntry:\n    import pyvista\n    cells, types, x = plot.create_vtk_mesh(V)\n    grid = pyvista.UnstructuredGrid(cells, types, x)\n    grid.point_data[\"u\"] = uh.x.array.real\n    grid.set_active_scalars(\"u\")\n    plotter = pyvista.Plotter()\n    plotter.add_mesh(grid, show_edges=True)\n    warped = grid.warp_by_scalar()\n    plotter.add_mesh(warped)\n    if pyvista.OFF_SCREEN:\n        pyvista.start_xvfb(wait=0.1)\n        plotter.screenshot(\"uh_poisson.png\")\n    else:\n        plotter.show()\n\nexcept ModuleNotFoundError:\n    print(\"'pyvista' is required to visualise the solution\")\n    print(\"Install 'pyvista' with pip: 'python3 -m pip install pyvista'\")\n\n","repo_name":"Denn1sMay/ma","sub_path":"neumann_test.py","file_name":"neumann_test.py","file_ext":"py","file_size_in_byte":2912,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"70435616146","text":"#!/usr/bin/python3\n\nimport cv2\nimport sys\nfrom flask import Flask, render_template, Response\nfrom camera import VideoCamera\nfrom flask_cors import CORS\nimport time\nimport threading\nimport requests\nimport os\nimport datetime\nimport uuid\nimport picamera as pic\nfrom add import get_ip_address\nfrom sensors import Sensors\nfrom http_module import Http\nfrom mongo import MongoDB\nfrom value import Value\nfrom image import Image\n\nphoto_interval = 15\nvalues_interval = 120\n\nvideo_camera = VideoCamera(flip=True)\nobject_classifier = cv2.CascadeClassifier(\n    \"/home/pi/Desktop/Integradora/Camera/models/facial_recognition_model.xml\")\n\n# App Globals (do not edit)\napp = Flask(__name__)\ncors = CORS(app, resources={r\"/*\": {\"origins\": \"*\"}})\n\nlast_found = 0\nlast_pres = 0\nlast_values = 0\n\napi_url = 'http://23.21.161.238/v1/api/'\ndefault_name = 'SECURITY CAMERA'\ncode = 'HOLAPROFES'  # 'L12UL74M44J7418'\n\nsensors = Sensors()\nhttp = Http(api_url, default_name, code)\ndb = MongoDB()\n\n\ndef check_for_objects():\n    global last_found\n    global last_pres\n    global last_values\n    try:\n        status = http.register()\n        if status == 500:\n            print(\"Server error\")\n        elif status == 422:\n            print(\"Camera already registered\")\n            try:\n                http.changeIp()\n                print(\"Ip address updated\")\n            except:\n                print(\"Error changing camera ip address: \", sys.exc_info()[0])\n        elif status == 201:\n            print(\"Camera registered succesfully!\")\n    except:\n        print(\"Error registering camera: \", sys.exc_info()[0])\n\n    try:\n        status = http.startVideo()\n        if status == 500:\n            print(\"Server error\")\n        elif status == 200 or status == 404:\n            print(\"Video started\")\n            try:\n                values = db.getValues()\n                http.mongoValues(values)\n                images = db.getImages()\n                http.mongoImages(images)\n                db.dropCollection('values')\n                db.dropCollection('images')\n                print(\"Internal values transfered\")\n            except:\n                print(\"Error saving mongo values: \", sys.exc_info()[0])\n    except:\n        print(\"Error starting video: \", sys.exc_info()[0])\n\n    while True:\n        frame, found_obj = video_camera.get_object(object_classifier)\n        presence = sensors.getPIR()\n        if found_obj and (time.time() - last_found) > photo_interval:\n            date = datetime.datetime.now().strftime(\"%y%m%d_%H%M%S\")\n            filename = \"_\".join([\"image\", date])\n            route = filename + str(uuid.uuid4()) + \".jpg\"\n            distance = sensors.getDistance()\n            try:\n                http.saveImage(frame, 1, distance, route)\n                print(\"Person image saved!\")\n            except:\n                print(\"Error sending image, now saving locally: \",\n                      sys.exc_info()[0])\n                video_camera.save_frame(route)\n                image = Image(route, datetime.datetime.now(\n                ).astimezone().isoformat(), distance, 1)\n                db.addRegistro(\"images\", image)\n            last_found = time.time()\n        elif presence and (time.time() - last_pres) > photo_interval:\n            date = datetime.datetime.now().strftime(\"%y%m%d_%H%M%S\")\n            filename = \"_\".join([\"image\", date])\n            route = filename + str(uuid.uuid4()) + \".jpg\"\n            distance = sensors.getDistance()\n            try:\n                http.saveImage(frame, 2, distance, route)\n                print(\"Presence image saved!\")\n            except:\n                print(\"Error sending image, now saving locally: \",\n                      sys.exc_info()[0])\n                video_camera.save_frame(route)\n                image = Image(route, datetime.datetime.now(\n                ).astimezone().isoformat(), distance, 2)\n                db.addRegistro(\"images\", image)\n            last_pres = time.time()\n        if (time.time() - last_values) > values_interval:\n            try:\n                dht = sensors.getTempHum()\n                value = Value(dht[1], dht[0],\n                              datetime.datetime.now().astimezone().isoformat())\n                try:\n                    http.saveValues(value.temperature, value.humidity)\n                    print(\"Values saved!\")\n                except:\n                    print(\"Error saving values in server, now saving locally\",\n                          sys.exc_info()[0])\n                    db.addRegistro(\"values\", value)\n            except:\n                print(\"DHT sensor connection error\", sys.exc_info()[0])\n\n            last_values = time.time()\n\n\n@app.route('/')\ndef index():\n    return render_template('index.html')\n\n\n@app.route('/video')\ndef video():\n    resp = Response()\n    resp.headers[\"Access-Control-Allow-Origin\"] = \"*\"\n    resp.headers[\"Access-Control-Allow-Methods\"] = \"GET, POST, PUT, DELETE, PATCH, OPTIONS\"\n    resp.headers[\"Access-Control-Allow-Headers\"] = \"Origin, X-Requested-With, Content-Type, Accept, Authorization\"\n    return resp\n\n\ndef gen(camera):\n    while True:\n        #frame, found_obj = video_camera.get_object(object_classifier)\n        frame = camera.get_frame()\n        yield (b'--frame\\r\\n'\n               b'Content-Type: image/jpeg\\r\\n\\r\\n' + frame + b'\\r\\n\\r\\n')\n\n\n@app.route('/video_feed')\ndef video_feed():\n    return Response(gen(video_camera),\n                    mimetype='multipart/x-mixed-replace; boundary=frame')\n\n\nif __name__ == '__main__':\n    t = threading.Thread(target=check_for_objects, args=())\n    t.daemon = True\n    t.start()\n    app.run(host='127.0.0.1', debug=False)\n","repo_name":"HumbertoCanales/3rd-eye-camera","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5639,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"35478187973","text":"import turtle\n\nturtle1 = turtle.Turtle()\nturtle1.shape(\"turtle\")\nlb_x, lb_y = map(int, turtle.textinput(\"\", \"왼쪽 하단 모서리 좌표 x, y :\").split(\" \"))\nrt_x, rt_y = map(int, turtle.textinput(\"\", \"왼쪽 하단 모서리 좌표 x, y :\").split(\" \"))\nturtle1.penup()\nturtle1.goto(lb_x, lb_y)\nturtle1.pendown()\nturtle1.goto(rt_x, lb_y)\nturtle1.goto(rt_x, rt_y)\nturtle1.goto(lb_x, rt_y)\nturtle1.goto(lb_x, lb_y)\nturtle1.penup()\nu_x, u_y = map(int, turtle.textinput(\"\", \"왼쪽 하단 모서리 좌표 x, y :\").split(\" \"))\nturtle1.goto(u_x, u_y)\nturtle1.dot(30)\nturtle1.right(135)\nturtle1.forward(50)\nturtle1.left(135)\nif lb_x < u_x < rt_x and lb_y < u_y < rt_y:\n    turtle1.write(\"점은 사각형 내부에 있습니다.\")\nelse:\n    turtle1.write(\"점은 사각형 외부에 있습니다.\")\n\nturtle.done()\n","repo_name":"woohyun212/Data_Science_and_AI_introductory_Coding_Solution","sub_path":"chapter4/ex4_9.py","file_name":"ex4_9.py","file_ext":"py","file_size_in_byte":811,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"10191205979","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[30]:\n\n\n# Python program to explain os.mkdir() method \n\t\n# importing os module \nimport os \nimport datetime\nfrom datetime import datetime\nimport socket\n\t\n# Directory \n\ndef process_data(name_file_folder,data):\n\n    today = datetime.now()\n    names = name_file_folder\n    directory = names+today.strftime('%d-%m-%Y')\n\n\n    # Parent Directory path \n    namehost = socket.gethostname()\n\n    if (namehost == 'DESKTOP-M4LTLO4'):\n        print(\"Acer\")\n        parent_dir = \"E:/Scrapy/\"\n    else:\n        print('HP')\n\n\n    # Path \n    path = os.path.join(parent_dir, directory) \n\n    # Create the directory \n    # 'GeeksForGeeks' in \n    # '/home / User / Documents' \n    try:\n            os.mkdir(path)                               \n            print(\"Directory '% s' created\" % directory) \n    except FileExistsError:\n            print(\"Directory \" , directory ,  \" already exists\")    \n\n    # if directory / file that \n    # is to be created already \n    # exists then 'FileExistsError' \n    # will be raised by os.mkdir() method \n\n    # Similarly, if the specified path \n    # is invalid 'FileNotFoundError' Error \n    # will be raised \n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"pauls007/scrap","sub_path":"processnames.py","file_name":"processnames.py","file_ext":"py","file_size_in_byte":1205,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"31681473477","text":"#!/usr/bin/env python\r\n\r\nimport argparse, itertools, sys\r\nimport pickle as pickle\r\n\r\nimport numpy as np\r\nfrom util import Prediction, tokenize_words\r\nfrom sklearn.ensemble import RandomForestClassifier\r\n        \r\ndef load(fd):\r\n    d = np.load(fd)\r\n    return d['X']\r\n\r\ndef load_classifier(fd):\r\n    results = pickle.load(fd)\r\n    return results['rf']\r\n\r\ndef opts():\r\n    parser = argparse.ArgumentParser(\r\n        description='Find optimal threshold for inserting words')\r\n    parser.add_argument('data', type=argparse.FileType('r'),\r\n        help='npz file with training data')\r\n    parser.add_argument('classifier', type=argparse.FileType('r'),\r\n        help='Input pickle file with classifier to re-use')\r\n    parser.add_argument('predictions', type=argparse.FileType('r'),\r\n        help='Input file with predicted words and locations')\r\n    return parser\r\n\r\nif __name__ == \"__main__\":\r\n    args = opts().parse_args()\r\n    \r\n    print(\"Loading test data\", file=sys.stderr)\r\n    X = load(args.data)\r\n    X = np.asarray(X, dtype=np.float32)\r\n    X = np.nan_to_num(X)   \r\n \r\n    print(\"Loading classifer\", file=sys.stderr)\r\n    clf = load_classifier(args.classifier)\r\n    \r\n    print(\"Predicting decisions\", file=sys.stderr)\r\n    d = clf.predict(X)\r\n    \r\n    print(\"Performing decisions on stdin\", file=sys.stderr)\r\n    for di, line, pred in zip(d, sys.stdin, args.predictions):\r\n        pred = Prediction.parse(pred)\r\n        words = tokenize_words(line)\r\n        if di == 0: #  do nothing\r\n            pass\r\n        elif di == 1: # insert space\r\n            words.insert(pred.location, ' ')\r\n        else: # insert word\r\n            words.insert(pred.location, pred.word)\r\n        print(' '.join(words))","repo_name":"Keesiu/meta-kaggle","sub_path":"data/external/repositories_2to3/113677/KaggleBillionWordImputation-master/scripts/predict_sentences.py","file_name":"predict_sentences.py","file_ext":"py","file_size_in_byte":1707,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"72640325587","text":"import os\nimport shutil\nimport random\n\n# 源文件夹和目标文件夹的路径\nsource_folder = '/root/autodl-fs/sun_temp/datasets/dataset_cls/civitai_imgs'\ndestination_folder = '/root/autodl-fs/sun_temp/datasets/dataset_cls/civitai_imgs_20000'\n\n# 获取源文件夹中所有文件的列表\nall_files = os.listdir(source_folder)\n\n# 如果源文件夹中的文件少于20000个，你可以选择所有文件\nif len(all_files) <= 20000:\n    selected_files = all_files\nelse:\n    # 使用集合来跟踪已选择的文件，确保不重复选择\n    selected_files = set()\n\n    # 随机选择20000个不重复的文件\n    while len(selected_files) < 20000:\n        file_name = random.choice(all_files)\n        selected_files.add(file_name)\n\n# 确保目标文件夹存在，如果不存在则创建它\nif not os.path.exists(destination_folder):\n    os.makedirs(destination_folder)\n\n# 复制选中的文件到目标文件夹\nfor file_name in selected_files:\n    source_file_path = os.path.join(source_folder, file_name)\n    destination_file_path = os.path.join(destination_folder, file_name)\n    shutil.copy(source_file_path, destination_file_path)\n\nprint(f'已从{source_folder}复制{len(selected_files)}个文件到{destination_folder}')\n","repo_name":"sunhaha123/civitai_cls","sub_path":"select_pictures.py","file_name":"select_pictures.py","file_ext":"py","file_size_in_byte":1237,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"11706792332","text":"def is_free(pos):\n    if board[pos] == \" \":\n        return True\n    return False\n\n\ndef print_board():\n    for i in range(1, 10):\n        print(f\"| {board[i]} \", end=\"\")\n        if i % 3 == 0:\n            print(\"|\")\n\n\ndef check_rows():\n    for i in range(1, 8, 3):\n        res = \"\"\n        for j in range(3):\n            res += board[i+j]\n        if res == \"XXX\":\n            return \"X\"\n        elif res == \"OOO\":\n            return \"O\"\n    return None\n\n\ndef check_columns():\n    for i in range(3):\n        res = \"\"\n        for j in range(1, 8, 3):\n            res += board[i+j]\n        if res == \"XXX\":\n            return \"X\"\n        elif res == \"OOO\":\n            return \"O\"\n    return None\n\n\ndef check_diagonals():\n    main_d = \"\"\n    sec_d = \"\"\n    for i in range(1, 10, 4):\n        main_d += board[i]\n    for j in range(7, 2, -2):\n        sec_d += board[j]\n    if main_d == \"XXX\" or sec_d == \"XXX\":\n        return \"X\"\n    elif main_d == \"OOO\" or sec_d == \"OOO\":\n        return \"O\"\n    return None\n\n\ndef check_winner():\n    if check_rows() is not None:\n        return check_rows()\n    if check_diagonals() is not None:\n        return check_diagonals()\n    if check_columns() is not None:\n        return check_columns()\n\n\ndef call_by_symbol(sym):\n    if sym == player_1[1]:\n        return player_1[0]\n    return player_2[0]\n\n\ndef choice_picker(player, choice):\n    if is_free(choice):\n        board[choice] = player[1]\n\n\ndef winner():\n    winner_sym = check_winner()\n    if winner_sym is not None:\n        winner = call_by_symbol(winner_sym)\n        return f\"{winner} won!\"\n    return None\n\n\nplayer_1 = [\"\", \"X\"]\nplayer_2 = [\"\", \"O\"]\n\nplayer_1[0] = input(\"Player one name: \")\nplayer_2[0] = input(\"Player two name: \")\n\nplayer_1[1] = input(f\"{player_1[0]} would you like to play with 'X' or 'O'? \")\nif player_1[1] == \"X\":\n    player_2[1] = \"O\"\n\nboard = {}\nprint(\"This is the numeration of the board:\")\nfor i in range(1, 10):\n    print(f\"| {i} \", end=\"\")\n    if i % 3 == 0:\n        print(\"|\")      # Moving to a new line\n    board[i] = \" \"\nprint(f\"{player_1} starts first!\")\n\nwhile True:\n    p1_choice = int(input(f\"{player_1} choose a free position [1-9]: \"))\n    choice_picker(player_1, p1_choice)\n    print_board()\n    win = winner()\n    if win is not None:\n        print(win)\n        break\n\n    p2_choice = int(input(f\"{player_2} choose a free position [1-9]: \"))\n    choice_picker(player_2, p2_choice)\n    print_board()\n    win = winner()\n    if win is not None:\n        print(win)\n        break","repo_name":"meroo893/Tic-tac-toe","sub_path":"TIc-Tac-Toe.py","file_name":"TIc-Tac-Toe.py","file_ext":"py","file_size_in_byte":2499,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"72898155346","text":"import time\n\nimport requests\nimport random\nimport multiprocessing\nimport timeit\n\n\n# This code is to test the ms application which I have made\n# It runs a set of random equations and triggers it\ndef thread_func(x):\n    for j in range(10):\n        endpoint = \"http://localhost:8080/expressions\"\n        string_data = str(random.randint(1, 9)) + \" * \" + str(random.randint(1, 9))\n        response = requests.post(endpoint, data=string_data, headers={\"Content-Type\": \"text/plain\"})\n        print(response.status_code, response.content, f\"part of {x}\", string_data)\n        for i in range(100):\n            string_data = str(random.randint(1, 9)) + \" * \" + str(random.randint(1, 9))\n            response = requests.post(endpoint, data=string_data, headers={\"Content-Type\": \"text/plain\"})\n            print(response.status_code, response.content, f\"part of {x}\", string_data)\n        response = requests.post(endpoint, data=\"end\", headers={\"Content-Type\": \"text/plain\"})\n        print(response.status_code, response.content, f\"part of {x}\", string_data)\n\n    time.sleep(1)\n\n\ndef main():\n    with multiprocessing.Pool(processes=50) as pool:\n        pool.map(thread_func, [x for x in range(50)])\n\n\nif __name__ == '__main__':\n    execution_time = timeit.timeit(main, number=1)\n    print(f\"The function took {execution_time} seconds to execute.\")\n","repo_name":"mathew-stark/onlinesales-test","sub_path":"Expression Eval/api test.py","file_name":"api test.py","file_ext":"py","file_size_in_byte":1337,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"3378606271","text":"#! /usr/bin/env python\n#coding=utf-8\nfrom django.conf.urls import patterns, url\nfrom .views import push,pull,player_status\n\n\nurlpatterns = patterns('',\n    url(r'^record/push/$', push, name='push'),\n    url(r'^record/pull/$', pull, name='pull'),\n    url(r'^player_status/$', player_status, name='status'),                       \n    )\n","repo_name":"scotfu/game","sub_path":"records/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":335,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"70195506705","text":"'''\nCreates words clouds for the top cutest categories based on the comments\n    on posts with that animal.\n'''\nimport ast\nfrom wordcloud import WordCloud\nimport matplotlib.pyplot as plt\n\ndef add_comments(animal, posts_for_animal):\n    '''\n    Adds all the comments for an animal into one big string.\n\n    Args:\n        animal: A string that represents an animal name.\n        post_for_animal: A string that is the path to a\n                txt file that has a dictionary that has\n                all the posts sorted into which animal it is.\n    Returns:\n        text: A string that is all the comments for posts with that\n            animal in it.\n    '''\n    text = ''\n    with open(posts_for_animal, 'r', encoding='utf8') as animals_id:\n        animals_dict = ast.literal_eval(str(animals_id.read()))\n    # Adds every comment from the animal to the string.\n    for post_id in animals_dict[animal]:\n        with open(f'data/comments/{post_id}.txt', 'r', encoding='utf8')\\\n             as comment:\n            text += str(comment.read())\n    return text\n\ndef create_word_cloud(animal):\n    '''\n    Takes in an animal name and creates and shows a word cloud image\n        of the most common words in the comments for posts\n        with that animal.\n    Args:\n        animal: A string that is an animal name.\n    '''\n    text = add_comments(animal, 'data/animals_posts.txt')\n    #Plots the word cloud and saves it to visualizations folder.\n    plt.figure( figsize=(20,10) )\n    word_cloud = WordCloud(width=1600, height=800).generate(text)\n    plt.imshow(word_cloud, interpolation='bilinear')\n    plt.axis('off')\n    plt.tight_layout(pad=0)\n    plt.savefig(f'visualizations/{animal}wordcloud.png')\n    plt.show()\n#Creates a word cloud for the top 4 cutest animals based on the bar graphs.\ncreate_word_cloud('dog')\ncreate_word_cloud('cat')\ncreate_word_cloud('human')\ncreate_word_cloud('bird')\n","repo_name":"olincollege/cute-animals-reddit","sub_path":"word_clouds.py","file_name":"word_clouds.py","file_ext":"py","file_size_in_byte":1892,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"42575131584","text":"from tkinter import *\nfrom random import randint\n\nroot = Tk()\n\ncanvas = Canvas(root, width='300', height='300')\ncanvas.pack()\n\n# create a line drawing function that takes 2 parameters:\n# the x and y coordinates of the line's starting point\n# and draws a 50 long horizontal line from that point.\n# draw 3 lines with that function.\n\n\ndef line_drawing(x_coordinate, y_coordinate):\n    line = canvas.create_line(x_coordinate, y_coordinate, x_coordinate+50, y_coordinate)\n    return\n\n\nline_drawing(randint(0, 250), randint(0, 300))\nline_drawing(randint(0, 250), randint(0, 300))\nline_drawing(randint(0, 250), randint(0, 300))\n\nroot.mainloop()","repo_name":"green-fox-academy/judashgriff","sub_path":"Week 3/Day 3/horizontal.py","file_name":"horizontal.py","file_ext":"py","file_size_in_byte":637,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"6650020101","text":"import sys\nimport torch\nfrom autoencoder.encoder import VariationalEncoder\nfrom settings import MODE\n\nclass EncodeState():\n    def __init__(self, latent_dim):\n        # Initialize the encoder\n        self.latent_dim = latent_dim\n        self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n        \n        try:\n            self.conv_encoder = VariationalEncoder(self.latent_dim).to(self.device)\n            self.conv_encoder.load()\n            self.conv_encoder.eval()\n\n            for params in self.conv_encoder.parameters():\n                params.requires_grad = False\n        except Exception as e:\n            print(e)\n            print('Encoder could not be initialized.')\n            sys.exit()\n    \n    def process(self, observation):\n        \"\"\"\n        Process the observation and prepare it for the dueling dqn network.\n        The network need observations as array.\n        But process transformation ar doing over tensors because it is faster.\n\n\n        Args:\n            observation: current observation.\n\n        Returns:\n            observations.\n        \"\"\"\n\n        if MODE == 1:\n\n            # Normalize observation and convert to tensor.\n            image_obs = torch.tensor(observation[0]/255, dtype=torch.float).to(self.device)\n            # Permute the image to prepare for pytorch.\n            image_obs = image_obs.permute(2,1,0)\n            # Convert tensor into numpy array\n            image_obs = image_obs.cpu().detach().numpy()\n            \n            driving_obs = torch.tensor(observation[1], dtype=torch.float).to(self.device)\n            driving_obs = driving_obs.cpu().detach().numpy()\n\n            # image_obs = observation[0]/255\n            # image_obs = np.transpose(observation[0]/255, (2,1,0))\n\n            # driving_obs = observation[1]\n\n            return  image_obs, driving_obs\n\n        else:\n            # Normalize observation and convert to tensor.\n            image_obs = torch.tensor(observation[0]/255, dtype=torch.float).to(self.device)\n            # Prepare for pytorch.\n            image_obs = image_obs.unsqueeze(0)\n            image_obs = image_obs.permute(0,3,2,1)\n            # Preprocess the image with the encoder\n            image_obs = self.conv_encoder(image_obs)\n            # Concat image and driving observation.\n            driving_obs = torch.tensor(observation[1], dtype=torch.float).to(self.device)\n            observation = torch.cat((image_obs.view(-1), driving_obs), -1)\n\n            # Convert tensor into numpy array\n            observation = observation.cpu().detach().numpy()\n\n            return observation\n        \n        ","repo_name":"Pituel/CARLA_RL_TFM_UOC","sub_path":"encoder_init.py","file_name":"encoder_init.py","file_ext":"py","file_size_in_byte":2627,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"16177760624","text":"from __future__ import division\n\nimport pickle\n\nimport io\nimport os\nfrom collections import Counter, defaultdict\n\nfrom magpie.base.document import Document\n\n\ndef save_to_disk(path_to_disk, obj, overwrite=False):\n  \"\"\" Pickle an object to disk \"\"\"\n  dirname = os.path.dirname(path_to_disk)\n  if not os.path.exists(dirname):\n    raise ValueError(\"Path \" + str(dirname) + \" does not exist\")\n\n  if not overwrite and os.path.exists(path_to_disk):\n    raise ValueError(\"File \" + str(path_to_disk) + \"already exists\")\n\n  pickle.dump(obj, open(path_to_disk, 'wb'))\n\n\ndef load_from_disk(path_to_disk):\n  \"\"\" Load a pickle from disk to memory \"\"\"\n  if not os.path.exists(path_to_disk):\n    raise ValueError(\"File \" + path_to_disk + \" does not exist\")\n\n  return pickle.load(open(path_to_disk, 'rb'))\n\n\ndef get_all_answers(data_dir, filtered_by=None):\n  \"\"\"\n  Extract ground truth answers from *.lab files in a given directory\n  :param data_dir: path to the directory with .lab files\n  :param filtered_by: whether to filter the answers.\n\n  :return: dictionary of the form e.g. {'101231': set('lab1', 'lab2') etc.}\n  \"\"\"\n  answers = dict()\n\n  files = {filename[:-4] for filename in os.listdir(data_dir)}\n  for f in files:\n    answers[f] = get_answers_for_doc(f + '.txt',\n                                     data_dir,\n                                     filtered_by=filtered_by)\n\n  return answers\n\n\ndef get_answers_for_doc(doc_name, data_dir, filtered_by=None):\n  \"\"\"\n  Read ground_truth answers from a .lab file corresponding to the doc_name\n  :param doc_name: the name of the document, should end with .txt\n  :param data_dir: directory in which the documents and answer files are\n  :param filtered_by: whether to filter the answers.\n\n  :return: set of unicodes containing answers for this particular document\n  \"\"\"\n  filename = os.path.join(data_dir, doc_name[:-4] + '.lab')\n\n  if not os.path.exists(filename):\n    raise ValueError(\"Answer file \" + filename + \" does not exist\")\n\n  with io.open(filename, 'r') as f:\n    answers = {line.rstrip('\\n') for line in f}\n\n  if filtered_by:\n    answers = {kw for kw in answers if kw in filtered_by}\n\n  return answers\n\n\ndef calculate_label_distribution(data_dir, filtered_by=None):\n  \"\"\"\n  Calculate the distribution of labels in a directory. Function can be used\n  to find the most frequent and not used labels, so that the target\n  vocabulary can be trimmed accordingly.\n  :param data_dir: directory path with the .lab files\n  :param filtered_by: a set of labels that defines the vocabulary\n\n  :return: list of KV pairs of the form (14, ['lab1', 'lab2']), which means\n           that both lab1 and lab2 were labels in 14 documents\n  \"\"\"\n  answers = [\n      kw for v in get_all_answers(\n          data_dir,\n          filtered_by=filtered_by).values() for kw in v]\n  counts = Counter(answers)\n\n  histogram = defaultdict(list)\n  for kw, cnt in counts.items():\n    histogram[cnt].append(kw)\n\n  return histogram\n\n\ndef calculate_number_of_labels_distribution(data_dir, filtered_by=None):\n  \"\"\" Look how many papers are there with 3 labels, 4 labels etc.\n   Return a histogram. \"\"\"\n  answers = get_all_answers(data_dir, filtered_by=filtered_by).values()\n  lengths = [len(ans_set) for ans_set in answers]\n  return Counter(lengths).items()\n","repo_name":"TovlyDeutsch/Linguistic-Features-for-Readability","sub_path":"magpie/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3264,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"48"}
+{"seq_id":"16926949838","text":"import numpy as np\n\n# 유클리디안 거리\ndef euclidean_dist(x,y):\n    return np.sqrt(np.sum((x-y)**2))\n\ndoc1 = np.array((2,3,0,1))\ndoc2 = np.array((1,2,3,1))\ndoc3 = np.array((2,1,2,2))\ndocQ = np.array((1,1,0,1))\n\nprint('문서1과 문서Q의 거리 :',euclidean_dist(doc1,docQ))\nprint('문서2과 문서Q의 거리 :',euclidean_dist(doc2,docQ))\nprint('문서3과 문서Q의 거리 :',euclidean_dist(doc3,docQ))\n\n#자카드 유사도\n# 두 문장의 교집합 / 합집합\n\ndoc1 = \"apple banana everyone like likey watch card holder\"\ndoc2 = \"apple banana coupon passport love you\"\n\ntok_1 = doc1.split(\" \")\ntok_2 = doc2.split(\" \")\nprint(tok_1)\nprint(tok_2)\n\nunion = set(tok_1).union(set(tok_2))\nprint('문서1과 문서2의 합집합 :',union)\n\nintersection = set(tok_1).intersection(set(tok_2))\nprint('문서1과 문서2의 교집합 :',intersection)\n\nprint('자카드 유사도 :',len(intersection)/len(union))\n","repo_name":"Son-Seung-Tae/STUDY_NLP","sub_path":"vector similarity/various similarity.py","file_name":"various similarity.py","file_ext":"py","file_size_in_byte":914,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"23127703812","text":"# Project 2: Michelle Tan\n# Instructor: Prof. Parkinson\n# Date: 2/3/2021\n\nfrom typing import Any\n\nfrom stack_array import Stack\n\n\n# You should not change this Exception class!\nclass PostfixFormatException(Exception):\n    pass\n\ndef postfix_eval(input_str: str) -> Any:\n    \"\"\"Evaluates a postfix expression\"\"\"\n    \"\"\"Input argument:  a string containing a postfix expression where tokens \n    are space separated.  Tokens are either operators + - * / ** << >> or numbers (integers or floats)\n    Returns the result of the expression evaluation. \n    Raises an PostfixFormatException if the input is not well-formed\"\"\"\n    stack = Stack(30)\n    if input_str == \"\":\n        raise PostfixFormatException('Insufficient operands')\n    op_list = [\"+\", \"-\", \"*\", \"/\", \"<<\", \">>\", \"**\"]\n    split_list = input_str.split()\n    for i in split_list:\n        new_val = i.lstrip(\"-\")\n        new_val = new_val.replace(\".\", \"\", 1)\n        if i in op_list:\n            try:\n                num_val = stack.pop()\n                num_val_initial = stack.pop()\n            except IndexError:\n                raise PostfixFormatException(\"Insufficient operands\")\n            if i == \"+\":\n                stack.push(num_val_initial + num_val)\n            if i == \"-\":\n                stack.push(num_val_initial - num_val)\n            if i == \"*\":\n                stack.push(num_val_initial * num_val)\n            if i == \"/\":\n                if num_val == 0:\n                    raise ValueError(\"0 not divisible\")\n                stack.push(num_val_initial / num_val)\n            if i == \"**\":\n                stack.push(num_val_initial ** num_val)\n            if i == \"<<\":\n                t1 = type(num_val)\n                t2 = type(num_val_initial)\n                if t1 == float or t2 == float:\n                    raise PostfixFormatException(\"Illegal bit shift operand\")\n                stack.push(num_val_initial << num_val)\n            if i == \">>\":\n                t1 = type(num_val)\n                t2 = type(num_val_initial)\n                if t1 == float or t2 == float:\n                    raise PostfixFormatException(\"Illegal bit shift operand\")\n                stack.push(num_val_initial >> num_val)\n        elif new_val.isdigit():\n            if \".\" in i:\n                stack.push(float(i))\n            else:\n                stack.push(int(i))\n        else:\n            raise PostfixFormatException(\"Invalid token\")\n    val = stack.pop()\n    if not stack.is_empty():\n        raise PostfixFormatException(\"Too many operands\")\n    return val\n\n\ndef infix_to_postfix(input_str: str) -> Any:\n    \"\"\"Converts an infix expression to an equivalent postfix expression\"\"\"\n    \"\"\"Input argument:  a string containing an infix expression where tokens are \n    space separated.  Tokens are either operators + - * / ** << >> or numbers (integers or floats)\n    Returns a String containing a postfix expression \"\"\"\n    stack = Stack(30)\n    if input_str == '':\n        return ''\n    op_list = [\"+\", \"-\", \"*\", \"/\", \"<<\", \">>\", \"**\"]\n    order = {}\n    order[\"+\"] = 1\n    order[\"-\"] = 1\n    order[\"*\"] = 2\n    order[\"/\"] = 2\n    order[\"**\"] = 3\n    order[\"<<\"] = 4\n    order[\">>\"] = 4\n    pfix_str = ''\n    split_list = input_str.split()\n    for i in split_list:\n        new_val = i.lstrip(\"-\")\n        new_val = new_val.replace(\".\", \"\", 1)\n        if new_val.isdigit() and pfix_str == \"\":\n            pfix_str = pfix_str + i\n        elif i in op_list:\n            if not stack.is_empty():\n                p = stack.peek()\n            while 0 < stack.size():\n                p = stack.peek()\n                if p == \"(\":\n                    break\n                if i == \"**\":\n                    if order[p] <= order[i]:\n                        break\n                    else:\n                        p1 = stack.pop()\n                        pfix_str = pfix_str + \" \" + p1\n                elif order[p] < order[i]:\n                    break\n                else:\n                    p2 = stack.pop()\n                    pfix_str = pfix_str + \" \" + p2\n            stack.push(i)\n        elif i == \"(\":\n            stack.push(i)\n        elif new_val.isdigit():\n            pfix_str = pfix_str + \" \" + i\n        elif i == \")\":\n            p = stack.peek()\n            while p != \"(\":\n                pfix_str = pfix_str + \" \" + stack.pop()\n                if not stack.is_empty():\n                    p = stack.peek()\n            stack.pop()\n    while not stack.is_empty():\n        pop3 = stack.pop()\n        pfix_str = pfix_str + \" \" + pop3\n    return pfix_str\n\n\ndef prefix_to_postfix(input_str: str) -> Any:\n    \"\"\"Converts a prefix expression to an equivalent postfix expression\"\"\"\n    \"\"\"Input argument: a string containing a prefix expression where tokens are \n    space separated.  Tokens are either operators + - * / ** << >> or numbers (integers or floats)\n    Returns a String containing a postfix expression(tokens are space separated)\"\"\"\n    stack = Stack(30)\n    if input_str == \"\":\n        return (\"\")\n    op_list = [\"+\", \"-\", \"*\", \"/\", \"<<\", \">>\", \"**\"]\n    split_list = input_str.split()\n    track = len(split_list) - 1\n    while track >= 0:\n        new_val = split_list[track].lstrip(\"-\")\n        new_val = new_val.replace(\".\", \"\", 1)\n        if new_val.isdigit():\n            stack.push(split_list[track])\n            track = track - 1\n        elif split_list[track] in op_list:\n            first = stack.pop()\n            second = stack.pop()\n            stack.push(first + \" \" + second + \" \" + split_list[track])\n            track = track - 1\n        else:\n            break\n    postfix = stack.pop()\n    return postfix\n","repo_name":"mtan22/CPE202","sub_path":"projects/project2/exp_eval.py","file_name":"exp_eval.py","file_ext":"py","file_size_in_byte":5619,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"28002338889","text":"import os\nfrom pathlib import Path\nimport logging\n\nlogging.basicConfig(level=logging.INFO,format='[%(asctime)s]:%(message)s')\n\npackage_name=\"deepClassifier\"\n\nlist_of_files=[\n    \".github/workflows/.gitkeep\",\n    f\"src/{package_name}/__init__.py\",\n    f\"src/{package_name}/components/__init__.py\",\n    f\"src/{package_name}/utils/__init__.py\",\n    f\"src/{package_name}/constants/__init__.py\",\n    f\"src/{package_name}/config/__init__.py\",\n    f\"src/{package_name}/pipeline/__init__.py\",\n    f\"src/{package_name}/entity/__init__.py\",\n    \"tests/__init__.py\",\n    \"tests/unit/__init__.py\",  # let's say you have created any class and you want to specifically test that class , then we use unit tests  \n    \"tests/integration/__init__.py\",  # when you are testing working of the pipeline\n    \"configs/config.yaml\",\n    \"dvc.yaml\",  # dvc=data versioning control file\n    \"params.yaml\",\n    \"init_setup.sh\",\n    \"requirements.txt\",\n    \"requirements_dev.txt\",\n    \"setup.py\",\n    \"setup.cfg\",\n    \"pyproject.toml\",\n    \"tox.ini\", # used for testing of the project locally\n    \"research/trials.ipynb\",\n]\n\n\nfor filepath in list_of_files:\n    filepath=Path(filepath)\n    filedir,filename=os.path.split(filepath)\n    if filedir!=\"\":\n        os.makedirs(filedir,exist_ok=True)\n        logging.info(f\"Creating directory {filedir} for file  {filename} \")\n    if (not os.path.exists(filepath)) or (os.path.getsize(filepath)==0) :\n        with open(filepath,\"w\") as f:\n            pass # create an empty file\n            logging.info(f\"Creating empty file : {filepath}\")\n    else:\n        logging.info(f\"{filename} already exits\")\n","repo_name":"anjalijoshi02/CNN_Classifier","sub_path":"template.py","file_name":"template.py","file_ext":"py","file_size_in_byte":1616,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"10572440995","text":"import jax.numpy as jnp\nfrom ott.geometry import pointcloud\n\ndef pairwise_dists(point_cloud : pointcloud, squared = False):\n    norms = jnp.sum(jnp.square(point_cloud.x), axis = 1)\n    dists_sq = jnp.maximum(norms[:, None] + norms[None, :] - 2 * point_cloud.x @ point_cloud.x.T, 0)\n    if squared:\n        return dists_sq\n    else:\n        return jnp.sqrt(dists_sq)\n\ndef histogram_distances(point_cloud_x : pointcloud, point_cloud_y : pointcloud, p = 1):\n    \"\"\"\n    Computes the distances needed to compute the second lower bound from\n    [Memoli 2011] in a vectorized and jitable format. The general strategy here is taken\n    from the scipy function `scipy.stats._cdf_distance`.\n    \"\"\"\n    dists_x = pairwise_dists(point_cloud_x)\n    dists_y = pairwise_dists(point_cloud_y)\n\n    N_x, N_y = dists_x.shape[0], dists_y.shape[0]\n    dists_x_sorted = jnp.sort(dists_x, axis = 1)\n    dists_y_sorted = jnp.sort(dists_y, axis = 1)\n\n    dists_x_wide = jnp.tile(dists_x_sorted[:, None, :], [1, N_y, 1])\n    dists_y_wide = jnp.tile(dists_y_sorted[None, :, :], [N_x, 1, 1])\n\n    all_values = jnp.concatenate([dists_x_wide, dists_y_wide], axis = 2)\n    all_values_sorter = jnp.argsort(all_values, axis = 2)\n\n    # These two methods should be equivalent. I'm not sure which is faster\n    # all_values_sorted = jnp.sort(all_values)\n    all_values_sorted = jnp.take_along_axis(all_values, all_values_sorter, axis = 2) # I'm guessing this one\n\n    deltas = jnp.diff(all_values_sorted, axis = 2)\n\n    dist_x_proto_pdf = jnp.concatenate([jnp.ones(dists_x_wide.shape), jnp.zeros(dists_y_wide.shape)], axis = 2)\n    # dist_y_proto_pdf = jnp.concatenate([jnp.zeros(dists_x_wide.shape), jnp.ones(dists_y_wide.shape)], axis = 2)\n\n    dist_x_pdf = jnp.take_along_axis(dist_x_proto_pdf, all_values_sorter, axis = 2)\n    # dist_y_pdf = jnp.take_along_axis(dist_y_proto_pdf, all_values_sorter, axis = 2)\n    dist_y_pdf = 1 - dist_x_pdf\n\n    dist_x_cdf = jnp.cumsum(dist_x_pdf / N_x, axis = 2)[:, :, :-1]\n    dist_y_cdf = jnp.cumsum(dist_y_pdf / N_y, axis = 2)[:, :, :-1]\n    if p == 1:\n        hist_dis_xy = jnp.sum(jnp.multiply(jnp.abs(dist_x_cdf - dist_y_cdf), deltas), axis = 2)\n    elif p == 2:\n        hist_dis_xy = jnp.sqrt(2) * jnp.sqrt(jnp.sum(jnp.multiply(jnp.square(dist_x_cdf - dist_y_cdf), deltas), axis = 2))\n    else:\n        hist_dis_xy = jnp.power(jnp.sum(jnp.multiply(jnp.power(jnp.abs(dist_x_cdf - dist_y_cdf), p),\n                                       deltas), axis = 2), 1/p)\n\n    return hist_dis_xy","repo_name":"Daniel-Packer/GW-speed","sub_path":"lower_bound.py","file_name":"lower_bound.py","file_ext":"py","file_size_in_byte":2496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"21454459390","text":"#!/usr/bin/env python\n\"\"\"Tests for the unregister command\n\nEnsure that a users controller can be unregistered.\n\nTest plan:\n - add user\n - run juju register command from previous step\n - verify controller shows up in juju list-controllers\n - 'juju unregister' the controller\n - verify the controller is no longer listed in juju list-controllers\n - verify that juju switch does not show the unregistered controller as\n   the current controller; will show \"ERROR no currently specified model\"\n\n\"\"\"\n\nfrom __future__ import print_function\n\nimport argparse\nimport json\nimport logging\nimport subprocess\nimport sys\nfrom textwrap import dedent\n\nfrom assess_user_grant_revoke import (\n    User,\n)\nfrom deploy_stack import (\n    BootstrapManager,\n)\nfrom utility import (\n    JujuAssertionError,\n    add_basic_testing_arguments,\n    configure_logging,\n    temp_dir,\n)\n\n\n__metaclass__ = type\n\n\nlog = logging.getLogger(\"assess_unregister\")\n\n\ndef assess_unregister(client):\n    user = User('testuser', 'read', [])\n    user_controller_name = '{}_controller'.format(user.name)\n    with temp_dir() as fake_home:\n        user_client = client.register_user(user, fake_home)\n        assert_controller_list(\n            user_client,\n            [user_controller_name])\n\n        user_client.juju(\n            'unregister', ('--yes', user_controller_name), include_e=False)\n\n        assert_controller_list(user_client, [])\n\n        assert_switch_raises_error(user_client)\n\n    # Ensure original controller still exists.\n    assert_controller_list(client, [client.env.controller.name])\n\n\ndef assert_switch_raises_error(client):\n    try:\n        client.get_juju_output('switch', include_e=False)\n    except subprocess.CalledProcessError as e:\n        if 'no currently specified model' not in e.stderr:\n            raise JujuAssertionError(\n                '\"juju switch\" command failed for an unexpected reason: '\n                '{}'.format(e.stderr))\n        log.info('\"juju switch\" failed as expected')\n        return\n    raise JujuAssertionError('\"juju switch failed to error as expected.\"')\n\n\ndef assert_controller_list(client, controller_list):\n    \"\"\"Assert that clients controller list only contains names provided.\n\n    :param client: ModelClient to retrieve controllers of.\n    :param controller_list: list of strings for expected controller names.\n\n    \"\"\"\n    json_output = client.get_juju_output(\n        'list-controllers', '--format', 'json', include_e=False)\n    output = json.loads(json_output)\n\n    try:\n        controller_names = output['controllers'].keys()\n    except AttributeError:\n        # It's possible that there are 0 controllers for this client.\n        controller_names = []\n\n    if controller_names != controller_list:\n        raise JujuAssertionError(\n            dedent(\"\"\"\\\n            Unexpected controller names.\n            Expected: {}\n            Got: {}\"\"\".format(\n                controller_list, controller_names)))\n\n\ndef parse_args(argv):\n    parser = argparse.ArgumentParser(description=\"Test unregister feature.\")\n    add_basic_testing_arguments(parser)\n    return parser.parse_args(argv)\n\n\ndef main(argv=None):\n    args = parse_args(argv)\n    configure_logging(args.verbose)\n    bs_manager = BootstrapManager.from_args(args)\n    with bs_manager.booted_context(args.upload_tools):\n        assess_unregister(bs_manager.client)\n    return 0\n\n\nif __name__ == '__main__':\n    sys.exit(main())\n","repo_name":"juju/1.25-upgrade","sub_path":"juju2/acceptancetests/assess_unregister.py","file_name":"assess_unregister.py","file_ext":"py","file_size_in_byte":3408,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"48"}
+{"seq_id":"6868200555","text":"#!/usr/bin/env python\n# license removed for brevity\nimport rospy\nfrom geometry_msgs.msg import Twist\nimport serial\nimport csv\nfrom time import sleep\n\n\narduino_port = \"/dev/ttyACM0\" #serial port of Arduino\nbaud = 9600 #arduino uno runs at 9600 baud\nfileName=\"data10march.csv\" #name of the CSV file generated\nser = serial.Serial(arduino_port, baud)\nprint(\"Connected to Arduino port:\" + arduino_port)\nsleep(.1)\nA=-62*2400*7/(32*22*0.655)\nB=-62*2400*7/(16*22*0.655)\nC=26*2400/(21*360)\n\ndef encoder_pub():\n  #global msg\n  msg = Twist()\n  pub = rospy.Publisher('encoder', Twist, queue_size=10)\n  rospy.init_node('encoderPub', anonymous=True)\n  rate = rospy.Rate(10) # 10hz\n  while not rospy.is_shutdown():\n    ser.flushInput()\n    getData=ser.readline()\n    data = getData.decode()\n    readings = data.split(\",\")\n    print(readings)\n    if len(readings)==3:\n      msg.linear.x = int(readings[0])/A\n      msg.linear.y = int(readings[1])/B\n      msg.angular.z = int(readings[2])/C\n      #rospy.loginfo(hello_str)\n      pub.publish(msg)\n    rate.sleep()\n\nif __name__ == '__main__':\n    try:\n        encoder_pub()\n    except rospy.ROSInterruptException:\n        pass\n","repo_name":"Niyas-A/SBAB","sub_path":"src/man_cntrl/src/read_encoder.py","file_name":"read_encoder.py","file_ext":"py","file_size_in_byte":1157,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"30403903639","text":"import math\n\n# Distance formula returns distance between 2 points\ndef distance(p1, p2):\n    d = math.sqrt(pow(p1[0] - p2[0], 2) + pow(p1[1] - p2[1], 2))\n    return d\n\n# Dictionaries are created to allow for ease of future edits\n# If in future points were entered differently then all that would need to be created for\n# compatibility is dictionaries for each frame\ndef create_dicts(matrix):\n    frames = []\n\n    # Hard coded order of points to ensure dictionary compatibility with all versions of Python\n    entries = [\"nose\", \"neck\", \"Rshoulder\", \"Relbow\", \"Lshoulder\", \"Lelbow\", \"Rhip\", \"Rknee\", \"Lhip\", \"Lknee\"]\n\n    for entry in matrix:\n        frame = {\n            \"nose\" : None,\n            \"neck\" : None,\n            \"Rshoulder\" : None,\n            \"Relbow\" : None,\n            \"Lshoulder\" : None,\n            \"Lelbow\" : None,\n            \"Rhip\" : None,\n            \"Rknee\" : None,\n            \"Lhip\" : None,\n            \"Lknee\" : None\n        }\n        point = 0\n        for x in entry:\n            frame[entries[point]] = x\n            point += 1\n        frames.append(frame)\n    \n    return frames\n\ndef get_direction(frame):\n    nose = frame[\"nose\"]\n    neck = frame[\"neck\"]\n\n    if (nose[0] >neck[0]):\n        return \"R\"\n    elif (nose[0] < neck[0]):\n        return \"L\"\n    else:\n        return \"F\"\n\ndef get_spine_length(frame):\n    tailbone = ((frame[\"Lhip\"][0] + frame[\"Rhip\"][0]) / 2, (frame[\"Lhip\"][1] + frame[\"Rhip\"][1]) / 2)\n    return distance(tailbone, frame[\"neck\"])\n\n# Testing\n# mat = [[(1,1),(2,2),(3,3),None,(5,5),(6,6),(7,7),(8,8),(9,9),(10,10)]]\n# print(create_dicts(mat))","repo_name":"nikitakesha13/walmart_vision","sub_path":"Archive/measurements.py","file_name":"measurements.py","file_ext":"py","file_size_in_byte":1598,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"4132887572","text":"import turtle\nimport time\nimport random\nimport platform\nimport random\n\ndelay=0.1\n\n# Score\nscore = 0\nhigh_score = 0\n\n\n#Set up the screen\nwn = turtle.Screen()\nwn.title(\"Snake Game\")\nwn.bgcolor(\"blue\")\nwn.setup(width=600, height=600)\nwn.tracer(0)\nwn.bgpic(\"Suicide.gif\")\nwn.bgpic(\"imp.gif\")\nwn.bgpic(\"Distraction.gif\")\nwn.bgpic(\"airship.gif\")\nwn.bgpic(\"lava.gif\")\n\n\n\n\n# Register the shapes\nturtle.register_shape(\"Sussy_girl.gif\")\nturtle.register_shape(\"crewmini.gif\")\nturtle.register_shape(\"amogus.gif\")\n\n# Set the score to  0\nscore = 0\n\n# Quick Note: False means 0\n\n# Draw the score\npen=turtle.Turtle()\npen.speed(0)\npen.shape(\"square\")\npen.color(\"red\")\npen.penup()\npen.hideturtle()\npen.goto(0, 260)\npen.write(\"Score: 0  High Score 0\", align =\"center\", font=(\"Courier\", 24, \"normal\"))\n\n#Snake Head\nhead = turtle.Turtle()\nhead.speed(0)\nhead.shape('Sussy_girl.gif')\nhead.color(\"red\")\nhead.penup()\nhead.goto(0,0)\nhead.direction = \"stop\"\n\n#Snake food\nfood = turtle.Turtle()\nfood.speed(0)\nfood.shape('crewmini.gif')\nfood.color(\"red\")\nfood.penup()\nfood.goto(0,100)\n\n#Enemy\nenemy = turtle.Turtle()\nenemy.speed(0)\nenemy.shape(\"amogus.gif\")\nenemy.penup()\nx = random.randint(-70, 70)\ny = random.randint(-30, 30)\nenemy.goto(x, y)\n\n\n\n\n\n\n\nsegments = []\n\n\n\n\n\n#Functions\ndef go_up():\n\tif head.direction != \"down\":\n\t\thead.direction = \"up\"\n\ndef go_down():\n\tif head.direction != \"up\":\n\t\thead.direction = \"down\"\n\ndef go_left():\n\tif head.direction != \"right\":\n\t\thead.direction = \"left\"\t\n\ndef go_right():\n\tif head.direction != \"left\":\n\t\thead.direction = \"right\"\n\ndef move():\n\tif head.direction ==\"up\":\n\t\ty=head.ycor()\n\t\thead.sety(y + 20)\n\n\tif head.direction ==\"down\":\n\t\ty=head.ycor()\n\t\thead.sety(y - 20)\n\n\tif head.direction ==\"left\":\n\t\tx=head.xcor()\n\t\thead.setx(x - 20)\n\n\tif head.direction ==\"right\":\n\t\tx=head.xcor()\n\t\thead.setx(x + 20)\n\n#Keyboard bindings\nwn.listen()\nwn.onkeypress(go_up, \"Up\")\nwn.onkeypress(go_down, \"Down\")\nwn.onkeypress(go_left, \"Left\")\nwn.onkeypress(go_right, \"Right\")\n\n\n\n\n\n#Main Game Loop\nwhile True:\n\twn.update()\n\t# Check for a collision between the border\n\tif head.xcor()>290 or head.xcor()<-290 or head.ycor()>290 or head.ycor()<-290:\n\t\ttime.sleep(1)\n\t\thead.goto(0, 0)\n\t\thead.direction = \"stop\"\n\n\t\t#Hide the segments\n\t\tfor segment in segments:\n\t\t\tsegment.goto(1000, 1000)\n\t\t#Clear the segments list\n\t\tsegments.clear()\n\t\tpen.clear()\n\t\tpen.write(\"Score: 0  High Score 0\", align=\"center\", font=(\"Courier\", 24, \"normal\"))\t\n\t\tscore = 0\n\t\twn.bgpic(\"lava.gif\")\n\t\t\n\t\t\n\n\n\t#Check for a collision with the food\n\tif head.distance(food)<20:\n\t\t#Move the food to a random position\n\t\tx = random.randint(-290, 290)\n\t\ty = random.randint(-290, 290)\n\t\tfood.goto(x, y)\n\n\t\t\n\t\t\n\t\t#Add a segment\n\t\tnew_segment = turtle.Turtle()\n\t\tnew_segment.speed(0)\n\t\tnew_segment.shape(\"crewmini.gif\")\n\t\tnew_segment.color(\"purple\")\n\t\tnew_segment.penup()\n\t\tsegments.append(new_segment)\n\n\n\t\tscore+=1\t\n\t\tif score > high_score:\n\t\t\thigh_score = score\n\t\tpen.clear()\n\t\tpen.write(\"Score: {}  High Score: {}\".format(score, high_score), align=\"center\", font=(\"Courier\", 24, \"normal\"))\n\t\tif score > 5:\n\t\t\twn.bgpic(\"airship.gif\")\n\t\tif score > 10:\n\t\t\twn.bgpic(\"Distraction.gif\")\n\t\tif score > 15:\n\t\t\twn.bgpic(\"imp.gif\")\t\n\t\tif score > 25:\n\t\t\twn,bgpic(\"Suicide.gif\")\t\t\n\n\n\n\n\n\t#Check for a collision with the enemy\n\tx = len(segments)\n\tprint (x)\n\n\ts=segments.pop(0)\n\n\tif x>0 and head.distance(enemy)<20:\n\t\tsegments.pop(0)\n\t\t\n\t\tsegments.goto(1000,1000)\n\t\t\n\t\tif x<0 and head.distance(enemy)<20:\n\t\t\ttime.sleep(1)\n\t\t\thead.goto(0,0)\n\t\t\thead.direction=\"stop\"\n\t\t\t\t\n\n\n\n\n\t\t\n\t\t\n\t\n\n\n\n\n\t#Move the end segments first in reverse order\n\tfor index in range(len(segments)-1, 0, -1):\n\t\tx = segments[index-1].xcor()\n\t\ty = segments[index-1].ycor()\n\t\tsegments[index].goto(x, y)\n\n\t#Move segment 0 to the place where the head is\n\tif len(segments)>0:\n\t\tx = head.xcor()\n\t\ty = head.ycor()\n\t\tsegments[0].goto(x, y)\n\n\n\tmove()\n\t# Check for head collisions with the TOXIC TAIL YEAH BOIII\n\tfor segment in segments:\n\t\tif segment.distance(head) < 20:\n\t\t\ttime.sleep(1)\n\t\t\thead.goto(0, 0)\n\t\t\t\n\t\t\thead.direction = \"stop\"\n\n\t\t\t\n\n\t\t\t# Hide the segments of the TOXIC TAIL\n\t\t\tfor segment in segments:\n\t\t\t\tsegment.goto(1000, 1000)\n\n\t\t\t# Clear the toxic tail list\n\t\t\tsegments.clear()\n\t\t\tpen.clear()\n\t\t\tpen.write(\"Score: 0  High Score 0\", align=\"center\", font=(\"Courier\", 24, \"normal\"))\t\n\t\t\tscore = 0\n\n\n\n\n\ttime.sleep(delay)\n\nwn.mainloop()","repo_name":"briangao892/PythonGames","sub_path":"Snake_Game.py","file_name":"Snake_Game.py","file_ext":"py","file_size_in_byte":4338,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"31135887610","text":"# Genuary 2022, Jan 1\n# Task: Draw 10,000 of something.\n# Idea: A grid of 100 x 100 that explodes into random positions\n\n# import python libraries, install with 'pip install ....'\nimport pandas as pd\nimport random as rn\n\n# create lists for x and y points and cross join to make 10,000 points, each point forms a grid 100 x 100\nx = list(range(100))\ny = list(range(100))\n\nx_coords = [a for a in x for b in y]\ny_coords = [b for a in x for b in y]\n\n# create 10,000 randomised x and y points\nx_rand = [rn.uniform(0, 99) for p in range(0, len(x)*len(y))]\ny_rand = [rn.uniform(0, 99) for p in range(0, len(x)*len(y))]\n\n# create an id for all the points\npoint_id = list(range(len(x)*len(y)))\n\n# create two data frames, one normalised, one randomised, differeniate by column stage = 1 or 2\nd = {'stage': 1, 'point_id': point_id,'x':x_coords,'y':y_coords}\nd_rand = {'stage': 2, 'point_id': point_id,'x':x_rand,'y':y_rand}\nstage_1_df = pd.DataFrame(data=d)\nstage_2_df = pd.DataFrame(data=d_rand)\n\n# stack data frames together with concat\ndf = pd.concat([stage_1_df,stage_2_df])\n\n# write data to csv\ndf.to_csv('data\\\\01_draw_10k.csv', encoding='utf-8-sig', index=False)\n","repo_name":"wjsutton/genuary_2022","sub_path":"01_draw_10k.py","file_name":"01_draw_10k.py","file_ext":"py","file_size_in_byte":1158,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"72094659986","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Dec 13 09:33:12 2020\n\n@author: Shrita\n\"\"\"\n\nimport pandas as pd\nimport pickle\npd.options.display.max_columns = 500\ndf1 = pd.read_csv('C:\\\\Users\\\\Shrita\\\\Desktop\\\\Career-recomendation-system\\\\Codes\\\\job_rec.csv')\ndf1 = df1[df1['IT'] == True]\ncol = ['RequiredQual', 'Eligibility', 'Title', 'JobDescription', 'JobRequirment']\ndf1 = df1[col]\n\ndef repl(title):\n    tokens = title.split()\n    for i,x in enumerate(tokens):\n        if x == 'Senior':\n            tokens = tokens[1:]\n        elif x == 'Junior':\n            tokens = tokens[1:]\n            \n    title2 = ' '.join(tokens)\n    return title2\n\ndf1['Title'] = df1['Title'].apply(lambda x: repl(x))\n\n\ndef repl(s):\n    if 'C++ Software Developer' in s:\n        s = s.replace('C++ Software Developer', 'C++ Developer')\n    elif ('Quality Assurance Engineer' in s) or ('Software QA Engineer' in s):\n        s = s.replace('Quality Assurance Engineer', 'QA Engineer')\n        s = s.replace('Software QA Engineer', 'QA Engineer')\n    elif '.Net Developer' in s:\n        s = s.replace('.Net Developer', '.NET Developer')\n    elif 'Java Software Developer' in s:\n        s = s.replace('Java Software Developer', 'Java Developer')\n    elif 'Senior Software Developer' in s:\n        s = s.replace('Senior Software Developer', 'Software Developer')\n    elif 'Database Administrator' in s:\n        s = s.replace('Database Administrator', 'Database Developer')\n    elif 'PHP Software Developer' in s:\n        s = s.replace('PHP Software Developer', 'PHP Developer')\n    elif '.NET Software Developer' in s:\n        s = s.replace('.NET Software Developer', '.NET Developer')\n    elif 'Java Software Engineer' in s:\n        s = s.replace('Java Software Engineer', 'Java Developer')\n    elif ('C#.NET Developer' in s) or ('C# .NET Developer' in s):\n        s = s.replace('C#.NET Developer', '.NET Developer')\n        s = s.replace('C# .NET Developer', '.NET Developer')\n    elif 'ASP.NET Developer' in s:\n        s = s.replace('ASP.NET Developer', 'Java Developer')\n    \n        \n    else:\n        pass\n    \n    return s\n\n\n\ndf1['Title'] = df1['Title'].apply(lambda x: repl(x))\n\nclasses = df1['Title'].value_counts()[:14]\nkeys = classes.keys().to_list()\n\ndf1 = df1[df1['Title'].isin(keys)]\ndf1['Title'].value_counts()\n\n#dropping eligibility because more than 30% of the values are missing\ndf1.drop('Eligibility' , axis=1 , inplace =True)\n\nfrom sklearn import preprocessing\nle2 = preprocessing.LabelEncoder()\ndf1['title_le'] = le2.fit_transform(df1['Title'])\n\n\npickle.dump(le2, open('le2.pkl','wb'))\n\nfrom sklearn.feature_extraction import DictVectorizer\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.model_selection import train_test_split\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\nimport nltk\nfrom nltk.corpus import stopwords\nfrom nltk import word_tokenize          \nfrom nltk.stem import WordNetLemmatizer \nclass LemmaTokenizer(object):\n    def __init__(self):\n        # lemmatize text - convert to base form \n        self.wnl = WordNetLemmatizer()\n        # creating stopwords list, to ignore lemmatizing stopwords \n        self.stopwords = stopwords.words('english')\n    def __call__(self, doc):\n        return [self.wnl.lemmatize(t) for t in word_tokenize(doc) if t not in self.stopwords]\n\n# removing new line characters, and certain hypen patterns                  \ndf1['RequiredQual']=df1['RequiredQual'].apply(lambda x: x.replace('\\n', ' ').replace('\\r', '').replace('- ', ''). replace(' - ', ' to '))\n\n\nimport nltk\nfrom nltk.tokenize import word_tokenize\n\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.preprocessing import LabelEncoder\n# train features and labels \ny = df1['title_le']\nX = df1['RequiredQual']\n# tdif feature rep \nvectorizer = TfidfVectorizer(tokenizer=LemmaTokenizer(), stop_words='english')\nvectorizer.fit(X)\n# transoforming text to tdif features\ntfidf_matrix = vectorizer.transform(X)\n# sparse matrix to dense matrix for training\nX_tdif = tfidf_matrix.toarray()\n\npickle.dump(vectorizer, open('vectorizer.pkl','wb'))\n\n\nX_train_words, X_test_words, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=10)\n\nX_train = vectorizer.transform(X_train_words)\nX_train = X_train.toarray()\n\nX_test = vectorizer.transform(X_test_words)\nX_test = X_test.toarray()\n\nfrom sklearn.linear_model import LogisticRegression\n\nlogistic = LogisticRegression(C = 100, penalty = 'l2')\n\nlogistic.fit(X_train, y_train)\ny_pred = logistic.predict(X_test)\nfrom sklearn.metrics import accuracy_score\naccuracy_score(y_test, y_pred)\n\npickle.dump(logistic, open('model2.pkl','wb'))\n\n","repo_name":"Shrita10/Career-recomendation-system","sub_path":"Codes/model2.py","file_name":"model2.py","file_ext":"py","file_size_in_byte":4760,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"48"}
+{"seq_id":"14290117837","text":"from django.urls import path\nfrom . import views\n\n\n# Refer to the corresponding view function for more detials of the url routes\nurlpatterns = [ \n    path('', views.getRoutes, name=\"index\"),\n    path('add/', views.addJob, name=\"addJob\" ),\n    path('delete/<int:id>', views.removeJob, name=\"removeJob\" ),\n    path('get-jobs/', views.getJobs, name='getJobs'),\n    path('company/jobs/', views.getCompanyJobs, name='getCompanyJobs'),\n]\n","repo_name":"anubhav06/hackViolet22","sub_path":"backend/jobPortal/api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":432,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"48"}
+{"seq_id":"5410906505","text":"import RPi.GPIO as GPIO\nimport time \nimport os\n\n\nspeaker_pin = 12\ncontrol_pin = 15\nweb_pin = 16\n\nGPIO.setmode(GPIO.BOARD)\nGPIO.setup(speaker_pin,GPIO.OUT)\nGPIO.setup(control_pin, GPIO.IN)\nGPIO.setup(web_pin, GPIO.IN)\n\nis_shout = False\n\ntry:\n\twhile True:\n\t\t#a = GPIO.input(control_pin)\n\t\t#if a == 1:\n\t\t#   GPIO.output(speaker_pin, GPIO.HIGH)\n\t\t#else:\n\t\t#   GPIO.output(speaker_pin, GPIO.LOW)\n\t\tif GPIO.input(control_pin):\n\t\t\tis_shout = True\n\t\tif not GPIO.input(web_pin):\n\t\t\tis_shout = False\n\n\t\tif is_shout:\n\t\t\tos.system('omxplayer -o local -p hello.mp3 > /dev/null 2>&1')\n\t\tprint(GPIO.input(control_pin), is_shout, GPIO.input(web_pin))\n\t\tGPIO.output(speaker_pin, (GPIO.input(control_pin) or is_shout) and GPIO.input(web_pin))\n\t\ttime.sleep(0.0001)\n\t\tGPIO.output(speaker_pin, GPIO.LOW)\n\t\ttime.sleep(0.0001)\n\t\t\nexcept KeyboardInterrupt:\n\tprint(\"Cleanup\")\n\t\nfinally:\n\tGPIO.cleanup()","repo_name":"raylee0703/Embedded_system_project","sub_path":"alert_gpio.py","file_name":"alert_gpio.py","file_ext":"py","file_size_in_byte":877,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"27605921800","text":"# -*- coding: utf-8 -*-\r\n# @Time    : 2018/12/27 11:27\r\n# @Author  : fovegage\r\n# @Email   : fovegage@gmail.com\r\n# @File    : break_Stu.py\r\n# @Software: PyCharm\r\n\r\n# 将输出 w 之前的内容\r\nstr = 'hello world'\r\nfor s in str:\r\n    if s == \"w\":\r\n        break\r\n    print(s)\r\n\r\n\r\n# 同理他将输出 5 之前的内容\r\ni = 0\r\nwhile i < 10:\r\n    i += 1\r\n    if i == 5:\r\n        break\r\n    print(i)","repo_name":"fovegage/learn-python","sub_path":"Python基础/语法/break_Stu.py","file_name":"break_Stu.py","file_ext":"py","file_size_in_byte":398,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"48"}
+{"seq_id":"23127674281","text":"import os\nimport xml.etree.ElementTree as ET\nfrom multiprocessing import Pool\nimport itertools\nfrom utils.utils import *\nimport pickle5 as pickle\nfrom tqdm import tqdm\nimport numpy as np\nimport pyproj\nfrom geographiclib.geodesic import Geodesic\nimport re\nimport argparse\nimport random\ngeodesic = pyproj.Geod(ellps='WGS84')\nfrom datetime import datetime\n\ndef measure_noise(print_results=False):\n    \"\"\"\n    Measures the average noise for every noise configuration in meters\n    \"\"\"\n    dataset_nonoise = SHDataset(noise=False)\n    G1,T1,G2,T2 = dataset_nonoise.read_snapshots(0)\n    \n    results = []\n    \n    for i in range(4):\n        dataset = SHDataset(noise=True, noise_config=i)\n        G1_,T1_,G2_,T2_ = dataset.read_snapshots(0)\n        \n        noise = []\n        \n        for j in tqdm(range(len(T2['T'])), desc=f'Calculating noise in meters for noise config {i}'):\n            t = T2['T'][j]\n            t_ = T2_['T'][j]\n            dists = measure_noise_t(t, t_)\n            noise.append(dists)\n        \n        results.append({\n            'config': i,\n            'meters': np.mean(noise)\n        })\n        if print_results:\n            print(f'Dataset noise config {i} has noise {np.mean(noise)}')\n\n    return results\n\n\"\"\"\nThis class implements Ornstein-Uhlenbeck process, for adding noise to trajectories\n\"\"\"\nclass OUActionNoise(object):\n    def __init__(self, mu, sigma=3.0, theta=1.0, dt=.0001, x0=None):\n        self.theta = theta\n        self.mu = mu\n        self.sigma = sigma\n        self.dt = dt\n        self.x0 = x0\n        self.reset()\n\n    def __call__(self):\n        x = self.x_prev + self.theta * (self.mu - self.x_prev) * self.dt + \\\n            self.sigma * np.sqrt(self.dt) * np.random.normal(size=self.mu.shape)\n\n        self.x_prev = x\n        return x\n\n    def reset(self):\n        self.x_prev = self.x0 if self.x0 is not None else np.zeros_like(self.mu)\n\n\"\"\"\nThis class implements the main Synthetic Highways dataset. It reads raw datafiles from the\ndataset directory and processes them to pickle files per map and per noise configuration\n\"\"\"\nclass SHDataset(object):\n    def __init__(self, dataset_dir='./dataset/', split_threshold=200, ref_coord=(52.356758, 4.894004),\n                coord_scale_factor=1, noise=True, noise_config=0, resample_timedp=False, resample_everyn=2,\n                 save_cleaned_data=True, min_traj_len=4, traj_trim_size=2, multiprocessing=False):\n        \"\"\"\n        Initializes the dataset\n\n        -------\n        Params\n        -------\n        dataset_dir : str\n            Directory of the dataset\n        split_threshold : number\n            What threshold to use when splitting/cleaning up trajectories.\n        \"\"\"\n        if noise_config > 3:\n            raise ValueError(f\"There are only 4 noise configs. Please specify a noise configuration in the range[0,1,2,3]\")\n        \n        # Initialize global variables\n        self.save_cleaned_data = save_cleaned_data\n        self.split_threshold = split_threshold\n        self.ref_coord = ref_coord\n        self.coord_scale_factor = coord_scale_factor\n        self.min_traj_len = min_traj_len\n        self.traj_trim_size = traj_trim_size\n        self.noise = noise\n        self.pnoise_shift = 0.05\n        sigmas = [0.0000125+(i*0.000025) for i in range(4)]\n        thetas = sigmas\n        self.multiprocessing = multiprocessing\n        self.resample_timedp = resample_timedp\n        self.resample_everyn = resample_everyn\n        \n        self.noise_mu = np.array([0,0])\n        self.noise_sigma = sigmas[noise_config]\n        self.noise_theta = thetas[noise_config]\n        self.noise_dt = .1\n        self.noise_function = OUActionNoise(self.noise_mu, self.noise_sigma, self.noise_theta, self.noise_dt, x0=[0,0])\n        self.dataset_dir = dataset_dir\n        self.rawdata_dir = os.path.join(self.dataset_dir, 'raw_data')\n        \n        # Organize the xml files in the raw data directory\n        self.organize_xml_filenames()\n\n        # Check if clean data directory exists\n        self.cleandata_dir = os.path.join(self.dataset_dir, 'clean_data')\n        if not os.path.exists(self.cleandata_dir):\n            os.mkdir(self.cleandata_dir)\n\n        # For each map, if cleaned file doesn't exist, save it\n        for i, fnames in enumerate(self.maps):\n            map_name = fnames['map_name']\n            cleaned_fname = os.path.join(self.cleandata_dir, f'{map_name}_cleaned_nonoise.hdf5')\n            \n            # Create cleaned dataset w/o noise and save to pickle if it doesn't exist\n            if not os.path.isfile(cleaned_fname) and self.save_cleaned_data:\n                G1,T1,G2,T2 = self.load_snapshots(i)\n\n                with open(cleaned_fname, 'wb') as handle:\n                    print(f'Writing snapshots to pickle file: {cleaned_fname}')\n                    pickle.dump((G1,T1,G2,T2), handle, protocol=pickle.HIGHEST_PROTOCOL)\n            \n            self.maps[i]['cleaned_data'] = cleaned_fname\n            # # Add noise to trajectories for current configuration, if noise is selected/enabled\n            # if self.noise:\n            #     cleaned_fname_noise = os.path.join(self.cleandata_dir, f'{map_name}_cleaned_noise_config{noise_config+1}.hdf5')\n            #     if not os.path.isfile(cleaned_fname_noise):\n            #         self.maps[i]['cleaned_data'] = cleaned_fname\n            #         G1,T1,G2,T2 = self.read_snapshots(i)\n            #         T1['T'] = self.add_noise_parallel(T1['T'])\n            #         T2['T'] = self.add_noise_parallel(T2['T'])\n\n            #         with open(cleaned_fname_noise, 'wb') as handle:\n            #             print(f'Writing snapshots to pickle file: {cleaned_fname_noise}')\n            #             pickle.dump((G1,T1,G2,T2), handle, protocol=pickle.HIGHEST_PROTOCOL)\n\n            #     self.maps[i]['cleaned_data'] = cleaned_fname_noise\n            # else:\n            #     self.maps[i]['cleaned_data'] = cleaned_fname\n\n    def organize_xml_filenames(self):\n        # Read raw data XML filenames\n        self.files = [os.path.join(self.rawdata_dir, fname) for fname in sorted(os.listdir(self.rawdata_dir)) if '.xml' in fname]\n\n        # First find all unique map names\n        map_names = []\n        for file in self.files:\n            match = re.findall(r'/([A-Z0-9 .]+)_1_map.xml', file, flags=re.IGNORECASE)\n            if len(match) == 1:\n                map_names.append(match[0])\n\n        # Then for each map name, find all batches for trajectories and path filenames\n        # and organize them into self.maps\n        self.maps = []\n        map_names = sorted(list(set(map_names)))\n        for map_name in map_names:\n            T1_fnames = []\n            T2_fnames = []\n            P1_fnames = []\n            P2_fnames = []\n            G1_fname = None\n            G2_fname = None\n            changes_fname = None\n            for file in self.files:\n                if map_name in file and 'batch' in file and '1_trajectories' in file:\n                    T1_fnames.append(file)\n                if map_name in file and 'batch' in file and '2_trajectories' in file:\n                    T2_fnames.append(file)\n                if map_name in file and 'batch' in file and '1_paths' in file:\n                    P1_fnames.append(file)\n                if map_name in file and 'batch' in file and '2_paths' in file:\n                    P2_fnames.append(file)\n                if map_name in file and 'batch' not in file and '1_map.xml' in file:\n                    G1_fname = file\n                if map_name in file and 'batch' not in file and '2_map.xml' in file:\n                    G2_fname = file\n                if map_name in file and 'batch' not in file and 'changes' in file:\n                    changes_fname = file\n            self.maps.append({\n                'map_name': map_name,\n                'snapshot1': {\n                    'map': G1_fname,\n                    'trajectories': T1_fnames,\n                    'paths': P1_fnames\n                },\n                'snapshot2': {\n                    'map': G2_fname,\n                    'trajectories': T2_fnames,\n                    'paths': P2_fnames\n                },\n                'changes': changes_fname\n            })\n\n\n    def read_snapshots(self, i, bbox=None):\n        \"\"\" \n        Main function for fetching data from a single snapshot\n        \"\"\"\n        cleaned_fname = self.maps[i]['cleaned_data']\n        with open(cleaned_fname, 'rb') as handle:\n            G1,T1,G2,T2 = pickle.load(handle)\n\n        if bbox is not None:\n            G1,T1,G2,T2 = filter_bbox_snapshots(G1,T1,G2,T2, bbox)\n\n        if self.resample_timedp:\n            T1['T'] = self.resample_timedpoints(T1['T'])\n            T2['T'] = self.resample_timedpoints(T2['T'])\n\n        if self.noise:\n            T1['T'] = self.add_noise_parallel(T1['T'])\n            T2['T'] = self.add_noise_parallel(T2['T'])\n\n        return G1,T1,G2,T2\n\n    def parse_trajectories_parallel(self, traj_fnames):\n        \"\"\"\n        This function takes a list of trajectory filenames (batches) and processes them in parallel\n        \"\"\"\n        if len(traj_fnames) == os.cpu_count():\n            pool = Pool(os.cpu_count())\n        elif len(traj_fnames) < os.cpu_count():\n            pool = Pool(len(traj_fnames))\n        elif len(traj_fnames) > os.cpu_count():\n            raise NotImplementedError(f'There are more trajectory batches than there are cpu cores. This feature has not yet been implemented')\n\n        if self.multiprocessing:\n            results = []\n            pbar = tqdm(pool.imap_unordered(self.parse_trajectories, traj_fnames), total=len(traj_fnames))\n            pbar.set_description('Reading trajectories')\n            for result in pbar:\n                results.append(result)\n            pool.close()\n            pool.join()\n            pbar.close()\n            trajectories = list(itertools.chain.from_iterable(results))\n        else:\n            trajectories = []\n            pbar = tqdm(traj_fnames)\n            pbar.set_description('Reading trajectories')\n            for fname in pbar:\n                trajectories = [*trajectories, *self.parse_trajectories(fname)]\n\n        return trajectories\n\n    def parse_paths_parallel(self, path_fnames):\n        \"\"\"\n        This function takes a list of path filenames (batches) and processes them in parallel\n        \"\"\"\n        if len(path_fnames) == os.cpu_count():\n            pool = Pool(os.cpu_count())\n        elif len(path_fnames) < os.cpu_count():\n            pool = Pool(len(path_fnames))\n        elif len(path_fnames) > os.cpu_count():\n            raise NotImplementedError(f'There are more path batches than there are cpu cores. This feature has not yet been implemented')\n\n        if self.multiprocessing:\n            results = []\n            pbar = tqdm(pool.imap_unordered(self.parse_paths, path_fnames), total=len(path_fnames))\n            pbar.set_description('Reading paths')\n            for result in pbar:\n                results.append(result)\n            pool.close()\n            pool.join()\n            pbar.close()\n\n            paths = dict(itertools.chain.from_iterable(map(dict.items, results)))\n        else:\n            paths = []\n            pbar = tqdm(path_fnames)\n            pbar.set_description('Reading paths')\n            for fname in pbar:\n                paths = [*paths, self.parse_paths(fname)]\n\n        return paths\n\n                \n    def load_snapshots(self, i):\n        \"\"\"\n        Loads the snapshots of city with the given index\n        ------\n        Params\n        ------\n        i : int\n            The index of which city/savegame to load\n        \"\"\"\n\n        # Get the filenames containing the map and trajectories of the two snapshots\n        map1_fname = self.maps[i]['snapshot1']['map']\n        traj1_fnames = self.maps[i]['snapshot1']['trajectories']\n        path1_fnames = self.maps[i]['snapshot1']['paths']\n        map2_fname = self.maps[i]['snapshot2']['map']\n        traj2_fnames = self.maps[i]['snapshot2']['trajectories']\n        path2_fnames = self.maps[i]['snapshot2']['paths']\n        \n        # Read the files in (this will take a while)\n        G1 = self.parse_map(map1_fname)\n        T1 = self.parse_trajectories_parallel(traj1_fnames)\n        P1 = self.parse_paths_parallel(path1_fnames)\n        G2 = self.parse_map(map2_fname)\n        T2 = self.parse_trajectories_parallel(traj2_fnames)\n        P2 = self.parse_paths_parallel(path2_fnames)\n        \n        return G1, {'T': T1, 'P': P1}, G2, {'T': T2, 'P': P2}\n    \n    def parse_map(self, xml_fname, service='Road'):\n        \"\"\"\n        Reads a map from the given filename, only retrieving the specified road types, returning\n        a dictionary with two entries (nodes and edges), which contain lists of objects\n        ------\n        Params\n        ------\n        xml_fname : str\n            XML file which to open\n        service : str\n            The service type (or segment/node type) which to extract\n        \"\"\"\n        \n        # Read XML file\n        map_tree = ET.parse(xml_fname)\n        map_root = map_tree.getroot()\n        nodes = map_root[0]\n        segments = map_root[1]\n        \n        # Initialize NetworkX graph\n        G = nx.Graph()\n        \n        # Add nodes with service specified by 'service' argument\n        for node in nodes:\n            if node.attrib['Service'] == service:\n                coords = cart_to_wgs84(self.ref_coord, \n                                       float(node[0].attrib['x']), float(node[0].attrib['z']),\n                                      scale_factor=self.coord_scale_factor)\n                G.add_node(\n                    int(node.attrib['Id']),\n                    lat=coords[0],\n                    lon=coords[1],\n                    elev=float(node.attrib['Elevation']),\n                    road_service=node.attrib['Service'],\n                    road_subservice=node.attrib['SubService'],\n                    color='blue'\n                )\n                \n        nodes = G.nodes(data=True)\n\n        # Add edges if both nodes are present in graph\n        for i, segment in enumerate(segments):\n            \n            start_nodeid = int(segment[0].attrib['NodeId'])\n            end_nodeid = int(segment[1].attrib['NodeId'])\n            \n            if start_nodeid in G.nodes and end_nodeid in G.nodes:\n                n1, n2 = nodes[start_nodeid], nodes[end_nodeid]\n                lat1, lon1, lat2, lon2 = n1['lat'], n1['lon'], n2['lat'], n2['lon']\n\n                # Calculate forward and backward azimuth/heading and edge length\n                fwd_azimuth, back_azimuth, dist = geodesic.inv(lon1, lat1, lon2, lat2)\n                fwd_azimuth = 360 + fwd_azimuth if fwd_azimuth < 0 else fwd_azimuth\n                back_azimuth = 360 + back_azimuth if back_azimuth < 0 else back_azimuth\n\n                # Calculate middlepoint of edge\n                l = Geodesic.WGS84.InverseLine(lat1, lon1, lat2, lon2)\n\n                # Compute the midpoint\n                m = l.Position(0.5 * l.s13)\n\n                G.add_edge(\n                    start_nodeid, end_nodeid,\n                    segment_id=int(segment.attrib['SegmentId']),\n                    prefabid=int(segment.attrib['PrefabId']),\n                    prefabname=segment.attrib['PrefabName'],\n                    fwd_azimuth=fwd_azimuth,\n                    back_azimuth=back_azimuth,\n                    length=dist,\n                    middle_coordinate={\n                        'lat': m['lat2'],\n                        'lon': m['lon2']\n                    },\n                    endpoints={\n                        'lat1': lat1,\n                        'lon1': lon1,\n                        'lat2': lat2,\n                        'lon2': lon2,\n                    },\n                    color='blue'\n                )\n\n\n                \n        return G\n    \n    def parse_trajectories(self, xml_fname):\n        \"\"\"\n        Reads the trajectories from the given XML filename\n        ------\n        Params\n        ------\n        xml_fname : str\n            The XML filename which to read\n        \"\"\"\n\n        # Read the XML file\n        traj_tree = ET.parse(xml_fname)\n        traj_root = traj_tree.getroot()\n        vehicles = traj_root[0]\n\n        # Create numpy trajectory datatype\n        trajectory_dtype = np.dtype([(\"lat\", \"f8\"), (\"lon\", \"f8\"), (\"speed\", \"f4\"), \n                                (\"x\", \"f4\"), (\"y\", \"f4\"), (\"z\", \"f4\"),\n                                (\"heading\", \"f4\"), (\"timestamp\", \"datetime64[s]\"), (\"gt_segment\", \"int\"),\n                                (\"path_id\", \"int\"), (\"pathpos_idx\", \"int\"), (\"vehtype\", 'S5')])\n        \n        # Convert XML formatted trajectories to list of lists of objects\n        trajectories = []\n        for vehicle in vehicles:\n            if vehicle.attrib['VehicleType'] != 'Car':\n                continue\n                \n            trajectory = np.zeros(len(vehicle), dtype=trajectory_dtype)\n            t_len = 0\n\n            for i, pos in enumerate(vehicle):\n\n                # Check if vehicle type still is consistent\n                vehicle_type = pos.attrib['VehicleType']\n\n                # Process current position info\n                lat, lon, speed = float(pos.attrib['x']), float(pos.attrib['z']), float(pos.attrib['speed'])\n                n1, n2, n3 = float(pos[0].attrib['n1']), float(pos[0].attrib['n2']), float(pos[0].attrib['n3'])\n                x, y, z = float(pos[1].attrib['x']), float(pos[1].attrib['y']), float(pos[1].attrib['z']) \n                heading = get_heading(float(pos[1].attrib['z']), float(pos[1].attrib['x']))\n                timestamp = datetime.strptime(pos.attrib['timestamp'], \"%m/%d/%Y %I:%M:%S %p\")\n                gt_segment = int(pos[2].attrib['Segment'])\n                path_id = int(pos[2].attrib['PathId'])\n                pathpos_idx = int(pos[2].attrib['PathPosIdx'])\n                \n                # Skip point if it's velocity vector == 0 and heading (xyz) == [0,0,1]\n                if [n1,n2,n3] == [0,0,0] and [x,y,z] == [0,0,1]:\n                    continue\n                \n                # Aggregate information into object and append to trajectory\n                trajectory[t_len] = lat, lon, speed, x, y, z, heading, timestamp, gt_segment, path_id, pathpos_idx, vehicle_type\n                t_len += 1\n\n            # Make sure trajectory matches minimum length\n            if t_len < self.min_traj_len:\n                continue\n            \n            # Trim off trajectory to actual length\n            trajectory = trajectory[:t_len]\n            \n            # Remove instances in trajectory where vehicle type isn't Car\n            veh_types = trajectory['vehtype'].view('S5')\n            idxs_vtype = np.argwhere(veh_types != b'Car')[:,0]\n            if idxs_vtype.size > 0:\n                trajectory = np.delete(trajectory, idxs_vtype, axis=0)\n\n            # Split trajectory based on locations\n            t_pos = np.stack([trajectory['lat'].view('f8'), trajectory['lon'].view('f8')], axis=1)\n            speeds = np.linalg.norm(np.diff(t_pos, axis=0), axis=1)\n            idxs_speed = np.argwhere(speeds > self.split_threshold)[:,0] + 1\n            \n            # Add splits to trajectories if it's split\n            if idxs_speed.size > 0:\n                # Split trajectory based on speed\n                splits_speed = np.split(trajectory, idxs_speed, axis=0)\n                # For each split\n                for split in splits_speed:\n                    # Trim the split\n                    split_trimmed = split[self.traj_trim_size:-self.traj_trim_size]\n                    # Check if trimmed split is of minimum trajectory length\n                    if len(split_trimmed) >= self.min_traj_len:\n                        trajectories.append(split)\n\n            # Otherwise, append single trajectory\n            else:\n                trajectory_trimmed = trajectory[self.traj_trim_size:-self.traj_trim_size]\n                if len(trajectory_trimmed) >= self.min_traj_len:\n                    trajectories.append(trajectory_trimmed)\n        \n        # Convert coordinates to wgs84\n        trajectories = self.to_wgs84(trajectories)\n        \n        return trajectories\n\n    def parse_paths(self, xml_fname):\n        \"\"\"\n        Reads the ground truth paths from the given XML filename\n        ------\n        Params\n        ------\n        xml_fname : str\n            The XML filename which to read\n        \"\"\"\n        # Read the XML file\n        paths_tree = ET.parse(xml_fname)\n        paths_root = paths_tree.getroot()\n\n        paths = {}\n        for path in paths_root:\n            path_id = int(path.attrib['PathId'])\n            segments = [int(segment.attrib['SegmentId']) for segment in path]\n            paths[path_id] = segments\n\n        return paths\n\n    def to_wgs84(self, T):\n        \"\"\"\n        Converts trajectories from game coordinates to wgs84 lat, lon coordinates\n        \"\"\"\n\n        i = 0\n        x,y = zip(*[(t['lat'], t['lon']) for traj in T for t in traj])\n        coords = cart_to_wgs84(self.ref_coord, x, y, scale_factor=self.coord_scale_factor)\n        new_T = []\n        for traj in T:\n            new_coords = coords[i:i+traj.size]\n            traj['lat'], traj['lon'] = new_coords[:,0], new_coords[:,1]\n            new_T.append(traj)\n            i+=traj.size\n        return new_T\n\n\n    def add_noise_parallel(self, T):\n        \"\"\"\n        Adds noise to trajectories using multiple threads\n        \"\"\"\n\n        if self.multiprocessing:\n            T_batches = np.array_split(T, os.cpu_count())\n            pool = Pool(os.cpu_count())\n            results = []\n            pbar = tqdm(pool.imap_unordered(self.add_noise, T_batches), total=len(T_batches))\n            pbar.set_description('Adding noise to trajectories')\n            for result in pbar:\n                results.append(result)\n\n            pool.close()\n            pool.join()\n            pbar.close()\n            trajectories_w_noise = list(itertools.chain.from_iterable(results))\n        else:\n            trajectories_w_noise = self.add_noise(T, pbar=True)\n\n        return trajectories_w_noise\n\n    def add_noise(self, T, pbar=False):\n        \"\"\"\n        Adds noise to trajectories\n        \"\"\"\n        if pbar:\n            new_T = []\n            pbar = tqdm(T)\n            pbar.set_description('Adding noise to trajectories')\n            for t in pbar:\n                new_T.append(self.add_noise_t(t))\n            return new_T\n\n        else:\n            new_T = []\n            \n            for t in T:\n                new_T.append(self.add_noise_t(t))\n            return new_T\n\n    def add_noise_t(self, t):\n        \"\"\"\n        Adds noise to a single trajectory\n        \"\"\"\n        ou_noise = np.array([self.noise_function() for i in range(len(t))])\n        old_coords = np.array([(p['lat'], p['lon']) for p in t])\n        new_coords = old_coords + ou_noise\n\n        shift_probs = np.expand_dims(np.random.binomial(1, self.pnoise_shift, len(t)), axis=1)\n        shift_noise_lat = np.random.normal(0, self.noise_sigma, len(t))\n        shift_noise_lon = np.random.normal(0, self.noise_sigma, len(t))\n        shift_noise = np.stack((shift_noise_lat, shift_noise_lon), axis=1)\n        shift_noise = shift_noise * shift_probs\n\n        final_noise = new_coords + shift_noise\n\n        t['lat'], t['lon'] = final_noise[:,0], final_noise[:,1]\n\n        self.noise_function.reset()\n\n        return t\n\n    def resample_timedpoints(self, T):\n        new_T = []\n        for t in T:\n            new_t = t[0:-1:self.resample_everyn]\n            if len(new_t) > self.min_traj_len:\n                new_T.append(new_t)\n        return new_T\n\n        \n    def __len__(self):\n        return len(self.maps)\n\n\nif __name__ == '__main__':\n    # Define and parse cmd line arguments\n    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n\n    parser.add_argument('--dataset_dir', default='./dataset/', type=str, help='Dataset root directory')\n    parser.add_argument('--noise', default=False, action='store_true', help='Add noise to trajectories')\n    parser.add_argument('--noise_config', default=0, type=int, help='Which noise configuration to use')\n    parser.add_argument('--split_threshold', default=200, type=int, help='What threshold to use when splitting up trajectories')\n    parser.add_argument('--preprocess_dataset', default=True, action='store_true', help='Whether to preprocess the entire dataset')\n\n    args = parser.parse_args()\n\n    # Preprocess the entire dataset\n    if args.preprocess_dataset:\n\n        # First process all maps without noise\n        print('Processing no noise dataset')\n        dataset = SHDataset(noise=False, dataset_dir=args.dataset_dir, noise_config=0)\n\n        # Then process all the different noise configurations\n        for i in range(4):\n            print(f'Processing dataset w/ noise config {i}')\n            dataset = SHDataset(noise=True, dataset_dir=args.dataset_dir, noise_config=i)\n\n    # Load dataset and plot a snapshot as a test\n    dataset = SHDataset(noise=args.noise, dataset_dir=args.dataset_dir, noise_config=args.noise_config)\n    G1,T1,G2,T2 = dataset.read_snapshots(0, bbox=(52.355, 52.365, 4.860, 4.900))\n\n    T1['T'] = random.sample(T1['T'], k=1000)\n    T2['T'] = random.sample(T2['T'], k=1000)\n\n    plot_graph(snapshot_to_nxgraph(G1, T2['T']), figsize=(10,10))","repo_name":"KJ-Waller/SyntheticHighways","sub_path":"ChangeDetection/SHDataset.py","file_name":"SHDataset.py","file_ext":"py","file_size_in_byte":25338,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"6897376367","text":"import pygame\nfrom pygame.locals import *\nfrom pygame import *\nimport globals\nimport pygame as pg\nfrom pygame.mixer import Sound\nimport time\n\n\npg.init()\n# definindo cores\nBLACK = (0, 0, 0)\nWHITE = (255, 255, 255)\nBLUE = (0, 0, 255)\nGREEN = (0, 255, 0)\nRED = (255, 0, 0)\nROXO = (127,0,127)\nLARANJA = (255, 127, 0)\nAMARELO = (255,255,0)\nCOR1 = (153, 0, 153)\nPELE = (251, 230, 226)\nCorSelecionada = (0, 255, 0)\nglobal cor1\ncor1 = (0, 0, 0)\n\ncont = 1\n\nfase = 0\n\npygame.init()\n\npygame.mixer.init()\n\nfundo = pygame.mixer.music.load(globals.get_path() + '\\\\Sound\\\\gameplay.mpeg')\n\nclick = pygame.mixer.Sound(globals.get_path() + '\\\\Sound\\\\click.wav')\n\npygame.mixer.music.play()\n\nscreen = pygame.display.set_mode((800, 700))\n# carregando fonte\nfont = pygame.font.SysFont(None, 55)\n\npygame.display.set_caption('COLORANDO')\n\n# preenchendo o fundo com preto\nscreen.fill(PELE)\n\ndef regras():\n      flag = True\n      while True:\n         if(flag== False):\n            break\n         else:\n            for event in pygame.event.get():\n                pos = pygame.mouse.get_pos()\n                pressed1,pressed2,pressed3 = pygame.mouse.get_pressed()\n                btn0 = botoesMenu(BLACK,300,500,372/2,149/2)\n                txtFase3 = pygame.image.load(globals.get_path()+'\\\\View\\\\fase1\\\\txtFase1.png').convert_alpha()\n                screen.fill(PELE)\n                novo_txtFase3 = pygame.transform.scale(txtFase3,(int(1363/2),int(541/2)))\n                screen.blit(novo_txtFase3,(60,150))\n                screen.blit(botao_continuar('btnContinuar'),(300,500))\n                pygame.display.update()\n                if event.type == QUIT:\n                  pygame.quit()\n                  exit()\n                elif btn0.collidepoint(pos) and pressed1:\n                    click.play()\n                    flag = False\n                    break\n\ndef botao_continuar(botao):\n       btn = pygame.image.load(globals.get_path()+'\\\\View\\\\corrida\\\\'+botao+'.png').convert_alpha()\n       novo_botao = pygame.transform.scale(btn,(int(372/2),int(149/2)))\n       return novo_botao\n\ndef dimensao_botao(botao):\n        btn = pygame.image.load(globals.get_path()+'\\\\View\\\\fase1\\\\'+botao+'.png').convert_alpha()\n        novo_botao = pygame.transform.scale(btn,(int(400),int(1500/2)))\n        return novo_botao\n\ndef titulo():\n        titulo = pygame.image.load(globals.get_path()+'\\\\View\\\\fase1\\\\titulo.png').convert_alpha()\n        novo_titulo = pygame.transform.scale(titulo,(int(726/2), int(217/2)))\n        screen.blit(novo_titulo,(210,50))\n\ndef torneira():\n        titulo = pygame.image.load(globals.get_path()+'\\\\View\\\\fase1\\\\torneira.png').convert_alpha()\n        novo_titulo = pygame.transform.scale(titulo,(int(650), int(500)))\n        screen.blit(novo_titulo,(-200,0))\n\ndef balde():\n        titulo = pygame.image.load(globals.get_path()+'\\\\View\\\\fase1\\\\balde.png').convert_alpha()\n        novo_titulo = pygame.transform.scale(titulo,(int(500), int(700)))\n        screen.blit(novo_titulo,(2,0))\n\ndef botoesMenu(color,x,y,width,height):\n        return pygame.draw.rect(screen, color, [x, y, width, height])\n\ndef instrucao():\n        titulo = pygame.image.load(globals.get_path()+'\\\\View\\\\fase1\\\\txtCorFormada.png').convert_alpha()\n        screen.blit(titulo,(50,40))\n\ndef btnConfirmar():\n        titulo = pygame.image.load(globals.get_path()+'\\\\View\\\\fase1\\\\btnConfirmar.png').convert_alpha()\n        novo_titulo = pygame.transform.scale(titulo,(int(115), int(215)))\n        screen.blit(novo_titulo,(500,495))\n\ndef menu():\n        btn1 = botoesMenu(BLACK,630,240,40,70)#azul\n        btn2 = botoesMenu(BLACK,630,340,40,70)#amarelo\n        btn3 = botoesMenu(BLACK,630,440,40,70)#vermelho\n        btn4 = botoesMenu(BLACK,730,240,40,70)#verde\n        btn5 = botoesMenu(BLACK,730,340,40,70)#laranja\n        btn6 = botoesMenu(BLACK,730,440,40,70)#roxo\n        btn7 = botoesMenu(BLACK,620,580,60,100)#juntar\n        btn8 = botoesMenu(BLACK,720,560,60,130)#lixo\n        btn9 = botoesMenu(BLACK,515,560,60,120)#confirma\n        screen.fill(PELE)\n\n        #titulo()\n        instrucao()\n        torneira()\n        balde()\n        #Quadrados que indicarão a cor a ser desenhada\n        #screen.blit(dimensao_botao('quad_verde'),(460,-300))\n        #screen.blit(dimensao_botao('quad_laranja'),(460,-300))\n        #screen.blit(dimensao_botao('quad_roxo'),(460,-300))\n        if(fase ==0):\n             screen.blit(dimensao_botao('quad_verde'),(460,-300))\n        elif(fase == 1):\n             screen.blit(dimensao_botao('quad_laranja'),(460,-300))\n        else:\n             screen.blit(dimensao_botao('quad_roxo'),(460,-300))\n        screen.blit(dimensao_botao('btnAzul'),(450,-100))\n        screen.blit(dimensao_botao('btnAmarelo'),(450,0))\n        screen.blit(dimensao_botao('btnVermelho'),(450,100))\n        if(fase >=1):\n            screen.blit(dimensao_botao('btnVerde'),(550,-100))\n        if(fase >= 2):\n            screen.blit(dimensao_botao('btnLaranja'),(550,0))\n        if(fase >= 3):\n           screen.blit(dimensao_botao('btnRoxo'),(550,100))\n\n        pygame.display.update()\n\n        return btn1,btn2,btn3,btn4,btn5,btn6,btn7,btn8,btn9\n\ndef derramando1(cor,x, y, widht,height):\n    aux = 1\n    while(aux < 365):\n        botoesMenu(cor,188,313,60,aux)\n        pygame.display.flip()\n        aux= aux + 1\n    botoesMenu(cor,x, y, widht,height)\n    pygame.display.flip()\ndef derramando2(cor,x, y, widht,height):\n    aux = 1\n    while(aux!= 195):\n        botoesMenu(cor,188,313,60,aux)\n        pygame.display.flip()\n        aux= aux + 1\n    botoesMenu(cor,x, y, widht,height)\n    pygame.display.flip()\n\ndef primeiro():\n    while True:\n            for event in pygame.event.get():\n                    pygame.display.update()\n                    pos = pygame.mouse.get_pos()\n                    pressed1, pressed2, pressed3 = pygame.mouse.get_pressed()\n                    if menu_botoes[0].collidepoint(pos) and pressed1:\n                        derramando1(BLUE,19, 508, 468, 170)\n                        botoesMenu(PELE,188,313,60,195)\n                        pygame.display.flip()\n                        return  BLUE\n                    elif menu_botoes[1].collidepoint(pos) and pressed1:\n                        derramando1(AMARELO,19, 508, 468, 170)\n                        botoesMenu(PELE,188,313,60,195)\n                        pygame.display.flip()\n                        return AMARELO\n                    elif menu_botoes[2].collidepoint(pos) and pressed1:\n                        derramando1(RED,19, 508, 468, 170)\n                        botoesMenu(PELE,188,313,60,195)\n                        pygame.display.flip()\n                        return RED\n                    elif menu_botoes[3].collidepoint(pos) and pressed1:\n                       if(fase>= 1):\n                           derramando1(GREEN,19, 508, 468, 170)\n                           botoesMenu(PELE,188,313,60,195)\n                           pygame.display.flip()\n                           return GREEN\n                    elif menu_botoes[4].collidepoint(pos) and pressed1:\n                        if(fase>= 2):\n                           derramando1(LARANJA,19, 508, 468, 170)\n                           botoesMenu(PELE,188,313,60,195)\n                           pygame.display.flip()\n                           return LARANJA\n                    elif menu_botoes[5].collidepoint(pos) and pressed1:\n                         if(fase>= 3):\n                           derramando1(ROXO,19, 508, 468, 170)\n                           botoesMenu(PELE,188,313,60,195)\n                           pygame.display.flip()\n                           return ROXO\n                    if event.type == QUIT:\n                        pygame.quit()\n                        exit()\n\ndef segundo():\n    while True:\n            for event in pygame.event.get():\n                    pygame.display.update()\n                    pos = pygame.mouse.get_pos()\n                    pressed1, pressed2, pressed3 = pygame.mouse.get_pressed()\n                    if menu_botoes[0].collidepoint(pos) and pressed1:\n                        derramando2(BLUE,19, 373, 468, 170)\n                        botoesMenu(PELE,188,313,60,60)\n                        pygame.display.flip()\n                        return  BLUE\n                    elif menu_botoes[1].collidepoint(pos) and pressed1:\n                        derramando2(AMARELO,19, 373, 468, 170)\n                        botoesMenu(PELE,188,313,60,60)\n                        pygame.display.flip()\n                        return AMARELO\n\n                    elif menu_botoes[2].collidepoint(pos) and pressed1:\n                        derramando2(RED,19, 373, 468, 170)\n                        botoesMenu(PELE,188,313,60,60)\n                        pygame.display.flip()\n                        return RED\n                    elif menu_botoes[3].collidepoint(pos) and pressed1:\n                       if(fase>= 1):\n                           derramando2(GREEN,19, 373, 468, 170)\n                           botoesMenu(PELE,188,313,60,60)\n                           pygame.display.flip()\n                           return GREEN\n                    elif menu_botoes[4].collidepoint(pos) and pressed1:\n                        if(fase>= 2):\n                           derramando2(LARANJA,19, 373, 468, 170)\n                           botoesMenu(PELE,188,313,60,60)\n                           pygame.display.flip()\n                           return LARANJA\n                    elif menu_botoes[5].collidepoint(pos) and pressed1:\n                        if(fase>= 3):\n                           derramando2(ROXO,19, 373, 468, 170)\n                           botoesMenu(PELE,188,313,60,60)\n                           pygame.display.flip()\n                           return ROXO\n                    if event.type == QUIT:\n                        pygame.quit()\n                        exit()\ndef misturar(cor1,cor2):\n        screen.blit(dimensao_botao('btnMistura'),(450,250))\n        while True:\n            for event in pygame.event.get():\n                    pygame.display.update()\n                    pos = pygame.mouse.get_pos()\n                    pressed1, pressed2, pressed3 = pygame.mouse.get_pressed()\n                    if menu_botoes[6].collidepoint(pos) and pressed1:\n                        resultado = globals.misturar_cores(cor1,cor2)\n                        botoesMenu(resultado,19, 373, 468, 305)\n                        botoesMenu(PELE,620,560,70,120)\n                        pygame.display.update()\n                        return resultado\n                    if event.type == QUIT:\n                        pygame.quit()\n                        exit()\ndef clicarConfirmarOuExcluir():\n        botoesMenu(PELE,620,560,70,120)\n        pygame.display.update()\n        btnConfirmar()\n        screen.blit(dimensao_botao('btnExclui'),(550,250))\n        while True:\n            for event in pygame.event.get():\n                    pygame.display.update()\n                    pos = pygame.mouse.get_pos()\n                    pressed1, pressed2, pressed3 = pygame.mouse.get_pressed()\n                    if menu_botoes[7].collidepoint(pos) and pressed1:\n                        screen.fill(PELE)\n                        menu()\n                        return 0\n                    elif menu_botoes[8].collidepoint(pos) and pressed1:\n                         screen.fill(PELE)\n                         menu()\n                         return 1\n                    elif event.type == QUIT:\n                        pygame.quit()\n                        exit()\n\nregras()\n\nmenu()\nmenu_botoes = menu()\n\nwhile True:\n        if(cont == 1):\n                menu()\n                cor1 = primeiro()\n                cont= cont + 1\n        elif(cont == 2):\n                cor2 = segundo()\n                cont= cont + 1\n                pygame.display.flip()\n        elif(cont == 3):\n            resultado = misturar(cor1,cor2)\n            decisao = clicarConfirmarOuExcluir()\n            if(decisao==1):\n                  if (fase == 0):\n                        if(resultado == GREEN):\n                              \n                              fase = fase + 1\n                              cont = 1\n                        else:\n                              cont = 1\n                  elif(fase == 1):\n                        if(resultado == LARANJA):\n                              fase = 2\n                              cont = 1\n                        else:\n                              cont = 1\n                  elif(fase == 2):\n                        if(resultado == ROXO):\n                              fase = 3\n                              cont = 4\n                        else:\n                              cont = 1\n                  else:\n                        cont = 1\n            else:\n                cont = 1\n        elif(cont == 4):\n              break\n        for event in pygame.event.get():\n           if event.type == QUIT:\n              pygame.quit()\n              exit()\nimport main\n","repo_name":"DouglasAndC/GAME-COLORANDO-PYGAME","sub_path":"Main/fase_1.py","file_name":"fase_1.py","file_ext":"py","file_size_in_byte":12987,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"6016213064","text":"# Views for our character app\n\nfrom django.http import HttpResponseNotFound\nfrom django.http import Http404\nfrom django.shortcuts import render\nfrom django.conf import settings\n\nfrom evennia.utils.search import search_script_tag\nfrom evennia.utils.utils import inherits_from\nfrom evennia import create_script\n\nfrom codes.web.cruac.forms import EditForm\nfrom django.http import HttpResponseRedirect\nfrom datetime import datetime\nfrom evennia.objects.models import ObjectDB\nfrom django.conf import settings\nfrom evennia.utils import create\nfrom urllib.parse import unquote\nfrom urllib.parse import quote\n\ndef string_to_list(string):\n    if type(string) is list:\n        result = String\n    else:\n        temp_string = string[1:-1]\n        result=[]\n        for item in temp_string.split(','):\n            if item.strip().isnumeric():\n                entry = int(item.strip())\n            else:\n                entry = item.strip()\n            if entry != '':\n                result.append(entry)\n    return result\n\nclass cruac_class:\n    longname = ''\n    rank = 0\n    prereq = ''\n    reference = ''\n    info = ''\n    restricted = False\n\n    def update(self,longname,rank,prereq,reference,info,restricted):\n        self.longname = longname\n        self.rank = rank\n        self.prereq = prereq\n        self.reference = reference\n        self.info = info\n        self.restricted = restricted\n\ndef sheet(request, object_id):\n\n    object_id = unquote(object_id)\n\n    try:\n        data = search_script_tag('cruac_rite_stat')\n    except IndexError:\n        raise Http404(\"I couldn't find a character with that ID.\")\n\n    stats = []\n    for stat in data:\n        if stat.db.longname[0:len(object_id)].lower() == object_id.lower():\n            stats.append(stat)\n    if len(stats) == 0:\n        return render(request, 'cruac/error.html', {'message': 'No matching cruac: '+object_id})\n    if len(stats) > 1:\n        return render(request, 'cruac/error.html', {'message': 'Too many matching cruac'})\n    cruac = cruac_class()\n    longname = stats[0].db.longname\n    rank = stats[0].db.rank\n    prereq = stats[0].db.prereq\n    reference = stats[0].db.reference\n    if stats[0].db.info:\n        info = stats[0].db.info.replace('|/','\\n')\n    else:\n        info = chr(160)\n    restricted = stats[0].db.restricted\n    cruac.update(longname,rank,prereq,reference,info,restricted)\n    if request.method == 'POST':\n         return render(request, 'cruac/error.html', {'message': 'POST'})\n    return render(request, 'cruac/sheet.html', {'cruac': cruac, 'request':request, 'id':quote(object_id)})\n\ndef editor(request, object_id):\n\n    object_id = unquote(object_id)\n\n    try:\n        data = search_script_tag('cruac_rite_stat')\n    except IndexError:\n        raise Http404(\"I couldn't find a character with that ID.\")\n\n    stats = []\n    for stat in data:\n        if stat.db.longname[0:len(object_id)].lower() == object_id.lower():\n            stats.append(stat)\n    if len(stats) == 0:\n        return render(request, 'cruac/error.html', {'message': 'No matching cruac'})\n    if len(stats) > 1:\n        return render(request, 'cruac/error.html', {'message': 'Too many matching cruac'})\n    cruac = stats[0]\n    starting_data = {'longname':cruac.db.longname,\n                     'rank':cruac.db.rank,\n                     'prereq':cruac.db.prereq,\n                     'reference':cruac.db.reference,\n                     'info':cruac.db.info,\n                     'restricted':cruac.db.restricted,\n                     'link':object_id}\n    form = EditForm(initial = starting_data)\n    return render(request, 'cruac/editor.html', {'form': form, 'cruac_id':object_id })\n\ndef editted(request):\n    user = request.user\n    if user.is_staff:\n        if request.method == 'POST':\n            form = EditForm(request.POST)\n            if form.is_valid():\n                try:\n                    data = search_script_tag('cruac_rite_stat')\n                except IndexError:\n                    raise Http404(\"I couldn't find a character with that ID.\")\n                stats = []\n                n = form.cleaned_data['link']\n                for stat in data:\n                    if stat.db.longname[0:len(n)].lower() == n.lower():\n                        stats.append(stat)\n                if len(stats) == 0:\n                    return render(request, 'cruac/error.html', {'message': len(data) + ' No matching cruac: ' + n})\n                if len(stats) > 1:\n                    return render(request, 'cruac/error.html', {'message': 'Too many matching cruac'})\n                cruac = stats[0]\n                app = cruac.update(longname=form.cleaned_data['longname'],\n                                   rank=int(form.cleaned_data['rank']),\n                                   prereq=form.cleaned_data['prereq'],\n                                   reference=form.cleaned_data['reference'],\n                                   info=form.cleaned_data['info'],\n                                   restricted=form.cleaned_data['restricted'])\n                return HttpResponseRedirect('/cruac/view/'+quote(n))\n            else:\n                return render(request, 'cruac/error.html', {'message': 'Invalid form'})\n        else:\n            return render(request, 'cruac/error.html', {'message': 'Not POST'})\n    else:\n        return render(request, 'cruac/error.html', {'message': 'Not staff'})\n\ndef create(request):\n\n    starting_data = {'longname':'',\n                     'rank':0,\n                     'prereq':'',\n                     'reference':'',\n                     'info':'',\n                     'restricted':False,\n                     'link':''}\n    form = EditForm(initial = starting_data)\n    return render(request, 'cruac/create.html', {'form': form})\n\ndef created(request):\n    user = request.user\n    if user.is_staff:\n        if request.method == 'POST':\n            form = EditForm(request.POST)\n            if form.is_valid():\n                name = form.cleaned_data['longname'].replace('\\'','').replace(' ','_')\n                s = create_script('typeclasses.scripts.CruacRiteScript',\n                                   key=name)\n                s.db.longname=form.cleaned_data['longname']\n                s.db.rank=int(form.cleaned_data['rank'])\n                s.db.prereq=form.cleaned_data['prereq']\n                s.db.reference=form.cleaned_data['reference']\n                s.db.info=form.cleaned_data['info']\n                s.db.restricted=form.cleaned_data['restricted']\n                return HttpResponseRedirect('/cruac/view/'+quote(s.db.longname))\n            else:\n                M = str(form)\n                return render(request, 'cruac/error.html', {'message': 'Invalid form\\n'+M})\n        else:\n            return render(request, 'cruac/error.html', {'message': 'Not POST'})\n    else:\n         return render(request, 'cruac/error.html', {'message': 'Not staff'})\n\ndef list(request):\n    data = search_script_tag('cruac_rite_stat')\n    cruac = []\n    for stat in data:\n        cruac.append(stat.db.longname)\n    cruac.sort()\n    return render(request, 'cruac/list.html', {'cruac':cruac })\n","repo_name":"esampson/codes","sub_path":"codes/web/cruac/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7110,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"916941467","text":"from datetime import timedelta\n\nfrom erepublik import Citizen, constants, utils\n\nCONFIG = {\n    \"email\": \"player@email.com\",\n    \"password\": \"Pa$5w0rd!\",\n    \"interactive\": True,\n    \"debug\": True,\n    \"work\": True,\n    \"ot\": True,  # Work OverTime\n    \"wam\": True,  # WorkAsManager\n    \"train\": True,\n}\n\n\n# noinspection DuplicatedCode\ndef main():\n    player = Citizen(email=CONFIG[\"email\"], password=CONFIG[\"password\"], auto_login=False)\n    player.config.interactive = CONFIG[\"interactive\"]\n    player.config.work = CONFIG[\"work\"]\n    player.config.train = CONFIG[\"train\"]\n    player.config.ot = CONFIG[\"ot\"]\n    player.config.wam = CONFIG[\"wam\"]\n    player.set_debug(CONFIG.get(\"debug\", False))\n    player.login()\n    now = player.now.replace(second=0, microsecond=0)\n    dt_max = constants.max_datetime\n    tasks = {}\n    if player.config.work:\n        tasks.update({\"work\": now})\n    if player.config.train:\n        tasks.update({\"train\": now})\n    if player.config.ot:\n        tasks.update({\"ot\": now})\n    if player.config.wam:\n        tasks.update({\"wam\": now.replace(hour=14, minute=0)})\n    while True:\n        try:\n            player.update_all()\n            if tasks.get(\"work\", dt_max) <= now:\n                player.write_log(\"Doing task: work\")\n                player.update_citizen_info()\n                player.work()\n                if player.config.ot:\n                    tasks[\"ot\"] = now\n                player.collect_daily_task()\n                next_time = utils.good_timedelta(now.replace(hour=0, minute=0, second=0), timedelta(days=1))\n                tasks.update({\"work\": next_time})\n\n            if tasks.get(\"train\", dt_max) <= now:\n                player.write_log(\"Doing task: train\")\n                player.update_citizen_info()\n                player.train()\n                player.collect_daily_task()\n                next_time = utils.good_timedelta(now.replace(hour=0, minute=0, second=0), timedelta(days=1))\n                tasks.update({\"train\": next_time})\n\n            if tasks.get(\"wam\", dt_max) <= now:\n                player.write_log(\"Doing task: Work as manager\")\n                success = player.work_as_manager()\n                if success:\n                    next_time = utils.good_timedelta(\n                        now.replace(hour=14, minute=0, second=0, microsecond=0), timedelta(days=1)\n                    )\n                else:\n                    next_time = utils.good_timedelta(now.replace(second=0, microsecond=0), timedelta(minutes=30))\n\n                tasks.update({\"wam\": next_time})\n\n            if tasks.get(\"ot\", dt_max) <= now:\n                player.update_job_info()\n                player.write_log(\"Doing task: work overtime\")\n                if now > player.my_companies.next_ot_time:\n                    player.work_ot()\n                    next_time = now + timedelta(minutes=60)\n                else:\n                    next_time = player.my_companies.next_ot_time\n                tasks.update({\"ot\": next_time})\n\n            closest_next_time = dt_max\n            next_tasks = []\n            for task, next_time in sorted(tasks.items(), key=lambda s: s[1]):\n                next_tasks.append(f\"{next_time.strftime('%F %T')}: {task}\")\n                if next_time < closest_next_time:\n                    closest_next_time = next_time\n            sleep_seconds = int(utils.get_sleep_seconds(closest_next_time))\n            if sleep_seconds <= 0:\n                player.write_log(f\"Loop detected! Offending task: '{next_tasks[0]}'\")\n            player.write_log(\"My next Tasks and there time:\\n\" + \"\\n\".join(sorted(next_tasks)))\n            player.write_log(\n                f\"Sleeping until (eRep): {closest_next_time.strftime('%F %T')}\" f\" (sleeping for {sleep_seconds}s)\"\n            )\n            seconds_to_sleep = sleep_seconds if sleep_seconds > 0 else 0\n            player.sleep(seconds_to_sleep)\n        except Exception as e:\n            player.report_error(f\"Task main loop ran into error: {e}\")\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"eeriks/erepublik","sub_path":"examples/eat_work_train.py","file_name":"eat_work_train.py","file_ext":"py","file_size_in_byte":4026,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"48"}
+{"seq_id":"8956684340","text":"#!/home/libo03/.jumbo/bin/python\n# -*- coding:utf-8 -*-\n\n# pop3.py  \nimport models.config as conf\nimport models.logger as log\nimport sys,os,string,binascii\nimport time\nimport poplib  \n  \nclass mail_client:\n\tdef __init__(self, mail_conf, logger):\n\t\tself.logger = logger \n\t\tself.mail_conf = mail_conf\n\t\t\n\t\tmail_account = self.mail_conf.get_mail_account()\n\t\tself.pp = poplib.POP3_SSL(mail_account['pop3_addr'])  \n\t\tself.pp.user(mail_account['pop3_user'])  \n\t\tself.pp.pass_(mail_account['pop3_pass'])  \n\n\tdef __del__(self):\n\t\tself.pp.quit()\n\t\n\tdef getStat(self):\n\t\tstat = self.pp.stat()\n\t\tself.logger.info(\"stat current mail account is %s\" %str(stat) )\n\t\treturn stat\n\n\tdef setCursor(self, cursor):\n\t\tself.mail_conf.set_mail_cursor(cursor)\n\t\tself.logger.info(\"set current mail cursor is %d\" %(cursor)) \n\n\tdef resetCursor(self):\n\t\tself.mail_conf.set_mail_cursor(0)\n\t\tself.logger.warning(\"RESET current mail cursor to 0\") \n\n\tdef getMails(self):\n\t\tcursor = self.mail_conf.get_mail_cursor()\n\t\tmax_query_count = self.mail_conf.get_max_query_count()\n\t\tstat = self.getStat()\n\t\ttotal_count = int(stat[0])\n\n\t\tmail_dir_info = self.mail_conf.get_mail_dir_info()\n\t\tmail_dir_path = mail_dir_info['path'] + '/' + mail_dir_info['incoming_dir_name'] + '/'\n\n\t\ti = 0;\n\t\tif (total_count - cursor) >= max_query_count :\n\t\t\tfor i in range(cursor, max_query_count + cursor):\n\t\t\t\tself.__getMail(i, mail_dir_path)\n\t\t\t\tnew_cursor = i + 1\n\t\t\t\tself.mail_conf.set_mail_cursor( new_cursor )\n\t\telif total_count > cursor :\n\t\t\tfor i in range(cursor, total_count):\n\t\t\t\tself.__getMail(i, mail_dir_path)\n\t\t\t\tnew_cursor = i + 1\n\t\t\t\tself.mail_conf.set_mail_cursor( new_cursor )\n\n\n\n\tdef __getMail(self, current_cursor, mail_dir_path):\n\t\tmail_content = ''\n\t\tmail_data = self.pp.retr(current_cursor)\n\t\tfor line in mail_data[1]:\n\t\t\tmail_content += line + \"\\n\"\n\t\tself.__saveToFile(current_cursor, mail_content, mail_dir_path)\n\n\tdef __saveToFile(self, index, mail_content, mail_dir_path):\n\t\tfile_name = str(int(time.time() * 100)) + '_' + str(index)\n\t\tfile_full_name = mail_dir_path + file_name\n\t\tf = file(file_full_name, 'w')\n\t\tf.write(mail_content)\n\t\tf.close()\n\n\nif __name__ == '__main__':\n\tlogger = log.log_control('/tmp/mail_agent.log') \n\tmail_conf = conf.MailConf(logger) \n\tmail_conf.read_conf()\n\n\tr = mail_client(mail_conf, logger)\n\tr.getMails()\n","repo_name":"rambolee/helpdesk","sub_path":"lib/mail_tools/get_popmail/pop3.py","file_name":"pop3.py","file_ext":"py","file_size_in_byte":2303,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"48"}
+{"seq_id":"41354204998","text":"import os\nimport shutil\nimport struct\nimport sys\nimport tempfile\nimport unittest\n\nfrom binman import elf\nfrom u_boot_pylib import command\nfrom u_boot_pylib import test_util\nfrom u_boot_pylib import tools\nfrom u_boot_pylib import tout\n\nbinman_dir = os.path.dirname(os.path.realpath(sys.argv[0]))\n\n\nclass FakeEntry:\n    \"\"\"A fake Entry object, usedfor testing\n\n    This supports an entry with a given size.\n    \"\"\"\n    def __init__(self, contents_size):\n        self.contents_size = contents_size\n        self.data = tools.get_bytes(ord('a'), contents_size)\n\n    def GetPath(self):\n        return 'entry_path'\n\n\nclass FakeSection:\n    \"\"\"A fake Section object, used for testing\n\n    This has the minimum feature set needed to support testing elf functions.\n    A LookupSymbol() function is provided which returns a fake value for amu\n    symbol requested.\n    \"\"\"\n    def __init__(self, sym_value=1):\n        self.sym_value = sym_value\n\n    def GetPath(self):\n        return 'section_path'\n\n    def LookupImageSymbol(self, name, weak, msg, base_addr):\n        \"\"\"Fake implementation which returns the same value for all symbols\"\"\"\n        return self.sym_value\n\n    def GetImage(self):\n        return self\n\ndef BuildElfTestFiles(target_dir):\n    \"\"\"Build ELF files used for testing in binman\n\n    This compiles and links the test files into the specified directory. It uses\n    the Makefile and source files in the binman test/ directory.\n\n    Args:\n        target_dir: Directory to put the files into\n    \"\"\"\n    if not os.path.exists(target_dir):\n        os.mkdir(target_dir)\n    testdir = os.path.join(binman_dir, 'test')\n\n    # If binman is involved from the main U-Boot Makefile the -r and -R\n    # flags are set in MAKEFLAGS. This prevents this Makefile from working\n    # correctly. So drop any make flags here.\n    if 'MAKEFLAGS' in os.environ:\n        del os.environ['MAKEFLAGS']\n    try:\n        tools.run('make', '-C', target_dir, '-f',\n                  os.path.join(testdir, 'Makefile'), 'SRC=%s/' % testdir)\n    except ValueError as e:\n        # The test system seems to suppress this in a strange way\n        print(e)\n\n\nclass TestElf(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        cls._indir = tempfile.mkdtemp(prefix='elf.')\n        tools.set_input_dirs(['.'])\n        BuildElfTestFiles(cls._indir)\n\n    @classmethod\n    def tearDownClass(cls):\n        if cls._indir:\n            shutil.rmtree(cls._indir)\n\n    @classmethod\n    def ElfTestFile(cls, fname):\n        return os.path.join(cls._indir, fname)\n\n    def testAllSymbols(self):\n        \"\"\"Test that we can obtain a symbol from the ELF file\"\"\"\n        fname = self.ElfTestFile('u_boot_ucode_ptr')\n        syms = elf.GetSymbols(fname, [])\n        self.assertIn('_dt_ucode_base_size', syms)\n\n    def testRegexSymbols(self):\n        \"\"\"Test that we can obtain from the ELF file by regular expression\"\"\"\n        fname = self.ElfTestFile('u_boot_ucode_ptr')\n        syms = elf.GetSymbols(fname, ['ucode'])\n        self.assertIn('_dt_ucode_base_size', syms)\n        syms = elf.GetSymbols(fname, ['missing'])\n        self.assertNotIn('_dt_ucode_base_size', syms)\n        syms = elf.GetSymbols(fname, ['missing', 'ucode'])\n        self.assertIn('_dt_ucode_base_size', syms)\n\n    def testMissingFile(self):\n        \"\"\"Test that a missing file is detected\"\"\"\n        entry = FakeEntry(10)\n        section = FakeSection()\n        with self.assertRaises(ValueError) as e:\n            elf.LookupAndWriteSymbols('missing-file', entry, section)\n        self.assertIn(\"Filename 'missing-file' not found in input path\",\n                      str(e.exception))\n\n    def testOutsideFile(self):\n        \"\"\"Test a symbol which extends outside the entry area is detected\"\"\"\n        if not elf.ELF_TOOLS:\n            self.skipTest('Python elftools not available')\n        entry = FakeEntry(10)\n        section = FakeSection()\n        elf_fname = self.ElfTestFile('u_boot_binman_syms')\n        with self.assertRaises(ValueError) as e:\n            elf.LookupAndWriteSymbols(elf_fname, entry, section)\n        self.assertIn('entry_path has offset 8 (size 8) but the contents size '\n                      'is a', str(e.exception))\n\n    def testMissingImageStart(self):\n        \"\"\"Test that we detect a missing __image_copy_start symbol\n\n        This is needed to mark the start of the image. Without it we cannot\n        locate the offset of a binman symbol within the image.\n        \"\"\"\n        entry = FakeEntry(10)\n        section = FakeSection()\n        elf_fname = self.ElfTestFile('u_boot_binman_syms_bad')\n        count = elf.LookupAndWriteSymbols(elf_fname, entry, section)\n        self.assertEqual(0, count)\n\n    def testBadSymbolSize(self):\n        \"\"\"Test that an attempt to use an 8-bit symbol are detected\n\n        Only 32 and 64 bits are supported, since we need to store an offset\n        into the image.\n        \"\"\"\n        if not elf.ELF_TOOLS:\n            self.skipTest('Python elftools not available')\n        entry = FakeEntry(10)\n        section = FakeSection()\n        elf_fname =self.ElfTestFile('u_boot_binman_syms_size')\n        with self.assertRaises(ValueError) as e:\n            elf.LookupAndWriteSymbols(elf_fname, entry, section)\n        self.assertIn('has size 1: only 4 and 8 are supported',\n                      str(e.exception))\n\n    def testNoValue(self):\n        \"\"\"Test the case where we have no value for the symbol\n\n        This should produce -1 values for all three symbols, taking up the\n        first 16 bytes of the image.\n        \"\"\"\n        if not elf.ELF_TOOLS:\n            self.skipTest('Python elftools not available')\n        entry = FakeEntry(28)\n        section = FakeSection(sym_value=None)\n        elf_fname = self.ElfTestFile('u_boot_binman_syms')\n        count = elf.LookupAndWriteSymbols(elf_fname, entry, section)\n        self.assertEqual(5, count)\n        expected = (struct.pack('<L', elf.BINMAN_SYM_MAGIC_VALUE) +\n                    tools.get_bytes(255, 20) +\n                    tools.get_bytes(ord('a'), 4))\n        self.assertEqual(expected, entry.data)\n\n    def testDebug(self):\n        \"\"\"Check that enabling debug in the elf module produced debug output\"\"\"\n        if not elf.ELF_TOOLS:\n            self.skipTest('Python elftools not available')\n        try:\n            tout.init(tout.DEBUG)\n            entry = FakeEntry(24)\n            section = FakeSection()\n            elf_fname = self.ElfTestFile('u_boot_binman_syms')\n            with test_util.capture_sys_output() as (stdout, stderr):\n                elf.LookupAndWriteSymbols(elf_fname, entry, section)\n            self.assertTrue(len(stdout.getvalue()) > 0)\n        finally:\n            tout.init(tout.WARNING)\n\n    def testMakeElf(self):\n        \"\"\"Test for the MakeElf function\"\"\"\n        outdir = tempfile.mkdtemp(prefix='elf.')\n        expected_text = b'1234'\n        expected_data = b'wxyz'\n        elf_fname = os.path.join(outdir, 'elf')\n        bin_fname = os.path.join(outdir, 'bin')\n\n        # Make an Elf file and then convert it to a fkat binary file. This\n        # should produce the original data.\n        elf.MakeElf(elf_fname, expected_text, expected_data)\n        objcopy, args = tools.get_target_compile_tool('objcopy')\n        args += ['-O', 'binary', elf_fname, bin_fname]\n        stdout = command.output(objcopy, *args)\n        with open(bin_fname, 'rb') as fd:\n            data = fd.read()\n        self.assertEqual(expected_text + expected_data, data)\n        shutil.rmtree(outdir)\n\n    def testDecodeElf(self):\n        \"\"\"Test for the MakeElf function\"\"\"\n        if not elf.ELF_TOOLS:\n            self.skipTest('Python elftools not available')\n        outdir = tempfile.mkdtemp(prefix='elf.')\n        expected_text = b'1234'\n        expected_data = b'wxyz'\n        elf_fname = os.path.join(outdir, 'elf')\n        elf.MakeElf(elf_fname, expected_text, expected_data)\n        data = tools.read_file(elf_fname)\n\n        load = 0xfef20000\n        entry = load + 2\n        expected = expected_text + expected_data\n        self.assertEqual(elf.ElfInfo(expected, load, entry, len(expected)),\n                         elf.DecodeElf(data, 0))\n        self.assertEqual(elf.ElfInfo(b'\\0\\0' + expected[2:],\n                                     load, entry, len(expected)),\n                         elf.DecodeElf(data, load + 2))\n        shutil.rmtree(outdir)\n\n    def testEmbedData(self):\n        \"\"\"Test for the GetSymbolFileOffset() function\"\"\"\n        if not elf.ELF_TOOLS:\n            self.skipTest('Python elftools not available')\n\n        fname = self.ElfTestFile('embed_data')\n        offset = elf.GetSymbolFileOffset(fname, ['embed_start', 'embed_end'])\n        start = offset['embed_start'].offset\n        end = offset['embed_end'].offset\n        data = tools.read_file(fname)\n        embed_data = data[start:end]\n        expect = struct.pack('<IIIII', 2, 3, 0x1234, 0x5678, 0)\n        self.assertEqual(expect, embed_data)\n\n    def testEmbedFail(self):\n        \"\"\"Test calling GetSymbolFileOffset() without elftools\"\"\"\n        try:\n            old_val = elf.ELF_TOOLS\n            elf.ELF_TOOLS = False\n            fname = self.ElfTestFile('embed_data')\n            with self.assertRaises(ValueError) as e:\n                elf.GetSymbolFileOffset(fname, ['embed_start', 'embed_end'])\n            with self.assertRaises(ValueError) as e:\n                elf.DecodeElf(tools.read_file(fname), 0xdeadbeef)\n            with self.assertRaises(ValueError) as e:\n                elf.GetFileOffset(fname, 0xdeadbeef)\n            with self.assertRaises(ValueError) as e:\n                elf.GetSymbolFromAddress(fname, 0xdeadbeef)\n            with self.assertRaises(ValueError) as e:\n                entry = FakeEntry(10)\n                section = FakeSection()\n                elf.LookupAndWriteSymbols(fname, entry, section, True)\n\n            self.assertIn(\n                \"Section 'section_path': entry 'entry_path': Cannot write symbols to an ELF file without Python elftools\",\n                str(e.exception))\n        finally:\n            elf.ELF_TOOLS = old_val\n\n    def testEmbedDataNoSym(self):\n        \"\"\"Test for GetSymbolFileOffset() getting no symbols\"\"\"\n        if not elf.ELF_TOOLS:\n            self.skipTest('Python elftools not available')\n\n        fname = self.ElfTestFile('embed_data')\n        offset = elf.GetSymbolFileOffset(fname, ['missing_sym'])\n        self.assertEqual({}, offset)\n\n    def test_read_loadable_segments(self):\n        \"\"\"Test for read_loadable_segments()\"\"\"\n        if not elf.ELF_TOOLS:\n            self.skipTest('Python elftools not available')\n        fname = self.ElfTestFile('embed_data')\n        segments, entry = elf.read_loadable_segments(tools.read_file(fname))\n\n    def test_read_segments_fail(self):\n        \"\"\"Test for read_loadable_segments() without elftools\"\"\"\n        try:\n            old_val = elf.ELF_TOOLS\n            elf.ELF_TOOLS = False\n            fname = self.ElfTestFile('embed_data')\n            with self.assertRaises(ValueError) as e:\n                elf.read_loadable_segments(tools.read_file(fname))\n            self.assertIn(\"Python: No module named 'elftools'\",\n                          str(e.exception))\n        finally:\n            elf.ELF_TOOLS = old_val\n\n    def test_read_segments_bad_data(self):\n        \"\"\"Test for read_loadable_segments() with an invalid ELF file\"\"\"\n        if not elf.ELF_TOOLS:\n            self.skipTest('Python elftools not available')\n        fname = self.ElfTestFile('embed_data')\n        with self.assertRaises(ValueError) as e:\n            elf.read_loadable_segments(tools.get_bytes(100, 100))\n        self.assertIn('Magic number does not match', str(e.exception))\n\n    def test_get_file_offset(self):\n        \"\"\"Test GetFileOffset() gives the correct file offset for a symbol\"\"\"\n        if not elf.ELF_TOOLS:\n            self.skipTest('Python elftools not available')\n        fname = self.ElfTestFile('embed_data')\n        syms = elf.GetSymbols(fname, ['embed'])\n        addr = syms['embed'].address\n        offset = elf.GetFileOffset(fname, addr)\n        data = tools.read_file(fname)\n\n        # Just use the first 4 bytes and assume it is little endian\n        embed_data = data[offset:offset + 4]\n        embed_value = struct.unpack('<I', embed_data)[0]\n        self.assertEqual(0x1234, embed_value)\n\n    def test_get_file_offset_fail(self):\n        \"\"\"Test calling GetFileOffset() without elftools\"\"\"\n        try:\n            old_val = elf.ELF_TOOLS\n            elf.ELF_TOOLS = False\n            fname = self.ElfTestFile('embed_data')\n            with self.assertRaises(ValueError) as e:\n                elf.GetFileOffset(fname, 0)\n            self.assertIn(\"Python: No module named 'elftools'\",\n                      str(e.exception))\n        finally:\n            elf.ELF_TOOLS = old_val\n\n    def test_get_symbol_from_address(self):\n        \"\"\"Test GetSymbolFromAddress()\"\"\"\n        if not elf.ELF_TOOLS:\n            self.skipTest('Python elftools not available')\n        fname = self.ElfTestFile('elf_sections')\n        sym_name = 'calculate'\n        syms = elf.GetSymbols(fname, [sym_name])\n        addr = syms[sym_name].address\n        sym = elf.GetSymbolFromAddress(fname, addr)\n        self.assertEqual(sym_name, sym)\n\n    def test_get_symbol_from_address_fail(self):\n        \"\"\"Test calling GetSymbolFromAddress() without elftools\"\"\"\n        try:\n            old_val = elf.ELF_TOOLS\n            elf.ELF_TOOLS = False\n            fname = self.ElfTestFile('embed_data')\n            with self.assertRaises(ValueError) as e:\n                elf.GetSymbolFromAddress(fname, 0x1000)\n            self.assertIn(\"Python: No module named 'elftools'\",\n                          str(e.exception))\n        finally:\n            elf.ELF_TOOLS = old_val\n\n    def test_is_valid(self):\n        \"\"\"Test is_valid()\"\"\"\n        self.assertEqual(False, elf.is_valid(b''))\n        self.assertEqual(False, elf.is_valid(b'1234'))\n\n        fname = self.ElfTestFile('elf_sections')\n        data = tools.read_file(fname)\n        self.assertEqual(True, elf.is_valid(data))\n        self.assertEqual(False, elf.is_valid(data[4:]))\n\n    def test_get_symbol_offset(self):\n        fname = self.ElfTestFile('embed_data')\n        syms = elf.GetSymbols(fname, ['embed_start', 'embed'])\n        expected = syms['embed'].address - syms['embed_start'].address\n        val = elf.GetSymbolOffset(fname, 'embed', 'embed_start')\n        self.assertEqual(expected, val)\n\n        with self.assertRaises(KeyError) as e:\n            elf.GetSymbolOffset(fname, 'embed')\n        self.assertIn('__image_copy_start', str(e.exception))\n\n    def test_get_symbol_address(self):\n        fname = self.ElfTestFile('embed_data')\n        addr = elf.GetSymbolAddress(fname, 'region_size')\n        self.assertEqual(0, addr)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"u-boot/u-boot","sub_path":"tools/binman/elf_test.py","file_name":"elf_test.py","file_ext":"py","file_size_in_byte":14837,"program_lang":"python","lang":"en","doc_type":"code","stars":3245,"dataset":"github-code","pt":"48"}
+{"seq_id":"37395100654","text":"\"\"\"\r\nGiven an array of integers, find the maximal absolute difference between any two of\r\nits adjacent elements.\r\n\r\nExample:\r\nFor inputArray = [2, 4, 1, 0], the output should be\r\nsolution(inputArray) = 3.\r\n\r\n[execution time limit] 4 seconds (py3)\r\n[input] array.integer inputArray\r\n\r\n[output] integer\r\nThe maximal absolute difference.\r\n\r\nTests passed: 13/13.\r\n\"\"\"\r\ndef solution(inputArray):\r\n    result = 0\r\n    resultArray = []\r\n    \r\n    for i, el in enumerate(inputArray):\r\n        a = i\r\n        x = i+1\r\n        b = i+2\r\n\r\n        if b < len(inputArray):\r\n            diff1 = abs(inputArray[x] - inputArray[a])\r\n            diff2 = abs(inputArray[x] - inputArray[b])\r\n            maxEachIteration = max(diff1, diff2)\r\n            resultArray.append(maxEachIteration)\r\n            result = max(resultArray)\r\n              \r\n    return result\r\n","repo_name":"SatoshiJIshida/projects","sub_path":"Python - Coding Challenges/arraymaximaladjacentdifference.py","file_name":"arraymaximaladjacentdifference.py","file_ext":"py","file_size_in_byte":847,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"27993117567","text":"'''\n# ex: 1\nimport streamlit as st\nfrom langchain.llms import OpenAI\nfrom langchain.embeddings import OpenAIEmbeddings\nfrom langchain.vectorstores import Chroma\nfrom langchain.chains import ConversationalRetrievalChain\nfrom langchain.document_loaders import PyPDFLoader\n\nst.title(\"Welcome to Health Universe!\")\nst.write(\"This is a sample application.\")\n\nalarm_clock = st.slider('hour', 0, 23, 17) # min: 0h, max: 23h, default: 17h\nst.header(alarm_clock) # print in large text\n\n# Inputs\n## Text input (not sidebar)\nopenai_api_key = st.sidebar.text_input(\"OpenAI API Key\", type=\"password\")\n\n\nfrom langchain.document_loaders import PyPDFLoader\n\nloader = PyPDFLoader(\"benefit-plans.pdf\")\npages = loader.load_and_split()\n\n\ndocument = pages[0]\n\nst.write(document)\n\n\n'''\n'''\n\nfrom dotenv import load_dotenv\nimport streamlit as st\nfrom PyPDF2 import PdfReader\nfrom langchain.text_splitter import CharacterTextSplitter\nfrom langchain.embeddings import OpenAIEmbeddings\nfrom langchain.vectorstores import FAISS\nfrom langchain.llms import OpenAI\nfrom langchain.chains.question_answering import load_qa_chain\nfrom langchain.callbacks import get_openai_callback\n\n\ndef main():\n    load_dotenv()\n    st.set_page_config(page_title=\"Ask Your PDF\", page_icon=\"🗂️\")\n    st.title(\"Ask Your PDF 💬\")\n\n    # Upload the file\n    pdf = st.file_uploader(\"Upload your PDF\", type=[\"pdf\"])\n\n    # Extract text from the uploaded file\n    if pdf is not None:\n        pdf_reader = PdfReader(pdf)\n        text = \"\"\n        for page in pdf_reader.pages:\n            text += page.extract_text()\n    \n        # Create chunks\n        text_splitter = CharacterTextSplitter(\n            separator=\"\\n\",\n            chunk_size=1000,\n            chunk_overlap=200,\n            length_function=len\n        )\n        chunks = text_splitter.split_text(text)\n\n        # Create the embeddings\n        embeddings = OpenAIEmbeddings()\n\n        # Build the vector store\n        knowledge_base = FAISS.from_texts(chunks, embeddings)\n\n        # Chat functionality\n        user_input = st.text_input(\"Ask a question about your PDF:\")\n        if user_input:\n            docs = knowledge_base.similarity_search(user_input)\n\n            chain = load_qa_chain(OpenAI(), chain_type=\"stuff\")\n\n            with get_openai_callback() as cb:\n                response = chain.run(input_documents=docs, question=user_input)\n                print(cb)\n\n            st.write(response)\n\n\nif __name__ == '__main__':\n    main()\n\n'''\n\nimport streamlit as st\nfrom dotenv import load_dotenv\nimport pickle\nfrom PyPDF2 import PdfReader\nfrom streamlit_extras.add_vertical_space import add_vertical_space\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\nfrom langchain.embeddings.openai import OpenAIEmbeddings\nfrom langchain.vectorstores import FAISS\nfrom langchain.llms import OpenAI\nfrom langchain.chains.question_answering import load_qa_chain\nfrom langchain.callbacks import get_openai_callback\nimport os\n\n# Sidebar contents\nwith st.sidebar:\n    st.title('Chat with Your Insurance Plan')\n    st.markdown('''\n    ## About\n    Chat with your insurance plan in pdf to discover the right information for your decision make\n\n    ''')\n    add_vertical_space(5)\n\nload_dotenv()\n\ndef main():\n    st.header(\"Chat with pdf\")\n\n    # upload a pdf file\n    pdf = st.file_uploader(\"Upload your PDF\", type='pdf')\n    \n    #st.write(pdf)\n    if pdf is not None:\n        #pdf reader\n        pdf_reader = PdfReader(pdf)\n        \n        text = \"\"\n        for page in pdf_reader.pages:\n            text += page.extract_text()\n\n        text_splitter = RecursiveCharacterTextSplitter(\n            chunk_size=1000,\n            chunk_overlap=200,\n            length_function=len\n            )\n        chunks = text_splitter.split_text(text=text)\n \n        # # embeddings\n        store_name = pdf.name[:-4]\n        st.write(f'{store_name}')\n        # st.write(chunks)\n \n        if os.path.exists(f\"{store_name}.pkl\"):\n            with open(f\"{store_name}.pkl\", \"rb\") as f:\n                VectorStore = pickle.load(f)\n            # st.write('Embeddings Loaded from the Disk')s\n        else:\n            embeddings = OpenAIEmbeddings()\n            VectorStore = FAISS.from_texts(chunks, embedding=embeddings)\n            with open(f\"{store_name}.pkl\", \"wb\") as f:\n                pickle.dump(VectorStore, f)\n \n        # embeddings = OpenAIEmbeddings()\n        # VectorStore = FAISS.from_texts(chunks, embedding=embeddings)\n \n        # Accept user questions/query\n        query = st.text_input(\"Ask questions about your PDF file:\")\n        # st.write(query)\n \n        if query:\n            docs = VectorStore.similarity_search(query=query, k=3)\n \n            llm = OpenAI()\n            chain = load_qa_chain(llm=llm, chain_type=\"stuff\")\n            with get_openai_callback() as cb:\n                response = chain.run(input_documents=docs, question=query)\n                print(cb)\n            st.write(response)\n \nif __name__ == '__main__':\n    \n    main()\n\n","repo_name":"mike-anh-pham/health4ai8hackathon","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4989,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"3302788481","text":"import sys\n\nclass Dungeon(object):\n    \"\"\"docstring for Dungeon.\"\"\"\n    def __init__(self, n, m):\n        super(Dungeon, self).__init__()\n        self.intersection_n = int(n)\n        self.corridor_n = int(m)\n        self.matrix = []\n\n    def add_line(self, x, y, f):\n        x, y = int(x), int(y)\n        #Sort from start -> end, factor\n        sorted_line = [min(x,y), max(x,y), float(f)]\n        self.matrix.append(sorted_line)\n\n    def calculate_factor(self):\n        # Sort the corridors by start and stop intersection \n        self.matrix.sort(key = lambda l: (l[0], l[1]))\n        #print(self.matrix)\n        starts, stops, factors = zip(*self.matrix)\n\n        possible_stops = set(stops)\n        intersections_best_factor = {intersection: None for intersection in possible_stops}\n        intersections_best_factor.update({0: 1.0})\n\n        # For every possible \n        for start, stop, factor in self.matrix:\n            factor_pre = intersections_best_factor.get(start)\n            if not factor_pre:\n                # There no factor for the starting intersection, it might be a blind alley\n                continue\n            factor_post = factor_pre * factor\n            # Check if the current factor is better\n            if intersections_best_factor[stop] is not None:\n                if factor_post < intersections_best_factor[stop]:\n                    continue\n            intersections_best_factor[stop] = factor_post\n        \n        #print(intersections_best_factor)\n        \n        return intersections_best_factor[self.intersection_n - 1]\n\n\n\ncurrent_dungeon = None\n\nfor i in sys.stdin:\n    line = i.split()\n    \n    if len(line) == 2:\n        if current_dungeon:\n            # Output the factor\n            final_factor = current_dungeon.calculate_factor()\n            print('{:6.4f}'.format(final_factor))\n        #Start a new dungeon\n        current_dungeon = Dungeon(*line)\n    elif len(line) == 3:\n        current_dungeon.add_line(*line)","repo_name":"wernersa/kattis","sub_path":"problems/getshorty.py","file_name":"getshorty.py","file_ext":"py","file_size_in_byte":1965,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"31603390775","text":"from bs4 import BeautifulSoup\nimport requests\n\nbase_url = \"http://questions.menstrupedia.com\"\n\ndef make_query():\n    user_query = input (\"Input your query regarding menstrutation: \")\n    query = user_query.split(\" \")\n    query = \"+\".join(query)\n    return query\n\ndef fetch_questions(query):\n    url = base_url + \"/search/?q=\"+query+\"&Submit=search&t=question\"\n    page  = requests.get(url)\n    data = BeautifulSoup(page.content, \"html.parser\")\n    ques = data.findAll(class_=\"question-summary-wrapper\")\n\n    c=0\n    url_list = []\n    string_list = []\n\n    if len(ques) == 0:\n        print (\"No questions found\")\n        return (\"Result not found\")\n\n    for i in ques:\n        qstring = \"\"\n\n        if c == 5:\n            break\n        c+=1\n        Q = i.find(\"h2\").get_text()\n        h = i.find(\"a\")\n        ans_url = (base_url + h[\"href\"]+\"\\n\")\n        url_list.append(ans_url)\n        qstring += \"*\" + Q + \"*\" + \"\\n\"\n        qstring += h[\"title\"]+\"\\n\" + \"\\n\"\n        qstring += \"*Answer link* \" + ans_url+ \"\\n\"\n        string_list.append(qstring)\n    return (url_list, string_list)\n\ndef fetch_answer(url_list):\n    user_ans = int(input(\"Which question number answere you want \"))\n    ans_page = requests.get(url_list[user_ans-1])\n    ans_data = BeautifulSoup(ans_page.content, \"html.parser\")\n    ans = ans_data.findAll(class_=\"answer-body\")\n\n    if len(ans) == 0:\n        print (\"No Answer found\")\n\n    j = 0\n    for i in ans:\n        if (j==3):\n            break\n        j += 1\n        A = i.find(\"p\").get_text()\n        print (str(j)+ \". \" + A + \"\\n\")\n        print (\"\\n$$$----------------------hyeeee Cool Answer-----------------------------------------$$$\\n\")\n\n\nif __name__ == \"__main__\":\n    query = make_query()\n    question_urls = fetch_questions(query)[0]\n    if len(question_urls) > 0:\n        fetch_answer(question_urls)","repo_name":"Ranipandit/MentrualBot","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":1832,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"72238251666","text":"from tkinter import *\nfrom random import *\nfrom tkinter.filedialog import askopenfilename\n\nname_of_game = \"\"\nsolution = list()\ninitial_board = list()\nboard = list()\ndifficulty = \"\"\nselected_X = 0\nselected_Y = 0\nload = False\n\n\ndef main():\n    setup_window = Tk()\n    setup_window.wm_title(\"Sudoku Setup\")\n    game_name = StringVar(None, \"Sudoku\")\n    game_name_label = Label(setup_window, text=\"Game Name:\")\n    game_name_label.pack()\n    game_name_entry = Entry(setup_window,textvariable=game_name, bd=5)\n    game_name_entry.pack()\n    game_difficulty_label = Label(setup_window, text=\"Game Difficulty:\")\n    game_difficulty_label.pack()\n    game_difficulty_list = Spinbox(setup_window,values=('Easy', 'Medium', 'Hard'), bd=5, state='readonly')\n    game_difficulty_list.pack()\n\n    def start_call_back():\n\n        def key(event):\n            handle_key_event(event, game_canvas)\n\n        def callback(event):\n            handle_mouse_event(event, game_canvas)\n\n        def submit_call_back():\n            submit_window = Tk()\n            valid = True\n            for i in range(0, 9):\n                for j in range(0, 9):\n                    if board[i][j] == 0:\n                        redraw_with_selected(game_canvas, board, i, j)\n                        valid = False\n\n            for i in range(0, 9):\n                candidates = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n                for j in range(0, 9):\n                    if board[i][j] in candidates:\n                        candidates.remove(board[i][j])\n                if len(candidates) > 0:\n                    valid = False\n            for j in range(0, 9):\n                candidates = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n                for i in range(0, 9):\n                    if board[i][j] in candidates:\n                        candidates.remove(board[i][j])\n                if len(candidates) > 0:\n                    valid = False\n\n            if valid:\n                for i in range(0, 9):\n                    for j in range(0, 9):\n                        redraw_with_selected(game_canvas, board, i, j)\n                submit_label = Label(submit_window, text=\"Your solution is correct!\\nWould you like to play again?\")\n                submit_label.pack()\n\n                def yes_callback():\n                    submit_window.destroy()\n                    game_canvas.delete(\"all\")\n                    setup_game(name_of_game,difficulty, game_canvas, False)\n                    game_window.deiconify()\n\n                yes_button = Button(submit_window, text=\"Yes\", command=yes_callback)\n                yes_button.pack()\n\n                def no_callback():\n                    submit_window.destroy()\n                    setup_window.deiconify()\n\n                no_button = Button(submit_window, text=\"No\", command=no_callback)\n                no_button.pack()\n            else:\n                submit_err_label = Label(submit_window, text=\"Oops! The solution is incorrect!\\nDo you want to continue?\")\n                submit_err_label.pack()\n\n                def yes_err_callback():\n                    game_window.deiconify()\n                    submit_window.destroy()\n\n                yes_err_button = Button(submit_window, text=\"Yes\", command=yes_err_callback)\n                yes_err_button.pack()\n\n                def no_err_callback():\n                    setup_window.deiconify()\n                    submit_window.destroy()\n\n                no_err_button = Button(submit_window, text=\"No\", command=no_err_callback)\n                no_err_button.pack()\n            game_window.withdraw()\n            submit_window.mainloop()\n\n        def save_call_back():\n            save_file_window = Tk()\n            save_file_window.wm_title(\"Save File\")\n            save_file_label = Label(save_file_window, text=\"Enter filename:\")\n            save_file_label.pack()\n            save_name = StringVar(None, \"File Name\")\n            save_name_entry = Entry(save_file_window, textvariable=save_name, bd=5)\n            save_name_entry.pack()\n\n            def save_file_call_back():\n                save_game(save_name_entry.get())\n                save_file_window.destroy()\n\n            save_button = Button(save_file_window, text=\"Save\", command=save_file_call_back)\n            save_button.pack()\n\n        game_namestr = game_name.get()\n        diff = game_difficulty_list.get()\n        setup_window.withdraw()\n        game_window = Tk()\n        game_window.wm_title(game_namestr)\n        game_window.bind(\"<Key>\", key)\n        game_window.bind(\"<Button-1>\", callback)\n        game_window.resizable(width=False, height=False)\n\n        def on_closing():\n            exit(0)\n\n        game_window.protocol(\"WM_DELETE_WINDOW\", on_closing)\n        game_canvas = Canvas(game_window, bg=\"white\", height=9*50, width=9*50)\n        setup_game(game_namestr, diff, game_canvas, load)\n        game_submit_button = Button(game_window, text=\"Submit Sudoku\", command=submit_call_back)\n        game_submit_button.pack()\n        game_save_button = Button(game_window, text=\"Save Game\", command=save_call_back)\n        game_save_button.pack()\n        game_window.mainloop()\n\n    def load_call_back():\n        global name_of_game, board, initial_board, solution, difficulty, load\n        load = True\n        for i in range(0, 9):\n            initial_board.append([0] * 9)\n        for i in range(0, 9):\n            board.append([0] * 9)\n        for i in range(0, 9):\n            solution.append([0] * 9)\n        filename = \"\"\n        try:\n            filename = askopenfilename()\n            file = open(filename, \"r\")\n            name_of_game = file.readline()\n            difficulty = file.readline()\n            for i in range(0, 9):\n                for j in range(0, 9):\n                    board[i][j] = int(file.readline())\n            for i in range(0, 9):\n                for j in range(0, 9):\n                    initial_board[i][j] = int(file.readline())\n            for i in range(0, 9):\n                for j in range(0, 9):\n                    solution[i][j] = int(file.readline())\n            file.close()\n            start_call_back()\n        except:\n            load = False\n\n    game_start_button = Button(setup_window, text=\"Start Game\", command=start_call_back)\n    game_start_button.pack()\n    game_load_button = Button(setup_window, text=\"Load Game\", command=load_call_back)\n    game_load_button.pack()\n    setup_window.mainloop()\n\n\ndef setup_game(name, diff, canvas, loaded):\n    global board, initial_board, solution, name_of_game, difficulty\n    if not loaded:\n        name_of_game = name\n        difficulty = diff\n        solution = generate_valid_sudoku()\n        #print_grid(solution)\n        grid = create_puzzle_difficulty(solution, difficulty)\n        board = grid\n        for i in range(0, 9):\n            initial_board.append([0] * 9)\n        for i in range(0, 9):\n            for j in range(0,9):\n                initial_board[i][j] = grid[i][j]\n    if loaded:\n        grid = board\n    canvas = draw_grid(canvas)\n    canvas = draw_selected_grid(canvas, 0, 1)\n    canvas = draw_labels(grid, canvas)\n    canvas.pack()\n\n\ndef save_game(filename):\n    global name_of_game, board, initial_board, solution, difficulty\n    file = open(filename, \"w\")\n    file.write(name_of_game+\"\\n\")\n    file.write(difficulty+\"\\n\")\n    for i in range(0, 9):\n        for j in range(0, 9):\n            file.write(str(board[i][j])+\"\\n\")\n    for i in range(0, 9):\n        for j in range(0, 9):\n            file.write(str(initial_board[i][j])+\"\\n\")\n    for i in range(0, 9):\n        for j in range(0, 9):\n            file.write(str(solution[i][j])+\"\\n\")\n    file.close()\n\n\ndef redraw_with_selected(canvas, grid, i, j):\n    canvas.delete(\"all\")\n    canvas = draw_labels(grid, draw_selected_grid(draw_grid(canvas), i, j))\n    return canvas\n\n\ndef draw_selected_grid(canvas, i, j):\n    global solution, initial_board\n    if initial_board[j][i] == 0:\n        canvas.create_rectangle(j*50, i*50, j*50+50, i*50+50, fill=\"green\", stipple=\"gray50\")\n    else:\n        canvas.create_rectangle(j * 50, i * 50, j * 50 + 50, i * 50 + 50, fill=\"red\", stipple=\"gray50\")\n    return canvas\n\n\ndef draw_grid(canvas):\n    width = 9*50\n    for i in range(0, 10):\n        if i == 3 or i == 6:\n            canvas.create_line(i*50, 0, i*50, width, fill=\"black\", width=3)\n            canvas.create_line(0, i*50, width, i*50, fill=\"black\", width=3)\n        else:\n            canvas.create_line(i*50, 0, i*50, width, fill=\"black\")\n            canvas.create_line(0, i*50, width, i*50, fill=\"black\")\n    return canvas\n\n\ndef draw_labels(grid, canvas):\n    c = Canvas()\n    for i in range(0, 9):\n        for j in range(0, 9):\n            if grid[i][j] != 0:\n                canvas.create_text(i*50+25, j*50+25, text=str(grid[i][j]))\n    return canvas\n\n\ndef create_puzzle_difficulty(grid, diff):\n    current_x = 0\n    current_y = 0\n    for i in range(0, 9):\n        blanked = list()\n        missing = 0\n        if diff == \"Easy\":\n            missing = randint(2, 3)\n        if diff == \"Medium\":\n            missing = randint(4, 5)\n        if diff == \"Hard\":\n            missing = randint(7, 8)\n        for j in range(0, missing):\n            pos = (randint(current_x, current_x+2), randint(current_y, current_y+2))\n            while pos in blanked:\n                pos = (randint(current_x, current_x + 2), randint(current_y, current_y + 2))\n            if pos not in blanked:\n                grid[pos[0]][pos[1]] = 0\n                blanked.append(pos)\n        if i == 0 or i == 1 or i == 3 or i == 4 or i == 6 or i == 7:\n            current_y = current_y + 3\n        if i == 2 or i == 5:\n            current_y = 0\n            current_x = current_x+3\n    return grid\n\n\ndef generate_valid_sudoku():\n    i = 0\n    grid = list()\n    for i in range(0, 9):\n        grid.append([0] * 9)\n    while not is_filled(grid):\n        posx = randint(0, 8)\n        posy = randint(0, 8)\n        candidates = get_candidates(grid, posx, posy)\n        if len(candidates) == 0:\n            i = i+1\n            if i == 200:\n                i = 0\n                grid = list()\n                for i in range(0, 9):\n                    grid.append([0] * 9)\n            continue\n        if len(candidates) > 0:\n            num = randint(0, len(candidates)-1)\n            grid[posx][posy] = candidates[num]\n    return grid\n\n\ndef is_filled(grid):\n    for i in range(0, 9):\n        for j in range(0, 9):\n            if grid[i][j] == 0:\n                return False\n    return True\n\n\ndef print_grid(grid):\n    for i in range(0, 9):\n        line = \"\"\n        for j in range(0, 9):\n            line = line + str(grid[j][i])\n        print(line)\n\n\ndef get_candidates(grid, x, y):\n    candidates = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n    for i in range(0, 9):\n        if i != x:\n            if grid[i][y] in candidates:\n                candidates.remove(grid[i][y])\n    for j in range(0, 9):\n        if j != y:\n            if grid[x][j] in candidates:\n                candidates.remove(grid[x][j])\n    a = 0\n    b = 0\n    if x > 2:\n        a = 3\n    if x > 5:\n        a = 6\n    if y > 2:\n        b = 3\n    if y > 5:\n        b = 6\n    for i in range(a, a+3):\n        for j in range(b, b+3):\n            if i != x and j != y:\n                if grid[i][j] in candidates:\n                    candidates.remove(grid[i][j])\n\n    return candidates\n\n\ndef handle_key_event(event, canvas):\n    global selected_X, selected_Y, board\n    if event.char == '1':\n        if initial_board[selected_X][selected_Y] == 0:\n            board[selected_X][selected_Y] = 1\n    if event.char == '2':\n        if initial_board[selected_X][selected_Y] == 0:\n            board[selected_X][selected_Y] = 2\n    if event.char == '3':\n        if initial_board[selected_X][selected_Y] == 0:\n            board[selected_X][selected_Y] = 3\n    if event.char == '4':\n        if initial_board[selected_X][selected_Y] == 0:\n            board[selected_X][selected_Y] = 4\n    if event.char == '5':\n        if initial_board[selected_X][selected_Y] == 0:\n            board[selected_X][selected_Y] = 5\n    if event.char == '6':\n        if initial_board[selected_X][selected_Y] == 0:\n            board[selected_X][selected_Y] = 6\n    if event.char == '7':\n        if initial_board[selected_X][selected_Y] == 0:\n            board[selected_X][selected_Y] = 7\n    if event.char == '8':\n        if initial_board[selected_X][selected_Y] == 0:\n            board[selected_X][selected_Y] = 8\n    if event.char == '9':\n        if initial_board[selected_X][selected_Y] == 0:\n            board[selected_X][selected_Y] = 9\n    if event.char == '0':\n        if initial_board[selected_X][selected_Y] == 0:\n            board[selected_X][selected_Y] = 0\n\n    if event.char == 'w':\n        selected_Y = selected_Y - 1\n    if event.char == 'a':\n        selected_X = selected_X - 1\n    if event.char == 's':\n        selected_Y = selected_Y + 1\n    if event.char == 'd':\n        selected_X = selected_X + 1\n    if selected_Y < 0:\n        selected_Y = 0\n    if selected_X < 0:\n        selected_X = 0\n    if selected_X > 8:\n        selected_X = 8\n    if selected_Y > 8:\n        selected_Y = 8\n    redraw_with_selected(canvas, board, selected_Y, selected_X)\n\n\ndef handle_mouse_event(event, canvas):\n    global selected_X, selected_Y, board\n    xymax = 9 * 50\n    x_quantum = xymax / 9\n    y_quantum = xymax / 9\n\n    selected_X = int(event.x / x_quantum)\n    selected_Y = int(event.y / y_quantum)\n    redraw_with_selected(canvas, board, selected_Y, selected_X)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"yerodin/TKinter-Sudoku","sub_path":"sudoku.py","file_name":"sudoku.py","file_ext":"py","file_size_in_byte":13499,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"2062666203","text":"import os\nfrom qgis.core import QgsProject, Qgis\nfrom qgis.PyQt import uic\nfrom qgis.PyQt import QtWidgets\nfrom qgis.PyQt.QtCore import QUrl, QByteArray\nfrom qgis.PyQt.QtNetwork import QNetworkRequest, QNetworkReply,  QNetworkAccessManager\n\nclass StateDownload:\n\n    def __init__(cls, parent=None, iface=None):\n        cls.parent = parent\n        cls.iface = iface\n        cls.nam = QNetworkAccessManager()\n        cls.nam.finished.connect(cls.reply_finished)\n        # https://docs.qgis.org/2.8/en/docs/pyqgis_developer_cookbook/communicating.html#showing-progress\n\n    def reply_finished(cls, reply):\n        if reply != None:\n            possibleRedirectUrl = reply.attribute(QNetworkRequest.RedirectionTargetAttribute)\n             \n            if possibleRedirectUrl != None: # if value, request is redirected.\n                request = QNetworkRequest(possibleRedirectUrl)\n                result = cls.nam.get(request)  \n                result.downloadProgress.connect(lambda done,  all,  reply=result: cls.progress(done,  all,  reply))\n            else:             \n                if reply.error() != None and reply.error() != QNetworkReply.NoError:\n                    cls.is_error = reply.errorString()\n                    reply.abort()\n                    reply.deleteLater()\n                        \n                elif reply.error() ==  QNetworkReply.NoError:\n                    result = reply.readAll()\n                    f = open(cls.filename, 'wb')\n                    f.write(result)\n                    f.close()      \n                    \n                    out_gpkg = cls.unzip(cls.filename)\n                    (dir, file) = os.path.split(out_gpkg)\n                    \n                    try:\n                        if not cls.layer_exists(file):\n                            cls.iface.addVectorLayer(out_gpkg, file, 'ogr')\n                    except:\n                        pass\n                        \n                    \n                    cls.iface.messageBar().pushMessage(\n                        \"Success\", f\"Downloaded file saved at {cls.dir}\",\n                        level=Qgis.Success, duration=5\n                    )\n                        \n                    reply.deleteLater()\n\n    def layer_exists(cls,  name):            \n        # Return True if layer of given name exists in current instance.\n        if len(QgsProject.instance().mapLayersByName(name)) != 0:\n            return True\n        else:\n            return False\n            \n    def unzip(cls,  zip_file):\n        import zipfile\n        (dir, file) = os.path.split(zip_file)\n\n        if not dir.endswith(':') and not os.path.exists(dir):\n            os.mkdir(dir)\n        \n        try:\n            zf = zipfile.ZipFile(zip_file)\n    \n            # extract files to directory structure\n            for i, name in enumerate(zf.namelist()):\n                if not name.endswith('/'):\n                    outfile = open(os.path.join(dir, name), 'wb')\n                    outfile.write(zf.read(name))\n                    outfile.flush()\n                    outfile.close()\n                    return os.path.join(dir, name)\n        except:\n            return None\n\n    def get_state_data(cls, fips, dest):\n        url = QUrl(f\"https://nsi.sec.usace.army.mil/downloads/nsi_2022/nsi_2022_{fips}.gpkg.zip\")\n        saveName = f\"nsi_2022_{fips}.gpkg.zip\"\n        fullPath = f\"{dest}\\{saveName}\"\n        cls.dir = dest\n        cls.filename = fullPath\n        #cls.load_to_canvas = True\n        req = QNetworkRequest(url)\n        reply = cls.nam.get(req)\n\n\nclass APIDownload:\n\n    def __init__(cls, parent=None, iface=None):\n        cls.parent = parent\n        cls.iface = iface\n        cls.root_url = \"https://nsi.sec.usace.army.mil/nsiapi/structures\"\n        cls.stats_url = \"https://nsi.sec.usace.army.mil/nsiapi/stats\"\n        cls.nam = QNetworkAccessManager()\n        cls.multi = False\n        \n        cls.nam.finished.connect(cls.api_reply_finished)\n        cls.bbox_coords = None\n        cls.nam_bbox = QNetworkAccessManager()\n        cls.nam_bbox.finished.connect(cls.api_stat_reply_finished)\n        \n    \n    def api_reply_finished(cls, reply):\n        if reply.error() != None and reply.error() != QNetworkReply.NoError:\n            cls.is_error = reply.errorString()\n            cls.iface.messageBar().pushMessage(\n                        \"Error\", cls.is_error,\n                        level=Qgis.Critical\n                    )\n            reply.abort()\n            reply.deleteLater()\n                \n        elif reply.error() ==  QNetworkReply.NoError:\n            if cls.multi:\n                pass\n            else:\n            \n                result = reply.readAll() # result should be JSON response from API\n            \n                with open(cls.filename, 'wb') as f:\n                    f.write(result)\n            \n                (dir, file) = os.path.split(cls.filename)\n                try:\n                    if not cls.layer_exists(file):\n                        cls.iface.addVectorLayer(cls.filename, file, 'ogr')\n                except:\n                    pass\n            \n            reply.deleteLater()\n    \n    def api_stat_reply_finished(cls, reply):\n        if reply.error() != None and reply.error() != QNetworkReply.NoError:\n            cls.is_error = reply.errorString()\n            cls.iface.messageBar().pushMessage(\n                        \"Error\", cls.is_error,\n                        level=Qgis.Critical\n                    )\n            reply.abort()\n            reply.deleteLater()\n                \n        elif reply.error() ==  QNetworkReply.NoError:\n            result = reply.readAll().data().decode() # convert to plain text for adding to json file\n            geojson_output = cls.create_stats_geojson(result)\n            # modify json here into geojson with bbox coords and other required fields.\n            \n            with open(cls.filename, 'w') as f:\n                f.write(geojson_output)\n            \n            (dir, file) = os.path.split(cls.filename)\n            try:\n                if not cls.layer_exists(file):\n                    cls.iface.addVectorLayer(cls.filename, file, 'ogr')\n            except:\n                pass\n            \n            cls.bbox_coords = None\n            reply.deleteLater()\n    \n    def create_stats_geojson(cls, result):\n        json_text0 = \"\"\"{\n            \"type\": \"FeatureCollection\",\n            \"name\": \"NSI Stats Bounding Box\",\n            \"crs\": { \"type\": \"name\", \"properties\": { \"name\": \"urn:ogc:def:crs:OGC:1.3:CRS84\" } },\n            \"features\": [\n            { \"type\": \"Feature\", \n            \"properties\":\n            \"\"\"\n        \n        json_text1 = \"\"\",\n            \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ \n            \"\"\"\n        \n        json_text2 = \"\"\" ] ] }\n            }\n            ]\n            }\"\"\"\n            \n        json_text = f\"{json_text0}{result}{json_text1}{cls.bbox_coords}{json_text2}\"\n        return(json_text)\n            \n    def layer_exists(cls,  name):            \n        # Return True if layer of given name exists in current instance.\n        if len(QgsProject.instance().mapLayersByName(name)) != 0:\n            return True\n        else:\n            return False\n            \n    def get_structs_fips(cls, fips, dest, saveAs='.geojson'):\n        if fips is None:\n            pass\n        else:\n            url = QUrl(f\"{cls.root_url}?fips={fips}&fmt=fs\")\n            print(f\"Request url is {url}\")\n            saveName = f\"nsi_2022_{fips}{saveAs}\"\n            fullPath = f\"{dest}/{saveName}\"\n            cls.dir = dest\n            cls.filename = fullPath\n            req = QNetworkRequest(url)\n            reply = cls.nam.get(req)\n            \n    def get_structs_bbox(cls, bbox, dest ,saveAs='.geojson'):\n        url = QUrl(f\"{cls.root_url}?bbox={bbox}&fmt=fs\")\n        saveName = f\"nsi_2022_bbox_{bbox}{saveAs}\"\n        fullPath = f\"{dest}/{saveName}\"\n        cls.dir = dest\n        cls.filename = fullPath\n        req = QNetworkRequest(url)\n        reply = cls.nam.get(req)\n        \n    def get_stats_bbox(cls, bbox, coords, dest ,saveAs='.json'):\n        url = QUrl(f\"{cls.stats_url}?bbox={bbox}\")\n        saveName = f\"nsi_2022_stats_bbox_{bbox}{saveAs}\"\n        fullPath = f\"{dest}/{saveName}\"\n        cls.dir = dest\n        cls.filename = fullPath\n        cls.bbox_coords = coords\n        req = QNetworkRequest(url)\n        reply = cls.nam_bbox.get(req)\n    \n    def get_structs_shape(cls, layer_name, geojson, dest, saveAs='.geojson'):\n        url = QUrl(cls.root_url)\n        saveName = f\"nsi_2022_{layer_name}{saveAs}\"\n        fullPath = f\"{dest}/{saveName}\"\n        cls.dir = dest\n        cls.filename = fullPath\n        data = QByteArray()\n        data.append(geojson)\n        req = QNetworkRequest(url)\n        req.setHeader(QNetworkRequest.KnownHeaders.ContentTypeHeader,\n            'application/json')\n        reply = cls.nam.post(req, data)","repo_name":"sebastianrowan/get_nsi_data","sub_path":"nsiDownload.py","file_name":"nsiDownload.py","file_ext":"py","file_size_in_byte":8927,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"30477574813","text":"'''\nИзмените программу 056 так, чтобы перед повторным предположением она сообщала пользователю, \nявляется ли его предположение больше или меньше загаданного числа.\n'''\n\nimport random    \n\nnum = random.randint(1, 10)    \nusernum = int(input('Enter the number from 1 to 10: '))    \n\nwhile num != usernum:\n    if usernum > num:\n        print('Too high')  \n    elif usernum < num:\n        print('Too low')  \n    usernum = int(input('No, try again: '))    \nprint('Correct!')    \n\n","repo_name":"Mikopashpe/Python_Exercises","sub_path":"Ex057_random.py","file_name":"Ex057_random.py","file_ext":"py","file_size_in_byte":608,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"11335115174","text":"import pygame\nimport numpy as np\nfrom sys import exit\n\n\npygame.init()\n\n# Screen\nsize = width, height = 600, 600\nscreen = pygame.display.set_mode(size)\npygame.display.set_caption('Background')\nclock = pygame.time.Clock()\n\nsun = pygame.Rect(width/2 - 50, height/2 - 50, 100, 100)   # pygame.Rect(x, y, width, height) El punto (0,0) del Rectangle es la esquina superior izquiera!\n\ndef circle_orbit(theta):   # función que retorna el valor de x e y según el theta entregado para generar circunferencia\n    x = np.cos(theta)*150 + width/2   # + width/2 para establecer x en el centro más el valor de cos(theta)*radio\n    y = np.sin(theta)*150 + height/2   # + height/2 para establecer x en el centro más el valor de cos(theta)*radio\n    return x, y\n\ntheta = np.linspace(0, 2*np.pi, 100)\nt_index = 0\n\nrunning = True\n# Main loop\nwhile running:\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            pygame.quit()\n            exit()\n            \n    # Background        \n    screen.fill('#000000')\n    \n    pygame.draw.line(screen, '#FFFFFF', (width/2, height/2), circle_orbit(theta[t_index]))\n    pygame.draw.ellipse(screen, '#FFC700', sun)                \n    pygame.draw.circle(screen, '#FFFFFF', (width/2, height/2), radius=(circle_orbit(2*np.pi)[0] - width/2), width=1)\n    pygame.draw.circle(screen, '#0069BE', circle_orbit(theta[t_index]), radius=10)\n    \n    t_index += 1\n    if t_index > len(theta)-1:\n        t_index = 0\n    \n    pygame.display.flip()\n    clock.tick(30)","repo_name":"sbarthou/Orbit","sub_path":"simpleOrbit.py","file_name":"simpleOrbit.py","file_ext":"py","file_size_in_byte":1508,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"43604599966","text":"import pandas as pd\r\nimport numpy as np\r\nfrom sklearn.metrics import mean_squared_error # 均方误差\r\nfrom sklearn.metrics import mean_absolute_error # 平方绝对误差\r\nfrom sklearn.metrics import r2_score # R square\r\nfrom sklearn.preprocessing import PolynomialFeatures\r\nfrom random import randrange, seed\r\nimport matplotlib.pyplot as plt\r\n\r\n# 以下函数用来划分数据集，可以选择分层划分或者随机划分\r\ndef cross_validation_split(data: pd.DataFrame, folds = 3, Target_Ratio = False, Random_State = 0):\r\n    if Target_Ratio:\r\n        group = data.groupby(data.columns[-1])\r\n        dataset_split = []\r\n        for _ in range(folds):  # 在 dataset_split 中添加[]\r\n            dataset_split.append([])\r\n        for tmp in range(folds):\r\n            for indx in group.size().index:   # groupby的值(list)\r\n                for _ in cross_validation_split(group.get_group(indx),folds,Random_State)[tmp]:\r\n                    dataset_split[tmp].append(_)\r\n        return dataset_split\r\n    else:\r\n        dataset = data.values.tolist()\r\n        dataset_split = list()\r\n        dataset_copy = list(dataset)\r\n        fold_size = int(len(dataset) / folds)\r\n        for i in range(folds):\r\n            fold = list()\r\n            while len(fold) < fold_size:\r\n                seed(Random_State)\r\n                index = randrange(len(dataset_copy))  # 随机生成下标\r\n                fold.append(dataset_copy.pop(index))\r\n            dataset_split.append(fold)\r\n        return dataset_split\r\n\r\ndef takey(ll):\r\n    # 用于从分割后的数据中分离出标签与属性\r\n    X = []\r\n    y = []\r\n    for tmp in ll:\r\n        y.append(tmp[-1])\r\n        X.append(tmp[:-1])\r\n    return np.array(X), np.array(y)  # 前一元素是属性，后一元素是标签\r\n\r\n# 读入数据集\r\nx = np.arange(0, 4*np.pi, 0.25)    # 等距从0到4Π取数据点，跨度为0.25.共51个数据点\r\ny = np.sin(x) + 0.25 * np.random.rand(51) - 0.25*np.random.rand(51)  # 在sinx函数的基础上，生成51个1以下的随机实数，且加上正负权重0.25.使得在目标点上下抖动\r\ndata1 = pd.read_csv('../datasets/data1.csv', index_col = 0)\r\n\r\n\r\n# 数据预处理：\r\nnewx = []\r\ncopyx = x\r\nfor i in x:\r\n    newx.append([i])\r\n\r\nfolds = 5\r\nX_train = []\r\nmaes = []   # 存储MAE值\r\nr2s = []    # 存储r2score值\r\nrmses = []  # 存储RMSE值\r\n\r\nnewdata1 = cross_validation_split(data1, folds=5) # 5则划分数据集\r\n\r\nX_test = newdata1[0]\r\ncopy = [1, 2, 3, 4]\r\nfor y in copy:\r\n    for z in newdata1[y]:\r\n        X_train.append(z)\r\n\r\nx_test, y_test = takey(X_test)\r\nx_train, y_train = takey(X_train)\r\nnewy_test = []\r\nfor x in y_test:\r\n    newy_test.append([x])\r\nnewy_train = []\r\nfor x in y_train:\r\n    newy_train.append([x])\r\n\r\n# 开始多项式特征拟合，以及多元线性回归\r\nfrom sklearn.linear_model import LinearRegression\r\n\r\nprees = []    # 用来存放所用样本在当前对应阶数上的预测值，用于画预测的曲线\r\nfor dd in range(1, 7):\r\n    poly_reg = PolynomialFeatures(degree=dd)\r\n    X_ploy_train = poly_reg.fit_transform(x_train)\r\n    X_ploy_test = poly_reg.fit_transform(x_test)\r\n    tt = poly_reg.fit_transform(newx)\r\n\r\n    lin2 = LinearRegression()\r\n\r\n    lin2.fit(X_ploy_train, newy_train)\r\n\r\n    prees.append(lin2.predict(tt))\r\n    predicts = lin2.predict(X_ploy_test)\r\n\r\n    rmses.append(np.sqrt(mean_squared_error(y_test, predicts)))\r\n    maes.append(mean_absolute_error(y_test, predicts))\r\n    r2s.append(r2_score(y_test, predicts))\r\n\r\n# 画不同阶数的预测曲线\r\nplt.figure(figsize=(15, 10))\r\n\r\nfor i in range(len(prees)):\r\n    plt.subplot(int('23' + str((i+1))))\r\n    plt.plot(copyx, prees[i])\r\n    plt.ylabel('value')\r\n    plt.xlabel('degree = '+ str(i+1))\r\n\r\nplt.savefig('./plots/degree.svg', format='svg')  # 保存为矢量图\r\n\r\n\r\n# 画不同阶数对应的评价标准的柱状图\r\nplt.figure(figsize=(14, 6))\r\nplt.xlabel('degree')\r\n# r2值图， X轴是参数取值\r\nplt.bar([x-0.15 for x in range(1, 7)], maes, width=0.3, color=\"green\",label='MAEs')\r\nplt.bar([x+0.15 for x in range(1, 7)], rmses, width=0.3, label='RMSEs')\r\nplt.legend(loc='upper left')\r\n\r\nplt.savefig('./plots/evaluation_bar.svg', format='svg')  # 保存为矢量图\r\n\r\n\r\n# 画不同阶数对应的评价标准的折线图\r\nplt.figure(figsize=(15, 5))\r\n\r\nplt.subplot(131)\r\nplt.plot([x for x in range(1,7)],maes,marker = \"o\",markersize=5,markerfacecolor = 'black')\r\nplt.ylabel('mae value')\r\nplt.xlabel('degree')\r\n\r\nplt.subplot(132)\r\nplt.plot([x for x in range(1,7)],rmses,marker = \"o\",markersize=5,markerfacecolor = 'black')\r\nplt.ylabel('rmse value')\r\nplt.xlabel('degree')\r\n\r\nplt.subplot(133)\r\nplt.plot([x for x in range(1,7)],r2s,marker = \"o\",markersize=5,markerfacecolor = 'black')\r\nplt.ylabel('r2 value')\r\nplt.xlabel('degree')\r\n\r\nplt.savefig('./plots/evaluation_curve.svg', format='svg') # 保存为矢量图","repo_name":"Yang2018/superMT","sub_path":"source code/Polynomial regression.py","file_name":"Polynomial regression.py","file_ext":"py","file_size_in_byte":4850,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"13610650591","text":"import os\n\nfrom glazier.lib.actions.base import BaseAction\nfrom glazier.lib.actions.base import ValidationError\n\nfrom glazier.lib import errors\n\n\nclass Error(errors.GlazierError):\n  pass\n\n\nclass FileNotFound(Error):\n\n  def __init__(self, path: str):\n    super().__init__(\n        error_code=errors.ErrorCode.FILE_NOT_FOUND,\n        message=f'The following path does not exist: {path}')\n\n\nclass PrintFromFile(BaseAction):\n  \"\"\"Print text from a file at the specified path.\"\"\"\n\n  def Run(self):\n    \"\"\"Reads a file and prints the content.\"\"\"\n    path: str = self._args[0]\n    ignore_error: bool = False\n\n    if len(self._args) > 1:\n      ignore_error = self._args[1]\n\n    if not os.path.exists(path):\n      if ignore_error:\n        return\n      raise FileNotFound(path)\n\n    content = self._get_content(path)\n    print(content)\n\n  def _get_content(self, path: str):\n    \"\"\"Get content from the specified file.\n\n    Args:\n      path: The full path to the file.\n\n    Returns:\n      File content.\n\n    Raises:\n      FileNotFound: No file exists at the determined path.\n    \"\"\"\n    f = open(path, 'r', encoding='utf-8')\n    content = f.read()\n    f.close()\n    return content\n\n  def Validate(self):\n    self._TypeValidator(self._args, list)\n\n    if not 1 <= len(self._args) <= 2:\n      message = (f'Invalid PrintFromFile args \"{self._args}\" '\n                 f'with length of {len(self._args)}')\n      raise ValidationError(message)\n\n    self._TypeValidator(self._args[0], str)\n    if len(self._args) > 1:\n      self._TypeValidator(self._args[1], bool)\n","repo_name":"google/glazier","sub_path":"glazier/lib/actions/art.py","file_name":"art.py","file_ext":"py","file_size_in_byte":1548,"program_lang":"python","lang":"en","doc_type":"code","stars":1200,"dataset":"github-code","pt":"48"}
+{"seq_id":"39609688887","text":"from unittest.mock import patch\n\nfrom django.conf import settings\nfrom django.core.management import call_command\nfrom model_bakery import baker\nfrom packaging import version as pyver\n\nfrom agents.models import Agent\nfrom agents.tasks import auto_self_agent_update_task, send_agent_update_task\nfrom logs.models import PendingAction\nfrom tacticalrmm.constants import (\n    AGENT_DEFER,\n    AgentMonType,\n    AgentPlat,\n    GoArch,\n    PAAction,\n    PAStatus,\n)\nfrom tacticalrmm.test import TacticalTestCase\n\n\nclass TestAgentUpdate(TacticalTestCase):\n    def setUp(self) -> None:\n        self.authenticate()\n        self.setup_coresettings()\n        self.setup_base_instance()\n\n    @patch(\"agents.management.commands.update_agents.send_agent_update_task.delay\")\n    @patch(\"agents.management.commands.update_agents.token_is_valid\")\n    @patch(\"agents.management.commands.update_agents.get_core_settings\")\n    def test_update_agents_mgmt_command(self, mock_core, mock_token, mock_update):\n        mock_token.return_value = (\"token123\", True)\n\n        baker.make_recipe(\n            \"agents.online_agent\",\n            site=self.site1,\n            monitoring_type=AgentMonType.SERVER,\n            plat=AgentPlat.WINDOWS,\n            version=\"2.0.3\",\n            _quantity=6,\n        )\n\n        baker.make_recipe(\n            \"agents.online_agent\",\n            site=self.site3,\n            monitoring_type=AgentMonType.WORKSTATION,\n            plat=AgentPlat.LINUX,\n            version=\"2.0.3\",\n            _quantity=5,\n        )\n\n        baker.make_recipe(\n            \"agents.online_agent\",\n            site=self.site2,\n            monitoring_type=AgentMonType.SERVER,\n            plat=AgentPlat.WINDOWS,\n            version=settings.LATEST_AGENT_VER,\n            _quantity=8,\n        )\n\n        mock_core.return_value.agent_auto_update = False\n        call_command(\"update_agents\")\n        mock_update.assert_not_called()\n\n        mock_core.return_value.agent_auto_update = True\n        call_command(\"update_agents\")\n\n        ids = list(\n            Agent.objects.defer(*AGENT_DEFER)\n            .exclude(version=settings.LATEST_AGENT_VER)\n            .values_list(\"agent_id\", flat=True)\n        )\n\n        mock_update.assert_called_with(agent_ids=ids, token=\"token123\", force=False)\n\n    @patch(\"agents.models.Agent.nats_cmd\")\n    @patch(\"agents.models.get_agent_url\")\n    def test_do_update(self, mock_agent_url, mock_nats_cmd):\n        mock_agent_url.return_value = \"https://example.com/123\"\n\n        # test noarch\n        agent_noarch = baker.make_recipe(\n            \"agents.online_agent\",\n            site=self.site1,\n            monitoring_type=AgentMonType.SERVER,\n            plat=AgentPlat.WINDOWS,\n            version=\"2.1.1\",\n        )\n        r = agent_noarch.do_update(token=\"\", force=True)\n        self.assertEqual(r, \"noarch\")\n\n        # test too old\n        agent_old = baker.make_recipe(\n            \"agents.online_agent\",\n            site=self.site2,\n            monitoring_type=AgentMonType.SERVER,\n            plat=AgentPlat.WINDOWS,\n            version=\"1.3.0\",\n            goarch=GoArch.AMD64,\n        )\n        r = agent_old.do_update(token=\"\", force=True)\n        self.assertEqual(r, \"not supported\")\n\n        win = baker.make_recipe(\n            \"agents.online_agent\",\n            site=self.site1,\n            monitoring_type=AgentMonType.SERVER,\n            plat=AgentPlat.WINDOWS,\n            version=\"2.1.1\",\n            goarch=GoArch.AMD64,\n        )\n\n        lin = baker.make_recipe(\n            \"agents.online_agent\",\n            site=self.site3,\n            monitoring_type=AgentMonType.WORKSTATION,\n            plat=AgentPlat.LINUX,\n            version=\"2.1.1\",\n            goarch=GoArch.ARM32,\n        )\n\n        # test windows agent update\n        r = win.do_update(token=\"\", force=False)\n        self.assertEqual(r, \"created\")\n        mock_nats_cmd.assert_called_with(\n            {\n                \"func\": \"agentupdate\",\n                \"payload\": {\n                    \"url\": \"https://example.com/123\",\n                    \"version\": settings.LATEST_AGENT_VER,\n                    \"inno\": f\"tacticalagent-v{settings.LATEST_AGENT_VER}-windows-amd64.exe\",\n                },\n            },\n            wait=False,\n        )\n        action1 = PendingAction.objects.get(agent__agent_id=win.agent_id)\n        self.assertEqual(action1.action_type, PAAction.AGENT_UPDATE)\n        self.assertEqual(action1.status, PAStatus.PENDING)\n        self.assertEqual(action1.details[\"url\"], \"https://example.com/123\")\n        self.assertEqual(\n            action1.details[\"inno\"],\n            f\"tacticalagent-v{settings.LATEST_AGENT_VER}-windows-amd64.exe\",\n        )\n        self.assertEqual(action1.details[\"version\"], settings.LATEST_AGENT_VER)\n\n        mock_nats_cmd.reset_mock()\n\n        # test linux agent update\n        r = lin.do_update(token=\"\", force=False)\n        mock_nats_cmd.assert_called_with(\n            {\n                \"func\": \"agentupdate\",\n                \"payload\": {\n                    \"url\": \"https://example.com/123\",\n                    \"version\": settings.LATEST_AGENT_VER,\n                    \"inno\": f\"tacticalagent-v{settings.LATEST_AGENT_VER}-linux-arm.exe\",\n                },\n            },\n            wait=False,\n        )\n        action2 = PendingAction.objects.get(agent__agent_id=lin.agent_id)\n        self.assertEqual(action2.action_type, PAAction.AGENT_UPDATE)\n        self.assertEqual(action2.status, PAStatus.PENDING)\n        self.assertEqual(action2.details[\"url\"], \"https://example.com/123\")\n        self.assertEqual(\n            action2.details[\"inno\"],\n            f\"tacticalagent-v{settings.LATEST_AGENT_VER}-linux-arm.exe\",\n        )\n        self.assertEqual(action2.details[\"version\"], settings.LATEST_AGENT_VER)\n\n        # check if old agent update pending actions are being deleted\n        # should only be 1 pending action at all times\n        pa_count = win.pendingactions.filter(\n            action_type=PAAction.AGENT_UPDATE, status=PAStatus.PENDING\n        ).count()\n        self.assertEqual(pa_count, 1)\n\n        for _ in range(4):\n            win.do_update(token=\"\", force=False)\n\n        pa_count = win.pendingactions.filter(\n            action_type=PAAction.AGENT_UPDATE, status=PAStatus.PENDING\n        ).count()\n        self.assertEqual(pa_count, 1)\n\n    def test_auto_self_agent_update_task(self):\n        auto_self_agent_update_task()\n\n    @patch(\"agents.models.Agent.do_update\")\n    def test_send_agent_update_task(self, mock_update):\n        baker.make_recipe(\n            \"agents.online_agent\",\n            site=self.site2,\n            monitoring_type=AgentMonType.SERVER,\n            plat=AgentPlat.WINDOWS,\n            version=\"2.1.1\",\n            goarch=GoArch.AMD64,\n            _quantity=6,\n        )\n        ids = list(\n            Agent.objects.defer(*AGENT_DEFER)\n            .exclude(version=settings.LATEST_AGENT_VER)\n            .values_list(\"agent_id\", flat=True)\n        )\n        send_agent_update_task(agent_ids=ids, token=\"\", force=False)\n        self.assertEqual(mock_update.call_count, 6)\n\n    @patch(\"agents.views.token_is_valid\")\n    @patch(\"agents.tasks.send_agent_update_task.delay\")\n    def test_update_agents(self, mock_update, mock_token):\n        mock_token.return_value = (\"\", False)\n        url = \"/agents/update/\"\n        baker.make_recipe(\n            \"agents.online_agent\",\n            site=self.site2,\n            monitoring_type=AgentMonType.SERVER,\n            plat=AgentPlat.WINDOWS,\n            version=\"2.1.1\",\n            goarch=GoArch.AMD64,\n            _quantity=7,\n        )\n        baker.make_recipe(\n            \"agents.online_agent\",\n            site=self.site2,\n            monitoring_type=AgentMonType.SERVER,\n            plat=AgentPlat.WINDOWS,\n            version=settings.LATEST_AGENT_VER,\n            goarch=GoArch.AMD64,\n            _quantity=3,\n        )\n        baker.make_recipe(\n            \"agents.online_agent\",\n            site=self.site2,\n            monitoring_type=AgentMonType.WORKSTATION,\n            plat=AgentPlat.LINUX,\n            version=\"2.0.1\",\n            goarch=GoArch.ARM32,\n            _quantity=9,\n        )\n\n        agent_ids: list[str] = list(\n            Agent.objects.only(\"agent_id\").values_list(\"agent_id\", flat=True)\n        )\n\n        data = {\"agent_ids\": agent_ids}\n        expected: list[str] = [\n            i.agent_id\n            for i in Agent.objects.only(\"agent_id\", \"version\")\n            if pyver.parse(i.version) < pyver.parse(settings.LATEST_AGENT_VER)\n        ]\n\n        r = self.client.post(url, data, format=\"json\")\n        self.assertEqual(r.status_code, 200)\n\n        mock_update.assert_called_with(agent_ids=expected, token=\"\", force=False)\n\n        self.check_not_authenticated(\"post\", url)\n\n    @patch(\"agents.views.token_is_valid\")\n    @patch(\"agents.tasks.send_agent_update_task.delay\")\n    def test_agent_update_permissions(self, update_task, mock_token):\n        mock_token.return_value = (\"\", False)\n\n        agents = baker.make_recipe(\"agents.agent\", _quantity=5)\n        other_agents = baker.make_recipe(\"agents.agent\", _quantity=7)\n\n        url = \"/agents/update/\"\n\n        data = {\n            \"agent_ids\": [agent.agent_id for agent in agents]\n            + [agent.agent_id for agent in other_agents]\n        }\n\n        # test superuser access\n        self.check_authorized_superuser(\"post\", url, data)\n        update_task.assert_called_with(\n            agent_ids=data[\"agent_ids\"], token=\"\", force=False\n        )\n        update_task.reset_mock()\n\n        user = self.create_user_with_roles([])\n        self.client.force_authenticate(user=user)\n\n        self.check_not_authorized(\"post\", url, data)\n        update_task.assert_not_called()\n\n        user.role.can_update_agents = True\n        user.role.save()\n\n        self.check_authorized(\"post\", url, data)\n        update_task.assert_called_with(\n            agent_ids=data[\"agent_ids\"], token=\"\", force=False\n        )\n        update_task.reset_mock()\n\n        # limit to client\n        # user.role.can_view_clients.set([agents[0].client])\n        # self.check_authorized(\"post\", url, data)\n        # update_task.assert_called_with(agent_ids=[agent.agent_id for agent in agents])\n        # update_task.reset_mock()\n\n        # add site\n        # user.role.can_view_sites.set([other_agents[0].site])\n        # self.check_authorized(\"post\", url, data)\n        # update_task.assert_called_with(agent_ids=data[\"agent_ids\"])\n        # update_task.reset_mock()\n\n        # remove client permissions\n        # user.role.can_view_clients.clear()\n        # self.check_authorized(\"post\", url, data)\n        # update_task.assert_called_with(\n        #     agent_ids=[agent.agent_id for agent in other_agents]\n        # )\n","repo_name":"amidaware/tacticalrmm","sub_path":"api/tacticalrmm/agents/tests/test_agent_update.py","file_name":"test_agent_update.py","file_ext":"py","file_size_in_byte":10761,"program_lang":"python","lang":"en","doc_type":"code","stars":2312,"dataset":"github-code","pt":"48"}
+{"seq_id":"1401752031","text":"import re\nimport pandas as pd\nimport logging\nimport pprint\n\nroute_regex = re.compile(r'^(?P<prefix>\\S*)\\t(?P<gateway>\\S*)\\t(?P<out_iface>\\S*)\\t(?P<metric>\\S*)\\t(?P<type>\\S*)',\n                         re.MULTILINE)\n\nprotocol_translate = {\n    'IGP': 'ospf',\n    'BGP': 'bgp',\n    'STATIC': 'static'\n}\n\n\ndef parse_cbgp(srt, name, adj=None):\n    \"\"\"\n    Parse the routing table received from C-BGP\n    :param srt: Source routing table\n    :param name: Name of the router\n    :param adj: Adjacency information\n    :return: Routing table as pandas dataframe\n    \"\"\"\n    logger = logging.getLogger('network-testing')\n    logger.info(f'Parsing routing table {pprint.pformat(srt)}')\n\n    rt = pd.DataFrame(\n        columns=[\"Node\", \"VRF\", \"Network\", \"Next_Hop\", \"Next_Hop_IP\", \"Next_Hop_Interface\", \"Protocol\", \"Metric\",\n                 \"Admin_Distance\", \"Tag\"])\n\n    for entry in route_regex.finditer(srt):\n        network = entry.group('prefix')\n        next_hop = None\n        next_ip = 'AUTO/NONE(-1l)'\n        next_if = 'dynamic'\n        protocol = protocol_translate[entry.group('type')]\n        if entry.group('gateway') == '0.0.0.0':\n            if protocol == 'static':\n                next_if = adj[name]['interfaces'][entry.group('out_iface')]\n            else:\n                next_ip = entry.group('out_iface')\n                iface = adj[name]['interfaces'][next_ip]\n                next_hop = adj[name][iface]\n        else:\n            next_ip = entry.group('gateway')\n            if next_ip in adj[name]:\n                next_hop = adj[name][next_ip]\n            elif next_ip in adj:\n                next_hop = adj[next_ip]\n        metric = int(entry.group('metric'))\n        res = {\n            'Node': name,\n            'VRF': 'default',\n            'Network': network,\n            'Next_Hop': next_hop,\n            'Next_Hop_IP': next_ip,\n            'Next_Hop_Interface': next_if,\n            'Protocol': protocol,\n            'Metric': metric,\n            'Admin_Distance': 0,\n            'Tag': None\n        }\n\n        rt = rt.append(res, ignore_index=True)\n\n    return rt\n","repo_name":"nsg-ethz/Metha","sub_path":"Systems/CBGP/cbgp_parser.py","file_name":"cbgp_parser.py","file_ext":"py","file_size_in_byte":2089,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"48"}
+{"seq_id":"39898662206","text":"#Provided by prof zeke and yilong musk\r\n#Free Code For Speedy Order Sandwiches Payslip\r\n\r\nimport tkinter as tk\r\nfrom tkinter import *\r\nfrom tkinter import ttk\r\nclass Frames(object):\r\n    def mainFrame(s, r):\r\n        r.title('SANDWICHES YUMM')\r\n        r.geometry(\"850x500\")\r\n        r.config(bg='gray')\r\n\r\n        frame1=Frame(r,width=300,height=100,bg='gray',  \r\n        highlightcolor=\"gray\",highlightbackground=\"white\",  \r\n        highlightthickness=3).grid(padx = 260, pady = 160)\r\n        \r\n        lblinfo=Label(font=('arial',24,'bold','underline'),text=\"Speedy Order Sandwiches\",bd=10,fg=\"Black\", bg='gray')\r\n        lblinfo.grid(row=0,column=0)\r\n        lblinfo.place(x=230, y=10)\r\n        n = Label(text ='What kind of sandwich do you like?', anchor=\"e\", justify=LEFT,\r\n                  font=('Arial',9,'bold'),bg='gray')\r\n        n.place(x=320, y=100)\r\n        s.sw=tk.StringVar()\r\n        pc=ttk.Combobox(r, textvariable=s.sw)\r\n        pc['values']= ('Hotdog','Burger','Club House')\r\n        pc.place(x=350, y=120)\r\n        n = Label(text ='Would you like fries with that?', anchor=\"e\", justify=LEFT,\r\n                  font=('Arial',9,'bold'), bg='gray')\r\n        n.place(x=330, y=150)\r\n        s.r = StringVar()\r\n        a = Radiobutton(r, text='Yes', variable=s.r, value='A', bg='gray')\r\n        a.place(x=340, y=185)\r\n        b = Radiobutton(r, text='No', variable=s.r, value='B', bg='gray')\r\n        b.place(x=440, y=185)\r\n        c = Button(r, text=\"Compute\", command=s.cp)\r\n        c.place(x=400, y=450)\r\n        cl = Button(r, text=\"Clear\", command=s.cl)\r\n        cl.place(x=360, y=450)\r\n        s.var1 = IntVar()\r\n        Checkbutton(r, text=\"Catsup\", variable=s.var1, onvalue=1, offvalue=0, bg='gray').place(x=330,y=278)\r\n        s.var2 = IntVar()\r\n        Checkbutton(r, text=\"Mustard\", variable=s.var2, onvalue=1, offvalue=0, bg='gray').place(x=400, y=278)\r\n        s.var3 = IntVar()\r\n        Checkbutton(r, text=\"Pickles\", variable=s.var3, onvalue=1, offvalue=0, bg='gray').place(x=470, y=278)\r\n        n = Label(text ='(Php 10.00)', anchor=\"e\", justify=LEFT,\r\n                  font=('Arial',7), bg='gray')\r\n        n.place(x=330, y=300)\r\n        n = Label(text ='(Php 12.00)', anchor=\"e\", justify=LEFT,\r\n                  font=('Arial',7), bg='gray')\r\n        n.place(x=400, y=300)\r\n        n = Label(text ='(Php 15.00)', anchor=\"e\", justify=LEFT,\r\n                  font=('Arial',7), bg='gray')\r\n        n.place(x=470, y=300)\r\n        s.r.set(None)\r\n        s.ov=Entry(r)\r\n        n = Label(text ='(Php 20.00)', anchor=\"e\", justify=LEFT,\r\n                  font=('Arial',9,'bold'), bg='gray')\r\n        n.place(x=380, y=220)\r\n        n = Label(text ='Condiments:', anchor=\"e\", justify=LEFT,\r\n                  font=('Arial',9,'bold'), bg='gray')\r\n        n.place(x=240, y=280)\r\n        n = Label(text ='Sizes:', anchor=\"e\", justify=LEFT,\r\n                  font=('Arial',9,'bold'), bg='gray')\r\n        n.place(x=300, y=320)\r\n        s.pa=tk.StringVar()\r\n        pd=ttk.Combobox(r, textvariable=s.pa)\r\n        pd['values']= ('Small','Medium','Large')\r\n        pd.place(x=350, y=320)\r\n        n = Label(text ='Amount Payable(12% VAT Included):', anchor=\"e\", justify=LEFT,\r\n                  font=('Arial',9,'bold'), bg='gray')\r\n        n.place(x=240,y=350)\r\n        s.ns = Entry(r)\r\n        s.ns.place(x=450,y=350)\r\n\r\n    def cp(s):\r\n        sel = s.sw.get()\r\n        print(sel)\r\n        if sel =='Hotdog':\r\n            p = 50\r\n        elif sel =='Burger':\r\n            p = 60\r\n        elif sel =='Club House':\r\n            p = 90\r\n        if s.r.get()=='A':\r\n            print('with fries +20')\r\n            p+=20\r\n        if s.var1.get()==1:\r\n            print('with c')\r\n            p+=10\r\n        if s.var2.get()==1:\r\n            print('with m')\r\n            p+=12\r\n        if s.var3.get()==1:\r\n            print('with p')\r\n            p+=15\r\n        p*=1.12\r\n        print(p)\r\n        s.ns.delete(0, END)\r\n        s.ns.insert(0,p)\r\n\r\n    def cl(s):\r\n        s.sw.set('')\r\n        s.pa.set('')\r\n        s.r.set(None)\r\n        s.var1.set(0)\r\n        s.var2.set(0)\r\n        s.var3.set(0)\r\n        print(s.sw.get())\r\n        s.ns.delete(0, END)\r\n\r\nr = Tk()\r\na = Frames()\r\na.mainFrame(r)\r\nr.mainloop()\r\n","repo_name":"ProfZeke/Speedy-Order-Sandwiches","sub_path":"Speedy-Order-Sandwiches.py","file_name":"Speedy-Order-Sandwiches.py","file_ext":"py","file_size_in_byte":4229,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"24101984290","text":"# -*- coding:UTF-8 -*- \r\n\r\nimport copy\r\n\r\ndef get_charge_pos(board) :\r\n\tway = [(-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 1), (1, -1), (1, 0), (1, 1)]\r\n\tret = []\r\n\t\r\n\tfor i in range(15) :\r\n\t\tfor j in range(15) :\r\n\t\t\tif board[i][j] != 0 :\r\n\t\t\t\tfor w in way :\r\n\t\t\t\t\tpos = (i + w[0], j + w[1])\r\n\t\t\t\t\tif pos[0] not in range(15) or pos[1] not in range(15) :\r\n\t\t\t\t\t\tcontinue\r\n\t\t\t\t\tif (board[pos[0]][pos[1]] == 0) and (pos not in ret) :\r\n\t\t\t\t\t\tret.append(pos)\r\n\t\t\t\t\t\t\r\n\treturn ret\r\n\t\r\ndef get_line_score(line) :\r\n\t#连五 ： 100000, 活四，双冲四，冲四活三 ： 10000, 双活三 ： 5000, 活三眠三 ： 1000\r\n\t#眠四 ： 500, 活三 ： 200, 双活二 ： 100, 眠三 ： 50, 活二眠二 ： 10, 活二 ： 5\r\n\t#眠二 ：3, 死四 ： -5, 死三 ： -5, 死二 ： -5\r\n\t\r\n\tscore = 0\r\n\t\r\n\t#连五\r\n\tfor i in line :\r\n\t\tif i.find('22222') != -1 :\r\n\t\t\tscore += 100000\r\n\t\t\tbreak\r\n\t\r\n\t#活四\r\n\tfor i in line :\r\n\t\tif i.find('022220') != -1 :\r\n\t\t\tscore += 50000\r\n\t\t\tbreak\r\n\t\r\n\t#双冲四\r\n\tcount = 0\r\n\tfor i in line :\r\n\t\tfor size in ['022221', '122220', '20222', '22202', '22022'] :\r\n\t\t\tif i.find(size) != -1 :\r\n\t\t\t\tcount += 1\r\n\t\t\t\tbreak\r\n\t\t\t\t\r\n\t\tif count == 2 :\r\n\t\t\tscore += 10000\r\n\t\t\tbreak\r\n\t\r\n\t#冲四活三\r\n\tft = [0,0]\r\n\tfor i in line :\r\n\t\tif not ft[0] :\r\n\t\t\tfor size in ['022221', '122220', '20222', '22202', '22022'] :\r\n\t\t\t\tif i.find(size) != -1 :\r\n\t\t\t\t\tft[0] = 1\r\n\t\t\t\t\tbreak\r\n\t\t\r\n\t\tif not ft[1] :\r\n\t\t\tfor size in ['02220', '2022', '2202'] :\r\n\t\t\t\tif i.find(size) != -1 :\r\n\t\t\t\t\tft[1] = 1\r\n\t\t\t\t\tbreak\r\n\t\t\r\n\t\tif ft[0] and ft[1] :\r\n\t\t\tscore += 10000\r\n\t\t\tbreak\r\n\t\r\n\t#双活三\r\n\tcount = 0\r\n\tfor i in line :\r\n\t\tfor size in ['02220', '2022', '2202'] :\r\n\t\t\tif i.find(size) != -1 :\r\n\t\t\t\tcount += 1\r\n\t\t\t\tbreak\r\n\t\t\t\t\r\n\t\tif count == 2 :\r\n\t\t\tscore += 10000\r\n\t\t\tbreak\r\n\t\t\t\r\n\t#活三眠三\r\n\ttt = [0,0]\r\n\tfor i in line :\r\n\t\tif not tt[0] :\r\n\t\t\tfor size in ['02220', '2022', '2202'] :\r\n\t\t\t\tif i.find(size) != -1 :\r\n\t\t\t\t\ttt[0] = 1\r\n\t\t\t\t\tbreak\r\n\t\t\t\t\r\n\t\tif not tt[1] :\r\n\t\t\tfor size in ['002221', '122200', '020221', '122020', '022021', '120220', '20022', '22002', '20202', '1022201'] :\r\n\t\t\t\tif i.find(size) != -1 :\r\n\t\t\t\t\ttt[1] = 1\r\n\t\t\t\tbreak\r\n\t\t\r\n\t\tif tt[0] and tt[1] :\r\n\t\t\tscore += 1000\r\n\t\t\tbreak\r\n\t\r\n\t#眠四\r\n\t\r\n\t#活三\r\n\tcount = 0\r\n\tfor i in line :\r\n\t\tfor size in ['02220', '2022', '2202'] :\r\n\t\t\tif i.find(size) != -1 :\r\n\t\t\t\tcount += 1\r\n\t\t\t\tbreak\r\n\tscore += count * 200\r\n\t\r\n\t#双活二\r\n\tcount = 0\r\n\tfor i in line :\r\n\t\tfor size in ['002200', '02020', '2002'] :\r\n\t\t\tif i.find(size) != -1 :\r\n\t\t\t\tcount += 1\r\n\t\t\t\tbreak\r\n\t\tif count == 2 :\r\n\t\t\tscore += 100\r\n\t\t\tbreak\r\n\t\r\n\t#眠三\r\n\tcount = 0\r\n\tfor i in line :\r\n\t\tfor size in ['002221', '122200', '020221', '122020', '022021', '120220', '20022', '22002', '20202', '1022201'] :\r\n\t\t\tif i.find(size) != -1 :\r\n\t\t\t\tcount += 1\r\n\t\t\t\tbreak\r\n\tscore += count * 50\r\n\t\r\n\t#活二眠二\r\n\tdd = [0,0]\r\n\tfor i in line :\r\n\t\tif not dd[0] :\r\n\t\t\tfor size in ['002200', '02020', '2002'] :\r\n\t\t\t\tif i.find(size) != -1 :\r\n\t\t\t\t\tdd[0] = 1\r\n\t\t\t\t\tbreak\t\t\t\r\n\t\t\r\n\t\tif not dd[1] :\r\n\t\t\tfor size in ['000221', '122000', '002021', '120200', '020021', '120020', '20002'] :\r\n\t\t\t\tif i.find(size) != -1 :\r\n\t\t\t\t\tdd[1] = 1\r\n\t\t\t\t\tbreak\r\n\t\t\t\t\t\r\n\t\tif dd[0] and dd[1] :\r\n\t\t\tscore += 10\r\n\t\t\tbreak\r\n\t\r\n\t#活二\r\n\tcount = 0\r\n\tfor i in line :\r\n\t\tfor size in ['002200', '02020', '2002'] :\r\n\t\t\tif i.find(size) != -1 :\r\n\t\t\t\tcount += 1\r\n\t\t\t\tbreak\r\n\tscore += count * 5\r\n\t\r\n\t#眠二\r\n\tcount = 0\r\n\tfor i in line :\r\n\t\tfor size in ['000221', '122000', '002021', '120200', '020021', '120020', '20002'] :\r\n\t\t\tif i.find(size) != -1 :\r\n\t\t\t\tcount += 1\r\n\t\t\t\tbreak\r\n\tscore += count * 3\r\n\t\r\n\t#死四，死三，死二\r\n\tcount = 0\r\n\tfor i in line :\r\n\t\tif i.find('122221') != -1 :\r\n\t\t\tcount += 1\r\n\t\tif i.find('12221') != -1 :\r\n\t\t\tcount += 1\r\n\t\tif i.find('1221') != -1 :\r\n\t\t\tcount += 1\r\n\tscore += count * -5\r\n\t\r\n\treturn score\r\n\t\t\r\n\t\r\ndef get_score(pos, board) :\r\n\t\r\n\tori = copy.deepcopy(board)\r\n\tori[pos[0]][pos[1]] = 2\r\n\t\r\n\t#横，竖\r\n\th = str(ori[pos[0]])[1:-1].replace(',', '').replace(' ', '')\r\n\ts = str([ori[i][pos[1]] for i in range(15)])[1:-1].replace(',', '').replace(' ', '')\r\n\t\r\n\t#左斜\r\n\tlx = str([ori[i][i - pos[0] + pos[1]] for i in range(15) if (i - pos[0] + pos[1]) in range(15)])[1:-1].replace(',', '').replace(' ', '')\r\n\t\r\n\t#右斜\r\n\trx = str([ori[i][pos[0] + pos[1] - i] for i in range(15) if (pos[0] + pos[1] - i) in range(15)])[1:-1].replace(',', '').replace(' ', '')\r\n\t\r\n\treturn get_line_score([h, s, lx, rx])\t\t\t\t\r\n\t\r\ndef opp_board(board) :\r\n\to_board = [[0] * 15 for i in range(15)]\t\r\n\t\r\n\tfor i in range(15) :\r\n\t\tfor j in range(15) :\r\n\t\t\tif board[i][j] != 0 :\r\n\t\t\t\to_board[i][j] = 1 if board[i][j] == 2 else 2\r\n\t\t\t\t\r\n\treturn o_board\r\n\r\ndef get_pos(board) :\r\n\tpos = get_charge_pos(board)\r\n\t\r\n\tget = (-1, -1)\r\n\tscore = -float(\"inf\")\r\n\t\r\n\tfor p in pos :\r\n\t\to_board = opp_board(board)\r\n\t\ts = get_score(p, board) + get_score(p, o_board)\r\n\t\tif s > score :\r\n\t\t\tget = p\r\n\t\t\tscore = s\r\n\t\t\t\r\n\treturn get\r\n","repo_name":"SDCS-Zyx/SDCS_Zyx","sub_path":"Gobang/computer.py","file_name":"computer.py","file_ext":"py","file_size_in_byte":4886,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"48"}
+{"seq_id":"11611289262","text":"import streamlit as st\nimport pickle\nimport numpy as np\n\nmodel = pickle.load(open('./models/calories_burnt_prediction.pkl','rb'))\nst.title('XGB REGRESSOR')\n\nhtml_temp = \"\"\"\n<div style=\"background:#025246 ;padding:10px\">\n<h2 style=\"color:white;text-align:center;\"> Calories Burnt Predictor </h2>\n</div>\n\"\"\"\n\nst.markdown(html_temp, unsafe_allow_html = True)\n\ndef predict_age(Boole, Gender, Age, Height, Weight, Duration, Heart_Rate,Body_Temp):\n    input=np.array([[Boole, Gender, Age, Height, Weight, Duration, Heart_Rate,Body_Temp]])\n    prediction = model.predict(input)\n    \n    return  prediction\n\nst.header('Enter the following details to predict the Calories Burnt')\nBoole = st.checkbox(\"True/False\",value=False)\nGender= st.number_input('Gender')\nAge= st.number_input('Age')\nHeight = st.number_input('Height')\nWeight = st.number_input('Weight')\nDuration= st.number_input('Duration')\nHeart_Rate = st.number_input('Heart_Rate')\nBody_Temp = st.number_input('Body_Temp')\n\n\nif st.button(\"Predict the Calories Burnt\"):\n        output = predict_age(Boole, Gender, Age, Height, Weight, Duration, Heart_Rate,Body_Temp)\n        st.success('You Probably burnt {} Calories\\n'.format(output))\n        \n        \n","repo_name":"Sajithrajan03/YouFit-Backend","sub_path":"Streamlit_App/pages/3_Calories Burnt Predictor.py","file_name":"3_Calories Burnt Predictor.py","file_ext":"py","file_size_in_byte":1202,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"5243311499","text":"# Name: Dinesha Priyadarshanee Kanatte Kankanamalage \r\n# Student Number: 10570495\r\n\r\n# Imports for the implementations.\r\nimport tkinter\r\nimport tkinter.messagebox\r\nimport json\r\nfrom os import path\r\n\r\nclass ProgramGUI:     \r\n    \r\n    def __init__(self):        \r\n        self.main = tkinter.Tk()\r\n        self.main.title(\"Word Find Log Viewer\")\r\n        self.main.geometry('550x150') #Size of the application.                  \r\n               \r\n        # Read the log file at first the program is created and relevant data will be stored accordingly.\r\n        readFile()      \r\n\r\n        # Creating 4 different frames to add the labels and buttons.\r\n        self.f1 = tkinter.Frame(self.main)\r\n        self.f2 = tkinter.Frame(self.main)\r\n        self.f3 = tkinter.Frame(self.main)\r\n        self.f4 = tkinter.Frame(self.main)\r\n \r\n        # Frame 1 - label \"Letters\" and the label for 9 letter sample to be shown.\r\n        self.labelLetters = tkinter.Label(self.f1, text='Letters:  ', font=2)\r\n        self.labelLetters.pack(side = 'left')\r\n        self.labelLetterValues = tkinter.Label(self.f1)\r\n        self.labelLetterValues.pack(side = 'right')\r\n        \r\n        # Frame 2 - label \"Words\" and the label for the user given words to be shown.\r\n        self.labelWords = tkinter.Label(self.f2, text='Words:  ', font=2)\r\n        self.labelWords.pack(side = 'left')\r\n        self.labelWordValues = tkinter.Label(self.f2)\r\n        self.labelWordValues.pack(side = 'right')\r\n\r\n        # Frame 3 - label \"Score\" and the label for the score to be shown.\r\n        self.labelScore = tkinter.Label(self.f3, text='Score:  ', font=2)\r\n        self.labelScore.pack(side = 'left')\r\n        self.labelScoreValue = tkinter.Label(self.f3)\r\n        self.labelScoreValue.pack(side = 'right')\r\n\r\n        # Filling up data from the log file to the labels created to show the letters, words and score.\r\n        show_log(self)\r\n\r\n        # Frame 4 - Button to show the previous record, if available.\r\n        self.prevButton = tkinter.Button(self.f4, text='Previous', font=2, command=self.previous_log)\r\n        self.prevButton.pack(side = 'left')        \r\n        \r\n        # Button to show the next record, if available \r\n        self.nextButton = tkinter.Button(self.f4, text='Next', font=2, command=self.next_log)\r\n        self.nextButton.pack(side = 'right')\r\n\r\n        # Create a label to show the current record number and number of records. Ex. Log 1/3\r\n        self.labelLogRecord = tkinter.Label(self.f4, font=1)\r\n        self.labelLogRecord.pack()\r\n        # Update the current record number according to the list index.\r\n        updateLogRecord(self)\r\n\r\n        self.f1.pack()\r\n        self.f2.pack()\r\n        self.f3.pack()\r\n        self.f4.pack(fill=None, expand=True)\r\n        \r\n        tkinter.mainloop()\r\n\r\n    # This method is invoked when the button 'Previous' is clicked.\r\n    def previous_log(self):\r\n        global current_log\r\n\r\n        # If the current log was accessing the first record, there are no previous record to show and warning is given.\r\n        if current_log-1 < 0:\r\n            tkinter.messagebox.showwarning('End of File', 'No previous log.')\r\n        else:\r\n            increaseIndex(False)#Increase or decrease the index of the log accessing.\r\n            show_log(self)#Letters, words and score are updated accordingly.     \r\n            updateLogRecord(self)#Log record number is updated.\r\n\r\n    # This method is invoked when the button 'Next' is clicked.\r\n    def next_log(self):\r\n        global current_log\r\n\r\n        # If the current log was accessing the last record, there are no next records to show and warning is given.\r\n        if current_log+1 > len(logs)-1:\r\n            tkinter.messagebox.showwarning('End of File', 'No next log.')\r\n        else:\r\n            increaseIndex(True)#Increase or decrease the index of the log accessing.\r\n            show_log(self)#Letters, words and score are updated accordingly.\r\n            updateLogRecord(self)#Log record number is updated.\r\n\r\nglobal current_log\r\ncurrent_log = 0\r\nlogs = [] \r\n\r\n# Read the txt file and load the content to a list 'logs'.\r\ndef readFile():\r\n    if path.isfile('log.txt') is True:\r\n        with open('log.txt', 'r') as fp:\r\n            listObj = json.load(fp)\r\n            for obj in listObj:\r\n                logs.append(obj)\r\n            fp.close()\r\n\r\n# Update and show the log data by setting the values to the relevant labels.\r\ndef show_log(self):\r\n    dic = logs[current_log]\r\n    l = str(' , '.join(dic[\"letters\"]))#remove square brackets\r\n    w = str(' , '.join(dic[\"words\"]))\r\n    self.labelLetterValues.config(text=l, font=2)\r\n    self.labelWordValues.config(text=w, font=2)\r\n    self.labelScoreValue.config(text=dic[\"score\"], font=2)\r\n\r\n# Increase or decrease the index of the log list according to the current place accessing.\r\ndef increaseIndex(isIncrease):\r\n    global current_log\r\n    if isIncrease:\r\n        current_log += 1\r\n    else:\r\n        current_log -= 1\r\n\r\n# Update the current log index to be shown in the label.\r\ndef updateLogRecord(self):\r\n    text_val = 'log '+ str(current_log+1) + '/' + str(len(logs))\r\n    self.labelLogRecord.config(text=text_val)\r\n        \r\ngui = ProgramGUI()\r\n","repo_name":"dineshapriyadarshanee/CodingFun","sub_path":"log_viewer.py","file_name":"log_viewer.py","file_ext":"py","file_size_in_byte":5209,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"25896663377","text":"\"\"\"\nHelper functions of the Thrift plugin Framework API for C++ target language.\n\"\"\"\n\ndef _invoke_codegen_rule_name(plugin, target_name, thrift_src):\n    \"\"\"Returns the name of the rule that invokes the plugin codegen binary.\"\"\"\n    return \"{}-cpp2-{}-{}\".format(target_name, plugin.name, thrift_src)\n\ndef _generate_invoke_codegen_rule(\n        plugin,\n        codegen_rule_name,\n        target_name,\n        thrift_src,\n        target_base_path,\n        include_target):\n    \"\"\"Generates a rule that invokes the plugin codegen binary. Returns the name\n    of the generated rule.\n    \"\"\"\n    rule_name = _invoke_codegen_rule_name(plugin, target_name, thrift_src)\n    cmd = \"$(exe {}) --target-base-path {} --out-path $OUT $SRCS --include-path $(location {})\".format(\n        codegen_rule_name,\n        target_base_path,\n        include_target,\n    )\n\n    native.genrule(name = rule_name, out = \".\", srcs = [thrift_src], cmd = cmd)\n\n    return rule_name\n\ndef _copy_from_codegen_rule_name(plugin, target_name, thrift_src, file):\n    \"\"\"Returns the name of the rule of the copied out artifact.\"\"\"\n    return \"{}={}\".format(\n        _invoke_codegen_rule_name(plugin, target_name, thrift_src),\n        file,\n    )\n\ndef _generate_copy_from_codegen_rule(plugin, target_name, thrift_src, file):\n    \"\"\"Generates a rule that copies a generated file from the plugin codegen output\n    directory out into its own target. Returns the name of the generated rule.\n    \"\"\"\n    invoke_codegen_rule_name = _invoke_codegen_rule_name(\n        plugin,\n        target_name,\n        thrift_src,\n    )\n    plugin_path_prefix = \"gen-cpp2-{}\".format(plugin.name)\n    rule_name = _copy_from_codegen_rule_name(plugin, target_name, thrift_src, file)\n\n    cmd = \" && \".join(\n        [\n            \"mkdir `dirname $OUT`\",\n            \"cp $(location :{})/{} $OUT\".format(invoke_codegen_rule_name, file),\n        ],\n    )\n\n    native.genrule(\n        name = rule_name,\n        out = \"{}/{}\".format(plugin_path_prefix, file),\n        cmd = cmd,\n    )\n\n    return rule_name\n\ndef _generate_rules(\n        plugin,\n        codegen_rule_name,\n        expected_out_headers,\n        expected_out_srcs,\n        target_base_path,\n        target_name,\n        lang,\n        thrift_srcs,\n        compiler_args,\n        include_target,\n        deps,\n        additional_target_deps = [],\n        requires_transitive_plugin_build = True):\n    \"\"\"Generates the list of rules required to build Thrift C++ plugin.\n\n    Three types of rules will be generated:\n\n    1) For each Thrift file (module), a rule will be created that invokes the\n    Thrift plugin's code generator to generate custom source/header files.\n\n    2) For each generated source/header file, a rule will be created to copy\n    the file out from the plugin codegen's output directory into its own rule.\n\n    3) Finally, a single cpp_library will be created to encapsulate all the\n    generated source and header files. This final cpp_library can then be\n    consumed by plugin users.\n    \"\"\"\n\n    # Will be needed later for arg parsing and include path on the codegen side\n    _ignore = [compiler_args]\n\n    if lang != \"cpp2\":\n        return\n\n    header_targets = []\n    src_targets = []\n\n    for thrift_src in thrift_srcs:\n        # Rule that invokes the plugin codegen\n        _generate_invoke_codegen_rule(\n            plugin,\n            codegen_rule_name,\n            target_name,\n            thrift_src,\n            target_base_path,\n            include_target,\n        )\n\n    # Rules that copy out the plugin-generated artifacts\n    for thrift_src, headers in expected_out_headers.items():\n        for header in headers:\n            header_targets.append(\n                _generate_copy_from_codegen_rule(\n                    plugin,\n                    target_name,\n                    thrift_src,\n                    header,\n                ),\n            )\n\n    for thrift_src, srcs in expected_out_srcs.items():\n        for src in srcs:\n            src_targets.append(\n                _generate_copy_from_codegen_rule(plugin, target_name, thrift_src, src),\n            )\n\n    generated_target_deps = additional_target_deps + [\n        \"//{}:{}-{}\".format(target_base_path, target_name, lang),\n    ]\n\n    if requires_transitive_plugin_build:\n        for dep in deps:\n            generated_target_deps.append(\"{}-{}-{}\".format(dep, lang, plugin.name))\n\n    # Master rules that combine the generated artifacts for all .thrift\n    # files into a single cpp_library.\n    cpp_library(\n        name = \"{}-{}-{}\".format(target_name, lang, plugin.name),\n        srcs = [\":{}\".format(src) for src in src_targets],\n        headers = [\":{}\".format(header) for header in header_targets],\n        deps = generated_target_deps,\n    )\n\nframework_cpp2_base = struct(generate_rules = _generate_rules)\n","repo_name":"elaa0505/buckit","sub_path":"infra_macros/fbcode_macros/build_defs/thrift_plugins/framework_cpp2_base.bzl","file_name":"framework_cpp2_base.bzl","file_ext":"bzl","file_size_in_byte":4811,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"48"}
+{"seq_id":"762360697","text":"import itertools\nimport math\nfrom typing import Final\n\n\nclass Shape(object):\n    id_iter = itertools.count()\n\n    def __init__(self):\n        # PEP 591: Final for Linters/Type Checkers https://peps.python.org/pep-0591/\n        # https://stackoverflow.com/questions/50533812/what-is-the-best-way-to-define-constant-variables-python-3/65706030#65706030\n        self.ID: Final[int] = next(Shape.id_iter)+1\n\n    def __str__(self):\n        return str(self.ID) + \": \" + self.__class__.__name__ + \", perimeter: \" + str(self.perimeter()) \\\n               + \", area: \" + str(self.area())\n\n    def perimeter(self):\n        return \"undefined\"\n\n    def area(self):\n        return \"undefined\"\n\n\nclass Circle(Shape):\n    def __init__(self, radius):\n        super().__init__()\n        self.radius = radius\n\n    def perimeter(self):\n        return 2 * math.pi * self.radius if self.radius >= 0 else \"undefined\"\n\n    def area(self):\n        return math.pi * self.radius**2 if self.radius >= 0 else \"undefined\"\n\n\nclass Ellipse(Shape):\n    def __init__(self, first, second):\n        super().__init__()\n        self.a = first if first >= second else second\n        self.b = first if first < second else second\n\n    def area(self):\n        return math.pi * self.a * self.b if (self.a >= 0 and self.b >= 0) else \"undefined\"\n\n    def eccentricity(self):\n        try:\n            c = math.sqrt(self.a**2 - self.b**2)\n        except ValueError:\n            return \"undefined\"\n        return c if (self.a >= 0 and self.b >= 0) else \"undefined\"\n\n\nclass Rhombus(Shape):\n    def __init__(self, p, q):\n        super().__init__()\n        self.p = p\n        self.q = q\n\n    def perimeter(self):\n        return 2 * math.sqrt(self.p**2 + self.q**2) if (self.p >= 0 and self.q >= 0) else \"undefined\"\n\n    def area(self):\n        return self.p * self.q / 2 if (self.p >= 0 and self.q >= 0) else \"undefined\"\n\n    def inradius(self):\n        try:\n            r = self.p * self.q / self.perimeter()\n        except ValueError:\n            return \"undefined\"\n        except TypeError:\n            return \"undefined\"\n        return r if r >= 0 else \"undefined\"\n\n\nif __name__ == '__main__':\n    shapes = [Shape() for i in range(10)]\n    print(\"(1b) ID:\", end=' ')\n    for shape in shapes:\n        print(shape.ID, end=' ')\n    print()\n    print(shapes[0].ID, end=' ')\n    shapes[0].ID = 15  # Should show warning for many Linters/Type Checkers\n    print(shapes[0].ID)\n    print()\n\n    print(\"(1c): \" + str(shapes[0]))\n    print()\n\n    circle = Circle(4)\n    print(circle)\n    print()\n\n    ellipse = Ellipse(4, 2)\n    print(ellipse)\n    print(\"(3d) linear eccentricity: \" + str(ellipse.eccentricity()))\n    ellipse.a = 1\n    print(ellipse)\n    print(\"(3d) linear eccentricity: \" + str(ellipse.eccentricity()))\n    print()\n\n    rhombus = Rhombus(4, 2)\n    print(rhombus)\n    print(\"(4d) in-radius: \" + str(rhombus.inradius()))\n    rhombus.p = -1\n    print(rhombus)\n    print(\"(4d) in-radius: \" + str(rhombus.inradius()))\n","repo_name":"RemineralizedWater/comp348_a3","sub_path":"comp348_a3_q5.py","file_name":"comp348_a3_q5.py","file_ext":"py","file_size_in_byte":2975,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"39016554709","text":"# Untitled.py\n# Created by Kids on 10/10/20.\n\nnames_ranked = {\"zoe\" : \"meh\", \"pam\" : \"okay\", \"steve\" : \"fine\", \"theo\" : \"da best\"}\ndef rank_returner(name):\n\tif name in names_ranked:\n\t\treturn name +  \" is \" + names_ranked[name]\n\telse:\n\t\treturn \"wrong!!!!!!!\"\ndef rank_returner_try(name):\n\ttry:\n\t\treturn names_ranked[name]\n\texcept KeyError:\n\t\treturn \"wrong!!!!!!!!!!!!!!!!!\"\ndef rank_retrun(name):\n\treturn names_ranked[name]\t\t\nif __name__ == \"__main__\":\n\tanswer = input(\"please give me a name\\n\")\n\tprint(rank_returner_try(answer))","repo_name":"CoolAsTapatio/coding-lessons","sub_path":"skills/trying_try_except.py","file_name":"trying_try_except.py","file_ext":"py","file_size_in_byte":528,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"2987835355","text":"from fastapi import FastAPI\n\nfrom config import get_swagger_settings\nfrom db.session import DbSessionProvider\nfrom routes import folders, names\n\n\ndef get_application() -> FastAPI:\n    \"\"\"\n        Initialize and return FastAPI instance\n    :return: FastAPI instance\n    \"\"\"\n    # configure swagger settings\n    app_settings = get_swagger_settings()\n\n    # Initialize FastAPI\n    app_instance = FastAPI(**app_settings)\n\n    @app_instance.on_event(\"startup\")\n    def startup_event():\n        DbSessionProvider.setup()\n\n    @app_instance.on_event(\"shutdown\")\n    async def shutdown_event():\n        await DbSessionProvider.teardown()\n\n    # Add api routes\n    app_instance.include_router(folders.router, tags=[\"Folders\"], prefix='/api')\n    app_instance.include_router(names.router, tags=[\"Grouping names\"], prefix='/api')\n\n    return app_instance\n\n\napp = get_application()\n","repo_name":"shardbread/home_challenge_clone","sub_path":"app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":870,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"11655792618","text":"num = list(map(int,input().split()))\n\nans = sum(set(num))*2 - sum(num)\nprint(abs(ans))\n\n'''\n---------------\n少し雑に作りすぎたかも\n特に最後のabsかぶせたとことか雑の極み\n\n'''\n","repo_name":"Taka0007/AtCoder","sub_path":"ABC/ABC-027/ABC-027A.py","file_name":"ABC-027A.py","file_ext":"py","file_size_in_byte":202,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"10707312130","text":"\"\"\"\n@File    : 8.字符串转整数.py\n@Time    : 2019-11-25 08:35\n@Author  : 李浩然\n@Software: PyCharm\n@Email: 1901210423@pku.edu.cn\n\"\"\"\n\n\nclass Solution:\n    def myAtoi(self, str) -> int:\n        nums='0123456789'\n        char='-+'\n        ans=[]\n        str=str.lstrip()\n        for i in range(len(str)):\n            if i==0 and str[i] in char:\n                ans.append(str[i])\n            elif str[i] in nums:\n                ans.append(str[i])\n            else:\n                break\n        if len(ans)==0:\n            return 0\n        elif len(ans)==1 and ans[0] in char:\n            return 0\n        else:\n            number=''\n            for i in ans:\n                number=number+i\n            digit=int(number)\n            if digit>pow(2,31)-1:\n                return pow(2,31)-1\n            elif digit<pow(-2,31):\n                return pow(-2,31)\n            else:\n                return digit\n\n\nS=Solution()\n\nstr=\"-91283472332 \"\nprint(S.myAtoi(str))","repo_name":"watermelon-lee/leetcode","sub_path":"code/8.字符串转整数.py","file_name":"8.字符串转整数.py","file_ext":"py","file_size_in_byte":970,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"48"}
+{"seq_id":"8880530439","text":"import gin\nimport torch.nn as nn\n\nfrom utils.utils import create_fully_connected_network\n\n# @gin.configurable\n# def train_critic(critic, data, optimizer, num_train_loop_iterations=80):\n#     optimizer.zero_grad()\n#     observations = data[\"observations\"]\n#     discounted_rewards = data[\"discounted_rewards\"]\n\n#     for _ in range(num_train_loop_iterations):\n#         values = critic(observations)\n#         loss = ((values - discounted_rewards) ** 2).mean()\n#         loss.backward()\n#         optimizer.step()\n\n#     return loss.item()\n\n\n@gin.configurable\nclass Critic(nn.Module):\n    def __init__(self, input_dim, hidden_sizes):\n        super().__init__()\n        sizes = [input_dim, *hidden_sizes, 1]\n        self.fc_net = create_fully_connected_network(sizes)\n\n    def forward(self, observation):\n        logits = self.fc_net(observation)\n        return logits\n","repo_name":"probably-nothing1/ActorCritic","sub_path":"src/models/Critic.py","file_name":"Critic.py","file_ext":"py","file_size_in_byte":867,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"30923872572","text":"# pip install -q transformers\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\ncheckpoint = \"bigcode/starcoder\"\n#device = \"cuda\" # for GPU usage or \"cpu\" for CPU usage\n\ntokenizer = AutoTokenizer.from_pretrained(checkpoint)\nmodel = AutoModelForCausalLM.from_pretrained(checkpoint)\n\ninputs = tokenizer.encode(\"def print_hello_world():\", return_tensors=\"pt\")\noutputs = model.generate(inputs)\nprint(tokenizer.decode(outputs[0]))\n","repo_name":"dlimeng/ai_chatgpt","sub_path":"11 starcoder/starcoderTest.py","file_name":"starcoderTest.py","file_ext":"py","file_size_in_byte":438,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"23702629441","text":"#\nfrom .base import GreenFunction\nimport numpy as np\n\n\nclass CoulombHamiltonian(GreenFunction):\n    def __init__(self, delta, A, R):\n\n        if type(delta) is not np.float:\n            raise ValueError(\"Energy is not a float!\")\n        if type(A) is not np.float:\n            raise ValueError(\"Bandwidth is not a float!\")\n        if type(R) is not np.int:\n            raise ValueError(\"Range is not an integer!\")\n\n        Hdiag = np.zeros((2 * R + 1,))\n        for s in range(2 * R + 1):\n            r = s - R\n            if r == 0:\n                Hdiag[s] = -A - delta\n            else:\n                Hdiag[s] = -A / np.abs(r)\n\n        self.M = np.diag(Hdiag)\n","repo_name":"marickmanrho/ct_absorption_visualization","sub_path":"igf/coulomb.py","file_name":"coulomb.py","file_ext":"py","file_size_in_byte":665,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"872080264","text":"import json\nimport logging\nimport urllib.parse\n\nimport requests\nimport websocket\n\nfrom squeaky_hinge_config import *\n\n\ndef fetch_conversations(include_messages):\n    logging.info(f\"START inbox flow\")\n\n    with open(\"hinge_creds.json\") as f:\n        data = json.load(f)\n\n    hinge_token = data[\"hinge_token\"]\n    user_id = data[\"user_id\"]\n    hinge_install_id = data[\"hinge_install_id\"]\n    logging.info(f\"Using Hinge token {hinge_token}\")\n    logging.info(f\"Using user ID {user_id}\")\n    logging.info(f\"Using Hinge install ID {hinge_install_id}\")\n\n    logging.info(f\"Using X-App-Version {hinge_android_package}\")\n    logging.info(f\"Using X-Device-Platform {hinge_apk_sha1sum}\")\n    resp = requests.post(\n        \"https://prod-api.hingeaws.net/message/authenticate\",\n        headers={\n            \"Authorization\": f\"Bearer {hinge_token}\",\n            \"X-App-Version\": hinge_app_version,\n            \"X-Device-Platform\": hinge_device_platform,\n            \"X-Install-Id\": hinge_install_id,\n        },\n        json={\"refresh\": False},\n    )\n    logging.info(\n        f\"Invoked message/authenticate and got response code {resp.status_code} and body: {resp.text}\"\n    )\n\n    if not resp.ok:\n        raise Exception(\n            f\"got response code {resp.status_code} when fetching Sendbird access token\"\n        )\n\n    sendbird_auth_data = resp.json()\n    sendbird_access_token = sendbird_auth_data[\"token\"]\n    logging.info(f\"Got SendBird access token {sendbird_access_token}\")\n\n    logging.info(f\"Using SendBird application ID {sendbird_application_id}\")\n    ws = websocket.create_connection(\n        f\"wss://ws-{sendbird_application_id.lower()}.sendbird.com?\"\n        + urllib.parse.urlencode(\n            {\n                \"ai\": sendbird_application_id,\n                \"user_id\": user_id,\n                \"access_token\": sendbird_access_token,\n            }\n        ),\n    )\n\n    sendbird_ws_message = ws.recv()\n    logging.info(f\"Opened SendBird websocket and got message {sendbird_ws_message}\")\n    sendbird_ws_data = json.loads(sendbird_ws_message.removeprefix(\"LOGI\"))\n\n    sendbird_session_key = sendbird_ws_data[\"key\"]\n    logging.info(f\"Got SendBird session key {sendbird_session_key}\")\n\n    resp = requests.get(\n        f\"https://api-{sendbird_application_id.lower()}.sendbird.com/v3/users/{user_id}/my_group_channels\",\n        headers={\n            \"Accept\": \"application/json\",\n            \"Session-Key\": sendbird_session_key,\n        },\n        params={\n            \"show_member\": \"true\",\n            \"show_frozen\": \"true\",\n            \"public_mode\": \"all\",\n            \"member_state_filter\": \"all\",\n            \"hidden_mode\": \"unhidden_only\",\n            \"show_delivery_receipt\": \"true\",\n            \"distinct_mode\": \"all\",\n            \"unread_filter\": \"all\",\n            \"token\": \"\",\n            \"show_read_receipt\": \"true\",\n            \"super_mode\": \"all\",\n            \"show_metadata\": \"true\",\n            \"limit\": \"20\",\n            \"show_empty\": \"true\",\n            \"order\": \"latest_last_message\",\n        },\n    )\n    logging.info(\n        f\"Invoked my_group_channels and got response code {resp.status_code} and body: {resp.text}\"\n    )\n\n    if not resp.ok:\n        raise Exception(\n            f\"got response code {resp.status_code} when fetching conversations\"\n        )\n\n    conversations = resp.json()\n\n    if include_messages:\n        for channel in conversations['channels']:\n            channel['messages'] = fetch_messages(sendbird_session_key, channel)\n\n    with open(\"conversations.json\", \"w\") as f:\n        json.dump(conversations, f, indent=2)\n        f.write(\"\\n\")\n\n    logging.info(f\"SUCCESS inbox flow\")\n\n\ndef fetch_messages(sendbird_session_key, channel):\n    logging.info(f\"START message flow {channel['channel_url']}\")\n\n    messages = []\n    last_message_time = channel['invited_at']\n\n    while last_message_time < channel['last_message']['created_at']:\n        resp = requests.get(\n            f\"https://api-{sendbird_application_id.lower()}.sendbird.com/v3/group_channels/{channel['channel_url']}/messages\",\n            headers={\n                \"Accept\": \"application/json\",\n                \"Session-Key\": sendbird_session_key,\n            },\n            params={\n                \"message_ts\": last_message_time,\n                \"next_limit\": 200,\n            },\n        )\n        logging.info(f\"Invoked group_channels and got response code {resp.status_code}\")\n\n        if not resp.ok:\n            logging.info(\n                f\"Invoked group_channels and got response code {resp.status_code} and body: {resp.text}\"\n            )\n            raise Exception(\n                f\"got response code {resp.status_code} when fetching messages\"\n            )\n\n        parsed = resp.json()\n        messages += parsed['messages']\n        last_message_time = messages[-1]['created_at']\n\n    logging.info(f\"SUCCESS message flow {channel['channel_url']}\")\n    return messages\n","repo_name":"radian-software/squeaky-hinge","sub_path":"conversations.py","file_name":"conversations.py","file_ext":"py","file_size_in_byte":4904,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"48"}
+{"seq_id":"43304705236","text":"from gtts import gTTS\nfrom playsound import playsound\n\nfile_name = 'sample.mp3'\n\n# 영어 문장\n# text = 'Imagine that you have just arrived at a hotel after a tiring 7-hour overnight flight.'\n# tts_en = gTTS(text = text, lang = 'en')\n# tts_en.save(file_name)\n# playsound(file_name)\n\n# 한글 문장\n# text = '윤길아 자니? 일어나 학교가야지 맘마먹자'\n# tts_ko = gTTS(text = text, lang = 'ko')\n# tts_ko.save(file_name)\n# playsound(file_name)    # mp3파일 재생\n\n\n\n# 긴 문장(파일에서 불러와서 처리) \nwith open('sample.txt', 'r', encoding = 'utf-8') as f:\n    text = f.read()\n\ntts_ko = gTTS(text = text, lang = 'ko')\ntts_ko.save(file_name)\nplaysound(file_name)","repo_name":"jangjiseon/AISpeaker","sub_path":"text_to_speech.py","file_name":"text_to_speech.py","file_ext":"py","file_size_in_byte":691,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"41283880469","text":"#Hernández Alvarado Miguel Kam Yaxkin\n\nimport Biblioteca_v2_yaxkin09\n\nprint(\"-------------------------\")\n\nGuerra_y_paz=Biblioteca_v2_yaxkin09.Leon_Tolstoi(\"Guerra_y_paz\", \"En inventario\", \"Novela\")\nGuerra_y_paz.Duracion()\nGuerra_y_paz.Personajes()\nGuerra_y_paz.Critica()\n\nprint(\"-------------------------\")\n\nLos_Miserables=Biblioteca_v2_yaxkin09.Victor_Hugo(\"Los_Miserables\", \"Prestado\", \"Novela\")\nLos_Miserables.Critica()\nLos_Miserables.Epoca()\nLos_Miserables.Narrativa()\n\nprint(\"-------------------------\")\n\nEl_castillo_ambulante=Biblioteca_v2_yaxkin09.Diana_Wynne_Jones(\"El_castillo_ambulante\", \"Retraso\", \"Fantasía\")\nEl_castillo_ambulante.Pelicula()\nEl_castillo_ambulante.Comedia()\nEl_castillo_ambulante.Idea()\n\nprint(\"-------------------------\")\n\nEl_gran_diseño=Biblioteca_v2_yaxkin09.Stephen_Hawking(\"El_gran_diseño\", \"Prestado\", \"Ciencia\")\nEl_gran_diseño.Solicitudes()\nEl_gran_diseño.Divulgacion()\nEl_gran_diseño.Contemporaneo()\n\nprint(\"-------------------------\")\n\nTrilogia_de_la_fundacion=Biblioteca_v2_yaxkin09.Isaac_Asimov(\"Trilogia_de_la_fundacion\", \"En inventario\", \"Ciencia ficción\")\nTrilogia_de_la_fundacion.Eventos()\nTrilogia_de_la_fundacion.Tiempo()\nTrilogia_de_la_fundacion.Personajes()\n\nprint(\"-------------------------\")\n\nLa_invasion_desde_Marte=Biblioteca_v2_yaxkin09.Orson_Welles(\"La_invasion_desde_Marte\", \"Prestado\", \"Investigación\")\nLa_invasion_desde_Marte.Estadistica()\nLa_invasion_desde_Marte.Evento()\nLa_invasion_desde_Marte.Guion()\n\nprint(\"-------------------------\")\n\nEl_cazador_de_historias=Biblioteca_v2_yaxkin09.Eduardo_Galeano(\"El_cazador_de_historias\", \"Retraso\", \"Microcuento\")\nEl_cazador_de_historias.Microcuento()\nEl_cazador_de_historias.Anecdotas()\nEl_cazador_de_historias.Contemporaneo()\n\nprint(\"-------------------------\")\n\nCalculus=Biblioteca_v2_yaxkin09.Michael_Spivak(\"Calculus\", \"En inventario\", \"Matemáticas\")\nCalculus.Formalidad()\nCalculus.Lectores()\nCalculus.Popularidad()\n\nprint(\"-------------------------\")\n\nLos_dias_que_no_se_nombran=Biblioteca_v2_yaxkin09.Jose_Emilio_Pacheco(\"Los_dias_que_no_se_nombran\", \"Prestado\", \"Poesía\")\nLos_dias_que_no_se_nombran.Contemporaneo()\nLos_dias_que_no_se_nombran.Edicion()\nLos_dias_que_no_se_nombran.Tematica()\n\nprint(\"-------------------------\")\n\nEl_arte_de_amar=Biblioteca_v2_yaxkin09.Ovidio(\"El_arte_de_amar\", \"Retraso\", \"No especificado\")\nEl_arte_de_amar.Popularidad()\nEl_arte_de_amar.Antiguedad()\nEl_arte_de_amar.Escolar()\n\nprint(\"-------------------------\")\nprint(\"\\n\")","repo_name":"emilianoNM/Clase2018Tecnicas","sub_path":"Clase19022018/testBiblioteca_v2_yaxkin09.py","file_name":"testBiblioteca_v2_yaxkin09.py","file_ext":"py","file_size_in_byte":2473,"program_lang":"python","lang":"es","doc_type":"code","stars":3,"dataset":"github-code","pt":"48"}
+{"seq_id":"44290721976","text":"# Operadores \r\n# De comparacion: <, >, <=, >=, == (operador igual)\r\n# Logico: are, or, not \r\n\r\n# boolean: True / False\r\n\r\nvariable = 10 # operador asignado\r\nnombre = \"pepe\"\r\n\r\n#user = input(\"Ingresar usuario: \")\r\n#password = input(\"Ingresar password: \")\r\n\r\n# Si user es igual a pepe y password es igual a 1234 mostrar bienvenido user\r\n# Si no mostrar usted no es pepe.\r\n\r\n#if user == \"pepe\" and password == \"1234\":\r\n #   print(\"Bienvenido, \" + user)\r\n#else:\r\n #   print(\"Usted no es pepe\")\r\n\r\n#print(\"hola mundo\")\r\n\r\n# Si es mayor de edad o VIP puede pasar\r\n\r\nedad = int(input(\"Ingrese su edad: \"))\r\nVIP = bool(input(\"¿Es usted vip? True/False: \"))\r\n\r\nif edad >= 18 or VIP == True: # el ¨or¨ es menos restrictivo entonces hay que tener cuidado al usarlo\r\n    print(\"Usted puede pasar\")\r\nelse: \r\n    print(\"Fuera de aca\")\r\n\r\n","repo_name":"FeHa1/curso-de-Python","sub_path":"Curso-UP/tres.py","file_name":"tres.py","file_ext":"py","file_size_in_byte":827,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"10933923195","text":"\nimport re\nimport os\nimport sys\nimport ast\nimport json\n\ndef score(filename):\n  with open(filename) as F:\n    last_line = None\n    for line in F:\n      last_line = line.strip()\n  obj = ast.literal_eval(last_line)\n  return obj\n\nnumber = re.compile(\"[0-9]+\")\n\nif __name__ == \"__main__\":\n  if len(sys.argv) > 1:\n    eval_file_list = []\n    directory = sys.argv[1]\n    for d in os.listdir(directory):\n      epoch = number.findall(d)\n      if d.endswith(\".eval\") and epoch:\n        eval_file = os.path.join(directory, d, \"out.eval\")\n        if os.path.isfile(eval_file):\n          eval_file_list.append((int(epoch[0]), eval_file))\n    eval_file_list.sort()\n    print(\"| epoch | Bleu_4 | CIDEr | METEOR | ROUGE_L |\")\n    print(\"|:------|-------:|------:|-------:|--------:|\")\n    for epoch, eval_file in eval_file_list:\n      try:\n        obj = score(eval_file)\n        B4 = obj[\"Bleu_4\"]\n        C = obj[\"CIDEr\"]\n        M = obj[\"METEOR\"]\n        R = obj[\"ROUGE_L\"]\n        print(\"| %d | %.4f | %.4f | %.4f | %.4f |\" % (epoch, B4, C, M, R))\n      except Exception as e:\n        #print(e)\n        pass\n\n\n\n    \n          \n\n        \n  else:\n    sys.exit(\"Usage: python %s [model_dir]\" % sys.argv[0])\n","repo_name":"wangheda/ImageCaption-UnderFitting","sub_path":"scripts/print_markdown_table.py","file_name":"print_markdown_table.py","file_ext":"py","file_size_in_byte":1191,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"48"}
+{"seq_id":"5238232720","text":"\nimport os\nimport sys\n\nTEST_DIR = os.path.dirname(__file__)\nsys.path.append(os.path.join(TEST_DIR, os.path.pardir))\nsys.path.insert(0, os.path.join(os.path.dirname(__file__), 'faux'))\nimport fauxroot\n\nsys.path.append(os.path.join(os.path.dirname(__file__), 'good-config.1'))\nimport fauxconfig\nsys.path.pop()\n\nimport consts\nimport migrate\nimport migration.services as ms\n\nfrom migrate_support import copyIntoRoot\n\ndef test_xinetdconf():\n    def cmpConf(filename, expectedFiles, expectedDirs, expectedServices):\n        conf = open(os.path.join(TEST_DIR, \"upgrade\", filename))\n        try:\n            xc = ms.XinetdConf()\n            xc.keyFilter = [\"id\", \"disable\"]\n            for line in conf:\n                xc.feedLine(line)\n\n            assert xc.includedFiles == expectedFiles\n            assert xc.includedDirs == expectedDirs\n            assert xc.services == expectedServices\n        finally:\n            conf.close()\n    \n    cases = [\n        (\"xinetd.conf.0\", [], ['/etc/xinetd.d'], {}),\n\n        (\"vmware-authd\", [], [], {\n                \"vmware-authd\" : { \"disable\" : \"no\" }\n                }),\n        \n        (\"time\", [], [], {\n                \"time-stream\" : { \"disable\" : \"yes\", \"id\" : \"time-stream\" }\n                }),\n        \n        ]\n\n    for case in cases:\n        yield (cmpConf,) + case\n\ndef test_migrate():\n    try:\n        fauxroot.resetLogs()\n\n        srcDir = os.path.join(TEST_DIR, \"upgrade\")\n        root = os.path.join(TEST_DIR, \"good-config.1\")\n        esx3 = consts.HOST_ROOT + consts.ESX3_INSTALLATION\n    \n        copyIntoRoot(os.path.join(srcDir, \"xinetd.conf.1\"),\n                     root,\n                     os.path.join(esx3, \"etc/xinetd.conf\"))\n        copyIntoRoot(os.path.join(srcDir, \"time\"),\n                     root,\n                     os.path.join(esx3, \"etc/xinetd.d/time\"))\n        copyIntoRoot(os.path.join(srcDir, \"time\"),\n                     root,\n                     os.path.join(consts.HOST_ROOT, \"etc/xinetd.d/time-stream\"))\n        copyIntoRoot(os.path.join(srcDir, \"vmware-authd\"),\n                     root,\n                     os.path.join(esx3, \"etc/xinetd.d/vmware-authd\"))\n        copyIntoRoot(os.path.join(srcDir, \"vmware-authd\"),\n                     root,\n                     os.path.join(consts.HOST_ROOT,\n                                  \"etc/xinetd.d/vmware-authd\"))\n        \n        fauxroot.FAUXROOT = [root]\n\n        migrate.migratePath(\"/etc/xinetd.conf\")\n\n        assert fauxroot.SYSTEM_LOG == [\n            ['/sbin/chkconfig', 'time-stream', 'off'],\n            ['/sbin/chkconfig', 'vmware-authd', 'on'],\n            ]\n    finally:\n        fauxroot.FAUXROOT = None\n","repo_name":"vmware-archive/weasel","sub_path":"test/test_migrateServices.py","file_name":"test_migrateServices.py","file_ext":"py","file_size_in_byte":2656,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"48"}
+{"seq_id":"13314726527","text":"#!/usr/bin/python3\nfrom AnalyzeResults import analyze_timings\nimport pickle\nimport os\nimport matplotlib.ticker as mticker\n\nimport matplotlib as mpl\nmpl.rcParams.update({'font.size': 14})\nmpl.use('Agg')\nimport matplotlib.pyplot as plt\nimport argparse\n\n\ndef plot_run(results, save_path):\n    fig, ax = plt.subplots()\n    ax2 = ax.twinx()\n    \n    plt.tight_layout()\n    fig.set_size_inches(5,3)\n\n    pts = sorted(results, key=lambda x: x[0])\n    xs = [mem/1024 for mem, cold_pct, dropped_pct in pts]\n    colds = [cold_pct for mem, cold_pct, dropped_pct in pts]\n    drops = [dropped_pct for mem, cold_pct, dropped_pct in pts]\n\n    ax.plot(xs, colds, color=\"blue\")\n    print(\"Cold %s\", colds)\n\n    ax2.plot(xs, drops, color=\"red\")\n    print(\"Dropped %s\", drops)\n    \n    ax.set_ylabel(\"% Cold Starts\", color=\"blue\")\n    ax.tick_params(axis='y', colors='blue')\n    ax.set_ylim((0,None))\n\n    ax2.set_ylabel(\"% Dropped\", color=\"red\")\n    ax2.tick_params(axis='y', colors='red')\n    ax2.set_ylim((0,None))\n    ax.set_xlabel(\"System Memory (GB)\")\n\n    plt.savefig(save_path, bbox_inches=\"tight\")\n    plt.close(fig)\n\ndef get_info_from_file(filename):\n    policy, num_funcs, mem, run = filename[:-5].split(\"-\")\n    return policy, int(num_funcs), int(mem), run\n\ndef load_data(path):\n    with open(path, \"r+b\") as f:\n        return pickle.load(f)\n\ndef plot_all(args):\n    results = []\n    num_funcs = args.numfuncs\n    filt = \"{}-{}-\".format(args.policy, num_funcs)\n    char_filt = \"-{}.pckl\".format(args.char)\n    for file in os.listdir(args.pckldir):\n        if filt in file and char_filt in file:\n            policy, num_funcs, mem, run = get_info_from_file(file)\n            pth = os.path.join(args.pckldir, file)\n            policy, evdict, miss_stats, lambdas, capacity_misses, len_trace = load_data(pth)\n\n            data = analyze_timings(policy, lambdas, miss_stats)\n            total_misses = sum(capacity_misses.values())\n            total_cold = 0\n            total_warm = 0\n            for func in data.keys():\n              if func != \"global\":\n                total_warm += data[func][\"hits\"]\n                total_cold += data[func][\"misses\"]\n            print(data[\"global\"], total_misses, total_warm, total_cold, len_trace)\n\n            cold_pct = data[\"global\"][\"server_cold\"] *100\n            dropped_pct = (total_misses / len_trace) * 100\n            # print(file, \"Cold starts %:\", cold_pct, \"; Dropped %:\", dropped_pct)\n            results.append((mem, cold_pct, dropped_pct))\n    save_path = os.path.join(args.plotdir, \"results-{}-{}-{}.png\".format(args.policy, num_funcs, args.char))\n    plot_run(results, save_path)\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(description='plot FaasCache Simulation')\n    parser.add_argument(\"--pckldir\", type=str, default=\"/data/alfuerst/verify-test/\", required=False)\n    parser.add_argument(\"--plotdir\", type=str, default=\"../figs/\", required=False)\n    parser.add_argument(\"--numfuncs\", type=int, default=20, required=False)\n    parser.add_argument(\"--char\", type=str, default=\"a\", required=False)\n    parser.add_argument(\"--policy\", type=str, default=\"RAND\", required=False)\n    \n    args = parser.parse_args()\n    plot_all(args)\n","repo_name":"amolsangar/cloud-functions-eviction-policy-simulator","sub_path":"faas-cloud-comp/code/analyze/PlotResults.py","file_name":"PlotResults.py","file_ext":"py","file_size_in_byte":3203,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"11696142764","text":"from __future__ import division, print_function\n\n# Basic Python modules\nimport math\nfrom pyproj import Proj\n\nclass Projection(object):\n    def __init__(self, dx, dim, offsets, uproj=\"utm\", uzone=11):\n\n        self.valid = False\n        self.proj = uproj\n        self.dx = dx\n        self.dim = dim\n\n        if self.proj == 'utm':\n            self.projobj = Proj(proj='utm', zone=uzone, ellps='WGS84')\n            if offsets != None:\n                self.xoffset = -1.0 * offsets[0]\n                self.yoffset = -1.0 * offsets[1]\n            else:\n                self.xoffset = 0\n                self.yoffset = 0\n\n            self.valid = True\n        else:\n            self.projobj = None\n\n    def is_valid(self):\n        return self.valid\n\n    def cleanup(self):\n        return\n\n    def get_raw_xy_from_geo(self, lon, lat):\n        x, y = self.projobj(lon, lat)\n        x = x + self.xoffset\n        y = y + self.yoffset\n        return (x, y)\n\n    def get_xy_from_geo(self, lon, lat):\n        if self.proj == 'utm':\n            x, y = self.projobj(lon, lat)\n            x = x + self.xoffset\n            y = y + self.yoffset\n            x = int(x / float(self.dx) + 0.5)\n            y = int(y / float(self.dx) + 0.5)\n            #print(\"x: %f, y: %f, self.dim[0]: %f, self.dim[1]: %f\" %\n            #      (x, y, self.dim[0], self.dim[1]))\n            if (x >= self.dim[0]) or (y >= self.dim[1]):\n                return (-1.0, -1.0)\n            return (x, y)\n        else:\n            return (-1.0, -1.0)\n\n    def get_geo_from_xy(self, x, y):\n        if self.proj == 'utm':\n            #print x,y\n            lon, lat = self.projobj(x * self.dx - self.xoffset,\n                                    y * self.dx - self.yoffset,\n                                    inverse=True)\n            return (lon, lat)\n        else:\n            return (-1.0, -1.0)\n\n    def get_dist_km(self, lon1, lat1, lon2, lat2):\n        dist = 0.0\n        if self.proj == 'CMU':\n            x1, y1 = self.projobj(lon1, lat1)\n            x2, y2 = self.projobj(lon2, lat2)\n\n            dist = math.sqrt((x2 - x1) * (x2 - x1) +\n                             (y2 - y1) * (y2 - y1)) / 1000.0\n\n        return dist\n","repo_name":"SCECcode/bbp","sub_path":"bbp/comps/Projection.py","file_name":"Projection.py","file_ext":"py","file_size_in_byte":2183,"program_lang":"python","lang":"en","doc_type":"code","stars":39,"dataset":"github-code","pt":"48"}
+{"seq_id":"32281367619","text":"from flask_restful import Resource, Api\nfrom marshmallow import fields, validate, ValidationError, validates_schema\nfrom marshmallow_sqlalchemy import ModelSchema\nfrom sqlalchemy import Column, String, Integer\n\nfrom rse_api.decorators import schema_out, register_resource, schema_in_out\nfrom rse_db.utils import get_declarative_base\n\nBase = get_declarative_base()\n\n\n# Our Database Model\n# This model handles all our interaction with DB\nclass ParameterModel(Base):\n    id = Column(Integer(), primary_key=True, autoincrement=True)\n    name = Column(String(255))\n    min = Column(Integer())\n    max = Column(Integer())\n\n    @staticmethod\n    def find_one(id):\n        pass\n\n    @staticmethod\n    def save(data, session=None):\n        # do any special data validations\n        # manipulations or whatever\n        # then save\n        if session is None:\n            session = ParameterModel.query.session\n        instance = ParameterModel(**data)\n        session.add(instance)\n        session.commit()\n\n\n# Schema for handling serialization and data validation on inputs\nclass ParameterSchema(ModelSchema):\n    id = fields.Integer(required=True)\n    name = fields.String(required=True)\n    min = fields.Integer(validate=validate.Range(1, 4))\n    max = fields.Integer(validate=validate.Range(1, 4))\n\n    @validates_schema\n    def check_min_less_than_max(self, data):\n        if data['min'] >= data['max']:\n            message = \"The Max must be larger than the min\"\n            raise ValidationError({\n                'min': message,\n                'max': message\n            })\n\n    class Meta:\n        model = ParameterModel\n\n\n# Resource to handle HTTP interactions\n@register_resource('/parameters')\nclass Parameters(Resource):\n\n    @schema_out(ParameterSchema())\n    def get(self, id):\n        return ParameterModel.find_one(id)\n\n    @schema_in_out(ParameterSchema(exclude=['id']), ParameterSchema())\n    def post(self, data):\n        return ParameterModel.save(data)\n\n    @schema_in_out(ParameterSchema(), ParameterSchema())\n    def put(self, data):\n        data = ParameterSchema(strict=True).load(data,\n                                             instance=ParameterModel.find_one(id),\n                                             session=ParameterModel.query.session,\n                                             partial=True)\n        ParameterModel.query.session.add(data)\n        ParameterModel.query.session.commit()\n\n","repo_name":"InstituteforDiseaseModeling/rse_api","sub_path":"tests/examples/flat_example.py","file_name":"flat_example.py","file_ext":"py","file_size_in_byte":2422,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"5488400093","text":"#! python3\n##0\n## 0                000000   0         000000     0  0         000000000   00000000    00000000   0          000000\n##  0              00     0  0         0           00             00       0      0    0      0   0          0\n##   0             00     0  0         00000       00   000000    00       0      0    0      0   0          00000\n##    0            00     0  0         0          0  0            00       0      0    0      0   0              0\n##     0000000      000000   0000000   000000    0    0           00       00000000    00000000   0000000   000000\n##\n## authors = Monkeyboy2805\nimport os, time, py_compile, shutil, sys, platform, threading, subprocess, random\nprint (\"Module LolexToolsMethods is running, using modules os, time, py_compile, shutil, sys, platform, threading.\")\ns = os.sep\nsys.path.insert(0, \"./project/old/\")\nif platform.system() == \"Windows\":\n    if sys.version_info.minor > 5:\n        py = \"py .\" + os.sep\n        pyo = \"py\"\n    else:\n        py = \"python .\" + os.sep\n        pyo = \"python\"\nelif platform.system() == \"Linux\":\n    py = \"python3 \" + os.sep\n    pyo = \"python3\"\nstopping = False\nclass uos:\n    useros = platform.system()\n    if os.path.exists(\"/system/build.prop\") == True and not(os.path.isdir(\"/system/build.prop\")) and platform.system() == \"Linux\":\n        useros = \"Android\"\nclass threads:\n    shutdown = False\n    logoff = False\n    restart = False\n    hibernate = False\ntry:\n    import restartsettings, logoffsettings, hibernatesettings, exitsettings, shutdownsettings, menusettings, LolexToolsOptions, theme\nexcept(ImportError) as e:\n    print((str(e)) + \" occured.\")\n    time.sleep(1)\ntry:\n    import ver\nexcept(ImportError) as e:\n    print(\"Please redownload this repository to access all features.\")\n    print(e)\n    time.sleep(5)\nclass title_updater:\n    threads = 2\n    notificationsmsg = []\n    notificationsdelay = []\n    a = round(time.time(), 0)\n    units = [\"weeks\", \"days\", \"hours\", \"minutes\", \"seconds\"]\n    months = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]\n    month_unit = [3, 0, 3, 2, 3, 2, 3, 3, 2, 3, 2, 3]\n    start_month_str = time.asctime(time.localtime(time.time())).split(\" \")[1]\n    start_month = -1\n    for i in range(0, len(months) - 1):\n        if start_month_str == months[i]:\n            start_month = i - 1\n            break\n    if uos.useros == \"Windows\":\n        title_cmd_start = \"TITLE \"\n        title_cmd_end = \"\"\n    elif uos.useros == \"Linux\" or uos.useros == \"Android\":\n        title_cmd_start = \"\\x1b]2;\"\n        title_cmd_end = \"\\x07\"\ndef get_current_datetime():\n    a = time.asctime(time.localtime(time.time()))\n    a = a.split(\" \")\n    b = a[3] + \" \"\n    del a[3]\n    for i in range(0, 4):\n        b = b + a[i] + \" \"\n    return b\ndef titleUpdater():\n    start = title_updater.a\n    end = title_updater.a\n    load = 0\n    global stopping\n    while stopping != True:\n        if stopping == True:\n            break\n        if len(title_updater.notificationsmsg) == 0:\n            start = time.time()\n            #os.system(\"TITLE Lolex-Tools|    \" + (str(title_updater.threads)) + \" threads|  Uptime: \" + (str(round(time.time(), 0) - title_updater.a)) + \" seconds\" + (str(time.localtime(time.asctime(time.time())))))\n            newtitle = title_updater.title_cmd_start + \" Lolex-Tools    \" + (str(title_updater.threads)) + \" threads  Uptime: \" #+ (str(round(time.time(), 0) - title_updater.a)) + \" seconds    \" + (str(time.time()))\n            #print(round(time.time(), 0) - title_updater.a)\n            new_time = round(time.time(), 0) - title_updater.a\n            if type(new_time) is float or type(new_time) is int:\n                timer = convert_time_all(True, seconds = new_time)\n            else:\n                timer = \"Sorry! There was a temporary error with the timer.\"\n            #print(timer)\n            #t = len(timer) - 1\n            #while t != -1:\n                #newtitle = newtitle[t] + (str(timer[t]) + \"\" + (title_updater.units[t]))\n                #print(newtitle)\n                #t = t - 1\n            #for i in range(0, len(timer) - 1):\n                    #j = len(timer) - i - 1\n                    #if timer[j] != 0:\n                            #newtitle = newtitle + str(timer[i]) + \"\" + (str(title_updater.units[i]))\n            newtitle = newtitle + (str(timer))\n            #newtitle = newtitle + title_updater.title_cmd_end\n            newtitle = newtitle + \"    \" + (str(get_current_datetime())) + \"    Load: \" + (str(load)) + \"%\" + title_updater.title_cmd_end\n            # FIX THE LOAD THING ...\n            if uos.useros == \"Linux\" or uos.useros == \"Android\":\n                    sys.stdout.write(newtitle)\n            else:\n                    os.system((str(newtitle)))\n            time.sleep(1)\n            end = time.time()\n            if type(load) is not str:\n                load = round((float((end - start - 1) * 100)), 2)\n                if load > 100:\n                    send_notification(\"WARNING: Overloading. Took \" + str(round(float(load - 100)/100), 2) + \" seconds too long too tick.\", 3)\n                    load = 100\n        else:\n            if uos.useros == \"Linux\" or uos.useros == \"Android\":\n                sys.stdout.write(title_updater.title_cmd_start + (str(title_updater.notificationsmsg[0])) + title_updater.title_cmd_end)\n            else:\n                os.system(title_updater.title_cmd_start + (str(title_updater.notificationsmsg[0])) + title_updater.title_cmd_end)\n            time.sleep(title_updater.notificationsdelay[0])\n            del title_updater.notificationsdelay[0]\n            del title_updater.notificationsmsg[0]\n    if uos.useros == \"Linux\" or uos.useros == \"Android\":\n        sys.stdout.write(\"\\x1b]2;Lolex-Tools: EXITING...\\x07\")\n    else:\n        os.system(\"TITLE Lolex-Tools: EXITING...\")\ndef _init_():\n        title_thread = threading.Thread(target = titleUpdater, args = [])\n        title_thread.start()\ndef version():\n        print(ver.version)\ndef convert_time_all(rtstr, seconds = 0, minutes = 0, hours = 0, days = 0, weeks = 0, start_month = title_updater.start_month, months = 0):\n    # Have a look into datetime for this\n    string_out = \"\"\n    if seconds >= 60:\n        minutes = minutes + seconds//60\n        seconds = seconds%60\n    if minutes >= 60:\n        hours = hours + minutes//60\n        minutes = minutes%60\n    if hours >= 24:\n        days = days + hours//24\n        hours = hours%24\n    if days >= 7:\n        weeks = weeks + days//7\n        days = days%7\n    #while weeks >= 4: #and days >= title_updater.month_unit[(start_month + (months%12))%12]:\n        #weeks = weeks - 4\n        # Based on a month being 4 weeks\n        #days = days - title_updater.month_unit[(start_month + (months%12))%12]\n        #months = months + 1\n    if rtstr == True:\n        #if months != 0:\n            #if months != 1:\n               #string_out = string_out + (str(months)) + \" months \"\n            #else:\n                #string_out = string_out + (str(months)) + \" month \"\n        if weeks != 0:\n            if weeks != 1:\n                string_out = string_out + (str(weeks)) + \" weeks \"\n            else:\n                string_out = string_out + (str(weeks)) + \" week \"\n        if days != 0:\n            if days != 1:\n                string_out = string_out + (str(days)) + \" days \"\n            else:\n                string_out = string_out + (str(days)) + \" day \"\n        if hours != 0:\n            if hours != 1:\n                string_out = string_out + (str(hours)) + \" hours \"\n            else:\n                string_out = string_out + (str(hours)) + \" hour \"\n        if minutes != 0:\n            if minutes != 1:\n                string_out = string_out + (str(minutes)) + \" minutes \"\n            else:\n                string_out = string_out + (str(minutes)) + \" minute \"\n        if seconds != 0:\n            if seconds != 1:\n                string_out = string_out + (str(seconds)) + \" seconds \"\n            else:\n                string_out = string_out + (str(seconds)) + \" second \"\n        string_out = string_out.replace(\".0\", \"\")\n        return string_out\n    #return months,\n    return weeks, days, hours, minutes, seconds\ndef send_notification(msg, delay):\n    title_updater.notificationsmsg.append(\"Lolex-Tools NOTIFICATION: \" + (str(msg)))\n    title_updater.notificationsdelay.append(delay)\ndef str_to_int(string):\n    try:\n        a = int(string)\n    except(ValueError):\n        return False\n    return True\ndef flicker():\n    a = input(\"Are you sure you wish to continue? 1 (yes) or 0 (no).Please don't continue if you have epilepsy.\")\n    if str_to_int(a) == True and int(a) == 1:\n        howlongtoflashfor = input(\"How many loops do you wish to occur?\")\n        if str_to_int(howlongtoflashfor) == True:\n            howlongtoflashfor = int(howlongtoflashfor)\n        else:\n            howlongtoflashfor = random.randint(1, 1000)\n            print(\"TYPE of loops was not a number, selected \" + (str(howlongtoflashfor)))\n    print(\"Here is a list of colours available:\")\n    print(\"a - Neon Green\")\n    print(\"b - Light Blue\")\n    print(\"c - Neon Red\")\n    print(\"d - Light Purple/Pink\")\n    print(\"e - Neon Yellow\")\n    print(\"f - White\")\n    print(\"1 - Dark Blue\")\n    print(\"2 - Dark Green\")\n    print(\"3 - Light Non-Neon Blue\")\n    print(\"4 - Dark Red/Brown\")\n    print(\"5 - Dark Purple\")\n    print(\"6 - Non Neon Yellow\")\n    print(\"7 - White/Light Gray\")\n    print(\"8 - Dark Gray\")\n    print(\"9 - Dark Neon Blue\")\n    done = \"0\"\n    colour = []\n    while done != \"1\":\n        colour.append(\"color \" + input(\"Please enter your colour \"))\n        done = input(\"Please enter 1 if you are finished adding colours.\")\n    #colour = (\"color \" + input(\"Please enter your first colour \"), \"color \" + input(\"Please enter your second colour \"), \"color \" + input(\"Please enter your third colour \"), \"color \" + input(\"Please enter your fourth colour \"), \"color \" + input(\"Please enter your fifth colour  \"))\n    flickerthread = threading.Thread(target = flickerp2, args = [colour, howlongtoflashfor])\n    flickerthread.start()\ndef flickerp2(colour, howlongtoflashfor):\n    currentflashes = int(0)\n    global stopping\n    while howlongtoflashfor != currentflashes:\n        if stopping == True:\n            break\n        for i in range(0, len(colour) - 1):\n            os.system(colour[i])\n            print(\"STARTED\")\n            pass\n##        os.system (colour[0])\n##        pass\n##        os.system (colour[1])\n##        pass\n##        os.system (colour[2])\n##        pass\n##        os.system (colour[3])\n##        pass\n##        os.system (colour[4])\n##        pass\n        currentflashes = currentflashes + 1\n    os.system(theme.theme)\ndef windowspage(page, layout):\n    if page == 0:\n        print(\"1  = Settings\")\n        if restartsettings.hidden == False:\n            print(\"2  = Restart\")\n        if logoffsettings.hidden == False:\n            print(\"3  = Logoff\")\n            print(\"4  = Alternative logoff method\")\n        if hibernatesettings.hidden == False:\n            print(\"5  = Hibernate\")\n    elif page == 1:\n        if shutdownsettings.hidden == False:\n            print(\"6  = Shutdown\")\n            print(\"7  = Alternative shutdown method\")\t\t \n        print(\"8  = Colour Flicker\")\n        print(\"9  = Call CMD\")\n        print(\"10 = Call documents\")\n    elif page == 2:\n        print(\"11 = Call a Python shell\")\n        print(\"12 = Call Task Manager\")\n        print(\"13 = Create folders\")\n        print(\"14 = Remove folders\")\n        print(\"15 = Create files\")\n    elif page == 3:\n        print(\"16 = Restart this script\")\n        print(\"17 = Perform arithmetic operations\")\n        print(\"18 = Call Remote Desktop\")\n        print(\"19 = Call Powershell\")\n        print(\"20 = Print SystemInfo\")\n    elif page == 4:\n        print(\"21 = Start Installer\")\n        if exitsettings.hidden == False:\n            print(\"22 = Exit\")\n    elif page == 5:\n        print(\"EXPERIMENTAL FEATURES:\")\n        #print(\"23 = Update\")\n        print(\"23 = Start File Explorer\\n\")\n    if layout == 1:\n        print(\"24 = Next Page\")\n        print(\"25 = Back a Page\")\n    else:\n        if page < 5:\n            page = page + 1\n        else:\n            return;\n        windowspage(page, layout)\ndef linuxpage(page, layout):\n    if page == 0:\n        print(\"1  = Settings\")\n        if restartsettings.hidden == False:\n            print(\"2  = Restart\")\n        if logoffsettings.hidden == False:\n            print(\"3  = Logoff\")\n        if hibernatesettings.hidden == False:\n            print(\"4  = Hibernate\")\n        if shutdownsettings.hidden == False:\n            print(\"5  = Shutdown\")\n    elif page == 1:\n        print(\"6  = Call a Python Shell\")\n        print(\"7  = Create folders\")\n        print(\"8  = Remove folders\")\n        print(\"9  = Create files\")\n        print(\"10 = Restart this script\")\n    elif page == 2:\n        print(\"11 = Perform arithmetic operations\")\n        print(\"12 = Start Installer\")\n        print(\"13 = Show Uptime and Average load\")\n        print(\"14 = Print SystemInfo\")\n        if exitsettings.hidden == False:\n            print(\"15 = Exit\")\n    elif page == 3:\n        print(\"EXPERIMENTAL FEATURES:\")\n        print(\"16 = Start File Explorer\\n\")\n    if layout == 1:\n        print(\"17 = Next Page\")\n        print(\"18 = Back a Page\")\n    else:\n        if page < 3:\n            page = page + 1\n        else:\n            return;\n        linuxpage (page, layout)\ndef androidpage(page, layout):\n    if page == 0:\n        print(\"1 = Settings\")\n        if restartsettings.hidden == False:\n            print(\"2 = Restart\")\n        if shutdownsettings.hidden == False:\n            print(\"3 = Shutdown\")\n        print(\"4 = Call a Python Shell\")\n        print(\"5 = Create folders\")\n    elif page == 1:\n        print(\"6 = Remove folders\")\n        print(\"7 = Create files\")\n        print(\"8 = Restart this script\")\n        print(\"9 = Perform arithmetic operations\")\n        print(\"10 = Start Installer\")\n    elif page == 2:\n        print(\"11 = Show Uptime and Average load\")\n        print(\"12 = Print SystemInfo\")\n        if exitsettings.hidden == False:\n            print(\"13 = Exit\")\n        print(\"EXPERIMENTAL FEATURES:\")\n        print(\"14 = Start File Explorer\\n\")\n    if layout == 1:\n        print(\"15 = Next Page\")\n        print(\"16 = Back a Page\")\n    else:\n        if page < 2:\n            page = page + 1\n        else:\n            return;\n        androidpage(page, layout)\ndef restart():\n    restart_confirm = input(\"Please enter 1 to confirm restart.\")\n    if restart_confirm == \"1\":\n        waittime = float(input(\"How long, in minutes do you wish to wait?\"))\n        while waittime < 0:\n            waittime = float(input(\"Please select a time bigger than 0 minutes.\\nHow long, in minutes, do you wish to wait?\"))\n        restartthreader = threading.Thread(target = restartthread, args = [waittime])\n        restartthreader.start()\ndef logoff(type_of):\n    logoff_confirm = input(\"Please enter 1 to confirm logoff.\")\n    if logoff_confirm == \"1\":\n        waittime = float(input(\"How long, in minutes, do you wish to wait?\"))\n        while waittime < 0:\n            waittime = float(input(\"Please select a time bigger than 0 minutes.\\nHow long, in minutes, do you wish to wait?\"))\n        loggeroff = threading.Thread(target = logoffthread, args = [waittime, type_of])\n        loggeroff.start()\ndef logoffthread(waittime, type_of):\n    if threads.logoff == True:\n        print(\"LOGOFF thread: Logoff already scheduled!\")\n        return;\n    threads.logoff = True\n    waittime = waittime * 60\n    while waittime > 0.1 and stopping == False:\n        time.sleep(0.1)\n        waittime = waittime - 0.1\n    if stopping != True:\n        print(\"LOGOFF thread: Logging off...\")\n        if uos.useros != \"Linux\":\n            if type_of == 0:\n                os.system(\"shutdown -l -f\")\n            else:\n                subprocess.Popen(\"logoff.exe\")\n        else:\n            os.system(\"gnome-session-quit --force\")\n    else:\n        print(\"LOGOFF thread: Stopping...\")\n    threads.logoff = False\ndef hibernate():\n    hibernate_confirm = input(\"Please enter 1 to confirm logoff.\")\n    if hibernate_confirm == \"1\":\n        waittime = float(input(\"How long, in minutes, do you wish to wait?\"))\n        while waittime < 0 and waittime > 65505:\n            waittime = float(input(\"Please select a time between 0 - 65505 minutes.\\nHow long, in minutes, do you wish to wait?\"))\n        hibernatethreader = threading.Thread(target = hibernatethread, args = [waittime])\n        hibernatethreader.start()\ndef hibernatethread(waittime):\n    if threads.hibernate == True:\n        print(\"HIBERNATE thread: Hibernate already scheduled!\")\n        return;\n    threads.hibernate = True\n    waittime = waittime * 60\n    while waittime > 0.1 and stopping == False:\n        time.sleep(0.1)\n        waittime = waittime - 0.1\n    if stopping != True:\n        print(\"HIBERNATE thread: Hibernating...\")\n        if uos.useros == \"Windows\":\n            os.system(\"shutdown -h -f\")\n        elif uos.useros == \"Linux\":\n            os.system(\"systemctl suspend\")\n    else:\n        print(\"HIBERNATE thread: Stopping...\")\n    threads.hibernate = False\ndef restartthread(waittime):\n    if threads.restart == True:\n        print(\"RESTART thread: restart already scheduled!\")\n        return;\n    threads.restart = True\n    waittime = waittime * 60\n    while waittime > 0.1 and stopping == False:\n        time.sleep(0.1)\n        waittime = waittime - 0.1\n    if stopping != True:\n        print(\"RESTART thread: Restarting device...\")\n        if uos.useros != \"Linux\":\n            os.system(\"shutdown -r -f\")\n        else:\n            if uos.useros == \"Android\":\n                if os.system(\"su -c reboot\") != 0:\n                    os.system(\"reboot\")\n            else:\n                    os.system(\"systemctl reboot -i\")\n    else:\n        print(\"RESTART thread: Stopping...\")\n    threads.restart = False\ndef shutdown(type_of):\n    shutdown_confirm = input(\"Please enter 1 to shutdown.\")\n    if shutdown_confirm == \"1\":\n        waittime = float(input(\"How long, in minutes, do you wish to wait?\"))\n        while waittime < 0 or waittime > 65505:\n            waittime = float(input(\"Please select a time between 0- 65505 minutes.\\nHow long, in minutes, do you wish to wait?\"))\n        shutdownthreader = threading.Thread(target = shutdownthread, args = [waittime, type_of])\n        shutdownthreader.start()\ndef shutdownthread(waittime, type_of):\n    if threads.shutdown == True:\n        print(\"SHUTDOWN thread: Shutdown already scheduled!\")\n        return;\n    threads.shutdown = True\n    waittime = waittime * 60\n    while waittime > 0.1 and stopping == False:\n        time.sleep(0.1)\n        waittime = waittime - 0.1\n    if stopping != True:\n        print(\"SHUTDOWN thread: shutting device down...\")\n        if uos.useros == \"Windows\":\n            if type_of == 0:\n                os.system (\"shutdown -s -f\")\n            elif type_of == 1:\n                subprocess.Popen(\"shutdown.exe\")\n        elif uos.useros == \"Linux\":\n            os.system(\"poweroff\")\n        elif uos.useros == \"Android\":\n            if os.system(\"su -c reboot -p\") != 0:\n                if os.system(\"/system/bin/reboot -p\") != 0:\n                    print(\"Failed to execute reboot binary.\")\n    else:\n        print(\"SHUTDOWN thread: Stopping...\")\n    threads.shutdown = False\ndef pyshell():\n    if uos.useros == \"Android\" or uos.useros == \"Linux\":\n        os.system(\"python3\")\n    elif uos.useros == \"Windows\":\n        option = input(\"Please enter 1 for the Python Shell or 0 for the IDLE shell.\")\n        if option == \"0\":\n            subprocess.call(pyo + \".exe\")\n        elif option == \"1\":\n            subprocess.Popen(pyo + \"w.exe\")\ndef scriptrestart():\n    global stopping\n    confirmscriptrestart = input(\"Please input 1 to confirm restarting of this script. Please enter 2 to start into the new version of the project.\")\n    if confirmscriptrestart == \"1\":\n        stopping = True\n        os.system(py + \"project/old/start.py\")\n        os._exit(0)\n    elif confirmscriptrestart == \"2\":\n        stopping = True\n        os.system(py + \"project/new/start.py\")\n        os._exit(0)\ndef numops():\n    print(\"Here is a list of operations:\")\n    print(\"1 = Add\")\n    print(\"2 = Take\")\n    submode = int(input(\"Please enter the number of the operation you wish to perform.\"))\n    if submode == 1 or submode == 2:\n        addortake()\ndef addortake():\n    startnum = int(input(\"Please enter your starting number.\"))\n    addortakenum = int(input(\"Please input the number to be added.\"))\n    endnum = int(input(\"Please enter your end number.\"))\n    waittime = float(input(\"How many seconds do you wish to wait before each operation is performed?\"))\n    while waittime > 4194304 or waittime < 0:\n        waittime = float(input(\"Please select a time in between 0 and 4194304 seconds.\\nHow long, in minutes do you wish to wait?\"))\n    if endnum > startnum:\n        while endnum > startnum:\n            print(startnum)\n            if addortakenum < int(0):\n                startnum = startnum - addortakenum\n            elif addortakenum > int(0):\n                startnum = startnum + addortakenum\n            time.sleep(waittime)\n    if startnum > endnum:\n        while startnum > endnum:\n            print (startnum)\n            if addortakenum < 0:\n                startnum = startnum + addortakenum\n            if addortakenum > 0:\n                startnum = startnum = startnum - addortakenum\n            time.sleep(waittime)\n    print (\"The closest number to your target end number was:\" + (str(startnum)))\n    time.sleep(1)\ndef dumpme():\n    if uos.useros == \"Windows\":\n        os.system(r\".\\project\\old\\resources\\systeminf\")\n    elif uos.useros == \"Linux\":\n        os.system(\"sudo lshw\")\n    else:\n        if os.system(\"su -c dumpsys\") != 0:\n            print(\"Cannot load as much information due to lack of root.\")\n            if os.system(\"/system/bin/dumpsys\") != 0:\n                            print(\"Failed to execute dumpsys binary. Please check your root and SELinux statuses.\")\ndef enterinstall():\n    confirm = int(input(\"Please confirm (with a 1) to enter the installer.\"))\n    if confirm == 1:\n        global stopping\n        stopping = True\n        os.system(py + \"project\" + s + \"old\" + s + \"setup\" + s + \"generic\" + s + \"LolexToolsInstaller.py\")\n        exit(0)\ndef uptime():\n    if uos.useros == \"Android\":\n        if os.system(\"su -c uptime\") != 0:\n            if os.system(\"/system/bin/uptime\") != 0:\n                print(\"Failed to run uptime script.\")\n    else:\n        os.system(\"uptime\")\ndef compiler(name):\n    try:\n        os.remove(\"./project/old/\" + name + \".pyc\")\n    except(IOError):\n        pass\n    py_compile.compile(name + \".py\", \"./project/old/\" + name + \".pyc\")\n    os.remove(\"./project/old/\" + name + \".py\")\ndef modehide(name, state):\n    if state == False:\n        newstate = True\n    else:\n        newstate = False\n    try:\n        os.remove(\"./project/old/\" + name + \".py\")\n        os.remove(\"./project/old/\" + name + \".pyc\")\n    except(IOError):\n        pass\n    with open (\"./project/old/\" + name + \"settings.py\",\"a\") as outf: \n        outf.write(\"hidden = \")\n        outf.write(str(newstate))\ndef bak(name, path, reinstall, attrestore, regenerate):\n    import LolexToolsOptions\n    try:\n        os.remove(path + name + \".py\")\n    except(IOError, OSError):\n        pass\n    try:\n        os.remove(path + name + \".pyc\")\n    except(IOError, OSError):\n        pass\n    if regenerate == 1 and name == \"LolexToolsOptions\":\n        attrestore = 1\n        regenerate = 0\n        reinstall = 0\n    elif name == \"verifonboot\" or name == \"theme\" or name == \"startplugins\" or \"settings\" in name:\n        regenerate = 1\n        attrestore = 0\n        reinstall = 0\n    if \"settings\" in name:\n        if name != \"menusettings\":\n            with open (path + name + \".py\", \"a\") as outf: outf.write(\"hidden = False\")\n        else:\n            with open (path + \"menusettings.py\", \"a\") as outf: outf.write(\"layout = 0\")\n        if LolexToolsOptions.compiler == True:\n            compiler(path + name + \".py\")\n        #os.system(py + s + \"main\" + s + \"LolexTools.py\")\n        #exit(None)\n    elif name == \"verifonboot\":\n        import LolexToolsOptions\n        if LolexToolsOptions.onepintotal > 1:\n            oneswappins = True\n        else:\n            oneswappins = False\n        if LolexToolsOptions.onewordtotal > 1:\n            oneswapwords = True\n        else:\n            oneswapwords = False\n        if LolexToolsOptions.twopintotal > 1:\n            twoswappins = True\n        else:\n            twoswappins = False\n        if LolexToolsOptions.twowordtotal > 1:\n            twoswapwords = True\n        else:\n            twoswapwords = False\n        with open (\"./project/old/verifonboot.py\", \"a\") as outf:\n            outf.write(\"compiledon = \")\n            outf.write(str(ver.version))\n            outf.write(\"\\nruntimeone = 0\\nruntimetwo = 0\\nwordtimeone = 0\\nwordtimetwo = 0\")\n            outf.write(\"\\noneswappins = \")\n            outf.write(str(oneswappins))\n            outf.write(\"\\noneswapwords = \")\n            outf.write(str(oneswapwords))\n            outf.write(\"\\ntwoswappins = \")\n            outf.write(str(twoswappins))\n            outf.write(\"\\ntwoswapwords = \")\n            outf.write(str(twoswapwords))\n    elif name == \"theme\":\n        # will add theme changing at some point\n        with open (\"./project/old/theme.py\", \"a\") as outf: pass\n    elif name == \"startplugins\":\n        # will add ability to add plugins at some point\n        with open (\"./project/old/startplugins.py\", \"a\") as outf: pass\n    if name == \"verifonboot\" or name == \"theme\" or name == \"startplugins\":\n        if LolexToolsOptions.compiler == True:\n            compiler(name + \".py\")\n        # os.system(py + \"main\" + s + \"LolexTools.py\")\n        #exit(None)\n    elif attrestore == 1:\n        backup = os.listdir(\"./project/old/Backup\")\n        arraypos = 0\n        found = False\n        while arraypos < len(backup):\n            currsub = os.listdir(backup[arraypos])\n            tarraypos = 0\n            while tarraypos < len(backup):\n                if name + \".pyc\" in currsub[tarraypos] and (\".pycnotpy\" + (str(sys.version_info[0])) + (str(sys.version_info[1])) in currsub[tarraypos]) == False:\n                    found = True\n                    os.rename(\"./project/old/Backup\" + backup[arraypos] + cursub[tarraypos], \"./project/old/\" + name + \".pyc\")\n                    if LolexToolsOptions.compiler == True:\n                        compiler(name + \".py\")\n                    #os.system(py + \"main\" + s + \"LolexTools.py\")\n                    #exit(None)\n        if found == False:\n            os.system(py + \"project\" + s + \"old\" + s + \"setup\" + s + \"generic\" + s + \"LolexToolsInstaller.py\")\n            exit(0)\ndef dirdisc(rtfiles, rtfolders, stpath, validator):\n    stpath = correctpath(stpath)\n    folders = [stpath]\n    files = []\n    arraypos = 0\n    while arraypos < len(folders):\n        path = correctpath(folders[arraypos])\n        if validate(path) == True:\n            cont = os.listdir(path)\n        else:\n            cont = []\n        tarraypos = 0\n        while tarraypos < len(cont):\n            if validator == True:\n                filev = validatefile(path + cont[tarraypos])\n                folderv = validate(path + cont[arraypos])\n            else:\n                filev = os.path.isfile(path + cont[tarraypos])\n                folderv = os.path.isdir(path + cont[tarraypos])\n            if filev == True:\n                files.append(path + cont[tarraypos])\n            if folderv == True:\n                folders.append(path + cont[tarraypos] + \"/\")\n            if folderv == True and filev == True:\n                for i in range(0, len(cont) - 1):\n                    if cont[i] == cont[tarraypos] and i != tarraypos:\n                        del cont[i]\n                        break;\n            tarraypos = tarraypos + 1\n        arraypos = arraypos + 1\n    if rtfiles == 1 and rtfolders == 1:\n        folders.append(\"END_OF_ARRAY<>\")\n        return folders + files;\n    elif rtfiles == 1:\n        return files;\n    elif rtfolders == 1:\n        return folders;                                                                                                           \ndef correctfile(path):\n    print(path)\n    slash = \"\\\\\"\n    if path[0] == \".\" and path[1] == \"/\":\n        tlen = 2\n        path = correctpath(os.getcwd()) + path[:midlen] + path[midlen + 2:]\n    if len(path) > 2:\n        path = path.replace(slash[0], \"/\")\n        path = path.replace(\"//\", \"/\")\n        print(2, path)\n    if path[len(path) - 1] == \"/\":\n        path = path[:len(path) - 1]\n        print(3, path)\n    return path;\ndef validatefile(path):\n    if validate(path) == True:\n        return \"WrongFunction\";\n    path = correctfile(path)\n    class a:\n        a = path\n    valid = True\n    try:\n        open(a.a, \"a\")\n    except(IOError, OSError) as e:\n        print(e)\n        valid = False\n    return valid;\ndef correctpath(path):\n    slash = \"\\\\\"\n    class zz:\n        p = path\n    if len(zz.p) > 2:\n        zz.p = zz.p.replace(slash[0], \"/\")\n        if zz.p[0] == \".\" and zz.p[1] == \"/\":\n            tlen = 2\n            zz.p = correctpath(os.getcwd()) + zz.p[:midlen] + zz.p[midlen + 2:]\n    if len(zz.p) != 0:\n        if zz.p[len(zz.p) - 1] != \"/\":\n            zz.p = zz.p.replace(zz.p, zz.p + \"/\")\n        if len(zz.p) > 2:\n            for i in range(0, len(zz.p) - 1):\n                            zz.p = zz.p.replace(\"//\", \"/\")\n    return zz.p;\ndef validate(path):\n    path = correctpath(path)\n    class v:\n        p = path\n    readin = True\n    try:\n        os.path.isdir(v.p)\n    except(IOError, OSError):\n        readin = False\n    if readin == True and not os.path.isdir(v.p):\n        readin = False\n    elif readin == True:\n        try:\n            os.listdir(v.p)\n        except(IOError, OSError):\n            readin = False\n    return readin;\nclass expl:\n    path = \"/\"\n    newpath = \"/\"\n    file = \"\"\n    loaded = False\n    if (platform.system() != \"Windows\" and platform.system() != \"Linux\"):\n        clear = \"<>\"\n    elif uos.useros == \"Windows\":\n        clear = \"cls\"\n    elif platform.system() == \"Linux\":\n        clear = \"clear\"\ndef loading(text1):\n    start = int(0)\n    while expl.loaded == False:\n        print(\"\\n\\n\\n\\n\\n\\n                      \" + (str(text1)) + \"\\n          (\" + (str(start)) + \") seconds elapsed.\")\n        time.sleep(1)\n        os.system(expl.clear)\n        start = int(start) + int(1)\n        if expl.loaded == True:\n            return;\n        time.sleep(5)\ndef explorer(tofinishop, rtnofiles, rtnofolders, otext, path, allowexit):\n    expl.path = path\n    file = 0\n    auto = 0\n    if expl.clear == \"<>\":\n            return 1;\n    while True:\n        expl.loaded = False\n        o = False\n        op = False\n        expl.path = correctpath(expl.path)\n        y = validate(expl.path)\n        if y == False:\n            auto = 1\n        if expl.path == \"/\":\n            expl.path = os.getcwd()[:2] + \"/\"\n            print(expl.path)\n            a = input(\"DEBUG_1\")\n            expl.file = \"..\"\n        if auto == 0:\n            os.system(expl.clear)\n        print(\"\\n \" + expl.path + \"\\n\")\n        if otext == 0:\n            otext = \"Start/finish operations on this folder\"\n        if allowexit == 0:\n            exittext = \"You cannot exit until the current operation is complete\"\n        else:\n            exittext = \"Exit file explorer\"\n        loadingthread = threading.Thread(target=loading, args=[\"Loading: Large directories may take a while...\"])\n        loadingthread.start()\n        array = os.listdir(expl.path)\n        expl.loaded = True\n        time.sleep(1.5)\n        print(\"\\n\\n///o - \" + otext + \"\\n./project/old/ - Go to the CWD\\n/// - Reload\\n///? - Help\\n.. - Up a level\\n///exit - \" + exittext + \"\\n///s - Search for files/folders in this directory\")\n        for i in range(0, len(array)):\n            if i == len(array):\n                break\n            if len(array[i]) > 2:\n                if array[i][0] != \"~\" and array[i][1] != \"$\":\n                    print(array[i])\n        expl.file = input(\"\\nSelect/Open (Name): \")\n        expl.file = expl.file.lower()\n        if expl.file == \"///\":\n            pass\n        if expl.file == \"..\" or auto == 1 or expl.path == \"/\":\n            ## SORT OUT SO IT LOOKS LIKE A REAL PATH AT SOME POINT\n            if len(expl.path) > 1 and \"/\" in expl.path and not (len(expl.path) == 3 and expl.path[2] == \"/\" and expl.path.count(\"/\") == 1):\n                new = expl.path.split(\"/\")\n                del new[len(new) -1]\n                actual = new[0]\n                if len(new) > 1:\n                    for i in range(0 , len(new) - 1):\n                        actual = \"/\" + new[i]\n                    expl.path = actual\n                elif expl.path == \"/\" or (len(expl.path) == 3 and expl.path[2] == \"/\" and expl.path.count(\"/\") == 1):\n                    os.system(expl.clear)\n                    real = False\n                    op = False\n                    o = False\n                    while real != True:\n                        ## FIX THIS FOR EXITING\n                        currentdrives = []\n                        for i in range(ord(\"A\"), ord(\"Z\")):\n                            success = True\n                            try:\n                                    os.listdir(chr(i) + \":/\")\n                            except(IOError):\n                                    success = False\n                            if success == True:\n                                    currentdrives.append(chr(i) + \":/\")\n                        for j in range(0, len(currentdrives) - 1):\n                            print(currentdrives[j])\n                        expl.path = input(\"Please input your drive letter.\")\n                        for k in range(0, len(currentdrives) - 1):\n                            if expl.path == currentdrives[k]:\n                                    real = True\n                                    break;\n                else:\n                    actual = \"/\"\n                    expl.path = actual\n                if expl.path == \"/\" or len(expl.path) < 3:\n                    actual = \"/\"\n                    expl.path = actual\n                if auto != 0:\n                    auto = 0\n        if expl.file == \"///?\":\n            print(\"Type names from here to enter/select files and folders.\")\n            time.sleep(5)\n        elif expl.file == \"///exit\":\n            if allowexit == 1:\n                return 1;\n            else:\n                print(\"Operation not completed!\")\n                time.sleep(5)\n        elif expl.file != \"///s\" and expl.file != \"///o\" and expl.file != \"..\" and expl.file != \"./project/old/\":\n            o = False\n            found = 0\n            arraypos = 0\n            possibles = []\n            if validate(expl.path) == False:\n                auto = 1\n            else:\n                array = os.listdir(expl.path)\n                arraylen = len(array)\n                while arraypos < arraylen:\n                    if array[arraypos].lower() == expl.file:\n                        possibles.append(array[arraypos])\n                        found = found + 1\n                    arraypos = arraypos + 1\n                if found == 2:\n                    if tofinishop == 1 or rtnofiles == 1:\n                        which = 1\n                    elif rtnofolders == 1:\n                        which = 0\n                    else:\n                        which = int(input(\"Please enter 1 for the folder, 0 for the file.\"))\n                    while which != 1 or which != 0:\n                            which = which - 2\n                    del possibles[which]\n                    found = 1\n                if found == 1:\n                    expl.newpath = expl.path + possibles[0]\n                    if validate(expl.path) == False:\n                        auto = 1\n                    else:\n                        file = 0\n                    if os.path.isdir(expl.newpath) == False:\n                        file = 1\n                        try:\n                            open(expl.newpath, \"a\")\n                        except(IOError, OSError):\n                            file = 2\n                    else:\n                        try:\n                            os.listdir(expl.newpath + \"/\")\n                        except(IOError, OSError):\n                            file = 3\n                        if file != 0 and tofinishop == 1:\n                            file = 4\n                            print(\"Please select a (valid) folder to complete the operation.\")\n                            time.sleep(3)\n                        if file == 2:\n                            print(\"Could not access file!\")\n                        elif file == 3:\n                            print(\"Could not access folder!\")\n                            time.sleep(3)\n                        elif file == 1:\n                            if rtnofiles != 1:\n                                if rtnofiles == -1:\n                                    return [expl.path, possibles[0]]\n                                return expl.newpath;\n                            o = True\n                        elif file == 0:\n                            expl.newpath = correctpath(expl.newpath)\n                            expl.path = expl.newpath\n                            if tofinishop == True or rtnofolders == 0:\n                                    return expl.path;\n                if found == 0 or \"~$\" in expl.file:\n                    print(\"No such file or directory found!\")\n                    file = 5\n        elif expl.file == \"///s\" or expl.file == \"///o\":\n            o = True\n        expl.newpath = expl.path\n        if file == 1:\n            expl.newpath = correctfile(expl.newpath)\n        if o == True:\n            op = 0\n            while op != 7:\n                if expl.file == \"///s\":\n                    pass\n                else:\n                    print(\"\\n\" + expl.newpath + \"\\n\")\n                    print(\"Here is a list of operations available: \\n1 = Delete\\n2 = Create file/folder\\n3 = Copy file/folder\\n4 = Rename file/folder\\n7 = Back\")\n                    op = int(input(\"Please enter the number of the operation you wish to execute.\"))\n                if op == 0 and expl.file == \"///s\":\n                    search = searchpath(input(\"Please enter the characters to search for.\"), expl.path, int(input(\"Please enter 1 to exlude this name, or 0 to include it.\")), int(input(\"Please enter 1 to only include results with the characters at the end, or 0 to not.\")), int(input(\"Please enter 1 to include folders, or 0 to not.\")), int(input(\"Please enter 1 to include files, or 0 to not,\")))\n                    if search == \"INVALID<>\":\n                        print(\"Folder could not be accessed or no return was selected.\")\n                        time.sleep(3)\n                        break;\n                    elif searchpath.rtfiles == 1 and searchpath.rtfolders == searchpath.files:\n                        if search == \"None\" or (len(search) == 1 and search[0] == \"END_OF_ARRAY<>\"):\n                            print(search)\n                    else:\n                        print(\"Files:\")\n                        for i in range(0, len(search) - 1):\n                            if search[i] != \"END_OF_ARRAY<>\":\n                                print( i + \" = \" + search[i])\n                            elif searchpath.rtfolders == 1:\n                                endfiles = i - 1\n                                j = i + 1\n                                break;\n                        print(\"Folders:\")\n                        for k in range(j, len(search) - 1):\n                                print(k + \" = \" + search[k])\n                elif op == 1:\n                    confirm = int(input(\"Please enter 1 to confirm delete.\"))\n                    if confirm == int(1):\n                        if os.path.isdir(expl.newpath) == True:\n                            print(\"Deleting...\")\n                            try:\n                                shutil.rmtree(expl.newpath)\n                            except(IOError, OSError) as e:\n                                print(e)\n                                print(\"Couldn't delete folder.\")\n                                time.sleep(3)\n                            ### EXITS FOR SOME REASON ON DIR RELOAD\n                            op = 7\n                            auto = 1\n                        else:\n                            print(\"Deleting...\")\n                            try:\n                                os.remove(expl.newpath)\n                            except(IOError, OSError) as f:\n                                print(f)\n                                print(\"Couldn't delete folder.\")\n                                time.sleep(3)\n                        break;\n                elif op == 2:\n                        if validatefile(expl.newpath) == True:\n                            print(\"Cannot create files/folders inside files!\")\n                        else:\n                            oq = int(input(\"1 = Make a folder\\n2 = Make a file\\nPlease enter the number of the object you would like to create.\"))\n                        class create:\n                            if oq == int(1):\n                                typ = \"folder\"\n                            elif oq == int(2):\n                                typ = \"file\"\n                            else:\n                                typ = \"folder\"\n                            newname = input(\"Please enter the name of your new \" + typ + \"  \")\n                        if oq == int(1):\n                            array.append(create.newname)\n                            try:\n                                print(\"Creating folder...\")\n                                os.mkdir(expl.path + create.newname)\n                            except(IOError, OSError) as g:\n                                print(g)\n                                time.sleep(3)\n                                del array[len(array) - 1]\n                        elif oq == int(2):\n                            array.append(create.newname)\n                            try:\n                                print(\"Creating file...\")\n                                open(expl.path + create.newname, \"a\")\n                            except(IOError, OSError) as h:\n                                print(h)\n                                time.sleep(3)\n                                del array[len(array) - 1]\n                elif op == 3:\n                    class copy:\n                        a = explorer(0, 1, 0, \"Select a path to paste\", 0)\n                    if copy.a != 3:\n                        if os.path.isdir(expl.newpath):\n                            try:\n                                shutil.copytree(expl.newpath, a)\n                            except(IOError, OSError):\n                                print(\"Could not copy folder!!!\")\n                                time.sleep(3)\n                        elif os.path.isfile(expl.newpath):\n                            try:\n                                shutil.copy(expl.newpath, a)\n                            except(IOError, OSError):\n                                print(\"Could not copy file!!!\")\n                                time.sleep(3)\n                elif op == 4:\n                    class rename:\n                        c = \"\"\n                    if len(expl.newpath) > 0:\n                        b = expl.newpath.split(\"/\")\n                        del b[len(b) -1]\n                        for i in range(0, len(b) - 1):\n                            rename.c = b[i] + \"/\"\n                    try:\n                        os.rename(expl.newpath, \"/\" + rename.c + input(\"What do you wish the new name to be?\"))\n                    except(IOError, OSError) as e:\n                        print(e)\n                        print(\"Could not rename file/folder!\")\n                        op = 7                    \n                if op == 7:\n                    if tofinishop == 1:\n                        return expl.path;\n                    break;\n        if expl.file == \"./project/old/\":\n            expl.path = expl.path.replace(expl.path, os.getcwd() + \"/\")\n            expl.newpath = expl.path\n            auto = 1\ndef addplugins(rewrite):\n    if rewrite == True:\n        try:\n            os.remove(\"./project/old/startplugins.py\")\n        except(IOError, OSError):\n            pass\n        try:\n            os.remove(\"./project/old/startplugins.pyc\")\n        except(IOError, OSError):\n            pass\n        w = open(\"./project/old/startplugins.py\", \"a\")\n        w.write(\"import sys\\nsys.path.insert(0, './project/old/lib')\\nfrom LolexToolsMethods import *\")\n    done = \"0\"\n    while done != \"1\":\n        success = True\n        print(\"You will now enter the explorer to choose your plugin.\")\n        time.sleep(1.5)\n        namepath = explorer(0, -1, 1, 0, \"/\", 0)\n        if namepath[1].endswith(\".py\") != True and namepath[1].endswith(\".pyc\") != True:\n            success = False\n        if success == True:\n            path = namepath[0]\n            name = namepath[1]\n            name = name.replace(\".py\", \"\")\n            name = name.replace(\".pyc\", \"\")\n            if \" \" in name or \"\t\" in name or name.count(\".\") > 1 or \"#\" in name or \"@\" in name or \"LolexTools\" in name or \"{\" in name or \"}\" in name or \"/\" in name or \"&\" in name or \"|\" in name:\n                print(\"Plugin name contains an/several characters that are invalid. Could not load plugin.\")\n            else:\n                w.write('\\ntry:\\n    sys.path.insert(\"0, ' + path + '\")\\n    from ' + (str(name)) + ' import *\\nexcept(ImportError) as e:\\n    print(\"Could not load plugin ' + name + ' due to the error below.\")\\n    print(e)')\n        done = input(\"Please enter 1 if you are done, 0 if not.\")\n","repo_name":"raven0034/Lolex-Tools","sub_path":"project/old/lib/LolexToolsMethods.py","file_name":"LolexToolsMethods.py","file_ext":"py","file_size_in_byte":46061,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"1106013492","text":"#!/usr/bin/env python3\n\n# --- imports\n\nimport sys\nimport os\nfrom WGALP.utils.input_manager import InputManager\nfrom sub_workflows.decontamination import Decontamination\nfrom WGALP.utils.input_manager import check_files\nfrom WGALP.utils.input_manager import check_folders\n\n# --- input arguments\n\ndef prepare_input(args):\n    input_data = InputManager(program_description=\"Remove reads that map to a contaminant reference, but not to any target reference\" + \"\\n\" + \"references must be in .fasta format and BWA indexed (command: bwa index path/to/file.fasta)\")\n    input_data.add_arg(\"--fastq\", \"path\", description=\"single raw read file to be cleaned (.fastq) [use only with NON paired end data]\")\n    input_data.add_arg(\"--fastq-fwd\", \"path\", description=\"forward raw reads (.fastq) [use only with paired end data]\")\n    input_data.add_arg(\"--fastq-rev\", \"path\", description=\"revese raw reads (.fastq) [use only with paired end data]\")\n    input_data.add_arg(\"--references\", \"list\", description=\"[Required] list of the possible references for the sequenced genome (.fasta, BWA indexed)\")\n    input_data.add_arg(\"--contaminants\", \"list\", description=\"[Required] list of the contaminant\")\n    input_data.add_arg(\"--output\", \"dir\", description=\"[Required] the folder in which the output will be saved\")\n    input_data.parse(args)\n    return input_data\n\n\n# --- input sanity checks\n\ndef sanity_check(fastq, fastq_fwd, fastq_rev, references, contaminants, output_dir):\n    # mode check\n    if fastq is not None and (fastq_fwd is not None and fastq_rev is not None):\n        raise Exception(\"Choose either PE [--fastq-rev and --fastq-fwd] or non PE [--fastq] mode, use --help for more information\")\n\n    if fastq is None and not (fastq_fwd is not None and fastq_rev is not None):\n        raise Exception(\"For PE mode both fwd and rev fastq files must be specified, use --help for more information\")\n\n    # files/dirs check\n    check_files(references + contaminants)\n    if fastq is None:\n        check_files([fastq_fwd, fastq_rev])\n    else:\n        check_files([fastq])\n    try:\n        check_folders([output_dir])\n    except Exception:\n        if os.path.isfile(output_dir):\n            raise Exception(\"--output argument must be a directory and not a file\")\n        else:\n            os.mkdir(output_dir)\n\n# --- core functions\n\ndef run_decontamination(fastq, references, contaminants, output_dir):\n    # sanity check\n    check_files([fastq])\n    check_files(references)\n    check_files(contaminants)\n    check_folders([output_dir])\n    # \n    fastq_basename = os.path.splitext(os.path.basename(fastq))[0]\n    args = {\n        \"references\" : references,\n        \"contaminants\" : contaminants,\n        \"fastq\" : fastq,\n    }\n    dec = Decontamination(fastq_basename, output_dir)\n    out = dec.run(args)\n    dec.delete_checkpoint()\n    return out\n\ndef run_decontaminationPE(fastq_fwd, fastq_rev, references, contaminants, output_dir):\n    # sanity check\n    check_files([fastq_fwd])\n    check_files([fastq_rev])\n    check_files(references)\n    check_files(contaminants)\n    check_folders([output_dir])\n    # \n    fastq_basename = os.path.splitext(os.path.basename(os.path.commonprefix([fastq_fwd, fastq_rev])))[0]\n    args = {\n        \"references\" : references,\n        \"contaminants\" : contaminants,\n        \"fastq_fwd\" : fastq_fwd,\n        \"fastq_rev\" : fastq_rev\n    }\n    dec = Decontamination(fastq_basename, output_dir)\n    out = dec.run(args)\n    dec.delete_checkpoint()\n    return out\n\n# --- caller function\n\ndef decontamination_workflow(args):\n\n    in_manager = prepare_input(args)\n\n    fastq = in_manager[\"--fastq\"][\"value\"]\n    fastq_fwd = in_manager[\"--fastq-fwd\"][\"value\"]\n    fastq_rev = in_manager[\"--fastq-rev\"][\"value\"]\n    references = in_manager[\"--references\"][\"value\"]\n    contaminants = in_manager[\"--contaminants\"][\"value\"]\n    output_dir = in_manager[\"--output\"][\"value\"]\n    \n    sanity_check(fastq, fastq_fwd, fastq_rev, references, contaminants, output_dir)\n\n    if fastq is None:\n        output = run_decontaminationPE(fastq_fwd, fastq_rev, references, contaminants, output_dir)\n        print(\"task completed successfully\")\n        print(\"decontaminated reads are at the following locations:\")\n        print(\"\\t\" + \"cleaned_fastq_fwd\" + \" : \" + output[\"cleaned_fastq_fwd\"])\n        print(\"\\t\" + \"cleaned_fastq_rev\" + \" : \" + output[\"cleaned_fastq_rev\"])\n    else:\n        output = run_decontamination(fastq, references, contaminants, output_dir)\n        print(\"task completed successfully\")\n        print(\"decontaminated reads are at the following locations:\")\n        print(\"\\t\" + \"cleaned_fastq\" + \" : \" + output[\"cleaned_fastq\"])\n\n    return output\n\n\nif __name__ == \"__main__\":\n    decontamination_workflow(sys.argv[1:])    \n\n\n    ","repo_name":"redsnic/WGA-LP","sub_path":"mini_programs/decontamination_workflow.py","file_name":"decontamination_workflow.py","file_ext":"py","file_size_in_byte":4753,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"48"}
+{"seq_id":"21169835790","text":"from pyspark import SparkConf, SparkContext\nimport re\nfrom pyspark.sql import Row, SQLContext\nfrom datetime import datetime\nfrom pyspark.sql.functions import *\n\n\nconf = SparkConf().setAppName(\"Nasa Log Analytics of Aug-1995 & July-1995 in Spark-DataFrame\").setMaster(\"yarn-client\")\nsc = SparkContext(conf=conf)\nsqlContext = SQLContext(sc)\n\n\nreg = '^(\\S+)\\s(\\S+)\\s(\\S+)\\s\\[(\\d{2}/\\w{3}/\\d{4}:\\d{2}:\\d{2}:\\d{2}\\s-\\d{4})]\\s\"(\\S+)\\s(\\S+)(.*)\"\\s(\\d{3})\\s(\\S+)$'\n\nlogs = sqlContext.read.text(\"/InputFiles/Spark/access*\")\n#logs.filter(logs.value.isNull()).count()\n\ndef load_df(logs):\n    logsDf = logs.select(regexp_extract('value', reg, 1).alias('Host'),\n                         regexp_extract('value', reg, 4).alias('Timestamp'),\n                         regexp_extract('value', reg, 5).alias('Method'),\n                         regexp_extract('value', reg, 6).alias('Endpoint'),\n                         regexp_extract('value', reg, 8).cast('integer').alias('Status'),\n                         regexp_extract('value', reg, 9).cast('integer').alias('Content_size'))\n    return logsDf\n\ndef filter_func(col_name,logsDf):\n    return sum(logsDf[col_name].isNull().cast('integer')).alias(col_name)\n    \n\nlogsDf = load_df(logs)\n#logsDf.show(10,truncate=False)\n\n#checking the null values column wise\ncountNull = [filter_func(col,logsDf) for col in logsDf.columns]\ntempDf = logsDf.coalesce(1).agg(*countNull)\n#tempDf.show()\n\n\n#33905 null values in Content_size and it is : '-' and replacing it with 0\n#logs.filter(~logs['value'].rlike(r'\\s(\\d+)$')).show()\nlogsDf = logsDf.na.fill({'Content_size' : 0})\n\ncountNull = [filter_func(col,logsDf) for col in logsDf.columns]\ntempDf = logsDf.coalesce(1).agg(*countNull)\n#tempDf.show()\n\n#dropping the null values of Status :\nlogsDf = logsDf[logsDf.Status.isNotNull()]\n\n#ignoring the timezone in timestamp and dropping the Timestamp and adding the new cloumn Time\nlogsDf = logsDf.select('*', split(logsDf.Timestamp, \" \")[0].alias('Time')).drop('Timestamp')\nlogsDf.cache()\n\n\n#content_size Statistics :\ncontentSizeSummary = logsDf.coalesce(1).describe(['Content_size'])\nprint(\"Content statistics of count , mean ,standard deviation , min, max : \")\ncontentSizeSummary.show()\n\n#HTTP Status wise count :\nHTTPcount = logsDf.coalesce(1).groupBy('Status').count().sort('Status')\nprint(\"Differnt HTTP Status wise count in ascending order :\")\nHTTPcount.show()\n\n#Analyzing top 10 frequent hosts :\ntop10Hosts = logsDf.coalesce(1).groupBy('Host').count().sort('count', ascending = False).limit(10)\nprint(\"Top 10 frequest hosts :\")\ntop10Hosts.show()\n\n#top 10 error endpoint ,i.e., Status is other than '200' :\nerrorEndpoints = logsDf.coalesce(1).filter(logsDf.Status != 200).groupBy('Endpoint').count().sort('count', ascending = False).limit(10)\nprint(\"Top 10 error endpoints whose status is other than 200 :\")\nerrorEndpoints.show(truncate = False)\n\n#unique hosts daily in entire log (includes the day of the month and the associated number of unique hosts for that day) :\nhostDay = logsDf.coalesce(1).select(logsDf.Host , split(logsDf.Time, \":\")[0].alias('Day'))\nuniqueHostDay = hostDay.dropDuplicates()\nhostPerDay = uniqueHostDay.coalesce(1).groupBy('Day').count()\nprint(\"Count of unique number of hosts per day :\")\nhostPerDay.show(truncate = False)\n\n#Hourly basis 404 errors count:\nnotFound = logsDf.coalesce(1).filter(logsDf.Status == 404)\nhourly404Count = notFound.coalesce(1).groupBy(split(split(notFound.Time, \":\")[1], \":\")[0].alias('Hour')).count().sort('Hour')\nprint(\"Hourly basis 404 error count :\")\nhourly404Count.show(truncate = False)\n\nfrom pyspark.sql.functions import col\n\n#Average number of request daily on per host per day:\nhostDay = logsDf.coalesce(1).select(logsDf.Host , split(logsDf.Time, \":\")[0].alias('Day'))\nuniqueHostDay = hostDay.coalesce(1).dropDuplicates()\nhostPerDay = uniqueHostDay.coalesce(1).groupBy('Day').count().select(col('Day'), col('count').alias('Total_Hosts'))\nreqPerDay = hostDay.coalesce(1).groupBy('Day').count().select(col('Day'), col('count').alias('Total_Requests'))\navgReqPerDay = hostPerDay.coalesce(1).join(reqPerDay, 'Day')\navgReqPerDay = avgReqPerDay.coalesce(1).withColumn ('Avg_Req', (col('Total_Requests')/col('Total_Hosts')))\nprint(\"Average number of request daily on per host per day:\")\navgReqPerDay.show(truncate = False)\n\n\n","repo_name":"AyanChatterjee20/Spark","sub_path":"Project/nasaLogAnalysisDF.py","file_name":"nasaLogAnalysisDF.py","file_ext":"py","file_size_in_byte":4295,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"18077355239","text":"import argparse\nimport numpy as np\nimport multiprocessing as mp\nimport cv2\nimport os\n\ndef run_eval(data_list, pred_root, gt_root, num_cls):\n    def compute_confusion_matrix(names, label_root, pred_root, num_cls, num_threads=16, arr_=None):\n        if num_threads == 1:\n            mat = np.zeros((num_cls, num_cls), np.float32)\n            for name in names:\n                gt = cv2.imread(os.path.join(label_root, name+'.png'), 0).astype(np.int32)\n                pred = cv2.imread(os.path.join(pred_root, name+'.png'), 0).astype(np.int32)\n                if gt.shape != pred.shape:\n                    info(None, \"NAME {}, gt.shape != pred.shape: [{} vs. {}]\".format(name, gt.shape, pred.shape), 'red')\n                    continue\n\n                valid = gt < num_cls\n                mat += np.bincount(gt[valid] * num_cls + pred[valid], minlength=num_cls**2).reshape(num_cls, -1)\n\n            if arr_ is not None:\n                arr_mat = np.frombuffer(arr_.get_obj(), np.float32)\n                arr_mat += mat.ravel()\n            return mat\n        else:\n            workload = np.full((num_threads,), len(names)//num_threads, np.int32)\n            if workload.sum() < len(names):\n                workload[:(len(names) - workload.sum())] += 1\n            workload = np.cumsum(np.hstack([0, workload]))\n            \n            names_split = [names[i:j] for i, j in zip(workload[:-1], workload[1:])]\n\n            arr_ = mp.Array('f', np.zeros((num_cls * num_cls,), np.float32))\n            mat = np.frombuffer(arr_.get_obj(), np.float32).reshape(num_cls, -1)\n\n            jobs = [mp.Process(target=compute_confusion_matrix, args=(_names, label_root, pred_root, num_cls, 1, arr_)) \\\n                    for _names in names_split]\n            res = [job.start() for job in jobs]\n            res = [job.join() for job in jobs]\n            return mat.copy()\n\n    def compute_eval_results(confmat):\n        iou = np.diag(confmat) / np.maximum(confmat.sum(axis=0) + confmat.sum(axis=1) - np.diag(confmat), 1e-10)\n        return iou\n\n    # \n    with open(data_list) as f:\n        names = [x.strip() for x in f.readlines()]\n\n    confmat = compute_confusion_matrix(names, gt_root, pred_root, num_cls)\n    iou = compute_eval_results(confmat)\n\n    msg = \"mIOU: {}\\n{}\\n\\n\".format(iou.mean(), iou)\n    print(msg)\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n\n    parser.add_argument('--num-cls', type=int, default=21)\n    parser.add_argument('--data-list', type=str, default='./data/VOC2012/val.txt')\n    parser.add_argument('--prediction-root',  type=str, required=True)\n    parser.add_argument('--groundtruth-root', type=str, required=True)\n\n    args = parser.parse_args()\n    run_eval(args.data_list, args.prediction_root, args.groundtruth_root, args.num_cls)\n\n","repo_name":"js-fan/CIAN","sub_path":"scripts/eval_segment.py","file_name":"eval_segment.py","file_ext":"py","file_size_in_byte":2782,"program_lang":"python","lang":"en","doc_type":"code","stars":34,"dataset":"github-code","pt":"48"}
+{"seq_id":"19519536869","text":"\r\nimport random\r\n\r\nclass Carta:\r\n\t\r\n\tdef __init__(self, valor, palo):\r\n\t\tself.valor = valor\r\n\t\tself.palo = palo\r\n\t\tself.etiqueta = str(valor + ' ' + palo)\r\n\r\n\t@staticmethod\r\n\tdef cargar_cartas():\r\n\t\tbaraja = []\r\n\t\t'''\r\n\t\t''' \r\n\t\tpalos = ['rojo', 'azul', 'amarillo', 'verde']\r\n\t\tvalores = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'Skip', '+2', 'Reverso']\r\n\t\tfor p in palos:\r\n\t\t\tfor v in valores:\r\n\t\t\t\tbaraja.append(Carta(v, p)) # v + \" \" + p, \r\n\t\t\t\r\n\t\tespeciales = ['+4', 'Ginyu', 'Joker']\r\n\t\tfor e in especiales:\r\n\t\t\tbaraja.append(Carta(e, 'negro')) # e + \" \" + 'negro',\r\n\r\n\t\treturn baraja\r\n\r\n\t@staticmethod\r\n\tdef mezcla(baraja):\r\n\t\trandom.shuffle(baraja)\r\n\t\treturn baraja\r\n\r\n\tdef imprimir_carta(self, numeracion = None):\r\n\t\tprint((str(numeracion) + '. ' if numeracion != None else '') + self.etiqueta)\r\n#\r\n","repo_name":"numbasan-san/pyUno","sub_path":"carta.py","file_name":"carta.py","file_ext":"py","file_size_in_byte":817,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"1116872957","text":"class TicTacToe:\n    def __init__(self):\n        self.board = [[\"-\" for i in range(3)] for j in range(3)]\n\n    def display_board(self):\n        for row in self.board:\n            print(\" \".join(row))\n\n    def update_board(self, row, col, player):\n        self.board[row][col] = player\n\n    def is_full(self):\n        for row in self.board:\n            if \"-\" in row:\n                return False\n        return True\n\n    def has_won(self, player):\n        for row in self.board:\n            if row == [player, player, player]:\n                return True\n\n        for col in range(3):\n            if all([row[col] == player for row in self.board]):\n                return True\n\n        if self.board[0][0] == self.board[1][1] == self.board[2][2] == player:\n            return True\n\n        if self.board[0][2] == self.board[1][1] == self.board[2][0] == player:\n            return True\n\n        return False\n\n    def get_valid_moves(self):\n        moves = []\n        for i in range(3):\n            for j in range(3):\n                if self.board[i][j] == \"-\":\n                    moves.append((i, j))\n        return moves\n\n    def minimax(self, player):\n        if self.has_won(\"X\"):\n            return 1\n        if self.has_won(\"O\"):\n            return -1\n        if self.is_full():\n            return 0\n\n        scores = []\n        for row, col in self.get_valid_moves():\n            self.update_board(row, col, player)\n            if player == \"X\":\n                scores.append(self.minimax(\"O\"))\n            else:\n                scores.append(self.minimax(\"X\"))\n            self.update_board(row, col, \"-\")\n\n        if player == \"X\":\n            return max(scores)\n        else:\n            return min(scores)\n\n    def best_move(self, player):\n        best_score = None\n        best_move = None\n        for row, col in self.get_valid_moves():\n            self.update_board(row, col, player)\n            score = self.minimax(\"O\" if player == \"X\" else \"X\")\n            self.update_board(row, col, \"-\")\n\n            if player == \"X\":\n                if best_score is None or score > best_score:\n                    best_score = score\n                    best_move = (row, col)\n            else:\n                if best_score is None or score < best_score:\n                    best_score = score\n                    best_move = (row, col)\n\n        return best_move\n\n\ngame = TicTacToe()\ngame.display_board()\nwhile not game.has_won(\"X\") and not game.has_won(\"O\") and not game.is_full():\n    x,y = map(int,input().split())\n    game.update_board(x, y, 'X')\n    game.display_board()\n    print(\"\")\n    computer_move = game.best_move('O')\n    game.update_board(*computer_move, 'O')\n    game.display_board()\n    print(\"\")\nif game.has_won(\"X\"):\n    print(\"X has won!\")\nelif game.has_won(\"O\"):\n    print(\"O has won!\")\nelif game.is_full():\n    print(\"The game has ended in a draw.\")","repo_name":"asdfqrt/barkingdog","sub_path":"혼자 공부 알고리즘/Min max 알고리즘.py","file_name":"Min max 알고리즘.py","file_ext":"py","file_size_in_byte":2873,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"27126550210","text":"import logging\nimport six\nimport time\nimport struct\n\nimport event\nfrom ryu.base import app_manager\nfrom ryu.controller import ofp_event\nfrom ryu.controller.handler import set_ev_cls\nfrom ryu.controller.handler import MAIN_DISPATCHER, DEAD_DISPATCHER\nfrom ryu.lib import addrconv, hub\nfrom ryu.lib.mac import DONTCARE_STR\nfrom ryu.lib.dpid import dpid_to_str, str_to_dpid\nfrom ryu.lib.port_no import port_no_to_str\nfrom ryu.lib.packet import packet, ethernet\nfrom ryu.lib.packet import lldp, ether_types\nfrom ryu.ofproto.ether import ETH_TYPE_LLDP\nfrom ryu.ofproto.ether import ETH_TYPE_CFM\nfrom ryu.ofproto import nx_match\nfrom ryu.ofproto import ofproto_v1_0\nfrom ryu.ofproto import ofproto_v1_2\nfrom ryu.ofproto import ofproto_v1_3\nfrom ryu.ofproto import ofproto_v1_4\n\n# --- Import from the original ryu topo detection module ---\nfrom ryu.topology.switches import Port, Switch, Link, Host\nfrom ryu.topology.switches import HostState, PortState, PortDataState, LinkState\nfrom ryu.topology.switches import LLDPPacket\n\n\nLOG = logging.getLogger(__name__)\n\n\nclass Host(Host):\n    \"\"\" Overwrite the host object class to store the name of the host \"\"\"\n    def __init__(self, mac, port, name):\n        super(Host, self).__init__(mac, port)\n        self.name = name\n\n    def to_dict(self):\n        d = {'name': self.name,\n             'mac': self.mac,\n             'ipv4': self.ipv4,\n             'ipv6': self.ipv6,\n             'port': self.port.to_dict()}\n        return d\n\n    def __eq__(self, host):\n        return (self.mac == host.mac and self.port == host.port and\n            self.name == host.name)\n\n    def __str__(self):\n        msg = 'Host<name=%s, mac=%s, port=%s,' % (self.name, self.mac, str(self.port))\n        msg += ','.join(self.ipv4)\n        msg += ','.join(self.ipv6)\n        msg += '>'\n        return msg\n\n\nclass SpecialLinkData():\n    \"\"\" Class that defines info related to special links (hosts and inter-domian)\n    to allow timing out elements.\n    \"\"\"\n    def __init__(self, obj):\n        self.obj = obj\n        self.timestamp = None\n        self.received_lldp()\n\n\n    def is_host(self):\n        return isinstance(self.obj, Host)\n\n\n    def is_inter_dom_link(self):\n        return isinstance(self.obj, InterDomPort)\n\n\n    def received_lldp(self):\n        self.timestamp = time.time()\n\nclass InterDomPort():\n    \"\"\" Class that defines information related to a inter domain port (switch and\n    port info from other domain).\n    \"\"\"\n    def __init__(self, dpid, port_no):\n        self.dpid = dpid\n        self.port_no = port_no\n\n\n    def to_dict(self):\n        return {'dpid': dpid_to_str(self.dpid),\n                'port_no': port_no_to_str(self.port_no)}\n\n    # for Switch.del_port()\n    def __eq__(self, other):\n        return self.dpid == other.dpid and self.port_no == other.port_no\n\n    def __ne__(self, other):\n        return not self.__eq__(other)\n\n    def __hash__(self):\n        return hash((self.dpid, self.port_no))\n\n    def __str__(self):\n        return 'InterDomPort<dpid=%s, port_no=%s>' % \\\n            (self.dpid, self.port_no)\n\n\nclass LLDPPacket(LLDPPacket):\n\n    HOST_NAME_PREFIX = \"host:\"\n    HOST_NAME_FMT = HOST_NAME_PREFIX + \"%s\"\n\n    @staticmethod\n    def lldp_packet(dpid, port_no, dl_addr, ttl):\n        pkt = packet.Packet()\n\n        dst = lldp.LLDP_MAC_NEAREST_BRIDGE\n        src = dl_addr\n        ethertype = ETH_TYPE_LLDP\n        eth_pkt = ethernet.ethernet(dst, src, ethertype)\n        pkt.add_protocol(eth_pkt)\n\n        tlv_chassis_id = lldp.ChassisID(\n            subtype=lldp.ChassisID.SUB_LOCALLY_ASSIGNED,\n            chassis_id=(LLDPPacket.CHASSIS_ID_FMT %\n                        dpid_to_str(dpid)).encode('ascii'))\n\n        tlv_port_id = lldp.PortID(subtype=lldp.PortID.SUB_PORT_COMPONENT,\n                                  port_id=struct.pack(\n                                      LLDPPacket.PORT_ID_STR,\n                                      port_no))\n\n        tlv_ttl = lldp.TTL(ttl=ttl)\n        tlv_end = lldp.End()\n\n        tlvs = (tlv_chassis_id, tlv_port_id, tlv_ttl, tlv_end)\n        lldp_pkt = lldp.lldp(tlvs)\n        pkt.add_protocol(lldp_pkt)\n\n        pkt.serialize()\n        return pkt.data\n\n\n    @staticmethod\n    def lldp_parse(data):\n        pkt = packet.Packet(data)\n        i = iter(pkt)\n        eth_pkt = six.next(i)\n        assert type(eth_pkt) == ethernet.ethernet\n\n        lldp_pkt = six.next(i)\n        if type(lldp_pkt) != lldp.lldp:\n            raise LLDPPacket.LLDPUnknownFormat()\n\n        # Validate and parse the chassis ID\n        tlv_chassis_id = lldp_pkt.tlvs[0]\n        if tlv_chassis_id.subtype != lldp.ChassisID.SUB_LOCALLY_ASSIGNED:\n            raise LLDPPacket.LLDPUnknownFormat(\n                msg='unknown chassis id subtype %d' % tlv_chassis_id.subtype)\n        chassis_id = tlv_chassis_id.chassis_id.decode('utf-8')\n        if not chassis_id.startswith(LLDPPacket.CHASSIS_ID_PREFIX):\n            raise LLDPPacket.LLDPUnknownFormat(\n                msg='unknown chassis id format %s' % chassis_id)\n        src_dpid = str_to_dpid(chassis_id[LLDPPacket.CHASSIS_ID_PREFIX_LEN:])\n\n        # Validate and parse the source Port ID\n        tlv_port_id = lldp_pkt.tlvs[1]\n        if tlv_port_id.subtype != lldp.PortID.SUB_PORT_COMPONENT:\n            raise LLDPPacket.LLDPUnknownFormat(\n                msg='unknown port id subtype %d' % tlv_port_id.subtype)\n        port_id = tlv_port_id.port_id\n        if len(port_id) != LLDPPacket.PORT_ID_SIZE:\n            raise LLDPPacket.LLDPUnknownFormat(\n                msg='unknown port id %d' % port_id)\n        (src_port_no, ) = struct.unpack(LLDPPacket.PORT_ID_STR, port_id)\n\n        # Check if we have a host name (i.e. it's a LLDP host discovery packet)\n        system_name = None\n        src_addr = None\n        if len(lldp_pkt.tlvs) == 6:\n            # Extracta and validate the system name\n            tlv_system_name = lldp_pkt.tlvs[3]\n            if tlv_system_name.__class__ != lldp.SystemName:\n                return src_dpid, src_port_no, None\n            system_name = tlv_system_name.system_name\n            if not system_name.startswith(LLDPPacket.HOST_NAME_PREFIX):\n                return src_dpid, src_port_no, None\n            system_name = system_name.replace(LLDPPacket.HOST_NAME_PREFIX, \"\")\n\n            # Extract and validate the address\n            tlv_ip = lldp_pkt.tlvs[4]\n            if tlv_ip.__class__ != lldp.PortID:\n                return src_dpid, src_port_no, None\n            if tlv_ip.subtype != lldp.PortID.SUB_NETWORK_ADDRESS:\n                return src_dpid, src_port_no, None\n            src_addr = tlv_ip.port_id\n\n            # Return the LLDP packet info with the host details tripple\n            return src_dpid, src_port_no, (system_name, src_addr, eth_pkt.src)\n        else:\n\n            # Return a normal result\n            return src_dpid, src_port_no, None\n\n\nclass SwitchesDiscovery(app_manager.RyuApp):\n    OFP_VERSIONS = [ofproto_v1_0.OFP_VERSION, ofproto_v1_2.OFP_VERSION,\n                    ofproto_v1_3.OFP_VERSION, ofproto_v1_4.OFP_VERSION]\n    _EVENTS = [event.EventSwitchEnter, event.EventSwitchLeave,\n               event.EventSwitchReconnected,\n               event.EventPortAdd, event.EventPortDelete,\n               event.EventPortModify,\n               event.EventLinkAdd, event.EventLinkDelete,\n               event.EventHostAdd, event.EventHostDelete,\n               event.EventInterDomLinkAdd, event.EventInterDomLinkDelete]\n\n    DEFAULT_TTL = 120  # unused. ignored.\n    LLDP_PACKET_LEN = len(LLDPPacket.lldp_packet(0, 0, DONTCARE_STR, 0))\n\n    # Wait n seconds before sending LLDP packet for subsequent ports that need\n    # to be polled in a poll interval (prevent packet out request flodding)\n    LLDP_SEND_GUARD = .05\n    # Every n second flood LLDP discovery packets on every port of every switch\n    LLDP_SEND_PERIOD_PER_PORT = .4\n    # Check every n seconds for failed ports\n    TIMEOUT_CHECK_PERIOD = 2.\n    # Link failed if no LLDP packet received after n seconds\n    LINK_TIMEOUT = TIMEOUT_CHECK_PERIOD * 2\n    # Consider a link as dead if we sent n LLDP packets without reciving any\n    LINK_LLDP_DROP = 4\n\n\n    # XXX ----- EXPLANATION LLDP AND LINK TIMEOUT MECHANISM ----- XXX\n    #\n    # Generate LLDP packets per port every `LLDP_SEND_PERIOD_PER_PORT` seconds.\n    #\n    # ``lldp_loop`` checks for expired ports (send LLDP packet) by comparing\n    # timestamp with send period. List of ports is ordered where last sent\n    # moved to end of list. If in a iteration need to send LLDP packet on\n    # multiple ports, delay subseqent packet out commands by `LLDP_SEND_GUARD`\n    # seconds. Sleep until signaled or until the next port expires.\n    #\n    # `link_loop` times out the ports and generates the port down notification.\n    # Loop runs every `TIMEOUT_CHECK_PERIOD` seconds. A LLDP link is considered\n    # failed if controller sent at-lest `LINK_LLDP_DROP` packets on port but\n    # received none. Links expire if no LLDP packet received for `LINK_TIMEOUT`\n    # seconds.\n    #\n    # Default Settings:\n    #   LLDP send period is 0.9s, timeout-check period is 5 seconds and link\n    #   timeout is set to timeout-check * 2 (10 seconds). This implies that\n    #   with default settings we can detect a failed link after 10 seconds\n    #   of inactivity.\n    #\n    # Default Settings Variables:\n    #   TimeCheckPeriod = round(LLDPSendPerP * LinkLLDPDrop) = round(4.5) = 5s\n    #   LinkTimeout = TimeCheckPeriod * 2 = 5 * 2  = 10s\n    #\n    # Faster Detection:\n    #   LLDPSendPerP = 0.4\n    #   LinkLLDPDrop = 4\n    #   =>  TimeChekPeriod = round(0.4 * 4) = round(1.6) = 2s\n    #       LinkTimeout = 2 * 2 = 4s\n    # ---------------------------------------------------------------\n\n\n    # Declare as failed a host and inter-domain link if an LLDP packet was not\n    # received in n seconds.\n    HOST_TIMEOUT = 2\n    INTER_DOM_LINK_TIMEOUT = 2\n\n    # XXX: The timeout check period is 5 seconds (check every 5 seconds for\n    # failed links). A LLDP link is considered failed if the controller sent 5\n    # LLDP packets without reciving any LLDP packets. Before we perform the\n    # failed link test, we requre that a port did not receive a LLDP packet\n    # for 10 seconds (check period * 2). This implies that in reality we\n    # can detect that a link has failed after 10 seconds of inactivity.\n\n    def __init__(self, *args, **kwargs):\n        super(SwitchesDiscovery, self).__init__(*args, **kwargs)\n\n        self.name = 'topoDiscovery'\n        self.dps = {}                 # datapath_id => Datapath class\n        self.port_state = {}          # datapath_id => ports\n        self.ports = PortDataState()  # Port class -> PortData class\n        self.links = LinkState()      # Link class -> timestamp\n        self.hosts = HostState()      # mac address -> Host class list\n        self.is_active = True\n        self.link_discovery = True\n\n        self.special_links = {}\n\n        self.pause_detection = True\n        self.pause_detection_state = hub.Event()\n\n        # XXX: If default pause detection overwrite provided, update\n        # the flag value.\n        if \"pause_detection\" in kwargs:\n            self.pause_detection = kwargs[\"pause_detection\"]\n\n        self.install_flow = self.CONF.install_lldp_flow\n        self.explicit_drop = self.CONF.explicit_drop\n        self.lldp_event = hub.Event()\n        self.link_event = hub.Event()\n        self.threads.append(hub.spawn(self.lldp_loop))\n        self.threads.append(hub.spawn(self.link_loop))\n\n\n    @set_ev_cls(event.EventTopoDiscoveryState)\n    def topo_discovery_change_state(self, req):\n        if req.isPause:\n            self._pause()\n        else:\n            self._resume()\n\n        # Wait up to a second for any in progress LLDP packets\n        # to finish\n        self.pause_detection_state.wait(timeout=1)\n        self.pause_detection_state.clear()\n\n        # Notify the requestor that the operationw as completed\n        rep = event.EventTopoDiscoveryStateReply(req.src)\n        self.reply_to_request(req, rep)\n\n\n    def _pause(self):\n        self.pause_detection = True\n\n\n    def _resume(self):\n        # TODO: Should we also reset the timestamp for the normal links\n        # Reset the timestamps for all links\n        for (key, data) in self.special_links.items():\n            data.received_lldp()\n\n        self.pause_detection = False\n        self.lldp_event.set()\n\n\n    def close(self):\n        self.is_active = False\n        if self.link_discovery:\n            self.lldp_event.set()\n            self.link_event.set()\n            hub.joinall(self.threads)\n\n\n    def _register(self, dp):\n        assert dp.id is not None\n\n        self.dps[dp.id] = dp\n        if dp.id not in self.port_state:\n            self.port_state[dp.id] = PortState()\n            for port in dp.ports.values():\n                self.port_state[dp.id].add(port.port_no, port)\n\n\n    def _unregister(self, dp):\n        if dp.id in self.dps:\n            if (self.dps[dp.id] == dp):\n                del self.dps[dp.id]\n                del self.port_state[dp.id]\n\n\n    def _get_switch(self, dpid):\n        if dpid in self.dps:\n            switch = Switch(self.dps[dpid])\n            for ofpport in self.port_state[dpid].values():\n                switch.add_port(ofpport)\n            return switch\n\n\n    def _get_port(self, dpid, port_no):\n        switch = self._get_switch(dpid)\n        if switch:\n            for p in switch.ports:\n                if p.port_no == port_no:\n                    return p\n\n\n    def _port_added(self, port):\n        lldp_data = LLDPPacket.lldp_packet(\n            port.dpid, port.port_no, port.hw_addr, self.DEFAULT_TTL)\n        self.ports.add_port(port, lldp_data)\n        # LOG.debug('_port_added dpid=%s, port_no=%s, live=%s',\n        #           port.dpid, port.port_no, port.is_live())\n\n\n    def _link_down(self, port):\n        try:\n            dst, rev_link_dst = self.links.port_deleted(port)\n        except KeyError:\n            # LOG.debug('key error. src=%s, dst=%s',\n            #           port, self.links.get_peer(port))\n            return\n        link = Link(port, dst)\n        self.send_event_to_observers(event.EventLinkDelete(link))\n        if rev_link_dst:\n            rev_link = Link(dst, rev_link_dst)\n            self.send_event_to_observers(event.EventLinkDelete(rev_link))\n        self.ports.move_front(dst)\n\n\n    def _special_link_down(self, port):\n        \"\"\" Remove a special link and generate notifications \"\"\"\n        try:\n            data = self.special_links[port]\n        except KeyError:\n            return\n\n        # Check the type and remove the link correctly\n        if data.is_host():\n            del self.special_links[port]\n            self._host_down(data.obj)\n        elif data.is_inter_dom_link():\n            del self.special_links[port]\n            self._inter_dom_down(port, data.obj)\n\n\n    def _host_down(self, host):\n        \"\"\" Notify observers that a host link went down \"\"\"\n        self.send_event_to_observers(event.EventHostDelete(host))\n        del self.hosts[host.mac]\n\n\n    def _inter_dom_down(self, key, idp):\n        \"\"\" Notify observers that an inter domain link went down \"\"\"\n        inter_dom_link = Link(key, idp)\n        self.send_event_to_observers(event.EventInterDomLinkDelete(inter_dom_link))\n\n\n    def _is_edge_port(self, port):\n        for link in self.links:\n            if port == link.src or port == link.dst:\n                return False\n\n        return True\n\n\n    @set_ev_cls(ofp_event.EventOFPStateChange,\n                [MAIN_DISPATCHER, DEAD_DISPATCHER])\n    def state_change_handler(self, ev):\n        dp = ev.datapath\n        assert dp is not None\n        LOG.debug(dp)\n\n        if ev.state == MAIN_DISPATCHER:\n            dp_multiple_conns = False\n            if dp.id in self.dps:\n                LOG.warning('Multiple connections from %s', dpid_to_str(dp.id))\n                dp_multiple_conns = True\n                (self.dps[dp.id]).close()\n\n            self._register(dp)\n            switch = self._get_switch(dp.id)\n            LOG.debug('register %s', switch)\n\n            if not dp_multiple_conns:\n                self.send_event_to_observers(event.EventSwitchEnter(switch))\n            else:\n                evt = event.EventSwitchReconnected(switch)\n                self.send_event_to_observers(evt)\n\n            if not self.link_discovery:\n                return\n\n            if self.install_flow:\n                ofproto = dp.ofproto\n                ofproto_parser = dp.ofproto_parser\n\n                # TODO:XXX need other versions\n                if ofproto.OFP_VERSION == ofproto_v1_0.OFP_VERSION:\n                    rule = nx_match.ClsRule()\n                    rule.set_dl_dst(addrconv.mac.text_to_bin(\n                                    lldp.LLDP_MAC_NEAREST_BRIDGE))\n                    rule.set_dl_type(ETH_TYPE_LLDP)\n                    actions = [ofproto_parser.OFPActionOutput(\n                        ofproto.OFPP_CONTROLLER, self.LLDP_PACKET_LEN)]\n                    dp.send_flow_mod(\n                        rule=rule, cookie=0, command=ofproto.OFPFC_ADD,\n                        idle_timeout=0, hard_timeout=0, actions=actions,\n                        priority=0xFFFF)\n                elif ofproto.OFP_VERSION >= ofproto_v1_2.OFP_VERSION:\n                    match = ofproto_parser.OFPMatch(\n                        eth_type=ETH_TYPE_LLDP,\n                        eth_dst=lldp.LLDP_MAC_NEAREST_BRIDGE)\n                    # OFPCML_NO_BUFFER is set so that the LLDP is not\n                    # buffered on switch\n                    parser = ofproto_parser\n                    actions = [parser.OFPActionOutput(ofproto.OFPP_CONTROLLER,\n                                                      ofproto.OFPCML_NO_BUFFER\n                                                      )]\n                    inst = [parser.OFPInstructionActions(\n                            ofproto.OFPIT_APPLY_ACTIONS, actions)]\n                    mod = parser.OFPFlowMod(datapath=dp, match=match,\n                                            idle_timeout=0, hard_timeout=0,\n                                            instructions=inst,\n                                            priority=0xFFFF)\n                    dp.send_msg(mod)\n                else:\n                    LOG.error('cannot install flow. unsupported version. %x',\n                              dp.ofproto.OFP_VERSION)\n\n            # Do not add ports while dp has multiple connections to controller.\n            if not dp_multiple_conns:\n                for port in switch.ports:\n                    if not port.is_reserved():\n                        self._port_added(port)\n\n            self.lldp_event.set()\n\n        elif ev.state == DEAD_DISPATCHER:\n            # dp.id is None when datapath dies before handshake\n            if dp.id is None:\n                return\n\n            switch = self._get_switch(dp.id)\n            if switch:\n                if switch.dp is dp:\n                    self._unregister(dp)\n                    LOG.debug('unregister %s', switch)\n                    evt = event.EventSwitchLeave(switch)\n                    self.send_event_to_observers(evt)\n\n                    if not self.link_discovery:\n                        return\n\n                    for port in switch.ports:\n                        if not port.is_reserved():\n                            self.ports.del_port(port)\n                            self._link_down(port)\n                    self.lldp_event.set()\n\n\n    @set_ev_cls(ofp_event.EventOFPPortStatus, MAIN_DISPATCHER)\n    def port_status_handler(self, ev):\n        msg = ev.msg\n        reason = msg.reason\n        dp = msg.datapath\n        ofpport = msg.desc\n\n        if reason == dp.ofproto.OFPPR_ADD:\n            # LOG.debug('A port was added.' +\n            #           '(datapath id = %s, port number = %s)',\n            #           dp.id, ofpport.port_no)\n            self.port_state[dp.id].add(ofpport.port_no, ofpport)\n            self.send_event_to_observers(\n                event.EventPortAdd(Port(dp.id, dp.ofproto, ofpport)))\n\n            if not self.link_discovery:\n                return\n\n            port = self._get_port(dp.id, ofpport.port_no)\n            if port and not port.is_reserved():\n                self._port_added(port)\n                self.lldp_event.set()\n\n        elif reason == dp.ofproto.OFPPR_DELETE:\n            # LOG.debug('A port was deleted.' +\n            #           '(datapath id = %s, port number = %s)',\n            #           dp.id, ofpport.port_no)\n            self.send_event_to_observers(\n                event.EventPortDelete(Port(dp.id, dp.ofproto, ofpport)))\n\n            if not self.link_discovery:\n                return\n\n            port = self._get_port(dp.id, ofpport.port_no)\n            if port and not port.is_reserved():\n                self.ports.del_port(port)\n                self._link_down(port)\n                self.lldp_event.set()\n\n            if port in self.special_links:\n                self._special_link_down(port)\n\n            self.port_state[dp.id].remove(ofpport.port_no)\n\n        else:\n            assert reason == dp.ofproto.OFPPR_MODIFY\n            # LOG.debug('A port was modified.' +\n            #           '(datapath id = %s, port number = %s)',\n            #           dp.id, ofpport.port_no)\n            self.port_state[dp.id].modify(ofpport.port_no, ofpport)\n            self.send_event_to_observers(\n                event.EventPortModify(Port(dp.id, dp.ofproto, ofpport)))\n\n            if not self.link_discovery:\n                return\n\n            port = self._get_port(dp.id, ofpport.port_no)\n            if port and not port.is_reserved():\n                if self.ports.set_down(port):\n                    self._link_down(port)\n                self.lldp_event.set()\n\n            if (msg.desc.state == dp.ofproto.OFPPS_LINK_DOWN and\n                                    port in self.special_links):\n                self._special_link_down(port)\n\n\n\n    @staticmethod\n    def _drop_packet(msg):\n        buffer_id = msg.buffer_id\n        if buffer_id == msg.datapath.ofproto.OFP_NO_BUFFER:\n            return\n\n        dp = msg.datapath\n        # TODO:XXX\n        if dp.ofproto.OFP_VERSION == ofproto_v1_0.OFP_VERSION:\n            dp.send_packet_out(buffer_id, msg.in_port, [])\n        elif dp.ofproto.OFP_VERSION >= ofproto_v1_2.OFP_VERSION:\n            dp.send_packet_out(buffer_id, msg.match['in_port'], [])\n        else:\n            LOG.error('cannot drop_packet. unsupported version. %x',\n                      dp.ofproto.OFP_VERSION)\n\n\n    @set_ev_cls(ofp_event.EventOFPPacketIn, MAIN_DISPATCHER)\n    def lldp_packet_in_handler(self, ev):\n        if not self.link_discovery:\n            return\n\n        msg = ev.msg\n        try:\n            src_dpid, src_port_no, host_info = LLDPPacket.lldp_parse(msg.data)\n        except LLDPPacket.LLDPUnknownFormat:\n            # This handler can receive all the packets which can be\n            # not-LLDP packet. Ignore it silently\n            return\n\n        # Retrieve the details of the sw that received the LLDP packet (dst)\n        dst_dpid = msg.datapath.id\n        if msg.datapath.ofproto.OFP_VERSION == ofproto_v1_0.OFP_VERSION:\n            dst_port_no = msg.in_port\n        elif msg.datapath.ofproto.OFP_VERSION >= ofproto_v1_2.OFP_VERSION:\n            dst_port_no = msg.match['in_port']\n        else:\n            LOG.error('cannot accept LLDP. unsupported version. %x',\n                      msg.datapath.ofproto.OFP_VERSION)\n\n        # ------ Host link processing -----\n\n        # Check if this is a host discovery packet\n        if host_info is not None:\n            # Unpack the host info and create a new host object\n            host_name, host_ip, host_mac = host_info\n            port = self._get_port(dst_dpid, dst_port_no)\n            host = Host(host_mac, port, host_name)\n\n            # If this is a new host add it\n            if host_mac not in self.hosts:\n                self.hosts.add(host)\n\n                # Set the IP of the host\n                if \":\" in host_ip:\n                    self.hosts.update_ip(host, ip_v6=host_ip)\n                else:\n                    self.hosts.update_ip(host, ip_v4=host_ip)\n\n                # Notify listeners that a new hosts has connected\n                ev = event.EventHostAdd(host)\n                self.send_event_to_observers(ev)\n\n                # Add the host to the list of special links\n                self.special_links[port] = SpecialLinkData(host)\n            elif self.hosts[host_mac].port != port:\n                # Set the IP of the host\n                if \":\" in host_ip:\n                    self.hosts.update_ip(host, ip_v6=host_ip)\n                else:\n                    self.hosts.update_ip(host, ip_v4=host_ip)\n\n                # Move the special link entry to reflect the new port\n                del self.special_links[self.hosts[host_mac].port]\n                self.special_links[port] = SpecialLinkData(host)\n\n                # Move the host and send event notifying that host has changed\n                ev = event.EventHostMove(src=self.hosts[host_mac], dst=host)\n                self.hosts[host_mac] = host\n                self.send_event_to_observers(ev)\n            else:\n                self.special_links[port].received_lldp()\n\n            return\n\n        # ------ Inter-domain link processing -----\n\n        src = self._get_port(src_dpid, src_port_no)\n        if src and src.dpid == dst_dpid:\n            # If the LLDP packet was received on the same switch it was\n            # flooded on (loop) ignore it. The packet is neither a normal\n            # topo discovery discovery packet or a inter-domain packet\n            # (the LLDP port will not exist for inter-domain packets).\n            return\n        elif not src:\n            # Don't know where packet was received so stop\n            dst = self._get_port(dst_dpid, dst_port_no)\n            if not dst:\n                return\n\n            # If the link exists flag that we received a lldp packet for it\n            # otherwise add the link\n            src = InterDomPort(src_dpid, src_port_no)\n            if dst in self.special_links:\n                self.special_links[dst].received_lldp()\n            else:\n                self.special_links[dst] = SpecialLinkData(src)\n\n                # Notify listeners that a new inter-domain link was found\n                inter_dom_link = Link(dst, src)\n                ev = event.EventInterDomLinkAdd(inter_dom_link)\n                self.send_event_to_observers(ev)\n\n            return\n\n        # ------ Standard link (intra-domain) processing -----\n        try:\n            self.ports.lldp_received(src)\n        except KeyError:\n            # There are races between EventOFPPacketIn and\n            # EventDPPortAdd. So packet-in event can happend before\n            # port add event. In that case key error can happend.\n            # LOG.debug('lldp_received error', exc_info=True)\n            pass\n\n        dst = self._get_port(dst_dpid, dst_port_no)\n        if not dst:\n            return\n\n        old_peer = self.links.get_peer(src)\n        # LOG.debug(\"Packet-In\")\n        # LOG.debug(\"  src=%s\", src)\n        # LOG.debug(\"  dst=%s\", dst)\n        # LOG.debug(\"  old_peer=%s\", old_peer)\n        if old_peer and old_peer != dst:\n            old_link = Link(src, old_peer)\n            del self.links[old_link]\n            self.send_event_to_observers(event.EventLinkDelete(old_link))\n\n        link = Link(src, dst)\n        if link not in self.links:\n            self.send_event_to_observers(event.EventLinkAdd(link))\n\n            # remove hosts if it's not attached to edge port\n            host_to_del = []\n            for host in self.hosts.values():\n                if not self._is_edge_port(host.port):\n                    host_to_del.append(host.mac)\n\n            for host_mac in host_to_del:\n                del self.hosts[host_mac]\n\n        if not self.links.update_link(src, dst):\n            # reverse link is not detected yet.\n            # So schedule the check early because it's very likely it's up\n            self.ports.move_front(dst)\n            self.lldp_event.set()\n        if self.explicit_drop:\n            self._drop_packet(msg)\n\n\n    def send_lldp_packet(self, port):\n        try:\n            port_data = self.ports.lldp_sent(port)\n        except KeyError:\n            # ports can be modified during our sleep in self.lldp_loop()\n            # LOG.debug('send_lld error', exc_info=True)\n            return\n        if port_data.is_down:\n            return\n\n        dp = self.dps.get(port.dpid, None)\n        if dp is None:\n            # datapath was already deleted\n            return\n\n        # LOG.debug('lldp sent dpid=%s, port_no=%d', dp.id, port.port_no)\n        # TODO:XXX\n        if dp.ofproto.OFP_VERSION == ofproto_v1_0.OFP_VERSION:\n            actions = [dp.ofproto_parser.OFPActionOutput(port.port_no)]\n            dp.send_packet_out(actions=actions, data=port_data.lldp_data)\n        elif dp.ofproto.OFP_VERSION >= ofproto_v1_2.OFP_VERSION:\n            actions = [dp.ofproto_parser.OFPActionOutput(port.port_no)]\n            out = dp.ofproto_parser.OFPPacketOut(\n                datapath=dp, in_port=dp.ofproto.OFPP_CONTROLLER,\n                buffer_id=dp.ofproto.OFP_NO_BUFFER, actions=actions,\n                data=port_data.lldp_data)\n            dp.send_msg(out)\n        else:\n            LOG.error('cannot send lldp packet. unsupported version. %x',\n                      dp.ofproto.OFP_VERSION)\n\n\n    def lldp_loop(self):\n        while self.is_active:\n            self.lldp_event.clear()\n            if not self.pause_detection:\n                now = time.time()\n                timeout = None\n                ports_now = []\n                ports = []\n                for (key, data) in self.ports.items():\n                    if data.timestamp is None:\n                        ports_now.append(key)\n                        continue\n\n                    expire = data.timestamp + self.LLDP_SEND_PERIOD_PER_PORT\n                    if expire <= now:\n                        ports.append(key)\n                        continue\n\n                    timeout = expire - now\n\n                    # XXX: Normally, this break would be strange, however, the\n                    # port dataset is ordered. When an LLDP packet is sent on\n                    # a port, the port is moved to the end of the list. Ports\n                    # due for sending packets will queue up at the front so\n                    # if the front port is not due for flooding a packet,\n                    # subsequent ports will not be due either.\n                    break\n\n                for port in ports_now:\n                    self.send_lldp_packet(port)\n                for port in ports:\n                    self.send_lldp_packet(port)\n                    hub.sleep(self.LLDP_SEND_GUARD)      # don't burst\n\n\n                # Check for expired special links\n                special_expired = []\n                for (key, data) in self.special_links.items():\n                    if data.is_host():\n                        expire = data.timestamp + self.HOST_TIMEOUT\n                        if expire <= now:\n                            del self.special_links[key]\n                            self._host_down(data.obj)\n                    elif data.is_inter_dom_link():\n                        expire = data.timestamp + self.INTER_DOM_LINK_TIMEOUT\n                        if expire <= now:\n                            del self.special_links[key]\n                            self._inter_dom_down(key, data.obj)\n\n                if timeout is not None and ports:\n                    timeout = 0     # We have already slept\n                #LOG.info('lldp sleep %s', timeout)\n\n                # If timeout is None set it to the LLDP send period (never\n                # wait indefinetly in case the code deadlocks)\n                if timeout is None:\n                    timeout = self.LLDP_SEND_PERIOD_PER_PORT\n\n                self.lldp_event.wait(timeout=timeout)\n            else:\n                self.pause_detection_state.set()\n                self.lldp_event.wait()\n                self.pause_detection_state.set()\n\n\n    def link_loop(self):\n        while self.is_active:\n            self.link_event.clear()\n\n            if not self.pause_detection:\n                now = time.time()\n                deleted = []\n                for (link, timestamp) in self.links.items():\n                    # LOG.debug('%s timestamp %d (now %d)', link, timestamp, now)\n                    if timestamp + self.LINK_TIMEOUT < now:\n                        src = link.src\n                        if src in self.ports:\n                            port_data = self.ports.get_port(src)\n                            # LOG.debug('port_data %s', port_data)\n                            if port_data.lldp_dropped() > self.LINK_LLDP_DROP:\n                                deleted.append(link)\n\n                for link in deleted:\n                    self.links.link_down(link)\n                    # LOG.debug('delete %s', link)\n                    self.send_event_to_observers(event.EventLinkDelete(link))\n\n                    dst = link.dst\n                    rev_link = Link(dst, link.src)\n                    if rev_link not in deleted:\n                        # It is very likely that the reverse link is also\n                        # disconnected. Check it early.\n                        expire = now - self.LINK_TIMEOUT\n                        self.links.rev_link_set_timestamp(rev_link, expire)\n                        if dst in self.ports:\n                            self.ports.move_front(dst)\n                            self.lldp_event.set()\n\n            self.link_event.wait(timeout=self.TIMEOUT_CHECK_PERIOD)\n\n\n    @set_ev_cls(event.EventSwitchRequest)\n    def switch_request_handler(self, req):\n        # LOG.debug(req)\n        dpid = req.dpid\n\n        switches = []\n        if dpid is None:\n            # reply all list\n            for dp in self.dps.values():\n                switches.append(self._get_switch(dp.id))\n        elif dpid in self.dps:\n            switches.append(self._get_switch(dpid))\n\n        rep = event.EventSwitchReply(req.src, switches)\n        self.reply_to_request(req, rep)\n\n\n    @set_ev_cls(event.EventLinkRequest)\n    def link_request_handler(self, req):\n        # LOG.debug(req)\n        dpid = req.dpid\n\n        if dpid is None:\n            links = self.links\n        else:\n            links = [link for link in self.links if link.src.dpid == dpid]\n        rep = event.EventLinkReply(req.src, dpid, links)\n        self.reply_to_request(req, rep)\n\n\n    @set_ev_cls(event.EventHostRequest)\n    def host_request_handler(self, req):\n        dpid = req.dpid\n        hosts = []\n        if dpid is None:\n            for mac in self.hosts:\n                hosts.append(self.hosts[mac])\n        else:\n            hosts = self.hosts.get_by_dpid(dpid)\n\n        rep = event.EventHostReply(req.src, dpid, hosts)\n        self.reply_to_request(req, rep)\n","repo_name":"wandsdn/helix","sub_path":"topo_discovery/lldp_discovery.py","file_name":"lldp_discovery.py","file_ext":"py","file_size_in_byte":35004,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"48"}
+{"seq_id":"5655613117","text":"import jax.numpy as jnp\nimport matplotlib.pyplot as plt\n\nfrom experiments.shared.data import Data, ExperimentData\nfrom src.distributions import Gaussian\nfrom src.gps.base.base import GPBaseParameters\nfrom src.gps.base.regression_base import GPRegressionBase\n\n\ndef plot_data(\n    save_path: str,\n    train_data: Data = None,\n    test_data: Data = None,\n    validation_data: Data = None,\n    inducing_data: Data = None,\n    title: str = None,\n    prediction_x: jnp.ndarray = None,\n    mean: jnp.ndarray = None,\n    covariance: jnp.ndarray = None,\n):\n    fig, ax = plt.subplots(figsize=(13, 6.5))\n    fig.tight_layout()\n    if mean is not None and covariance is not None and prediction_x is not None:\n        ax.plot(prediction_x.reshape(-1), mean.reshape(-1), label=\"mean\")\n        stdev = jnp.sqrt(covariance)\n        ax.fill_between(\n            prediction_x.reshape(-1),\n            (mean - 1.96 * stdev).reshape(-1),\n            (mean + 1.96 * stdev).reshape(-1),\n            facecolor=(0.8, 0.8, 0.8),\n            label=\"error bound (95%)\",\n        )\n    if title is not None:\n        ax.set_title(title)\n    if train_data is not None:\n        ax.scatter(\n            train_data.x,\n            train_data.y,\n            label=\"train\",\n            alpha=0.3,\n            color=\"tab:blue\",\n        )\n    if validation_data is not None:\n        ax.scatter(\n            validation_data.x,\n            validation_data.y,\n            label=\"validation\",\n            alpha=0.3,\n            color=\"tab:green\",\n        )\n    if test_data is not None:\n        ax.scatter(\n            test_data.x,\n            test_data.y,\n            label=\"test\",\n            alpha=0.3,\n            color=\"tab:orange\",\n        )\n    if inducing_data is not None:\n        ax.scatter(\n            inducing_data.x,\n            inducing_data.y,\n            label=\"inducing\",\n            color=\"black\",\n        )\n    ax.set_xlabel(\"x\")\n    ax.set_ylabel(\"y\")\n    ax.legend()\n    fig.savefig(save_path, bbox_inches=\"tight\")\n    plt.close(fig)\n\n\ndef plot_prediction(\n    experiment_data: ExperimentData,\n    inducing_data: Data,\n    gp: GPRegressionBase,\n    gp_parameters: GPBaseParameters,\n    save_path: str,\n    title: str = None,\n):\n    gaussian_prediction = Gaussian(\n        **gp.predict_probability(\n            parameters=gp_parameters,\n            x=experiment_data.full.x,\n        ).dict()\n    )\n    plot_data(\n        train_data=experiment_data.train,\n        test_data=experiment_data.test,\n        validation_data=experiment_data.validation,\n        inducing_data=inducing_data,\n        title=title,\n        prediction_x=experiment_data.full.x,\n        mean=gaussian_prediction.mean,\n        covariance=gaussian_prediction.covariance,\n        save_path=save_path,\n    )\n","repo_name":"jswu18/gvi-gaussian-process","sub_path":"experiments/regression/plotters.py","file_name":"plotters.py","file_ext":"py","file_size_in_byte":2754,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"48"}
+{"seq_id":"35837163012","text":"from sympy import *\nimport numpy as np\n\n\nclass SymbolicLimitStateFunctions:\n\n    def __init__(self, variable_number, limit_state_functions):\n        symbol_code = ''\n        for i in range(variable_number-1):\n            symbol_code += 'x' + str(i+1) + ','\n        symbol_code += 'x' + str(variable_number)\n        x = [0]*(variable_number+1)\n        x[1:] = symbols(symbol_code)\n\n        symbolic_functions = []\n        for component in limit_state_functions:\n            symbolic_functions.append(eval(component))\n\n        self.variable_number = variable_number\n        self.component_number = len(limit_state_functions)\n        self.symbol_code = symbol_code\n        self.symbolic_limit_state_functions = symbolic_functions\n\n    def g(self, x_input):\n        x = [0]*(self.variable_number+1)\n        x[1:] = symbols(self.symbol_code)\n        g_input = [0]*self.variable_number\n        g = np.zeros(self.component_number)\n\n        # make tuple to put in subs function and compute g(x)\n        for i in range(self.variable_number):\n            g_input[i] = (x[i+1], x_input[i][0])\n        for i in range(self.component_number):\n            g[i] = float(self.symbolic_limit_state_functions[i].subs(g_input))\n\n        return g\n\n    def gradient_g(self, x_input):\n        x = [0]*(self.variable_number+1)\n        x[1:] = symbols(self.symbol_code)\n        gradient_g_input = [0]*self.variable_number\n        gradient_g = np.zeros((self.component_number, self.variable_number))\n\n        # make tuple to put in subs function and compute gradient_g(x)\n        for i in range(self.variable_number):\n            gradient_g_input[i] = (x[i+1], x_input[i][0])\n        for i in range(self.component_number):\n            for k in range(self.variable_number):\n                gradient_g_k = self.symbolic_limit_state_functions[i].diff(x[k+1])\n                gradient_g[i][k] = float(gradient_g_k.subs(gradient_g_input))\n\n        return gradient_g\n","repo_name":"99taeha/STRAP","sub_path":"strap/symbolic_limit_state_functions.py","file_name":"symbolic_limit_state_functions.py","file_ext":"py","file_size_in_byte":1935,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"48"}
+{"seq_id":"1112921418","text":"__author__ = \"Apoorv Malik\"\n__email__ = \"malikap@oregonstate.edu\"\n\n\n# link: https://leetcode.com/problems/max-consecutive-ones-ii/\n\nfrom typing import List\n\n\nclass Solution:\n    def findMaxConsecutiveOnes(self, nums: List[int]) -> int:\n        best_score, current_score, restricted_score = 0, 0, 0\n        for e in nums:\n            if e:\n                current_score += 1\n                restricted_score += 1\n            else:\n                current_score = restricted_score + 1\n                restricted_score = 0\n            best_score = max(best_score, current_score)\n        return best_score\n","repo_name":"1998apoorvmalik/leetcode","sub_path":"487.py","file_name":"487.py","file_ext":"py","file_size_in_byte":602,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"11666090004","text":"import pytest\nfrom robot import robot, robotStatus\nfrom map_gen import MAP_SET, BASIC_MAP\n\nBATTERY_A_START_VAL = 13\n@pytest.mark.dependency(name=\"BATA\", depends=[\"MVB\", \"MVC\"], scope=\"session\")\ndef test_BATA():\n    robot_t = robot(BASIC_MAP(20)[0], 0, 0, BATTERY_A_START_VAL)\n    for i in range(1, 13):\n        robot_t.right()\n        assert robot_t.get_battery() == BATTERY_A_START_VAL - i\n        assert robot_t.get_status()  == robotStatus.ALIVE\n    \n    for i in range(20):\n        robot_t.right()\n        assert robot_t.get_battery() == 0\n        assert robot_t.get_status()  == robotStatus.DEAD\n\n    assert (robot_t.get_x(), robot_t.get_y()) == (0, 13)\n\nBATTERY_B_START_VAL = 2\nBATTERY_B_PICK_VAL  = 11\nBATTERY_B_END_VAL   = 9\n@pytest.mark.dependency(name=\"BATB\", depends=[\"MVB\", \"MVC\"], scope=\"session\")\ndef test_BATB():\n    robot_t = robot(MAP_SET[2], 3, 0, BATTERY_B_START_VAL)\n    robot_t.up()\n    assert robot_t.get_battery() == BATTERY_B_PICK_VAL\n    \n    for _ in range(20):\n        robot_t.right(2).left(2)\n\n    assert robot_t.get_battery() == BATTERY_B_END_VAL\n    assert robot_t.get_status()  == robotStatus.CRASH\n    \nBATTERY_C_START_VAL = 2\nBATTERY_C_END_VAL   = 8\n@pytest.mark.dependency(name=\"BATC\", depends=[\"MVB\", \"MVC\"], scope=\"session\")\ndef test_BATC():\n    robot_t = robot(MAP_SET[3], 2, 1, BATTERY_C_START_VAL)\n    robot_t.left().down().up().right()\n\n    assert robot_t.get_status()  == robotStatus.ALIVE\n    assert robot_t.get_battery() == BATTERY_C_END_VAL\n\nLOOP_D_CONST        = 49\nBATTERY_D_START_VAL = 10 ** 4\nMAP_SIZE            = 10 ** 2\nBATTERY_D_END_VAL   = 100000\ndef f(val): return val == BATTERY_D_START_VAL\ndef g(val): return val > BATTERY_D_START_VAL\nset_BATD = [(robot(BASIC_MAP(MAP_SIZE)[1], 0, 0, BATTERY_D_START_VAL), f, robotStatus.WATER, robotStatus.WATER),\n            (robot(BASIC_MAP(MAP_SIZE)[2], 0, 0, BATTERY_D_START_VAL), f, robotStatus.CRASH, robotStatus.CRASH),\n            (robot(BASIC_MAP(MAP_SIZE)[3], 0, 0, BATTERY_D_START_VAL), g, robotStatus.ALIVE, robotStatus.CRASH)]\n@pytest.mark.dependency(name=\"BATD\", depends=[\"BATA\", \"BATB\", \"BATC\"], scope=\"session\")\n@pytest.mark.parametrize(\"robot_t, func_t, status_ta, status_tb\", set_BATD)\ndef test_BATD(robot_t, func_t, status_ta, status_tb):\n    for _ in range(LOOP_D_CONST):\n        robot_t.right(MAP_SIZE - 1).down().left(MAP_SIZE - 1).down()\n        assert func_t(robot_t.get_battery())\n        assert robot_t.get_status() == status_ta\n    robot_t.right(MAP_SIZE - 1).down().left(MAP_SIZE - 1).down()\n    assert robot_t.get_status() == status_tb\n    if status_ta == robotStatus.ALIVE:\n        assert robot_t.get_battery() == BATTERY_D_END_VAL\n        assert (robot_t.get_x(), robot_t.get_y()) == (MAP_SIZE - 1, 0)\n\nLOOP_E_CONST        = 50\nBATTERY_E_START_VAL = 10 ** 5\nBATTERY_E_END_VAL   = 90001\n@pytest.mark.dependency(name=\"BATE\", depends=[\"BATD\"], scope=\"session\")\ndef test_BATE():\n    robot_t = robot(BASIC_MAP(MAP_SIZE)[0], 0, 0, BATTERY_E_START_VAL)\n    for i in range(1, LOOP_E_CONST):\n        robot_t.right(MAP_SIZE - 1).down().left(MAP_SIZE - 1).down()\n        assert robot_t.get_battery() == BATTERY_E_START_VAL - 2 * (i * MAP_SIZE)\n        assert robot_t.get_status()  == robotStatus.ALIVE\n\n    robot_t.right(MAP_SIZE - 1).down().left(MAP_SIZE - 1)\n    assert robot_t.get_battery() == BATTERY_E_END_VAL\n    assert robot_t.get_status()  == robotStatus.ALIVE\n","repo_name":"Rusiek/BIT_Academy-Practice","sub_path":"OOP/Task1/tst_move/test_04_battery.py","file_name":"test_04_battery.py","file_ext":"py","file_size_in_byte":3379,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"40681637749","text":"from collections import defaultdict\n\n\ndef degreeOfArray(arr):\n    ref = defaultdict(list)\n    for i, num in enumerate(arr):\n        ref[num].append(i)\n    ref2 = defaultdict(int)\n    max_num = 0\n    for key in ref:\n        max_num = max(len(ref[key]), max_num)\n    min_dist = float(\"inf\")\n    for key in ref:\n        if(len(ref[key]) == max_num):\n            min_dist = min(min_dist, ref[key]\n                           [len(ref[key])-1] - ref[key][0] + 1)\n    return min_dist\n","repo_name":"GaronSmith/python-data-structures-algorithms","sub_path":"hacker_rank/degree_of_array.py","file_name":"degree_of_array.py","file_ext":"py","file_size_in_byte":477,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"48"}
+{"seq_id":"15173780060","text":"#Bamlak Tekle\n#Date: 09/26/22\n#Class Section: 8\n#Assignment#3\n#problem 2\n\n#ask the user to input the length for the sides three times (shortest to longest)\nshortside= int(input(\"Enter the length of the shortest side: \"))\ninterside= int(input(\"Enter the length of the intermediate side: \"))\nlongside= int(input(\"Enter the length of the longest side: \"))\n#use the if and else function\n#if all sides are equal, print that it is an equilateral triangle\n#use math equations to calculate the area for the right and equilateral triangles\nif shortside==interside==longside:\n    print(\"It is an equilateral triangle\")\n    equiarea= (3**0.5)*(shortside**2)/4\n    print(equiarea)\nelif longside**2==(interside**2) + (shortside**2):\n    print(\"It is a right triangle\")\n    rightarea= 0.5*(shortside*interside)\n    print(rightarea)\nelif longside>=shortside+interside:\n    print(\"It is not valid triangle\")\nelse:\n    print(\"It is a valid triangle, but not a right triangle or an equilateral triangle\")\n    \n","repo_name":"bamlaktekle/library","sub_path":"TekleBamlak_library.py","file_name":"TekleBamlak_library.py","file_ext":"py","file_size_in_byte":992,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"35738996779","text":"import collections\nimport os\n\nimport numpy as np\nimport torch\nimport torchvision\nfrom torch.utils.data import DataLoader\nfrom common.base_model import BaseModel, show_gpu_memory_info\nfrom projects.expert_neuron.dataset import Dataset\nfrom projects.expert_neuron.modules.completer import Completer\n\nfrom settings import settings\n\n\nclass ExpertNeuron(BaseModel):\n    def __init__(self, **_):\n        super(ExpertNeuron, self).__init__()\n        self.expert_one = Completer(in_channels=24, out_channels=3, input_size=settings.coordmap_input_size,\n                                    output_size=settings.coordmap_input_size, features_map=settings.coordmap_features_map)\n        self.expert_two = Completer(in_channels=6, out_channels=3, input_size=settings.coordmap_input_size,\n                                    output_size=settings.coordmap_input_size, features_map=settings.coordmap_features_map)\n        self.train_epochs = settings.segmentation_train_epochs\n        self.train_dataset = Dataset(\n            master_dir=settings.coordmap_train_images_dir,\n            image_size=settings.coordmap_input_size,\n            len_dataset=-1,\n            preprocessing=self.expert_one.get_preprocessing()\n        )\n        self.valid_dataset = Dataset(\n            master_dir=settings.coordmap_valid_images_dir,\n            image_size=settings.coordmap_input_size,\n            len_dataset=settings.valid_batch_size * 2,\n            preprocessing=self.expert_one.get_preprocessing()\n        )\n        self.train_loader = DataLoader(self.train_dataset,\n                                       shuffle=True,\n                                       batch_size=settings.train_batch_size,\n                                       num_workers=settings.train_num_workers)\n        self.valid_loader = DataLoader(self.valid_dataset,\n                                       shuffle=False,\n                                       batch_size=settings.valid_batch_size,\n                                       num_workers=settings.train_num_workers)\n        self.criterion = torch.nn.BCELoss()\n        b1, b2 = .5, .999\n        self.optimizer_one = torch.optim.Adam(self.expert_one.parameters(), lr=settings.segmentation_lr[0],\n                                              betas=(b1, b2))\n        self.optimizer_two = torch.optim.Adam(self.expert_two.parameters(), lr=settings.segmentation_lr[0],\n                                              betas=(b1, b2))\n\n        self.lr_decay_steps = 0\n\n        if not os.path.isdir(settings.models_weights_dir):\n            os.makedirs(settings.models_weights_dir)\n        self.expert_one_weights_filepath = os.path.join(settings.models_weights_dir,\n                                                        self.expert_one.get_weights_filename())\n        self.expert_two_weights_filepath = os.path.join(settings.models_weights_dir,\n                                                        self.expert_two.get_weights_filename())\n\n    def initialize(self, logger, device: torch.device, device2: torch.device):\n        super(ExpertNeuron, self).initialize(logger, device, device2)\n        if os.path.isfile(self.expert_one_weights_filepath):\n            print(f'Loading model weights for net from {self.expert_one_weights_filepath}')\n            try:\n                self.expert_one.load_state_dict(torch.load(self.expert_one_weights_filepath, map_location='cpu'),\n                                                strict=False)\n            except Exception as e:\n                print(str(e))\n        if os.path.isfile(self.expert_two_weights_filepath):\n            print(f'Loading model weights for net from {self.expert_two_weights_filepath}')\n            try:\n                self.expert_two.load_state_dict(torch.load(self.expert_two_weights_filepath, map_location='cpu'),\n                                                strict=False)\n            except Exception as e:\n                print(str(e))\n\n        self.expert_one = self.expert_one.to(device)\n        self.expert_two = self.expert_two.to(device2)\n        # show_gpu_memory_info()\n        self.expert_one.zero_grad()\n        self.expert_two.zero_grad()\n\n    def train(self, epoch: int):\n        self.adjust_lr(epoch)\n        self.expert_one.train()\n        self.expert_two.train()\n        losses_queue = collections.deque(maxlen=settings.segmentation_log_interval_batches)\n        for batch_idx, (data_one, data_two, target) in enumerate(self.train_loader):\n            data_one, data_two, = data_one.to(self.device), data_two.to(self.device2)\n\n            output_one = self.expert_one(data_one)\n            output_two = self.expert_two(data_two)\n            loss_dissimilarity_one = self.criterion(output_one, output_two.detach().to(self.device))\n            # loss_dissimilarity_two = self.criterion(output_two, output_one.detach())\n\n            loss_target_one = self.criterion(output_one, target.to(self.device))\n            loss_target_two = self.criterion(output_two, target.to(self.device2))\n\n            loss_one = loss_dissimilarity_one + loss_target_one\n            loss_two = loss_target_two  # loss_dissimilarity_two\n\n            loss = loss_one.detach().cpu() + loss_two.detach().cpu()\n            losses_queue.append(loss.item())\n            loss_one.backward()\n            loss_two.backward()\n            # loss_target_two.backward()\n            self.optimizer_one.step()\n            self.optimizer_two.step()\n            self.expert_one.zero_grad()\n            self.expert_two.zero_grad()\n\n            # Log batch results\n            if (batch_idx + 1) % settings.segmentation_log_interval_batches == 0:\n                mean_loss = sum(losses_queue) / len(losses_queue)\n                self.logger.report_scalar(\n                    \"train\", \"loss\", iteration=(epoch * len(self.train_loader) + batch_idx + 1), value=mean_loss)\n\n                print(f'Train Epoch: {epoch}/{settings.segmentation_train_epochs} '\n                      f'[{(batch_idx + 1) * settings.train_batch_size}/{len(self.train_loader.dataset)} '\n                      f'({100. * (batch_idx + 1) / len(self.train_loader):.0f}%)]\\t'\n                      f'Loss: {mean_loss:.6f}')\n\n    def validate(self, epoch: int) -> float:\n        self.expert_one.eval()\n        self.expert_two.eval()\n        losses_queue = collections.deque()\n        with torch.no_grad():\n            for batch_idx, (data_one, data_two, target) in enumerate(self.valid_loader):\n                data_one, data_two = data_one.to(self.device), data_two.to(self.device2)\n\n                output_one = self.expert_one(data_one)\n                output_two = self.expert_two(data_two)\n                loss_dissimilarity_one = self.criterion(output_one, output_two.detach().to(self.device))\n                # loss_dissimilarity_two = self.criterion(output_two, output_one.detach())\n\n                loss_target_one = self.criterion(output_one, target.to(self.device))\n                loss_target_two = self.criterion(output_two, target.to(self.device2))\n\n                loss_one = loss_dissimilarity_one + loss_target_one\n                loss_two = loss_target_two # + loss_dissimilarity_two\n\n                loss = loss_one.detach().cpu() + loss_two.detach().cpu()\n                losses_queue.append(loss.item())\n\n                # Save as image the last batch prediction\n                if epoch % settings.segmentation_save_image_interval_epochs == 0:\n                    self.save_prediction_image(epoch, batch_idx, data_one, data_two, output_one, output_two, target,\n                                               'validation')\n\n        mean_loss = sum(losses_queue) / len(losses_queue)\n        self.logger.report_scalar(\"validation\", \"loss\", iteration=epoch, value=mean_loss)\n        print(f'Validation Epoch: {epoch}\\tLoss: {mean_loss:.6f}')\n        return mean_loss\n\n    def save_prediction_image(self, epoch, batch_idx, data_one, data_two, output_one, output_two, target, series: str):\n        data_one_grid = torchvision.utils.make_grid(data_one.detach().cpu()[:, :3, :, :],\n                                                    nrow=data_one.size(0)).permute(1, 2, 0).numpy()\n        data_one_grid2 = torchvision.utils.make_grid(data_one.detach().cpu()[:, 3:6, :, :],\n                                                     nrow=data_one.size(0)).permute(1, 2, 0).numpy()\n        data_two_grid = torchvision.utils.make_grid(data_two.detach().cpu()[:, :3, :, :],\n                                                    nrow=data_two.size(0)).permute(1, 2, 0).numpy()\n        data_two_grid2 = torchvision.utils.make_grid(data_two.detach().cpu()[:, 3:, :, :],\n                                                     nrow=data_two.size(0)).permute(1, 2, 0).numpy()\n        data_one_out_grid = torchvision.utils.make_grid(output_one.detach().cpu(),\n                                                        nrow=output_one.size(0)).permute(1, 2, 0).numpy()\n        data_two_out_grid = torchvision.utils.make_grid(output_two.detach().cpu(),\n                                                        nrow=output_two.size(0)).permute(1, 2, 0).numpy()\n        target_grid = torchvision.utils.make_grid(target.detach().cpu(),\n                                                  nrow=target.size(0)).permute(1, 2, 0).numpy()\n        segmentator_real_grid = np.vstack(\n            (data_one_grid, data_one_grid2, data_two_grid, data_two_grid2, data_one_out_grid, data_two_out_grid,\n             target_grid))\n        self.logger.report_image(\"segmentation\", f\"{series} {batch_idx}\", iteration=epoch,\n                                 image=segmentator_real_grid)\n\n    def can_save_weights(self, epoch) -> bool:\n        return epoch % 5 == 0 and epoch > 1\n\n    def save_modules_weights(self):\n        torch.save(self.expert_one.state_dict(), self.expert_one_weights_filepath)\n        torch.save(self.expert_two.state_dict(), self.expert_two_weights_filepath)\n\n    def adjust_lr(self, epoch: int):\n        if self.lr_decay_steps < len(settings.segmentation_lr_decay_interval_epochs) and \\\n                epoch > settings.segmentation_lr_decay_interval_epochs[self.lr_decay_steps]:\n            self.lr_decay_steps += 1\n            old_lr = self.optimizer_one.param_groups[0]['lr']\n            new_lr = settings.segmentation_lr[\n                min(self.lr_decay_steps, len(settings.segmentation_lr) - 1)]\n            if new_lr != old_lr:\n                self.optimizer_one.param_groups[0]['lr'] = new_lr\n                self.optimizer_two.param_groups[0]['lr'] = new_lr\n                print(f'Changed learning rate of generator from {old_lr} to {new_lr}')\n        self.logger.report_scalar(\"learning rate\", \"lr\", iteration=epoch,\n                                  value=self.optimizer_one.param_groups[0]['lr'])\n\n    def test(self):\n        self.expert_one.eval()\n        self.expert_two.eval()\n\n        test_dataset = Dataset(\n            master_dir=settings.coordmap_valid_images_dir,\n            image_size=settings.coordmap_input_size,\n            len_dataset=-1,\n            preprocessing=self.expert_one.get_preprocessing()\n        )\n        test_loader = DataLoader(test_dataset,\n                                 shuffle=False,\n                                 batch_size=1,\n                                 num_workers=2)\n\n        original_image = (self.valid_dataset.master / 255).astype(np.float32)\n        h, w, c = original_image.shape\n        final_image_one = np.zeros((h, w, c), dtype=np.float32)\n        final_image_two = np.zeros((h, w, c), dtype=np.float32)\n        with torch.no_grad():\n            for batch_idx, (data_one, data_two, target) in enumerate(test_loader):\n                data_one, data_two = data_one.to(self.device), data_two.to(self.device)\n\n                output_one = self.expert_one(data_one)\n                output_image = output_one.detach().cpu()[0, ...].permute(1, 2, 0).numpy()\n                final_image_one[\n                test_dataset.yy[batch_idx] + int(test_dataset.image_size):test_dataset.yy[\n                                                                              batch_idx] + int(\n                    self.valid_dataset.image_size * 2),\n                test_dataset.xx[batch_idx] + int(test_dataset.image_size):test_dataset.xx[\n                                                                              batch_idx] + int(\n                    self.valid_dataset.image_size * 2),\n                :] = output_image\n\n                output_two = self.expert_two(data_two)\n                output_image = output_two.detach().cpu()[0, ...].permute(1, 2, 0).numpy()\n                final_image_two[\n                test_dataset.yy[batch_idx] + int(test_dataset.image_size):test_dataset.yy[\n                                                                              batch_idx] + int(\n                    self.valid_dataset.image_size * 2),\n                test_dataset.xx[batch_idx] + int(test_dataset.image_size):test_dataset.xx[\n                                                                              batch_idx] + int(\n                    self.valid_dataset.image_size * 2),\n                :] = output_image\n                if batch_idx % 50 == 0:\n                    print(batch_idx, '/', len(test_loader))\n\n        self.logger.report_image(\"test\", \"reconstructed image 1\", iteration=1, image=final_image_one)\n        self.logger.report_image(\"test\", \"reconstructed image 2\", iteration=1, image=final_image_two)\n        self.logger.report_image(\"test\", \"reconstructed image diff\", iteration=1,\n                                 image=np.abs(final_image_one - final_image_two))\n","repo_name":"Daniangio/model_training","sub_path":"projects/expert_neuron/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":13551,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"4580915225","text":"# https://leetcode.com/problems/maximum-subarray/\n\n# Kadanes's Algorithm\n\nclass Solution:\n    def maxSubArray(self, nums: List[int]) -> int:\n        maxSum = nums[0]\n        currentSum = 0\n        for num in nums:\n            if currentSum < 0:\n                currentSum = 0\n            currentSum += num\n            maxSum = max(currentSum, maxSum)\n        return maxSum","repo_name":"Akshay-Tarde/LeetCodeQuestions","sub_path":"arrays/maximum-subarray.py","file_name":"maximum-subarray.py","file_ext":"py","file_size_in_byte":372,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"15308522101","text":"import os\n\nimport uvicorn\nfrom fastapi import FastAPI\nfrom fastapi.middleware.cors import CORSMiddleware\n\nfrom api_app.settings import get_app_settings\nfrom api_app.api import router as api_router\nfrom api_app.exceptions import attribute_error_handler\n\n\ndef get_application():\n    settings = get_app_settings()\n\n    application = FastAPI(**settings.fastapi_kwargs)\n\n    application.add_middleware(\n        CORSMiddleware,\n        allow_origins=settings.allowed_hosts,\n        allow_credentials=settings.cors_allow_credentials,\n        allow_methods=settings.cors_allow_methods,\n        allow_headers=settings.cors_allow_headers,\n    )\n\n    application.add_exception_handler(AttributeError, attribute_error_handler)\n    application.include_router(api_router, prefix=settings.api_prefix)\n\n    return application\n\n\napp = get_application()\n\n\nif __name__ == \"__main__\":\n    host = os.environ.get(\"HOST\", \"localhost\")\n    port = int(os.environ.get(\"PORT\", 8000))\n    uvicorn.run(app, host=host, port=port)\n","repo_name":"EldarKhayarov/russian-hate-speech-classifier","sub_path":"api_app/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1000,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"32517996226","text":"from sqlalchemy import Column, String, Integer, Boolean, ForeignKey\nfrom applications import db\n\nfrom sqlalchemy.orm import relationship, backref\n\nimport json\n\nfrom ..utils.constants import ElectiveStatus\n\n\nclass PlanCourseScheduling(db.Model):\n\t__tablename__ = 'plan_course_scheduling'\n\n\tid = Column(Integer, primary_key=True)\n\n\t\"\"\"\n\t\t某个老师的排课\n\t\"\"\"\n\n\t# # 开课单位\n\t# insitution_name = Column(String(256))\n\t# # 中文简称\n\t# short_name = Column(String(256))\n\t\n\t# 课程\n\tplan_course_id = Column(Integer, ForeignKey('plan_course.id'))\n\tplan_course = relationship('PlanCourse',\n\t\tforeign_keys=[plan_course_id],\n\t\tbackref=backref('plan_course_scheduling', lazy='dynamic'))\n\n\t# 学期\n\tbase_term_id = Column(Integer, ForeignKey('base_term.id'))\n\tbase_term = relationship('BaseTerm',\n\t\tforeign_keys=[base_term_id],\n\t\tbackref=backref('plan_course_scheduling', lazy='dynamic'))\n\n\t# 上课时间\n\tteaching_datetimes = Column(String(1080))\n\t# 教学地点\n\tteaching_place = Column(String(256))\n\t# 教学资源\n\tteaching_resource = Column(String(256))\n\t# 教学班所在校区\n\tcampus = Column(String(256))\n\t# 起始周\n\tstart_week = Column(String(256))\n\t# 终止周\n\tend_week = Column(String(256))\n\t# 周学时\n\thours_of_week = Column(String(256))\n\n\t# 计划人数\n\texpected_members = Column(Integer)\n\t# # 已选人数\n\t# actual_members = Column(Integer)\n\t\n\t# 是否限定专业\n\tfor_base_major_ids = relationship('BaseMajor',\n\t\tbackref=backref('limit_plan_course_scheduling_ids', lazy='dynamic'),\n\t\tsecondary='plan_course_scheduling_and_base_major'\n\t)\n\t# 是否限定年级\n\tfor_grade_grade_ids = relationship('GradeGrade',\n\t\tbackref=backref('limit_plan_course_scheduling_ids', lazy='dynamic'),\n\t\tsecondary='plan_course_scheduling_and_grade_grade'\n\t)\n\t# 是否限定班级\n\tfor_grade_class_ids = relationship('GradeClass',\n\t\tbackref=backref('limit_plan_course_scheduling_ids', lazy='dynamic'),\n\t\tsecondary='plan_course_scheduling_and_grade_class'\n\t)\n\t\n\t# 教师\n\tteacher_id = Column(Integer, ForeignKey('user.id'))\n\tteacher = relationship('User',\n\t\tforeign_keys=[teacher_id],\n\t\tbackref=backref('plan_course_scheduling_ids', lazy='dynamic'))\n\n\t# 开课说明（备注）\n\tremark = Column(String(256))\n\n\tdef __str__(self):\n\n\t\tinfo = []\n\t\tif self.teacher:\n\t\t\tinfo.append(self.teacher.username)\n\t\tif self.plan_course:\n\t\t\tinfo.append(self.plan_course.base_curriculum.name)\n\n\t\treturn \",\".join(info)\n\n\t@property\n\tdef actual_members(self):\n\t\tclass_members_mount = sum([i.class_member_ids.count() for i in self.for_grade_class_ids])\n\t\telective_members_mount = self.task_elective_ids.count()\n\t\ttotal = class_members_mount + elective_members_mount\n\t\treturn total\n\n\t# 以后做缓存\n\t@property\n\tdef my_elected_ids(self):\n\t\tfrom ..utils.login import current_user\n\t\tif not current_user:\n\t\t\treturn None\n\t\tif current_user.task_elective_ids:\n\t\t\treturn [elective.plan_course_scheduling_id for elective in current_user.task_elective_ids]\n\t\telse:\n\t\t\treturn []\n\n\t@property\n\tdef did_I_elect(self):\n\t\tif self.my_elected_ids == None:\n\t\t\treturn None\n\t\telif self.id in self.my_elected_ids:\n\t\t\treturn True\n\t\telse:\n\t\t\treturn False\n\n\n\t@property\n\tdef teaching_datetimes_simple(self):\n\n\t\ttry:\n\t\t\tjson_data = json.loads(self.teaching_datetimes)\n\t\texcept Exception as e:\n\t\t\treturn \"\"\n\n\t\tnew_dict = {'周一':[],'周二':[],'周三':[],'周四':[],'周五':[],}\n\t\tfor _class in ['第一节','第二节','第三节','第四节','第五节','第六节',]:\n\t\t\tfor _day in ['周一','周二','周三','周四','周五',]:\n\t\t\t\tif json_data[_class][_day] not in [0,'0']:\n\t\t\t\t\tnew_dict[_day].append(_class)\n\n\n\t\tfrom ..utils.week import combine_series\n\n\t\tdays = []\n\t\tfor _day in ['周一','周二','周三','周四','周五',]:\n\t\t\tif new_dict[_day]:\n\t\t\t\tclasses = combine_series(\n\t\t\t\t\tseries_tuple=['第一节','第二节','第三节','第四节','第五节','第六节',],\n\t\t\t\t\tunknown_tuple=new_dict[_day],\n\t\t\t\t)\n\t\t\t\tdays.append(\"{}:{}\".format(_day,\",\".join(classes)))\n\n\t\treturn days\n\n\n\t@property\n\tdef approved_members(self):\n\t\tclass_members_mount = sum([i.class_member_ids.count() for i in self.for_grade_class_ids])\n\t\telective_members_mount = self.task_elective_ids.filter_by(\n\t\t\tauditive_status=ElectiveStatus.approved).count()\n\t\ttotal = class_members_mount + elective_members_mount\n\t\treturn total","repo_name":"cllen/graduation-design","sub_path":"1code/tms-flask/server/applications/tms/models/plan_course_scheduling.py","file_name":"plan_course_scheduling.py","file_ext":"py","file_size_in_byte":4272,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"74589501264","text":"import copy\nimport os\nimport sys\nimport time\nimport torch\nimport torch.utils.data\nfrom torch import nn\nimport warnings\nfrom torch.utils.tensorboard import SummaryWriter\n\n# from torchvision import models\nsys.path.insert(0, \"./vision\")\ntry:\n    from torchvision import models\nexcept ImportError:\n    print(\"Error importing pytorch's torchvision repository!\")\n\nsys.path.append(os.path.abspath(\"./vision/references/classification\"))\ntry:\n    import utils\n    from train import load_data\nexcept ImportError:\n    print(\"Error import pytorch's vision repository!\")\n\n# Sparsity Toolkit\nsys.path.append(\"./apex\")\ntry:\n    from apex.contrib.sparsity import ASP\nexcept ImportError:\n    print(\"Error importing `apex`!\")\n\n# Quantization Toolkit\nfrom pytorch_quantization import nn as quant_nn\nfrom pytorch_quantization import calib\nimport tqdm\n\n\ndef prune_trained_model_custom(model, optimizer, allow_recompute_mask=False, allow_permutation=True,\n                               compute_sparse_masks=True):\n    \"\"\" Adds mask buffers to model (init_model_for_pruning), augments optimize, and computes masks if .\n    Source: https://github.com/NVIDIA/apex/blob/52c512803ba0a629b58e1c1d1b190b4172218ecd/apex/contrib/sparsity/asp.py#L299\n    Modifications:\n      1) Abstracted 'allow_recompute_mask' and 'allow_permutation' arguments\n      2) Enabled sparse mask computation as optional\n    \"\"\"\n    asp = ASP()\n    asp.init_model_for_pruning(\n        model,\n        mask_calculator=\"m4n2_1d\",\n        verbosity=2,\n        whitelist=[torch.nn.Linear, torch.nn.Conv2d],\n        allow_recompute_mask=allow_recompute_mask,\n        allow_permutation=allow_permutation\n    )\n    asp.init_optimizer_for_pruning(optimizer)\n    if compute_sparse_masks:\n        asp.compute_sparse_masks()\n    return asp\n\n\ndef data_loading(data_path, batch_size, torchvision_args, train_data_size=None, test_data_size=None, val_data_size=None,\n                 distributed=False, drop_last=False):\n    traindir = os.path.join(data_path, 'train')\n    valdir = os.path.join(data_path, 'val')\n    dataset, dataset_test, train_sampler, test_sampler = load_data(traindir, valdir, torchvision_args)\n    dataset_val = copy.deepcopy(dataset_test)\n    val_sampler = copy.deepcopy(test_sampler)\n\n    # Take subset if != None\n    if train_data_size:\n        dataset = torch.utils.data.Subset(dataset, list(range(0, train_data_size)))\n        if torchvision_args.distributed:\n            train_sampler = torch.utils.data.distributed.DistributedSampler(dataset)\n        else:\n            train_sampler = torch.utils.data.RandomSampler(dataset)\n    if test_data_size:\n        dataset_test = torch.utils.data.Subset(dataset_test, list(range(0, test_data_size)))\n        if torchvision_args.distributed:\n            test_sampler = torch.utils.data.distributed.DistributedSampler(dataset_test)\n        else:\n            test_sampler = torch.utils.data.RandomSampler(dataset_test)\n    if val_data_size:\n        dataset_val = torch.utils.data.Subset(dataset_val, list(range(0, val_data_size)))\n        if torchvision_args.distributed:\n            val_sampler = torch.utils.data.distributed.DistributedSampler(dataset_val)\n        else:\n            val_sampler = torch.utils.data.RandomSampler(dataset_val)\n\n    # Make dataloader\n    train_data_loader = torch.utils.data.DataLoader(\n        dataset, batch_size=batch_size,\n        sampler=train_sampler, drop_last=drop_last)\n\n    test_data_loader = torch.utils.data.DataLoader(\n        dataset_test, batch_size=batch_size,\n        sampler=test_sampler, drop_last=drop_last)\n\n    val_data_loader = torch.utils.data.DataLoader(\n        dataset_val, batch_size=batch_size,\n        sampler=val_sampler, drop_last=drop_last)\n\n    return train_data_loader, test_data_loader, val_data_loader\n\n\ndef eval_baseline(args, criterion, data_loader_test):\n    model = models.__dict__[args.model_name](pretrained=True)\n    model.to(args.device)\n\n    acc1, acc5, loss = None, None, None\n    if args.eval_baseline:\n        print(\"---------- Evaluating baseline model ----------\")\n        with torch.no_grad():\n            acc1, acc5, loss = evaluate(model, criterion, data_loader_test, device=args.device, print_freq=args.print_freq)\n\n    if args.save_baseline:\n        ckpt_path = os.path.join(args.output_dir, \"baseline.pth\")\n        torch.save({\n            'model_state_dict': model.state_dict(),\n            \"acc1_val\": acc1,\n            \"acc5_val\": acc5,\n            \"loss_val\": loss,\n            'args': args\n        }, ckpt_path)\n        export_onnx(model, ckpt_path.replace(\".pth\", \".onnx\"), args.batch_size, val_crop_size=args.val_crop_size)\n\n    return model, acc1, acc5\n\n\ndef train_one_epoch(model, criterion, optimizer, data_loader, device, epoch, args, steps_per_epoch=None,\n                    model_ema=None, scaler=None):\n    \"\"\"\n    Codebase: torch/vision/references/classification/train.py\n    Modification: added steps_per_epoch.\n    \"\"\"\n    model.train()\n    metric_logger = utils.MetricLogger(delimiter=\"  \")\n    metric_logger.add_meter(\"lr\", utils.SmoothedValue(window_size=1, fmt=\"{value}\"))\n    metric_logger.add_meter(\"img/s\", utils.SmoothedValue(window_size=10, fmt=\"{value}\"))\n\n    header = f\"Epoch: [{epoch}]\"\n    for i, (image, target) in enumerate(metric_logger.log_every(data_loader, args.print_freq, header)):\n        if steps_per_epoch is not None and i >= steps_per_epoch:\n            break\n        start_time = time.time()\n        image, target = image.to(device), target.to(device)\n        with torch.cuda.amp.autocast(enabled=scaler is not None):\n            output = model(image)\n            loss = criterion(output, target)\n\n        optimizer.zero_grad()\n        if scaler is not None:\n            scaler.scale(loss).backward()\n            if args.clip_grad_norm is not None:\n                # we should unscale the gradients of optimizer's assigned params if do gradient clipping\n                scaler.unscale_(optimizer)\n                nn.utils.clip_grad_norm_(model.parameters(), args.clip_grad_norm)\n            scaler.step(optimizer)\n            scaler.update()\n        else:\n            loss.backward()\n            if args.clip_grad_norm is not None:\n                nn.utils.clip_grad_norm_(model.parameters(), args.clip_grad_norm)\n            optimizer.step()\n\n        if model_ema and i % args.model_ema_steps == 0:\n            model_ema.update_parameters(model)\n            if epoch < args.lr_warmup_epochs:\n                # Reset ema buffer to keep copying weights during warmup period\n                model_ema.n_averaged.fill_(0)\n\n        acc1, acc5 = utils.accuracy(output, target, topk=(1, 5))\n        batch_size = image.shape[0]\n        metric_logger.update(loss=loss.item(), lr=optimizer.param_groups[0][\"lr\"])\n        metric_logger.meters[\"acc1\"].update(acc1.item(), n=batch_size)\n        metric_logger.meters[\"acc5\"].update(acc5.item(), n=batch_size)\n        metric_logger.meters[\"img/s\"].update(batch_size / (time.time() - start_time))\n    return metric_logger\n\n\ndef train_loop(model, model_without_ddp, criterion, optimizer, data_loader, data_loader_val, device, epoch, args, summary_writer_dir,\n               save_ckpt_path, steps_per_epoch=None, model_ema=None, scaler=None, lr_scheduler=None, opset=13):\n    def _save_model(ep, ckpt_path, acc1_val, acc5_val, current_val_loss):\n        torch.save({\n            'epoch': ep,\n            'model_state_dict': model_without_ddp.state_dict(),\n            'optimizer_state_dict': optimizer.state_dict(),\n            'lr_scheduler_state_dict': lr_scheduler.state_dict() if lr_scheduler is not None else None,  # ADDED\n            'loss': criterion,\n            \"acc1_val\": acc1_val,\n            \"acc5_val\": acc5_val,\n            \"loss_val\": current_val_loss,\n            'args': args  # ADDED\n        }, ckpt_path)\n        # Export to ONNX\n        export_onnx(model_without_ddp, ckpt_path.replace(\".pth\", \".onnx\"), args.batch_size,\n                    val_crop_size=args.val_crop_size, opset_version=opset)\n\n    summary_writer = SummaryWriter(summary_writer_dir)\n    best_val_loss = float(\"inf\")\n\n    tick = time.time()\n    for e in range(0, epoch):\n        print(\"Epoch {}/{}\".format(e, epoch))\n        if args.distributed:\n            data_loader.sampler.set_epoch(e)\n            torch.distributed.barrier()\n        metric_logger = train_one_epoch(\n            model, criterion, optimizer, data_loader, device, e, args, steps_per_epoch=steps_per_epoch,\n            model_ema=model_ema, scaler=scaler\n        )\n        if lr_scheduler is not None:\n            lr_scheduler.step()\n        if args.distributed:\n            torch.distributed.barrier()\n\n        # ======== Validation step ========\n        acc1_val, acc5_val, loss_val = evaluate(model, criterion, data_loader_val, device, print_freq=args.print_freq)\n        # Save the BEST model\n        current_val_loss = loss_val\n        if current_val_loss < best_val_loss:\n            best_val_loss = current_val_loss\n            _save_model(e, save_ckpt_path, acc1_val, acc5_val, current_val_loss)\n\n        # ======== Summary Writer (Tensorboard) log ========\n        summary_writer.add_scalar(\"lr\", metric_logger.lr.value, e)\n\n        summary_writer.add_scalar(\"Loss_train/epoch\", metric_logger.loss.global_avg, e)\n        summary_writer.add_scalar(\"Accuracy_top1_train/epoch\", metric_logger.acc1.global_avg, e)\n        summary_writer.add_scalar(\"Accuracy_top5_train/epoch\", metric_logger.acc5.global_avg, e)\n\n        summary_writer.add_scalar(\"Loss_val/epoch\", loss_val, e)\n        summary_writer.add_scalar(\"Accuracy_top1_val/epoch\", acc1_val, e)\n        summary_writer.add_scalar(\"Accuracy_top5_val/epoch\", acc5_val, e)\n\n        # Save the FINAL model\n        _save_model(e, save_ckpt_path.replace(\"_best.pth\", \"_final.pth\"), acc1_val, acc5_val, current_val_loss)\n\n    tock = time.time()\n    time_min = (tock - tick) / (1000 * 60)\n    print(\"Training took {} minutes or {} hours!\".format(time_min, time_min / 60))\n\n\ndef evaluate(model, criterion, data_loader, device, print_freq=100, log_suffix=\"\"):\n    \"\"\"\n    Codebase: torch/vision/references/classification/train.py\n    Modifications:\n      1) returning Top-5 accuracy and loss on top of the Top-1 accuracy, and\n      2) commented out \"and torch.distributed.get_rank() == 0\" if.\n    \"\"\"\n    model.eval()\n    metric_logger = utils.MetricLogger(delimiter=\"  \")\n    header = f\"Test: {log_suffix}\"\n\n    num_processed_samples = 0\n    with torch.inference_mode():\n        for image, target in metric_logger.log_every(data_loader, print_freq, header):\n            image = image.to(device, non_blocking=True)\n            target = target.to(device, non_blocking=True)\n            output = model(image)\n            loss = criterion(output, target)\n\n            acc1, acc5 = utils.accuracy(output, target, topk=(1, 5))\n            # FIXME need to take into account that the datasets\n            # could have been padded in distributed setup\n            batch_size = image.shape[0]\n            metric_logger.update(loss=loss.item())\n            metric_logger.meters[\"acc1\"].update(acc1.item(), n=batch_size)\n            metric_logger.meters[\"acc5\"].update(acc5.item(), n=batch_size)\n            num_processed_samples += batch_size\n    # gather the stats from all processes\n\n    num_processed_samples = utils.reduce_across_processes(num_processed_samples)\n    print(\"Num processed samples: {}\".format(num_processed_samples))\n    print(\"data_loader len: {}\".format(len(data_loader.dataset)))\n    if (\n        hasattr(data_loader.dataset, \"__len__\")\n        and len(data_loader.dataset) != num_processed_samples\n        # and torch.distributed.get_rank() == 0\n    ):\n        # See FIXME above\n        warnings.warn(\n            f\"It looks like the dataset has {len(data_loader.dataset)} samples, but {num_processed_samples} \"\n            \"samples were used for the validation, which might bias the results. \"\n            \"Try adjusting the batch size and / or the world size. \"\n            \"Setting the world size to 1 is always a safe bet.\"\n        )\n\n    metric_logger.synchronize_between_processes()\n\n    print(f\"{header} Acc@1 {metric_logger.acc1.global_avg:.3f},\"\n          f\" Acc@5 {metric_logger.acc5.global_avg:.3f},\"\n          f\" loss {metric_logger.loss.global_avg:.5f}\")\n    return metric_logger.acc1.global_avg, metric_logger.acc5.global_avg, metric_logger.loss.global_avg\n\n\ndef export_onnx(model, onnx_filename, batch_onnx, val_crop_size=224, opset_version=13, verbose=False,\n                do_constant_folding=True, trace_model=False):\n    model.eval()\n    # We have to shift to pytorch's fake quant ops before exporting the model to ONNX\n    quant_nn.TensorQuantizer.use_fb_fake_quant = True\n\n    # Export ONNX for multiple batch sizes\n    print(\"Creating ONNX file: \" + onnx_filename)\n    dummy_input = torch.randn(batch_onnx, 3, val_crop_size, val_crop_size, device=\"cuda\")\n    try:\n        print(\"Exporting ONNX model with input {} to {} with opset {}!\".format(dummy_input.shape, onnx_filename, opset_version))\n        model_tmp = model\n        if isinstance(model, torch.nn.DataParallel) or isinstance(model, torch.nn.parallel.DistributedDataParallel):\n            #  '.module' is necessary here because model is wrapped in torch.nn.DataParallel\n            model_tmp = model.module\n        if trace_model:\n            model_tmp = torch.jit.trace(model_tmp, dummy_input)\n        torch.onnx.export(\n            model_tmp, dummy_input, onnx_filename,\n            verbose=verbose,\n            opset_version=opset_version,\n            do_constant_folding=do_constant_folding\n        )\n    except ValueError:\n        print(\"Failed to export to ONNX\")\n        return False\n\n    return True\n\n\ndef get_optimizer(args, parameters):\n    opt_name = args.opt.lower()\n    if opt_name.startswith(\"sgd\"):\n        optimizer = torch.optim.SGD(\n            parameters,\n            lr=args.sparse_lr,\n            momentum=args.sparse_momentum,\n            weight_decay=args.sparse_weight_decay\n        )\n    elif opt_name == \"rmsprop\":\n        optimizer = torch.optim.RMSprop(\n            parameters,\n            lr=args.sparse_lr,\n            momentum=args.sparse_momentum,\n            weight_decay=args.sparse_weight_decay,\n            eps=0.0316,\n            alpha=0.9\n        )\n    elif opt_name == \"adamw\":\n        optimizer = torch.optim.AdamW(\n            parameters,\n            lr=args.sparse_lr,\n            weight_decay=args.sparse_weight_decay\n        )\n    else:\n        raise RuntimeError(f\"Invalid optimizer {args.opt}. Only SGD, RMSprop and AdamW are supported.\")\n\n    return optimizer\n\n\ndef get_lr_scheduler(args, optimizer):\n    if args.lr_scheduler == \"multistep\":\n        lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[30, 60, 80], gamma=args.lr_gamma)\n    elif args.lr_scheduler == \"constant\":\n        lr_scheduler = torch.optim.lr_scheduler.ConstantLR(\n            optimizer, factor=args.lr_warmup_decay, total_iters=args.sparse_epoch\n        )\n    elif args.lr_scheduler == \"step\":\n        main_lr_scheduler = torch.optim.lr_scheduler.StepLR(\n            optimizer,\n            step_size=args.lr_step_size,\n            gamma=args.lr_gamma\n        )\n        if args.lr_warmup_epochs > 0:\n            warmup_lr_scheduler = torch.optim.lr_scheduler.ConstantLR(\n                optimizer, factor=args.lr_warmup_decay, total_iters=args.lr_warmup_epochs\n            )\n            lr_scheduler = torch.optim.lr_scheduler.SequentialLR(\n                optimizer,\n                schedulers=[warmup_lr_scheduler, main_lr_scheduler],\n                milestones=[args.lr_warmup_epochs]\n            )\n        else:\n            lr_scheduler = main_lr_scheduler\n    else:\n        raise (\"LR Scheduler {} not supported!\".format(args.lr_scheduler))\n\n    return lr_scheduler\n\n\n# ======== Calibration ========\n# Source: https://github.com/NVIDIA/TensorRT/blob/master/tools/pytorch-quantization/examples/calibrate_quant_resnet50.ipynb\ndef collect_stats(model, data_loader, num_batches):\n    \"\"\"Feed data to the network and collect statistic\"\"\"\n\n    # Enable calibrators\n    for name, module in model.named_modules():\n        if isinstance(module, quant_nn.TensorQuantizer):\n            if module._calibrator is not None:\n                module.disable_quant()\n                module.enable_calib()\n            else:\n                module.disable()\n\n    progress_bar = tqdm.tqdm(total=len(data_loader), leave=True, desc='Evaluation Progress')\n    for i, (image, _) in enumerate(data_loader):\n        model(image.to(torch.device(\"cuda:0\")))  # .cuda())\n        progress_bar.update()\n        if i >= num_batches:\n            break\n    progress_bar.update()\n\n    # Disable calibrators\n    for name, module in model.named_modules():\n        if isinstance(module, quant_nn.TensorQuantizer):\n            if module._calibrator is not None:\n                module.enable_quant()\n                module.disable_calib()\n            else:\n                module.enable()\n\n\ndef compute_amax(model, **kwargs):\n    \"\"\"Load calib result\"\"\"\n    for name, module in model.named_modules():\n        if isinstance(module, quant_nn.TensorQuantizer):\n            if module._calibrator is not None:\n                if isinstance(module._calibrator, calib.MaxCalibrator):\n                    module.load_calib_amax(strict=False)\n                else:\n                    module.load_calib_amax(strict=False, **kwargs)\n    model.cuda()\n","repo_name":"NVIDIA/DL4AGX","sub_path":"SparsityINT8/train_utils.py","file_name":"train_utils.py","file_ext":"py","file_size_in_byte":17418,"program_lang":"python","lang":"en","doc_type":"code","stars":122,"dataset":"github-code","pt":"48"}
+{"seq_id":"36067679807","text":"# Auction bidder\nfrom os import system\n\n# Create an empty list of bids\n\nbids_dict = {}\n\n# Create a function that stores name and bid\ndef bidder(name, bid):\n    bids_dict[name] = int(bid)\n\n# Create a function that returns the name and bid of the highest bid\ndef get_highest_bid(bids_dict):\n    \n    highest_bidder = \"\"\n    highest_bid = 0\n\n    for bidder in bids_dict:\n        if bids_dict[bidder] > highest_bid:\n            highest_bid = bids_dict[bidder]\n            highest_bidder = bidder\n\n    print(f\"The highest bidder is {highest_bidder} for ${highest_bid}.\")\n\n\n# Create a condition and loop while True\nnew_bid = True\n\nwhile new_bid:\n    new_name = input(\"What is your name? \")\n    new_bid = int(input(\"What is your bid? $\"))\n    bidder(new_name, new_bid)\n\n    if input(\"Are there more bidders? \") == \"No\":\n        new_bid = False\n    \n    system(\"clear\")\n    print(bids_dict)\n\n\n\n\nget_highest_bid(bids_dict)\n\n","repo_name":"grzlz/100days","sub_path":"9-dictionaries.py","file_name":"9-dictionaries.py","file_ext":"py","file_size_in_byte":915,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"27811897717","text":"import sqlite3\nfrom flask import json\n\nconnection = sqlite3.connect('database.db')\n\nwith open('schema.sql') as f:\n    connection.executescript(f.read())\n\ncur = connection.cursor()\n\ncur.execute(\"INSERT INTO marks (geometry, properties) VALUES (?, ?)\",\n            (json.dumps({\"type\": \"Point\", \"coordinates\": [59.874901, 29.827274]}),\n             json.dumps({\"balloonContentHeader\": \"<font size=3><b>12шка</b></font>\",\n                         \"balloonContentBody\": \"<p>Здесь я живу</p>\",\n                         \"balloonContentFooter\": \"<font size=1>Чтобы удалить метку щелкните по ней колесиком</font>\",\n                         \"clusterCaption\": \"12\",\n                         \"hintContent\": \"12\"}))\n            )\n\ncur.execute(\"INSERT INTO marks (geometry, properties) VALUES (?, ?)\",\n            (json.dumps({\"type\": \"Point\", \"coordinates\": [59.874232, 29.830302]}),\n             json.dumps({\"balloonContentHeader\": \"<font size=3><b>15шка</b></font>\",\n                         \"balloonContentBody\": \"<p><strong>Реклама кинопарапета</strong></p>\",\n                         \"balloonContentFooter\": \"<font size=1>Чтобы удалить метку щелкните по ней колесиком</font>\",\n                         \"clusterCaption\": \"15\",\n                         \"hintContent\": \"15\"}))\n            )\n\ncur.execute(\"INSERT INTO marks (geometry, properties) VALUES (?, ?)\",\n            (json.dumps({\"type\": \"Point\", \"coordinates\": [59.874855, 29.830275]}),\n             json.dumps({\"balloonContentHeader\": \"<font size=3><b>14ка</b></font>\",\n                         \"balloonContentBody\": \"<p>Здесь я жил</p>\",\n                         \"balloonContentFooter\": \"<font size=1>Чтобы удалить метку щелкните по ней колесиком</font>\",\n                         \"clusterCaption\": \"14\",\n                         \"hintContent\": \"14\"}))\n            )\n\ncur.execute(\"INSERT INTO marks (geometry, properties) VALUES (?, ?)\",\n            (json.dumps({\"type\": \"Point\", \"coordinates\": [59.873236, 29.829472]}),\n             json.dumps({\"balloonContentHeader\": \"<font size=3><b>Короткий путь</b></font>\",\n                         \"balloonContentBody\": \"<p>Чтобы быстро добраться в Ленту</p>\" +\n                                               \"<p>перелезайте через забор здесь.</p>\",\n                         \"balloonContentFooter\": \"<font size=1>Чтобы удалить метку щелкните по ней колесиком</font>\",\n                         \"clusterCaption\": \"Путь\",\n                         \"hintContent\": \"Короткий путь\"}))\n            )\n\nconnection.commit()\nconnection.close()\n","repo_name":"TihonkovSergey/ymap_simple_site","sub_path":"init_db.py","file_name":"init_db.py","file_ext":"py","file_size_in_byte":2805,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"19439891491","text":"#!/usr/bin/env python3\n# encoding: utf-8\n\nfrom distutils.core import setup, Extension\n\npyrtsignal_module = Extension('pyrtsignal', sources = ['pyrtsignal.cc'])\n\nsetup(name='pyrtsignal',\n      version='0.1.0',\n      description='Python real time signal module',\n      ext_modules=[pyrtsignal_module])\n","repo_name":"gvaduha/pyrtsignal","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":300,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"9016345931","text":"from django.shortcuts import render\nfrom django.shortcuts import redirect\nfrom django.http import HttpResponseRedirect\nfrom django.http import HttpResponse\nfrom blog.models import Ingresar_variables\nimport os\nimport pickle\n\n# Create your views here.\ndef ingresar_variables(request):\n    return render(request, 'blog/ingresar_variables.html', {})\n\ndef resultado(request):\n    if request.method == 'GET':\n        #form = PostForm(request.GET)\n        # if form.is_valid():\n        #     post = form.save(commit=False)\n        #     post.save()\n        # return HttpResponseRedirect('/resultado')\n        #    return redirect('resultado', pk=resultado.pk)\n        bolsas = request.GET['bolsas']\n        sexo = request.GET['sexo']\n        edad = request.GET['edad']\n        peso = request.GET['peso']\n        diagnostico = request.GET['diagnostico']\n        servicio = request.GET['servicio']\n        hemoglobina = request.GET['hemoglobina']\n        hematocrito = request.GET['hematocrito']\n        gsreceptor = request.GET['gsreceptor']\n        gsdonante = request.GET['gsdonante']\n        hiv = request.GET['hiv']\n        hb = request.GET['hb']\n        antihb = request.GET['antihb']\n        antihcv = request.GET['antihcv']\n        htlv = request.GET['htlv']\n        sifilis = request.GET['sifilis']\n        chagas = request.GET['chagas']\n        inputs = [int(bolsas), int(sexo), int(edad), int(peso), int(diagnostico), int(servicio), int(hemoglobina), int(hematocrito), int(gsreceptor), int(gsdonante), int(hiv), int(hb), int(antihb), int(antihcv), int(htlv), int(sifilis), int(chagas)]\n        print(inputs)\n        salida = calculo(inputs)\n        print(salida)\n\n        return render(request, 'blog/resultado.html',{'bolsas':bolsas, 'sexo':sexo, 'edad':edad, 'peso':peso, 'diagnostico':diagnostico, 'servicio':servicio, 'hemoglobina':hemoglobina, 'hematocrito':hematocrito, 'gsreceptor':gsreceptor, 'gsdonante':gsdonante, 'hiv':hiv, 'hb':hb, 'antihb':antihb, 'antihcv':antihcv, 'htlv':htlv, 'sifilis':sifilis, 'chagas':chagas, 'salida':salida})\n        \n    else:\n        #form = PostForm()\n        return HttpResponse('ERROR')\n    #return render(request, 'blog/resultado.html', {'form': form})\n\ndef calculo(inputs):\n    #numbers_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]\n    list_pickle_path = 'modelo.sav'\n    \n    # Create an variable to pickle and open it in write mode\n    list_pickle = open(list_pickle_path, 'wb')\n    salida = pickle.dump(inputs, list_pickle)\n    list_pickle.close()\n    return salida","repo_name":"LuisDiegoChunga/my-first-work","sub_path":"blog/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2537,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"22493630454","text":"\nclass ModelConfig(object):\n\n\tdef __init__(self):\n\t\tself.image_format = 'jpeg'\n\n\t\tself.batch_size = 5\n\t\tself.max_seq_len = 15\n\n\t\tself.image_size = [224, 224]\n\t\tself.num_image_channels = 3\n\n\t\tself.num_clstm_kernels = 256 #384 for two layers of ConvLSTMs\n\t\tself.clstm_kernel_size = [3, 3]\n\t\tself.num_convlstm_layers = 2\n\t\t# If < 1.0, the dropout keep probability applied to ConvLSTM variables.\n\t\tself.clstm_dropout_keep_prob = 0.7\n\n\t\tself.pretrained_model_file = None\n\n\t\tself.training_data_tfrecord_path = \\\n\t\t\t'/home/lishen/Experiments/CLSTMT/dataset/training_set/TFRecord/training_data.tf_record.soft_gt'\n\n\t\t# Approximate number of values per input shard. Used to ensure sufficient\n\t\t# mixing between shards in training.\n\t\tself.values_per_input_shard = 2300\n\t\t# Minimum number of shards to keep in the input queue.\n\t\tself.input_queue_capacity_factor = 2\n\t\t# Number of threads for prefetching SequenceExample protos.\n\t\tself.num_input_reader_threads = 1\n\n\t\t# Number of threads for image preprocessing. Should be a multiple of 2.\n\t\tself.num_preprocess_threads = 1\n\n\t\tself.num_seqs = 115 # total number of domains(training tracking sequences)\n\n\nclass TrainingConfig(object):\n\n\tdef __init__(self):\n\t\t\"\"\"Set the default training hyper-parameters.\"\"\"\n\n\t\t# Optimizer for training the model\n\t\tself.optimizer = \"SGD\"\n\t\tself.max_epoches = 100\n\t\tself.learning_rate = 0.001\n\n\nclass FinetuningConfig(object):\n\tdef __init__(self):\n\t\tself.learning_rate = 0.01\n\t\tself.use_domain_specific_finetuned_model = True\n\n\nclass TestingConfig(object):\n\n\tdef __init__(self):\n\t\tself.root_dir = '/home/lishen/Experiments/CLSTMT'\n\t\tself.code_root_dir = '/home/code/lishen/dataset'\n\t\tself.peep_ratio = 3.5\n\n\nclass VerificationModelConfig(object):\n\tdef __init__(self):\n\t\tself.pretrained_model_file = \"./weights/vgg16_verif.npy\"\n\t\tself.num_boxes_per_batch = None\n\n\t\t\n","repo_name":"MathsShen/DaSiamRPNWithOfflineTraining","sub_path":"code/configuration.py","file_name":"configuration.py","file_ext":"py","file_size_in_byte":1833,"program_lang":"python","lang":"en","doc_type":"code","stars":88,"dataset":"github-code","pt":"48"}
+{"seq_id":"31841819072","text":"n = int(input())\nd = {}\nfor i in range(n):\n    arr = input().split()\n    for j in range(len(arr)):\n        for z in range(j + 1, len(arr)):\n            if d.get(arr[j]) is None:\n                d[arr[j]] = set()\n            if d.get(arr[z]) is None:\n                d[arr[z]] = set()\n            d[arr[j]].add(arr[z])\n            d[arr[z]].add(arr[j])\n\n\n\nused = {}\n\nvisited = {}\nprev = {}\n\n\ndef dfs(start, visited, prev, g, i):\n    print(start, i)\n    visited[start] = True\n    for u in g[start]:\n        if not visited.get(u):\n            prev[u] = start\n            dfs(u, visited, prev, g, i)\n            i += 1\n\n\ndfs('Isenbaev', visited, prev, d, 0)\n","repo_name":"kholmatov/algorithm","sub_path":"isenbaev.py","file_name":"isenbaev.py","file_ext":"py","file_size_in_byte":654,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"74905409425","text":"# 입력 조건\n#     첫쨰 줄에 떡의 개수 N과 요철한 떡의 길이 M이 주어진다. (1 <= N <= 1000000, 1<= M <= 2000000000)\n#     둘째 줄에는 떡의 개별 높이가 주어진다. \n#     떡 높이의 총합은 항상 M 이상이므로, 손님은 필요한 양 만큼 떡을 사갈 수 있다.\n#     높이는 10억보다 작거나 같은 양의 정수 또는 0이다.\n\n# 적어도 M만큼의 떡을집에 가져가기 위해 절단기에 설정할 수 있는 높이의 최댓값을 출력한다.\n\n# 입력 예시\n#     4 6\n#     19 15 10 17\n\n# 출력 예시\n#     15\n    \n    \n\n\nstock, order = list(map(int, input().split()))\nitems = set(map(int, input().split()))\n\nstart = 1\nend = max(items) \nresult = 0\nwhile(start<=end):\n    cut = (end + start) //2\n    leftRicecake = 0\n    for i in items:\n        if i - cut > 0:\n            leftRicecake += i - cut \n    if leftRicecake < order:\n        end = cut -1 \n    else:\n        result = cut\n        start = cut + 1\n        \n\nprint(result)    \n                \n\n\n","repo_name":"limnyn/python_codingtest","sub_path":"codingtest/7-8_떡볶이떡만들기.py","file_name":"7-8_떡볶이떡만들기.py","file_ext":"py","file_size_in_byte":1028,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"74997210705","text":"from datetime import datetime\nfrom pytube import Channel, YouTube\n\n\ndef get_videos_from_channel(url):\n    \"\"\" Yields all videos associated with the given YouTube channel url.\n\n    Args:\n        url (str): A YouTube channel url.\n    Yields:\n        (pytube.Youtube): Youtube video instances.\n    \"\"\"\n    channel = Channel(url)\n    for video in channel.videos_generator():\n        yield video\n\n\ndef get_videos_from_channel_within_timeframe(url, start_date, end_date):\n    \"\"\" Yields videos associated with the given YouTube channel url within the\n        specified time frame.\n\n    Args:\n        url (str): A YouTube channel url.\n        start_date (datetime.datetime): The start date.\n        end_date (datetime.datetime): The end date.\n    Yields:\n        (pytube.Youtube): Youtube video instances.\n    \"\"\"\n    for video in get_videos_from_channel(url):  \n        date = video.publish_date\n        if date >= start_date and date <= end_date:\n            yield video\n\n\ndef get_recent_videos_from_channel(url, delta):\n    \"\"\" Yields recent videos associated with the given YouTube channel url.\n\n    Args:\n        url (str): A YouTube channel url.\n        delta (datetime.timedelta): The preceeding period to resolve videos.\n    Yields:\n        (pytube.Youtube): Youtube video instances.\n    \"\"\"\n    now = datetime.now()\n    for video in get_videos_from_channel(url):\n        if video.publish_date < now-delta:\n            break\n        yield video\n\n\ndef download_captions(url, language_codes=None):\n    \"\"\" Downloads the captions from a youtube video and returns these in srt\n        format.\n    \n    Args:\n        url (str): A valid youtube URL.\n        language_code (tuple(str)): The language of the captions to extract.\n            Defaults to ('a.en', 'en') - autogenerated English, English. \n    Returns:\n        Optional[str]: Captions encoded in srt format.\n    \"\"\"\n    language_codes = language_codes or (\"a.en\", \"en\")\n    video = YouTube(url)\n    for code in language_codes:\n        caption = video.captions.get(code)\n        if caption:\n            return caption.generate_srt_captions()\n    return None\n\n\n\"\"\"\nNasty hack:\n    Because of the bug reported here in pytube-11.0.2:\n    https://stackoverflow.com/questions/68780808/xml-to-srt-conversion-not-working-after-installing-pytube\n    We are patching the xml_caption_to_srt function with the following 'fix'...\n\n    TODO: Remove when pytube fixes this bug.\n\"\"\"\n\nimport xml.etree.ElementTree as ElementTree\nfrom html import unescape\nfrom pytube import Caption\n\n\ndef xml_caption_to_srt(self, xml_captions: str) -> str:\n    \"\"\"Convert xml caption tracks to \"SubRip Subtitle (srt)\".\n\n    :param str xml_captions:\n        XML formatted caption tracks.\n    \"\"\"\n    segments = []\n    root = ElementTree.fromstring(xml_captions)\n    for i, child in enumerate(list(root.findall('body/p'))):\n        text = ''.join(child.itertext()).strip()\n        if not text:\n            continue\n        caption = unescape(text.replace(\"\\n\", \" \").replace(\"  \", \" \"),)\n        try:\n            duration = float(child.attrib[\"d\"])\n        except KeyError:\n            duration = 0.0\n        start = float(child.attrib[\"t\"])\n        end = start + duration\n        sequence_number = i + 1  # convert from 0-indexed to 1.\n        line = \"{seq}\\n{start} --> {end}\\n{text}\\n\".format(\n            seq=sequence_number,\n            start=self.float_to_srt_time_format(start/1000.),\n            end=self.float_to_srt_time_format(end/1000.),\n            text=caption,\n        )\n        segments.append(line)\n    return \"\\n\".join(segments).strip()\n\nCaption.xml_caption_to_srt = xml_caption_to_srt\n","repo_name":"robbiefordyce/windwords","sub_path":"src/python/windwords/youtube.py","file_name":"youtube.py","file_ext":"py","file_size_in_byte":3623,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"33266386504","text":"import sys\nfrom collections import deque\n\nT = int(sys.stdin.readline())\n\nfor _ in range(T):\n    \n    p = sys.stdin.readline()        # 수행할 함수\n    n = int(sys.stdin.readline())   # 배열에 들어있는 수의 개수\n    arr = sys.stdin.readline().rstrip()[1:-1].split(\",\")    # 배열\n\n    queue = deque(arr)              # 덱 생성\n\n    reverse_flag = 0                # R 플래그\n    error_flag = 0                  # 에러 플래그\n\n    if n == 0:\n        queue = []\n\n    for cmd in p:\n        if cmd == \"R\":\n            reverse_flag += 1\n        elif cmd == \"D\":\n            if len(queue) < 1:\n                error_flag = 1\n                print(\"error\")\n                break\n            else:\n                if reverse_flag % 2 == 0:\n                    queue.popleft()\n                else:\n                    queue.pop()\n\n    if error_flag == 0:\n        if reverse_flag % 2 == 0:\n            print(\"[\" + \",\".join(queue) + \"]\")\n        else:\n            queue.reverse()\n            print(\"[\" + \",\".join(queue) + \"]\")\n        \n    \n","repo_name":"noitrighthere/Python-Algorithm","sub_path":"백준/백준_5430_AC.py","file_name":"백준_5430_AC.py","file_ext":"py","file_size_in_byte":1058,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"30225363104","text":"import unittest\nfrom unittest import mock\nfrom twitter_api import TwitterApi\n\nimport config.config as config\n\n\nclass TwitterApiTest(unittest.TestCase):\n\n    def setUp(self):\n        app = config.App()\n\n    def test_convertTwitterDate(self):\n        twitter_api = TwitterApi()\n        date_twitter = 'Sun Sep 27 15:50:28 +0000 2020'\n\n        actual = twitter_api.convertTwitterDate(date_twitter)\n\n        expected = '15:50 PM - 27 Sep 2020'\n\n        self.assertEqual(expected, actual)\n\n    def test_createAuthHeader(self):\n        twitter_api = TwitterApi()\n\n        actual = twitter_api.createAuthHeader()\n\n        expected = {\"Authorization\": \"Bearer bearer_token\"}\n\n        self.assertEqual(expected, actual)\n\n    def test_extractHashtags(self):\n        twitter_api = TwitterApi()\n\n        hashtags = [\n            {'text': 'three'},\n            {'text': 'seagrass'}\n        ]\n\n        actual = twitter_api.extractHashtags(hashtags)\n\n        expected = ['three', 'seagrass']\n\n        self.assertEqual(expected, actual)\n\n    def test_createTweetSearchUrl(self):\n        twitter_api = TwitterApi()\n\n        actual = twitter_api.createTweetSearchUrl('influencer', '5')\n\n        expected = 'https://api.twitter.com/1.1/search/tweets.json?q=%23influencer&count=5'\n\n        self.assertEqual(expected, actual)\n\n    def test_createUserTweetsUrl(self):\n        twitter_api = TwitterApi()\n\n        actual = twitter_api.createUserTweetsUrl('nasa', '3')\n\n        expected = 'https://api.twitter.com/1.1/statuses/user_timeline.json?screen_name=nasa&count=3'\n\n        self.assertEqual(expected, actual)\n\n    @mock.patch(\"requests.get\")\n    @mock.patch(\"twitter_api.TwitterApi.createTweetSearchUrl\")\n    @mock.patch(\"twitter_api.TwitterApi.createAuthHeader\")\n    def test_getTweetsByHashtag_obtainResultFromApiSuccessfully(self, mocked_header, mocked_url, mocked_get):\n        twitter_api = TwitterApi()\n\n        mocked_url.return_value = \"api_url\"\n        mocked_header.return_value = \"header\"\n\n        json_response = \"json_response\"\n        mocked_get.return_value.status_code = 200\n        mocked_get.return_value.text = json_response\n\n        twitter_api.getTweetsByHashtag('hashtag', '3')\n\n        mocked_get.assert_called_with(\"api_url\", headers=\"header\")\n\n    @mock.patch(\"requests.get\")\n    @mock.patch(\"twitter_api.TwitterApi.createTweetSearchUrl\")\n    @mock.patch(\"twitter_api.TwitterApi.createAuthHeader\")\n    def test_getTweetsByHashtag_handleExceptionOnApiBadRequest(self, mocked_header, mocked_url, mocked_get):\n        twitter_api = TwitterApi()\n\n        mocked_url.return_value = \"api_url\"\n        mocked_header.return_value = \"header\"\n\n        json_response = \"json_response\"\n        mocked_get.return_value.status_code = 400\n        mocked_get.return_value.text = json_response\n\n        with self.assertRaises(Exception):\n            twitter_api.getTweetsByHashtag('hashtag', '3')\n\n        mocked_get.assert_called_with(\"api_url\", headers=\"header\")\n\n    @mock.patch(\"requests.get\")\n    @mock.patch(\"twitter_api.TwitterApi.createUserTweetsUrl\")\n    @mock.patch(\"twitter_api.TwitterApi.createAuthHeader\")\n    def test_getTweetsByUsername_obtainResultFromApiSuccessfully(self, mocked_header, mocked_url, mocked_get):\n        twitter_api = TwitterApi()\n\n        mocked_url.return_value = \"api_url\"\n        mocked_header.return_value = \"header\"\n\n        json_response = \"json_response\"\n        mocked_get.return_value.status_code = 200\n        mocked_get.return_value.text = json_response\n\n        twitter_api.getTweetsByUsername('hashtag', '3')\n\n        mocked_get.assert_called_with(\"api_url\", headers=\"header\")\n\n    @mock.patch(\"requests.get\")\n    @mock.patch(\"twitter_api.TwitterApi.createUserTweetsUrl\")\n    @mock.patch(\"twitter_api.TwitterApi.createAuthHeader\")\n    def test_getTweetsByUsername_handleExceptionOnApiBadRequest(self, mocked_header, mocked_url, mocked_get):\n        twitter_api = TwitterApi()\n\n        mocked_url.return_value = \"api_url\"\n        mocked_header.return_value = \"header\"\n\n        json_response = \"json_response\"\n        mocked_get.return_value.status_code = 400\n        mocked_get.return_value.text = json_response\n\n        with self.assertRaises(Exception):\n            twitter_api.getTweetsByUsername('nasa', '3')\n\n        mocked_get.assert_called_with(\"api_url\", headers=\"header\")\n\n    def test_extractTweetInfo(self):\n        tweets = [\n            {\n                \"created_at\": \"Thu Apr 06 15:28:43 +0000 2017\",\n                \"text\": \"tweet_text_first\",\n                \"entities\": {\n                    \"hashtags\": [{\"text\": \"first\"}, {\"text\": \"second\"}],\n                },\n                \"user\": {\n                    \"id\": 111,\n                    \"name\": \"name_one\",\n                    \"screen_name\": \"screen_name_one\",\n                },\n                \"retweet_count\": 10,\n                \"favorite_count\": 11,\n            },\n            {\n                \"created_at\": \"Fri Apr 16 15:28:43 +0000 2017\",\n                \"text\": \"tweet_text_second\",\n                \"entities\": {\n                    \"hashtags\": [{\"text\": \"one\"}, {\"text\": \"two\"}],\n                },\n                \"user\": {\n                    \"id\": 222,\n                    \"name\": \"name_two\",\n                    \"screen_name\": \"screen_name_two\",\n                },\n                \"retweet_count\": 20,\n                \"favorite_count\": 21,\n            }\n        ]\n\n        twitter_api = TwitterApi()\n        actual = twitter_api.extractTweetInfo(tweets)\n\n        expected = [\n            {\n                'account': {\n                    'fullname': 'name_one',\n                    'href': '/screen_name_one',\n                    'id': 111,\n                },\n                'date': '15:28 PM - 06 Apr 2017',\n                'hashtags': ['first', 'second'],\n                'likes': 11,\n                'retweets': 10,\n                'text': 'tweet_text_first',\n            },\n            {\n                'account': {\n                    'fullname': 'name_two',\n                    'href': '/screen_name_two',\n                    'id': 222,\n                },\n                'date': '15:28 PM - 16 Apr 2017',\n                'hashtags': ['one', 'two'],\n                'likes': 21,\n                'retweets': 20,\n                'text': 'tweet_text_second',\n            },\n        ]\n\n        self.assertEqual(expected, actual)\n","repo_name":"antoniosv/twittertoy","sub_path":"app/tests/twitter_api_test.py","file_name":"twitter_api_test.py","file_ext":"py","file_size_in_byte":6358,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"42289421305","text":"#!/usr/bin/env python\n\n# Distributed under the MIT License.\n# See LICENSE.txt for details.\n\nimport multiprocessing as mp\nimport os\nimport shutil\n\nimport click\nimport h5py\nimport numpy as np\nimport pandas as pd\nimport rich\n\nfrom spectre.Visualization.ReadH5 import available_subfiles\n\n\ndef write_dat_data(dat_path, h5_filename, out_dir, precision):\n    with h5py.File(h5_filename, \"r\") as h5file:\n        if not dat_path.endswith(\".dat\"):\n            dat_path = dat_path + \".dat\"\n        dat_file = h5file.get(dat_path)\n        if dat_file is None:\n            subfiles = available_subfiles(h5_filename, extension=\".dat\")\n            raise ValueError(\n                f\"Unable to find subfile {dat_path}. Known \"\n                f\"files are:\\n{subfiles}\"\n            )\n        legend = dat_file.attrs[\"Legend\"]\n        dat_data = np.array(dat_file)\n\n    header = \"\\n\".join(f\"[{i}] \" + \"{}\" for i in range(len(legend))).format(\n        *legend\n    )\n\n    format_string = f\"%.{precision}e\"\n\n    # Write to stdout\n    if not out_dir:\n        print(\n            pd.DataFrame(dat_data).to_string(\n                index=False,\n                header=legend,\n                float_format=format_string,\n                justify=\"left\",\n            )\n        )\n        return\n\n    dat_dir = os.path.join(out_dir, os.path.dirname(dat_path))\n    os.makedirs(dat_dir, exist_ok=True)\n    dat_filename = os.path.join(out_dir, dat_path)\n\n    np.savetxt(\n        dat_filename, dat_data, delimiter=\" \", fmt=format_string, header=header\n    )\n\n\ndef extract_dat_files(\n    filename,\n    out_dir,\n    num_cores,\n    precision,\n    list=False,\n    force=False,\n    subfiles=None,\n):\n    \"\"\"Extract dat files from an H5 file\n\n    Extract all Dat files inside a SpECTRE HDF5 file. The resulting files will\n    be put into the 'OUT_DIR'. The directory structure will be identical to the\n    group structure inside the HDF5 file.\n    \"\"\"\n    if list:\n        import rich.columns\n\n        subfiles = available_subfiles(filename, extension=\".dat\")\n        rich.print(rich.columns.Columns(subfiles))\n        return\n\n    if subfiles:\n        all_dat_files = subfiles\n    else:\n        all_dat_files = available_subfiles(filename, extension=\".dat\")\n\n    if out_dir:\n        if not os.path.exists(out_dir):\n            os.mkdir(out_dir)\n        else:\n            if force:\n                shutil.rmtree(out_dir, ignore_errors=True)\n                os.mkdir(out_dir)\n            else:\n                raise ValueError(\n                    f\"Could not make directory '{out_dir}'. Already exists.\"\n                )\n    else:\n        if subfiles and len(subfiles) > 1:\n            raise ValueError(\n                \"If no output directory is specified, can only write \"\n                f\"one subfile to stdout, not {len(subfiles)}\"\n            )\n\n    num_dat_files = len(all_dat_files)\n\n    # Only use multiprocessing if we are writing to a directory and using more\n    # than one core. Otherwise avoid the overhead\n    if out_dir and num_cores > 1:\n        with mp.Pool(processes=num_cores) as pool:\n            pool.starmap(\n                write_dat_data,\n                zip(\n                    all_dat_files,\n                    [filename] * num_dat_files,\n                    [out_dir] * num_dat_files,\n                    [precision] * num_dat_files,\n                ),\n            )\n    else:\n        for dat_filename in all_dat_files:\n            write_dat_data(dat_filename, filename, out_dir, precision)\n\n    if out_dir:\n        print(f\"Successfully extracted all Dat files into '{out_dir}'\")\n\n\n@click.command(name=\"extract-dat\", help=extract_dat_files.__doc__)\n@click.argument(\n    \"filename\",\n    type=click.Path(exists=True, file_okay=True, dir_okay=False, readable=True),\n)\n@click.argument(\n    \"out_dir\",\n    type=click.Path(file_okay=False, dir_okay=True, writable=True),\n    required=False,\n)\n@click.option(\n    \"--num-cores\",\n    \"-j\",\n    default=1,\n    show_default=True,\n    help=\"Number of cores to run on.\",\n)\n@click.option(\n    \"--precision\",\n    \"-p\",\n    default=16,\n    show_default=True,\n    help=\"Precision with which to save (or print) the data.\",\n)\n@click.option(\n    \"--force\",\n    \"-f\",\n    is_flag=True,\n    help=\"If the output directory already exists, overwrite it.\",\n)\n@click.option(\n    \"--list\",\n    \"-l\",\n    is_flag=True,\n    help=\"List all dat files in the HDF5 file and exit.\",\n)\n@click.option(\n    \"--subfile\",\n    \"-d\",\n    \"subfiles\",\n    multiple=True,\n    help=\"Full path of subfile to extract (including extension).\",\n)\ndef extract_dat_command(**kwargs):\n    _rich_traceback_guard = True  # Hide traceback until here\n    extract_dat_files(**kwargs)\n\n\nif __name__ == \"__main__\":\n    extract_dat_command(help_option_names=[\"-h\", \"--help\"])\n","repo_name":"sxs-collaboration/spectre","sub_path":"src/IO/H5/Python/ExtractDatFromH5.py","file_name":"ExtractDatFromH5.py","file_ext":"py","file_size_in_byte":4754,"program_lang":"python","lang":"en","doc_type":"code","stars":135,"dataset":"github-code","pt":"48"}
+{"seq_id":"41384139459","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nfrom a08e03LaxWend import a08e03LaxWend\nfrom matplotlib import pyplot as plt\nfrom matplotlib import animation\nimport numpy as np \n\nc = 1\nxmin=-15\nxmax = 15\nf = lambda x: 1./((x-4)**2 +1) + 1./((x+4)**2 + 1)\ng = lambda x: x*np.exp(-1./2*x**2)\nN = 2**6\nh = 1.0 /N\nk = 1./2*h\ntt = int(10.0/k)+1\nx = np.linspace(xmin,xmax,(xmax-xmin)/h+1)\nt = np.linspace(0,10,tt)\n\ndef init(line,):\n    line.set_data([], [])\n    return (line,)\n\ndef animate(i,t,x,xmin,xmax,c,f,g,h,k,ax,line,):\n    y = a08e03LaxWend(t[i],xmin,xmax,c,f,g,h,k)\n    ax.set_ylim((min(y),max(y)))\n    line.set_data(x, y)\n    return (line,)\n\nfig, ax = plt.subplots()\nax.set_xlim(( -15, 15))\nline, = ax.plot([], [], lw=2)\nline, = init(line,)\nanim = animation.FuncAnimation(fig, animate,fargs=(t,x,xmin,xmax,c,f,g,h,k,ax,line,),\n                               frames=tt, interval=11, blit=True)\nanim.save('animation_wave_Lax5.mp4', fps=30, extra_args=['-vcodec', 'libx264']) ","repo_name":"mozzsa/numerical-mathematics","sub_path":"assignment8/a08e03movie.py","file_name":"a08e03movie.py","file_ext":"py","file_size_in_byte":977,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"9426624732","text":"##parameters=REQUEST\n## Script (Python) \"sendmail.py\"\n# $Id$\n\n#\n# Create a new mail session object\n#\ncontext.portal_webMail.createMailSession(REQUEST)\n\n#\n# Sending message...\n#\nx = 0\nread_flag = int(REQUEST.form.get('ack_read', 0))\ntry:\n    x = context.portal_webMail.sendMail(REQUEST, read_flag)\nexcept 'SMTP_connection_error':\n    msg = \"smtp_connection_error\"\n    portal_url = context.portal_url()\n    return REQUEST.RESPONSE.redirect(portal_url + '/webmail_error?error_message=' + msg)\n\n\nif x == 0:\n    # All is good\n    context.portal_webMail.endMailSession(REQUEST)\n    return context.webmail_show(REQUEST)\nelse:\n    # To define ?\n    # this only happens when the email do not have any recipients\n    # so I gusess we should redirect to the edit message form, with\n    # a message...\n    msg = \"cpswebmail_enter_recipient\"\n    portal_url = context.portal_url()\n    REQUEST.RESPONSE.redirect(portal_url + '/edit_message?portal_status_message=' + msg)\n\n    return 1\n\n","repo_name":"nuxeo-cps/legacy--CPSWebMail","sub_path":"skins/cpswebmail_default/sendmail.py","file_name":"sendmail.py","file_ext":"py","file_size_in_byte":971,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"13580318151","text":"import sys\nimport heapq\ninput = sys.stdin.readline\n\nT = int(input())\nfor _ in range(T):\n    min_heap = []\n    max_heap = []\n\n    k = int(input())\n    visited = [False] * k\n\n    for id in range(k):\n        letter, n = input().split()\n        n = int(n)\n        if letter == \"I\":\n            heapq.heappush(min_heap, (n, id))\n            heapq.heappush(max_heap, (-n, id))\n            visited[id] = True\n        if letter == \"D\":\n          if max_heap and n == 1:\n            d = heapq.heappop(max_heap)\n            min_heap.remove(-d)\n          elif min_heap and n == -1:\n            d = heapq.heappop(min_heap)\n            max_heap.remove(-d)\n    if not min_heap:\n      print(\"EMPTY\")\n    elif len(min_heap)==1:\n      x = heapq.heappop(min_heap)\n      print(x, x)\n    else:\n      print(-(heapq.heappop(max_heap)), heapq.heappop(min_heap))\n","repo_name":"hynjxn/algorithm-study-in-python","sub_path":"BOJ/BOJ-7662.py","file_name":"BOJ-7662.py","file_ext":"py","file_size_in_byte":839,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"23324197548","text":"import os\nimport json\n\nbase = os.path.dirname(os.path.abspath(__file__))\nINPUT_OTHER_SOURCE = os.path.normpath(os.path.join(\n    base, \"./data/source/projectnextnlp-chat-dialogue-corpus/json/rest1046\"))\n\ndef load_others():\n    files = os.listdir(INPUT_OTHER_SOURCE)\n    result = []\n    for filename in files:\n        path = os.path.join(INPUT_OTHER_SOURCE, filename)\n        file = open(path, \"r\")\n        copus_json = json.load(file)\n        for turn in copus_json[\"turns\"]:\n            if turn[\"speaker\"] == \"U\":\n                result.append(turn[\"utterance\"])\n\n    return result\n\ndef main():\n    others = load_others()\n    for other in others:\n        print(other)\n\nif __name__ == '__main__':\n    main()\n","repo_name":"benishouga/distributed-representation-superimposer","sub_path":"dr-superimposer/load_chat_corpus_to_others.py","file_name":"load_chat_corpus_to_others.py","file_ext":"py","file_size_in_byte":708,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"69956119505","text":"from lib763.fs import (\n    save_str_to_file,\n    load_str_from_file,\n    get_all_file_path_in,\n    get_file_name,\n)\nfrom extract_fgold_func import P_META_TEST_DIR, J_META_TEST_DIR\n\nsave_str_to_file(\n    load_str_from_file(\"../colab/patch/code_tokenizer.txt\"),\n    \"./preprocessing/src/code_tokenizer.py\",\n)\nfor path in get_all_file_path_in(\"../colab/patch\"):\n    if \".py\" in path:\n        save_str_to_file(\n            load_str_from_file(path),\n            P_META_TEST_DIR + get_file_name(path),\n        )\n    elif \".java\" in path:\n        save_str_to_file(\n            load_str_from_file(path),\n            J_META_TEST_DIR + get_file_name(path),\n        )\n","repo_name":"naru-99/langMorphDA","sub_path":"experiment/transcoder/colab/patch_repo_error.py","file_name":"patch_repo_error.py","file_ext":"py","file_size_in_byte":658,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"23059797451","text":"import tkinter\n\n\ndef button1_command():...\n\n\ndef print_hello(event):...\n\n\n\ndef init_main_window():\n    global root, button1, button2,label,scale\n\n    root = tkinter.Tk()\n\n    button1 = tkinter.Button(root,text=\"Button 1\", command=button1_command)\n    button1.bind(\"<Button>\", print_hello)\n\n\n\n    button2 = tkinter.Button(root,text=\"Button 2\")\n    button2.bind(\"<Button>\", print_hello)\n\n    variable = tkinter.IntVar(0)\n    label = tkinter.Label(root,textvariable=variable)\n    scale = tkinter.Scale(root, orient=tkinter.HORIZONTAL, length=300,\n            from_=0, to = 100,tickinterval=10, resolution=5,variable=variable)\n\n    text = tkinter.Entry(root,textvariable=variable)\n\n    for obj in button1,button2,label,scale,text:\n        obj.pack()\nif __name__ ==  \"__main__\":\n    init_main_window()\n\n    root.mainloop()\n\n\n","repo_name":"khorkhordin/khorkhordin","sub_path":"wid1.py","file_name":"wid1.py","file_ext":"py","file_size_in_byte":820,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"10564957266","text":"from OFS.interfaces import IApplication\nfrom plone.distribution.api import distribution as dist_api\nfrom plone.distribution.api import site as site_api\nfrom plone.distribution.core import Distribution\nfrom plone.protect.interfaces import IDisableCSRFProtection\nfrom Products.CMFPlone.browser.admin import AddPloneSite as AddPloneSiteView\nfrom Products.CMFPlone.browser.admin import AppTraverser\nfrom zope.component import adapts\nfrom zope.component import queryMultiAdapter\nfrom zope.interface import alsoProvides\nfrom zope.publisher.browser import BrowserView\nfrom zope.publisher.interfaces import IRequest\n\nimport json\n\n\nclass RestTraverser(AppTraverser):\n    \"\"\"Traverser supporting REST calls in Zope Application root.\"\"\"\n\n    adapts(IApplication, IRequest)\n\n    def publishTraverse(self, request, name):\n        if not name.startswith(\"@\"):\n            return super().publishTraverse(request, name)\n\n        service = queryMultiAdapter(\n            (self.context, request), name=request._rest_service_id + name\n        )\n        if service is None:\n            # No service, fallback to regular view\n            view = queryMultiAdapter((self.context, request), name=name)\n            if view is not None:\n                return view\n            raise\n        return service\n\n\nclass Overview(BrowserView):\n    \"\"\"Overview page for Zope Root.\"\"\"\n\n\nclass AddPloneSite(AddPloneSiteView):\n    \"\"\"Add Plone site.\"\"\"\n\n    distribution: Distribution = None\n    default_data: dict = None\n\n    def __call__(self):\n        context = self.context\n        form = self.request.form\n        distribution_name = form.get(\"distribution\", \"default\")\n        site_id = form.get(\"site_id\")\n        self.distribution = dist_api.get(distribution_name)\n        submitted = form.get(\"form.submitted\", False)\n        if submitted:\n            # CSRF protect. DO NOT use auto CSRF protection for adding a site\n            alsoProvides(self.request, IDisableCSRFProtection)\n            site = site_api.create(\n                context,\n                distribution_name,\n                profile_id=\"Products.CMFPlone:plone\",\n                answers=form,\n            )\n            self.request.response.redirect(site.absolute_url())\n            return \"\"\n        else:\n            self.default_data = json.dumps(\n                {\n                    \"site_id\": site_id,\n                    \"default_language\": self.browser_language(),\n                    \"portal_timezone\": \"UTC\",\n                },\n                indent=2,\n            )\n        return self.index()\n","repo_name":"plone/plone.distribution","sub_path":"src/plone/distribution/browser/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":2547,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"48"}
+{"seq_id":"7103481473","text":"\"\"\"\nBTC - Darkmarket Transaction Correlation\nModule for data export into CSV file.\nOrganisation: Brno University of Technology - Faculty of Information Technology\nAuthor: Daniel Konecny (xkonec75)\nDate: 05. 05. 2022\n\"\"\"\n\nimport sys\n\nimport pandas as pd\nfrom pathlib import Path\n\n\nclass DataExporter:\n    def __init__(self, export_file_path):\n        self.file_path = Path(export_file_path)\n        self.df = None\n\n    def export_data(self, data):\n        self.df = pd.DataFrame(\n            data,\n            columns=['product_id', 'variant_id', 'tx_id', 'address', 'price_diff', 'heuristics']\n        )\n        self.df.to_csv(self.file_path, index=False)\n\n    def print_data(self, data):\n        self.df = pd.DataFrame(\n            data,\n            columns=['product_id', 'variant_id', 'tx_id', 'address', 'price_diff', 'heuristics']\n        )\n        self.df.to_csv(sys.stdout, index=False)\n","repo_name":"danielkonecny/btc-tx-correlation","sub_path":"src/DataExporter.py","file_name":"DataExporter.py","file_ext":"py","file_size_in_byte":894,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"71897347985","text":"# «Найди Вальдо». Дан случайный словарь, в котором,\n# на одном из уровней вложенности есть ключ «Waldo».\n# Написать функцию, которая бы принимала случайный\n# словарь и возвращала бы другой словарь, в котором был бы только один ключ «Waldo» и\n# значение этого  ключа из оригинального словаря.\n\nsimple = {\n    \"_id\": \"5eca0224bbf81beccba525d5\",\n    \"index\": 0,\n    \"guid\": \"7c53ffb4-5152-4e58-bfd7-49c7b00a92d2\",\n    \"isActive\": False,\n    \"balance\": \"$1,905.45\",\n    \"picture\": \"http://placehold.it/32x32\",\n    \"age\": 23,\n    \"eyeColor\": \"blue\",\n    \"name\": \"Mendez Love\",\n    \"gender\": \"male\",\n    \"company\": \"NIQUENT\",\n    \"email\": \"mendezlove@niquent.com\",\n    \"phone\": \"+1 (803) 421-3635\",\n    \"address\": \"883 Garnet Street, Roy, Federated States Of Micronesia, 288\",\n    \"about\": \"Magna do sunt id mollit non nulla est. Dolor ea occaecat minim veniam sit in Lorem excepteur et sint minim ea esse. Consequat incididunt tempor labore proident exercitation dolor quis Lorem. Veniam ipsum pariatur pariatur id officia.\\r\\n\",\n    \"registered\": \"2015-07-21T07:09:56 -03:00\",\n    \"latitude\": -85.74001,\n    \"longitude\": -36.020207,\n    \"tags\": [\n      \"ipsum\",\n      \"pariatur\",\n      \"officia\",\n      \"incididunt\",\n      \"labore\",\n      \"exercitation\",\n      \"occaecat\"\n    ],\n    \"friends\": [\n      {\n        \"id\": 0,\n        \"name\": \"Gardner Quinn\"\n      },\n      {\n        \"id\": 1,\n        \"name\": \"Antonia Mercer\",\n        \"waldo\": \"Я здесь!\",\n      },\n      {\n        \"id\": 2,\n        \"name\": \"Allie Sharp\"\n      }\n    ],\n    \"greeting\": \"Hello, Mendez Love! You have 6 unread messages.\",\n    \"favoriteFruit\": \"banana\"\n  }\n\n\ndef waldo(simple):\n    for key, value in simple.items():\n        # key, value\n        if key == \"waldo\":\n            print({\"waldo\": value})\n        if isinstance(value, dict):\n            waldo(value)\n        if isinstance(value, list):\n            for x in value:\n                if isinstance(x, dict):\n                    waldo(x)\n    # if \"waldo\" in simple:\n    #     return simple.get(\"waldo\")\n\n\nprint(waldo(simple))\n","repo_name":"ArkadiyVoronov/QA_python_automation","sub_path":"first_lesson/task.py","file_name":"task.py","file_ext":"py","file_size_in_byte":2279,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"36622723793","text":"# -*- coding: utf-8 -*-\r\n# @Author: Zengjq\r\n# @Date:   2018-09-21 12:54:57\r\n# @Last Modified by:   Zengjq\r\n# @Last Modified time: 2020-03-21 21:37:09\r\n\r\nimport scrapy\r\nfrom cartoonmad.items import CartoonmadItem\r\nimport os\r\nfrom scrapy.crawler import CrawlerProcess\r\nfrom custom_url import urls as custom_urls\r\nimport re\r\n\r\n\r\nclass ChapterSpider(scrapy.Spider):\r\n    name = 'manga'\r\n    allowed_domains = ['cartoonmad.com']\r\n    download_folder = 'cartoonmad'\r\n\r\n    custom_settings = {\r\n        'MEDIA_ALLOW_REDIRECTS': True,\r\n    }\r\n\r\n    def start_requests(self):\r\n        manga_no = getattr(self, 'no', None)\r\n        manga_url = getattr(self, 'url', None)\r\n        urls = []\r\n        print('manga_no', manga_no)\r\n        if manga_no != None:\r\n            mangas = manga_no.split(' ')\r\n            for manga in mangas:\r\n                new_url = 'https://www.cartoonmad.com/comic/' + manga + '.html'\r\n                if new_url not in urls:\r\n                    urls.append(new_url)\r\n\r\n        if manga_url != None:\r\n            mangas = manga_url.split(' ')\r\n            for new_url in mangas:\r\n                if new_url not in urls:\r\n                    urls.append(new_url)\r\n\r\n        if (manga_no is None or manga_no == '') and (manga_url == None or manga_url == ''):\r\n            # urls = ['https://www.cartoonmad.com/comic/3899.html']\r\n            print('使用特定url')\r\n            urls = custom_urls\r\n        print('准备开始')\r\n        for url in urls:\r\n            print(url)\r\n            # yield scrapy.Request(url, self.parse)\r\n            yield scrapy.Request(url, self.parse)\r\n\r\n    def parse(self, response):\r\n        \"\"\"\r\n        scrapy shell https://www.cartoonmad.com/comic/2500.html\r\n        \"\"\"\r\n        # with open('tmp.txt', 'wb') as f:\r\n        #     f.write(response.body)\r\n        # 漫画id\r\n        manga_no = response.url.split('/')[-1].split('.')[0]\r\n        # 名称含有中文\r\n        manga_name = str(response.css('title::text').extract()[0][:-14].strip().replace('?', '').strip())\r\n        manga_save_folder = os.path.join(self.download_folder, manga_no + '_' + manga_name)\r\n        print('parse()', manga_no, manga_name)\r\n\r\n        # chapters = response.css(\"body > table > tr:nth-child(1) > td:nth-child(2) > table > tr:nth-child(4) > td > table > tr:nth-child(2) > td:nth-child(2) > table:nth-child(3) > tr > td a\")\r\n        chapters = response.css(\"fieldset\")[1].css(\"tr > td a\")\r\n\r\n        chapters_list = []\r\n        for chapter in chapters:\r\n            chapter_name = chapter.css('::text').extract()[0]\r\n            chapter_no = chapter_name.split(' ')[1]\r\n            chapters_list.append([chapter_name, chapter_no])\r\n        # print('chapters_list', chapters_list)\r\n\r\n        # 页数比较麻烦 selector 怎么取都会取多\r\n        # chapters_pages = response.css(\"body > table > tr:nth-child(1) > td:nth-child(2) > table > tr:nth-child(4) > td > table > tr:nth-child(2) > td:nth-child(2) > table:nth-child(3) > tr > td font::text\")\r\n        chapters_pages = response.css(\"fieldset\")[1].css(\"tr > td font::text\")\r\n        # 每个章节的页数\r\n        chapters_pages_count = []\r\n        # 简单粗暴的处理\r\n        for x in range(len(chapters)):\r\n            page_count = chapters_pages[x].extract()[1:-2]\r\n            chapters_pages_count.append(page_count)\r\n\r\n        # 特殊处理\r\n        url_id = int(manga_no)\r\n        if url_id == 1893:\r\n            print('缺少页数特殊处理')\r\n            chapters_pages_count[0] = 11\r\n            chapters_pages_count[1] = 21\r\n        elif url_id == 2500:\r\n            print('缺少章节, 地址修正无效')\r\n            chapters_list = chapters_list[:5] + chapters_list[6:]\r\n            chapters_pages_count = chapters_pages_count[:5] + chapters_pages_count[6:]\r\n            print('chapters_list', chapters_list)\r\n        elif url_id == 3908:\r\n            print('缺少页数特殊处理')\r\n            chapters_pages_count[0] = 11\r\n\r\n        # print('chapters', chapters)\r\n        for index, chapter in enumerate(chapters):\r\n            chapter_link = 'https://www.cartoonmad.com' + response.css(\"body > table > tr:nth-child(1) > td:nth-child(2) > table > tr:nth-child(4) > td > table > tr:nth-child(2) > td:nth-child(2) > table:nth-child(3) > tr > td a::attr(href)\")[index].extract()\r\n            chapter_name = chapter.css('::text').extract()[0]\r\n            chapter_no = chapter_name.split(' ')[1]\r\n\r\n            check_path = 'download/' + manga_save_folder + '/' + chapter_name\r\n            if os.path.exists(check_path) and (int(chapters_pages_count[index]) == len(os.listdir(check_path))):\r\n                print('文件夹已存在, 文件都下载过了, 跳过')\r\n                continue\r\n\r\n            print('chapter_link', chapter_link)\r\n            yield scrapy.Request(chapter_link, meta={'manga_no': manga_no, 'chapter_no': chapter_no, 'manga_name': manga_name, 'chapter_name': chapter_name, 'chapters_pages_count': chapters_pages_count, 'chapters_list': chapters_list, 'manga_save_folder': manga_save_folder, 'chapters_index': index}, callback=self.parse_page)\r\n\r\n    def parse_page(self, response):\r\n        \"\"\"\r\n        scrapy shell https://www.cartoonmad.com/comic/469500002025001.html\r\n        scrapy shell https://www.cartoonmad.com/comic/169800012046001.html\r\n        scrapy shell https://www.cartoonmad.com/comic/872600014021002.html\r\n        scrapy shell https://www.cartoonmad.com/comic/872600012021001.html\r\n        scrapy shell https://www.cartoonmad.cc/comic/870100022025003.html\r\n        \"\"\"\r\n\r\n        manga_no = response.meta['manga_no']\r\n        chapter_no = response.meta['chapter_no']\r\n        manga_name = response.meta['manga_name']\r\n        chapter_name = response.meta['chapter_name']\r\n        chapters_pages_count = response.meta['chapters_pages_count']\r\n        chapters_list = response.meta['chapters_list']\r\n        manga_save_folder = response.meta['manga_save_folder']\r\n        chapters_index = response.meta['chapters_index']\r\n        print('parse_page()', manga_no, manga_name)\r\n\r\n        # 跳过广告\r\n        if '漫畫讀取中' in response.text:\r\n            print('是广告页...跳过')\r\n            pat = '''var link = '(.*?)';'''\r\n            res = re.search(pat, response.text)\r\n            chapter_link = res[1]\r\n            print('chapter_link', chapter_link)\r\n            yield scrapy.Request(chapter_link, meta={'manga_no': manga_no, 'chapter_no': chapter_no, 'manga_name': manga_name, 'chapter_name': chapter_name, 'chapters_pages_count': chapters_pages_count, 'chapters_list': chapters_list, 'manga_save_folder': manga_save_folder}, callback=self.parse_page)\r\n\r\n        # image_url = response.css(\"img::attr(src)\")[7].extract()\r\n        # print response.url\r\n        # if 'cartoonmad.com' not in image_url or '/image/panen.png' in image_url:\r\n        #     image_url = response.css(\"img::attr(src)\")[6].extract()\r\n        print('response', response)\r\n        image_urls = response.css(\"img::attr(src)\").extract()\r\n        print('image_urls', image_urls)\r\n        image_url = ''\r\n\r\n        # https://www.cartoonmad.com/comic/comicpic.asp?file=/4695/000/001\r\n        # https://www.cartoonmad.com/home75378/4695/000/001.jpg\r\n        #\r\n        # https://www.cartoonmad.com/comic/comicpic.asp?file=/3080/001/001&rimg=1\r\n        # https://web3.cartoonmad.com/home13712/3080/001/001.jpg\r\n        image_url_prefix = ''\r\n        is_asp_request = False\r\n        has_suffix = False\r\n        for x in image_urls:\r\n            # new rule\r\n            if x in ['https://www.cartoonmad.com/image/rad1.gif', 'https://www.cartoonmad.com/image/panen.png', 'https://www.cartoonmad.com/image/rad.gif']:\r\n                continue\r\n            if 'cc.fun8.us' in x:\r\n                continue\r\n            if 'comicpic.asp' in x:\r\n                # print('是asp')\r\n                is_asp_request = True\r\n                if '&rimg=1' in x:\r\n                    has_suffix = True\r\n                break\r\n            elif 'cartoonmad' in x:\r\n                image_url = x\r\n                image_url_parts = image_url.split('/')\r\n                # print image_url\r\n                # print image_url_parts\r\n                image_url_prefix = image_url_parts[0] + '//' + image_url_parts[2] + '/' + image_url_parts[3] + '/'\r\n                break\r\n\r\n        # for index, chapter in enumerate(chapters_list):\r\n        index = chapters_index\r\n        chapter = chapters_list[index]\r\n        chapter_name = chapter[0]\r\n        chapter_no = chapter[1]\r\n\r\n        # 下载图片\r\n        # https://web.cartoonmad.com/c37sn562e81/3899/001/010.jpg\r\n        # https://www.cartoonmad.com/comic/comicpic.asp?file=/8726/001/002\r\n\r\n        item = CartoonmadItem()\r\n        item['imgfolder'] = manga_save_folder + '/' + chapter_name\r\n        # print('chapters_pages_count', chapters_pages_count)\r\n        for y in range(1, int(chapters_pages_count[index]) + 1):\r\n            if is_asp_request:\r\n                # print('是asp')\r\n                item['imgurl'] = 'https://www.cartoonmad.com/comic/comicpic.asp?file=/' + manga_no + '/' + chapter_no + '/' + str(y).zfill(3)\r\n                if has_suffix:\r\n                    item['imgurl'] += '&rimg=1'\r\n            else:\r\n                # print('不是asp')\r\n                item['imgurl'] = [image_url_prefix + manga_no + '/' + chapter_no + '/' + str(y).zfill(3) + '.jpg']\r\n            # print('download image: ', item['imgurl'])\r\n            item['imgname'] = str(y).zfill(3) + '.jpg'\r\n            img_file_path = item['imgfolder'] + '/' + item['imgname']\r\n            # skip files that already downloaded\r\n            # print('检测图片是否存在', img_file_path)\r\n            if os.path.exists('download/' + img_file_path):\r\n                print('skip', img_file_path)\r\n                continue\r\n\r\n            if not os.path.exists('download/' + item['imgfolder']):\r\n                print('创建目录')\r\n                os.makedirs(os.getcwd() + '/download/' + item['imgfolder'])\r\n            headers = {\r\n                \"Accept\": \"image/webp,image/apng,image/*,*/*;q=0.8\",\r\n                \"Accept-Encoding\": \"gzip, deflate, br\",\r\n                \"Accept-Language\": \"zh-CN,zh;q=0.9,ja;q=0.8,en;q=0.7\",\r\n                \"Connection\": \"keep-alive\",\r\n                \"DNT\": \"1\",\r\n                \"Host\": \"www.cartoonmad.com\",\r\n                \"Referer\": response.url,\r\n                \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.86 Safari/537.36\",\r\n            }\r\n            item['imgheaders'] = headers\r\n            print('图片下载地址', item['imgurl'])\r\n            yield item\r\n","repo_name":"alexbelij/manga_crawler","sub_path":"cartoonmad/spiders/ImgSpider.py","file_name":"ImgSpider.py","file_ext":"py","file_size_in_byte":10666,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"40831637675","text":"# Implement per-token output video performer\n# Code is based on\n#     https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py\n#     https://github.com/yitu-opensource/T2T-ViT/blob/main/models/token_performer.py\n\n\nimport math\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom collections import OrderedDict\nfrom timm.models.vision_transformer import Mlp, DropPath, trunc_normal_\nfrom performer_pytorch.performer_pytorch import linear_attention, gaussian_orthogonal_random_matrix, softmax_kernel, partial\n\nfrom models.transformer import VideoTransformer\n\n\n\"\"\"Attention from VisionTransformer\"\"\"\nclass Attention(nn.Module):\n    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):\n        super().__init__()\n        self.num_heads = num_heads\n        head_dim = dim // num_heads\n        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights\n        self.scale = qk_scale or head_dim ** -0.5\n\n        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)\n        self.attn_drop = nn.Dropout(attn_drop)\n        self.proj = nn.Linear(dim, dim)\n        self.proj_drop = nn.Dropout(proj_drop)\n\n    def forward(self, x):\n        B, N, C = x.shape\n        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)\n        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)\n\n        attn = (q @ k.transpose(-2, -1)) * self.scale\n        attn = attn.softmax(dim=-1)\n        attn = self.attn_drop(attn)\n\n        x = (attn @ v).transpose(1, 2).reshape(B, N, C)\n        x = self.proj(x)\n        x = self.proj_drop(x)\n        return x\n\n\ndef nonnegative_softmax_kernel_feature_creator(\n        data: torch.Tensor,\n        projection_matrix: torch.Tensor,\n        batch_dims_t,\n        is_query: bool,\n        normalize_data: bool=True,\n        eps: float=0.0001):\n    \"\"\"\n    Constructs nonnegative kernel features for fast softmax attention.\n    Args:\n      data: input for which features are computes\n      projection_matrix: random matrix used to compute features\n      batch_dims_t: tuple of batch dimensions\n      is_query: predicate indicating whether input data corresponds to queries or\n        keys\n      normalize_data: predicate indicating whether data should be normalized,\n      eps: numerical stabilizer.\n    Returns:\n      Random features for fast softmax attention.\n    \"\"\"\n    if normalize_data:\n        # We have e^{qk^T/sqrt{d}} = e^{q_norm k_norm^T}, where\n        # w_norm = w * data_normalizer for w in {q,k}.\n        data_normalizer = 1.0 / (math.sqrt(math.sqrt(data.shape[-1])))\n    else:\n        data_normalizer = 1.0\n\n    ratio = 1.0 / math.sqrt(projection_matrix.shape[0])\n    data_mod_shape = data.shape[0:len(batch_dims_t)] + projection_matrix.shape\n    data_thick_random_matrix = torch.zeros(data_mod_shape, device=projection_matrix.device) + projection_matrix\n\n    data_dash = torch.matmul(\n        data_normalizer * data,\n        data_thick_random_matrix.transpose(-1, -2).unsqueeze(1)\n    )\n\n    diag_data = torch.square(data)\n    diag_data = torch.sum(diag_data, dim=data.ndim - 1)\n    diag_data = (diag_data / 2.0) * data_normalizer * data_normalizer\n    diag_data = diag_data.unsqueeze(-1)\n\n    if is_query:\n        data_dash = ratio * (\n            torch.exp(data_dash - diag_data -\n                    torch.max(data_dash, dim=-1, keepdim=True)[0]) + eps)\n    else:\n        data_dash = ratio * (\n            torch.exp(data_dash - diag_data - torch.max(data_dash)) + eps)\n\n    return data_dash\n\n\nclass PerformerAttention(nn.Module):\n    # A warpper class for SelfAttention module to make it compatible with VisionTransformer\n    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):\n        super().__init__()\n        self.num_heads = num_heads\n        head_dim = dim // num_heads\n        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights\n        self.scale = qk_scale or head_dim ** -0.5\n\n        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)\n        self.attn_drop = nn.Dropout(attn_drop)\n        self.proj = nn.Linear(dim, dim)\n        self.proj_drop = nn.Dropout(proj_drop)\n\n        self.nb_features = int(head_dim * math.log(head_dim))\n        self.create_projection = partial(gaussian_orthogonal_random_matrix, nb_rows=self.nb_features,\n                                         nb_columns=head_dim, scaling=0, qr_uniform_q=False)\n        projection_matrix = self.create_projection()\n        self.register_buffer('projection_matrix', projection_matrix)\n\n        self.feature_redraw_interval = 100\n        self.register_buffer('calls_since_last_redraw', torch.tensor(0))\n\n    @torch.no_grad()\n    def redraw_projection_matrix(self, device):\n        projections = self.create_projection(device=device)\n        self.projection_matrix.copy_(projections)\n        del projections\n\n    def _forward(self, x):\n        B, N, C = x.shape\n        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)\n        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)\n\n        create_kernel = partial(softmax_kernel, projection_matrix=self.projection_matrix, device='cuda')\n        q1 = create_kernel(q, is_query=True)\n        k1 = create_kernel(k, is_query=False)\n\n        q2 = nonnegative_softmax_kernel_feature_creator(q, self.projection_matrix, [0], is_query=True)\n        import ipdb; ipdb.set_trace()\n\n        x = linear_attention(q, k, v).transpose(1, 2).reshape(B, N, C)\n        x = self.proj(x)\n        x = self.proj_drop(x)\n        return x\n\n    def forward(self, x):\n        self.calls_since_last_redraw += 1\n        if self.calls_since_last_redraw % self.feature_redraw_interval == 0:\n            self.calls_since_last_redraw.zero_()\n            self.redraw_projection_matrix(x.device)\n        return self._forward(x)\n\n\nclass PerformerBlock(nn.Module):\n\n    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,\n                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):\n        super().__init__()\n        self.norm1 = norm_layer(dim)\n        self.attn = PerformerAttention(\n            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)\n        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here\n        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()\n        self.norm2 = norm_layer(dim)\n        mlp_hidden_dim = int(dim * mlp_ratio)\n        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)\n\n    def forward(self, x):\n        x = x + self.drop_path(self.attn(self.norm1(x)))\n        x = x + self.drop_path(self.mlp(self.norm2(x)))\n        return x\n\n\nclass VideoPerformer(VideoTransformer):\n    def __init__(self,\n                 *args,\n                 depth=12, num_heads=12, mlp_ratio=4., qkv_bias=False,\n                 qk_scale=None, drop_rate=0., attn_drop_rate=0., drop_path_rate=0.,\n                 norm_layer=nn.LayerNorm,\n                 # pretrained=False,\n                 **kwargs):\n        super().__init__(*args,\n                         depth=depth, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,\n                         drop_rate=drop_rate, attn_drop_rate=attn_drop_rate, drop_path_rate=drop_path_rate, norm_layer=norm_layer,\n                         **kwargs)\n        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule\n        self.blocks = nn.ModuleList([\n            PerformerBlock(\n                dim=self.embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,\n                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)\n            for i in range(depth)])\n\n\n        # if pretrained:\n        #     patch_size=kwargs['patch_size']\n        #     embed_dim=kwargs['embed_dim']\n        #     transformer=None\n        #     # if patch_size==16 and embed_dim==768 and depth==8 and mlp_ratio==3. and qkv_bias==False and norm_layer==nn.LayerNorm:\n        #     #     from timm.models.vision_transformer import vit_small_patch16_224\n        #     #     transformer=lambda:vit_small_patch16_224(True)\n        #     # transformer =\n        #     # assert transformer is not None\n        #     from timm.models.vision_transformer import vit_base_patch16_224\n        #     transformer=vit_base_patch16_224(pretrained=True)\n        #     model=self\n        #     for old_key in transformer.state_dict():\n        #         if old_key in ['head.weight','head.bias']:\n        #             continue\n        #         elif old_key == 'pos_embed':\n        #             new_key=old_key\n        #             model.state_dict()[new_key][0,0]=transformer.state_dict()[old_key][0,0]\n        #             num_patch = transformer.state_dict()[old_key].size(1) - 1\n        #             video_length = (model.state_dict()[new_key].size(1)-1) // num_patch\n        #             for i in range(video_length):\n        #                 model.state_dict()[new_key][0,1+i*num_patch:1+(i+1)*num_patch] = transformer.state_dict()[old_key][0,1:]\n        #             print(new_key,'<-',old_key)\n        #         elif 'attn' not in old_key:\n        #             new_key=old_key\n        #             model.state_dict()[new_key][:]=transformer.state_dict()[old_key]\n        #             print(new_key,'<-',old_key)\n        #         elif 'proj' in old_key:\n        #             new_key=old_key.replace('proj','layer.to_out')\n        #             model.state_dict()[new_key][:]=transformer.state_dict()[old_key]\n        #             print(new_key,'<-',old_key)\n        #         else:\n        #             new_key=old_key.replace('qkv','layer.to_q')\n        #             model.state_dict()[new_key][:]=transformer.state_dict()[old_key][:embed_dim]\n        #             print(new_key,'<-',old_key+'[:%d]'%embed_dim)\n        #             new_key=new_key.replace('weight','bias')\n        #             model.state_dict()[new_key][:]=0\n        #             print(new_key,'<-',0)\n        #             new_key=old_key.replace('qkv','layer.to_k')\n        #             model.state_dict()[new_key][:]=transformer.state_dict()[old_key][embed_dim:2*embed_dim]\n        #             print(new_key,'<-',old_key+'[%d:%d]'%(2*embed_dim,embed_dim))\n        #             new_key=new_key.replace('weight','bias')\n        #             model.state_dict()[new_key][:]=0\n        #             print(new_key,'<-',0)\n        #             new_key=old_key.replace('qkv','layer.to_v')\n        #             model.state_dict()[new_key][:]=transformer.state_dict()[old_key][2*embed_dim:]\n        #             print(new_key,'<-',old_key+'[%d:]'%(2*embed_dim))\n        #             new_key=new_key.replace('weight','bias')\n        #             model.state_dict()[new_key][:]=0\n        #             print(new_key,'<-',0)\n\n\nif __name__ == '__main__':\n    # test VideoPerformer\n    import torchprof\n    device = 'cuda'\n    N, L = 1, 2\n    x = torch.randn(N, L, 3, 224, 224).to(device)\n    cls_label = torch.randint(6, (N,), dtype=torch.long).to(device)\n    token_label = torch.randint(8, (N, L), dtype=torch.long).to(device)\n    model_kwargs = dict(\n        patch_size=16, embed_dim=768, depth=8, num_heads=8, mlp_ratio=3.,\n        qkv_bias=False, norm_layer=nn.LayerNorm)\n    model = VideoPerformer(pretrained=True,video_length=L, num_token_classes=8, fuse_patch=True, img_size=224, num_classes=6, **model_kwargs).to(device)\n    with torchprof.Profile(model, use_cuda=False, profile_memory=True) as prof:\n        cls_logits, token_logits = model(x)\n        loss_cls = F.cross_entropy(cls_logits, cls_label)\n        loss_token = F.cross_entropy(token_logits.flatten(start_dim=0, end_dim=1), token_label.flatten())\n        loss = loss_cls + loss_token\n        loss.backward()\n    print(prof.display(show_events=False))\n","repo_name":"xieshuqin/DenseTokenSupervision","sub_path":"models/new_performer.py","file_name":"new_performer.py","file_ext":"py","file_size_in_byte":12199,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"40980254711","text":"import errno\nimport os\nimport re\nimport shutil\nimport urllib2\n\n\ndef custom_splitext(filename):\n    cur_filename_no_ext, cur_file_ext = os.path.splitext(filename)\n    while True:\n        filename_no_ext, file_ext = os.path.splitext(cur_filename_no_ext)\n        if file_ext == '':\n            return cur_filename_no_ext, cur_file_ext\n        else:\n            cur_filename_no_ext = filename_no_ext\n            cur_file_ext = file_ext + cur_file_ext\n\n\ndef get_new_enumerated_filename(filename):\n    filename_no_ext, file_ext = custom_splitext(filename)\n    trailing_digits = re.compile('.+(\\(\\d+\\))$')\n    match = re.match(trailing_digits, filename_no_ext)\n    if match:\n        group = match.groups()[0]\n        new_number = str(int(group[1:-1]) + 1)\n        filename = filename_no_ext[:-len(group)] + '(' + new_number + ')' + file_ext\n    else:\n        filename = filename_no_ext + '(1)' + file_ext\n    return filename\n\n\ndef path_is_dir(path):\n    return path[-1] == '/'\n\n\ndef get_filename_and_file_ext(file_path):\n    _, file_ext = os.path.splitext(file_path)\n    file_name = file_path.split('/')[-1]\n    return file_name, file_ext\n\n\ndef remove_file_or_dir(path):\n    try:\n        shutil.rmtree(path)\n    except OSError as e:\n        # If the error was caused because the source wasn't a directory\n        if e.errno == errno.ENOTDIR:\n            os.remove(path)\n        else:\n            print('Unable to delete %s. Error: %s' % (path, e))\n\n\ndef copy_file_or_dir(src, dest):\n    try:\n        shutil.copytree(src, dest)\n    except OSError as e:\n        # If the error was caused because the source wasn't a directory\n        if e.errno == errno.ENOTDIR:\n            shutil.copy(src, dest)\n        else:\n            print('Unable to copy %s to %s. Error: %s' % (e, src, dest))\n\n\ndef convert_track_name_str_to_list(track_name_str):\n    if track_name_str == '.':\n        return None\n\n    track_name_str = track_name_str.strip()\n    track_name_list = re.split(':|\\^|\\|', track_name_str)\n    track_name_list = [urllib2.unquote(part) for part in track_name_list]\n    return track_name_list\n\n\ndef convert_to_boolean(text):\n    text_lower = text.lower()\n    if text_lower == 'true':\n        return True\n    elif text_lower == 'false':\n        return False\n    elif text_lower == '.':  # use default\n        return None\n    else:\n        raise TypeError","repo_name":"brynjagr/gtrackcore","sub_path":"gtrackcore/util/suite/CommonFunctions.py","file_name":"CommonFunctions.py","file_ext":"py","file_size_in_byte":2344,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"48"}
+{"seq_id":"41029261134","text":"# coding=utf-8\nimport pytest\nfrom ladybug_geojson.slippy.map import ( tile_from_lat_lon,\n    get_recurrent_tiles )\n\n\ndef test_tile_from_lat_lon():\n    coord = (41.894599, 12.483092)\n    pt = tile_from_lat_lon(*coord, 15)\n    \n    assert 17520 in pt\n\ndef test_get_recurrent_tiles():\n\n    tiles = get_recurrent_tiles(17515, 12173, 15, 16)\n    assert (35031, 24346) in tiles\n    assert len(tiles) == 4\n","repo_name":"ladybug-tools/ladybug-geojson","sub_path":"tests/test_slippy.py","file_name":"test_slippy.py","file_ext":"py","file_size_in_byte":399,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"48"}
+{"seq_id":"33199682575","text":"import numpy as np\nfrom .transformations import translation_matrix, rotation_matrix, concatenate_matrices\n\nclass Mesh(object):\n\n    def __init__(self):\n        self.vertices = []\n        self.world_coords = []\n        self.screen_coords = []\n        self.vertex_uv_coords = []\n        self.texture = None\n\n        self.vertex_normals = []\n        self.world_normals = []\n\n        self.faces = []\n        self.face_normals = []\n\n        self.position = [0,0,0]\n        self.orientation = [0,0,0]\n\n        self.color = (255,255,255)\n        # r = 255 * (0.25 + (idx % len(faces)) * 0.75 / len(faces))\n        # g = 255 * (0.5 + (idx % len(faces)) * 0.5 / len(faces))\n        # b = 255 * 1-(0.0 + (idx % len(faces)) * 1.0 / len(faces))\n\n    def get_model_matrix(self):\n        trans = translation_matrix(self.position)\n        r_x = rotation_matrix(self.orientation[0], [1,0,0])\n        r_y = rotation_matrix(self.orientation[1], [0,1,0])\n        r_z = rotation_matrix(self.orientation[2], [0,0,1])\n        return concatenate_matrices(trans, r_z, r_y, r_x)\n\n\ndef load_model_from_json(jsonFile):\n    import json\n    meshes = []\n    with open(jsonFile) as fp:\n        jsonObject = json.load(fp)\n\n        materials = {mat['name']:mat for mat in jsonObject['materials']}\n\n        for jsonMesh in jsonObject['meshes']:\n            m = Mesh()\n            vertices = jsonMesh['vertices']\n            indices = jsonMesh['indices']\n            verticesStep = 6 + jsonMesh['uvCount']*2\n\n            for i in range(0, len(vertices), verticesStep):\n                m.vertices.append(vertices[i:i+3])\n                m.vertex_normals.append(vertices[i+3:i+3+3])\n                m.vertex_uv_coords.append(vertices[i+3+3:i+3+3+2])\n\n            for i in range(0, len(indices), 3):\n                m.faces.append(indices[i:i+3])\n\n            m.position = jsonMesh['position']\n            m.vertex_normals = np.array(m.vertex_normals)\n            m.vertex_uv_coords = np.array(m.vertex_uv_coords)\n            if 'materialId' in jsonMesh:\n                mId = jsonMesh['materialId']\n                jsonMaterial = materials[mId]\n                try:\n                    filename = jsonMaterial['diffuseTexture']['name']\n                    m.texture = Texture(os.path.join('models', filename))\n                except KeyError:\n                    pass\n                try:\n                    m.color = np.array(jsonMaterial['ambient'])\n                    m.color = np.array((1,1,1))\n\n                except:\n                    pass\n\n            meshes.append(m)\n\n    return meshes\n","repo_name":"browatbn2/MAD","sub_path":"utils/M3D/mesh.py","file_name":"mesh.py","file_ext":"py","file_size_in_byte":2563,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"12269771731","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Sep 23 11:52:07 2018\n\n@author: Gehaha\n\"\"\"\n\nimport logging\n\nclass Pigment:\n    \"颜料\"\n    def __init__(self,color):\n        self.__color = color\n        self.__user = \"\"\n        \n    def getColor(self,user):\n        self.__user = user\n        return self.__color\n    \n    def setUser(self,user):\n        self.__user = user\n        return self\n    \n    def showInfo(self):\n        print(self.__user + \"取得\" + self.__color + \"色颜料\")\n        \nclass PigmengFactory:\n    \"资料的工厂类\"\n    def __init__(self):\n        self.__sigmentSet = {\n            \"红\": Pigment(\"红\"),\n            \"黄\": Pigment(\"黄\"),\n            \"蓝\": Pigment(\"蓝\"),\n            \"绿\": Pigment(\"绿\"),\n            \"紫\": Pigment(\"紫\"),\n        }\n    \n    def getPigment(self,color):\n        pigment = self._sigmentSet.get(color)\n        if pigment is None:\n            logging.error(\"没有%s颜色的颜料！\",color)\n            \n        return pigment\n    \ndef testPigment():\n    factory = PigmengFactory()\n    pigmentRed = factory.getPigment (\"红\").setUser(\"梦之队\")\n    pigmentRed.showInfo()\n    \n    pigmentYellow = factory.getPigment (\"黄\").setUser(\"梦之队\")\n    pigmentYellow.showInfo()\n    \n    pigmentBlue1 = factory.getPigment (\"蓝\").setUser(\"梦之队\")\n    pigmentBlue1.showInfo()\n    \n    pigmentBlue2 = factory.getPigment (\"\").setUser(\"梦之队\")\n    pigmentBlue2.showInfo()\n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    ","repo_name":"GeHaha/AboutPython","sub_path":"PythonDaily/罗伟富设计模式/E17享元/Flyweight_Pattern.py","file_name":"Flyweight_Pattern.py","file_ext":"py","file_size_in_byte":1526,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"37218781984","text":"\nfrom PIL import Image\nimport numpy as np\nimport torch\nimport imageio\nimport os\nimport copy\nfrom haven import haven_utils as hu\n# from src import proposals\nfrom src import datasets\nfrom skimage.io import imread\nfrom scipy.io import loadmat\nimport torchvision.transforms.functional as FT\nimport numpy as np\nimport torch\nfrom skimage import morphology\nfrom skimage.io import imread\nimport torchvision.transforms.functional as FT\nfrom skimage.transform import rescale\nimport torchvision\nfrom torchvision.transforms import transforms\nimport pylab as plt\nfrom skimage.color import label2rgb\nfrom skimage.segmentation import slic\nfrom haven import haven_utils as hu\nfrom haven import haven_img as hi\nfrom src import utils as ut\n# from repos.aranxta_code.extract_cost import CsObject\n# from repos.selectivesearch.selectivesearch import selective_search\n# from src import region_methods\n# import pycocotools.mask as mask_util\nfrom skimage.segmentation import mark_boundaries\nimport pandas as pd \n\n\nclass Pascal:\n    def __init__(self, datadir, split, supervision, exp_dict=None, sbd=False):\n     \n        self.split = split\n        self.exp_dict = exp_dict\n        self.supervision = supervision\n        # self.n_classes = 21\n        self.n_classes = 21\n        self.datadir = datadir\n        if split == \"train\":\n            # berkeley addition of images\n            if sbd == True:\n                \n                dataset = torchvision.datasets.SBDataset(os.path.join(datadir, 'sbdataset'),\n                                                           image_set='train',\n                                                           download=False)\n            else:\n                dataset = torchvision.datasets.VOCSegmentation(datadir,\n                                                            year='2012',\n                                                            image_set='train',\n                                                            download=False)\n            \n        elif split in [\"val\", 'test']:\n            dataset = torchvision.datasets.VOCSegmentation(datadir,\n                                                            image_set='val',\n                                                            download=False)\n            \n        self.point_dict = hu.load_json(os.path.join(datadir, 'VOCdevkit',                 \n                                    'pascal2012_trainval_main.json'))\n        self.dataset = dataset\n        self.transforms = None\n                                                                         \n    def __len__(self):\n        return len(self.dataset)\n\n    def __getitem__(self, index):\n        # index = 0\n        img_path = self.dataset.images[index]\n        name = os.path.split(img_path)[-1].split('.')[0]\n\n        img_pil = Image.open(img_path).convert(\"RGB\")\n        W, H = img_pil.size\n        points_list = self.point_dict[name]\n        points_mask = np.zeros((H, W))\n        for p in points_list:\n            if p['y'] >= H or p['x'] >= W:\n                continue\n            points_mask[int(p['y']), int(p['x'])] = p['cls']\n        \n        images = torchvision.transforms.ToTensor()(np.array(img_pil))\n        mean = [0.485, 0.456, 0.406]\n        std = [0.229, 0.224, 0.225]\n\n        images = transforms.Normalize(mean=mean, std=std)(images)\n        mask_path = self.dataset.masks[index]\n\n        if '.mat' in mask_path:\n            mask_pil = Image.fromarray(hu.load_mat(mask_path)['GTcls'][0]['Segmentation'][0])\n        else:\n            mask_pil = Image.open(mask_path)\n            \n        masks= torch.as_tensor(np.array(mask_pil))\n        \n        \n        points_mask[points_mask == 0] = 255\n        y_list, x_list = np.where(masks==0)\n        if len(y_list):\n            for i in range(len(points_list)):\n                yi, xi = datasets.get_random(y_list, x_list, seed=i+1)\n                points_list += [{'cls':0, 'x':xi, 'y':yi}]\n                points_mask[yi, xi] = 0\n\n        # hu.save_image(fname='tmp.png', img=torch.as_tensor(np.array(img_pil))/255., \n        #             points=(points_mask!=255).astype('uint8'))\n\n        batch = {\"images\": images,\n        \"img_pil\": img_pil,\n                'points':torch.as_tensor(points_mask),\n                'point_list':points_list,\n                # 'inst':inst,\n                #  'flipped':flipped,\n                 \"masks\": masks,\n                #  \"original\":inv_transform(images),\n                 \"meta\": {\"index\": index,\n                        'hash':hu.hash_dict({'id':index, 'split':self.split}),\n                          \"name\": self.dataset.images[index],\n                          \"size\": images.shape[-2:],\n                          \"image_id\": index,\n                          \"split\": self.split}}\n\n        return batch\n\n\ndef get_blob_list(mask_dict, points_mask, img_pil, split_inst=False):\n    blob_list = []\n    mask = preds = mask_dict['preds']\n    probs = mask_dict['probs']\n    assert probs.shape[1] == preds.shape[0]\n    assert probs.shape[2] == preds.shape[1]\n    \n    imask = np.zeros(mask.shape)\n    cmask = np.zeros(mask.shape)\n    \n    blob_id = 1\n    for c in np.unique(mask):\n        if c == 0:\n            continue\n        probs_class = probs[c]\n        point_ind = points_mask == c\n        inst_mask = morphology.label(mask==c)\n        for l in np.unique(inst_mask):\n            if l == 0:\n                continue\n            blob_ind = inst_mask == l\n            locs = np.where(blob_ind * point_ind)\n            y_list, x_list = locs\n            n_points = len(y_list)\n            if n_points == 0:\n                continue\n            if n_points > 1 and split_inst: \n                # split multiple points\n                img_points = hi.points_on_image(y_list, x_list, img_pil)\n                img_masks = hi.mask_on_image(img_pil, mask)\n                img_masks = hi.mask_on_image(img_points.copy(), blob_ind)\n                hu.save_image('tmp.jpg', (img_points)*0.5 + hu.f2l(hi.gray2cmap(probs_class))*0.5)\n                hu.save_image('tmp.jpg', img_masks)\n                \n                for yi, xi in zip(y_list, x_list):\n                    imask, cmask, blob_list, blob_id = add_mask(yi, xi, points_mask, \n                                                            blob_ind, \n                                                            n_points, blob_list, imask, cmask,\n                                                            blob_id)\n            else:\n                # add for that single point\n                yi, xi = y_list[0], x_list[0]\n                imask, cmask, blob_list, blob_id = add_mask(yi, xi, points_mask, \n                                                            blob_ind, \n                                                            n_points, blob_list, imask, cmask,\n                                                            blob_id)\n                \n\n    return blob_list, cmask.astype('uint8'), imask.astype('uint8')\n\ndef get_blob_list_v2(mask_dict, points_mask, img_pil):\n    blob_list = []\n    preds = mask_dict['preds']\n    probs = mask_dict['probs']\n\n    masker = Masker(preds)\n\n    for c in np.unique(preds):\n        if c == 0:\n            continue\n        point_ind = points_mask == c\n        inst_mask = morphology.label(preds==c)\n        for l in np.unique(inst_mask):\n            if l == 0:\n                continue\n            blob_ind = inst_mask == l\n            locs = np.where(blob_ind * point_ind)\n            y_list, x_list = locs\n            n_points = len(y_list)\n            if n_points == 0:\n                continue\n            if n_points > 1:\n                # split multiple points\n                mask_list = watersplit(probs[l,:,:], (blob_ind * point_ind), img_pil, blob_ind)\n                assert(len(mask_list) == n_points)\n                for mask_dict in mask_list:\n                    yi = mask_dict['yi']\n                    xi = mask_dict['xi']\n\n                    masker.add_mask(yi, xi, points_mask, mask_dict['mask'],\n                                    n_points)\n            else:\n                # add for that single point\n                yi, xi = y_list[0], x_list[0]\n                masker.add_mask(yi, xi, points_mask, blob_ind,\n                                    n_points)\n\n    y_list, x_list = np.where(points_mask)\n    # \n    masker.save(img_pil, y_list, x_list, preds )\n    # mask\n    blob_list, cmask, imask =  masker.get_masks()\n    masker.save(img_pil, y_list, x_list, imask )\n    return blob_list, cmask.astype('uint8'), imask.astype('uint8')\n\nclass Masker:\n    def __init__(self,mask):\n        self.imask = np.zeros(mask.shape, 'uint8')\n        self.cmask = np.zeros(mask.shape, 'uint8')\n        self.blob_list = []\n        self.blob_id = 1\n\n    def add_mask(self, yi, xi, points_mask, blob_ind, n_points):\n        ci = points_mask[yi, xi]\n        self.blob_list += [{'mask':blob_ind, 'point':{'y':yi, 'xi':xi}, 'cls':ci}]\n        self.imask[blob_ind] = self.blob_id\n        self.cmask[blob_ind] = ci\n        self.blob_id += 1\n\n\n    def get_masks(self):\n        return self.blob_list, self.cmask, self.imask\n\n    def save(self, img_pil, y_list, x_list, mask):\n        img_p = hi.points_on_image( y_list, x_list, img_pil)\n\n        img_maskspil = hi.mask_on_image(img_p, mask.astype('uint8'), add_bbox=True)\n        hu.save_image('masker.jpg', img_maskspil)\n\ndef watersplit(_probs, _points, img_pil, blob_ind):\n    import numpy as np\n    from skimage.morphology import watershed\n    from skimage.segmentation import find_boundaries\n    from scipy import ndimage\n    from scipy import ndimage as ndi\n\n    points = _points.copy()\n    img_pilb = img_pil.convert('L')\n\n    # points[points!=0] = np.arange(1, points.sum()+1)\n    points = ndi.label(points)[0]\n    points = points.astype(float)\n\n    # probs = ndimage.black_tophat(_probs.copy(), 7)\n    probs = ndimage.black_tophat(_probs.clone(), 7)\n    # seg =  watershed(probs, points, mask=img_pilb)\n    water_mask =  watershed(probs, points, mask=blob_ind)\n    mask_list = []\n    for p in np.unique(points):\n        if p == 0:\n            continue \n        y_list, x_list = np.where(points==p)\n        mask_list += [{'yi':y_list[0], 'xi':x_list[0], 'mask':water_mask==p}]\n    \n    # for i, mask_dict in enumerate(mask_list):\n    #     img_maskspil = hi.mask_on_image(img_pil, mask_dict['mask'])\n    #     img_points = hi.points_on_image([mask_dict['yi']], [mask_dict['xi']], img_maskspil)\n    #     hu.save_image('water_mask_%d.jpg' % i, img_points)\n    return mask_list","repo_name":"IssamLaradji/affinity_lcfcn","sub_path":"src/datasets/pascal.py","file_name":"pascal.py","file_ext":"py","file_size_in_byte":10478,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"48"}
+{"seq_id":"17932162633","text":"import pickle\n\n# Tải mô hình từ file\nfilename = 'cnn.pkl'\nwith open(filename, 'rb') as file:\n    model = pickle.load(file)\n\n# Dữ liệu văn bản mới cần phân loại\ntext_samples = [\n    \"This is a positive review.\",\n    \"I don't like this product.\",\n    \"The movie was great!\",\n    \"The book was boring.\",\n]\nprint(model)\n\n# Sử dụng mô hình để dự đoán nhãn của các văn bản mới\npredictions = model.predict(text_samples)\n\n# In kết quả dự đoán\nfor text, label in zip(text_samples, predictions):\n    print(f\"Văn bản: '{text}'\")\n    print(f\"Nhãn dự đoán: {label}\")\n    print()","repo_name":"dzungdducws/Vietnamese-sentiment-analysis","sub_path":"kien/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":623,"program_lang":"python","lang":"vi","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"27730759240","text":"\nimport collections\nimport copy\nimport json\nimport os\n\nfrom bogt import io  # noqa\nfrom bogt import spec\n\n\nEMPTY_LIVESET = {\n    \"version\": \"1.0.0\",\n    \"liveSetData\": {\n        \"orderNumber\": 1,\n        \"path\": None,\n        \"image\": None,\n        \"id\": \"\",\n        \"name\": \"\",\n        \"url\": None\n    },\n    \"patchList\": [],\n    \"device\": \"GT\"\n}\n\n\ndef load_tsl_from_file(path, conf):\n    if os.path.isfile(path):\n        with open(path) as f:\n            return LiveSet(conf, json.load(f), path=path)\n    if os.path.exists(path):\n        raise Exception('%s is not a file' % path)\n    return LiveSet(conf, path=path)\n\n\ndef write_patch_order(patch, info_out):\n    fx_chain = spec.table('FX CHAIN')\n    start_fx = []\n    end_fx = []\n    a_fx = []\n    b_fx = []\n    positions = patch['params']['chainParams']['positionList']\n    state = 'start'\n    for p in positions:\n        fx = fx_chain[p]\n        if fx == 'DIV1':\n            state = 'a'\n            continue\n        elif fx == 'MIX1_DIV2':\n            state = 'b'\n            continue\n        elif fx == 'MIX2':\n            state = 'end'\n            continue\n        if state == 'start':\n            start_fx.append(fx)\n        elif state == 'a':\n            a_fx.append(fx)\n        elif state == 'b':\n            b_fx.append(fx)\n        elif state == 'end':\n            end_fx.append(fx)\n\n    info_out.write('             |\\n')\n    for fx in start_fx:\n        info_out.write('        %s\\n' % fx.center(12))\n        info_out.write('             |\\n')\n    info_out.write('       ------------ \\n')\n    info_out.write('      |            |\\n')\n    for i in range(max(len(a_fx), len(b_fx))):\n        afx = a_fx and a_fx.pop(0) or None\n        bfx = b_fx and b_fx.pop(0) or None\n        if afx:\n            info_out.write(' %s' % afx.center(12))\n        else:\n            info_out.write('      |     ')\n        if bfx:\n            info_out.write(' %s\\n' % bfx.center(12))\n        else:\n            info_out.write('       |     \\n')\n        info_out.write('      |            |\\n')\n    info_out.write('       ------------ \\n')\n    info_out.write('             |\\n')\n    for fx in end_fx:\n        info_out.write('        %s\\n' % fx.center(12))\n        info_out.write('             |\\n')\n\n\nclass LiveSet(object):\n\n    def __init__(self, conf, data=None, path=None):\n        self.data = data or dict(EMPTY_LIVESET)\n        self.conf = conf\n        self.path = path\n        self.patches = collections.OrderedDict()\n        for p in self.data['patchList']:\n            self._add_patch(p)\n        self._sync_data()\n\n    def _add_patch(self, p):\n        order_num = len(self.patches) + 1\n        p['orderNumber'] = order_num\n        key = '%s: %s' % (order_num, p.get('name').strip())\n        self.patches[key] = p\n        return key\n\n    def _sync_data(self):\n        self.data['patchList'] = self.patches.values()\n\n    def add_patch(self, p):\n        key = self._add_patch(copy.deepcopy(p))\n        self._sync_data()\n        return key\n\n    def remove_patch(self, patch_key):\n        del self.patches[patch_key]\n        i = 1\n        for p in self.patches.itervalues():\n            p['orderNumber'] = i\n            i += 1\n        self._sync_data()\n\n    def to_midi(self, session, patch_key, preset_name=None):\n        patch = self.patches[patch_key]\n        session.patch_to_midi(patch, preset_name)\n\n    def store(self):\n        with open(self.path, 'w') as f:\n            json.dump(self.data, f, indent=2)\n","repo_name":"steveb/bogt","sub_path":"bogt/tsl.py","file_name":"tsl.py","file_ext":"py","file_size_in_byte":3452,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"22556565842","text":"#This script is intended to find the top and the mid pedestal of the H mod plasma profile for the pre and post processing of the simulation\n\n#Developed by Max Curie on 02/03/2020\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport scipy.optimize as opt\nimport re\nfrom max_stat_tool import *\nfrom max_building_guide import *\n# some_file.py\nimport sys\n\nplot0  = 1  # type 1 if want to plot the result\nreport0 = 1 # type 1 if want a report of the result\n\n#The command of this script will be \"python max_parity_calculator.py 0001(# of the scan)\" \n\ndef parity_finder_long(zgrid,f,name,plot,report): #this function is for local linear simulation with long range of z \n\n    f=np.real(f)\n    #For the case the zgrid is evenly distributed. \n    zmin=np.min(zgrid)\n    zmax=np.max(zgrid)\n    nz=len(zgrid)\n    #print(zmax)\n    #print(zmin)\n\n    z0=int(nz/(zmax-zmin)) #nz from zmin to zmax\n    parity_name=['even','odd']\n    location=[-1,0,1]\n    parity=np.zeros((len(location)+1,2))\n    \n    parity_plot_abs0=[]\n    parity_plot_abs1=[]\n    for j in range(len(location)):\n        for i in range(z0):\n            ztemp=int(nz/2+location[j]*z0) #around -1,0,1\n            #even parity sum(f(x)-f(-x)) = 0 if even \n            parity[j][0]=parity[j][0]+abs(f[ztemp+i-1]-f[ztemp-i])\n            #odd parity sum(f(x)+f(-x)) = 0 if odd \n            parity[j][1]=parity[j][1]+abs(f[ztemp+i-1]+f[ztemp-i])\n\n\n    #********Doing the same thing for loop***********\n    z_loop, f_loop=loop(zgrid,f,-1,1)\n    for i in range(z0):\n        ztemp=z0\n        parity_plot_abs0.append(abs(f_loop[ztemp+i-1]-f_loop[ztemp-i]))\n        parity_plot_abs1.append(abs(f_loop[ztemp+i-1]+f_loop[ztemp-i]))\n        #even parity sum(f(x)-f(-x)) = 0 if even \n        ntemp=len(location)\n        parity[ntemp][0]=parity[ntemp][0]+abs(f_loop[ztemp+i-1]-f_loop[ztemp-i])\n        #odd parity sum(f(x)+f(-x)) = 0 if odd \n        parity[ntemp][1]=parity[ntemp][1]+abs(f_loop[ztemp+i-1]+f_loop[ztemp-i])\n    #***********Find the ratio at different locations****************\n    ratio=np.zeros((len(location)+1)*2)\n    for i in range(len(location)+1):\n        ratio[2*i+0]=parity[i][0]/(parity[i][0]+parity[i][1]) #percentage of oddness\n        ratio[2*i+1]=parity[i][1]/(parity[i][0]+parity[i][1]) #percentage of evenness\n    \n    #ratio[2*0+0,2*0+1]=-1    {percentage of oddness, percentage of eveness}\n    #ratio[2*1+0,2*1+1]= 0    {percentage of oddness, percentage of eveness}\n    #ratio[2*2+0,2*2+1]= 1    {percentage of oddness, percentage of eveness}\n    #ratio[2*3+0,2*3+1]= loop {percentage of oddness, percentage of eveness}\n    location0=location[int(np.argmin(ratio[0:len(location)*2-1])/2)]\n    parity0=parity_name[int(np.argmin(ratio[0:len(location)*2-1])%2)]\n\n\n    #********calc the ratio for loop**********************\n   \n    if parity[3][1]<parity[3][0]:\n        parity1='odd'\n    else:\n        parity1='even'\n\n    #*********Print out report*****************\n    if report==1:\n        for i in range(len(location)+1):\n            if i < len(location):\n                print('Around z=',location[i])\n            else:\n                print('For the loop')        \n            print(ratio[2*i+0]*100,'% Odd', ratio[2*i+1]*100, \"% Even\")\n\n        print('The location of the center is', location0)\n        print('The function is largely', parity0)\n\n        print('Based on the loop, the function is largely ',parity1)\n    \n        if parity1==parity0:\n            print('Result checked')\n        else:\n            print('location=', location0 ,'and loop mismatch, please check the Parity_plot.png or function.png to determine the parity manually')\n    #*********End of Print out report**********\n\n    #********Plot the result*******************\n    if plot ==1:\n        x_zoom,y_zoom = zoom1D(zgrid,f,-2,2)\n        plt.clf()\n        plt.title('Parity of'+ name+ 'calculation')\n        plt.xlabel(r'$z/\\pi$',fontsize=10)\n        plt.ylabel(r'$f$',fontsize=13)\n        #plt.plot(np.arange(0,1,1/len(parity_plot0)),parity_plot_abs0,label=\"Even function abs\")\n        #plt.plot(np.arange(0,1,1/len(parity_plot0)),parity_plot_abs1,label=\"Odd function abs\")\n        plt.plot(x_zoom,y_zoom,label=name)\n        plt.plot(z_loop,f_loop,label=name+'_loop')\n        plt.axhline(y=0, color=\"red\")\n        plt.axvline(x=location0, label='Center of the symmetry', color=\"red\")\n        #plt.axvline(x=midped, label=\"Mid-pedestal\", color=\"red\")\n        #lt.axvline(x=topped, label=\"Top-pedestal\", color=\"green\")\n        plt.legend()\n        plt.savefig('Parity_plot_'+ name +'.png')\n        plt.show()\n\n        plt.clf()\n        plt.title(name)\n        plt.xlabel(r'$z/\\pi$',fontsize=10)\n        plt.ylabel(r'$f$',fontsize=13)\n        #plt.plot(z_loop,f_loop,label=\"loop_f\")\n        plt.plot(zgrid,f,label=name)\n        #plt.axvline(x=midped, label=\"Mid-pedestal\", color=\"red\")\n        #lt.axvline(x=topped, label=\"Top-pedestal\", color=\"green\")\n        plt.legend()\n        plt.savefig('function_'+name+'.png')\n        plt.show()\n        \n        ntemp=int(np.argmin(ratio[0:len(location)*2-1])/2)\n        ratio0=np.zeros(2)\n        ratio0[0]=ratio[ntemp*2+0]\n        ratio0[1]=ratio[ntemp*2+1]\n\n    return parity0,location0,ratio0\n\ndef parity_finder_short(zgrid,f,name,plot,report): #this function is for local linear simulation with short range of z \n\n    f=np.real(f)\n    #For the case the zgrid is evenly distributed. \n    zmin=np.min(zgrid)\n    zmax=np.max(zgrid)\n\n    if abs(zmin) > abs(zmax):\n        idx=find_nearest_index(zgrid,-zmax)\n        new_z=zgrid[idx:-1]\n        f=f[idx:-1]\n    elif abs(zmin) > abs(zmax):\n        idx=find_nearest_index(zgrid,-zmin)\n        new_z=zgrid[0:idx]\n        f=f[0:idx]\n    else:\n        new_z=zgrid\n        f=f\n\n    idx=find_nearest_index(new_z,0)\n    #print(f'index of z=0 is {idx}')\n    #print(f'index of nz/2 is {int(len(new_z)/2)}')\n\n    z0=int(len(new_z)/2)\n\n    location0=0\n    parity=np.zeros(2)\n\n\n    for i in range(z0):\n        ztemp=int(z0) #around -1,0,1\n        #even parity sum(f(x)-f(-x)) = 0 if even \n        parity[0]=parity[0]+abs(f[ztemp+i-1]-f[ztemp-i])\n        #odd parity sum(f(x)+f(-x)) = 0 if odd \n        parity[1]=parity[1]+abs(f[ztemp+i-1]+f[ztemp-i])\n\n    #Determine function's parity\n    if parity[1]<parity[0]:\n        parity1='odd'\n    else:\n        parity1='even'\n\n    if plot ==1:\n        plt.clf()\n        plt.title('Parity of'+name+' calculation')\n        plt.xlabel(r'$z/\\pi$',fontsize=10)\n        plt.ylabel(name,fontsize=13)\n        #plt.plot(np.arange(0,1,1/len(parity_plot0)),parity_plot_abs0,label=\"Even function abs\")\n        #plt.plot(np.arange(0,1,1/len(parity_plot0)),parity_plot_abs1,label=\"Odd function abs\")\n        plt.plot(new_z,f,label=name)\n        plt.axhline(y=0, color=\"red\")\n        plt.axvline(x=location0, label='Center of the symmetry', color=\"red\")\n        #plt.axvline(x=midped, label=\"Mid-pedestal\", color=\"red\")\n        #lt.axvline(x=topped, label=\"Top-pedestal\", color=\"green\")\n        plt.legend()\n        plt.savefig('Parity_plot_'+name+'.png')\n        plt.show()\n\n    ratio=np.zeros(2)\n    ratio[0]=parity[0]/(parity[0]+parity[1]) #percentage of oddness\n    ratio[1]=parity[1]/(parity[0]+parity[1]) #percentage of evenness\n\n    return parity1,location0,ratio\n\ndef parity_finder_LL(zgrid,f,name,plot,report):\n    zmin=np.min(zgrid)\n    zmax=np.max(zgrid)\n    if zmax>=2 and zmin<= -2: \n        return parity_finder_long(zgrid,f,name,plot,report)\n    else:\n        return parity_finder_short(zgrid,f,name,plot,report)\n\n\ndef parity_finder_GL(xgrid,zgrid,f,name,plot,report): #this function is for global linear simulation\n    print(np.shape(f)) #f(z,x)\n    print(np.shape(xgrid))\n    print(np.shape(zgrid))\n    \n    location_dis=np.zeros(len(xgrid))  #distribution of the location in term of radius\n    f_dis=np.zeros(len(xgrid))         #distribution of the function in term of radius\n    ratio_dis=np.zeros((len(xgrid),2)) #distribution of the ratio in term of radius\n    ratio0=np.zeros(2)\n    location_avg=0\n    f_temp=0\n    parity0=0\n\n    \n    for i in range(len(xgrid)):\n        parity,location0,ratio = parity_finder_LL(zgrid,f[:,i],name,plot=0,report=0)\n\n        location_dis[i] = location0\n        ratio_dis[i,0]  = ratio[0]\n        ratio_dis[i,1]  = ratio[1]\n        f_dis[i]        = np.mean(abs(f[:,i]))\n\n        f_temp=f_temp+f_dis[i]\n\n        location_avg=location_avg+location0*f_dis[i]\n        ratio0[0]=ratio0[0]+ratio[0]*f_dis[i]\n        ratio0[1]=ratio0[1]+ratio[1]*f_dis[i]\n\n    location_avg=location_avg/f_temp\n    ratio0[0]=ratio0[0]/f_temp\n    ratio0[1]=ratio0[1]/f_temp\n\n    if ratio0[0]>ratio[1]:\n        parity0 = 'odd'\n    else:\n        parity0 = 'even'\n\n    if report==1:\n        print('The location of the center is z=',location_avg)\n        print('The function is largely =', parity0)\n    if plot==1:\n        plt.clf()\n        plt.figure(figsize=(8.0,9.5))\n        fig=plt.gcf()\n        #Top of the plot\n        plt.subplot(2,1,1)\n        plt.title('Location of center of symmetry for '+name)\n        plt.xlabel(r'$r/a$',fontsize=10)\n        plt.ylabel(name,fontsize=13)\n        plt.contourf(xgrid,zgrid,f,70)\n        plt.plot(xgrid,location_dis,label=\"Center of the symmetry\",color=\"purple\")\n        plt.axhline(y=location_avg, label='Average Center of the symmetry',color=\"red\")\n        plt.legend()\n\n        #Bottom of the plot\n        plt.subplot(2,1,2)\n        plt.title('Parity calculation for ' +name)\n        plt.xlabel(r'$r/a$',fontsize=10)\n        plt.ylabel(name,fontsize=13)\n        plt.plot(xgrid,ratio_dis[:,0],label=\"ratio of odd\",color=\"purple\")\n        plt.plot(xgrid,norm(f_dis),label=\"average of f(arb.)\",color=\"green\")\n        plt.legend()\n\n        plt.tight_layout()\n        #plt.savefig('Parity_plot.png')\n        plt.show()\n        \n    return parity0,location_avg,ratio0\n    #Parity of the location\n    #location of the symmetry center\n    #Parity of the loop sum\n    #ratio    {percentage of oddness, percentage of eveness}\n\n#***************Starts of the run****************\n#Running the functions\n  #Initialization\ntime, suffix, pars, field = initi_with_input()\n  #Determine if the run is location simulation\nif 'x_local' in pars:\n    if pars['x_local']:\n        x_local = True\n    else:\n        x_local = False \nelse:\n    x_local = True\nif x_local:\n    phi,apar,zgrid = phi_and_apar(suffix,field,pars)\nelse:\n    phi,apar,xgrid,zgrid = phi_and_apar(suffix,field,pars)\n\nprint('For phi')\nname=\"phi\"\nif x_local:\n    parity_phi,location_phi,ratio_phi = parity_finder_LL(zgrid,phi,name,plot=plot0,report=report0)\nelse:\n    parity_phi,location_phi,ratio_phi = parity_finder_GL(xgrid,zgrid,phi,name,plot=plot0,report=report0)\nprint('******************************')\nprint('For Apar')\nname=\"apar\"\nif x_local:\n    parity_apar,location_apar,ratio_apar = parity_finder_LL(zgrid,apar,name,plot=plot0,report=report0)\nelse:\n    parity_apar,location_apar,ratio_apar = parity_finder_GL(xgrid,zgrid,apar,name,plot=plot0,report=report0)\nprint('***********************************************')\nprint('************Summary of parity******************')\nif location_phi==location_apar:\n    print('Center of the symmetry is z=',location_phi)\nelse:\n    print('Center of symmetry for phi= ', location_phi, ', apar=', location_apar ,', WARNING, please check the output graph manually')\n\nif parity_phi=='even' and parity_apar=='odd':\n    print('phi=even (', ratio_phi[1]*100, '%), apar=odd  (', ratio_apar[0]*100, '%)  ---->   Ballooning parity')\nelif parity_phi=='odd' and parity_apar=='even':\n    print('phi=odd  (', ratio_phi[0]*100, '%), apar=even (', ratio_apar[1]*100 ,'%)  ---->   Tearing parity')\nelse:\n    print('phi:  odd ', ratio_phi[0]*100 ,'%,  even ',ratio_phi[1]*100,'%')\n    print('apar: odd ', ratio_apar[0]*100,'%,  even ',ratio_apar[1]*100,'%')\n    print('phi=', parity_phi, ', apar=' ,parity_apar,'   ---->   WARNING, please check the output graph manually')\n\nprint('**************End of Summary*******************')\nprint('***********************************************')\n\n\n","repo_name":"drdrhatch/IFS_scripts","sub_path":"max_parity_calculator.py","file_name":"max_parity_calculator.py","file_ext":"py","file_size_in_byte":11989,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"48"}
+{"seq_id":"40299187060","text":"\"\"\"Multiplexes video and audio inputs to encoded byte stream.\"\"\"\n\nfrom typing import Iterable, Iterator\n\nimport audio\nimport frame_grabber\nimport machine\nimport opcodes\nimport screen\nimport video\nfrom palette import Palette\nfrom video_mode import VideoMode\n\n\nclass Movie:\n    def __init__(\n            self, filename: str,\n            every_n_video_frames: int = 1,\n            audio_bitrate: int = 14700,\n            audio_normalization: float = None,\n            max_bytes_out: int = None,\n            video_mode: VideoMode = VideoMode.HGR,\n            palette: Palette = Palette.NTSC,\n    ):\n        self.filename = filename  # type: str\n        self.every_n_video_frames = every_n_video_frames  # type: int\n        self.max_bytes_out = max_bytes_out  # type: int\n        self.video_mode = video_mode  # type: VideoMode\n        self.palette = palette  # type: Palette\n\n        self.audio = audio.Audio(\n            filename, bitrate=audio_bitrate,\n            normalization=audio_normalization)  # type: audio.Audio\n\n        self.frame_grabber = frame_grabber.FileFrameGrabber(\n            filename, mode=video_mode, palette=self.palette)\n        self.video = video.Video(\n            self.frame_grabber,\n            ticks_per_second=self.audio.sample_rate,\n            mode=video_mode,\n            palette=self.palette\n        )  # type: video.Video\n\n        # Byte offset within TCP stream\n        self.stream_pos = 0  # type: int\n\n        # Current audio tick opcode count within movie stream.\n        self.ticks = 0  # type: int\n\n        # Tracks internal state of player virtual machine\n        self.state = machine.Machine()\n\n        # Currently operating on AUX memory bank?\n        self.aux_memory_bank = False\n\n    def encode(self) -> Iterator[opcodes.Opcode]:\n        \"\"\"\n\n        :return:\n        \"\"\"\n        video_frames = self.frame_grabber.frames()\n        op_seq = None\n\n        yield opcodes.Header(mode=self.video_mode)\n\n        last_memory_bank = self.aux_memory_bank\n        for au in self.audio.audio_stream():\n            self.ticks += 1\n            new_video_frame = self.video.tick(self.ticks)\n            if new_video_frame:\n                try:\n                    main, aux = next(video_frames)\n                except StopIteration:\n                    break\n\n                should_encode_frame = (\n                        (self.video.frame_number - 1) %\n                        self.every_n_video_frames == 0\n                )\n                if should_encode_frame:\n                    if self.video_mode == VideoMode.DHGR:\n                        target_pixelmap = screen.DHGRBitmap(\n                            main_memory=main,\n                            aux_memory=aux,\n                            palette=self.palette\n                        )\n                    else:\n                        target_pixelmap = screen.HGRBitmap(\n                            main_memory=main,\n                            palette=self.palette\n                        )\n\n                    print(\"Starting frame %d\" % self.video.frame_number)\n                    op_seq = self.video.encode_frame(\n                        target_pixelmap, is_aux=self.aux_memory_bank)\n                    self.video.out_of_work = {True: False, False: False}\n\n            if self.aux_memory_bank != last_memory_bank:\n                # We've flipped memory banks, start new opcode sequence\n                last_memory_bank = self.aux_memory_bank\n                op_seq = self.video.encode_frame(\n                    target_pixelmap, is_aux=self.aux_memory_bank)\n\n            # au has range -15 .. 16 (step=1)\n            # Tick cycles are units of 2\n            tick = au * 2  # -30 .. 32 (step=2)\n            tick += 34  # 4 .. 66 (step=2)\n\n            (page, content, offsets) = next(op_seq)\n\n            yield opcodes.TICK_OPCODES[(tick, page)](content, offsets)\n\n    def _emit_bytes(self, _op: opcodes.Opcode) -> Iterable[int]:\n        \"\"\"Emit compiled bytes corresponding to a player opcode.\n\n        Also tracks byte stream position.\n        \"\"\"\n        for b in self.state.emit(_op):\n            yield b\n            self.stream_pos += 1\n\n    def emit_stream(self, ops: Iterable[opcodes.Opcode]) -> Iterator[int]:\n        \"\"\"Emit compiled byte stream corresponding to opcode stream.\n\n        Inserts padding opcodes at 2KB stream boundaries, to instruct player\n        to manage the TCP socket buffer.\n\n        :param ops:\n        :return:\n        \"\"\"\n        for op in ops:\n            if self.max_bytes_out and self.stream_pos >= self.max_bytes_out:\n                yield from self.done()\n                return\n\n            yield from self._emit_bytes(op)\n\n            # Keep track of where we are in TCP client socket buffer\n            socket_pos = self.stream_pos % 2048\n            if socket_pos >= 2044:\n                # 2 op_ack address bytes + 2 payload bytes from ACK must\n                # terminate 2K stream frame\n                if self.video_mode == VideoMode.DHGR:\n                    # Flip-flop between MAIN and AUX banks\n                    self.aux_memory_bank = not self.aux_memory_bank\n\n                yield from self._emit_bytes(opcodes.Ack(self.aux_memory_bank))\n                assert self.stream_pos % 2048 == 0, self.stream_pos % 2048\n\n        yield from self.done()\n\n    def done(self) -> Iterator[int]:\n        \"\"\"Terminate byte stream by emitting terminal opcode and padding to 2KB.\n\n        :return:\n        \"\"\"\n        yield from self._emit_bytes(opcodes.Terminate())\n\n        # Player expects to fill 2K TCP buffer so pad it out\n        for _ in range(2048 - (self.stream_pos % 2048)):\n            yield 0x00\n","repo_name":"KrisKennaway/ii-vision","sub_path":"transcoder/movie.py","file_name":"movie.py","file_ext":"py","file_size_in_byte":5651,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"48"}
+{"seq_id":"43026271335","text":"from wygameboard import Board\nfrom wymodel import baseline_model\nfrom wyreplay import Replay\n\nimport numpy as np\nfrom keras.models import load_model\nimport tensorflow as tf\n\nfrom copy import deepcopy\nfrom queue import Queue\nfrom random import choice, random\nfrom threading import Thread, Lock\n\n\nDEBUG = False\nEPSILON = .1\nEPOCHS = 10**7\nBATCH_SIZE = 128\nFILENAME = \"reversi3.h5\"\nTHREADS = 8\n\n\nempty = 0\nblack = 1\nwhite = -1\n\n\nif DEBUG:\n    from os import system\n\n\nclass AtomicWeights:\n    def __init__(self, data=None):\n        self._mutex = Lock()\n        self._data = data\n\n    def set(self, data):\n        with self._mutex:\n            self._data = data\n\n    def get(self):\n        with self._mutex:\n            return deepcopy(self._data)\n\n\ndef update_screen(board):\n    system('clear')\n    print(board)\n\n\nclass ColorRemember:\n    def __init__(self):\n        self._mem = [[],[]]\n        self._act = [[],[]]\n        self._mm = 1\n\n    def _add(self, index, state, action):\n        self._mem[index].append(state)\n        self._act[index].append(action)\n\n        if len(self._mem[index]) > self._mm:\n            del self._mem[index][0]\n\n        if len(self._act[index]) > self._mm:\n            del self._act[index][0]\n\n    def remember(self, state, action, color):\n        if color == black:\n            self._add(0, state, action)\n        else:\n            self._add(1, state, action)\n\n    def get(self, color):\n        if color == black:\n            if len(self._mem[0]) >= self._mm:\n                return self._mem[0], self._act[0][0]\n        else:\n            if len(self._mem[1]) >= self._mm:\n                return self._mem[1], self._act[1][0]\n        return None, None\n\n\ndef train_self(weights, queue, epochs, thread_num):\n    remember = Replay()\n\n    with tf.Session(graph = tf.Graph()) as sess:\n        target_model = baseline_model()\n        target_model.set_weights(weights.get())\n\n        for e in range(epochs + 1):\n            remember.clear()\n            board = Board()\n            cr = ColorRemember()\n\n            game_over = False\n            force_random = False\n            color = black\n\n            input_t = [np.array(board.get_state(), ndmin=2), np.array([color])]\n\n            while not game_over:\n                input_past = input_t\n\n                if DEBUG:\n                    update_screen(board)\n\n                if random() <= EPSILON or force_random:\n                    action = choice(board.get_valid_moves(color))\n                    force_random = False\n                else:\n                    q = target_model.predict(input_past)\n                    if color == black:\n                        action = np.argmax(q[0])\n                    else:\n                        action = np.argmin(q[0])\n                try:\n                    board.update(action, color)\n                except:\n                    force_random = True\n                    continue\n\n                if board.has_valid_move(-color):\n                    color = -color\n                elif board.has_valid_move(color):\n                    pass\n                else:\n                    game_over = True\n\n                input_t = [np.array(board.get_state(), ndmin=2), np.array([color])]\n\n                cr.remember(deepcopy(input_past), action, input_past[1][0])\n\n                reward = 0\n                if game_over:\n                    score = board.score()\n                    if score[0] > score[1]:\n                        reward = 1\n                    elif score[0] < score[1]:\n                        reward = -1\n\n                    potential_memory, potential_action = cr.get(input_t[1][0])\n                    remember.remember([deepcopy(potential_memory[0]), potential_action, reward, deepcopy(input_t)], game_over)\n                    boards, colors, targets = remember.get_batch(target_model, batch_size=BATCH_SIZE)\n                    queue.put(([boards, colors], targets))\n                    break\n\n                potential_memory, potential_action = cr.get(input_t[1][0])\n                if potential_memory:\n                    remember.remember([deepcopy(potential_memory[0]), potential_action, reward, deepcopy(input_t)], game_over)\n\n            if e % 1 == 0 and thread_num == 0:\n                print(\"Finished game {}\".format(e))\n                return\n\n            if e % 100 == 0:\n                target_model.set_weights(weights.get())\n\n\nif __name__ == \"__main__\":\n    try:\n        model = load_model(FILENAME)\n    except:\n        model = baseline_model()\n\n    thread_store = []\n    queue = Queue()\n    aw = AtomicWeights()\n    aw.set(model.get_weights())\n\n    for x in range(THREADS):\n        t = Thread(target=train_self, args=(aw, queue, EPOCHS, x,))\n        t.daemon = True\n        t.start()\n        thread_store.append(t)\n\n    for i in range(THREADS * EPOCHS):\n        item = queue.get()\n\n        model.train_on_batch(item[0], item[1])\n        aw.set(model.get_weights())\n\n        queue.task_done()\n\n        if i % (100 * THREADS) == 0:\n            model.save(FILENAME)\n\n    for thread in thread_store:\n        thread.join()\n\n    model.save(FILENAME)\n","repo_name":"wileyyugioh/reversi-ai","sub_path":"wytrain.py","file_name":"wytrain.py","file_ext":"py","file_size_in_byte":5104,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"1730935468","text":"import websocket\nimport json\nimport requests\nimport time\nimport json\nfrom indicators import MACD, RSI, VolumeIndicator\nfrom helper import TRADE_SIZE, OpenOrder, TAKER_TRADING_FEE\nimport numpy as np\nimport pandas as pd\nfrom dotenv import load_dotenv\nfrom binance.client import Client\nfrom datetime import datetime, timedelta\nimport os\nimport threading\nload_dotenv()\n\n\ndef init_wallet():\n    global wallet\n    wallet['USDT'] = INIT_USDT\n    wallet['BNB'] = INIT_BNB\n    wallet['BTCUSDT'] = 0\n\n    print(\"wallet initialized\")\n\n\ndef getBnbPrice():\n    # Get BNB price to calculate exact fees\n    url = 'https://api.binance.com/api/v3/avgPrice?symbol=BNBUSDT'\n    return float(requests.get(url).json()['price'])\n\n\ndef calculate_function(long):\n    global stream\n    latest_price = stream.iloc[-1]['Price']\n\n    if long:\n        stop_loss = latest_price * (1 - RISK_PERCENTAGE / 100)\n        target = latest_price / (1 + RRRATIO * RISK_PERCENTAGE / 100)\n    else:\n        stop_loss = latest_price(1 + RISK_PERCENTAGE / 100)\n        target = latest_price / (1 - RRRATIO * RISK_PERCENTAGE / 100)\n    return target, stop_loss\n\n\n'''\nFunction to get past data and init indicators based on hat past data\nreturns indicator objects and the previous data -> data, macd, rsi, volume indicator\n'''\n\n\ndef get_data():\n    # Define the column names for the DataFrame\n    columns = ['open_time', 'open', 'high', 'low', 'close', 'volume']\n\n    # Create an empty DataFrame\n    df = pd.DataFrame(columns=columns)\n    # Get Past one hour data and store into database\n\n    # Calculate the start and end timestamps for the desired 1-hour period\n    end_time = str(datetime.utcnow())\n    start_time = str(datetime.utcnow() - timedelta(hours=1))\n    interval = Client.KLINE_INTERVAL_1MINUTE\n\n    # Retrieve the Kline data from the Binance API\n    klines = client.get_historical_klines(\n        symbol=symbol,\n        interval=interval,\n        start_str=str(start_time),\n        end_str=str(end_time)\n    )\n\n    # Iterate over the retrieved Kline data and insert it into the database\n    for kline in klines:\n        # Convert milliseconds to seconds\n        open_time = pd.to_datetime(int(kline[0]), unit='ms')\n        open_price = float(kline[1])\n        high_price = float(kline[2])\n        low_price = float(kline[3])\n        close_price = float(kline[4])\n        volume = float(kline[5])\n\n        # Append a new row to the DataFrame\n        row = pd.DataFrame([[open_time, open_price, high_price,\n                             low_price, close_price, volume]], columns=columns)\n        df = pd.concat([df, row], ignore_index=True)\n\n    # Init Indicators\n    macd = MACD(data=df)\n    rsi = RSI(data=df)\n    volumeIndicator = VolumeIndicator(data=df)\n\n    return [df, macd, rsi, volumeIndicator]\n\n\n'''\nFunction to get the BNB fees for transaction\nreturns float value representing fees in BNB\n'''\n\n\ndef get_fees():\n    bnb_price = getBnbPrice()\n    # Buying for TRADE_SIZE usdt\n    bnb_fees = TRADE_SIZE * TAKER_TRADING_FEE/bnb_price\n    return bnb_fees\n\n\n'''\nfunction to check prerequisite\nreturns bool value\n'''\n\n\ndef prereq():\n    global wallet\n    print(wallet)\n    if wallet['BNB'] < get_fees():\n        print(\"ADD MORE BNB\")\n    return wallet['USDT'] > TRADE_SIZE and wallet['BNB'] > get_fees()\n\n\n'''\nfunction to execute orders with binance or locally\nFirst created locally\n'''\n\n\ndef buy_order(order):\n    # BUY ORDER ON Binance Wallet\n    pass\n\n\ndef sell_order(order):\n    # SELL ORDER ON BINANCE WALLET\n    pass\n\n\ndef execute_order(order, triggered):\n    print(\"exec order checking\")\n    global stream, open_orders, open_position\n\n    latest_row = stream.iloc[-1]\n    trade_time, trade_price, trade_fees = latest_row['Time'], latest_row['Price'], get_fees(\n    )\n\n    if triggered == False:\n        # just place order with order type and keep it market order.\n        order.trade_executed(trade_price, trade_time, trade_fees)\n        # Check order type\n        if order.long:\n            if TEST:\n                # BUY ORDER\n                # changes to the wallet\n                wallet['USDT'] -= 10\n                wallet['BNB'] -= trade_fees\n                wallet['BTC'] += order.btcQuant\n            else:\n                buy_order(order)\n        else:\n            if TEST:\n                # SELL ORDER\n                wallet['USDT'] += 10\n                wallet['BNB'] -= trade_fees\n                wallet['BTC'] -= order.btcQuant\n            else:\n                sell_order(order)\n\n    else:\n        # market order\n        order.complete_trade(trade_price, trade_time, trade_fees)\n        open_position = False\n        # Check order type\n        if order.long:\n            if TEST:\n                # closing BUY ORDER => sell btc\n                # changes to the wallet\n                wallet['USDT'] += trade_price * order.btcQuant\n                wallet['BNB'] -= trade_fees\n                wallet['BTC'] -= order.btcQuant\n            else:\n                buy_order(order)\n        else:\n            if TEST:\n                # closing SELL ORDER => buy back btc\n                wallet['USDT'] -= trade_price * order.btcQuant\n                wallet['BNB'] -= trade_fees\n                wallet['BTC'] += order.btcQuant\n            else:\n                sell_order(order)\n    print(\"exec order checked\")\n\n\n'''\n    Function to handle message from socket on price ticker\n    also checks if open trade is triggered or not\n'''\n\n\ndef handle_price_message(ws, msg):\n    global stream, open_position, open_orders\n    symbol, time, price = msg['s'], msg[\"E\"], msg['c']\n\n    df = {\n        'Symbol': symbol,\n        'Time': pd.to_datetime(time, unit='ms'),\n        'Price': float(price)\n    }\n    row = pd.DataFrame(df, index=[0])\n    stream = pd.concat([stream, row], ignore_index=True)\n\n    # check close position on latest candle\n    if open_position:\n        temp = open_orders[0]\n        if temp.long and (price < temp.sl or price > temp.target):\n            execute_order(temp, True)\n        if not temp.long and (price > temp.sl or price < temp.target):\n            execute_order(temp, True)\n\n\n'''\n    Function to handle message from on candlestick\n'''\n\n\ndef handle_kline_message(ws, msg):\n    # print(\"Type is - \")\n    # when candle is closed\n    if msg['k']['x'] == False:\n        pass\n    elif msg['k']['x'] == True:\n        print(\"Candle Received\")\n\n        # reset datastream\n        global stream, data, open_position, macdIndicator, rsiIndicator, volumeIndicator, open_orders\n        stream = pd.DataFrame(columns=['Symbol', 'Time', 'Price'])\n\n        columns = ['open_time', 'open', 'high', 'low', 'close', 'volume']\n        # Extract the relevant data from the WebSocket message\n        open_time = pd.to_datetime(int(msg['k']['t']), unit='ms')\n        open_price = float(msg['k']['o'])\n        high_price = float(msg['k']['h'])\n        low_price = float(msg['k']['l'])\n        close_price = float(msg['k']['c'])\n        volume = float(msg['k']['v'])\n        # Append a new row to the DataFrame\n        row = pd.DataFrame([[open_time, open_price, high_price,\n                             low_price, close_price, volume]], columns=columns)\n        data = pd.concat([data, row],  ignore_index=True)\n\n        # update indicators and get signals\n        signals = {}\n        signals['MACD'] = macdIndicator.update_trade_signal(data)\n        signals['RSI'] = rsiIndicator.update_trade_signal(data)\n        signals['volume'] = volumeIndicator.get_signal(data)\n\n        if open_position == False and prereq():\n            print(\"PRINTING The SIGNALS\")\n            print(signals)\n            # CONDITION FOR LONG TRADE\n            if signals['MACD'] == \"Buy\" and signals['volume']:\n                open_position = True\n                long = True\n                print(\"-------------------LONG TRADE----------------------------\")\n                # calculate target and stop loss\n                target, sl = calculate_function(long)\n                # trade_time = pd.to_datetime(msg['E'], unit='ms')\n                order = OpenOrder(long, target, sl)\n                open_orders = [order]\n                # execute trade and get the buy price\n                execute_order(order, False)\n\n            # CONDITION FOR SHORT TRADE\n            elif signals['MACD'] == \"Sell\" and signals['volume']:\n                open_position = True\n                long = False\n                print(\"-------------------SHORT TRADE----------------------------\")\n                # calculate target and stop loss\n                target, sl = calculate_function(long)\n\n                # trade_time = pd.to_datetime(msg['E'], unit='ms')\n                order = OpenOrder(long, target, sl)\n                open_orders = [order]\n                # execute trade and get the buy price\n                execute_order(order, False)\n\n            else:\n                print(\"-------------------NO TRADE----------------------------\")\n        # Position is already open, cannot take another position for now\n        else:\n            print(\"Already one position is open\")\n\n\n'''\n    Function to initialize socket\n'''\n\n\ndef init_socket():\n    base_url = 'wss://stream.binance.com:9443/stream?streams=btcusdt@ticker/btcusdt@kline_1m'\n    streams = {\n        'price': 'btcusdt@ticker',\n        'candle': 'btcusdt@kline_1m'\n    }\n\n    def on_open(ws):\n        print(\"open\")\n\n    def on_message(ws, msg):\n        data = json.loads(msg)\n        stream_name = data['stream']\n\n        if stream_name == streams['price']:\n            handle_price_message(ws, data['data'])\n\n        if stream_name == streams['candle']:\n            handle_kline_message(ws, data['data'])\n\n    def on_error(ws, err):\n        print(\"Error with the socket - \", err)\n        # TODO - consider already open offers.\n\n    def on_close(ws):\n        # WebSocket connection closed\n        print(\"WebSocket connection closed\")\n    stream_socket = websocket.WebSocketApp(base_url,\n                                           on_open=on_open,\n                                           on_message=on_message,\n                                           on_error=on_error,\n                                           on_close=on_close\n                                           )\n\n    stream_socket.run_forever()\n\n\n# Constants\nINIT_USDT = 1000\nINIT_BNB = 10  # Needed to pay fees\n\nRRRATIO = 2  # Risk Reward Ratio\nRISK_PERCENTAGE = 2\n\nTEST = True\n\napiKey = os.getenv('API_KEY')\nsecretKey = os.getenv('SECRET_KEY')\n\n\"\"\"\nA little explanation on how leverage positions are coded.\nOpening a position is buying from now on, even it is a short position, it is\nstill considered buying.\nClosing a position is selling from now on, even it is a short position, it is\nstill considered selling.\n\nWhen buying:\n    - The total USDT spent recorded on the code is the initial margin price.\n    - The number of coins owned are the equivalent of margin*LEVERAGE.\n    - Example: Buy 50€ of VET at 0.08 with LEVERAGE=5\n        - usdt spent: 50 USDT\n        - actual position price (not logged on tradeHistory): 50*5 = 250 USDT\n        - coins owned: 250 USDT/0.08(USDT/VET) = 3125 VET\n\nWhen selling:\n    - The total USDT earned is the initial margin + profit.\n\n\"\"\"\n\n\n\"\"\" Indicates how many coins are in the wallet\n    wallet[crypto] = (amount, priceOfBuy, long)\n        long = True if long position\n               False if short position \n\"\"\"\nwallet = {}\n\ninit_wallet()\n\nsymbol = 'BTCUSDT'\nclient = Client(api_key=apiKey, api_secret=secretKey)\nopen_position = False\n# to store the open order object\nopen_orders = []\ndata, macdIndicator, rsiIndicator, volumeIndicator = get_data()\n\nstream = pd.DataFrame(columns=['Symbol', 'Time', 'Price'])\n\n# print('data')\n# print(data.tail)\n\n# print('indicators')\n# print('MACD')\n# print(macd.macd_data)\n\n# print('RSI')\n# print(rsi.rsi_values)\n\n# print(\"volume\")\n# print(volume.volume)\n# threading.Thread(target=receive_stream).start()\nthreading.Thread(target=init_socket).start()\n\nwhile True:\n    time.sleep(20)\n    print(stream.shape, data.shape)\n","repo_name":"rahulgupta046/Trading-BOT","sub_path":"crypto_bot_EMA.py","file_name":"crypto_bot_EMA.py","file_ext":"py","file_size_in_byte":11977,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"4997953994","text":"#!/usr/bin/env  python3\n\n#######################################################################################\n#                                                                                     #\n#       Program purpose: Takes a positive integer and returns the sum of the cube of  #\n#                        all the positive integers smaller than the specified number. #\n#       Program Author : Happi Yvan <ivensteinpoker@gmail.com>                        #\n#       Creation Date  : September 3, 2019                                            #\n#                                                                                     #\n#######################################################################################\n\nimport math\n\ndef find_sum_of_cubes(intVal):\n    cubeSum = 0\n    for i in range(1, intVal, 1):\n        cubeSum += math.pow(i, 3)\n    return cubeSum\n\nif __name__ == \"__main__\":\n    userInt = int(input(\"Enter some integer: \"))\n    print(f\"Sum of cubes of all positive integer less than {userInt}: \"\n          f\"{int(find_sum_of_cubes(userInt))}\")\n","repo_name":"ivenpoker/Python-Projects","sub_path":"Projects/Online Workouts/w3resource/Basic - Part-I/program-149.py","file_name":"program-149.py","file_ext":"py","file_size_in_byte":1074,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"25998493474","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\"\"\"\nImplementation of the paper \"https://arxiv.org/abs/1707.00067\".\nFirstly, the GAN network described in the Section 3 is implemented\n\"\"\"\nclass Generator(nn.Module):\n\tdef __init__(self, opt):\n\t\tsuper(Generator, self).__init__()\n\n\t\tself.down_blocks = nn.ModuleList()\n\t\tself.num_down = opt.num_down\n\n\t\tin_ch = opt.input_ch\n\t\tout_ch = opt.ngf\n\n\t\tfor ii in range(self.num_down): \n\t\t\tself.down_blocks.append(DownBlock(in_ch=in_ch, out_ch=out_ch, is_first = (ii == 0), is_last = (ii == self.num_down-1)))\n\t\t\tin_ch = out_ch\n\t\t\tout_ch = out_ch * 2\n\n\t\tbn_ch = opt.ngf * (2**(self.num_down-1))\n\n\t\tself.up_blocks = nn.ModuleList()\n\t\tin_ch = bn_ch\n\t\tout_ch = in_ch // 2\n\t\tfor ii in range(self.num_down): \n\t\t\tself.up_blocks.append(UpBlock(in_ch=in_ch, out_ch=out_ch,  is_first = (ii == 0), is_last = (ii == self.num_down-1)))\n\t\t\tif ii > 0:\n\t\t\t\tin_ch = in_ch // 2\n\t\t\tout_ch = out_ch // 2 \n\n\t\tself.last_layer = nn.ModuleList()\n\t\tself.last_layer.append(nn.ReLU(True))\n\t\t# self.last_layer.append(nn.ConvTranspose2d(out_ch*2, opt.output_ch, kernel_size=3, stride=1, padding=1))\n\t\t# self.last_layer.append(nn.Sigmoid())\t\t\n\t\tself.last_layer.append(nn.Conv2d(out_ch*2, opt.output_ch, kernel_size=3, stride=1, padding=1))\n\t\tself.last_layer.append(nn.Tanh())\n\t\t\n\tdef forward(self, x):\n\t\tskip_cons = {}\n\t\tlvl = 0\n\t\tfor down_layer in self.down_blocks:\n\t\t\t#print('\\n\\ndown layer inp shape', x.shape)\n\t\t\tx = down_layer(x)\n\t\t\t#print('down layer out shape', x.shape)\n\t\t\tskip_cons[f'skip_{lvl}'] = x\n\t\t\tlvl += 1\n\n\t\t# last downsampled and fused layer is botleneck layer\n\t\tinp = skip_cons[f'skip_{lvl-1}'] \n\n\t\tfor ii in range(self.num_down):\t\t\t\n\t\t\tlayer = self.up_blocks[ii]\n\t\t\t#print(f'\\nup layer-{ii} inp shape', inp.shape)\n\t\t\tout = layer(inp)\n\t\t\t#print('up layer out shape', out.shape)\n\t\t\tif ii < (self.num_down -1):\n\t\t\t\tskip = skip_cons[f'skip_{lvl-2-ii}']\n\t\t\t\t#print('skip connection shape', skip.shape)\n\t\t\t\tinp = torch.cat([out, skip], 1)\n\n\t\tfor layer in self.last_layer:\n\t\t\tout = layer(out)\n\t\t\n\t\treturn out\n\n\nclass Conv3DBlock(nn.Module):\n\tdef __init__(self, opt):\n\t\tsuper(Conv3DBlock, self).__init__()\n\t\t\n\t\tself.in_ch = opt.input_ch\n\t\tself.out_ch = opt.input_ch\n\t\tself.num_source = opt.num_source\n\n\t\trelu = nn.ReLU(True)\n\t\tconv = nn.Conv3d(self.in_ch, self.out_ch, kernel_size=(2,3,3), padding=(0,1,1), bias=False)\n\t\tnorm = nn.BatchNorm3d(self.out_ch)\n\n\t\tmodel = [conv, relu, norm]\n\t\tself.model = nn.Sequential(*model)\n\n\tdef forward(self, x):\n\t\t# Convert 4D tensor (2D image) into 5D tensor (3D image)\n\t\t# print('\\nConv3D Block input shape: ', x.shape)\n\t\tbs, ch, h, w = x.shape\n\t\tinp = torch.zeros([bs, self.in_ch, self.num_source, h, w]).cuda()\n\t\tfor ii in range(self.num_source):\n\t\t\tinp[:,:,ii,:,:] = x[:, ii*self.in_ch:(ii+1)*self.in_ch,:,:]\n\t\t# print('\\n4D inp shape: ', inp.shape)\n\t\t\n\t\tout = inp\n\t\tfor _ in range(self.num_source-1):\n\t\t\tout = self.model(out)\n\t\t\t#print('\\noutput shape', out.shape)\n\t\treturn torch.squeeze(out, 2)\n\t\t \nclass DownBlock(nn.Module):\n\tdef __init__(self, in_ch, out_ch, is_first=False, is_last=False):\n\t\tsuper(DownBlock, self).__init__()\n\n\t\tdownrelu = nn.LeakyReLU(0.2, True)\n\t\tdownconv = nn.Conv2d(in_ch, out_ch, kernel_size=4, stride=2, padding=1, bias=False)\n\t\t# downconv2 = nn.Conv2d(out_ch, out_ch, kernel_size=4, stride=1, padding=1, bias=False)\n\t\tdownnorm = nn.BatchNorm2d(out_ch)\n\n\t\tif is_first:\n\t\t\tmodel = [downconv]\n\t\telif is_last:\n\t\t\tmodel = [downrelu, downconv]\n\t\telse:\n\t\t\tmodel = [downrelu, downconv, downnorm]\n\n\t\tself.model = nn.Sequential(*model)\n\n\tdef forward(self, x):\n\t\t# #print('\\n\\ndownsampling input', x.shape)\n\t\tout = self.model(x)\n\t\t# #print('\\ndownsampling out', out.shape)\n\t\treturn out\n\n\nclass UpBlock(nn.Module):\n\tdef __init__(self, in_ch, out_ch,  is_first=False, is_last=False):\n\t\tsuper(UpBlock, self).__init__()\n\n\t\tuprelu = nn.ReLU(True)\n\t\t# # #print('\\n\\nunder convtranspose inch outch', in_ch, out_ch)\n\t\tupconv = nn.ConvTranspose2d(in_ch, out_ch, kernel_size=4, stride=2, padding=1, bias=False)\t\t\n\t\tupnorm = nn.BatchNorm2d(out_ch)\n\n\t\t\n\t\tif is_first:\n\t\t\tmodel = [uprelu, upconv, upnorm]\n\t\telif is_last:\n\t\t\tmodel = [uprelu, upconv, upnorm]\n\t\telse:\t\t\t\n\t\t\tmodel = [uprelu, upconv, upnorm, nn.Dropout(0.5)]\n\n\t\tself.model = nn.Sequential(*model)\n\n\tdef forward(self, x):\n\t\t# #print('\\n\\nupsampling input', x.shape)\n\t\tout = self.model(x)\n\t\t# #print('\\nupsampling out', out.shape)\n\t\treturn out\n\n\n\n# class Discriminator(nn.Module):\n# \tdef __init__(self):\n# \t\tsuper(Discriminator, self).__init__()\n\n# \t\t\n\n\n\"\"\"\nfrom pix2pix paper:\nhttps://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py\n\"\"\"\n# Defines the PatchGAN discriminator with the specified arguments.\nclass NLayerDiscriminator(nn.Module):\n    def __init__(self, opt, norm_layer=nn.BatchNorm2d, use_sigmoid=True, use_bias=False):\n        super(NLayerDiscriminator, self).__init__()\n        # if type(norm_layer) == functools.partial:\n        #     use_bias = norm_layer.func == nn.InstanceNorm2d\n        # else:\n        #     use_bias = norm_layer == nn.InstanceNorm2d\n        \n\n        kw = 4\n        padw = 1\n       \tnum_inp = (opt.num_source+1)\n\n        sequence = [\n            nn.Conv2d(opt.input_ch*num_inp, opt.ndf, kernel_size=kw, stride=2, padding=padw),\n            nn.LeakyReLU(0.2, True)\n        ]\n\n        nf_mult = 1\n        nf_mult_prev = 1\n        for n in range(1, opt.n_layers):\n        # #print('n layers', opt.n_layers)\n        # for n in range(1, 3):\n            nf_mult_prev = nf_mult\n            nf_mult = min(2**n, 8)\n            sequence += [\n                nn.Conv2d(opt.ndf * nf_mult_prev, opt.ndf * nf_mult,\n                          kernel_size=kw, stride=2, padding=padw, bias=use_bias),\n                norm_layer(opt.ndf * nf_mult),\n                nn.LeakyReLU(0.2, True)\n            ]\n\n        nf_mult_prev = nf_mult\n        nf_mult = min(2**opt.n_layers, 8)\n        sequence += [\n            nn.Conv2d(opt.ndf * nf_mult_prev, opt.ndf * nf_mult,\n                      kernel_size=kw, stride=1, padding=padw, bias=use_bias),\n            norm_layer(opt.ndf * nf_mult),\n            nn.LeakyReLU(0.2, True)\n        ]\n\n        sequence += [nn.Conv2d(opt.ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)]\n\n        if use_sigmoid:\n            sequence += [nn.Sigmoid()]\n\n        self.model = nn.Sequential(*sequence)\n\n    def forward(self, input):\n        # #print('input shape', input.shape)\n        # #print('\\noutput shape', self.model(input).shape)\n        # exit()\n        return self.model(input)\n","repo_name":"littlelittlelotus/CS168","sub_path":"networks.py","file_name":"networks.py","file_ext":"py","file_size_in_byte":6521,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"14465637561","text":"# https://www.acmicpc.net/problem/10988\n\nl = list(input())\ni = 1\nk = 0\nfor n in range(0, len(l) // 2):\n    if(l[n] != l[len(l) - n - 1]):\n        i = 0\n        k += 1\n\nprint(i)","repo_name":"dhrjsfud0522/cbei","sub_path":"심화1/10988.py","file_name":"10988.py","file_ext":"py","file_size_in_byte":176,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"69915432147","text":"\"\"\"\n    Argo Workflows API\n\n    Argo Workflows is an open source container-native workflow engine for orchestrating parallel jobs on Kubernetes. For more information, please see https://argoproj.github.io/argo-workflows/  # noqa: E501\n\n    The version of the OpenAPI document: VERSION\n    Generated by: https://openapi-generator.tech\n\"\"\"\n\n\nimport copy\nimport logging\nimport multiprocessing\nimport sys\nimport urllib3\n\nfrom http import client as http_client\nfrom argo_workflows.exceptions import ApiValueError\n\n\nJSON_SCHEMA_VALIDATION_KEYWORDS = {\n    'multipleOf', 'maximum', 'exclusiveMaximum',\n    'minimum', 'exclusiveMinimum', 'maxLength',\n    'minLength', 'pattern', 'maxItems', 'minItems'\n}\n\nclass Configuration(object):\n    \"\"\"NOTE: This class is auto generated by OpenAPI Generator\n\n    Ref: https://openapi-generator.tech\n    Do not edit the class manually.\n\n    :param host: Base url\n    :param api_key: Dict to store API key(s).\n      Each entry in the dict specifies an API key.\n      The dict key is the name of the security scheme in the OAS specification.\n      The dict value is the API key secret.\n    :param api_key_prefix: Dict to store API prefix (e.g. Bearer)\n      The dict key is the name of the security scheme in the OAS specification.\n      The dict value is an API key prefix when generating the auth data.\n    :param username: Username for HTTP basic authentication\n    :param password: Password for HTTP basic authentication\n    :param discard_unknown_keys: Boolean value indicating whether to discard\n      unknown properties. A server may send a response that includes additional\n      properties that are not known by the client in the following scenarios:\n      1. The OpenAPI document is incomplete, i.e. it does not match the server\n         implementation.\n      2. The client was generated using an older version of the OpenAPI document\n         and the server has been upgraded since then.\n      If a schema in the OpenAPI document defines the additionalProperties attribute,\n      then all undeclared properties received by the server are injected into the\n      additional properties map. In that case, there are undeclared properties, and\n      nothing to discard.\n    :param disabled_client_side_validations (string): Comma-separated list of\n      JSON schema validation keywords to disable JSON schema structural validation\n      rules. The following keywords may be specified: multipleOf, maximum,\n      exclusiveMaximum, minimum, exclusiveMinimum, maxLength, minLength, pattern,\n      maxItems, minItems.\n      By default, the validation is performed for data generated locally by the client\n      and data received from the server, independent of any validation performed by\n      the server side. If the input data does not satisfy the JSON schema validation\n      rules specified in the OpenAPI document, an exception is raised.\n      If disabled_client_side_validations is set, structural validation is\n      disabled. This can be useful to troubleshoot data validation problem, such as\n      when the OpenAPI document validation rules do not match the actual API data\n      received by the server.\n    :param server_index: Index to servers configuration.\n    :param server_variables: Mapping with string values to replace variables in\n      templated server configuration. The validation of enums is performed for\n      variables with defined enum values before.\n    :param server_operation_index: Mapping from operation ID to an index to server\n      configuration.\n    :param server_operation_variables: Mapping from operation ID to a mapping with\n      string values to replace variables in templated server configuration.\n      The validation of enums is performed for variables with defined enum values before.\n    :param ssl_ca_cert: str - the path to a file of concatenated CA certificates\n      in PEM format\n\n    :Example:\n\n    API Key Authentication Example.\n    Given the following security scheme in the OpenAPI specification:\n      components:\n        securitySchemes:\n          cookieAuth:         # name for the security scheme\n            type: apiKey\n            in: cookie\n            name: JSESSIONID  # cookie name\n\n    You can programmatically set the cookie:\n\nconf = argo_workflows.Configuration(\n    api_key={'cookieAuth': 'abc123'}\n    api_key_prefix={'cookieAuth': 'JSESSIONID'}\n)\n\n    The following cookie will be added to the HTTP request:\n       Cookie: JSESSIONID abc123\n    \"\"\"\n\n    _default = None\n\n    def __init__(self, host=None,\n                 api_key=None, api_key_prefix=None,\n                 access_token=None,\n                 username=None, password=None,\n                 discard_unknown_keys=False,\n                 disabled_client_side_validations=\"\",\n                 server_index=None, server_variables=None,\n                 server_operation_index=None, server_operation_variables=None,\n                 ssl_ca_cert=None,\n                 ):\n        \"\"\"Constructor\n        \"\"\"\n        self._base_path = \"http://localhost:2746\" if host is None else host\n        \"\"\"Default Base url\n        \"\"\"\n        self.server_index = 0 if server_index is None and host is None else server_index\n        self.server_operation_index = server_operation_index or {}\n        \"\"\"Default server index\n        \"\"\"\n        self.server_variables = server_variables or {}\n        self.server_operation_variables = server_operation_variables or {}\n        \"\"\"Default server variables\n        \"\"\"\n        self.temp_folder_path = None\n        \"\"\"Temp file folder for downloading files\n        \"\"\"\n        # Authentication Settings\n        self.access_token = access_token\n        self.api_key = {}\n        if api_key:\n            self.api_key = api_key\n        \"\"\"dict to store API key(s)\n        \"\"\"\n        self.api_key_prefix = {}\n        if api_key_prefix:\n            self.api_key_prefix = api_key_prefix\n        \"\"\"dict to store API prefix (e.g. Bearer)\n        \"\"\"\n        self.refresh_api_key_hook = None\n        \"\"\"function hook to refresh API key if expired\n        \"\"\"\n        self.username = username\n        \"\"\"Username for HTTP basic authentication\n        \"\"\"\n        self.password = password\n        \"\"\"Password for HTTP basic authentication\n        \"\"\"\n        self.discard_unknown_keys = discard_unknown_keys\n        self.disabled_client_side_validations = disabled_client_side_validations\n        self.logger = {}\n        \"\"\"Logging Settings\n        \"\"\"\n        self.logger[\"package_logger\"] = logging.getLogger(\"argo_workflows\")\n        self.logger[\"urllib3_logger\"] = logging.getLogger(\"urllib3\")\n        self.logger_format = '%(asctime)s %(levelname)s %(message)s'\n        \"\"\"Log format\n        \"\"\"\n        self.logger_stream_handler = None\n        \"\"\"Log stream handler\n        \"\"\"\n        self.logger_file_handler = None\n        \"\"\"Log file handler\n        \"\"\"\n        self.logger_file = None\n        \"\"\"Debug file location\n        \"\"\"\n        self.debug = False\n        \"\"\"Debug switch\n        \"\"\"\n\n        self.verify_ssl = True\n        \"\"\"SSL/TLS verification\n           Set this to false to skip verifying SSL certificate when calling API\n           from https server.\n        \"\"\"\n        self.ssl_ca_cert = ssl_ca_cert\n        \"\"\"Set this to customize the certificate file to verify the peer.\n        \"\"\"\n        self.cert_file = None\n        \"\"\"client certificate file\n        \"\"\"\n        self.key_file = None\n        \"\"\"client key file\n        \"\"\"\n        self.assert_hostname = None\n        \"\"\"Set this to True/False to enable/disable SSL hostname verification.\n        \"\"\"\n\n        self.connection_pool_maxsize = multiprocessing.cpu_count() * 5\n        \"\"\"urllib3 connection pool's maximum number of connections saved\n           per pool. urllib3 uses 1 connection as default value, but this is\n           not the best value when you are making a lot of possibly parallel\n           requests to the same host, which is often the case here.\n           cpu_count * 5 is used as default value to increase performance.\n        \"\"\"\n\n        self.proxy = None\n        \"\"\"Proxy URL\n        \"\"\"\n        self.no_proxy = None\n        \"\"\"bypass proxy for host in the no_proxy list.\n        \"\"\"\n        self.proxy_headers = None\n        \"\"\"Proxy headers\n        \"\"\"\n        self.safe_chars_for_path_param = ''\n        \"\"\"Safe chars for path_param\n        \"\"\"\n        self.retries = None\n        \"\"\"Adding retries to override urllib3 default value 3\n        \"\"\"\n        # Enable client side validation\n        self.client_side_validation = True\n\n        # Options to pass down to the underlying urllib3 socket\n        self.socket_options = None\n\n    def __deepcopy__(self, memo):\n        cls = self.__class__\n        result = cls.__new__(cls)\n        memo[id(self)] = result\n        for k, v in self.__dict__.items():\n            if k not in ('logger', 'logger_file_handler'):\n                setattr(result, k, copy.deepcopy(v, memo))\n        # shallow copy of loggers\n        result.logger = copy.copy(self.logger)\n        # use setters to configure loggers\n        result.logger_file = self.logger_file\n        result.debug = self.debug\n        return result\n\n    def __setattr__(self, name, value):\n        object.__setattr__(self, name, value)\n        if name == 'disabled_client_side_validations':\n            s = set(filter(None, value.split(',')))\n            for v in s:\n                if v not in JSON_SCHEMA_VALIDATION_KEYWORDS:\n                    raise ApiValueError(\n                        \"Invalid keyword: '{0}''\".format(v))\n            self._disabled_client_side_validations = s\n\n    @classmethod\n    def set_default(cls, default):\n        \"\"\"Set default instance of configuration.\n\n        It stores default configuration, which can be\n        returned by get_default_copy method.\n\n        :param default: object of Configuration\n        \"\"\"\n        cls._default = copy.deepcopy(default)\n\n    @classmethod\n    def get_default_copy(cls):\n        \"\"\"Return new instance of configuration.\n\n        This method returns newly created, based on default constructor,\n        object of Configuration class or returns a copy of default\n        configuration passed by the set_default method.\n\n        :return: The configuration object.\n        \"\"\"\n        if cls._default is not None:\n            return copy.deepcopy(cls._default)\n        return Configuration()\n\n    @property\n    def logger_file(self):\n        \"\"\"The logger file.\n\n        If the logger_file is None, then add stream handler and remove file\n        handler. Otherwise, add file handler and remove stream handler.\n\n        :param value: The logger_file path.\n        :type: str\n        \"\"\"\n        return self.__logger_file\n\n    @logger_file.setter\n    def logger_file(self, value):\n        \"\"\"The logger file.\n\n        If the logger_file is None, then add stream handler and remove file\n        handler. Otherwise, add file handler and remove stream handler.\n\n        :param value: The logger_file path.\n        :type: str\n        \"\"\"\n        self.__logger_file = value\n        if self.__logger_file:\n            # If set logging file,\n            # then add file handler and remove stream handler.\n            self.logger_file_handler = logging.FileHandler(self.__logger_file)\n            self.logger_file_handler.setFormatter(self.logger_formatter)\n            for _, logger in self.logger.items():\n                logger.addHandler(self.logger_file_handler)\n\n    @property\n    def debug(self):\n        \"\"\"Debug status\n\n        :param value: The debug status, True or False.\n        :type: bool\n        \"\"\"\n        return self.__debug\n\n    @debug.setter\n    def debug(self, value):\n        \"\"\"Debug status\n\n        :param value: The debug status, True or False.\n        :type: bool\n        \"\"\"\n        self.__debug = value\n        if self.__debug:\n            # if debug status is True, turn on debug logging\n            for _, logger in self.logger.items():\n                logger.setLevel(logging.DEBUG)\n            # turn on http_client debug\n            http_client.HTTPConnection.debuglevel = 1\n        else:\n            # if debug status is False, turn off debug logging,\n            # setting log level to default `logging.WARNING`\n            for _, logger in self.logger.items():\n                logger.setLevel(logging.WARNING)\n            # turn off http_client debug\n            http_client.HTTPConnection.debuglevel = 0\n\n    @property\n    def logger_format(self):\n        \"\"\"The logger format.\n\n        The logger_formatter will be updated when sets logger_format.\n\n        :param value: The format string.\n        :type: str\n        \"\"\"\n        return self.__logger_format\n\n    @logger_format.setter\n    def logger_format(self, value):\n        \"\"\"The logger format.\n\n        The logger_formatter will be updated when sets logger_format.\n\n        :param value: The format string.\n        :type: str\n        \"\"\"\n        self.__logger_format = value\n        self.logger_formatter = logging.Formatter(self.__logger_format)\n\n    def get_api_key_with_prefix(self, identifier, alias=None):\n        \"\"\"Gets API key (with prefix if set).\n\n        :param identifier: The identifier of apiKey.\n        :param alias: The alternative identifier of apiKey.\n        :return: The token for api key authentication.\n        \"\"\"\n        if self.refresh_api_key_hook is not None:\n            self.refresh_api_key_hook(self)\n        key = self.api_key.get(identifier, self.api_key.get(alias) if alias is not None else None)\n        if key:\n            prefix = self.api_key_prefix.get(identifier)\n            if prefix:\n                return \"%s %s\" % (prefix, key)\n            else:\n                return key\n\n    def get_basic_auth_token(self):\n        \"\"\"Gets HTTP basic authentication header (string).\n\n        :return: The token for basic HTTP authentication.\n        \"\"\"\n        username = \"\"\n        if self.username is not None:\n            username = self.username\n        password = \"\"\n        if self.password is not None:\n            password = self.password\n        return urllib3.util.make_headers(\n            basic_auth=username + ':' + password\n        ).get('authorization')\n\n    def auth_settings(self):\n        \"\"\"Gets Auth Settings dict for api client.\n\n        :return: The Auth Settings information dict.\n        \"\"\"\n        auth = {}\n        if 'BearerToken' in self.api_key:\n            auth['BearerToken'] = {\n                'type': 'api_key',\n                'in': 'header',\n                'key': 'Authorization',\n                'value': self.get_api_key_with_prefix(\n                    'BearerToken',\n                ),\n            }\n        return auth\n\n    def to_debug_report(self):\n        \"\"\"Gets the essential information for debugging.\n\n        :return: The report for debugging.\n        \"\"\"\n        return \"Python SDK Debug Report:\\n\"\\\n               \"OS: {env}\\n\"\\\n               \"Python Version: {pyversion}\\n\"\\\n               \"Version of the API: VERSION\\n\"\\\n               \"SDK Package Version: 0.0.0-pre\".\\\n               format(env=sys.platform, pyversion=sys.version)\n\n    def get_host_settings(self):\n        \"\"\"Gets an array of host settings\n\n        :return: An array of host settings\n        \"\"\"\n        return [\n            {\n                'url': \"http://localhost:2746\",\n                'description': \"No description provided\",\n            },\n            {\n                'url': \"https://localhost:2746\",\n                'description': \"No description provided\",\n            }\n        ]\n\n    def get_host_from_settings(self, index, variables=None, servers=None):\n        \"\"\"Gets host URL based on the index and variables\n        :param index: array index of the host settings\n        :param variables: hash of variable and the corresponding value\n        :param servers: an array of host settings or None\n        :return: URL based on host settings\n        \"\"\"\n        if index is None:\n            return self._base_path\n\n        variables = {} if variables is None else variables\n        servers = self.get_host_settings() if servers is None else servers\n\n        try:\n            server = servers[index]\n        except IndexError:\n            raise ValueError(\n                \"Invalid index {0} when selecting the host settings. \"\n                \"Must be less than {1}\".format(index, len(servers)))\n\n        url = server['url']\n\n        # go through variables and replace placeholders\n        for variable_name, variable in server.get('variables', {}).items():\n            used_value = variables.get(\n                variable_name, variable['default_value'])\n\n            if 'enum_values' in variable \\\n                    and used_value not in variable['enum_values']:\n                raise ValueError(\n                    \"The variable `{0}` in the host URL has invalid value \"\n                    \"{1}. Must be {2}.\".format(\n                        variable_name, variables[variable_name],\n                        variable['enum_values']))\n\n            url = url.replace(\"{\" + variable_name + \"}\", used_value)\n\n        return url\n\n    @property\n    def host(self):\n        \"\"\"Return generated host.\"\"\"\n        return self.get_host_from_settings(self.server_index, variables=self.server_variables)\n\n    @host.setter\n    def host(self, value):\n        \"\"\"Fix base path.\"\"\"\n        self._base_path = value\n        self.server_index = None\n","repo_name":"argoproj/argo-workflows","sub_path":"sdks/python/client/argo_workflows/configuration.py","file_name":"configuration.py","file_ext":"py","file_size_in_byte":17369,"program_lang":"python","lang":"en","doc_type":"code","stars":13722,"dataset":"github-code","pt":"48"}
+{"seq_id":"19331764173","text":"import pygame\r\nimport os\r\n\r\n\r\npygame.init()\r\nSTAT_FONT = pygame.font.SysFont(\"comicsans\", 30)\r\nGAME_OVER_FONT = pygame.font.SysFont(\"comicsans\", 100)\r\n\r\n#change these to change the game window sizing\r\nWIN_WIDTH = 500 \r\nWIN_HEIGHT = 600\r\n\r\n\r\nclass Entity:\r\n    \"\"\"\r\n    Class to represent objects in the game\r\n    \"\"\"\r\n    def __init__(self, x, y, image_surface, speed):\r\n        self.x = x\r\n        self.y = y\r\n        self.img = image_surface\r\n        self.active = False #for bullet only\r\n        self.move_right = True #only use in alien object\r\n        self.speed = speed\r\n\r\n    def draw(self, pygame_window):\r\n        pygame_window.blit(self.img, (self.x, self.y))\r\n    \r\n    def get_mask(self):\r\n        return pygame.mask.from_surface(self.img)\r\n\r\n\r\nclass Alien(Entity):\r\n    \"\"\"\r\n    Extends entity class to represent an alien\r\n    \"\"\"\r\n    def move(self, win_width):\r\n        if self.move_right:\r\n            self.x += self.speed\r\n\r\n        else:\r\n            self.x -= self.speed\r\n        \r\n    def update(self, pygame_window, win_width):\r\n        self.move(win_width)\r\n        self.draw(pygame_window)\r\n    \r\n    def collide_bullet(self, bullet):\r\n        alien_mask = self.get_mask()\r\n        offset = (-1*(round(self.x)-bullet.x), -1*(round(self.y)- bullet.y))\r\n        point = alien_mask.overlap(bullet.get_mask(), offset)\r\n        if point:\r\n            bullet.y = -20\r\n        return point\r\n    \r\n\r\nclass Bullet(Entity):\r\n    \"\"\"\r\n    Extends bullet class to represent the bullet in the game\r\n    \"\"\"\r\n    def set_pos(self, x, y):\r\n        self.x = x\r\n        self.y = y\r\n\r\n    def move(self):\r\n        if self.y < WIN_HEIGHT and self.y > 0:\r\n            self.active = True\r\n        else:\r\n            self.active = False\r\n        self.y -= self.speed\r\n        \r\n    def draw(self, window):\r\n        self.move()\r\n        if self.active:\r\n            window.blit(self.img, (self.x, self.y))\r\n        \r\n\r\n\r\nclass Game:\r\n    \"\"\"\r\n    A game of space invaders\r\n    \"\"\"\r\n\r\n    ALIEN_IMG = pygame.transform.scale(pygame.image.load(os.path.join('images', 'alien.png')), (35,25))\r\n    PLAYER_IMG = pygame.transform.scale(pygame.image.load(os.path.join('images', 'player.png')), (42, 26))\r\n    BULLET_IMG = pygame.transform.scale(pygame.image.load(os.path.join('images', 'bullet.png')), (6, 12))\r\n    FPS = 60\r\n\r\n    def __init__(self, win_width, win_height):\r\n        \r\n        self.win_width = win_width\r\n        self.alien_speed = 0.5\r\n        self.window = pygame.display.set_mode((win_width, win_height))\r\n        self.game_over = False     \r\n        self.player = Entity(win_width//2, win_height-50, self.PLAYER_IMG, 2)\r\n        self.aliens = self.add_aliens()\r\n        self.clock = pygame.time.Clock()\r\n        self.bullet = Bullet(0,-1000, self.BULLET_IMG, 5)\r\n        self.score = 0  \r\n        \r\n\r\n    def add_aliens(self):\r\n        aliens = []\r\n        y = 0\r\n        for j in range(0,5):\r\n            x = 0\r\n            for i in range(0,9):\r\n                aliens.append(Alien(x,y,self.ALIEN_IMG, self.alien_speed))\r\n                x += 50\r\n            y += 40\r\n        return aliens  \r\n\r\n    def alien_handling(self):\r\n        for alien in self.aliens:    \r\n            hit = alien.collide_bullet(self.bullet)\r\n            if hit:\r\n                self.aliens.remove(alien)\r\n                self.score += 10\r\n            alien.update(self.window, self.win_width)\r\n            if alien.y >= self.player.y:\r\n                self.game_over = True\r\n\r\n        for alien in self.aliens:\r\n            if alien.x < 0:\r\n                for alien in self.aliens:\r\n                    alien.move_right = True\r\n                    alien.y += 8\r\n                break\r\n            elif alien.x > self.win_width-35:\r\n                for alien in self.aliens:\r\n                    alien.move_right = False\r\n                break\r\n        if len(self.aliens) == 0:\r\n            self.alien_speed += 0.5\r\n            self.aliens = self.add_aliens()\r\n        \r\n\r\n    def draw(self):\r\n        self.window.fill((0,0,0))\r\n        score_text = STAT_FONT.render(\"Score: \" + str(self.score), 1, (255,255,255))\r\n        self.window.blit(score_text, (self.win_width-10-score_text.get_width(), WIN_HEIGHT-20))\r\n        self.player.draw(self.window)\r\n        self.bullet.draw(self.window)\r\n        self.alien_handling()\r\n        pygame.display.update()\r\n\r\n\r\n    def input_handling(self):\r\n        keys = pygame.key.get_pressed()\r\n        if keys[pygame.K_SPACE]:\r\n            if not self.bullet.active:\r\n                self.bullet.set_pos(self.player.x+21, self.player.y)\r\n        if keys[pygame.K_LEFT]:\r\n            self.player.x -= self.player.speed\r\n        elif keys[pygame.K_RIGHT]:\r\n            self.player.x += self.player.speed\r\n        \r\n\r\n        for event in pygame.event.get():\r\n            if event.type == pygame.QUIT:\r\n                self.game_over = True\r\n\r\n    def game_over_screen(self):\r\n        self.window.fill((0,0,0))\r\n        text = GAME_OVER_FONT.render(\"GAME OVER\", 1, (255, 255, 255))\r\n        self.window.blit(text, (((WIN_WIDTH-text.get_width())//2, WIN_HEIGHT//2+10)))  \r\n        pygame.display.update()\r\n    \r\n\r\n    def run(self):\r\n        while not self.game_over:\r\n            self.input_handling()\r\n            self.draw()\r\n            self.clock.tick(self.FPS)\r\n        while self.game_over:\r\n            self.game_over_screen()\r\n            for event in pygame.event.get():\r\n                if event.type == pygame.QUIT:\r\n                    pygame.quit()\r\n                    \r\n    \r\n        \r\n                 \r\n","repo_name":"p-lahanas/space-invaders","sub_path":"entities.py","file_name":"entities.py","file_ext":"py","file_size_in_byte":5545,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"28013440582","text":"import numpy as np\nimport itertools\nimport sys\n\n# unitary transformation from Weyl to Dirac basis\n# | 1  1 |\n# |-1  1 |\nWEYL_DIRAC = np.array([[1,0,1,0],[0,1,0,1],[-1,0,1,0],[0,-1,0,1]])\n# unitary transformation from Weyl to Dirac basis\n# | 1 -1 |\n# | 1  1 |\nDIRAC_WEYL = np.array([[1,0,-1,0],[0,1,0,-1],[1,0,1,0],[0,1,0,1]])\n\n# assumes Nf=2\nNF=2\n# top down approach to find fully symmetric\n# Nc spin-flavor wave function\n# with desired spin S and isospin I\n# example input\n# Nc I   Iz    S   Sz\n# 3 0.5 0.5  0.5  -0.5\n\n# Use binary labeling, for example  for SU(3) spin-flavor\n# |f1 s1 f2 s2 f3 s3 >\n# u=0,d=1\n# ex. |000000> = |u^ u^ u^ > = 0 = (1,0,0,0,..,0) 1 in 0th position\n# ex. |000010> = |u^ u^ d^ > = 2 = (0,0,1,0,..,0) 1 in 2nd position\n\n\ndef get_evals(Nc, I, Iz, S, Sz):\n    # what the values of I and S could be given Nc\n    Qvals_possible = [(Nc - 2 * i) * 1 / 2 for i in np.arange(int(Nc / 2) + 1)]\n    # evals to check for in calculation of operators I^2 and S^2\n    Q_evals = [Qval * (Qval + 1) for Qval in Qvals_possible]\n\n    symmetric_val = max(Qvals_possible)\n    # check I and S compatible\n    if symmetric_possible(I, S, symmetric_val):\n        print(\"I and S okay\")\n    else:\n        print(\"These values of I and S cannot be combined in a symmetric way\")\n        sys.exit(3)\n\n    # get possible S and I subspace vals\n    IS_sub_possible = get_Qsub_possible(Iz, Sz, Qvals_possible, symmetric_val)\n    IS_sub_evals = [[Ival*(Ival+1), Sval*(Sval+1)] for Ival, Sval in IS_sub_possible]\n\n    return Q_evals, IS_sub_evals\n\n\n# is it possible to combine I and S to make a symmetric wavefunction?\ndef symmetric_possible(I, S, symmetric_val):\n    I_symm = bool(I == symmetric_val)\n    S_symm = bool(S == symmetric_val)\n    return bool(I_symm == S_symm)  # both symmetric or neither symmetric possible\n\n\n# what the values of I and S could be given symmetric (possible_symm)\n# and Iz, Sz (ex. Sz=-1/2 is part of S=1/2 and S=3/2) (possible_Q)\ndef get_Qsub_possible(Iz, Sz, Qvals_possible, symmetric_val):\n    Qsub_possible = []\n    for IQval in Qvals_possible:\n        for SQval in Qvals_possible:\n            # check to see if IQval and SQVal pair can be combined symmetrically\n            possible_symm = symmetric_possible(IQval, SQval, symmetric_val)\n            # check that Qval not smaller than Iz/Sz, ex if Iz=3/2, can't have I=1/2\n            possible_Q = bool(IQval >= abs(Iz) and SQval >= abs(Sz))\n            if possible_symm and possible_Q:  # if symmetric and z component works\n                Qsub_possible.append([IQval, SQval])\n    return Qsub_possible\n\n\n# given a position n, in a state vector, find the spin (up or down)\n# of the kth quark (spin or flavor)\ndef spin_of_k_position(Nc, n, k):\n    binary = bin(n)[2:]  # remove the \"b0\" symbol\n    # append 0s to beginning so all the same length\n    binary = \"0\" * (2 * Nc - len(binary)) + binary\n    spin = binary[k]\n    if spin == \"0\":\n        return \"u\"\n    if spin == \"1\":\n        return \"d\"\n\n\n# compute Iz, Sz matrix for each individual quark\n# Iiz |f1 s1 f2 s2 f3 s3> = Iz(fi) |state>\n# ex. I3\n# +1/2 for all values of n such that n written in binary has a\n# 0 in the 5th place (f3 place), ex. 11 11 01, ex. 00, 01, 00\n# -1/2 for all values of n such that n written in binary has a\n# 1 in the 5th place (f3 place) ex. 11 11 10, ex. 00, 01, 11\ndef get_Siz(Nc, dim, k):\n    Siz = np.zeros((dim, dim))\n    for n in range(dim):\n        spin = spin_of_k_position(Nc, n, k)\n        if spin == \"u\":\n            Siz[n, n] = 1 / 2\n        if spin == \"d\":\n            Siz[n, n] = -1 / 2\n    return Siz\n\n\ndef flip_at_k(Nc, n, k):\n    binary = bin(n)[2:]  # remove the \"b0\" symbol\n    # append 0s to beginning so all the same length\n    binary = \"0\" * (2 * Nc - len(binary)) + binary\n    # flip 0<->1 at kb\n    prefix = binary[:k]\n    flip = str((int(binary[k]) + 1) % 2)\n    suffix = binary[k + 1:]\n    m = int(prefix + flip + suffix, 2)\n    return m\n\n\n# transform from u to d or d to u\n# of the kth quark (spin or flavor)\ndef get_S_plus_minus(Nc, dim, k):\n    S_plus = np.zeros((dim, dim))\n    S_minus = np.zeros((dim, dim))\n    for n in range(dim):  # row indicating where we are taking state to\n        spin_n = spin_of_k_position(Nc, n, k)\n        m = flip_at_k(Nc, n, k)\n        if spin_n == \"u\":  # S_plus flips to u\n            S_plus[n, m] = 1  # take m(d) to n(u)\n        if spin_n == \"d\":  # S_minus flips to d\n            S_minus[n, m] = 1  # take m(u) to n(d)\n    return S_plus, S_minus\n\n\ndef compute_Ssq_total(Nc, dim, Sisq, Siz, Si_pm):\n    Ssq = np.zeros((dim, dim))\n    # contribution from Ii^2\n    for i in range(Nc):\n        Ssq += Sisq[i]\n    # contribution from Iiz (diagonal, so order doesn't matter, factor of 2)\n    index_combinations = list(itertools.combinations(np.arange(Nc), 2))\n    for comb_indices in index_combinations:\n        ind1 = comb_indices[0]\n        ind2 = comb_indices[1]\n        Ssq += 2 * np.matmul(Siz[ind1], Siz[ind2])\n    # contributions from Iix,Iiy (in terms of Ii+ and Ii-), not diagonal\n    index_permutations = list(itertools.permutations(np.arange(Nc), 2))\n    for perm_indices in index_permutations:\n        ind1 = perm_indices[0]\n        ind2 = perm_indices[1]\n        Ssq += 0.5 * np.matmul(Si_pm[ind1][0], Si_pm[ind2][1])\n        Ssq += 0.5 * np.matmul(Si_pm[ind1][1], Si_pm[ind2][0])\n    return Ssq\n\n\n# check that list1 is a subset of list2\ndef check_subsets(list1, list2, tol=1e-6):\n    for item1 in list(list1):\n        in_list2 = False\n        for item2 in list(list2):\n            if abs(item1 - item2) < tol:\n                in_list2 = True\n                break\n                # if can't find item1, not a subset\n        if not in_list2:\n            return False\n    return True\n\n\n# check that a set of lists are equal\n# by checking l1 subset of l2 for all permutations l1,2l\ndef check_equiv_lists(lists):\n    indices = list(np.arange(len(lists)))\n    perms = itertools.permutations(indices, 2)\n    for perm in list(perms):\n        if check_subsets(lists[perm[0]], lists[perm[1]]) is False:\n            return False\n    return True\n\n\ndef check_eigenvalues(Ivalues, I_evals, Svalues, S_evals):\n    passed = True\n    if check_equiv_lists([Ivalues, I_evals]) is False:\n        passed = False\n        print(\"\\n\\nUNEXPECTED or MISSING ISOSPIN VALUE\")\n        print(\"expected\")\n        print(I_evals)\n        print(\"found\")\n        print(Ivalues)\n    if check_equiv_lists([Svalues, S_evals]) is False:\n        passed = False\n        print(\"UNEXPECTED or MISSING SPIN VALUE\")\n        print(\"expected\")\n        print(S_evals)\n        print(\"found\")\n        print(Svalues)\n        print(\"\\n\\n\")\n    if passed:\n        print(\"good\\n\")\n\n\ndef check_sub_eigenvalues(Ivalues, Svalues, IS_sub_evals):\n    # first just check that there are not I or S found that don't belong at all\n    I_sub_vals = list(set([IS_val[0] for IS_val in IS_sub_evals]))\n    S_sub_vals = list(set([IS_val[1] for IS_val in IS_sub_evals]))\n    check_eigenvalues(Ivalues, I_sub_vals, Svalues, S_sub_vals)\n\n    # give warning of how I and S should be combined\n    print(\"NOTE that in order to make symmetric wavefunction,\\n \"\n          \"can only combine following isospin and spin eigenvalues\")\n    for Ival, Sval in IS_sub_evals:\n        print(f'I*(I+1) = {Ival}, S*(S+1) = {Sval}')\n    print()\n\n\n# Generic way to construct\ndef get_base_state(Nc, Sz):\n    # start at maximal, 0=up, 1=down\n    state = [\"0\"]*Nc\n    Sz_state = 1 / 2 * Nc\n    # then reduce\n    for i in range(Nc):\n        if Sz_state == Sz:  # spin done\n            break\n        else:  # keep reducing spin\n            state[i] = \"1\"\n            Sz_state -= 1  # switching u to d\n    return state\n\n\ndef get_all_states(Nc, Ibase_state, Sbase_state):\n    # duu found above, now get all perms of that, duu,udu,uud\n    Iperms = list(set(list(itertools.permutations(Ibase_state, Nc))))\n    Sperms = list(set(list(itertools.permutations(Sbase_state, Nc))))\n    # combine I and S into one set of states\n    IS_all = []\n    for Iperm in Iperms:\n        for Sperm in Sperms:\n            state_string = ''\n            for quark in range(Nc):\n                state_string += Iperm[quark]\n                state_string += Sperm[quark]\n            IS_all.append(state_string)\n    IS_all = list(set(IS_all))\n    return IS_all\n\n\ndef check_symmetric(Nc, state1, state2):\n    symmetric = False\n    # break apart into quarks\n    quarks = [state2[i:i + 2] for i in range(0, 2 * Nc, 2)]\n    quark_permutations = itertools.permutations(quarks, Nc)\n    # put string back together\n    quark_permutations = [''.join(perm) for perm in quark_permutations]\n    if state1 in quark_permutations:\n        symmetric = True\n    return symmetric\n\n\ndef get_symmetric_states(Nc, all_states):\n    if len(all_states) == 1:\n        return [all_states]\n    # make list of sublists containing states related by interchanging 2 quarks\n    symmetric_states = []\n    # loop over all states to place them into a sublist\n    for i, state2 in enumerate(all_states):\n        # i=0 0th state is already placed into a sublist\n        if i == 0:\n            pass\n        # states to compare this state to are a representative state from each sublist\n        state1_list = [symmetric_states[n][0] for n in range(len(symmetric_states))]\n        # loop over representatives list until a match is found\n        found_symmetric = False\n        for n, state1 in enumerate(state1_list):\n            if check_symmetric(Nc, state1, state2):\n                # if found match, put this state into the correct sublist\n                found_symmetric = True\n                symmetric_states[n].append(state2)\n                break\n        # if no match is found, start a new sublist\n        if found_symmetric is False:\n            symmetric_states.append([state2])\n\n    return symmetric_states\n\n\n########################################################\n# Find Iz Sz subspace of totally symmetric vectors\ndef get_subspace(Nc, dim, Iz, Sz):\n    Ibase_state = get_base_state(Nc, Iz)  # for example duu when Nc=3, Iz=1/2\n    Sbase_state = get_base_state(Nc, Sz)  # for example dduu when Nc=4, Sz=0\n\n    # get all combinations\n    # ex: duu,udu,uud for flavor, same for spin, total 9 combinations\n    all_states = get_all_states(Nc, Ibase_state, Sbase_state)\n    # make list of sublists containing states related by interchanging 2 quarks\n    symmetric_states = get_symmetric_states(Nc, all_states)\n    # define subspace from set of symmetric states (add and normalize)\n    subspace = np.zeros((len(symmetric_states), dim))\n    for i, perms in enumerate(symmetric_states):\n        normalization = np.sqrt(len(perms))\n        for perm in perms:\n            # convert string of binary state to integer index\n            index = int(perm, 2)\n            subspace[i][index] += 1 / normalization\n    return subspace, symmetric_states\n\n\ndef convert_binary_to_state(Nc, state):\n    state_dict = {'0': 'u', '1': 'd'}\n    state_string_binary = [state[0][i:i + 2] for i in range(0, 2 * Nc, 2)]\n    state_string = ''\n    for i, string_binary in enumerate(state_string_binary):\n        state_string += state_dict[string_binary[0]] + state_dict[string_binary[1]]\n        if i != Nc - 1:\n            state_string += ','\n    return state_string\n\n\ndef print_input(Nc, I, Iz, S, Sz):\n    print()\n    print(f\"Nc = {Nc}\")\n    print(f\" I = {I}\")\n    print(f\"Iz = {Iz}\")\n    print(f\" S = {S}\")\n    print(f\"Sz = {Sz}\")\n    print()\n\n\ndef check_fullspace_values(output, Nc, I, Iz, S, Sz, Ivalues, Svalues):\n    if output is not None:\n        print('Checking eigenvalues of I^2 and S^2 ...')\n        print()\n        Q_evals, _ = get_evals(Nc, I, Iz, S, Sz)\n        check_eigenvalues(Ivalues, Q_evals, Svalues, Q_evals)\n\n\ndef check_subspace_values(output, Nc, I, Iz, S, Sz, Isub_values, Ssub_values):\n    if output is not None:\n        print()\n        print(\"Checking eigenvalues of I^2 and S^2 subspaces\\n ...\")\n        _, IS_sub_evals = get_evals(Nc, I, Iz, S, Sz)\n        check_sub_eigenvalues(Isub_values, Ssub_values, IS_sub_evals)\n\n\ndef output_results(output, Nc, symmetric_basis, Isub_values, Isub_vectors, Ssub_values, Ssub_vectors):\n    if output is not None:\n        print()\n        print(\"Basis\")\n        print('-----')\n        for i, state in enumerate(symmetric_basis):\n            print(f'v{i + 1}: {convert_binary_to_state(Nc, state)} type (sum over {len(state)} permutations)')\n        print()\n        print('Solution')\n        print('--------')\n        for i, (Ivalue, Ivector, Svalue, Svector) in enumerate(zip(Isub_values, Isub_vectors, Ssub_values, Ssub_vectors)):\n            print(f'I^2 eigenvalue: {Ivalue}')\n            print('I^2 eigen vector')\n            print(Ivector / Ivector[0])\n            print()\n            print(f'S^2 eigenvalue: {Svalue}')\n            print('S^2 eigen vector')\n            print(Svector / Svector[0])\n            print()\n\n\ndef human(Nc, state):\n    state_dict = {'0': 'u', '1': 'd'}\n    string = ''\n    for q in range(Nc):\n        string += state_dict[state[q * 2]]\n        string += state_dict[state[q * 2 + 1]]\n        if q != Nc-1:\n            string += ','\n    return string\n\n\ndef get_linear_comb(dim, I, S, basis, Isub_values, Isub_vectors):\n    if I!=S: # this shouldn't happen until Nc>4\n        print(\"Sorry, don't know which state to get because I != S\")\n        sys.exit(3)\n        return 0\n\n    else:\n        Isub_values = [round(val,2) for val in Isub_values]\n        vectorI = Isub_vectors[list(Isub_values).index(I*(I+1))]\n        linear_comb = np.zeros(dim)\n        basis_norm = 1/np.sqrt(sum([v**2 for v in vectorI]))\n        for v, component in zip(basis, vectorI):\n            for state in v:\n                linear_comb[int(state, 2)] += basis_norm*component\n\n        return linear_comb, vectorI, basis_norm,\n\n\n\n\ndef Weyl_to_Dirac(spin, basis):\n    # assume spin up -> (1,0,0,0) Weyl\n    # assume spin down -> (0,1,0,0) Weyl\n    if spin == '0':\n        if basis == 'Dirac':\n            return np.array([1,0,-1,0])\n        if basis == '2Weyl':\n            return np.array([1,0])\n        if basis == '4Weyl':\n            return np.array([1,0,1,0])\n\n    if spin == '1':\n        if basis == 'Dirac':\n            return np.array([0,1,0,-1])\n        if basis == '2Weyl':\n            return np.array([0,1])\n        if basis == '4Weyl':\n            return np.array([0,1,0,1])\n\n\ndef spin_d_from_basis(basis):\n    if basis == 'Dirac' or basis == '4Weyl':\n        spin_d = 4\n    if basis == '2Weyl':\n        spin_d = 2\n    return spin_d\n\n\ndef get_Gamma_matrices(Nc, linear_comb, basis):\n    '''\n    # note that Gamma absorbs the numerical normalizations, to be divided out\n    '''\n    # first get the dimension of the spin matrices (spin_d x spin_d)\n    spin_d = spin_d_from_basis(basis)\n    # ASSUMES Nc=4?\n    # construct rank 4 Gamma matrices (in Dirac basis)\n    # one matrix for each combination of flavors\n    max_num_flavor_combs = NF**Nc  # ex. uuuu, uudd, udud, uuud etc.\n    # u_alpha u_beta d_sigma d_delta * Gamma_alpha,beta,sigma,delta\n    Gamma = np.zeros((max_num_flavor_combs, spin_d**Nc))\n    spins_each_flavor = [[] for each in range(max_num_flavor_combs)]\n    factors_each_flavor = [[] for each in range(max_num_flavor_combs)]\n    Gamma_index_list = []\n    # loop over elements of linear combination\n    for i, factor in enumerate(linear_comb):\n        if factor != 0:\n            # convert index to state ex 5 = '0101' = udud\n            state_bin = bin(i)[2:]\n            # add extra zeros to the front\n            state_bin = '0'*(2*Nc - len(state_bin)) + state_bin\n\n            # look at the flavors involved in this state, ex '0101' -> '0','0' = u,u\n            flavor_state = [state_bin[i] for i in range(0, 2*Nc, 2)]\n            # convert flavor state to int\n            flavor_state_index = int(''.join(flavor_state), 2)\n            Gamma_index_list.append(flavor_state_index)\n            # look at the spins involved in this state, ex '0101' -> '0','0' = u,u\n            spin_state = [state_bin[i] for i in range(1, 2*Nc + 1, 2)]\n            # get 4d spinor for each\n            spin_Dirac = [Weyl_to_Dirac(spin, basis) for spin in spin_state]\n            spins_each_flavor[flavor_state_index].append(spin_state)\n            factors_each_flavor[flavor_state_index].append(factor)\n            for alpha in range(spin_d):\n                for beta in range(spin_d):\n                    for sigma in range(spin_d):\n                        for delta in range(spin_d):\n                            # convert spin indices to index using base spin_d (binary if 2Weyl basis)\n                            spin_index = int(''.join([str(alpha), str(beta), str(sigma), str(delta)]), spin_d)\n                            # f1_alpha f2_beta f3_sigma f4_delta * Gamma[f1,f2,f3,f4]_alpha,beta,sigma,delta\n                            # product of spin_Dirac elements is either 0 or 1 or -1\n                            # for case of Weyl spinors: if product is not zero, this is a unique spin flavor state\n                            # for case of Dirac spinors: += in order to add other terms with same flavor but diff spin\n                            Gamma[flavor_state_index, spin_index] += factor * spin_Dirac[0][alpha]\\\n                                                                            * spin_Dirac[1][beta] \\\n                                                                            * spin_Dirac[2][sigma] \\\n                                                                            * spin_Dirac[3][delta]\n    return Gamma, Gamma_index_list, spins_each_flavor, factors_each_flavor\n\n\ndef confirm_Dirac_states(Nc, dim, Gamma, factors_each_flavor, linear_comb, basis):\n    # first get the dimension of the spin matrices (spin_d x spin_d)\n    spin_d = spin_d_from_basis(basis)\n    # ASSUMES NC=4\n    linear_comb_reconstructed = np.zeros((dim))\n    for flavor_index, matrix in enumerate(Gamma):\n        # get flavor state\n        flavor_state = bin(flavor_index)[2:]\n        # add extra zeros to the front\n        flavor_state = '0'*(Nc - len(flavor_state)) + flavor_state\n        # get Dirac spin state from Gamma matrix indices\n        for alpha in range(spin_d):\n            for beta in range(spin_d):\n                for sigma in range(spin_d):\n                    for delta in range(spin_d):\n                        # convert spin indices to index using base spin_d (binary if 2Weyl basis)\n                        spin_index = int(''.join([str(alpha), str(beta), str(sigma), str(delta)]), spin_d)\n                        # +/-1 or 0 * numerical normalization factors\n                        value = Gamma[flavor_index, spin_index]\n                        # numerical factor coefficient\n                        if abs(value) > 1e-6:\n                            # combine this flavor state and this spin state to flavor-spin state\n                            # note that this ONLY makes sense for 2Weyl and 4Weyl\n                            # 4Weyl since only nonzero value for upper 2 components of spinor\n                            state = flavor_state[0] + str(alpha) \\\n                                  + flavor_state[1] + str(beta) \\\n                                  + flavor_state[2] + str(sigma) \\\n                                  + flavor_state[3] + str(delta)\n                            # convert binary\n                            state_index = int(state, 2)\n                            linear_comb_reconstructed[state_index] = value\n    return bool(list(linear_comb_reconstructed) == list(linear_comb))\n\n\ndef test_Dirac(Nc, dim, basis='Dirac'):\n    # example linear combination: 1/sqrt(2) [ 00,00,00,00  +  00,00,00,01 ]\n    broken = False\n    linear_comb_easy = np.zeros((dim))\n    linear_comb_easy[0] = 1/np.sqrt(2)\n    linear_comb_easy[1] = 1/np.sqrt(2)\n\n    Dirac_Gamma, _, _, _ = get_Gamma_matrices(Nc, linear_comb_easy, basis)\n    # get Dirac Gamma corresponding to flavor uuuu = 0\n\n    Gamma_reconstruct = np.zeros((4**Nc))\n    # all spin up -> u u u u spins\n    # spin_up = [1,0,-1,0]\n    # spin indices nonzero\n    # 0 = +1\n    # 2 = -1\n    norm = 1/np.sqrt(2)\n\n    Gamma_reconstruct[int(''.join(['0', '0', '0', '0']), 4)] += norm\n    Gamma_reconstruct[int(''.join(['0', '0', '0', '2']), 4)] += -norm\n    Gamma_reconstruct[int(''.join(['0', '0', '2', '0']), 4)] += -norm\n    Gamma_reconstruct[int(''.join(['0', '0', '2', '2']), 4)] += norm\n\n    Gamma_reconstruct[int(''.join(['0', '2', '0', '0']), 4)] += -norm\n    Gamma_reconstruct[int(''.join(['0', '2', '0', '2']), 4)] += norm\n    Gamma_reconstruct[int(''.join(['0', '2', '2', '0']), 4)] += norm\n    Gamma_reconstruct[int(''.join(['0', '2', '2', '2']), 4)] += - norm\n\n    Gamma_reconstruct[int(''.join(['2', '0', '0', '0']), 4)] += -norm\n    Gamma_reconstruct[int(''.join(['2', '0', '0', '2']), 4)] += norm\n    Gamma_reconstruct[int(''.join(['2', '0', '2', '0']), 4)] += norm\n    Gamma_reconstruct[int(''.join(['2', '0', '2', '2']), 4)] += -norm\n    Gamma_reconstruct[int(''.join(['2', '2', '0', '0']), 4)] += norm\n    Gamma_reconstruct[int(''.join(['2', '2', '0', '2']), 4)] += -norm\n    Gamma_reconstruct[int(''.join(['2', '2', '2', '0']), 4)] += -norm\n    Gamma_reconstruct[int(''.join(['2', '2', '2', '2']), 4)] += norm\n\n\n    # one spin down -> u u u d spins\n    # spin_up = [1,0,-1,0]\n    # spin_down = [0,1,0,-1]\n    # 0,1 = +1\n    # 2,3 = -1\n    Gamma_reconstruct[int(''.join(['0', '0', '0', '1']), 4)] += norm\n    Gamma_reconstruct[int(''.join(['0', '0', '0', '3']), 4)] += -norm\n    Gamma_reconstruct[int(''.join(['0', '0', '2', '1']), 4)] += -norm\n    Gamma_reconstruct[int(''.join(['0', '0', '2', '3']), 4)] += norm\n\n    Gamma_reconstruct[int(''.join(['0', '2', '0', '1']), 4)] += -norm\n    Gamma_reconstruct[int(''.join(['0', '2', '0', '3']), 4)] += norm\n    Gamma_reconstruct[int(''.join(['0', '2', '2', '1']), 4)] += norm\n    Gamma_reconstruct[int(''.join(['0', '2', '2', '3']), 4)] += -norm\n\n    Gamma_reconstruct[int(''.join(['2', '0', '0', '1']), 4)] += -norm\n    Gamma_reconstruct[int(''.join(['2', '0', '0', '3']), 4)] += norm\n    Gamma_reconstruct[int(''.join(['2', '0', '2', '1']), 4)] += norm\n    Gamma_reconstruct[int(''.join(['2', '0', '2', '3']), 4)] += -norm\n    Gamma_reconstruct[int(''.join(['2', '2', '0', '1']), 4)] += norm\n    Gamma_reconstruct[int(''.join(['2', '2', '0', '3']), 4)] += -norm\n    Gamma_reconstruct[int(''.join(['2', '2', '2', '1']), 4)] += -norm\n    Gamma_reconstruct[int(''.join(['2', '2', '2', '3']), 4)] += norm\n\n    if list(Gamma_reconstruct) != list(Dirac_Gamma[0]):\n        broken = True\n\n    if broken:\n        print()\n        print(f\"ERROR: problem with converting linear combination in {basis} basis!\")\n        print()\n        sys.exit(3)\n\n\ndef test_Weyl(Nc, dim, basis='2Weyl'):\n    # example linear combination: 1/sqrt(2) [ 00,00,00,00  +  00,00,00,01 ]\n    broken = False\n    linear_comb_easy = np.zeros((dim))\n    linear_comb_easy[0] = 1/np.sqrt(2)\n    linear_comb_easy[1] = 1/np.sqrt(2)\n\n    Dirac_Gamma, _, _, factors_each_flavor = get_Gamma_matrices(Nc, linear_comb_easy, basis)\n\n    # get Dirac Gamma corresponding to flavor uuuu = 0\n    Gamma_uuuu = Dirac_Gamma[0]\n    Gamma_uuuu_uuuu = Gamma_uuuu[0]  # spin_index = int(uuuu, 2 or 4) = 0\n    Gamma_uuuu_uuud = Gamma_uuuu[1]  # spin_index = int(uuud, 2 or 4) = 1\n    if abs(Gamma_uuuu_uuuu - 1/np.sqrt(2)) > 1e-6:\n        broken = True\n    if abs(Gamma_uuuu_uuud - 1/np.sqrt(2)) > 1e-6:\n        broken = True\n    if confirm_Dirac_states(Nc, dim, Dirac_Gamma, factors_each_flavor, linear_comb_easy, basis) is False:\n        broken = True\n    if broken:\n        print()\n        print(f\"ERROR: problem with converting linear combination in {basis} basis!\")\n        print()\n        sys.exit(3)\n\n\ndef check_Gamma(Nc, dim, linear_comb, basis):\n    # test to make sure get_Gamma_matrices function works using baby example\n    test_Weyl(Nc, dim, '2Weyl')\n    test_Dirac(Nc, dim, 'Dirac')\n\n    Gamma_matrices, Gamma_index_list, spin_each_flavor, factor_each_flavor = get_Gamma_matrices(Nc, linear_comb, basis)\n\n    # reconstruct linear combination from spin matrices\n    if basis == '4Weyl' or basis == '2Weyl':\n        correct = confirm_Dirac_states(Nc, dim, Gamma_matrices, factor_each_flavor, linear_comb, basis)\n        if not correct:\n            print(\"Problem with converting to Dirac\")\n            sys.exit(3)\n    else:\n        print('Have not checked spin matrix')\n\n\n\ndef get_Gamma_workflow(Nc, linear_comb, basis):\n    \"\"\"\n    :param Nc: int, number of colors\n    :param linear_comb: 1D np array of floats, int(flavor_spin_state, 2) = index,\n                        example, ud,du,ud,ud = 01,10,01,01 = int('01100101',2)=101\n                        -> linear_comb[101] = coefficient of ud,du,ud,ud term\n    :param basis: string, one of 4Dirac, 4Weyl, 2Dirac, determines basis of spin matrix\n    :return: dictionary of dictionaries\n                spin_matrices['f1f2f3f4']['factor'] = coefficient of this term, float\n                spin_matrices['f1f2f3f4']['matrix'] = 4D np array of size 2^4 or 4^4 depending on basis\n                such that\n                        f1f2f3f4_term =\n                        f1_alpha f2_beta f3_sigma f4_delta\n                        * spin_matrices['f1f2f3f4']['matrix][alpha, beta, sigma, delta]\n                and fully symmetric operator is given by\n                Baryon = sum_{f1,f2,f3,f4 permutations} ( spin_matrices['f1f2f3f4']['factor'] * f1f2f3f4_term )\n    \"\"\"\n\n    # first get the dimension of the spin matrices (spin_d x spin_d)\n    spin_d = spin_d_from_basis(basis)\n    # get relevant data\n    Gamma_matrices, Gamma_index_list, spin_each_flavor, factor_each_flavor = get_Gamma_matrices(Nc, linear_comb, basis)\n    print('In gamma workflow, gamma matrix shape is')\n    print(Gamma_matrices.shape)\n    # construct dictionary\n    spin_matrices_dict = {}\n    for i, (spins, factor, spin_matrix) in enumerate(zip(spin_each_flavor, factor_each_flavor, Gamma_matrices)):\n        # only include relevant flavor combinations in the dictionary\n        if len(spins) != 0:\n            flavor_state = bin(i)[2:]\n            flavor_state = '0' * (Nc - len(flavor_state)) + flavor_state\n            coefficient = max(factor)\n            # separate coefficient from spin matrix containing only 0s and +/-1s\n            spin_matrix = spin_matrix/coefficient\n            spin_matrix = np.reshape(spin_matrix, newshape=(spin_d, spin_d, spin_d, spin_d))\n            spin_matrices_dict[flavor_state] = {'factor': factor, 'matrix': spin_matrix}\n\n    return spin_matrices_dict\n\n\ndef print_human_form(human_form, Nc, I, Iz, S, Sz, symmetric_basis, spinor_basis, vectorI, basis_norm, linear_comb):\n    if human_form:\n        print()\n        print()\n        print('----- HUMAN READABLE ---- TWO WAYS ------')\n        print(f'Flavor-spin wave function for I={I}, Iz={Iz}, S={S}, Sz={Sz}')\n        print()\n        print()\n        print()\n        for term, (v, component) in enumerate(zip(symmetric_basis, vectorI)):\n            # print each vector on a line\n            print_string = f'{basis_norm * component} x [ '\n            for i, state in enumerate(v):\n                print_string += human(Nc, state)\n                if i != len(v) - 1:\n                    print_string += ' + '\n            print_string += ' ]'\n            print(print_string)\n\n            if term != len(symmetric_basis) - 1:\n                print(' + ')\n        print()\n        print()\n\n        Gamma_matrices, Gamma_index_list, spin_each_flavor, factor_each_flavor = get_Gamma_matrices(Nc, linear_comb,\n                                                                                                    spinor_basis)\n        for i, (spins, factor) in enumerate(zip(spin_each_flavor, factor_each_flavor)):\n            if len(spins) != 0:\n                flavor_state = bin(i)[2:]\n                flavor_state = '0' * (Nc - len(flavor_state)) + flavor_state\n                print(f'FLAVOR: {flavor_state}')\n                print('SPINS x factor')\n                print([''.join(spin) for spin in spins])\n                print([str(round(f, 2)) for f in factor])\n            print()\n        print()\n\n\ndef check_permutations(Nc, q1, q2, flavors, spin_matrices_dict):\n    # coeff_orig = spin_matrices_dict[flavors]['factor']\n    # coeff_perm = spin_matrices_dict[flavors_perm]['factor']\n    # if coeff_perm == coeff_orig:\n\n    ind = np.arange(Nc)\n    # if q1 < q2:\n    #     axes_perm = ind[:q1] + ind[q2] + ind[q1+1:q2] + ind[q1] + ind[q2+1:]\n    # else: # q1 > q2:\n    #     axes_perm = ind[:q2] + ind[q1] + ind[q2+1:q1] + ind[q2] + ind[q1+1:]\n    # print(q1, q2)\n    # print(axes_perm)\n    # matrix_orig = spin_matrices_dict[flavors]['matrix']\n    # matrix_perm = matrix_orig.transpose(matrix_orig, axes=axes_perm)\n    #\n    # return np.array_equal(matrix_perm, matrix_orig)\n\n\n# Nc = 4 hardwired function\ndef spin_matrix_symmetries(Nc, spin_matrices_dict):\n    \"\"\"\n    find where spin_matrices are equal under exchange of particles\n    :param spin_matrices_dict: as described in get_Gamma_workflow\n                spin_matrices['f1f2f3f4']['factor'] = coefficient of this term, float\n                spin_matrices['f1f2f3f4']['matrix'] = 4D np array of size 2^4 or 4^4 depending on basis\n    :return:\n    \"\"\"\n    # get flavor combinations\n    for flavors in spin_matrices_dict.keys():\n        print(flavors)\n        # just look at Nc-1 consecutive permutations\n        for q1 in range(Nc-1):\n            q2 = q1 + 1\n            check_permutations(Nc, q1, q2, flavors, spin_matrices_dict)\n        print()\n\n\n# Nc =4 hardwired function\ndef check_S2_I2(Nc, spin_matrices_dict):\n    # matrix constructed from code\n    spin_mat = spin_matrices_dict['0000']['matrix']\n\n    # construct matrix by hand, check that Dirac matrix for S=2 I=2 is as expected\n    # for Dirac 'up', nonzero indices are 0 and 2\n    # Dirac up = [1,0,-1,0]\n    # get all combinations of 4 0s and 2s equivalent to all binary up to 16=2**4, and replace 1s with 2s\n    # keep track of -1 factors - appear in result when odd number of 1s (which become 2s) appear in binary\n    # get list of indices where spin matrix should be +/- 1\n    matrix_by_hand = np.zeros((4, 4, 4, 4))\n    for i in range(16):\n        # get original binary\n        indices_str = bin(i)[2:]\n        indices_str = '0' * (Nc - len(indices_str)) + indices_str\n        print(indices_str)\n\n        # replace 1s with 2s, and count number of 2s\n        indices = []\n        num_2s = 0\n        for index in indices_str:\n            if index == '0':\n                indices.append(int(index))\n            if index == '1':\n                indices.append(2)\n                num_2s += 1\n        matrix_by_hand[indices[0], indices[1], indices[2], indices[3]] = (-1) ** num_2s\n\n    return np.allclose(spin_mat, matrix_by_hand)\n\n\ndef check_Dirac_matrix_S2_I2(Nc, Iz, Sz, spinor_basis, spin_matrices_dict):\n    if Sz == 2 and Iz == 2 and spinor_basis == 'Dirac':\n        correct = check_S2_I2(Nc, spin_matrices_dict)\n        if correct:\n            print('Good job!')\n        else:\n            print('Yikes! Sz=2, Iz=2 wrong.')\n\n\ndef spin_matrix_to_file(Nc, I, Iz, S, Sz, spinor_basis, spin_matrices_dict):\n    if spinor_basis == 'Dirac':\n        spin_d = 4\n        print('Saving matrices to files.')\n    else:\n        print('Not saving the spin matrices to files.')\n        return\n    # text file in format\n    # alpha beta sigma delta matrix[alpha, beta, sigma, delta]\n    # num rows = 4**Nc, num columns = Nc + 1\n    for flavors in spin_matrices_dict.keys():\n        matrix = spin_matrices_dict[flavors]['matrix']\n        with open(f'Nc{Nc}_I{I}_Iz{Iz}_S{S}_Sz{Sz}_{flavors}.txt', 'w+') as f:\n            # instead of Nc for loops, just get all permutations with repeats of spin indices\n            all_indices = itertools.product(np.arange(spin_d), repeat=Nc)\n            for i, indices in enumerate(all_indices):\n                for ind in indices:\n                    f.write(f'{ind} ')\n                f.write(f'{matrix[tuple(indices)]}\\n')\n            f.close()\n\n\ndef main(spinor_basis='4Weyl', output=False, human_form=True):\n    Nc = int(sys.argv[1])\n    I = float(sys.argv[2])\n    Iz = float(sys.argv[3])\n    S = float(sys.argv[4])\n    Sz = float(sys.argv[5])\n    print_input(Nc, I, Iz, S, Sz)\n    # check_I_S(Nc, I, S)\n    # 2 choices for each flavor, 2 choices for each spin, Nc quarks in a baryon\n    dim = (2 * 2) ** Nc\n    ########################################################\n    # First compute I^2, S^2 matrix for each individual quark\n    # note that for any given state of Nc qaurks, I^2 of any indiv quark is still I(I+1)\n    # Iisq |state> = I*(I+1) |state> (always 1/2 since fermion!)\n    Iisq = [1 / 2 * (1 / 2 + 1) * np.identity(dim)]*Nc\n    Sisq = [1 / 2 * (1 / 2 + 1) * np.identity(dim)]*Nc\n    ########################################################\n    # compute Iz operator for each quark\n    Iiz = [get_Siz(Nc, dim, 2 * i) for i in range(Nc)]  # (2*i)th place of |f1 s1 f2 s2 f3 s3 ..> = fi\n    Siz = [get_Siz(Nc, dim, 2 * i + 1) for i in range(Nc)]  # (2*i+1)th place of |f1 s1 f2 s2 f3 s3 ..> = si\n    ########################################################\n    # compute I+ and I-, S+ and S-\n    # example I1p|00 00 10> = |00 00 00>\n    Ii_pm = [get_S_plus_minus(Nc, dim, 2 * i) for i in range(Nc)]  # (2*i)th place of |f1 s1 f2 s2 f3 s3 ..> = fi\n    Si_pm = [get_S_plus_minus(Nc, dim, 2 * i + 1) for i in range(Nc)]  # (2*i+1)th place of |f1 s1 f2 s2 f3 s3 ..> = si\n    ########################################################\n    # compute total I^2 and S^2 matrix, using I^2 = (I1 + I2 + I3 + ...)^2\n    Isq = compute_Ssq_total(Nc, dim, Iisq, Iiz, Ii_pm)\n    Ssq = compute_Ssq_total(Nc, dim, Sisq, Siz, Si_pm)\n    ########################################################\n    # compute eigen values of I^2 and S^2 (as check)\n    Ivalues, Ivectors = np.linalg.eig(Isq)\n    Svalues, Svectors = np.linalg.eig(Ssq)\n    check_fullspace_values(output, Nc, I, Iz, S, Sz, Ivalues, Svalues)\n\n    ########################################################\n    # Find S^2 and I^2 basis within the subspace\n    subspace, symmetric_basis = get_subspace(Nc, dim, Iz, Sz)\n    Isq_subspace = np.matmul(np.matmul(subspace, Isq), np.transpose(subspace))\n    Ssq_subspace = np.matmul(np.matmul(subspace, Ssq), np.transpose(subspace))\n\n    Isub_values, Isub_vectors = np.linalg.eig(Isq_subspace)\n    Ssub_values, Ssub_vectors = np.linalg.eig(Ssq_subspace)\n    Isub_vectors = Isub_vectors.transpose()\n    Ssub_vectors = Ssub_vectors.transpose()\n\n    ########################################################\n    # check to make sure eigen values are correct\n    check_subspace_values(output, Nc, I, Iz, S, Sz, Isub_values, Ssub_values)\n\n    ########################################################\n    # output results: basis, vectors with corresponding eigenvalues\n    output_results(output, Nc, symmetric_basis, Isub_values, Isub_vectors, Ssub_values, Ssub_vectors)\n\n    # get result as a 1D array indicating linear combination of all possible flavor spin state terms\n    linear_comb, vectorI, basis_norm = get_linear_comb(dim, I, S, symmetric_basis, Isub_values, Isub_vectors)\n\n    # get the set of rank Nc Dirac matrices that combine all of the spins for each flavor combo\n    #check_Gamma(Nc, dim, linear_comb, spinor_basis)\n\n    # print human readable result\n    print_human_form(human_form, Nc, I, Iz, S, Sz, symmetric_basis, spinor_basis, vectorI, basis_norm, linear_comb)\n\n    # get spin matrix dictionary for next part of workflow\n    spin_matrices_dict = get_Gamma_workflow(Nc, linear_comb, spinor_basis)\n\n    # find symmetries of spin matrices\n    # symm = spin_matrix_symmetries(Nc, spin_matrices_dict)\n    # print(symm)\n\n    # check that DIRAC spin matrix is correct for simple case\n    #check_Dirac_matrix_S2_I2(Nc, Iz, Sz, spinor_basis, spin_matrices_dict)\n\n    # save Gamma matrix to text file for use in C code\n    spin_matrix_to_file(Nc, I, Iz, S, Sz, spinor_basis, spin_matrices_dict)\n\n\nif __name__ == \"__main__\":\n    main(spinor_basis='4Weyl', output=True, human_form=True)\n","repo_name":"chrisculver/su4ops","sub_path":"kimmy_Nc_baryon.py","file_name":"kimmy_Nc_baryon.py","file_ext":"py","file_size_in_byte":35978,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"25274280771","text":"total = 0\r\nprint(\"Let's add your numbers\")\r\nwhile total < 51:\r\n\tnum = int(input(\"Add a number: \"))\r\n\ttotal = total + num\r\n\tprint(f\"The total is {total}\")\t\r\n\tif total == 50:\r\n\t\tbreak\r\n\tcontinue\r\n\r\n\t\r\n\r\n\r\n\r\n\r\n\t\r\n","repo_name":"moon-yung/LCT-Python-Challenger-I","sub_path":"challenge7.py","file_name":"challenge7.py","file_ext":"py","file_size_in_byte":210,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"14690731380","text":"# https://www.practicepython.org/exercise/2017/01/24/33-birthday-dictionaries.html\n# This exercise is Part 1 of 4 of the birthday data exercise series. The other exercises are: Part 2, Part 3, and Part 4.\n# For this exercise, we will keep track of when our friend’s birthdays are, and be able to find that information based on their name.\n# Create a dictionary (in your file) of names and birthdays.\n# When you run your program it should ask the user to enter a name, and return the birthday of that person back to them.\n# The interaction should look something like this:\n# >>> Welcome to the birthday dictionary. We know the birthdays of:\n# Albert Einstein\n# Benjamin Franklin\n# Ada Lovelace\n# >>> Who's birthday do you want to look up?\n# Benjamin Franklin\n# >>> Benjamin Franklin's birthday is 01/17/1706.\n\n\nclass Birthday:\n\n    def __init__(self) -> None:\n        self.birthday = {}\n        self.func_print_names()\n\n    def func_new(self) -> None:\n        \"\"\"\n        \n        \"\"\"\n        name = input(\"name: \")\n        date = \"birthday: \"\n        self.birthday[name] = input(date)\n\n    def func_append_dict(self, dict_to_append: dict) -> None:\n        \"\"\"\n        Append dictionary to self.birthday\n        \"\"\"\n        self.birthday.update(dict_to_append)\n\n    def func_print_names(self) -> None:\n        \"\"\"\n        Print the names from dictionary\n        \"\"\"\n        text_suffix = \"We know the birthdays of:\"\n        if len(self.birthday) == 0:\n            text_suffix = \"The birthday dictionary is empty yet.\"\n        print(f\"Welcome to the birthday dictionary. {text_suffix}\")\n        for n in [*self.birthday]:\n            print(n)\n\n    def func_print_birthday(self, name=False) -> None:\n        \"\"\"\n        Print the birthday of name\n        \"\"\"\n        if not name:\n            name = input(\"Who's birthday do you want to look up? \")\n        if name in [*self.birthday]:\n            print(f\"{name}'s birthday: {self.birthday[name]}\")\n        else:\n            print(f\"No data about {name}\")\n\n\nif __name__ == \"__main__\":\n    # value\n    dict_to_append = {\n        \"Eugene Paul Wigner\": \"17/11/1902\",\n        \"John von Neumann\": \"28/12/1903\"\n    }\n\n    # test\n    birthday = Birthday()\n    birthday.func_new()\n    birthday.func_print_names()\n    birthday.func_print_birthday()\n    birthday.func_print_birthday(\"otto\")\n    birthday.func_print_birthday(\"\")\n    birthday.func_append_dict(dict_to_append)\n    birthday.func_print_names()\n    birthday.func_print_birthday(\"Eugene Paul Wigner\")\n","repo_name":"akekesi/Practice_Python","sub_path":"practicepython_33.py","file_name":"practicepython_33.py","file_ext":"py","file_size_in_byte":2499,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"1923413032","text":"#!/usr/bin/env python3\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom bagpy import bagreader\nimport pandas as pd\nfrom sklearn.preprocessing import StandardScaler\nfrom scipy.io import savemat\n\n\ndef find_bag_lenght(file_path):\n    b = bagreader(file_path)\n    ACT_POS_MSG = b.message_by_topic('/actual_position')\n    EXPERIMENT_WINDOW_MSG = b.message_by_topic(\"/experiment_window\")\n    act_pos_pd = pd.read_csv(ACT_POS_MSG)\n    experiment_window_pd = pd.read_csv(EXPERIMENT_WINDOW_MSG)\n    act_pos = act_pos_pd['pose.position.x']\n    experiment_window = experiment_window_pd['data']\n    lenghts = []\n    lenghts.append(len(act_pos))\n    lenghts.append(len(experiment_window))\n    minimum_lenght = np.amin(lenghts)\n    final_lenght = 0\n    for i in range(0, minimum_lenght):\n        if experiment_window[i]:\n            final_lenght += 1\n    return final_lenght\n\n\ndef bag_read(file_path):\n    b = bagreader(file_path)\n\n    # read messages for each topic\n    # H_ERR_MSG = b.message_by_topic('/human_error')\n    ACT_POS_MSG = b.message_by_topic('/actual_position')\n    REF_POS_MSG = b.message_by_topic('/reference_position')\n    # POS_ERR_MSG = b.message_by_topic('/position_error')\n    FORCE_MSG = b.message_by_topic('/human_force')\n    EXPERIMENT_WINDOW_MSG = b.message_by_topic(\"/experiment_window\")\n\n    # import csv as panda dataframe\n    # h_err_pd = pd.read_csv(H_ERR_MSG)\n    act_pos_pd = pd.read_csv(ACT_POS_MSG)\n    ref_pos_pd = pd.read_csv(REF_POS_MSG)\n    # pos_err_pd = pd.read_csv(POS_ERR_MSG)\n    force_pd = pd.read_csv(FORCE_MSG)\n    experiment_window_pd = pd.read_csv(EXPERIMENT_WINDOW_MSG)\n\n    # create vectors\n    time = act_pos_pd['Time'] - act_pos_pd.loc[0, 'Time']\n    # h_err_x = h_err_pd['pose.position.x']\n    # h_err_y = h_err_pd['pose.position.y']\n    act_pos_x = act_pos_pd['pose.position.x']\n    act_pos_y = act_pos_pd['pose.position.y']\n    ref_pos_x = ref_pos_pd['pose.position.x']\n    ref_pos_y = ref_pos_pd['pose.position.y']\n    # pos_err_x = pos_err_pd['pose.position.x']\n    # pos_err_y = pos_err_pd['pose.position.y']\n    h_force_x = force_pd['wrench.force.x']\n    h_force_y = force_pd['wrench.force.y']\n    experiment_window = experiment_window_pd['data']\n\n    for i in range(len(act_pos_x)):\n        ref_pos = np.sqrt((np.power(ref_pos_x, 2) + np.power(ref_pos_y, 2)))\n        act_pos = np.sqrt((np.power(act_pos_x, 2) + np.power(act_pos_y, 2)))\n        h_force = np.sqrt((np.power(h_force_x, 2) + np.power(h_force_y, 2)))\n        # pos_err = np.sqrt((np.power(pos_err_x, 2) + np.power(pos_err_y, 2)))\n        # h_err = np.sqrt((np.power(h_err_x, 2) + np.power(h_err_y, 2)))\n\n    # SIZE ADJUSTMENTS\n    ref_pos_input = ref_pos\n    act_pos_input = act_pos\n\n    time_adj = []\n    h_err_adj = []\n    force_adj = []\n    ref_pos_adj = []\n    act_pos_adj = []\n    lenghts = []\n    error_adj = []\n    error_adj_dot = []\n    lenghts.append(len(h_force))\n    lenghts.append(len(ref_pos_input))\n    lenghts.append(len(act_pos_input))\n    lenghts.append(len(experiment_window))\n    minimum_lenght = np.amin(lenghts)\n\n    window_vector = np.zeros(len(experiment_window))\n    for i in range(0, minimum_lenght):\n        if experiment_window[i]:\n            window_vector[i] = 1\n\n    for idx in range(0, minimum_lenght):\n        if window_vector[idx] == 1:\n            time_adj.append(time[idx])\n            force_adj.append(h_force[idx])\n            ref_pos_adj.append(ref_pos_input[idx])\n            act_pos_adj.append(act_pos_input[idx])\n            error_adj.append(ref_pos_input[idx] - act_pos_input[idx])\n\n    for i in range(1, len(time_adj)):\n        time_adj[i] = time_adj[i] - time_adj[0]\n    time_adj[0] = 0\n\n    # CONVERT TO NP ARRAYS\n    input_array = np.array(error_adj)\n    output_array = np.array(force_adj)\n    time_array = np.array(time_adj)\n    input_norm = []\n    output_norm = []\n    for idx in range(0, len(error_adj)):\n        input_norm.append((input_array[idx] - np.mean(input_array))/np.std(input_array))\n        output_norm.append((output_array[idx] - np.mean(output_array))/np.std(output_array))\n    input_norm_array = np.array(input_norm)\n    output_norm_array = np.array(output_norm)\n    return input_norm_array, output_norm_array, time_array\n\n\ndef arrays_to_dataframe(input, output, time):\n    # Init dataframes timeseries\n    # input_df = pd.DataFrame({'time': time, 'data': input})\n    input_df = {'time': time, 'data': input}\n    output_df = pd.DataFrame({'time': time, 'data': output})\n    output_df = {'time': time, 'data': output}\n\n    # Add delayed copies\n    delays = [125, 625, 1250]\n    # input_del = [np.empty(len(input)), np.empty(len(input)), np.empty(len(input))]\n    output_del = [np.empty(len(output)), np.empty(len(output)), np.empty(len(output))]\n    idx = 0\n    for d in delays:\n        # input_del[idx][:d] = 0\n        # input_del[idx][d:len(input)] = input[d:len(input)]\n        output_del[idx][:d] = 0\n        output_del[idx][d:len(output)] = output[d:len(output)]\n        idx += 1\n    # input_df_d1 = pd.DataFrame({'time': time, 'data': input_del[0]})\n    output_df_d1 = pd.DataFrame({'time': time, 'data': output_del[0]})\n    # output_df_d1 = {'time': time, 'data': output_del[0]}\n    # input_df_d2 = pd.DataFrame({'time': time, 'data': input_del[1]})\n    output_df_d2 = pd.DataFrame({'time': time, 'data': output_del[1]})\n    # output_df_d2 = {'time': time, 'data': output_del[1]}\n    # input_df_d3 = pd.DataFrame({'time': time, 'data': input_del[2]})\n    output_df_d3 = pd.DataFrame({'time': time, 'data': output_del[2]})\n    # output_df_d3 = {'time': time, 'data': output_del[2]}\n\n    # # NORMALIZE\n    # # Fit scalers\n    # input_scalers = {}\n    # output_scalers = {}\n    # for x in input_df.columns:\n    #     input_scalers[x] = StandardScaler().fit(input_df[x].values.reshape(-1, 1))\n    #     output_scalers[x] = StandardScaler().fit(output_df[x].values.reshape(-1, 1))\n    return input_df, output_df, output_df_d1, output_df_d2, output_df_d3\n\n\nsubjects_complete = ['/Alessandro_Scano', '/Claudia_Pagano', '/Francesco_Airoldi', '/Matteo_Malosio', '/Michele',\n                     '/Giorgio_Nicola', '/Paolo_Franceschi', '/Marco_Faroni', '/Stefano_Mutti', '/Trunal']\nsubjects = ['/Claudia_Pagano', '/Marco_Faroni']\nbag_folder_base = '/home/adriano/projects/bag/controller_adriano'\nfile_extension = '.bag'\nexperiment_type = 'step'\nunderscore = '_'\n\nn_of_iterations = 10\nsubjects = subjects_complete\n\nlenghts = []\nfor n in range(0, len(subjects)):\n    bag_folder = bag_folder_base + subjects[n] + '/step/'\n    for i in range(0, n_of_iterations):\n        experiment_number = str(i+1)\n        file_name = experiment_type + underscore + experiment_number\n        bag_input = bag_folder + file_name + file_extension\n        len_row_col = find_bag_lenght(bag_input)\n        lenghts.append(len_row_col)\n\nmin_len = np.amin(lenghts)\n\n\nx = []  # np.empty((len(subjects)*n_of_iterations, min_len))\ny = []  # np.empty((len(subjects)*n_of_iterations, min_len))\nfor n in range(0, len(subjects)):\n    bag_folder = bag_folder_base + subjects[n] + '/step/'\n    for i in range(0, n_of_iterations):\n        experiment_number = str(i+1)\n        file_name = experiment_type + underscore + experiment_number\n        bag_input = bag_folder + file_name + file_extension\n        x_n_i, y_n_i, t_n_i = bag_read(bag_input)\n        if len(x_n_i) > min_len:\n            x_n_i = x_n_i[:min_len]\n            y_n_i = y_n_i[:min_len]\n            t_n_i = t_n_i[:min_len]\n        x_n, y_n, y_n_d1, y_n_d2, y_n_d3 = arrays_to_dataframe(x_n_i, y_n_i, t_n_i)\n        x.append(x_n)  # [i + n*n_of_iterations, :] = x_n_i\n        x.append(x_n)\n        x.append(x_n)\n        x.append(x_n)\n        y.append(y_n)  # [i + n*n_of_iterations, :] = y_n_i\n        y.append(y_n_d1)\n        y.append(y_n_d2)\n        y.append(y_n_d3)\n\n\ndataset = pd.DataFrame({'x': x, 'y': y})\n# dataset = {'x': x, 'y': y}\n# print(\"dataset shape: \", dataset.shape)\ndataset.to_pickle('dataset_error_force_del_norm_1705.pkl')\n# savemat(file_name='data_error_force_delsecsint_norm.pkl', mdict=dataset)\n","repo_name":"adrianoscibilia/human_control_modeling","sub_path":"create_dataset.py","file_name":"create_dataset.py","file_ext":"py","file_size_in_byte":8021,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"15151256692","text":"#\n#\n#\n# Company - Verifone\n# Link - https://www.verifone.com/en/careers/search?locations%5B%5D=8729&keyword=\n#\nfrom A_OO_get_post_soup_update_dec import DEFAULT_HEADERS, update_peviitor_api\nfrom L_00_logo import update_logo\n#\nimport requests\nfrom bs4 import BeautifulSoup\n#\nimport uuid\n\n\ndef get_data_from_verifone():\n    \"\"\"\n    ... get data from verifone with one requests.\n    \"\"\"\n\n    response = requests.get(url='https://www.verifone.com/en/global/careers/jobs?title=&departments=All&locations=686',\n                            headers=DEFAULT_HEADERS)\n    soup = BeautifulSoup(response.text, 'lxml')\n\n    soup_data = soup.find_all('tr')\n\n    lst_with_data = []\n    for dt in soup_data:\n        link = dt.find('a')\n        title = dt.find('a')\n        location = dt.find('td', attrs={'class': 'views-field views-field-field-offices'})\n\n        if link is not None:\n            lst_with_data.append({\n                \"id\": str(uuid.uuid4()),\n                \"job_title\": title.text.strip(),\n                \"job_link\": link['href'].strip(),\n                \"company\": \"verifone\",\n                \"country\": \"Romania\",\n                \"city\": location.text.strip()\n                })\n\n    return lst_with_data\n\n\n# update data on peviitor!\n@update_peviitor_api\ndef scrape_and_update_peviitor(company_name, data_list):\n    \"\"\"\n    Update data on peviitor API!\n    \"\"\"\n\n    return data_list\n\n\ncompany_name = 'verifone'\ndata_list = get_data_from_verifone()\nscrape_and_update_peviitor(company_name, data_list)\n\nprint(update_logo('verifone',\n                  'https://datasym.co.uk/wp-content/uploads/2017/09/verifone-logo-300x120.png'\n                  ))\n","repo_name":"peviitor-ro/Scrapers_start_with_digi","sub_path":"sites/verifone_scraper.py","file_name":"verifone_scraper.py","file_ext":"py","file_size_in_byte":1655,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"48"}
+{"seq_id":"31790017594","text":"from flask import Flask, render_template, request, jsonify, session, redirect\nimport stripe\nfrom flask_app.models.users import User\nfrom flask_app import app\n\nstripe.api_key = 'sk_test_51NDup2FabksylCi8SFvbhtLIVxxBS1gZ3MUvH1lq9sKc8tjJgllKghz1gPVsm6rybRXsQ3kVdoIssPDdaDFii2AK00NH08t73i'\n\n\n@app.route('/create-checkout-session', methods=['POST'])\ndef create_checkout_session():\n    user_id = request.json['userId']\n    user = User.get_one({'id': user_id})\n    if user:\n        session = stripe.checkout.Session.create(\n            payment_method_types=['card'],\n            line_items=[\n                {\n                    # Replace with your Stripe Price ID for the monthly subscription\n                    'price': 'price_1NEP8ZFabksylCi8e5lcSdZj',\n                    'quantity': 1,\n                },\n            ],\n            mode='subscription',\n            success_url='http://frontendtutor.com/success34iubbuyhgugiuBIBv2?' + \\\n            user_id,  # Pass user ID as a query parameter\n            cancel_url='http://frontendtutor.com/',\n            customer=user.stripe_customer_id,\n            metadata = {\n                        'selected_product': 'premium38'\n                    }\n        )\n\n        return jsonify({'sessionId': session.id})\n    else:\n        return jsonify({'error': 'User not found'})\n\n\n@app.route('/success34iubbuyhgugiuBIBv2', methods=['GET'])\ndef succcesss():\n    return redirect('/')\n\n\n@app.route('/create-checkout-session/premium', methods=['POST'])\ndef create_checkout_sessionadvanced():\n    user_id = request.json['userId']\n    user = User.get_one({'id': user_id})\n    if user:\n        session = stripe.checkout.Session.create(\n            payment_method_types=['card'],\n            line_items=[\n                {\n                    'price': 'price_1NEP9zFabksylCi8BHQMlGme',\n                    'quantity': 1,\n                },\n            ],\n            mode='subscription',\n            success_url='http://frontendtutor.com/success34iubbuyhgugiuBIBv2?' +\n            user_id,\n            cancel_url='http://frontendtutor.com/',\n            customer=user.stripe_customer_id,\n            metadata = {\n                        'selected_product': 'premium70'\n                    }\n        )\n\n        return jsonify({'sessionId': session.id})\n    else:\n        return jsonify({'error': 'User not found'})\n\n\n","repo_name":"Jsingh651/FrontendTutor","sub_path":"flask_app/controllers/paymentController.py","file_name":"paymentController.py","file_ext":"py","file_size_in_byte":2347,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"71725171666","text":"# coding:utf-8\nclass UnionFind(object):\n    def __init__(self, n):\n        self.parent = {}\n        for i in range(n):\n            self.parent[i] = i\n\n    def find(self, x):\n        if x != self.parent[x]:\n            self.parent[x] = self.find(self.parent[x])\n        return self.parent[x]\n\n    def union(self, x1, x2):\n        root1 = self.find(x1)\n        root2 = self.find(x2)\n        # 要合并，retunr false\n        if root1 != root2:\n            self.parent[root2] = root1\n            return False\n        # 父亲一样，不合并，return True\n        return True\n\nclass Solution(object):\n    def findRedundantDirectedConnection(self, edges):\n        \"\"\"\n        :type edges: List[List[int]]\n        :rtype: List[int]\n        \"\"\"\n        n = len(edges)\n        uf = UnionFind(n + 1)\n\n        # 记录最后一条重复的边\n        last = []\n        parent = {}\n        candidates = []\n        for st, ed in edges:\n\n            # 当前节点已经有父亲了，说明此时有两个点指向ed\n            if ed in parent:\n                candidates.append([parent[ed], ed])\n                candidates.append([st, ed])\n            # 当前节点没父亲，说明是一条单向的线\n            else:\n                parent[ed] = st\n                # 当发现st,ed早已在同一个联通分路的时候，我们要记录当前这个多连的边\n                if uf.union(st, ed):\n                    last = [st, ed]\n\n        # 情况1，度都为1，找出重复链接的边\n        if not candidates:\n            return last\n\n        # 情况3，存在度为2的边，且之前在遍历度为1的时候发生过重复，那么删掉度1重复的\n        if last:\n            candidates[0]\n\n        # 情况2，不存在度为2的边，且之前在遍历度为1的时候没有重复，那么删掉度2后来的边\n        return candidates[1]\n\n\n\"\"\"\n视频思路：理解三种情况\nhttps://www.bilibili.com/video/BV13t41157K5?from=search&seid=1317598284124498974\n\n文字思路：\n1. 都是度为1， 则找出构成环的最后一条边\n2. 有度为2的两条边(A->B, C->B)，则删除的边一定是在其中\n    先不将C->B加入并查集中，若不能构成环，则C->B是需要删除的点边，\n    反之，则A->B是删除的边(去掉C->B还能构成环，则C->B一定不是要删除的边)\n\n所以分为三个情况：\n情况1：都是度为1， 则找出构成环的最后一条边\n情况2：遍历到有度为2的两条边，假如说，在遍历度为1的边的时候，已经发现了重复，那么删除度为2且在之前已经重复的边\n情况3：遍历到有度为2的两条边，假如说，在遍历度为1的边的时候，已经发现了重复，那么最后构成度为2的边是导致重复的边\n\n链接：https://leetcode-cn.com/problems/redundant-connection-ii/solution/python-jian-ji-dai-ma-bing-cha-ji-de-yun-yong-by-y/\n\n\"\"\"","repo_name":"Andrewlearning/Leetcoding","sub_path":"leetcode/Graph/Union find并查集(检测无向图是否有环)/685. 去除一条边使不成环(有向图，不常规).py","file_name":"685. 去除一条边使不成环(有向图，不常规).py","file_ext":"py","file_size_in_byte":2883,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"28463316120","text":"#Author Mollie Goldblatt mgoldbl1@umbc.edu 8/25/2022\n\nimport PySimpleGUI as sg\nimport theta_solver as ts\nimport wrist_shape as ws\nimport numpy as np\nimport matplotlib as mpl\n\nmpl.use('TkAgg')\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2Tk\nfrom mpl_toolkits.mplot3d import Axes3D\nimport matplotlib.pyplot as plt\nimport math\n\n\n# helper function for set_axes_equal\ndef _set_axes_radius(ax, origin, radius):\n    x, y, z = origin\n    ax.set_xlim3d([x - radius, x + radius])\n    ax.set_ylim3d([y - radius, y + radius])\n    ax.set_zlim3d([z - radius, z + radius])\n\n# https://stackoverflow.com/questions/13685386/matplotlib-equal-unit-length-with-equal-aspect-ratio-z-axis-is-not-equal-to\n# (answer by AndrewCox)\ndef set_axes_equal(ax):\n    \"\"\"Set 3D plot axes to equal scale.\n\n    Make axes of 3D plot have equal scale so that spheres appear as\n    spheres and cubes as cubes.  Required since `ax.axis('equal')`\n    and `ax.set_aspect('equal')` don't work on 3D.\n    \"\"\"\n    limits = np.array([\n        ax.get_xlim3d(),\n        ax.get_ylim3d(),\n        ax.get_zlim3d(),\n    ])\n    origin = np.mean(limits, axis=1)\n    radius = 0.5 * np.max(np.abs(limits[:, 1] - limits[:, 0]))\n    _set_axes_radius(ax, origin, radius)\n\n# returns x, y, and z coordinates for the uncut cylinder, so matplotlib can plot it with plotsurface.\n# axis point 1 is the center of the tube at the bottom of the uncut cylinder\n# axis point 2 is the center of the tube at the top of the uncut cylinder\ndef cylinder(r_outer, axis_point1, axis_point2):\n    b = np.array([0, 1, 0])\n    if axis_point1[-1] == axis_point2[-1]:\n        a = np.array([1, 0, 0])\n    else:\n        m = (axis_point2[2] - axis_point1[2])/(axis_point2[0] - axis_point1[0])\n        a = np.array([m/math.sqrt(m**2 +1), 0, -1/math.sqrt(m**2 +1)])\n\n    t = np.linspace(0, 1, 10)\n        \n    omega = np.linspace(0, 2*math.pi, 20)\n\n    ts, omegas = np.meshgrid(t, omega)\n\n    centers_x = (1-ts)*axis_point1[0] + ts*axis_point2[0]\n    centers_y = (1-ts)*axis_point1[1] + ts*axis_point2[1]\n    centers_z = (1-ts)*axis_point1[2] + ts*axis_point2[2]\n\n    xs = centers_x + np.cos(omegas)*r_outer*a[0] + np.sin(omegas)*r_outer*b[0]\n    ys = centers_y + np.cos(omegas)*r_outer*a[1] + np.sin(omegas)*r_outer*b[1]\n    zs = centers_z + np.cos(omegas)*r_outer*a[2] + np.sin(omegas)*r_outer*b[2]\n\n    return xs, ys, zs\n\n# plots the uncut section \ndef plot_uncut(axis_point1, axis_point2, r_outer, ax):\n    xs, ys, zs = cylinder(r_outer, axis_point1, axis_point2)\n    ax.plot_surface(xs, ys, zs, color='orange')\n\n# axis point 1 is the center of the tube at the bottom of the cut section.\n# axis point 2 is the center of the tube at the top of the cut section.\n# the bend center is the center of the circle that the cut section bends around.\n# the distance from the bend center to axis point 1 is R = 1/kappa.\n# the distance from the bend center to axis point 2 is R = 1/kappa.\n# axis point 1, axis point 2, and the bend center are the corners of an isosceles triangle.\n# the angles of the isosceles triangle are theta (bend center), beta (axis point 1), beta (axis point2).\n# the angle alpha is the angle between the x axis and the line: axis point 1-axis point 2.\n# the angle gamma is beta - alpha.\n# gamma is the angle between the x axis and the line: axis point 1-bend center.\n\n\ndef find_gamma(axis_point1, axis_point2, theta):\n    if axis_point1[0] == axis_point2[0] and axis_point1[2] < axis_point2[2]:\n        return math.pi/2\n    elif axis_point1[0] == axis_point2[0] and axis_point1[2] > axis_point2[2]:\n        return -math.pi/2\n    else:\n        m = (axis_point2[2] - axis_point1[2])/(axis_point2[0] - axis_point1[0])\n        beta = (math.pi-theta)/2\n        alpha = math.atan(m)\n        gamma = beta - alpha\n        if axis_point1[0] < axis_point2[0]:\n            return gamma\n        else:\n            return gamma - math.pi\n\n# find the bend center from axis point 1, R = 1/kappa, and gamma\ndef find_bend_center(axis_point1, R, gamma):\n    xbc = axis_point1[0]+R*math.cos(gamma)\n    zbc = axis_point1[2] - R*math.sin(gamma)\n    bend_center = np.array([xbc, 0, zbc])\n    return bend_center\n\n# plot a cut section of the tube from kappa, theta, axis point 1, and axis point 2.\n# psi is an angle that gives points on the arc that connects axis point 1 and axis point 2.\n# psi goes from 0 to theta.\n# omega is an angle that gives points on the tube in part of a circle around a values of psi.\n# omega goes from -phi/2 to phi/2\n# phi is the angle of the sector from equation A.5 in masters thesis by N. Pacheco.\n# unit vector [a0, a1, a2] points from the bend center to a point on the arc that connects axis point 1 and axis point 2. \n\ndef plot_cut(kappa, theta, axis_point1, axis_point2, phi, r_outer, ax):\n    \n    R = 1/kappa\n    gamma = find_gamma(axis_point1, axis_point2, theta)\n    bend_center = find_bend_center(axis_point1, R, gamma)\n    psi = np.linspace(0, theta, 8)\n    omega = np.linspace(-phi/2, phi/2, 8)\n    psis, omegas = np.meshgrid(psi, omega)\n\n    x_centers = bend_center[0] - R*np.cos(gamma+psis)\n    z_centers = bend_center[2] + R*np.sin(gamma+psis)\n    y_centers = 0 * x_centers\n     \n\n    a0 = -np.cos(gamma+psis)\n    a1 = 0\n    a2 = np.sin(gamma+psis)\n    \n    b = np.array([0, 1, 0])\n    \n    xs = x_centers + r_outer * np.cos(omegas) * a0 + r_outer * np.sin(omegas) * b[0]\n    ys = y_centers + r_outer * np.cos(omegas) * a1 + r_outer * np.sin(omegas) * b[1]\n    zs = z_centers + r_outer * np.cos(omegas) * a2 + r_outer * np.sin(omegas) * b[2]\n\n    ax.plot_surface(xs, ys, zs, color='#90ee90')\n    \n# plots the uncut and cut sections of the wrist  \ndef plot_3d_wrist(r_outer, x_array, z_array, n, fignum, kappa_array, theta_array, phi_array):\n    kappa_array_mm = kappa_array/1000\n    \n    fig3d = plt.figure(num=fignum)\n    ax3d = plt.axes(projection ='3d')\n    fig3d.set_figheight(8.5)\n    fig3d.set_figwidth(10.5)\n\n    for index in range(0, 2*n+1):\n        if index % 2 != 0:\n            axis_point1 = np.array([x_array[index], 0, z_array[index]])\n            axis_point2 = np.array([x_array[index + 1], 0, z_array[index + 1]])\n            \n            plot_uncut(axis_point1, axis_point2, r_outer, ax3d)\n        elif index % 2 == 0 and index != 2*n:\n            axis_point1 = np.array([x_array[index], 0, z_array[index]])\n            axis_point2 = np.array([x_array[index + 1], 0, z_array[index + 1]])\n            \n            plot_cut(kappa_array_mm[index//2], theta_array[index//2], axis_point1, axis_point2, phi_array[index//2], r_outer, ax3d)          \n            \n             \n            \n    ax3d.set_box_aspect([1,1,1])\n    set_axes_equal(ax3d)\n    ax3d.set_xlabel('X axis')\n    ax3d.set_ylabel('Y axis')\n    ax3d.set_zlabel('Z axis')\n   \n\n    ax3d.view_init(0, -90)\n\n    return fig3d\n","repo_name":"comet-lab/summer-research-2022","sub_path":"Three_D_Wrist.py","file_name":"Three_D_Wrist.py","file_ext":"py","file_size_in_byte":6786,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"69844948306","text":"'''\nCreated on 23 Sep 2015\n\n@author: si\n'''\nimport random\nimport time\nimport threading\nimport os\n\nfrom flask import abort, Blueprint, make_response, jsonify, current_app, request\nfrom sqlalchemy.sql import text\n\nfrom flask_app import db, Foo\n\napi_views = Blueprint('api_views', __name__)\n\n# @api_views.errorhandler(404)\n# def not_found(error):\n#     return make_response(jsonify( { 'error': 'Not found' } ), 404)\n# \n# @api_views.after_request\n# def after_request(response):\n#     # avoid transaction isolation - flush should work but doesn't\n#     db.session.remove()\n#     return response\n\n@api_views.route('/')\ndef hello():\n    try:\n        thread_name = threading.current_thread().name\n    except:\n        thread_name = \"No_thread\"\n    try:\n        pid = str(os.getpid())\n    except:\n        pid = \"no_pid\"\n    return \"Hello World! %s-%s\" % (thread_name, pid)\n\n\n@api_views.route('/foox/<int:id>/', methods = ['GET'])\ndef get_foox(id):\n\n    foo = Foo.query.filter_by(id=id).first()\n    if foo == None:\n        abort(404)\n\n    sql = \"delete from foo where id<>%s\" % id\n    db.session.execute(sql)\n    \n#     db.session.execute(\"select sleep(0.2)\")\n\n    if db.session.query(Foo).filter_by(id=id).count() != 1:\n        abort(500)\n    \n    db.session.rollback()\n    \n    try:\n        thread_name = threading.current_thread().name\n    except:\n        thread_name = \"No_thread\"\n    try:\n        pid = str(os.getpid())\n    except:\n        pid = \"no_pid\"\n    \n    processed_by = \"%s-%s\" % (pid, thread_name)\n\n    x = foo.serialize\n    x['http_by_proc-thread'] = processed_by\n    return jsonify(x)\n\n\n@api_views.route('/foo/<int:id>/', methods = ['GET'])\ndef get_foo(id):\n    db.session().execute(\"select sleep(0.5)\")\n    foo = Foo.query.filter_by(id=id).first()\n    if foo == None:\n        abort(404)\n    return jsonify(foo.serialize)\n\n@api_views.route('/foo/', methods = ['POST'])\ndef make_foo():\n    try:\n        thread_name = threading.current_thread().name\n    except:\n        thread_name = \"No_thread\"\n    try:\n        pid = str(os.getpid())\n    except:\n        pid = \"no_pid\"\n    \n    processed_by = \"%s-%s\" % (pid, thread_name)\n\n    r = request.get_json()\n\n    if 'title' not in r:\n        d = {'reason': 'title not set',\n             'http_by_proc-thread': processed_by,\n             }\n        return jsonify(d), 400\n\n    d = {   'title' : r['title'],\n            'proc_id' : str(os.getpid()),\n            'thread_name' : threading.current_thread().name,\n    }\n\n    foo = Foo(**d)\n    db.session.add(foo)\n    db.session.commit()\n    d['id'] = foo.id\n    d['success'] = True\n    d['http_by_proc-thread'] = processed_by\n    return jsonify(d)\n","repo_name":"caffeinate/test-pylot","sub_path":"FlaskGunicornSqlAlchemy/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2641,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"2793687147","text":"for tc in range(1,1+int(input())):\n    n,a,b= list(map(int, input().split()))\n    minV = 987654321\n    sum =0\n    n1 = int(n**0.5)\n    for r in range(n1,n+1):\n        for c in range(1,n1+1):\n            if r * c <= n:\n                sum = a*abs(r-c) + b*(n-(r*c))\n                if sum < minV:\n                    minV = sum\n\n    print(\"#{} {}\".format(tc,minV))\n\n\n","repo_name":"silverjjj/algorithm","sub_path":"SW Expert Academy/SWex1491.py","file_name":"SWex1491.py","file_ext":"py","file_size_in_byte":366,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"74121346706","text":"# https://atcoder.jp/contests/arc019/submissions/14270400\n# B - こだわりの名前\nimport sys\n\nsys.setrecursionlimit(10 ** 7)\nf_inf = float('inf')\nmod = 10 ** 9 + 7\n\n\ndef resolve():\n    A = input()\n    n = len(A)\n\n    cnt = 0 if n % 2 == 0 else 1\n    for i in range(n // 2):\n        if A[i] == A[-(i + 1)]:\n            cnt += 2\n\n    if cnt == n:\n        if n % 2 != 0:\n            res = 25 * (n - 1)\n        else:\n            res = 25 * n\n    else:\n        if (n - cnt) // 2 == 1:\n            res = 25 * n - (n - cnt)\n        else:\n            res = 25 * n\n\n    print(res)\n\n\nif __name__ == '__main__':\n    resolve()\n","repo_name":"happa64/AtCoder_Beginner_Contest","sub_path":"ARC/ARC019/ARC019-B.py","file_name":"ARC019-B.py","file_ext":"py","file_size_in_byte":619,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"30201513564","text":"\"\"\"add_last_ar_year\n\nRevision ID: 11a256335c79\nRevises: 738437025c05\nCreate Date: 2021-01-21 11:02:47.150439\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import postgresql\n\n# revision identifiers, used by Alembic.\nrevision = '11a256335c79'\ndown_revision = '738437025c05'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('businesses', sa.Column('last_ar_year', sa.Integer(), nullable=True))\n    op.add_column('businesses_version', sa.Column('last_ar_year', sa.Integer(), nullable=True))\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_column('businesses', 'last_ar_year')\n    op.drop_column('businesses_version', 'last_ar_year')\n    # ### end Alembic commands ###\n","repo_name":"bcgov/lear","sub_path":"legal-api/migrations/versions/11a256335c79_add_last_ar_year.py","file_name":"11a256335c79_add_last_ar_year.py","file_ext":"py","file_size_in_byte":870,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"48"}
+{"seq_id":"32545001064","text":"import pygame\n\nclass NavePrincipal(pygame.sprite.Sprite):\n    def __init__(self) -> None:\n        super().__init__()\n        self.image = pygame.image.load(\"imagenes/nave_espacial3.png\")\n        self.image = pygame.transform.scale(self.image, (70,70))\n        self.rect = self.image.get_rect()\n        self.rect.centery = 740\n        self.rect.centerx = 400\n        self.vida = 3\n        self.score = 0\n    def update(self):\n        lista_teclas = pygame.key.get_pressed()\n        if lista_teclas[pygame.K_LEFT] and self.rect.left > 0:\n            self.rect.centerx -= 6\n        if lista_teclas[pygame.K_RIGHT] and self.rect.right < 800:          \n            self.rect.centerx += 6\n\n\n    \n\n   \n        \n\n\n\n\n","repo_name":"LucasCarmagnola/Galaxia","sub_path":"SEGUNDO PARCIAL LABO/nave_principal.py","file_name":"nave_principal.py","file_ext":"py","file_size_in_byte":708,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"32402630191","text":"import nltk\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport re\nfrom glob import glob\nimport os\n\n\ndef Merger(path):\n    speeches = []\n    years = []\n    countries = []\n    sessions = []\n    years_cleaned = []\n    \n    folders = os.listdir(path)\n    for root, dirs, files in os.walk(path):\n        for name in files:\n            if name.endswith('.txt'):\n                with open(os.path.join(root, name), 'r', encoding='utf-8') as f:\n                    data = f.read()\n                    try:\n                        speeches.append(data)\n                    except:\n                        speeches.append(np.nan)\n                countries.append(name.split('_')[0])\n                years.append(name.split('_')[-1])\n                sessions.append(name.split('_')[1])\n                \n    for year in years:\n        years_cleaned.append(year.replace('.txt', ''))\n        \n    dic = {\n            'Year': years_cleaned,\n            'Session': sessions,\n            'Country': countries, \n            'Speech': speeches\n            }\n\n    df = pd.DataFrame(dic)\n    return df","repo_name":"Christine-Z/Twenty_years_UN_speeches_analysis","sub_path":"Code/Merger.py","file_name":"Merger.py","file_ext":"py","file_size_in_byte":1107,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"24151797717","text":"# Uses python3\n\ndef max_gold(W, n, arr_w):\n    ## assigning value to each gold bar. 1kg gold bar has value $1\n    ## value is same as weight since more the gold more the value\n#     arr_v = arr_w\n    \n    value_table = [[0 for j in range(W + 1)] for i in range(n + 1)]\n    \n    for i in range(1, n + 1):\n        \n        for w in range(1, W + 1):\n            \n            value_table[i][w] = value_table[i-1][w]\n            \n            if arr_w[i-1] <= w:\n            \n                val = value_table[i-1][w - arr_w[i-1]] + arr_w[i-1]\n                \n                if val > value_table[i][w]:\n                    value_table[i][w] = val\n            \n    \n    \n    return value_table[n][W]\n\nif __name__ == '__main__':\n    W, n = map(int, input().split())\n    arr_w = list(map(int, input().split()))\n    max_val = max_gold(W, n, arr_w)\n    print(max_val)\n","repo_name":"tarunkukreja003/Algorithmic-Toolbox-Coursera-Solutions","sub_path":"feAw7ZC0SHOgMO2QtLhz8g_5e2cf32276ab4d838e5a2be642f85567_course1_2001022001/week6_dynamic_programming2/1_maximum_amount_of_gold/knapsack.py","file_name":"knapsack.py","file_ext":"py","file_size_in_byte":859,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"74402359825","text":"#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n\r\n# @Author  : mofei\r\n# @Time    : 2018/9/12 16:44\r\n# @File    : comment_count.py\r\n# @Software: PyCharm\r\n\r\n# 同步article的comment_count字段\r\nfrom blog.models import Article\r\n\r\nfor a in Article.objects.all():\r\n    a.comment_count = a.comment_set.count()\r\n    for c in a.comment_set.all():\r\n        a.comment_count += c.reply_set.count()\r\n    a.save()","repo_name":"mofei952/blogproject","sub_path":"blog/scripts/comment_count.py","file_name":"comment_count.py","file_ext":"py","file_size_in_byte":402,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"188524370","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        ('stock', '0005_auto_20150210_1413'),\n    ]\n\n    operations = [\n        migrations.AlterField(\n            model_name='matos',\n            name='date_ajout',\n            field=models.DateTimeField(auto_now_add=True, verbose_name=b\"Date d'ajout\"),\n            preserve_default=True,\n        ),\n        migrations.AlterField(\n            model_name='matos',\n            name='date_modif',\n            field=models.DateTimeField(auto_now=True, verbose_name=b'Date de modification'),\n            preserve_default=True,\n        ),\n    ]\n","repo_name":"stakha/regietool","sub_path":"stock/migrations/0006_auto_20150210_2220.py","file_name":"0006_auto_20150210_2220.py","file_ext":"py","file_size_in_byte":709,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"10824348589","text":"################################################################################\n# Get TF-IDF matrix\n################################################################################\n\n# Load the necessary packages\nimport pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.feature_extraction.text import TfidfVectorizer\n\nclean_data = pd.read_csv('/Volumes/Elements/Text/nlp_cleaned_data.csv')\n\n# Recode flair labels to avoid doubling up on flairs \nclean_data.replace(':CENTG: - Centrist','centrist', inplace=True)\nclean_data.replace(':centrist: - Centrist','centrist', inplace=True)\nclean_data.replace(':centrist: - Grand Inquisitor','centrist', inplace=True)\nclean_data.replace(':left: - Left', 'left', inplace=True)\nclean_data.replace(':libright: - LibRight', 'libright', inplace=True)\nclean_data.replace(':libright2: - LibRight', 'libright', inplace=True)\nclean_data.replace(':right: - Right',  'right', inplace=True)\nclean_data.replace(':libleft: - LibLeft', 'libleft', inplace=True)\nclean_data.replace(':lib: - LibCenter', 'libcenter', inplace=True)\nclean_data.replace(':auth: - AuthCenter','authcenter', inplace=True)\nclean_data.replace(':authleft: - AuthLeft','authleft', inplace=True)\nclean_data.replace(':authright: - AuthRight','authright', inplace=True)\n\n\n# Assign features and response appropriately (for TF-IDF we use the cleaned comments)\nX = clean_data['comment']\ny = clean_data['user.flair']\n\n# Ensure data is a string\nX = X.apply(lambda x: np.str_(x))\n\n# Split data into 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# Set up TF-IDF object to create vocab, count and transform data to TF-IDF features\ntf_idf_vec = TfidfVectorizer(smooth_idf = True, use_idf = True,  max_df = 0.9, min_df = 0.01, max_features = 10000)\n\ntf_idf_vec.fit(X_train)\n\nmatrix = tf_idf_vec.transform(X)\ndf = pd.DataFrame(matrix.toarray(), columns = tf_idf_vec.get_feature_names())\n\ndf = pd.concat([y, df], axis=1)\ndf.to_csv('/Volumes/Elements/Text/tf_idf_matrix.csv')\n","repo_name":"MichaelKitchener98/RedditIdeology-Honours2021","sub_path":"EDA/create_tf_idf_viz_data.py","file_name":"create_tf_idf_viz_data.py","file_ext":"py","file_size_in_byte":2078,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"998658286","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\n\n\nclass AdversarialNorm2d(nn.Module):\n    def __init__(self, num_features, model_params):\n        super().__init__()\n        self.f_num = np.prod(num_features)\n\n        self.clf = nn.Sequential()\n        self.clf.add_module('conv1', nn.Conv2d(self.f_num, model_params['advers_f_num'], 3))\n        self.clf.add_module('conv1_act', nn.LeakyReLU(0.1, inplace=True))\n        self.clf.add_module('conv2', nn.Conv2d(model_params['advers_f_num'], model_params['advers_f_num'], 3))\n        self.clf.add_module('conv2_act', nn.LeakyReLU(0.1, inplace=True))\n        self.clf.add_module('pool', nn.AdaptiveAvgPool2d(1))\n        self.head = nn.Linear(model_params['advers_f_num'], 1)\n        \n        \n    def forward(self, x):\n        \n        x = self.clf(x)\n        x = self.head(x.squeeze(2).squeeze(2))\n        return x\n        \n\nclass Adversarial_VGGLike(nn.Module):\n    def __init__(self, model_params):\n        super().__init__()\n        self.elu_alpha = model_params['elu_alpha']\n        \n        self.conv1 = nn.Conv2d(3, model_params['filters'][0], kernel_size=(3,3), padding=(0,0), stride=(1,1))\n        self.conv2 = nn.Conv2d(model_params['filters'][0], model_params['filters'][1], kernel_size=(3,3), padding=(0,0), stride=(1,1))\n        self.conv3 = nn.Conv2d(model_params['filters'][1], model_params['filters'][2], kernel_size=(3,3), padding=(0,0), stride=(1,1))\n        self.conv4 = nn.Conv2d(model_params['filters'][2], model_params['filters'][3], kernel_size=(3,3), padding=(0,0), stride=(1,1))\n        self.conv5 = nn.Conv2d(model_params['filters'][3], model_params['filters'][4], kernel_size=(3,3), padding=(0,0), stride=(1,1))\n        self.conv6 = nn.Conv2d(model_params['filters'][4], model_params['filters'][5], kernel_size=(3,3), padding=(0,0), stride=(1,1))\n\n        self.adv_norm1 = AdversarialNorm2d((8), model_params)\n        self.adv_norm2 = AdversarialNorm2d((12), model_params)\n        self.adv_norm3 = AdversarialNorm2d((20), model_params)\n        self.adv_norm4 = AdversarialNorm2d((28), model_params)\n        \n        \n        self.clf = nn.Sequential()\n        self.clf.add_module(\n            'lin', nn.Linear(model_params['filters'][-1], model_params['filters'][-1])\n            )\n        self.clf.add_module(\n            'lin_actv',\n            nn.ELU(model_params['elu_alpha'])\n            )\n        self.clf.add_module(\n            'clf', nn.Linear(model_params['filters'][-1], model_params['classes_nb'])\n            )\n    \n    \n    def forward(self, x, typeof_forward=0, d_count=None):\n        \"\"\"\n            typeof_forward (int):\n                0 - forward without adversarial (usefull for validation model)\n                1 - forward only generator\n                2 - forward only adversarial\n                3 - forward everything\n            \n        \"\"\"\n\n        if typeof_forward == 0:\n            x = F.elu(self.conv1(x), alpha=self.elu_alpha)\n            x = F.elu(self.conv2(x), alpha=self.elu_alpha)\n            x = F.max_pool2d(x, 2)\n            x = F.elu(self.conv3(x), alpha=self.elu_alpha)\n            x = F.elu(self.conv4(x), alpha=self.elu_alpha)\n            x = F.max_pool2d(x, 2)\n            x = F.elu(self.conv5(x), alpha=self.elu_alpha)\n            x = F.elu(self.conv6(x), alpha=self.elu_alpha)\n            x = x.view(x.shape[0], -1)\n            x = self.clf(x)\n            \n            return x\n\n        elif typeof_forward == 1:\n            x = self.conv1(x)\n            x1 = F.elu(x, alpha=self.elu_alpha)\n\n            x = self.conv2(x1)\n            x2 = F.elu(x, alpha=self.elu_alpha)\n\n            x = F.max_pool2d(x2, 2)\n\n            x = self.conv3(x)\n            x3 = F.elu(x, alpha=self.elu_alpha)\n\n            x = self.conv4(x3)\n            x4 = F.elu(x, alpha=self.elu_alpha)  \n\n            return [x1, x2, x3, x4]\n        \n        elif typeof_forward == 2:\n            x1, x2, x3, x4 = x\n\n            z1 = self.adv_norm1(x1)\n            if d_count == 1:\n                return z1\n            z2 = self.adv_norm2(x2)\n            if d_count == 2:\n                return z2\n            z3 = self.adv_norm3(x3)\n            if d_count == 3:\n                return z3\n            z4 = self.adv_norm4(x4)\n            if d_count == 4:\n                return z4\n            \n            return [z1, z2, z3, z4]\n\n        elif typeof_forward == 3:\n            x = self.conv1(x)\n            z1 = self.adv_norm1(x)\n            x = F.elu(x, alpha=self.elu_alpha)\n\n            x = self.conv2(x)\n            z2 = self.adv_norm2(x)\n            x = F.elu(x, alpha=self.elu_alpha)\n\n            x = F.max_pool2d(x, 2)\n\n            x = self.conv3(x)\n            z3 = self.adv_norm3(x)\n            x = F.elu(x, alpha=self.elu_alpha)\n\n            x = self.conv4(x)\n            z4 = self.adv_norm4(x)\n            x = F.elu(x, alpha=self.elu_alpha)\n\n            x = F.max_pool2d(x, 2)\n\n            x = self.conv5(x)\n            x = F.elu(x, alpha=self.elu_alpha)\n\n            x = self.conv6(x)\n            x = F.elu(x, alpha=self.elu_alpha)\n\n            x = x.view(x.shape[0], -1)        \n            x = self.clf(x)\n            \n            return x, [z1, z2, z3, z4]","repo_name":"7rick03ligh7/Adversarial_Normalization","sub_path":"src/models/ad_model.py","file_name":"ad_model.py","file_ext":"py","file_size_in_byte":5194,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"17739389087","text":"import logging\nimport os\nimport time\n\nimport numpy as np\nfrom tqdm import tqdm\nimport os\n\nimport torch\nimport torch.nn as nn\nimport torch.distributed as dist\nfrom torch.nn import functional as F\n\nfrom lib.utils.utils import AverageMeter\nfrom lib.utils.utils import get_confusion_matrix\nfrom lib.utils.utils import adjust_learning_rate\nfrom lib.utils.utils import get_world_size, get_rank\n\n\n\ndef reduce_tensor(inp):\n    \"\"\"\n    Reduce the loss from all processes so that \n    process with rank 0 has the averaged results.\n    \"\"\"\n    world_size = get_world_size()\n    if world_size < 2:\n        return inp\n    with torch.no_grad():\n        reduced_inp = inp\n        dist.reduce(reduced_inp, dst=0)\n    return reduced_inp\n\ndef train(config, epoch, num_epoch, epoch_iters, base_lr, num_iters,\n         trainloader, optimizer, model, writer_dict, final_output_dir):\n    \n    # Training\n    model.train()\n    batch_time = AverageMeter()\n    ave_loss = AverageMeter()\n    tic = time.time()\n    cur_iters = epoch*epoch_iters\n    writer = writer_dict['writer']\n    global_steps = writer_dict['train_global_steps']\n    world_size = get_world_size()\n\n    for i_iter, (images, labels, qtable) in enumerate(trainloader):\n        # images, labels, _, _ = batch\n        images = images.cuda()\n        labels = labels.long().cuda()\n\n        losses, _ = model(images, labels, qtable)  # _ : output of the model (see utils.py)\n        loss = losses.mean()\n\n        reduced_loss = reduce_tensor(loss)\n\n        model.zero_grad()\n        loss.backward()\n        optimizer.step()\n\n        # measure elapsed time\n        batch_time.update(time.time() - tic)\n        tic = time.time()\n\n        # update average loss\n        ave_loss.update(reduced_loss.item())\n\n        lr = adjust_learning_rate(optimizer,\n                                  base_lr,\n                                  num_iters,\n                                  i_iter+cur_iters)\n\n        if i_iter % config.PRINT_FREQ == 0:\n            print_loss = ave_loss.average() / world_size\n            msg = 'Epoch: [{}/{}] Iter:[{}/{}], Time: {:.2f}, ' \\\n                  'lr: {:.6f}, Loss: {:.6f}' .format(\n                      epoch, num_epoch, i_iter, epoch_iters, \n                      batch_time.average(), lr, print_loss)\n            logging.info(msg)\n            \n            writer.add_scalar('train_loss', print_loss, global_steps)\n            global_steps += 1\n            writer_dict['train_global_steps'] = global_steps\n\n\ndef validate(config, testloader, model, writer_dict, valid_set=\"valid\"):\n    \n    rank = get_rank()\n    world_size = get_world_size()\n    model.eval()\n    ave_loss = AverageMeter()\n    confusion_matrix = np.zeros(\n        (config.DATASET.NUM_CLASSES, config.DATASET.NUM_CLASSES))\n    avg_mIoU = AverageMeter()\n    avg_p_mIoU = AverageMeter()\n\n    with torch.no_grad():\n        for _, (image, label, qtable) in enumerate(tqdm(testloader)):\n            size = label.size()\n            image = image.cuda()\n            label = label.long().cuda()\n\n            losses, pred = model(image, label, qtable)\n            pred = F.upsample(input=pred, size=(\n                        size[-2], size[-1]), mode='bilinear')\n            loss = losses.mean()\n            reduced_loss = reduce_tensor(loss)\n            ave_loss.update(reduced_loss.item())\n\n            current_confusion_matrix = get_confusion_matrix(\n                label,\n                pred,\n                size,\n                config.DATASET.NUM_CLASSES,\n                config.TRAIN.IGNORE_LABEL)\n            confusion_matrix += current_confusion_matrix\n            # mIoU\n            pos = current_confusion_matrix.sum(1)  # ground truth label count\n            res = current_confusion_matrix.sum(0)  # prediction count\n            tp = np.diag(current_confusion_matrix)  # Intersection part\n            IoU_array = (tp / np.maximum(1.0, pos + res - tp))  # Union part\n            mean_IoU = IoU_array.mean()\n            avg_mIoU.update(mean_IoU)\n            TN = current_confusion_matrix[0, 0]\n            FN = current_confusion_matrix[1, 0]\n            FP = current_confusion_matrix[0, 1]\n            TP = current_confusion_matrix[1, 1]\n            p_mIoU = 0.5 * (FN / np.maximum(1.0, FN + TP + TN)) + 0.5 * (FP / np.maximum(1.0, FP + TP + TN))\n            avg_p_mIoU.update(np.maximum(mean_IoU, p_mIoU))\n\n    confusion_matrix = torch.from_numpy(confusion_matrix).cuda()\n    reduced_confusion_matrix = reduce_tensor(confusion_matrix)\n\n    confusion_matrix = reduced_confusion_matrix.cpu().numpy()\n    pos = confusion_matrix.sum(1)\n    res = confusion_matrix.sum(0)\n    tp = np.diag(confusion_matrix)\n    pixel_acc = tp.sum() / pos.sum()\n    mean_acc = (tp / np.maximum(1.0, pos)).mean()\n    IoU_array = (tp / np.maximum(1.0, pos + res - tp))\n    mean_IoU = IoU_array.mean()\n    print_loss = ave_loss.average()/world_size\n\n    if rank == 0:\n        writer = writer_dict['writer']\n        global_steps = writer_dict['valid_global_steps']\n        writer.add_scalar(valid_set+'_loss', print_loss, global_steps)\n        writer.add_scalar(valid_set+'_mIoU', mean_IoU, global_steps)\n        writer.add_scalar(valid_set+'_avg_mIoU', avg_mIoU.average(), global_steps)\n        writer.add_scalar(valid_set+'_avg_p-mIoU', avg_p_mIoU.average(), global_steps)\n        writer.add_scalar(valid_set+'_pixel_acc', pixel_acc, global_steps)\n        writer_dict['valid_global_steps'] = global_steps + 1\n    return print_loss, mean_IoU, avg_mIoU.average(), avg_p_mIoU.average(), IoU_array, pixel_acc, mean_acc, confusion_matrix\n\n","repo_name":"mjkwon2021/CAT-Net","sub_path":"lib/core/function.py","file_name":"function.py","file_ext":"py","file_size_in_byte":5544,"program_lang":"python","lang":"en","doc_type":"code","stars":160,"dataset":"github-code","pt":"48"}
+{"seq_id":"7933728455","text":"from selenium import webdriver\nimport codecs\n\nWEBPAGE_IDENTIFIER = '@misc'\n\n\ndef get_urls():\n    with codecs.open('references.bib', 'r', encoding='utf8') as ref:\n        line = ref.readline()\n        cnt = 1\n        urls = []\n        while line:\n            if not line.strip().startswith(WEBPAGE_IDENTIFIER):\n                line = ref.readline()\n                continue\n            else:\n                while line:\n                    line = ref.readline()\n                    if not line.strip().startswith('url'):\n                        continue\n                    else:\n                        url = line.replace('url', '').replace('=', '').replace('{', '').replace('}', '') \\\n                            .replace(',', '').strip()\n                        if url.endswith('/'):\n                            url = url[:-1]\n                        urls.append(url)\n                        break\n                line = ref.readline()\n            cnt += 1\n    return urls\n\n\ndef capture_webpage(url: str):\n    print(\"capturing:\", url)\n    pic_name = url.replace('http://', '').replace('https://', '').replace('/', '-').replace('www.', '') \\\n                   .replace('.html', '') + \".png\"\n    options = webdriver.ChromeOptions()\n    options.headless = True\n    driver = webdriver.Chrome(options=options)\n    driver.get(url)\n    s = lambda x: driver.execute_script('return document.body.parentNode.scroll' + x)\n    driver.set_window_size(s('Width'), s(\n        'Height'))  # May need manual adjustment\n    driver.find_element_by_tag_name('body').screenshot(pic_name)\n    driver.quit()\n    print(\"saved:\", pic_name)\n\n\nif __name__ == '__main__':\n    urls = get_urls()\n    print(urls)\n    for url in urls:\n        capture_webpage(url)\n","repo_name":"sadoMasupilami/bibtex2png","sub_path":"capturer.py","file_name":"capturer.py","file_ext":"py","file_size_in_byte":1735,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"18711751385","text":"from django.db import models\nfrom django.db.models.signals import pre_save, post_save\nfrom django.dispatch import receiver\nfrom R4C.task import send_email\n\n\nclass Robot(models.Model):\n    serial = models.CharField(max_length=5, blank=True, null=True)\n    model = models.CharField(max_length=2)\n    version = models.CharField(max_length=2)\n    created = models.DateTimeField()\n\n\n@receiver(pre_save, sender=Robot)\ndef robot_pre_save(sender, instance: Robot, **kwargs):\n    instance.model = instance.model.upper()\n    instance.version = instance.version.upper()\n    instance.serial = f\"{instance.model}-{instance.version}\"\n\n\n@receiver(post_save, sender=Robot)\ndef order_post_save(sender, instance: Robot, **kwargs):\n    from orders.models import Order\n\n    orders = Order.objects.filter(robot_serial=instance.serial, is_active=True)\n    for order in orders:\n        subject = f\"Заказ для робота {order.robot_serial}\"\n        message = f\"Добрый день!\\nНедавно вы интересовались нашим роботом модели {instance.model}, версии {instance.serial}. Этот робот теперь в наличии. Если вам подходит этот вариант - пожалуйста, свяжитесь с нами.\"\n        recipient_list = order.customer.email\n\n        send_email.delay(subject, message, recipient_list)\n        order = Order.objects.get(pk=order.id)\n        order.is_active = False\n        order.save()\n","repo_name":"Nooumakk/Robots-for-consumers","sub_path":"robots/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1482,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"37074571019","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom layers import GraphConvolution, GraphAttentionLayer\nfrom torch.nn.init import kaiming_uniform_, xavier_uniform_\n\n\n\n\nclass GCN(nn.Module):\n    def __init__(self, nfeat, nhid, nclass, nedge, dropout):\n        super(GCN, self).__init__()\n        self.gc1 = GraphConvolution(nfeat, nhid)\n        self.gc2 = GraphConvolution(nhid, nclass)\n        self.dropout = dropout\n        self.linear = nn.Linear(nclass*3+nedge, nclass)\n        kaiming_uniform_(self.linear.weight, nonlinearity='relu')\n\n    def forward(self, x, adj, tx, tid):\n        x = F.relu(self.gc1(x, adj))\n        x = F.dropout(x, self.dropout, training=self.training)\n        x = self.gc2(x, adj)\n\n        ex_uin = tid[:, :, 0].unsqueeze(2).repeat(1, 1, x.shape[2]).long()\n        bee_uin = tid[:, :, 1].unsqueeze(2).repeat(1, 1, x.shape[2]).long()\n        frc_uin = tid[:, :, 2].unsqueeze(2).repeat(1, 1, x.shape[2]).long()\n        ex_x = torch.gather(x, 1, ex_uin)\n        bee_x = torch.gather(x, 1, bee_uin)\n        frc_x = torch.gather(x, 1, frc_uin)\n\n        x = torch.cat((ex_x, bee_x, frc_x, tx), dim=2).squeeze()\n        x = F.relu(self.linear(x))\n\n        return F.log_softmax(x, dim=1)\n\n\nclass GAT(nn.Module):\n    def __init__(self, nfeat, nhid, nclass, nedge, dropout, alpha=0.2, nheads=4):\n        \"\"\"Dense version of GAT.\"\"\"\n        super(GAT, self).__init__()\n        self.dropout = dropout\n\n        self.attentions = [GraphAttentionLayer(nfeat, nhid, dropout=dropout, alpha=alpha, concat=True) for _ in range(nheads)]\n        for i, attention in enumerate(self.attentions):\n            self.add_module('attention_{}'.format(i), attention)\n\n        self.out_att = GraphAttentionLayer(nhid * nheads, nclass, dropout=dropout, alpha=alpha, concat=False)\n        self.linear = nn.Linear(nclass*3+nedge, nclass)\n        kaiming_uniform_(self.linear.weight, nonlinearity='relu')\n\n    def forward(self, x, adj, tx, tid):\n        x = F.dropout(x, self.dropout, training=self.training)\n        x = torch.cat([att(x, adj) for att in self.attentions], dim=2)\n        x = F.dropout(x, self.dropout, training=self.training)\n        x = F.elu(self.out_att(x, adj))\n\n        ex_uin = tid[:, :, 0].unsqueeze(2).repeat(1, 1, x.shape[2]).long()\n        bee_uin = tid[:, :, 1].unsqueeze(2).repeat(1, 1, x.shape[2]).long()\n        frc_uin = tid[:, :, 2].unsqueeze(2).repeat(1, 1, x.shape[2]).long()\n        ex_x = torch.gather(x, 1, ex_uin)\n        bee_x = torch.gather(x, 1, bee_uin)\n        frc_x = torch.gather(x, 1, frc_uin)\n\n        x = torch.cat((ex_x, bee_x, frc_x, tx), dim=2).squeeze()\n        x = F.relu(self.linear(x))\n\n        return F.log_softmax(x, dim=1)\n\n\n\nclass HeteroLayer(nn.Module):\n    def __init__(self, nfeat, nhid, dropout, alpha=0.2) -> None:\n        super().__init__()\n        self.dropout = dropout\n        self.gc1 = GraphAttentionLayer(nfeat, nhid, dropout=dropout, alpha=alpha, concat=True)\n        self.gc2 = GraphAttentionLayer(nhid, nhid, dropout=dropout, alpha=alpha, concat=True)\n\n    def forward(self, x, adj):\n        x = F.elu(self.gc1(x, adj))\n        x = F.dropout(x, self.dropout, training=self.training)\n        x = F.elu(self.gc2(x, adj))\n        return x\n\nclass NodeLayer1(nn.Module):\n    def __init__(self, nfeat, nout, dropout, device):\n        super().__init__()\n        self.nout = nout\n        self.complaintGat = HeteroLayer(nfeat, nout, dropout)\n        self.rev_complaintGat = HeteroLayer(nfeat, nout, dropout)\n        self.payGat = HeteroLayer(nfeat, nout, dropout)\n        self.rev_payGat = HeteroLayer(nfeat, nout, dropout)\n        self.cooperateGat = HeteroLayer(nfeat, nout, dropout)\n        self.rev_cooperateGat = HeteroLayer(nfeat, nout, dropout)\n        self.HeteroConv = [self.complaintGat, self.rev_complaintGat, self.payGat, self.rev_payGat, self.cooperateGat, self.rev_cooperateGat]\n        self.device = device\n\n    def forward(self, x, adj):\n        # x: [batch_size, n, nx]\n        # adj: [batch_size, 6, n, n, ne]\n        h = torch.FloatTensor([]).to(self.device)\n\n        for i in range(6):\n            et = adj[:, i, :, :].squeeze()\n            h = torch.cat((h, self.HeteroConv[i](x, et)), dim=2)\n\n        if torch.any(torch.isnan(h)):\n            print('self.HeteroConv', h)\n            exit()\n    \n        return h","repo_name":"zhengwen1106/MIDLG","sub_path":"baseline_GNN/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":4314,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"30380113277","text":"s = \"Microsoft libera atualização de emergência no  Windows para corrigir falha de segurança. Empresa enviou correção \" \\\n    \"para vulnerabilidade  conhecida como PrintNightmare, revelada há uma semana, para diversas versões do sistema  \" \\\n    \"operacional. Recomendação é instalação imediata.\"\n\nlowcase = s.lower()\nprint(lowcase)\n\nremoveCommaFullStop = s.replace(',', \"\"), s.replace('.', '')\nprint(removeCommaFullStop)\n\nsplit = s.split(' ')\nprint(split)\n\ncomprimento = len(s)\nprint(comprimento)\n\nss = \"maça pera uva\"\ndividir = ss.split(' ')\ndividir.reverse()\nprint(\" \".join(dividir))\n","repo_name":"Thiago-CSantos/Faculdade-Python","sub_path":"aula03_03/referenteExercicio9.py","file_name":"referenteExercicio9.py","file_ext":"py","file_size_in_byte":601,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"44569605034","text":"# Importamos librerías necesarias\nimport time\nimport digitalio\nimport board\nimport usb_hid\nfrom adafruit_hid.keyboard import Keyboard\nfrom adafruit_hid.keycode import Keycode\nfrom adafruit_hid.mouse import Mouse\n\n# Definimos pulsadores a utilizar y los ubicamos en los GPIO correspondientes\nbtn1_pin = board.GP15\nbtn2_pin = board.GP16\n\n# Comenzamos la emulación de teclado y mouse\nkeyboard = Keyboard(usb_hid.devices)\nmouse = Mouse(usb_hid.devices)\n\n# Usando el led ya soldado en la placa, lo prendo cuando haya actividad\nled = digitalio.DigitalInOut(board.GP25)\nled.direction = digitalio.Direction.OUTPUT\nled.value = False\n\n# Definimos el formato de los botones (por defecto, van a permanecer en 1 o HIGH ya que estan soldados entre el GPIO y GND)\nbtn1 = digitalio.DigitalInOut(btn1_pin)\nbtn1.direction = digitalio.Direction.INPUT\nbtn1.pull = digitalio.Pull.UP\n\nbtn2 = digitalio.DigitalInOut(btn2_pin)\nbtn2.direction = digitalio.Direction.INPUT\nbtn2.pull = digitalio.Pull.UP\n\n# Los efectos de los botones -> al ponerse en 0 o LOW se activa el proceso dentro del if\nwhile True:\n    if not(btn1.value):\n        print(\"Botón 1\")\n        mouse.move(y=600)\n        time.sleep(0.1)\n        mouse.move(y=-55)\n        time.sleep(0.1)\n        mouse.move(x=-1)\n        time.sleep(0.1)\n        mouse.move(x=1)\n        click = 500\n        while click > 0:\n            led.value = True\n            mouse.move(x=1)\n            mouse.click(Mouse.LEFT_BUTTON)\n            mouse.move(x=-1)\n            time.sleep(0.02)\n            click = click - 1\n        led.value = False\n    \n    if not(btn2.value):\n        print(\"Botón 2\")\n        mouse.move(y=600)\n        time.sleep(0.1)\n        mouse.move(y=-55)\n        time.sleep(0.1)\n        mouse.move(x=-1)\n        time.sleep(0.1)\n        mouse.move(x=1)\n        click = 1500\n        while click > 0:\n            led.value = True\n            mouse.move(x=1)\n            mouse.click(Mouse.LEFT_BUTTON)\n            mouse.move(x=-1)\n            time.sleep(0.02)\n            click = click - 1\n        led.value = False\n        \n    time.sleep(0.1)\n","repo_name":"TenoTrash/Ganando_al_Eggfarm","sub_path":"code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":2079,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"5794452235","text":"import random\nimport string\nimport json\nimport requests\nfrom datetime import datetime\nfrom django.http import HttpResponse, JsonResponse\nfrom apps.menu.models import *\nfrom apps.location.models import *\nfrom apps.account.models import *\nfrom django.contrib.auth.models import User\nfrom apps.citizen.models import Citizen\nfrom decouple import config\nimport telerivet\n\n\nclass TelerivetWrapper:\n    #BASE_URL   = \"http://127.0.0.1:8000/\"\n    BASE_URL   = \"https://jinadi.happen.co.tz/\"\n    API_KEY = 'MA9447RTQAZAT6MWZXX393D9KCU3HEUR'\n    PROJECT_ID = 'PJ219d5087c1ab3829'\n\n    def __init__(self):\n        self.headers = {\n            \"Authorization\": f\"Bearer {self.API_KEY}\",\n            \"Content-Type\": \"application/json\",\n        }\n        self.API_URL = self.BASE_URL\n\n\n    def call_init_menu(self, **kwargs):\n        \"\"\" Init First Menu \"\"\"\n\n        \"\"\"args\"\"\"\n        phone        = kwargs['phone']\n        key          = kwargs['key']\n        designation  = kwargs['designation']\n\n        \"\"\"menu\"\"\"\n        if key.upper() == 'MTENDAJI':\n            menu = Menu.objects.get(flag=\"Mtendaji_Name\") \n        elif key.upper() == 'MWENYEKITI':\n            menu = Menu.objects.get(flag=\"Mwenyekiti_Name\")\n        elif key.upper() == 'MJUMBE':\n            menu = Menu.objects.get(flag=\"Mjumbe_Name\")\n        elif key.upper() == 'MWANANCHI':\n            menu = Menu.objects.get(flag='Mwananchi_Name')\n        elif key.upper() == 'JEMBE':\n            if designation == 'MWENYEKITI':\n                menu = Menu.objects.get(flag=\"Jembe_Mwenyekiti_Mwanzo\")\n            elif designation == 'MTENDAJI':\n                menu = Menu.objects.get(flag=\"Jembe_Mtendaji_Mwanzo\")    \n            elif designation == 'MWANANCHI' or designation == 'MJUMBE':\n                menu = Menu.objects.get(flag=\"Jembe_Mwananchi_Mwanzo\")  \n\n        \"\"\"random code\"\"\"\n        uuid = ''.join(random.choices(string.ascii_uppercase, k=12))\n\n        \"\"\"Create menu session\"\"\"\n        session_id = self.create_menu_session(phone=phone, menu_id=menu.id, uuid=uuid)\n\n        message = self.process_menu(menu.id, phone, uuid, designation)\n\n        return message\n\n    def call_registration_menu(self, **kwargs):\n        \"\"\"Registration Menu\"\"\"\n\n        \"\"\"args\"\"\"\n        phone = kwargs['phone']\n        key   = kwargs['key']\n\n        \"\"\"random code\"\"\"\n        uuid = ''.join(random.choices(string.ascii_uppercase, k=12))\n\n        \"\"\"menu\"\"\"\n        if key.upper() == 'MTENDAJI':\n            menu = Menu.objects.get(flag=\"Mtendaji_Name\")\n            designation = \"MTENDAJI\"\n        elif key.upper() == 'MWENYEKITI':\n            menu = Menu.objects.get(flag=\"Mwenyekiti_Name\")\n            designation = \"MWENYEKITI\"   \n        elif key.upper() == 'MJUMBE':\n            menu = Menu.objects.get(flag=\"Mjumbe_Name\")\n            designation = \"MJUMBE\"\n        elif key.upper() == 'MWANANCHI':\n            menu = Menu.objects.get(flag='Mwananchi_Name')\n            designation = \"MWANANCHI\"\n\n        \"\"\"Create new temporary data\"\"\"\n        citizen = Citizen()\n        citizen.phone = phone \n        citizen.designation = designation \n        citizen.save()\n\n        \"\"\"create new session\"\"\"\n        session_id = self.create_menu_session(phone=phone, menu_id=menu.id, uuid=uuid)\n\n        \"\"\"process menu\"\"\"\n        message = self.process_menu(menu.id, phone, uuid, designation)\n\n        return message\n\n\n    def check_menu_link(self, menu_id, key):\n        \"\"\"Check Menu Link\"\"\"\n        validate = self.validate_pull(menu_id, key)\n\n        if validate == 'VALID':\n            sub_menu = SubMenu.objects.filter(menu_id=menu_id)\n\n            if sub_menu.count() > 0:\n                sub_menu_key = SubMenu.objects.filter(menu_id=menu_id, view_id=key).first()\n\n                if (sub_menu_key):\n                    menu_link = MenuLink.objects.filter(\n                        menu_id=menu_id, sub_menu_id=sub_menu_key.id)\n\n                    if(menu_link):\n                        return 'NEXT_MENU'\n                    else:\n                        return 'INVALID_INPUT'\n                else:\n                    return 'INVALID_INPUT'       \n            else:\n                menu_link = MenuLink.objects.filter(menu_id=menu_id)\n\n                if(menu_link):\n                    return 'NEXT_MENU'\n                else:\n                    return 'END_MENU'\n        else:\n            return 'INVALID_INPUT'; \n\n\n    def validate_pull(self, menu_id, key):\n        \"\"\"Validate pull information\"\"\"\n        menu = Menu.objects.filter(pk=menu_id).first()\n\n        if(menu):\n            if menu.pull == 1:\n                if menu.url == 'wards/':\n                    \"\"\"validate wards\"\"\"\n                    ward = Ward.objects.filter(pk=key)\n\n                    if(ward.count() > 0):\n                        return 'VALID'\n                    else:\n                        return 'INVALID' \n\n                if menu.url == 'villages/':\n                    \"\"\"validate village\"\"\"\n                    village = Village.objects.filter(pk=key)\n\n                    if(village.count() > 0):\n                        return 'VALID'\n                    else:\n                        return 'INVALID'\n            else:\n                return 'VALID'                              \n        else:\n            return 'INVALID' \n\n\n    def next_menu(self, **kwargs):\n        \"\"\"Triggering Next Menu\"\"\"\n\n        \"\"\"args\"\"\"\n        phone       = kwargs['phone']\n        uuid        = kwargs['uuid']\n        menu_id     = kwargs['menu_id']\n        key         = kwargs['key']\n        designation = kwargs['designation']\n\n        \"\"\"action\"\"\"\n        action = None\n\n        \"\"\"sub menu\"\"\"\n        sub_menu_key = SubMenu.objects.filter(menu_id=menu_id, view_id=key)\n\n        message = \"\"\n        if (sub_menu_key):\n            menu_link = MenuLink.objects.filter(\n                menu_id=menu_id, sub_menu_id=sub_menu_key[0].id)\n\n            if(menu_link):\n                \"\"\"create menu session\"\"\"\n                session_id = self.create_menu_session(phone=phone,menu_id=menu_link[0].link_id,uuid=uuid)\n\n                \"\"\"process menu\"\"\"\n                message = self.process_menu(menu_link[0].link_id, phone, uuid, designation) \n\n                \"\"\"menu action\"\"\"\n                menu = Menu.objects.get(pk=menu_link[0].link_id)\n\n                if menu.action is not None:\n                    action = menu.action           \n            else:\n                \"\"\"message\"\"\"\n                message = \"Invalid input\"\n        else:\n            menu_link = MenuLink.objects.filter(menu_id=menu_id)\n\n            if(menu_link):\n                \"\"\"create menu session\"\"\"\n                session_id = self.create_menu_session(phone=phone,menu_id=menu_link[0].link_id,uuid=uuid)\n\n                \"\"\"process menu\"\"\"\n                message = self.process_menu(menu_link[0].link_id, phone, uuid, designation)\n\n                \"\"\"menu action\"\"\"\n                menu = Menu.objects.get(pk=menu_link[0].link_id)\n\n                if menu.action is not None:\n                    action = menu.action\n            else:\n                \"\"\"message\"\"\"\n                message = \"Invalid input\"\n\n        return JsonResponse({'status': 'success', 'message': message, 'action': action})\n\n    def process_menu(self, menu_id, phone, uuid, designation):\n        \"\"\"Process Menu\"\"\"\n        message = \"\"\n\n        menu = Menu.objects.get(pk=menu_id)\n        message = menu.title\n\n        if menu.flag == 'Mwananchi_Verify_ID':\n            menu_session = MenuSession.objects.filter(flag=\"Mwananchi_ID_Number\", phone=phone).last()\n\n            if menu_session:\n                message = message.replace(\"ID_Number\", menu_session.values)\n\n        if menu.flag == 'Mtendaji_Verify_ID':\n            menu_session = MenuSession.objects.filter(flag=\"Mtendaji_ID_Number\", phone=phone).last()\n\n            if menu_session:\n                message = message.replace(\"ID_Number\", menu_session.values)\n\n        if menu.flag == 'Mwenyekiti_Verify_ID':\n            menu_session = MenuSession.objects.filter(flag=\"Mwenyekiti_ID_Number\", phone=phone).last()\n\n            if menu_session:\n                message = message.replace(\"ID_Number\", menu_session.values)\n\n        if menu.flag == 'Mjumbe_Verify_ID':\n            menu_session = MenuSession.objects.filter(flag='Mjumbe_ID_Number', phone=phone).last()\n\n            if menu_session:\n                message = message.replace(\"ID_Number\", menu_session.values)\n\n        if menu.flag == 'Jembe_Mwenyekiti_Teua_Qn' or menu.flag == 'Jembe_Mwenyekiti_Ondoa_Qn' \\\n             or menu.flag == 'Jembe_Mtendaji_Teua_Qn' or menu.flag == 'Jembe_Mtendaji_Ondoa_Qn' \\\n                or menu.flag == 'Jembe_Mwenyekiti_Maoni_Aina' or menu.flag == 'Jembe_Mtendaji_Maoni_Aina' or menu.flag == 'Jembe_Mwananchi_Maoni_Aina':\n            if menu.flag == 'Jembe_Mwenyekiti_Teua_Qn':\n                menu_session =  MenuSession.objects.filter(flag=\"Jembe_Mwenyekiti_Teua\", phone=phone).last() \n\n            elif menu.flag == 'Jembe_Mwenyekiti_Ondoa_Qn': \n                menu_session =  MenuSession.objects.filter(flag=\"Jembe_Mwenyekiti_Ondoa\", phone=phone).last() \n\n            elif menu.flag == 'Jembe_Mtendaji_Teua_Qn':\n                menu_session =  MenuSession.objects.filter(flag=\"Jembe_Mtendaji_Teua\", phone=phone).last() \n\n            elif menu.flag == 'Jembe_Mtendaji_Ondoa_Qn':\n                menu_session =  MenuSession.objects.filter(flag=\"Jembe_Mtendaji_Ondoa\", phone=phone).last() \n\n            elif menu.flag == 'Jembe_Mwenyekiti_Maoni_Aina':\n                menu_session =  MenuSession.objects.filter(flag=\"Jembe_Mwenyekiti_Maoni\", phone=phone).last() \n\n            elif menu.flag == 'Jembe_Mtendaji_Maoni_Aina':\n                menu_session =  MenuSession.objects.filter(flag=\"Jembe_Mtendaji_Maoni\", phone=phone).last()\n\n            elif menu.flag == 'Jembe_Mwananchi_Maoni_Aina':\n                menu_session =  MenuSession.objects.filter(flag=\"Jembe_Mwananchi_Maoni\", phone=phone).last()\n\n            \"\"\"Split values\"\"\"\n            values = menu_session.values\n            arr_key = values.split(' ', maxsplit=2)\n\n            unique_id = arr_key[0]\n            pin = arr_key[1]\n\n            \"\"\"verify pin\"\"\"\n            qry_citizen = Citizen.objects.filter(phone=phone, password=pin)\n\n            if qry_citizen.count() > 0:\n                \"\"\"query JEMBE data\"\"\"\n                jembe_data = self.get_citizen_data(unique_id=unique_id,designation=\"MWANANCHI\")\n                message = message.replace(\"Jembe_Data\", jembe_data)\n            else:\n                jembe_data = \"Umekosea neno siri!\"  \n                message = message.replace(\"Jembe_Data\", jembe_data) \n                 \n\n        \"\"\"Finalizing thread\"\"\"\n        if menu.pull == 1:\n            \"\"\"Construct URL\"\"\"\n            URL = self.BASE_URL + menu.url\n\n            \"\"\"response\"\"\"\n            response = requests.get(URL, params={\"message_id\": uuid, \"designation\": designation})\n\n            sub_message = \"\"\n            for data in response.json()['response']:\n                sub_message += data\n\n            message = message + \"\\r\\n\" + sub_message  \n\n        elif menu.pull == 0:     \n            sub_menus = SubMenu.objects.filter(menu_id=menu_id).order_by('view_id')\n\n            if(sub_menus):\n                sub_message = \"\"\n                for val in sub_menus:\n                    sub_message += val.view_id + \". \" + val.title + \"\\r\\n\"\n\n                message = message + \"\\r\\n\" + sub_message\n        return message \n\n\n    def create_menu_session(self, **kwargs):\n        \"\"\"create menu session\"\"\" \n\n        \"\"\"args\"\"\" \n        phone   = kwargs['phone']\n        menu_id = kwargs['menu_id']\n        uuid    = kwargs['uuid']\n\n        \"\"\"query menu\"\"\"\n        menu  = Menu.objects.get(pk=menu_id)\n\n        \"\"\"create menu session\"\"\"\n        session = MenuSession() \n        session.phone = phone  \n        session.code = uuid\n        session.menu_id = menu.id\n        session.flag = menu.flag\n        session.save()\n\n        \"\"\"return session ID\"\"\"\n        return session.id\n\n\n    def send_message(self, **kwargs):\n        \"\"\"Send message to telerivet\"\"\"\n        \"\"\"args\"\"\"\n        sender  = kwargs[\"sender\"]\n        message = kwargs[\"message\"]\n\n        tr = telerivet.API(self.API_KEY)\n        project = tr.initProjectById(self.PROJECT_ID)\n\n        sent_msg = project.sendMessage(\n            to_number = sender,\n            content = message\n        )\n        print(sent_msg)\n\n        return HttpResponse({'error': False, 'success_msg': \"Message sent!\"})\n\n\n    def process_data(self, **kwargs):\n        \"\"\"Process data for processing\"\"\"\n\n        \"\"\"args\"\"\"\n        uuid  = kwargs[\"uuid\"]\n\n        \"\"\"menu sessions\"\"\"\n        menu_sessions = MenuSession.objects.filter(code=uuid)\n\n        if menu_sessions:\n            arr_data = {}\n            for menu_session in menu_sessions:\n                menu = Menu.objects.get(pk=menu_session.menu_id)\n                sub_menu = SubMenu.objects.filter(menu_id=menu.id)\n\n                menu_value = ''\n                if sub_menu:\n                    sub_menu_value = SubMenu.objects.filter(menu_id=menu.id, view_id=menu_session.values).first()\n\n                    if sub_menu_value:\n                        menu_value = sub_menu_value.title\n                else:\n                    menu_value = menu_session.values\n\n                \"\"\"assign all data to array\"\"\"\n                if menu.label is not None and menu_value is not None:\n                    arr_data[menu.label] = menu_value\n\n            \"\"\"response\"\"\"\n            return JsonResponse({'status': 'success', 'arr_data': arr_data})\n        else:\n            return JsonResponse({'status': 'failed', 'error_msg': 'No any data!'})\n            \n\n    def get_citizen_data(self, **kwargs):\n        \"\"\"Get citizen data\"\"\"\n        qry_data = \"\"\n\n        \"\"\"args\"\"\"\n        uuid   = kwargs[\"unique_id\"]\n        desgn  = kwargs[\"designation\"]\n\n        \"\"\"query citizen\"\"\"\n        qry_citizen = Citizen.objects.filter(unique_id=uuid, designation=desgn)\n\n\n        if qry_citizen.count() > 0:\n            qry_citizen = qry_citizen.first()\n\n            \"\"\"message\"\"\"\n            qry_data = \"Taarifa za jembe:\\n\" \\\n                \"Namba: \"+ str(qry_citizen.unique_id) +\"\\n\" \\\n                    \"Jina: \"+ qry_citizen.name +\"\\n\" \\\n                        \"Simu: \" +qry_citizen.phone +\"\\n\" \\\n                                \"Kitambulisho: \" + qry_citizen.id_type+\"\\n\" \\\n                                    \"Kitamb. Namba: \"+ qry_citizen.id_number+\"\\n\"\\\n                                        \"Kata: \" + qry_citizen.ward.name+\"\\n\" \\\n                                            \"Mtaa/Kijiji: \"+ qry_citizen.village.name+\"\\n\" \\\n                                                \"Kitongoji: \" + qry_citizen.hamlet + \"\\n\"\n        else:\n            qry_data = \"Hakuna mwananchi mwenye namba \" + uuid                                      \n\n\n        return qry_data                                        \n            ","repo_name":"renfrid/django-telerivet","sub_path":"apps/api/classes.py","file_name":"classes.py","file_ext":"py","file_size_in_byte":14931,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"37279320437","text":"import pandas as pd\nfrom vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n\ndef vader_analyse(text):\n    analyser = SentimentIntensityAnalyzer()\n    prediction = analyser.polarity_scores(text)\n    score = prediction['compound']\n\n    if score >= 0.05:\n        return \"Positive\"\n\n    if score <= -0.05:\n        return \"Negative\"\n        \n    return \"Neutral\"\n\n\n\ndef get_accuracy(length):\n\n    \n    good_ones = 0\n    cpt = 0\n\n    df = pd.read_csv('Reddit_Data.csv')\n\n    if(length <= 0):\n        length = 1\n    if(length > len(df)):\n        length = len(df)\n\n    for i in range(0, length):\n        text = str(df[\"clean_comment\"][i])\n        score = int(df[\"category\"][i])\n\n        # Return the sentiment based on the column \"category\"\n        if score == 1:\n            sentiment =  \"Positive\"\n        else :\n            if score == -1:\n                sentiment =  \"Negative\"\n            else:\n                sentiment =  \"Neutral\"\n    \n        # Return the sentiment based on the column \"clean_comment\"\n        predicted_sentiment = vader_analyse(text)\n\n        # calculate the accuracy\n        cpt += 1\n        if sentiment == predicted_sentiment:\n            good_ones += 1\n\n    return (good_ones / cpt)","repo_name":"Tr4hern/Data_Engineering","sub_path":"work/vader.py","file_name":"vader.py","file_ext":"py","file_size_in_byte":1219,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"33150732576","text":"import sys\nsys.setrecursionlimit(10**6)\n\nanswer = [[]]\ndef tornado(nn, ts, s):\n    global answer  \n    if ts // 2 < s + 1:\n        return  \n    for i in range(ts - (s * 2) - 1):\n        nn += 1\n        answer[s][s + i] = nn\n        answer[s + i][-s - 1] = nn\n        answer[-s - 1][-s - 1 - i] = nn\n        answer[-s - 1 - i][s] = nn\n    \n \n    if ts % 2 == 1:\n        answer[ts // 2][ts // 2] = nn + 1\n\n    tornado(nn, ts, s + 1)\n    \n\ndef solution(n, clockwise):\n    global answer\n    \n    answer = [[0] * n for _ in range(n)]\n    tornado(0, n, 0)\n    \n    temp = [[0] * n for _ in range(n)]\n    if not clockwise:\n        for i in range(n):\n            temp[i] = answer[n-1-i]\n        return temp\n    return answer\n\nprint(solution(9, False))","repo_name":"HeoYou/algorithm-python","sub_path":"220312-test1-2.py","file_name":"220312-test1-2.py","file_ext":"py","file_size_in_byte":743,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"73812101587","text":"from config import env\nimport sys\n\nWEBHOOK_PATH = f'/webhook/{env.BOT_TOKEN}'\nWEBAPP_HOST = '0.0.0.0'\n\nARGS_LEN = len(sys.argv)\nWEBAPP_PORT = sys.argv[1] if ARGS_LEN > 1 else env.PORT\nWEBHOOK_HOST = sys.argv[2] if ARGS_LEN > 2 else env.BASE_URI\nIS_WEBHOOK = WEBAPP_PORT and WEBHOOK_HOST\nIS_PING_PONG = IS_WEBHOOK and bool(\n    env.PING_PONG) and env.PING_PONG.lower() == 'true'\nWEBHOOK_URL = f'{WEBHOOK_HOST}{WEBHOOK_PATH}' if IS_WEBHOOK else None\n\nTELEGRAM_MESSAGE_MAX_SIZE = 4096\n\nRANDOM_CAT_API_URL = \"https://api.thecatapi.com/v1/images/search\"\nRANDOM_DOG_API_URL = \"https://api.thedogapi.com/v1/images/search\"\n\nIMAGE_AUTHOR_HTML_LINK = \"<a href='https://www.deviantart.com/cybercinnamonroll'>cybercinnamonroll</a>\"\nCOOKIE_KEY = \"the_mafia_host_bot_token\"\n","repo_name":"HewstonFox/TheMafiaHostBotV2","sub_path":"bot/constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":760,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"43054384527","text":"#write a python script to print all the perfect numbers\n#between 5 to 100\n\nfor n in range(5,10000):\n    sum=0\n    for i in range(1,n//2+1):\n        if n%i==0:\n            sum=sum+i;\n\n    if sum==n:\n        print(n);\n\n\n    \n            \n","repo_name":"akshaywaghmode2099/Loop_Python","sub_path":"perfect_range.py","file_name":"perfect_range.py","file_ext":"py","file_size_in_byte":236,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"29846369000","text":"import time\n\nteam = input(\"TIME: \");\njogo = input(\"JOGO: \");\n\nautoLine = 0;  # \nautoBottom = 0;  # abo\nautoOutter = 0;  # aou\nautoInner = 0;  # ain\nautoPegouPowerCell = 0;  # ape\nautoErroPowerCell = 0;  # aer\n\nteleopBottom = 0;  # tbo\nteleopOutter = 0;  # tou\nteleopInner = 0;  # tin\nteleopErroPowerCell = 0;  # ter\nteleopParouDeFuncionar = 0;  # tpa\n\ncicloTarget = 0;  # cta\ncicloLine = 0;  # cau\ncicloTrincheira = 0;  # ctr\ncicloRendezvous = 0;  # cre\ncicloAtrasTrincheira = 0;  # cat\n\nendEstaciona = 0;  # ees\nendSobe = 0;  # eso\nendNivelaSozinho = 0;  # eni\nendFalhouSubida = 0;  # efa\n\ninitialTime = time.time();\n\nlog = []\nwhile(True):\n    userInput = input();\n    log.append([(time.time() - initialTime), userInput]);\n\n    try:\n        number = int(userInput.split()[1]);\n        userInput = userInput.split()[0];\n    except:\n        number = 1\n\n    if(userInput == \"ali\"):\n        autoLine += 5\n    elif(userInput == \"abo\"):\n        autoBottom += number\n    elif(userInput == \"aou\"):\n        autoOutter += number\n    elif(userInput == \"ain\"):\n        autoInner += number\n    elif(userInput == \"ape\"):\n        autoPegouPowerCell += number\n    elif(userInput == \"aer\"):\n        autoErroPowerCell += number\n        \n\n    elif(userInput == \"tbo\"):\n        teleopBottom += number\n    elif(userInput == \"tou\"):\n        teleopOutter += number\n    elif(userInput == \"tin\"):\n        teleopInner += number\n    elif(userInput == \"ter\"):\n        teleopErroPowerCell += number\n    elif(userInput == \"tpa\"):\n        teleopParouDeFuncionar += 1\n        \n    elif(userInput == \"cta\"):\n        cicloTarget += number\n    elif(userInput == \"cli\"):\n        cicloLine += number\n    elif(userInput == \"ctr\"):\n        cicloTrincheira += number\n    elif(userInput == \"cre\"):\n        cicloRendezvous += number\n    elif(userInput == \"cat\"):\n        cicloAtrasTrincheira += number\n        \n\n    elif(userInput == \"ees\"):\n        endEstaciona += 5\n    elif(userInput == \"eso\"):\n        endSobe += 25\n    elif(userInput == \"eni\"):\n        endNivelaSozinho += 15\n    elif(userInput == \"efa\"):\n        endFalhouSubida += 1\n   \n    elif(userInput == \"exit\"):\n        break;\n\n    else:\n        log[len(log)-1][1] = \"ERRO: \" + log[len(log)-1][1]\n\nfor i in range(len(log)):    \n    print(log[i]);\n\n\nprint();\nprint(\"TIME: {}\".format(team));\nprint(\"JOGO: {}\".format(jogo));\nprint();\nprint(\"autoLine: {}\".format(autoLine));\nprint(\"autoBottom: {}\".format(autoBottom));\nprint(\"autoOutter: {}\".format(autoOutter));\nprint(\"autoInner: {}\".format(autoInner));\nprint(\"autoPegouPowerCell: {}\".format(autoPegouPowerCell));\nprint(\"autoErroPowerCell: {}\".format(autoErroPowerCell));\n\nprint(\"teleopBottom: {}\".format(teleopBottom));\nprint(\"teleopOutter: {}\".format(teleopOutter));\nprint(\"teleopInner: {}\".format(teleopInner));\nprint(\"teleopErroPowerCell: {}\".format(teleopErroPowerCell));\nprint(\"teleopParouDeFuncionar: {}\".format(teleopParouDeFuncionar));\n\nprint(\"cicloTarget: {}\".format(cicloTarget));\nprint(\"cicloLine: {}\".format(cicloLine));\nprint(\"cicloTrincheira: {}\".format(cicloTrincheira));\nprint(\"cicloRendezvous: {}\".format(cicloRendezvous));\nprint(\"cicloAtrasTrincheira: {}\".format(cicloAtrasTrincheira));\n\nprint(\"endEstaciona: {}\".format(endEstaciona));\nprint(\"endSobe: {}\".format(endSobe));\nprint(\"endNivelaSozinho: {}\".format(endNivelaSozinho));\nprint(\"endFalhouSubida: {}\".format(endFalhouSubida));\n\n\n\n\n\n\n","repo_name":"Bertonebreno/FRC-Strategy-System-2020","sub_path":"fred_system.py","file_name":"fred_system.py","file_ext":"py","file_size_in_byte":3378,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"9961410185","text":"import smtplib\nimport sqlite3\nfrom email.message import EmailMessage\n\nimport pandas as pd\nfrom jinja2 import Environment, FileSystemLoader\nfrom pandas_schema import Schema, Column\nfrom pandas_schema.validation import IsDtypeValidation, DateFormatValidation\n\nimport setting\n\n\ndef get_email_client() -> object:\n    \"\"\"\n    Get email client\n    :return: email client to send email\n    \"\"\"\n    server = smtplib.SMTP(\n        setting.EMAIL['host'], setting.EMAIL['port']\n    )\n    server.starttls()\n    server.login(\n        setting.EMAIL['user'], setting.EMAIL['password']\n    )\n\n    return server\n\n\ndef email_render(average: dict, template_name: str) -> str:\n    \"\"\"\n    Render email template\n    :param average:\n    :param template_name:\n    :return:\n    \"\"\"\n    env = Environment(loader=FileSystemLoader('./templates'))\n    template = env.get_template(template_name)\n\n    return template.render(average)\n\n\ndef send_email(user_email: str, average: dict) -> dict:\n    \"\"\"\n    Send email to user\n    :param user_email:\n    :param average:\n    :return:\n    \"\"\"\n    email_client = get_email_client()\n    email = EmailMessage()\n    email['Subject'] = 'Summary Story account'\n    email['From'] = setting.EMAIL['sender']\n    email['To'] = user_email\n    email.set_content(\n    email_render(average, 'summary_email.html'),\n        subtype='html'\n    )\n\n    return email_client.send_message(email)\n\n\ndef get_df_from_file(file_path: str) -> [bool, object]:\n    \"\"\"\n    Open file and return pandas dataframe\n    :param file_path: path to file\n    :return: pandas dataframe\n    \"\"\"\n    df = pd.read_csv(\n        file_path,\n        parse_dates=['Date'],\n        dtype={'Transaction': float, 'Id': int}\n    )\n\n    schema = Schema([\n        Column('Id', [IsDtypeValidation(int)]),\n        Column('Date', [DateFormatValidation('%Y-%m-%d')]),\n        Column('Transaction', [IsDtypeValidation(float)])\n    ])\n\n    errors = schema.validate(df)\n    if errors:\n        for error in errors:\n            print(error)\n        return False, None\n    return True, df\n\n\ndef get_average_from_df(df: object) -> dict:\n    \"\"\"\n    Get average from dataframe\n    Total balance / number of transactions\n    Number of transactions group by month\n    Average debit amount:\n    Average credit amount:\n    :return\n    dict with average values\n    \"\"\"\n    average = {\n        'total_balance': df['Transaction'].sum(),\n        'number_of_transactions': df['Transaction'].count(),\n        'average_debit_amount': df[df['Transaction'] < 0]['Transaction'].mean(),\n        'average_credit_amount': df[df['Transaction'] > 0]['Transaction'].mean(),\n        'number_of_transactions_group_by_month': df.groupby(\n           df['Date'].dt.strftime('%B')\n        )['Transaction'].count().to_dict()\n    }\n\n    return average\n\n\ndef save_df_to_sql(df, table_name='transactions'):\n    con = sqlite3.connect('transactions.db')\n    df.to_sql(table_name, con, if_exists='replace', index=False)\n\n\ndef prosses_file(user_email, file_path):\n    print('get_df_from_file')\n    success, df = get_df_from_file(file_path)\n    if not success:\n        raise Exception('Error in file')\n    print('save_df_to_sql')\n    save_df_to_sql(df)\n    print('get_average')\n    average = get_average_from_df(df)\n    print('send_email')\n    send_email(user_email, average)\n    print('Done')\n\n\nif __name__ == '__main__':\n    user_email = setting.SUMMARY_USER\n    file_path = setting.FILE_PATH\n\n    prosses_file(user_email, file_path)\n","repo_name":"skomlev/stori","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3449,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"14029289908","text":"# 파이썬 변수 동시 선언( 이렇게 해야하는거 처음 앎... )\nm_col, m_row, lst_max = 0, 0, -1 # 최대값을 -1로 설정하는 이유는? 자연수 배열이므로 0이 최대 값일시 오류 발생 가능\n\nfor i in range(9):\n  lst = list(map(int, input().split())) # 시간제한이 빡센 문제가 아니므로 반복문 내 input() 사용 가능\n  for j in range(9):\n    if lst[j] > lst_max:\n      m_col = i + 1 # 리스트의 인덱스 값이므로 몇번째 인지 출력하기 위해 각 값에 + 1\n      m_row = j + 1\n      lst_max = lst[j]\n\nprint(lst_max) # 이차원 배열의 최대 값 출력\nprint(m_col, m_row) ","repo_name":"chaewu/PS","sub_path":"BOJ/BOJ_2566.py","file_name":"BOJ_2566.py","file_ext":"py","file_size_in_byte":642,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"28878358908","text":"import torch\nimport torch.nn as nn\nimport os\nimport sys\n\nscript_dir = os.path.dirname(__file__)\nhelper_dir = os.path.join(script_dir, \"../models\")\nsys.path.append(helper_dir)\nfrom metaformer import GraphMaskConvolution\n\ninput_size = 5\nchannels_in = 9  # size * size\nbatch = 1\n\nmask = GraphMaskConvolution(\n    size=3,\n    embed_dim=input_size,\n    graph_layer=\"symm_nnn\",\n)\n\n\nx_batched = torch.linspace(\n    1,\n    batch * channels_in * input_size,\n    batch * channels_in * input_size,\n).reshape(batch, channels_in, input_size)\nprint(x_batched)\n\n\noptimizer = torch.optim.SGD(\n    mask.parameters(),\n    lr=0.01,\n)\nloss_fn = nn.L1Loss()\n\n# test batched\noptimizer.zero_grad()\nresult = mask(x_batched)\nloss = loss_fn(result, torch.ones_like(result))\nloss.backward()\noptimizer.step()\n\nprint(result)\nprint(mask.factors)\nprint(mask.center_weight_template)\nprint(mask.nn_weight_template)\nprint(mask.nnn_weight_template)\nprint(mask(x_batched))\n","repo_name":"jonas-kell/bachelor-thesis-experiments","sub_path":"experiments/graph_layer.py","file_name":"graph_layer.py","file_ext":"py","file_size_in_byte":937,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"21736005552","text":"# Problem Link: https://www.hackerrank.com/challenges/itertools-permutations/problem\n# ----------------------------------------------------------------------------------\n\n\nfrom itertools import permutations\n\nu_input = list(map(str, input().split()))\ns = sorted(u_input[0])\nn = int(u_input[1])\n\np = list(permutations(s, n))\nfor i in p:\n    print(*i, sep='')","repo_name":"ybg345/hacker-rank-solutions","sub_path":"Python/06_Itertools/itertools.permutations().py","file_name":"itertools.permutations().py","file_ext":"py","file_size_in_byte":356,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"70500925906","text":"import os\nimport numpy as np\nimport pandas as pd\n\n\ndef get_fsl_confounds(sub_id, run_id):\n    confound_file = rf'/mnt/workdir/DCM/BIDS/derivatives/fmriprep_volume_fmapless/fmriprep/{sub_id}/func/' \\\n                    rf'{sub_id}_task-game1_run-{run_id}_desc-confounds_timeseries.tsv'\n    save_dir = r'/mnt/workdir/DCM/BIDS/derivatives/FSL/preprocessed_data/confound'\n    save_template = fr'{sub_id}_game1_run-0{run_id}_confounds.txt'\n    motions_df = pd.read_csv(confound_file,sep='\\t')\n    motion_columns = [#'trans_x', 'trans_y', 'trans_z', 'rot_x', 'rot_y', 'rot_z',\n                       'csf', 'white_matter']\n    motions = motions_df[motion_columns].to_numpy()\n    motion_outpath = os.path.join(save_dir,save_template)\n    np.savetxt(motion_outpath,motions,delimiter='  ')\n\n\nsub_id = r'sub-079'\nruns = range(1,7)\n\nfor run_id in runs:\n    get_fsl_confounds(sub_id,run_id)","repo_name":"YukunQu/DCM","sub_path":"analysis/mri/preprocess/fmriprep2fsl/fsl_confound.py","file_name":"fsl_confound.py","file_ext":"py","file_size_in_byte":879,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"29093542772","text":"import tkinter as tk\nfrom tkinter import ttk as ttk\n\n\nclass ScrollableListbox(tk.Frame):\n    def __init__(self, parent, *args, **kwargs):\n        super().__init__(parent)\n\n        y_scrollbar = ttk.Scrollbar(self)\n        x_scrollbar = ttk.Scrollbar(self, orient=tk.HORIZONTAL)\n\n        self._var = tk.Variable()\n        self._listbox = tk.Listbox(self, *args,\n                                   listvariable=self._var,\n                                   xscrollcommand=x_scrollbar.set,\n                                   yscrollcommand=y_scrollbar.set,\n                                   **kwargs)\n\n        y_scrollbar['command'] = self._listbox.yview\n        x_scrollbar['command'] = self._listbox.xview\n\n        self.grid_columnconfigure(0, weight=1)  # the same effect as expand=1 for pack\n        self.grid_rowconfigure(0, weight=1)  # the same effect as expand=1 for pack\n        self._listbox.grid(row=0, column=0, sticky=tk.N+tk.S+tk.E+tk.W)\n        y_scrollbar.grid(row=0, column=1, sticky=tk.N+tk.S)\n        x_scrollbar.grid(row=1, column=0, sticky=tk.E+tk.W)\n        self.insert = lambda index, *items: self._listbox.insert(index, *items)\n\n    @property\n    def values(self):\n        return self._var.get()\n\n    @values.setter\n    def values(self, values):\n        self._var.set(tuple(values))\n\n    def clear(self):\n        self._listbox.delete(0, tk.END)\n        self._listbox.update()\n\n    def curselection(self):\n        return self._listbox.curselection()\n\n    def append(self, item):\n        self._listbox.insert(tk.END, item)\n        self._listbox.yview_moveto('1.0')\n        self._listbox.update()\n","repo_name":"AlekseySP/df-translation-client","sub_path":"widgets/scrollable_listbox.py","file_name":"scrollable_listbox.py","file_ext":"py","file_size_in_byte":1616,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"48"}
+{"seq_id":"73738357267","text":"'''\nVERY smol widget, that holds simple UI for changing cal coefficient\n'''\n\nfrom PyQt5 import QtWidgets\nfrom customButton import cButton\nclass changeCalCoeffWindow(QtWidgets.QWidget):\n    def __init__(self):\n        super().__init__()\n        self.setVisible(False)\n        self.layout = QtWidgets.QGridLayout(self)\n        self.cancel = cButton(\"Cancel\")\n        self.apply = cButton(\"Set\")\n        self.textEdit = QtWidgets.QDoubleSpinBox(self)\n        self.layout.addWidget(self.textEdit, 0,0,1,2)\n        self.layout.addWidget(self.cancel, 1,0)\n        self.layout.addWidget(self.apply, 1,1)\n        self.cancel.clicked.connect(self.__disappearThis)\n    \n    def setText(self, value):\n        self.textEdit.setValue(value)\n    \n    def getValue(self):\n        text = self.textEdit.cleanText() \n        return self.textEdit.valueFromText(text)     \n\n    def __disappearThis(self):\n        self.setVisible(False)","repo_name":"dachshund-ncu/simpleSingleDishDataReductor","sub_path":"UI/manualCalCoeffSetter.py","file_name":"manualCalCoeffSetter.py","file_ext":"py","file_size_in_byte":915,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"2487207446","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jun  2 10:32:49 2020\n\n@author: findl\n\"\"\"\n\nimport numpy as np\nfrom sklearn import preprocessing\n\nX = np.array([\n    [2.1, 5, 150],\n    [1.2, 6, 170],\n    [4.4, 4, 130],\n    [5.4, 4, 100],\n    [12.1, 7, 208],\n    [5, 5, 140],\n    [6.3, 5, 145],\n    [0.3, 5, 160],\n    [0.7, 4, 125],\n    [5.5, 4, 135],\n    [3.2, 4, 141],\n    [11.1, 5, 98],\n    ])\n\ny = np.array([4.3, 7.3, 2.2, 1.9, 10.1, 3, 3, 6.6, 8, 4.2, 4.1, 8.3])\n\nx_test = np.array([4,5, 150])\n\nfinal = np.abs(x_test - X)\ndistance = np.sum(final, axis = 1)\n\nsort_index = np.argsort(distance) [0:3]\n\nclosest = y[sort_index]\n\nnp.mean(closest)\n\n\ndef __featureScaling__(X, u, std):\n        return (X - u) / std\n\n\nu = np.mean(X, axis=0)\nstd = np.std(X, axis=0)\n\nX_normalized = __featureScaling__(X, u, std)\nX_normalized[:,[1,2]]= X[:,[1,2]]\n\nx_test_normalized = __featureScaling__(np.array([x_test]), u, std)\nx_test_normalized[:,[1,2]]= np.array([x_test])[:,[1,2]].flatten()\n\nx_test_normalized = x_test_normalized.flatten()\n\n\n\n\n# X_normalized[:,0] = preprocessing.normalize(np.array([X[:,0]]), norm='l2')\nfinal = np.abs(x_test_normalized - X_normalized)\ndistance_normalized = np.sum(final, axis = 1)\n\nsort_index_normalized = np.argsort(distance_normalized) [0:3]\n\nclosest = y[sort_index_normalized]\n\nnp.mean(closest)\n\nu = np.mean(X, axis=0)\nstd = np.std(X, axis=0)\nXe = __featureScaling__(X, u, std)\n","repo_name":"findlay-forsblom/2DV516-MachineLearning","sub_path":"ff222ey_A4/Test/TentaTest2.py","file_name":"TentaTest2.py","file_ext":"py","file_size_in_byte":1391,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"74998392786","text":"import TISE_solver as T\nimport numpy as np\nfrom importlib import reload\nfrom scipy.integrate import odeint\nimport matplotlib.pyplot as plt\nfrom scipy import constants as const\n\nhbar = T.hbar\ne = 1\neigEnergy1i = T.eigEnergy1i\neigFunc1i = T.eigFunc1i\nxvec1i = T.xvec1i\nc = 1\nE0 = 2.5e-1\nts = np.linspace(0,50,1001)\nnmax = 2\n\nTISE_hash_check = ''\n\nwith open('./TISE_solver.py','rb') as f:\n    bytes = f.read()\n    TISE_hash_check = T.sha256(bytes).hexdigest()\n\nif(T.TISE_hash != TISE_hash_check):\n    TISE_solver = reload(T)\n\ndef inner_product(a,b):\n    return a.conj().dot(b)\n\ndef TDSE(s,t,H):\n    sz = [s[2*i] + 1j*s[2*i+1] for i in range(int(len(s)/2))]\n    dsdt = np.array(-1j*H(t).dot(sz),dtype=np.complex128)\n    ret = np.array([[np.real(dsdt[i]), np.imag(dsdt[i])] for i in range(len(dsdt))]).flatten()\n    return ret\n\nomega0 = (eigEnergy1i[1] - eigEnergy1i[0])/hbar\ndipole_m_elements = [[inner_product(eigFunc1i[i],xvec1i*eigFunc1i[j]) for i in range(nmax)] for j in range(nmax)]\nOmega0 = e*inner_product(eigFunc1i[1],xvec1i*eigFunc1i[0])*E0/hbar\nOmega = lambda t : Omega0*(np.exp(1j*omega*t) + np.exp(-1j*omega*t))\nOmegas = np.array([[e*dipole_m_elements[i][j]*E0/hbar if i != j else 0 for i in range(nmax)] for j in range(nmax)])\nomegas = np.array([[(eigEnergy1i[i] - eigEnergy1i[j])/hbar for i in range(nmax)] for j in range(nmax)])\n\nomega = 1*omega0 - Omega0\nomega1 = (eigEnergy1i[2] - eigEnergy1i[1])/hbar\n\nRabi_H_RWA = lambda t : np.array([[0,hbar*np.conj(Omega0)/2],[hbar*Omega0/2,-hbar*(omega - omega0)]],dtype=np.complex128)\nRabi_H = lambda t : np.array([[eigEnergy1i[0],hbar*np.conj(Omega(t))/2],[hbar*Omega(t)/2,eigEnergy1i[1]]],dtype=np.complex128)\nN_level_Rabi_H = lambda t : np.array([[hbar*Omegas[i][j]*(np.exp(1j*omega*t) + np.exp(-1j*omega*t))/2 for i in range(nmax)] for j in range(nmax)])\nN_level_Rabi_H_2 = lambda t : np.array([[hbar*Omegas[i][j]*(np.exp(1j*omega1*t) + np.exp(-1j*omega1*t))/2 for i in range(nmax)] for j in range(nmax)])\nDouble = lambda t : N_level_Rabi_H_2(t) + N_level_Rabi_H(t) + np.diag([eigEnergy1i[i] for i in range(nmax)])\n\ns0 = [0,0]*nmax\ns0[0] = 1\nsol = odeint(TDSE,s0,ts, args=(lambda t : N_level_Rabi_H(t) + np.diag([eigEnergy1i[i] for i in range(nmax)]),))\nsol2 = odeint(TDSE,[1,0,0,0],ts, args=(Rabi_H_RWA,))\n\nfig2, ax2 = plt.subplots()\n# ax2.plot(ts,(sol[:,0] + 1j*sol[:,1])*(sol[:,0] - 1j*sol[:,1]),label = 'RWA $c_0$')\n# ax2.plot(ts,(sol[:,2] + 1j*sol[:,3])*(sol[:,2] - 1j*sol[:,3]),label = 'RWA $c_1$')\nax2.plot(ts,(sol2[:,0] + 1j*sol2[:,1])*(sol2[:,0] - 1j*sol2[:,1]),label ='RWA $c_0$')\nax2.plot(ts,(sol2[:,2] + 1j*sol2[:,3])*(sol2[:,2] - 1j*sol2[:,3]),label ='RWA $c_1$')\nfor i in range(nmax):\n    ax2.plot(ts,abs(sol[:,2*i] + 1j*sol[:,2*i + 1])**2,label ='$c_'+str(i)+'$')\n# ax2.plot(ts,(sol[:,2] + 1j*sol[:,3])*(sol[:,2] - 1j*sol[:,3]),label ='3-level $c_1$')\n# ax2.plot(ts,(sol[:,4] + 1j*sol[:,5])*(sol[:,4] - 1j*sol[:,5]),label ='3-level $c_2$')\nax2.legend()\nplt.show()","repo_name":"Lala5th/Trapped_ion_gate_simulation","sub_path":"Rabi_test/TDSE_solver.py","file_name":"TDSE_solver.py","file_ext":"py","file_size_in_byte":2936,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"5329316120","text":"#!/usr/bin/env python\n#------------------------------------------------------------\n# Purpose: Demonstrate, using kmpfit, that if you find best-fit\n# parameters, the errors derived from the covariance matrix \n# correspond to an increase in chi^2 of 1.\n# Vog, 23 Nov 2011\n#------------------------------------------------------------\nimport numpy\nfrom matplotlib.pyplot import figure, show, rc\nfrom numpy.random import normal, randint\nfrom kapteyn import kmpfit\n\n\ndef residuals(p, data):\n   x, y, err = data\n   a, b = p\n   model = a + b*x\n   return (y-model)/err\n\n\n# Artificial data\n#----------------\nN = 100\na0 = 2; b0 = 3\nx = numpy.linspace(0.0, 2.0, N)\ny = a0 + b0*x + normal(0.0, 0.4, N)  # Mean,sigma,N\nderr = normal(0.0, 0.5, N)\nerr = 0.9+derr\n\nfitobj = kmpfit.Fitter(residuals=residuals, data=(x, y, err))\nfitobj.fit(params0=[1,1])\n\nif (fitobj.status <= 0):\n   print('error message =', fitobj.errmsg)\n   raise SystemExit\n\nprint(\"\\n\\n======== Results kmpfit for Y = A + B*X =========\")\nprint(\"Params:        \", fitobj.params)\nprint(\"Errors from covariance matrix         : \", fitobj.xerror)\nprint(\"Uncertainties assuming reduced Chi^2=1: \", fitobj.stderr)\nprint(\"Chi^2 min:     \", fitobj.chi2_min)\n\np1, p2 = fitobj.params\ne1, e2 = fitobj.xerror\n# Next we take one of the parameters to be fixed and change its value\n# with the amount of one of the estimated errors (covariance, scaled or bootstrap)\n# If we fit again, then, according to Bevington (Data Reduction and Error\n# Analysis for the Physical Sciences  Section 11-5), one should expect the\n# Chi square value to increase with 1.0\n\nfitobj.parinfo[0] = dict(fixed=True)\nfitobj.fit(params0=[p1+e1,1])\nprint(\"\\nFix first parameter and set its value to fitted value+error\")\nprint(\"Params:        \", fitobj.params)\nprint(\"Chi^2 min:     \", fitobj.chi2_min)\nprint(\"Errors from covariance matrix         : \", fitobj.xerror)\n\nfitobj.parinfo = [{'fixed':False}, {'fixed':True}]\nfitobj.fit(params0=[1, p2+e2])\nprint(\"\\nFix second parameter and set its value to fitted value+error\")\nprint(\"Params:        \", fitobj.params)\nprint(\"Chi^2 min:     \", fitobj.chi2_min)\nprint(\"Errors from covariance matrix         : \", fitobj.xerror)","repo_name":"kapteyn-astro/kapteyn","sub_path":"doc/source/EXAMPLES/kmpfit_errors_chi2delta.py","file_name":"kmpfit_errors_chi2delta.py","file_ext":"py","file_size_in_byte":2179,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"48"}
+{"seq_id":"5969756695","text":"### RPSLS\r\nimport random\r\none_more = \"y\"\r\n\r\ndef name_to_number(name):\r\n    if name == \"rock\":\r\n        name = 0\r\n    elif name == \"Spock\":\r\n        name = 1\r\n    elif name == \"paper\":\r\n        name = 2\r\n    elif name == \"lizard\":\r\n        name = 3\r\n    elif name == \"scissors\":\r\n        name = 4\r\n    else:\r\n        return False\r\n        \r\n    return name\r\n\r\n\r\ndef number_to_name(number):\r\n    \r\n    if number == 0:\r\n        number = \"rock\"\r\n    elif number == 1:\r\n        number = \"Spock\"\r\n    elif number == 2:\r\n        number = \"paper\"\r\n    elif number == 3:\r\n        number = \"lizard\"\r\n    elif number == 4:\r\n        number = \"scissors\"\r\n    else:\r\n        return False\r\n    \r\n    return number\r\n\r\n\r\ndef rpsls(player_choice):\r\n    player_choice_name = number_to_name(player_choice)\r\n    print (\"\\n-------------\")\r\n    print (\"The player chooses %s\" %(player_choice_name))\r\n    random_no = random.randrange(4)\r\n    comp_choice = number_to_name(random_no)\r\n    \r\n    print (\"The computer chooses %s\" % (comp_choice))\r\n\r\n    if random_no < int(player_choice):\r\n        result = (random_no - int(player_choice))%5\r\n    else:\r\n        result = (int(player_choice) - random_no)%5\r\n\r\n    if result == 1 or result == 2:\r\n        if random_no < player_choice:\r\n###            smaller_no = comp_choice\r\n ###           bigger_no = player_choice\r\n            print (\"Computer wins!\")\r\n            \r\n        else:\r\n ###           smaller_no = player_choice\r\n ###           bigger_no = comp_choice\r\n            print (\"Player wins!\")\r\n            \r\n    elif result == 3 or result == 4:\r\n        if random_no < player_choice:\r\n ###           smaller_no = comp_choice\r\n ###           bigger_no = player_choice\r\n            print (\"Player wins!\")\r\n        else:\r\n ###           smaller_no = player_choice\r\n ###           bigger_no = comp_choice\r\n            print (\"Computer wins!\")\r\n\r\n    else:\r\n        print (\"It's a tie!\")\r\n\r\nwhile one_more == \"y\":\r\n    print (\"One more? - y/n -\")\r\n    one_more = input()\r\n    if one_more == \"y\":\r\n        start_game()\r\n    else:\r\n        break\r\n        \r\n\r\ndef start_game():\r\n    print (\"Please select number 0-4\")\r\n    choice = int(input())\r\n    rpsls(choice)\r\n\r\nstart_game()\r\n","repo_name":"mariogelo/Coursera","sub_path":"RPSLS.py","file_name":"RPSLS.py","file_ext":"py","file_size_in_byte":2204,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"5810040641","text":"\ndef is_tree(N, edges):\n    if N != len(edges) + 1:\n        return \"The Graph is not a tree\"\n    graph = {}\n    visited = set()\n    \n    #build the graph\n    for u, v in edges:\n        if u not in graph:\n            graph[u] = []\n        if v not in graph:\n            graph[v] = []\n        graph[u].append(v)\n        graph[v].append(u)\n    \n    #Perform DFS to check for cycles\n    def dfs(node,parent):\n        visited.add(node)\n        for neighbor in graph[node]:\n            if neighbor == parent:\n                continue\n            if neighbor in visited:\n                return \"The graph is not a tree\"\n            if dfs(neighbor, node) == \"The graph is not a tree\":\n                return \"The graph is not a tree\"\n        return \"The graph is a tree\"\n    #check is there are cycles and all nodes are reachable\n    return dfs(1, -1)\n#Provided input\nN = 3\nedges = [(1,2),(2,3)]\n\n#check is the graph is a tree and print the output\nprint(is_tree(N,edges))","repo_name":"SarathiManikandan0/Interview_Q-A_Python","sub_path":"Interview Q&A Python/Algorithm/dfscyclecheck.py","file_name":"dfscyclecheck.py","file_ext":"py","file_size_in_byte":964,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"48"}
+{"seq_id":"27149988320","text":"\n\ndef winstreak(my_List):\n    my_List = []\n    \n    #define streak counter \n    previous = None\n    streak = 0\n    previous_success = True\n\n    #repetitive counter\n    for i in my_list:\n        if (i-1)==previous:\n\n            if previous_success:\n                streak+=1\n            else:\n                streak =1\n\n        previous = i\n    print(streak)\n\n    return winstreak()","repo_name":"collinskoech11/PythonRefresher","sub_path":"final_q9.py","file_name":"final_q9.py","file_ext":"py","file_size_in_byte":381,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"40717818308","text":"from __future__ import annotations\nimport datetime\nfrom dataclasses import dataclass, field\nfrom kiota_abstractions.serialization import AdditionalDataHolder, Parsable, ParseNode, SerializationWriter\nfrom kiota_abstractions.store import BackedModel, BackingStore, BackingStoreFactorySingleton\nfrom typing import Any, Callable, Dict, List, Optional, TYPE_CHECKING, Union\n\n@dataclass\nclass HostSslCertificatePort(AdditionalDataHolder, BackedModel, Parsable):\n    # Stores model information.\n    backing_store: BackingStore = field(default_factory=BackingStoreFactorySingleton(backing_store_factory=None).backing_store_factory.create_backing_store, repr=False)\n\n    # Stores additional data not described in the OpenAPI description found when deserializing. Can be used for serialization as well.\n    additional_data: Dict[str, Any] = field(default_factory=dict)\n    # The first date and time that this port was observed. The Timestamp type represents date and time information using ISO 8601 format and is always in UTC time. For example, midnight UTC on Jan 1, 2014 is 2014-01-01T00:00:00Z.\n    first_seen_date_time: Optional[datetime.datetime] = None\n    # The most recent date and time that this port was observed. The Timestamp type represents date and time information using ISO 8601 format and is always in UTC time. For example, midnight UTC on Jan 1, 2014 is 2014-01-01T00:00:00Z.\n    last_seen_date_time: Optional[datetime.datetime] = None\n    # The OdataType property\n    odata_type: Optional[str] = None\n    # The port number.\n    port: Optional[int] = None\n    \n    @staticmethod\n    def create_from_discriminator_value(parse_node: Optional[ParseNode] = None) -> HostSslCertificatePort:\n        \"\"\"\n        Creates a new instance of the appropriate class based on discriminator value\n        param parse_node: The parse node to use to read the discriminator value and create the object\n        Returns: HostSslCertificatePort\n        \"\"\"\n        if not parse_node:\n            raise TypeError(\"parse_node cannot be null.\")\n        return HostSslCertificatePort()\n    \n    def get_field_deserializers(self,) -> Dict[str, Callable[[ParseNode], None]]:\n        \"\"\"\n        The deserialization information for the current model\n        Returns: Dict[str, Callable[[ParseNode], None]]\n        \"\"\"\n        fields: Dict[str, Callable[[Any], None]] = {\n            \"firstSeenDateTime\": lambda n : setattr(self, 'first_seen_date_time', n.get_datetime_value()),\n            \"lastSeenDateTime\": lambda n : setattr(self, 'last_seen_date_time', n.get_datetime_value()),\n            \"@odata.type\": lambda n : setattr(self, 'odata_type', n.get_str_value()),\n            \"port\": lambda n : setattr(self, 'port', n.get_int_value()),\n        }\n        return fields\n    \n    def serialize(self,writer: SerializationWriter) -> None:\n        \"\"\"\n        Serializes information the current object\n        param writer: Serialization writer to use to serialize this model\n        Returns: None\n        \"\"\"\n        if not writer:\n            raise TypeError(\"writer cannot be null.\")\n        writer.write_datetime_value(\"firstSeenDateTime\", self.first_seen_date_time)\n        writer.write_datetime_value(\"lastSeenDateTime\", self.last_seen_date_time)\n        writer.write_str_value(\"@odata.type\", self.odata_type)\n        writer.write_int_value(\"port\", self.port)\n        writer.write_additional_data_value(self.additional_data)\n    \n\n","repo_name":"microsoftgraph/msgraph-beta-sdk-python","sub_path":"msgraph_beta/generated/models/security/host_ssl_certificate_port.py","file_name":"host_ssl_certificate_port.py","file_ext":"py","file_size_in_byte":3421,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"48"}
+{"seq_id":"38826734864","text":"import streamlit as st\r\nfrom streamlit_option_menu import option_menu\r\nfrom predict_page import show_predict_page\r\nfrom home_page import show_home_page\r\nfrom contact_page import show_contact_page\r\nfrom feedback_page import show_feedback_page\r\n\r\n#page = st.sidebar.selectbox(\"Explore Or Predict\", (\"Predict\", \"Explore\"))\r\nselected = option_menu(\r\n    menu_title=None,\r\n    options=[\"Home\",\"Predict\",\"Contact\",\"Feedback\"],\r\n    icons = [\"house\", \"search\", \"envelope\",\"envelope-paper\"],\r\n    menu_icon=\"cast\",\r\n    default_index=0,\r\n    orientation=\"horizontal\",\r\n)\r\nif selected == \"Home\":\r\n    show_home_page()\r\nelif selected == \"Predict\":\r\n    show_predict_page()\r\nelif selected == \"Contact\":\r\n    show_contact_page()\r\nelif selected == \"Feedback\":\r\n    show_feedback_page()\r\n","repo_name":"msc137808/pi_application","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":774,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"26252832976","text":"import datetime\nimport requests\nimport ssl\nimport OpenSSL\nimport socket\nimport csv\nimport boto3\n\n\nlocal_profiles = {'prod', 'dev'}\nclient = boto3.client('route53')\ngo_daddy_key = \"\"\ngo_daddy_secret = \"\"\nauth_key = 'sso-key ' + go_daddy_key + ':' + go_daddy_secret\n\ndump = [['Host', 'Issuer', 'Start', 'End', 'Expired', 'Subject']]\n\ndef rchop(string, ending):\n  if string.endswith(ending):\n    return string[:-len(ending)]\n  return string\n\ndef cert_check(hostname):\n    hostname = rchop(hostname, \".\")\n    print(hostname)\n    port = 443\n    row = []\n    row.append(hostname)\n    try:\n        socket.setdefaulttimeout(1)\n        cert = ssl.get_server_certificate(\n            (hostname, port), ssl_version=ssl.PROTOCOL_TLSv1)\n        x509 = OpenSSL.crypto.load_certificate(OpenSSL.crypto.FILETYPE_PEM, cert)\n    except (ValueError, socket.error, socket.gaierror, socket.herror, socket.timeout):\n        row.append(\"No Avilable Certificate Or Endpoint Public Access\")\n        return row\n\n    try:\n        row.append(x509.get_issuer())\n        row.append(x509.get_notBefore().decode())\n        row.append(x509.get_notAfter().decode())\n        row.append(x509.has_expired())\n        row.append(x509.get_subject())\n    except:\n        row.append(\"No Avilable Certificate Or Endpoint Public Access\")\n        return row\n\n    return row\n\nfor profile in local_profiles:\n    session = boto3.Session(profile_name=profile)\n    client = session.client('route53')\n    zones = client.list_hosted_zones()\n\n    for zone in zones['HostedZones']:\n\n        if zone['Config']['PrivateZone'] != True:\n            records = client.list_resource_record_sets(HostedZoneId=zone['Id'])\n            for record in records['ResourceRecordSets']:\n                if record['Type'] in ['SOA', 'MX', 'NS', 'TXT', 'SRV']:\n                    continue\n                dump.append(cert_check(record['Name']))\n\n\nresponse = requests.get(\n            'https://api.godaddy.com/v1/domains?statuses=ACTIVE',\n            headers={'Authorization': auth_key},)\n\nfor domain in response.json():\n    url = 'https://api.godaddy.com/v1/domains/' + domain['domain'] + '/records'\n    response = requests.get(\n            url,\n            headers={'Authorization': auth_key},)\n    if 'code' in response.json():\n        continue\n    for record in response.json():\n        if record['type'] in ['SOA', 'MX', 'NS', 'TXT', 'SRV']:\n            continue\n        if record['name'] in ['@']:\n            fqdn = domain['domain']\n            dump.append(cert_check(fqdn))\n            continue\n        try:\n            fqdn = record['name'] + \".\" + domain['domain']\n        except:\n            fqdn = domain['domain']\n        dump.append(cert_check(fqdn))\n\nwith open('cert_report.csv', 'w') as csvFile:\n    writer = csv.writer(csvFile)\n    writer.writerows(dump)\n\ncsvFile.close()\n","repo_name":"orzaida87/Prism","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2815,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"11754765788","text":"from data import BachChoralesDataset\nfrom model import *\n\nfrom torch.utils.data import Dataset, DataLoader\nimport torch\nimport torch.nn\n\nimport matplotlib.pyplot as plt\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\n\ndef train_loop(model, opt, loss_fn, dataloader):\n\n    model.train()\n    total_loss = 0\n\n    for h_notes, m_notes in dataloader:\n\n        h_notes = h_notes.to(device)\n        m_notes = m_notes.to(device)\n\n        m_notes_input = m_notes[:, :-1]\n        m_notes_expect = m_notes[:, 1:]\n\n        sequence_length = m_notes_input.size(1)\n        tgt_mask = model.get_tgt_mask(sequence_length).to(device)\n\n        pred = model(h_notes, m_notes_input, tgt_mask)\n\n        # Permute pred to have batch size first again\n        pred = pred.permute(1, 2, 0)\n\n        loss = loss_fn(pred, m_notes_expect)\n\n        opt.zero_grad()\n        loss.backward()\n        opt.step()\n\n        total_loss += loss.detach().item()\n\n    return total_loss / len(dataloader)\n\n\ndef validation_loop(model, loss_fn, dataloader):\n\n    model.eval()\n    total_loss = 0\n\n    with torch.no_grad():\n        for h_notes, m_notes in dataloader:\n            h_notes = h_notes.to(device)\n            m_notes = m_notes.to(device)\n\n            m_notes_input = m_notes[:, :-1]\n            m_notes_expect = m_notes[:, 1:]\n\n            sequence_length = m_notes_input.size(1)\n            tgt_mask = model.get_tgt_mask(sequence_length).to(device)\n\n            pred = model(h_notes, m_notes_input, tgt_mask)\n\n            # Permute pred to have batch size first again\n            pred = pred.permute(1, 2, 0)\n            loss = loss_fn(pred, m_notes_expect)\n\n            total_loss += loss.detach().item()\n\n    return total_loss / len(dataloader)\n\n\ndef fit(model, opt, loss_fn, train_dataloader, val_dataloader, epochs):\n\n    # Used for plotting later on\n    train_loss_list, validation_loss_list = [], []\n\n    print(\"Training and validating model\")\n    for epoch in range(epochs):\n        print(\"-\" * 25, f\"Epoch {epoch + 1}\", \"-\" * 25)\n\n        train_loss = train_loop(model, opt, loss_fn, train_dataloader)\n        train_loss_list += [train_loss]\n\n        validation_loss = validation_loop(model, loss_fn, val_dataloader)\n        validation_loss_list += [validation_loss]\n\n        print(f\"Training loss: {train_loss:.4f}\")\n        print(f\"Validation loss: {validation_loss:.4f}\")\n        print()\n\n        torch.save(model, f\"model/Epoch_{epoch + 1}.pt\")\n\n    return train_loss_list, validation_loss_list\n\n\ndef main():\n    data = BachChoralesDataset('dataset')\n\n    train_data, val_data= torch.utils.data.random_split(data, [0.8, 0.2])\n\n    train_dataloader = DataLoader(\n        dataset=train_data,\n        batch_size=1,\n        shuffle=True\n    )\n\n    val_dataloader = DataLoader(\n        dataset=val_data,\n        batch_size=1,\n        shuffle=True\n    )\n\n    model = Transformer(\n        num_tokens=88 + 1 + 1 + 1, dim_model=512, num_heads=8, num_encoder_layers=3, num_decoder_layers=3, dropout_p=0.1\n    ).to(device)\n\n    opt = torch.optim.Adam(model.parameters(), lr=1e-4, eps=1e-9)\n    loss_fn = nn.CrossEntropyLoss()\n\n    train_loss_list, validation_loss_list = fit(model, opt, loss_fn, train_dataloader, val_dataloader, 20)\n\n    x = list(range(1, 21))\n\n    plt.style.use('seaborn-deep')\n    plt.plot(x, train_loss_list, label='Train Loss')\n    plt.plot(x, validation_loss_list, label='Validation Loss')\n    plt.xlabel('Epoch')\n    plt.legend()\n    plt.show()\n\n\nif __name__ == '__main__':\n\n    main()\n","repo_name":"yangqu2000/TransformerDuet","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3489,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"30932921732","text":"# from Teste.Testador import Testes\n# t = Testes('C:/Users/Tiago/PycharmProjects/URIonline/Teste/Respostas.txt')\n\nwhile True:\n    ordem = int(input())\n    if ordem == 0:\n        break\n    matriz = []\n    i, f = 1, ordem\n    for x in range(ordem):\n        aux = []\n        if x == 0:\n            aux = list(range(1, ordem + 1))\n        elif x + 1 == ordem:\n            aux = list(range(ordem, 0, -1))\n        else:\n            for p in range(i, 0, -1):\n                aux.append(p)\n            for m in range(2, f + 1):\n                aux.append(m)\n        i += 1\n        f -= 1\n        matriz.append(aux)\n\n    for linha in range(ordem):\n        saida = ''\n        for coluna in range(ordem):\n            saida += ' %3d' % matriz[linha][coluna]\n        print(saida[1:])\n    print('')\n","repo_name":"dlimatiago/uri-online","sub_path":"Iniciante/URI_1478.py","file_name":"URI_1478.py","file_ext":"py","file_size_in_byte":785,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"14024117182","text":"import logging\nimport math\nimport datetime\nimport astro_constants\nimport astro_utils\nimport astro_sun\n\n# Initialise logging module\nlogging.root.handlers = []\nlogging.basicConfig(\n    format='%(asctime)s %(levelname)-8s %(message)s',\n    # datefmt='%Y-%m-%d %H:%M:%S',\n    level=logging.INFO\n    # level=logging.DEBUG\n    )\n\ndef main():\n    print('')\n    print('Sunrise/Sunset')\n    print('')\n\n    year = 1979\n    month = 9\n    day = 7\n\n    lat = 51.860435\n    long = -1.596303\n\n    astro_sun.sunRiseSet(year, month, day, lat, long)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"ajpowell/practical_astronomy","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":572,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"20608656061","text":"\"\"\"Code for scraping from FromHot.\"\"\"\nimport logging\nimport time\n\nimport requests\nfrom bs4 import BeautifulSoup\n\nfrom streamscrape.utils import get_ip_address\n\nlogger = logging.getLogger(__name__)\n\nHOME = \"http://www.fromhot.com\"\n\n\ndef scrape():\n    \"\"\"Scrape FromHot.\n\n    :return: A list of {\"timestamp\": _, \"url\": _, \"ip\": _}\n    \"\"\"\n    all_urls = []\n    page = requests.get(HOME)\n    soup = BeautifulSoup(page.text, \"html.parser\")\n\n    # Search through \"Open Video\" links\n    for video_link in soup.find_all(\"a\", title=\"Open Video\"):\n        try:\n            url = video_link.attrs[\"href\"]\n        except KeyError:\n            continue\n        event_data = {\n            \"timestamp\": int(time.time()),\n            \"url\": url,\n            \"ip\": get_ip_address(url),\n        }\n        logger.debug(\"URL: {}\".format(event_data))\n        all_urls.append(event_data)\n    return all_urls\n","repo_name":"hudson-ayers/safe-sports-streams","sub_path":"src/streamscrape/other/fromhot.py","file_name":"fromhot.py","file_ext":"py","file_size_in_byte":887,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"1508738709","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Dec 12 11:30:31 2017\n\n@author: diana\n\"\"\"\n\nfrom conlluFilesOperations import ConlluFileReader, ConlluFileWriter\nfrom sentenceDependencies import createSentenceDependencies\nfrom dataProcessor import buildDictionaries\nimport torch\nfrom mst import mst\nimport numpy as np\nfrom torch.autograd import Variable\n\nuseEnglish = False # change to false for Dutch\n\nmodelId = \"{}17_12\".format(\"\" if useEnglish else \"Dutch_\")  # change this each run\nfilename = \"results/DependencyParserModel_{}.pkl\".format(modelId)\nmodel = torch.load(filename)\n\ntestFileName = r\"UD_English/en-ud-test.conllu\" if useEnglish else r\"UD_Dutch/nl-ud-test.conllu\"\ntestSentencesReader = ConlluFileReader(testFileName)\ntestSentences = testSentencesReader.readSentencesDependencies('<root>')\n\ntrainFileName = r\"UD_English/en-ud-train.conllu\" if useEnglish else r\"UD_Dutch/nl-ud-train.conllu\"\n\n # These are needed for sentence prep\ntrainSentencesReader = ConlluFileReader(trainFileName)\ntrainingSet = trainSentencesReader.getSentenceDependenciesUnknownMarker('<unk>')\n\n\n######################### Remove this when done testing\n\n#trainSentencesReader = ConlluFileReader(r\"UD_English/en-ud-train.conllu\")\n#trainingSet = trainSentencesReader.readSentencesDependencies('<root>')\n#\n### Just for dummy data testing \n#nSentencesToUse = 5\n#nSentencesSoFar = 0\n#newSentencesDependencies = []\n#i = 0\n#while(nSentencesSoFar < nSentencesToUse):\n#    if(7<= len(trainingSet[i].tokens) <= 9):\n#        newSentencesDependencies.append(trainingSet[i])\n#        nSentencesSoFar += 1\n#    i += 1\n#\n#trainingSet = newSentencesDependencies\n\n########################\n\n\nw2i, t2i, _, _, _, i2l = buildDictionaries(trainingSet, '<unk>')\n\nsentencesDepsPredictions = []\n\nfor s in testSentences:\n    # Input prep\n    sentenceInWords, sentenceInTags = s.getSentenceInWordsAndInTags() # Getting tokens and tags\n\n    wordsToIndices = [w2i[w] for w in sentenceInWords]\n    words_tensor = torch.LongTensor(wordsToIndices)\n    \n    tagsToIndices = [t2i[t] for t in sentenceInTags]\n    tags_tensor = torch.LongTensor(tagsToIndices)\n    \n    scoreMatrix = model.predictArcs(Variable(words_tensor), Variable(tags_tensor))    \n    headsForWords = mst(scoreMatrix.data.numpy())    \n    \n    labelsMatrix = model.predictLabels(torch.LongTensor(headsForWords))\n    labelsForWords = np.argmax(labelsMatrix.data.numpy(), axis=1)\n    \n    sentencesDepsPredictions.append(createSentenceDependencies(sentenceInWords, sentenceInTags, headsForWords, [i2l[l] for l in labelsForWords]))\n\nwriter = ConlluFileWriter('output/predictions_{}.conllu'.format(modelId))\nwriter.write(sentencesDepsPredictions)","repo_name":"sabinavanrooij/nlp-dep-parser","sub_path":"evaluate.py","file_name":"evaluate.py","file_ext":"py","file_size_in_byte":2675,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"18368491397","text":"import numpy as np\nfrom PIL import Image\n\n# open theta matrix file Theta11\nTheta11 = np.loadtxt('../Theta11')\nTheta22 = np.loadtxt('../Theta22')\n\n\ndef predict_image(image_path):\n    img = np.array(Image.open(image_path))\n    img = img.reshape(1600, 1)\n    return predict(img)\n\n\ndef sigmoid(x):\n    return 1 / (1 + np.exp(-x))\n\n\ndef predict(image_pixels):\n    input_layer = np.append(1, image_pixels)\n\n    hidden_layer = sigmoid(np.dot(Theta11, input_layer))\n    hidden_layer = np.append(1, hidden_layer)\n\n    output_layer = sigmoid(np.dot(Theta22, hidden_layer))\n\n    # find index of max value in output_layer\n    ind = np.argmax(output_layer)\n    labels = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']\n    return labels[ind]\n","repo_name":"platon-p/nn-sign-language","sub_path":"predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":731,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"44131597721","text":"#!/usr/bin/env python3\nimport string\nimport sys\nimport json\nimport os\nimport itertools\nfrom distutils.util import strtobool\n\nclass Checker():\n    \"\"\"\n    This class is a simple plagiarism checker for case insensitive matching.\n    \"\"\"\n    def __init__(self, input_text, master_text, check_synonyms=True, chck_len=6):\n        \"\"\"\n        Feed two strings of text into this class. The second string\n        is the text to check against for plagiarism.\n        \"\"\"\n        self.input_text = input_text\n        self.master_text = master_text\n        self.check_synonyms = bool(strtobool(check_synonyms))\n        self.chck_len = chck_len\n        self.synonyms = json.load(open(os.path.dirname(os.path.abspath(__file__)) +'/support/src.json', 'r'))\n        self.prep()\n        self.check()\n\n    def prep(self):\n        translator = str.maketrans('', '', string.punctuation)\n        #remove punctuation\n        self.input_list = self.input_text.translate(translator)\n\n        #put the input text into a list\n        self.input_list = self.input_list.split()\n\n        # make a master list\n        self.mlist = self.master_text.translate(translator)\n        self.mlist = self.mlist.split()\n        \n    def check_syns(self):\n        self.mutator_list = []\n        for pos, word in enumerate(self.check_str):\n            \n            if word in self.synonyms.keys():\n                temp_syns = []\n\n                for key, value in self.synonyms[word].items():\n                    temp_syns += value\n\n                self.mutator_list.append(list(set(temp_syns)))\n                    \n            else:\n                self.mutator_list.append([word])\n            \n        # iterate all possible combos           \n        self.master_list = list(itertools.product(*self.mutator_list))\n        \n        for some_list in self.master_list:\n            # check for plagiarism\n            result = any(list(some_list) == self.mlist[k:self.chck_len+k] for k in range(len(self.mlist) -1))\n            if result:\n                print (\"[!] Found plagiarism:\", list(some_list))\n                print (\"=\" * 50)\n\n    def check(self):\n        # take the chck_len width, make a subset and check in master iterate\n        print (\"[*] Processing...\")\n        if self.check_synonyms is True:\n            print(\"[*] Checking Synonyms\")\n        \n        result =''\n        \n        for i,j in enumerate(self.input_list):\n            self.master_list = []\n            self.check_str = self.input_list[i:self.chck_len+i]\n            if len(self.check_str) < self.chck_len:\n                continue\n            #print(\" [*] Block:\", self.check_str)\n            if self.check_str not in self.master_list:\n                self.master_list.append(self.check_str)\n            \n            # capture each of the synonyms and assign them on location\n            # Example [[1, A, B, C, D],[2],[3, A, B], [4, A, B , C]]\n            if self.check_synonyms is True:\n                self.check_syns()\n            else:\n                result = any(list(self.check_str) == self.mlist[k:self.chck_len+k] for k in range(len(self.mlist) -1))\n    \n                if result:\n                    print (\"[!] Found plagiarism:\", list(self.check_str))\n                    print (\"=\" * 50)        \n\n\nif __name__ == \"__main__\":\n    # This is meant to be used as an imported class so you must provide files and check_length here \n    # FYI, this is just for author testing.. feel free to use this, but no support...\n\n    quick = Checker(open(sys.argv[1], 'r').read(), open(sys.argv[2], 'r').read(), sys.argv[3], int(sys.argv[4]))\n","repo_name":"secretsquirrel/PlagiarismChecker","sub_path":"check.py","file_name":"check.py","file_ext":"py","file_size_in_byte":3581,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"48"}
+{"seq_id":"43561922968","text":"import string\n\ndef alphabet_position(letter):\n    value = 0\n    letter = letter.upper()\n    Capital = string.ascii_uppercase\n\n    for i in range(len(Capital)):\n        if letter == Capital[i]:\n            value = ord(Capital[i]) - 65\n\n    return value\n\ndef rotate_character(char, rot):\n    \"\"\"Checks to see if the answer is a string data type and Checks to see if\n    the string in an actual letter.  Rotates the letter within 26 if it is an\n    actual letter\"\"\"\n    if type(char) is str and char.isalpha() == True:\n        char_value = alphabet_position(char)\n        char_position = (char_value + rot) % 26\n        if ord(char) < 96:\n            return chr(char_position + 65)\n        else:\n            return chr(char_position + 97)\n    else:  #if answer is not a letter, returns the answer\n        return char","repo_name":"mcwilli94/web-caesar","sub_path":"helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":813,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"38498839938","text":"import json\nimport os\n\nfrom pattern_TS import detectDS\n\nfrom pattern_BN import detectBN\nfrom pattern_DC import detectDC\nfrom pattern_OF import detectOF\nfrom pattern_RE import detectRE\nfrom pattern_UE import detectUE\nfrom libs import splitOpcode\nfrom libs import decompressSourcemap\nfrom libs import load\n\nfrom libs import readContracts\n\n\ndef readtxt(file):\n    with open(file, 'r')as f:\n        data = f.read()\n        return data\n\n\ndef findTarget(contents, contract, opcodeList, List, length):\n    opcode_output = []\n    #print(length)\n    for i in range(len(List)):\n        if i >= length:\n            break\n        offset = List[i][0]\n        l = List[i][1]\n        if opcodeList[i][1].strip() == 'JUMPI':\n            offset = List[i][0]\n            l = List[i][1]\n            snippet = contract[offset:offset + l]\n            #print(snippet)\n            #print(contents)\n            if not snippet.strip().startswith(\"contract\"):\n                    for j in contents:\n                        for k in j:\n                            if str(k).rstrip(';') in snippet or snippet in str(k):\n                                opcode_output.append(int(opcodeList[i][0], 16))\n    return opcode_output\n\n\ndef findPosition(inputFileDir, file):\n    inputFilePath = inputFileDir + file\n    FileName = file.split('.')[0]\n    inputFileName = inputFileDir.split(\"../\")[1] + file\n    inputAsmPath = inputFileDir+FileName+'.asm'\n    opcode_outputs = {}\n    name = file.split(\".\")[0]\n    opcode_output2 = []\n    List2 = []\n    l2 = []\n    opcode_output = []\n    targets = {}\n    contents=[]\n    try :\n        data = load(inputFilePath+'.json')\n        srcmap_runtime = data[\"contracts\"][inputFileName + \":\" + name][\"srcmap-runtime\"]\n    except:\n        print(\"empty json file!\")\n        return \"\",False\n    contract = readContracts(inputFilePath)\n    try :\n        opcodes_runtime = readtxt(inputAsmPath)\n    except:\n        print(\"empty asm file!\")\n        return \"\", False\n    contents.append(detectDS(inputFilePath))\n    contents.append(detectBN(inputFilePath))\n    contents.append(detectOF(inputFilePath))\n    contents.append(detectRE(inputFilePath))\n    contents.append(detectDC(inputFilePath))\n    contents.append(detectUE(inputFilePath))\n    srcList_runtime = srcmap_runtime\n    try:\n        List2 = decompressSourcemap(srcList_runtime)\n        opcodeList = splitOpcode(opcodes_runtime)\n        l2 = len(List2)\n    except:\n        print(\"empty runtime file!\")\n        return \"\",False\n\n    try:\n      opcode_output2 = findTarget(contents, contract, opcodeList, List2, len(opcodeList))\n    except:\n        print(\"empty asm file!\")\n        return \"\", False\n    for i in opcode_output2:\n        opcode_output.append(i)\n    # opcode_output.append(opcode_output2)\n    # for i in range(len(List1)):\n    #     if i>len(opcodes)-len(opcodes_runtime):\n    #         break\n    #     if opcodeList[i][1].strip() =='JUMPI':\n    #         offset = List1[i][0]\n    #         l = List1[i][1]\n    #         snippet = contract[offset:offset + l]\n    #         if not snippet.strip().startswith(\"contract\"):\n    #             for k in contents[0]:\n    #                 if str(k) in snippet:\n    #                     opcode_output.append(int(opcodeList[i][0], 16))\n    #                     print(snippet)\n    # for i in range(len(List2)):\n    #     if opcodeList2[i][1].strip() == 'JUMPI':\n    #         offset = List2[i][0]\n    #         l = List2[i][1]\n    #         snippet = contract[offset:offset + l]\n    #         if not snippet.strip().startswith(\"contract\"):\n    #             for k in contents[0]:\n    #                 if str(k) in snippet:\n    #                     opcode_output.append(int(opcodeList2[i][0], 16))\n    #                     print(snippet)\n    opcode_outputs[name] = list(set(opcode_output))\n    # print(opcode_outputs)\n    return opcode_outputs,True\n\n\ndef TSDetection():\n    inputFileDir = \"../contracts/\"\n    outputFileDir = \"../branch_msg/targets.json\"\n    outputs = {}\n    Files = os.listdir(inputFileDir)\n    for dir in Files:\n        dir = inputFileDir + dir + '/'\n        files = os.listdir(dir)\n        for file in files:\n            if len(file.split(\".\")) <= 2 and file.split(\".\")[1] == \"sol\":\n                output,flag = findPosition(dir, file)\n                if not flag:\n                    continue\n                outputs.update(output)\n    with open(outputFileDir, 'w') as f:\n        json.dump(outputs, f)\n\nif __name__ == '__main__':\n    TSDetection()","repo_name":"Messi-Q/IR-Fuzz","sub_path":"tools/get_targetLoc.py","file_name":"get_targetLoc.py","file_ext":"py","file_size_in_byte":4473,"program_lang":"python","lang":"en","doc_type":"code","stars":32,"dataset":"github-code","pt":"48"}
+{"seq_id":"72981575185","text":"from common import lines\n\nimport collections\nfrom queue import PriorityQueue\n\n\ndef search(graph, start, end):\n    seen = set()\n\n    remaining = PriorityQueue()\n    remaining.put((0, (0, 0)))\n\n    while remaining:\n        cost, vertex = remaining.get()\n        if vertex in seen:\n            continue\n\n        seen.add(vertex)\n\n        for neighbour, distance in graph[vertex]:\n            if neighbour == end:\n                return cost + distance\n\n            if neighbour in seen:\n                continue\n\n            remaining.put((cost + distance, neighbour))\n\n\ndef make_graph(grid):\n    graph = collections.defaultdict(list)\n\n    for i, row in enumerate(grid):\n        for j, n in enumerate(row):\n            for a, b in [(-1, 0), (0, -1), (1, 0), (0, 1)]:\n                if 0 <= i + a < len(grid) and 0 <= j + b < len(row):\n                    graph[i, j].append(((i + a, j + b), grid[i + a][j + b]))\n\n    return graph\n\ngrid = [\n    [int(c) for c in line]\n    for line in lines\n]\n\ngraph = make_graph(grid)\nprint('Part 1:', search(graph, min(graph), max(graph)))\n\ngrid = [\n    [\n        ((n + a + b - 1) % 9 + 1)\n        for a in range(5)\n        for n in row\n    ]\n    for b in range(5)\n    for row in grid\n]\n\ngraph = make_graph(grid)\nprint('Part 2:', search(graph, min(graph), max(graph)))\n","repo_name":"avayert/aoc2021","sub_path":"src/15.py","file_name":"15.py","file_ext":"py","file_size_in_byte":1300,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"8671506763","text":"#import akshare as ak\n#stock_zh_a_hist_df = ak.stock_zh_a_hist(symbol=\"000001\", period=\"daily\", start_date=\"20170301\", end_date='20210907', adjust=\"\")\n#print(stock_zh_a_hist_df)\nimport akshare as ak\nfrom pandas import Series,DataFrame\nimport pandas as pd\n#前复权拉去股票数\ndef fetch_index(index):\n    stock_df = ak.stock_zh_index_daily_tx(symbol=index)\n    stock_df[\"pcnt\"] = (stock_df[\"close\"] - stock_df[\"close\"].shift(1)) / stock_df['close'] * 100\n    stock_df[\"pcnt\"].fillna(0)\n\n    # 长线指标 1d, 10d, 20d, 60d, 120d, 240d\n    peried = [1,5,10,20,60,120,240]\n    for p in peried:\n        stock_high_max=compute_m_day_max(stock_df, p)\n        stock_low_min=compute_m_day_min(stock_df, p)\n        stock_df['pcnt_'+str(p)+'d'] = (stock_high_max - stock_low_min) / stock_df['close'] * 100\n\n    stock_high_max=compute_m_day_max(stock_df, 250)\n    stock_low_min=compute_m_day_min(stock_df, 250)\n    stock_df['max_price'] = stock_high_max\n    stock_df['min_price'] = stock_low_min\n    stock_high_max_500=compute_m_day_max(stock_df, 500)\n    stock_low_min_500=compute_m_day_min(stock_df, 500) \n    stock_df['max_price_500d'] = stock_high_max_500\n    stock_df['min_price_500d'] = stock_low_min_500\n\n    stock_df.to_csv(index + \".csv\", date_format='%Y-%m-%d')\n\ndef compute_m_day_max(df, day):\n    max_df = df['high']\n    for i in range(1,day+1):\n        max_df = DataFrame([df['high'].shift(i), max_df]).T.max(axis=1)\n    return max_df\n\ndef compute_m_day_min(df, day):\n    min_df = df['low']\n    for i in range(1,day+1):\n        min_df = DataFrame([df['low'].shift(i), min_df]).T.min(axis=1)\n    return min_df\n\nif __name__ == '__main__':\n    stock = pd.read_csv('stock.csv')\n    for index in stock['code']:\n        print('start fetch: ' + index)\n        fetch_index(index)","repo_name":"foxapple/etf-quant","sub_path":"data/fetch.py","file_name":"fetch.py","file_ext":"py","file_size_in_byte":1779,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"38710491974","text":"model_params = {\n    'naive-bayes': {},\n    'naive-bayes-g': {'priors': [0.9, 0.1]},\n    'naive-bayes-m': {'fit_prior': True, 'alpha': 0.01, 'class_prior': [0.8, 0.2]},\n    'naive-bayes-b': {'binarize': 10, 'fit_prior': True, 'alpha': 0.01, 'class_prior': [0.8, 0.2]},\n    'decision-tree': {'criterion': 'gini', 'max_features': None, 'min_impurity_decrease': .0001},\n    'knn': {},\n    'svm': {'dual': True, 'max_iter': 200},\n    'rf': {'criterion': 'gini', 'n_estimators': 200, 'min_impurity_decrease': 0.0,\n           'min_samples_split': 8, 'min_samples_leaf': 2},\n    'et': {'criterion': 'gini', 'n_estimators': 200, 'min_impurity_decrease': 0.0,\n           'min_samples_split': 8, 'min_samples_leaf': 2},\n    'logreg': {'fit_intercept': True, 'penalty': 'l2', 'dual': False},\n    'xgb': {}\n}\n","repo_name":"RedTachyon/Sklearn-modelling","sub_path":"settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":797,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"38952680740","text":"# _*_ coding: utf-8 _*_\n\nfrom django.conf.urls import url\nfrom .views import PatentListView, PatentDetailView, AddPatentView, ModifyView\n\n# from django.urls import path, re_path\n\napp_name = \"patent\"\n\nurlpatterns = [\n    # 专利展示\n    url(r'^list/$', PatentListView.as_view(), name='list'),\n    url(r'^detail/(?P<patent_id>\\d+)/', PatentDetailView.as_view(), name='detail'),\n    url(r'^add_patent/$', AddPatentView.as_view(), name='add_patent'),\n    url(r'^modify/(?P<patent_id>\\d+)/', ModifyView.as_view(), name='modify'),\n\n]\n","repo_name":"kingvern/txplatform","sub_path":"apps/patent/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":531,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"34057768367","text":"# takes in user input, and checks to see if it is a valid choice.  Returns int representation of choice\ndef isValid(input):\n    if input == \"Salmonella enterica\":\n        return 1\n    elif input == \"Bacillus cereus\":\n        return 2\n    elif input == \"Listeria\":\n        return 3\n    elif input == \"Brochothrix thermosphacta\":\n        return 4\n    elif input == \"None\":\n        return 0\n    else:\n        return -1\n\n\ndef isNumber(str):\n    try:\n        float(str)\n        return True\n    except ValueError:\n        return False\n\n\ndef dataFilter():\n    selection = -1\n    filters = [0, -1, -1]  # array to hold filers, 0 - name filter, 1 - lower bound, 2 - upper bound\n\n    while selection == -1:\n        selection = isValid(input(\"Enter the type of Bacteria you would like to filter (enter 'None' if you don't wish to filter):\"))\n        if selection == -1: print(\"Invalid Input, Please Try Again\")\n\n    filters[0] = selection\n\n    selection = \"scooby doo\"\n\n    while not isNumber(selection) and not selection == \"-1\":  # lower bound\n        selection = input(\"Enter the lower bound for the range filter (enter '-1' value if you don't wish to filter):\")\n        if not isNumber(selection) and selection != \"-1\": print(\"Please Input A Valid Integer\")\n\n    filters[1] = float(selection)\n\n    if float(selection) < 0:  # if selection is neg, then return filters\n        return filters\n\n    selection = \"chocolate chip scooby snack\"\n    isLessThan = True\n\n    while not isNumber(selection) or isLessThan:  # upper bound\n        selection = input(\"Enter the upper bound for the range filter:\")\n        if not isNumber(selection): print(\"Please Input A Valid Integer\")\n        elif float(selection) < filters[1]: print(\"Upper Bound Must Be Greater Than Lower Bound\")\n        else: isLessThan = False\n\n    filters[2] = float(selection)\n\n    return filters\n\n\ndef filterArray(data, filtered, filter):  # takes in data and filter arrays, and returns filtered array\n\n    filtered = data.copy()\n\n    if filter[0] > 0:  # if has item to filter\n        filtered = filtered[filter[0] != filtered[:, 2]]\n\n    if filter[1] >= 0:\n        filtered = filtered[filter[1] <= filtered[:, 1]]\n        filtered = filtered[filter[2] >= filtered[:, 1]]\n\n    return filtered","repo_name":"joshrudi/02631_Project1","sub_path":"filter.py","file_name":"filter.py","file_ext":"py","file_size_in_byte":2247,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"35089563530","text":"import copy\nfrom typing import Optional, List\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn, Tensor\n\nimport warnings\nfrom typing import Tuple, Optional\n\nimport torch\nfrom torch import Tensor\nfrom torch.nn.modules.linear import Linear\nfrom torch.nn.init import xavier_uniform_\nfrom torch.nn.init import constant_\nfrom torch.nn.init import xavier_normal_\nfrom torch.nn.parameter import Parameter\nfrom torch.nn.modules.module import Module\nfrom torch.nn import functional as F\n\nimport warnings\nimport math\n\nfrom torch._C import _infer_size, _add_docstr\nfrom torch.nn import _reduction as _Reduction\nfrom torch.nn.modules import utils\nfrom torch.nn.modules.utils import _single, _pair, _triple, _list_with_default\nfrom torch.nn import grad\nfrom torch import _VF\nfrom torch._jit_internal import boolean_dispatch, List, Optional, _overload, Tuple\ntry:\n    from torch.overrides import has_torch_function, handle_torch_function\nexcept:\n    from torch._overrides import has_torch_function, handle_torch_function\nTensor = torch.Tensor\n\nfrom torch.nn.functional import linear, pad, softmax, dropout\n\nclass MultiheadAttention(Module):\n    r\"\"\"Allows the model to jointly attend to information\n    from different representation subspaces.\n    See reference: Attention Is All You Need\n    .. math::\n        \\text{MultiHead}(Q, K, V) = \\text{Concat}(head_1,\\dots,head_h)W^O\n        \\text{where} head_i = \\text{Attention}(QW_i^Q, KW_i^K, VW_i^V)\n    Args:\n        embed_dim: total dimension of the model.\n        num_heads: parallel attention heads.\n        dropout: a Dropout layer on attn_output_weights. Default: 0.0.\n        bias: add bias as module parameter. Default: True.\n        add_bias_kv: add bias to the key and value sequences at dim=0.\n        add_zero_attn: add a new batch of zeros to the key and\n                       value sequences at dim=1.\n        kdim: total number of features in key. Default: None.\n        vdim: total number of features in value. Default: None.\n        Note: if kdim and vdim are None, they will be set to embed_dim such that\n        query, key, and value have the same number of features.\n    Examples::\n        >>> multihead_attn = nn.MultiheadAttention(embed_dim, num_heads)\n        >>> attn_output, attn_output_weights = multihead_attn(query, key, value)\n    \"\"\"\n    bias_k: Optional[torch.Tensor]\n    bias_v: Optional[torch.Tensor]\n\n    def __init__(self, embed_dim, num_heads, dropout=0., bias=True, add_bias_kv=False, add_zero_attn=False, kdim=None, vdim=None):\n        super(MultiheadAttention, self).__init__()\n        self.embed_dim = embed_dim\n        self.kdim = kdim if kdim is not None else embed_dim\n        self.vdim = vdim if vdim is not None else embed_dim\n        self._qkv_same_embed_dim = self.kdim == embed_dim and self.vdim == embed_dim\n\n        self.num_heads = num_heads\n        self.dropout = dropout\n        self.head_dim = embed_dim // num_heads\n        assert self.head_dim * num_heads == self.embed_dim, \"embed_dim must be divisible by num_heads\"\n\n        vdim = vdim if vdim is not None else embed_dim\n        self.out_proj = Linear(vdim , vdim)\n\n        self.in_proj_bias = None\n        self.in_proj_weight = None\n        self.bias_k = self.bias_v = None\n        self.q_proj_weight = None\n        self.k_proj_weight = None\n        self.v_proj_weight = None\n\n        self.add_zero_attn = add_zero_attn\n\n        self._reset_parameters()\n\n    def _reset_parameters(self):\n        constant_(self.out_proj.bias, 0.)\n\n    def __setstate__(self, state):\n        # Support loading old MultiheadAttention checkpoints generated by v1.1.0\n        if '_qkv_same_embed_dim' not in state:\n            state['_qkv_same_embed_dim'] = True\n\n        super(MultiheadAttention, self).__setstate__(state)\n\n    def forward(self, query, key, value, key_padding_mask=None,\n                need_weights=True, attn_mask=None):\n        # type: (Tensor, Tensor, Tensor, Optional[Tensor], bool, Optional[Tensor]) -> Tuple[Tensor, Optional[Tensor]]\n        r\"\"\"\n    Args:\n        query, key, value: map a query and a set of key-value pairs to an output.\n            See \"Attention Is All You Need\" for more details.\n        key_padding_mask: if provided, specified padding elements in the key will\n            be ignored by the attention. When given a binary mask and a value is True,\n            the corresponding value on the attention layer will be ignored. When given\n            a byte mask and a value is non-zero, the corresponding value on the attention\n            layer will be ignored\n        need_weights: output attn_output_weights.\n        attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all\n            the batches while a 3D mask allows to specify a different mask for the entries of each batch.\n    Shape:\n        - Inputs:\n        - query: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is\n          the embedding dimension.\n        - key: :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is\n          the embedding dimension.\n        - value: :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is\n          the embedding dimension.\n        - key_padding_mask: :math:`(N, S)` where N is the batch size, S is the source sequence length.\n          If a ByteTensor is provided, the non-zero positions will be ignored while the position\n          with the zero positions will be unchanged. If a BoolTensor is provided, the positions with the\n          value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged.\n        - attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length.\n          3D mask :math:`(N*\\text{num_heads}, L, S)` where N is the batch size, L is the target sequence length,\n          S is the source sequence length. attn_mask ensure that position i is allowed to attend the unmasked\n          positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend\n          while the zero positions will be unchanged. If a BoolTensor is provided, positions with ``True``\n          is not allowed to attend while ``False`` values will be unchanged. If a FloatTensor\n          is provided, it will be added to the attention weight.\n        - Outputs:\n        - attn_output: :math:`(L, N, E)` where L is the target sequence length, N is the batch size,\n          E is the embedding dimension.\n        - attn_output_weights: :math:`(N, L, S)` where N is the batch size,\n          L is the target sequence length, S is the source sequence length.\n        \"\"\"\n        if not self._qkv_same_embed_dim:\n            return multi_head_attention_forward(\n                query, key, value, self.embed_dim, self.num_heads,\n                self.in_proj_weight, self.in_proj_bias,\n                self.bias_k, self.bias_v, self.add_zero_attn,\n                self.dropout, self.out_proj.weight, self.out_proj.bias,\n                training=self.training,\n                key_padding_mask=key_padding_mask, need_weights=need_weights,\n                attn_mask=attn_mask, use_separate_proj_weight=True,\n                q_proj_weight=self.q_proj_weight, k_proj_weight=self.k_proj_weight,\n                v_proj_weight=self.v_proj_weight, out_dim=self.vdim)\n        else:\n            return multi_head_attention_forward(\n                query, key, value, self.embed_dim, self.num_heads,\n                self.in_proj_weight, self.in_proj_bias,\n                self.bias_k, self.bias_v, self.add_zero_attn,\n                self.dropout, self.out_proj.weight, self.out_proj.bias,\n                training=self.training,\n                key_padding_mask=key_padding_mask, need_weights=need_weights,\n                attn_mask=attn_mask, out_dim=self.vdim)\n\n\ndef multi_head_attention_forward(query: Tensor,\n                                 key: Tensor,\n                                 value: Tensor,\n                                 embed_dim_to_check: int,\n                                 num_heads: int,\n                                 in_proj_weight: Tensor,\n                                 in_proj_bias: Tensor,\n                                 bias_k: Optional[Tensor],\n                                 bias_v: Optional[Tensor],\n                                 add_zero_attn: bool,\n                                 dropout_p: float,\n                                 out_proj_weight: Tensor,\n                                 out_proj_bias: Tensor,\n                                 training: bool = True,\n                                 key_padding_mask: Optional[Tensor] = None,\n                                 need_weights: bool = True,\n                                 attn_mask: Optional[Tensor] = None,\n                                 use_separate_proj_weight: bool = False,\n                                 q_proj_weight: Optional[Tensor] = None,\n                                 k_proj_weight: Optional[Tensor] = None,\n                                 v_proj_weight: Optional[Tensor] = None,\n                                 static_k: Optional[Tensor] = None,\n                                 static_v: Optional[Tensor] = None,\n                                 out_dim: Optional[Tensor] = None\n                                 ) -> Tuple[Tensor, Optional[Tensor]]:\n    r\"\"\"\n    Args:\n        query, key, value: map a query and a set of key-value pairs to an output.\n            See \"Attention Is All You Need\" for more details.\n        embed_dim_to_check: total dimension of the model.\n        num_heads: parallel attention heads.\n        in_proj_weight, in_proj_bias: input projection weight and bias.\n        bias_k, bias_v: bias of the key and value sequences to be added at dim=0.\n        add_zero_attn: add a new batch of zeros to the key and\n                       value sequences at dim=1.\n        dropout_p: probability of an element to be zeroed.\n        out_proj_weight, out_proj_bias: the output projection weight and bias.\n        training: apply dropout if is ``True``.\n        key_padding_mask: if provided, specified padding elements in the key will\n            be ignored by the attention. This is an binary mask. When the value is True,\n            the corresponding value on the attention layer will be filled with -inf.\n        need_weights: output attn_output_weights.\n        attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all\n            the batches while a 3D mask allows to specify a different mask for the entries of each batch.\n        use_separate_proj_weight: the function accept the proj. weights for query, key,\n            and value in different forms. If false, in_proj_weight will be used, which is\n            a combination of q_proj_weight, k_proj_weight, v_proj_weight.\n        q_proj_weight, k_proj_weight, v_proj_weight, in_proj_bias: input projection weight and bias.\n        static_k, static_v: static key and value used for attention operators.\n    Shape:\n        Inputs:\n        - query: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is\n          the embedding dimension.\n        - key: :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is\n          the embedding dimension.\n        - value: :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is\n          the embedding dimension.\n        - key_padding_mask: :math:`(N, S)` where N is the batch size, S is the source sequence length.\n          If a ByteTensor is provided, the non-zero positions will be ignored while the zero positions\n          will be unchanged. If a BoolTensor is provided, the positions with the\n          value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged.\n        - attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length.\n          3D mask :math:`(N*num_heads, L, S)` where N is the batch size, L is the target sequence length,\n          S is the source sequence length. attn_mask ensures that position i is allowed to attend the unmasked\n          positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend\n          while the zero positions will be unchanged. If a BoolTensor is provided, positions with ``True``\n          are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor\n          is provided, it will be added to the attention weight.\n        - static_k: :math:`(N*num_heads, S, E/num_heads)`, where S is the source sequence length,\n          N is the batch size, E is the embedding dimension. E/num_heads is the head dimension.\n        - static_v: :math:`(N*num_heads, S, E/num_heads)`, where S is the source sequence length,\n          N is the batch size, E is the embedding dimension. E/num_heads is the head dimension.\n        Outputs:\n        - attn_output: :math:`(L, N, E)` where L is the target sequence length, N is the batch size,\n          E is the embedding dimension.\n        - attn_output_weights: :math:`(N, L, S)` where N is the batch size,\n          L is the target sequence length, S is the source sequence length.\n    \"\"\"\n    if not torch.jit.is_scripting():\n        tens_ops = (query, key, value, in_proj_weight, in_proj_bias, bias_k, bias_v,\n                    out_proj_weight, out_proj_bias)\n        if any([type(t) is not Tensor for t in tens_ops]) and has_torch_function(tens_ops):\n            return handle_torch_function(\n                multi_head_attention_forward, tens_ops, query, key, value,\n                embed_dim_to_check, num_heads, in_proj_weight, in_proj_bias,\n                bias_k, bias_v, add_zero_attn, dropout_p, out_proj_weight,\n                out_proj_bias, training=training, key_padding_mask=key_padding_mask,\n                need_weights=need_weights, attn_mask=attn_mask,\n                use_separate_proj_weight=use_separate_proj_weight,\n                q_proj_weight=q_proj_weight, k_proj_weight=k_proj_weight,\n                v_proj_weight=v_proj_weight, static_k=static_k, static_v=static_v)\n    tgt_len, bsz, embed_dim = query.size()\n    assert embed_dim == embed_dim_to_check\n    # allow MHA to have different sizes for the feature dimension\n    assert key.size(0) == value.size(0) and key.size(1) == value.size(1)\n\n    head_dim = embed_dim // num_heads\n    v_head_dim = out_dim // num_heads\n    assert head_dim * num_heads == embed_dim, \"embed_dim must be divisible by num_heads\"\n    scaling = float(head_dim) ** -0.5\n\n    q = query * scaling\n    k = key\n    v = value\n\n    if attn_mask is not None:\n        assert attn_mask.dtype == torch.float32 or attn_mask.dtype == torch.float64 or \\\n            attn_mask.dtype == torch.float16 or attn_mask.dtype == torch.uint8 or attn_mask.dtype == torch.bool, \\\n            'Only float, byte, and bool types are supported for attn_mask, not {}'.format(attn_mask.dtype)\n        if attn_mask.dtype == torch.uint8:\n            warnings.warn(\"Byte tensor for attn_mask in nn.MultiheadAttention is deprecated. Use bool tensor instead.\")\n            attn_mask = attn_mask.to(torch.bool)\n\n        if attn_mask.dim() == 2:\n            attn_mask = attn_mask.unsqueeze(0)\n            if list(attn_mask.size()) != [1, query.size(0), key.size(0)]:\n                raise RuntimeError('The size of the 2D attn_mask is not correct.')\n        elif attn_mask.dim() == 3:\n            if list(attn_mask.size()) != [bsz * num_heads, query.size(0), key.size(0)]:\n                raise RuntimeError('The size of the 3D attn_mask is not correct.')\n        else:\n            raise RuntimeError(\"attn_mask's dimension {} is not supported\".format(attn_mask.dim()))\n        # attn_mask's dim is 3 now.\n\n    # convert ByteTensor key_padding_mask to bool\n    if key_padding_mask is not None and key_padding_mask.dtype == torch.uint8:\n        warnings.warn(\"Byte tensor for key_padding_mask in nn.MultiheadAttention is deprecated. Use bool tensor instead.\")\n        key_padding_mask = key_padding_mask.to(torch.bool)\n\n    if bias_k is not None and bias_v is not None:\n        if static_k is None and static_v is None:\n            k = torch.cat([k, bias_k.repeat(1, bsz, 1)])\n            v = torch.cat([v, bias_v.repeat(1, bsz, 1)])\n            if attn_mask is not None:\n                attn_mask = pad(attn_mask, (0, 1))\n            if key_padding_mask is not None:\n                key_padding_mask = pad(key_padding_mask, (0, 1))\n        else:\n            assert static_k is None, \"bias cannot be added to static key.\"\n            assert static_v is None, \"bias cannot be added to static value.\"\n    else:\n        assert bias_k is None\n        assert bias_v is None\n\n    q = q.contiguous().view(tgt_len, bsz * num_heads, head_dim).transpose(0, 1)\n    if k is not None:\n        k = k.contiguous().view(-1, bsz * num_heads, head_dim).transpose(0, 1)\n    if v is not None:\n        v = v.contiguous().view(-1, bsz * num_heads, v_head_dim).transpose(0, 1)\n\n    if static_k is not None:\n        assert static_k.size(0) == bsz * num_heads\n        assert static_k.size(2) == head_dim\n        k = static_k\n\n    if static_v is not None:\n        assert static_v.size(0) == bsz * num_heads\n        assert static_v.size(2) == v_head_dim\n        v = static_v\n\n    src_len = k.size(1)\n\n    if key_padding_mask is not None:\n        assert key_padding_mask.size(0) == bsz\n        assert key_padding_mask.size(1) == src_len\n\n    if add_zero_attn:\n        src_len += 1\n        k = torch.cat([k, torch.zeros((k.size(0), 1) + k.size()[2:], dtype=k.dtype, device=k.device)], dim=1)\n        v = torch.cat([v, torch.zeros((v.size(0), 1) + v.size()[2:], dtype=v.dtype, device=v.device)], dim=1)\n        if attn_mask is not None:\n            attn_mask = pad(attn_mask, (0, 1))\n        if key_padding_mask is not None:\n            key_padding_mask = pad(key_padding_mask, (0, 1))\n\n    attn_output_weights = torch.bmm(q, k.transpose(1, 2))\n    assert list(attn_output_weights.size()) == [bsz * num_heads, tgt_len, src_len]\n\n    if attn_mask is not None:\n        if attn_mask.dtype == torch.bool:\n            attn_output_weights.masked_fill_(attn_mask, float('-inf'))\n        else:\n            attn_output_weights += attn_mask\n\n\n    if key_padding_mask is not None:\n        attn_output_weights = attn_output_weights.view(bsz, num_heads, tgt_len, src_len)\n        attn_output_weights = attn_output_weights.masked_fill(\n            key_padding_mask.unsqueeze(1).unsqueeze(2),\n            float('-inf'),\n        )\n        attn_output_weights = attn_output_weights.view(bsz * num_heads, tgt_len, src_len)\n\n    # attn_output_weights = softmax(\n    #     attn_output_weights, dim=-1)\n    attn_output_weights = softmax(\n            attn_output_weights - attn_output_weights.max(dim=-1, keepdim=True)[0], dim=-1)\n    attn_output_weights = dropout(attn_output_weights, p=dropout_p, training=training)\n\n    attn_output = torch.bmm(attn_output_weights, v)\n    assert list(attn_output.size()) == [bsz * num_heads, tgt_len, v_head_dim]\n    attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, out_dim)\n    attn_output = linear(attn_output, out_proj_weight, out_proj_bias)\n\n    if need_weights:\n        # average attention weights over heads\n        attn_output_weights = attn_output_weights.view(bsz, num_heads, tgt_len, src_len)\n        return attn_output, attn_output_weights.sum(dim=1) / num_heads\n    else:\n        return attn_output, None\n\n","repo_name":"IDEA-Research/DINO","sub_path":"models/dino/attention.py","file_name":"attention.py","file_ext":"py","file_size_in_byte":19570,"program_lang":"python","lang":"en","doc_type":"code","stars":1768,"dataset":"github-code","pt":"48"}
+{"seq_id":"39927022808","text":"# -*- coding: utf-8 -*-\n\nimport re\nimport urlparse\nimport urllib2\nimport time\nfrom datetime import datetime\nimport robotparser\nimport Queue\n\n\ndef link_crawler(seed_url, link_regex=None, delay=5, max_depth=-1, headers=None, \nuser_agent='wswp', proxy=None, num_retries=1):\n    \"\"\"\n    Crawl from the given seed URL following links matched by link_regex\n    \"\"\"\n    # the queue of URL's that still need to be crawled\n    crawl_queue = Queue.deque([seed_url]) \n    # the URL's that have been seen and at what depth\n    seen = {seed_url: 0}\n    # track how many URL's have been downloaded\n    num_urls = 0\n    rp = get_robots(seed_url)\n    throttle = Throttle(delay)\n    headers = headers or {}\n    if user_agent:\n        headers['User-agent'] = user_agent\n\n    while crawl_queue:\n        url = crawl_queue.pop()\n        html = download(url)\n        # filter for links matching our regular expression\n        for link in get_links(html):\n            if re.match(link_regex, link):\n                # form absolute link\n                link = urlparse.urljoin(seed_url, link)\n                # check if have already seen this link\n                if link not in seen:\n                    seen.add(link)\n                    crawl_queue.append(link)\n\n\nclass Throttle:\n    \"\"\"\n    Throttle downloading by sleeping between requests to same domain\n    \"\"\"\n    def __init__(self, delay):\n        # amount of delay between downloads for each domain\n        self.delay = delay\n        # timestamp of when a domain was last accessed\n        self.domains = {}\n\ndef get_robots(url):\n    \"\"\"\n    Initialize robots parser for this domain\n    \"\"\" \n    rp = robotparser.RobotFileParser()\n    rp.set_url(urlparse.urljoin(url, '/robots.txt'))\n    rp.read()\n    return rp\n\n\ndef get_links(html):\n    \"\"\"\n    Return a list of links from html\n    \"\"\"\n    # a regular expression to extract all links from the webpage\n    webpage_regex = re.compile('<a[^>]+href=[\"\\'](.*?)[\"\\']'), re.IGNORECASE\n    # list of all links from webpage_regex\n    return webpage_regex.findall(html)\n\n\nif __name__ == '__main__':\n    link_crawler('http://example.webscraping.com', '/(index|view)')\n\n","repo_name":"Tweibro/crawler","sub_path":"link_crawler3.py","file_name":"link_crawler3.py","file_ext":"py","file_size_in_byte":2145,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"70178633745","text":"#!/usr/bin/env python3\n# encoding: utf-8\n\n\nfrom flask import Blueprint, render_template, flash, url_for, redirect\nfrom flask_login import current_user, login_user\nfrom jobplus.forms import UserinfoForm, SeekerRegisterForm\nfrom jobplus.models import Seeker\n\nseeker = Blueprint('seeker', __name__, url_prefix='/user')\n\n\n@seeker.route('/profile', methods=['GET', 'POST'])\ndef profile():\n    if not current_user.is_seeker:\n        return redirect(url_for('front.index'))\n    form = UserinfoForm()\n    form.load_seeker_info()\n    if not Seeker.query.filter_by(user_id=current_user.id).first():\n        flash('请完善个人信息', 'success')\n    if form.validate_on_submit():\n        form.update_user()\n        flash('更新成功', 'success')\n    return render_template('user.html', form=form)\n\n\n@seeker.route('/register', methods=['GET', 'POST'])\ndef register():\n    form = SeekerRegisterForm()\n    if current_user.is_authenticated:\n        # 如果用户已登录，并尝试打开注册页面，会被跳转到profile\n        if current_user.is_seeker:\n            return redirect(url_for('.profile'))\n        else:\n            return redirect(url_for('front.index'))\n    if form.validate_on_submit():\n        user = form.create_user()\n        # 注册后登录\n        login_user(user, True)\n        flash('注册成功，请完善个人信息', 'success')\n        return redirect(url_for('.profile'))\n    return render_template('seeker_register.html', form=form)\n\n","repo_name":"LouPlus/jobplus3-6","sub_path":"jobplus/handlers/seeker.py","file_name":"seeker.py","file_ext":"py","file_size_in_byte":1467,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"48"}
+{"seq_id":"16835355487","text":"from __future__ import print_function\n\nimport numpy as np\nimport unittest\nimport sys\nsys.path.append(\"..\")\nfrom op_test import OpTest\nimport paddle\nimport paddle.fluid as fluid\nfrom paddle.fluid import core\n\npaddle.enable_static()\nSEED = 2021\n\n\nclass TestFillConstantBatchSizeLike(OpTest):\n    def setUp(self):\n        self.set_npu()\n        self.place = paddle.NPUPlace(0)\n        self.op_type = \"fill_constant_batch_size_like\"\n        self.init_shape()\n        self.init_value()\n        self.init_dtype()\n        self.init_force_cpu()\n        self.init_dim_idx()\n\n        self.inputs = {\n            'Input': np.random.random(self.input_shape).astype(\"float32\")\n        }\n        self.attrs = {\n            'shape': self.shape,\n            'value': self.value,\n            'str_value': self.str_value,\n            'dtype': self.dtype,\n            'force_cpu': self.force_cpu,\n            'input_dim_idx': self.input_dim_idx,\n            'output_dim_idx': self.output_dim_idx\n        }\n        self.outputs = {\n            'Out': np.full(self.output_shape, self.output_value,\n                           self.output_dtype)\n        }\n\n    def set_npu(self):\n        self.__class__.use_npu = True\n\n    def init_shape(self):\n        self.input_shape = [4, 5]\n        self.shape = [123, 92]\n        self.output_shape = (4, 92)\n\n    def init_value(self):\n        self.value = 3.8\n        self.str_value = ''\n        self.output_value = 3.8\n\n    def init_dtype(self):\n        self.dtype = core.VarDesc.VarType.FP32\n        self.output_dtype = np.float32\n\n    def init_force_cpu(self):\n        self.force_cpu = False\n\n    def init_dim_idx(self):\n        self.input_dim_idx = 0\n        self.output_dim_idx = 0\n\n    def test_check_output(self):\n        self.check_output_with_place(self.place)\n\n\nclass TestFillConstantBatchSizeLike2(TestFillConstantBatchSizeLike):\n    def init_shape(self):\n        # test shape\n        self.input_shape = [4, 5, 6, 7]\n        self.shape = [10, 123, 92]\n        self.output_shape = (4, 123, 92)\n\n\nclass TestFillConstantBatchSizeLike3(TestFillConstantBatchSizeLike):\n    def init_value(self):\n        # use 'str_value' rather than 'value'\n        self.value = 3.8\n        self.str_value = '4.5'\n        self.output_value = 4.5\n\n\nclass TestFillConstantBatchSizeLike4(TestFillConstantBatchSizeLike):\n    def init_value(self):\n        # str_value = 'inf'\n        self.value = 3.8\n        self.str_value = 'inf'\n        self.output_value = float('inf')\n\n\nclass TestFillConstantBatchSizeLike5(TestFillConstantBatchSizeLike):\n    def init_value(self):\n        # str_value = '-inf'\n        self.value = 3.8\n        self.str_value = '-inf'\n        self.output_value = -float('inf')\n\n\nclass TestFillConstantBatchSizeLike6(TestFillConstantBatchSizeLike):\n    def init_dtype(self):\n        self.dtype = core.VarDesc.VarType.FP16\n        self.output_dtype = np.float16\n\n    def test_check_output(self):\n        self.check_output_with_place(self.place, atol=1e-2)\n\n\nclass TestFillConstantBatchSizeLike7(TestFillConstantBatchSizeLike):\n    def init_dtype(self):\n        self.dtype = core.VarDesc.VarType.INT32\n        self.output_dtype = np.int32\n\n\nclass TestFillConstantBatchSizeLike8(TestFillConstantBatchSizeLike):\n    def init_force_cpu(self):\n        self.force_cpu = True\n\n\nclass TestFillConstantBatchSizeLike9(TestFillConstantBatchSizeLike):\n    def init_shape(self):\n        self.input_shape = [4, 5]\n        self.shape = [123, 92]\n        self.output_shape = (123, 4)\n\n    def init_dim_idx(self):\n        self.input_dim_idx = 0\n        self.output_dim_idx = 1\n\n\nclass TestFillConstantBatchSizeLikeLodTensor(TestFillConstantBatchSizeLike):\n    # test LodTensor\n    def setUp(self):\n        self.set_npu()\n        self.place = paddle.NPUPlace(0)\n        self.op_type = \"fill_constant_batch_size_like\"\n        self.init_shape()\n        self.init_value()\n        self.init_dtype()\n        self.init_force_cpu()\n        self.init_dim_idx()\n\n        lod = [[3, 2, 5]]\n        self.inputs = {\n            'Input': (np.random.random(self.input_shape).astype(\"float32\"), lod)\n        }\n        self.attrs = {\n            'shape': self.shape,\n            'value': self.value,\n            'str_value': self.str_value,\n            'dtype': self.dtype,\n            'force_cpu': self.force_cpu,\n            'input_dim_idx': self.input_dim_idx,\n            'output_dim_idx': self.output_dim_idx\n        }\n        self.outputs = {\n            'Out': np.full(self.output_shape, self.output_value,\n                           self.output_dtype)\n        }\n\n    def init_shape(self):\n        self.input_shape = [10, 20]\n        self.shape = [123, 92]\n        self.output_shape = (3, 92)\n\n\nclass TestFillConstantBatchSizeLikeLodTensor2(\n        TestFillConstantBatchSizeLikeLodTensor):\n    # test LodTensor with 'input_dim_idx' != 0\n    def init_shape(self):\n        self.input_shape = [10, 20]\n        self.shape = [123, 92]\n        self.output_shape = (20, 92)\n\n    def init_dim_idx(self):\n        self.input_dim_idx = 1\n        self.output_dim_idx = 0\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"EnnSou/ooss-paddle2.3","sub_path":"python/paddle/fluid/tests/unittests/npu/test_fill_constant_batch_size_like_op_npu.py","file_name":"test_fill_constant_batch_size_like_op_npu.py","file_ext":"py","file_size_in_byte":5100,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"48"}
+{"seq_id":"38753448437","text":"import smtplib\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\nfrom email.mime.application import MIMEApplication\nimport pandas as pd\nimport joblib\n\ndef UsarModelo(Excel_a_Analizar):\n    #Importamos el modelo\n    print(\"Estamos usando el modelo\")\n    ModeloClf=joblib.load('arbol_entrenado.pkl')\n    DataFrame=pd.read_excel(Excel_a_Analizar,sheet_name='Hoja1')\n    vIndependientes = DataFrame.iloc[:,6:16]\n    prediction = ModeloClf.predict(vIndependientes)\n    DataFrame['LAVADODEACTIVOS'] = prediction\n    NuevoExcel = 'datos_con_predicciones.xlsx'  # Ruta del archivo Excel generado\n    DataFrame.to_excel(NuevoExcel, index=False)\n    return NuevoExcel\n\n# Lista de destinatarios (agrega las direcciones de correo que desees)\ndestinatarios = ['teresa.atencio.robles@hotmail.com', 'brunzev98@gmail.com']\n\ndef Enviar(Excel_a_Enviar):\n    # Configuración del correo\n    print(\"Estamos enviando\")\n    smtp_server = 'smtp.outlook.com'\n    smtp_port = 587\n    smtp_username = 'teresa_atencio@usmp.pe'\n    smtp_password = '159875321t'\n    sender_email = 'teresa_atencio@usmp.pe'\n    #recipient_email = 'teresa.atencio.robles@hotmail.com'\n    subject = 'Archivo con las transaciones sospechozas'\n    message = 'Adjunto encontrarás el archivo con las predicciones.'\n\n    # Crear el mensaje de correo\n    msg = MIMEMultipart()\n    msg['From'] = sender_email\n    #msg['To'] = recipient_email\n    msg['Subject'] = subject\n    msg.attach(MIMEText(message, 'plain'))\n\n    # Adjuntar el archivo Excel\n    with open(Excel_a_Enviar, \"rb\") as excel_file:\n         excel_attachment = MIMEApplication(excel_file.read(), _subtype=\"xlsx\")\n         excel_attachment.add_header('Content-Disposition', f'attachment; filename={Excel_a_Enviar}')\n         msg.attach(excel_attachment)\n\n    # Conectar y enviar el correo\n    for recipient_email in destinatarios:\n        msg['To'] = recipient_email\n        try:\n            server = smtplib.SMTP(smtp_server, smtp_port)\n            server.starttls()\n            server.login(smtp_username, smtp_password)\n            server.sendmail(sender_email, recipient_email, msg.as_string())\n            server.quit()\n            print(\"Correo enviado exitosamente!\")\n        except Exception as e:\n            print(\"Error al enviar el correo:\", str(e))\n","repo_name":"Brunzev/Proyecto22","sub_path":"ScriptIA.py","file_name":"ScriptIA.py","file_ext":"py","file_size_in_byte":2305,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"4089269219","text":"import os\nimport glob\n\n################\n# File formats #\n################\n\nfor filename in glob.glob(os.path.join(\"_build\", \"html\", \"FileFormats\", \"*.html\")):\n    with open(filename, encoding=\"utf-8\") as inp:\n        text = inp.readlines()\n    with open(filename, \"w\", encoding=\"utf-8\") as output:\n        for line in text:\n            if line.startswith(\"<kbd><span class=\\\"option\\\">-\"):\n                output.write(\"<kbd><span class=\\\"option\\\">\" + line[27:])\n            else:\n                output.write(line)\n\n# Fix LaTeX\nfilename = os.path.join(\"_build\", \"latex\", \"OpenBabel.tex\")\nstartfixing = False\nif os.path.isfile(filename):\n    input = open(filename, \"r\")\n    text = input.readlines()\n    output = open(filename, \"w\")\n    for line in text:\n        if line.startswith(\"\\chapter{Supported File Formats and Options}\"):\n            startfixing = True\n        if startfixing and line.startswith(\"\\item [-\"):\n            output.write(\"\\item [\" + line[8:])\n        else:\n            output.write(line)\n\n#################\n# ---errorlevel #\n#################\n\ndef fix(filename, before, after):\n    inputfile = filename\n    fixed = False\n    if os.path.isfile(inputfile):\n        with open(inputfile, encoding=\"utf-8\") as inp:\n            text = inp.readlines()\n        with open(inputfile, \"w\", encoding=\"utf-8\") as output:\n            for line in text:\n                if line.startswith(before):\n                    output.write(after + line[len(before):])\n                    fixed = True\n                else:\n                    output.write(line)\n    return fixed\n\nfix(os.path.join(\"_build\", \"html\", \"Command-line_tools\", \"babel.html\"),\n    \"<kbd><span class=\\\"option\\\">--errorlevel\",\n    \"<kbd><span class=\\\"option\\\">---errorlevel\")\nfix(os.path.join(\"_build\", \"latex\", \"OpenBabel.tex\"),\n    \"\\item [-{-}errorlevel\",\n    \"\\item [-{-}{-}errorlevel\")\n\nprint(\"Fixed HTML and LaTeX\")\n","repo_name":"openbabel/documentation","sub_path":"FixFileFormatHTML.py","file_name":"FixFileFormatHTML.py","file_ext":"py","file_size_in_byte":1890,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"48"}
+{"seq_id":"19381004623","text":"import time\n\n# ДЗ: исправить ошибки подсчета времени, добавить комментарии, описать какую парадигму использовали.\n\n'''В данном коде используется объектно-ориентированная парадигма программирования. Класс Stopwatch определяет \nсостояние и поведение объекта секундомера. Класс содержит методы для запуска, паузы, возобновления, остановки и \nполучения времени работы секундомера. Код также содержит основной блок, который создает экземпляр класса Stopwatch \nи в цикле предоставляет пользователю возможность управлять секундомером.\n'''\n\nclass Stopwatch:\n    def __init__(self):  # конструктор класса\n                # Инициализация переменных состояния секундомера\n        self.start_time = None  \n        self.bool_pause_time = False  \n        self.pause_start_time = None \n        self.total_paused_time = 0  \n\n    def start(self):  # запускает или возобновляет секундомер\n        if not self.start_time:  \n            self.start_time = time.time()  \n        elif self.bool_pause_time:  \n            self.total_paused_time += time.time() - self.pause_start_time  \n            self.bool_pause_time = False \n\n    def pause(self):  # приостанавливает работу секундомера \n        if self.start_time and not self.bool_pause_time:  \n            self.bool_pause_time = True \n            self.pause_start_time = time.time()  \n\n    def resume(self):  # возобновляет работу секуномера после паузы\n        if self.bool_pause_time:  \n            self.bool_pause_time = False  \n            self.total_paused_time += time.time() - self.pause_start_time  \n\n    def stop(self):  # останавливает секундомер и сбрасывает все переменные\n        self.start_time = None \n        self.bool_pause_time = False\n        self.pause_start_time = None\n        self.total_paused_time = 0\n\n\n    def get_time(self):    # Получение времени работы секундомера\n        if self.start_time:\n            current_time = time.time()\n            if self.bool_pause_time:\n                return self.pause_start_time - self.start_time - self.total_paused_time\n            else:\n                return current_time - self.start_time - self.total_paused_time\n\n    def get_time_format(self):      # Получение времени работы секундомера в формате \"минуты:секунды\"\n        time = int(self.get_time())\n        min = time // 60\n        sec = time % 60\n        return f\"{min:02}: {sec:02}\"\n\n\nif __name__ == \"__main__\":  #\n    name = Stopwatch()\n    while True: #вывод доступных опций\n        print(\"1 - start\")\n        print(\"2 - pause\")\n        print(\"3 - continue\")\n        print(\"4 - stop\")\n        print(\"5 - exit\")\n\n        choice = input(\"Choose number: \")\n        if choice == \"1\":\n            name.start()\n        elif choice == \"2\":\n            name.pause()\n        elif choice == \"3\":\n            name.resume()\n        elif choice == \"4\":\n            name.stop()\n        elif choice == \"5\":\n            print(\"Exit\")\n            break\n# После выхода из цикла выводится общее время работы секундомера\n    total = name.get_time_format()\n    print(\"time\", total)","repo_name":"natalia3101/Paradigms","sub_path":"paradigmas/lesson6/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3808,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"19902410016","text":"from time import time\nfrom functools import reduce, wraps\n\n\ndef profile(func):\n    @wraps(func)\n    def wrapper(*args, **kwargs):\n        begin = time()\n        r = func(*args, **kwargs)\n        end = time()\n        print('실행 시간: {:.3f}초'.format(end - begin))\n        return r\n    return wrapper\n\n\n@profile\ndef factorial(n):\n    return reduce(lambda x, y: x * y, range(1, n + 1))\n\n\nprint(factorial(10000))\n","repo_name":"giinie/CSBootcamp","sub_path":"src/learn_python_the_right_way/decorator_profile.py","file_name":"decorator_profile.py","file_ext":"py","file_size_in_byte":416,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"38108303114","text":"import os, sys\nsys.path.insert(0, os.path.dirname(os.path.abspath(__file__)) + \"/../\")\nimport csv\nimport datetime\nimport math\nimport random\nimport time\nfrom collections import OrderedDict\nfrom itertools import product\nfrom pymcda.learning.meta_mrsort3 import MetaMRSortPop3\nfrom pymcda.learning.heur_mrsort_init_profiles import HeurMRSortInitProfiles\nfrom pymcda.learning.lp_mrsort_weights import LpMRSortWeights\nfrom pymcda.learning.heur_mrsort_profiles4 import MetaMRSortProfiles4\nfrom pymcda.learning.lp_mrsort_mobius import LpMRSortMobius\nfrom pymcda.learning.heur_mrsort_profiles_choquet import MetaMRSortProfilesChoquet\nfrom pymcda.types import CriterionValue, CriteriaValues\nfrom pymcda.types import Alternatives, Criteria, PerformanceTable\nfrom pymcda.types import AlternativesAssignments, Categories\nfrom pymcda.types import AlternativeAssignment\nfrom pymcda.electre_tri import MRSort\nfrom pymcda.generate import generate_alternatives\nfrom pymcda.generate import generate_categories_profiles\nfrom pymcda.generate import generate_random_profiles\nfrom pymcda.generate import generate_random_criteria_weights\nfrom pymcda.pt_sorted import SortedPerformanceTable\nfrom pymcda.utils import compute_ca\nfrom pymcda.utils import compute_confusion_matrix\nfrom test_utils import test_result, test_results\nfrom test_utils import load_mcda_input_data\nfrom test_utils import save_to_xmcda\n\nDATADIR = os.getenv('DATADIR', '%s/pymcda-data' % os.path.expanduser('~'))\n\nheur_init_profiles = HeurMRSortInitProfiles\nlp_weights = LpMRSortWeights\nheur_profiles = MetaMRSortProfiles4\n\ndef compute_assignments_majority(models_ca, pt):\n    models =  sorted(models_ca, key = lambda k: models_ca[k],\n                      reverse = True)\n\n    models_aa = {}\n    for model in models[:len(models)/2]:\n        models_aa[model] = model.get_assignments(pt)\n\n    aa = AlternativesAssignments()\n    for ap in pt:\n        aid = ap.id\n        cat_scores = {}\n        for model in models[:int(math.ceil(len(models) / 2))]:\n            cat = models_aa[model][aid].category_id\n            if cat in cat_scores:\n                cat_scores[cat] += models_ca[model]\n            else:\n                cat_scores[cat] = models_ca[model]\n\n        b = AlternativeAssignment(aid,\n                                  max(cat_scores,\n                                      key = lambda cat: cat_scores[cat]))\n        aa.append(b)\n\n    return aa\n\ndef compute_auc_majority(models_ca, pt):\n    models =  sorted(models_ca, key = lambda k: models_ca[k],\n                      reverse = True)\n\n    auc = 0\n    for model in models[:int(math.ceil(len(models) / 2))]:\n        aa = model.get_assignments(pt)\n        auc += model.auc(aa, pt) * models_ca[model]\n\n    return auc / (len(models) / 2)\n\ndef run_test(seed, data, pclearning, nloop, nmodels, nmeta):\n    random.seed(seed)\n\n    # Separate learning data and test data\n    pt_learning, pt_test = data.pt.split(2, [pclearning, 100 - pclearning])\n    aa_learning = data.aa.get_subset(pt_learning.keys())\n    aa_test = data.aa.get_subset(pt_test.keys())\n\n    # Initialize a random model\n    cat_profiles = generate_categories_profiles(data.cats)\n    worst = data.pt.get_worst(data.c)\n    best = data.pt.get_best(data.c)\n    b = generate_alternatives(len(data.cats) - 1, 'b')\n    bpt = None\n    cvs = None\n    lbda = None\n\n    model = MRSort(data.c, cvs, bpt, lbda, cat_profiles)\n\n    # Run the metaheuristic\n    t1 = time.time()\n\n    pt_sorted = SortedPerformanceTable(pt_learning)\n\n    # Algorithm\n    meta = MetaMRSortPop3(nmodels, model.criteria,\n                          model.categories_profiles.to_categories(),\n                          pt_sorted, aa_learning,\n                          heur_init_profiles,\n                          lp_weights,\n                          heur_profiles)\n\n    for i in range(0, nloop):\n        model, ca_learning = meta.optimize(nmeta)\n\n    t_total = time.time() - t1\n\n    aa_learning2 = compute_assignments_majority(meta.models, pt_learning)\n    ca_learning = compute_ca(aa_learning, aa_learning2)\n    auc_learning = compute_auc_majority(meta.models, pt_learning)\n    diff_learning = compute_confusion_matrix(aa_learning, aa_learning2,\n                                           model.categories)\n\n    # Compute CA of test setting\n    if len(aa_test) > 0:\n        aa_test2 = compute_assignments_majority(meta.models, pt_test)\n        ca_test = compute_ca(aa_test, aa_test2)\n        auc_test = compute_auc_majority(meta.models, pt_test)\n        diff_test = compute_confusion_matrix(aa_test, aa_test2,\n                                           model.categories)\n\n    else:\n        ca_test = 0\n        auc_test = 0\n        ncat = len(data.cats)\n        diff_test = OrderedDict([((a, b), 0) for a in model.categories \\\n                                             for b in model.categories])\n\n    # Compute CA of whole set\n    aa2 = compute_assignments_majority(meta.models, data.pt)\n    ca = compute_ca(data.aa, aa2)\n    auc = compute_auc_majority(meta.models, data.pt)\n    diff_all = compute_confusion_matrix(data.aa, aa2, model.categories)\n\n    t = test_result(\"%s-%d-%d-%d-%d-%d\" % (data.name, seed, nloop, nmodels,\n                                           nmeta, pclearning))\n\n    model.id = 'learned'\n    aa_learning.id, aa_test.id = 'learning_set', 'test_set'\n    pt_learning.id, pt_test.id = 'learning_set', 'test_set'\n    save_to_xmcda(\"%s/%s.bz2\" % (directory, t.test_name),\n                  aa_learning, aa_test, pt_learning, pt_test, *meta.models)\n\n    t['seed'] = seed\n    t['na'] = len(data.a)\n    t['nc'] = len(data.c)\n    t['ncat'] = len(data.cats)\n    t['pclearning'] = pclearning\n    t['nloop'] = nloop\n    t['nmodels'] = nmodels\n    t['nmeta'] = nmeta\n    t['na_learning'] = len(aa_learning)\n    t['na_test'] = len(aa_test)\n    t['ca_learning'] = ca_learning\n    t['ca_test'] = ca_test\n    t['ca_all'] = ca\n    t['auc_learning'] = auc_learning\n    t['auc_test'] = auc_test\n    t['auc_all'] = auc\n\n    for k, v in diff_learning.items():\n        t['learn_%s_%s' % (k[0], k[1])] = v\n    for k, v in diff_test.items():\n        t['test_%s_%s' % (k[0], k[1])] = v\n    for k, v in diff_all.items():\n        t['all_%s_%s' % (k[0], k[1])] = v\n\n    t['t_total'] = t_total\n\n    return t\n\ndef run_tests(nseeds, data, pclearning, nloop, nmodels, nmeta, filename):\n    # Create the CSV writer\n    f = open(filename, 'wb')\n    writer = csv.writer(f)\n\n    # Write the test options\n    writer.writerow(['data', data.name])\n    writer.writerow(['heur_init_profiles', heur_init_profiles.__name__])\n    writer.writerow(['lp_weights', lp_weights.__name__])\n    writer.writerow(['heur_profiles', heur_profiles.__name__])\n    writer.writerow(['nloop', nloop])\n    writer.writerow(['nmodels', nmodels])\n    writer.writerow(['nmeta', nmeta])\n    writer.writerow(['pclearning', pclearning])\n\n    # Create a test_results instance\n    results = test_results()\n\n    # Initialize the seeds\n    seeds = range(nseeds)\n\n    # Run the algorithm\n    initialized = False\n    for _pclearning, _nloop, _nmodels, _nmeta, seed in product(pclearning,\n                                        nloop, nmodels, nmeta, seeds):\n        t1 = time.time()\n        t = run_test(seed, data, _pclearning, _nloop, _nmodels, _nmeta)\n        t2 = time.time()\n\n        if initialized is False:\n            fields = t.get_attributes()\n            writer.writerow(fields)\n            initialized = True\n\n        t.tocsv(writer, fields)\n        f.flush()\n        print(\"%s (%5f seconds)\" % (t, t2 - t1))\n\n        results.append(t)\n\n    # Perform a summary\n    writer.writerow(['', ''])\n\n    t = results.summary(['na', 'nc', 'ncat', 'pclearning', 'na_learning',\n                         'na_test', 'nloop', 'nmodels', 'nmeta'],\n                        ['ca_learning', 'ca_test', 'ca_all',\n                         'auc_learning', 'auc_test', 'auc_all', 't_total'])\n    t.tocsv(writer)\n\nif __name__ == \"__main__\":\n    from optparse import OptionParser\n    from test_utils import read_single_integer, read_multiple_integer\n    from test_utils import read_csv_filename\n\n    parser = OptionParser(usage = \"python %s [options]\" % sys.argv[0])\n    parser.add_option(\"-c\", \"--choquet\", action = \"store_true\",\n                      dest = \"choquet\", default = False,\n                      help = \"use MR-Sort Choquet\")\n    parser.add_option(\"-i\", \"--csvfile\", action = \"store\", type=\"string\",\n                      dest = \"csvfile\",\n                      help = \"csv file with data\")\n    parser.add_option(\"-p\", \"--pclearning\", action = \"store\", type=\"string\",\n                      dest = \"pclearning\",\n                      help = \"Percentage of data to use in learning set\")\n    parser.add_option(\"-m\", \"--nmodels\", action = \"store\", type=\"string\",\n                      dest = \"nmodels\",\n                      help = \"Size of the population (of models)\")\n    parser.add_option(\"-l\", \"--max-loops\", action = \"store\", type=\"string\",\n                      dest = \"max_loops\",\n                      help = \"Max number of loops of the whole \"\n                             \"metaheuristic\")\n    parser.add_option(\"-o\", \"--max_oloops\", action = \"store\", type=\"string\",\n                      dest = \"max_oloops\",\n                      help = \"max number of loops for the metaheuristic \" \\\n                             \"used to find the profiles\")\n    parser.add_option(\"-s\", \"--nseeds\", action = \"store\", type=\"string\",\n                      dest = \"nseeds\",\n                      help = \"number of seeds\")\n    parser.add_option(\"-f\", \"--filename\", action = \"store\", type=\"string\",\n                      dest = \"filename\",\n                      help = \"filename to save csv output\")\n    (options, args) = parser.parse_args()\n\n    while not options.csvfile:\n        options.csvfile = raw_input(\"CSV file containing data ? \")\n    data = load_mcda_input_data(options.csvfile)\n    if data is None:\n        exit(1)\n\n    if options.choquet is True:\n        lp_weights = LpMRSortMobius\n        heur_profiles = MetaMRSortProfilesChoquet\n\n    options.pclearning = read_multiple_integer(options.pclearning,\n                                               \"Percentage of data to \" \\\n                                               \"use in the learning set\")\n    options.max_oloops = read_multiple_integer(options.max_oloops, \"Max \" \\\n                                               \"number of loops for \" \\\n                                               \"profiles' metaheuristic\")\n    options.nmodels = read_multiple_integer(options.nmodels,\n                                            \"Population size (models)\")\n    options.max_loops = read_multiple_integer(options.max_loops, \"Max \" \\\n                                              \"number of loops for the \" \\\n                                              \"whole metaheuristic\")\n    options.nseeds = read_single_integer(options.nseeds, \"Number of seeds\")\n\n    dt = datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n    default_filename = \"%s/test_meta_mrsort2-%s-%s.csv\" \\\n                       % (DATADIR, data.name, dt)\n    options.filename = read_csv_filename(options.filename, default_filename)\n\n    directory = options.filename + \"-data\"\n    if not os.path.exists(directory):\n        os.makedirs(directory)\n\n    run_tests(options.nseeds, data, options.pclearning, options.max_loops,\n              options.nmodels, options.max_oloops, options.filename)\n\n    print(\"Results saved in '%s'\" % options.filename)\n","repo_name":"minpegdwende/MRSort-jupyter-notebook","sub_path":"oso-pymcda/apps/test_meta_mrsort2_csv.py","file_name":"test_meta_mrsort2_csv.py","file_ext":"py","file_size_in_byte":11435,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"27735464133","text":"from sys import stdin\n\n\nDIRECTIONS = [\n    (-1, 0, 0),\n    (0, -1, 0),\n    (0, 0, -1),\n    (1, 0, 0),\n    (0, 1, 0),\n    (0, 0, 1),\n]\n\n\ndef add(a, b):\n    ax, ay, az = a\n    bx, by, bz = b\n    return ax + bx, ay + by, az + bz\n\n\ndef get_surface_area(cubes):\n    return sum(add(c, d) not in cubes for c in cubes for d in DIRECTIONS)\n\n\ndef main():\n    droplet = {tuple(int(w) for w in l.split(\",\")) for l in stdin}\n    print(get_surface_area(droplet))\n\n    min_x = min(x for x, y, z in droplet) - 1\n    min_y = min(y for x, y, z in droplet) - 1\n    min_z = min(z for x, y, z in droplet) - 1\n\n    max_x = max(x for x, y, z in droplet) + 1\n    max_y = max(y for x, y, z in droplet) + 1\n    max_z = max(z for x, y, z in droplet) + 1\n\n    box = {\n        (x, y, z)\n        for x in range(min_x, max_x + 1)\n        for y in range(min_y, max_y + 1)\n        for z in range(min_z, max_z + 1)\n    }\n\n    stack = [(min_x, min_y, min_z)]\n    mold = set()\n\n    while stack:\n        cube = stack.pop()\n\n        if cube in mold:\n            continue\n\n        mold.add(cube)\n\n        for direction in DIRECTIONS:\n            neighbor = add(cube, direction)\n\n            if neighbor not in box or neighbor in droplet:\n                continue\n\n            stack.append(neighbor)\n\n    print(get_surface_area(mold) - get_surface_area(box))\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"elemel/advent-of-code","sub_path":"python/2022/day_18/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1359,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"6587776190","text":"#Assume, you have been given two lists. [Your program should work for any two given lists; change the following lists and check whether your program works correctly for the code you have written]\n#Write a Python program that creates a new list with all the even elements of both of the given lists and prints the new list.\n#You may create a third list to store the even elements of the given lists.\n\nlist_one = [1, 2, 3, 4, 5, 6, 7, 8, 9]\nlist_two = [10, 11, 12, -13, -14, -15, -16]\n\nlist_one = [x for x in list_one if x % 2 == 0]\nlist_two = [x for x in list_two if x % 2 == 0]\nlist_one.extend(list_two)\nprint(list_one)","repo_name":"arnob016/PyClass","sub_path":"Lab Task 4 OOP2/lab task 4 8.py","file_name":"lab task 4 8.py","file_ext":"py","file_size_in_byte":619,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"48"}
+{"seq_id":"11631001581","text":"\"\"\"Basic Utils\"\"\"\nimport os\nimport time\nfrom multiprocessing import Pool\nfrom multiprocessing.pool import ThreadPool\n\n\ndef save_res(res: list, out_path: str) -> None:\n    \"\"\"save a result record to file\n    format should be: [ip, port, user, passwd, device, vulnerability]\n    \"\"\"\n    with open(out_path, 'a') as f:\n        f.write(f\"{','.join(res)}\\n\")\n\n\ndef run_time(func):\n    def wrapper(*args, **kwargs):\n        t0 = time.time()\n        res = func(*args, **kwargs)\n        print(f\"\\n>Time used: {time_formatter(time.time() - t0)}\")\n        return res\n    return wrapper\n\n\n# @run_time\ndef multi_process(func, items, processes=40):\n    \"\"\"multiprocess API\"\"\"\n    with Pool(processes) as pool:\n        res = pool.map_async(func, items).get()\n    return res\n\n\n# @run_time\ndef multi_thread(func, items, processes=40):\n    \"\"\"multiprocess API\"\"\"\n    with ThreadPool(processes) as pool:\n        res = pool.map_async(func, items).get()\n    return res\n\n\ndef output_formatter(info, color='green', bold=False, underline=False, flash=False):\n    \"\"\"output format\n    head + [bold/underline/flash] + color + info + tail\n    \"\"\"\n    head = '\\033['\n    tail = '\\033[0m'\n    _bold = '1;'\n    _underline = '4;'\n    _flash = '5;'\n    colors = {\n        'red' : '31m',\n        'green' : '32m',\n        'yellow' : '33m',\n        'blue' : '34m',\n        'pink' : '35m',\n        'cyan' : '36m',\n        'white' : '37m',\n    }\n    bold = _bold if bold else ''\n    underline = _underline if underline else ''\n    flash = _flash if flash else ''\n    color = colors[color] if color in colors else colors['green']\n\n    return head + bold + underline + flash + color + str(info) + tail\n\n\ndef printf(info, color='green', bold=False, underline=False, flash=False, *args, **kwargs):\n    print(output_formatter(info, color=color, bold=bold, underline=underline, flash=flash), *args, **kwargs)\n\n\ndef time_formatter(t: float) -> str:\n    \"\"\"format the time\"\"\"\n    if t > 60 * 60: return f\"{int(t / (60 * 60))}h \" + time_formatter(t % (60 * 60))\n    elif t > 60: return f\"{int(t / 60)}m \" + time_formatter(t % 60)\n    else: return f\"{int(t)}s\"\n\n\ndef process_bar(cidx=[0]):\n    def wrapper(total, done, found=0, timer=False, start_time=0):\n        \"\"\"since tqdm cant be used when we use mutiprocess\"\"\"\n        # icon\n        icon_list = '⇐⇖⇑⇗⇒⇘⇓⇙'\n        icon = output_formatter(icon_list[cidx[0]], color='green', bold=True)\n        cidx[0] = cidx[0] + 1 if cidx[0] < len(icon_list) - 1 else 0\n        icon = f\"[{icon}]\"\n\n        # time\n        if timer and start_time != 0:\n            time_used = time.time() - start_time\n            done = done + 1 if done == 0 else done  # avoid the devision number is zero\n            time_pred = time_used * (total / done)\n            time_used = output_formatter(time_formatter(time_used), color='cyan', bold=True)\n            time_pred = output_formatter(time_formatter(time_pred), color='white', bold=True)\n            _time = f\"Time: {time_used}/{time_pred}\"\n\n        # count\n        _total = output_formatter(total, color='blue', bold=True)\n        _done = output_formatter(done, color='blue', bold=True)\n        _percent = output_formatter(f\"{round(done / total * 100, 1)}%\", color='pink', bold=True)\n        _found = 'Found ' + output_formatter(found, color='red', bold=True) if found else ''\n        count = f\"{_done}/{_total}({_percent}) {_found}\"\n\n        print(f\"\\r{icon} {count}  {_time:<55}\", end='')\n    return wrapper\n\n\nif __name__ == '__main__':\n    found, t0 = 0, time.time()\n    bar = process_bar()\n    for i in range(1, 10001):\n        time.sleep(.05)\n        found += 1 if not i % 10 else 0\n        bar(10000, i, found, timer=True, start_time=t0)\n    print()\n","repo_name":"no1se2/No1sevuln","sub_path":"utils/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":3706,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"24848117660","text":"from django.urls import path\nfrom . import views\n#from users import views as user_views\n\nurlpatterns = [\n    path('', views.home, name='blog-home'),\n    path('task/', views.task, name='task-list'),\n    path('album/', views.album, name='album-list'),\n    #path('register', user_views.register, name='register'),\n]","repo_name":"masumwebz/blog","sub_path":"blog/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":312,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"6250743339","text":"# 2- Создайте программу для игры с конфетами человек против человека.\n# Условие задачи: На столе лежит 2021 конфета(или сколько вы зададите). Играют два игрока делая\n# ход друг после друга. Первый ход определяется жеребьёвкой. За один ход можно забрать\n# не более чем 28 конфет(или сколько вы зададите). Тот, кто берет последнюю конфету - проиграл.\n# Предусмотрите последний ход, ибо там конфет остается меньше.\n\n# a) Добавьте игру против бота\n# b) Подумайте как наделить бота \"интеллектом\"\n\nfrom random import randint\nfrom re import X\nimport math\n\n\ndef input_int_numbers(number) -> int:\n    \"\"\"\n    Проверка целого числа\n    \"\"\"\n    index = True\n    while index:\n        try:\n            number = int(number)\n            index = False\n        except ValueError:\n            print(\"Таким число конфет быть не может.\")\n            number = input(\"Попрубуйте ввести число конфет еще раз : \")\n            input_int_numbers(number)\n    return int(number)\n\n\ndef game_candies(number_candies, queue_players):\n    if queue_players == 1:\n        number_candies = first_player_go(number_candies)\n        queue_players = 0\n    else:\n        number_candies = second_player_go(number_candies)\n        queue_players = 1\n    if number_candies <= 0:\n        if queue_players==1:\n            print(\"you win\")\n        else :\n            print(\"you lose\")\n    else:\n            game_candies(number_candies,queue_players)\n\n\ndef first_player_go(number_candies):\n    take_you = input(\"Сколько вы конфет возьмете?\\n\")\n    take_you=input_int_numbers(take_you)\n    take_you = control_taken_sweets(take_you,number_candies)\n    number_candies = number_candies-take_you\n    return number_candies\n\n\ndef second_player_go(number_candies):\n    take_second=number_candies%28-1\n    if math.trunc(number_candies/28)>0:\n        take_second=number_candies%28-2\n    if take_second==0:\n        take_second=1\n    number_candies -= int(take_second)\n    print(\n        f\"Второй игрок взял {take_second}, осталось {number_candies} конфет.\")\n    return number_candies\n\n\ndef control_taken_sweets(taken_num, number_candies):\n    if 0 < taken_num < 29 and taken_num <= number_candies:\n        return int(taken_num)\n    else:\n        taken_num=input(\"Такое количество конфет вы не можете взять.\\n\\\n        Сколько вы конфет возьмете?\\n\")\n        taken_num=input_int_numbers(taken_num)\n        taken_num=control_taken_sweets(taken_num, number_candies)\n    return int(taken_num)\n\n\nnumber_candies = input(\"\\\n            На столе лежат конфеты.\\n\\\n            За один ход можно забрать не более чем 28 конфет.\\n\\\n            Тот, кто берет последнюю конфету - проиграл.\\n\\\nВведите начальное количество конфет : \")\ninput_int_numbers(number_candies)\ngame_candies(int(number_candies), queue_players=randint(0,2))\n\n","repo_name":"AlexandrFeldsherov/taskFive","sub_path":"task002.py","file_name":"task002.py","file_ext":"py","file_size_in_byte":3448,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"27785046129","text":"\n# coding: utf-8\n\n# In[3]:\n\n\nimport numpy as np\nimport pandas as pd        \nimport tensorflow as tf\nimport xgboost\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.metrics import classification_report\n\n\n# In[4]:\n\n\ndf = pd.read_csv(r'C:\\Users\\jfh470\\Desktop\\titanic\\train.csv')\n# randomizes data\ndf = df.sample(frac=1).reset_index(drop=True)\n\n# fills empty spaces\ndf['Embarked']=df['Embarked'].fillna('S') \ndf['Age']=df['Age'].fillna(df['Age'].dropna().median())\ndf['Fare'] = df['Fare'].fillna(df['Fare'].dropna().median)\n\n# changes categorical data to numerical\nlabel_encoder = LabelEncoder()\ndf['Sex'] = label_encoder.fit_transform(df['Sex'])\ndf['Embarked'] = label_encoder.fit_transform(df['Embarked'])\n\n# drops unnecessary columns\ndf = df.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1)\n\n# splits data into test and train\ndf_train, df_test = np.split(df, [int(.7*len(df))])\n\ndf_test = df_test.reset_index(drop=True)\n\nprint(df_train.info())\ndf_test.info()\n\n\n# In[5]:\n\n\nX_train = df_train.drop(['Survived'], axis=1)\ny_train = df_train['Survived']\n\n\n# In[6]:\n\n\nX_train.head(5)\n\n\n# In[7]:\n\n\nX_train = X_train.values\nX_test = df_test.drop(['Survived'], axis=1).values\ny_test = df_test['Survived'].values\n\nprint(X_test)\n\n\n# Gradient Boosting\n\n# In[6]:\n\n\nfrom sklearn.ensemble import GradientBoostingClassifier\nlearning_rates = [0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1]\nn_estimators = [50, 100, 150, 200, 250]\nfor learning_rate in learning_rates:\n    for n_estimator in n_estimators:\n        gb = GradientBoostingClassifier(n_estimators=n_estimator, learning_rate=learning_rate, max_features=7, max_depth=7)\n        gb.fit(X_train, y_train)\n        #     gb_predict = pd.DataFrame(gb.predict(X_test), columns=['Gradient Boost Predictions'])\n        print(n_estimator, \" estimators, learning rate of \", learning_rate, \", accuracy = \", gb.score(X_test, y_test))\n\n\n# Random Forest\n\n# In[8]:\n\n\nfrom sklearn.ensemble import RandomForestClassifier\n\nrandom_forest = RandomForestClassifier(n_estimators=871, max_features=5)\nrandom_forest.fit(X_train, y_train)\nrand_for_predict = random_forest.predict(X_test)\nprint(rand_for_predict)\nrand_for_predict = pd.DataFrame(rand_for_predict, columns=['Random Forest Predictions'])\nprint(random_forest.score(X_test, y_test))\n\n\n\n# XGBoost\n\n# In[9]:\n\n\nfrom xgboost import XGBClassifier\nxgb_model = xgboost.XGBClassifier(n_estimators=500, learning_rate=0.001, use_label_encoder=False)\nxgb_model.fit(X_train, y_train)\nxgb_predict = pd.DataFrame(xgb_model.predict(X_test), columns=['XGBoost Predictions'])\n\nprint(xgb_predict)\nxgb_model.score(X_test, y_test)\n\n\n# Neural Network\n\n# In[10]:\n\n\ndef get_batch(x_data, y_data, batch_size):\n    idxs = np.random.randint(0, len(y_data), batch_size)\n    return x_data[idxs,:], y_data[idxs]\n\n\n# In[11]:\n\n\nepochs = 250\nbatch_size = 100\nX_test = tf.Variable(X_test)\n\n\n# In[12]:\n\n\nw1 = tf.Variable(tf.random.normal([7, 128], stddev=0.03), name='w1')\nb1 = tf.Variable(tf.random.normal([128]), name='b1')\n\nw2 = tf.Variable(tf.random.normal([128, 10]), name='w2')\nb2 = tf.Variable(tf.random.normal([10]), name='b2')\n\n\n# In[13]:\n\n\ndef nn_model(x_input, w1, b1, w2, b2):\n    x = tf.add(tf.matmul(tf.cast(x_input, tf.float32), w1), b1)\n#     print(x)\n    x = tf.nn.relu(x)\n    logits = tf.add(tf.matmul(x, w2), b2)\n    return logits\n\n\n# In[14]:\n\n\ndef loss_fn(logits, labels):\n    cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=labels, logits=logits))\n    return cross_entropy\n\n\n# In[15]:\n\n\noptimizer = tf.keras.optimizers.Adam()\n\n\n# In[16]:\n\n\ntotal_batch = int(len(y_train)/batch_size)\naccuracy = 0\nprevious = -2\nfor epoch in range(epochs):\n    if accuracy > previous + 1:\n        avg_loss = 0\n        nn_predict = list()\n        for i in range(total_batch):\n            batch_x, batch_y = get_batch(X_train, y_train, batch_size=batch_size)\n            batch_x = tf.Variable(batch_x)\n            batch_y = tf.Variable(batch_y)\n            batch_y = tf.one_hot(batch_y, 10)\n            with tf.GradientTape() as tape:\n                logits = nn_model(batch_x, w1, b1, w2, b2)\n                loss = loss_fn(logits, batch_y)\n            gradients = tape.gradient(loss, [w1, b1, w2, b2])\n            optimizer.apply_gradients(zip(gradients, [w1, b1, w2, b2]))\n            avg_loss += loss/total_batch\n            test_logits = nn_model(X_test, w1, b1, w2, b2)\n        max_idxs = tf.argmax(test_logits, axis=1)\n        test_acc = np.sum(max_idxs.numpy() == y_test) / len(y_test)\n        print(f\"Epoch: {epoch + 1}, loss={avg_loss:.3f}, test set accuracy={test_acc*100:.3f}%\")\n        if (epoch + 1) % 5 == 0:\n            previous = accuracy\n            accuracy = test_acc*100\n        nn_predict.append(max_idxs.numpy())\n    else:\n        break\nnn_predict = pd.DataFrame(nn_predict, index=['Neural Network Predictions']).transpose()\nprint(nn_predict)\n\n\n# In[17]:\n\n\noutput = pd.concat([df_test, rand_for_predict, xgb_predict, nn_predict], axis=1)\nprint(output.head(5))\n# output.to_csv('titanic_output.csv')\n\n\n# In[18]:\n\n\nprint(\"Scores for Neural Network: \\n\"+ classification_report(output['Survived'], output['Neural Network Predictions']))\n\n","repo_name":"c-hloehuang/Kaggle-Titanic","sub_path":"Titanic.py","file_name":"Titanic.py","file_ext":"py","file_size_in_byte":5140,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"73896765907","text":"'''\nCreated on Jul 7, 2013\n\n@author: Yubin Bai\n\nAll rights reserved.\n'''\n\nimport time\nfrom multiprocessing.pool import Pool\nfrom itertools import combinations\nfrom math import sqrt\nfrom heapq import *\nparallelSolve = False\nINF = 1 << 31\n\n\ndef solve(par):\n    def mst():\n\n        def find(i):\n            if parent[i] != i:\n                parent[i] = find(parent[i])\n            return parent[i]\n\n        def union(i, j):\n            p1 = find(i)\n            p2 = find(j)\n            if rank[p1] < rank[p2]:\n                parent[p1] = p2\n            else:\n                parent[p2] = p1\n                if rank[p1] == rank[p2]:\n                    rank[p1] += 1\n\n        # make disjoint set\n        parent = range(P)\n        rank = [0] * P\n        # kruskal\n        heapify(graph)\n        mst = []\n        while len(mst) < P - S:\n            c, start, end = heappop(graph)\n            if find(start) != find(end):\n                mst.append(c)\n                union(start, end)\n        return max(mst)\n\n    S, P, stations = par\n    graph = []\n    for s1, s2 in combinations(range(P), 2):\n        d = (stations[s1][0] - stations[s2][0]) ** 2 + (\n            stations[s1][1] - stations[s2][1]) ** 2\n        graph.append([sqrt(d), s1, s2])\n\n    return '%.2f' % mst()\n\n\nclass Solver:\n\n    def getInput(self):\n        self.numOfTests = int(self.fIn.readline())\n        self.input = []\n        for i in range(self.numOfTests):\n            S, P = map(int, self.fIn.readline().split())\n            stations = []\n            for i in range(P):\n                stations.append(map(float, self.fIn.readline().split()))\n            self.input.append((S, P, stations))\n\n    def __init__(self):\n        self.fIn = open('input.txt')\n        self.fOut = open('output.txt', 'w')\n        self.results = []\n\n    def parallel(self):\n        self.getInput()\n        p = Pool(4)\n        millis1 = int(round(time.time() * 1000))\n        self.results = p.map(solve, self.input)\n        millis2 = int(round(time.time() * 1000))\n        print(\"Time in milliseconds: %d \" % (millis2 - millis1))\n        self.makeOutput()\n\n    def sequential(self):\n        self.getInput()\n        millis1 = int(round(time.time() * 1000))\n        for i in self.input:\n            self.results.append(solve(i))\n        millis2 = int(round(time.time() * 1000))\n        print(\"Time in milliseconds: %d \" % (millis2 - millis1))\n        self.makeOutput()\n\n    def makeOutput(self):\n        for test in range(self.numOfTests):\n            self.fOut.write(\"%s\\n\" % self.results[test])\n        self.fIn.close()\n        self.fOut.close()\n\nif __name__ == '__main__':\n    solver = Solver()\n    if parallelSolve:\n        solver.parallel()\n    else:\n        solver.sequential()\n","repo_name":"yubinbai/pcuva-problems","sub_path":"UVa 10369 - Arctic Networks/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2723,"program_lang":"python","lang":"en","doc_type":"code","stars":47,"dataset":"github-code","pt":"48"}
+{"seq_id":"19554646815","text":"import numpy as np\nimport random\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns  # 用于合并绘图\n\n# 定义车辆信息数据框和属性\ndf_Vehicle = pd.DataFrame(columns=('vehicleID',  # 车辆id\n                                   'mode',  # 车辆类型，0普通车辆，1电动车（不需要充电）2电动车正常充电3电动车占用充电桩当做停车位\n                                   'pkID',  # 停车场id\n                                   'arrTime',  # 车辆到达时间，以分钟为单位，将8:00~20:00分成780个时间点\n                                   'pkStatus', ### 新增属性，是否已经进入停车场,0未进入，1在停车场，2离开停车场\n                                   'entPkTime',  # 进入车位时间\n                                   'pkSpaceID',  # 进入车位编号\n                                   'stayTime',  # 停车时间\n                                   'depTime',  # 离开车位时间\n                                   'reservedTime', ### 驻留时间\n                                   'inQueque', ### 新增属性，是否在排队\n                                   'waitingTime'  # 等待时间\n                                   ))\n# 定义停车位信息数据框和属性\ndf_ParkingSpace = pd.DataFrame(columns=('pkSpaceID',  # 停车位编号\n                                        'pkID',  # 停车场编号\n                                        'typePkMode',  # 停车场类型, 0普通停车场，1充电桩停车场\n                                        'pksAvailable',  # 当前是否可用\n                                        'inusedTimes',  # 累计使用次数\n                                        'accInusedTime',  # 累计使用时间\n                                        'reservedTime',  # 剩余使用时间\n                                        'vehIDSeries'  # 使用过的车辆编号\n                                        ))\n# 定义停车场信息数据框和属性\ndf_Parkinglot = pd.DataFrame(columns=('pkID',  # 停车场编号\n                                      'typePkMode',  # 停车场类型\n                                      'numPkSpace',  # 停车场车位数\n                                      'timePnt',  # 时间节点\n                                      'initPkOcc',  # 初始已使用车位数\n                                      '_0curPkOcc',  # 当前使用车辆数\n                                      '_0queLength'  # 排队车辆数\n                                      ))\n\n# 停车场初始化，普通停车位，已使用普通停车位，充电桩，已使用充电桩\n'''\n这里有个问题，充电桩占车位比例差异较大，如果按照各个停车场电动车停放比例一致来考虑，\n则部分充电桩可能会排队较长。当然，这也可以用来测试充电桩布局均匀的重要性。\n'''\n\n# 第一列普通停车位车位数，第二列初始占用普通停车位\n# 第三列充电停车位，第三列初始占用充电停车位\nparkingIndex = [[53, 34, 8, 2],\n                [150, 22, 8, 2],\n                [54, 34, 6, 2],\n                [57, 15, 3, 1],\n                [140, 44, 10, 3]]\n\n# 车辆初始化\n# 各时段到达车辆分布，需要结合调查值做修正\npark_arr = [[19, 9, 8, 9, 8, 8, 2, 1, 3, 3, 1, 2, 2],\n            [19, 11, 7, 10, 10, 5, 1, 7, 3, 2, 4, 2, 2],\n            [17, 9, 11, 10, 11, 7, 3, 4, 2, 5, 3, 3, 3],\n            [14, 11, 12, 11, 11, 3, 8, 5, 4, 2, 4, 2, 2],\n            [12, 9, 6, 8, 10, 10, 2, 3, 4, 3, 2, 2, 3]]\n\nev_rat = 0.2  # 电动车比例\nev_charge = 0.5  # 需要充电的电动车占比\nev_charge_illegal = 0.2  # 使用充电桩作为常规车位车辆占比\npv_pks = 0.3  # pv 车位所占比例\n\naverage_charge_time = 30  # 单位：分钟\naverage_parking_time = 240  # 单位：分钟； 已废弃，使用调查值。\n\nstayTime = [507, 399, 509, 400, 430]  # 每个停车场的平均停留时间，单位：分钟\n\nvehNum = 0\npkSpaceNum = 0\n\n# 停车位初始化与车辆初始编号\n# 辅助计数变量\nj = 0\nfor i in range(5):\n    # 普通车位初始化\n    for k in range(parkingIndex[i][0]):\n        df_ParkingSpace = df_ParkingSpace.append([{'pkSpaceID': pkSpaceNum + k + 1,\n                                                   'pkID': str(i + 1) + 'a',\n                                                   'typePkMode': 0,\n                                                   'pksAvailable': 1,\n                                                   'inusedTimes': 0,\n                                                   'accInusedTime': 0,\n                                                   'reservedTime': 0,\n                                                   'vehIDSeries': ''}], ignore_index=True)\n\n    normalParkingSpaceListOcc = random.sample(list(range(parkingIndex[i][0])), parkingIndex[i][1])\n    for k in normalParkingSpaceListOcc:\n        j = j + 1\n        _staytime = np.random.poisson(lam=stayTime[i])\n        df_ParkingSpace.loc[df_ParkingSpace.pkSpaceID == pkSpaceNum + k + 1, 'inusedTimes'] = 1\n        df_ParkingSpace.loc[df_ParkingSpace.pkSpaceID == pkSpaceNum + k + 1, 'pksAvailable'] = 0\n        df_ParkingSpace.loc[df_ParkingSpace.pkSpaceID == pkSpaceNum + k + 1, 'reservedTime'] = _staytime\n        df_ParkingSpace.loc[df_ParkingSpace.pkSpaceID == pkSpaceNum + k + 1, 'vehIDSeries'] += str(vehNum + j)\n\n        df_Vehicle = df_Vehicle.append([{'vehicleID': vehNum + j, # 车辆编号\n                                         'mode': 0,\n                                         'pkID': str(i + 1) + 'a',\n                                         'arrTime': 0,\n                                         'pkStatus': 1,\n                                         'entPkTime': 0,\n                                         'pkSpaceID': pkSpaceNum + k + 1,\n                                         'stayTime': _staytime,\n                                         'reservedTime':_staytime,\n                                         'depTime': _staytime,\n                                         'inQueque': 0,\n                                         'waitingTime': 0\n                                         }], ignore_index=True\n                                       )\n    pkSpaceNum += parkingIndex[i][0]\n\n    # 充电桩停车位初始化\n    for k in range(parkingIndex[i][2]):\n        if k < int(parkingIndex[i][2]*pv_pks):\n            pvSta = 1\n        else:\n            pvSta = 2\n\n        df_ParkingSpace = df_ParkingSpace.append([{'pkSpaceID': pkSpaceNum + k + 1,\n                                                   'pkID': str(i + 1) + 'b',\n                                                   'typePkMode': pvSta,  # pv充电桩1， 普通充电桩2\n                                                   'pksAvailable': 1,\n                                                   'inusedTimes': 0,\n                                                   'accInusedTime': 0,\n                                                   'reservedTime': 0,\n                                                   'vehIDSeries':'' }], ignore_index=True)\n\n    evParkingSpaceListOcc = random.sample(list(range(parkingIndex[i][2])), parkingIndex[i][3])\n    for k in evParkingSpaceListOcc:\n        j = j + 1\n        _staytime = np.random.poisson(lam=stayTime[i])\n        df_ParkingSpace.loc[df_ParkingSpace.pkSpaceID == pkSpaceNum + k + 1, 'inusedTimes'] = 1\n        df_ParkingSpace.loc[df_ParkingSpace.pkSpaceID == pkSpaceNum + k + 1, 'pksAvailable'] = 0\n        df_ParkingSpace.loc[df_ParkingSpace.pkSpaceID == pkSpaceNum + k + 1, 'reservedTime'] = _staytime\n        df_ParkingSpace.loc[df_ParkingSpace.pkSpaceID == pkSpaceNum + k + 1, 'vehIDSeries'] += str(vehNum + j)\n\n        df_Vehicle = df_Vehicle.append([{'vehicleID': vehNum + j,\n                                         'mode': 2,\n                                         'pkID': str(i + 1) + 'b',\n                                         'arrTime': 0,\n                                         'pkStatus': 1,\n                                         'entPkTime': 0,\n                                         'pkSpaceID': pkSpaceNum + k + 1,\n                                         'stayTime': _staytime,\n                                         'reservedTime': _staytime,\n                                         'depTime': _staytime,\n                                         'inQueque': 0,\n                                         'waitingTime': 0\n                                         }], ignore_index=True\n                                       )\n\n    pkSpaceNum += parkingIndex[i][2]\n    # vehNum += parkingIndex[i][1]\n    # vehNum += parkingIndex[i][3]\n\nfor i in range(5):\n    vehNum += parkingIndex[i][1]\n    vehNum += parkingIndex[i][3]\n\n# 辅助变量，统计车位数\nn = 0\n# 随机生成车辆，对车辆分配车辆类型、停车场、车辆达到时间和停车时间等属性。\nfor i in range(5):\n    park_arr_timeid = []\n    ev_sample = []\n    ev_sample_illegal = []\n\n    # 把小时划分为分钟\n    time_0 = list(range(60))\n    for j in range(13):\n        # 按照到达的车辆数，在每个小时的60个数字进行有放回的抽样，因为可能存在多辆车同时到达的情形。\n        # 0～60 抽，60～120抽\n        temp_arr = np.random.choice(time_0, park_arr[i][j], replace=True) + 60 * j + 1\n        # 合并成为一个停车到达时间序列\n        park_arr_timeid.extend(temp_arr)\n\n    # 对停车到达时间序列进行排序\n    park_arr_timeid.sort()\n\n    # 获取电动车辆编号\n    ev_sample_id = random.sample(list(range(len(park_arr_timeid))), int(len(park_arr_timeid) * ev_rat))\n    ev_sample_id.sort()\n\n    # 获取需充电车辆编号；这里按全日到达车辆来抽样电动车辆，避免很多小时电动车到达为0.\n    ev_sample_id_normal = random.sample(ev_sample_id, int(len(ev_sample_id) * ev_charge))\n    ev_sample_id_normal.sort()\n\n    # 获使用充电桩当做普通停车位的车辆编号\n    ev_sample_id_illegal = random.sample(ev_sample_id_normal, int(len(ev_sample_id_normal) * ev_charge_illegal))\n    ev_sample_id_illegal.sort()\n\n    # 车辆初始化\n    ## 普通停车位\n    for k in range(len(park_arr_timeid)):\n        if k not in ev_sample_id_normal:\n            n += 1\n            df_Vehicle = df_Vehicle.append([{'vehicleID': vehNum + n,\n                                             'mode': 0,\n                                             'pkID': str(i + 1) + 'a',\n                                             'arrTime': park_arr_timeid[k],\n                                             'pkStatus': 0,\n                                             'stayTime': np.random.poisson(lam=stayTime[i]),\n                                             'waitingTime':0\n                                             }], ignore_index=True)\n\n    ## 正常充电停车车辆\n    for k in ev_sample_id_normal:\n        if k not in ev_sample_id_illegal:\n            n += 1\n            df_Vehicle = df_Vehicle.append([{'vehicleID': vehNum + n,\n                                             'mode': 2,\n                                             'pkID': str(i + 1) + 'b',\n                                             'arrTime': park_arr_timeid[k],\n                                             'pkStatus': 0,\n                                             'stayTime': np.random.poisson(lam=average_charge_time),\n                                             'waitingTime':0\n                                             }], ignore_index=True)\n\n    ## 使用充电车位作为普通停车位车辆\n    for k in ev_sample_id_illegal:\n        n += 1\n        df_Vehicle = df_Vehicle.append([{'vehicleID': vehNum + n,\n                                         'mode': 3,\n                                         'pkID': str(i + 1) + 'b',\n                                         'arrTime': park_arr_timeid[k],\n                                         'pkStatus': 0,\n                                         'stayTime': np.random.poisson(lam=stayTime[i]),\n                                         'waitingTime':0\n                                         }], ignore_index=True)\n\nfor i in range(5):\n    for j in ['a', 'b']:\n        if j == 'a':\n            df_Parkinglot = df_Parkinglot.append([{'pkID': str(i + 1) + j,\n                                                   'typePkMode': 0,\n                                                   'numPkSpace': parkingIndex[i][0],\n                                                   'timePnt': 0,\n                                                   'initPkOcc': parkingIndex[i][1],\n                                                   '_0curPkOcc': parkingIndex[i][1],\n                                                   '_0queLength': 0\n                                                   }], ignore_index=True)\n        if j == 'b':\n            df_Parkinglot = df_Parkinglot.append([{'pkID': str(i + 1) + j,\n                                                   'typePkMode': 1,\n                                                   'numPkSpace': parkingIndex[i][2],\n                                                   'timePnt': 0,\n                                                   'initPkOcc': parkingIndex[i][3],\n                                                   '_0curPkOcc': parkingIndex[i][3],\n                                                   '_0queLength': 0\n                                                   }], ignore_index=True)\n\n# 停车仿真\n\n# 对车辆按照到达时间进行排序\ndf_Vehicle = df_Vehicle.sort_values(by='arrTime')\nvehNum = len(df_Vehicle)\n\ncurRec = 0\nfor i in range(780):\n\n    hour = int(i / 60) + 8\n    minutes = i - 60 * (hour -8)\n\n    if i % 5 == 0:\n        print(\"-----running %02d 时 %02d 分-----\" %(hour, minutes))\n\n    # # 停车场信息初始化\n    lastOcc = '_' + str(i) + 'curPkOcc'\n    lastQueLen = '_' + str(i) + 'queLength'\n\n    df_Parkinglot_iter = df_Parkinglot.loc[:,['pkID',lastOcc,lastQueLen]]\n\n    for k in range(vehNum):\n\n        if df_Vehicle.iloc[k, df_Vehicle.columns.get_loc('arrTime')] <= i:\n            # 过滤掉初始入库车辆\n            # 已入库\n            if df_Vehicle.iloc[k, df_Vehicle.columns.get_loc('pkStatus')] == 1:\n\n                # 更新已入库车辆信息\n                df_Vehicle.iloc[k, df_Vehicle.columns.get_loc('reservedTime')] -= 1\n\n                # 更新停车位信息\n                df_ParkingSpace.loc[df_ParkingSpace.pkSpaceID == df_Vehicle.iloc[\n                    k, df_Vehicle.columns.get_loc('pkSpaceID')], 'accInusedTime'] += 1\n                df_ParkingSpace.loc[df_ParkingSpace.pkSpaceID == df_Vehicle.iloc[\n                    k, df_Vehicle.columns.get_loc('pkSpaceID')], 'reservedTime'] -= 1\n\n                # 更新停车场临时信息\n                if df_Vehicle.iloc[k, df_Vehicle.columns.get_loc('reservedTime')] == 0:\n                    df_ParkingSpace.loc[df_ParkingSpace.pkSpaceID == df_Vehicle.iloc[\n                        k, df_Vehicle.columns.get_loc('pkSpaceID')], 'pksAvailable'] = 1\n                    df_Vehicle.loc[df_Vehicle.reservedTime == 0, 'pkStatus'] = 2\n                    df_Parkinglot_iter.loc[\n                        df_Parkinglot_iter.pkID == df_Vehicle.iloc[k, df_Vehicle.columns.get_loc('pkID')], lastOcc] -= 1\n            # 刚刚到达\n            elif df_Vehicle.iloc[k, df_Vehicle.columns.get_loc('pkStatus')] == 0:\n\n                # 获取当前车辆车辆\n                vhID = df_Vehicle.iloc[k, df_Vehicle.columns.get_loc('vehicleID')]\n\n                # 更新待入库车辆信息\n                # 建立可使用车位集\n                listAvParking_df = df_ParkingSpace[\n                    (df_ParkingSpace.pksAvailable == 1) & (\n                                df_ParkingSpace.pkID == df_Vehicle.iloc[k, df_Vehicle.columns.get_loc('pkID')])]\n\n                listAvParking = listAvParking_df['pkSpaceID'].values.tolist()\n\n                if len(listAvParking):\n\n                    newArrival = random.sample(listAvParking, 1)\n                    newArrival = newArrival[0]\n\n                    # print(newArrival)\n\n                    # update parkingspace info\n                    df_ParkingSpace.loc[df_ParkingSpace.pkSpaceID == newArrival, 'pksAvailable'] = 0\n                    df_ParkingSpace.loc[df_ParkingSpace.pkSpaceID == newArrival, 'inusedTimes'] += 1\n                    df_ParkingSpace.loc[df_ParkingSpace.pkSpaceID == newArrival, 'vehIDSeries'] +=(',' + str(vhID))\n                    df_ParkingSpace.loc[df_ParkingSpace.pkSpaceID == newArrival, 'reservedTime'] = \\\n                        df_Vehicle.iloc[k, df_Vehicle.columns.get_loc('stayTime')]\n\n                    # 更新车辆信息\n                    df_Vehicle.iloc[k, df_Vehicle.columns.get_loc('entPkTime')] = i\n                    df_Vehicle.iloc[k, df_Vehicle.columns.get_loc('pkStatus')] = 1\n                    df_Vehicle.iloc[k, df_Vehicle.columns.get_loc('reservedTime')] = \\\n                        df_Vehicle.iloc[k, df_Vehicle.columns.get_loc('stayTime')]\n                    df_Vehicle.iloc[k, df_Vehicle.columns.get_loc('pkSpaceID')] = newArrival\n                    df_Vehicle.iloc[k, df_Vehicle.columns.get_loc('depTime')] = i + df_Vehicle.iloc[k, df_Vehicle.columns.get_loc('stayTime')]\n\n                    #更新停车库信息\n                    if df_Vehicle.iloc[k, df_Vehicle.columns.get_loc('inQueque')] == 1:\n                        df_Parkinglot_iter.loc[df_Parkinglot_iter.pkID == df_Vehicle.iloc[k, df_Vehicle.columns.get_loc('pkID')], lastOcc] += 1\n                        df_Parkinglot_iter.loc[df_Parkinglot_iter.pkID == df_Vehicle.iloc[\n                            k, df_Vehicle.columns.get_loc('pkID')], lastQueLen] -= 1\n                    else:\n                        df_Parkinglot_iter.loc[df_Parkinglot_iter.pkID == df_Vehicle.iloc[k, df_Vehicle.columns.get_loc('pkID')], lastOcc] += 1\n\n                else:\n                    # 更新车辆信息\n                    df_Vehicle.iloc[k, df_Vehicle.columns.get_loc('inQueque')] = 1\n                    df_Vehicle.iloc[k, df_Vehicle.columns.get_loc('waitingTime')] += 1\n\n                    if df_Vehicle.iloc[k, df_Vehicle.columns.get_loc('arrTime')] == i:\n                        #更新停车场信息\n                        df_Parkinglot_iter.loc[df_Parkinglot_iter.pkID == df_Vehicle.iloc[k, df_Vehicle.columns.get_loc('pkID')], lastQueLen] += 1\n\n        # 进入车辆选择\n        else:\n            break\n\n    # 更新停车库信息\n    curOcc = '_' + str(i + 1) + 'curPkOcc'\n    curQueLen = '_' + str(i + 1) + 'queLength'\n\n    df_Parkinglot_iter.rename(columns={lastOcc: curOcc, lastQueLen: curQueLen}, inplace=True)\n\n    df_Parkinglot = pd.merge(df_Parkinglot, df_Parkinglot_iter, on='pkID', how='inner')\n\n# 输出结果文件\noutputfile1 = \"01vehicle_record.csv\"\ndf_Vehicle.to_csv(outputfile1, sep=',', header=True, index=False)\n\noutputfile2 = \"02parkingSpace_record.csv\"\ndf_ParkingSpace.to_csv(outputfile2, sep=',', header=True, index=False)\n\noutputfile3 = \"03parkinglot_record.csv\"\ndf_Parkinglot.to_csv(outputfile3, sep=',', header=True, index=False)\n\nprint('----------- simulation progress finished -----------')\n","repo_name":"HexinZhang/VE450","sub_path":"Simulation.py","file_name":"Simulation.py","file_ext":"py","file_size_in_byte":19186,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"21308701740","text":"import torch\nfrom matplotlib import pyplot as plt\n\n\n# предикт по всем данным\ndef show_results(y_true, y_pred, sum_plans, name=None):\n    plt.plot(y_true, label='Целевое значение')\n    plt.plot(y_pred, label='Прогноз ИИ модели', color='orange')\n    plt.plot(sum_plans, label='Плановое значение Primavera', color='g')\n\n    plt.grid()\n    plt.legend(loc='best')\n    plt.xlabel('месяцы')\n    plt.ylabel('машиночасы')\n    if not name: name = 'Cравнение применения ИИ моделей для прогнозирования техники'\n    plt.title(name);\n\n\ndef get_predict(PD_DATA, feature_contractor_dict_ids, stages_dict_ids, mech_res_ids, model, model_type, contractor_id,\n                contr_id_real, project_id, resource_id, print_result=False, plot_result=True):\n    a = list(map(str, range(0, 373)))\n    b = ['proj_id', 'contr_id']\n    b.extend(a)\n    # if model_type is ModelType.Linear_3MONTH:\n    #     b.extend(['month', 'year', 'res_id', 'm_1', 'm_2', 'm_3', 'target'])\n    # else:\n    b.extend(['month', 'year', 'res_id', 'target'])\n    df_for_predict = PD_DATA.loc[\n        (PD_DATA.contr_id == contractor_id) & (PD_DATA.proj_id == project_id) & (PD_DATA.res_id == resource_id), b]\n\n    features_for_predict = torch.tensor(df_for_predict.iloc[:, :-1].values).to(torch.float)\n    target = df_for_predict.iloc[:, -1].values\n\n    plans = df_for_predict.iloc[:, 2:-4]\n\n    sum_plans = plans.sum(axis=1)\n\n    sum_plans = sum_plans.values\n\n    predict = []\n\n    with torch.no_grad():\n        for month in range(features_for_predict.shape[0]):\n            a = model(features_for_predict[month])\n            predict.append(a.tolist())\n\n    if print_result:\n        if target.shape[0] != 0:\n            plan_targ_predict = {'resource': [], 'month': [], 'predict': [], 'target': [], 'plan': []}\n            plan_targ_predict['resource'].append(list(mech_res_ids.keys())[list(mech_res_ids.values()).index(resource_id)])\n            for i in range(len(predict)):\n                # print(f'month = {i+1}, predict = {predict[i] :.2f}, target = {target[i] :.2f}')\n                plan_targ_predict['month'].append(i)\n                plan_targ_predict['predict'].append(predict[i])\n                plan_targ_predict['target'].append(target[i])\n                plan_targ_predict['plan'].append(sum_plans[i])\n            print(plan_targ_predict)\n            return plan_targ_predict\n        else:\n            return 0\n\n    if plot_result:\n        if target.shape[0] != 0:\n            print(f'Resource_id = {resource_id}')\n            Contractor = list(feature_contractor_dict_ids.keys())[list(feature_contractor_dict_ids.values()).index(contr_id_real)]\n            project = list(stages_dict_ids.keys())[list(stages_dict_ids.values()).index(project_id)]\n            resource = list(mech_res_ids.keys())[list(mech_res_ids.values()).index(resource_id)]\n\n            show_results(target, predict, sum_plans,\n                         name=f'Contractor = {Contractor},'\n                              f' project = {project} \\n resource = {resource}')\n        return 0","repo_name":"MashaMihalkova/test_track_model_pytorch","sub_path":"model/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":3157,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"70500941906","text":"# -*- coding: utf-8 -*-\n\"\"\"\n\n@author: QYK\n\"\"\"\nimport os\nimport numpy as np\nimport pandas as pd\nfrom analysis.mri.preprocess.fsl.preprocess_melodic import list_to_chunk\nfrom analysis.mri.voxel_wise.nilearn.firstLevel_analysis import load_ev, prepare_data, get_reg_index, first_level_glm\nfrom joblib import Parallel, delayed\n\n\ndef set_contrasts(design_matrix):\n    # set contrast\n    contrast_name = ['M1', 'M2_error', 'decision_error','M2_corr','decision_corr']\n    for onset in ['m2', 'decision']:\n        for even_odd in ['even']:\n            for bin in range(1, 13, 1):\n                if bin in range(1, 13, 2):\n                    contrast_name.append(onset + '_align_' + str(bin) + \"_\" + even_odd)\n                elif bin in range(2, 13, 2):\n                    contrast_name.append(onset + '_misalign_' + str(bin) + \"_\" + even_odd)\n\n    # base contrast\n    contrasts_set = {}\n    for contrast_id in contrast_name:\n        contrast_index = get_reg_index(design_matrix, contrast_id)\n        if len(contrast_index) == 0:\n            print(contrast_id, ' have no regressor!')\n            continue\n\n        contrast_vector = np.zeros(design_matrix.shape[1])\n        contrast_vector[contrast_index] = 1\n        contrasts_set[contrast_id] = contrast_vector\n\n    # advanced contrast\n    # align and misalign and alignPhi contrast\n    for onset in ['m2','decision']:\n        for odevity in ['even']:\n            align = np.zeros(design_matrix.shape[1])\n            missalign = np.zeros(design_matrix.shape[1])\n            for cid, cvt in contrasts_set.items():\n                if (cid.startswith(f'{onset}_align')) and (odevity in cid):\n                    align += cvt\n                elif (cid.startswith(f'{onset}_misalign')) and (odevity in cid):\n                    missalign += cvt\n                else:\n                    continue\n            contrasts_set[f'{onset}_align_{odevity}'] = align\n            contrasts_set[f'{onset}_misalign_{odevity}'] = missalign\n            contrasts_set[f'{onset}_alignPhi_{odevity}'] = align - missalign\n        # contrasts_set[f'{onset}_align'] = contrasts_set[f'{onset}_align_odd'] + contrasts_set[f'{onset}_align_even']\n        # contrasts_set[f'{onset}_misalign'] = contrasts_set[f'{onset}_misalign_odd'] + contrasts_set[f'{onset}_misalign_even']\n        #contrasts_set[f'{onset}_alignPhi'] = contrasts_set[f'{onset}_align'] - contrasts_set[f'{onset}_misalign']\n    #contrasts_set['alignPhi'] = contrasts_set['m2_alignPhi'] + contrasts_set['decision_alignPhi']\n    return contrasts_set\n\n\ndef run_glm(subj, configs):\n    # read parameters\n    dataroot = configs['dataroot']\n    ifold = configs['ifold']\n    # skill the subject who already have results\n    datasink = os.path.join(dataroot, 'sub-{}'.format(subj))\n    if os.path.exists(datasink):\n        print(f\"sub-{subj} already have results.\")\n    else:\n        print(\"-------{} start!--------\".format(subj))\n        functional_imgs, design_matrices = prepare_data(subj, ifold, configs, load_ev, True, False)\n        first_level_glm(datasink, functional_imgs, design_matrices, set_contrasts)\n\n\nif __name__ == \"__main__\":\n    # specify configure parameters\n    configs = {'TR': 3.0,\n               'task': 'game1',\n               'glm_type': 'cv_test_align_spct',\n               'run_list': [1, 2, 3, 4, 5, 6],\n               'event_dir': r'/mnt/data/DCM/result_backup/2023.5.14/Events',\n               'events_name': r'sub-{}_task-game1_run-{}_events.tsv',\n               'func_dir': r'/mnt/workdir/DCM/BIDS/derivatives/fmriprep_volume_fmapless/fmriprep',\n               'func_name': 'func/sub-{}_task-game1_run-{}_space-MNI152NLin2009cAsym_res-2_desc-preproc_bold_trimmed.nii.gz',\n               'regressor_name': r'sub-{}_task-game1_run-{}_desc-confounds_timeseries_trimmed.tsv'}\n\n    # specify subjects\n    participants_tsv = r'/mnt/workdir/DCM/BIDS/participants.tsv'\n    participants_data = pd.read_csv(participants_tsv, sep='\\t')\n    data = participants_data.query(f'{configs[\"task\"]}_fmri>=0.5')\n    pid = data['Participant_ID'].to_list()\n    subjects = [p.split('-')[-1] for p in pid]\n\n    subjects_chunk = list_to_chunk(subjects,60)\n    for ifold in range(6, 7):\n        # creat dataroot\n        dataroot = r'/mnt/workdir/DCM/BIDS/derivatives/Nilearn_test/{}/{}/Setall/{}fold'.format(configs['task'],\n                                                                                           configs['glm_type'], ifold)\n        if not os.path.exists(dataroot):\n            os.makedirs(dataroot)\n\n        configs['ifold'] = ifold\n        configs['dataroot'] = dataroot\n        for chunk in subjects_chunk:\n            results_list = Parallel(n_jobs=60)(delayed(run_glm)(subj, configs) for subj in chunk)\n","repo_name":"YukunQu/DCM","sub_path":"analysis/mri/voxel_wise/nilearn/cv/cv_test_12bin_spct.py","file_name":"cv_test_12bin_spct.py","file_ext":"py","file_size_in_byte":4708,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"4073516579","text":"from rest_framework.routers import DefaultRouter\n\nfrom rest.loader import load_viewsets\n\n\ndef get_router():\n    router = DefaultRouter(trailing_slash=False)\n\n    for args in load_viewsets(\"RESTAPI_VIEWSETS\"):\n        router.register(*args)\n\n    return router\n\nROUTER = get_router()\n","repo_name":"openattic/openattic","sub_path":"backend/rest/router.py","file_name":"router.py","file_ext":"py","file_size_in_byte":282,"program_lang":"python","lang":"en","doc_type":"code","stars":56,"dataset":"github-code","pt":"48"}
+{"seq_id":"9273316105","text":"#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jul  6 14:29:24 2018\n\n@author: luke\n\"\"\"\n\n# Basic application to load a mesh from file and view it in a window\n\n# Python imports\nimport sys, os\n\n## Imports from this project\n# hack to allow local imports without creaing a module or modifying the path variable\nsys.path.append(os.path.join(os.path.dirname(__file__), '..', 'core')) \nfrom InputOutput import *\nfrom MeshDisplay import MeshDisplay\n\n#from HalfEdgeMesh import *\nfrom HalfEdgeMesh_ListImplementation import *\n\n\ndef main(inputfile,show):\n\n    # Get the path for the mesh to load, either from the program argument if\n    # one was given, or a dialog otherwise\n    if(len(sys.argv) > 1):\n        filename = sys.argv[1]\n    elif inputfile is not None:\n        filename = inputfile\n    else:\n        print(\"ERROR: No file name specified. Proper syntax is 'python testview.py path/to/your/mesh.obj'.\")\n        exit()\n\n    # Read in the mesh\n    mesh = HalfEdgeMesh(readMesh(filename),\n                        staticGeometry=False)\n\n    # Create a viewer window\n    winName = 'meshview -- ' + os.path.basename(filename)\n    meshDisplay = MeshDisplay(windowTitle=winName)\n    meshDisplay.setMesh(mesh)\n    if show:\n        meshDisplay.startMainLoop()\n    return mesh, meshDisplay\n\nif __name__ == \"__main__\": \n    \n    \n    mesh, meshDisplay = main(\n                            inputfile='../meshes/bunny.obj',\n                            show=True)\n    ","repo_name":"opnumten/Python-discrete-differential-geometry","sub_path":"IO.py","file_name":"IO.py","file_ext":"py","file_size_in_byte":1470,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"48"}
+{"seq_id":"27338839691","text":"import settings\nfrom handlers import add_order, remove_order, time_to_eat, get_chat_id\n\nfrom telegram.ext import (\n    CommandHandler,\n    Filters,\n    MessageHandler,\n    PicklePersistence,\n    Updater,\n)\n\n\ndef main():\n    persistence = PicklePersistence(filename=\"food_persistence\")\n    updater = Updater(token=settings.BOT_TOKEN, persistence=persistence)\n\n    dispatcher = updater.dispatcher\n    dispatcher.add_handler(MessageHandler(filters=(Filters.reply & Filters.text), callback=add_order))\n    dispatcher.add_handler(CommandHandler(\"chat_id\", get_chat_id))\n    dispatcher.add_handler(CommandHandler(\"cancel\", remove_order))\n\n    job_queue = updater.job_queue\n    job_queue.run_repeating(time_to_eat, interval=60, first=0)\n\n    updater.start_polling()\n    updater.idle()\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"Keydol/food_tg","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":817,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"72981574225","text":"from common import lines\n\n\ngraph = {}\n\nfor line in lines:\n    start, end = line.split('-')\n    graph.setdefault(start, []).append(end)\n    graph.setdefault(end, []).append(start)\n\n\ndef traverse(node, seen, twice):\n    if node == 'end':\n        return True\n\n    if node in seen:\n        if node == 'start':\n            return False\n\n        if twice:\n            return False\n\n        twice = True\n\n    if node.islower():\n        seen = seen | {node}\n\n    return sum(traverse(neighbour, seen, twice) for neighbour in graph[node])\n\nprint('Part 1:', traverse('start', set(), True))\nprint('Part 1:', traverse('start', set(), False))\n","repo_name":"avayert/aoc2021","sub_path":"src/12.py","file_name":"12.py","file_ext":"py","file_size_in_byte":629,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"9130189281","text":"'''\nYou are given two non-empty linked lists representing two non-negative integers. The digits are stored in reverse order and each of their nodes contain a single digit. Add the two numbers and return it as a code.\n\nYou may assume the two numbers do not contain any leading zero, except the number 0 itself.\n\nExample:\nInput: (2 -> 4 -> 3) + (5 -> 6 -> 4)\nOutput: 7 -> 0 -> 8\nExplanation: 342 + 465 = 807.\n'''\n\nclass ListNode(object):\n    def __init__(self, x):\n         self.val = x\n         self.next = None\n\nclass Solution(object):\n    #\n    def addTwoNumbers1(self, l1, l2):\n        dummy = cur = ListNode(0)\n        carry = 0\n        while l1 or l2 or carry:\n            if l1:\n                carry += l1.val\n                l1 = l1.next\n            if l2:\n                carry += l2.val\n                l2 = l2.next\n            cur.next = ListNode(carry % 10)\n            cur = cur.next\n            carry //= 10\n        return dummy.next\n\n    def addTwoNumbers2(self, l1, l2):\n        \"\"\"\n        :type l1: ListNode\n        :type l2: ListNode\n        :rtype: ListNode\n        \"\"\"\n        lres = ListNode(-1)\n        cur = lres\n        cur_l1, cur_l2 = l1, l2\n        val_l1, val_l2 = 0, 0\n        flag = 0\n\n        while cur_l1 != None or cur_l2 != None or flag == 1:\n            if cur_l1 == None:\n                val_l1 = 0\n            else:\n                val_l1 = cur_l1.val\n            if cur_l2 == None:\n                val_l2 = 0\n            else:\n                val_l2 = cur_l2.val\n\n            val = val_l1 + val_l2 + flag\n            if val >= 10:\n                val = val % 10\n                flag = 1\n            else:\n                flag = 0\n            node = ListNode(val)\n            cur.next = node\n            cur = cur.next\n            if cur_l1 != None:\n                cur_l1 = cur_l1.next\n            if cur_l2 != None:\n                cur_l2 = cur_l2.next\n        return lres.next","repo_name":"shanyiWang/LeetCode","sub_path":"code/2. Add Two Numbers.py","file_name":"2. Add Two Numbers.py","file_ext":"py","file_size_in_byte":1916,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"31099639922","text":"# maketimeseries.py\n#\n# take the word vectors and make a happiness timeseries\n#\n# USAGE\n#\n# python maketimeseries.py word-vectors/harry8.csv timeseries/harry8.csv\n\nimport os\nimport sys\n\nfrom django.conf import settings\nfrom hedonometer.models import Book\nfrom labMTsimple.storyLab import emotionFileReader, emotionV, stopper\n\nsys.path.append(\"/home/prod/hedonometer\")\nos.environ[\"DJANGO_SETTINGS_MODULE\"] = \"mysite.settings\"\n\n\ndef chopper(fullVec, labMT, labMTvector, outfile):\n    minWindows = 10\n    timeseries = [0 for i in range(len(fullVec[0]) + 1)]\n    # print(len(timeseries))\n\n    textFvec = [0 for j in range(len(fullVec))]\n    for i in range(0, minWindows / 2):\n        textFvec = [textFvec[j] + fullVec[j][i] for j in range(len(fullVec))]\n        # print(\"adding point {0}\".format(i))\n\n    for i in range(minWindows / 2, minWindows):\n        # print(\"scoring\")\n        stoppedVec = stopper(textFvec, labMTvector, labMTwordList, stopVal=2.0)\n        timeseries[i - minWindows / 2] = emotionV(stoppedVec, labMTvector)\n        # print(\"adding point {0}\".format(i))\n        textFvec = [textFvec[j] + fullVec[j][i] for j in range(len(fullVec))]\n\n    for i in range(minWindows, len(timeseries) - 1):\n        # print(\"scoring\")\n        stoppedVec = stopper(textFvec, labMTvector, labMTwordList, stopVal=2.0)\n        timeseries[i - minWindows / 2] = emotionV(stoppedVec, labMTvector)\n        # print(\"adding point {0}\".format(i))\n        # print(\"removing point {0}\".format(i-minWindows))\n        textFvec = [\n            textFvec[j] + fullVec[j][i] - fullVec[j][i - minWindows] for j in range(len(fullVec))\n        ]\n\n    for i in range(len(timeseries) - 1, len(timeseries) + minWindows / 2):\n        # print(\"scoring\")\n        stoppedVec = stopper(textFvec, labMTvector, labMTwordList, stopVal=2.0)\n        timeseries[i - minWindows / 2] = emotionV(stoppedVec, labMTvector)\n        # print(\"removing point {0}\".format(i-minWindows))\n        textFvec = [textFvec[j] - fullVec[j][i - minWindows] for j in range(len(fullVec))]\n\n    # print(\"done\")\n\n    # print(timeseries[0:11])\n    # print(timeseries[-11:])\n\n    g = open(outfile, \"w\")\n    g.write(\"{0}\".format(timeseries[0]))\n    for i in range(1, len(timeseries)):\n        g.write(\",\")\n        # g.write(\"{0:.5f}\".format(timeseries[i]))\n        g.write(\"{0}\".format(timeseries[i]))\n    g.write(\"\\n\")\n    g.close()\n\n\nif __name__ == \"__main__\":\n    inf = sys.argv[1]\n\n    b = Book.objects.filter(filename__exact=inf.split(\".\")[0])\n    if len(b) > 0:\n        lang = b[0].language\n        labMT, labMTvector, labMTwordList = emotionFileReader(\n            stopval=0.0, fileName=\"labMT2\" + lang + \".txt\", returnVector=True\n        )\n\n        f = open(\"word-vectors/\" + inf, \"r\")\n        fullVec = [map(int, line.split(\",\")) for line in f]\n        f.close()\n\n        print(\"processing \" + b[0].title)\n        chopper(fullVec, labMT, labMTvector, \"timeseries/\" + inf)\n        print(\"done\")\n    else:\n        print(\"could not find book for file \" + inf)\n","repo_name":"andyreagan/hedonometer-data-munging","sub_path":"bookdata/maketimeseries.py","file_name":"maketimeseries.py","file_ext":"py","file_size_in_byte":3001,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"48"}
+{"seq_id":"44051147888","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\nLaunch AWS EMR cluster for running SparkVar jobs.\n\"\"\"\n\nimport sys\nimport os\nimport boto3\nimport botocore\nimport argparse\nimport json\nimport datetime\nimport math\n\n# Parse arguments\ndef parse_args():\n    Defaults = {'MinMapQual':30, 'MinCov':3, 'MinPurity':0.98, 'HetsCov':10, 'HetsPurity':0.7, \\\n                'IndelCov':1, 'IndelPurity':0.5, 'IndelPerc':0.1 }\n\n    core_node_list = ['cc2.8xlarge', 'c3.4xlarge', 'c3.8xlarge', 'c4.4xlarge',\n                      'c4.8xlarge', 'm4.10xlarge', 'm4.16xlarge', 'm1.xlarge']\n\n    ap = argparse.ArgumentParser()\n\n    # Common options\n    group0 = ap.add_argument_group('Required')\n    group0.add_argument(\"--job_id\", metavar='STR', default='SparkVar_job',\n        help=\"optional job id\")\n    group0.add_argument(\"--samples\", metavar='STR', required=True,\n        help=\"a file of list of samples (s3 object) to process (one sample for each line)\")\n    group0.add_argument(\"--reference\", metavar='STR', required=True,\n        help=\"name of reference sequence file (fasta)\")\n    group0.add_argument(\"--ref_s3_path\", metavar='STR', required=True,\n        help=\"s3 object path of reference sequence\")\n    group0.add_argument(\"--bwt_prefix\", metavar='STR', required=True,\n        help=\"Prefix of bowtie2 reference index\")\n    group0.add_argument(\"--bwt_s3_path\", metavar='STR', required=True,\n        help=\"s3 object path for bowtie2 reference index\")\n    group0.add_argument(\"--output\", metavar='STR', required=True,\n        help=\"s3 object path for output\")\n    group0.add_argument(\"--pipeline\", metavar='STR', required=True,\n        help=\"s3 object path for data processing pipeline\")\n    group0.add_argument(\"--ec2_subnet_id\", metavar='STR', required=True,\n        help=\"EC2 subnet ID for EMR cluster\")\n    group0.add_argument(\"--emr_managed_master_security_group\", metavar='STR', required=True,\n        help=\"The identifier of the Amazon EC2 security group for the master node\")\n    group0.add_argument(\"--emr_managed_slave_security_group\", metavar='STR', required=True,\n        help=\"The identifier of the Amazon EC2 security group for the slave nodes\")\n    group0.add_argument(\"--service_access_security_group\", metavar='STR', required=True,\n        help=\"The identifier of the Amazon EC2 security group for the Amazon EMR service to access clusters in VPC private subnets\")\n\n    # alignment options\n    group1 = ap.add_argument_group('Alignment')\n    #group1.add_argument(\"--lib_type\", metavar='STR', default='paired_end', choices=['single_end', 'paired_end'],\n    #    help=\"Type of DNA library, choices are [single_end, paired_end]. [paired_end]\")\n    group1.add_argument(\"--aln_only\", default=False, action=\"store_true\",\n        help=\"Option to only perform alignment (force '--save_BAMs'). [False]\")\n    group1.add_argument(\"--bt2_param\", metavar='\\'STR\\'',\n        help=\"option to bowtie2 parameters.\")\n\n    # SNP-calling options\n    group2 = ap.add_argument_group('Variant calling')\n    group2.add_argument(\"--qual\", metavar='INT', type=int, default=Defaults['MinMapQual'],\n        help=\"Option to set the minimum mapping quality for SNP call. [{}]\".format(Defaults['MinMapQual']))\n    group2.add_argument(\"--indel\", default=False, action='store_true',\n        help=\"Flag to call INDELs too, defaults to not call indels. [False]\")\n    group2.add_argument(\"--hets\", default=False, action='store_true',\n        help=\"Flag to call heterozygous SNPs. [False]\")\n    group2.add_argument(\"--vcf\", default=False, action='store_true',\n        help=\"Option to call variants using samtools/bcftools. [tabular]\")\n    group2.add_argument(\"--mpileup_param\", metavar='\\'STR\\'',\n        help=\"Option to set samtools mpileup parameters.\")\n    group2.add_argument(\"--min_cov\", metavar='INT', default=Defaults['MinCov'], type=int,\n        help=\"Option to set the minimum coverage for snp call. [{}]\".format(Defaults['MinCov']))\n    group2.add_argument(\"--min_purity\", metavar='FLOAT', default=Defaults['MinPurity'], type=float,\n        help=\"Option to set the minimum purity for snp call. [{}]\".format(Defaults['MinPurity']))\n    group2.add_argument(\"--hets_cov\", metavar='INT', default=Defaults['HetsCov'], type=int,\n        help=\"Option to set the minimum coverage for hets call. [{}]\".format(Defaults['HetsCov']))\n    group2.add_argument(\"--hets_purity\", metavar='FLOAT', default=Defaults['HetsPurity'], type=float,\n        help=\"Option to set the maximum purity for hets call. [{}]\".format(Defaults['HetsPurity']))\n    group2.add_argument(\"--indel_cov\", metavar='INT', default=Defaults['IndelCov'], type=int,\n        help=\"Option to set the minimum coverage for indel call. [{}]\".format(Defaults['IndelCov']))\n    group2.add_argument(\"--indel_purity\", metavar='FLOAT', default=Defaults['IndelPurity'], type=float,\n        help=\"Option to set the minimum purity for indel call. [{}]\".format(Defaults['IndelPurity']))\n    group2.add_argument(\"--indel_perc\", metavar='FLOAT', default=Defaults['IndelPerc'], type=float,\n        help=\"Option to set the minimum percent for displaying an indel allele. [{}]\".format(Defaults['IndelPerc']))\n\n    # EMR Cluster configuration options\n    group3 = ap.add_argument_group('EMR configuration:')\n    group3.add_argument(\"--EMR_release\", metavar='STR', default=\"emr-5.5.0\",\n        help=\"The release label for the Amazon EMR release. [emr-5.5.0]\")\n    group3.add_argument(\"--master_node_type\", metavar='STR', default=\"m1.xlarge\",\n        help=\"EC2 instance type of the master node. [m1.xlarge]\")\n    group3.add_argument(\"--master_node_number\", metavar='INT', default=1,\n        help=\"Number of master nodes to assign to the EMR cluster. [1]\")\n    group3.add_argument(\"--master_node_ebs\", metavar='INT', type=int,\n        help=\"EBS volume attched to master nodes (Gb). EBS volume of 500 Gb is attached for EBS-only instance types [None]\")\n    group3.add_argument(\"--additional_master_security_groups\", metavar='STR', default=\"\",\n        help=\"Additional Amazon EC2 security group IDs for the master node (delimited by ',' if multiple IDs were given). [None]\")\n    group3.add_argument(\"--core_node_type\", metavar='STR', default=\"m4.10xlarge\",\n        help=\"EC2 instance type of the core nodes. Options are \"+', '.join(core_node_list)+'. [m4.10xlarge]')\n    group3.add_argument(\"--core_node_number\", metavar='INT', default=8,\n        help=\"Number of core nodes to assign to the EMR cluster. [8]\")\n    group3.add_argument(\"--core_node_ebs\", metavar='INT', type=int,\n        help=\"EBS volume attched to core nodes (Gb). EBS volume of 500 Gb is attached for EBS-only instance types [None]\")\n    group3.add_argument(\"--additional_slave_security_groups\", metavar='STR', default=\"\",\n        help=\"Additional Amazon EC2 security group IDs for the slave nodes (delimited by ',' if multiple IDs were given). [None]\")\n    group3.add_argument(\"--ssh_key\", metavar='STR', default=None,\n        help=\"SSH key to use with master node. [None]\")\n    group3.add_argument(\"--profile\", metavar='STR', default=None,\n        help=\"Named profiles stored in the config and credentials files. [default]\")\n    group3.add_argument(\"--emr_cluster_name\", metavar='STR', default=\"My Cluster\",\n        help=\"Name used to identify the EMR cluster. [My Cluster]\")\n    group3.add_argument(\"--job_flow_role\", metavar='STR', default=\"EMR_EC2_DefaultRole\",\n        help=\" An IAM role for an EMR cluster. [EMR_EC2_DefaultRole]\")\n    group3.add_argument(\"--tag\", metavar='STR', default=\"\",\n        help=\"Name used to tag EC2 instances assocaited with EMR cluster. [None]\")\n    group3.add_argument(\"--auto_terminate\", default=False, action=\"store_true\",\n        help=\"Option to terminate the EMR cluster upon completion of all steps. [False]\")\n    group3.add_argument(\"--deploy_mode\", metavar='STR', default='cluster', choices=['client', 'cluster'],\n        help=\"Where to run the Spark application from. Options are [client, cluster]. [cluster]\")\n    group3.add_argument(\"--log\", metavar='STR', default=None,\n        help=\"s3 object path for log infomation. [None]\")\n\n    # Spark options\n    group4 = ap.add_argument_group('Spark:')\n    group4.add_argument(\"--executor_memory\", metavar='STR',\n        help=\"Amount of memory per executor (e.g. 5G). [inferred from type of instances]\")\n    group4.add_argument(\"--executor_cores\", metavar='INT', type=int,\n        help=\"Number of cores per executor. [inferred from type of instances]\")\n    group4.add_argument(\"--number_partitions\", metavar='INT', type=int,\n        help=\"Number of data partitions. [inferred from type of instances]\")\n\n    # File Storage options\n    group5 = ap.add_argument_group('File storage:')\n    group5.add_argument(\"--save_BAMs\", default=False, action=\"store_true\",\n        help=\"Flag to save BAM files after processing is complete. [False]\")\n    group5.add_argument(\"--index\", default=False, action=\"store_true\",\n        help=\"Option to index the final bam file. [False]\")\n\n    args = vars(ap.parse_args())\n\n    if args['core_node_type'].lower() not in core_node_list:\n        print (\"Here are the choices for 'core_node_type':\")\n        for i in core_node_list: print (i)\n        sys.exit(1)\n\n    return args\n\n# Function to build list of EMR applications to install\ndef build_applications():\n    return [\n        {\n            'Name': 'Spark',\n        },\n        {\n            'Name': 'Ganglia',\n        },\n    ]\n\n# Function to build Bootstrap Actions\ndef build_bootstrap(args):\n    # Build the Bootstrap JSON\n    bootstrap_action = [\n        {\n            'Name': \"Install Tools\",\n            'ScriptBootstrapAction': {\n                'Path': \"{}application/SparkVar/install_tools_bootstrap_param.sh\".format(args['pipeline']),\n                'Args': [args[\"pipeline\"], args[\"ref_s3_path\"], args['bwt_s3_path']]\n            }\n        }\n    ]\n\n    return bootstrap_action\n\n# Function to build the configuration JSON for the EMR cluster\ndef build_configuration():\n    return [\n        {\n            \"Classification\":\"spark-env\",\n            \"Properties\":{},\n            \"Configurations\":[\n                {\n                    \"Classification\":\"export\",\n                    \"Properties\":{\n                        \"PYSPARK_PYTHON\":\"/usr/bin/python3\",\n                        \"PYTHONHASHSEED\":\"0\"\n                    },\n                    \"Configurations\":[]\n                }\n            ]\n        },\n        {\n            \"Classification\":\"spark\",\n            \"Properties\":{\n                \"maximizeResourceAllocation\":\"true\"\n            },\n            \"Configurations\":[]\n        },\n        {\n            \"Classification\":\"hadoop-env\",\n            \"Properties\":{},\n            \"Configurations\":[\n                {\n                    \"Classification\":\"export\",\n                    \"Properties\":{\n                        \"PYTHONHASHSEED\":\"0\"\n                    },\n                    \"Configurations\":[]\n                }\n            ]\n        },\n        {\n            \"Classification\":\"spark-defaults\",\n            \"Properties\":{\n                \"spark.yarn.appMasterEnv.PYTHONHASHSEED\":\"0\"\n            },\n            \"Configurations\":[]\n        }\n    ]\n\n# Function to build EMR EC2 Instance-Groups\ndef instance_groups(args):\n    master = {\n        'Name': 'Master - 1',\n        'Market': 'ON_DEMAND',\n        'InstanceRole': 'MASTER',\n        'InstanceType': args[\"master_node_type\"],\n        'InstanceCount': int(float(args[\"master_node_number\"])),\n    }\n    if args['master_node_ebs']:\n        master['EbsConfiguration'] = ebs_config(args['master_node_ebs'])\n    elif args['master_node_type'] in ['c4.4xlarge', 'c4.8xlarge', 'm4.10xlarge', 'm4.16xlarge']:\n        master['EbsConfiguration'] = ebs_config(500)\n\n    core = {\n        'Name': 'Core - 2',\n        'Market': 'ON_DEMAND',\n        'InstanceRole': 'CORE',\n        'InstanceType': args[\"core_node_type\"],\n        'InstanceCount': int(float(args[\"core_node_number\"])),\n    }\n    if args['core_node_ebs']:\n        core['EbsConfiguration'] = ebs_config(args['core_node_ebs'])\n    elif args['core_node_type'] in ['c4.4xlarge', 'c4.8xlarge', 'm4.10xlarge', 'm4.16xlarge']:\n        core['EbsConfiguration'] = ebs_config(500)\n\n    return [master, core]\n\ndef ebs_config(volume_size):\n    return  {\n                'EbsBlockDeviceConfigs': [\n                    {\n                        'VolumeSpecification': {\n                            'VolumeType': 'gp2',\n                            #'Iops': 10000,\n                            'SizeInGB': volume_size\n                        },\n                         'VolumesPerInstance': 1\n                    },\n                 ],\n                 'EbsOptimized': False\n            }\n    \n# Function to build Tags\ndef build_tags(args):\n    if args[\"tag\"] is None:\n        return None\n    else:\n        return [\n            {\n                'Key': 'Name',\n                'Value': args[\"tag\"]\n            }\n        ]\n\n# Function to set the Spark-Submit parameters based on the EC2 instance type and reference assembly\ndef get_spark_parameters(args):\n    # Get the total size of BT2 files associated with the reference assembly\n    if args['profile']:\n        session = boto3.Session(profile_name=args['profile'])\n    else:\n        session = boto3.Session() # use default profile\n    \n    client = session.client('s3')\n    \n    r = client.list_objects_v2(Bucket=(args['bwt_s3_path'].split('/'))[2],\n                               Prefix='/'.join((args['bwt_s3_path'].split('/'))[3:])\n                               )\n\n    # Sum up the sizes of the individual bt2 files\n    size = 0\n    for item in r['Contents']:\n        if item['Key'].endswith('bt2'):\n            size += item['Size']\n\n    # Convert to GB\n    bt2_size = round(float(size) / (1024**3),2)\n\n    # List of memory availabe to EMR EC2 instances within a Spark application\n    # (http://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-hadoop-task-config.html)\n    instance_types = {\n        'm1.xlarge': {'vcpu': 4, 'memory': 12288},\n        'cc2.8xlarge': {'vcpu': 32, 'memory': 56320},\n        'c3.8xlarge': {'vcpu': 32, 'memory': 53248},\n        'c4.8xlarge': {'vcpu': 36, 'memory': 53248},\n        'm4.10xlarge': {'vcpu': 40, 'memory': 155648},\n        'm4.16xlarge': {'vcpu': 64, 'memory': 253952},\n    }\n\n    # Get memory and vCPU count for the instance type\n    instance = args['core_node_type']\n    available_memory = (float(instance_types[instance]['memory']) / 1024) / 1.1\n    vcpus = instance_types[instance]['vcpu']\n    blocking_memory = int(math.ceil(0.75 * instance_types[instance]['memory'] / 1024))\n\n    # Find the number of executors to minimize the number of wasted vCPUs\n    config = {'wasted_cpus': 1000, 'vcpus_per_executor': 0, 'executors': 0}\n    for executor_memory in range(int(math.ceil(bt2_size)), int(math.floor(available_memory))):\n        # Calculate the number of executors\n        executors = math.floor(available_memory / (executor_memory))\n\n        # Calculate how many cores are available to each executor\n        cores = math.floor(vcpus / executors)\n\n        # Calculate how many Bowtie2 instances can run on the executor\n        bowties = math.floor(executor_memory / bt2_size)\n        if bowties > 5:\n            bowties = 5\n\n        # Calculate how many Bowtie2 instances can run on the instance\n        total_bowties = bowties * executors\n\n        # Calculate the number of threads for each Bowtie instance\n        threads = math.floor(cores / bowties)\n\n        # Get the number of wasted vCPUs\n        waste = vcpus - (threads * bowties * executors)\n\n        # Update config if this configuration minimizes wasted vCPU and maximizes cores/executor\n        if (waste < config['wasted_cpus']) and (cores > config['vcpus_per_executor']):\n            excess_memory = available_memory - (executors * executor_memory)\n\n            additional_per_executor = math.floor(excess_memory / executors)\n            #excess_memory -= additional_per_executor\n\n            config = {\n                'wasted_cpus': waste,\n                'vcpus_per_executor': cores,\n                'executors': executors,\n                'memory_per_executor': executor_memory + additional_per_executor,\n                'bowties_per_executor': bowties,\n                'vcpus_per_bowtie': threads,\n                'bowtie_instances': total_bowties,\n                'unused_memory': round(excess_memory,1)\n            }\n\n    # Return the selected --executor-memory and --executor-cores settings, and the memory setting required\n    # for one executor to fit on the cluster\n    return int(config['memory_per_executor']), int(config['bowties_per_executor']), blocking_memory\n\n# Function to build Steps\ndef build_steps(args):\n    executor_memory, executor_cores, blocking_memory = get_spark_parameters(args)\n\n    if args['deploy_mode'] == 'cluster':\n        SparkVar_path = '{}application/SparkVar/SparkVar.py'.format(args['pipeline'])\n    else:\n        SparkVar_path = '/mnt/SparkVar.py'\n\n    # Get the list of samples\n    with open(args['samples']) as fp:\n        sample_list = fp.read().splitlines()\n    samples = ','.join(sample_list)\n\n    SparkVar_cmd = [\"spark-submit\",\n                    \"--deploy-mode\", \"{}\".format(args['deploy_mode']),\n                    \"--master\",\"yarn\",\n                    \"--executor-memory\",\"{}G\".format(executor_memory),\n                    \"--executor-cores\",\"{}\".format(executor_cores),\n                    \"--jars\",\"/usr/lib/spark/examples/jars/spark-examples.jar\",\n                    \"{}\".format(SparkVar_path),\n                    \"--job_id\",\"{}\".format(args['job_id']),\n                    \"--sample\",\"{}\".format(samples),\n                    \"--ref\",\"{}\".format(args[\"reference\"]),\n                    \"--bt2\",\"{}\".format(args[\"bwt_prefix\"]),\n                    \"--output\",\"{}\".format(args[\"output\"]),\n                    #\"--lib_type\",\"{}\".format(args[\"lib_type\"]),\n                    \"--qual\",\"{}\".format(args[\"qual\"]),\n                    \"--min_cov\",\"{}\".format(args[\"min_cov\"]),\n                    \"--min_purity\",\"{}\".format(args[\"min_purity\"]),\n                    \"--hets_cov\",\"{}\".format(args[\"hets_cov\"]),\n                    \"--hets_purity\",\"{}\".format(args[\"hets_purity\"]),\n                    \"--indel_cov\",\"{}\".format(args[\"indel_cov\"]),\n                    \"--indel_purity\",\"{}\".format(args[\"indel_purity\"]),\n                    \"--indel_perc\",\"{}\".format(args[\"indel_perc\"]),\n                   ]\n    if args['aln_only']: SparkVar_cmd += [\"--aln_only\"]\n    if args['indel']: SparkVar_cmd += [\"--indel\"]\n    if args['hets']: SparkVar_cmd += [\"--hets\"]\n    if args['vcf']: SparkVar_cmd += [\"--vcf\"]\n    if args['save_BAMs']: SparkVar_cmd += [\"--save_BAMs\"]\n    if args['index']: SparkVar_cmd += [\"--index\"]\n    if args['number_partitions']: SparkVar_cmd += [\"--number_partitions\", args['number_partitions']]\n    if args['bt2_param']: SparkVar_cmd += [\"--bt2_param\", \"'\"+args['bt2_param']+\"'\"]\n    if args['mpileup_param']: SparkVar_cmd += [\"--mpileup_param\", \"'\"+args['mpileup_param']+\"'\"]\n    if args['executor_memory']: SparkVar_cmd[6:7] = [args['executor_memory']]\n    if args['executor_cores']: SparkVar_cmd[8:9] = [str(args['executor_cores'])]\n    \n    steps = [\n        {\n            \"Name\": \"SparkVar\",\n            \"ActionOnFailure\": \"CONTINUE\",\n            'HadoopJarStep': {\n                \"Jar\":\"command-runner.jar\",\n                \"Args\": SparkVar_cmd\n            }\n        }\n    ] \n    \n    return steps\n\ndef main():\n    # Get command line arguments\n    args = parse_args()\n    print (' '.join(build_steps(args)[0]['HadoopJarStep']['Args']))\n        \n    # Build EMR cluster and initiate steps\n    if args['profile']:\n        session = boto3.Session(profile_name=args['profile'])\n    else:\n        session = boto3.Session() # use default named profile\n\n    client = session.client('emr')\n\n    kwargs = {'Applications' : build_applications(),\n              'BootstrapActions': build_bootstrap(args),\n              'Configurations' : build_configuration(),\n              'Instances' : {\n                               'InstanceGroups': instance_groups(args),\n                               'Ec2KeyName': args[\"ssh_key\"],\n                               'KeepJobFlowAliveWhenNoSteps': (True if args[\"auto_terminate\"]==False else False),\n                               'TerminationProtected': False,\n                               'Ec2SubnetId': args['ec2_subnet_id'],\n                               'EmrManagedMasterSecurityGroup': args['emr_managed_master_security_group'],\n                               'EmrManagedSlaveSecurityGroup': args['emr_managed_slave_security_group'],\n                               'ServiceAccessSecurityGroup': args['service_access_security_group'],\n                               'AdditionalMasterSecurityGroups': args['additional_master_security_groups'].split(','),\n                               'AdditionalSlaveSecurityGroups': args['additional_slave_security_groups'].split(','),\n                             },\n              'JobFlowRole' : args['job_flow_role'],\n              'Name' : args['emr_cluster_name'],\n              'ReleaseLabel' : args['EMR_release'],\n              'ScaleDownBehavior' : 'TERMINATE_AT_INSTANCE_HOUR',\n              'ServiceRole' : args['job_flow_role'],\n              'Steps' : build_steps(args),\n              'Tags' : build_tags(args),\n              'VisibleToAllUsers' : True\n             }\n    if args['log']: kwargs['LogUri'] = args['log']\n\n    response = client.run_job_flow(**kwargs)\n\n    if 'JobFlowId' in response:\n        cluster_id = response['JobFlowId']\n\n        # Print status message for user\n        msg = \"A cluster with {} x {} master node and {} x {} core nodes has been started. \".format(args['master_node_number'], args['master_node_type'],\n                                                                                                   args['core_node_number'], args['core_node_type'])\n        msg += \"Your cluster id is {}.\".format(cluster_id)\n\n        print(msg)\n    else:\n        print(\"Your cluster failed to start. Sorry!\")\n\n# Main executable\nif __name__ == \"__main__\":\n    main()\n","repo_name":"huiguo7/SparkVar","sub_path":"emr-launch.py","file_name":"emr-launch.py","file_ext":"py","file_size_in_byte":22070,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"20784533727","text":"import subprocess\nimport re\nimport collections\nimport socket\n\nIpTableRule = collections.namedtuple(\n    'IpTableRule', 'target, prot, opt, source, destination, filters')\n\ndef build_cmd(rule):\n    cmd = []\n    cmd.extend([\"-j\", rule.target])\n    cmd.extend([\"-s\", rule.source])\n    cmd.extend([\"-d\", rule.destination])\n    cmd.extend([\"-p\", rule.prot])\n    for filt in rule.filters:\n        if filt in [\"tcp\", \"udp\", \"icmp\"]:\n            continue\n        if filt.startswith(\"dpt:\"):\n            cmd.extend([\"--dport\", filt.split(\":\")[1]])\n        if filt.startswith(\"spt:\"):\n            cmd.extend([\"--sport\", filt.split(\":\")[1]])\n    return cmd\n\ndef add_rule(rule, table=\"INPUT\"):\n    cmd = [\"iptables\", \"-A\", table]\n    cmd.extend(build_cmd(rule))\n    subprocess.check_call(cmd)\n\ndef remove_rule(rule, table=\"INPUT\"):\n    cmd = [\"iptables\", \"-D\", table]\n    cmd.extend(build_cmd(rule))\n    subprocess.check_call(cmd)\n\ndef list_rules(table=\"INPUT\"):\n    p = subprocess.Popen([\"iptables\", \"-L\", table, \"-n\"], stdout=subprocess.PIPE)\n    (stdout, stderr) = p.communicate()\n    if p.returncode != 0:\n        raise Exception(\"Error executing iptables.\")\n\n    found = []\n\n    # Skip two lines of the header.\n    rules = stdout.split(\"\\n\")[2:]\n    for rule in rules:\n        m = re.match(\"([^ ]+)\\s+([^ ]+)\\s+([^ ]+)\\s+([^ ]+)\\s+([^ ]+)\\s+(.+)\", rule)\n        if m:\n            target      = m.group(1)\n            prot        = m.group(2)\n            opt         = m.group(3)\n            source      = m.group(4)\n            destination = m.group(5)\n            filters     = m.group(6).split()\n\n            found.append(IpTableRule(target, prot, opt, source, destination, filters))\n\n    return found\n\ndef modify_host(source=\"0.0.0.0/0\", destination=\"0.0.0.0/0\", action=\"ACCEPT\", prot=\"tcp\", port=0):\n    rule = IpTableRule(action, prot, \"--\", source, destination, [])\n    if port:\n        rule.filters.append(\"dpt:%d\" % port)\n    add_rule(rule)\n\nZOOKEEPER_LOCAL = \"127.0.0.1\"\nZOOKEEPER_PORTS = [2181, 2888, 3888]\n\ndef zookeeper_clear(extra_ports=[]):\n    rules = list_rules()\n    ports = ZOOKEEPER_PORTS[:]\n    ports.extend(extra_ports)\n    for rule in rules:\n        for port in ports:\n            if \"dpt:%d\" % port in rule.filters:\n                remove_rule(rule)\n                break\n\ndef zookeeper_allow(host, extra_ports=[]):\n    ports = ZOOKEEPER_PORTS[:]\n    ports.extend(extra_ports)\n    for port in ports:\n        for prot in (\"tcp\", \"udp\"):\n            try:\n                modify_host(source=(\"%s/32\" % host), action=\"ACCEPT\", prot=prot, port=port)\n            except socket.error:\n                return\n\ndef zookeeper_reject(extra_ports=[]):\n    ports = ZOOKEEPER_PORTS[:]\n    ports.extend(extra_ports)\n    for port in ports:\n        for prot in (\"tcp\", \"udp\"):\n            modify_host(source=\"0.0.0.0/0\", action=\"REJECT\", prot=prot, port=port)\n\ndef setup(hosts=[], extra_ports=[]):\n    zookeeper_clear(extra_ports=extra_ports)\n    zookeeper_allow(ZOOKEEPER_LOCAL, extra_ports=extra_ports)\n    addresses = [ZOOKEEPER_LOCAL]\n    for host in hosts:\n        try:\n            address = socket.gethostbyname(host)\n        except socket.error:\n            continue\n        if not(address in addresses):\n            zookeeper_allow(address, extra_ports=extra_ports)\n            addresses.append(address)\n    zookeeper_reject(extra_ports=extra_ports)\n","repo_name":"rmahmood/reactor-core","sub_path":"reactor/server/iptables.py","file_name":"iptables.py","file_ext":"py","file_size_in_byte":3356,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"41008865554","text":"\npretty_name = \"Reddit\"\nservice_name = \"reddit\"\nurl = \"https://www.reddit.com/\"\n\ndef search(keyword):\n    result = {}\n\n    result['title'] = pretty_name\n    result['url'] = url + keyword\n    result['source'] = service_name\n    result['keyword'] = keyword\n\n    return result","repo_name":"jmnavarr/py_django_poc","sub_path":"social/libraries/reddit.py","file_name":"reddit.py","file_ext":"py","file_size_in_byte":273,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"38040472151","text":"import numpy as np\nfrom fuzzy.sets.calculated import CalculatedFuzzySet\n\nfrom defuzzy.defuzzifier import Defuzzifier\nfrom fuzzy.domain.simple_domain import SimpleDomain\nfrom fuzzy.i_fuzzy import FuzzySet\nfrom fuzzy.utils import debug\n\n\nclass COGDefuzzifier(Defuzzifier):\n    def __init__(self, debug=False):\n        self.debug = debug\n\n    def defuzzify(self, inferred_set: FuzzySet) -> float:\n        if self.debug:\n            debug.s_print(inferred_set, \"Zaključak: \")\n        domain = inferred_set.get_domain()\n        domain_elems = np.linspace(domain.first, domain.last, num=100)\n        approximated_set = np.array([inferred_set.get_value_at(elem) for elem in domain_elems])\n        numerator = np.trapz(approximated_set * np.array(domain_elems), domain_elems)\n        denominator = np.trapz(approximated_set, domain_elems)\n        if denominator == 0:\n            return 0\n        return numerator / denominator\n\nif __name__ == \"__main__\":\n    defuzz = COGDefuzzifier()\n    muset = CalculatedFuzzySet(SimpleDomain(0, 1), lambda x : x ** 2)\n    print(defuzz.defuzzify(muset))","repo_name":"mihaelnikic/FuzzyTORCS","sub_path":"defuzzy/cog_def.py","file_name":"cog_def.py","file_ext":"py","file_size_in_byte":1083,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"35708723514","text":"import sys\nfrom pathlib import Path\nimport re\nimport os\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport tqdm\nfrom torch_geometric.loader import DataLoader\nfrom torch_geometric.data import InMemoryDataset, Data\nfrom torch.utils.data import Sampler\n\nfrom shapely.geometry import Polygon\nfrom osgeo import gdal, gdal_array\ngdal.UseExceptions()\nimport laspy\nfrom laxpy.tree import LAXTree\nfrom laxpy.file import LAXParser\n\n\nfrom pn2_scalar_regressor import Net\nimport rasterizer\n\nclass PointCloudsInFiles(InMemoryDataset):\n    \"\"\"Point cloud dataset where one data point is a file.\"\"\"\n\n    def __init__(self, lasfiles, biomassfile, max_points=20000):\n        self.lasfiles = lasfiles\n        self.biomassfile = biomassfile\n        self.max_points = max_points\n\n        ds = gdal.Open(self.biomassfile)\n        ulx, xres, xskew, uly, yskew, yres = ds.GetGeoTransform()\n        lrx = ulx + (ds.RasterXSize * xres)\n        lry = uly + (ds.RasterYSize * yres)\n\n        self.XSize = ds.RasterXSize\n        self.YSize = ds.RasterYSize\n        self.ulx = ulx\n        self.uly = uly\n        self.xres = xres\n        self.yres = yres\n\n        band = ds.GetRasterBand(1)\n        nodata = band.GetNoDataValue()\n        arr = band.ReadAsArray()\n        arr[arr == nodata] = np.nan\n        self.valid_1d_indices = np.where(np.isfinite(arr.flatten()))[0]\n        self.biomass = arr\n\n\n\n        self.bboxes = []\n        for lasfile in self.lasfiles:\n            laxfile = re.sub(r'^(.*).la[sz]$', r'\\1.lax', str(lasfile))\n            parser = LAXParser(laxfile)\n            tree = LAXTree(parser)\n            minx, maxx, miny, maxy = parser.bbox\n            bbox_las = Polygon([(minx, miny), (minx, maxy), (maxx, maxy), (maxx, miny)])\n            self.bboxes.append(bbox_las)\n\n        super().__init__()\n\n    def __len__(self):\n        return len(self.valid_1d_indices)\n\n    def __getitem__(self, idx):\n        if torch.is_tensor(idx):\n            idx = idx.tolist()\n\n        xpos, ypos = np.unravel_index(self.valid_1d_indices[idx], (self.YSize, self.XSize))\n        y_label = self.biomass[xpos, ypos]\n        bbox = [self.ulx + (ypos+0.0) * self.xres,\n                self.uly + (xpos+0.0) * self.yres,\n                self.ulx + (ypos+1.0) * self.xres,\n                self.uly + (xpos+1.0) * self.yres]\n\n        print(bbox, y_label)\n        q_polygon = Polygon([(bbox[0], bbox[1]), (bbox[2], bbox[1]), (bbox[2], bbox[3]), (bbox[0], bbox[3])])\n\n        pcloud = {'xyz': []}\n        for lasfile_id, lasfile in enumerate(self.lasfiles):\n            bbox_las = self.bboxes[lasfile_id]\n            if not q_polygon.intersects(bbox_las):\n                continue\n\n            laxfile = re.sub(r'^(.*).la[sz]$', r'\\1.lax', str(lasfile))\n            parser = LAXParser(laxfile)\n            tree = LAXTree(parser)\n\n            lashandle = laspy.read(lasfile)\n\n            # x, y = lashandle.x, lashandle.y\n            # valid_points = np.logical_and(\n            #     np.logical_and(x > bbox[0], x <= bbox[2]),\n            #     np.logical_and(y > bbox[3], y <= bbox[1]),\n            # )\n            # # valid_indices = np.where(valid_points)\n            # pcloud['xyz'].append(lashandle.xyz[valid_points, :])\n\n\n            for cell_index, polygon in tree.cell_polygons.items():  # use quadtree for preselection\n                if q_polygon.intersects(polygon):\n                    candidate_indices = parser.create_point_indices(cell_index)\n                    x, y = lashandle.x[candidate_indices], lashandle.y[candidate_indices]\n                    valid_points = np.logical_and(\n                        np.logical_and(x > bbox[0], x <= bbox[2]),\n                        np.logical_and(y > bbox[3], y <= bbox[1]),\n                    )\n                    valid_indices = candidate_indices[valid_points]\n                    pcloud['xyz'].append(lashandle.xyz[valid_indices, :])\n        if len(pcloud['xyz']) > 0:\n            pcloud['xyz'] = np.concatenate(pcloud['xyz'], axis=0)\n        if pcloud['xyz'].shape[0] < self.max_points:\n            pcloud['xyz'] = pcloud['xyz'][np.random.choice(range(pcloud['xyz'].shape[0]), self.max_points, replace=True), :]\n        elif pcloud['xyz'].shape[0] > self.max_points:\n            pcloud['xyz'] = pcloud['xyz'][np.random.choice(range(pcloud['xyz'].shape[0]), self.max_points, replace=False), :]\n\n        sample = Data(pos=torch.from_numpy(pcloud['xyz']).float(),\n                      y=torch.as_tensor(y_label, dtype=torch.float))\n\n        return sample\n\nclass PointCloudsRasterExtract2Ep(InMemoryDataset):\n    \"\"\"Point cloud dataset where one data point is a file.\"\"\"\n\n    def __init__(self, lasfiles_ep1, lasfiles_ep2, biomassfile, max_points=20000, backup_extract=None, skip_n=0):\n        self.lasfiles_ep1 = lasfiles_ep1\n        self.lasfiles_ep2 = lasfiles_ep2\n        self.biomassfile = biomassfile\n        self.max_points = max_points\n        self.skip_n = skip_n\n\n        ds = gdal.Open(self.biomassfile)\n        ulx, xres, xskew, uly, yskew, yres = ds.GetGeoTransform()\n        lrx = ulx + (ds.RasterXSize * xres)\n        lry = uly + (ds.RasterYSize * yres)\n\n        self.XSize = ds.RasterXSize\n        self.YSize = ds.RasterYSize\n        self.ulx = ulx\n        self.uly = uly\n        self.xres = xres\n        self.yres = yres\n\n        band = ds.GetRasterBand(1)\n        nodata = band.GetNoDataValue()\n        arr = band.ReadAsArray()\n        arr[arr == nodata] = np.nan\n        self.biomass = arr\n\n\n\n        self.bboxes = []\n        self.lasindices = np.full_like(self.biomass, None, dtype=object)\n\n        if backup_extract is not None and os.path.exists(backup_extract):\n            print(\"Loading previously extracted point indices from disk...\")\n            self.lasindices = np.load(backup_extract, allow_pickle=True)\n\n        else:\n            for fileid, lasfile in enumerate(tqdm.tqdm(self.lasfiles_ep1, \"scanning input las files\")):\n                lashandle = laspy.read(lasfile)\n                xy = lashandle.xyz[:, :2]\n                minxy = np.min(xy, axis=0)\n                maxxy = np.max(xy, axis=0)\n                if maxxy[0] < ulx or minxy[0] > lrx:\n                    continue\n                if maxxy[1] < lry or minxy[1] > uly:\n                    continue\n                raster = rasterizer.Rasterizer(data=xy, raster_size=(xres, yres), method=None)\n                XVoxelCenter, XVoxelContains, idxVoxelUnique, _ = raster.rasterize(origin=(ulx, uly))\n                for cid, (centerx, centery, contains) in enumerate(zip(*XVoxelCenter, XVoxelContains)):\n                    # get respective biomass\n                    px = int((centerx - ulx)/xres)\n                    py = int((centery - uly)/yres)\n                    if px < 0 or px >= self.XSize or py < 0 or py >= self.YSize:\n                        continue\n                    bm = arr[py, px]\n                    if not np.isnan(bm):\n                        if self.lasindices[py, px] is None:\n                            self.lasindices[py, px] = lashandle.xyz[contains, :]\n                        else:\n                            self.lasindices[py, px] = np.concatenate([self.lasindices[py, px],\n                                                                      lashandle.xyz[contains, :]])\n            if backup_extract is not None:\n                self.lasindices.dump(backup_extract)\n\n        self.valid_1d_indices = np.where(np.logical_and(np.isfinite(arr.flatten()), self.lasindices.flatten()))[0]\n        print(f\"{len(self)} training samples found.\")\n        super().__init__()\n\n    def __len__(self):\n        return len(self.valid_1d_indices) - self.skip_n\n\n    def __getitem__(self, idx):\n        if torch.is_tensor(idx):\n            idx = idx.tolist()\n        idx += self.skip_n\n\n        xpos, ypos = np.unravel_index(self.valid_1d_indices[idx], (self.YSize, self.XSize))\n        y_label = self.biomass[xpos, ypos]\n        lasindices = self.lasindices[xpos, ypos]\n\n        xyz = lasindices\n        # for fileid, contains in lasindices.items():\n        #     lashandle = laspy.read(self.lasfiles[fileid])\n        #     xyz.append(lashandle.xyz[contains])\n        # xyz = np.concatenate(xyz)\n        sample = Data(pos=torch.from_numpy(xyz).float(),\n                      y=torch.as_tensor(y_label, dtype=torch.float))\n\n        return sample\n\n\n\ndef main(args):\n    train_dataset = PointCloudsRasterExtract2Ep(lasfiles_ep1=list(Path(r\"D:\\temp\\harm_2012_norm\").glob(\"*.laz\")),\n                                                biomassfile=r\"D:\\temp\\RF_PRF_biomass_Ton_DRY_masked_train.tif\",\n                                                backup_extract=r\"D:\\temp\\train_presel.npy\",\n                                                skip_n=0  #14000+3000+22000,\n                                                )\n    torch.random.seed(42)\n    train_dataset.shuffle()\n    train_loader = DataLoader(train_dataset, batch_size=2, shuffle=False,\n                              num_workers=6)\n\n    test_dataset = PointCloudsRasterExtract2Ep(lasfiles_ep1=list(Path(r\"D:\\temp\\harm_2012_norm\").glob(\"*.laz\")),\n                                               biomassfile=r\"D:\\temp\\RF_PRF_biomass_Ton_DRY_masked_test.tif\",\n                                               backup_extract=r\"D:\\temp\\test_presel.npy\"\n                                               )\n    test_loader = DataLoader(test_dataset, batch_size=2, shuffle=False,\n                              num_workers=6)\n\n    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n    print(f\"Using {device} device.\")\n    model = Net(num_features=0).to(device)\n    optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)\n\n\n    def train(path):\n        model.train()\n        loss_list = []\n        for i, data in enumerate(train_loader):\n            data = data.to(device)\n            optimizer.zero_grad()\n            out = model(data)[:, 0]\n            loss = F.mse_loss(out, data.y)\n            loss.backward()\n            optimizer.step()\n            if (i + 1) % 1 == 0:\n                print(f'[{i + 1}/{len(train_loader)}] MSE Loss: {loss.to(\"cpu\"):.4f} ')\n                loss_list.append(loss.detach().to(\"cpu\").numpy())\n            if (i + 1) % 1000 == 0:\n                print(f'Saving file...')\n                torch.save(model, path)\n                print(f'mean loss last 1000 it: {np.mean(loss_list[-1000:])}')\n        print(f'mean loss this epoch: {np.mean(loss_list)}')\n        return np.mean(loss_list)\n\n    @torch.no_grad()\n    def test(loader, ep_id):\n        model.eval()\n        losses = []\n        for idx, data in enumerate(loader):\n            data = data.to(device)\n            outs = model(data)[:, 0]\n            loss = F.mse_loss(outs, data.y)\n            print(\"Sample differences in p95:\")\n            print(data.y.to('cpu').numpy() - outs.to('cpu').numpy())\n            losses.append(float(loss.to(\"cpu\")))\n        return float(np.mean(losses))\n\n\n    for epoch in range(1, 1001):\n        model_path = rf'D:\\temp\\models\\deepbiomass.model'\n        if os.path.exists(model_path):\n            model = torch.load(model_path)\n        train_mse = train(model_path)\n        torch.save(model, model_path)\n\n        mse = test(test_loader, epoch)\n        with open(model_path.replace('.model', '.csv'), 'a') as f:\n            f.write(\n            f'{epoch}, {train_mse}, {mse}\\n'\n            )\n        print(f'Epoch: {epoch:02d}, Mean test MSE: {mse:.4f}')\n        print(f'Epoch: {epoch:02d}, Mean train MSE: {train_mse:.4f}')\n\n\nif __name__ == '__main__':\n    main(sys.argv[1:])","repo_name":"lwiniwar/DeepBiomass","sub_path":"main_2ep.py","file_name":"main_2ep.py","file_ext":"py","file_size_in_byte":11543,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"71291530705","text":"import json\nimport os\nimport shutil\nimport sys\nsys.path.append(\"..\")\nimport unittest\nfrom main_project.local.data_handling_locally import DataHandlingLocal\n\nclass TestDataHandling(unittest.TestCase):\n    \n    def setUp(self):\n        self.test_path = \"../test_raw_data_path\"\n        self.data_handler = DataHandlingLocal(self.test_path)\n        self.test_complete_dictionary = { \n            \"Name\" : \"\",\n            \"Unique_ID\" : [\"10530943\", \"12081395\"], \n            \"UUID\" :\"\", \n            \"Product_Image\" : [\"https://static.thcdn.com/images/large/original//productimg/1600/1600/10530943-1224889444460882.jpg\", \"https://static.thcdn.com/images/large/original//productimg/1600/1600/12081397-1324792209160155.jpg\"]\n            }\n            \n    def test1_create_raw_data_folder(self):\n        self.data_handler.create_raw_data_folder() \n        self.assertTrue(os.path.exists(self.test_path))\n        print(\"Raw data file path exists\")   \n        \n    def test2_save_data_locally(self):\n        self.data_handler.save_data_locally(self.test_complete_dictionary)\n        self.assertTrue(os.path.exists(self.test_path + '/data.json'))\n        with open(self.test_path + '/data.json', 'r') as all_data:\n            complete_data = json.load(all_data) \n        json_keys = complete_data.keys()\n        your_dictionary_keys = list(self.test_complete_dictionary.keys())\n        self.assertEqual(set(json_keys), set(your_dictionary_keys))\n        print(\"Json exists and has the correct keys\")\n\n    def test3_download_image(self):\n        self.data_handler.download_image(self.test_complete_dictionary)\n        self.assertTrue(os.path.exists(self.test_path + '/images'))\n        print(\"Images file path exists\")\n        \n    def test4_download_image(self):\n        self.data_handler.download_image(self.test_complete_dictionary)\n        image_files_list = os.listdir(self.test_path + '/images')\n        for file_name in image_files_list :\n            name, extension = os.path.splitext(file_name)\n            self.assertEqual(extension, \".jpg\")\n            file_name_index = image_files_list.index(file_name)\n            self.assertEqual(name, self.test_complete_dictionary[\"Unique_ID\"][file_name_index])\n        print(\"Images have been stored as .jpg files with the correct names\")     \n    \n    @classmethod\n    def tearDownClass(cls):\n        shutil.rmtree(\"../test_raw_data_path\")\n                \nif __name__ == \"__main__\":\n    unittest.main()","repo_name":"AhmadAlalti/AiCore_Data_Collection_Pipeline","sub_path":"test/test_local.py","file_name":"test_local.py","file_ext":"py","file_size_in_byte":2444,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"26443675025","text":"import sc2\n\nfrom sc2 import BotAI, Race, UnitTypeId, AbilityId\nfrom sc2.ids.upgrade_id import UpgradeId\nfrom sc2.position import Point2\n\n\n\nclass CompetitiveBot(BotAI):\n    NAME: str = \"CompetitiveBot\"\n    \"\"\"This bot's name\"\"\"\n    RACE: Race = Race.Protoss\n    \"\"\"This bot's Starcraft 2 race.\n    Options are:\n        Race.Terran\n        Race.Zerg\n        Race.Protoss\n        Race.Random\n    \"\"\"\n\n    def __init__(self):\n        sc2.BotAI.__init__(self)\n        sc2.proxy = False\n\n    async def on_start(self):\n        print(\"Game started\")\n        # Do things here before the game starts\n\n    async def on_step(self, iteration):\n        await self.distribute_workers()\n        await self.build_worker()\n        await self.build_pylon()\n        await self.build_gateways()\n        await self.build_gas()\n        await self.build_cyber_core()\n        await self.train_stalkers()\n        await self.build_four_gates()\n        await self.chrono()\n        await self.warp_research()\n        await self.attack()\n        await self.stalker_micro()\n\n        # Populate this function with whatever your bot should do!\n        pass\n\n    # build workers whenever base are idle and each base has less than 22 workers\n    async def build_worker(self):\n        probe_per_nexus = 22\n        nexus = self.townhalls.ready.random\n        if (\n                self.can_afford(UnitTypeId.PROBE)\n                and nexus.is_idle\n                and self.workers.amount < self.townhalls.amount * probe_per_nexus\n        ):\n            nexus.train(UnitTypeId.PROBE)\n\n    # build pylon facing the enemy and near nexus\n    async def build_pylon(self):\n        pylon_from_nexus_dis = 10\n        remaining_supply = 3\n        nexus = self.townhalls.ready.random\n        pos = nexus.position.towards(self.enemy_start_locations[0], pylon_from_nexus_dis)\n\n        if (\n                self.supply_left < remaining_supply\n                and self.already_pending(UnitTypeId.PYLON) == 0\n                and self.can_afford(UnitTypeId.PYLON)\n        ):\n            await self.build(UnitTypeId.PYLON, near=pos)\n\n        # build proxy pylon after 4 gates\n        if (\n                self.structures(UnitTypeId.GATEWAY).amount\n                + self.structures(UnitTypeId.WARPGATE).amount == 4\n                and self.can_afford(UnitTypeId.PYLON)\n                and not sc2.proxy\n        ):\n            proxy_pos = self.game_info.map_center.towards(self.enemy_start_locations[0],20)\n            await self.build(UnitTypeId.PYLON, near = proxy_pos)\n            sc2.proxy = True\n\n    # build gateway next to a random pylon\n    async def build_gateways(self):\n        if (\n                self.structures(UnitTypeId.PYLON).ready\n                and self.can_afford(UnitTypeId.GATEWAY)\n                and not self.structures(UnitTypeId.GATEWAY)\n        ):\n            # if pylon is ready then build gate next to pylon\n\n            pylon = self.structures(UnitTypeId.PYLON).ready.random\n            await self.build(UnitTypeId.GATEWAY, near = pylon)\n\n    # build gas\n    async def build_gas(self):\n        gas_to_nexus = 15\n        if self.structures(UnitTypeId.GATEWAY):\n            for nexus in self.townhalls.ready:\n                vgs = self.vespene_geyser.closer_than(gas_to_nexus, nexus)\n                for vg in vgs:\n                    if not self.can_afford(UnitTypeId.ASSIMILATOR):\n                        break\n                    worker = self.select_build_worker(vg.position)\n                    if (worker is None):\n                        break\n                    if not self.gas_buildings or not self.gas_buildings.closer_than(1, vg):\n                        worker.build(UnitTypeId.ASSIMILATOR, vg)\n                        worker.stop(queue=True)\n\n    async def build_cyber_core(self):\n        if (self.structures(UnitTypeId.GATEWAY).ready\n                and self.can_afford(UnitTypeId.CYBERNETICSCORE)\n                and not self.structures(UnitTypeId.CYBERNETICSCORE)\n\n                and not self.already_pending(UnitTypeId.CYBERNETICSCORE)):\n            pylon = self.structures(UnitTypeId.PYLON).ready.random\n            await self.build(UnitTypeId.CYBERNETICSCORE, near=pylon)\n\n\n    async def train_stalkers(self):\n        for gateway in self.structures(UnitTypeId.GATEWAY).ready:\n            if self.can_afford(UnitTypeId.STALKER) and gateway.is_idle:\n                gateway.train(UnitTypeId.STALKER)\n\n        for warpgate in self.structures(UnitTypeId.WARPGATE).ready:\n            if self.can_afford(UnitTypeId.STALKER) and warpgate.is_idle:\n                pylon = self.structures(UnitTypeId.PYLON).closest_to(self.enemy_start_locations[0])\n                warpgate.warp_in(UnitTypeId.STALKER, pylon.position.random_on_distance(3))\n\n\n    async def build_four_gates(self):\n        num_gates = 4\n        if (self.structures(UnitTypeId.PYLON).ready\n                and self.can_afford(UnitTypeId.GATEWAY)\n                and self.structures(UnitTypeId.GATEWAY).amount +\n                     self.structures(UnitTypeId.WARPGATE).amount < num_gates):\n\n            pylon = self.structures(UnitTypeId.PYLON).ready.random\n            await self.build(UnitTypeId.GATEWAY, near=pylon)\n\n\n    async def chrono(self):\n        chrono_energy = 50\n        if self.structures(UnitTypeId.PYLON):\n            nexus = self.structures(UnitTypeId.NEXUS).ready.random\n            if (\n                    not self.structures(UnitTypeId.CYBERNETICSCORE).ready\n                    and self.structures(UnitTypeId.PYLON).amount > 0\n                    and nexus.energy >= chrono_energy\n            ):\n                nexus(AbilityId.EFFECT_CHRONOBOOSTENERGYCOST, nexus)\n            elif (\n                    nexus.energy >= chrono_energy\n                    and 1 > self.already_pending_upgrade(UpgradeId.WARPGATERESEARCH) > 0):\n                cybercore = self.structures(UnitTypeId.CYBERNETICSCORE).ready.random\n                nexus(AbilityId.EFFECT_CHRONOBOOSTENERGYCOST, cybercore)\n\n\n    async def warp_research(self):\n        if (\n            self.structures(UnitTypeId.CYBERNETICSCORE).ready\n            and self.can_afford(AbilityId.RESEARCH_WARPGATE)\n            and self.already_pending_upgrade(UpgradeId.WARPGATERESEARCH) == 0\n        ):\n            cybercore = self.structures(UnitTypeId.CYBERNETICSCORE).ready.random\n            cybercore.research(UpgradeId.WARPGATERESEARCH)\n\n    async def attack(self):\n        wanted_stalker_count = 5\n        stalker_count = self.units(UnitTypeId.STALKER).amount\n        stalkers = self.units(UnitTypeId.STALKER)\n        if stalker_count > wanted_stalker_count:\n            for stalker in stalkers:\n                stalker.attack(self.enemy_start_locations[0])\n\n    async def stalker_micro(self):\n        stalkers = self.units(UnitTypeId.STALKER)\n        enemy_loc = self.enemy_start_locations[0]\n        if self.structures(UnitTypeId.PYLON).ready:\n            for stalker in stalkers:\n                if stalker.weapon_ready:\n                    stalker.attack(enemy_loc)\n                else:\n                    stalker.move(self.structures(UnitTypeId.PYLON).closest_to(enemy_loc))\n\n    def on_end(self, result):\n        print(\"Game ended.\")\n        # Do things here after the game ends\n","repo_name":"tonychang04/sc2AI","sub_path":"bot/bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":7209,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"22867984376","text":"from collections import Counter\nfrom typing import List\nclass Solution:\n    def minimumRounds(self, tasks: List[int]) -> int:\n\n        hash_table = Counter(tasks)\n        count = 0\n        for task in hash_table.values():\n            if task < 2:\n                return -1\n            if task % 3 == 0:\n                count += task // 3\n\n            elif task == 2:\n                count += 1\n\n            else:\n                count += abs(task // -3)\n        return count\n\n\"\"\"TESTS:\n1)Runtime 2083 ms\nBeats 45.41%\nMemory 28.1 MB\nBeats 96.23%\n2)Runtime 2081 ms\nBeats 45.46%\nMemory 28.1 MB\nBeats 97.83%\"\"\"\n\n","repo_name":"BenitoSwaggolini/Leetcode_tasks","sub_path":"medium/2244. Minimum Rounds to Complete All Tasks/r.py","file_name":"r.py","file_ext":"py","file_size_in_byte":608,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"48"}
+{"seq_id":"31669510981","text":"#The dataset is taken from the coursera assignment 1 of deep learning specialization course 1\r\n\r\nimport h5py\r\n\r\n\r\ndef load_dataset():\r\n    train_dataset = h5py.File('./dataset/train_catvnoncat.h5', \"r\")\r\n    train_set_x_orig = np.array(train_dataset[\"train_set_x\"][:])  # your train set features\r\n    train_set_y_orig = np.array(train_dataset[\"train_set_y\"][:])  # your train set labels\r\n\r\n    test_dataset = h5py.File('./dataset/test_catvnoncat.h5', \"r\")\r\n    test_set_x_orig = np.array(test_dataset[\"test_set_x\"][:])  # your test set features\r\n    test_set_y_orig = np.array(test_dataset[\"test_set_y\"][:])  # your test set labels\r\n\r\n    classes = np.array(test_dataset[\"list_classes\"][:])  # the list of classes\r\n\r\n    train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))\r\n    test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))\r\n\r\n    return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes\r\n\r\n\r\nimport model\r\nimport numpy as np\r\n\r\n#load dataset\r\ntrain_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes= load_dataset()\r\n\r\n\r\n#reshape and normalize dataset\r\ntrain_set_x=np.reshape( train_set_x_orig, (train_set_x_orig.shape[0], -1))\r\ntrain_set_x=train_set_x/255.0\r\n\r\ntest_set_x=np.reshape( test_set_x_orig, (test_set_x_orig.shape[0], -1))\r\ntest_set_x=test_set_x/255.0\r\n\r\ntrain_set_y=np.reshape(train_set_y_orig, (train_set_y_orig.shape[1], 1))\r\ntest_set_y=np.reshape(test_set_y_orig, (test_set_y_orig.shape[1], 1))\r\n\r\n#declare the model\r\nlg= model.BinaryLogisticRegression()\r\n\r\n#train the model\r\nlg.fit(train_X=train_set_x, train_Y=train_set_y, learningRate=0.005, numOfIterations=1500)\r\n\r\n\r\n#evaluate the model\r\npredictions=lg.predict(test_X=test_set_x)\r\n\r\n#print test accuracy\r\nprint(\"Test Accuracy: {}%\".format(100 - np.mean(np.abs(predictions - test_set_y)) * 100))\r\n\r\n#now let's make prediction for our own cat image (image taken from google images).\r\nprint(\"\\n\")\r\nfrom PIL import Image\r\nimage = Image.open('./dataset/catpic.jpeg')\r\nimage= image.resize((64,64)) #the images in the training set were also 64 by 64.\r\n\r\n# convert image to numpy array\r\ndata = np.asarray(image)\r\ndata=np.reshape(data, (1, -1))\r\n\r\nprint(data.shape)\r\npredicted_label=lg.predict(data)\r\nprint(\"y = \" + str(np.squeeze(predicted_label)) + \", your algorithm predicts a \\\"\" + classes[int(np.squeeze(predicted_label)),].decode(\"utf-8\") +  \"\\\" picture.\")\r\n","repo_name":"BigWheel92/Logistic-Regression","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2426,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"20411433269","text":"\"\"\" Module for working with point representation of spatial variables, such as cells. \"\"\"\n\n\nimport numpy as np\n\n\nclass Point:\n\n    def __init__(self, c: np.ndarray, cs_names: list = None, **kwargs):\n        \"\"\" Creates a new point object.\n\n        Args:\n            c: Of shape n_coord_systems*n_dims.  Each row gives the location of a point in a given coordinate\n            system. For many applications, points may be specified in a single coordinate system in which case c\n            would have only one row.\n\n            cs_names: An optional list of names to associate with each coordinate system. If not none, the length of\n            cs_names must equal the number of coordinate systems used to specify the position of the point.\n\n            **kwargs: Additional keyword arguments that will be added as attributes of the object.\n\n        Raises:\n            ValueError: If the length of cs_names does not equal the number of coordinate systems in c.\n        \"\"\"\n\n        if c.ndim == 1:\n            c = np.expand_dims(c, 0)\n\n        n_coord_systems = c.shape[0]\n\n        if cs_names is not None:\n            if len(cs_names) != n_coord_systems:\n                raise(ValueError('if cs_names is not None, cs_names must have exactly one entry for each coordinate system in c'))\n\n        self.c = c\n        self.cs_names = cs_names\n\n        for k in kwargs:\n            setattr(self, k, kwargs[k])\n\n    @classmethod\n    def from_dict(cls, d:dict):\n        \"\"\" Create a new point object from a dictionary.\n\n        Args:\n            d: A dictionary with the keys 'c' and 'cs_names'\n\n        Returns:\n            A new Point object\n        \"\"\"\n        return cls(**d)\n\n    def to_dict(self):\n        \"\"\" Create a dictionary from a Point object.\n\n        This is useful for saving the object in a manner which will still allow to be loaded in the\n        future in case the class definition of Point changes.\n\n        Returns:\n            d: A dictionary with object data.\n        \"\"\"\n        return vars(self)\n\n","repo_name":"wbishopJanelia/janelia_core","sub_path":"janelia_core/dataprocessing/point.py","file_name":"point.py","file_ext":"py","file_size_in_byte":2017,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"7613419330","text":"import unittest\nfrom unittest.case import TestCase\n\nfrom airflow.models import DagBag\nfrom application_context import DAGS_PATH\n\n\nclass TestHelloWorldDAG(TestCase):\n    dag_id = 'hello_world'\n\n    def setUp(self):\n        self.dagbag = DagBag(\n            dag_folder=DAGS_PATH\n        )\n\n    def test_task_count(self):\n        dag = self.dagbag.get_dag(self.dag_id)\n        self.assertEqual(len(dag.tasks), 3)\n\n    def test_contain_tasks(self):\n        dag = self.dagbag.get_dag(self.dag_id)\n        tasks = dag.tasks\n        task_ids = list(map(lambda task: task.task_id, tasks))\n        self.assertListEqual(task_ids, ['dummy_task', 'hello_task', 'multiplyby5_task'])\n\n    @unittest.skip\n    def test_dependencies_of_dummy_task(self):\n        dag = self.dagbag.get_dag(self.dag_id)\n        dummy_task = dag.get_task('dummy_task')\n\n        upstream_task_ids = list(map(lambda task: task.task_id, dummy_task.upstream_list))\n        self.assertListEqual(upstream_task_ids, [])\n        downstream_task_ids = list(map(lambda task: task.task_id, dummy_task.downstream_list))\n        self.assertListEqual(sorted[downstream_task_ids], sorted['hello_task', 'multiplyby5_task'])\n\n    def test_dependencies_of_hello_task(self):\n        dag = self.dagbag.get_dag(self.dag_id)\n        hello_task = dag.get_task('hello_task')\n\n        upstream_task_ids = list(map(lambda task: task.task_id, hello_task.upstream_list))\n        self.assertListEqual(upstream_task_ids, ['dummy_task'])\n        downstream_task_ids = list(map(lambda task: task.task_id, hello_task.downstream_list))\n        self.assertListEqual(downstream_task_ids, [])\n\n\nsuite = unittest.TestLoader().loadTestsFromTestCase(TestHelloWorldDAG)\nunittest.TextTestRunner(verbosity=2).run(suite)\n","repo_name":"SVFBr/base-airflow-project","sub_path":"tests/test_hello_world.py","file_name":"test_hello_world.py","file_ext":"py","file_size_in_byte":1740,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"27430778512","text":"import unittest\nfrom survey import AnonymousSurvey\n\nclass TestAnonymousSurvey(unittest.TestCase):\n    \"\"\"Tests for the class AnonymousSurvey\"\"\"\n\n# This sets up (get it) the rest of our class. We have values we can import into all our other tests\n\n    def setUp(self):\n        \"\"\"\n        Create a survey and a set of responses for use in all test methods.\n        \"\"\"\n        question = \"What language did you first learn to speak?\"\n        self.my_survey = AnonymousSurvey(question)\n        self.responses = ['English', 'Spanish', 'Mandarin']\n\n# commented out the bits we don't need any more since they're already defined in the previous \n# setUp function from earlier. \n\n    def test_store_single_response(self):\n        \"\"\"Test that a single response is stored properly.\"\"\"\n        # We can replace allllll of this stuff with the set up we have earlier. But \n        # since we did the set up, we need to reference these items as self.whatever\n        # otherwise they aren't defined.\n#        question = \"What language did you first learn to speak?\"\n#        my_survey = AnonymousSurvey(question)\n#        my_survey.store_response('English')\n#        self.assertIn('English', my_survey.responses)\n        self.my_survey.store_response(self.responses[0])\n        self.assertIn(self.responses[0], self.my_survey.responses)\n\n# And again, we've defined this stuff so we can just import it, we have to add the \n# self.stuff to everything since it needs to bring the stuff in from its class\n# instead of defining it new each time. \n\n    def test_store_three_responses(self):\n        \"\"\"Test that three individual responses are stored publicly.\"\"\"\n        # We can replace allllll of this stuff with the set up we have earlier. But \n        # since we did the set up, we need to reference these items as self.whatever\n        # otherwise they aren't defined.\n#        question = \"What language did you first learn to speak?\"\n#        my_survey = AnonymousSurvey(question)\n#        responses = ['English', 'Spanish', 'Mandarin']\n#        for response in responses:\n#            my_survey.store_response(response)\n#        for response in responses:\n#            self.assertIn(response, my_survey.responses)\n        for response in self.responses:\n            self.my_survey.store_response(self.responses)\n        for response in self.responses:\n            self.assertIn(response, self.my_survey.responses)\n\nunittest.main()","repo_name":"remotephone/pythoncrashcourse","sub_path":"ch11/test_survey.py","file_name":"test_survey.py","file_ext":"py","file_size_in_byte":2419,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"44286719256","text":"#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n# Python GetFEM interface\r\n#\r\n# Copyright (C)  2015-2020 Yves Renard.\r\n#\r\n# This file is a part of GetFEM\r\n#\r\n# GetFEM  is  free software;  you  can  redistribute  it  and/or modify it\r\n# under  the  terms  of the  GNU  Lesser General Public License as published\r\n# by  the  Free Software Foundation;  either version 3 of the License,  or\r\n# (at your option) any later version along with the GCC Runtime Library\r\n# Exception either version 3.1 or (at your option) any later version.\r\n# This program  is  distributed  in  the  hope  that it will be useful,  but\r\n# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY\r\n# or  FITNESS  FOR  A PARTICULAR PURPOSE.  See the GNU Lesser General Public\r\n# License and GCC Runtime Library Exception for more details.\r\n# You  should  have received a copy of the GNU Lesser General Public License\r\n# along  with  this program;  if not, write to the Free Software Foundation,\r\n# Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301, USA.\r\n#\r\n############################################################################\r\n\r\nimport numpy as np\r\n\r\nimport getfem as gf\r\n\r\n# Deformation of a plate under the coupling of thermal, elasticity, and\r\n# electric effects.\r\n#\r\n#\r\n#     ______________________________________\r\n#   /|         __       __       __         |->\r\n#   /|        /  \\     /  \\     /  \\        |->\r\n#   /|       |    |   |    |   |    |       |-> F\r\n#   /|        \\__/     \\__/     \\__/        |->\r\n#   /|______________________________________|->\r\n#     \r\n#\r\n# Elastic problem: The plate is clamped at rhe left boundary and a\r\n#   traction density of force F is prescribed at the right boundary.\r\n# Electric problem: The potential is prescribed to be 0V at the right\r\n#   boundary and 0.1V at the left boundary.\r\n# Thermal problem: A thermal insulation condition is prescribed at the\r\n#   left and hole boudnaries. The remaining boundary and the plate itself\r\n#   is supposed to be submitted to heat transfer with respect to the\r\n#   air at 20oC.\r\n# Coupling terms:\r\n#   - Joule heating: source term  sigma|Grad_V|^2\r\n#   - Dependance of the thermal conductivity in temperature :\r\n#     sigma = 1/(rho_0(1+alpha(theta-T0)))\r\n#     with T0 = 20oC, rho_0 the resistance temperature coefficient at T0\r\n#     and alpha the second resistance temperature coefficient.\r\n#   - Thermal expansion:\r\n#     stress_tensor = clambdastar div(u) I + 2 cmu epsilon(u) - beta theta I\r\n#     with beta = alpha_th E/(1-2nu), alpha_th being the thermal\r\n#     expansion coefficient.\r\n# The first two coupling terms are nonlinear ones.\r\n\r\n\r\n#\r\n# Physical parameters\r\n#\r\nepsilon = 1.       # Thickness of the plate (cm)\r\nE = 21E6           # Young Modulus (N/cm^2)\r\nnu = 0.3           # Poisson ratio\r\nclambda = E*nu/((1+nu)*(1-2*nu)) # First Lame coefficient (N/cm^2)\r\ncmu = E/(2*(1+nu))               # Second Lame coefficient (N/cm^2)\r\nclambdastar = 2*clambda*cmu/(clambda+2*cmu) # Lame coefficient for Plane stress (N/cm^2)\r\nF = 100E2          # Force density at the right boundary (N/cm^2)\r\nkappa = 4.         # Thermal conductivity (W/(cm K))\r\nD = 10.            # Heat transfer coefficient (W/(K cm^2))\r\nair_temp = 20.     # Temperature of the air in oC.\r\nalpha_th = 16.6E-6 # Thermal expansion coefficient (/K).\r\nT0 = 20.           # Reference temperature in oC.\r\nrho_0 = 1.754E-8   # Resistance temperature coefficient at T0 = 20oC\r\nalpha = 0.0039     # Second resistance temperature coefficient.\r\n\r\n#\r\n# Numerical parameters\r\n#\r\nh = 2.                    # Approximate mesh size\r\nelements_degree = 2       # Degree of the finite element methods\r\nexport_mesh = True        # Draw the mesh after mesh generation or not\r\nsolve_in_two_steps = 2    # Solve the elasticity problem separately (1)\r\n                          # or in a coupled way (0) or both and compare (2)\r\n\r\n#\r\n# Mesh generation. Meshes can also been imported from several formats.\r\n#\r\nmo1 = gf.MesherObject('rectangle', [0., 0.], [100., 25.])\r\nmo2 = gf.MesherObject('ball', [25., 12.5], 8.)\r\nmo3 = gf.MesherObject('ball', [50., 12.5], 8.)\r\nmo4 = gf.MesherObject('ball', [75., 12.5], 8.)\r\nmo5 = gf.MesherObject('union', mo2, mo3, mo4)\r\nmo  = gf.MesherObject('set minus', mo1, mo5)\r\n\r\nprint('Mesh generation')\r\ngf.util('trace level', 2)   # No trace for mesh generation\r\nmesh = gf.Mesh('generate', mo, h, 2)\r\n\r\n#\r\n# Boundary selection\r\n#\r\nfb1 = mesh.outer_faces_in_box([1., 1.], [99., 24.])    # Boundary of the holes\r\nfb2 = mesh.outer_faces_with_direction([ 1., 0.], 0.01) # Right boundary\r\nfb3 = mesh.outer_faces_with_direction([-1., 0.], 0.01) # Left boundary\r\nfb4 = mesh.outer_faces_with_direction([0.,  1.], 0.01) # Top boundary\r\nfb5 = mesh.outer_faces_with_direction([0., -1.], 0.01) # Bottom boundary\r\nfb6 = mesh.outer_faces_in_ball([25., 12.5], 8.+0.01*h) # Left hole boundary\r\nfb7 = mesh.outer_faces_in_ball([50., 12.5], 8.+0.01*h) # Center hole boundary\r\nfb8 = mesh.outer_faces_in_ball([75., 12.5], 8.+0.01*h) # Right hole boundary\r\n\r\nRIGHT_BOUND=1; LEFT_BOUND=2; TOP_BOUND=3; BOTTOM_BOUND=4; HOLE_BOUND=5;\r\nHOLE1_BOUND = 6; HOLE2_BOUND = 7; HOLE3_BOUND = 8;\r\n\r\nmesh.set_region( RIGHT_BOUND, fb2)\r\nmesh.set_region(  LEFT_BOUND, fb3)\r\nmesh.set_region(   TOP_BOUND, fb4)\r\nmesh.set_region(BOTTOM_BOUND, fb5)\r\nmesh.set_region(  HOLE_BOUND, fb1)\r\nmesh.set_region( HOLE1_BOUND, fb6)\r\nmesh.set_region( HOLE2_BOUND, fb7)\r\nmesh.set_region( HOLE3_BOUND, fb8)\r\nmesh.region_subtract( RIGHT_BOUND, HOLE_BOUND)\r\nmesh.region_subtract(  LEFT_BOUND, HOLE_BOUND)\r\nmesh.region_subtract(   TOP_BOUND, HOLE_BOUND)\r\nmesh.region_subtract(BOTTOM_BOUND, HOLE_BOUND)\r\n\r\nmesh.region_merge(HOLE1_BOUND, HOLE2_BOUND)\r\nmesh.region_merge(HOLE1_BOUND, HOLE3_BOUND)\r\n\r\nnp.testing.assert_array_equal(mesh.region(HOLE_BOUND), mesh.region(HOLE1_BOUND))\r\n\r\nif (export_mesh):\r\n    mesh.export_to_vtk('mesh.vtk');\r\n    print('\\nYou can view the mesh for instance with');\r\n    print('mayavi2 -d mesh.vtk -f ExtractEdges -m Surface \\n');\r\n\r\n#\r\n# Definition of finite elements methods and integration method\r\n#\r\n\r\nmfu = gf.MeshFem(mesh, 2)  # Finite element for the elastic displacement\r\nmfu.set_classical_fem(elements_degree)\r\nmft = gf.MeshFem(mesh, 1)  # Finite element for temperature and electrical field\r\nmft.set_classical_fem(elements_degree)\r\nmfvm = gf.MeshFem(mesh, 1) # Finite element for Von Mises stress interpolation\r\nmfvm.set_classical_discontinuous_fem(elements_degree)\r\nmim = gf.MeshIm(mesh, elements_degree*2)   # Integration method\r\n\r\n\r\n#\r\n# Model definition\r\n#\r\n\r\nmd=gf.Model('real');\r\nmd.add_fem_variable('u', mfu)       # Displacement of the structure\r\nmd.add_fem_variable('theta', mft)   # Temperature\r\nmd.add_fem_variable('V', mft)       # Electric potential\r\n\r\n# Membrane elastic deformation\r\nmd.add_initialized_data('cmu', [cmu])\r\nmd.add_initialized_data('clambdastar', [clambdastar])\r\nmd.add_isotropic_linearized_elasticity_brick(mim, 'u', 'clambdastar', 'cmu')\r\n\r\nmd.add_Dirichlet_condition_with_multipliers(mim, 'u', elements_degree-1, LEFT_BOUND)\r\nmd.add_initialized_data('Fdata', [F*epsilon, 0])\r\nmd.add_source_term_brick(mim, 'u', 'Fdata', RIGHT_BOUND)\r\n\r\n# Electrical field\r\nsigmaeps = '(eps/(rho_0*(1+alpha*(theta-T0))))'\r\nmd.add_initialized_data('eps', [epsilon])\r\nmd.add_initialized_data('rho_0', [rho_0])\r\nmd.add_initialized_data('alpha', [alpha])\r\nmd.add_initialized_data('T0', [T0])\r\nmd.add_nonlinear_term(mim, sigmaeps+'*(Grad_V.Grad_Test_V)')\r\nmd.add_Dirichlet_condition_with_multipliers(mim, 'V', elements_degree-1, RIGHT_BOUND)\r\nmd.add_initialized_data('DdataV', [0.1])\r\nmd.add_Dirichlet_condition_with_multipliers(mim, 'V', elements_degree-1, LEFT_BOUND, 'DdataV')\r\n\r\n# Thermal problem\r\nmd.add_initialized_data('kaeps', [kappa*epsilon])\r\nmd.add_generic_elliptic_brick(mim, 'theta', 'kaeps')\r\nmd.add_initialized_data('D2', [D*2])\r\nmd.add_initialized_data('D2airt', [air_temp*D*2])\r\nmd.add_mass_brick(mim, 'theta', 'D2')\r\nmd.add_source_term_brick(mim, 'theta', 'D2airt')\r\nmd.add_initialized_data('Deps', [D/epsilon])\r\nmd.add_initialized_data('Depsairt', [air_temp*D/epsilon])\r\nmd.add_Fourier_Robin_brick(mim, 'theta', 'Deps', TOP_BOUND)\r\nmd.add_source_term_brick(mim, 'theta', 'Depsairt', TOP_BOUND)\r\nmd.add_Fourier_Robin_brick(mim, 'theta', 'Deps', BOTTOM_BOUND)\r\nmd.add_source_term_brick(mim, 'theta', 'Depsairt', BOTTOM_BOUND)\r\n\r\n# Joule heating term\r\nmd.add_nonlinear_term(mim, '-'+sigmaeps+'*Norm_sqr(Grad_V)*Test_theta')\r\n\r\n# Thermal expansion term\r\nmd.add_initialized_data('beta', [alpha_th*E/(1-2*nu)])\r\nmd.add_linear_term(mim, 'beta*(T0-theta)*Trace(Grad_Test_u)')\r\n\r\n\r\n#\r\n# Model solve\r\n#\r\n\r\nif (solve_in_two_steps >= 1):\r\n  md.disable_variable('u')\r\n  print('First problem with', md.nbdof(), ' dofs')\r\n  md.solve('max_res', 1E-9, 'max_iter', 100, 'noisy')\r\n  md.enable_variable('u')\r\n  md.disable_variable('theta')\r\n  md.disable_variable('V')\r\n  print('Second problem with ', md.nbdof(), ' dofs')\r\n  md.solve('max_res', 1E-9, 'max_iter', 100, 'noisy')\r\n  md.enable_variable('theta')\r\n  md.enable_variable('V')\r\n  U1 = md.variable('u')\r\n  \r\nif (solve_in_two_steps == 0):\r\n  print('Global problem with ', md.nbdof(), ' dofs')\r\n  md.solve('max_res', 1E-9, 'max_iter', 100, 'noisy')\r\n\r\nif (solve_in_two_steps == 2):\r\n  print('Global problem with ', md.nbdof(), ' dofs')\r\n  md.set_variable('u', md.variable('u')*0.);\r\n  md.solve('max_res', 1E-9, 'max_iter', 100, 'noisy')\r\n  U2 = md.variable('u')\r\n  print (np.linalg.norm(U2-U1));\r\n  if (np.linalg.norm(U2-U1) > 1E-10):\r\n      print(\"Too big difference between solve in one and two steps\"); exit(1)\r\n\r\n#\r\n# Solution export\r\n#  \r\nU = md.variable('u')\r\nV = md.variable('V')\r\nTHETA = md.variable('theta')\r\nVM = md.compute_isotropic_linearized_Von_Mises_or_Tresca('u', 'clambdastar', 'cmu', mfvm)\r\nCO = np.reshape(md.interpolation('-'+sigmaeps+'*Grad_V', mfvm), (2, mfvm.nbdof()), 'F')\r\n\r\nmfvm.export_to_vtk('displacement_with_von_mises.vtk', mfvm,  VM, 'Von Mises Stresses', mfu, U, 'Displacements')\r\nprint('You can view solutions with for instance:\\nmayavi2 -d displacement_with_von_mises.vtk -f WarpVector -m Surface')\r\nmft.export_to_vtk('temperature.vtk', mft, THETA, 'Temperature')\r\nprint('mayavi2 -d temperature.vtk -f WarpScalar -m Surface')\r\nmft.export_to_vtk('electric_potential.vtk', mft, V, 'Electric potential')\r\nprint('mayavi2 -d electric_potential.vtk -f WarpScalar -m Surface')\r\n","repo_name":"getfem-doc/getfem","sub_path":"interface/tests/python/demo_thermo_elasticity_electrical_coupling.py","file_name":"demo_thermo_elasticity_electrical_coupling.py","file_ext":"py","file_size_in_byte":10327,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"48"}
+{"seq_id":"31432995487","text":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom wordcloud import WordCloud\nimport seaborn as sns\nfrom ggplot import *\nplt.style.use('default')\n\ngit_df = pd.read_csv(\"TopStaredRepositories.csv\", parse_dates=['Last Update Date'], dayfirst=True)\ngit_df.head()\n\ngit_df.info()\n\ngit_df_max = git_df['Number of Stars'].str.contains('k').all()\nprint(git_df_max)\n\ngit_df['Number of Stars']=git_df['Number of Stars'].str.replace('k','').astype(float)\nprint(git_df.head())\n\nprint(git_df['Number of Stars'].describe())\n\nclassified_repos=[]\nfor i in range(8,300,7):\n    x = git_df[(git_df['Number of Stars'] >= i) & (git_df['Number of Stars'] <(i+7.0))]\n    classified_repos.append(len(x))\n\nindexes = []\n\nfor i in range (8000,300000, 7000):\n    x = '[' + str(i) +','+ (str(i+7000)) + ')'\n    indexes.append(x)\n\ndivided_repos = pd.Series(data=classified_repos, index=indexes)\n#divided_repos.plot(kind='bar', figsize=(15,10), color=['red'],legend=True, label='Number of repositories')\n\nx=git_df['Language'].value_counts()\nprint(x)\n\n\nplt.figure()\nx.plot(kind='barh',figsize=(15,10),grid=True, label='Number of repositories',legend='No of repos',title='No of repositories vs language used')\nplt.show()\n\n\nx[:20].plot.pie(label=\"Division of the top 20 languages\",fontsize=10,figsize=(10,10),legend=True)\n\nplt.show()","repo_name":"ruchiraPeiris/Starc-Recruiter","sub_path":"Github/github-repo-analysis.py","file_name":"github-repo-analysis.py","file_ext":"py","file_size_in_byte":1324,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"1788886212","text":"tareas = self.env['project.task'].search([('x_contador_etapas', '=', 2)])\nfor tarea in tareas:\n    proxima_etapa = pe = False\n    for etapa in tarea.etapa_ids:\n        if pe:\n            proxima_etapa = etapa\n            break\n        if etapa == tarea.stage_id:\n            pe = True\n    try:\n        if proxima_etapa:\n            tarea.stage_id = proxima_etapa\n    except:\n        continue\n        \n\n#Modelo: Project.task\n#Grupos de ejec: RRHH/Emple\n#Planificaciones: 1 vez al dia, hora","repo_name":"francofranzini/ValidacionesTD","sub_path":"AplicacionesDeUsuario/PasarTareaAutomaticamente.py","file_name":"PasarTareaAutomaticamente.py","file_ext":"py","file_size_in_byte":488,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"13072018499","text":"\"\"\"\n\nMutable Default Argument\n\nدر پایتون، هنگام تعریف فانکشن، هیچوقت نباید از آبجکت های تغییرپذیر بعنوان دیفالت آرگیومنت درنظر گرفت\nداستان از این قراره که، دیفالت آرگیومنت هایی که تغییرپذیرند مثل لیست، فقط و فقط یکبار ایجاد می شوند و\nآن هم هنگام ایجاد خود فانکشن است! در نتیجه هربار که ما فانکشن رو صدا بزنیم، آبجکت میوتیبل از اول ساخته\nنمی شود و بلکه دوباره از همان اولیه استفاده خواهد شد\n\nراه حل چیه؟\nبیایم دیفالت رو برابر نان قرار بدیم و چک کنیم اگر نان بود بیاد و یک لیست رو بسازه\n\n\"\"\"\n\n# Wrong Way\n# def show(name, person=[]):\n#     person.append(name)\n#     return person\n\n\n# s1 = show('Mahdi')\n# print(s1) # output => ['Mahdi']\n# s2 = show('Rose')\n# print(s2) # output => ['Mahdi', 'Rose']\n# s3 = show('Ali')\n# print(s3) # output => ['Mahdi', 'Rose', 'Ali']\n\n# -------\n\n\n# Right Way\ndef show(name, person=None):\n    if person is None:\n        person = []\n    person.append(name)\n    return person\n\n\ns1 = show('Mahdi')\nprint(s1) # output => ['Mahdi']\ns2 = show('Rose')\nprint(s2) # output => ['Rose']\ns3 = show('Ali')\nprint(s3) # output => ['Ali']","repo_name":"mahdi-zarepour/Python3","sub_path":"Intermediate-Topics/mutable_default_argument/mutable_default_argument.py","file_name":"mutable_default_argument.py","file_ext":"py","file_size_in_byte":1414,"program_lang":"python","lang":"fa","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"13458127591","text":"#https://hackerrank-challenge-pdfs.s3.amazonaws.com/22937-magic-square-forming-English?AWSAccessKeyId=AKIAR6O7GJNX5DNFO3PV&Expires=1650210950&Signature=F7odKPABOiJb7a49y9grXaPY4zw%3D&response-content-disposition=inline%3B%20filename%3Dmagic-square-forming-English.pdf&response-content-type=application%2Fpdf\n\nsquares = [(2, 7, 6, 9, 5, 1, 4, 3, 8),\n(2, 9, 4, 7, 5, 3, 6, 1, 8),\n(4, 3, 8, 9, 5, 1, 2, 7, 6),\n(4, 9, 2, 3, 5, 7, 8, 1, 6),\n(6, 1, 8, 7, 5, 3, 2, 9, 4),\n(6, 7, 2, 1, 5, 9, 8, 3, 4),\n(8, 1, 6, 3, 5, 7, 4, 9, 2),\n(8, 3, 4, 1, 5, 9, 6, 7, 2)]\nu_squares=[]\nfor i in squares:\n    u_squares.append((list(i)))\ndef formingMagicSquare(s):\n    s_=[]\n    for i in s:\n        for j in i:\n            s_.append(j)\n    diff=0\n    cost=[]\n    for x in u_squares:\n        for n in range(len(s_)):\n                diff=abs(s_[n]-x[n])+diff\n        cost.append(diff)\n        diff=0\n    return min(cost)\n","repo_name":"psd-psd/Seeker_python","sub_path":"Forming a Magic Square.py","file_name":"Forming a Magic Square.py","file_ext":"py","file_size_in_byte":897,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"14523009881","text":"from pwn import *\n\n\ndef hamming_distance(a, b):\n\tcount = 0\n\tfor i in bin(a ^ b):\n\t\tif i == '1': count += 1\n\treturn count\n\n\nport = int(input(\"Port >> \"))\naddress = (\"localhost\", port)\n\nconn = remote(address[0], address[1])\n\nfor _ in range(100):\n\n\ta, b = tuple(map(int, conn.recvuntil(b\">> \").decode().split(\"\\n\")[-2].split(\":\")[1].strip().split(\" \")))\n\tconn.send(f\"{hamming_distance(a, b)}\\n\".encode())\n\nprint(conn.recvall())","repo_name":"0awawa0/DonNU_CTF","sub_path":"2020_2021/DonNU CTF 2021/Coding/coding3/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":424,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"11104399110","text":"import numpy as np\nimport gym\nimport time\n\nnp.random.seed(1)\n\nenv = gym.make('CartPole-v0')\nenv = env.unwrapped\nN_ACTIONS = env.action_space.n\nN_STATES = env.observation_space.shape[0]\n\ndef choose_action():\n    action = np.random.randint(0, N_ACTIONS)\n    return action\n\nfor i_episode in range(4000):\n    s = env.reset()\n    ep_r = 0\n    for step in range(180):\n        env.render()\n        a = choose_action()\n\n        # take action\n        s_, r, done, info = env.step(a)\n        ep_r += r\n        s = s_\n    print('Ep: ', i_episode,'| Ep_r: ', ep_r)\n\n","repo_name":"BaiLiping/DRL","sub_path":"Demo/without_train.py","file_name":"without_train.py","file_ext":"py","file_size_in_byte":554,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"2009484901","text":"import math\nimport numpy as np\n\ndef sin_table(lower,upper,entries):\n    x_array= np.linspace(lower,upper, entries)\n    sin_x_array= np.sin(x_array)\n    merged= np.column_stack((x_array,sin_x_array))\n    return merged\n\ndef main():\n\n    print('x \\t\\t|\\t\\t sin(x)')\n    table= sin_table(0,2*math.pi,1000)\n    for i in table:\n        print(f'{i[0]:.4f}\\t\\t|\\t\\t{i[1]:.4f}')\n\nif __name__ == \"__main__\":\n    main()\n\n\n","repo_name":"nityamthakur/astr-19","sub_path":"cj1_s5.py","file_name":"cj1_s5.py","file_ext":"py","file_size_in_byte":411,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"13015020027","text":"from django.shortcuts import render\nfrom web_admin_client.menu import menu\n\n\ndef days_off(request):\n    context = {\n        'menu': menu,\n        'title': 'Days off'\n    }\n    return render(request, 'days_off/days_off.html', context=context)\n","repo_name":"AleksandrPopov/web_admin_client","sub_path":"web_admin_client/days_off/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":242,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"23612796886","text":"from decimal import Decimal\nimport datetime\nimport operator\nfrom functools import wraps\n\nfrom dateutil.relativedelta import relativedelta\nfrom sql import Column, Null, Window, Literal\nfrom sql.aggregate import Sum, Max\nfrom sql.conditionals import Coalesce, Case\n\nfrom trytond.model import (\n    ModelView, ModelSQL, DeactivableMixin, fields, Unique, sequence_ordered)\nfrom trytond.wizard import Wizard, StateView, StateAction, StateTransition, \\\n    Button\nfrom trytond.report import Report\nfrom trytond.tools import reduce_ids, grouped_slice\nfrom trytond.pyson import Eval, If, PYSONEncoder, Bool\nfrom trytond.transaction import Transaction\nfrom trytond.pool import Pool\nfrom trytond import backend\n\n__all__ = ['TmiMetaGroup',\n    'TmiGroup']\n\nclass TmiMetaGroup(sequence_ordered(), ModelSQL, ModelView):\n    'Account Type'\n    __name__ = 'tmi.meta.group'\n\n    name = fields.Char('Name', size=None, required=True)\n    parent = fields.Many2One('tmi.meta.group', 'Parent',\n        ondelete=\"RESTRICT\",\n        domain=[\n            ('company', '=', Eval('company')),\n            ],\n        depends=['company'])\n    childs = fields.One2Many('tmi.meta.group', 'parent', 'Children',\n        domain=[\n            ('company', '=', Eval('company')),\n        ],\n        depends=['company'])\n    currency_digits = fields.Function(fields.Integer('Currency Digits'),\n            'get_currency_digits')\n    tithe = fields.Function(fields.Numeric('Tithe',\n        digits=(16, Eval('currency_digits', 2)), depends=['currency_digits']),\n        'get_amount')\n    type = fields.Selection('tmi.group',\n        [\n        ('conference','Conference'),\n        ('division','Division'),\n        ('union','Union'),\n        ('field','Field'),\n        ('zona','Zone'),\n        ('district','District'),\n        ('church','Church'),\n        ('small_group','Small Group'),\n        ]\n        ,'Type')\n    company = fields.Many2One('company.company', 'Company', required=True,\n            ondelete=\"RESTRICT\")\n\n    def get_currency_digits(self, name):\n        return self.company.currency.digits\n\n    @classmethod\n    def get_amount(cls, types, name):\n        pool = Pool()\n        Group = pool.get('tmi.group')\n\n        res = {}\n        for type_ in types:\n            res[type_.id] = Decimal('0.0')\n\n        childs = cls.search([\n                ('parent', 'child_of', [t.id for t in types]),\n                ])\n        meta_sum = {}\n        for type_ in childs:\n            meta_sum[type_.id] = Decimal('0.0')\n\n        groups = Group.search([\n                ('meta', 'in', [t.id for t in childs]),\n                ])\n        for group in groups:\n            meta_sum[group.meta.id] += (group.name)\n\n        for type_ in types:\n            childs = cls.search([\n                    ('parent', 'child_of', [type_.id]),\n                    ])\n            for child in childs:\n                res[type_.id] += type_sum[child.id]\n            exp = Decimal(str(10.0 ** -type_.currency_digits))\n            res[type_.id] = res[type_.id].quantize(exp)\n            if type_.display_balance == 'credit-debit':\n                res[type_.id] = - res[type_.id]\n        return res\n\n\nclass TmiGroup(ModelView, ModelSQL):\n    'TMI Group'\n    __name__ = 'tmi.group'\n\n    name = fields.Char('Group',required=True)\n    code = fields.Char('Code')\n    meta = fields.Many2One('tmi.meta.group', 'Meta', ondelete=\"RESTRICT\",\n        required=True, \n        domain=[\n            ('company', '=', Eval('company')),\n            ], depends=['company'])\n    type = fields.Selection('tmi.group',\n        [\n        ('church','Church'),\n        ('small_group','Small Group'),\n        ]\n        ,'Type')\n    company = fields.Many2One('company.company', 'Company', required=True,\n            ondelete=\"RESTRICT\")\n    currency = fields.Function(fields.Many2One('currency.currency',\n        'Currency'), 'get_currency')\n    currency_digits = fields.Function(fields.Integer('Currency Digits'),\n        'get_currency_digits')\n    tithe = fields.Function(fields.Numeric('Tithe',\n        digits=(16,Eval('currency_digits',2))), 'get_amount')\n    baptism = fields.Function(fields.Numeric('Baptism',\n        digits=(16,Eval('currency_digits',2))), 'get_amount')\n    church_planting = fields.Function(fields.Numeric('Church Planting',\n        digits=(16,Eval('currency_digits',2))), 'get_amount')\n    gathering = fields.Function(fields.Numeric('Gathering',\n        digits=(16,Eval('currency_digits',2))), 'get_amount')\n    small_group = fields.Function(fields.Numeric('Small Group',\n        digits=(16,Eval('currency_digits',2))), 'get_amount')\n    organizing_church = fields.Function(fields.Numeric('Organizing Church',\n        digits=(16,Eval('currency_digits',2))), 'get_amount')\n    praise_thanksgiving = fields.Function(fields.Numeric('Praise and Thanksgiving', \n        digits=(16,Eval('currency_digits',2))), 'get_amount')\n    offering = fields.Function(fields.Numeric('Offering',\n        digits=(16,Eval('currency_digits',2))), 'get_amount')\n\n\n    @classmethod\n    def __setup__(cls):\n        super(TmiGroup, cls).__setup__()\n        cls._order.insert(0, ('name', 'ASC'))\n\n    def get_rec_name(self, name):\n        if self.code:\n            return self.type + ' - ' + self.name + ' - ' + self.code \n        else:\n            return self.type + ' - ' + self.name\n\n    def get_currency(self, name):\n        return self.company.currency.id\n\n    def get_currency_digits(self, name):\n        return self.company.currency.digits\n\n    @classmethod\n    def get_amount(cls, groups, names):\n        '''\n        Function to compute tithe, baptism, church_planting, gathering, small_group, \n        organizing_church, praise_thanksgiving, offering for TMI Group.        \n        '''\n        pool = Pool()\n        MoveLine = pool.get('tmi.move.line')\n        cursor = Transaction().connection.cursor()\n\n        result = {}\n        ids = [a.id for a in groups]\n        for name in names:\n            if name not in {'tithe', 'baptism', 'church_planting', \\\n                    'gathering','small_group', 'organizing_church', \\\n                    'praise_thanksgiving', 'offering'}:\n                raise ValueError('Unknown name: %s' % name)\n            result[name] = dict((i, Decimal(0)) for i in ids)\n\n        table = cls.__table__()\n        line = MoveLine.__table__()\n        line_query = MoveLine.query_get(line)\n        columns = [table.id]\n        for name in names:\n            columns.append(Sum(Coalesce(Column(line, name), 0)))\n        for sub_ids in grouped_slice(ids):\n            red_sql = reduce_ids(table.id, sub_ids)\n            cursor.execute(*table.join(line, 'LEFT',\n                    condition=line.group == table.id\n                    ).select(*columns,\n                    where=red_sql & line_query,\n                    group_by=table.id))\n            for row in cursor.fetchall():\n                group_id = row[0]\n                for i, name in enumerate(names, 1):\n                    # SQLite uses float for SUM\n                    if not isinstance(row[i], Decimal):\n                        result[name][group_id] = Decimal(str(row[i]))\n                    else:\n                        result[name][group_id] = row[i]\n        for group in groups:\n            for name in names:\n                exp = Decimal(str(10.0 ** -group.currency_digits))\n                result[name][group.id] = (\n                    result[name][group.id].quantize(exp))\n        return result\n","repo_name":"iehoshia/tmi","sub_path":"tmi_group.py","file_name":"tmi_group.py","file_ext":"py","file_size_in_byte":7428,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"7729581662","text":"import yaml\nimport yaml.scanner\n\nfrom ..err import SerializeError\nfrom ..abc import *\nfrom ..core import register_format\n\n@register_format('yaml', '.yaml', '.yml')\nclass YamlSerializer(ISerializer):\n    format_name = 'yaml'\n\n    def loads(self, s: str, options: dict):\n        kwargs = {}\n        kwargs.update(Options.pop_origin_kwargs(options))\n        self.check_options(options)\n        try:\n            return yaml.safe_load(s, **kwargs)\n        except yaml.scanner.ScannerError as e:\n            raise SerializeError(e)\n\n    def dumps(self, obj, options: dict) -> str:\n        kwargs = {\n            'allow_unicode': not Options.pop_ensure_ascii(options),\n            'indent': Options.pop_indent(options),\n        }\n        kwargs.update(Options.pop_origin_kwargs(options))\n        self.check_options(options)\n        return yaml.dump(obj, **kwargs)\n","repo_name":"Cologler/anyser-python","sub_path":"anyser/impls/yaml.py","file_name":"yaml.py","file_ext":"py","file_size_in_byte":857,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"8395103881","text":"import os\nfrom os import path\nimport discord\nfrom discord.ext import commands\nfrom dotenv import load_dotenv\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy import Column, Integer, String\nfrom sqlalchemy.orm import sessionmaker\nimport time\nfrom RPLCD.gpio import CharLCD\nimport RPi.GPIO as GPIO\nfrom threading import Thread\n\nclass lcdThread(Thread):\n    def __init__(self,author,message):\n        Thread.__init__(self)\n        self.author = author\n        self.message = message\n        self.users = {'Decunut':3,'quicho':5}\n    def run(self):\n        #This is so that the light works \n        GPIO.setwarnings(False)\n        GPIO.setmode(GPIO.BOARD)\n        userLightColor = 3\n        if self.author in self.users:\n            userLightColor = self.users[self.author]\n        GPIO.setup(userLightColor,GPIO.OUT)\n        GPIO.output(userLightColor,True)\n    \n        #this is the lcd stuff\n        lcd = CharLCD(cols=16, rows=2, pin_rs=22, pin_e=18, pins_data=[16, 11, 12, 15],numbering_mode=GPIO.BOARD)\n        lcd.cursor_pos = (1,0)\n        lcd.write_string(self.author)\n        for i in range(0,2):\n            currentPositionLCD = 0\n            for char in self.message:\n                lcd.cursor_pos = (0,currentPositionLCD)\n                #increasing the cursor position\n                currentPositionLCD +=1\n                currentPositionLCD = currentPositionLCD % 16\n                lcd.write_string(char)\n                time.sleep(.1)\n            time.sleep(.5)\n        GPIO.output(userLightColor,False)\n        lcd.clear()\n        \nload_dotenv()\nTOKEN = os.getenv('DISCORD_TOKEN')\nserver = os.getenv('DISCORD_GUILD')\n\n#client = discord.Client()\n#right here I am telling that every command starts with !\n#and telling it that the commaonds are case insensitive so they can type in any way of the command in terms of casing and it will work.\nbot = discord.ext.commands.Bot(command_prefix=\"!\",case_insensitive=True)\n\n#This basically tells us that every time we connect to the discord server we print in python that we have connected to the discord server\n@bot.event\nasync def on_ready():\n    print(f'{bot.user} has connected to Discord!')\n\n#Here we only enter when the name is in there\n@bot.command(name='cofee', help='!cofee <args> lights a light on cofees room and puts the message on a screen')\nasync def cofee(ctx,*args):\n    name = ctx.message.author.name\n    print(ctx.message.author.id)\n    messageCon = ' '.join(args)\n    print(messageCon)\n    words=lcdThread(name,messageCon)\n    words.start()    \n\n#Here we only enter when the name is in there\n@bot.command(name='pot', help='!pot returns potato')\nasync def pot(ctx,*args):\n    await ctx.send('potato')\n    await ctx.send('second potato')\n\n#We only enter here when the name is there\n@bot.command(name='pin', help=\"!pin <message> pins the message to the pin thing in chat\")\nasync def pin(ctx, *args):\n    #args has everythin after the command\n    #getting all the arguments and joining them together with a spacebar and putting it in content\n    content = ' '.join(args)\n    message = await ctx.send( (content) )\n    await message.pin()\n    #content = ' '.join(args)\n    #await ctx.send(content)\n\n@bot.command(name='top', help=\"!top <number> returns the numberth pin to the chat. If no number then just top pin\")\nasync def top(ctx, *args):\n    #Top tells us to get the top Pin\n    pinNum = 0\n    #checking if they gave an argument and if it is numberic\n    if len(args) == 1 and args[0].isnumeric():\n        pinNum = int(args[0]) -  1\n    #this gets us all the pins that the\n    allPinned = await ctx.author.pins()\n    allChannels = ctx.guild.channels\n    theChannel = 0\n    for channel in allChannels:\n        if channel.name =='general':\n            theChannel = channel\n    #I now have the channel that I want\n    pinnedMessages = await theChannel.pins()\n    if len(pinnedMessages) < pinNum:\n        await ctx.send('We do not have that many pinned messages')\n    else:\n        await ctx.send(pinnedMessages[pinNum].content)\n\n@bot.command( help='!gold <name> If you do not provide a name you will see the list of golds. If you do then you will add a gold.')\nasync def gold(ctx,*args):\n    #we are getting all the database stuff\n    engine = create_engine('sqlite:///society.db')\n\n    from models import Person\n\n    Session = sessionmaker(bind=engine)\n    session = Session()\n\n    if (len(args) == 0):\n        content = ' '\n        for name in session.query(Person.name):\n            content = content + (name.name) + '\\n'\n        await ctx.send(content)\n    else:\n        #there are arguments so we add the gold\n        newGold = ' '.join(args)\n        gold = Person(color='Gold',name=newGold)\n        session.add(gold)\n        session.commit()\n        await ctx.send( ('Added: '+ newGold) )\n    #we are closing the session.\n    session.close()\n\n@bot.command( help='!book <title> returns information about title of the book. If there is no title provided then return current book information')\nasync def book(ctx, *args):\n    #we are getting all the database stuff\n    engine = create_engine('sqlite:///society.db')\n\n    from models import BookRead\n\n    Session = sessionmaker(bind=engine)\n    session = Session()\n\n    #if they just want to current book that is being read with the description and purchase info\n    if (len(args) == 0):\n        currentBook = session.query(BookRead).filter_by(currBook = True).first()\n        await ctx.send(currentBook)\n    #returns the book title and description and the purchase info for that book\n    else:\n        #getting the title of the book\n        bookTitle = ' '.join(args)\n        #getting all the information for the book they are going to read\n        book = session.query(BookRead).filter_by(title = bookTitle).first()\n        await ctx.send(book)\n    session.close()\n\n#command that will return all the books we have read for book club\n@bot.command( help='!books <dm> returns all the books we have read in the book club. If you include word dm then it will be dmed to you rather than in the chat')\nasync def books(ctx, *args):\n    #getting the db stuff\n    engine = create_engine('sqlite:///society.db')\n    from models import BookRead\n    Session = sessionmaker(bind = engine)\n    session = Session()\n\n    allBooks = session.query(BookRead)\n    arg1 =' '\n    if(len(args) >=1):\n        arg1 = args[0]\n    if arg1.lower() == 'dm':\n        for book in allBooks:\n            await ctx.author.create_dm()\n            await ctx.author.dm_channel.send(book)\n    #they have elected not to put it as a dm so it will be in chat\n    else:\n        for book in allBooks:\n            await ctx.send(book)\n\n#command that adds a book to the reading thing\n@bot.command( help='!reading <title> adds this book to the BookRead table ')\nasync def reading(ctx, *args):\n    if (len(args)==0):\n        await ctx.send('You need to provide a title for the book we are going to read. Try !help reading ')\n        return\n\n    if ctx.author.permissions_in(ctx.message.channel).administrator:\n        #we are getting all the database stuff\n        engine = create_engine('sqlite:///society.db')\n        from models import BookRead\n        Session = sessionmaker(bind=engine)\n        session = Session()\n        #getting the book title from the args\n        bookTitle = ' '.join(args)\n        #adding that book to the Bookreading table and setting it as the currentBook\n        newBook = BookRead(title = bookTitle,currBook=True )\n        session.add(newBook)\n        session.commit()\n        session.close()\n        await ctx.send( ('Added book as the current book we are reading: ' + bookTitle ) )\n    else:\n        await ctx.send('You must be an admin to add a new book we are reading')\n\n#command that adds a book to the reading thing\n@bot.command( help='!description <title> // <description> adds a description to a book with this title. Is separated by a literal // . Space after title and before description. ')\nasync def description(ctx, *args):\n    #we are getting all the database stuff\n    if (len(args)==0):\n        await ctx.send('You need to provide a title for the book. Try !help description ')\n        return\n\n    if ctx.author.permissions_in(ctx.message.channel).administrator:\n        #still getting the database stuff\n        engine = create_engine('sqlite:///society.db')\n        from models import BookRead\n        Session = sessionmaker(bind=engine)\n        session = Session()\n\n        #we are searching for the index where // is\n        index = 0\n        for word in args:\n            print(word)\n            if word == '//':\n                break\n            index +=1\n\n        #For args I need to search for the / then when I find it I add everything before it to the book title\n        #and everything after to the description\n        bookTitle = ' '.join(args[0:index])\n        bookDescription = ' '.join(args[(index+1):])\n\n        #getting the row for the place that matches the book we are reading\n        bookReading = session.query(BookRead).filter_by(title = bookTitle).first()\n        #changing what the description is for the book\n        bookReading.description = bookDescription\n        #committing the changes for the database and\n        session.commit()\n        session.close()\n        await ctx.send( ('{} has a description now: '.format(bookTitle) ) )\n    else:\n        await ctx.send(\"You must be an admin to add a description to the book we are reading\")\n\n#command that adds a book to the reading thing\n@bot.command( help='!purchase <title> // <purchase info> adds a description to a book with this title. Is separated by a literal // there needs to be a spaces like in the example. ')\nasync def purchase(ctx, *args):\n    #we are getting all the database stuff\n    if (len(args)==0):\n        await ctx.send('You need to provide a title for the book. Try !help purchase ')\n        return\n\n    #You must be an admin to issue this command\n    if ctx.author.permissions_in(ctx.message.channel).administrator:\n        #getting the db stuff\n        engine = create_engine('sqlite:///society.db')\n        from models import BookRead\n        Session = sessionmaker(bind=engine)\n        session = Session()\n\n        #we are searching for the index where // is\n        index = 0\n        for word in args:\n            print(word)\n            if word == '//':\n                break\n            index +=1\n\n        #For args I need to search for the / then when I find it I add everything before it to the book title\n        #and everything after is the purchas info\n        bookTitle = ' '.join(args[0:index])\n        bookPurchase = ' '.join(args[(index+1):])\n\n        #getting the row for the place that matches the book we are reading\n        bookReading = session.query(BookRead).filter_by(title = bookTitle).first()\n        #changing what the description is for the book\n        bookReading.purchaseInfo = bookPurchase\n        #committing the changes for the database and\n        session.commit()\n        session.close()\n        await ctx.send( ('{} has its purchase info now: '.format(bookTitle) ) )\n    else:\n        await ctx.send('You must be an admin to add purchase info.')\n\n@bot.command(help=\"!finished <title> makes the book with that title a finished book\")\nasync def finished(ctx,*args):\n    if (len(args) == 0):\n        await ctx.send('Use !help finished to learn to use this command')\n        return\n    else:\n        #doing the database things\n        if( ctx.author.permissions_in(ctx.message.channel).administrator):\n            engine = create_engine('sqlite:///society.db')\n            from models import BookRead\n            Session = sessionmaker(bind=engine)\n            session = Session()\n\n            #title of the book\n            bookTitle = ' '.join(args)\n            #getting the book\n            book = session.query(BookRead).filter_by(title=bookTitle).first()\n\n            #we enter this if statement if we could not find the book\n            if book is None:\n                await ctx.send('We could not find a book with title: {}'.format(bookTitle))\n                return\n            #we enter here if we do find a book with that title\n            else:\n                book.currBook = False\n                await ctx.send('We have finished book {}'.format(bookTitle))\n                session.commit()\n            session.close()\n        else:\n            await ctx.send(\"You must be an admin to issue this command \")\n\n@bot.command(help='!hw <description> without the description you get what reading is due next. With description it updates the description')\nasync def hw(ctx,*args):\n    #if hw is already here\n    if (path.exists('hw.txt')):\n        #deciding whether we are updating description or reading what is next due\n        if (len(args) != 0 )  :\n            if ctx.author.permissions_in(ctx.message.channel).administrator:\n                print ( ctx.author.permissions_in(ctx.message.channel).administrator)\n                hw = open('hw.txt','w')\n                content = ' '.join(args)\n                hw.write(content)\n                await ctx.send('Updated reading')\n                hw.close()\n            else:\n                await ctx.send('You are not an admin. You cannnot make an update. Sorry :(')\n        else:\n            hw = open('hw.txt','r')\n            content = hw.read()\n            await ctx.send(content)\n            hw.close()\n    #here we are making the hw file\n    else:\n        hw = open('hw.txt','w')\n        hw.write('Next reading is going to be here')\n        hw.close()\n        await ctx.send('Next reading is going to be here')\n\n@bot.command(help='!considering . Prints the list of book considering.')\nasync def considering(ctx):\n    #getting the book list\n    engine = create_engine('sqlite:///society.db')\n    from models import BookList\n    Session = sessionmaker(bind=engine)\n    session = Session()\n    consideringBooks = session.query(BookList)\n    for book in consideringBooks:\n        await ctx.send(book)\n\n@bot.command(help=\"!remove <title> . Removes this book from the books we are considering.\")\nasync def remove(ctx, *args):\n    if (len(args) == 0):\n        await ctx.send('Try using !help remove to learn how to properly use it.')\n    else:\n        #getting the book list\n        engine = create_engine('sqlite:///society.db')\n        from models import BookList\n        Session = sessionmaker(bind=engine)\n        session = Session()\n        book = session.query(BookList).filter_by(title = ' '.join(args)).first()\n        if(book is None):\n            await ctx.send('Could not find book with title: {}\\nCheck out the spelling again!'.format(' '.join(args)))\n        else:\n            session.delete(book)\n            session.commit()\n            await ctx.send('We have removed that book')\n        session.close()\n\n\n@bot.command(help=\"!consider <title> // <description> . Adds this book to the books we are considering list. You don't need to put a description\")\nasync def consider(ctx, *args):\n    if (len(args) == 0):\n        await ctx.send('Try using !help consider for some help')\n    else:\n        #getting the book list\n        engine = create_engine('sqlite:///society.db')\n        from models import BookList\n        Session = sessionmaker(bind=engine)\n        session = Session()\n        #we are searching for the index where // is\n        index = 0\n        for word in args:\n            if word == '//':\n                break\n            index +=1\n\n        #getting the bookTitle along with the book description\n        bookTitle = ' '.join(args[0:index])\n        bookDescription = ' '.join(args[index+1:])\n        if(index == -1):\n            bookDescription = 'No description for the book.'\n\n        #adding the book we want to consider for the reading\n        considerBook = BookList(title=bookTitle,description=bookDescription)\n        session.add(considerBook)\n        session.commit()\n        session.close()\n        await ctx.send('You have added {} for consideration in the book club'.format(bookTitle))\n\n    #this is when I was keeping track of stuff with a textfile rather than a database\n    # if( path.exists('gold.txt')):\n    #     #if there are no arguemnts we just read the list of golds\n    #     if (len(args) == 0):\n    #         gold = open('gold.txt','r')\n    #         content = ''\n    #         for line in gold:\n    #             content += line\n    #         await ctx.send(content)\n    #\n    #     #otherwise there are arguments so we add the gold\n    #     else:\n    #         newGold = ' '.join(args)\n    #         gold = open('gold.txt','a')\n    #         gold.write( ( newGold +'\\n') )\n    #         gold.close()\n    #         await ctx.send( ('Added: ' + newGold )  )\n    #\n    # #we enter here if the file does not exists\n    # else:\n    #     gold = open('gold.txt','w')\n    #     gold.write('Darrow Lykos\\n')\n    #     if len(args) == 0:\n    #         await ctx.send('Added: Darrow Lykos as gold')\n    #     else:\n    #         gold.write(' '.join(args))\n\nprint(server)\nbot.run(TOKEN)\n","repo_name":"Herrera-pgoat/discordbot","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":17009,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"14567147405","text":"from inspect import FrameInfo\nfrom typing import Text\nfrom dash_bootstrap_components._components.Col import Col\nfrom dash_bootstrap_components._components.Row import Row\nfrom future.utils import with_metaclass\n\nfrom pandas.io.formats import style\nfrom dash_core_components.Dropdown import Dropdown\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport pickle\nimport os\nimport re\nimport sys\nsys.path.append('../process data/')\nimport scipy.stats as stats\nfrom plotly.subplots import make_subplots\nimport plotly.graph_objects as go\nimport plotly.figure_factory as ff\nimport plotly.offline as pyo\nimport plotly.express as px\nfrom encode_processed_data import encode_data\n\nimport dash\nimport dash_table\nimport dash_core_components as dcc\nimport dash_html_components as html\nimport dash_bootstrap_components as dbc\nimport dash_daq as daq\nimport plotly.graph_objects as go\nfrom dash.dependencies import Input, Output\nfrom summary_plots_and_figures import summary_plots_and_figures\n\n\n# Since we're adding callbacks to elements that don't exist in the app.layout,\n# Dash will raise an exception to warn us that we might be\n# doing something wrong.\n# In this case, we're adding the elements through a callback, so we can ignore\n# the exception.\napp = dash.Dash(__name__, suppress_callback_exceptions=True, external_stylesheets=[dbc.themes.BOOTSTRAP])\n\n\n# ================================================== https://dash.plotly.com/urls ==================================================\n\n\n# ---- fetch data object ----x\nwith open('../data objects/batch_processing_object_with_encodings.pkl', 'rb') as file2:\n    # ed = pickle.load(file2)\n    ed = pd.read_pickle(file2)\nspf = summary_plots_and_figures(ed)\nspf.create_performance_groupings(10)\nspf.random_participants_sample(10)\n# ---- fetch data object ----x\n\n# # ---- colour schemes ----x\ncolours_choice = [\n            {'label': 'Plasma',     'value': 'Plasma'},\n            {'label': 'Plotly3',    'value': 'Plotly3'},\n            {'label': 'viridis',    'value': 'viridis'},\n            {'label': 'Inferno',    'value': 'Inferno'},\n            {'label': 'Turbo',      'value': 'Turbo'},\n            {'label': 'Blackbody',  'value': 'Blackbody'},\n            {'label': 'RdBu',       'value': 'RdBu'},\n            {'label': 'Aggrnyl',    'value': 'Aggrnyl'},\n            {'label': 'RdYlBu',     'value': 'RdYlBu'},\n            {'label': 'Portland',   'value': 'Portland'},\n            {'label': 'Tropic',     'value': 'Tropic'},\n        ]\nclrs = {\n    'Plasma':      sum([px.colors.sequential.Plasma*3], []),\n    'Plotly3':     sum([px.colors.sequential.Plotly3*3], []),\n    'viridis':     sum([px.colors.sequential.Viridis*3], []),\n    'Inferno':     sum([px.colors.sequential.Inferno*3], []),\n    'Turbo':       sum([px.colors.sequential.Turbo*3], []),\n    'Blackbody':   sum([px.colors.sequential.Blackbody*3], []),\n    'RdBu':        sum([px.colors.sequential.RdBu*3], []),\n    'Aggrnyl':     sum([px.colors.sequential.Aggrnyl*3], []),\n    'RdYlBu':      sum([px.colors.diverging.RdYlBu*3], []),\n    'Portland':    sum([px.colors.diverging.Portland*3], []),\n    'Tropic':      sum([px.colors.diverging.Tropic*3], [])}\n# # ---- colour schemes ----x\n\n\n\napp.layout = dbc.Container([\n    dcc.Location(id='url', refresh=False),\n    html.Div(id='page-content')\n])\n\n\n# =================================================== Universal Elements ==================================================\nnavigation_menu = dbc.Col([ \n    html.Br(), html.Hr(),\n    dbc.Row([\n    dbc.Col([\n        # html.Br(),\n        dbc.Col(dcc.Link('Home page', href='/')),\n        dbc.Col(dcc.Link('Model Free Analysis', href='/page-1')),\n        dbc.Col(dcc.Link('Theoretical Statistics & Neuroscience', href='/page-2'))], width=8),\n        dbc.Col([html.Img(src=app.get_asset_url('uct-uhs.png'),style={'width':'60%'})], width=4),\n    ]),\n    html.Hr(), html.Br()\n])\n# =================================================== Universal Elements ==================================================\n\n\n\n# ====================================================== Landing Page =====================================================\nindex_page = dbc.Container([\n    html.Div('Neuropsychological Data Analysis', style={'color': '#555555', 'fontSize': 25, 'margin':'15px', 'text-align': 'center'}),\n    dbc.Row([html.Div('We\\'re a cognitive neuroscience research unit comparing human & Artificial Intelligence learning in dynamic decision making tasks under uncertainty. We\\'re utilising Statistical Learning to reverse engineer neurological executive functions. This interactive tools aims to aid in one\\'s intuitive understanding about relationships in the data. In affliantion with University of Helsinki & University of Cape Town.', style={'color': '#555555', 'fontSize': 14, 'margin-right':'20px', 'text-align': 'center'})], justify=\"center\", align=\"center\"),\n    navigation_menu,\n    dbc.Row(html.Iframe(id=\"embedded-pdf\", src=\"assets/description.pdf\", height='900', width='90%'), justify=\"center\", align=\"center\")\n])\n# ====================================================== Landing Page =====================================================\n\n\n\n# ================================================== Model-Free Analysis ==================================================\npage_1_layout = dbc.Container([\n    html.Div('Model Free Analysis', style={'color': '#555555', 'fontSize': 25, 'margin':'15px', 'text-align': 'left'}),\n    html.Div('This dashboard allows one to build a statistical intuition about the relationships in the data.', style={'color': '#555555', 'fontSize': 14, 'margin-right':'20px', 'text-align': 'left'}),\n    navigation_menu,\n    dbc.Row([\n        dbc.Col([\n            html.Div('', style={'margin-top':'30px'}),\n            dbc.Row([\n                dbc.Col(html.Div('Variable X'), width=4),\n                dbc.Col([dcc.Dropdown(id='varx', options=spf.continuous_vars, value='wcst_accuracy')])\n                ]),\n            dbc.Row([\n                dbc.Col(html.Div('Variable Y'), width=4),\n                dbc.Col([dcc.Dropdown(id='vary', options=spf.continuous_vars,value='navon_perc_correct')])\n                ]),\n            dbc.Row([\n                dbc.Col(html.Div('Group'), width=4),\n                dbc.Col([dcc.Dropdown(id='group', options=spf.categorical_vars, value='')])\n                ])      \n        ], width=4),\n        dbc.Col(dcc.Graph(id='summary_plot_1'))\n    ]),\n    dbc.Row([\n        dbc.Col([\n            dbc.Row([\n                dbc.Col(html.Div('Colour scheme'), width=4),\n                dbc.Col([dcc.Dropdown(id='colours', options=colours_choice, value='Plasma')])\n                            ]),\n            dbc.Row([\n                dbc.Col(html.Div('Trial batch size'), width=4),\n                dbc.Col(dcc.Slider(id='wcst_groups', min=1, max=10, step=1, value=1)) # options: [1,2,3,4,5]\n                        ]),\n        ], width=6),\n        dbc.Col([\n                daq.ToggleSwitch(id='vlines', value=False, label='Show forced error points', color='#ABE2FB', labelPosition='left'),\n                dbc.Row([\n                    dbc.Col(html.Div('N groups'), width=3),\n                    dbc.Col(dcc.Slider(id='n_groups', min=0, max=10, step=1, value=5))]),\n        ])\n    ]),\n    dbc.Col(dcc.Graph(id='wcst_plot')),\n    dbc.Row([\n        dbc.Col([\n            html.Div(id='anova_table'), \n            html.Div([html.Div(id='summary_table')])],\n            width=8),\n        dbc.Col(dcc.Graph(id='group_pie')),\n    ]),\n    dbc.Col(dcc.Graph(id='summary_plot_2'))\n])\n\n@app.callback(\n    Output(component_id='summary_table',                component_property='children'),\n    Output(component_id='anova_table',                  component_property='children'),\n    Output(component_id='wcst_plot',                    component_property='figure'),\n    Output(component_id='group_pie',                    component_property='figure'),\n    Output(component_id='summary_plot_1',               component_property='figure'),\n    Output(component_id='summary_plot_2',               component_property='figure'),\n\n    Input(component_id='colours',                       component_property='value'),\n    Input(component_id='wcst_groups',                   component_property='value'),\n    Input(component_id='n_groups',                      component_property='value'),\n    Input(component_id='varx',                          component_property='value'),\n    Input(component_id='vary',                          component_property='value'),\n    Input(component_id='group',                         component_property='value'),\n    Input(component_id='vlines',                        component_property='value')\n    )\n\n\ndef page_1_dropdown(colours, wcst_groups, n_groups, varx, vary, group, vlines):\n    spf.create_performance_groupings(n_groups)\n    spf.random_participants_sample(n_groups)\n    spf.compute_wcst_performance_trial_bins(n_groups, use_seq=True, g=wcst_groups)\n    x    = spf.compute_summary_stats(data=spf.ed.summary_table, value_var=varx, group_var=group, resetIndex=True)\n    dt1  = html.Div([\n        html.Div('Average Scores per Group', style={'color': '#555555', 'fontSize': 20, 'margin':'10px'}),\n        dash_table.DataTable(\n            id='table',\n            columns=[{\"name\": i, \"id\": i} for i in x.columns],\n            data=x.to_dict('records'),\n            style_cell={'overflow': 'scroll', 'textOverflow': 'ellipsis'},\n            style_table={'overflow': 'scroll'},\n            style_header=dict(backgroundColor=\"#ddf3fc\"),\n            style_data=dict(backgroundColor=\"#fffafa\")\n        )])\n    x    = spf.ANOVA(data=spf.ed.summary_table, group_var=group, value_var=varx)\n    dt2  = html.Div([\n            html.Div('ANOVA: ' + varx, style={'color': '#555555', 'fontSize': 20, 'margin':'10px'}),\n            dash_table.DataTable(\n                id='table',\n                columns=[{\"name\": i, \"id\": i} for i in x.columns],\n                data=x.to_dict('records'),\n                style_cell={'overflow': 'scroll', 'textOverflow': 'ellipsis'},\n                style_table={'overflow': 'scroll'},\n                style_header=dict(backgroundColor=\"#ddf3fc\"),\n                style_data=dict(backgroundColor=\"#fffafa\")\n            )\n        ])\n    wcst = spf.wcst_performance_plot(group=group, colours=clrs[colours], mean_plot=True, show_vlines=vlines)['figure'] \n    grp  = spf.basic_pie_chart(df=spf.ed.summary_table, dummy_var=group, colors=clrs[colours], labels=None, title='Group Distribution: '+group) \n    sm1  = spf.scatter_plot(data=spf.ed.summary_table, group_var=group, xvar=varx, yvar=vary, xlab=varx, cols=clrs[colours], ylab=vary, title='Raw Data')\n    sm2  = spf.distribution_plot(data=spf.ed.summary_table, nbinsx=50, xvar=varx, group_var=group, title='Distributions over: ' + varx, cols=clrs[colours]) \n    return dt1, dt2, wcst, grp, sm1, sm2\n# ================================================== Model-Free Analysis ==================================================\n\n\n\n\n# =================================================== Theoretical Models ===================================================\npage_2_layout = dbc.Container([\n    html.Div('Theoretical Neuroscience', style={'color': '#555555', 'fontSize': 25, 'margin':'15px', 'text-align': 'left'}),\n    html.Div('Theoretical Neuroscience is increasingly taken as mathematical approximations to neurological behaviour. In our study we adopt this strategy to simulate executive functions with computational models.', style={'color': '#555555', 'fontSize': 14, 'margin-right':'20px', 'text-align': 'left'}),\n    navigation_menu,\n    html.H1('Page 2'),\n    dcc.RadioItems(\n        id='page-2-radios',\n        options=[{'label': i, 'value': i} for i in ['Orange', 'Blue', 'Red']],\n        value='Orange'\n    ),\n])\n\n@app.callback(dash.dependencies.Output('page-2-content', 'children'),\n              [dash.dependencies.Input('page-2-radios', 'value')])\ndef page_2_radios(value):\n    return 'You have selected \"{}\"'.format(value)\n\n\n# Update the index\n@app.callback(dash.dependencies.Output('page-content', 'children'),\n              [dash.dependencies.Input('url', 'pathname')])\ndef display_page(pathname):\n    if pathname == '/page-1':\n        return page_1_layout\n    elif pathname == '/page-2':\n        return page_2_layout\n    else:\n        return index_page\n    # You could also return a 404 \"URL not found\" page here\n\n\nif __name__ == '__main__':\n    app.run_server(debug=True, host='127.0.0.1')\n\n\n\n\n\n\n\n\n\n\n\n\n\n# # ------------------------- Anylsis: Model Free -------------------------x\n# model_free = dbc.Container([\n#     dbc.Row([\n#         dbc.Col([\n#             html.Div('', style={'margin-top':'30px'}),\n#             dbc.Row([\n#                 dbc.Col(html.Div('Variable X'), width=4),\n#                 dbc.Col([dcc.Dropdown(id='varx', options=spf.continuous_vars, value='wcst_accuracy')])\n#                 ]),\n#             dbc.Row([\n#                 dbc.Col(html.Div('Variable Y'), width=4),\n#                 dbc.Col([dcc.Dropdown(id='vary', options=spf.continuous_vars,value='navon_perc_correct')])\n#                 ]),\n#             dbc.Row([\n#                 dbc.Col(html.Div('Group'), width=4),\n#                 dbc.Col([dcc.Dropdown(id='group', options=spf.categorical_vars, value='')])\n#                 ])      \n#         ], width=4),\n#         dbc.Col(dcc.Graph(id='summary_plot_1'))\n#     ]),\n#     dbc.Row([\n#     dbc.Col([\n#         dbc.Row([\n#             dbc.Col(html.Div('Colour scheme'), width=4),\n#             dbc.Col([dcc.Dropdown(id='colours', options=colours_choice, value='Plasma')])\n#                         ]),\n#         dbc.Row([\n#             dbc.Col(html.Div('Trial batch size'), width=4),\n#             dbc.Col(dcc.Slider(id='wcst_groups', min=1, max=10, step=1, value=1)) # options: [1,2,3,4,5]\n#                     ]),\n#     ], width=6),\n#     dbc.Col([\n#             daq.ToggleSwitch(id='vlines', value=False, label='Show forced error points', color='#ABE2FB', labelPosition='left'),\n#             dbc.Row([\n#                 dbc.Col(html.Div('N groups'), width=3),\n#                 dbc.Col(dcc.Slider(id='n_groups', min=0, max=10, step=1, value=5))]),\n#     ])\n#     ]),\n#     dbc.Col(dcc.Graph(id='wcst_plot')),\n#     dbc.Row([\n#         dbc.Col([\n#             html.Div(id='anova_table'), \n#             html.Div([html.Div(id='summary_table')])],\n#             width=8),\n#         dbc.Col(dcc.Graph(id='group_pie')),\n#     ]),\n#     dbc.Col(dcc.Graph(id='summary_plot_2'))\n# ]),\n# # ------------------------- Anylsis: Model Free -------------------------x\n\n# app.layout = dbc.Container([\n#     html.Div('Neuropsychological Data Analysis', style={'color': '#555555', 'fontSize': 25, 'margin':'15px', 'text-align': 'center'}),\n#     dbc.Row([html.Div('We\\'re a cognitive neuroscience research unit comparing human & Artificial Intelligence learning in dynamic decision making tasks under uncertainty. We\\'re utilising Statistical Learning to reverse engineer neurological executive functions. This interactive tools aims to aid in one\\'s intuitive understanding about relationships in the data. In affliantion with University of Helsinki & University of Cape Town.', style={'color': '#555555', 'fontSize': 12, 'margin-right':'20px', 'text-align': 'center'})], justify=\"center\", align=\"center\"),\n#     html.Br(),\n#     dbc.Row([\n#         dbc.Col([\n#             dbc.Col(dcc.Location(id='url', refresh=False)),\n#             dbc.Col(dcc.Link('Model Free Analysis', href='/model-free')),\n#             dbc.Col(dcc.Link('Theoretical Statistics & Neuroscience', href='/theory')),\n#             dbc.Col(html.Label([html.A('Connect with us!',\n#                 href='https://raw.githubusercontent.com/ZachWolpe/Dynocog/main/images/description.png')],\n#                 style={'color': '#555555', 'fontSize': 12, 'margin-right':'20px', 'text-align': 'center'}))\n#         ], width=8),\n#         dbc.Col([\n#         html.Img(src=app.get_asset_url('uct-uhs.png'),style={'width':'60%'}\n#         )], width=4),\n#     ]),\n\n   \n\n    \n\n\n\n#     # content will be rendered in this element\n#     html.Div(id='page-content')\n# ])\n\n\n\n\n# # @app.callback(dash.dependencies.Output('page-content', 'children'),\n# #               [dash.dependencies.Input('url', 'pathname')])\n\n# @app.callback(\n#     Output(component_id='page-content', component_property='children'),\n    \n#     # Output(component_id='summary_table',                component_property='children'),\n#     # Output(component_id='anova_table',                  component_property='children'),\n#     # Output(component_id='wcst_plot',                    component_property='figure'),\n#     # Output(component_id='group_pie',                    component_property='figure'),\n#     # Output(component_id='summary_plot_1',               component_property='figure'),\n#     # Output(component_id='summary_plot_2',               component_property='figure'),\n\n#     Input(component_id='url', component_property='pathname'),\n#     # Input(component_id='colours',                       component_property='value'),\n#     # Input(component_id='wcst_groups',                   component_property='value'),\n#     # Input(component_id='n_groups',                      component_property='value'),\n#     # Input(component_id='varx',                          component_property='value'),\n#     # Input(component_id='vary',                          component_property='value'),\n#     # Input(component_id='group',                         component_property='value'),\n#     # Input(component_id='vlines',                        component_property='value')\n#     )\n\n    \n# def display_page(pathname#, colours, wcst_groups, n_groups, varx, vary, group, vlines\n# ):\n#     print(pathname)\n#     if pathname=='/model-free':\n#         return model_free\n#     return dbc.Container([\n#         html.H3('You are on page {}'.format(pathname)), \n#     ])\n\n\n\n# if __name__=='__main__':\n#     app.run_server(debug=True, host = '127.0.0.1') ","repo_name":"ZachWolpe/Dynocog","sub_path":"Python Implementation/final instance/dashboard/dashboard_multitab.py","file_name":"dashboard_multitab.py","file_ext":"py","file_size_in_byte":17976,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"584105069","text":"import logging, fastapi, pydantic, os\n\n\nlog_file = os.getenv(\"LOG_FILE\")\nlog_format = os.getenv(\"LOG_FORMAT\")\nlog_level = os.getenv(\"LOG_LEVEL\")\nlogging.basicConfig(\n    filename=log_file, format=log_format, level=log_level\n)\n\n\napp = fastapi.FastAPI()\n\n\nclass ProfileSchema(pydantic.BaseModel):\n\tf_name: str\n\tl_name: str\n\n\n@app.get(\"/profiles\")\nasync def get_profiles(request: fastapi.Request):\n\treturn {\n\t\t\"service\": \"profiles\",\n\t\t\"endpoint\": \"GET /profiles\",\n\t\t\"authentication\": \"required\",\n\t\t\"status\": \"success\",\n\t\t\"current_user\": request.headers[\"user\"]\n\t}\n\n@app.post(\"/profiles\")\nasync def add_profile(request: fastapi.Request):\n\treturn {\n\t\t\"service\": \"profiles\",\n\t\t\"endpoint\": \"POST /profiles\",\n\t\t\"authentication\": \"required\",\n\t\t\"status\": \"success\",\n\t\t\"current_user\": request.headers[\"user\"]\n\t}\n\n@app.get(\"/profiles/{profile_id}\")\nasync def get_profile(request: fastapi.Request, profile_id: str):\n\treturn {\n\t\t\"service\": \"profiles\",\n\t\t\"endpoint\": f\"GET /profiles/{profile_id}\",\n\t\t\"authentication\": \"required\",\n\t\t\"status\": \"success\",\n\t\t\"current_user\": request.headers[\"user\"]\n\t}\n\n@app.patch(\"/profiles/{profile_id}\")\nasync def update_profile(request: fastapi.Request, profile_id: str):\n\treturn {\n\t\t\"service\": \"profiles\",\n\t\t\"endpoint\":  f\"PATCH /profiles/{profile_id}\",\n\t\t\"authentication\": \"required\",\n\t\t\"status\": \"success\",\n\t\t\"current_user\": request.headers[\"user\"]\n\t}\n\n@app.delete(\"/profiles/{profile_id}\")\nasync def delete_profile(request: fastapi.Request, profile_id: str):\n\treturn {\n\t\t\"service\": \"profiles\",\n\t\t\"endpoint\":  f\"DELETE /profiles/{profile_id}\",\n\t\t\"authentication\": \"required\",\n\t\t\"status\": \"success\",\n\t\t\"current_user\": request.headers[\"user\"]\n\t}","repo_name":"Lemonpill/dockerized-gateway","sub_path":"profiles/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1664,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"12235057364","text":"import streamlit as st\nimport pandas as pd\nimport requests\nimport json\nfrom tencentcloud.common import credential\nfrom tencentcloud.common.profile.client_profile import ClientProfile\nfrom tencentcloud.common.profile.http_profile import HttpProfile\nfrom tencentcloud.common.exception.tencent_cloud_sdk_exception import TencentCloudSDKException\nfrom tencentcloud.tts.v20190823 import tts_client, models\nimport base64\nimport uuid\nimport api\n\n# st.markdown(\n#     f\"\"\"\n#     <style>\n#     .stApp {{\n#     background-image: url('http://pic.bizhi360.com/bpic/84/10584.jpg');\n#     background-size: cover\n#     }}\n#     </style>\n#     \"\"\",\n#     unsafe_allow_html=True)\n\n\nst.title('语音文学机器人')\n\n# 定义的可选数据\ndf1 = pd.DataFrame({\n    'person_name': ['智逍遥', '智瑜', '智聆', '智美', '智云', '智莉',\n                    '智言', '智娜', '智琪', '智芸', '智华', '智燕',\n                    '智丹', '智辉', '智宁', '智萌', '智甜', '智蓉',\n                    '智靖', '智彤', '智刚', '智瑞', '智虹', '智萱',\n                    '智皓', '智薇', '智希', '智梅', '智洁', '智凯',\n                    '智柯', '智奎', '智芳', '智蓓', '智莲', '智依',\n                    '智川']})\n\nperson_jieshi = {'智逍遥': '阅读男声', '智瑜': '情感女声', '智聆': '通用女声',\n                 '智美': '客服女声', '智云': '通用男声', '智莉': '通用女声',\n                 '智言': '助手女声', '智娜': '客服女声', '智琪': '客服女声',\n                 '智芸': '知性女声', '智华': '通用男声', '智燕': '新闻女声',\n                 '智丹': '新闻女声', '智辉': '新闻男声', '智宁': '新闻男声',\n                 '智萌': '男童声', '智甜': '女童声', '智蓉': '情感女声',\n                 '智靖': '情感男声', '智彤': '粤语女声', '智刚': '新闻男声',\n                 '智瑞': '新闻男声', '智虹': '新闻女声', '智萱': '聊天女声',\n                 '智皓': '聊天男声', '智薇': '聊天女声', '智希': '通用女声',\n                 '智梅': '通用女声', '智洁': '通用女声', '智凯': '通用男声',\n                 '智柯': '通用男声', '智奎': '通用男声', '智芳': '通用女声',\n                 '智蓓': '客服女声', '智莲': '通用女声', '智依': '通用女声',\n                 '智川': '四川女声'}\n\npreson_set_property = {'智逍遥': '100510000', '智瑜': '101001', '智聆': '101002',\n                       '智美': '101003', '智云': '101004', '智莉': '101005', '智言': '101006',\n                       '智娜': '101007', '智琪': '101008', '智芸': '101009', '智华': '101010',\n                       '智燕': '101011', '智丹': '101012', '智辉': '101013', '智宁': '101014',\n                       '智萌': '101015', '智甜': '101016', '智蓉': '101017', '智靖': '101018',\n                       '智彤': '101019', '智刚': '101020', '智瑞': '101021', '智虹': '101022',\n                       '智萱': '101023', '智皓': '101024', '智薇': '101025', '智希': '101026',\n                       '智梅': '101027', '智洁': '101028', '智凯': '101029', '智柯': '101030',\n                       '智奎': '101031', '智芳': '101032', '智蓓': '101033', '智莲': '101034',\n                       '智依': '101035', '智川': '101040'}\n\n\n\n# 选择要生成的类型\nstyle_n = 0\nstyle = st.radio(\"请选择您想要的类型\", ('诗', '词', '对联'))\nif style == '诗':\n    style_n = 1\nelif style == '词':\n    style_n = 2\nelse:\n    style_n = 3\n\n\n# 一个api写两个函数，一个是请求一个是调用\n\n\ndef cipost(word):\n    url = \"http://119.3.249.76:8080/gen/ci\"\n    parm = {\"text\": word}\n    res = requests.post(url, json.dumps(parm))\n    # return res.text\n    if json.loads(res.text)['code'] == 200:\n        return json.loads(res.text)['result']\n    else:\n        return '出错'\n\n\ndef shipost(word):\n    parm = {\"text\": word}\n    couplet_url = \"http://119.3.249.76:8080/gen/poem\"\n    couplet_res = requests.post(couplet_url, json.dumps(parm))\n    # return couplet_res.text\n    if json.loads(couplet_res.text)['code'] == 200:\n        return json.loads(couplet_res.text)['result']\n    else:\n        return '出错'\n\n\ndef duilianpost(word):\n    parm = {\"text\": word}\n    couplet_url = \"http://119.3.249.76:8080/gen/couplet\"\n    couplet_res = requests.post(couplet_url, json.dumps(parm))\n    # return couplet_res.text\n    if json.loads(couplet_res.text)['code'] == 200:\n        # for i in json.loads(couplet_res.text)['result']:\n        #     s = s.join(i)\n        s = json.loads(couplet_res.text)['result'][0] + ',' + json.loads(couplet_res.text)['result'][1]\n        return s\n    else:\n        return '出错'\n\n\ntitle = st.text_input('请输入开头————例如：春')\nif st.button('生成文本'):\n    with open('word.txt', 'w') as f:\n        if style_n == 1:\n            # print(shipost(title))\n            f.write(shipost(title))\n        elif style_n == 2:\n            # print(cipost(title))\n            f.write(cipost(title))\n        elif style_n == 3:\n            # print(duilianpost(title))\n            f.write(duilianpost(title))\n        f.close()\nwith open('word.txt', 'r') as f:\n    st.write(f.read())\n    f.close()\n\n\nwith open('word.txt', 'r') as f:\n    s = f.read()\n    st.write()\n    f.close()\noption = st.selectbox(\n    '请选择使用的人物音色：',\n    df1['person_name'])\njieshi = person_jieshi[option]\nst.write('您选的人物特点: ', jieshi)\npreaon_property = preson_set_property[option]\n\n\n# 生成语音的SDK\n\n\nif st.button('生成语音'):\n    with open('word.txt', 'r') as f:\n        s = f.read()\n    api.tencent_cloud_com(preaon_property,s)\n    audio_file = open('E:testten.mp3', 'rb')\n    audio_bytes = audio_file.read()\n    st.audio(audio_bytes, format='audio/ogg')\n\n","repo_name":"duanyuejing/Art-Robots-V3.0","sub_path":"index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":5901,"program_lang":"python","lang":"zh","doc_type":"code","stars":2,"dataset":"github-code","pt":"48"}
+{"seq_id":"32073288192","text":"import pygame, sys\n\n#Game Init\npygame.init()\nWIDTH,HEIGHT = 800,400\ndisplay_surface = pygame.display.set_mode((WIDTH,HEIGHT))\npygame.display.set_caption(\"2D game\")\n\n#Background\nbg_surf = pygame.Surface((WIDTH,HEIGHT))\nbg_surf.fill((0,0,100))\nbg_rect = bg_surf.get_rect()\n\n# Player\nplayer_surf = pygame.Surface((100,100))\nplayer_surf.fill((255,255,0))\nplayer_rect = player_surf.get_rect()\n\n\nwhile True:\n\t#Event Loop\n\tfor event in pygame.event.get():\n\t\tif event.type == pygame.QUIT:\n\t\t\tpygame.quit()\n\t\t\tsys.exit()\n\t\tif event.type == pygame.KEYDOWN or event.type == pygame.KEYUP:\n\t\t\tif event.key == pygame.K_a:\n\t\t\t\tplayer_rect.x -= 10\n\t\t\tif event.key == pygame.K_d:\n\t\t\t\tplayer_rect.x += 10\n\t\t\tif event.key == pygame.K_w:\n\t\t\t\tplayer_rect.y -= 10\n\t\t\tif event.key == pygame.K_s:\n\t\t\t\tplayer_rect.y += 10\n\n\n\n\n\tdisplay_surface.blit(bg_surf,bg_rect)\n\tdisplay_surface.blit(player_surf,player_rect)\n\n\t#Draw the final frame\n\tpygame.display.update()","repo_name":"OtavioLira/Udemy-Learning-Python-with-Games","sub_path":"Asteroid Shooter/code/FlatGame.py","file_name":"FlatGame.py","file_ext":"py","file_size_in_byte":935,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"2610742934","text":"\"\"\"\n>>> a = np.arange(20)\n>>> (a == snappy_unpack_blob(snappy_pack_blob(a))).all()\nTrue\n\n>>> a = np.arange(20, dtype=np.uint8)\n>>> snappy_unpack_blob(snappy_pack_blob(a)).dtype == a.dtype\nTrue\n\n>>> a = np.arange(20, dtype=np.float32)\n>>> b = snappy_unpack_blob(snappy_pack_blob(a))\n>>> a.dtype == b.dtype == np.float32\nTrue\n\n>>> all(ai == bi for ai, bi in zip(a, b))\nTrue\n\n>>> snappy_unpack_blob(snappy_pack_blob(None)) is None\nTrue\n\n>>> exp = ['A', 'XSDFSDFSD', \"A/A,B\"]\n>>> obs = snappy_unpack_blob(snappy_pack_blob(np.array(exp)))\n>>> assert exp == list(obs), (list(obs), exp)\n\n>>> try:\n...    b = snappy_pack_blob(a)\n... except zlib.error:\n...    pass\n\n\n>>> b = snappy_unpack_blob(snappy_pack_blob(np.array([False, True, False, True])))\n>>> b[2] = True\n\"\"\"\nfrom __future__ import absolute_import\nimport sys\nimport zlib\ntry:\n    import pickle as pickle\nexcept ImportError:\n    import pickle\n\ntry:\n    from cyordereddict import OrderedDict\nexcept:\n    from collections import OrderedDict\n\n\nif sys.version_info[0] == 3:\n    buffer = memoryview\n    def unpack_genotype_blob(blob):\n        return pickle.loads(zlib.decompress(blob), encoding='latin1')\n    def zdumps(obj):\n        return zlib.compress(pickle.dumps(obj, pickle.HIGHEST_PROTOCOL), 9)\nelse:\n    def unpack_genotype_blob(blob):\n        return pickle.loads(zlib.decompress(blob))\n    def zdumps(obj):\n        return zlib.compress(pickle.dumps(obj, pickle.HIGHEST_PROTOCOL), 9)\n\ndef pack_blob(obj):\n    return buffer(zdumps(obj))\n\ndef unpack_ordereddict_blob(blob):\n    blob_val = pickle.loads(zlib.decompress(blob))\n    if blob_val is not None:\n        return OrderedDict(blob_val)\n    return None\n\n\ntry:\n    import snappy\nexcept ImportError:\n    pass\n\nimport numpy as np\n\n# we use the numpy type char as the first item we save to know the dtype when we\n# decompress.\nlookup = {dt(1).dtype.char: dt for dt in (np.uint8, np.uint32, np.int32,\n          np.float32, np.int64, np.float64, np.bool_)}\n\nif sys.version_info[0] == 3:\n    SEP = '\\0'\n\n    def snappy_pack_blob(obj, sep=SEP):\n        if obj is None: return ''\n        c = obj.dtype.char\n        if c == 'S' or c == 'U':\n            return b'U' + snappy.compress(sep.join(obj))\n        return buffer(c.encode('utf-8') + snappy.compress(obj.tobytes(), 'utf8'))\n\n    def snappy_unpack_blob(blob, sep=SEP):\n        if len(blob) == 0: return None\n        if blob[0] in (ord('S'), ord('U')):\n            return np.array(snappy.decompress(blob[1:], \"utf8\").split(sep))\n        dt = lookup[chr(blob[0])]\n        arr = np.frombuffer(snappy.decompress(blob[1:]), dtype=dt)\n        # hack since arrays arent writable from buffer and we need this for comp_het\n        # phasing.\n        if blob[0] == ord('?'):\n            arr = np.array(arr)\n            arr.setflags(write=True)\n        return arr\n\nelse:\n\n    SEP = '\\0'\n    def snappy_pack_blob(obj, sep=SEP):\n        if obj is None: return ''\n        c = obj.dtype.char\n        if c == 'S': return 'S' + snappy.compress(sep.join(obj))\n        return buffer(c + snappy.compress(obj.tobytes()))\n\n    def snappy_unpack_blob(blob, sep=SEP):\n        if len(blob) == 0: return None\n        if blob[0] == 'S':\n            return np.array(snappy.decompress(blob[1:]).split(sep))\n        dt = lookup[blob[0]]\n        arr = np.frombuffer(snappy.decompress(blob[1:]), dtype=dt)\n        # hack since arrays arent writable from buffer and we need this for comp_het\n        # phasing.\n        if blob[0] == '?':\n            arr.setflags(write=True)\n        return arr\n\nif __name__ == \"__main__\":\n    import doctest\n    print(doctest.testmod())\n","repo_name":"arq5x/gemini","sub_path":"gemini/compression.py","file_name":"compression.py","file_ext":"py","file_size_in_byte":3588,"program_lang":"python","lang":"en","doc_type":"code","stars":305,"dataset":"github-code","pt":"48"}
+{"seq_id":"7314267265","text":"from aiogram import F, Router\nfrom aiogram.filters import Command\nfrom aiogram.fsm.context import FSMContext\nfrom aiogram.types import (\n    Message,\n    ReplyKeyboardRemove,\n)\n\nrouter = Router(name='cancel_command')\n   \n   \n@router.message(Command(\"отмена\"))\n@router.message(F.text.casefold() == \"отмена\")\nasync def cancel_handler(message: Message, state: FSMContext) -> None:\n    \"\"\"\n    Отмена действий\n    \"\"\"\n    current_state = await state.get_state()\n    if current_state is None:\n        return\n    \n    await state.clear()\n    await message.answer(\n        \"Операция отменена.\",\n        reply_markup=ReplyKeyboardRemove(),\n    )","repo_name":"KosmosoJ/testing_bot","sub_path":"handlers/cancel.py","file_name":"cancel.py","file_ext":"py","file_size_in_byte":679,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"48"}
+{"seq_id":"11620634379","text":"import sys\n\nimport BankInvesting\nimport ConsolePrint as Cp\nimport SignIn\nimport SignUp\nimport Utils as utils\n\n\ndef start_system():\n    tries = 4\n    user = get_user()\n    while user is None and tries != 0:\n        tries -= 1\n        Cp.printYellow(f\"Tentativas restantes {tries + 1}\")\n        user = get_user()\n\n    if tries == 0:\n        Cp.printRed(\"Número de tentativas atingidas, tente novamente mais tarde!\")\n        sys.exit()\n    show_menu(user)\n    BankInvesting.start_simulation()\n\n\ndef show_menu(user):\n    Cp.printBlue(\"******* Sink Banking and Services inc. - Menu *******\")\n    Cp.printBlue(f\"Você está logado. Usuário: {user.name}\")\n    print()\n    cpf = ask_for_cpf()\n    while not confirm_cpf(cpf):\n        cpf = ask_for_cpf()\n\n\ndef ask_for_cpf():\n    hard_cpf = input(\"Infome o CPF, apenas números:\")\n    if hard_cpf.lower() == \"sair\":\n        sys.exit()\n    valid_cpf = utils.is_valid_cpf(hard_cpf)\n    while not valid_cpf:\n        Cp.printYellow(\"CPF inválido, tente novamente!\")\n        new_cpf = input(\"Infome o CPF, apenas números:\")\n        if new_cpf.lower() == \"sair\":\n            sys.exit()\n        if utils.is_valid_cpf(new_cpf):\n            hard_cpf = utils.clean_cpf_if_contains_dash_or_dot(new_cpf)\n        valid_cpf = utils.is_valid_cpf(hard_cpf)\n    return utils.apply_cpf_format(hard_cpf)\n\n\ndef confirm_cpf(cpf):\n    print(f\"Confirma o CPF: {cpf}?\")\n    print(f\"S:Sim N:Não\")\n    option = input(\"Digite sua opção:\").upper().replace(\" \", \"\")\n    while not utils.is_yes_or_no(option):\n        option = input(\"Desculpe, não entendi. Tente novamente: \").upper()\n    if option == \"S\":\n        return True\n    else:\n        return False\n\n\ndef get_user():\n    Cp.printBlue(\"******* Sink Banking and Services inc. *******\")\n    print(\"Bem-vindo(a) ao Sink Banking and Services, os melhores serviços bancários para você.\")\n    print()\n    print(\"Você já tem uma conta? \\nS:Sim N:Não\")\n    user = SignIn.sign_in() if ask_user_has_account() else SignUp.sign_up()\n    return user\n\n\ndef ask_user_has_account():\n    option = input(\"Digite sua opção:\").upper().replace(\" \", \"\")\n    while not utils.is_yes_or_no(option):\n        option = input(\"Desculpe, não entendi. Tente novamente: \").upper()\n    if option == \"S\":\n        return True\n    else:\n        return False\n\n\nif __name__ == '__main__':\n    start_system()\n","repo_name":"vicgles/SinkBanking","sub_path":"MainApplication.py","file_name":"MainApplication.py","file_ext":"py","file_size_in_byte":2354,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"72079722705","text":"import re\n\nsplit_data = '\\n' # Doing this to strip each line by default\ncompleted = True\nraw_data = None # Not To be touched\n\ndef part1(data):\n\tpossible = 0\n\tfor line in data:\n\t\t# This will get all the numbers and sort them from smallest to largest\n\t\tnums = sorted(int(x) for x in re.split(r'\\s+', line))\n\t\t# Next to validate triangles, the smaller 2 sides must be greater than the larger side\n\t\tif nums[0] + nums[1] > nums[2]:\n\t\t\tpossible += 1\n\treturn possible\n\ndef part2(data):\n\t# We simply have to rearrange the given data to solve this part\n\t# Converting the rows to columns\n\tnew_data = [[], [], []]\n\tfor line in data:\n\t\tnums = re.split(r'\\s+', line)\n\t\tfor i in range(3):\n\t\t\tnew_data[i].append(int(nums[i])) \n\t# Flattening the list\n\tnew_data = [x for y in new_data for x in y] # IDK how this works but it does\n\t# Now we can chunk the list so we can have lists of sides\n\tchunks = [new_data[i:i+3] for i in range(0, len(new_data), 3)]\n\t# Now we can validate the triangles\n\tpossible = 0\n\tfor chunk in chunks:\n\t\t# This will get all the numbers and sort them from smallest to largest\n\t\tnums = sorted(chunk)\n\t\t# Next to validate triangles, the smaller 2 sides must be greater than the larger side\n\t\tif nums[0] + nums[1] > nums[2]:\n\t\t\tpossible += 1\n\treturn possible\n","repo_name":"fschatbot/Advent-Calendar-Python","sub_path":"2016/3.py","file_name":"3.py","file_ext":"py","file_size_in_byte":1263,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"48"}
+{"seq_id":"2985199078","text":"#!/usr/bin/env python3\n\n\"\"\"Useless comment\"\"\"\n\nimport tensorflow.keras as K\ninception_block = __import__('0-inception_block').inception_block\n\n\ndef inception_network():\n    \"\"\"\n    Create the GoogleNet model\n    :return: The GoogleNet model\n    \"\"\"\n    x = K.Input(shape=(224, 224, 3))\n\n    conv_1 = K.layers.Conv2D(filters=64,\n                             kernel_size=(7, 7),\n                             strides=(2, 2),\n                             padding=\"same\",\n                             activation=\"relu\")(x)\n    max_pool_1 = K.layers.MaxPooling2D(pool_size=(3, 3),\n                                       strides=(2, 2),\n                                       padding=\"same\")(conv_1)\n\n    conv_2 = K.layers.Conv2D(filters=64,\n                             kernel_size=(1, 1),\n                             padding=\"same\",\n                             activation=\"relu\")(max_pool_1)\n    conv_3 = K.layers.Conv2D(filters=192,\n                             kernel_size=(3, 3),\n                             padding=\"same\",\n                             activation=\"relu\")(conv_2)\n    max_pool_2 = K.layers.MaxPooling2D(pool_size=(3, 3),\n                                       strides=(2, 2),\n                                       padding=\"same\")(conv_3)\n\n    inception_2a = inception_block(max_pool_2, [64, 96, 128, 16, 32, 32])\n    inception_2b = inception_block(inception_2a, [128, 128, 192, 32, 96, 64])\n\n    max_pool_3 = K.layers.MaxPooling2D(pool_size=(3, 3),\n                                       strides=(2, 2),\n                                       padding=\"same\")(inception_2b)\n\n    inception_4a = inception_block(max_pool_3, [192, 96, 208, 16, 48, 64])\n    inception_4b = inception_block(inception_4a, [160, 112, 224, 24, 64, 64])\n    inception_4c = inception_block(inception_4b, [128, 128, 256, 24, 64, 64])\n    inception_4d = inception_block(inception_4c, [112, 144, 288, 32, 64, 64])\n    inception_4e = inception_block(inception_4d, [256, 160, 320, 32, 128, 128])\n\n    max_pool_4 = K.layers.MaxPooling2D(pool_size=(3, 3),\n                                       strides=(2, 2),\n                                       padding=\"same\")(inception_4e)\n\n    inception_5a = inception_block(max_pool_4, [256, 160, 320, 32, 128, 128])\n    inception_5b = inception_block(inception_5a, [384, 192, 384, 48, 128, 128])\n\n    avg_pooling = K.layers.AvgPool2D(pool_size=(7, 7),\n                                     padding=\"same\")(inception_5b)\n\n    dropout = K.layers.Dropout(rate=0.4)(avg_pooling)\n\n    fc_1 = K.layers.Dense(units=1000, activation=\"softmax\")(dropout)\n\n    return K.models.Model(inputs=x, outputs=fc_1)\n","repo_name":"Camaltra/holbertonschool-machine_learning","sub_path":"supervised_learning/deep_cnns/1-inception_network.py","file_name":"1-inception_network.py","file_ext":"py","file_size_in_byte":2621,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"70846704785","text":"from collections import deque\nINF = 10**8\nh, w = map(int,input().split())\ngraph = [list(input()) for _ in range(h)]\ninform_graph = [[[INF,INF] for _ in range(w)] for _ in range(h)]\nvisited = [[[False,False] for _ in range(w)] for _ in range(h)]\n\ndx = [0,-1,1,0]\ndy = [1,0,0,-1]\nd = deque()\nd.append((0,0,0))\ninform_graph[0][0][0] = 1\nvisited[0][0][0] = True\nwhile d:\n  i,j,cnt = d.popleft()\n  for idx in range(4):\n    nx = i + dx[idx]\n    ny = j + dy[idx]\n    if 0 <= nx < h and 0 <= ny < w:\n      if graph[nx][ny] == '0' and visited[nx][ny][cnt] == False:\n        visited[nx][ny][cnt] = True\n        inform_graph[nx][ny][cnt] = inform_graph[i][j][cnt] + 1\n        d.append((nx,ny,cnt))\n      elif graph[nx][ny] == '1' and cnt == 0 and visited[nx][ny][1] == False:\n        visited[nx][ny][1] = True\n        inform_graph[nx][ny][1] = inform_graph[i][j][0] + 1\n        d.append((nx,ny,1))\n\nanswer = min(inform_graph[h-1][w-1])\nif answer == INF:\n  print(-1)\nelse:\n  print(answer)","repo_name":"hvvany/TIL","sub_path":"Algorithm/Baekjoon/code_folder/2206.py","file_name":"2206.py","file_ext":"py","file_size_in_byte":976,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"48"}
+{"seq_id":"7023505021","text":"# -*- coding: cp1251 -*-\n\nprint(\"¬ведите сумму:\", end=\" \")\nmoney = int(input())\n\natm = {1000: 5, 100: 3, 50: 1, 10: 2}\n\nsum = 0\noutput = \"\"\n\nfor k in atm.keys():\n    for i in range(1, atm[k] + 1):\n        if sum == money:\n            output += str(k) + \"*\" + str(i - 1)\n            break\n        elif i == atm[k]:\n            output += str(k) + \"*\" + str(i)\n        sum += k\n\n    if sum == money:\n        break\n    output += \" + \"\n\nif sum != money:\n     print(\"ќпераци¤ не может быть выполнена!\")\n     output = \"\"\nelse:\n    print(output)\n","repo_name":"Oleja302/Python_Labs","sub_path":"Lab_1/9.py","file_name":"9.py","file_ext":"py","file_size_in_byte":538,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"48"}
+{"seq_id":"11700839504","text":"from __future__ import division, print_function\n\n# Import Python modules\nimport os\nimport sys\n\n# Import Broadband modules\nimport bband_utils\nimport bbp_formatter\nimport arias_duration\nimport plot_seismograms\nimport plot_rotd50\nfrom install_cfg import InstallCfg\nfrom station_list import StationList\n\nclass GenPlots(object):\n\n    def __init__(self, i_r_stations, i_format,\n                 i_comparison_label, sim_id=0):\n        self.sim_id = sim_id\n        self.r_stations = i_r_stations\n        self.format = i_format\n        self.comp_label = i_comparison_label\n\n    def run(self):\n        print(\"Generating Plots\".center(80, '-'))\n\n        # Initialize basic variables\n        install = InstallCfg.getInstance()\n        sim_id = self.sim_id\n        sta_base = os.path.basename(os.path.splitext(self.r_stations)[0])\n\n        self.log = os.path.join(install.A_OUT_LOG_DIR,\n                                str(sim_id),\n                                \"%d.gen_plots.log\" % (sim_id))\n\n\n        # Input, tmp, and output directories\n        a_tmpdir = os.path.join(install.A_TMP_DATA_DIR, str(sim_id))\n        a_outdir = os.path.join(install.A_OUT_DATA_DIR, str(sim_id))\n        a_tmpdir_seis = os.path.join(install.A_TMP_DATA_DIR, str(sim_id),\n                                     \"obs_seis_%s\" % (sta_base))\n\n        # Station file\n        a_statfile = os.path.join(install.A_IN_DATA_DIR,\n                                  str(sim_id),\n                                  self.r_stations)\n        # List of observed seismogram files\n        filelist = os.listdir(a_tmpdir_seis)\n\n        slo = StationList(a_statfile)\n        site_list = slo.getStationList()\n\n        for site in site_list:\n            stat = site.scode\n\n            # Look for the files we need\n            bbpfile = os.path.join(a_tmpdir_seis, \"%s.bbp\" % stat)\n            expected_file = os.path.join(a_outdir, \"%d.%s.vel.bbp\" %\n                                         (sim_id, stat))\n            if (not os.path.exists(expected_file) or\n                not os.path.exists(bbpfile)):\n                # just skip this station\n                continue\n\n            print(\"==> Plotting seismogram comparison for station: %s\" % (stat))\n            if self.format == 'vel':\n                # We have velocity, nothing we need to do\n                filename1 = bbpfile\n            elif self.format == 'acc':\n                # We have acceleration, must integrate first\n                # Create path names and check if their sizes are within bounds\n                nsfile = os.path.join(a_tmpdir, \"temp.acc.000\")\n                ewfile = os.path.join(a_tmpdir, \"temp.acc.090\")\n                udfile = os.path.join(a_tmpdir, \"temp.acc.ver\")\n                bband_utils.check_path_lengths([nsfile, ewfile, udfile],\n                                               bband_utils.GP_MAX_FILENAME)\n\n                cmd = (\"%s/wcc2bbp \" % (install.A_GP_BIN_DIR) +\n                       \"nsfile=%s ewfile=%s udfile=%s \" %\n                       (nsfile, ewfile, udfile) +\n                       \"wcc2bbp=0 < %s >> %s 2>&1\" %\n                       (bbpfile, self.log))\n                bband_utils.runprog(cmd, abort_on_error=True, print_cmd=False)\n\n                for comp in ['000', '090', 'ver']:\n                    # Create path names and check if their sizes are\n                    # within bounds\n                    filein = os.path.join(a_tmpdir,\n                                          \"temp.acc.%s\" % (comp))\n                    fileout = os.path.join(a_tmpdir,\n                                           \"temp.vel.%s\" % (comp))\n\n                    bband_utils.check_path_lengths([filein, fileout],\n                                                   bband_utils.GP_MAX_FILENAME)\n\n                    cmd = (\"%s/integ_diff integ=1 \" % (install.A_GP_BIN_DIR) +\n                           \"filein=%s fileout=%s >> %s 2>&1\" %\n                           (filein, fileout, self.log))\n                    bband_utils.runprog(cmd, abort_on_error=True,\n                                        print_cmd=False)\n\n                # Create path names and check if their sizes are within bounds\n                nsfile = os.path.join(a_tmpdir, \"temp.vel.000\")\n                ewfile = os.path.join(a_tmpdir, \"temp.vel.090\")\n                udfile = os.path.join(a_tmpdir, \"temp.vel.ver\")\n                vel_bbp_file = os.path.join(a_tmpdir, \"temp.%s.vel\" % stat)\n\n                bband_utils.check_path_lengths([nsfile, ewfile, udfile],\n                                               bband_utils.GP_MAX_FILENAME)\n\n                cmd = (\"%s/wcc2bbp wcc2bbp=1 \" % install.A_GP_BIN_DIR +\n                       \"nsfile=%s ewfile=%s udfile=%s > %s 2>> %s\" %\n                       (nsfile, ewfile, udfile, vel_bbp_file, self.log))\n                bband_utils.runprog(cmd, abort_on_error=True, print_cmd=False)\n                filename1 = vel_bbp_file\n\n            # Generate arias duration files for calculated data\n            calc_acc = os.path.join(a_outdir, \"%d.%s.acc.bbp\" %\n                                    (sim_id, stat))\n            calc_peer_n = os.path.join(a_tmpdir, \"%d.%s_N.acc\" %\n                                       (sim_id, stat))\n            calc_peer_e = os.path.join(a_tmpdir, \"%d.%s_E.acc\" %\n                                       (sim_id, stat))\n            calc_peer_z = os.path.join(a_tmpdir, \"%d.%s_Z.acc\" %\n                                       (sim_id, stat))\n            # Convert calculated acc seismogram into peer format\n            bbp_formatter.bbp2peer(calc_acc, calc_peer_n,\n                                   calc_peer_e, calc_peer_z)\n\n            # Now calculate arias duration for each component\n            for comp in [\"N\", \"E\", \"Z\"]:\n                file_in = os.path.join(a_tmpdir, \"%d.%s_%s.acc\" %\n                                       (sim_id, stat, comp))\n                file_out = os.path.join(a_tmpdir, \"%d.%s_%s.arias\" %\n                                        (sim_id, stat, comp))\n                arias_duration.ad_from_acc(file_in, file_out)\n\n            # Generate arias duration files for observed data\n            obs_acc = os.path.join(a_tmpdir_seis, \"%s.bbp\" % stat)\n            obs_peer_n = os.path.join(a_tmpdir, \"obs.%s_N.acc\" %\n                                      (stat))\n            obs_peer_e = os.path.join(a_tmpdir, \"obs.%s_E.acc\" %\n                                      (stat))\n            obs_peer_z = os.path.join(a_tmpdir, \"obs.%s_Z.acc\" %\n                                      (stat))\n            # Convert observed acc seismogram into peer format\n            bbp_formatter.bbp2peer(obs_acc, obs_peer_n,\n                                   obs_peer_e, obs_peer_z)\n\n            # Now calculate arias duration for each component\n            for comp in [\"N\", \"E\", \"Z\"]:\n                file_in = os.path.join(a_tmpdir, \"obs.%s_%s.acc\" %\n                                       (stat, comp))\n                file_out = os.path.join(a_tmpdir, \"obs.%s_%s.arias\" %\n                                        (stat, comp))\n                arias_duration.ad_from_acc(file_in, file_out)\n\n            # Plot seismograms with arias duration\n            filename2 = os.path.join(a_outdir, \"%d.%s.vel.bbp\" %\n                                     (sim_id, stat))\n            outfile = os.path.join(a_outdir, \"%s_%d_%s_overlay.png\" %\n                                   (self.comp_label,\n                                    sim_id, stat))\n            obs_arias_n = os.path.join(a_tmpdir, \"obs.%s_N.arias\" %\n                                       (stat))\n            obs_arias_e = os.path.join(a_tmpdir, \"obs.%s_E.arias\" %\n                                       (stat))\n            obs_arias_z = os.path.join(a_tmpdir, \"obs.%s_Z.arias\" %\n                                       (stat))\n            calc_arias_n = os.path.join(a_tmpdir, \"%d.%s_N.arias\" %\n                                        (sim_id, stat))\n            calc_arias_e = os.path.join(a_tmpdir, \"%d.%s_E.arias\" %\n                                        (sim_id, stat))\n            calc_arias_z = os.path.join(a_tmpdir, \"%d.%s_Z.arias\" %\n                                        (sim_id, stat))\n\n            plot_seismograms.plot_overlay_with_arias(stat, filename1,\n                                                     filename2,\n                                                     obs_arias_n,\n                                                     obs_arias_e,\n                                                     obs_arias_z,\n                                                     calc_arias_n,\n                                                     calc_arias_e,\n                                                     calc_arias_z,\n                                                     self.comp_label,\n                                                     \"run %d\" % sim_id,\n                                                     outfile)\n\n        # Now create rd50 comparison plots\n        for site in site_list:\n            stat = site.scode\n            print(\"==> Plotting RotD50 comparison for station: %s\" % (stat))\n\n            # Now process rd50 files\n            expected_rd50_file = os.path.join(a_outdir, \"%d.%s.rd50\" %\n                                              (sim_id, stat))\n            if not os.path.exists(expected_rd50_file):\n                # just skip it\n                print(\"Skipping rotd50/psa5 for station %s...\" % (stat))\n                continue\n\n            # See if .rd50 file exists for comparison. If it don't\n            # exist, skip it\n            rd50_file = None\n            if (\"%s.rd50\" % (stat)) in filelist:\n                rd50_file = \"%s.rd50\" % (stat)\n            else:\n                # Skip this station\n                continue\n\n            # Plot rotd50 results\n            rd50_filename1 = os.path.join(a_tmpdir_seis, rd50_file)\n            rd50_filename2 = os.path.join(a_outdir, \"%d.%s.rd50\" %\n                                          (sim_id, stat))\n            outfile = os.path.join(a_outdir, \"%s_%d_%s_rotd50.png\" %\n                                   (self.comp_label, sim_id, stat))\n\n            plot_rotd50.plot_rd50(stat, rd50_filename1, rd50_filename2,\n                                  self.comp_label, sim_id, outfile,\n                                  site.low_freq_corner,\n                                  site.high_freq_corner,\n                                  quiet=True)\n\n        print(\"Generating Plots Completed\".center(80, '-'))\n\nif __name__ == \"__main__\":\n    PROG_BASE = os.path.basename(sys.argv[0])\n    if len(sys.argv) != 6:\n        print(\"Usage: %s \" % (PROG_BASE) +\n              \"station_list obs_dir obs_format comp_label sim_id\")\n        sys.exit(1)\n    print(\"Testing Module: %s\" % (PROG_BASE))\n    ME = GenPlots(sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4],\n                  sim_id=int(sys.argv[5]))\n    ME.run()\n    sys.exit(0)\n","repo_name":"SCECcode/bbp","sub_path":"bbp/comps/gen_plots.py","file_name":"gen_plots.py","file_ext":"py","file_size_in_byte":10881,"program_lang":"python","lang":"en","doc_type":"code","stars":39,"dataset":"github-code","pt":"48"}