diff --git "a/1999.jsonl" "b/1999.jsonl"
new file mode 100644--- /dev/null
+++ "b/1999.jsonl"
@@ -0,0 +1,643 @@
+{"seq_id":"543859537","text":"# Copyright (c) The Diem Core Contributors\n# SPDX-License-Identifier: Apache-2.0\n\n\"\"\"This module defines util functions for encoding and decoding offchain specific JWS messages.\n\nThe `serialize` and `deserialize` functions handle JWS message with the following requirements:\n\n  1. Protected header must be `{\"alg\": \"EdDSA\"}`\n  2. Characters encoding must be `utf-8`\n  3. JWS encoding must be `compact`\n\n\"\"\"\n\nimport base64, json, typing\n\nfrom . import CommandRequestObject, CommandResponseObject, to_json, from_json\n\n\nPROTECTED_HEADER: bytes = base64.urlsafe_b64encode(b'{\"alg\":\"EdDSA\"}')\nENCODING: str = \"UTF-8\"\n\nT = typing.TypeVar(\"T\")\n\n\ndef serialize(\n    obj: typing.Union[CommandRequestObject, CommandResponseObject],\n    sign: typing.Callable[[bytes], bytes],\n    headers: typing.Optional[typing.Dict[str, typing.Any]] = None,\n) -> bytes:\n    return serialize_string(to_json(obj), sign, headers=headers)\n\n\ndef deserialize(\n    msg: bytes,\n    klass: typing.Type[T],\n    verify: typing.Callable[[bytes, bytes], None],\n) -> T:\n    decoded_body, sig, signing_msg = deserialize_string(msg)\n    verify(sig, signing_msg)\n    return from_json(decoded_body, klass)\n\n\ndef serialize_string(\n    json: str, sign: typing.Callable[[bytes], bytes], headers: typing.Optional[typing.Dict[str, typing.Any]] = None\n) -> bytes:\n    header = PROTECTED_HEADER if headers is None else encode_headers(headers)\n    payload = base64.urlsafe_b64encode(json.encode(ENCODING))\n    sig = sign(signing_message(payload, header=header))\n    return b\".\".join([header, payload, base64.urlsafe_b64encode(sig)])\n\n\ndef deserialize_string(msg: bytes) -> typing.Tuple[str, bytes, bytes]:\n    parts = msg.split(b\".\")\n    if len(parts) != 3:\n        raise ValueError(\n            \"invalid JWS compact message: %s, expect 3 parts: <header>.<payload>.<signature>\" % msg.decode(ENCODING)\n        )\n\n    header, body, sig = parts\n    header_text = decode(header).decode(ENCODING)\n    try:\n        protected_headers = json.loads(header_text)\n    except json.decoder.JSONDecodeError as e:\n        raise ValueError(f\"invalid JWS message header: {header_text}\") from e\n\n    if not isinstance(protected_headers, dict) or protected_headers.get(\"alg\") != \"EdDSA\":\n        raise ValueError(f\"invalid JWS message header: {header}, expect alg is EdDSA\")\n\n    return (\n        decode(body).decode(ENCODING),\n        decode(sig),\n        signing_message(body, header=header),\n    )\n\n\ndef signing_message(payload: bytes, header: bytes) -> bytes:\n    return b\".\".join([header, payload])\n\n\ndef encode_headers(headers: typing.Dict[str, typing.Any]) -> bytes:\n    return base64.urlsafe_b64encode(json.dumps(headers).encode(ENCODING))\n\n\ndef decode(msg: bytes) -> bytes:\n    return base64.urlsafe_b64decode(fix_padding(msg))\n\n\ndef fix_padding(input: bytes) -> bytes:\n    return input + b\"=\" * (4 - (len(input) % 4))\n","sub_path":"src/diem/offchain/jws.py","file_name":"jws.py","file_ext":"py","file_size_in_byte":2857,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"441518196","text":"#!/usr/bin/env python\n\nimport rospy\nimport cv2\nimport sys\nimport numpy as np\n'''\nlower_yellow = np.array([20,0,0])\nupper_yellow = np.array([45,255,255])\nlower_red_1 = np.array([0,30,25])\nupper_red_1 = np.array([20,255,255])\nlower_red_2 = np.array([140,0,0])\nupper_red_2 = np.array([179,255,255])\n'''\nlower_yellow = np.array([20,0,0])\nupper_yellow = np.array([29,255,255])\nlower_red_1 = np.array([0,20,0])\nupper_red_1 = np.array([9,255,255])\nlower_red_2 = np.array([166,0,0])\nupper_red_2 = np.array([179,255,255])\n\n\nkernal = np.ones((5, 5), np.uint8)\n\ndef findYellowCnts(img):\n    mask = cv2.inRange(img, lower_yellow, upper_yellow)\n    blur = cv2.GaussianBlur(mask, (11,11),0)\n    canny = cv2.Canny(blur, 30, 150)\n    closing = cv2.morphologyEx(canny, cv2.MORPH_CLOSE, kernal)\n    opening = cv2.morphologyEx(closing, cv2.MORPH_CLOSE, kernal)\n    (_,cnts, _) = cv2.findContours(opening, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)\n    #print(\"Count {} Yellow\". format(len(cnts)))\n    if(len(cnts) > 0):\n        cnts = sorted(cnts, key = cv2.contourArea, reverse = True)\n        return cnts[0]\n    else:\n        return None\n\ndef findRedCnts(img):\n    mask_1 = cv2.inRange(img, lower_red_1, upper_red_1)\n    mask_2 = cv2.inRange(img, lower_red_2, upper_red_2)\n    mask = mask_1 + mask_2\n    blur = cv2.GaussianBlur(mask, (11,11),0)\n    canny = cv2.Canny(blur, 30, 150)\n    closing = cv2.morphologyEx(canny, cv2.MORPH_CLOSE, kernal)\n    opening = cv2.morphologyEx(closing, cv2.MORPH_CLOSE, kernal)\n    (_,cnts, _) = cv2.findContours(opening, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)\n    #print(\"Count {} Red\". format(len(cnts)))\n    if(len(cnts) > 0):\n        cnts = sorted(cnts, key = cv2.contourArea, reverse = True)\n        return cnts[0]\n    else:\n        return None\n        \ndef findCentroid(cnts):\n    M = cv2.moments(cnts)\n    cx = int(M[\"m10\"]/(M[\"m00\"]+1e-6))\n    cy = int(M[\"m01\"]/(M[\"m00\"]+1e-6))\n    return cx, cy\n\n\ndef focusCenter(img, cx_y, cx_r):\n    bar_cx = (float(cx_y) + float(cx_r)) / 2\n    view_cx = img.shape[1]/2\n    delta_lr = (view_cx - bar_cx) / img.shape[1]\n    #print(cx_y,cx_r,bar_cx,view_cx,img.shape[1])\n    \n    '''\n    print(\"delta_lr: {}\".format(delta_lr))\n    if(delta_lr > 20):\n        print(\"GO LEFT\")\n    elif(delta_lr < -20):\n        print(\"GO RIGHT\")\n    else:\n        print(\"GO FORWARD\")  \n    '''\n    return delta_lr\n\ndef headTilt(img, cy_y, cy_r):\n    bar_cy = (cy_y + cy_r) / 2\n    view_cy = img.shape[0]/2\n    delta_head = (view_cy - bar_cy) / img.shape[0] \n    '''\n    print(\"delta_head: {}\".format(delta_head))\n\n    if(delta_head > 10):\n        print(\"HEAD UP\")\n    elif(delta_head < -10):\n        print(\"HEAD DOWN\")\n    else:\n        print(\"HEAD KEEP\")  \n    '''\n    return delta_head\n\ndef slope(cx_y, cy_y, cx_r, cy_r):\n    \n    bar_slope = (cy_y - cy_r) / (cx_y - cx_r + 1e-6)\n    '''\n    print(\"bar_slope: {}\".format(bar_slope))\n    if(bar_slope > 0.1):\n        print(\"Turn facing right\")\n    elif(bar_slope < -0.1):\n        print(\"Turn facing left\")\n    else:\n        print(\"Keep facing forward\")\n    '''\n    return bar_slope\n\n        \n'''\ncap = cv2.VideoCapture(0)\nwhile not rospy.is_shutdown():\n    ret, frame = cap.read()\n    frame = cv2.resize(frame, (0,0),fx=0.5,fy=0.5, interpolation=cv2.INTER_CUBIC)\n    hsv_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n    cnts_yellow = findYellowCnts(hsv_frame)\n    cnts_red = findRedCnts(hsv_frame)\n    cx_y, cy_y = findCentroid(cnts_yellow)\n    cx_r, cy_r = findCentroid(cnts_red)\n\n    \n    focusCenter(hsv_frame, cx_y, cx_r)\n    headTilt(hsv_frame, cy_y, cy_r)\n    \n    slope(cx_y, cy_y, cx_r, cy_r)\n    \n    cv2.drawContours(hsv_frame, cnts_yellow, -1, (255,0,0), 2)\n    cv2.drawContours(hsv_frame, cnts_red, -1, (10,235,290), 2)\n    \n    cv2.circle(hsv_frame, (int((cx_y + cx_r) / 2),  int((cy_y + cy_r) / 2)),5,(130, 40, 255), -1)\n    cv2.circle(hsv_frame, (int(frame.shape[1]/2), int(frame.shape[0]/2)),5,(130, 40, 255), -1)\n    \n    cv2.circle(hsv_frame, (cx_y, cy_y),5,(130, 40, 255), -1)\n    cv2.circle(hsv_frame, (cx_r, cy_r),5,(130, 40, 255), -1)\n\n    cv2.imshow('Current view',hsv_frame)\n       \n    cv2.waitKey(33)\n'''    \n","sub_path":"src/fira_weightlifting/scripts/vision.py","file_name":"vision.py","file_ext":"py","file_size_in_byte":4131,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"298567665","text":"from multiprocessing import Process\nfrom multiprocessing import Queue\nimport os\nimport time\nimport random\n\n#写数据进程执行的代码\ndef write(q):\n\tprint('Process to write : %s' % os.getpid())\n\tfor value in ['A', 'B', 'C']:\n\t\tprint('Put %s to queue...' % value)\n\t\tq.put(value)\n\t\ttime.sleep(random.random())\n\t\t\n\t\t\n#读数据进程执行的代码\ndef read(q):\n\tprint('Process to read ：%s' % os.getpid())\n\twhile(True):\n\t\tvalue = q.get(True)\n\t\tprint(\"Get %s from queeu.\" % value)\n\t\t\nif __name__ == '__main__':\n\t\n\t#父进程创建queue，并开始各个进程：\n\tq = Queue()\n\tpw = Process(target = write, args = (q,))\n\tpr = Process(target = read, args = (q,))\n\tpw.start()\n\tpr.start()\n\tpw.join()\n\tpr.terminate()#强行停止\n\t","sub_path":"process/process_queue.py","file_name":"process_queue.py","file_ext":"py","file_size_in_byte":729,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"216352590","text":"import argparse\nimport sys\nimport os\nimport cv2\nimport warnings\n\nwarnings.filterwarnings(\"ignore\", category=FutureWarning)\n\ntry:\n  import tensorflow as tf\n  from tensorflow import keras\n  # NB: Eager execution needs to be disabled before any model loading.\n  tf.compat.v1.disable_eager_execution ()\nexcept:\n  import keras\n\nfrom utils import *\n\ndef deepconcolic(test_object, outs):\n  report_args = { 'save_input_func': test_object.save_input_func,\n                  'inp_ub': test_object.inp_ub,\n                  'outs': outs}\n  if test_object.criterion=='nc':       ## neuron cover\n    from nc import setup as nc_setup\n    if test_object.norm=='linf':\n      from pulp_norms import LInfPulp\n      from nc_pulp import NcPulpAnalyzer\n      engine = nc_setup (test_object = test_object,\n                         setup_analyzer = NcPulpAnalyzer,\n                         input_metric = LInfPulp ())\n    elif test_object.norm=='l0':\n      from nc_l0 import NcL0Analyzer\n      engine = nc_setup (test_object = test_object,\n                         setup_analyzer = NcL0Analyzer,\n                         input_shape = test_object.raw_data.data[0].shape,\n                         eval_batch = test_object.eval_batch)\n    else:\n      print('\\n not supported norm... {0}\\n'.format(test_object.norm))\n      sys.exit(0)\n    engine.run (**report_args)\n  elif test_object.criterion=='ssc':\n    from ssc import SScAttackBasedAnalyzer, setup as ssc_setup\n    engine = ssc_setup (test_object = test_object,\n                        setup_analyzer = SScAttackBasedAnalyzer,\n                        ref_data = test_object.raw_data)\n    engine.run (**report_args)\n  elif test_object.criterion=='ssclp':\n    from pulp_norms import LInfPulp\n    from mcdc_pulp import SScPulpAnalyzer\n    from ssc import setup as ssc_setup\n    engine = ssc_setup (test_object = test_object,\n                        setup_analyzer = SScPulpAnalyzer,\n                        input_metric = LInfPulp ())\n    engine.run (**report_args)\n  elif test_object.criterion=='svc':\n    outs = setup_output_dir (outs)\n    from run_ssc import run_svc\n    print('\\n== Starting DeepConcolic tests for {0} =='.format (test_object))\n    run_svc(test_object, outs)\n  else:\n    print('\\n not supported coverage criterion... {0}\\n'.format(test_object.criterion))\n    sys.exit(0)\n\n\ndef main():\n\n  parser=argparse.ArgumentParser(description='Concolic testing for neural networks' )\n  parser.add_argument(\n    '--model', dest='model', default='-1', help='the input neural network model (.h5)')\n  parser.add_argument(\"--inputs\", dest=\"inputs\", default=\"-1\",\n                    help=\"the input test data directory\", metavar=\"DIR\")\n  parser.add_argument(\"--outputs\", dest=\"outputs\", default=\"-1\",\n                    help=\"the outputput test data directory\", metavar=\"DIR\")\n  parser.add_argument(\"--training-data\", dest=\"training_data\", default=\"-1\",\n                    help=\"the extra training dataset\", metavar=\"DIR\")\n  parser.add_argument(\"--criterion\", dest=\"criterion\", default=\"nc\",\n                    help=\"the test criterion\", metavar=\"nc, ssc...\")\n  parser.add_argument(\"--labels\", dest=\"labels\", default=\"-1\",\n                    help=\"the default labels\", metavar=\"FILE\")\n  parser.add_argument(\"--mnist-dataset\", dest=\"mnist\", help=\"MNIST dataset\", action=\"store_true\")\n  parser.add_argument(\"--cifar10-dataset\", dest=\"cifar10\", help=\"CIFAR-10 dataset\", action=\"store_true\")\n  parser.add_argument(\"--vgg16-model\", dest='vgg16', help=\"vgg16 model\", action=\"store_true\")\n  parser.add_argument(\"--norm\", dest=\"norm\", default=\"l0\",\n                    help=\"the norm metric\", metavar=\"linf, l0\")\n  parser.add_argument(\"--input-rows\", dest=\"img_rows\", default=\"224\",\n                    help=\"input rows\", metavar=\"INT\")\n  parser.add_argument(\"--input-cols\", dest=\"img_cols\", default=\"224\",\n                    help=\"input cols\", metavar=\"INT\")\n  parser.add_argument(\"--input-channels\", dest=\"img_channels\", default=\"3\",\n                    help=\"input channels\", metavar=\"INT\")\n  parser.add_argument(\"--cond-ratio\", dest=\"cond_ratio\", default=\"0.01\",\n                    help=\"the condition feature size parameter (0, 1]\", metavar=\"FLOAT\")\n  parser.add_argument(\"--top-classes\", dest=\"top_classes\", default=\"1\",\n                    help=\"check the top-xx classifications\", metavar=\"INT\")\n  parser.add_argument(\"--layer-index\", dest=\"layer_indexes\",\n                      nargs=\"+\", type=int,\n                      help=\"to test a particular layer\", metavar=\"INT\")\n  parser.add_argument(\"--feature-index\", dest=\"feature_index\", default=\"-1\",\n                    help=\"to test a particular feature map\", metavar=\"INT\")\n\n  args=parser.parse_args()\n\n\n  criterion=args.criterion\n  norm=args.norm\n  cond_ratio=float(args.cond_ratio)\n  top_classes=int(args.top_classes)\n\n  raw_data=None\n  img_rows, img_cols, img_channels = int(args.img_rows), int(args.img_cols), int(args.img_channels)\n\n  dnn = None\n  inp_ub = 1\n  save_input = None\n  if args.model!='-1':\n    dnn = keras.models.load_model (args.model)\n    dnn.summary()\n    save_input = save_an_image\n  elif args.vgg16:\n    dnn = keras.applications.VGG16 ()\n    inp_ub = 255\n    dnn.summary()\n    save_input = save_an_image\n  else:\n    print (' \\n == Please specify the input neural network == \\n')\n    sys.exit(0)\n\n  if args.inputs!='-1':\n    \n    xs=[]\n    print ('Loading input data... ', end = '', flush = True)\n    for path, subdirs, files in os.walk(args.inputs):\n      for name in files:\n        fname=(os.path.join(path, name))\n        if fname.endswith('.jpg') or fname.endswith('.png'):\n          try:\n            image = cv2.imread(fname)\n            image = cv2.resize(image, (img_rows, img_cols))\n            image = image.astype('float')\n            xs.append((image))\n          except: pass\n    x_test = np.asarray(xs)\n    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, img_channels)\n    raw_data = raw_datat(x_test, None)\n    print (len(xs), 'loaded.')\n  elif args.mnist:\n    from keras.datasets import mnist\n    print ('Loading MNIST data... ', end = '', flush = True)\n    img_rows, img_cols, img_channels = 28, 28, 1\n    (x_train, y_train), (x_test, y_test) = mnist.load_data()\n    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, img_channels)\n    x_test = x_test.astype('float32')\n    x_test /= 255\n    raw_data = raw_datat(x_test, y_test)\n    print ('done.')\n  elif args.cifar10:\n    from keras.datasets import cifar10\n    print ('Loading CIFAR10 data... ', end='', flush = True)\n    img_rows, img_cols, img_channels = 32, 32, 3\n    (x_train, y_train), (x_test, y_test) = cifar10.load_data()\n    x_test=x_test[0:3000]\n    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, img_channels)\n    x_test = x_test.astype('float32')\n    x_test /= 255\n    raw_data = raw_datat(x_test, y_test)\n    print ('done.')\n  else:\n    print (' \\n == Please input dataset == \\n')\n    sys.exit(0)\n\n  outs=None\n  if args.outputs!='-1':\n    outs=args.outputs\n  else:\n    print (' \\n == Please specify the output directory == \\n')\n    sys.exit(0)\n\n\n  test_object=test_objectt(dnn, raw_data, criterion, norm)\n  test_object.cond_ratio = cond_ratio\n  test_object.top_classes = top_classes\n  test_object.inp_ub = inp_ub\n  test_object.save_input_func = save_input\n  if args.layer_indexes is not None:\n    try:\n      test_object.layer_indices=[]\n      for layer_index in tuple(args.layer_indexes):\n        layer = dnn.get_layer (index = int (layer_index))\n        test_object.layer_indices.append (dnn.layers.index (layer))\n    except ValueError as e:\n      sys.exit (e)\n    if args.feature_index!='-1':\n      test_object.feature_indices=[]\n      test_object.feature_indices.append(int(args.feature_index))\n      print ('feature index specified:', test_object.feature_indices)\n  if args.training_data!='-1':\n    tdata=[]\n    print ('To load the extra training data...')\n    for path, subdirs, files in os.walk(args.training_data):\n      for name in files:\n        fname=(os.path.join(path, name))\n        if fname.endswith('.jpg') or fname.endswith('.png'):\n          try:\n            image = cv2.imread(fname)\n            image = cv2.resize(image, (img_rows, img_cols))\n            image=image.astype('float')\n            tdata.append((image))\n          except: pass\n    print ('The extra training data loaded: ', len(tdata))\n    test_object.training_data=tdata\n\n  if args.labels!='-1':\n    labels=[]\n    lines = [line.rstrip('\\n') for line in open(args.labels)]\n    for line in lines:\n      for l in line.split():\n        labels.append(int(l))\n    test_object.labels=labels\n\n  test_object.check_layer_indices ()\n  deepconcolic(test_object, outs)\n\nif __name__==\"__main__\":\n  try:\n    main ()\n  except KeyboardInterrupt:\n    sys.exit('Interrupted.')\n","sub_path":"src/deepconcolic.py","file_name":"deepconcolic.py","file_ext":"py","file_size_in_byte":8764,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"28917100","text":"def largeGroupPositions(self, S):\n    \"\"\"\n    :type S: str\n    :rtype: List[List[int]]\n    \"\"\"\n    slow = 0\n    fast = 0\n    result = []\n    while slow < len(S):\n        count = 0\n        temp = []\n        while fast < len(S) and S[fast] == S[slow]:\n            fast += 1\n            count += 1\n        if count >= 3:\n            temp.append(slow)\n            temp.append(fast-1)\n            result.append(temp)\n        slow = fast\n    return result","sub_path":"Leetcode Problem 830.py","file_name":"Leetcode Problem 830.py","file_ext":"py","file_size_in_byte":449,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"210604142","text":"import turtle\nt = turtle.Turtle()\n\ns = turtle.Screen()\nc = 0\nwhile 1:\n\tfor i in range(4):\n\t\tt.forward(250)\n\t\tt.right(90)\n\tt.right(3)\n\tc += 1\n\tif c>= 360/3:\n\t\tcontinue\n\nturtle.done()\n\n\n","sub_path":"Square_pattern.py","file_name":"Square_pattern.py","file_ext":"py","file_size_in_byte":184,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"319731088","text":"import torch\nimport functools\n\n__all__ = [\n    \"Downsample2d\",\n]\n\nclass Downsample2d(torch.nn.Module):\n    def __init__(self,\n        scale: float=0.5,\n        mode: str=\"bilinear\"\n    ):\n        super(Downsample2d, self).__init__()\n        self.downsample = functools.partial(torch.nn.functional.interpolate,\n            scale_factor=scale,\n            mode=mode\n        )\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        return self.downsample(x)","sub_path":"moai/nn/sampling/spatial/downsample/interpolate.py","file_name":"interpolate.py","file_ext":"py","file_size_in_byte":464,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"295933732","text":"from flask_restplus import Namespace, Resource, fields\nfrom flask import *\nimport  DataLayer.DocumentDb.Queries as queries\nimport DataLayer.Repositories\nimport os\napi = Namespace('Alerts', description='Alerts related operations')\nimport os\nimport platform\nimport DataLayer.DocumentDb.ConnectionManager\n\n\nalert_advanced_data = api.model('advanced_alerts_data', {\n    'refkeynumber': fields.String(required=True),\n    'name': fields.String(required=True),\n})\n\n\ncreate_alert_response_model = api.model('create_alert_response_model', {\n    'alert_id': fields.String(required=True, description='The alert identifier'),\n    'InternalData': fields.List(fields.Nested(alert_advanced_data)),\n    'alert_name': fields.String(required=True, description='The alert name')\n\n})\n\n\n\nget_all_alerts_response_model = api.model('get_all_alerts_response', {\n    'id': fields.String(required=True, description='The alert name'),\n    'alert_name': fields.String(required=True, description='The alert name'),\n    'alert_category': fields.String(required=True, description='The alert name')\n})\n\n\nclass xx(object):\n    def __init__(self):\n        self.refkeynumber=\"sdfsdf\"\n        self.name =\"dsafsdf\"\n\n\nclass alertres(object):\n    def __init__(self,alertid,alertname,internaldata):\n        self.alert_id = alertid\n        self.alert_name = alertname\n        self.InternalData = internaldata\n        self.test_avi = \"sdfsdf\"\n        self.data=\"dataaa!\"\n\n\ncreate_alert_request_model = api.model('create_alert_request_model', {\n         'Alert_Name': fields.String(description='Name of the Alert', required=True),\n         'Alert_Id': fields.String(description='Id of the alert', required=True),\n         'InternalData': fields.List(fields.Nested(alert_advanced_data), required=True),\n         'Data': fields.String(description='Id of the alert', required=False),\n    })\n\n\nAlerts = [\n    {'id': 'this_is_alert_id', 'name': 'xsg33ghd3w2667e233'},\n]\n\n\n\n# @api.route('/aaa')\n# class Cat(Resource):\n#     @api.doc('list_alerts')\n#     #@api.marshal_list_with(create_alert_response_model)\n#     def get(self):\n#         '''List all alerts'''\n#         mydocument = queries.get_alert()\n#         return mydocument\n\n@api.route('/<id>')\n@api.param('id', 'The alert identifier')\n@api.response(404, 'alert not found')\nclass Cat(Resource):\n    @api.doc('Get_Alert_By_Id')\n    @api.marshal_with(create_alert_response_model)\n    def get(self, id):\n        '''Fetch an alert given its identifier'''\n        for cat in Alerts:\n            if cat['id'] == id:\n                return cat\n        api.abort(404)\n\n\n@api.route('/')\n@api.response(201, 'alert created')\nclass Cat(Resource):\n    @api.doc('create_new_alert')\n    @api.marshal_with(create_alert_response_model)\n    @api.expect(create_alert_request_model)\n    def post(self):\n        '''Creates new alert'''\n        x = xx()\n        xxxx =list()\n        xxxx.append(x)\n        adf= alertres(\"id\",\"name\",xxxx)\n        json =  request.get_json(force=True)\n        return adf\n        #return jsonpickle.encode(adf)\n        '''Fetch an alert given its identifier'''\n\n\n    @api.doc('list_alerts')\n    @api.marshal_with(get_all_alerts_response_model)\n    #@api.marshal_list_with(create_alert_response_model)\n    def get(self):\n        '''List all alerts'''\n        a = DataLayer.Repositories.AlertsRepository.get_all_alerts()\n        return a\n        # return mydocument\n\n\n\n@api.route('/health')\n@api.response(200, 'server is healthy')\nclass HealthCheck(Resource):\n    @api.doc('check alert server health')\n    def post(self):\n        '''Checks alert server health state'''\n        xxx =os.path.dirname(os.path.realpath(__file__))\n        yyy = platform.system()\n        return \"im 0kay :). i mean it\"\n        '''Fetch an alert given its identifier'''\n\n\n\n\n","sub_path":"Controllers/Alerts.py","file_name":"Alerts.py","file_ext":"py","file_size_in_byte":3765,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"594345769","text":"import cv2\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\nimg = cv2.imread('225.jpeg', 0)\nret, thresh = cv2.threshold(img, 127, 255, 0)\nimage, contours, hierarchy = cv2.findContours(thresh, 1, 2)\n\ncnt = contours[0]\n\n# 边界矩形的长宽比\n\nx, y, w, h = cv2.boundingRect(cnt)\naspect_ratio = float(w) / h\nprint(aspect_ratio)\n\n# 轮廓面积与边界面积的比 Extent\n\narea = cv2.contourArea(cnt)\nx, y, w, h = cv2.boundingRect(cnt)\nrect_area = w * h\nextent = float(area) / rect_area\nprint(extent)\n\n\n# Solidity 轮廓面积与凸包面积的比\n\narea = cv2.contourArea(cnt)\nhull = cv2.convexHull(cnt)\nhull_area = cv2.contourArea(hull)\nSolidity = float(area) / hull_area\nprint(Solidity)\n\n# Equivalent Diameter 与轮廓相等的圆形面积的直径\narea = cv2.contourArea(cnt)\nequi_diameter = np.sqrt(4 * area / np.pi)\nprint(equi_diameter)\n\n\n# 方向,对象的方向，会返回长轴和短轴的长度\n(x, y), (MA, ma), angle = cv2.fitEllipse(cnt)\nprint((x, y), (MA, ma), angle)\n\n\n# 掩模和像素点\n\nimg = cv2.imread('225.jpeg')\nimgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\nmask = np.zeros(imgray.shape, np.uint8)\n\n# 这里参数一定要选择 -1 ，绘制填充的整个轮廓\npixelpoints1 = cv2.drawContours(mask, [cnt], 0, 255, -1)\n# np.nonzero 返回非零的元素��引\n'''要按元素而不是维度对索引进行分组，请使用：\ntranspose(nonzero(a))\n结果总是一个二维数组，每个非零元素都有一行。'''\npixelpoints2 = np.transpose(np.nonzero(mask))  # ????\n\nprint(pixelpoints1)  # openCV返回的是(x,y)的格式，而Numpy 返回的是(row，colum)，也就是非零元素的位置\nprint(pixelpoints2)\n# 两者互换，row = x，colunm = y\n\n# 最大值和最小值以及它们的位置\nmin_val, max_val, min_loc, max_loc = cv2.minMaxLoc(imgray, mask=mask)\nprint(min_val, max_val, min_loc, max_loc)\n\n# 颜色以及平均灰度\n# 我们可以使用相同的掩模求一个对象的平均颜色或平均灰度\n\nmean_val = cv2.mean(img, mask=mask)\nprint(mean_val)\n\n\n# 极点\n# 一个对象最上面，最下面，最左边，最右边的点\nleftmost = tuple(cnt[cnt[:, :, 0].argmin()][0])\nrightmost = tuple(cnt[cnt[:, :, 0].argmax()][0])\ntopmost = tuple(cnt[cnt[:, :, 1].argmin()][0])\nbottommost = tuple(cnt[cnt[:, :, 1].argmax()][0])\nprint(leftmost, rightmost, topmost, bottommost)\n","sub_path":"OpenCv/opencv16_3.py","file_name":"opencv16_3.py","file_ext":"py","file_size_in_byte":2332,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"649242283","text":"# coding: utf-8\n\nimport os\nimport commands\nfrom dcmp.clouddisks.utils import clouddisk_root_path, default_quota, check_domainuser\n\n\nclass Quota(object):\n\tdef __init__(self, name, limit=default_quota):\n\t\tos.environ['value'] = str(limit)\n\t\tos.environ['username'] = str(name)\n\t\tos.environ['cloudpath'] = str(clouddisk_root_path)\n\t\tself.name = name\n\n\tdef set_quota(self):\n\t\tif not check_domainuser(self.name):\n\t\t\treturn False\n\t\telse:\n\t\t\tstatus, result = commands.getstatusoutput('xfs_quota -x -c \"limit -u bsoft=$value bhard=$value $username\" $cloudpath')\n\t\tif status == 0:\n\t\t\treturn True\n\t\telse:\n\t\t\treturn False","sub_path":"dcmp/clouddisks/cloudDisk/quota.py","file_name":"quota.py","file_ext":"py","file_size_in_byte":608,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"388350330","text":"# This is my code to train n model and saved them\n\nimport random\nimport os\nimport tensorflow as tf\nimport numpy as np\nimport shutil\nimport tempfile\n\nfrom tensorflow.python import pywrap_tensorflow\n\nfrom baseline_constants import BYTES_WRITTEN_KEY, BYTES_READ_KEY, LOCAL_COMPUTATIONS_KEY\nfrom mh_constants import VARIABLE_PARAMS, MODEL_PARAMS\nfrom mlhead_utilfuncs import input_fn\nfrom kmean_model import KmeanModel\n\nclass Mlhead_Clus_Server:\n    \n    def __init__(self, client_model, dataset, model, num_clusters):\n        self.modeldir = \"/scratch/tmpmodel\"\n        if not os.path.exists(self.modeldir):\n            os.makedirs(self.modeldir)\n#        if num_clusters < 2:\n#            raise Exception(\"Sorry, cluster number must be 2 or more\")\n        if num_clusters > 10:\n            raise Exception(\"Sorry, cluster number must less than 10\") \n        if num_clusters != -1:\n            self.model = client_model.get_params()  # global model of the server.\n            self.selected_clients = [] # this variable keeps random, unclustered clients\n            self.set_model_path(dataset, model)\n            self._x_dimensions = self.get_model_x_dimensions(dataset, model)\n            self._variable = self.get_model_variable(dataset, model)\n            self._num_clusters = num_clusters\n            self._learned = None\n            self._shuffledkeys = None\n        \"\"\"\n        cluster_membership is a list of cluster dictionary,\n        each contains {'member':list of clients, \n            'center': a center vector, 'attention': a attention vector,\n            'loss': mean valdation loss of each client of this cluster}\n        \"\"\"\n        self._cluster_membership = list()\n        for _ in range(num_clusters):\n            self._cluster_membership.append({\"member\": [], \"center\": None, \"attention\": [], \"loss\": None})        \n\n    @property\n    def path(self):\n        return self._path\n    \n    @property\n    def x_dimensions(self):\n        return self._x_dimensions\n    \n    @property\n    def variable(self):\n        return self._variable\n\n    @property\n    def selected(self):\n        return [c for c in self.selected_clients]\n    \n    @property\n    def clusters(self):\n        return [c for c in self._clusters_membership]\n\n    def select_clients(self, my_round, possible_clients):\n        \"\"\"Selects num_clients clients randomly from possible_clients.\n        \n        Note that within function, num_clients is set to\n            min(num_clients, len(possible_clients)).\n        Args:\n            possible_clients: Clients from which the server can select.\n            num_clients: Number of clients to select; default 20\n        Return:\n            list of (num_train_samples, num_test_samples)\n        \"\"\"\n        num_clients = len(possible_clients)\n        np.random.seed(my_round * 50)\n        self.selected_clients = np.random.choice(possible_clients, num_clients, replace=False)\n\n        return [(c.num_train_samples, c.num_test_samples) for c in self.selected_clients]\n\n    def train_model(self):\n        \"\"\"Trains self.model on given clients.\n\n        \"\"\"\n        clients = self.selected_clients\n\n        tot_clients = len(clients)\n        done_idx = 0\n        for counter, c in enumerate(clients, 1):\n            \"\"\"\n            Note: this is a trick. and it's equal to clear the session of client\n            and make sure the graph has been re-initialized.\n            \"\"\"\n            c_file = self.get_chkpfile( 'write_%s.ckpt' % c.id )\n\n            \"\"\"\n            I think this one will run faster than training\n            since now I have already initial the value with normal\n            distribution but no training at first round\n            \"\"\"\n            \n            if os.path.exists(c_file):\n                os.remove(c_file)\n            c.save_model(c_file)               \n\n            done_percentil = float(done_idx + 1) * 25\n            if self.get_percentil(counter, tot_clients) >= done_percentil:\n                done_idx += 1\n                print( \"%g%% clients has done\" % done_percentil)\n\n    def get_percentil(self, counter, len):\n        fraction = float((counter + 1) / len)\n        return fraction * 100   \n\n\n    def set_model_path(self, dataset, model):\n    \tself._path = os.path.join('/scratch/leaf/ckpt_runtime', dataset, model)\n    \tif not os.path.exists(self._path):\n        \tos.makedirs(self._path)\n            \n    def get_model_x_dimensions(self, dataset, model):\n        key = \"%s.%s\" % (dataset, model)\n        d = MODEL_PARAMS[ key ]\n        return d[0] * d[1]\n    \n    def get_model_variable(self, dataset, model):\n        key = \"%s.%s\" % (dataset, model)\n        v = VARIABLE_PARAMS[ key ] \n        return v\n\n    def get_chkpfile(self, id_ckpt):\n        return os.path.join(self._path, id_ckpt)\n\n    def train_iteation(self, data):\n        train_data = lambda: input_fn(data)\n        self._kmeans.train(train_data)\n        cluster_centers = self._kmeans.cluster_centers()\n        # print(\"cluster centers:\", cluster_centers)\n        score = self._kmeans.score(train_data)\n        return score\n    \n    def get_init_point_data(self):\n        #points = np.random.normal(loc=0.5, scale=0.5, size= (len(self.selected), self._x_dimensions)) \n        points = np.random.uniform(-0.149, 0.149, (len(self.selected), self._x_dimensions))\n        c_dict = {}\n        for x, client in enumerate(self.selected):\n            c_dict[client.id] = points[x]\n        self._shuffledkeys = list(c_dict.keys())\n        return c_dict\n\n    def run_clustering(self, prev_score, data):\n        seed = np.random.randint(5667799881, size=1)[0]\n        features = np.concatenate([np.array(data[x]) for x in self._shuffledkeys])\n        features = np.reshape(features, (len(self._shuffledkeys), self._x_dimensions))\n        model = KmeanModel(features.shape[0], self._x_dimensions, self._num_clusters, seed)\n        self._learned = model.assign_clusters(features)\n        random.shuffle(self._shuffledkeys)\n        return None, len(self._learned)\n        \n    def train_kmeans(self, prev_score, data):\n        \"\"\"We are using pre-made tensorflow estimators to \n            train and predict.\n           \n           Args:\n               prev_score: a sum of the distance between each sample\n               to their nearest center\n               data: list of weights of user model\n           Return:\n               updated score\n        \n        \"\"\"\n\n        seed = np.random.randint(5667799881, size=1)[0]\n        temp_name = next(tempfile._get_candidate_names())\n        temp_modeldir = os.path.join(self.modeldir, temp_name)\n        if not os.path.exists(temp_modeldir): \n            os.makedirs(temp_modeldir)\n        self._kmeans = tf.contrib.factorization.KMeansClustering(\n            model_dir=temp_modeldir,\n            random_seed=seed,\n            num_clusters=self._num_clusters, use_mini_batch=False)        \n\n       # composed of weights of every client model \n\n        score = self.train_iteation(data)\n        \n        # evaluate the samples to compute the distance between\n        # each sample and each center, forming a matrix have each \n        # row for each sample, and the column is distance to each\n        # center.\n        y = self._kmeans.transform(lambda: input_fn(data))\n        point_distance = list(y)\n        self._learned = np.argmin(point_distance , 1)\n        # removing not used files of this model\n        #shutil.rmtree(temp_modeldir, ignore_errors=True)\n        return None, score\n\n    \n    def eval_clustermembership(self, num_clusters):\n        \"\"\"Transfrom the input data,\n        get the min distance of each point to cluster centers, \n        then return the index of cluster center whose distance for a sample\n        is the mininum\n        \n        return a list of (num_clients, clients)\n        \"\"\"\n        # print(str(self._learned))\n\n        for _, cluster in enumerate(self._cluster_membership):\n            cluster[\"member\"] = list()\n        \n        sort_clients = list()\n        # to sync the sorted clients with shuffle step\n        for i in self._shuffledkeys:\n            for client in self.selected_clients:\n                if client.id == i:\n                    sort_clients.append(client)\n                    break\n                    \n        for x in range(len(self._learned)):\n            grp_id = self._learned[x]            \n            self._cluster_membership[grp_id][\"member\"].append(sort_clients[x])\n        return [ (len(cluster[\"member\"]), cluster[\"member\"]) for cl_id, cluster in enumerate(self._cluster_membership)]\n            ","sub_path":"all_baselines/fed-cluster/models/mlhead_clus_server.py","file_name":"mlhead_clus_server.py","file_ext":"py","file_size_in_byte":8537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"104290533","text":"name = input(\"Enter file:\")\nif len(name) < 1 : name = \"mbox-short.txt\"\nhandle = open(name)\ndict ={}\nfor line in handle:\n    if not line.startswith(\"From \"):\n        continue\n    email = (line.split()[1]).strip()\n    dict[email] = dict.get(email,0) + 1\n \nmax = None\nmost_profilic = None \nfor email, count in dict.items():\n    if max is None or max< count:\n        max = count\n        most_profilic = email\n\nprint(most_profilic, max)","sub_path":"Python Data Structures/Assignment7.py","file_name":"Assignment7.py","file_ext":"py","file_size_in_byte":431,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"233433275","text":"#!/usr/bin/env python\n#\n# __init__.py -\n#\n# Author: Paul McCarthy <pauldmccarthy@gmail.com>\n#\n\nimport          os\nimport          gc\nimport          time\nimport          shutil\nimport          logging\nimport          tempfile\nimport          traceback\nimport          contextlib\n\nimport          wx\n\nimport matplotlib as mpl\nmpl.use('WxAgg')  # noqa\n\nimport fsleyes_props                as props\nimport fsl.utils.idle               as idle\nimport                                 fsleyes\nimport fsleyes.frame                as fslframe\nimport fsleyes.main                 as fslmain\nimport fsleyes.actions.frameactions as frameactions  # noqa\nimport fsleyes.gl                   as fslgl\nimport fsleyes.colourmaps           as colourmaps\nimport fsleyes.displaycontext       as dc\nimport fsleyes.overlay              as fsloverlay\n\n\nfrom .compare_images import compare_images\n\n\n# Under GTK, a single call to\n# yield just doesn't cut it\ndef realYield(centis=10):\n    for i in range(int(centis)):\n        wx.YieldIfNeeded()\n        time.sleep(0.01)\n\n\n@contextlib.contextmanager\ndef tempdir():\n    \"\"\"Returnsa context manager which creates and returns a temporary\n    directory, and then deletes it on exit.\n    \"\"\"\n\n    testdir = tempfile.mkdtemp()\n    prevdir = os.getcwd()\n    try:\n\n        os.chdir(testdir)\n        yield testdir\n\n    finally:\n        os.chdir(prevdir)\n        shutil.rmtree(testdir)\n\n\ninitialised = [False]\n\ndef run_with_fsleyes(func, *args, **kwargs):\n    \"\"\"Create a ``FSLeyesFrame`` and run the given function. \"\"\"\n\n    logging.getLogger().setLevel(logging.WARNING)\n\n    gc.collect()\n    idle.idleReset()\n\n    propagateRaise = kwargs.pop('propagateRaise', True)\n    startingDelay  = kwargs.pop('startingDelay',  500)\n    finishingDelay = kwargs.pop('finishingDelay', 5)\n    callAfterApp   = kwargs.pop('callAfterApp',   None)\n\n    result = [None]\n    raised = [None]\n    frame  = [None]\n    app    = [None]\n\n    def init():\n        fsleyes.initialise()\n        props.initGUI()\n        colourmaps.init()\n        initialised[0] = True\n        fslgl.bootstrap((2, 1))\n        wx.CallAfter(run)\n\n    def finish():\n        frame[0].Close(askUnsaved=False, askLayout=False)\n        app[0].ExitMainLoop()\n\n    def run():\n\n        overlayList = fsloverlay.OverlayList()\n        displayCtx  = dc.DisplayContext(overlayList)\n        lockGroup   = dc.OverlayGroup(displayCtx, overlayList)\n\n        displayCtx.overlayGroups.append(lockGroup)\n\n        frame[0]    = fslframe.FSLeyesFrame(None,\n                                            overlayList,\n                                            displayCtx)\n\n        app[0].SetOverlayListAndDisplayContext(overlayList, displayCtx)\n        app[0].SetTopWindow(frame[0])\n\n        frame[0].Show()\n\n        try:\n            if func is not None:\n                result[0] = func(frame[0],\n                                 overlayList,\n                                 displayCtx,\n                                 *args,\n                                 **kwargs)\n\n        except Exception as e:\n            traceback.print_exc()\n            raised[0] = e\n\n        finally:\n            wx.CallLater(finishingDelay, finish)\n\n    app[0] = fslmain.FSLeyesApp()\n    dummy  = wx.Frame(None)\n    panel  = wx.Panel(dummy)\n    sizer  = wx.BoxSizer(wx.HORIZONTAL)\n    sizer.Add(panel, flag=wx.EXPAND, proportion=1)\n    dummy.SetSizer(sizer)\n\n    if callAfterApp is not None:\n        callAfterApp()\n\n    dummy.SetSize((100, 100))\n    dummy.Layout()\n    dummy.Show()\n\n    if not initialised[0]:\n        wx.CallLater(startingDelay,\n                     fslgl.getGLContext,\n                     parent=panel,\n                     ready=init)\n    else:\n        wx.CallLater(startingDelay, run)\n\n    app[0].MainLoop()\n    dummy.Close()\n\n    time.sleep(1)\n\n    if raised[0] and propagateRaise:\n        raise raised[0]\n\n    return result[0]\n\n\ndef run_with_viewpanel(func, vptype, *args, **kwargs):\n    def inner(frame, overlayList, displayCtx, *a, **kwa):\n        panel = frame.addViewPanel(vptype)\n        return func(panel, overlayList, displayCtx, *a, **kwa)\n    return run_with_fsleyes(inner, *args, **kwargs)\n\n\ndef run_with_orthopanel(func, *args, **kwargs):\n    \"\"\"Create a ``FSLeyesFrame`` with an ``OrthoPanel`` and run the given\n    function.\n    \"\"\"\n    from fsleyes.views.orthopanel import OrthoPanel\n    return run_with_viewpanel(func, OrthoPanel, *args, **kwargs)\n\n\ndef run_with_lightboxpanel(func, *args, **kwargs):\n    \"\"\"Create a ``FSLeyesFrame`` with a ``LightBoxPanel`` and run the given\n    function.\n    \"\"\"\n    from fsleyes.views.lightboxpanel import LightBoxPanel\n    return run_with_viewpanel(func, LightBoxPanel, *args, **kwargs)\n\n\ndef run_with_scene3dpanel(func, *args, **kwargs):\n    \"\"\"Create a ``FSLeyesFrame`` with a ``Scene3DPanel`` and run the given\n    function.\n    \"\"\"\n    from fsleyes.views.scene3dpanel import Scene3DPanel\n    return run_with_viewpanel(func, Scene3DPanel, *args, **kwargs)\n\n\ndef run_with_timeseriespanel(func, *args, **kwargs):\n    \"\"\"Create a ``FSLeyesFrame`` with a ``TimeSeriesPanel`` and run the given\n    function.\n    \"\"\"\n    from fsleyes.views.timeseriespanel import TimeSeriesPanel\n    return run_with_viewpanel(func, TimeSeriesPanel, *args, **kwargs)\n\n\ndef run_with_histogrampanel(func, *args, **kwargs):\n    \"\"\"Create a ``FSLeyesFrame`` with a ``HistogramPanel`` and run the given\n    function.\n    \"\"\"\n    from fsleyes.views.histogrampanel import HistogramPanel\n    return run_with_viewpanel(func, HistogramPanel, *args, **kwargs)\n\n\ndef run_with_powerspectrumpanel(func, *args, **kwargs):\n    \"\"\"Create a ``FSLeyesFrame`` with a ``PowerSpectrumPanel`` and run the\n    given function.\n    \"\"\"\n    from fsleyes.views.powerspectrumpanel import PowerSpectrumPanel\n    return run_with_viewpanel(func, PowerSpectrumPanel, *args, **kwargs)\n","sub_path":"tests/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":5830,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"612613184","text":"\"\"\"\nMichael S. Emanuel\nMon Apr 30 14:29:09 2018\n\"\"\"\n\nfrom sys import argv\nfrom Database_Connection import getConnection\nfrom Euler.Primes import PrimeTable\nfrom Euler.Timing import Timer\n\n\ndef demo():\n    \"\"\"Demonstrate getting a connection and executing a SQL statement.\"\"\"\n    conn = getConnection()\n    # print(conn)\n    curs = conn.cursor()\n    query = '''\n    SELECT\n      p.p\n    FROM\n      dbo.Prime as p;\n    '''\n    # print(query)\n    curs.execute(query)\n    for row in curs:\n        print(row)\n    conn.close()\n\n\ndef truncatePrimes(curs) -> None:\n    \"\"\"Truncate the staging table r.Prime.\"\"\"\n    sql = '''TRUNCATE TABLE r.Prime'''\n    curs.execute(sql)\n    curs.commit()\n\n\ndef insertPrimes(curs, pt: PrimeTable, p0: int, p1: int) -> int:\n    \"\"\"Insert primes between p0 and p1 into the staging table r.Prime.\"\"\"\n    # Extend primes up to p1 if necessary\n    pt.buildPrimesUpTo(p1)\n    # Prime index for start and end\n    i0: int = pt.primeIndex(p0)\n    i1: int = pt.primeIndex(p1)\n    # Sql string to insert one prime\n    sqlInsert = '''\n    INSERT INTO r.Prime (p) VALUES (?)\n    '''\n    # Iterate over primes index i0 to i1\n    for i in range(i0, i1):\n        p: int = pt.nthPrime(i+1)\n        curs.execute(sqlInsert, p)\n    # Commit\n    curs.commit()\n    # Return last prime inserted\n    return p\n\n\ndef insertPrimesByIndex(curs, pt: PrimeTable, i0: int, i1: int) -> int:\n    \"\"\"Insert primes indexed i0 to i1 into the staging table r.Prime.\"\"\"\n    # Prime index for start and end\n    # i0: int = pt.primeIndex(p0)\n    # i1: int = pt.primeIndex(p1)\n    # Sql string to insert one prime\n    sqlInsert = '''\n    INSERT INTO r.Prime (p) VALUES (?)\n    '''\n    # Iterate over primes index i0 to i1\n    for i in range(i0, i1):\n        p: int = pt.nthPrime(i+1)\n        curs.execute(sqlInsert, p)\n    # Commit\n    curs.commit()\n    # Return last prime inserted\n    return p\n\n\ndef importPrimes(curs, pMax) -> None:\n    \"\"\"Import primes from the staging table r.Prime to dbo.Prime.\"\"\"\n    # Sql string to insert one prime\n    sqlImport = '''\n    EXEC ImportPrimes @pMax=?;\n    '''\n    curs.execute(sqlImport, pMax)\n    # Commit\n    curs.commit()\n\n\ndef main():\n    # Unpack arguments\n    argc = len(argv)-1\n    if argc != 2:\n        print('Usage: python Database_InsertPrimes.py pMin pMax.')\n        print('This will insert all primes from and including pMin, up to but not including pMax.')\n        return\n    pMin: int = int(argv[1])\n    pMax: int = int(argv[2])\n    # Get DB connection\n    conn = getConnection()\n    # Cursor\n    curs = conn.cursor()\n    # Status update\n    tc: Timer = Timer()\n    # Instantiate PrimeTable\n    pt = PrimeTable(pMin)\n    msg: str = f'Prime table loaded up to {pMin}.'\n    tc.status(msg)\n\n    # Insert primes one page at a time\n    pageSize: int = 10**6\n    kMin: int = pMin // pageSize\n    kMax: int = pMax // pageSize\n    for pageNum in range(kMin, kMax):\n        # Start and end of the page to be inserted\n        p0: int = pageNum * pageSize\n        p1: int = (pageNum+1) * pageSize\n        # Insert these primes into r.Prime\n        p: int = insertPrimes(curs, pt, p0, p1)\n        # Import them into dbo.Prime with prime index\n        importPrimes(curs, p1)\n        # Status update\n        n: int = (pageNum+1)*pageSize\n        primeCount: int = len(pt.prime)\n        msg = f'Completed search to n={n}. Have {primeCount} primes through p={p}.'\n        tc.status(msg, pageNum-kMin, kMax-kMin)\n    # Close cursor\n    curs.close()\n    # Close connection\n    conn.close()\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"Database_InsertPrimes.py","file_name":"Database_InsertPrimes.py","file_ext":"py","file_size_in_byte":3552,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"367289909","text":"import pandas as pd\nimport gc\nimport catboost\n\ndef subsample(data, fraction):\n    data_label_1 = data.loc[data.is_click == 1].sample(frac=fraction)\n    data_label_0 = data.loc[data.is_click == 0].sample(frac=fraction)\n    data = pd.concat([data_label_0, data_label_1])\n    data = data.sample(frac=1)\n    return data\n\n\ndef train_test_split(data):\n    train = data.loc[(data.refresh_day >= 3) & (data.refresh_day <= 10)]\n    train = subsample(train, 0.1)\n\n    valid = data.loc[data.refresh_day == 11]\n    valid = subsample(valid, 0.1)\n\n    test = data.loc[data.refresh_day == 12]\n    test = subsample(test, 0.1)\n    return train, valid, test\n\n\ndef main():\n    data1 = pd.read_csv('../data/raw_data.csv', index_col=False)\n    data2 = pd.read_csv('../data/raw_data2.csv', index_col=False)\n    data3 = pd.read_csv('../data/raw_data3.csv', index_col=False)\n    data = pd.concat([data1, data2, data3])\n    del data1, data2, data3\n    gc.collect()\n    data['category'].fillna('other', inplace=True)\n    train, valid, test = train_test_split(data)\n    del data\n    gc.collect()\n    train.to_csv('../data/train_raw.csv', index=False)\n    valid.to_csv('../data/valid_raw.csv', index=False)\n    test.to_csv('../data/test_raw.csv', index=False)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"feature/split.py","file_name":"split.py","file_ext":"py","file_size_in_byte":1272,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"79771651","text":"from setuptools import find_packages, setup\n\nwith open('README.md') as file:\n    long_description = file.read()\n\nsetup(\n    name=\"le-utils\",\n    packages = find_packages(),\n    version=\"0.0.9c24\",\n    description=\"LE Utils and constants shared across Kolibri, Ricecooker and the Content Curation Server.\",\n    long_description=long_description,\n    license=\"MIT\",\n    url=\"https://github.com/learningequality/le-utils\",\n    download_url=\"https://github.com/learningequality/le-utils/tarball/0.0.9c14\",\n    keywords=\"le-utils le_utils le utils kolibri ricecooker content curation\",\n    package_data={\"le_utils\": [\"resources/*.json\"], },\n    classifiers=[\n        \"Development Status :: 2 - Pre-Alpha\",\n        \"Programming Language :: Python\",\n        \"License :: OSI Approved :: MIT License\",\n        \"Topic :: Utilities\",\n    ],\n    author=\"Jordan Yoshihara\",\n    author_email=\"jordan@learningequality.org\", )\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":911,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"518035206","text":"import FWCore.ParameterSet.Config as cms\n\nprocess = cms.Process(\"Ana\")\nprocess.load('FWCore.MessageService.MessageLogger_cfi')\n##-------------------- Communicate with the DB -----------------------\nprocess.load('Configuration.StandardSequences.Services_cff')\nprocess.load('Configuration.StandardSequences.FrontierConditions_GlobalTag_cff')\nprocess.GlobalTag.globaltag = 'GR_R_42_V19::All'\n#process.load(\"CondCore.DBCommon.CondDBCommon_cfi\")\n#from CondCore.DBCommon.CondDBSetup_cfi import *\n#process.jec = cms.ESSource(\"PoolDBESSource\",\n#      DBParameters = cms.PSet(\n#        messageLevel = cms.untracked.int32(0)\n#        ),\n#      timetype = cms.string('runnumber'),\n#      toGet = cms.VPSet(\n#      cms.PSet(\n#            record = cms.string('JetCorrectionsRecord'),\n#            tag    = cms.string('JetCorrectorParametersCollection_Jec11V2_AK5PF'),\n#            label  = cms.untracked.string('AK5PF')\n#            ),\n#      cms.PSet(\n#            record = cms.string('JetCorrectionsRecord'),\n#            tag    = cms.string('JetCorrectorParametersCollection_Jec11V2_AK7PF'),\n#            label  = cms.untracked.string('AK7PF')\n#            ),\n#      cms.PSet(\n#            record = cms.string('JetCorrectionsRecord'),\n#            tag    = cms.string('JetCorrectorParametersCollection_Jec11V2_AK5Calo'),\n#            label  = cms.untracked.string('AK5Calo')\n#            ),\n#      cms.PSet(\n#            record = cms.string('JetCorrectionsRecord'),\n#            tag    = cms.string('JetCorrectorParametersCollection_Jec11V2_AK7Calo'),\n#            label  = cms.untracked.string('AK7Calo')\n#            )\n#      ), \n#      connect = cms.string('sqlite:Jec11V2.db')\n#)\nprocess.load('Configuration.StandardSequences.MagneticField_38T_cff')\nprocess.load('Configuration.StandardSequences.Geometry_cff')\nprocess.load('RecoJets.Configuration.RecoPFJets_cff')\nprocess.load('RecoJets.Configuration.RecoJets_cff')\n##-------------------- Import the JEC services -----------------------\nprocess.load('JetMETCorrections.Configuration.DefaultJEC_cff')\n#process.es_prefer_jec = cms.ESPrefer('PoolDBESSource','jec')\n#############   Set the number of events #############\nprocess.maxEvents = cms.untracked.PSet(\n    input = cms.untracked.int32(100)\n)\n#############   Format MessageLogger #################\nprocess.MessageLogger.cerr.FwkReport.reportEvery = 1000\n#############   Define the source file ###############\nprocess.source = cms.Source(\"PoolSource\",\n    fileNames = cms.untracked.vstring(\n    '/store/data/Run2011A/Jet/AOD/PromptReco-v6/000/173/244/E2AB1AE4-50C7-E011-937E-003048F11CF0.root'\n    )\n)\n############# processed tree producer ##################\nprocess.TFileService = cms.Service(\"TFileService\",fileName = cms.string('ProcessedTree_data.root'))\n\nprocess.ak7 = cms.EDAnalyzer('ProcessedTreeProducer',\n    ## jet collections ###########################\n    pfjets          = cms.InputTag('ak7PFJets'),\n    calojets        = cms.InputTag('ak7CaloJets'),\n    ## database entry for the uncertainties ######\n    PFPayloadName   = cms.string('AK7PF'),\n    CaloPayloadName = cms.string('AK7Calo'),\n    ## calojet ID and extender for the JTA #######\n    calojetID       = cms.InputTag('ak7JetID'),\n    calojetExtender = cms.InputTag('ak7JetExtender'),\n    ## set the conditions for good Vtx counting ##\n    offlineVertices = cms.InputTag('offlinePrimaryVertices'),\n    goodVtxNdof     = cms.double(4), \n    goodVtxZ        = cms.double(24),\n    ## rho #######################################\n    srcCaloRho      = cms.InputTag('kt6CaloJets','rho'),\n    srcPFRho        = cms.InputTag('kt6PFJets','rho'),\n    ## preselection cuts #########################\n    maxY            = cms.double(5.0), \n    minPFPt         = cms.double(20),\n    minPFFatPt      = cms.double(10),\n    maxPFFatEta     = cms.double(2.5),\n    minCaloPt       = cms.double(20),\n    minNPFJets      = cms.int32(1),\n    minNCaloJets    = cms.int32(1), \n    minJJMass       = cms.double(-1),\n    ## trigger ###################################\n    printTriggerMenu = cms.untracked.bool(True),\n    processName     = cms.string('HLT'),\n    triggerName     = cms.vstring('HLT_Jet30_v1','HLT_Jet30_v2','HLT_Jet30_v3','HLT_Jet30_v4','HLT_Jet30_v5','HLT_Jet30_v6',\n                                  'HLT_Jet60_v1','HLT_Jet60_v2','HLT_Jet60_v3','HLT_Jet60_v4','HLT_Jet60_v5','HLT_Jet60_v6',\n                                  'HLT_Jet80_v1','HLT_Jet80_v2','HLT_Jet80_v3','HLT_Jet80_v4','HLT_Jet80_v5','HLT_Jet80_v6',\n                                  'HLT_Jet110_v1','HLT_Jet110_v2','HLT_Jet110_v3','HLT_Jet110_v4','HLT_Jet110_v5','HLT_Jet110_v6',\n                                  'HLT_Jet150_v1','HLT_Jet150_v2','HLT_Jet150_v3','HLT_Jet150_v4','HLT_Jet150_v5','HLT_Jet150_v6',\n                                  'HLT_Jet190_v1','HLT_Jet190_v2','HLT_Jet190_v3','HLT_Jet190_v4','HLT_Jet190_v5','HLT_Jet190_v6',\n                                  'HLT_Jet240_v1','HLT_Jet240_v2','HLT_Jet240_v3','HLT_Jet240_v4','HLT_Jet240_v5','HLT_Jet240_v6',\n                                  'HLT_Jet300_v1', 'HLT_Jet300_v2','HLT_Jet300_v3','HLT_Jet300_v4','HLT_Jet300_v5',\n                                  'HLT_Jet370_v1','HLT_Jet370_v2','HLT_Jet370_v3','HLT_Jet370_v4','HLT_Jet370_v5','HLT_Jet370_v6'),\n    triggerResults  = cms.InputTag(\"TriggerResults\",\"\",\"HLT\"),\n    triggerEvent    = cms.InputTag(\"hltTriggerSummaryAOD\",\"\",\"HLT\"),\n    ## jec services ##############################\n    pfjecService    = cms.string('ak7PFL1FastL2L3Residual'),\n    calojecService  = cms.string('ak7CaloL1L2L3Residual')\n)\n\nprocess.ak5 = process.ak7.clone(\n    pfjets           = 'ak5PFJets',\n    calojets         = 'ak5CaloJets',\n    PFPayloadName    = 'AK5PF',\n    CaloPayloadName  = 'AK5Calo',\n    calojetID        = 'ak5JetID',\n    calojetExtender  = 'ak5JetExtender',\n    pfjecService     = 'ak5PFL1FastL2L3Residual',\n    calojecService   = 'ak5CaloL1L2L3Residual',\n    printTriggerMenu = False \n)\n############# turn-on the fastjet area calculation needed for the L1Fastjet ##############\n############# applied only to PFJets because if CaloJets are re-recoed the JetID map will be lost #####\nprocess.kt6PFJets.doRhoFastjet = True\nprocess.kt6PFJets.Rho_EtaMax = cms.double(5.0)\nprocess.kt6CaloJets.doRhoFastjet = True\nprocess.kt6CaloJets.Rho_EtaMax = cms.double(5.0)\nprocess.ak7PFJets.doAreaFastjet = True\nprocess.ak7PFJets.Rho_EtaMax = cms.double(5.0)\nprocess.ak7PFJets.jetPtMin = cms.double(5.0)\nprocess.ak5PFJets.doAreaFastjet = True\nprocess.ak5PFJets.Rho_EtaMax = cms.double(5.0)\nprocess.ak5PFJets.jetPtMin = cms.double(5.0)\n\nprocess.path = cms.Path(process.kt6PFJets * process.kt6CaloJets * process.ak5PFJets * process.ak7PFJets * process.ak5 * process.ak7)\n\n\n","sub_path":"KKousour/QCDAnalysis/test/ProcessedTreeProducer_data_cfg.py","file_name":"ProcessedTreeProducer_data_cfg.py","file_ext":"py","file_size_in_byte":6700,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"429173476","text":"import cv2\nimport numpy as np\nimport PIL.Image as Image\n\nfrom carla.image_converter import labels_to_cityscapes_palette\n\nfrom erdos.op import Op\nfrom erdos.utils import setup_logging\n\n\nclass SegmentedVideoOperator(Op):\n    def __init__(self, name, log_file_name=None):\n        super(SegmentedVideoOperator, self).__init__(name)\n        self._logger = setup_logging(self.name, log_file_name)\n\n    @staticmethod\n    def setup_streams(input_streams, filter_name):\n        input_streams.filter_name(filter_name)\\\n            .add_callback(SegmentedVideoOperator.display_frame)\n        return []\n\n    def display_frame(self, msg):\n        frame_array = labels_to_cityscapes_palette(msg.data)\n        img = Image.fromarray(np.uint8(frame_array)).convert('RGB')\n        open_cv_image = np.array(img)\n        cv2.imshow(self.name, open_cv_image)\n        cv2.waitKey(1)\n\n    def execute(self):\n        self.spin()\n","sub_path":"examples/pylot/segmented_video_operator.py","file_name":"segmented_video_operator.py","file_ext":"py","file_size_in_byte":905,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"474853340","text":"'''\n1676번 팩토리얼 0의 개수\n문제:\nN!에서 뒤에서부터 처음 0이 아닌 숫자가 나올 때까지 0의 개수를 구하는 프로그램을 작성하시오.\n입력:\n첫째 줄에 N이 주어진다. (0 ≤ N ≤ 500)\n출력:\n첫째 줄에 구한 0의 개수를 출력한다.\n'''\n\nN = int(input())\nvalue = 1\nfor i in range(N, 0, -1):\n    value *= i\n\nvalue = str(value)\ncount = 0;\nfor i in range(len(value)-1, -1, -1):\n    if value[i] == '0':\n        count += 1\n    else:\n        break\n\nprint(count)","sub_path":"백준/1676.py","file_name":"1676.py","file_ext":"py","file_size_in_byte":513,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"270684312","text":"# coding: utf-8\nimport zerorpc\nimport logging\nimport sys\nimport lenovotf.util.deployHelper as dhelper\nimport os\nimport re\nimport lenovotf.util.confReader as reader\n\nlogging.basicConfig(level=logging.INFO)\n\nserver = None\ngendpoint = None\nWEB_SERVER_CODE_DIR = \"/data/tfcodes/upload/\"\n\n\nclass CentralRPCServer(object):\n    \"\"\"the rpc server run on central\"\"\"\n\n    def __init__(self, endpoint):\n        super(CentralRPCServer, self).__init__()\n        self.endpoint = endpoint\n\n    def do_train(self, app_id, train_params):\n        '''start distribute training, include:\n         1. start training on every worker/ps/chief nodes\n         '''\n        logging.info(\"ready to start training for app: %s\" % app_id)\n        # TODO 根据参数启动各节点训练任务，启动前需要对sy.path进行设置，将打包的依赖添加到应用的path中\n\n    def do_dis_code(self, app_id, cluster):\n        '''deprecated,根据appid 和集群信息分发代码'''\n        # TODO 对源码进行二次打包\n        logging.info(\"start to distribute codes to nodes...\")\n        source_dir = '/data/tfcode/%s' % app_id\n        target_dir = '/data/tfcode/%s' % app_id\n        cmd = \"fab -f ../../util/deployHelper.py deploy_code:%s,%s,%s\" % (app_id, source_dir, cluster)\n        os.system(cmd)\n        # dhelper.deploy_code(source_dir=source_dir, cluster=cluster, app_id=app_id)\n        logging.info(\"end to distribute codes to nodes %s...\" % cluster)\n\n    def do_disCode(self, filename, cluster):\n        '''根据源码文件和集群信息分发代码'''\n        # 对源码进行二次打包，这里文件名是用户环境源码文件完整路径，需要进行处理\n        filename = re.sub(\"\\\\\\\\\", \"/\", filename)\n        # XXX.zip\n        filename = re.split(\"/\", filename)[-1]\n        logging.info(\"repackaging codes for file %s....\" % filename)\n        fullname = reader.get(\"web\", \"upload_store_path\") + filename\n        pkcmd = \"python ./repackage.py %s\" % fullname\n        os.system(pkcmd)\n\n        # {\n        #     'cluster': {'chief': ['192.168.11.39:2222'],\n        #                 'ps': ['192.168.11.39:2223', '192.168.11.39:2224'],\n        #                 'worker': ['192.168.11.39:2224', '192.168.11.39:2225']},\n        #     'hosts': [{'host': '192.168.11.39:2222', 'task': {'type': 'chief', 'index': 0}},\n        #               {'host': '192.168.11.39:2224', 'task': {'type': 'worker', 'index': 0}},\n        #               {'host': '192.168.11.39:2225', 'task': {'type': 'worker', 'index': 1}}]\n        # }\n        hosts = cluster['hosts']\n        host_list = []\n        for host in hosts:\n            host_list.append(re.split(\":\", host['host'])[0])\n        # self.upload_code(file, cluster)\n        logging.info(\"start to distribute source code：%s to nodes%s\" % (fullname, host_list))\n        re_packed_name = re.split(\"\\.\", fullname)[0] + \"_with_dependences.zip\"\n        cmd = \"fab -f ./deployHelper.py deploy_code:%s,%s\" % (re_packed_name, cluster)\n        os.system(cmd)\n        # dhelper.deploy_code(source_dir=source_dir, cluster=cluster, app_id=app_id)\n        logging.info(\"end to distribute codes %s to nodes %s...\" % (re_packed_name, host_list))\n\n\ndef deploy_model(self, app_id):\n    logging.info(\"ready to start deploying model for app: %s\" % app_id)\n    # TODO 模型上线\n\n\ndef start(endpoint):\n    global gendpoint\n    global server\n    gendpoint = endpoint\n    logging.info('starting WorkerRPCServer on ' + endpoint)\n    server = zerorpc.Server(CentralRPCServer(endpoint))\n    server.bind(endpoint)\n    # self.server.bind('tcp://0.0.0.0:4243')\n    server.run()\n\n\ndef stop(self):\n    logging('stopping WorkerRPCServer on %s' % self.endpoint)\n    server.close()\n    global gendpoint\n    gendpoint = None\n\n\nif __name__ == '__main__':\n    endpoint = 'tcp://0.0.0.0:14243'\n    if len(sys.argv) > 1:\n        # print(sys.argv[1])\n        start(sys.argv[1])\n    else:\n        start(endpoint)\n","sub_path":"lenovotf/rpc/central/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":3925,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"384833138","text":"from math import sin, cos, pi\nfrom random import random\n\nimport pyglet\n\nimport stars\n\nclass TestGalaxy(object):\n    def __init__(self, galaxy_map):\n        self.galaxy = galaxy_map\n        self.batch = self.galaxy.batch\n        self.points = []\n        with open('points.txt', 'r') as f:\n            for line in f:\n                self.points.append(float(line[:-1]))\n        self.stars = [stars.Star(self) for x in range(len(self.points))]\n        self.x, self.y = 300, 300\n        \n    def disperse_stars(self):\n        for s in self.stars:\n            ind = self.stars.index(s)\n            s.x = ind\n            s.y = self.points[ind] * 10\n","sub_path":"game/modules/naive_galaxy.py","file_name":"naive_galaxy.py","file_ext":"py","file_size_in_byte":643,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"180047437","text":"from django.contrib import admin\nfrom companys.models import Companys, Job, Welfare\n\n\n# Register your models here.\n\n\nclass CompanysAdmin(admin.ModelAdmin):\n    list_display = ('name', 'type', 'logoImg', 'addr', 'introduce', 'personnel', 'company_start_time')\n\n    search_fields = ('name', 'type')\n\n    list_per_page = 50\n\n\nclass JobAdmin(admin.ModelAdmin):\n    list_display = ('job', 'company', 'min_salary', 'max_salary', 'describe', 'working_years', 'education', 'recruitment')\n\n    search_fields = ('job', 'education')\n\n    list_per_page = 50\n\n\nclass WelfareAdmin(admin.ModelAdmin):\n    list_display = ('name', 'is_delete')\n\n    search_fields = ('name',)\n\n    list_per_page = 50\n\n\nadmin.site.register(Companys, CompanysAdmin)\nadmin.site.register(Job, JobAdmin)\nadmin.site.register(Welfare, WelfareAdmin)\n\nadmin.site.site_header = '仿无忧管理系统'\nadmin.site.site_title = '登录系统后台'\nadmin.site.index_title = '后台管理'\n","sub_path":"apps/companys/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":942,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"359326464","text":"import argparse\nimport tensorflow as tf\nfrom config import *\nfrom os.path import join, exists\nfrom os import makedirs\nimport pickle\nimport math\n\nfrom sklearn.model_selection import train_test_split\n\nFLAGS = None\n\ndef standardize(X):\n    return (X - X.mean())/ X.std()\n\n\ndef load_data():\n    \"\"\"\n    load data from pickle\n    :return:\n    \"\"\"\n    with open(join(FLAGS.source_data), 'rb') as f:\n        data_x = pickle.load(f)\n        data_y = pickle.load(f)\n\n        return standardize(data_x), data_y\n\n\ndef train(loss, global_step):\n    MOVING_AVERAGE_DECAY = 0.9999  # The decay to use for the moving average.\n\n    opt = tf.train.RMSPropOptimizer(0.0001)\n    grads = opt.compute_gradients(loss)\n\n    apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)\n\n    # Track the moving averages of all trainable variables.\n    variable_averages = tf.train.ExponentialMovingAverage(\n        MOVING_AVERAGE_DECAY, global_step)\n    variables_averages_op = variable_averages.apply(tf.trainable_variables())\n\n    with tf.control_dependencies([apply_gradient_op, variables_averages_op]):\n        train_op = tf.no_op(name='train')\n    return train_op\n\ndef get_data(data_x, data_y):\n    \"\"\"\n    split data from loaded data\n    :param data_x:\n    :param data_y:\n    :return: Arrays\n    \"\"\"\n    print('Data X Length', len(data_x), 'Data Y Length', len(data_y))\n    print('Data X Example', data_x[0])\n    print('Data Y Example', data_y[0])\n    \n    train_x, test_x, train_y, test_y = train_test_split(data_x, data_y, test_size=0.4, random_state=40)\n    dev_x, test_x, dev_y, test_y, = train_test_split(test_x, test_y, test_size=0.5, random_state=40)\n    \n    print('Train X Shape', train_x.shape, 'Train Y Shape', train_y.shape)\n    print('Dev X Shape', dev_x.shape, 'Dev Y Shape', dev_y.shape)\n    print('Test Y Shape', test_x.shape, 'Test Y Shape', test_y.shape)\n    return train_x, train_y, dev_x, dev_y, test_x, test_y\n\n\ndef main():\n    data_x, data_y = load_data()\n    n_classes = data_y.shape[-1]\n    train_x, train_y, dev_x, dev_y, test_x, test_y = get_data(data_x, data_y)\n    train_steps = math.ceil(train_x.shape[0] / FLAGS.train_batch_size)\n    dev_steps = math.ceil(dev_x.shape[0] / FLAGS.dev_batch_size)\n    test_steps = math.ceil(test_x.shape[0] / FLAGS.test_batch_size)\n    \n    global_step = tf.Variable(-1, trainable=False, name='global_step')\n    \n    # train and dev dataset\n    train_dataset = tf.data.Dataset.from_tensor_slices((train_x, train_y)).shuffle(10000)\n    train_dataset = train_dataset.batch(FLAGS.train_batch_size)\n    \n    dev_dataset = tf.data.Dataset.from_tensor_slices((dev_x, dev_y))\n    dev_dataset = dev_dataset.batch(FLAGS.dev_batch_size)\n    \n    test_dataset = tf.data.Dataset.from_tensor_slices((test_x, test_y))\n    test_dataset = test_dataset.batch(FLAGS.test_batch_size)\n    \n    print(train_dataset.output_types, test_dataset.output_shapes)\n    \n    # a reinitializable iterator\n    iterator = tf.data.Iterator.from_structure(train_dataset.output_types, train_dataset.output_shapes)\n    \n    train_initializer = iterator.make_initializer(train_dataset)\n    dev_initializer = iterator.make_initializer(dev_dataset)\n    test_initializer = iterator.make_initializer(test_dataset)\n    \n    # input Layer\n    with tf.variable_scope('inputs'):\n        # x.shape = [-1, 60, 160, 3]\n        x, y_label = iterator.get_next()\n    print(x.shape)\n    \n    keep_prob = tf.placeholder(tf.float32, [])\n    # layer1\n\n    def conv_layer(x, layer_num, kernel_num, kernel_size):\n        for _ in range(layer_num):\n            x = tf.layers.conv2d(x, kernel_num, kernel_size=kernel_size, padding=\"same\",\n                                 activation=tf.nn.relu,\n                                 kernel_initializer=tf.contrib.layers.xavier_initializer())\n        x = tf.layers.max_pooling2d(x, pool_size=2, strides=2, padding=\"same\")\n        return x\n\n    x = conv_layer(x, 3, 32, 3)\n    x = conv_layer(x, 3, 64, 3)\n    x = conv_layer(x, 3, 128, 3)\n\n    x = tf.layers.flatten(x)\n    x = tf.layers.dense(x, 1024, activation=tf.nn.relu)\n\n    x = tf.layers.dense(x, 1024, activation=tf.nn.relu)\n    # x = tf.layers.dropout(x, rate=keep_prob)\n    x = tf.layers.dense(x, n_classes)\n\n    # loss\n    cross_entropy = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(logits=x, labels=y_label))\n    max_index_predict = tf.argmax(tf.reshape(x, [-1, CAPTCHA_LENGTH, VOCAB_LENGTH]), axis=2)\n    print('Max Index Predict', max_index_predict)\n    max_index_label = tf.argmax(tf.reshape(y_label, [-1, CAPTCHA_LENGTH, VOCAB_LENGTH]), axis=2)\n    print('Max Index Label', max_index_label)\n\n    correct_predict = tf.equal(max_index_predict, max_index_label)\n    print('Correct predict', correct_predict)\n    accuracy = tf.reduce_mean(tf.reshape(tf.cast(correct_predict, tf.float32), [-1]))\n    \n    # Train\n    train_op = tf.train.RMSPropOptimizer(0.0001).minimize(cross_entropy, global_step=global_step)\n    # train_op = train(cross_entropy, global_step)\n    \n    # Saver\n    saver = tf.train.Saver()\n    \n    # Iterator\n    sess = tf.Session()\n    sess.run(tf.global_variables_initializer())\n    \n    # Global step\n    gstep = 0\n    \n    if FLAGS.train:\n\n        for epoch in range(FLAGS.epoch_num):\n            tf.train.global_step(sess, global_step_tensor=global_step)\n            # Train\n            sess.run(train_initializer)\n            for step in range(int(train_steps)):\n                loss, acc, gstep, _ = sess.run([cross_entropy, accuracy, global_step, train_op],\n                                               feed_dict={keep_prob: FLAGS.keep_prob})\n                # Print log\n                if step % FLAGS.steps_per_print == 0:\n                    print('Global Step', gstep, 'Step', step, 'Train Loss', loss, 'Accuracy', acc)\n            \n            if epoch % FLAGS.epochs_per_dev == 0:\n                # Dev\n                sess.run(dev_initializer)\n                for step in range(int(dev_steps)):\n                    if step % FLAGS.steps_per_print == 0:\n                        print('Dev Accuracy', sess.run(accuracy, feed_dict={keep_prob: 1}), 'Step', step)\n            \n            # Save model\n            if epoch % FLAGS.epochs_per_save == 0:\n                saver.save(sess, FLAGS.checkpoint_dir, global_step=gstep)\n    \n    else:\n        # Load model\n        ckpt = tf.train.get_checkpoint_state('ckpt')\n        saver.restore(sess, ckpt.model_checkpoint_path)\n        print('Restore from', ckpt.model_checkpoint_path)\n        sess.run(test_initializer)\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(description='Captcha')\n    parser.add_argument('--train_batch_size', help='train batch size', default=128)\n    parser.add_argument('--dev_batch_size', help='dev batch size', default=256)\n    parser.add_argument('--test_batch_size', help='test batch size', default=256)\n    parser.add_argument('--source_data', help='source size', default='./data/data.pkl')\n    parser.add_argument('--num_layer', help='num of layer', default=2, type=int)\n    parser.add_argument('--num_units', help='num of units', default=64, type=int)\n    parser.add_argument('--time_step', help='time steps', default=32, type=int)\n    parser.add_argument('--embedding_size', help='time steps', default=64, type=int)\n    parser.add_argument('--category_num', help='category num', default=5, type=int)\n    parser.add_argument('--learning_rate', help='learning rate', default=0.001, type=float)\n    parser.add_argument('--epoch_num', help='num of epoch', default=10000, type=int)\n    parser.add_argument('--epochs_per_test', help='epochs per test', default=100, type=int)\n    parser.add_argument('--epochs_per_dev', help='epochs per dev', default=2, type=int)\n    parser.add_argument('--epochs_per_save', help='epochs per save', default=2, type=int)\n    parser.add_argument('--steps_per_print', help='steps per print', default=2, type=int)\n    parser.add_argument('--steps_per_summary', help='steps per summary', default=100, type=int)\n    parser.add_argument('--keep_prob', help='train keep prob dropout', default=0.5, type=float)\n    parser.add_argument('--checkpoint_dir', help='checkpoint dir', default='ckpt/model.ckpt', type=str)\n    parser.add_argument('--summaries_dir', help='summaries dir', default='summaries/', type=str)\n    parser.add_argument('--train', help='train', default=True, type=bool)\n    \n    FLAGS, args = parser.parse_known_args()\n    \n    main()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8405,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"531375608","text":"def mq_to_arc(number_of_days=30):\n    from app.utils import get_stations\n    from microquake.core import UTCDateTime\n    from microquake.IMS import web_api\n    import pandas as pd\n    site = get_stations()\n    site.stations()[0]\n    site.stations()[0].loc\n\n    endtime = UTCDateTime.now()\n    starttime = endtime - number_of_days * 24 * 3600\n\n    base_url = 'http://10.95.74.35:8002/ims-database-server/databases/mgl'\n\n    cat = web_api.get_catalogue(base_url, starttime, endtime, site, event=True, blast=False)\n    events_xyz = []\n    events_mag = []\n    events_time =[]\n    for event in cat:\n        events_xyz.append(event.preferred_origin().loc)\n        events_mag.append(event.preferred_magnitude().mag)\n        events_time.append(event.preferred_origin().time)\n\n    df_xyz = pd.DataFrame(events_xyz)\n    df_mag = pd.DataFrame(events_mag)\n    df_time =pd.DataFrame(events_time)\n\n    df = pd.merge(df_xyz,df_mag,left_index=True,right_index=True)\n    df = pd.merge(df, df_time, left_index=True, right_index=True)\n\n    df = df.rename(columns={'0_x': 'x',1:'y',2:'z','0_y':'mag', 0: 'time'})\n    df = df[df['mag'] > -100]\n    df = df[df['z'] < 300]\n\n    pseudo_events = 10 ** (df['mag'] * 3 / 2)\n    df_pseudo_events = pseudo_events.to_frame('pseudo_events')\n\n    df_tarp_mag = 10**df['mag']\n    df_tarp_mag = df_tarp_mag.to_frame('df_tarp_mag')\n\n    df = pd.merge(df,df_tarp_mag,left_index=True,right_index=True)\n    df = pd.merge(df, df_pseudo_events, left_index=True, right_index=True)\n\n    return df\n\ndef eom_report(instrument_paths=['/Underground/MPBX/MDT MPBXs',\n                             '/Underground/MPBX/MDT SmartCables',\n                             '/Underground/MPBX/YieldPoint'],\n               unit_of_time='month', length_of_data=2):\n    '''\n    :param instrument_paths: list of instrument paths in canary\n    :param unit_of_time: used to filter data eg. day, week, month, year\n    :param length_of_data: number of unit_of_time to be return from today\n            defaults recalls all data from the previous month\n    :return:\n    '''\n\n    import gsct.canary.tools as tools\n    import gsct.montoring.report as report\n    unread_i = tools.unread_instrument(instrument_paths=instrument_paths,\n                                       length_of_data=length_of_data,\n                                       unit_of_time='year')\n\n    max_d = tools.maxdisp_xyz_latest(instrument_paths=instrument_paths,\n                                     unit_of_time=unit_of_time,\n                                     length_of_data=length_of_data,\n                                     dipfrom=-91)\n\n   # sd = report.mq_to_arc()\n\n    return unread_i, max_d#, sd","sub_path":"gsct/montoring/report.py","file_name":"report.py","file_ext":"py","file_size_in_byte":2658,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"538364948","text":"# 40. Combination Sum II\n# https://leetcode.com/problems/combination-sum-ii/description/\n\n# Runtime: 48 ms\n# Memory Usage: 13.2 MB\n# runtime beats 92.78 % of python3 submissions.\n# memory usage beats 66.34 % of python3 submissions.\n\nclass Solution:\n    def combinationSum2(self, candidates: List[int], target: int) -> List[List[int]]:\n        if not candidates:\n            return []\n        \n        candidates.sort()\n        ans = []\n        \n        def backtrack(comb, idx, target):\n            if target == 0:\n                ans.append(comb)\n                return\n            if idx >= len(candidates) or candidates[idx] > target:\n                return\n            for i in range(idx, len(candidates)):\n                if i > idx and candidates[i] == candidates[i-1]:\n                    continue\n                if target >= candidates[i]:\n                    backtrack(comb + [candidates[i]], i+1, target - candidates[i])\n        \n        backtrack([], 0, target)\n        \n        return ans","sub_path":"Algorithm/HW04_Backtracking/leetcode040_Combination_Sum_II.py","file_name":"leetcode040_Combination_Sum_II.py","file_ext":"py","file_size_in_byte":1001,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"60595076","text":"# Copyright 2021 The Chromium Authors. All rights reserved.\n# Use of this source code is governed by a BSD-style license that can be\n# found in the LICENSE file.\n\nfrom __future__ import print_function\n\nimport argparse\nimport os\nimport shutil\nimport subprocess\nimport sys\nimport tempfile\n\nfrom gpu_tests import path_util\n\nsys.path.insert(0,\n                os.path.join(path_util.GetChromiumSrcDir(), 'build', 'fuchsia'))\nfrom common_args import (AddCommonArgs, AddTargetSpecificArgs, ConfigureLogging,\n                         GetDeploymentTargetForArgs)\nfrom symbolizer import RunSymbolizer\n\n\ndef RunTestOnFuchsiaDevice(script_cmd):\n  \"\"\"Preps Fuchsia device with pave and package update, then runs script.\"\"\"\n\n  parser = argparse.ArgumentParser()\n  AddCommonArgs(parser)\n  AddTargetSpecificArgs(parser)\n  runner_script_args, test_args = parser.parse_known_args()\n  ConfigureLogging(runner_script_args)\n\n  # If out_dir is not set, assume the script is being launched\n  # from the output directory.\n  if not runner_script_args.out_dir:\n    runner_script_args.out_dir = os.getcwd()\n\n  # Create a temporary log file that Telemetry will look to use to build\n  # an artifact when tests fail.\n  temp_log_file = False\n  if not runner_script_args.system_log_file:\n    runner_script_args.system_log_file = os.path.join(tempfile.mkdtemp(),\n                                                      'system-log')\n    temp_log_file = True\n\n  package_names = ['web_engine_with_webui', 'web_engine_shell']\n  web_engine_dir = os.path.join(runner_script_args.out_dir, 'gen', 'fuchsia',\n                                'engine')\n\n  # Pass all other arguments to the gpu integration tests.\n  script_cmd.extend(test_args)\n  listener_process = None\n  symbolizer_process = None\n  try:\n    with GetDeploymentTargetForArgs(runner_script_args) as target:\n      target.Start()\n      fuchsia_device_address, fuchsia_ssh_port = target._GetEndpoint()\n      script_cmd.extend(\n          ['--chromium-output-directory', runner_script_args.out_dir])\n      script_cmd.extend(['--fuchsia-device-address', fuchsia_device_address])\n      script_cmd.extend(['--fuchsia-ssh-config', target._GetSshConfigPath()])\n      if fuchsia_ssh_port:\n        script_cmd.extend(['--fuchsia-ssh-port', str(fuchsia_ssh_port)])\n      script_cmd.extend(\n          ['--fuchsia-system-log-file', runner_script_args.system_log_file])\n      # Add to the script\n      if runner_script_args.verbose:\n        script_cmd.append('-v')\n\n      # Set up logging of WebEngine\n      listener_process = target.RunCommandPiped(['log_listener'],\n                                                stdout=subprocess.PIPE,\n                                                stderr=subprocess.STDOUT)\n      build_ids_paths = map(\n          lambda package_name: os.path.join(web_engine_dir, package_name,\n                                            'ids.txt'), package_names)\n      symbolizer_process = RunSymbolizer(\n          listener_process.stdout, open(runner_script_args.system_log_file,\n                                        'w'), build_ids_paths)\n\n      # Keep the Amber repository live while the test runs.\n      with target.GetAmberRepo():\n        # Install necessary packages on the device.\n        far_files = map(\n            lambda package_name: os.path.join(web_engine_dir, package_name,\n                                              package_name + '.far'),\n            package_names)\n        target.InstallPackage(far_files)\n        return subprocess.call(script_cmd)\n  finally:\n    if temp_log_file:\n      shutil.rmtree(os.path.dirname(runner_script_args.system_log_file))\n    if listener_process:\n      listener_process.kill()\n    if symbolizer_process:\n      symbolizer_process.kill()\n","sub_path":"content/test/gpu/fuchsia_util.py","file_name":"fuchsia_util.py","file_ext":"py","file_size_in_byte":3722,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"137221155","text":"from time import sleep\nfrom dbmodules.dbconnection import *\n#from multiprocessing import Process\nimport os, signal\nimport sys\nfrom commandFile import *\nimport threading\nimport itertools\n\n\noutput_lock = threading.Lock()\nttl = sys.argv[2]\n\ndbClient = MongoConnection()\n\n\nneighbors = dbClient.getNeighbors()\n\nif sys.argv[1] == \"4\":\n\n    pktId = find_peers(output_lock, neighbors, ttl)\n    dbClient.insertPeersPktId(pktId)\n\nelse:\n    searchStr = sys.argv[3]\n    pktId = sys.argv[4]\n    info = find_file(output_lock, neighbors, pktId, searchStr, ttl)\n    dbClient.insertFilePktId(info)\n\nsleep(5)\n\nif sys.argv[2] == \"3\":\n    dbClient.finishSearchFile(sys.argv[1])\nelse:\n    dbClient.finishSearchPeers(sys.argv[1])\n","sub_path":"helpermodules/sleepProcess.py","file_name":"sleepProcess.py","file_ext":"py","file_size_in_byte":708,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"643179832","text":"# syft absolute\nimport syft as sy\nfrom syft.lib.python.complex import Complex\n\n\ndef test_serde() -> None:\n    syft_complex = Complex(\"2+3j\")\n\n    serialized = sy.serialize(syft_complex)\n\n    deserialized = sy.deserialize(serialized)\n\n    assert isinstance(deserialized, Complex)\n    assert deserialized == syft_complex\n\n\ndef test_send(client: sy.VirtualMachineClient) -> None:\n    syft_complex = Complex(\"2+3j\")\n    ptr = syft_complex.send(client)\n    # Check pointer type\n    assert ptr.__class__.__name__ == \"ComplexPointer\"\n\n    # Check that we can get back the object\n    res = ptr.get()\n    assert res == syft_complex\n\n\ndef test_complex_bytes() -> None:\n    # Testing if multiple serialization of the similar object results in same bytes\n    value_1 = Complex(5, 3)\n    value_2 = Complex(5, 3)\n    assert sy.serialize(value_1, to_bytes=True) == sy.serialize(value_2, to_bytes=True)\n","sub_path":"packages/syft/tests/syft/lib/python/complex/complex_serde_test.py","file_name":"complex_serde_test.py","file_ext":"py","file_size_in_byte":887,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"159227304","text":"# polynomial regression\n# multiple varaiables\n\nfrom matplotlib import pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nimport numpy as np\n\n# parameteres\n# theta0, theta1,theta2\ntheta0 = 300\ntheta1 = 1\ntheta2 = 0.1\ntheta3 = 0.01\n\ndef gradient_descent(x:list, y:list):\n    cost_functions = []\n    x1 = x\n    x2 = []\n    x3 = []\n    # the number of training set\n    m = len(y)\n\n    # learning rate alpha\n    alpha = 0.0004\n\n    # caculate\n    for i in range(0, m):\n        x2.append(x[i] * x[i])\n        x3.append(x[i] * x[i] * x[i])\n\n    global theta0\n    global theta1 \n    global theta2\n    global theta3\n    \n    # gredient descent loop\n    k = 1\n    while k< 100001:\n        total = 0\n        for i in range(0, m):\n            total = total + pow((theta0 + theta1 * x1[i] + theta2 * x2[i] + theta3 * x3[i] - y[i]), 2)\n        cost_function = total / (2 * m)\n        cost_functions.append(cost_function)\n        print(f\"time:{k},cost:{cost_function}\")\n\n        total_theta0 = 0\n        for i in range(0, m):\n            total_theta0 = total_theta0 + (theta0 + theta1 * x1[i] + theta2 * x2[i] + theta3 * x3[i] - y[i])\n\n        total_theta1 = 0\n        for i in range(0, m):\n            total_theta1 = total_theta1 + (theta0 + theta1 * x1[i] + theta2 * x2[i] + theta3 * x3[i] - y[i]) *x1[i]\n\n        total_theta2 = 0\n        for i in range(0, m):\n            total_theta2 = total_theta2 + (theta0 + theta1 * x1[i] + theta2 * x2[i] + theta3 * x3[i] - y[i]) * x2[i]\n        \n        total_theta3 = 0\n        for i in range(0, m):\n            total_theta3 = total_theta3 + (theta0 + theta1 * x1[i] + theta2 * x2[i] + theta3 * x3[i] - y[i]) * x3[i]\n\n        temp0 = theta0 - alpha * total_theta0 / m\n        temp1 = theta1 - alpha * total_theta1 / m\n        temp2 = theta2 - alpha * total_theta2 / m\n        temp3 = theta3 - alpha * total_theta3 / m\n        theta0 = temp0\n        theta1 = temp1\n        theta2 = temp2\n        theta3 = temp3\n\n        k = k + 1\n    return cost_functions\n\ndef plot_cost_function(cost_functions:list):\n    x = np.arange(1,100001)\n    y = np.array(cost_functions)\n    plt.title(\"Gradient Descent\") \n    plt.xlabel(\"times\") \n    plt.ylabel(\"cost function\") \n    plt.plot(x, y) \n    plt.show()\n\ndef plot_figure(x:list, y:list):\n    # TODO\n    pass\n\nif __name__ == \"__main__\":\n    with open(\"data/distances.txt\", 'r') as f:\n        distances = f.read().split(\"\\n\")\n    with open(\"data/prices.txt\", 'r') as f:\n        prices = f.read().split(\"\\n\")\n    \n    m = len(prices)\n\n    for i in range(0, m):\n        distances[i] = float(distances[i])\n    for i in range(0, m):\n        prices[i] = float(prices[i])\n\n    # mean normalization\n    temp = []\n    for i in range(0, m):\n        mean = sum(distances) / m\n        temp.append(round((distances[i] - mean) / (max(distances) - min(distances)), 2))\n    normolization_distances = temp\n    # result\n    cost_function = gradient_descent(x=normolization_distances, y=prices)\n    plot_cost_function(cost_function)\n    plot_figure(x1=distances, y=prices)\n   ","sub_path":"week2/algorithm3.py","file_name":"algorithm3.py","file_ext":"py","file_size_in_byte":3024,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"277609992","text":"import os\nimport xmlrunner\nimport unittest\nimport requests\nfrom selenium import webdriver\nfrom pages.main_page import MainPage\n\n\ndata = {}\n\n\n\nclass GoogleTest1(unittest.TestCase):\n    def setUp(self):\n        if os.name == 'nt':\n            self.driver = webdriver.Chrome(executable_path='./chrome_driver/chromedriver.exe')\n        else:\n            self.driver = webdriver.Chrome(executable_path='./chrome_driver/chromedriver')\n        self.driver.implicitly_wait(10)\n\n        self.driver.get('http://google.com.ua')\n\n        self.main_page = MainPage(self.driver)\n        self.result = self.main_page.search('tanks')\n\n    def test_search_results_count(self):\n        count = self.result.search_results_count\n        data['search_results_count'] = count\n        self.assertGreaterEqual(12, count)  # or self.assertTrue(12 >= count)\n\n    def test_is_domain_in_results(self):\n        result_domains = self.result.search_results_domain_list\n        self.assertTrue('worldoftanks.ru' in result_domains)\n\n    def tearDown(self):\n        data['last_url'] = self.driver.current_url\n        self.driver.quit()\n\n\nclass GoogleTest2(unittest.TestCase):\n    def setUp(self):\n        HEADERS = {\n            'Accept-Language': 'ru-RU,ru;q=0.8,en-US;q=0.5,en;q=0.3',\n            'User-Agent': 'googlebot',\n            'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',\n            'Referer': ''\n        }\n        self.s = requests.Session()\n        self.s.headers.update(HEADERS)\n\n    def test_url_available(self):\n        try:\n            self.s.head(data['last_url'])\n        except Exception as e:\n            assert False, 'Network connection error'\n\n    def test_is_domain_in_html(self):\n        try:\n            r = self.s.get(data['last_url'])\n        except Exception as e:\n            assert False, 'Network connection error'\n\n        self.assertIn('worldoftanks.ru', r.text)\n\n    def test_search_results_count(self):\n        try:\n            r = self.s.get(data['last_url'])\n        except Exception as e:\n            assert False, 'Network connection error'\n        # results_count = len(findall(r'h3 class=\"r\"', r.text))  # using regexp\n        results_count = r.text.count('h3 class=\"r\"')\n        self.assertEqual(results_count, data['search_results_count'])\n\n\n@unittest.skip\nclass GoogleMailTest1(unittest.TestCase):\n    def setUp(self):\n        if os.name == 'nt':\n            self.driver = webdriver.Chrome(executable_path='./chrome_driver/chromedriver.exe')\n        else:\n            self.driver = webdriver.Chrome(executable_path='./chrome_driver/chromedriver')\n        self.driver.implicitly_wait(10)\n\n        self.driver.get('http://google.com.ua')\n        self.main_page = MainPage(self.driver)\n\n    def test_login_get_1st_email(self):\n        email_page = self.main_page.open_email()\n        if not email_page.is_signed_in():\n            email_page.login_with('ex3me0@gmail.com', 'crashc0derf0rmat')\n        print(email_page.get_first_email().text.strip())\n\n    def tearDown(self):\n        self.driver.quit()\n\n\nif __name__ == '__main__':\n    runner = xmlrunner.XMLTestRunner(output='reports')\n    unittest.main(testRunner=runner, warnings='ignore')","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3183,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"557292206","text":"from django.contrib import messages\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\nfrom django.contrib.auth.decorators import login_required\nfrom django.db.models import Q\nfrom django.http import HttpResponse, HttpResponseRedirect, Http404\nfrom django.shortcuts import render, get_object_or_404, redirect\nfrom django.contrib.contenttypes.models import ContentType\nfrom comments.models import Comment\nfrom .models import Post\nfrom .forms import PostForm\nfrom comments.forms import CommentForm\nfrom django.views.generic import DetailView, ListView\n\n\n\n\n@login_required(login_url=\"/login\")\ndef post_create(request): # Create post (PUT)\n    if not request.user.is_authenticated:\n        raise Http404\n    form = PostForm(request.POST or None, request.FILES or None)\n    if form.is_valid():\n        instance=form.save(commit=False)\n        instance.user = request.user\n        instance.save()\n        form.save_m2m()\n        print(form.cleaned_data)\n        # Will put message saying success\n        messages.success(request, \"Successfully created\")\n        return HttpResponseRedirect(instance.get_absolute_url())\n    # else:\n    #     messages.error(request, \"Not successfully created\")\n    tags = Post.tag.all()\n    lenTags = int(len(tags)/2)\n    tags_1st = tags[:lenTags]\n    tags_2nd = tags[lenTags:]\n    context = {\n        'title': 'Ask A Question',\n        \"form\": form,\n        'tags_1st': tags_1st,\n        'tags_2nd': tags_2nd\n    }\n    return render(request, \"posts/post_form.html\", context)\n\n\n\ndef post_detail(request, id): # Retrive post (GET)\n    instance=get_object_or_404(Post, id=id)\n\n    initial_data = {\n        \"content_type\": instance.get_content_type,\n        \"object_id\": instance.id,\n    }\n\n    form = CommentForm(request.POST or None, request.FILES or None, initial=initial_data)\n    if form.is_valid() and request.user.is_authenticated:\n        print(form.cleaned_data)\n        c_type = form.cleaned_data.get(\"content_type\")\n        content_type = ContentType.objects.get(model=c_type)\n        obj_id = form.cleaned_data.get(\"object_id\")\n        content_data = form.cleaned_data.get(\"content\")\n        model_pic = form.cleaned_data.get(\"image\")\n        new_comment, created = Comment.objects.get_or_create(\n            user = request.user,\n            content_type=content_type,\n            object_id = obj_id,\n            content=content_data,\n            model_pic=model_pic,\n        )\n\n        if created:\n            print(\"yeah\")\n\n\n    content_type=ContentType.objects.get_for_model(Post)\n    obj_id=instance.id\n    comments=Comment.objects.filter(content_type=content_type, object_id=obj_id)\n    tags = Post.tag.all()\n    lenTags = int(len(tags)/2)\n    tags_1st = tags[:lenTags]\n    tags_2nd = tags[lenTags:]\n    context={\n        'title': instance.title,\n        'instance': instance,\n        'comments': comments,\n        'comment_form': form,\n        'tags_1st': tags_1st,\n        'tags_2nd': tags_2nd,\n    }\n    return render(request, \"posts/post_detail.html\", context)\n\n\ndef post_list(request): # Retrive post (GET)\n    queryset_list = Post.objects.all().order_by(\"-timestamp\")\n    # print(\"queryset_list: \", len(queryset_list))\n    query = request.GET.get(\"q\")\n    if query:\n        queryset_list=queryset_list.filter(\n            Q(title__icontains=query) |\n            Q(content__icontains=query) |\n            Q(user__first_name__icontains=query) |\n            Q(user__last_name__icontains=query)\n        ).distinct()\n\n    paginator = Paginator(queryset_list, 10)  # Show 25 contacts per page\n    page = request.GET.get('page')\n    queryset = paginator.get_page(page)\n\n\n    tags = Post.tag.all()\n    print(tags)\n    lenTags = int(len(tags)/2)\n    tags_1st = tags[:lenTags]\n    tags_2nd = tags[lenTags:]\n    context={\n        'title': 'List',\n        'object_list': queryset,\n        'queryset_list': len(queryset_list),\n        'tags_1st': tags_1st,\n        'tags_2nd': tags_2nd\n    }\n\n    return render(request, \"posts/index.html\", context)\n\n\n\ndef post_update(request, id=None):\n    instance = get_object_or_404(Post, id=id)\n    form = PostForm(request.POST or None, request.FILES or None, instance=instance)\n    if form.is_valid():\n        instance = form.save(commit=False)\n        instance.save()\n        # Will put message saying success\n        messages.success(request, \"Successfully updated\")\n        return HttpResponseRedirect(instance.get_absolute_url())\n    context = {\n        'title': instance.title,\n        'instance': instance,\n        'form': form,\n    }\n    return render(request, \"posts/post_form.html\", context)\n\n\ndef post_delete(request, id=None):\n    if not request.user.is_authenticated:\n        raise Http404\n\n    instance = get_object_or_404(Post, id=id)\n    instance.delete()\n    messages.success(request, \"Successfully deleted\")\n    return redirect('posts:list')\n\n\ndef TagIndexView(request, slug):\n    print(slug)\n\n    queryset_list = Post.objects.filter(tag__slug=(slug)).order_by(\"-timestamp\")\n    print(queryset_list)\n    tags = Post.tag.all()\n    lenTags = int(len(tags)/2)\n    tags_1st = tags[:lenTags]\n    tags_2nd = tags[lenTags:]\n    context = {\n        'title': 'List',\n        'object_list': queryset_list,\n        'queryset_list': len(queryset_list),\n        'tags_1st': tags_1st,\n        'tags_2nd': tags_2nd\n    }\n\n    return render(request, \"posts/index.html\", context)\n\n","sub_path":"posts/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5375,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"503985650","text":"from ..plots import *\nfrom pymc import psample, Slice, Metropolis, find_hessian, sample\n\n\ndef test_plots():\n\n    # Test single trace\n    from pymc.examples import arbitrary_stochastic as asmod\n\n    with asmod.model as model:\n\n        start = model.test_point\n        h = find_hessian(start)\n        step = Metropolis(model.vars, h)\n        trace = sample(3000, step, start)\n\n        forestplot(trace)\n\n        autocorrplot(trace)\n\n\ndef test_multichain_plots():\n\n    from pymc.examples import disaster_model as dm\n\n    with dm.model as model:\n        # Run sampler\n        step1 = Slice([dm.early_mean, dm.late_mean])\n        step2 = Metropolis([dm.switchpoint])\n        start = {'early_mean': 2., 'late_mean': 3., 'switchpoint': 50}\n        ptrace = psample(1000, [step1, step2], start, threads=2)\n\n    forestplot(ptrace, vars=['early_mean', 'late_mean'])\n\n    autocorrplot(ptrace, vars=['switchpoint'])\n","sub_path":"pymc/tests/test_plots.py","file_name":"test_plots.py","file_ext":"py","file_size_in_byte":904,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"268840891","text":"import pandas as pd\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import train_test_split\nimport numpy as np\nfrom bs4 import BeautifulSoup\nfrom nltk.tokenize import WordPunctTokenizer\nfrom sklearn import preprocessing\nfrom sklearn import metrics\nimport re\nimport pickle\nfrom sklearn.model_selection import train_test_split\nfrom nltk.corpus import stopwords\nfrom autocorrect import Speller\n\nnltkstop = set(stopwords.words('english'))\nstop = ['an', 'and', 'a', 'for', 'was', 'we','they', 'do', 'of', 'am',\n        'who', 'as', 'from', 'had', 'in', 'those']\ncombined_pat = \"(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)\"\n\ndef tweet_cleaner(text):\n    soup = BeautifulSoup(text, 'lxml')\n    souped = soup.get_text()\n    stripped = re.sub(combined_pat, '', souped)\n    try:\n        clean = stripped.decode(\"utf-8-sig\").replace(u\"\\ufffd\", \"?\")\n    except:\n        clean = stripped\n    letters_only = re.sub(\"[^a-zA-Z]\", \" \", clean)\n    lower_case = letters_only.lower()\n    # During the letters_only process two lines above, it has created unnecessay white spaces,\n    # Tokenize and join together to remove unneccessary white spaces\n\n    words = tok.tokenize(lower_case)    \n    #words = tok.tokenize(lower_case)\n    #words = [spell(w) for w in words2]\n\n    #words = [w for w in words if w not in stopwords.words('english')]\n    return (\" \".join(words)).lower().strip()\n\ndf = pd.read_csv('./datasets/training80k.csv', names=['label', 'sentence'], sep='\\t', engine='python')\ntok = WordPunctTokenizer()\n\nX = df['sentence'].values\ny_train = df['label'].values\n\ndf_test = pd.read_csv('./datasets/test20k.csv', names=['label', 'sentence'], sep='\\t', engine='python')\n\n\nX_val, X_test, y_val, y_test = train_test_split(df_test['sentence'].values, df_test['label'].values,random_state = 9, test_size=0.5)\n\nspell = Speller(fast=True)\n\nX_train = [tweet_cleaner(t) for t in X]\nX_test = [tweet_cleaner(t) for t in X_test]\nX_val = [tweet_cleaner(t) for t in X_val]\n\ntfidf = TfidfVectorizer(max_df = 0.08, ngram_range = (1,2)) #Can add stopwords here\nX = tfidf.fit_transform(X_train)\nle = preprocessing.LabelEncoder()\nle.fit(y_train)\ntarget_labels = le.classes_\ntrain_y = le.transform(y_train)\n\ncls = LogisticRegression(verbose = 1, random_state=0, solver='lbfgs', max_iter=400)\ncls.fit(X, train_y)\nyp = cls.predict(X)\nacc = metrics.accuracy_score(train_y, yp)\nprint(\"  Accuracy on %s  is: %s\" % ('train', acc))\n\nvalid = tfidf.transform(X_val)\nyp = cls.predict(valid)\nacc = metrics.accuracy_score(y_val, yp)\nprint(\"  Accuracy on %s  is: %s\" % ('valid', acc))\n\ntest = tfidf.transform(X_test)\nyp = cls.predict(test)\nacc = metrics.accuracy_score(y_test, yp)\nprint(\"  Accuracy on %s  is: %s\" % ('test', acc))\n\n\n","sub_path":"logistic.py","file_name":"logistic.py","file_ext":"py","file_size_in_byte":2845,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"592965655","text":"from Circle import Circle\n\ndef main():\n    # Create a circle with radius 1\n    circle1 = Circle()\n    print(\"반지름이 \", circle1.radius,\n        \"인 원의 넓이는 \", circle1.getArea(), \"입니다.\")\n\n\n    # Create a circle with radius 25\n    circle2 = Circle(25)\n    print(\"반지름이 \", circle2.radius,\n        \"인 원의 넓이는 \", circle2.getArea(), \"입니다.\")\n\n    # Create a circle with radius 125\n    circle3 = Circle(125)\n    print(\"반지름이 \", circle3.radius,\n        \"인 원의 넓이는 \", circle3.getArea(), \"입니다.\")\n\n    # Modify circle radius\n    circle2.radius = 100\n    print(\"반지름이 \", circle2.radius,\n        \"인 원의 넓이는 \", circle2.getArea(), \"입니다.\")\n\nmain() # Call the main function\n","sub_path":"lec07/TestCircle.py","file_name":"TestCircle.py","file_ext":"py","file_size_in_byte":749,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"630743119","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.shortcuts import render\n\nfrom django.http import HttpResponse,Http404\nfrom django.template.loader import get_template\nimport random\nimport time\nfrom datetime import datetime\nfrom dell.models import DellServer\nfrom rest_framework import serializers, viewsets\n\ndef index(request):\n    template = get_template('index.html')\n    quotes = [ '今日事，今日毕',\n               '要收获,先付出',\n               '知识就是力量',\n               '无言的爱',\n               '无欲则刚',\n               '喜欢一个人没有道理',\n               '一个人的个性就是他的命']\n    html = template.render({'quote': random.choice(quotes)})\n    return HttpResponse(html)\n    \ndef bootstrap(request):\n    template = get_template('bootstrap.html')\n    quotes = [ '今日事，今日毕',\n               '要收获,先付出',\n               '知识就是力量',\n               '无言的爱',\n               '无欲则刚',\n               '喜欢一个人没有道理',\n               '一个人的个性就是他的命']\n    html = template.render({'quote': random.choice(quotes)})\n    return HttpResponse(html)\n\ndef bank(request):\n    template = get_template('bank.html')\n    quotes = [ '今日事，今日毕',\n               '要收获,先付出',\n               '知识就是力量',\n               '无言的爱',\n               '无欲则刚',\n               '喜欢一个人没有道理',\n               '一个人的个性就是他的命']\n    html = template.render({'quote': random.choice(quotes)})\n    return HttpResponse(html)\n\ndef alldellserver(request):\n    dellserver = DellServer.objects.all()\n    template = get_template('dellserver.html')\n    now = datetime.now()\n    servernum = len(dellserver)\n    html = template.render(locals())\n    return HttpResponse(html)\n\n\ndef server_info(request,stg):\n    try:\n        server = DellServer.objects.get(stg=stg)\n    except alldellserver.DoesNotExit:\n        raise Http404('Not find ')\n    expiretime2 = server.endTime\n    expiretime = str(server.endTime).split('+')[0]\n\n    template = get_template('serverdisp.html')\n    nowtime = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n    expire1 = datetime.strptime(expiretime,\"%Y-%m-%d %H:%M:%S\")\n    nowtime1 = datetime.strptime(nowtime,\"%Y-%m-%d %H:%M:%S\")\n    remaintime = str(expire1 - nowtime1)\n    avlTime  =  remaintime.split('days')[0]\n\n    html = template.render(locals())\n    return HttpResponse(html)\n\ndef search(request):\n    request.encoding = 'utf-8'\n    dellserver = []\n    if 'q' in request.GET:\n        searchstr =  request.GET['q'].encode('utf-8')\n        message = '你搜索的内容为: ' + request.GET['q'].encode('utf-8')\n        aproducts = DellServer.objects.all()\n        for each in range(len(aproducts)):\n            if searchstr in  aproducts[each].stg or searchstr in  aproducts[each].MachineDescription:\n                dellserver.append(aproducts[each])\n        if len(dellserver) > 0:\n            template = get_template('search.html')\n            servernum = len(dellserver)\n            html = template.render(locals())\n           #html = template.render({'dellserver': dellserver})\n            return HttpResponse(html)\n        else:\n            message = '你搜索的内容为空'\n            return HttpResponse(message)\n            \n        #p = Product.objects.get(sku=sku)\n    else:\n        message = '你搜索的内容为空'\n        return HttpResponse(message)\n\nclass DellSerializer(serializers.HyperlinkedModelSerializer):\n    class Meta:\n        model = DellServer\n        #fields = ('pmodel', 'nickname','description', 'year', 'price')\n        fields = ('stg','MachineDescription', 'ServiceLevelDescription', 'startTime', 'endTime')\n\nclass DellviewSet(viewsets.ModelViewSet):\n    queryset = DellServer.objects.all()\n    serializer_class = DellSerializer\n\n","sub_path":"django/idcs/dell/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3906,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"541704082","text":"school = [\"locker\" , \"classroom\"]\nprint (school)\nprint(\"You are the Class President of your school and you misplaced your bag after many meetings. Your job is to find your bag before the school closes!\")\nchoice = input(\"Your beginning location is the School. You have the choice of looking at your locker or the classroom your meetings were in. Which one? \\n\")\nchoice = choice.lower()\n\nif choice == \"locker\":\n    locker = [\"textbook\", \"gym clothes\"]\n    print(\"These are the items in your  \" + str(choice) + str(locker))\n    lockerChoice = input(\"You can check under the textbook or under the gym clothes. Which one? \\n\")\n    lockerChoice = lockerChoice.lower()\n    if lockerChoice == \"textbook\":\n        print(\"You looked under your textbook but there was nothing! Try again!\")\n    elif lockerChoice == \"gym clothes\":\n        print(\"Your gym clothes did not cover your bag! Try again!\")\nelif choice == \"classroom\":\n    classroom = [\"desk\", \"window sill\"]\n    print(\"These are the places where you usually place your bag down in the \" + str(choice) + str(classroom))\n    classroomChoice = input(\"Where would you like to look? \\n\")\n    classroomChoice = classroomChoice.lower()\n    if classroomChoice == \"desk\":\n        print(\"Sorry, there was nothing on your desk! Try again!\")\n    elif classroomChoice == \"window sill\":\n        print(\"You found your bag! Congrats!\")\n        \n    ","sub_path":"Text Adventure/textadventure.py","file_name":"textadventure.py","file_ext":"py","file_size_in_byte":1381,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"317282933","text":"from PyQt5.QtWidgets import QWidget, QApplication, QVBoxLayout, \\\n                            QLabel, QPushButton, QTableWidget, QTableWidgetItem\nfrom PyQt5.QtCore import Qt\nimport sys\nimport json\nimport requests\n\nclass 표위젯(QWidget):  \n    def __init__(self):\n        super().__init__()\n        self.initUI()\n\n    def initUI(self):        \n        self.table = QTableWidget(150, 5, self)\n        header = ['종목', '전일 종가', '변동가', '변동률', '시가']\n\n        bithumbUrl = 'https://api.bithumb.com/public/ticker/ALL_KRW' \n        # 빗썸 오픈 API : https://apidocs.bithumb.com/docs/ticker 참고\n        data = json.loads(requests.get(bithumbUrl).text)\n\n        # print(data)\n        for index, coin in enumerate(data['data']):  # data에서 data항목만 뽑아서 순서를 정해 줌\n            if coin == 'date':\n                break\n            self.table.setItem(index, 0, QTableWidgetItem(coin)) \n            self.table.setItem(index, 1, QTableWidgetItem(data['data']\\\n                                         [coin]['prev_closing_price']+'원'))  # 전일종가\n            self.table.setItem(index, 2, QTableWidgetItem(data['data']\\\n                                         [coin]['fluctate_24H']+'원'))        # 최근24시간 변동가\n            self.table.setItem(index, 3, QTableWidgetItem(data['data']\\\n                                         [coin]['fluctate_rate_24H']+\" %\"))   # 최근24시간 변동률\n            self.table.setItem(index, 4, QTableWidgetItem(data['data']\\\n                                         [coin]['opening_price']+\"원\"))       # 시가\n        \n        self.table.setHorizontalHeaderLabels(header)\n        self.table.cellClicked.connect(self.showCellPosition)\n\n        self.label = QLabel()\n        vbox = QVBoxLayout()\n        vbox.addWidget(self.table)\n        vbox.addWidget(self.label, alignment=Qt.AlignCenter)\n\n        self.setLayout(vbox)\n\n        self.setWindowTitle('QTableWidget')\n        self.setGeometry(300, 300, 720, 500)\n        self.show()\n\n    def showCellPosition(self):\n        행 = self.table.currentColumn()\n        열 = self.table.currentRow()\n        self.label.setText(f'{행} 행, {열} 열입니다.')\n    \napp = QApplication(sys.argv)\n실행인스턴스 = 표위젯()\napp.exec_()\n","sub_path":"419_표위젯(QTableWidget).py","file_name":"419_표위젯(QTableWidget).py","file_ext":"py","file_size_in_byte":2287,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"640960851","text":"__author__ = 'nghia'\n\nfrom pymir import AudioFile\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef read_wav(filePath, windowTime):\n    '''\n\n    read a music file & return\n\n    :param filePath:\n    :param windowTime:\n    :return: list (windows, wavData)\n    '''\n    wavData = AudioFile.open(filePath)\n    windows = wavData.frames(wavData.sampleRate*windowTime/1000)\n    return [windows, wavData]\n\n\ndef spectral_centroid(window):\n    return window.spectrum().centroid()\n\n\ndef short_term_energy(window):\n    xi = 0  # the short-term energy for the i-th window\n    for m in range(0, len(window) - 1):\n        hamming = 0.54 + 0.46 * np.cos(2 * np.pi * (len(window) - m - 1) / (len(window) - 1))  # use hamm)ing window\n        xi += np.power(window[m], 2) * np.power(hamming, 2)\n    return xi\n\n\ndef test():\n    windows = read_wav(\"input/example.wav\", 10)\n    X = [short_term_energy(window) for window in windows]\n    Y = [spectral_centroid(window) for window in windows]\n\n    # plot the first 1024 samples\n    plt.plot(Y)\n\n    # label the axes\n    plt.ylabel(\"Amplitude\")\n    plt.xlabel(\"Time (samples)\")\n\n    # set the title\n    plt.title(\"Flute Sample\")\n    # display the plot\n    plt.show()","sub_path":"MusicRecogProj/main/extract_features.py","file_name":"extract_features.py","file_ext":"py","file_size_in_byte":1195,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"369722153","text":"#On a chessboard, positions are marked with a letter between a and h for the column and a number between 1 and 8 for the row.\r\n#Given an input string with a letter and number, print whether it is in a corner, at the border, or in the inside of the chess board.\r\n\r\ninputStr = 'a1'\r\ncorner=['a1','h1','a8','h8']\r\nborder=['b1','c1','d1','e1','f1','g1','a2','a3','a4','a5','a6','a7','b8','c8','d8','e8','f8','g8','h2','h3','h4','h5','h6','h7']\r\nif inputStr in corner:\r\n        print(\"corner\")\r\nelif(inputStr in border):\r\n    print(\"Border\")\r\nelse:\r\n    print(\"Inside\")\r\n","sub_path":"problem2.py","file_name":"problem2.py","file_ext":"py","file_size_in_byte":566,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"240995566","text":"from lxml import html\nimport requests\nimport json\nfrom validity import valid\n# from flask import Flask\n\nclass get_recipe(object):\n    def __init__(self, list, pg):\n        self.list = list\n        self.pg = pg\n    def return_recipe(self):\n        api_key='a7117cb7dc7f8b768ec323b949e752dd'\n        # for i in range(len(self.list)):\n        #     ingr = ingr+self.list[i]+','\n        # ingr = ingr[:-1]\n        response = []\n        num = 0\n        page = requests.get('http://food2fork.com/api/search?key='+api_key+'&q='+self.list+'&page='+str(self.pg))\n        parsed = json.loads(page.text)\n        count = parsed['count']\n        for i in range(0,count):\n            if(valid(parsed['recipes'][i]['publisher'])):\n                temp  = json.dumps({'title':parsed['recipes'][i]['title'],'id':parsed['recipes'][i]['recipe_id']},separators=(',',':'))\n                response.append(temp)\n                num=num+1\n        return str(response).replace(\"'\",'')\n\nif __name__ == '__main__':\n    obj = get_recipe(\"shredded chicken\",1)\n    obj.return_recipe()\n","sub_path":"getrecipe.py","file_name":"getrecipe.py","file_ext":"py","file_size_in_byte":1056,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"182289908","text":"# -*- coding: utf-8 -*-\n# coding: utf8\n\nimport os\nimport sys\nimport io\nimport codecs\nimport locale\nimport subprocess\nfrom subprocess import *\nimport re\n\n\n\nIP = input(\"Введите IP кассы: \")\nFSRAR_ID_NEW = input(\"Введите Новый FSRAR_ID____БЕЗ НУЛЯ впереди!: \")\nFSRAR_ID_OLD = input(\"Введите Старый FSRAR_ID____БЕЗ НУЛЯ впереди!: \")\nRoadKassa = \"ssh -o\" + \" \" \"StrictHostKeyChecking=no\" + \" \" +  \"root@\" + IP\nroadCS = \"ssh root@192.168.0.15\"\nroadPuppet = \"ssh root@192.168.0.12\"\n\n\n\n##Подключение к SSH:\n    \nos.chdir(\"C:\\\\OpenSSH\")\n\n##Создание нового VPN сертификата на КС\n\ndef CreateCertVpnOnKS():\n\n    Command8 = \"/opt/certificate/gencert.sh\" + \" \" + \"cash-\" + FSRAR_ID_NEW + \"-\" + FSRAR_ID_NEW + \"1\"\n    Qwerty8 = roadCS + \" \"  + \"\\\"\" + Command8 + \"\\\"\"    \n    print(\"Создание нового VPN сертификата на КС: \" + Qwerty8 + \"\\n\")\n    res8 = Popen(Qwerty8, shell=True, stdout=PIPE)\n    out8 = str(res8.communicate()[0].decode(\"utf8\"))\n    print (\"Создание нового VPN сертификата на КС:\\n\\n \" + out8)    \n\n    \n\n##Удаление сертификатов на Puppet    \n\ndef DelCertPuppet():\n\n    Command6 = \"puppet cert clean\" + \" \" + \"cash-\" + FSRAR_ID_OLD + \"-\" + FSRAR_ID_OLD + \"1\"\n    Qwerty6 = roadPuppet + \" \"  + \"\\\"\" + Command6 + \"\\\"\"\n    print(\"Удаление старых сертификатов на Puppet: \" + Qwerty6 + \"\\n\")\n    res6 = Popen(Qwerty6, shell=True, stdout=PIPE)\n    out6 = str(res6.communicate()[0].decode(\"utf8\"))\n    print (\"Удаление старых сертификатов на Puppet:\\n\\n \" + out6)\n\n    Command9 = \"puppet cert clean\" + \" \" + \"cash-\" + FSRAR_ID_NEW + \"-\" + FSRAR_ID_NEW + \"1\"\n    Qwerty9 = roadPuppet + \" \"  + \"\\\"\" + Command9 + \"\\\"\"\n    print(\"Удаление новых(если есть) сертификатов на Puppet: \" + Qwerty9 + \"\\n\")\n    res9 = Popen(Qwerty9, shell=True, stdout=PIPE)\n    out9 = str(res9.communicate()[0].decode(\"utf8\"))\n    print (\"Удаление новых(если есть) сертификатов на Puppet:\\n\\n \" + out9)\n\n\n##Переименовываем кассу:\ndef per():\n\n    Command1 = \"sed -i 's/[0-9]\\{12\\}/\" + \"0\" + FSRAR_ID_NEW + \"/g'\" + \" \" + \"/linuxcash/cash/data/cash.reg\" + \" && \" + \"sed -i 's/[0-9]\\{11\\}/\" + FSRAR_ID_NEW + \"/g'\" + \" \" + \"/etc/hosts\" + \" && \" + \"sed -i 's/[0-9]\\{11\\}/\" + FSRAR_ID_NEW + \"/g'\" + \" \" + \"/etc/hostname\" + \" && \" + \"sed -i 's/[0-9]\\{11\\}/\" + FSRAR_ID_NEW + \"/g'\" + \" \" + \"/etc/puppet/puppet.conf\" + \" && \" + \"service hostname restart\"\n    Qwerty1 = RoadKassa + \" \"  + \"\\\"\" + Command1 + \"\\\"\"\n    print(\"Переименовываем кассу: \" + Qwerty1 + \"\\n\")\n    res1 = Popen(Qwerty1, shell=True, stdout=PIPE)\n    out1 = str(res1.communicate()[0].decode(\"utf8\"))\n    print (\"Переименовываем кассу:\\n\\n \" + out1)\n\n##Запускаем puppet agent:\n\ndef agent():\n\n    Command3 = \"rm -rf /var/lib/puppet/ssl && service puppet restart && sleep 20 && puppet agent -t\"\n    Qwerty3 = RoadKassa + \" \"  + \"\\\"\" + Command3 + \"\\\"\"\n    print(\"Запуск puppet: \" + Qwerty3 + \"\\n\")\n##    res3 = Popen(Qwerty3, shell=True, stdout=PIPE)\n##    out3 = str(res3.communicate()[0].decode(\"utf8\"))\n    print (\"Запуск puppet:\\n\\n \")\n    os.system(Qwerty3)\n\n\n##Удаляем старые сертификаты VPN из кассы:\n\ndef vpn():\n\n    Command2 = \"rm -f /etc/openvpn/\" + \"*\" + FSRAR_ID_OLD + \"*.*\" + \" \" + \"&&\" + \" \" + \"rm -f /var/run/openvpn/\" + \"*\" + FSRAR_ID_OLD + \"*.*\"\n    Qwerty2 = RoadKassa + \" \"  + \"\\\"\" + Command2 + \"\\\"\"\n    print(\"Удаляем старые сертификаты VPN из кассы: \" + Qwerty2 + \"\\n\")\n    res2 = Popen(Qwerty2, shell=True, stdout=PIPE)\n    out2 = str(res2.communicate()[0].decode(\"utf8\"))\n    print (\"Удаляем старые сертификаты VPN из кассы:\\n\\n \" + out2)\n\n\n##Инициализация данных в БД:\n\ndef BD():\n    \n    Command4 = \"mysql -uroot dictionaries < /linuxcash/cash/tools/tools_avail/initial_data/dictionaries-full.sql\"\n    Qwerty4 = RoadKassa + \" \"  + \"\\\"\" + Command4 + \"\\\"\"\n    print(\"Инициализация данных в БД: \" + Qwerty4 + \"\\n\")\n    res4 = Popen(Qwerty4, shell=True, stdout=PIPE)\n    out4 = str(res4.communicate()[0].decode(\"utf8\"))\n    print (\"Инициализация данных в БД:\\n\\n \" + out4)\n\n\n##Вывести список IP адресов:\n\ndef ip():\n\n    Command5 = \"sudo ifconfig | grep\" + \" \" + \"\\'\" + \"inet addr\" + \"\\'\"\n    Qwerty5 = RoadKassa + \" \"  + \"\\\"\" + Command5 + \"\\\"\"    \n    print(\"Вывести список IP адресов: \" + Qwerty5 + \"\\n\")\n    res5 = Popen(Qwerty5, shell=True, stdout=PIPE)\n    out5 = str(res5.communicate()[0].decode(\"utf8\"))\n    print (\"Вывести список IP адресов:\\n\\n \" + out5)\n\n\n##Удаление старого сертификата VPN на КС:\n\ndef DelCertVPN():\n\n    Command7 = \"cd /opt/certificate/\" + \" && \" +  \"svn del\" + \" \" + \"cash-\" + FSRAR_ID_OLD + \"-\" +  FSRAR_ID_OLD + \"1\" + \".crt\" + \" \" + \"cash-\" + FSRAR_ID_OLD + \"-\" +  FSRAR_ID_OLD + \"1\" + \".csr\" + \" \" + \"cash-\" + FSRAR_ID_OLD + \"-\" +  FSRAR_ID_OLD + \"1\" + \".key\" + \" && \" + \"svn commit -m\" + \" \" + \"\\'\" + \"del bag cert\" + \"\\'\"\n    Qwerty7 = roadCS + \" \"  + \"\\\"\" + Command7 + \"\\\"\"    \n    print(\"Удаление старого сертификата VPN на КС: \" + Qwerty7 + \"\\n\")\n    res7 = Popen(Qwerty7, shell=True, stdout=PIPE)\n    out7 = str(res7.communicate()[0].decode(\"utf8\"))\n    print (\"Удаление старого сертификата VPN на КС:\\n\\n \" + out7)    \n\nCreateCertVpnOnKS()     ##Создаем сертификат VPN на КС\nDelCertPuppet()         ##Удаляем старый и новый сертификат Puppet\nper()                   ##Прописываем везде новый FSRAR на кассе\nagent()                 ##Запуск агента Puppet\nvpn()                   ##Удалить старые сертификаты vpn\nBD()                    ##Инициализация данных в БД\nip()                    ##Вывести список IP адресов\nDelCertVPN()            ##Удалить старый сертификат VPN\n\n##Выход из консоли:\ninput('\\nНажмите Enter для выхода\\n')\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"ProjectPython/Django/MyCassAdmin/CommandOnKass/static/commandOnKass/Site/File/ChangeOrgazization.py","file_name":"ChangeOrgazization.py","file_ext":"py","file_size_in_byte":6440,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"254802840","text":"import numpy as np\nimport pickle\n\nmask = np.array([[0., -1., 1., 0., 1., 0., 1.]] * 3)  # no horizontal rotation\nmask2 = np.array([[0., 0., 0., 0., 0., 1.5, 0.]] * 3)  # horizontal hand\n\n\nclass Population():\n\n    def __init__(self, size, elite=None, mutation=None):\n        if elite is None:\n            self.individuals = [self._create_individual() for _ in range(size)]\n        else:\n            elite = [self._create_individual(e['weights'], e['id']) for e in elite]\n\n            self.individuals = elite + self._mutate(elite, mutation)\n            while len(self.individuals) < size:\n                self.individuals += [self._create_individual()]\n\n    def _mutate(self, elite, mutation):\n        if mutation == 'crossover':\n            if len(elite) % 2 == 1:\n                elite = elite[:-1]\n            children = [self._mutate_cross_over(elite[i], elite[i + 1]) for i in range(0, len(elite), 2)]\n            return list(sum(children, ()))\n        elif mutation == 'single_crossover':\n            if len(elite) % 2 == 1:\n                elite = elite[:-1]\n            children = [self._mutate_single_cross_over(elite[i], elite[i + 1]) for i in range(0, len(elite), 2)]\n            return list(sum(children, ()))\n        elif mutation == 'random':\n            return [self._mutate_individual(e) for e in elite]\n        else:\n            raise ValueError('unknown mutation: {}'.format(mutation))\n\n    def _create_individual(self, weights=None, id=None):\n        if weights is None:\n            return {'weights': self._get_syn_weights(), 'distance': -1., 'id': id}\n        else:\n            return {'weights': weights, 'distance': -1., 'id': id}\n\n    def _get_syn_weights(self):\n        weights = (np.random.rand(3, 7) * 5)\n        return (weights * mask + mask2).tolist()\n\n    def _mutate_individual(self, individual):\n        weights = individual['weights']\n        weights += np.random.rand(3, 7) - .5\n        weights *= mask\n        weights += mask2\n        return self._create_individual(weights.tolist())\n\n    def _mutate_cross_over(self, individual1, individual2):\n        weights1 = individual1['weights']\n        weights2 = individual2['weights']\n\n        mask1 = np.randon.randint(2, size=len(weights1))\n        mask2 = np.ones(len(weights2)) - mask1\n\n        new1 = self._create_individual((weights1 * mask1 + weights2 * mask2).tolist())\n        new2 = self._create_individual((weights1 * mask2 + weights2 * mask1).tolist())\n        return new1, new2\n\n    def _mutate_single_cross_over(self, individual1, individual2):\n        weights1 = individual1['weights']\n        weights2 = individual2['weights']\n\n        random = np.random.randint(len(weights1))\n\n        mask1 = np.concatenate((np.ones(random), np.zeros(len(weights1) - random)))\n\n        mask2 = np.ones(len(weights2)) - mask1\n\n        new1 = self._create_individual((weights1 * mask1 + weights2 * mask2).tolist())\n        new2 = self._create_individual((weights1 * mask2 + weights2 * mask1).tolist())\n        return new1, new2\n\n    def get_elite(self, size):\n        size = min(size, len(self.individuals))\n        return sorted(self.individuals, key=lambda i: i['distance'], reverse=True)[:size]\n\n    def get_population_size(self):\n        return len(self.individuals)\n\n    def load(self, filename):\n        with open(filename, 'r') as f:\n            individuals = pickle.load(f)\n        self.individuals = individuals\n\n\nclass EvolutionaryAlgo():\n\n    def __init__(self, generation_size, population_size, seed=None, mutation='random'):\n        self.seed = seed\n        self.population_size = population_size\n        self.generation_size = generation_size\n        self.generations = [Population(population_size)]\n        self.mutation = mutation\n\n    def set_distance(self, distance, generation, individual):\n        self.generations[generation].individuals[individual]['distance'] = distance\n\n    def set_id(self, generation, individual):\n        if self.get_id(generation, individual) is None:\n            id = (generation, individual)\n            self.generations[generation].individuals[individual]['id'] = id\n\n    def get_id(self, generation, individual):\n        return self.generations[generation].individuals[individual]['id']\n\n    def get_weights(self, generation, individual):\n        return self.generations[generation].individuals[individual]['weights']\n\n    def mutate(self):\n        elite = self.generations[-1].get_elite(self.population_size / 2)\n        self.generations += [Population(self.population_size, elite=elite, mutation=self.mutation)]\n\n    def _get_filename(self, generation):\n        return 'evolution_results/generation_{}.pickle'.format(generation)\n\n    def save(self, generation):\n        filename = self._get_filename(generation)\n        with open(filename, 'w') as f:\n            pickle.dump(self.generations[generation].individuals, f)\n        return filename\n\n    def load(self, generation):\n        filename = self._get_filename(generation)\n        pop = Population(self.population_size)\n        pop.load(filename)\n        self.population_size = pop.get_population_size()\n        self.generations = [pop]\n\n# evol = EvolutionaryAlgo(3,2)\n#\n# population = evol.generations[0]\n#\n# for ind in range(len(population.individuals)):\n#     evol.set_distance(1000.-ind, 0, ind)\n#\n# print(evol.generations[0].individuals)\n# population = evol.generations[0]\n#\n# print(sorted(population.individuals, key=lambda i: i['distance'], reverse=True))\n#\n# evol.mutate()\n# print(evol.generations[1].individuals)\n","sub_path":"evolution.py","file_name":"evolution.py","file_ext":"py","file_size_in_byte":5505,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"100046811","text":"#Time complexity - O(n)\n#space complexity - O(n)\n#Problem done in Leetcode - Yes\n#Any challenges faced - No\n\n#Algorithm :\n#we use two dictionaries to solve the problem .one dictionary is used to store the unique replacement characters from s to t and another dictionary is used to store the unique replacement characters from t to s.while storing the replacements if they dont match with ones that are already present in the dictionary then we return false else we return true.\n\n\nclass Solution(object):\n    def isIsomorphic(self, s, t):\n        \"\"\"\n        :type s: str\n        :type t: str\n        :rtype: bool\n        \"\"\"\n        d=dict()\n        d1=dict()\n        for i in range(len(s)):\n            if s[i] not in d:\n                d[s[i]]=t[i]\n            else:\n                if d[s[i]]!=t[i]:\n                    return False\n            if t[i] not in d1:\n                d1[t[i]]=s[i]\n            else:\n                if d1[t[i]]!=s[i]:\n                    return False\n        return True","sub_path":"Isomorphic_strings.py","file_name":"Isomorphic_strings.py","file_ext":"py","file_size_in_byte":1002,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"493854620","text":"# ZSS - Various file records\n\nclass ZSSRecord: # Generic file record\n    def __init__(self, qinfo):\n        self.item_name = qinfo.fileName()\n        self.item_path = qinfo.filePath()\n        self.isFile = int(qinfo.isFile())\n        self.isDir = int(qinfo.isDir())\n\n    def getDict(self):\n        return {\n            'item_name': self.item_name,\n            'item_path': self.item_path,\n            'isFile': self.isFile,\n            'isDir': self.isDir,\n        }\n\nclass ZSSRecordDocument(ZSSRecord): # File record - Document\n    def __init__(self, qinfo):\n        super().__init__(qinfo)\n        self.author = qinfo.owner()\n\n    def getDict(self):\n        return {\n            'item_name': self.item_name,\n            'item_path': self.item_path,\n            'isFile': self.isFile,\n            'isDir': self.isDir,\n            'author': self.author\n        }","sub_path":"experimental/zss_record.py","file_name":"zss_record.py","file_ext":"py","file_size_in_byte":862,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"209822785","text":"from account.decorators import super_admin_required\nfrom utils.api import APIView, validate_serializer\n\nfrom faq.models import FAQ\nfrom faq.serializers import (FAQSerializer, CreateFAQSerializer,\n                                      EditFAQSerializer)\nimport logging\nlogger=logging.getLogger(__name__)\n\nclass FAQAdminAPI(APIView):\n    @validate_serializer(CreateFAQSerializer)\n    @super_admin_required\n    def post(self, request):\n        \"\"\"\n        publish faq\n        \"\"\"\n        data = request.data\n        faq = FAQ.objects.create(question=data[\"question\"],\n                                                   answer=data[\"answer\"],\n                                                   created_by=request.user,\n                                                   visible=data[\"visible\"])\n        return self.success(FAQSerializer(faq).data)\n\n    @validate_serializer(EditFAQSerializer)\n    @super_admin_required\n    def put(self, request):\n        \"\"\"\n        edit faq\n        \"\"\"\n        data = request.data\n        try:\n            faq = FAQ.objects.get(id=data.pop(\"id\"))\n        except FAQ.DoesNotExist:\n            return self.error(\"FAQ does not exist\")\n\n        for k, v in data.items():\n            setattr(faq, k, v)\n        faq.save()\n\n        return self.success(FAQSerializer(faq).data)\n\n    @super_admin_required\n    def get(self, request):\n        \"\"\"\n        get faq list / get one faq\n        \"\"\"\n        faq_id = request.GET.get(\"id\")\n        if faq_id:\n            try:\n                faq = FAQ.objects.get(id=faq_id)\n                return self.success(FAQSerializer(faq).data)\n            except FAQ.DoesNotExist:\n                return self.error(\"FAQ does not exist\")\n        faq = FAQ.objects.all().order_by(\"-create_time\")\n        if request.GET.get(\"visible\") == \"true\":\n            faq = faq.filter(visible=True)\n        return self.success(self.paginate_data(request, faq, FAQSerializer))\n\n    @super_admin_required\n    def delete(self, request):\n        if request.GET.get(\"id\"):\n            FAQ.objects.filter(id=request.GET[\"id\"]).delete()\n        return self.success()\n","sub_path":"faq/views/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":2104,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"96505630","text":"'''\n小新媽媽上超市買水果 她都先把不喜歡的水果刪除 再將剩下的水果買\n回家 請為小新媽媽設計一個輸入不喜歡的水果後刪除的程式 並顯示剩\n下的水果 如果輸入 Enter 就結束輸入 。\n'''\n\nfruit_list = ['香蕉', '蘋果', '橘子', '鳳梨', '西瓜']\n\nwhile True:\n    fruit = input('請輸入要刪去的水果品項(輸入Enter刪除結束)')\n    if fruit != 'Enter':\n        try:\n            fruit_list.remove(fruit)\n        except:\n            print(\"The data you've inserted is not correct or not in the list, check again\")\n    else:\n        break\n\nprint('須購買水果品項清單')\nfor i, j in enumerate(fruit_list):\n    print(j)","sub_path":"W6_a3.py","file_name":"W6_a3.py","file_ext":"py","file_size_in_byte":697,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"551544167","text":"import ctypes\r\nimport hashlib\r\nimport base64\r\nimport os\r\nimport sys\r\n\r\nfrom _io import BytesIO\r\nfrom Tea.stream import BaseStream\r\n\r\n\r\ndef int_overflow(val):\r\n    maxint = 2147483647\r\n    if not -maxint - 1 <= val <= maxint:\r\n        val = (val + (maxint + 1)) % (2 * (maxint + 1)) - maxint - 1\r\n    return val\r\n\r\n\r\ndef unsigned_right_shift(n, i):\r\n    if n < 0:\r\n        n = ctypes.c_uint32(n).value\r\n\r\n    if i < 0:\r\n        return -int_overflow(n << abs(i))\r\n\r\n    return int_overflow(n >> i)\r\n\r\n\r\nclass CRC64:\r\n    POLY = 0xc96c5795d7870f42\r\n\r\n    def __init__(self):\r\n        self.value = 0\r\n\r\n    @property\r\n    def table(self):\r\n        return int(''.join(self._table))\r\n\r\n    @property\r\n    def _table(self):\r\n        table = []\r\n        for n in range(256):\r\n            crc = n\r\n            for j in range(8):\r\n                if crc & 1 == 1:\r\n                    crc = unsigned_right_shift(crc, 1) ^ self.POLY\r\n                else:\r\n                    crc = unsigned_right_shift(crc, 1)\r\n            table.append(crc)\r\n\r\n        return table\r\n\r\n    def get_value(self):\r\n        first = unsigned_right_shift(self.value, 1) / 5\r\n        second = self.value - first * 10\r\n        return first + second\r\n\r\n    def update(self, bt):\r\n        for b in bt:\r\n            self.value = ~self.value\r\n            self.value = self._table[(self.value ^ b) & 0xff] ^ unsigned_right_shift(self.value, 8)\r\n            self.value = ~self.value\r\n\r\n\r\nclass VerifyStream(BaseStream):\r\n    def __init__(self, file, res, size=1024):\r\n        super().__init__(size)\r\n        self.file = file\r\n        self.size = size\r\n        self.crc = CRC64()\r\n        self.ref = res\r\n        self.md5 = hashlib.md5()\r\n        if isinstance(file, BytesIO):\r\n            self.file_size = file.getbuffer().nbytes\r\n        else:\r\n            self.file_size = os.path.getsize(file.name)\r\n        self._file_size = self.file_size\r\n\r\n    def __len__(self):\r\n        return self.file_size\r\n\r\n    def __iter__(self):\r\n        return self\r\n\r\n    def __next__(self):\r\n        return self.read(size=self.size, loop=True)\r\n\r\n    def read(self, size=None, loop=False):\r\n        res = self.file.read(size)\r\n        if size is None:\r\n            size = sys.maxsize\r\n\r\n        if isinstance(res, str):\r\n            bres = res.encode('utf-8')\r\n        else:\r\n            bres = res\r\n\r\n        if size <= self._file_size:\r\n            self.crc.update(bres)\r\n            self.md5.update(bres)\r\n        else:\r\n            self.ref['md5'] = base64.b64encode(self.md5.digest()).decode('utf-8')\r\n            self.ref['crc'] = self.crc.get_value()\r\n\r\n        if not res:\r\n            self.refresh()\r\n            if loop:\r\n                raise StopIteration\r\n            else:\r\n                return res\r\n\r\n        if size == sys.maxsize or size >= self.file_size:\r\n            self.crc.update(bres)\r\n            self.md5.update(bres)\r\n            self.ref['md5'] = base64.b64encode(self.md5.digest()).decode('utf-8')\r\n            self.ref['crc'] = self.crc.get_value()\r\n            return res\r\n\r\n        self._file_size -= len(bres)\r\n        return res\r\n\r\n    def refresh(self):\r\n        self.crc = CRC64()\r\n        self.md5 = hashlib.md5()\r\n        self.file.seek(0, 0)\r\n        self._file_size = self.file_size\r\n\r\n    def close(self):\r\n        self.file.close()\r\n","sub_path":"util/python/alibabacloud_oss_util/verify_stream.py","file_name":"verify_stream.py","file_ext":"py","file_size_in_byte":3321,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"326410050","text":"import sys\nimport os\nimport time\n\npath = '/Users/poojitha/Desktop/273-Lab1/input/'\n\ndef ext_merge_sort(lis):\n    if len(lis)<2 :\n        return lis\n    else:\n        midval = len(lis)//2\n        l_list = ext_merge_sort(lis[:midval])\n        r_list = ext_merge_sort(lis[midval:])\n        f_list = merge_lists(l_list,r_list)\n        return f_list    \n\ndef merge_lists(l_list1,r_list1):\n    l_iter,r_iter =0,0\n    l_list,r_list= [],[]\n    l_list.extend(l_list1)\n    r_list.extend(r_list1)\n    res=[]\n    while l_iter < len(l_list) and r_iter < len(r_list):\n        if int(r_list[r_iter]) > int(l_list[l_iter]):\n            #print(l_list[l_iter])\n            res.append(l_list[l_iter])\n            l_iter += 1\n        else:\n            res.append(r_list[r_iter])\n            r_iter += 1\n    \n    res.extend(l_list[l_iter:])\n    res.extend(r_list[r_iter:])\n    #print (res)\n    return res\n\ndef writedata(sorted_data):\n    op_file = open('sorted.txt','a')\n    for x in sorted_data:\n        op_file.write(str(x))\n        op_file.write('\\n')\n    op_file.close()\n\ndef sortdata(unsorted_data):\n    sorted_data = ext_merge_sort(unsorted_data)\n    writedata(sorted_data)   \n\ndef readdata(): \n    file_names = ['unsorted_'+str(i)+'.txt' for i in range(1,11)]\n    unsorted_data =[]\n    time.sleep(1)\n    for fi in file_names:\n        fpath = os.path.join(path,fi)\n        f_read =open(fpath,'r')\n        unsorted_data += [int(x.strip()) for x in f_read.readlines()]\n    time.sleep(1)\n    f_read.close()\n    sortdata(unsorted_data)\n\ndef main():\n    readdata()   \n\nif __name__ ==  \"__main__\":\n    main()\n\n\n","sub_path":"ext_merge_sort.py","file_name":"ext_merge_sort.py","file_ext":"py","file_size_in_byte":1590,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"343860998","text":"# -*- coding: utf-8 -*-\n#\n# A Fabric file for installing, deploying and running Invenio\n#\n# Lars Holm Nielsen <lars.holm.nielsen@cern.ch>\n#\n# Copyright (C) 2012 CERN.\n#\n# This program is free software; you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation; either version 2 of the License, or\n# (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful, but\n# WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU\n# General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program; if not, see <http://www.gnu.org/licenses/>.\n\n\"\"\"\nFabric tasks for bootstrapping, installing, deploying and running Atlantis.\n\nExamples\n--------\n\nSetup virtual environment and database with Python 2.4 and Invenio 1.0.0:\n  fab loc_py24 invenio:v1.0.0 bootstrap\n  fab loc_py24 invenio:v1.0.0 invenio_create_demosite\n\nSetup virtual environment and database with Python 2.5 and Invenio master:\n  fab loc_py25 invenio:origin/master bootstrap\n\nDump (database and virtual environment), load and drop:\n  fab loc_py24 invenio:v1.0.0 dump\n  fab loc_py24 invenio:v1.0.0 load\n  fab loc_py24 invenio:v1.0.0 drop\n\"\"\"\n\nfrom fabric.api import task, abort\nimport os\n\nfrom inveniofab.env import env_create, env_get\nfrom inveniofab.venv import *\n\n@task\ndef loc(prefix, activate=True, version=None, db_suffix='', **kwargs):\n    \"\"\" Local environment (default Python) \"\"\"\n    env = env_create('loc', activate=activate, **kwargs)\n\n    env.CFG_SRCDIR = os.path.expanduser(\"~/src/\")\n\n    env.CFG_INVENIO_SRCDIR = os.path.join(env.CFG_SRCDIR, 'invenio')\n    env.CFG_INVENIO_PREFIX = prefix\n    env.CFG_INVENIO_PORT_HTTP = \"4000\"\n    env.CFG_INVENIO_PORT_HTTPS = \"4000\"\n    env.CFG_INVENIO_USER = env.user\n    env.CFG_INVENIO_APACHECTL = 'service apache2'\n    env.CFG_INVENIO_ADMIN = 'nobody@localhost'\n\n    env.CFG_INVENIO_REPOS = [\n        ('invenio', {\n            'repository' : 'http://invenio-software.org/repo/invenio/',\n            'ref': 'origin/maint-1.1',\n            'bootstrap_targets': ['all', 'install', 'install-mathjax-plugin', 'install-ckeditor-plugin', 'install-pdfa-helper-files', 'install-jquery-plugins', ],\n            'deploy_targets': ['all', 'check-upgrade', 'install', ],\n        }),\n    ]\n\n    env.CFG_DATABASE_DUMPDIR = prefix\n    env.CFG_DATABASE_HOST = 'localhost'\n    env.CFG_DATABASE_NAME = _shorten_mysqlname('atlantis%s' % db_suffix)\n    env.CFG_DATABASE_USER = _shorten_mysqlname('atlantis%s' % db_suffix)\n    env.CFG_DATABASE_PASS = _shorten_mysqlname('atlantis%s' % db_suffix)\n    env.CFG_DATABASE_DROP_ALLOWED = True\n\n    env.CFG_MISCUTIL_SMTP_HOST = '127.0.0.1'\n    env.CFG_MISCUTIL_SMTP_PORT = '1025'\n\n\n@task\ndef loc_py24(prefix=None, version=None, activate=True, **kwargs):\n    \"\"\" Local environment (Python 2.4) \"\"\"\n    prefix = _get_prefix(prefix, 'atlantis-py24')\n    kwargs['python'] = os.path.expanduser('~/.pythonbrew/pythons/Python-2.4.6/bin/python')\n    return loc(prefix, activate=activate, version=None, db_suffix='_py24', **kwargs)\n\n\n@task\ndef loc_py25(prefix=None, version=None, activate=True, **kwargs):\n    \"\"\" Local environment (Python 2.5) \"\"\"\n    prefix = _get_prefix(prefix, 'atlantis-py25')\n    kwargs['python'] = os.path.expanduser('~/.pythonbrew/pythons/Python-2.5.4/bin/python')\n    return loc(prefix, activate=activate, version=None, db_suffix='_py25', **kwargs)\n\n\n@task\ndef loc_py26(prefix=None, version=None, activate=True, **kwargs):\n    \"\"\" Local environment (Python 2.6) \"\"\"\n    prefix = _get_prefix(prefix, 'atlantis-py26')\n    kwargs['python'] = os.path.expanduser('~/.pythonbrew/pythons/Python-2.6.7/bin/python')\n    return loc(prefix, activate=activate, version=None, db_suffix='_py26', **kwargs)\n\n\n@task\ndef loc_py27(prefix=None, version=None, activate=True, **kwargs):\n    \"\"\" Local environment (Python 2.7) \"\"\"\n    prefix = _get_prefix(prefix, 'atlantis-py27')\n    kwargs['python'] = os.path.expanduser('~/.pythonbrew/pythons/Python-2.7.3/bin/python')\n    return loc(prefix, activate=activate, version=None, db_suffix='_py27', **kwargs)\n\n\n#\n# Helpers\n#\ndef _get_prefix(prefix, name):\n    if not prefix:\n        prefix = os.getenv('WORKON_HOME', '~/envs')\n        prefix = os.path.join(prefix, name)\n    return os.path.expandvars(os.path.expanduser(prefix))\n\ndef _shorten_mysqlname(name):\n    if len(name) > 16:\n        name = name.replace(\"_\", \"\", len(name) - 16)\n        return name[:16]\n    return name","sub_path":"fabfile.py","file_name":"fabfile.py","file_ext":"py","file_size_in_byte":4612,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"395864306","text":"#!/usr/bin/python\n# encoding: utf-8\n\nfrom __future__ import unicode_literals\n\nimport sys\n\nimport sqlite3\nfrom sqlite3 import Error\nfrom workflow import Workflow3, ICON_INFO, ICON_WARNING, ICON_ERROR\n\n\ndef main(wf):\n\n    github_school = \"https://git.txstate.edu\"\n    github_personal = \"https://github.com/kevin-funderburg\"\n    github_icon = \"icons/Icon-elusive-github@2x.png\"\n    database = r\"classdata.db\"\n    sql = \"SELECT * FROM fall2020;\"\n\n    # create a database connection\n    conn = create_connection(database)\n    with conn:\n        # execute_sql(conn, sql)\n        cur = conn.cursor()\n        cur.execute(sql)\n        rows = cur.fetchall()\n\n        for row in rows:\n            name = row[0]\n            folderPath = row[1]\n            bookPath = row[2]\n            oneNote = row[3]\n            website = row[4]\n            zoom = row[5]\n            it = wf.add_item(uid=name,\n                             title=name,\n                             subtitle=\"open OneNote section\",\n                             arg=oneNote,\n                             autocomplete=name,\n                             valid=True,\n                             icon=\"icon.png\",\n                             icontype=\"file\")\n            it.add_modifier('cmd',\n                            subtitle=\"go to class website: \" + website,\n                            arg=website,\n                            valid=True)\n            it.add_modifier('alt',\n                            subtitle=\"browse in Alfred\",\n                            arg=folderPath,\n                            valid=True)\n            it.add_modifier('shift',\n                            subtitle=bookPath,\n                            arg=bookPath,\n                            valid=True)\n            it.add_modifier('ctrl',\n                            subtitle=\"go to zoom meeting\",\n                            arg=zoom,\n                            valid=True)\n\n\n    it = wf.add_item(uid=github_school,\n                     title=\"github - texas state\",\n                     subtitle=github_school,\n                     arg=github_school,\n                     valid=True,\n                     icon=github_icon,\n                     icontype=\"file\")\n\n    it = wf.add_item(uid=github_personal,\n                     title=\"github - personal\",\n                     subtitle=github_personal,\n                     arg=github_personal,\n                     valid=True,\n                     icon=github_icon,\n                     icontype=\"file\")\n\n    wf.send_feedback()\n\n\ndef create_connection(db_file):\n    \"\"\" create a database connection to the SQLite database\n        specified by the db_file\n    :param db_file: database file\n    :return: Connection object or None\n    \"\"\"\n    conn = None\n    try:\n        conn = sqlite3.connect(db_file)\n    except Error as e:\n        print(e)\n\n    return conn\n\n\nif __name__ == '__main__':\n    wf = Workflow3()\n    log = wf.logger\n    sys.exit(wf.run(main))\n","sub_path":"getClassData.py","file_name":"getClassData.py","file_ext":"py","file_size_in_byte":2943,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"262800690","text":"\"\"\"\nFunctions and techniques for in-memory caching of persistent data,\nin particular, for using memcache.\n\"\"\"\nimport logging\n\nfrom google.appengine.ext import db\nfrom google.appengine.api import memcache\n\n\ndef fetch_path(*args, **kwargs):\n    cachekey = '-'.join(str(a) for a in args)\n    result = memcache.get(cachekey)\n\n    if result is None:\n        result = dbfetch_path(*args, **kwargs)\n        success = memcache.set(cachekey, result)\n        if not success:\n            logging.error(\"Memcache set failed for key: {0}\".format(cachekey))\n    return result\n\n\ndef dbfetch_path(*args, **kwargs):\n    d_k = db.Key.from_path(*args, **kwargs)\n    result = db.get(d_k)\n    return result\n","sub_path":"core/cache.py","file_name":"cache.py","file_ext":"py","file_size_in_byte":686,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"564669590","text":"import configparser\nfrom datetime import datetime\nimport os\nfrom pyspark.sql import SparkSession\nfrom pyspark.sql.types import TimestampType, StringType\nfrom pyspark.sql.functions import udf, col, monotonically_increasing_id\nfrom pyspark.sql.functions import year, month, dayofmonth, hour, weekofyear, date_format, dayofweek\n\n\n# config = configparser.ConfigParser()\n# config.read('dl.cfg')\n\n# os.environ['AWS_ACCESS_KEY_ID']=config['AWS_ACCESS_KEY_ID']\n# os.environ['AWS_SECRET_ACCESS_KEY']=config['AWS_SECRET_ACCESS_KEY']\n\n\ndef create_spark_session():\n    spark = SparkSession \\\n        .builder \\\n        .config(\"spark.jars.packages\", \"org.apache.hadoop:hadoop-aws:2.7.0\") \\\n        .getOrCreate()\n    return spark\n\n\ndef process_song_data(spark, input_data, output_data):\n    \"\"\"\n    The function that retrieves song data from s3, processes it and uploads it to other s3 bucket.\n    \n    Parameters: \n        spark (object): SparkSession which helps ro process big data.\n        input_data (string): path to s3 bucket where input data stored. \n        output_data (string): path to s3 bucket where data should be uploaded. \n    \n    \"\"\"\n    # get filepath to song data file\n    song_data = input_data + 'song_data/*/*/*/*.json'\n    \n    # read song data file\n    df = spark.read.json(song_data)\n    \n    df.printSchema()\n#     df.show(5)\n\n    # extract columns to create songs table\n    df.createOrReplaceTempView('songs_table')\n    \n    songs_table = spark.sql('''\n        SELECT song_id, title, artist_id, year, duration \n        FROM songs_table\n        ORDER BY song_id\n    ''')\n    songs_table.printSchema()\n    songs_table.show(5, truncate = False)\n    \n    # write songs table to parquet files partitioned by year and artist\n    songs_table_output_path = output_data + 'songs_table.parquet'\n    \n    songs_table.dropDuplicates().write.mode('overwrite').partitionBy('year', 'artist_id').parquet(songs_table_output_path)\n    print('Songs table done!!!!')\n\n    # extract columns to create artists table\n    df.createOrReplaceTempView('artist_table')\n    \n    artists_table = spark.sql('''\n        SELECT artist_id AS artist_id,\n               artist_name AS name,\n               artist_location AS location,\n               artist_latitude AS latitude,\n               artist_longitude AS longitude\n        FROM artist_table\n        ORDER BY artist_id desc\n    ''')\n    artists_table.printSchema()\n    artists_table.show(5, truncate = False)\n    \n    # write artists table to parquet files\n    artists_table_output_path = output_data + 'artists_table.parquet'\n    \n    artists_table.dropDuplicates().write.mode('overwrite').parquet(artists_table_output_path)\n    print('Artists table done!!!!')\n\n\ndef process_log_data(spark, input_data, output_data):\n    \"\"\"\n    The function that retrieves song data and log data from s3, processes it and uploads it to other s3 bucket.\n    \n    Parameters: \n        spark (object): SparkSession which helps ro process big data.\n        input_data (string): path to s3 bucket where input data stored. \n        output_data (string): path to s3 bucket where data should be uploaded. \n    \n    \"\"\"\n    # get filepath to log data file\n    log_data = input_data + \"log_data/*.json\"\n\n    # read log data file\n    df = spark.read.json(log_data)\n    \n    # filter by actions for song plays\n    df = df.filter(df.page == \"NextSong\")\n\n    # extract columns for users table    \n    df.createOrReplaceTempView('logs_table')\n    \n    users_table = spark.sql(\"\"\"\n        SELECT DISTINCT userId as user_id,\n               firstName as first_name,\n               lastName as last_name,\n               gender, \n               level\n        FROM logs_table\n        ORDER BY user_id\n    \"\"\")\n    \n    # write users table to parquet files\n    users_table_output_path = output_data + 'users_table.parquet'\n    \n    users_table.write.mode('overwrite').parquet(users_table_output_path)\n    print('Users table done!!!!')\n\n    # create timestamp column from original timestamp column\n    get_timestamp = udf(lambda x: datetime.fromtimestamp(x / 1000.0), TimestampType())\n    df = df.withColumn(\"timestamp\", get_timestamp(df.ts))\n    \n    # create datetime column from original timestamp column\n    get_datetime = udf(lambda x: datetime.fromtimestamp(x / 1000.0).strftime('%Y-%m-%d %H:%M:%S'), StringType())\n    df = df.withColumn(\"datetime\", get_datetime(df.ts))\n    \n    # extract columns to create time table\n    df.createOrReplaceTempView('logs_time_table')\n    \n    time_table = spark.sql(\"\"\"\n        SELECT DISTINCT datetime as start_time,\n               hour(timestamp) as hour,\n               dayofmonth(timestamp) as day,\n               weekofyear(timestamp) as week,\n               month(timestamp) as month,\n               year(timestamp) as year,\n               dayofweek(timestamp) as weekday\n        FROM logs_time_table\n        ORDER BY start_time\n    \"\"\")\n    \n    time_table.printSchema()\n    time_table.show(5, truncate = False)\n    \n    # write time table to parquet files partitioned by year and month\n    time_table_output_path = output_data + 'time_table.parquet'\n    \n    time_table.write.mode('overwrite').partitionBy('year', 'month').parquet(time_table_output_path)\n    print('Time table done!!!!')\n\n    # read in song data to use for songplays table\n    song_data = input_data + 'song_data/*/*/*/*.json'\n    song_df = spark.read.json(song_data)\n\n    # extract columns from joined song and log datasets to create songplays table\n    songplays_df = df.join(song_df, (song_df.title == df.song) & (song_df.artist_name == df.artist))\n    songplays_df = songplays_df.withColumn(\"songplay_id\", monotonically_increasing_id())\n    songplays_df.createOrReplaceTempView('songplays_table')\n    \n    songplays_table = spark.sql(\"\"\"\n        SELECT songplay_id,\n               datetime as start_time,\n               userId as user_id, \n               level, \n               song_id, \n               artist_id, \n               sessionId as session_id,\n               location,\n               userAgent as user_agent,\n               month(timestamp) as month,\n               year(timestamp) as year\n        FROM songplays_table\n    \"\"\")\n    songplays_table.printSchema()\n    songplays_table.show(5, truncate = False)\n\n    # write songplays table to parquet files partitioned by year and month\n    songplays_table_output_path = output_data + 'songplays_table.parquet'\n    \n    songplays_table.dropDuplicates().write.mode('overwrite').partitionBy('year', 'month').parquet(songplays_table_output_path)\n    print('Songplays table done!!!!')\n\n\ndef main():\n    spark = create_spark_session()\n    input_data = \"s3a://udacity-dend/\"\n    output_data = \"\"\n    \n    process_song_data(spark, input_data, output_data)    \n    process_log_data(spark, input_data, output_data)\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"etl.py","file_name":"etl.py","file_ext":"py","file_size_in_byte":6824,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"554171750","text":"import os\nimport urllib.request\n\ndef url_open(url):\n    request = urllib.request.Request(url)\n    request.add_header('User-Agent',\n                       'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36')\n    response = urllib.request.urlopen(url)\n    html = response.read()\n    return html\n\ndef get_page(url):\n    html = str(url_open(url).decode('utf-8'))\n    a = html.find('current-comment-page')+23\n    b = html.find(']',a)\n    return html[a:b]\n\ndef find_imgs(page_url):\n    html = str(url_open(page_url).decode('utf-8'))\n    img_addrs = []\n    a = html.find('img src=')\n    while a!=-1:\n        b = html.find('.jpg',a,a+255)\n        if b!=-1:\n            img_addrs.append('http:'+html[a+9:b+4])\n        else:\n            b=a+9\n        a = html.find('img src=',b)\n\n    for each in img_addrs:\n        print(each)\n\n    return img_addrs\n\n\ndef save_imgs(float, img_addrs):\n    for each in img_addrs:\n        filename = str(each).split('/')[-1]\n        with open(filename,'wb') as file:\n            img = url_open(each)\n            file.write(img)\n\n\ndef download_mz(folder='OOXX', pages=2):\n    os.mkdir(folder)\n    os.chdir(folder)\n\n    url = 'http://jandan.net/ooxx/'\n    page_num = int(get_page(url))\n\n    for i in range(pages):\n        page_num -= i\n        page_url = url + 'page-' + str(page_num) + '#comments'\n        img_addrs = find_imgs(page_url)\n        save_imgs(float,img_addrs)\n\n    print('下载完成')\n\nif __name__ == \"__main__\":\n    download_mz(2)","sub_path":"study/网络爬虫/爬煎蛋网妹子图/download_mz.py","file_name":"download_mz.py","file_ext":"py","file_size_in_byte":1531,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"618163777","text":"import env\nimport argparse\nimport pathlib\nfrom time import sleep\n\n\ndef make_sequence(S):\n    ret = []\n    for s in S:\n        ret.append(list(float(q) for q in s[1:-3].split(',')))\n    return ret\n\n\np = argparse.ArgumentParser()\np.add_argument('task')\np.add_argument('seq')\np.add_argument('--fps', type=int, default=5)\np.add_argument('--fallback', action='store_true')\n\nargs = p.parse_args()\nif args.fps <= 0:\n    raise ValueError('FPS too small')\n\nslp = 1/args.fps\nE, end, emp = env.load_task(pathlib.Path(args.task),\n                            render=True,\n                            fallback=args.fallback)\n\nR = E.robot\n\nwith open(args.seq, 'r') as seqfile:\n    S = seqfile.readlines()\n\nQ = make_sequence(S)\n\nfor q in Q:\n    sleep(slp)\n    R.state = q\n\ninput()\n\ndel R\ndel E\n","sub_path":"seq-viewer.py","file_name":"seq-viewer.py","file_ext":"py","file_size_in_byte":778,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"66682564","text":"# -*- coding:utf-8 -*-\n\nimport threading\nimport logging\nimport sys\nimport io\nimport os\n\nfrom time import sleep\nfrom PIL import Image\nimport numpy as np\nfrom cv2 import cvtColor, Canny\nfrom cv2 import COLOR_BGR2GRAY, bilateralFilter\n\nfrom paint import *\nimport config\n\n\nclass ScreenshotThread(threading.Thread):\n    def __init__(self, screenshot_queue, image_processing_queue):\n        threading.Thread.__init__(self)\n        self.screenshot_queue = screenshot_queue\n        self.image_processing_queue = image_processing_queue\n\n    def run(self):\n        while True:\n            with threading.Lock():\n                dahua = self.screenshot_queue.get()\n            if config.snapshots_enabled:\n                self.make_snapshots(dahua)\n            self.screenshot_queue.task_done()\n\n    def make_snapshots(self, dahua):\n        model = dahua.model\n        channels_count = dahua.channels_count\n        logging.debug(f' Make snapshot from {dahua.ip} (DM: {dahua.model}, channels: {channels_count})')\n        config.trash_cam[dahua.ip] = 0\n        dead_counter = 0\n        capturing = 0\n        total_channels = config.ch_count\n        for channel in range(channels_count):\n            # Ускорение / Performance\n            if dead_counter > 4 or config.trash_cam[dahua.ip] > 2:\n                logging.debug(f' {dead_counter} dead channels in a row. Skipping this cam')\n                break\n            try:\n                jpeg = dahua.get_snapshot(channel)\n                dead_counter = 0\n                capturing += 1\n                name = f\"{dahua.ip}_{dahua.port}_{dahua.login}_{dahua.password}_{channel + 1}_{model}.jpg\"\n                grabster = channels_count - capturing\n                print(fore_green(f\"Brute progress: [{config.state}] Grabbing snapshots for {dahua.ip}.. \\n\")  # Left {str(grabster)} channels.. Trash: {str(config.trash_cam[dahua.ip])}\\n\")\n                + back_yellow(f\"Writing snapshots.. Total saved {config.snapshots_counts} from {total_channels}\"), end='\\r')\n                sleep(0.05)\n                self.image_processing_queue.put([name, jpeg], block=False, timeout=20)\n                # self.image_processing(jpeg)\n            except Exception as e:\n                logging.debug(f' Channel {channel + 1} of {dahua.ip} is dead {str(e)}{\" \"*40}')\n                dead_counter += 1\n                continue\n        logging.debug(\"%s exit from make_snapshots()\" % dahua.ip)\n        return\n\n\nclass ImageProcessingThread(threading.Thread):\n    def __init__(self, image_processing_queue):\n        threading.Thread.__init__(self)\n        self.image_processing_queue = image_processing_queue\n\n    def run(self):\n        while True:\n            with threading.Lock():\n                # print(self.image_processing_queue.get())\n                name, image = self.image_processing_queue.get()\n            self.processing(name, image)\n            self.image_processing_queue.task_done()\n\n    def is_dark(self, image):\n        x = np.sum(image)/image.size\n        if x < 50:\n            return True\n        else:\n            return False\n\n    def is_interesting(self, image):\n        gray = cvtColor(image, COLOR_BGR2GRAY)\n        gray = bilateralFilter(gray, 11, 17, 17)\n        edged = Canny(gray, 30, 200)\n        if np.sum(edged[:, :]**2) < 2500:\n            return False\n        else:\n            return True\n\n    def processing(self, name, image_bytes):\n        n_name = name.split(\"_\")\n        n_ip = n_name[0]\n        try:\n            pil_image = Image.open(io.BytesIO(image_bytes))\n            image = np.array(pil_image)\n            if self.is_dark(image) or not self.is_interesting(image):\n                self.save_image(os.path.join('trash', name), image_bytes)\n                config.trash_cam[n_ip] += 1\n                return False\n            else:\n                self.save_image(name, image_bytes)\n                config.trash_cam[n_ip] = 0\n                return True\n        except Exception as e:\n            config.trash_cam[n_ip] += 1\n            #print(\"PIL Issue: \" + str(e))\n            logging.debug(f'{fore_red(\"Cannot save screenshot\")} - {name.split(\"_\")[0]} - {back_red(\"CORRUPTED FILE\")}{\" \"*40}')\n            pass\n\n    def save_image(self, name, image_bytes):\n        n_name = name.split(\"_\")\n        n_ip = n_name[0]\n        try:\n            with open(os.path.join(config.snapshots_folder, name), 'wb') as outfile:\n                outfile.write(image_bytes)\n            config.snapshots_counts += 1\n            logging.debug(f' {fore_green(f\"Saved snapshot - {name}\")}{\" \"*40}')\n        except Exception as e:\n            config.trash_cam[n_ip] += 1\n            #print(\" Outfile: \" + e)\n            logging.debug(f'{fore_red(\"Cannot save screenshot\")} - {name}{\" \"*40}')\n","sub_path":"snapshot.py","file_name":"snapshot.py","file_ext":"py","file_size_in_byte":4748,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"335172263","text":"import datetime\nimport io\nimport logging\n\nfrom requests.adapters import BaseAdapter\nfrom requests.models import Response\nfrom requests.structures import CaseInsensitiveDict\nfrom requests.utils import get_encoding_from_headers\n\ntry:\n    from http.client import responses\nexcept ImportError:\n    from httplib import responses\n\ntry:\n    from urllib.parse import urlparse\nexcept ImportError:\n    from urlparse import urlparse\n\ntry:\n    timedelta_total_seconds = datetime.timedelta.total_seconds\nexcept AttributeError:\n    def timedelta_total_seconds(timedelta):\n        return (\n            timedelta.microseconds + 0.0 +\n            (timedelta.seconds + timedelta.days * 24 * 3600) * 10 ** 6) / 10 ** 6\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass Content(object):\n\n    def __init__(self, content):\n        self._len = len(content)\n        self._read = 0\n        self._bytes = io.BytesIO(content)\n\n    def __len__(self):\n        return self._len\n\n    def read(self, amt=None):\n        if amt:\n            self._read += amt\n        return self._bytes.read(amt)\n\n    def readline(self):\n        line = self._bytes.readline()\n        self._read += len(line)\n        return line\n\n    def stream(self, amt=None, decode_content=None):\n        while self._read < self._len:\n            yield self.read(amt)\n\n    def release_conn(self):\n        pass\n\n\nclass WSGIAdapter(BaseAdapter):\n    server_protocol = 'HTTP/1.1'\n    wsgi_version = (1, 0)\n\n    def __init__(self, app, multiprocess=False, multithread=False, run_once=False, log_function=None):\n        self.app = app\n        self.multiprocess = multiprocess\n        self.multithread = multithread\n        self.run_once = run_once\n        self._log = log_function or self._log\n        self.errors = io.BytesIO()\n\n    def send(self, request, *args, **kwargs):\n        start = datetime.datetime.utcnow()\n\n        urlinfo = urlparse(request.url)\n\n        if not request.body:\n            data = b''\n        # requests>=2.11.0 makes request body a bytes object which no longer needs\n        # encoding\n        elif isinstance(request.body, bytes):\n            data = request.body\n        else:\n            data = request.body.encode('utf-8')\n\n        environ = {\n            'CONTENT_TYPE': request.headers.get('Content-Type', 'text/plain'),\n            'CONTENT_LENGTH': len(data),\n            'PATH_INFO': urlinfo.path,\n            'REQUEST_METHOD': request.method,\n            'SERVER_NAME': urlinfo.hostname,\n            'QUERY_STRING': urlinfo.query,\n            'SERVER_PORT': urlinfo.port or ('443' if urlinfo.scheme == 'https' else '80'),\n            'SERVER_PROTOCOL': self.server_protocol,\n            'wsgi.version': self.wsgi_version,\n            'wsgi.url_scheme': urlinfo.scheme,\n            'wsgi.input': Content(data),\n            'wsgi.errors': self.errors,\n            'wsgi.multiprocess': self.multiprocess,\n            'wsgi.multithread': self.multithread,\n            'wsgi.run_once': self.run_once,\n            'wsgi.url_scheme': urlinfo.scheme,\n        }\n\n        environ.update(dict(\n            ('HTTP_{0}'.format(name).replace('-', '_').upper(), value)\n            for name, value in request.headers.items()\n        ))\n\n        response = Response()\n\n        def start_response(status, headers):\n            response.status_code = int(status.split(' ')[0])\n            response.reason = responses.get(response.status_code, 'Unknown Status Code')\n            response.headers = CaseInsensitiveDict(headers)\n            response.encoding = get_encoding_from_headers(response.headers)\n            response.elapsed = datetime.datetime.utcnow() - start\n            self._log(response)\n\n        response.request = request\n        response.url = request.url\n\n        response.raw = Content(b''.join(self.app(environ, start_response)))\n\n        return response\n\n    def close(self):\n        pass\n\n    def _log(self, response):\n        if response.status_code < 400:\n            log = logger.info\n        elif response.status_code < 500:\n            log = logger.warning\n        else:\n            log = logger.error\n\n        summary = '{status} {method} {url} ({host}) {time}ms'.format(\n            status=response.status_code,\n            method=response.request.method,\n            url=response.request.path_url,\n            host=urlparse(response.url).hostname,\n            time=round(timedelta_total_seconds(response.elapsed) * 1000, 2),\n        )\n\n        log(summary)\n","sub_path":"wsgiadapter.py","file_name":"wsgiadapter.py","file_ext":"py","file_size_in_byte":4430,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"542827232","text":"import io\nimport string\nimport subprocess as sp\n\ndef shell_exec(cmd):\n    p = sp.run(cmd, stdout=sp.PIPE, stderr=sp.PIPE)\n    return p.stdout\n\n\ndef shell_get_strings(file, prefix):\n    cmd = ['strings', file]\n    sio = io.StringIO(shell_exec(cmd).decode())\n    strings = []\n    for line in sio:\n        strings.append(line.rstrip())\n    strings.sort()\n    return strings\n\n\ndef get_strings(data, prefix):\n    s = \"\"\n    printable = set(string.printable)\n    strings = set()\n    i = 0\n    for c in data:\n        if 0 == i % 2**20:\n            T(\"%d\", i >> 20)\n        if c in printable:\n            s += c\n        else:\n            if \"\" != s:\n                if s.startswith(prefix):\n                    strings.add(s)\n                s = \"\"\n        i += 1\n    strings = list(strings)\n    strings.sort()\n    return strings\n","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":822,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"415590879","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Feb  6 18:29:39 2015\n\n@author: npingel\n\"\"\"\nimport numpy as np\nfrom astropy.io import fits \nimport matplotlib.pyplot as pyplot\nimport matplotlib \n\nmatplotlib.rc('font', family='sans-serif') \nmatplotlib.rc('font', serif='Helvetica Neue') \nmatplotlib.rc('text', usetex='false') \nmatplotlib.rcParams.update({'font.size': 14})\n\nsourceList = ['NGC891','NGC925','NGC4414', 'NGC4565']\ndistList = [9.2,9.1,17.8,10.8]\n\nfor sourceIdx in range(0,len(sourceList)):\n    src = sourceList[sourceIdx]\n    dist = distList[sourceIdx]\n    print(\"Analyzing source: \"+src)\n    infData = '/Users/npingel/Desktop/Research/GBT-HALOGAS/MOM0/'+src+'/'+src+'_WSRT_MOM0_CONTSUB_SM.FITS'\n    sdData = '/Users/npingel/Desktop/Research/GBT-HALOGAS/MOM0/'+src+'/'+src+'_GBT_MOM0_CONTSUB_SM.FITS'       \n    intNoiseData = '/Users/npingel/Desktop/Research/GBT-HALOGAS/MOM0/'+src+'/'+src+'_WSRT_HI_NOISE_MAP_SM.FITS'\n    sdNoiseData = '/Users/npingel/Desktop/Research/GBT-HALOGAS/MOM0/'+src+'/'+src+'_GBT_HI_NOISE_MAP_SM.FITS'\n\n    sdHduList = fits.open(sdData)\n    interHduList = fits.open(infData)\n    sdErrHduList = fits.open(sdNoiseData)\n    intErrHduList = fits.open(intNoiseData)\n    if src == 'NGC891':\n        dv = 8.24\n    else:\n        dv = 4.12\n        \n    sdImage = np.nan_to_num(sdHduList[0].data)*1.82e18*dv\n    intImage = np.nan_to_num(interHduList[0].data)*1.82e18*dv\n    intNoiseIm = np.nan_to_num(intErrHduList[0].data)\n    sdNoiseIm = np.nan_to_num(sdErrHduList[0].data)\n    interXPixSize = interHduList[0].header['CDELT1']*3600\n    interYPixSize = interHduList[0].header['CDELT2']*3600\n    minHI = np.max(intNoiseIm)*dv*1.82e18*3\n\n\n    pixArea = 16/3600.\n    pixLinScale = 2*dist*np.tan(np.deg2rad(4./3600./2))*1e3\n    pixPhysArea = pixLinScale**2\n\n    sdXSize = sdImage.shape[1]\n    sdYSize = sdImage.shape[0]\n\n    infXSize = intImage.shape[1]\n    infYSize = intImage.shape[0]\n\n    levs = np.logspace(np.log10(minHI),20.0,10,endpoint='False')\n    sdAngLevs = [] \n    sdAngLevsErr = []\n    intAngLevs = []\n    intAngLevsErr = []\n\n    for ind in range(len(levs)):\n        print('Determining cumulative Area of level: '+str(levs[ind]))\n        sdCnt = 0.\n        intCnt= 0.\n        for i in range(infXSize):\n            for j in range(infYSize):\n                intVal = intImage[j,i]    \n                noiseVal = intNoiseIm[j,i]\n                if intVal >= levs[ind]:\n                    intCnt+=1\n\n  \n        for i in range(sdXSize):\n            for j in range(sdYSize):\n                sdVal = sdImage[j,i]\n                if sdVal >= levs[ind] and sdVal > 0:\n                    sdCnt+=1\n    \n        sdAngLevs.append(sdCnt*pixPhysArea)  \n        sdAngLevsErr.append(np.sqrt(sdCnt)*pixPhysArea)\n        intAngLevs.append(intCnt*pixPhysArea)\n        intAngLevsErr.append(np.sqrt(intCnt)*pixPhysArea)\n\n\n## print out area Levs\n#print('The GBT area in total sq. arcmins : ', sdMassLevs)\n#print('The WSRT area in total sq. arcmins : ', intMassLevs)\n#print('The ratio of the GBT and WSRT areas ', np.divide(sdMassLevs,intMassLevs))\n\n    sdAreaLevsErr_Log = sdAngLevsErr/(10*np.log(10.))\n    infAreaLevsErr_Log = sdAngLevsErr/(10*np.log(10.))\n\n##Plot\n    pyplot.figure()\n    pyplot.xlabel(r'Log$_{10}$(N$_{HI}$) Level [cm$^{-2}$]')\n    pyplot.ylabel('Cumulative Area [kpc$^2$]')\n    pyplot.title('Cumulative HI Area')\n    pyplot.xlim(17,20.5)\n    pyplot.errorbar(np.log10(levs),sdAngLevs,yerr=sdAngLevsErr,color='black', fmt='s',label='GBT')\n    pyplot.errorbar(np.log10(levs),sdAngLevs,yerr=sdAngLevsErr,color='black')\n    pyplot.errorbar(np.log10(levs),intAngLevs,yerr=intAngLevsErr,color='black', fmt='--o', label='WSRT') \n    pyplot.legend(loc=0,prop={'size':14},fontsize='large')\n    pyplot.errorbar(np.log10(levs),sdAngLevs,yerr=sdAngLevsErr,color='black',fmt='s')\n    pyplot.errorbar(np.log10(levs),sdAngLevs,yerr=sdAngLevsErr,color='black')\n    pyplot.errorbar(np.log10(levs),intAngLevs,yerr=intAngLevsErr,fmt='--o',color='black')\n    pyplot.savefig('/Users/npingel/Desktop/'+src+'Area_NHI.pdf', bbox_inches='tight')    \n    pyplot.show()\n\n","sub_path":"PxArea.py","file_name":"PxArea.py","file_ext":"py","file_size_in_byte":4075,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"137377933","text":"import os\r\nimport sys\r\nimport datetime\r\nimport time\r\n\r\ndef main():\r\n    f = open('test.txt', 'r')\r\n    o = open('test-fixed.txt', 'w')\r\n    for line in  f.readlines():\r\n        line = line.rstrip(\"\\r\\n\")\r\n        line = line.replace('\"', '\\\\\"')\r\n        o.write(line)\r\n    f.close()\r\n    o.close()\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n","sub_path":"Python/rmLine/fixFile.py","file_name":"fixFile.py","file_ext":"py","file_size_in_byte":341,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"412953550","text":"import sqlite3\r\nfrom math import*\r\nimport numpy\r\nimport scipy\r\nfrom numpy import*\r\nfrom scipy import spatial\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\n#Create connection to the db\r\ndb_name = 'test.db'\r\n# Try to connect\r\n\t\r\nconn = sqlite3.connect(db_name)\r\nconn.text_factory = str\r\nc = conn.cursor()\r\n\r\nclass Data:\r\n\tdef __init__(self,jobs,goals):\r\n\t\tself.jobs = jobs\r\n\t\tself.goals = goals\r\n\t\t\r\nclass Job:\r\n\tdef __init__(self,name,current_weight,recommended_weight,recommended_rank,max_growth,max_security,max_exploration,max_innovation,max_education):\r\n\t\tself.name = name\r\n\t\tself.current_weight = current_weight\r\n\t\tself.recommended_rank = recommended_rank;\r\n\t\tself.recommended_weight = recommended_weight;\r\n\t\tself.goals = [max_growth,max_security,max_exploration,max_innovation,max_education];\r\n\t\tself.max_growth = max_growth\r\n\t\tself.max_security = max_security\r\n\t\tself.max_exploration = max_exploration\r\n\t\tself.max_innovation = max_innovation\r\n\t\tself.max_education = max_education\r\n\t\t\r\nclass Goal:\r\n\tdef __init__(self,name,value):\r\n\t\tself.name = name\r\n\t\tself.value = value\r\n\r\n\r\n#Helper functions---------------------------------------------------------\r\ndef initialize_db(job_goals_values):\r\n\t# Create table\r\n\tc.execute('''CREATE TABLE goals\r\n\t\t\t\t (uid real,name text, value text)''')\r\n\t\t\t\t \r\n\tc.execute('''CREATE TABLE skills_recommendation\r\n\t\t\t\t (uid real,skill text)''')\r\n\t\t\t\t \r\n\tc.execute('''CREATE TABLE job_weight\r\n\t\t\t\t (uid real,name text, \r\n\t\t\t\t current_weight real, \r\n\t\t\t\t current_rank real, \r\n\t\t\t\t recommended_weight real, \r\n\t\t\t\t recommended_rank real, \r\n\t\t\t\t max_growth real, \r\n\t\t\t\t max_security real, \r\n\t\t\t\t max_exploration real, \r\n\t\t\t\t max_innovation real, \r\n\t\t\t\t max_education real)''')\r\n\r\n\t# Insert a row of data into goals table\r\n\tc.execute(\"INSERT INTO goals VALUES (0,'Security',1)\")\r\n\tc.execute(\"INSERT INTO goals VALUES (1,'Wealth',3)\")\r\n\tc.execute(\"INSERT INTO goals VALUES (2,'Exploration',4)\")\r\n\tc.execute(\"INSERT INTO goals VALUES (3,'Innovation',1)\")\r\n\tc.execute(\"INSERT INTO goals VALUES (4,'Expansioin',1)\")\r\n\r\n\r\n\t# Insert a row of data into job_weight\r\n\tc.execute(\"INSERT INTO job_weight VALUES (0,'Maintenance Worker',0.16,0,0.16,0,'%d','%d','%d','%d','%d')\" %(job_goals_values[0][0],job_goals_values[0][1],job_goals_values[0][2],job_goals_values[0][3],job_goals_values[0][4]))\r\n\tc.execute(\"INSERT INTO job_weight VALUES (1,'Lead Process Planner',0.16,1,0.16,1,'%d','%d','%d','%d','%d')\" %(job_goals_values[1][0],job_goals_values[1][1],job_goals_values[1][2],job_goals_values[1][3],job_goals_values[1][4]))\r\n\tc.execute(\"INSERT INTO job_weight VALUES (2,'Ressource Excavator',0.16,2,0.16,2,'%d','%d','%d','%d','%d')\" %(job_goals_values[2][0],job_goals_values[2][1],job_goals_values[2][2],job_goals_values[2][3],job_goals_values[2][4]))\r\n\tc.execute(\"INSERT INTO job_weight VALUES (3,'Medic',0.16,3,0.16,3,'%d','%d','%d','%d','%d')\" %(job_goals_values[3][0],job_goals_values[3][1],job_goals_values[3][2],job_goals_values[3][3],job_goals_values[3][4]))\r\n\tc.execute(\"INSERT INTO job_weight VALUES (4,'Harvester',0.16,4,0.16,4,'%d','%d','%d','%d','%d')\" %(job_goals_values[4][0],job_goals_values[4][1],job_goals_values[4][2],job_goals_values[4][3],job_goals_values[4][4]))\r\n\tc.execute(\"INSERT INTO job_weight VALUES (5,'Gardien',0.16,5,0.16,5,'%d','%d','%d','%d','%d')\" %(job_goals_values[5][0],job_goals_values[5][1],job_goals_values[5][2],job_goals_values[5][3],job_goals_values[5][4]))\r\n\t\t\r\n\r\n\tc.execute(\"INSERT INTO skills_recommendation VALUES (0,'Structure')\")\r\n\tc.execute(\"INSERT INTO skills_recommendation VALUES (1,'Electricity')\")\r\n\tc.execute(\"INSERT INTO skills_recommendation VALUES (2,'Machine')\")\r\n\tc.execute(\"INSERT INTO skills_recommendation VALUES (3,'Problem Solving')\")\r\n\tc.execute(\"INSERT INTO skills_recommendation VALUES (4,'Programming')\")\r\n\tc.execute(\"INSERT INTO skills_recommendation VALUES (5,'Battle')\")\r\n\r\n\t# Save (commit) the changes\r\n\tconn.commit()\r\n\r\ndef get_goal_value(uid,col):\r\n\tc.execute('SELECT ({coi}) FROM {tn} WHERE uid=\"{cn}\"'.\\\r\n        format(coi=col, tn='goals', cn=uid))\r\n\t\r\n\treturn c.fetchone()[0]\t\r\n\t\r\ndef get_job_value(uid,col):\r\n\tc.execute('SELECT ({coi}) FROM {tn} WHERE uid=\"{cn}\"'.\\\r\n\t\tformat(coi=col, tn='job_weight', cn=uid))\r\n\treturn c.fetchone()[0]\r\n\t\r\ndef populate_jobs(job_count):\r\n\tjobs = []\r\n\tfor i in range(0,job_count):\r\n\t\tname = get_job_value(i,\"name\");\r\n\t\tcurrent_weight = get_job_value(i,\"current_weight\");\r\n\t\tcurrent_rank = get_job_value(i,\"current_rank\");\r\n\t\trecommended_rank = get_job_value(i,\"recommended_rank\");\r\n\t\trecommended_weight = get_job_value(i,\"recommended_weight\");\r\n\t\tmax_growth = get_job_value(i,\"max_growth\");\r\n\t\tmax_security = get_job_value(i,\"max_security\");\r\n\t\tmax_exploration = get_job_value(i,\"max_exploration\");\r\n\t\tmax_innovation = get_job_value(i,\"max_innovation\");\r\n\t\tmax_education = get_job_value(i,\"max_education\");\r\n\t\tjob = Job(name,current_weight,recommended_weight,recommended_rank,max_growth,max_security,max_exploration,max_innovation,max_education)\r\n\t\tjobs.append(job)\r\n\treturn jobs\r\n\t\r\ndef populate_goals(goals_count):\r\n\tgoals = []\r\n\tfor i in range(0,goals_count):\r\n\t\tname = get_goal_value(i,\"name\");\r\n\t\tvalue = get_goal_value(i,\"value\");\r\n\t\tgoal = Goal(name,value)\r\n\t\tgoals.append(goal)\r\n\treturn goals\t\r\n\r\ndef populate_goals_value():\r\n\tgoals_array = []\r\n\t#Security, Wealth, Exploration, Innovation and Expansion\r\n\tgoals_array.append([1,2,2,3,2])\r\n\tgoals_array.append([1,1,1,3,4])\r\n\tgoals_array.append([1,4,3,1,1])\r\n\tgoals_array.append([3,3,1,2,1])\r\n\tgoals_array.append([1,3,3,1,2])\r\n\tgoals_array.append([5,2,1,1,1])\r\n\treturn goals_array\r\n\r\n\t\r\ndef populate_skill_array():\r\n\tskills_array = []\r\n\tskills_array.append([1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0])\r\n\tskills_array.append([1,0,1,1,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0])\r\n\tskills_array.append([0,1,1,0,1,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0])\r\n\tskills_array.append([0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,1,1])\r\n\tskills_array.append([0,0,0,0,0,0,0,1,0,0,1,0,0,1,1,1,0,0,1,0])\r\n\tskills_array.append([0,0,0,1,0,1,0,0,0,1,0,0,0,0,1,1,0,0,0,1])\r\n\treturn skills_array\r\n\t\r\ndef populate_skill_dict():\r\n\tskill_dict = {}\r\n\tskill_name = ['Structure','Electricity','Machine','Problem Solving','Programming',\r\n\t\t\t\t\t'Battle','Geography','Planning','Project Management','Peace Keeping',\r\n\t\t\t\t\t'Geology','Materials','Research','Biology','Manual Labor','Health',\r\n\t\t\t\t\t'Cleaning','Pharmacy','Nutrition','Psychology']\r\n\tfor i in range(0,20):\r\n\t\tskill_dict[i] = skill_name[i]\r\n\treturn skill_dict\r\n\t\r\ndef update_goals(new_goals):\r\n\tindex = 0\r\n\tfor goals in new_goals:\r\n\t\tc.execute(\"UPDATE goals SET value = '%d' WHERE uid = %d\" %(goals,index));\r\n\t\tindex = index + 1\r\n\tconn.commit();\r\n\t\r\ndef update_recommendation(skill_dict,ranked_skills):\r\n\tfor index in range(0,6):\r\n\t\tc.execute(\"UPDATE skills_recommendation SET skill = '%s' WHERE uid = %d\" %(skill_dict[ranked_skills[index][0]],index));\r\n\tconn.commit();\r\n\t\r\ndef update_job_col(job_ranking):\r\n\tindex = 0\r\n\tfor job_tuple in job_ranking:\r\n\t\tc.execute(\"UPDATE job_weight SET recommended_rank = '%f' WHERE uid= %d\" %(index,job_tuple[0]));\r\n\t\tc.execute(\"UPDATE job_weight SET recommended_weight = '%f' WHERE uid= %d\" %(job_tuple[1],job_tuple[0]));\r\n\t\tindex = index + 1\r\n\tconn.commit();\r\n\t\r\n\r\n#-------------------------------------------------------------------------\r\n\r\n#Job Ranking algorithm----------------------------------------------------\r\ndef calculate_euclidian_distance(target,current):\r\n\tA = numpy.array(target)\r\n\tB = numpy.array(current)\r\n\treturn scipy.spatial.distance.euclidean(A,B)\r\n\t\r\ndef rank_jobs(data):\r\n\tjob_ranking = {}\r\n\tdata_goals = []\r\n\ttotal = 0;\r\n\tfor goal in data.goals:\r\n\t\tdata_goals.append(float(goal.value));\r\n\t\r\n\ti = 0\r\n\tfor job in data.jobs:\r\n\t\tjob_ranking[i] = calculate_euclidian_distance(data_goals,job.goals)\r\n\t\ttotal = total + job_ranking[i]\r\n\t\ti += 1\r\n\tjob_ranking = {key: total-value for key, value in job_ranking.iteritems()}\t\r\n\tjob_ranking = sorted(job_ranking.items(), key=lambda t: t[1])\r\n\treturning_job_rank = {}\r\n\t\r\n\tindex = 0\r\n\ttotal = 0;\r\n\tfor job_tuple in job_ranking:\r\n\t\ttotal += job_tuple[1];\r\n\t\r\n\tfor job_tuple in job_ranking:\r\n\t\treturning_job_rank[job_tuple[0]]=(job_tuple[1]/total)\r\n\t\tindex = index + 1\r\n\t\t\r\n\treturning_job_rank = sorted(returning_job_rank.items(), key=lambda t: t[1],reverse=True)\r\n\treturn returning_job_rank\r\n#-------------------------------------------------------------------------\r\n\t\r\n#Skill Ranking Calculation\r\ndef calculate_skill_ranking(aggregated_skills,job_ranking,job_count,skill_count,data):\r\n\tcumulative_skills_score = {}\r\n\tfor i in range(0,skill_count):\r\n\t\tcumulative_skills_score[i] = 0\r\n\t\t\r\n\tfor i in range(0,job_count):\r\n\t\tfor j in range(0,skill_count):\r\n\t\t\tcumulative_skills_score[j] += (job_ranking[i][1] -data.jobs[i].recommended_weight)*(aggregated_skills[i][j])\r\n\t\r\n\tcumulative_skills_score= {key: value for key, value in cumulative_skills_score.iteritems()}\t\r\n\tcumulative_skills_score = sorted(cumulative_skills_score.items(), key=lambda t: t[1],reverse=True)\r\n\treturn cumulative_skills_score\r\n\t\t\r\n\r\n#Initialize the database\r\n#initialize_db(populate_goals_value())\r\n\r\n#Initialize the data and Simple euclidian distance\r\njob_count = 6\r\ngoals_count = 5\r\ndata = Data(populate_jobs(job_count),populate_goals(goals_count))\r\nupdate_goals([5,1,1,1,2])\r\njob_ranking = rank_jobs(data)\r\nindex = 0\r\nprint(\"Goals inputed : Security = 5, Wealth = 1, Exploration = 1 , Innovation = 1, Expension = 2\")\r\n\r\nprint(\"Job ranking :\")\r\nfor job_tuple in job_ranking:\r\n\tprint('Job : %s ranking is %d with new target = %f%%' %(data.jobs[job_tuple[0]].name,index,job_tuple[1]))\r\n\tindex = index + 1\r\nupdate_job_col(job_ranking);\r\n\t\r\n\r\n#Skill selection:\r\nskill_array = populate_skill_array()\r\nskill_count = 20\r\nranked_skills = calculate_skill_ranking(skill_array,job_ranking,job_count,skill_count,data)\r\n\r\nskill_dict = populate_skill_dict()\r\nfor i in range(0,skill_count):\r\n\tprint(\"Skill : %s is ranked %d\" %(skill_dict[ranked_skills[i][0]],i))\r\nupdate_recommendation(skill_dict,ranked_skills)\r\n\r\n\r\n\r\n#Show the plot with matplotlib\r\n#ranked_skills and skill_dict, got the skill list ranked\r\n#job_ranking is where my job are in ranked order\r\n\r\ndata = Data(populate_jobs(job_count),populate_goals(goals_count))\r\nrecommended_weight_plot = []\r\ncurrent_weight_plot = []\r\nfor i in range(0,6):\r\n\trecommended_weight_plot.append(data.jobs[i].recommended_weight);\r\n\tcurrent_weight_plot.append(data.jobs[i].current_weight)\r\nrecommended_weight_plot = numpy.array(recommended_weight_plot)\r\ncurrent_weight_plot = numpy.array(current_weight_plot)\r\n\r\n\r\nlabels = ['test','Maintenance Worker','Lead Process Planner','Ressource Excavator','Medic','Harvester','Guardian']\r\ndata = [current_weight_plot,\r\n  recommended_weight_plot]\r\n\r\nf, (ax1, ax2) = plt.subplots(2, 1, sharey=True)\r\nX = numpy.arange(6)\r\nrects_1 = ax1.bar(X -0.125, data[0], color = 'g', width = 0.25,label='Current')\r\nrects_2 = ax1.bar(X + 0.125, data[1], color = 'gold', width = 0.25,label='Recommended')\r\nax1.set_ylim([min(recommended_weight_plot)-0.002,max(recommended_weight_plot)+0.01])\r\nax1.set_xticklabels(labels)\r\nax1.set_title('Comparison of Actual and Recommended Job Partition')\r\nax1.legend(loc='upper left')\r\n\r\ndef autolabel(rects,data):\r\n\tindex = 0\r\n\tfor rect in rects:\r\n\t\theight = data[index]\r\n\t\tax1.text(rect.get_x() + rect.get_width()/2., 1.01*height,\r\n\t\t\t'%.2f' % (height),\r\n\t\t\tha='center', va='bottom')\r\n\t\tindex += 1\r\n\t\t\t\t\t\r\nautolabel(rects_1,data[0])\r\nautolabel(rects_2,data[1])\r\n\r\n# Pie chart, where the slices will be ordered and plotted counter-clockwise:\r\nlabels = []\r\nlabels.append('test')\r\nfor i in range(0,6):\r\n\tlabels.append(skill_dict[ranked_skills[i][0]])\r\n\t\r\nlabel_value = max(recommended_weight_plot)\r\ndata = [label_value, label_value, label_value, label_value, label_value,label_value]\r\n\r\nax2.bar(X, data[0], color = 'b', width = 0.25)\r\nax2.set_xticklabels(labels)\r\nax2.set_ylim([min(recommended_weight_plot)-0.002,max(recommended_weight_plot)+0.01])\r\nax2.set_title('Proposed Skills to Promote')\r\nmng = plt.get_current_fig_manager()\r\nmng.window.state('zoomed') #works fine on Windows!\r\n\r\nplt.show()\r\n\r\n\r\n\r\nconn.close()\r\n","sub_path":"ai part/repartition_hack.py","file_name":"repartition_hack.py","file_ext":"py","file_size_in_byte":12050,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"242591291","text":"\"\"\"\ntcp server\n\"\"\"\n\nfrom socket import *\n# tcp socket\ntcp_socket_s = socket(AF_INET,SOCK_STREAM)\ntcp_socket_s.bind(('0.0.0.0',65535))\n\n\"\"\n#listen socket  看成一个列队设置\ntcp_socket_s.listen(20)\n\n#wait accept client\nwhile 1:\n    print('waiting for connect...')\n    c_connfd,c_addr = tcp_socket_s.accept()  #与客户端connect呼应\n    print('ok,connet from',c_addr,'生成客户端连接套接字',c_connfd)\n\n\n    # recive and sendback\n    while 1:\n        re_data = c_connfd.recv(5)\n        print('收到来自',c_addr,\"的消息:\",re_data.decode())\n        c_connfd.send(b'ok,thanks')  #会产生BrokenPipeError\n        # 异常关闭或主动关闭\n        if re_data  == b\"##\" :\n            print(c_addr,'退出')\n            break\n        elif not re_data:\n            print(c_addr, '异常退出')\n            break\n\n    c_connfd.close()\n\n\n","sub_path":"fancy_month02/day11_tcp/day11_0111_note/tcp_server.py","file_name":"tcp_server.py","file_ext":"py","file_size_in_byte":856,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"308936886","text":"\"\"\"\nLayer Parser\n\nShould define a layer as a combination of rule selectors\nand a sequence of agendas\n\neg:\nΓ::a.layer:\n\t| $pipeline_var |\n\n\t;; *must* only produce a single context?\n\tan.agenda.set.$agenda(::Σ)?\n\ta.rule.selector.$x?\n\tanother.rule.selector.$y?\n\n\tdfs(@x, (::ρ, #a_tag))      -> $z(::[ρ])\n\tleaves(@y, (::ρ))           -> $q(::[ρ])\n\tmerge($z, $q)               -> $i\n\tLayerRunRules($i)           -> $b\n\tLayerRunAgenda($agenda, $b) -> $c\n\n\tLayerPerform($c)\nend\n\n\"\"\"\nimport logging as root_logger\nimport pyparsing as pp\n\nfrom acab.config import AcabConfig\n\nfrom acab.abstract.parsing import util as PU\nfrom acab.abstract.rule.production_operator import ProductionContainer\nfrom acab.abstract.pipeline.layer import Layer, make_layer\n\nlogging = root_logger.getLogger(__name__)\n\nutil = AcabConfig.Get()\nQUERY_S = util(\"Parsing.Structure\", \"QUERY_S\")\nTRANSFORM_S = util(\"Parsing.Structure\", \"TRANSFORM_S\")\nACTION_S = util(\"Parsing.Structure\", \"ACTION_S\")\n\nHOTLOAD_BASIC_SEN = pp.Forward()\nHOTLOAD_QUERY = pp.Forward()\nHOTLOAD_TRANSFORM = pp.Forward()\nHOTLOAD_ACTION = pp.Forward()\n\n# Layers should be a special case of rule\nconditions  = PU.N(QUERY_S , HOTLOAD_QUERY     + PU.gap)\ntransforms  = PU.N(TRANSFORM_S , HOTLOAD_TRANSFORM + PU.gap)\nvar_setting = PU.NG(ACTION_S   , HOTLOAD_ACTION    + PU.component_gap)\n\nlayer_body = PU.op(conditions) + PU.op(transforms) + PU.op(var_setting)\n\nlayer_stmt = PU.STATEMENT_CONSTRUCTOR(PU.LAYER_HEAD,\n                                      HOTLOAD_BASIC_SEN,\n                                      layer_body)\n\nlayer_body.setParseAction(make_layer)\n\nparse_point = layer_stmt\n# parse_point.setFailAction(lambda s, loc, expr, err: print(\"{}\\n{}\".format(str(err), err.markInputline())))\n\ndef parseString(s):\n    return parse_point.parseString(s)\n","sub_path":"acab/modules/structures/layer/LayerParser.py","file_name":"LayerParser.py","file_ext":"py","file_size_in_byte":1790,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"235800783","text":"#!/usr/bin/env python3\n\nimport sys\nimport markdown2\nimport requests\nfrom jinja2 import Template\nfrom bleach import linkify\n\nimport siteconf\n\nhtml_context = {'menu': siteconf.menu, 'menu_active': 'Home'}\n\nwith open('index.html', 'w') as idx:\n\n    r = requests.get(\n        'https://github.com/alttch/pptop/blob/master/README.md?raw=true')\n    if not r.ok:\n        raise RuntimeError('http code {}'.format(r.code))\n\n    data = markdown2.markdown(r.text)\n\n    with open('tpl/index_header.html') as fh:\n        template = Template(fh.read())\n        idx.write(template.render(html_context))\n\n    data = ('<p>' +\n            data[data.find('ppTOP is'):data.find('p.s. Code in ')].replace(\n                '<code>shell', '<code>'))\n\n    for d in data.split('\\n'):\n        if d.find('\"asciicast\"') != -1:\n            asciinema_id = d.split('\"')[1].split('/')[-1]\n            d = ((\n                '<div class=\"asciinema-container\"><script id=\"asciicast-{i}\" ' +\n                'src=\"https://asciinema.org/a/{i}.js\" ' +\n                'async data-size=\"9\"></script></div>').format(i=asciinema_id))\n        elif d.find(' href ') == -1 and d.find('https://') != -1:\n            d = linkify(d).replace(' rel=\"nofollow\"', '')\n        d = d.replace('<img src=\"https://img.shields.io/', '<img style=\"border: 0px\" src=\"https://img.shields.io/')\n        idx.write(d + '\\n')\n\n    with open('tpl/index_footer.html') as fh:\n        idx.write(fh.read())\n","sub_path":"generate.py","file_name":"generate.py","file_ext":"py","file_size_in_byte":1437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"44519315","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport collections\nimport os\n\nimport tensorflow as tf\n\nEN_BLACKLIST = '!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~\\''\n\ndef _convert_data(data):\n\tdata = data.lower()\n\tfor char in EN_BLACKLIST:\n\t\tdata = data.replace(char, \"\")\n\treturn data\n\ndef _read_words(filename):\n\twith tf.gfile.GFile(filename, \"r\") as f:\n\t\tdata = _convert_data(f.read())\n\t\treturn data.split()\n\ndef _build_vocab(filename):\n\tdata = _read_words(filename)\n\tcounter = collections.Counter(data)\n\tcount_pairs = sorted(counter.items(), key=lambda x: (-x[1], x[0]))\n\n\twords, _ = list(zip(*count_pairs))\n\tword_to_id = dict(zip(words, range(len(words))))\n\n\treturn word_to_id\n\ndef _read_line(filename):\n\twith tf.gfile.GFile(filename, \"r\") as f:\n\t\treturn [_convert_data(x.strip()) for x in f]\n\ndef _read_words_line(data):\n\treturn [line.split() for line in data]\n\ndef _file_to_word_ids(filename, word_to_id):\n\tdata = _read_line(filename)\n\tdt = []\n\tfor line in _read_words_line(data):\n\t\tdt.append([word_to_id[word] for word in line])\n\treturn dt\n\ndef ptb_raw_data(data_path=None):\n\n\tFAQ_path = os.path.join(data_path, \"FAQ_data.txt\")\n\n\tword_to_id = _build_vocab(FAQ_path)\n\tFAQ_id = _file_to_word_ids(FAQ_path, word_to_id)\n\tFAQ_string = _read_line(FAQ_path)\n\n\treturn FAQ_id, FAQ_string, word_to_id","sub_path":"ChatbotSimple/src/Reader.py","file_name":"Reader.py","file_ext":"py","file_size_in_byte":1350,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"362161199","text":"import pytest\n\nimport pygorpho as pg\nimport numpy as np\n\ndef test_dilate():\n    vol = np.zeros((7,7,7))\n    vol[3,3,3] = 1\n\n    lineSteps = np.array([[1,0,0],[0,1,0],[0,0,1]])\n    lineLens = np.array([3, 4, 5])\n\n    expected = np.zeros((7,7,7))\n    expected[2:5,2:6,1:6] = 1\n\n    actual1 = pg.flat.linear_morph(vol, lineSteps, lineLens, pg.DILATE)\n\n    np.testing.assert_equal(actual1, expected)\n\n    actual2 = pg.flat.linear_dilate(vol, lineSteps, lineLens)\n    np.testing.assert_equal(actual2, expected)\n\n\ndef test_erode():\n    vol = np.ones((7,7,7))\n    vol[3,3,3] = 0\n\n    lineSteps = np.array([[1,0,0],[0,1,0],[0,0,1]])\n    lineLens = np.array([3, 4, 5])\n\n    expected = np.ones((7,7,7))\n    expected[2:5,2:6,1:6] = 0\n\n    actual1 = pg.flat.linear_morph(vol, lineSteps, lineLens, pg.ERODE)\n    np.testing.assert_equal(actual1, expected)\n\n    actual2 = pg.flat.linear_erode(vol, lineSteps, lineLens)\n    np.testing.assert_equal(actual2, expected)\n\n\ndef test_invalid_op():\n    with pytest.raises(AssertionError):\n        pg.flat.linear_morph([], [], [], 99)\n\n\ndef test_resize():\n    vol = np.ones((3,3))\n    line_steps = np.array([1,0,0])\n    line_lens = np.ones(1)\n    res = pg.flat.linear_dilate(vol, line_steps, line_lens)\n    assert res.shape == vol.shape\n\n\ndef test_non_numpy_input():\n    vol = [0, 0, 1, 0, 0]\n    line_steps = [0, 1, 0]\n    line_lens = 3\n    expected = [0, 1, 1, 1, 0]\n    actual = pg.flat.linear_dilate(vol, line_steps, line_lens)\n    np.testing.assert_equal(actual, expected)\n\n\ndef test_valid_dims():\n    vol = []\n    line_steps = 1\n    line_lens = 1\n    with pytest.raises(AssertionError):\n        pg.flat.linear_dilate(vol, line_steps, line_lens)\n","sub_path":"tests/test_flat_linear.py","file_name":"test_flat_linear.py","file_ext":"py","file_size_in_byte":1677,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"418671675","text":"import ROOT\nimport numpy\nimport scipy\nimport StatisticalTest\n\nclass LogLikelihoodTest(StatisticalTest.StatisticalTest) :\n\n  def __init__(self) :\n    StatisticalTest.StatisticalTest.__init__(self)\n\n  def doTest(self, dataHist, bkgHist, firstBinToUse, lastBinToUse) :\n\n    dataCore = dataHist.histogram\n    bkgCore = bkgHist.histogram\n  \n    assert dataCore.GetNbinsX() == bkgCore.GetNbinsX()\n\n    # Find first and last bins with data\n    # If reasonable, overwrite with user's choice\n    firstBin = dataHist.firstBinWithData\n    lastBin = dataHist.lastBinWithData\n    if firstBinToUse>0 and firstBinToUse > firstBin and firstBinToUse < lastBin : firstBin = firstBinToUse\n    if lastBinToUse > firstBinToUse and lastBinToUse>0 and lastBinToUse > firstBin and lastBinToUse < lastBin :\n      lastBin = lastBinToUse\n\n    answer = 0.0\n    for bin in range (firstBin, lastBin+1) :\n\n      if self.excludeWindow :\n        if (bin > self.firstBinToExclude - 1 and bin < self.lastBinToExclude+1) : continue\n\n      data = dataCore.GetBinContent(bin)\n      if (data==0) : continue\n      bkg = bkgCore.GetBinContent(bin)\n      deltaB = bkgCore.GetBinError(bin)\n\n      if data < 0.0 or bkg < 0.0 :\n        thisterm = -1E10\n      elif data == 0.0 :\n        thisterm = -1.0*bkg\n      else :\n        thisterm = data * numpy.log(bkg) - bkg - scipy.special.gammaln(data+1)\n      answer = answer + thisterm\n\n    return answer","sub_path":"LogLikelihoodTest.py","file_name":"LogLikelihoodTest.py","file_ext":"py","file_size_in_byte":1404,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"527651623","text":"from functools import wraps\n\nfrom flask import current_app, Blueprint, flash, redirect, render_template, url_for, session, request, abort, jsonify\n\nfrom .forms import BookForm, BookFormEdit, AuthorForm, AuthorFormEdit, HiddenForm\nfrom .models import db, Book, Author\n\nlibrary = Blueprint(\"library\", __name__)\n\ndef login_required(F):\n    \"\"\"User login check decorator\"\"\"\n    @wraps(F)\n    def _F(*args, **kargs):\n        if not session.get('logged_in'):\n            abort(401)\n        else:\n            return F(*args, **kargs)\n    return _F\n\n\ndef auth_required(F):\n    \"\"\"User authentication check decorator\"\"\"\n    @wraps(F)\n    def _F(*args, **kargs):\n        if not session.get('auth'):\n            abort(401)\n        else:\n            return F(*args, **kargs)\n    return _F\n\n\n@library.route(\"/\")\ndef view_index():\n    return render_template(\"index.html\")\n\n\n@library.route(\"/books\")\n@auth_required\ndef list_books():\n    query = Book.query.filter(Book.id >= 0)\n    books = query.order_by(Book.title).all()\n    return render_template(\"books_list.html\", title=\"Books List\", books=books)\n\n\n@library.route(\"/authors\")\n@auth_required\ndef list_authors():\n    query = Author.query.filter(Author.id >= 0)\n    authors = query.order_by(Author.name).all()\n    return render_template(\"authors_list.html\", title=\"Authors List\", authors=authors)\n\n\n@library.route(\"/book/<int:book_id>\")\n@auth_required\ndef view_book(book_id=None):\n    book = Book.query.get_or_404(book_id)\n    title = \"Book: \" + book.title\n    form = HiddenForm(record_id=book_id)\n    return render_template(\"book.html\", book=book, title=title, form=form)\n\n\n@library.route(\"/delete_book\", methods=(\"POST\", ))\n@login_required\ndef delete_book():\n    form = HiddenForm()\n    if form.validate_on_submit():\n        book = Book.query.get_or_404(form.record_id.data)\n        db.session.delete(book)\n        db.session.commit()\n        flash(\"The book had been removed\")\n        return redirect(url_for(\"library.list_books\"))\n    return render_template(\"validation_error.html\", form=form)\n\n\n@library.route(\"/update_book/<int:book_id>\")\n@login_required\ndef update_book_form(book_id):\n    book = Book.query.get_or_404(book_id)\n    book_form = BookFormEdit()\n    book_form.book_id.default = book.id\n    book_form.title.default = book.title\n    book_form.authors.default = [a.id for a in book.authors]\n    book_form.process()\n    return render_template('add_book.html', form=book_form)\n\n\n@library.route(\"/author/<int:author_id>\")\n@auth_required\ndef view_author(author_id=None):\n    author = Author.query.get_or_404(author_id)\n    title = \"Author: \" + author.name\n    form = HiddenForm(record_id=author.id)\n    return render_template(\"author.html\", author=author, title=title, form=form)\n\n\n@library.route(\"/delete_author\", methods=(\"POST\", ))\n@login_required\ndef delete_author():\n    form = HiddenForm()\n    if form.validate_on_submit():\n        author = Author.query.get_or_404(form.record_id.data)\n        db.session.delete(author)\n        db.session.commit()\n        flash(\"The author had been removed\")\n        return redirect(url_for(\"library.list_authors\"))\n    return render_template(\"validation_error.html\", form=form)\n\n\n@library.route(\"/update_author/<int:author_id>\")\n@login_required\ndef update_author_form(author_id):\n    author = Author.query.get_or_404(author_id)\n    author_form = AuthorFormEdit()\n    author_form.author_id.default = author.id\n    author_form.name.default = author.name\n    author_form.books.default = [b.id for b in author.books]\n    author_form.process()\n    return render_template('add_author.html', form=author_form)\n\n\n@library.route(\"/add_book\")\n@login_required\ndef add_book_form():\n    book_form = BookForm()\n    return render_template('add_book.html', form=book_form)\n\n\n@library.route(\"/add_book\", methods=(\"POST\", ))\n@login_required\ndef add_book():\n    form = BookForm()\n    if form.validate_on_submit():\n        book = Book.query.get_or_404(form.book_id.data) if form.book_id.data else Book()\n        form.populate_obj(book)\n        if not book.id:\n            db.session.add(book)\n            flash_msg = \"Added book\"\n        else:\n            flash_msg = \"Updated book\"\n        db.session.commit()\n        flash(flash_msg)\n        return redirect(url_for(\"library.view_book\", book_id=book.id))\n    return render_template(\"validation_error.html\", form=form)\n\n\n@library.route(\"/add_author\")\n@login_required\ndef add_author_form():\n    author_form = AuthorForm()\n    return render_template('add_author.html', form=author_form)\n\n\n@library.route(\"/add_author\", methods=(\"POST\", ))\n@login_required\ndef add_author():\n    form = AuthorForm()\n    if form.validate_on_submit():\n        author = Author.query.get_or_404(form.author_id.data) if form.author_id.data else Author()\n        form.populate_obj(author)\n        if not author.id:\n            db.session.add(author)\n            flash_msg = \"Added author\"\n        else:\n            flash_msg = \"Updated author\"\n        db.session.commit()\n        flash(flash_msg)\n        return redirect(url_for(\"library.view_author\", author_id=author.id))\n    return render_template(\"validation_error.html\", form=form)\n\n\n@library.route('/login', methods=('GET', 'POST'))\ndef login():\n    error = None\n    if request.method == 'POST':\n        if request.form['username'] == current_app.config['USERNAME'] and \\\n        request.form['password'] == current_app.config['PASSWORD']:\n            session['logged_in'] = True\n            session['auth'] = True\n            flash('You were logged in')\n        elif request.form['username']:\n            session['auth'] = True\n            flash('You were authorized')\n        else:\n            error = 'Wrong credentials'\n            return render_template('login.html', error=error)\n        return redirect(url_for('library.view_index'))\n    return render_template('login.html', error=error)\n\n\n@library.route('/logout')\ndef logout():\n    if session.pop('logged_in', None): flash('You were logged out')\n    if session.pop('auth', None): flash('You were unauthorized')\n    return redirect(url_for('library.view_index'))\n\n\n@library.route('/search')\n@auth_required\ndef search_form():\n    return render_template('search.html')\n\n\n@library.route('/search', methods=('POST', ))\n@auth_required\ndef search():\n    if 'book_title' in request.form and request.form['book_title']:\n        list = Book.query.filter(Book.title.contains(request.form['book_title']))\n        return render_template(\"books_list.html\", title=\"Found Books\", books=list)\n    elif 'author_name' in request.form and request.form['author_name']:\n        list = Author.query.filter(Author.name.contains(request.form['author_name']))\n        return render_template(\"authors_list.html\", title=\"Found Authors\", authors=list)\n    error = \"Need a parameter\"\n    return render_template('search.html', error=error)\n\n\n@library.route('/search_book_api')\n@auth_required\ndef search_book_api():\n    req = request.args.get('term', 'test', type=str)\n    list = Book.query.filter(Book.title.contains(req)).order_by(Book.title).limit(10)\n    res_dict = dict([(str(b.id), b.title) for b in list])\n    return jsonify(**res_dict)\n\n\n@library.route('/search_author_api')\n@auth_required\ndef search_author_api():\n    req = request.args.get('term', 'test', type=str)\n    list = Author.query.filter(Author.name.contains(req)).order_by(Author.name).limit(10)\n    res_dict = dict([(str(a.id), a.name) for a in list])\n    return jsonify(**res_dict)","sub_path":"library/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7429,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"495419512","text":"import peewee as pw\n\nfrom . import BaseAction\nfrom ..db.controllers import MoviesController, ServerController, IMDBInfoController\nfrom ..util import get_imdb_info, cleanup_messages\nfrom . import logger\n\n\nclass SuggestAction(BaseAction):\n    action_name = \"suggest\"\n    controller = MoviesController()\n    server_controller = ServerController()\n    imdb_controller = IMDBInfoController()\n\n    async def action(self, msg):\n        server_id = msg.guild.id\n        server_row = self.server_controller.get_by_id(server_id)\n        message_timeout = server_row.message_timeout\n        if server_row.block_suggestions:\n            server_msg = await msg.channel.send(\n                \"Suggestions are currently disabled on the server\"\n            )\n            if message_timeout > 0:\n                await cleanup_messages([msg, server_msg], sec_delay=message_timeout)\n            return\n        suggestion = self.get_message_data(msg)\n        suggestion = suggestion.title()\n\n        imdb_row = None\n        if server_row.check_movie_names:\n            imdb_info = get_imdb_info(suggestion)\n            if not imdb_info:\n                server_msg = await msg.channel.send(\n                    \"Could not find the movie title you suggested in IMDb.\"\n                )\n                if message_timeout > 0:\n                    await cleanup_messages([msg, server_msg], sec_delay=message_timeout)\n                return\n\n            imdb_data = {\n                \"imdb_id\": imdb_info.movieID,\n                \"title\": imdb_info[\"title\"],\n                \"canonical_title\": imdb_info[\"canonical title\"],\n                \"year\": imdb_info[\"year\"],\n                \"thumbnail_poster_url\": imdb_info[\"cover url\"],\n                \"full_size_poster_url\": imdb_info[\"full-size cover url\"],\n            }\n            try:\n                imdb_row = self.imdb_controller.create(imdb_data)\n            except pw.IntegrityError as e:\n                # IMDB entry already added, so ignore error\n                logger.debug(\n                    \"IMDB entry insert error: {}\\n{}\".format(imdb_data, str(e))\n                )\n                pass\n\n        movie_data = {\n            \"server\": server_id,\n            \"movie_name\": suggestion,\n            \"suggested_by\": msg.author.name,\n            \"imdb_id\": imdb_row,\n        }\n        try:\n            self.controller.create(movie_data)\n        except pw.IntegrityError as e:\n            logger.debug(\"Movie insert error: {}\\n{}\".format(movie_data, str(e)))\n            server_msg = await msg.channel.send(\n                f\"{suggestion} has already been suggested in this server.\"\n            )\n            if message_timeout > 0:\n                await cleanup_messages([msg, server_msg], sec_delay=message_timeout)\n            return\n        server_msg = await msg.channel.send(\n            f\"Your suggestion of {suggestion} has been added to the list.\"\n        )\n        if message_timeout > 0:\n            await cleanup_messages([msg, server_msg], sec_delay=message_timeout)\n\n    @property\n    def help_text(self):\n        return (\n            \"Adds the supplied movie to the suggestions list. There is a chance this movie will now \"\n            \"show up on future votes\"\n        )\n\n    @property\n    def help_options(self):\n        return [\"[movie name]\"]\n","sub_path":"movienightbot/actions/suggest.py","file_name":"suggest.py","file_ext":"py","file_size_in_byte":3296,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"612593579","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Mar 26 11:16:32 2017\n\n\n\n\"\"\"\n\nimport goslate\n\nUGLY_TEXT  = \"Uglytext\"\nFIXED_TEXT = \"FixedText\"\n\n# read text\nfh = open(UGLY_TEXT, \"r\")\nuglyText = fh.read()\nfh.close()\n\n# fix text\nfixText1 = uglyText.replace(\"   \",\"\")\nfixText2 = fixText1.replace(\"\\n\",\" \")\n\n# consult google\ngs = goslate.Goslate()\nfixText3 = gs.translate(fixText2, 'es')\n\n# write traduction\nfh = open(FIXED_TEXT, \"w\")\nfh.write(fixText2)\nfh.write(\"\\n---------------Traduccion----------------\\n\")\nfh.write(fixText3)\nfh.close()\n","sub_path":"fix_ugly_text.py","file_name":"fix_ugly_text.py","file_ext":"py","file_size_in_byte":531,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"100195822","text":"\nimport os\nimport copy\nimport re\nimport csv\n\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\nfrom webdriver_manager.chrome import ChromeDriverManager\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver import ActionChains\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support import expected_conditions as EC\n\nimport time \n\nimport requests \nfrom bs4 import BeautifulSoup \nfrom selenium import webdriver \nfrom selenium.webdriver.common.keys import Keys \nimport time \n\nimport os\n\nfrom datetime import datetime\n\n#url of the page we want to scrape \nurls = []\n\nurl = \"https://www.psacard.com/pop/tcg-cards/2006/pokemon-ex-crystal-guardians/87132\"\nurls.append(url)\n\nurl = \"https://www.psacard.com/pop/tcg-cards/2007/pokemon-pop-series-5/101536\"\nurls.append(url)\n\nurl = 'https://www.psacard.com/pop/tcg-cards/2005/pokemon-japanese-golden-sky-silvery-ocean/100014'\nurls.append(url)\n\nurl = \"https://www.psacard.com/pop/tcg-cards/2006/pokemon-ex-dragon-frontiers/87126\"\nurls.append(url)\n\n# initiating the webdriver. Parameter includes the path of the webdriver. \n\nwith open('gold_stars.csv', \"a\") as fp:\n        wr = csv.writer(fp, dialect='excel')\n        wr.writerow(['name','1','2','3','4','5','6','7','8','9','10','Total_pop','date'])\n\n\n\n\ndef get_pop(url):\n    driverpath = os.path.realpath(r'/usr/local/bin/chromedriver')\n    chrome_options = Options()  \n\n    driver = webdriver.Chrome(driverpath, options=chrome_options) \n    driver.get(url)  \n      \n    # this is just to ensure that the page is loaded \n    time.sleep(5)  \n      \n    html = driver.page_source \n      \n    # this renders the JS code and stores all \n    # of the information in static HTML code. \n      \n    # Now, we could simply apply bs4 to html variable \n    soup = BeautifulSoup(html, \"html.parser\") \n\n\n    n = 0\n\n    text = ''\n\n    for k in soup.find_all('td'):\n        temp = []\n        \n        text += k.get_text()\n\n\n    text = text.split('\\n')\n\n    indeces = [0,12,20,24,28,32,36,40,44,48,52,56]\n\n    data = []\n    i = 59\n    \n    \n    today = datetime.today()\n\n    date = ''\n    date += (str(today.year) + '-' + str(today.month)) \n\n    set_name = url.split('/')\n    set_name = set_name[-2]\n    set_name = set_name[8:]\n\n    while i < len(text):\n      entry = []\n      temp = text[i:(i+62)]\n      temp[0] = temp[0] + ' ' + temp[1]\n      entry = []\n      for k in indeces:\n         entry.append(temp[k].strip())\n\n      entry.append(set_name)\n      entry.append(date)\n\n      data.append(entry)\n      i += 62\n\n\n\n \n\n    today = datetime.today()\n\n\n    file_name = set_name +  '.csv'\n    \n\n    with open(file_name, \"a\") as fp:\n        wr = csv.writer(fp, dialect='excel')\n        wr.writerow(['name','1','2','3','4','5','6','7','8','9','10','Total_pop','date'])\n        for i in data:\n            wr.writerow(i)\n\n\n    with open('gold_stars.csv', \"a\") as fp:\n        wr = csv.writer(fp, dialect='excel')\n        for i in data:\n            if \"Gold Star\" in i[0]:\n                wr.writerow(i)\n        \n\n\n\n\n    print(data)\n\n        \n        \n    driver.close()\n\n\n\nfor url in urls:\n    get_pop(url)\n\n\n","sub_path":"scrape_goldstars.py","file_name":"scrape_goldstars.py","file_ext":"py","file_size_in_byte":3152,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"20186369","text":"from tkinter import *\n\nroot=Tk()\n\nmiframe=Frame(root,width=500,height=400)\nmiframe.pack()\n\nmilabel=Label(miframe,text=\"Hola mundo\", fg=\"red\", font=(\"Comic Sans MS\",18)) #tamaño 18 de color rojo\nmilabel.place(x=100,y=200) #100 pixeles desde el borde izq de la pantalla hasta el texto. 200 pixeles desde la parte superior hasta el texto.\n\nmiImagen=PhotoImage(file=\"mouse.png\")\nLabel(miframe, image=miImagen).place(x=20,y=10)\n\nroot.mainloop()","sub_path":"FreeWork/Curse/py_gui/Labels&Images.py","file_name":"Labels&Images.py","file_ext":"py","file_size_in_byte":440,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"286734443","text":"from django.http import HttpResponse\nfrom django.shortcuts import get_object_or_404, render_to_response, render\nfrom django.contrib.auth.models import User\nfrom event.models import Product, Event\nfrom django.db.models import Sum\nfrom decimal import *\nfrom datetime import datetime\nimport simplejson as json\n\ndef index(request):\n    recent = Event.objects.order_by('-timestamp')[:10]\n    return render(request, 'index.html', {'events': recent})\n\ndef track(request, user_id, barcode):\n    b = get_object_or_404(Product, barcode=barcode)\n    u = get_object_or_404(User, username=user_id)\n    e = Event.objects.create(user=u, amount=1, value=1, product=b, action='C')\n    return HttpResponse()\n\ndef deposit(request):\n    data = list()\n    for p in Product.objects.all():\n        sumobject = Event.objects.filter(product=p).aggregate(Sum('amount'))\n        if not sumobject['amount__sum']:\n            sumobject['amount__sum'] = 0\n        data.append({'label': p.name, 'value': sumobject['amount__sum']})\n    return render(request, 'deposit.html', {'products': json.dumps(data).replace('&quot;', '\"')})\n\ndef sync(request):\n    drinks = []\n    for p in Product.objects.all():\n        drinks.append({'name': p.name, 'barcode': p.barcode})\n    return HttpResponse(json.dumps(drinks))\n\ndef product(request, product_id):\n    p = Product.objects.get(id=product_id)\n    data = dict()\n    data['name'] = p.name\n    data['img'] = p.url\n    return render(request, 'product.html', {'data': data})\n\ndef user(request, user_id):\n    u = get_object_or_404(User, username=user_id)\n    data = {'name': u.username, 'balance': Decimal(0)}\n    for a in Event.ACTIONS:\n        allEvents = Event.objects.filter(user=u, action=a[0])\n        obj = allEvents.aggregate(Sum('value'), Sum('amount'))\n        value = obj['value__sum']\n        amount = obj['amount__sum']\n        if not value:\n            value = Decimal(0)\n        if not amount:\n            amount = 0\n        data[a[0]] = amount\n        if a[0] == 'C':\n            data['balance'] -= value\n        else:\n            data['balance'] += value\n    allEvents = Event.objects.filter(user=u, action='C')\n    last = allEvents.latest(field_name='timestamp')\n    delta = datetime.utcnow() - last.timestamp.replace(tzinfo=None)\n    if delta.seconds < 1:\n        text = \"right now\"\n    elif delta.seconds < 60:\n        text = \"%d seconds ago\" %delta.seconds\n    elif delta.seconds < 3600:\n        text = \"%d seconds ago\" %(delta.seconds / 60)\n    elif delta.seconds < 86400:\n        text = \"%d hours ago\" %(delta.seconds/60**2)\n    else:\n        text = \"%d days ago\" %delta.days\n    data['last'] = last.product.name + \", \" + text\n    return render(request, 'user.html', {'data': data})\n\ndef productlist(request):\n    products = Product.objects.all().exclude(name='dummy')\n    return render(request, 'productlist.html', {'products': products})\n\ndef userlist(request):\n    users = User.objects.all().exclude(username='admin')\n    return render(request, 'userlist.html', {'users': users})\n","sub_path":"event/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3011,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"488799723","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jun 22 21:13:01 2015\n\n@author: Da Vinci\n\"\"\"\nimport os\nimport IndexList\nimport DocVectorList\n\n#print string.punctuation\nil = IndexList.IndexList()\ndcl = DocVectorList.DocVectorList()\nil.setDocVectorList(dcl)\ndcl.setIndexList(il)\nif os.name == 'posix':\n    dataPath = 'Reuters'\nelse:\n    dataPath = \"C:\\\\Users\\\\Da Vinci\\\\Desktop\\\\ir\\\\Reuters\"\n# il.readAllFromDir(\"./Reuters\")\n# il.writeIndexToFile(\"index_record.txt\")\n# dcl.writeDocVectorToFile(\"doc_vector.txt\")\n# il._printkey();\nil.readFromIndexFile('index_record.txt')\ndcl.readDocVectorFromFile('doc_vector.txt')\nprint('index file read done')\n#resset = il.getDocSet('wednesday', False)\n# print resset\n\n# il.readAllFromDir(dataPath)\n# il.writeIndexToFile(\"index_record.txt\")\n# dcl.writeDocVectorToFile(\"doc_vector.txt\")\n# il.writeDocIDToFile(\"docID.txt\")\n#il._printkey();\n","sub_path":"Instance.py","file_name":"Instance.py","file_ext":"py","file_size_in_byte":865,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"528577571","text":"from turtle import *\n\nlength = 10\nangle = 90\n\ndef draw_path(axiom):\n    for symbol in axiom:\n        if symbol == 'F':\n            forward(length)\n        elif symbol == '-':\n            left(angle)\n        elif symbol == '+':\n            right(angle)\n\ndef apply_rule(axiom,n):\n    rule = \"F-F+F+FF-F-F+F\"\n    for i in range(n):\n        axiom=axiom.replace(\"F\",rule)\n    return axiom\n\nspeed(0)\naxiom = \"F-F-F-F\"\ndraw_path(apply_rule(axiom,2))\nexitonclick()\n","sub_path":"Code/L-system.py","file_name":"L-system.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"21282654","text":"\ndef median(A, B):\n\tm, n = len(A), len(B)\n\n\tif m > n: # swap two arrays (we want len(A) < len(B))\n\t\tA, B, m, n = B, A, n, m\n\n\tif n == 0:\n\t\traise ValueError\n\n\n\timin = 0\n\timax = m\n\thalf_len = (m + n + 1) / 2 # +1 is for lengths\n\n\twhile imin <= imax:\n\t\t# reindex A with i, B with j\n\t\ti = (imin + imax) / 2\n\t\tj = half_len - i\n\t\t# note that we want i + j = half_len\n\n\t\tif i < m and B[j-1] > A[i]:\n\t\t\t# i is too small; must increase\n\t\t\t# bottom left to top right comparison\n\t\t\timin = i+1\n\t\telif i > 0 and A[i-1] > B[j]:\n\t\t\t# is is too large; must decrease\n\t\t\t# top right to bottom left comparison\n\t\t\timax = i-1\n\t\telse:\n\t\t\t# i is perfect\n\n\t\t\tif i == 0:\n\t\t\t\tmax_of_left = B[j-1]\n\t\t\telif j == 0:\n\t\t\t\tmax_of_left = A[i-1]\n","sub_path":"Reference/LeetCode/4 Median of Two Sorted Arrays.py","file_name":"4 Median of Two Sorted Arrays.py","file_ext":"py","file_size_in_byte":712,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"278718739","text":"def is_palindrome(n):\n    return n == int(str(n)[::-1])\n\ndef main(inpath):\n  outfile = open(\"out.txt\", \"w\")\n  lines = open(inpath, \"r\").read().split('\\n')\n  cases = int(lines[0])\n  palindromes = []\n  for i in xrange(1, 3 * 10**7 + 1):\n    if is_palindrome(i) and is_palindrome(i**2):\n      palindromes.append(i**2)\n  \n  for i in xrange(1, cases + 1):\n    [x, y] = lines[i].split(' ')\n    x = int(x)\n    y = int(y)\n\n    j = 0\n    k = len(palindromes) - 1\n    while x > palindromes[j]:\n      j += 1\n    while y < palindromes[k]:\n      k -= 1\n    outfile.write(\"Case #\" + str(i) + \": \" + str(k - j + 1) + \"\\n\")\n\nmain(\"input.txt\")\n","sub_path":"solutions_2463486_0/Python/handorff/problemb.py","file_name":"problemb.py","file_ext":"py","file_size_in_byte":627,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"471154024","text":"# -*- coding: utf-8 -*-\n# source : http://www.runoob.com/python/python-100-examples.html\n#Python 练习实例1\n# 题目：有四个数字：1、2、3、4，能组成多少个互不相同且无重复数字的三位数？各是多少？\n# 程序分析：可填在百位、十位、个位的数字都是1、2、3、4。组成所有的排列后再去 掉不满足条件的排列。\ndef py_exe01():\n    count = 0\n    for i in range(1, 5):\n        for j in range(1, 5):\n            for k in range(1, 5):\n                if i != j != k and i != k:\n                    print(i, j, k)\n                    count += 1\n                continue\n    print(\"The total num is {}\".format(count))\n\npy_exe01()\n\n#Python 练习实例2\n# 题目：企业发放的奖金根据利润提成。\n# 利润(I)低于或等于10万元时，奖金可提10%；\n# 利润高于10万元，低于20万元时，低于10万元的部分按10%提成，高于10万元的部分，可提成7.5%；\n# 20万到40万之间时，高于20万元的部分，可提成5%；\n# 40万到60万之间时高于40万元的部分，可提成3%；\n# 60万到100万之间时，高于60万元的部分，可提成1.5%，高于100万元时，\n# 超过100万元的部分按1%提成，从键盘输入当月利润I，求应发放奖金总数？\n\ndicMoney_re= [[100000, 0.1], [200000, 0.075], [400000, 0.05], [600000, 0.03], [1000000, 0.01]]\ndicMoney = [[1000000, 0.01], [600000, 0.015], [400000, 0.03], [200000, 0.05], [100000, 0.075], [0, 0.1]]\n\ndef py_exe02():\n    money_inhand = 0\n    money = int(input('Please input the money:'))\n    for i in range(0, len(dicMoney)):\n        if money > dicMoney[i][0]:\n            money_inhand += (money - dicMoney[i][0]) * dicMoney[i][1]\n            money =  dicMoney[i][0]\n    print(money_inhand)\n\n#Python 练习实例3\n# 一个整数，它加上100后是一个完全平方数，再加上168又是一个完全平方数，请问该数是多少？\n# x + 100 = m^2\n# x + 100 + 168 = n^2\n# m^2  - n^2 = 168\n# i = (m + n) j = (m - n)\n# i * j = 168\n# i (1, 168 / 2 + 1) j [2, 168)\ndef py_exe03():\n    for i in range(1, 85):\n        if  168 % i == 0:\n            j = 168 / i\n            if (i + j) % 2 == 0 and (i - j) % 2 == 0 and i > j:\n                m = (i + j) / 2\n                n = (i - j) / 2\n                x = n * n - 100\n                print(int(x))\n\npy_exe03()\n\n#Python 练习实例4\n# 输入某年某月某日，判断这一天是这一年的第几天？\np = [31,28,31,30,31,30,31,31,30,31,30,31] # 平年\nr = [31,29,31,30,31,30,31,31,30,31,30,31] # 闰年\n\ndef py_exe04():\n    year = int(input('year: '))\n    month = int(input('month: '))\n    day = int(input('day: '))\n    if year % 4 == 0:\n        if year % 100 == 0 and year % 400 != 0:\n            y = r\n        else:\n            y = p\n    else:\n        y = p\n\n    sum = day\n    for i in range(0, month - 1):\n        sum += y[month - 1]\n\n    print(\"The total days : {}\".format(sum))\n\npy_exe04()\n\n#Python 练习实例5\n# 题目：输入三个整数x,y,z，请把这三个数由小到大输出。\ndef py_exe05():\n    num = input('input the three numbers:(like 1,3,2)')\n    l = num.split(',')\n    l.sort()\n    print(l)\n\npy_exe05()\n\n#Python 练习实例6\n# 题目：斐波那契数列\n# 1 1 2 3 5 8 13 21\n# 效率最快方式\ndef fib(max):\n    n, a, b = 0, 0, 1\n    while n < max:\n        #print(b)\n        yield b\n        a, b = b, a+b\n        n += 1\n\ndef test_fib():\n    f = fib(10)\n    for i in f:\n        print(i)\ntest_fib()\n\ndef py_exe06(n):\n    a , b = 1 , 1\n    i = 1\n    while i < n:\n        a , b = b , a + b\n        i += 1\n    print(a)\n\npy_exe06(10)\n\n# 递归方式\ndef py_exe06_recu(n):\n    if n == 1 or n == 2:\n        return 1\n    else:\n        return py_exe06_recu(n - 2) + py_exe06_recu(n - 1)\n\nprint(py_exe06_recu(10))\n\n#Python 练习实例7\n# 题目：将一个列表的数据复制到另一个列表中。\ndef py_exe07():\n    a = [1, 2, 3]\n    print(a)\n    b = a[:]\n    print(b)\n\npy_exe07()\n\n#Python 练习实例8\n# 题目：输出 9*9 乘法口诀表\ndef py_exe08():\n    for i in range(1,10):\n        for j in range(1, i+1):\n            print(\"{} * {} = {}\".format(j, i, i*j, ), end=\"\\t\")\n            #print(\"%d * %d = %d\"%(j, i, i*j), end=\"\\t\")\n        print()\n\npy_exe08()\n\n#Python 练习实例9\n# 题目：暂停一秒输出\nimport  time\ndef py_exe09():\n    for i in range(10):\n        print(\"{}s\".format(i + 1))\n        time.sleep(1)\n\npy_exe09()\n\n#Python 练习实例10\n# 题目：暂停一秒输出，并格式化当前时间\ndef py_exe10():\n    print(time.strftime(\"%Y:%m:%d %H:%M:%S\", time.localtime()))\n    time.sleep(1)\n    print(time.strftime(\"%a, %b, %d %H:%M:%S %Y\",time.localtime()))\npy_exe10()","sub_path":"python练习/菜鸟教程_python100题/python_1_10.py","file_name":"python_1_10.py","file_ext":"py","file_size_in_byte":4657,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"105244174","text":"from ftplib import FTP\r\nimport sys\r\n\r\nglobal ftp_use\r\n\r\nclass FTP_COMMON():\r\n\tdef __init__(self,ftp_config):\r\n\t\tself.addr = ftp_config[\"addr\"]\r\n\t\tself.username = ftp_config[\"username\"]\r\n\t\tself.password = ftp_config[\"password\"]\r\n\t\t\r\n\t\tself.ftp = FTP()\r\n\t\tself.ftp.connect(self.addr)\r\n\t\tself.ftp.login(self.username,self.password)\r\n\t\r\n\tdef __del__(self,ftp_config):\r\n\t\tself.ftp.close()\r\n\t\r\n\tdef file_is_exsit_in_ftp(self,remote_path,filename):\r\n\t\tself.ftp.cwd(remote_path)\r\n\t\tslist = self.ftp.nlst()\r\n\t\tif filename in str(slist):\r\n\t\t\treturn True\r\n\t\telse:\r\n\t\t\treturn False\r\n\r\n\tdef rm_file_in_ftp(self,remote_path,filename):\r\n\t\ttry:\r\n\t\t\tself.ftp.cwd(remote_path)\r\n\t\t\tslist = self.ftp.nlst()\r\n\t\t\tif filename in str(slist):\r\n\t\t\t\tself.ftp.delete(filename)\r\n\t\t\telse:\r\n\t\t\t\tprint(\"%s is not exsit!\" % filename)\r\n\t\texcept Exception as e:\r\n\t\t\tprint('Exception : rm_file_in_ftp')\r\n\t\r\n\tdef down_file_from_ftp(self,remote_path,filename,bufsize=10000):\r\n\t\twith open(filename,'wb+') as fp:\r\n\t\t\ttry:\r\n\t\t\t\tself.ftp.retrbinary(\"RETR %s\"%remote_path+\"/\"+filename, fp.write, bufsize)\r\n\t\t\t\tfp.flush()  \r\n\t\t\texcept:\r\n\t\t\t\tprint(\"Download %s failed!\" % filename)\r\n\t\t\t\treturn False\r\n\t\treturn True\r\n\r\n\r\n\tdef upload_file_to_ftp(self,remote_path,filename,bufsize=10000):\r\n\t\tif self.file_is_exsit_in_ftp(remote_path,filename) == True:\r\n\t\t\t#print(\"%s is exsit in FTP! Delete it!\" % filename)\r\n\t\t\tself.rm_file_in_ftp(remote_path,filename)\r\n\t\twith open(filename,'rb') as file_object:\r\n\t\t\ttry:\r\n\t\t\t\tself.ftp.storbinary('STOR %s' % remote_path + '/' + filename, file_object, bufsize)\r\n\t\t\t\tself.ftp.set_debuglevel(0)\r\n\t\t\texcept Exception as e:\r\n\t\t\t\tprint(\"Upload %s failed!\" % filename)\r\n\t\t\t\treturn False\r\n\t\treturn True\r\n\r\n\tdef create_remote_dir(self,remote_path,new_dir):\r\n\t\tself.ftp.cwd(remote_path)\r\n\t\tslist = self.ftp.nlst()\r\n\t\tif new_dir not in str(slist):\r\n\t\t\tself.ftp.mkd(new_dir)\r\n\t\t\treturn True\r\n\t\telse:\r\n\t\t\treturn False\r\n\r\n\tdef rm_dir_in_ftp(self,remote_path):\t\r\n\t\tdir_name = remote_path.split(\"/\")[-1].strip()\r\n\t\ttry:\r\n\t\t\tself.ftp.cwd(remote_path)\r\n\t\t\tslist = self.ftp.nlst()\r\n\t\t\tfor file in slist:\r\n\t\t\t\tself.ftp.delete(file)\r\n\t\t\tself.ftp.cwd(\"..\")\r\n\t\t\tself.ftp.rmd(dir_name)\r\n\t\texcept:\r\n\t\t\tprint('Delete %s failed' % dir_name)\r\n\r\nif __name__ == '__main__':\r\n\tprint(str(sys.argv[0]) + \" enter\")\r\n\t\r\n\tftp_config = {\r\n\t\t\"addr\":\"ftp.asrmicro.com\",\r\n\t\t\"username\":\"otaupdate\",\r\n\t\t\"password\":\"9eLtBmea\"\r\n\t}\r\n\t\r\n\tftp = FTP_COMMON(ftp_config)\r\n\tftp.down_file_from_ftp(\"/Setting\",\"P_user.list\")\r\n\tftp.upload_file_to_ftp(\"/test/xxxxx\",\"P_user.list\",10000)\r\n\t#ftp.create_remote_dir(\"/test\",\"xxxxx\")\r\n\tftp.rm_files_in_ftp(\"/test/xxxxx\")\r\n\t\r\n\t\r\n\t\r\n\t","sub_path":"PythonTools/Common/ftp_common.py","file_name":"ftp_common.py","file_ext":"py","file_size_in_byte":2610,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"139777916","text":"from collections import namedtuple\nimport statistics as stat\nfrom math import pi\nimport sys\n\nimport lcg\nfrom distributions import ExponentialDistribution, GammaDistribution, GaussianDistribution, UniformDistribution, TriangularDistribution, SimpsonDistribution\nfrom histogram import draw_histogram\nfrom reader import read_positive_int\n\n\nLcgParameters = namedtuple('LcgParameters', ['initial', 'multiplyer', 'base'])\n\nDISTRIBUTIONS_DESCRIPTION = [\n    UniformDistribution(),\n    GaussianDistribution(),\n    ExponentialDistribution(),\n    GammaDistribution(),\n    TriangularDistribution(),\n    SimpsonDistribution()\n]\n\nDEFAULT_LCG_PARAMS = LcgParameters(\n    base=1046527,\n    initial=65537,\n    multiplyer=32771\n)\n\nRANDOM_VECTOR_LENGTH = 100000\n\ndef print_result(name, actual_result, reference_value_representation='', reference_value=None):\n    if actual_result is not None:\n        result = '{}: {}'.format(name, actual_result)\n        if reference_value:\n            result += ' (reference value = {}; delta: {})'.format(\n                reference_value_representation + ' = ' + str(reference_value)\n                if reference_value_representation\n                else\n                reference_value\n                , abs(reference_value - actual_result)\n                )\n    else:\n        result = 'Can not find {}'.format(name)\n    print(result)\n\n\ndef read_lcg_parameters():\n    return LcgParameters(\n        base=read_positive_int(\"m\"),\n        initial=read_positive_int(\"R0\"),\n        multiplyer=read_positive_int(\"a\")\n    )\n\n\ndef lcg_demo():\n    params = read_lcg_parameters()\n    result = list(lcg.random_vector(RANDOM_VECTOR_LENGTH, params))\n    print_result('mean', stat.mean(result), reference_value=1/2, reference_value_representation='1/2')\n    print_result('variance', stat.variance(result), reference_value=1/12, reference_value_representation='1/12')\n    print_result('standart deviation', stat.stdev(result))\n    print_result('2K/N', lcg.uniform_ratio(result), reference_value=pi/4, reference_value_representation='pi/4')\n    period = lcg.period(lambda length: lcg.random_vector(length, params))\n    print_result('period', period)\n    if period:\n        print_result('aperiodic interval', lcg.aperiodic_interval(lambda length: lcg.random_vector(length, params), period))\n    draw_histogram(result)\n\n\ndef print_menu():\n    for i, distribution in enumerate(DISTRIBUTIONS_DESCRIPTION):\n        print('\\t{} : {}'.format(i+1, distribution.name))\n    print('\\t0 : exit')\n\n\ndef read_command():\n    valid = False\n    while not valid:\n        try:\n            command = int(input('>> '))\n            valid = command >= 0 and command <= len(DISTRIBUTIONS_DESCRIPTION)\n        except ValueError:\n            print('Invalid input')\n            print_menu()\n    return command\n\n\ndef distributions_demo():\n    print_menu()\n    command = read_command()\n    while command != 0:\n        distribution = DISTRIBUTIONS_DESCRIPTION[command - 1]\n        result = list(distribution.generate(RANDOM_VECTOR_LENGTH, DEFAULT_LCG_PARAMS))\n        print_result('mean', stat.mean(result))\n        print_result('variance', stat.variance(result))\n        print_result('standart deviation', stat.stdev(result))\n        draw_histogram(result)\n        command = read_command()\n\n\ndef main():\n    modes = {\n        'lcg': lcg_demo,\n        'dist': distributions_demo\n    }\n    if (len(sys.argv) == 2) and (sys.argv[1] in modes):\n        command = modes[sys.argv[1]]\n    else:\n        print('Usage main.py dist|lcg')\n        return\n    command()\n\nif __name__ == '__main__':\n    main()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3568,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"225501943","text":"#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n#\r\n#  falloutModel.py\r\n#  \r\n#  Copyright 2015 Jad <jad.pamment@gmail.com>\r\n#  \r\n#  This program is free software; you can redistribute it and/or modify\r\n#  it under the terms of the GNU General Public License as published by\r\n#  the Free Software Foundation; either version 2 of the License, or\r\n#  (at your option) any later version.\r\n#  \r\n#  This program is distributed in the hope that it will be useful,\r\n#  but WITHOUT ANY WARRANTY; without even the implied warranty of\r\n#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\r\n#  GNU General Public License for more details.\r\n#  \r\n#  You should have received a copy of the GNU General Public License\r\n#  along with this program; if not, write to the Free Software\r\n#  Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston,\r\n#  MA 02110-1301, USA.\r\n#  \r\n#  \r\n__author__ = \"Jad\"\r\n__date__ = \"$15/11/2015 4:03:54 PM$\"\r\n\r\nimport falloutEvent \r\nfrom pygame.locals import *\r\nfrom random import *\r\nfrom time import gmtime, strftime\r\n\r\nclass RunTime():\r\n    \"\"\"\r\n    runtime details\r\n    \"\"\"\r\n    STATE_LOADING = 0\r\n    STATE_RUNNING = 1\r\n    STATE_SHUTDOWN = 2\r\n    \r\n    def __init__(self,evManager):\r\n        self.evManager = evManager\r\n        self.evManager.registerListener(self)\r\n        self.state = RunTime.STATE_LOADING\r\n        print(\"...Pip-Boy Loading...\")\r\n        \r\n    def run(self):\r\n        self.state = RunTime.STATE_RUNNING\r\n        ev = falloutEvent.FalloutStartedEvent()\r\n        self.evManager.post(ev)\r\n        print(\"...Pip-Boy Running...\")\r\n        \r\n    def getState(self):\r\n        return self.state\r\n        \r\n    def Notify(self, event):\r\n        if isinstance(event,falloutEvent.TickEvent):\r\n            if self.state == RunTime.STATE_LOADING:\r\n                self.run()\r\n\r\nclass FalloutClock():\r\n    #Default Clock object for Fallout Screen - drawn by default\r\n    def __init__(self,evManager):\r\n        self.evManager = evManager\r\n        self.evManager.registerListener(self)\r\n        self.clockText = \"\"\r\n        self.x = 0\r\n        self.y = 0\r\n        print(\"...Clock Initialised...\")\r\n        \r\n    def TickClock(self):\r\n        self.clockText = strftime(\"%Y-%m-%d %H:%M:%S\", gmtime())\r\n        return self.clockText\r\n    \r\n    def setPos(self,x,y):\r\n        self.x = x\r\n        self.y = y\r\n        \r\n    def getPos(self):\r\n        return (self.x,self.y)\r\n        \r\n    def Notify(self,event):\r\n        if isinstance(event,falloutEvent.FalloutStartedEvent):\r\n            self.TickClock()\r\n        elif isinstance(event,falloutEvent.TickEvent):\r\n            self.TickClock()\r\n            \r\n        \r\n            \r\n    \r\n            \r\n        \r\n","sub_path":"falloutModel.py","file_name":"falloutModel.py","file_ext":"py","file_size_in_byte":2690,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"216178225","text":"\"\"\"\nProject Toxic Span Detection\nPostprocessing for model LSTM with attention\n@authors: Julia Kłos, Patrycja Cieplicka\n@date: 12.01.2020\n\"\"\"\nimport numpy as np\n\ndef getWordsByAttention(attention, tokenized, word_vect, treshold):\n    \"\"\"\n    Function returning toxic words, given word vector, attention weights, word_indexes and treshold\n    \"\"\"\n    tokenized = tokenized[0] > 0\n    weights = attention[tokenized]\n    weights = weights > treshold\n    words = [word for k, word in enumerate(word_vect.split(' '))]\n    toxic_words = [b for a, b in zip(weights, words) if a]\n    return toxic_words\n\ndef wordAttentionWeights(sequenceSentence,weights):\n    \"\"\"\n    The same function as the AttentionLayer class - calculate the weights of attention layer\n    \"\"\"\n    uit = np.dot(sequenceSentence, weights[0]) + weights[1]\n    uit = np.tanh(uit)\n\n    ait = np.dot(uit, weights[2])\n    ait = np.squeeze(ait)\n    ait = np.exp(ait)\n    ait /= np.sum(ait)\n    \n    return ait","sub_path":"src/attention_exp.py","file_name":"attention_exp.py","file_ext":"py","file_size_in_byte":965,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"496326173","text":"#!/usr/bin/env python3\nimport os\nimport sys\nimport pprint\nimport argparse\nimport functools\nimport numpy as np\nimport tensorflow as tf\nfrom data_processing import datasets\nfrom model_code import models, utils\nimport tensorflow_addons as tfa\nimport matplotlib.pyplot as plt\n\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n\nmodel_index = {\n    'baseline': functools.partial(\n        models.Baseline, \n        input_shape=datasets.IMAGE_SHAPE),\n    'CNN' : models.CNN,\n}\n\n\ndef get_compiled_model(model_type, model_params=None, compile_params=None):\n    \"\"\"\n    Returns a compiled model with given model/compile parameters\n    \"\"\"\n    if model_params:\n        model = model_type(**model_params)\n    else:\n        model = model_type()\n\n    model.compile(**compile_params)\n\n    return model\n\n\n\n# -----------------------------------------------------------------------------\n# Subcommand functions\n# -----------------------------------------------------------------------------\ndef predict(args):\n    if args.use_h5:\n        model = tf.keras.models.load_model(args.model_path)\n    else:\n        model = model_index[args.model_type]()\n        model.load_weights(filepath=args.model_path).expect_partial()\n\n    if args.image.endswith('.txt'): # list of images\n        # with open(args.image, 'r') as file:\n        #     for image in file:\n        #         pred = utils.display_prediction(image.rstrip(), model,\n        #                                         augmentation=args.augmentation)\n        #         print(os.path.splitext(os.path.basename(image))[0], \n        #               *pred, sep='\\t')\n        dataset = datasets.DataWriter.get_basic_dataset(args.image, args.num_processes)\n        dataset = dataset.batch(args.batch_size, drop_remainder=False) \\\n                         .prefetch(buffer_size=tf.data.experimental.AUTOTUNE)\n\n        for filenames, images in dataset:\n            predictions = model(images)\n            for filename, prediction in zip(filenames, predictions):\n                f = os.path.splitext(os.path.basename(filename.numpy()))[0]\n                f = f.decode().split('_')\n                print(*f[:3], sep='\\t', end='\\t')\n                print(*prediction.numpy(), sep='\\t')\n    else:\n        pred = utils.display_prediction(args.image, model,\n                                        augmentation=args.augmentation)\n        print(os.path.splitext(os.path.basename(args.image))[0], \n              *pred, sep='\\t')\n\ndef evaluate(args):\n    if args.use_h5:\n        model = tf.keras.models.load_model(args.model_path)\n    else:\n        model = model_index[args.model_type]()\n        model.load_weights(filepath=args.model_path).expect_partial()\n    utils.evaluate_model(model, data_dir= args.data_dir, \n                         batch_size=args.batch_size)\n\ndef train(args):\n    pprint.pprint(vars(args))\n\n    # load data\n    training_set, n_train = datasets.DataReader(\n        augmentation=True,\n        num_processes=args.processes,\n        batch_size=args.batch_size,\n        data_list=args.train_list,\n        tfrec_list=args.train_tfrec_list).get_dataset()\n\n    val_set, n_val = datasets.DataReader(\n        augmentation=False,\n        num_processes=args.processes,\n        batch_size=args.batch_size,\n        data_list=args.val_list,\n        tfrec_list=args.val_tfrec_list).get_dataset()\n\n    print(f\"Train on {n_train} examples.  Validate on {n_val} examples.\")\n\n    # setup training\n    callbacks = [\n        tf.keras.callbacks.ModelCheckpoint(\n            filepath=args.save_to,\n            monitor='val_loss',\n            verbose=1,\n            save_best_only=True),\n    ]\n\n    lr_schedule = tf.keras.experimental.CosineDecayRestarts(\n        initial_learning_rate=args.lr,\n        alpha=0.0001,\n        t_mul=2.0,\n        m_mul=1.25,\n        first_decay_steps=np.ceil(n_train/(args.batch_size/4))) # ie. two epochs\n\n\n    # TODO eventually I'd like to optionally be able to load these from a JSON\n    model_params = {'num_classes': args.num_classes}\n    loss = tf.keras.losses.CategoricalCrossentropy\n    metrics=['CategoricalAccuracy']\n    compile_params = dict(\n        loss=loss(\n            label_smoothing=args.label_smoothing,\n        ),\n        optimizer=#tfa.optimizers.Lookahead(\n            tfa.optimizers.SGDW(\n                weight_decay=args.weight_decay,\n                learning_rate=lr_schedule,\n                momentum=args.momentum,\n                nesterov=True,\n            ),\n        #),\n        metrics=metrics)\n    model = get_compiled_model(\n        model_index[args.model_type], \n        model_params, \n        compile_params)\n\n    model.fit(\n        training_set,\n        steps_per_epoch=np.ceil(n_train/args.batch_size),\n        validation_data=val_set,\n        validation_steps=np.ceil(n_val/args.batch_size),\n        epochs=args.epochs,\n        callbacks=callbacks,\n        verbose=args.verbose\n    )\n\n\n# -----------------------------------------------------------------------------\n# Get arguments\n# -----------------------------------------------------------------------------\nparser = argparse.ArgumentParser()\nsubparsers = parser.add_subparsers(title='Subcommands')\n\n# prediction subcommand -------------------------------------------------------\npredict_parser = subparsers.add_parser(\n    'predict', help='Use a trained model to classify an image.',\n    formatter_class=argparse.ArgumentDefaultsHelpFormatter)\npredict_parser.add_argument(\n    '--model-path', '-mp', dest='model_path', type=str, required=True,\n    help='Path of trained model')\npredict_parser.add_argument(\n    '--model-type', '-mt', dest='model_type', type=str, required=False,\n    choices=model_index.keys(), default='CNN', help='Type of model to load.')\npredict_parser.add_argument(\n    '--use-h5', '-h5', dest='use_h5', action='store_true')\npredict_parser.add_argument(\n    '--augmentation', '-a', dest='augmentation', action='store_true',\n    help='Use test time augmentation.')\npredict_parser.add_argument(\n    '--image', '-i', dest='image', type=str, required=True,\n    help='Path of image')\npredict_parser.add_argument(\n    '--num_processes', '-n', dest='num_processes', type=int, default=1,\n    help='number of processes to use when loading images')\npredict_parser.add_argument(\n    '--batch_size', '-b', dest='batch_size', type=int, default=32)\npredict_parser.set_defaults(\n    func=predict)\n\n# evaluation subcommand -------------------------------------------------------\neval_parser = subparsers.add_parser(\n    'evaluate', help='Evaluate a trained model on a labelled dataset',\n    formatter_class=argparse.ArgumentDefaultsHelpFormatter)\neval_parser.add_argument(\n    '--model-path', '-mp', dest='model_path', type=str, required=True,\n    help=\"Path of trained model (before the dot '.')\")\neval_parser.add_argument(\n    '--model-type', '-mt', dest='model_type', type=str, required=False,\n    choices=model_index.keys(), default='CNN', help='Type of model to load.')\neval_parser.add_argument(\n    '--use-h5', '-h5', dest='use_h5', action='store_true')\neval_parser.add_argument(\n    '--data-dir', '-d', dest='data_dir', type=str, required=True,\n    help='Root data directory for test set.')\neval_parser.add_argument(\n    '--batch-size', '-b', dest='batch_size', type=int, required=False,\n    default=80, help='Number of images to feed to model at a time.')\neval_parser.set_defaults(\n    func=evaluate)\n\n# training subcommand ---------------------------------------------------------\ntrain_parser = subparsers.add_parser(\n    'train', help='Train a new model.',\n    formatter_class=argparse.ArgumentDefaultsHelpFormatter)\ntrain_parser.add_argument(\n    '--verbose', '-v', dest='verbose', type=int, default=1,\n    help='verbosity level passed to keras fit function.')\ntrain_parser.add_argument(\n    '--processes', '-p', dest='processes', type=int, default=1,\n    help='Number of processes used in fetching data.')\ntrain_parser.add_argument(\n    '--batch-size', '-b', dest='batch_size', type=int, required=False,\n    default=80, help='Number of images to feed to model at a time.')\ntrain_parser.add_argument(\n    '--epochs', '-e', dest='epochs', type=int, required=False,\n    default=100, help='Max number of epochs to train model.')\ntrain_parser.add_argument(\n    '--model-type', '-mt', dest='model_type', type=str, required=False,\n    default='CNN', help='Type of model to train.')\ntrain_parser.add_argument(\n    '--num-classes', '-n', dest='num_classes', type=int, required=False,\n    default='3', help='Number of possible class labels')\ntrain_parser.add_argument(\n    '--train-list', dest='train_list', type=str, required=True,\n    help='File containing list of images in train set.')\ntrain_parser.add_argument(\n    '--val-list', dest='val_list', type=str, required=True,\n    help='File containing list of images in val set.')\ntrain_parser.add_argument(\n    '--train-tfrec-list', dest='train_tfrec_list', type=str, required=True,\n    help='File containing list of train tfrecord paths (s3 or local).')\ntrain_parser.add_argument(\n    '--val-tfrec-list', dest='val_tfrec_list', type=str, required=True,\n    help='File containing list of val tfrecord paths (s3 or local).')\ntrain_parser.add_argument(\n    '--learning-rate', '-lr', dest='lr', type=float, required=False,\n    default=1e-4, help='Learning rate for optimizer.')\ntrain_parser.add_argument(\n    '--momentum', '-mom', dest='momentum', type=float, required=False,\n    default=0.9, help='Momentum term in SGD optimizer.')\ntrain_parser.add_argument(\n    '--weight-decay', '-w', dest='weight_decay', type=float, required=False,\n    default=0, help='Weight decay strength')\ntrain_parser.add_argument(\n    '--label-smoothing', '-ls', dest='label_smoothing', type=float, required=False,\n    default=0.0, help='Strength of label smoothing (0-1).')\ntrain_parser.add_argument(\n    '--save-to', '-s', dest='save_to', type=str, required=False,\n    default=None, help='filename if you want to save your trained model.')\ntrain_parser.set_defaults(\n    func=train)\n\nargs = parser.parse_args()\n\nif len(sys.argv) == 1:\n    parser.print_help()\n    parser.exit()\n\nargs.func(args)\n","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":10066,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"285326938","text":"from django.http.response import HttpResponse\nimport jwt\n\n# Django imports\nfrom django.contrib.auth import authenticate\nfrom django.contrib.auth.models import update_last_login\nfrom django.conf import settings\n\n# Third Party Imports\nfrom rest_framework import status\nfrom rest_framework.decorators import api_view\nfrom rest_framework.response import Response\n\n# Local Imports\nfrom users.serializers import (\n    UserSignupSerializer,\n    PasswordSerializer,\n    SigninSerializer,\n    UserSerializer,\n)\nfrom users.models import User\nfrom users.utils import generate_email, user_tokens\n\n# Create your views here.\n\n\ndef home(request):\n    return HttpResponse(\"Welcome to TODO API\")\n\n\n@api_view((\"POST\",))\ndef signup(request):\n    # custom serializer to validate user data\n    serializer = UserSignupSerializer(data=request.data)\n    serializer.is_valid(raise_exception=True)\n    validated_data = serializer.validated_data\n\n    email = validated_data.get(\"email\")\n    try:\n        user = User.objects.get(email=email)\n    except User.DoesNotExist:\n        user = serializer.create(validated_data)\n\n    if user.is_active is True:\n        content = {\"message\": f\"{email} already active\"}\n        return Response(content, status=status.HTTP_400_BAD_REQUEST)\n    generate_email(\n        user=user,\n        to_email=user.email,\n        subject=\"Verification Email - Example\",\n    )\n    content = {\"message\": f\"Activation link send to {email}\"}\n    return Response(content, status=status.HTTP_201_CREATED)\n\n\n@api_view((\"POST\",))\ndef resend_verification_email(request):\n    email = request.POST.get(\"email\")\n    try:\n        user = User.objects.get(email=email)\n    except User.DoesNotExist:\n        content = {\"message\": f\"{email} not exist\"}\n        return Response(content, status=status.HTTP_400_BAD_REQUEST)\n    if user.is_active is True:\n        content = {\"message\": f\"{email} already active\"}\n        return Response(content, status=status.HTTP_400_BAD_REQUEST)\n    generate_email(\n        user,\n        to_email=user.email,\n        subject=\"Verification Email - Example\",\n    )\n    content = {\"message\": f\"Activation link send to {email}\"}\n    return Response(content, status=status.HTTP_200_OK)\n\n\n@api_view((\"PUT\",))\ndef verify_user(request, token):\n    try:\n        payload = jwt.decode(token, settings.SECRET_KEY, algorithms=\"HS256\")\n\n    except jwt.DecodeError as identifier:\n        return Response(\n            {\"message\": \"Activation link invalid\"}, status=status.HTTP_400_BAD_REQUEST\n        )\n    except jwt.ExpiredSignatureError as identifier:\n        return Response(\n            {\"message\": \"Activation link expired\"}, status=status.HTTP_400_BAD_REQUEST\n        )\n\n    user = User.objects.get(id=payload[\"user_id\"])\n    if user.is_active is True:\n        content = {\"message\": f\"{user.email} already active,Sign In\"}\n        return Response(content, status=status.HTTP_400_BAD_REQUEST)\n    serializer = PasswordSerializer(data=request.data)\n    serializer.is_valid(raise_exception=True)\n    validated_data = serializer.validated_data\n    # create the user with requested password\n    user = serializer.create(validated_data, user)\n    # activate the user if password has been created\n    user.is_active = True\n    user.save()\n\n    response = {\n        \"message\": f\"{user.email} is now active.\",\n        \"User\": UserSerializer(user).data,\n        \"tokens\": user_tokens(user),\n    }\n    return Response(data=response, status=status.HTTP_200_OK)\n\n\n@api_view((\"POST\",))\ndef signin(request):\n    # custom serializer to validate user data\n    serializer = SigninSerializer(data=request.data)\n    serializer.is_valid(raise_exception=True)\n    validated_data = serializer.validated_data\n\n    email = validated_data.get(\"email\")\n    password = validated_data.get(\"password\")\n    try:\n        user = User.objects.get(email=email)\n    except User.DoesNotExist:\n        content = {\"message\": f\"{email} not exist\"}\n        return Response(content, status=status.HTTP_400_BAD_REQUEST)\n    if user.is_active is False:\n        content = {\"message\": f\"{email} not active, Sign Up\"}\n        return Response(content, status=status.HTTP_400_BAD_REQUEST)\n    user = authenticate(email=email, password=password)\n    if user:\n        update_last_login(None, user)\n        data = {\"user\": UserSerializer(user).data, \"tokens\": user_tokens(user)}\n        return Response(data)\n","sub_path":"users/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4361,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"579385038","text":"# Operadores Aritméticos\n# Soma +\nsomando = 10 + 10\n\n# Subtração –\nsubtraindo = 10 - 2\n\n# Multiplicação *\nmultiplicando = 10 * 5\n\n# Divisão /\ndividindo = 10 / 2\n\n# Potencia **\npotencia = 2 ** 4\n\n# Resto de uma divisão %\nresto1 = 15 % 2\nresto2 = 15 % 5\n\nn1 = 7\nn2 = 9\nn3 = 8\nn4 = 9\nmedia = (n1 + n2 + n3 + n4) / 4\n\nprint('Somando 10 + 10 =', somando)\nprint('Subtraindo 10 - 2 =', subtraindo)\nprint('Multiplicando 10 * 5 =', multiplicando)\nprint('Dividindo 10 / 2 =', dividindo)\nprint('2 elevado a 4 =', potencia)\nprint('O resto da divisao entre 15 por 2 é:', resto1)\nprint('O resto da divisao entre 15 por 5 é:', resto2)\n","sub_path":"aprendendo_a_sintaxe/operadores_aritmeticos.py","file_name":"operadores_aritmeticos.py","file_ext":"py","file_size_in_byte":631,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"412205573","text":"import os, csv, random\nimport requests\nimport urllib.request\nfrom datetime import datetime\nfrom bs4 import BeautifulSoup\nfrom colorama import init, Fore, Back, Style\n\n\ndef assortment_status():\n\tlines()\n\tprint(to_green(\"\\tЩо робимо?\"))\n\tlines()\n\tprint(\" 1. Список товарiв на складi.\")\n\tprint(\" 2. Поповнити кiлькiсть товарiв.\")\n\tprint(\" 3. Змiнити цiну товару.\")\n\tprint(\" 4. Додати новий товар до ассортименту.\")\n\tprint(\" 5. Видалити товар з ассортименту.\")\n\tprint(\" 6. Завершити роботу.\")\n\tlines()\n\t\ndef wrong(text):\n\tlines()\n\tprint(Fore.RED + text + Fore.RESET)\n\tlines()\n\ndef to_green(text):\n\ta = Fore.GREEN + str(text) + Fore.RESET\n\treturn a\n\ndef to_red(text):\n\ta = Fore.RED + str(text) + Fore.RESET\n\treturn a\n\ndef to_violet(text):\n\ta = Fore.MAGENTA + str(text) + Fore.RESET\n\treturn a\n\ndef to_yellow(text):\n\ta = Fore.YELLOW + str(text) + Fore.RESET\n\treturn a\n\ndef assortment_list():\n\tos.system(\"cls\")\n\twith open(\"data/storage_and_assortment.csv\", \"r\", encoding=\"utf-8\", newline=\"\") as file:\n\t\treader = csv.reader(file)\n\t\tprint(Fore.MAGENTA + \" Код\" + Fore.CYAN + \"\\t| \" + Fore.MAGENTA + \"К-сть\" + Fore.CYAN + \"\\t| \" + Fore.MAGENTA + \"Цiна\"+ Fore.CYAN + \"\\t| \\t\" + Fore.MAGENTA + \"Назва\" + Fore.RESET)\n\t\tlines()\n\t\tfor ind, val in enumerate(reader):\n\t\t\tif ind > 0:\n\t\t\t\tprint(Fore.GREEN + val[0] + Fore.CYAN + \"\\t|  \" + Fore.YELLOW + val[4] + Fore.CYAN + \"\\t|  \" + Fore.GREEN + val[5] + Fore.CYAN + \" \\t| \" + Fore.RESET + val[1])\n\t\tlines()\n\ndef yes_or_no():\n\tlines()\n\tprint(Fore.MAGENTA + \"\\t\\t\\t    Пiдтвердити операцiю?\")\n\tprint(\"\\n\\n\\t\\t\" + Fore.GREEN + \" Tак\\t\\t\\t\\t\\t\" + Fore.RED + \"Нi\\n\\n\\t\\t\" + Fore.GREEN + \"Enter\\t\\t\\t\\t\" + Fore.RED + \"будь-що + Enter\" + Fore.RESET)\n\tarrow()\n\ta = input()\n\treturn True if a == \"\" else False\n\ndef press_enter():\n\tprint(Fore.MAGENTA)\n\tinput(\"\\n\\t\\t\\tНатисни Enter, щоб продовжити\")\n\tprint(Fore.RESET)\n\ndef arrow():\n\tprint(Fore.GREEN + \"\\n-->\" + Fore.RESET, end=\" \")\n\ndef lines():\n\tprint(Fore.CYAN + \"-\"*79 + Fore.RESET)\n\ndef to_green(text):\n\ta = Fore.GREEN + text + Fore.RESET\n\treturn a\n\ndef reader_checker(number_of_column, value_to_compare):\n\tchecker = 0\n\twith open(\"data/storage_and_assortment.csv\", \"r\", encoding=\"utf-8\", newline=\"\") as file:\n\t\treader = csv.reader(file)\n\t\tfor row in reader:\n\t\t\tif row[number_of_column] == value_to_compare:\n\t\t\t\tchecker += 1\n\treturn checker\n\ndef add_exemplar():\n\t# Початок генерації і записування даних\n\n\t# Код товару\n\twhile True:\n\t\tos.system(\"cls\")\n\t\tunit_code_pre = \"\"\n\t\tlines()\n\t\tprint(to_green(\" Код товару\") + \" (6 цифр)\\n(пусте поле, щоб згенерувати випадковий)\\n(щоб вийти в головне меню без збереження змiн, введи - )\")\n\t\tlines()\n\t\tarrow()\n\t\tsome = input()\n\t\tif some == \"-\":\n\t\t\treturn True\n\t\tif some == \"\":\n\t\t\tfor i in range(6):\n\t\t\t\tunit_code_pre += str(random.randint(0, 9))\n\t\telif len(some) == 6 and some.isdigit():\n\t\t\tunit_code_pre = some\n\t\telse:\n\t\t\twrong(\" Щось неправильно, спробуй ще.\")\n\t\t\tpress_enter()\n\t\t\tcontinue\n\n\t\tif reader_checker(0, unit_code_pre) == 0:\n\t\t\tunit_code = unit_code_pre\n\t\t\tbreak\n\t\telse:\n\t\t\twrong(\" Такий код вже зайнято. Потрiбна повторна генерацiя, або ж напиши свiй.\")\n\t\t\tpress_enter()\n\t\t\tcontinue\n\n\t#Ім`я товару\n\twhile True:\n\t\tos.system(\"cls\")\n\t\tlines()\n\t\tprint(to_green(\" Назва товару\") + \" (10 i бiльше символiв)\\n(щоб вийти в головне меню без збереження змiн, введи - ):\")\n\t\tlines()\n\t\tarrow()\n\t\tsome = input()\n\t\tif some == \"-\":\n\t\t\treturn True\n\t\tif len(some) >= 10:\n\t\t\tif reader_checker(1, some) == 0:\n\t\t\t\tunit_name = some\n\t\t\t\tbreak\n\t\t\telse:\n\t\t\t\twrong(\" Така назва вже зайнята. Спробуй iншу.\")\n\t\t\t\tpress_enter()\n\t\t\t\tcontinue\n\t\telse:\n\t\t\twrong(\" Назва закоротка.\")\n\t\t\tpress_enter()\n\t\t\tcontinue\n\t\n\t#Посилання на торгову площадку\t\n\twhile True:\n\t\tos.system(\"cls\")\n\t\tlines()\n\t\tprint(to_green(\" Посилання на торгову площадку\") + \":\\n(щоб вийти в головне меню без збереження змiн, введи - )\")\n\t\tlines()\n\t\tarrow()\n\t\tsome = input()\n\t\tif some == \"-\":\n\t\t\treturn True\n\t\tif \"http\" in some:\n\t\t\tif reader_checker(2, some) == 0:\n\t\t\t\tlink_to_shop = some\n\t\t\t\tbreak\n\t\t\telse:\n\t\t\t\twrong(\" Таке посилання вже є у iншого товару. Добавити це посилання(так), чи спробувати iнше(нi)?\")\n\t\t\t\tif yes_or_no():\n\t\t\t\t\tlink_to_shop = some\n\t\t\t\t\tbreak\n\t\t\t\telse:\n\t\t\t\t\tcontinue\n\t\telse:\n\t\t\twrong(\" Посилання невiрне. Воно повинно починатись з \\\"http\\\"\")\n\t\t\tpress_enter()\n\t\t\tcontinue\n\n\t#Фото\n\tphoto_in_system = \"\"\n\tif \"aliexpress\" in link_to_shop:\n\t\tos.system(\"cls\")\n\t\tlines()\n\t\tprint(Fore.MAGENTA + \"Йде завантаження фото\" + Fore.RESET)\n\t\tlines()\n\t\tpage = requests.get(link_to_shop)\n\t\tif page.status_code >= 200 and page.status_code < 300:\n\t\t\tsoup = BeautifulSoup(page.text, \"html.parser\")\n\t\t\tphoto_link = str(soup.find_all(\"meta\", property=\"og:image\"))[16:-24]\n\t\t\tphoto_in_system = \"images/\" + unit_code + \".\" + photo_link.split(\".\")[-1]\n\t\t\turllib.request.urlretrieve(photo_link, photo_in_system)\n\t\t\tos.system(\"cls\")\n\t\t\tlines()\n\t\t\tprint(Fore.MAGENTA + \"Фото успiшно завантажено\" + Fore.RESET)\n\t\t\tlines()\n\t\t\tpress_enter()\n\t\telse:\n\t\t\tos.system(\"cls\")\n\t\t\twrong(\" Нажаль, фото не вдалось завантажити.\")\n\t\t\tprint(\" Ви можете завантажити його вручну, а потiм помiстити у папку \" + Fore.MAGENTA + \"/images\" + Fore.RESET)\n\t\t\tprint(\" Iм`я повинне бути: номер_товару.jpg\")\n\t\t\tpress_enter()\n\telse:\n\t\tos.system(\"cls\")\n\t\twrong(\" Нажаль, фото не вдалось завантажити.\")\n\t\tprint(\" Ви можете завантажити його вручну, а потiм помiстити у папку \" + Fore.MAGENTA + \"/images\" + Fore.RESET)\n\t\tprint(\" Iм`я повинне бути: номер_товару.jpg\")\n\t\tpress_enter()\n\n\t# Кількість товару на складі\n\twhile True:\n\t\tos.system(\"cls\")\n\t\tlines()\n\t\tprint(to_green(\"Кількість товару, яка прибула:\"))\n\t\tlines()\n\t\tprint(\" пусте поле, якщо \" + to_violet(\"0\"))\n\t\tlines()\n\t\tarrow()\n\t\tsome = input()\n\t\tif some == \"\":\n\t\t\tquantity_in_stock = 0\n\t\t\tbreak\n\t\telse:\n\t\t\tif some.isdigit():\n\t\t\t\tquantity_in_stock = int(some)\n\t\t\t\tbreak\n\t\t\telse:\n\t\t\t\twrong(\"Це повинно бути число\")\n\t\t\t\tpress_enter()\n\n\n\t# Ціна для продажу: \n\n\twhile True:\n\t\tos.system(\"cls\")\n\t\tlines()\n\t\tprint(to_green(\"Цiна для продажу (грн):\"))\n\t\tlines()\n\t\tarrow()\n\t\tselling_price = input()\n\t\tif selling_price.isdigit():\n\t\t\tbreak\n\t\telse:\n\t\t\twrong(\"Це має бути число.\")\n\t\t\tpress_enter()\n\t\t\tcontinue\n\t# Кінець генерації і записування даних\n\n\t# Додаємо дані до ассортименту\n\twith open(\"data/storage_and_assortment.csv\", \"a\", encoding=\"utf-8\", newline=\"\") as file:\n\t\twriter = csv.writer(file)\n\t\twriter.writerow([\n\t\t\tunit_code,\n\t\t\tunit_name,\n\t\t\tlink_to_shop,\n\t\t\tphoto_in_system,\n\t\t\tquantity_in_stock,\n\t\t\tselling_price\n\t\t])\n\tsave_and_load()\n\tos.system(\"cls\")\n\tlines()\n\tprint(Fore.MAGENTA + \"Товар додано до асортименту:\" + Fore.RESET)\n\tlines()\n\tprint(Fore.CYAN + unit_code + \" | \" + unit_name + \" | \" + photo_in_system + Fore.RESET)\n\n\tpress_enter()\n\n\ndef delete_exemplar():\n\twhile True:\n\t\tassortment_list()\n\t\tlines()\n\t\tprint(to_green(\"Номер товару для видалення:\\n\") + \"(порожнє поле, щоб вийти в головне меню)\\n(добав в кiнцi - ,щоб звiрити код товару з фото)\")\n\t\tlines()\n\t\tarrow()\n\t\tcode_to_delete = input()\n\t\tif code_to_delete == \"\":\n\t\t\tbreak\n\t\tfuture_list = []\n\t\tif code_to_delete.isdigit() and len(code_to_delete) == 6 or len(code_to_delete) == 7 and code_to_delete[-1] == \"-\" and code_to_delete[0:6].isdigit():\n\t\t\tif reader_checker(0, code_to_delete[0:6]) > 0:\n\t\t\t\twith open(\"data/storage_and_assortment.csv\", \"r\", encoding=\"utf-8\", newline=\"\") as file:\n\t\t\t\t\treader = csv.reader(file)\n\t\t\t\t\tfor row in reader:\n\t\t\t\t\t\tif row[0] != code_to_delete[0:6]:\n\t\t\t\t\t\t\tfuture_list.append(row)\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tif \"-\" in code_to_delete:\n\t\t\t\t\t\t\t\tos.startfile(row[3].replace(\"/\", \"\\\\\"))\n\t\t\t\t\t\t\tstuff_to_delete = row[0] + \" | \" + row[1] + \" | \" + row[2]\n\t\t\t\t\t\t\tphoto_to_delete = row[3]\n\t\t\t\tlines()\n\t\t\t\tos.system(\"cls\")\n\t\t\t\tlines()\n\t\t\t\tprint(Fore.MAGENTA + \"Видалити цей товар?\\n\" + Fore.YELLOW + stuff_to_delete + Fore.RESET)\n\t\t\t\tlines()\n\t\t\t\tif yes_or_no():\n\t\t\t\t\twith open(\"data/storage_and_assortment.csv\", \"w\", encoding=\"utf-8\", newline=\"\") as file:\n\t\t\t\t\t\twriter = csv.writer(file)\n\t\t\t\t\t\tfor i in future_list:\n\t\t\t\t\t\t\twriter.writerow(i)\n\t\t\t\t\tos.remove(photo_to_delete)\n\t\t\t\t\tsave_and_load()\n\t\t\t\t\tos.system(\"cls\")\n\t\t\t\t\tlines()\n\t\t\t\t\tprint(Fore.MAGENTA + \"Товар успiшно видалено.\" + Fore.RESET)\n\t\t\t\t\tlines()\n\t\t\t\t\tpress_enter()\n\t\t\t\t\tbreak\n\t\t\telse:\n\t\t\t\tlines()\n\t\t\t\tprint(Fore.RED + \"Товару з таким кодом не iснує.\" + Fore.RESET)\n\t\t\t\tlines()\n\t\t\t\tpress_enter()\n\t\telse:\n\t\t\tlines()\n\t\t\tprint(Fore.RED + \"Невiрне значення.\" + Fore.RESET)\n\t\t\tlines()\n\t\t\tpress_enter()\n\ndef add_quantity():\n\twhile True:\n\t\tfuture_list = []\n\t\tassortment_list()\n\t\tlines()\n\t\tprint(to_green(\"Який товар потрiбно поповнити? (номер товару)\\n\") + \"(порожнє поле, щоб вийти в головне меню)\\n(добав в кiнцi - ,щоб звiрити код товару з фото)\")\n\t\tlines()\n\t\tarrow()\n\t\tcode_to_quantify = input()\n\t\tif code_to_quantify == \"\":\n\t\t\tbreak\n\t\tif code_to_quantify.isdigit() and len(code_to_quantify) == 6 or len(code_to_quantify) == 7 and code_to_quantify[-1] == \"-\" and code_to_quantify[0:6].isdigit():\n\t\t\tif reader_checker(0, code_to_quantify[0:6]) > 0:\n\t\t\t\twith open(\"data/storage_and_assortment.csv\", \"r\", encoding=\"utf-8\", newline=\"\") as file:\n\t\t\t\t\treader = csv.reader(file)\n\t\t\t\t\tfor row in reader:\n\t\t\t\t\t\tif row[0] != code_to_quantify[0:6]:\n\t\t\t\t\t\t\tfuture_list.append(row)\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tif \"-\" in code_to_quantify:\n\t\t\t\t\t\t\t\tos.startfile(row[3].replace(\"/\", \"\\\\\"))\n\t\t\t\t\t\t\twhile True:\n\t\t\t\t\t\t\t\tos.system(\"cls\")\n\t\t\t\t\t\t\t\tlines()\n\t\t\t\t\t\t\t\tprint(Fore.YELLOW + row[0] + \" | \" + row[1] + Fore.RESET)\n\t\t\t\t\t\t\t\tprint(to_green(\"Скiльки товару прибуло?\\n(пусту поле, щоб вийти в головне меню.)\"))\n\t\t\t\t\t\t\t\tlines()\n\t\t\t\t\t\t\t\tarrow()\n\t\t\t\t\t\t\t\tquantity_to_add = input()\n\t\t\t\t\t\t\t\tif quantity_to_add == \"\":\n\t\t\t\t\t\t\t\t\treturn True\n\t\t\t\t\t\t\t\telif quantity_to_add.isdigit() or quantity_to_add[0] == \"-\" and quantity_to_add[1:].isdigit():\n\t\t\t\t\t\t\t\t\tquantity_to_add = int(quantity_to_add)\n\t\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\tlines()\n\t\t\t\t\t\t\t\t\tprint(Fore.RED + \"Це повинно бути число.\" + Fore.RESET)\n\t\t\t\t\t\t\t\t\tlines()\n\t\t\t\t\t\t\t\t\tpress_enter()\n\t\t\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\tos.system(\"cls\")\n\t\t\t\t\t\t\tlines()\n\t\t\t\t\t\t\tprint(Fore.YELLOW + row[0] + \" | \" + row[1] + Fore.RESET)\n\t\t\t\t\t\t\tlines()\n\t\t\t\t\t\t\tprint(Fore.RED + str(row[4]) + Fore.MAGENTA + \" --> \" + Fore.GREEN + str(int(quantity_to_add) + int(row[4])) + Fore.RESET)\n\t\t\t\t\t\t\tif yes_or_no():\n\t\t\t\t\t\t\t\trow[4] = int(row[4]) + quantity_to_add\n\t\t\t\t\t\t\t\tfuture_list.append(row)\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tfuture_list.append(row)\n\t\t\t\t\t\t\t\tlines()\n\t\t\t\t\t\t\t\tprint(Fore.RED + \"Операцiю вiдмiнено.\" + Fore.RESET)\n\t\t\t\t\t\t\t\tlines()\n\t\t\t\t\t\t\t\tpress_enter()\n\t\t\t\t\t\t\t\treturn True\n\t\t\t\t\twith open(\"data/storage_and_assortment.csv\", \"w\", encoding=\"utf-8\", newline=\"\") as file:\n\t\t\t\t\t\twriter = csv.writer(file)\n\t\t\t\t\t\tfor i in future_list:\n\t\t\t\t\t\t\twriter.writerow(i)\t\t\n\t\t\t\t\tsave_and_load()\n\t\t\t\t\tos.system(\"cls\")\n\t\t\t\t\tlines()\n\t\t\t\t\tprint(Fore.MAGENTA + \"Товар успiшно поповнено\" + Fore.RESET)\n\t\t\t\t\tlines()\n\t\t\t\t\tpress_enter()\n\t\t\telse:\n\t\t\t\tlines()\n\t\t\t\tprint(Fore.RED + \"Такого коду не iснує.\" + Fore.RESET)\n\t\t\t\tlines()\n\t\t\t\tpress_enter()\n\t\telse:\n\t\t\tlines()\n\t\t\tprint(Fore.RED + \"Невiрний код.\" + Fore.RESET)\n\t\t\tlines()\n\t\t\tpress_enter()\n\ndef change_price():\n\twhile True:\n\t\tfuture_list = []\n\t\tassortment_list()\n\t\tlines()\n\t\tprint(to_green(\"Для якого товару мiняємо цiну? (номер товару)\\n\") + \"(порожнє поле, щоб вийти в головне меню)\\n(добав в кiнцi - ,щоб звiрити код товару з фото)\")\n\t\tlines()\n\t\tarrow()\n\t\tcode_to_price = input()\n\t\tif code_to_price == \"\":\n\t\t\tbreak\n\t\tif code_to_price.isdigit() and len(code_to_price) == 6 or len(code_to_price) == 7 and code_to_price[-1] == \"-\" and code_to_price[0:6].isdigit():\n\t\t\tif reader_checker(0, code_to_price[0:6]) > 0:\n\t\t\t\twith open(\"data/storage_and_assortment.csv\", \"r\", encoding=\"utf-8\", newline=\"\") as file:\n\t\t\t\t\treader = csv.reader(file)\n\t\t\t\t\tfor row in reader:\n\t\t\t\t\t\tif row[0] != code_to_price[0:6]:\n\t\t\t\t\t\t\tfuture_list.append(row)\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tif \"-\" in code_to_price:\n\t\t\t\t\t\t\t\tos.startfile(row[3].replace(\"/\", \"\\\\\"))\n\t\t\t\t\t\t\twhile True:\n\t\t\t\t\t\t\t\tos.system(\"cls\")\n\t\t\t\t\t\t\t\tlines()\n\t\t\t\t\t\t\t\tprint(Fore.YELLOW + row[0] + Fore.CYAN + \" | \" + Fore.YELLOW + row[1] + Fore.RESET + \"\\nПоточна цiна на товар:  \" + row[5])\n\t\t\t\t\t\t\t\tlines()\n\t\t\t\t\t\t\t\tprint(to_green(\"Нова цiна на товар:\\n\") + \"(пусте поле, щоб вийти в головне меню.)\")\n\t\t\t\t\t\t\t\tlines()\n\t\t\t\t\t\t\t\tarrow()\n\t\t\t\t\t\t\t\tprice_to_be = input()\n\t\t\t\t\t\t\t\tif price_to_be == \"\":\n\t\t\t\t\t\t\t\t\treturn True\n\t\t\t\t\t\t\t\telif price_to_be.isdigit():\n\t\t\t\t\t\t\t\t\tprice_to_be = int(price_to_be)\n\t\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\tlines()\n\t\t\t\t\t\t\t\t\tprint(Fore.RED + \"Це повинно бути число.\" + Fore.RESET)\n\t\t\t\t\t\t\t\t\tlines()\n\t\t\t\t\t\t\t\t\tpress_enter()\n\t\t\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\tos.system(\"cls\")\n\t\t\t\t\t\t\tlines()\n\t\t\t\t\t\t\tprint(Fore.YELLOW + row[0] + \" | \" + row[1] + Fore.RESET)\n\t\t\t\t\t\t\tlines()\n\t\t\t\t\t\t\tprint(Fore.RED + str(row[5]) + Fore.MAGENTA + \" --> \" + Fore.GREEN + str(price_to_be) + Fore.RESET)\n\t\t\t\t\t\t\tif yes_or_no():\n\t\t\t\t\t\t\t\trow[5] =  price_to_be\n\t\t\t\t\t\t\t\tfuture_list.append(row)\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tfuture_list.append(row)\n\t\t\t\t\t\t\t\tlines()\n\t\t\t\t\t\t\t\tprint(Fore.RED + \"Операцiю вiдмiнено.\" + Fore.RESET)\n\t\t\t\t\t\t\t\treturn True\n\t\t\t\t\twith open(\"data/storage_and_assortment.csv\", \"w\", encoding=\"utf-8\", newline=\"\") as file:\n\t\t\t\t\t\twriter = csv.writer(file)\n\t\t\t\t\t\tfor i in future_list:\n\t\t\t\t\t\t\twriter.writerow(i)\n\t\t\t\t\tsave_and_load()\n\t\t\t\t\tos.system(\"cls\")\n\t\t\t\t\tlines()\n\t\t\t\t\tprint(Fore.MAGENTA + \"Цiну товару успiшно змiнено.\" + Fore.RESET)\n\t\t\t\t\tlines()\n\t\t\t\t\tpress_enter()\n\t\t\telse:\n\t\t\t\twrong(\"Такого коду не iснує.\")\n\t\t\t\tpress_enter()\n\t\t\t\tcontinue\n\t\telse:\n\t\t\twrong(\"Невiрний код.\")\n\t\t\tpress_enter()\n\t\t\tcontinue\n\ndef watch_foto():\n\tlines()\n\tprint(Fore.MAGENTA + \"Введи номер товару, щоб переглянути фото\" + Fore.RESET + \"\\n(порожнє поле, щоб вийти у головне меню)\")\n\tlines()\n\tarrow()\n\tsome = input()\n\tsome_to_show = some + \".\" + \"jpg\"\n\tif some == \"\":\n\t\treturn True\n\tif some.isdigit() and len(some) == 6:\n\t\tif reader_checker(0, some) > 0:\n\t\t\tif some_to_show in os.listdir(\"images/\"):\n\t\t\t\twith open(\"data/storage_and_assortment.csv\", \"r\", encoding=\"utf-8\", newline=\"\") as file:\n\t\t\t\t\treader = csv.reader(file)\n\t\t\t\t\tfor row in reader:\n\t\t\t\t\t\tif row[0] == some:\n\t\t\t\t\t\t\tos.startfile(row[3].replace(\"/\", \"\\\\\"))\n\t\t\t\t\t\t\tassortment_list()\n\t\t\telse:\n\t\t\t\twrong(\"Зображення з таким кодом не iснує.\")\n\t\t\t\tpress_enter()\n\t\telse:\n\t\t\twrong(\"Зображення з таким кодом не iснує.\")\n\t\t\tpress_enter()\n\telse:\n\t\twrong(\"Невiрне значення.\")\n\t\tpress_enter()\n\ndef save_and_load():\n\tos.system(\"cls\")\n\tos.system(\"git pull\")\n\tos.system(\"git add --all\")\n\tcommit = \"git commit -m \\\"\" + str(datetime.today())[0:16] + \"\\\"\"\n\tos.system(commit)\n\tos.system(\"git push\")\n\nos.system(\"git pull\")\n\ninit()\n\nif not os.path.exists(\"images\"):\n\tos.makedirs(\"images\")\nif not os.path.exists(\"data\"):\n\tos.makedirs(\"data\")\nif \"storage_and_assortment.csv\" not in os.listdir(\"data/\"):\n\twith open(\"data/storage_and_assortment.csv\", \"w\", encoding=\"utf-8\", newline=\"\") as file:\n\t\twriter = csv.writer(file)\n\t\twriter.writerow([\"Код товару\", \"Ім`я товару\", \"Посилання на торгову площадку\", \"Шлях до фото\", \"Кількість на складі\", \"Цiна\"])\n\nwhile True:\n\tos.system(\"cls\")\n\tassortment_status()\n\tarrow()\n\tnext_to_do = input()\n\tif next_to_do == \"1\":\n\t\twhile True:\n\t\t\tassortment_list()\n\t\t\tif watch_foto():\n\t\t\t\tbreak\n\telif next_to_do == \"2\":\n\t\tadd_quantity()\n\telif next_to_do == \"3\":\n\t\tchange_price()\n\telif next_to_do == \"4\":\n\t\tadd_exemplar()\n\telif next_to_do == \"5\":\n\t\tdelete_exemplar()\n\telif next_to_do == \"6\":\n\t\tos.system(\"cls\")\n\t\texit()","sub_path":"kaori_storage.py","file_name":"kaori_storage.py","file_ext":"py","file_size_in_byte":17207,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"345747694","text":"from Player import Player\nfrom Board import Board\nimport numpy as np\nimport IPython.core.debugger\ndbg = IPython.core.debugger.Pdb()\n\n\nclass Value_Iteration_AI(Player):\n    LOSING_STATES = -100\n    WINNING_STATES = 100\n\n    def __init__(self, opponent, player_id=1, discount_factor=0.5, board=None):\n        self.player_id = player_id\n        self.discount_factor = discount_factor\n        self.states = []\n        self.value_function = []\n        self.policy = []\n        self.opponent = opponent\n        self.board = board\n\n        if self.board is None:\n            self.board = Board()\n\n        self.value_iteration()\n\n    def reward_function(self, state_info1, state_info2):\n        if self.board.is_game_over():\n            if state_info2[1] == 0 and state_info2[3] == 0:    # winning state\n                return 100\n            elif state_info2[0] == 0 and state_info2[2] == 0:  # losing state\n                return -100\n            else:\n                return 0                                       # draw state\n        else:\n            # if my player eats a opponent's piece, gain 5. if my player eats a opponent's king, gain 10\n            gained_reward = 5*(state_info2[0] - state_info1[0]) + 10 * (state_info2[2] - state_info1[2])\n            # if opponent eats my piece, punish -5. if opponent eats my king, punish -10\n            lost_reward = 5*(state_info2[1] - state_info1[1]) + 10 * (state_info2[3] - state_info1[3])\n\n            return gained_reward - lost_reward\n\n    def get_reward(self, current_spots, next_spots):\n        current_status = self.board.get_states_from_boards_spots([current_spots])\n        next_status = self.board.get_states_from_boards_spots([next_spots])\n\n        return self.reward_function(current_status[0], next_status[0])\n\n    def get_transition_probabilities(self, actions, opponent_action):\n        # the probability of taking the action is calculated by 1 / (number of actions x number of opponent actions)\n        return 1 / (len(actions) * len(opponent_action))\n\n    def get_value(self, state):\n        try:    # if the state has already observed, find state's index and state's value and return them\n            index = self.states.index(state)\n\n            return self.value_function[index], index\n        except ValueError:      # if the state has not been observed yet, create a new state and add it to states array\n            self.states.append(state)\n            self.value_function.append(0)\n            index = len(self.value_function) - 1\n\n            return 0, index\n\n    def calculate_value_of_action(self, state, possible_moves, opponent_moves):\n        next_state = self.board.spots                     # determine next state\n        next_state_value = self.get_value(next_state)[0]  # obtain value of next state. If the state is not in the states array, this function creates the state and adds to the array\n        reward = self.get_reward(state, next_state)\n        prob = self.get_transition_probabilities(possible_moves, opponent_moves)\n\n        return prob * (reward + self.discount_factor * next_state_value)\n\n    def calculate_expected_value(self, state):\n        if self.board.is_game_over():\n            return [self.LOSING_STATES]\n\n        possible_moves = self.board.get_possible_next_moves()\n        expected_value = np.zeros(len(possible_moves))\n\n        for i in range(len(possible_moves)):\n            move = possible_moves[i]\n            self.board.set_spots(state)                             # recover board to state condition\n\n            self.board.make_move(move)                              # make my move\n            opponent_moves = [self.opponent.get_next_move()]  # determine possible opponent's moves\n\n            if self.board.is_game_over():\n                expected_value[i] = self.calculate_value_of_action(state, possible_moves, opponent_moves)\n                self.board.switch_turn()\n\n                continue\n\n            for opp_move in opponent_moves:                         # maybe there can be more than one opponent moves\n                self.board.make_move(opp_move)                      # make opponent move to obtain next state\n                expected_value[i] += self.calculate_value_of_action(state, possible_moves, opponent_moves)\n\n        return expected_value\n\n    def value_iteration(self, theta=0.0001):\n        self.states.append(self.board.spots)\n        self.value_function.append(0)\n\n        while True:\n            delta = 0\n\n            for state in self.states:\n                self.board.set_spots(state)  # make the board look like same as the state\n\n                v, index = self.get_value(state)\n\n                expected_value = self.calculate_expected_value(state)\n\n                self.value_function[index] = np.max(expected_value)\n\n                delta = max(delta, np.abs(v - self.value_function[index]))\n\n            if delta < theta:\n                break\n\n        self.board.reset_board()\n        self.calculate_policy()\n\n    def calculate_policy(self):\n        for state in self.states:\n            expected_value = self.calculate_expected_value(state)       # get values of actions\n            self.policy[state] = [0 for i in expected_value]            # init policy's values for this state\n\n            best_action = np.argmax(expected_value)                     # find best action in this state\n            self.policy[state, best_action] = 1.0                       # assign best action to 1\n\n    def game_completed(self):\n        pass\n\n    def get_next_move(self):\n        \"\"\"\n        Gets the desired next move from the AI.\n        \"\"\"\n        current_state = self.board.spots                            # determine current state\n        determine_policies = np.array(self.policy[current_state])   # obtain policy array for current state\n        possible_actions = self.board.get_possible_next_moves()     # obtain available actions\n\n        return possible_actions[determine_policies == 1]            # return selected action whose value is 1\n\n","sub_path":"Value_Iteration_AI.py","file_name":"Value_Iteration_AI.py","file_ext":"py","file_size_in_byte":5999,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"427931207","text":"from util import *\n\n\n@apply\ndef apply(given):\n    ((f, (z, xi, direction)), _f), (_xi, a, b) = given.of(All[Equal[Limit]])\n    assert direction == 0\n    assert xi == _xi\n    assert _f == f._subs(z, xi)\n    assert b >= a\n    return Any[xi:a:b](All[z:a:b](f >= _f))\n\n\n@prove\ndef prove(Eq):\n    from axiom import calculus\n\n    a = Symbol(real=True)\n    b = Symbol(real=True, domain=Interval(a, oo, left_open=True))\n    f = Function(real=True)\n    from axiom.calculus.all_eq.imply.all_any_eq.intermediate_value_theorem import is_continuous\n    Eq << apply(is_continuous(f, a, b))\n\n\nif __name__ == '__main__':\n    run()\n# created on 2020-06-13\n","sub_path":"axiom/calculus/is_continuous/imply/any_all_ge/extreme_value_theorem.py","file_name":"extreme_value_theorem.py","file_ext":"py","file_size_in_byte":639,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"218399992","text":"# Work with Python 3.6\r\nfrom discord.ext.commands import Bot\r\nimport discord\r\nfrom collections import defaultdict\r\n\r\nimport xlrd\r\nfrom xlrd import open_workbook\r\nfrom xlutils.copy import copy\r\nimport xlwt\r\nfrom xlwt import Workbook\r\n\r\nTOKEN = 'NjQwNjczMTQzNDg4MzgwOTI4.Xb-CLA.r-15GdxMJ8Ewv5hxCgWB1yMqsOI'\r\n\r\nloc = (\"JerkData.xls\")\r\n\r\nclient = discord.Client()\r\n\r\nbot = Bot('!')\r\npoints = defaultdict(int)\r\n\r\n@client.event\r\nasync def on_ready():\r\n    game = discord.Game(\"with Myself\")\r\n    await client.change_presence(status=discord.Status.idle, activity=game)\r\n    print(\"Logged in as: {0.user}\".format(client))\r\n\r\n@client.event\r\nasync def on_message(message):\r\n    onSheet = 0;\r\n    # we do not want the bot to reply to itself\r\n    if message.author == client.user:\r\n        return\r\n\r\n    wb = xlrd.open_workbook(loc)\r\n    sheet = wb.sheet_by_index(0);\r\n    mentionUser = message.author.mention;\r\n    rows = sheet.nrows;\r\n    userRow = 0;\r\n    userCount = 0;\r\n\r\n    for i in range(0, rows):\r\n        if sheet.cell_value(i, 0) == message.author.name:\r\n            userRow = i;\r\n            userCount = sheet.cell_value(i, 1);\r\n            onSheet = 1;\r\n\r\n    wb = copy(wb);\r\n    sheet = wb.get_sheet('Sheet 1');\r\n    if onSheet == 0:\r\n        sheet.write(rows, 0, message.author.name);\r\n        userRow = rows;\r\n\r\n    if message.content.startswith('jerk'):\r\n        userCount += 1.0;\r\n        sheet.write(userRow, 1, userCount);\r\n        await message.channel.send(\"{} has jerked off {} time.\".format(mentionUser, userCount));\r\n\r\n\r\n    wb.save('JerkData.xls') \r\nclient.run(TOKEN)","sub_path":"JerkBot/JerkBot.py","file_name":"JerkBot.py","file_ext":"py","file_size_in_byte":1581,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"484520111","text":"import pygame\nimport sys\nimport math\nimport time\nimport random\n\ndef dreptunghi(screen, x, y, l, w, color):\n    pygame.draw.line(screen, color, [x, y], [x + l, y])\n    pygame.draw.line(screen, color, [x+ l,y], [x + l,y + w])\n    pygame.draw.line(screen, color, [x + l,y + w], [x ,y + w])\n    pygame.draw.line(screen, color, [x ,y + w],[x ,y ])\n    \ndef paleta(screen, X, Y, L, W,color):\n    pygame.draw.line(screen, color, [X, Y], [X + L, Y])\n    pygame.draw.line(screen, color, [X + L,Y],[X +L,Y+ W])\n    pygame.draw.line(screen, color, [X + L,Y + W], [X,Y + W])\n    pygame.draw.line(screen, color, [X,Y + W],[X,Y])\n    \ndef minge(screen, x, y, r, color, w):\n    pygame.draw.circle(screen, color,[x, y], r, w)\n    \ndef intersects(mx, my, r, left, top, right, bottom):\n   if mx < left:\n       closestX = left\n   else:\n       if mx > right:\n           closestX = right\n       else:\n            closestX = mx\n\n   if my < top:\n       closestY = top\n   else:\n       if my > bottom:\n           closestY = bottom\n       else:\n            closestY = my\n\n   dx = closestX - mx\n   dy = closestY - my\n\n   return (dx * dx + dy * dy) <= r * r    \n\n#culorile sunt RED,GREEN,BLUE    \nRED = (255 ,0, 0)\nWHITE=(255,255,255)\nBLUE=(0,0,255)\nGREEN=(0,255,0)\nBLACK=(0,0,0)\npygame.init()\n\nscore=0\n\nsize=[500, 350]\nscreen=pygame.display.set_mode(size)\n\npx=200\npy=280\n\nr=7\n\nmx=250\nmy=50\n\n#ui=random.randint(0,360)* math.pi/180\nui=0\n\npdir=0\n\ncycle=0\n\nkeypush= -1\n\ncaramizi=[1,1,1,1,1,1,1,1,1]\n\nwon=False\nlose=False\n\nlives=3\ntextcount=0\n\npygame.display.set_caption(\"PRO-LEVEL BREAK-OUT\")\nfont=pygame.font.SysFont('Calibri',25,True,False)\n\n\nmdir=2\ndone=False\nclock=pygame.time.Clock()\n\npygame.key.set_repeat(1,10)\n\nwhile not done: \n    for event in pygame.event.get():\n        if event.type==pygame.QUIT:\n            done=True\n        keys = pygame.key.get_pressed()\n        \n        if keys[pygame.K_RIGHT]:\n            px=px + 5\n            if pdir<0:\n                pdir=0\n            pdir=pdir + 1\n            if pdir>5:\n                pdir=5\n            keypush=cycle\n            if px+100>=500:\n                px=400\n    \n        if keys[pygame.K_LEFT]: \n            px=px - 5\n            if pdir>0:\n                pdir=0\n            pdir=pdir - 1\n            if pdir<-5:\n                pdir=-5\n            keypush=cycle\n            if px<=0:\n                px=0\n                \n            \n        if abs(keypush-cycle) >15:\n            pdir=0\n            \n                \n                \n    screen.fill(WHITE)\n    dreptunghi(screen,0,300,500,5,BLACK)\n    textscore= font.render(\"score: \" + str(score),True,BLACK)\n    screen.blit(textscore,[225,325])\n    textlives= font.render(\"lives: \"+ str(lives),True,BLACK)\n    screen.blit(textlives,[10,325])\n    \n    \n    \n    if score==90:\n        wintext=font.render(\"YOU WIN! ;)\",True,GREEN)\n        screen.blit(wintext,[225,175])    \n        done=True\n        won=True\n        \n    if lives==0:\n        losetext=font.render(\"YOU LOSE! :(\",True,RED)\n        screen.blit(losetext,[225,175])\n        done=True\n        lose=True\n        \n    if my + r >= 300 :\n        lives=lives-1\n        textcount=200\n        \n    if textcount>0:\n        textcount=textcount-1     \n        textliveslost=font.render(\"- 1 LIFE\",True,BLACK)\n        screen.blit(textliveslost,[400,325])\n        mx=250\n        my=50\n        \n    for i in range(9):\n        if caramizi[i]==1:\n            dreptunghi(screen, 25 + i*50, 10, 50, 25, RED)\n    \n    paleta(screen, px, py,100,10,BLUE)\n    \n    mx=mx + mdir * math.sin(ui) \n    if mx <= 0 + r:\n        ui= - ui\n        \n    if mx>= 500 - r:\n        ui= - ui\n    my=my + mdir * math.cos(ui) \n    \n    if my <= 0 + r:\n        ui= math.pi - ui\n        \n        #AICI E PALETA\n    if intersects(mx, my, r, px,py,px+ 100,py+10) : \n       # if pdir!=0:\n           # ui=math.pi -ui + pdir * math.pi/16\n        #else:\n           # ui=math.pi -ui\n        #ui=math.pi - math.atan2(my,mx) \n        #ui=math.pi/2 + math.atan2(my,mx)\n        #ui=3*math.pi/2 - math.atan2(my,mx)\n        if px <= mx <= px + 50:\n            ui= 3*math.pi/2 - math.atan2(my,mx)\n        else:\n            ui=math.pi/2 + math.atan2(my,mx)\n        \n\n        \n    if my>= 300 - r :   \n        ui= math.pi -ui\n  # AICI E CARAMIDA  \n    for i in range(9):\n        \n        #if caramizi[i]==1 and (10 +25+r -2<=int(my)<=10 + 25 + r+2) and (25 + i*50<=mx<=25 + i*50 + 50)  :\n            #caramizi[i]=0\n            #score=score+10\n            #ui=math.pi - ui\n        if caramizi[i]==1 and intersects(mx,my,r,25+i*50,10,25+i*50+50,25):\n            caramizi[i]=0\n            score=score+10\n            ui=math.pi - math.atan2(my,mx)\n        if caramizi[i]==1 and (10-2<=my <= 10 +25+2) and  (mx + r-2<=25+i*50<=mx+r+2):\n            caramizi[i]=0\n            ui= -ui\n            score=score+10\n        if caramizi[i]==1 and (10-2<=my <= 10 +25+2) and  (mx + r-2<=25+50+i*50<=mx+r+2):\n            caramizi[i]=0\n            ui= -ui\n            score=score+10\n        \n    minge(screen,int(mx),int(my), r,GREEN, 0)\n     \n    pygame.display.flip()\n    \n    clock.tick(100)\n    cycle=cycle + 1\n    cycle=cycle %60 \n    \nif won:\n    time.sleep(5)\nif lose:\n    time.sleep(5)\n    \npygame.quit()\nsys.exit()    ","sub_path":"breakout4.py","file_name":"breakout4.py","file_ext":"py","file_size_in_byte":5214,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"387325622","text":"from eduvpn.utils import logger\nimport gi\ngi.require_version('Gtk', '3.0')  # noqa: E402\nfrom gi.repository import Gtk, GObject\n\n\n# ui thread\ndef error_helper(parent: GObject,  # type: ignore\n                 msg_big: str,\n                 msg_small: str) -> None:\n    \"\"\"\n    Shows a GTK error message dialog.\n    args:\n        parent (GObject): A GTK Window\n        msg_big (str): the big string\n        msg_small (str): the small string\n    \"\"\"\n    logger.error(f\"{msg_big}: {msg_small}\")\n    error_dialog = Gtk.MessageDialog(  # type: ignore\n        parent,\n        0,\n        Gtk.MessageType.ERROR,  # type: ignore\n        Gtk.ButtonsType.OK,  # type: ignore\n        str(msg_big),\n    )\n    error_dialog.format_secondary_text(str(msg_small))  # type: ignore\n    error_dialog.run()  # type: ignore\n    error_dialog.hide()  # type: ignore\n\n\ndef show_ui_component(builder, component: str, show: bool):\n    \"\"\"\n    Set the visibility of a UI component.\n    \"\"\"\n    component = builder.get_object(component)\n    if show:\n        component.show()  # type: ignore\n    else:\n        component.hide()  # type: ignore\n\n\ndef link_markup(link: str) -> str:\n    try:\n        scheme, rest = link.split(':', 1)\n        if rest.startswith('//'):\n            rest = rest[2:]\n    except ValueError:\n        return link\n    else:\n        return f'<a href=\"{link}\">{rest}</a>'\n","sub_path":"eduvpn/ui/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1362,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"160824229","text":"'''\nAggregate and summarize 33 million rows of accelerator and gyroscope\nsensor data from smart watches and mobile phones. Output processed data\nto HDF files to preserve metadata.\n'''\n# Join and summarize 33 million rows of smart watch and phone sensor\n# with data Pandas using MapReduce paradigm.\n# output to HDF5 files\n\n# file streaming. chunk as generator object.\n#\n# Group and summarize activity 33 million rows of sensor data by activity,\n# user, device and sensor type. Concatenate all summarized data into dataframe.\n# Output to HDF5 format (binary format for preserve metadata.\n#\n# Project assumes that that column names are consistent across CSVs. MapReduce\n# paradigm/approach to calculate mean, standard deviation and variance.\n#\n# Acceleration: rate of change of the velocity of an object.\n#\n# Gyroscope: measure rotational motion\n#\n# 3-axis: x left/right, y forward/backward, z up/down\n#\n# pandas, sensor data, aggregation, HDF5, python, multiindex\n#\n# More than 80% of the processing time is concentrated in grouping and\n# aggregating the data frame chunks. __next__, get_chunk, read functions\n\n\nimport os\n\nimport numpy as np\nimport pandas as pd\n\n\nclass SensorInput:\n    '''Read CSV data into data frame chunks.'''\n\n    def __init__(self, dir_data, csv_file, column_names, data_types,\n                 chunk_size):\n        self.dir_data = dir_data\n        self.csv_file = csv_file\n        self.column_names = column_names\n        self.data_types = data_types\n        self.chunk_size = chunk_size\n\n    def read_data(self):\n        '''Read CSV data into data frame chunks.\n\n        Returns\n        -------\n        df: pandas.io.parsers.TextFileReader\n        '''\n        csv_path = os.path.join(self.dir_data, self.csv_file)\n        df = pd.read_csv(csv_path, usecols=self.column_names,\n                         dtype=self.data_types, chunksize=self.chunk_size)\n        return df\n\n\nclass SensorProcess:\n    '''Process data frame chunks into aggregated and summarized data.'''\n\n    def __init__(self, agg_funcs, cols_axes, group_by):\n        self.agg_funcs = agg_funcs\n        self.cols_axes = cols_axes\n        self.group_by = group_by\n\n    @staticmethod\n    def _reduce_mean(count, mean):\n        '''Reduce mapped means.\n\n        Parameters\n        ----------\n        count : pandas.Series\n        mean : pandas.Series\n\n        Returns\n        -------\n        float\n        '''\n        return count.multiply(mean).sum() / count.sum()\n\n    def _reduce_variance(self, count, mean, variance):\n        '''Reduce mapped variances.\n\n        Parameters\n        ----------\n        count : pandas.Series\n        mean : pandas.Series\n        variance : pandas.Series\n\n        Returns\n        -------\n        reduced_variance : float\n        '''\n        reduced_mean = self._reduce_mean(count, mean)\n        delta = mean.subtract(reduced_mean).pow(2)\n        reduced_variance = ((count.multiply(variance).sum() +\n                             count.multiply(delta).sum()) /\n                            count.sum())\n        return reduced_variance\n\n    def _reduce_summary_statistics(self, s, g, cols):\n        '''Calculate summary statistics.\n\n        Parameters\n        ----------\n        s : dict\n        g : pandas.DataFrame\n            Data frame of grouped data.\n        cols : list\n            List of summary statistics columns names.\n\n        Returns\n        -------\n        s : dict\n        '''\n        count, max_, mean, min_, std, sum_, var = cols\n        s[count] = g[count].sum()\n        s[sum_] = g[sum_].sum()\n        s[mean] = self._reduce_mean(g[count], g[mean])\n        s[min_] = g[min_].min()\n        s[max_] = g[max_].max()\n        s[var] = self._reduce_variance(g[count], g[mean], g[var])\n        s[std] = np.sqrt(s[var])\n        return s\n\n    def _reduce_groups(self, group, cols_stats):\n        '''Apply summary statistics function to subsets of columns.\n\n        The summary statistics columns (e.g. count, sum, etc.) are\n        repeated for each x, y and z direction. Apply the summary\n        statistics function to each direction subgroup.\n\n        Parameters\n        ----------\n        group : pandas.DataFrame\n            Data frame of grouped data.\n        cols_stats : list\n            List of data frame column names.\n\n        Returns\n        -------\n        pandas.Series\n        '''\n        series = {}\n\n        for i in range(len(self.cols_axes)):\n            cols_direction = [col for col in cols_stats\n                              if col.startswith(self.cols_axes[i])]\n            cols_direction.sort()\n            series = self._reduce_summary_statistics(series, group,\n                                                     cols_direction)\n\n        index = cols_stats\n        return pd.Series(series, index=index)\n\n    def _reduce_chunks(self, df):\n        '''Apply reduce functions to grouped data frame.\n\n        Parameters\n        ----------\n        df : pandas.DataFrame\n            Data frame of aggregated data.\n\n        Returns\n        -------\n        df_reduced : pandas.DataFrame\n        '''\n        std_col_names = zip(self.cols_axes, ['std'] * len(self.cols_axes))\n        cols_std = [''.join(s) for s in std_col_names]\n        cols_stats = df.columns.tolist() + cols_std\n\n        df_reduced = (df.groupby(self.group_by)\n                      .apply(lambda group: self._reduce_groups(group,\n                                                               cols_stats)))\n        df_reduced.sort_index(axis='columns', inplace=True)\n        df_reduced.drop(self.group_by, axis='columns', inplace=True)\n        return df_reduced\n\n    def _aggregate_chunks(self, df):\n        '''Group and aggregate TextFileReader chunks.\n\n        Parameters\n        ----------\n        df : pandas.io.parsers.TextFileReader\n\n        Returns\n        -------\n        df_agg : pandas.DataFrame\n        '''\n        intermediate_stats = []\n\n        for chunk in df:\n            chunk = (chunk.groupby(self.group_by)\n                     .aggregate(self.agg_funcs)\n                     .reset_index())\n            chunk.columns = ['_'.join(col) if col[1] else col[0] for col\n                             in chunk.columns]\n            intermediate_stats.append(chunk)\n\n        df_agg = (pd.concat(intermediate_stats, axis='index')\n                  .reset_index(drop=True))\n        return df_agg\n\n    def process_sensor(self, df):\n        '''Run the processing functions.\n\n        Parameters\n        ----------\n        df : pandas.io.parsers.TextFileReader\n\n        Returns\n        -------\n        df : pandas.DataFrame\n        '''\n        df = self._aggregate_chunks(df)\n        df = self._reduce_chunks(df)\n        return df\n\n\nclass SensorOutput:\n    '''Output aggregated and summarized data to HDF to preserve metadata.'''\n\n    def __init__(self, dir_output, output_file_name):\n        self.dir_output = dir_output\n        self.output_file_name = output_file_name\n\n    def to_hdf(self, df):\n        '''Write summarized data frame to HDF.\n\n        Parameters\n        ----------\n        df : pandas.DataFrame\n\n        Returns\n        -------\n        None\n        '''\n        path_output = os.path.join(self.dir_output, self.output_file_name)\n        hdf_key = self.output_file_name.split('.')[0]\n        df.to_hdf(path_output, key=hdf_key, format='table', mode='w')\n\n\ndef main():\n    '''Run processing program.'''\n\n    dir_data = os.path.abspath('data/')\n    dir_output = os.path.abspath('output/')\n    csv_files = [\n        'Phones_accelerometer.csv',\n        'Phones_gyroscope.csv',\n        'Watch_accelerometer.csv',\n        'Watch_gyroscope.csv'\n    ]\n    hdf_files = [\n        'phone_accel.h5',\n        'phone_gyro.h5',\n        'watch_accel.h5',\n        'watch_gyro.h5'\n    ]\n\n    column_names = ('x', 'y', 'z', 'User', 'Model', 'Device', 'gt')\n    data_types = {\n        'x': np.float32,\n        'y': np.float32,\n        'z': np.float32,\n        'User': 'category',\n        'Model': 'category',\n        'Device': 'category',\n        'gt': 'category'\n    }\n    chunk_size = 100000\n\n    agg_funcs = ['count', 'sum', 'mean', 'min', 'max', 'var']\n    group_by = ['gt', 'User', 'Device']\n    cols_axes = ('x_', 'y_', 'z_')\n\n    input_output = zip(csv_files, hdf_files)\n\n    for input_file, output_file in input_output:\n\n        sensor_input = SensorInput(dir_data, input_file, column_names,\n                                   data_types, chunk_size)\n        df = sensor_input.read_data()\n\n        sensor_process = SensorProcess(agg_funcs, cols_axes, group_by)\n        df = sensor_process.process_sensor(df)\n\n        sensor_output = SensorOutput(dir_output, output_file)\n        sensor_output.to_hdf(df)\n\n\nif __name__ == '__main__':\n\n    main()\n","sub_path":"activity-sensors-batch-processing/batch_process_activity_sensors.py","file_name":"batch_process_activity_sensors.py","file_ext":"py","file_size_in_byte":8666,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"387335695","text":"l1 = int(input())\r\nw1 = int(input())\r\nh1 = int(input())\r\nl2 = int(input())\r\nw2 = int(input())\r\nh2 = int(input())\r\nlc = int(input())\r\nwc = int(input())\r\nhc = int(input())\r\nnumber2 = (lc >= l1 + l2) and (wc >= w1) and (wc >= w2)\r\nnumber3 = (lc >= l1 + w2) and (wc >= w1) and (wc >= l2)\r\nnumber4 = (lc >= w1 + w2) and (wc >= l1) and (wc >= l2)\r\nnumber5 = (lc >= w1 + l2) and (wc >= w2) and (wc >= l1)\r\nnumber6 = (wc >= l1 + l2) and (lc >= w1) and (lc >= w2)\r\nnumber7 = (wc >= l1 + w2) and (lc >= w1) and (lc >= l2)\r\nnumber8 = (wc >= w1 + w2) and (lc >= l1) and (lc >= l2)\r\nnumber9 = (wc >= w1 + l2) and (lc >= w2) and (lc >= l1)\r\nnumber0 = (lc >= l1) and (wc >= w1)\r\nnumber11 = (lc >= l2) and (wc >= w2)\r\nnumber12 = (lc >= w1) and (wc >= l1)\r\nnumber13 = (lc >= w2) and (wc >= l2)\r\nif (hc < h1) or (hc < h2):\r\n    print('NO')\r\nelif (number11 or number13) and (number12 or number13):\r\n    if hc >= (h1 + h2):\r\n        print('YES')\r\n    elif number2 or number3 or number4 or number5:\r\n        print('YES')\r\n    elif number6 or number7 or number8 or number9:\r\n        print('YES')\r\n    else:\r\n        print('NO')\r\nelse:\r\n    print('NO')\r\n","sub_path":"2_упаковка3.py","file_name":"2_упаковка3.py","file_ext":"py","file_size_in_byte":1131,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"560382207","text":"import TagScriptEngine\nfrom unittest import TestCase\n\nclass test_edgecase_functionality(TestCase):\n\n    def setUp(self):\n        \"\"\" Sets up engine and other variables that might be needed between tests \"\"\"\n        self.engine = TagScriptEngine.Engine()\n    def tearDown(self):\n        \"\"\" Cleans the plate to make tests consistent \"\"\"\n        self.engine.Clear_Variables()\n        self.engine = None\n\n    # Actual tests below\n    # ======\n    def test_edgecase_missing_var_in_math(self):\n\n        self.engine.Add_Variable(\"1\", \"30\")\n        self.engine.Add_Variable(\"2\", \"2\")\n        #self.engine.Add_Variable(\"3\", \"0\")\n        trouble = self.engine.Process(\"m{($1+1+0$3=)*$2}\")\n\n        self.assertEqual(\"62\", trouble)\n\n    def test_edgecase_missing_var_in_math_substitute(self):\n\n        self.engine.Add_Variable(\"1\", \"30\")\n        self.engine.Add_Variable(\"2\", \"2\")\n        #self.engine.Add_Variable(\"3\", \"0\")\n        trouble = self.engine.Process(\"m{($1+1+0$3=0)*$2}\")\n\n        self.assertEqual(\"62\", trouble)\n\n    def test_edgecase_variable_in_variable_assignment_isnt_replaced(self):\n        self.engine.Add_Variable(\"user\", \"Carl#0001\")\n\n        trouble = self.engine.Process(\"!{f=#{hello $user~hello $user}}$f\")\n\n        self.assertEqual(\"hello Carl#0001\", trouble)\n    \n    def test_edgecase_reusable_list_in_var_assignement(self):\n        seen_yen = False\n        seen_carl = False\n        seen_py = False\n        for _ in range(150):\n            phrase = self.engine.Process(\"#user{carl,yenni,pysnow} !{player1=$user} $player1\").strip(' ')\n            if \"pysnow\" in phrase:\n                seen_py = True\n            if \"carl\" in phrase:\n                seen_carl = True\n            if \"yenni\" in phrase:\n                seen_yen = True\n        self.assertTrue(seen_carl and seen_py and seen_yen)\n        ","sub_path":"TagScriptTests/test_edgecases.py","file_name":"test_edgecases.py","file_ext":"py","file_size_in_byte":1818,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"461178383","text":"import hashlib,os,pickle\r\nclass HashType:\r\n    MD5=0\r\n    SHA1=1\r\n    SHA256=2\r\n    SHA384=3\r\n    SHA512=4\r\nclass HashItem:\r\n    def __init__(self,path,hashType,hashValue):\r\n        self.meth=path\r\n        self.tipp=hashType\r\n        self.val=hashValue\r\n    def equals(self,otherGuy):\r\n        if type(otherGuy)==type(self):\r\n            if otherGuy.val:return self.val==otherGuy.val\r\n            else:return False\r\n        elif type(otherGuy)==bytes:\r\n            return self.val==otherGuy.val\r\ndef GetHashObjFromType(heshType):\r\n    if heshType==HashType.MD5:return hashlib.md5()\r\n    elif heshType==HashType.SHA1:return hashlib.sha1()\r\n    elif heshType==HashType.SHA256:return hashlib.sha256()\r\n    elif heshType==HashType.SHA384:return hashlib.sha384()\r\n    elif heshType==HashType.SHA512:return hashlib.sha512()\r\n    else:return None\r\ndef GetHashObjOfFyle(puth,heshType=HashType.MD5,buffSize=1024*1024*10):\r\n    hesh=GetHashObjFromType(heshType)\r\n    with open(puth,'rb') as inp:\r\n        data=inp.read(buffSize)\r\n        while len(data)>0:\r\n            hesh.update(data)\r\n            data=inp.read(buffSize)\r\n    return hesh\r\ndef GetHashDigest(put,heshType=HashType.MD5,buffSize=1024*1024*10):\r\n    return GetHashObjOfFyle(put,heshType,buffSize).digest()\r\ndef GetHexDigest(puth,heshType=HashType.MD5,buffSize=1024*1024*10):\r\n    return GetHashObjOfFyle(puth,heshType,buffSize).hexdigest()\r\nclass HashShite:\r\n    def __init__(self):\r\n        self.items=list()\r\n    def leard(self,peth,heshType=HashType.MD5,bufferSize=1024*1024*512,debug=True):\r\n        for root,dirs,fils in os.walk(peth):\r\n            for f in fils:\r\n                if debug:print(\"Getting hash value for %s\" % os.path.join(root,f))\r\n                hashVal=GetHashDigest(os.path.join(root,f),heshType,bufferSize)\r\n                if debug:print(hashVal)\r\n                self.items.append(HashItem(os.path.join(root,f),heshType,hashVal))\r\nheshShit=HashShite()\r\nheshShit.leard(\"Grand Theft Auto V\")\r\nwith open(\"heshes.shite\",'wb') as out:\r\n    pickle.dump(heshShit,out)\r\n","sub_path":"unsorted_guff/Hash Check/getHashes.py","file_name":"getHashes.py","file_ext":"py","file_size_in_byte":2047,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"392395397","text":"import re\n\n\nclass ShowInfo:\n\n    parser = re.compile(\n        r\"^(?P<exact>\\d+)$|^(?P<from>\\d+)\\+$|^(?P<begin>^\\d+)\\-(?P<end>\\d+)$\")\n\n    def __init__(self, begin=1, end=None, min_steps=None):\n        assert begin >= 1\n        assert end is None or end >= 1\n        self.begin = begin\n        self.end = end\n        if min_steps:\n            self._min_steps = min_steps\n        else:\n            self._min_steps = max(begin, end if end else 1)\n\n    @classmethod\n    def parse(cls, obj):\n        if obj is None:\n            return ShowInfo()\n        if isinstance(obj, int):\n            return ShowInfo(obj, obj)\n        if isinstance(obj, str):\n            m = cls.parser.match(obj)\n            if m is None:\n                raise Exception(\"Invalid format of 'show' string: {!r}\"\n                                .format(obj))\n            m = m.groupdict()\n\n            if m[\"exact\"] is not None:\n                exact = int(m[\"exact\"])\n                return ShowInfo(exact, exact)\n\n            if m[\"from\"] is not None:\n                return ShowInfo(int(m[\"from\"]))\n            return ShowInfo(int(m[\"begin\"]), int(m[\"end\"]))\n        else:\n            raise Exception(\"Invalid show argument\")\n\n    def min_steps(self):\n        return self._min_steps\n\n    def ensure_steps(self, steps):\n        min_steps = max(self._min_steps, steps)\n        return ShowInfo(self.begin, self.end, min_steps)\n\n    def is_visible(self, step):\n        return self.begin <= step and (self.end is None or step <= self.end)\n\n    def __repr__(self):\n        return \"<ShowInfo begin={} end={} min={}>\".format(\n            self.begin, self.end, self._min_steps)\n","sub_path":"elsie/show.py","file_name":"show.py","file_ext":"py","file_size_in_byte":1640,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"551144003","text":"import numpy as np\n\nclass Genome:\n    def __init__(self, x_dim, y_dim, random_weights=True):\n        self.genome_type = \"survived\"\n        self.x_dim = x_dim\n        self.y_dim = y_dim\n        self.h_dim = 1\n        self.score = 0\n        self.fitness = 0\n        # fixed bias for simplicity\n        # for optimal search, inputs should be normalized in advance\n        self.bias = 1\n\n        if random_weights:\n            self.w1 = np.random.random((\n                self.h_dim, self.x_dim + 1\n                )) * 2 - 1\n            self.w2 = np.random.random((\n                self.y_dim, self.h_dim\n                )) * 2 - 1\n        else:\n            self.w1 = np.zeros((self.h_dim, self.x_dim + 1))\n            self.w2 = np.zeros((self.y_dim, self.h_dim))\n        return\n\n    # x, h, and y are input vector, hidden vector, and output vector respectively\n    def predict(self, x):\n        # append bias to inputs\n        x = np.append(self.bias, x)\n\n        # multiply by weight and push to hidden layer\n        h = np.dot(self.w1, x.reshape(-1, 1))\n        # h = self.layer_output(x, self.w1)\n\n        # apply relu activation to h\n        # sigmoid activation commented out below\n        # h = 1 / (1 + np.exp(-1 * h))\n        h = h * (h > 0)\n\n        # multiply by weight and push to output\n        y = np.dot(self.w2, h.reshape(-1, 1))\n\n        # return formatted output\n        y = np.ndarray.flatten(y > 0)\n        return y\n","sub_path":"neat/genome.py","file_name":"genome.py","file_ext":"py","file_size_in_byte":1433,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"293337066","text":"from pycompss.api.task import task\nfrom pycompss.api.api import compss_wait_on\n\n\n@task(returns=1)\ndef create_greeting(message, use_storage):\n    \"\"\"\n    Instantiates a persistent object and populates it with the received\n    message.\n    :param message: String with the information to store in the psco.\n    :return: The populated persistent object.\n    \"\"\"\n    if use_storage:\n        from storage_model.classes import hello\n    else:\n        from model.classes import hello\n    hi = hello()\n    hi.message = message\n    if use_storage:\n        hi.make_persistent(\"greet\")\n    return hi\n\n\n@task(returns=1)\ndef greet(greetings):\n    \"\"\"\n    Retrieves the information contained in the given persistent object.\n    :param greetings: Persistent object.\n    :return: String with the psco content.\n    \"\"\"\n    content = greetings.message\n    return content\n\n\n@task(returns=1)\ndef check_greeting(content, message):\n    \"\"\"\n    Checcks that the given content is equal to the given message.\n    :param content: String with content.\n    :param message: String with message.\n    :return: Boolean (True if equal, False otherwise).\n    \"\"\"\n    return content == message\n\n\ndef parse_arguments():\n    \"\"\"\n    Parse command line arguments. Make the program generate\n    a help message in case of wrong usage.\n    :return: Parsed arguments\n    \"\"\"\n    import argparse\n    parser = argparse.ArgumentParser(description='Hello world.')\n    parser.add_argument('--use_storage', action='store_true',\n                        help='Use storage?')\n    return parser.parse_args()\n\n\ndef main(use_storage):\n    message = \"Hello world\"\n    greeting = create_greeting(message, use_storage)\n    content = greet(greeting)\n    result = check_greeting(content, message)\n    result_wrong = check_greeting(content, message + \"!!!\")\n    result = compss_wait_on(result)\n    result_wrong = compss_wait_on(result_wrong)\n    if result != result_wrong:\n        print(\"THE RESULT IS OK\")\n    else:\n        msg = \"SOMETHING FAILED!!!\"\n        print(msg)\n        raise Exception(msg)\n\n\nif __name__ == \"__main__\":\n    options = parse_arguments()\n    main(**vars(options))\n","sub_path":"python/examples_with_persistent_storage/hello_world/src/hello_world.py","file_name":"hello_world.py","file_ext":"py","file_size_in_byte":2127,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"422161841","text":"class Solution:\n    # @param A, a list of integers\n    # @return a boolean\n    # Method 1: > O(n):\n    \"\"\"\n    def canJump(A):\n    record = [0 for value in range(0, len(A))]\n    record[0] = 1\n    for i in range(0, len(A)-1):\n        if A[i] > 0:\n            if i + A[i] >= len(A)-1:\n                return True\n            else:\n                for j in range(1, A[i]+1):\n                    record[i+j] += 1\n    if record[len(A)-1] == 0:\n        return False    \n    \"\"\"\n    # Method 2: record the max steps\n    def canJump(self, A):\n        max_steps = 0\n        for i in range(0, len(A)):\n            if i <= max_steps:\n                max_steps = max(max_steps, i + A[i])\n                if max_steps >= len(A)-1:\n                    return True\n            else:\n                return False\n        return True\n        \n        \n        \n","sub_path":"Python/jump_game.py","file_name":"jump_game.py","file_ext":"py","file_size_in_byte":844,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"12947433","text":"#!/usr/bin/env python\n\nimport mimetypes, os, sys, time, subprocess, datetime, boto, urllib.parse, math\nfrom .lib import S3 as S3lib\nfrom io import StringIO\nfrom multiprocessing import Pool\nfrom housepy import config, log\n\n\ndef upload(source, dest=None, num_processes=2, split=50, force=True, reduced_redundancy=False, verbose=False):\n    if dest is None:\n        dest = source\n    log.info(\"s3.upload %s to %s/%s...\" % (source, config['s3']['bucket'], dest))\n    src = open(source)\n    s3 = boto.connect_s3(config['s3']['access_key_id'], config['s3']['secret_access_key'])\n    bucket = s3.lookup(config['s3']['bucket'])\n    key = bucket.get_key(dest)\n    if key is not None:\n        if not force:\n            raise ValueError(\"--> '%s' already exists\" % dest)\n    part_size = max(5 * 1024 * 1024, 1024 * 1024 * split)\n    src.seek(0,2)\n    size = src.tell()\n    num_parts = int(math.ceil(size / part_size))\n    if size < 5 * 1024 * 1024:\n        src.seek(0)\n        t1 = time.time()\n        k = boto.s3.key.Key(bucket, dest)\n        k.set_contents_from_file(src)\n        t2 = time.time() - t1\n        s = size/1024./1024.\n        log.info(\"--> finished uploading %0.2fM in %0.2fs (%0.2fMbps)\" % (s, t2, s/t2))\n        return\n    mpu = bucket.initiate_multipart_upload(dest, reduced_redundancy=reduced_redundancy)\n    log.info(\"--> initialized upload: %s\" % mpu.id)\n    def gen_args(num_parts, fold_last):\n        for i in range(num_parts+1):\n            part_start = part_size*i\n            if i == (num_parts-1) and fold_last is True:\n                yield (bucket.name, mpu.id, src.name, i, part_start, part_size*2)\n                break\n            else:\n                yield (bucket.name, mpu.id, src.name, i, part_start, part_size)\n    fold_last = ((size % part_size) < 5*1024*1024)\n    try:\n        pool = Pool(processes=num_processes)\n        t1 = time.time()\n        pool.map_async(do_part_upload, gen_args(num_parts, fold_last)).get(9999999)\n        t2 = time.time() - t1\n        s = size/1024./1024.\n        src.close()\n        mpu.complete_upload()\n        log.info(\"--> finished uploading %0.2fM in %0.2fs (%0.2fMbps)\" % (s, t2, s/t2))\n        return True\n    except Exception as err:\n        log.error(\"--> encountered an error, canceling upload\")        \n        log.error(log.exc(err))\n        mpu.cancel_upload()\n        return False\n        \n\n\ndef do_part_upload(args):\n    \"\"\"\n    Upload a part of a MultiPartUpload\n\n    Open the target file and read in a chunk. Since we can't pickle\n    S3Connection or MultiPartUpload objects, we have to reconnect and lookup\n    the MPU object with each part upload.\n\n    :type args: tuple of (string, string, string, int, int, int)\n    :param args: The actual arguments of this method. Due to lameness of\n                 multiprocessing, we have to extract these outside of the\n                 function definition.\n\n                 The arguments are: S3 Bucket name, MultiPartUpload id, file\n                 name, the part number, part offset, part size\n    \"\"\"\n\n    bucket_name, mpu_id, fname, i, start, size = args\n    s3 = boto.connect_s3(config['s3']['access_key_id'], config['s3']['secret_access_key'])\n    bucket = s3.lookup(bucket_name)\n    mpu = None\n    for mp in bucket.list_multipart_uploads():\n        if mp.id == mpu_id:\n            mpu = mp\n            break\n    if mpu is None:\n        raise Exception(\"--> could not find MultiPartUpload %s\" % mpu_id)\n    fp = open(fname, 'rb')\n    fp.seek(start)\n    data = fp.read(size)\n    fp.close()\n    if not data:\n        raise Exception(\"--> unexpectedly tried to read an empty chunk\")\n    def progress(x,y):\n        log.info(\"Part %d: %0.2f%%\" % (i+1, 1.*x/y))\n    t1 = time.time()\n    mpu.upload_part_from_file(StringIO(data), i+1, cb=progress)\n    t2 = time.time() - t1\n    s = len(data)/1024./1024.\n    log.info(\"--> uploaded part %s (%0.2fM) in %0.2fs at %0.2fMbps\" % (i+1, s, t2, s/t2))\n\n\ndef delete(path):\n    log.info(\"s3.delete\")        \n    conn = S3lib.AWSAuthConnection(config['s3']['access_key_id'], config['s3']['secret_access_key'])\n    log.info(\"--> deleting %s/%s\" % (config['s3']['bucket'], path))        \n    try:\n        response = conn.delete(config['s3']['bucket'], path)\n    except Exception as e:\n        log.error(\"--> failed: %s\" % log.exc(e))\n        return False\n    log.info(\"--> %s\" % response.message)\n    return True\n    \ndef list_contents():\n    log.info(\"s3.list\")\n    connection = boto.connect_s3(config['s3']['access_key_id'], config['s3']['secret_access_key'])\n    log.info(\"--> listing %s\" % (config['s3']['bucket']))        \n    try:\n        bucket = connection.get_bucket(config['s3']['bucket'])\n        contents = [key.name for key in bucket.list()]\n    except Exception as e:\n        log.error(\"--> failed: %s\" % log.exc(e))\n        return False\n    log.info(\"--> %s\" % contents)\n    return contents\n\ndef download(path, destination=None):\n    if destination is None:\n        destination = path\n    log.info(\"s3.download\")        \n    connection = boto.connect_s3(config['s3']['access_key_id'], config['s3']['secret_access_key'])\n    log.info(\"--> downloading %s/%s\" % (config['s3']['bucket'], path))        \n    try:\n        bucket = connection.get_bucket(config['s3']['bucket'])\n        key = bucket.get_key(path)    \n        key.get_contents_to_filename(destination)\n    except Exception as e:\n        log.error(\"--> failed: %s\" % log.exc(e))\n        return False\n    log.info(\"--> successfully wrote %s\" % destination)\n    return True\n    \n","sub_path":"s3.py","file_name":"s3.py","file_ext":"py","file_size_in_byte":5503,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"197970600","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n# Distributed under the terms of the GNU General Public License v2\n#\n# I'm very _very_ lazy.\n#\n# Typing: repoman commit -m \"cat/pkg: msg\" is a massive PITA to me.\n# This script is a convenient helper so that I don't have to invoke repoman and\n# feed him with the \"cat/pkg\" prefix every time.\n# It works out the prefix from the CWD.\n#\n# That's it really.\nimport os, sys, subprocess\n\ndef is_git(cwd):\n    git_repository = '{0}{1}/.git'.format(os.sep, os.sep.join(cwd[:-2]))\n    return os.path.isdir(git_repository)\n\ndef format_commit_message(directory, msg):\n    return '{0}: {1}'.format(directory, msg)\n\ndef repoman_commit(msg):\n    subprocess.call(['repoman', 'commit', '-m', msg])\n\ndef main(msg):\n    cwd = os.getcwd().split(os.sep)[1:]\n    if not is_git(cwd):\n        print('gcommit: Please cd into your Gentoo git repository. Aborting commit.')\n        sys.exit(1)\n\n    repoman_commit(format_commit_message(os.sep.join(cwd[-2:]), msg))\n\nif __name__ == '__main__':\n    try:\n        main(sys.argv[1])\n    except IndexError:\n        print('gcommit: Please specify a commit message!')\n        sys.exit(0)\n","sub_path":"gcommit.py","file_name":"gcommit.py","file_ext":"py","file_size_in_byte":1152,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"369556236","text":"import unittest\n\nfrom sort import mergesort\n\n\nclass SortingCorrectnessTest(unittest.TestCase):\n    known_values = (\n        (\n            [42, 23, 16, 15, 8, 4],\n            [4, 8, 15, 16, 23, 42]\n        ),\n        (\n            [],\n            []\n        ),\n        (\n            [1],\n            [1]\n        ),\n        (\n            ['h', 'b', 'o'],\n            ['b', 'h', 'o']\n        ),\n    )\n\n    def test_sorting_correctness(self):\n        \"\"\"should give known result with known input\"\"\"\n\n        for input_value, output_value in self.known_values:\n            \"\"\"Python Built-in sorting\"\"\"\n            self.assertEqual(\n                output_value,\n                sorted(input_value)\n            )\n\n            \"\"\"Merge Sort\"\"\"\n            self.assertEqual(\n                output_value,\n                mergesort(input_value)\n            )\n\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"sort/sort_test.py","file_name":"sort_test.py","file_ext":"py","file_size_in_byte":900,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"340013055","text":"import sys\n\npossibilites = [\" SET \", \" TO \", \" IF \", \" THEN \", \" ALSO IF \", \" OTHERWISE \", \" WHILE \", \" FOR \", \" IN \", \" ENDLOOP \", \" EXIT \", \" SAY \", \" GIVEBACK \", \" MAKEAFUNCTION \", \" WITHARGUMENTS \", \" CALL \", \" WITHPARAMETERS \"]\nreplacements = [\"# = \", \"#\\n\", \"if #:\\n\\t\", \"\", \"\\belif #:\\n\\t\", \"\\belse:\\n\\t#\\n\\b\", \"while #:\\n\\t\", \"for #\", \"in #:\\n\\t\", \"\\b\", \"break\\n\\b\", \"print ('#')\\n\", \"return #\\n\", \"def # ( \",\" # ):\\n\\t\", \"#(\", \"#)\"]\n\n\n\ndef syntaxify(s, possibilities, replacements):\n    s = \" \"+s\n    output = \"\"\n    locations = []\n    for possibility in possibilities:\n        length = 0\n        while length < len(s):\n            length = s.find(possibility, length)\n            if length == -1:\n                break\n            else:\n                locations.append(length)\n                length += 1\n    locationstep = 0\n    operatorsplit = []\n    locations.sort()\n    #print(locations)\n    for location in locations:\n        if len(locations)>(locationstep+1):\n            newstr=s[location:locations[locationstep+1]]\n            #print(\"newstr:\" + newstr)\n        else:\n            newstr=s[location:]\n        count = 0\n        cap = False\n        '''\n        for char in newstr.split():\n            while cap != False:\n                cap = char.isupper()\n                operatorsplit.append(char)\n                count += 1\n        operator = ''.join(operatorsplit)\n        operatorlocation = possibilities.index(operator)\n        outputinitial = replacements[operatorlocation]\n        '''\n        operator = \" \" + newstr.split()[0] + \" \"\n        possloc=possibilities.index(operator)\n        outputinitial = replacements[possloc]\n        #print (outputinitial)\n        output = output + outputinitial.replace(\"#\", newstr.split()[1])\n        #print (output)\n        locationstep += 1\n    return output\n    #if end != \"quit\":\n        #output = output+syntaxify(end, possibilites, replacements);\n        ##print (output)\n    #else:\n        #print (\"Have a nice day!\");\n\n'''\nif __name__ == \"__main__\":\n    if '-' in sys.argv:\n        read_from_stdin()\n    else:\n        prompt_user()\n'''\n\n\nfoutput = \"\"\ns = input(\"Hey! Start typing your code (but  make sure to have the right easySyntax):\")\n\noutput = syntaxify(s, possibilites, replacements)\nfoutput = foutput + output\nprint(\"Current Output:\\n\" + foutput)\nend = \"\"\nwhile end != 'quit':\n    end = input(\"Anything else? (type code/'quit')\")\n    output = syntaxify(end, possibilites, replacements)\n    foutput = foutput + output\n    print(\"Current Output:\\n\" + foutput)\n\nprint(\"Output:\\n\" + foutput)\nmycode = open('mycode.py', 'a')\nmycode.write(foutput)\nprint(\"File Made.\")\nprint('Have a nice day!')\n# syntaxify(s, possibilites, replacements)\n","sub_path":"coding.py","file_name":"coding.py","file_ext":"py","file_size_in_byte":2708,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"594193591","text":"from __future__ import absolute_import\n\nimport sys\n\nif sys.version_info[:2] < (2, 7):\n    import unittest2 as unittest\nelse:\n    import unittest\n\nfrom enstaller.main import main\n\nfrom .common import without_any_configuration\n\nclass TestEnstallerMainActions(unittest.TestCase):\n    @without_any_configuration\n    def test_print_version(self):\n        # XXX: this is lousy test: we'd like to at least ensure we're printing\n        # the correct version, but capturing the stdout is a bit tricky. Once\n        # we replace print by proper logging, we should be able to do better.\n        try:\n            main([\"--version\"])\n        except SystemExit as e:\n            self.assertEqual(e.code, 0)\n\n    @without_any_configuration\n    def test_help_runs_and_exits_correctly(self):\n        try:\n            main([\"--help\"])\n        except SystemExit as e:\n            self.assertEqual(e.code, 0)\n\n    @without_any_configuration\n    def test_print_env(self):\n        try:\n            main([\"--env\"])\n        except SystemExit as e:\n            self.assertEqual(e.code, 0)\n","sub_path":"tests/functional/test_simple.py","file_name":"test_simple.py","file_ext":"py","file_size_in_byte":1065,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"488440612","text":"import json\nimport os\nimport mmap\nimport pickle\nimport random\nimport time\nimport torch\n\nfrom threading import Lock\nfrom torch.utils.data import Dataset, DataLoader\nfrom torch.utils.data.sampler import RandomSampler, BatchSampler\nfrom itertools import accumulate\n\nclass LazyDataset(Dataset):\n    \"\"\"A dataset for lazy loading from disk.\n    \"\"\"\n\n    def __init__(self, path, use_mmap=True):\n        self.path = path\n        self.use_mmap = use_mmap\n\n        if not os.path.exists(path):\n            raise ValueError('File `%s` does not exist.' % path)\n\n        self.lazy_dir = self.get_lazy_dir()\n        self.lazy_file = os.path.join(self.lazy_dir, 'data')\n        self.lazy_index = os.path.join(self.lazy_dir, 'index.pkl')\n        if not os.path.exists(self.lazy_dir):\n            self.create_index()\n\n        self.file = open(self.lazy_file, 'rb')\n        if self.use_mmap:\n            print('Memory map `%s`' % path)\n            # In distributed setting, it's useful to cache file in ramdisk\n            self.file = mmap.mmap(self.file.fileno(), 0, prot=mmap.PROT_READ)\n\n        self.read_lock = Lock()\n\n        with open(self.lazy_index, 'rb') as f_index:\n            self.offsets = pickle.load(f_index)\n\n    def get_lazy_dir(self):\n        return os.path.splitext(self.path)[0]+'_lazy'\n\n    def create_index(self):\n        # In distributed setting, only first process creates the index..\n        # Note that we assume that the file is stored on a shared file system\n        if not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0:\n            os.makedirs(self.lazy_dir)\n            print(\"Creating index for lazy loading of `%s`\" % self.path)\n            offsets = list()\n            cnt = 0\n            with open(self.path, 'rb') as f_in, open(self.lazy_file, 'wb') as f_out:\n                for line in f_in:\n                    f_out.write(line)\n                    cnt += len(line)\n                    offsets.append(cnt)\n            with open(self.lazy_index, 'wb') as f_index:\n                pickle.dump(offsets, f_index)\n        else:\n            while not os.path.exists(self.lazy_index):\n                time.sleep(1)\n\n\n    def __getitem__(self, index):\n        start = 0 if index == 0 else self.offsets[index-1]\n        end = self.offsets[index] - 1\n\n        self.read_lock.acquire()\n        self.file.seek(start)\n        txt = self.file.read(end - start)\n        self.read_lock.release()\n        return txt.decode('utf-8')\n\n    def __len__(self):\n        return len(self.offsets)\n\n\nclass JSONDataset(Dataset):\n\n    def __init__(self, dataset, key=None):\n        self.dataset = dataset\n        self.key = key\n\n    def __getitem__(self, index):\n        item = json.loads(self.dataset[index])\n        if self.key:\n            item = item[self.key]\n        return item\n\n    def __len__(self):\n        return len(self.dataset)\n\nclass BertDataset(Dataset):\n\n    def __init__(self, dataset, tokenizer, max_seq_length=512, short_seq_prob=0.1,\n                 masked_lm_prob=0.15, max_predictions_per_seq=80):\n        self.dataset = dataset\n        self.tokenizer = tokenizer\n        self.max_seq_length = max_seq_length\n        self.short_seq_prob = short_seq_prob\n        self.masked_lm_prob = masked_lm_prob\n        self.max_predictions_per_seq = max_predictions_per_seq\n\n\n    def __getitem__(self, index):\n        # get rng state corresponding to index (allows deterministic random pair)\n        rng = random.Random(index)\n        # get seq length, subtract [CLS], .., [SEP], .. [SEP] tokens\n        target_seq_length = self.max_seq_length - 3\n\n        if rng.random() < self.short_seq_prob:\n            target_seq_length = rng.randint(2, target_seq_length)\n\n        # get sentence pair and label\n        tokens_a, tokens_b, is_random_next = self.create_random_instance(target_seq_length, rng)\n        self.truncate_seq_pair(tokens_a, tokens_b, target_seq_length, rng)\n\n        # [CLS], tokens_a.. [SEP] tokens_b... [SEP]\n        tokens = list()\n        segment_ids = list()\n        tokens.append('[CLS]')\n        for tok in tokens_a:\n            tokens.append(tok)\n            segment_ids.append(0)\n\n        tokens.append('[SEP]')\n        segment_ids.append(0)\n\n        for tok in tokens_b:\n            tokens.append(tok)\n            segment_ids.append(1)\n\n        tokens.append(\"[SEP]\")\n        segment_ids.append(1)\n\n        # pad sequences to max length\n        attention_mask = [1]*512\n        attention_mask[len(tokens):] = [0]*(512-len(tokens))\n        self.pad_seq(tokens, self.max_seq_length, '[PAD]')\n        self.pad_seq(segment_ids, self.max_seq_length, 0)\n\n        # randomly mask inputs\n        input_tokens, lm_labels = self.create_masked_lm_predictions(tokens, rng)\n\n        input_tokens = self.tokenizer.convert_tokens_to_ids(input_tokens)\n\n        sample = dict()\n        sample['input_tokens'] = torch.LongTensor(input_tokens)\n        sample['segment_ids'] = torch.LongTensor(segment_ids)\n        sample['attention_mask'] = torch.LongTensor(attention_mask)\n        sample['lm_labels'] = torch.LongTensor(lm_labels)\n        sample['is_random_next'] = torch.LongTensor([int(is_random_next)])\n        return sample\n\n    def pad_seq(self, seq, max_len, val):\n        while len(seq) < max_len:\n            seq.append(val)\n\n    def sentence_split(self, document):\n        \"\"\"split document into sentences\"\"\"\n        return [line for line in document.split('\\n') if line]\n\n    def get_random_doc(self, rng):\n        doc_idx = rng.randint(0, len(self.dataset) - 1)\n        doc = self.sentence_split(self.dataset[doc_idx])\n        doc = [self.tokenizer.tokenize(sentence) for sentence in doc]\n        return doc, doc_idx\n\n    def remaining_doc_length(self, doc):\n        segment_lengths = [len(sentence) for sentence in doc]\n        return list(accumulate(reversed(segment_lengths)))[::-1]\n\n    def create_random_instance(self, target_seq_length, rng):\n        \"\"\"Fetches a random sentence pair corresponding to rng state similar to\n        https://github.com/google-research/bert/blob/master/create_pretraining_data.py#L248-L294\"\"\"\n\n        # sample random doc, make sure it contains more than `target_seq_length` tokens\n        doc_a, doc_a_idx = self.get_random_doc(rng)\n        remaining_doc_length = self.remaining_doc_length(doc_a)\n        len_doc_a = remaining_doc_length[0]\n        while len_doc_a < target_seq_length:\n            doc_a, doc_a_idx = self.get_random_doc(rng)\n            remaining_doc_length = self.remaining_doc_length(doc_a)\n            len_doc_a = remaining_doc_length[0]\n\n        # start at random segment (but ensure that we keep more than `target_seq_length` tokens)\n        segment_id = rng.choice([i for i, l in enumerate(remaining_doc_length) if l >= target_seq_length])\n        segments = list()\n        num_tokens = 0\n        for segment in doc_a[segment_id:]:\n            segments.append(segment)\n            num_tokens += len(segment)\n            if num_tokens >= target_seq_length:\n                break\n\n        a_end = 1\n        if len(segments) > 1:\n            a_end = rng.randint(1, len(segments) - 1)\n\n        a_tokens = list()\n        for seg in segments[:a_end]:\n            a_tokens.extend(seg)\n\n        if len(segments) == 1 or random.random() > 0.5:\n            is_random_next = True\n            target_b_length = target_seq_length - len(a_tokens)\n\n            # ensure that doc_a != doc_b\n            doc_b, doc_b_idx = self.get_random_doc(rng)\n            remaining_doc_b_length = self.remaining_doc_length(doc_b)\n            while doc_a_idx == doc_b_idx or remaining_doc_b_length[0] < target_b_length:\n                doc_b, doc_b_idx = self.get_random_doc(rng)\n                remaining_doc_b_length = self.remaining_doc_length(doc_b)\n\n            remaining_doc_b_length = self.remaining_doc_length(doc_b)\n            segment_id = rng.choice([i for i, l in enumerate(remaining_doc_b_length) if l >= target_b_length])\n\n            b_tokens = list()\n            for seg in doc_b[segment_id:]:\n                b_tokens.extend(seg)\n                if len(b_tokens) >= target_b_length:\n                    break\n        else:\n            is_random_next = False\n            b_tokens = list()\n            for seg in segments[a_end:]:\n                b_tokens.extend(seg)\n\n        return a_tokens, b_tokens, is_random_next\n\n    def truncate_seq_pair(self, tokens_a, tokens_b, max_num_tokens, rng):\n        \"\"\"Truncates a pair of sequences to a maximum sequence length.\"\"\"\n        while True:\n            total_length = len(tokens_a) + len(tokens_b)\n            if total_length <= max_num_tokens:\n                break\n\n            trunc_tokens = tokens_a if len(tokens_a) > len(tokens_b) else tokens_b\n            assert len(trunc_tokens) >= 1\n\n            # We want to sometimes truncate from the front and sometimes from the\n            # back to add more randomness and avoid biases.\n            if rng.random() < 0.5:\n                del trunc_tokens[0]\n            else:\n                trunc_tokens.pop()\n\n    def create_masked_lm_predictions(self, tokens, rng):\n        cand_indexes = [i for i, tok in enumerate(tokens) if tok not in ('[SEP]', '[CLS]', '[PAD]')]\n        rng.shuffle(cand_indexes)\n        output_tokens = list(tokens)\n        num_to_predict = min(self.max_predictions_per_seq,\n                             max(1, int(round(len(cand_indexes) * self.masked_lm_prob))))\n\n        lm_labels = [-1]*len(tokens)\n        for index in cand_indexes[:num_to_predict]:\n            masked_token = None\n            # 80% of the time, replace with [MASK]\n            if rng.random() < 0.8:\n                masked_token = \"[MASK]\"\n            else:\n                # 10% of the time, keep original\n                if rng.random() < 0.5:\n                    masked_token = tokens[index]\n                # 10% of the time, replace with random word\n                else:\n                    random_tok_ind = rng.randint(0, len(self.tokenizer.vocab) - 1)\n                    masked_token = self.tokenizer.ids_to_tokens[random_tok_ind]\n\n            output_tokens[index] = masked_token\n            lm_labels[index] = self.tokenizer.vocab[tokens[index]]\n\n        return output_tokens, lm_labels\n\n    def __len__(self):\n        return len(self.dataset)\n\nclass PreprocessedBertDataset(Dataset):\n\n    def __init__(self, dataset, tokenizer, max_seq_length=512,\n                 masked_lm_prob=0.15, max_predictions_per_seq=80):\n        self.dataset = dataset\n        self.tokenizer = tokenizer\n        self.max_seq_length = max_seq_length\n        self.masked_lm_prob = masked_lm_prob\n        self.max_predictions_per_seq = max_predictions_per_seq\n\n    def __getitem__(self, index):\n        # random.seed(index)\n        sample = self.dataset[index]\n        tokens_a = sample['tokens_a']\n        tokens_b = sample['tokens_b']\n        is_random_next = sample['is_random_next']\n        target_seq_length = sample['target_seq_length']\n\n        self.truncate_seq_pair(tokens_a, tokens_b, target_seq_length, random)\n\n        # [CLS], tokens_a.. [SEP] tokens_b... [SEP]\n        tokens = list()\n        segment_ids = list()\n        tokens.append('[CLS]')\n        for tok in tokens_a:\n            tokens.append(tok)\n            segment_ids.append(0)\n\n        tokens.append('[SEP]')\n        segment_ids.append(0)\n\n        for tok in tokens_b:\n            tokens.append(tok)\n            segment_ids.append(1)\n\n        tokens.append(\"[SEP]\")\n        segment_ids.append(1)\n\n        # pad sequences to max length\n        attention_mask = [1] * self.max_seq_length\n        attention_mask[len(tokens):] = [0] * (self.max_seq_length - len(tokens))\n        self.pad_seq(tokens, self.max_seq_length, '[PAD]')\n        self.pad_seq(segment_ids, self.max_seq_length, 0)\n\n        # randomly mask inputs\n        input_tokens, lm_labels = self.create_masked_lm_predictions(tokens, random)\n\n        input_tokens = self.tokenizer.convert_tokens_to_ids(input_tokens)\n\n        sample = dict()\n        sample['input_tokens'] = torch.LongTensor(input_tokens)\n        sample['segment_ids'] = torch.LongTensor(segment_ids)\n        sample['attention_mask'] = torch.LongTensor(attention_mask)\n        sample['lm_labels'] = torch.LongTensor(lm_labels)\n        sample['is_random_next'] = torch.LongTensor([int(is_random_next)])\n        return sample\n\n    def truncate_seq_pair(self, tokens_a, tokens_b, max_num_tokens, rng):\n        \"\"\"Truncates a pair of sequences to a maximum sequence length.\"\"\"\n        while True:\n            total_length = len(tokens_a) + len(tokens_b)\n            if total_length <= max_num_tokens:\n                break\n\n            trunc_tokens = tokens_a if len(tokens_a) > len(tokens_b) else tokens_b\n            assert len(trunc_tokens) >= 1\n\n            # We want to sometimes truncate from the front and sometimes from the\n            # back to add more randomness and avoid biases.\n            if rng.random() < 0.5:\n                del trunc_tokens[0]\n            else:\n                trunc_tokens.pop()\n\n    def create_masked_lm_predictions(self, tokens, rng):\n        cand_indexes = [i for i, tok in enumerate(tokens) if tok not in ('[SEP]', '[CLS]', '[PAD]')]\n        rng.shuffle(cand_indexes)\n        output_tokens = list(tokens)\n        num_to_predict = min(self.max_predictions_per_seq,\n                             max(1, int(round(len(cand_indexes) * self.masked_lm_prob))))\n        lm_labels = [-1]*len(tokens)\n        for index in cand_indexes[:num_to_predict]:\n            masked_token = None\n            # 80% of the time, replace with [MASK]\n            if rng.random() < 0.8:\n                masked_token = \"[MASK]\"\n            else:\n                # 10% of the time, keep original\n                if rng.random() < 0.5:\n                    masked_token = tokens[index]\n                # 10% of the time, replace with random word\n                else:\n                    random_tok_ind = rng.randint(0, len(self.tokenizer.vocab) - 1)\n                    masked_token = self.tokenizer.ids_to_tokens[random_tok_ind]\n\n            output_tokens[index] = masked_token\n            lm_labels[index] = self.tokenizer.vocab[tokens[index]]\n\n        return output_tokens, lm_labels\n\n    def pad_seq(self, seq, max_len, val):\n        while len(seq) < max_len:\n            seq.append(val)\n\n    def __len__(self):\n        return len(self.dataset)\n\nif __name__ == '__main__':\n    from pytorch_pretrained_bert.tokenization import BertTokenizer\n\n    tokenizer = BertTokenizer.from_pretrained('bert-large-uncased', cache_dir='./data')\n\n    dataset = JSONDataset(LazyDataset('/home/hdvries/data/prep_128.txt'))\n    dataset = PreprocessedBertDataset(dataset, tokenizer, max_seq_length=128)\n\n\n    # wiki_dataset = LazyDataset('/home/nathan/data/wiki/enwiki.txt', use_mmap=True)\n    # wiki_dataset = JSONDataset(wiki_dataset, key='text')\n    #\n    # dataset = BertDataset(wiki_dataset, tokenizer)\n    sampler = BatchSampler(RandomSampler(dataset), 4, True)\n\n    iterator = DataLoader(dataset, batch_sampler=sampler, num_workers=1, pin_memory=True)\n\n    for batch in iterator:\n        print(batch['input_tokens'].shape)","sub_path":"pytorch_pretrained_bert/datasets.py","file_name":"datasets.py","file_ext":"py","file_size_in_byte":15095,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"595124078","text":"# pylint: disable=undefined-variable\nimport logging\n\n\nfrom telegram import Update\nfrom telegram.ext import CallbackContext\nfrom telegram.chataction import ChatAction\nfrom telegram.message import ParseMode\nfrom telegram.inline.inlinequeryresultarticle import InlineQueryResultArticle\nfrom telegram.inline.inputtextmessagecontent import InputTextMessageContent\n\nfrom telegram.ext.dispatcher import run_async\n\nfrom utils import validUrl, name_and_id, update_object_type, players\nfrom replies import error, info, reply_markup\nfrom db import data\nlogging.basicConfig(\n                    handlers=(\n                              logging.FileHandler('command_logs.log'),\n                              logging.StreamHandler()),\n                    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',\n                    level=logging.INFO)\n\nwelcome_text = (\"Minecraft Server Status Bot\\n\"\n\"\\n\"\n\"To check players online on the server use @msmpbot in the text input line in any Telegram chat.\\n\"\n\"\\n\"\n\"Commands:\\n\"\n\"/status - Server info\\n\"\n\"/players - Online players on the server\\n\"\n\"\\n\"\n\"To change the monitored server send me its address.\")\n\n# ==========================\n# Inline Query Handler\n# ==========================\n\ndef inline_status(update: Update, context: CallbackContext):\n    address = context.user_data['url']\n\n    q = list()\n\n    q.append(InlineQueryResultArticle(\n        id='1', title=_('Address: ') + address,\n        input_message_content=InputTextMessageContent(\n        message_text=_('Address: ') + address))\n        )\n\n    online, str_players = players(address, True)\n\n    text = ngettext('{0} player online', '{0} players online', online).format(online)\n\n    q.append(InlineQueryResultArticle(\n        id='2', title=text,\n        description=str_players,\n        input_message_content=InputTextMessageContent(\n        message_text=text if online == 0 else text + ': ' + str_players)))\n\n    update.inline_query.answer(q, cache_time=60)\n    logging.info(\"inline answer sent\")\n    \n\n\n# ==========================\n# Message Handler\n# ==========================\n\ndef message(update: Update, context: CallbackContext):\n    \"\"\"Usage: <IP-address> \"\"\"\n    user_data = context.user_data\n    \n    if not update.message.text: return\n        \n    text = update.message.text.lower()\n    if text == (\"ru\" or \"en\"):\n        user_data['lang'] = text\n        return\n\n    if text != 'default':\n        if not validUrl(text):\n            logging.info(\"Invalid URL, too long\")\n            return\n\n    data(update.effective_user.id, text)\n\n    user_data['url'] = data(update.effective_user.id)\n\n    update.message.reply_chat_action(ChatAction.TYPING)\n    update.message.reply_text(_('Server addess changed to ') + user_data['url'], ParseMode.MARKDOWN)\n\n\n# ==========================\n# Commands\n# ==========================\n\ndef cmd_start(update: Update, context: CallbackContext):\n    \"\"\"Usage: /start\"\"\"\n    message = update.message\n    message.reply_chat_action(ChatAction.TYPING)\n    message.reply_text(_(welcome_text), ParseMode.MARKDOWN)\n\n\ndef cmd_status(update: Update, context: CallbackContext):\n    \"\"\"Usage: /status url\"\"\"\n    update.message.reply_chat_action(ChatAction.TYPING)\n\n    url = context.user_data['url']\n\n    update.message.reply_text(\n        text=info.status(url),\n        reply_markup=reply_markup(),\n        parse_mode=ParseMode.MARKDOWN)\n\n    logging.info(\"/status %s online\" % url)\n\n\ndef cmd_players(update: Update, context: CallbackContext):\n    \"\"\"Usage: /players url\"\"\"\n    update.message.reply_chat_action(ChatAction.TYPING)\n\n    url = context.user_data['url']\n\n    update.message.reply_text(\n        text=info.players(url),\n        reply_markup=reply_markup(),\n        parse_mode=ParseMode.MARKDOWN)\n\n    logging.info(\"/players %s online\" % url)\n\n\n# ==========================\n# CallBacks\n# ==========================\n\ndef cb_status(update: Update, context: CallbackContext):\n    update.callback_query.message.edit_text(\n        text=info.status(context.user_data['url']),\n        reply_markup=reply_markup(),\n        parse_mode=ParseMode.MARKDOWN\n    )\n\n\ndef cb_players(update: Update, context: CallbackContext):\n    update.callback_query.message.edit_text(\n        text=info.players(context.user_data['url']),\n        reply_markup=reply_markup(),\n        parse_mode=ParseMode.MARKDOWN\n    )\n    update.callback_query.edit_message_text\n\n    \ndef cb_about(update: Update, context: CallbackContext):\n    update.callback_query.message.edit_text(\n        text=_(welcome_text),\n        reply_markup=reply_markup(),\n        parse_mode=ParseMode.MARKDOWN\n    )\n\n# ==========================\n# Before all handlers\n# ==========================\n\nfrom os import getenv\nimport gettext\ndomain = getenv(\"HEROKU_APP_NAME\")\nlang_code = None\n\ndef check_database(update : Update, context: CallbackContext):\n    user_data = context.user_data\n\n    try:\n        user_data['url']\n\n    except KeyError:\n        user_data['url'] = data(update.effective_user.id)\n        \n        global lang_code\n        lc = update.effective_user.language_code\n        \n        user_data['lang'] = (\n            lc \n            if lc and gettext.find(domain, \"locale\", [lc])\n            else 'en'\n        )\n    \n    if lang_code != user_data['lang']:\n        lang_code = user_data['lang']\n        \n        lang = gettext.translation(domain, \"locale\", [lang_code])\n        lang.install(['ngettext'])\n\n    logging.info(update_object_type(update).__class__.__name__ + \" recieved from \" + name_and_id(update.effective_user))\n    \n# ==========================\n# Error handler\n# ==========================\n\nfrom socket import timeout as TimeoutError\nfrom telegram.error import BadRequest\n\ndef error_handler(update : Update, context : CallbackContext):\n    if type(context.error) is TimeoutError:\n        logging.error(\"Timeout error\")\n    else:\n        logging.exception(context.error)\n\n        if type(context.error) is BadRequest: return\n\n    if update.inline_query:\n        error.inline(context.bot, update.inline_query.id)\n    elif update.message:\n        error.message(context.bot, update.message.chat_id, context.user_data['url'])\n    elif update.callback_query:\n        error.callback(context.bot, update.callback_query.message, context.user_data['url'])\n","sub_path":"commands.py","file_name":"commands.py","file_ext":"py","file_size_in_byte":6257,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"335780796","text":"class Solution:\n    def longestCommonPrefix(self, strs):\n        if len(strs) == 0:\n            return \"\"\n        j = len(strs[0])\n        # find the shortest word\n        for word in strs:\n            if len(word) < j:\n                j = len(word)\n        for i in range(0, j):\n            for w in range(0, len(strs)-1):\n                if strs[w][i] != strs[w+1][i]:\n                    return strs[0][:i]\n        return strs[0][:j]\n","sub_path":"0. Easy/0014. Longest Common Prefix/longest_prefix.py","file_name":"longest_prefix.py","file_ext":"py","file_size_in_byte":437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"222585734","text":"#This is a List of Items program.\n\n#add list\ndef addToInventory(inventory, addedItems):\n\n    for i in range(len(addedItems)):\n        inventory.setdefault(addedItems[i],0)\n        inventory[addedItems[i]] += 1\n\n    return inventory\n  \n#print List\ndef displayInventory(inventory):\n    print('Inventory:')\n    item_total = 0\n    for k,v in inventory.items():\n        print(str(v) + ' ' + k)\n        item_total += v\n    print('\\nTotal number of items: ' + str(item_total))\n\ndragonLoot = ['gold coin','dagger','gold coin','gold coin','ruby']\ninv = {'gold coin':42, 'rope':1}\ninv = addToInventory(inv,dragonLoot)\ndisplayInventory(inv)","sub_path":"PrintListItems.py","file_name":"PrintListItems.py","file_ext":"py","file_size_in_byte":629,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"132903587","text":"import shodan\nimport time\nimport pickle\nfrom tqdm import tqdm\n\napi = shodan.Shodan('APIKEY')\n\nip_list = []\nprocessed_ips = set()\n\ntry:\n    with open(\"processed_ips\", \"rb\") as fp:  # for handling many IPs, this way processed IPs will be stored and reloaded\n        processed_ips = pickle.load(fp)\nexcept FileNotFoundError:\n    print(\"There aren't any stored IPs.\")\n\nwith open(\"ip_list\", \"r\", encoding=\"utf-8\") as fp:  # IP list should be provided here each for each line\n    for line in fp:\n        ip_list.append(line.split(\" \")[1].strip())\n\nwith open(\"output_file\", \"a\", encoding=\"UTF-8\") as fp:  # output file\n    for ip in tqdm(ip_list):\n        if ip in processed_ips:\n            continue\n        try:\n            info = api.host(ip)\n            time.sleep(5)\n            # parsed = json.dumps(info)\n            # parsed = json.loads(parsed)\n            # print(json.dumps(parsed, indent=4, sort_keys=True))\n            try:\n                vulnerabilities = info[\"vulns\"]\n            except KeyError as e:\n                print(\"No vulns for {}\".format(info[\"ip_str\"]))\n                vulnerabilities = [\"None\"]\n            try:\n                asn = info[\"asn\"]\n            except KeyError as e:\n                asn = \"none\"\n            fp.write(\"{},{},{},{},{}\\n\".format(info[\"ip_str\"],\n                                               info[\"os\"],\n                                               asn,\n                                               \" \".join(str(port) for port in info[\"ports\"]),\n                                               \" \".join(vulnerabilities)))\n            with open(\"processed_ips\", \"wb\") as pfp:\n                processed_ips.add(ip)\n                pickle.dump(processed_ips, pfp)\n        except shodan.exception.APIError as e:\n            time.sleep(5)\n            with open(\"processed_ips\", \"wb\") as pfp:\n                processed_ips.add(ip)\n                pickle.dump(processed_ips, pfp)\n            print(\"For IP:\" + ip + \" this error is produced:\" + str(e))\n","sub_path":"shodan_bulk_search.py","file_name":"shodan_bulk_search.py","file_ext":"py","file_size_in_byte":1999,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"365255306","text":"import requests\nimport re\nimport sys\n\n#payload for post request in requests.post\npayload = {\n    'sessionID':'0',\n    'requestID':'0',\n    'type_origin':'any',\n    #from \"Mozarthaus/Kolping\n    'name_origin': sys.argv[1],\n    'type_destination':'any',\n    #to \"Königsplatz\"\n    'name_destination':'101',\n    'maxChanges':'0'\n}\n\n#function to remove all HTML from the o\ndef a(test_str):\n    ret = ''\n    skip1c = 0\n    skip2c = 0\n    for i in test_str:\n        if i == '<':\n            skip1c += 1\n        elif i == '(':\n            skip2c += 1\n        elif i == '>' and skip1c > 0:\n            skip1c -= 1\n        elif i == ')'and skip2c > 0:\n            skip2c -= 1\n        elif skip1c == 0 and skip2c == 0:\n            ret += i\n    return ret\n\n#post request gets result in a html page\nr = requests.post('https://efa.avv-augsburg.de/stawa2/XSLT_TRIP_REQUEST2', data=payload)\nr = r.text\n\n#applies function to remove all html tags\no = a(r)\n\ndetpos = o.find('Detail')\n\nfahrten = []\neinsfahrpos = o.find('1. Fahrt',detpos)\nfahrten.append(o[einsfahrpos+8:einsfahrpos+14])\nzweifahrpos = o.find('2. Fahrt',detpos)\nfahrten.append(o[zweifahrpos+8:zweifahrpos+14])\ndreifahrpos = o.find('3. Fahrt',detpos)\nfahrten.append(o[dreifahrpos+8:dreifahrpos+14])\nvierfahrpos = o.find('4. Fahrt',detpos)\nfahrten.append(o[vierfahrpos+8:vierfahrpos+14])\n\nprint (fahrten[0])\nprint (fahrten[1])\nprint (fahrten[2])\nprint (fahrten[3])\nprint ('--------')\nprint (o)\n\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1439,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"186558754","text":"# model settings\n_base_ = [\n    \"htc_without_semantic_r50_fpn_1x_coco.py\",\n    \"../../helper/dataset.py\",\n    \"../../helper/runtime.py\",\n    \"../../helper/schedule.py\",\n]\n\n# backbone\npretrained = (\n    \"https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth\"  # noqa\n)\nmodel = dict(\n    backbone=dict(\n        _delete_=True,\n        type=\"SwinTransformer\",\n        pretrain_img_size=384,\n        embed_dims=192,\n        depths=[2, 2, 18, 2],\n        num_heads=[6, 12, 24, 48],\n        window_size=12,\n        mlp_ratio=4,\n        qkv_bias=True,\n        qk_scale=None,\n        drop_rate=0.0,\n        attn_drop_rate=0.0,\n        drop_path_rate=0.2,\n        patch_norm=True,\n        out_indices=(0, 1, 2, 3),\n        with_cp=False,\n        convert_weights=True,\n        init_cfg=dict(type=\"Pretrained\", checkpoint=pretrained),\n    ),\n    neck=dict(in_channels=[192, 384, 768, 1536]),\n)\n\n\n# work_dir, wandb exp name\nexp = \"htc_swin_l\"\nwork_dir = f\"./work_dirs/{exp}\"\n\n# Wandb Log\nlog_config = dict(\n    hooks=[\n        dict(type=\"TextLoggerHook\"),\n        dict(\n            type=\"WandbLoggerHook\",\n            init_kwargs=dict(\n                project=\"object-detection-recycling-trash\", entity=\"boostcamp-2th-cv-02team\", name=f\"{exp}\"\n            ),\n        ),\n    ]\n)\n\nlr_config = dict(step=[8, 12])\nrunner = dict(type=\"EpochBasedRunner\", max_epochs=15)\n","sub_path":"template/mmdetection/configs/custom/models/htc/htc_swin_l.py","file_name":"htc_swin_l.py","file_ext":"py","file_size_in_byte":1404,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"261072570","text":"# 에라토스테네스의 체\n#\n# 비트마스크(2권 597p) 이용\n#\nfrom math import sqrt\n\nM, N = map(int, input().split())\nnumbers = [i for i in range(N + 1)]\nsieve = [1] * int((N + 7) / 8 + 1)\n\n\ndef isPrime(k):\n    global sieve\n    return sieve[k >> 3] & (1 << (k & 7))\n\n\ndef setComposite(k):\n    global sieve\n    sieve[k >> 3] &= ~(1 << (k & 7)) % 256\n\n\ndef eratosthenes():\n    global numbers\n    for i in range(2, int(sqrt(N)) + 1):\n        if isPrime(i):\n            for j in range(i * i, N + 1, i):\n                setComposite(j)\n\n\neratosthenes()\nfor i in range(16):\n    print(isPrime(i))\nprint(sieve)\n","sub_path":"SsangWoo/python/baekjoon/1929.py","file_name":"1929.py","file_ext":"py","file_size_in_byte":612,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"105880255","text":"import re\nimport string\n\nwith open(\"../../../data/2018/5/data.txt\") as f:\n    x = f.read().strip()\n\nrepat = '|'.join([''.join(i) for i in zip(list(string.ascii_uppercase), list(string.ascii_lowercase))] + [''.join(i) for i in zip(list(string.ascii_lowercase), list(string.ascii_uppercase))])\npattern = re.compile(repat)\nlengths = []\nfor letter in string.ascii_uppercase:\n    y = re.sub(\"%s|%s\" % (letter, letter.lower()), \"\", x)\n    while True:\n        if pattern.findall(y):\n            y = pattern.sub(\"\", y)\n        else:\n            lengths.append(len(y))\n            break\n\nprint(min(lengths))\n","sub_path":"python/2018/5/solution2.py","file_name":"solution2.py","file_ext":"py","file_size_in_byte":599,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"311401122","text":"expected_output = {\n    \"trustpoints\": {\n        \"CISCO_IDEVID_SUDI\": {\n            \"associated_trustpoints\": {\n                \"certificate\": {\n                    \"status\": \"Available\",\n                    \"serial_number_in_hex\": \"793B572700000003750B\",\n                    \"subject\": {\n                        \"name\": \"WS-C3850-24P-0057D21BC800\",\n                        \"pid\": \"WS-C3850-24P\",\n                        \"cn\": \"WS-C3850-24P-0057D21BC800\",\n                        \"serial_number\": \"FCW1947C0GF\",\n                    },\n                    \"issuer\": {\"cn\": \"Cisco Manufacturing CA SHA2\", \"o\": \"Cisco\"},\n                    \"crl_distribution_points\": \"http://www.cisco.com/security/pki/crl/cmca2.crl\",\n                    \"usage\": \"General Purpose\",\n                    \"validity_date\": {\n                        \"start_date\": \"00:34:52 UTC Nov 20 2015\",\n                        \"end_date\": \"00:44:52 UTC Nov 20 2025\",\n                    },\n                },\n                \"ca_certificate\": {\n                    \"status\": \"Available\",\n                    \"serial_number_in_hex\": \"02\",\n                    \"subject\": {\"cn\": \"Cisco Manufacturing CA SHA2\", \"o\": \"Cisco\"},\n                    \"issuer\": {\"cn\": \"Cisco Root CA M2\", \"o\": \"Cisco\"},\n                    \"crl_distribution_points\": \"http://www.cisco.com/security/pki/crl/crcam2.crl\",\n                    \"usage\": \"Signature\",\n                    \"validity_date\": {\n                        \"start_date\": \"13:50:58 UTC Nov 12 2012\",\n                        \"end_date\": \"13:00:17 UTC Nov 12 2037\",\n                    },\n                },\n            }\n        }\n    }\n}\n","sub_path":"src/genie/libs/parser/iosxe/tests/ShowCryptoPkiCertificates/cli/equal/golden_output_c3850_expected.py","file_name":"golden_output_c3850_expected.py","file_ext":"py","file_size_in_byte":1644,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"269024896","text":"import numpy as np\n\nimport matplotlib\nimport matplotlib.pyplot as plt\n\ndef main():\n\n\tN = 500\n\n\tnpzfiles = ['Q_output.npz','SARSA_output.npz','NN_output.npz','Optimal_output.npz','Random_output.npz']\n\tlegend_labels = ['Q Learning', 'SARSA', 'NN Q Learning', 'Optimal Strategy','Random Agent']\n\n\tfig1, ax1 = plt.subplots()\n\tfor ind,npzfile in enumerate(npzfiles):\n\t\tprint(npzfile)\n\n\t\tnpzdict = np.load('output/'+npzfile)\n\t\trewards = npzdict['rewards']\n\n\t\titerations = npzdict['iterations']\n\t\titerations_vec = np.array(range(N,iterations+1))\n\n\t\trunning_mean_reward = running_mean(x=rewards,N=N)\n\t\tax1.plot(iterations_vec, running_mean_reward, label=str(legend_labels[ind]))\n\n\tax1.grid()\n\tax1.set(xlabel = 'Iteration', ylabel = 'Mean reward over last 500 iterations', title='Various Agents - OpenAI Gym Nchain ', xlim=(0,iterations))\n\tax1.legend()\n\tfig1.tight_layout()\n\tfig1.savefig(\"output/reward.png\", dpi = 300)\n\ndef running_mean(x, N):\n\t#x is data, N is  window size.\n    cumsum = np.cumsum(np.insert(x, 0, 0)) \n    return (cumsum[N:] - cumsum[:-N]) / float(N)\n\nif __name__ == '__main__':\n\tmain()","sub_path":"nchain/plotting.py","file_name":"plotting.py","file_ext":"py","file_size_in_byte":1096,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"587620999","text":"import glob\r\nimport pandas as pd\r\nimport chardet\r\nimport codecs\r\nimport sys,xlrd\r\nfrom xlrd import open_workbook\r\nimport os.path\r\n\r\nvar = pd.DataFrame\r\nmyvar = pd.DataFrame\r\nrootDir = 'C:/Users/vinicius.dourado.MPT/Dropbox/tabnet/spabr/'\r\nfor dirName, subdirList, fileList in os.walk(rootDir, topdown=False):\r\n    print('Found directory: %s' % dirName)\r\n    count = 1\r\n    for fname in fileList:\r\n        filepath = os.path.join(rootDir, dirName, fname)\r\n        myvar = pd.read_csv(filepath, sep=\";\")\r\n        if (count == 1):\r\n            var = myvar\r\n        else:\r\n            var = pd.concat([var, myvar], axis=0)    \r\n        count +=1\r\n        \r\nvar = var[~(var['Municipio'].str.contains('Total|&'))]\r\n\r\nvar[\"cod_municipio\"], var[\"desc_municipio\"] = zip(*var[\"Municipio\"].str.split(' ',1).tolist())\r\ndel var['Municipio']\r\nvar[\"cod_municipio\"]  = var.cod_municipio.astype(int)\r\n\r\nvar[\"mes\"], var[\"ano\"] = zip(*var[\"periodo\"].str.split('_',1).tolist())\r\ndel var['periodo']\r\n\r\nvar['Quantidade aprovada'] = var['Quantidade aprovada'].replace(['-'], '0')\r\nvar['Valor aprovado'] = var['Valor aprovado'].replace(['-'], '0')\r\nvar\r\n\r\nvar.to_csv('C:/Users/vinicius.dourado.MPT/Dropbox/tabnet/spabr/Dados detalhados das AIH - por local internação - Brasil_2008emdiante.csv',sep=\";\" , columns=[\"cod_municipio\",\"desc_municipio\",\"Quantidade aprovada\",\"Valor aprovado\",\"mes\",\"ano\"], index=False)\r\n\r\n\r\n","sub_path":"agragacaodoscsv.py","file_name":"agragacaodoscsv.py","file_ext":"py","file_size_in_byte":1395,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"336300276","text":"#! /usr/bin/env python\n# -*- coding: utf-8 -*-\n# Date: 2018/8/2\n\n\"\"\"\n    阿里 api 调用出口\n\n        封装更易用的api\n\"\"\"\n\nfrom .pay.basic import AliPay\nfrom .yun.basic import AliYunClient\n\nfrom .config import Config\n\n\nclass AliApi(object):\n\n    config_class = Config\n\n    default_config = {\n        \"DEBUG\": True,\n        \"CLOUD\": {\n            \"APP_ID\": None,\n            \"SECRET\": None\n        },\n        \"PAY\": {\n            \"APP_ID\": None,\n            \"PUBLIC_KEY_PATH\": None,\n            \"PRIVATE_KEY_PATH\": None,\n            \"NOTIFY_URL\": None,\n            \"RETURN_URL\": None\n        }\n    }\n\n    def __init__(self, ):\n        self.config = self.make_config()\n\n    def make_config(self):\n        defaults = dict(self.default_config)\n        return self.config_class(defaults)\n\n    @property\n    def pay(self):\n\n        pay_config = self.config[\"PAY\"]\n        app_id = pay_config[\"APP_ID\"]\n        public_key_path = pay_config[\"PUBLIC_KEY_PATH\"]\n        private_key_path = pay_config[\"PRIVATE_KEY_PATH\"]\n        notify_url = pay_config[\"NOTIFY_URL\"]\n        return_url = pay_config[\"RETURN_URL\"]\n\n        return AliPay(\n            app_id=app_id, public_key_path=public_key_path,\n            private_key_path=private_key_path, notify_url=notify_url,\n            return_url=return_url, debug=self.config[\"DEBUG\"]\n        )\n\n    @property\n    def yun(self):\n\n        cloud_config = self.config[\"CLOUD\"]\n        app_id = cloud_config[\"APP_ID\"]\n        secret = cloud_config[\"SECRET\"]\n\n        return AliYunClient(\n            app_id=app_id, secret=secret\n        )\n","sub_path":"aliopenapi/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":1578,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"18316662","text":"from django.shortcuts import render, redirect, get_object_or_404\nfrom django.views.generic import (ListView, DetailView, View, TemplateView)\nfrom django.contrib.auth.models import User\nfrom django.contrib.auth import login, logout\nfrom django.contrib.auth.mixins import LoginRequiredMixin, PermissionRequiredMixin\nfrom django.contrib.auth.decorators import login_required\nfrom django.utils.decorators import method_decorator\nfrom django.http import HttpResponseRedirect, HttpResponse, JsonResponse\nfrom django.conf import settings\nfrom .models import Group, Membership, Post\nfrom .forms import GroupForm, PostForm\nfrom django.utils import timezone\nfrom django.template.response import TemplateResponse\n\n\nimport pdb\n\n\ndef home_page(request):\n    return render(request, template_name='homepage.html')\n\ndef user_login(request):\n    if request.method == \"GET\":\n        return render(request, template_name='login.html')\n    elif request.method == \"POST\":\n        username = request.POST.get('username')\n        password = request.POST.get('password')\n        try:\n            user = User.objects.get(username=username)\n            if user.check_password(password):\n                login(request, user)\n                return HttpResponseRedirect('/')\n            else:\n                return render(request, 'login.html', {'error': settings.LOGIN_ERROR_MESSAGE}, status=401)\n                # return HttpResponse(settings.LOGIN_ERROR_MESSAGE, status=401)\n                # return TemplateResponse(request, 'login.html',\n                #                     context={'error': settings.LOGIN_ERROR_MESSAGE},\n                #                     status=400)\n        except User.DoesNotExist:\n            return HttpResponse(settings.LOGIN_ERROR_MESSAGE, status=401)\n\ndef user_logout(request):\n    logout(request)\n    return HttpResponseRedirect('/')\n\ndef user_registration(request):\n    if request.method == \"GET\":\n        return render(request, template_name='registration.html')\n    elif request.method == \"POST\":\n        username = request.POST.get('username')\n        email = request.POST.get('email')\n        password1 = request.POST.get('password1')\n        password2 = request.POST.get('password2')\n        try:\n            user = User.objects.get(username=username)\n            return HttpResponse(settings.REGISTRATION_USER_EXISTS_ERROR_MESSAGE,\n                                status=400)\n        except User.DoesNotExist:\n            if password1 == password2:\n                user = User.objects.create_user(username, email)\n                user.set_password(password1)\n                user.save()\n                return HttpResponseRedirect('/')\n            else:\n                return HttpResponse(settings.REGISTRATION_PASSWORDS_ERROR_MESSAGE,\n                                    status=400)\n\n\nclass GroupsList(LoginRequiredMixin, ListView):\n\n    model = Group\n    template_name = 'groups/groups_list.html'\n\n\nclass GroupPage(LoginRequiredMixin, TemplateView):\n\n    def get(self, request, group_id):\n        template_name = 'groups/group_info.html'\n        group = get_object_or_404(Group, pk=group_id)\n        post_list = Post.objects.filter(group=group_id).values()\n        posts = [elem for elem in post_list]\n        user = request.user\n        is_member = self.is_member(user, group)\n        is_creator = self.is_creator(user.pk, group_id)\n        return render(request, template_name, {'is_member': is_member,\n                                               'is_creator': is_creator,\n                                               'group': group,\n                                               'posts': posts})\n\n    def post(self, request, group_id):\n        group = get_object_or_404(Group, pk=group_id)\n        user = request.user\n        is_member = self.is_member(user, group)\n        if is_member:\n            membership = Membership.objects.get(group=group, user=user)\n            membership.delete()\n        else:\n            Membership.objects.create(group=group, user=user,\n                                      date_joined=timezone.now())\n        return HttpResponseRedirect(f'/groups/{group_id}/')\n\n\n    @staticmethod\n    def is_member(user, group):\n        try:\n            Membership.objects.get(group=group, user=user)\n            is_member = True\n        except Membership.DoesNotExist:\n            is_member = False\n        return is_member\n\n    @staticmethod\n    def is_creator(user_id, group_id):\n        user = User.objects.get(pk=user_id)\n        group = Group.objects.get(pk=group_id)\n        creator = group.creator\n        if creator.pk == user.pk:\n            return True\n        return False\n\n\nclass GroupCreate(LoginRequiredMixin, TemplateView):\n\n    def get(self, request):\n        template_name = \"groups/group_create.html\"\n        form = GroupForm()\n        return render(request, template_name, {'form': form})\n\n    def post(self, request):\n\n        name = request.POST.get('name')\n        theme = request.POST.get('theme')\n        is_private = request.POST.get('private')\n\n        creator = request.user\n        data = {'name': name,\n                'theme': theme,\n                'creator': creator.pk}\n        form = GroupForm(data)\n        if form.is_valid():\n            group = form.instance\n            if is_private is None:\n                group.save()\n            else:\n                group.is_private = True\n                group.save()\n                membership = Membership(user=creator, group=group, date_joined=timezone.now())\n                membership.save()\n            return HttpResponseRedirect('/groups/')\n        return HttpResponse(\"Data is not valid\", status=400)\n\n\nclass GroupUpdate(LoginRequiredMixin, TemplateView):\n\n    def get(self, request, group_id):\n        group = Group.objects.get(pk=group_id)\n        form = GroupForm(instance=group)\n        user = request.user\n        template_name = \"groups/group_form.html\"\n        if GroupPage.is_creator(user.pk, group_id):\n            return render(request, template_name, {'form': form})\n        return render(request, template_name, {'form': form,\n                                            'error': \"Only creator is allowed to update the group\"})\n\n    def post(self, request, group_id):\n        user = request.user\n        group = get_object_or_404(Group, id=group_id)\n        if GroupPage.is_creator(user.pk, group_id):\n            name = request.POST.get('name')\n            theme = request.POST.get('theme')\n            data = {'name': name, 'theme': theme, 'creator': group.creator.pk}\n            form = GroupForm(data=data, instance=group)\n            if form.is_valid():\n                form.save()\n                # return render(request, \"groups/group_info.html\")\n                return HttpResponseRedirect(f'/groups/{group_id}/')\n        return render(request,  \"groups/group_form.html\", {'error': \"Only creator is allowed to update the group\"})\n\n\nclass GroupDelete(LoginRequiredMixin, TemplateView):\n    template_name = 'groups/group_form.html'\n\n    def post(self, request, group_id):\n        group = get_object_or_404(Group, pk=group_id)\n        try:\n            group.delete()\n            return HttpResponseRedirect('/groups/')\n        except:\n            return HttpResponse(\"Couldn't delete\", status=400)\n\n\n@login_required\ndef invite(request, group_id):\n    if request.method == \"POST\":\n        user_current = request.user\n        username = request.POST.get('invited_user')\n        group = get_object_or_404(Group, pk=group_id)\n        is_creator = GroupPage.is_creator(user_current.pk, group.pk)\n        data = {'group': group,\n                'is_member': True,\n                'is_creator': is_creator}\n        try:\n            user = User.objects.get(username=username)\n            try:\n                member = Membership.objects.get(user=user, group=group)\n                return render(request, 'groups/group_info.html',\n                              {**data,'message': \"User is a member already\"})\n            except Membership.DoesNotExist:\n                membership = Membership(user=user, group=group, date_joined=timezone.now())\n                membership.save()\n            return render(request, 'groups/group_info.html', {**data,'message': \"User was invited\"} )\n\n        except User.DoesNotExist:\n            return render(request, 'groups/group_info.html', {**data, 'message': \"User doesn't exist\",})\n\n\n\n# class PostPage(LoginRequiredMixin, TemplateView):\n#\n#     def get(self, request, **args):\n#         template_name = 'posts/posts_list.html'\n#\n#\n\nclass PostsList(LoginRequiredMixin, ListView):\n\n    model = Post\n    template_name = 'posts/posts_list.html'\n\nclass PostInfo(LoginRequiredMixin, DetailView):\n\n    model = Post\n    template_name = 'posts/post_info.html'\n\nclass PostCreate(LoginRequiredMixin, TemplateView):\n\n    def get(self, request, group_id):\n        template_name = 'posts/post_create.html'\n        form = PostForm()\n        creator = request.user\n        group = get_object_or_404(Group, pk=group_id)\n        if GroupPage.is_member(creator, group):\n            return render(request, template_name, {'form': form})\n        return render(request, 'posts/post_create.html',\n                      {'error': \"You must join group to create posts\"})\n\n    def post(self, request, group_id):\n        title = request.POST.get('title')\n        text = request.POST.get('text')\n        creator = request.user\n        publish = request.POST.get('publish')\n        group = get_object_or_404(Group, pk=group_id)\n        if GroupPage.is_member(creator, group):\n            data = {'title': title,\n                    'text': text,\n                    'creator': creator.pk,\n                    'group': group.pk}\n            form = PostForm(data)\n            if form.is_valid():\n                post = form.instance\n                if publish is None:\n                    post.date_created = timezone.now()\n                post.save()\n                if group.is_private == True:\n                    post.is_private = True\n                    post.save()\n                return HttpResponseRedirect('/posts/')\n        return render(request, 'posts/post_create.html', {'error': \"You must join group to create posts\"})\n\nclass PostUpdate(LoginRequiredMixin, TemplateView):\n\n    @staticmethod\n    def is_creator(user_id, post_id):\n        user = User.objects.get(pk=user_id)\n        post = Post.objects.get(pk=post_id)\n        creator = post.creator\n        if creator.pk == user.pk:\n            return True\n        return False\n\n    def get(self, request, post_id):\n        user_id = request.user.pk\n        template_name = 'posts/post_form.html'\n        post = get_object_or_404(Post, pk=post_id)\n        form = PostForm(instance=post)\n        if self.is_creator(user_id, post_id):\n            return render(request, template_name, {'form': form})\n        else:\n            return render(request, template_name, {'form': form, 'error':\"Only creator is allowed to update the group\" })\n\n\n    def post(self, request, post_id):\n        user_id = request.user.pk\n        if self.is_creator(user_id, post_id):\n            title = request.POST.get('title')\n            text = request.POST.get('text')\n            post = get_object_or_404(Post, pk=post_id)\n            data = {'title': title, 'text': text, 'creator': post.creator.pk,\n                    'group': post.group.pk}\n            form = PostForm(data=data, instance=post)\n            if form.is_valid():\n                form.save()\n                return HttpResponseRedirect(f'/posts/{post_id}')\n        return render(request, 'posts/post_form.html', {'error': \"Only creator is allowed to update the group\"})\n\nclass PostDelete(LoginRequiredMixin, TemplateView):\n\n    def post(self, request, post_id):\n        post = get_object_or_404(Post, pk=post_id)\n        try:\n            post.delete()\n            return HttpResponseRedirect('/posts/')\n        except:\n            return HttpResponse(\"Couldn't delete\", status=400)\n\n\nclass DraftsList(LoginRequiredMixin, ListView):\n\n    def get(self, request):\n        user = request.user\n        posts = Post.objects.filter(creator=user, date_created=None).values()\n        drafts = [elem for elem in posts]\n        template_name = 'posts/drafts_list.html'\n        return render(request, template_name, {'drafts': drafts})\n\n\n@login_required\ndef publish(request, draft_id):\n    if request.method == \"POST\":\n        post = Post.objects.get(pk=draft_id)\n        post.date_created = timezone.now()\n        post.save()\n        return HttpResponseRedirect('/drafts/')\n\n\n\n\n\n\n\n\n\n","sub_path":"blog/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":12532,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"548670481","text":"from core.test_ft import Fine_tuning as test_ft\nfrom scipy import misc\nimport numpy as np\n\nfile_name = 'cman'\nfile_name_clean = file_name + '_clean.png'\nfile_path = './data/'\n\nnoise_mean = 0\nnoise_sigma = 30\n\nif noise_sigma not in [15, 25, 30, 50, 75]:\n    print ('No weight file')\n    exit()\n    \nclean_image = misc.imread(file_path + file_name_clean)\nnoisy_image = clean_image + np.random.normal(noise_mean, noise_sigma, clean_image.shape)\n\nt_ft = test_ft(clean_image, noisy_image, noise_sigma)\ndenoised_img, psnr, ssim = t_ft.fine_tuning()\n\nmisc.imsave(file_name +'_denoised_ft.png', denoised_img)\n\nprint ('PSNR : ' + str(round(psnr,2)) + '\\nSSIM : ' + str(round(ssim,4)))","sub_path":"test_fc_aide_ft.py","file_name":"test_fc_aide_ft.py","file_ext":"py","file_size_in_byte":675,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"142581384","text":"# -*- coding: utf-8 -*-\nimport json\nimport traceback\n\nimport requests\n\n\nclass WikiDataItem:\n    def __init__(self, wd_item_id, init_at_once=True):\n        self.wd_item_id = wd_item_id\n        self.property_name_list = None\n        self.data_dict = None\n        self.relation_property_name_list = None\n        self.source_wd_dict_json = None\n        self.is_init = False\n        if init_at_once:\n            self.init_wikidata_item(self.wd_item_id)\n\n    def get_en_name(self):\n        if self.source_wd_dict_json:\n            if 'labels' in self.source_wd_dict_json.keys() and \"en\" in self.source_wd_dict_json['labels'].keys():\n                return self.source_wd_dict_json['labels'][\"en\"][\"value\"]\n        return None\n\n    @staticmethod\n    def __merge_value_from_dict_list(dict_list, value_key='value'):\n        values = []\n        for d in dict_list:\n            if value_key in d:\n                values.append(d[value_key])\n        # return ',,'.join(values)\n        return values\n\n    def __parse_wd_json_to_dict(self, wd_item_json):\n        wd_item_property_dict = {}\n        self.__extract_muilti_language_property(wd_item_json, 'aliases', wd_item_property_dict)\n        self.extract_muilti_language_property_from_dict(wd_item_json, 'labels', wd_item_property_dict)\n        self.extract_muilti_language_property_from_dict(wd_item_json, 'descriptions', wd_item_property_dict)\n\n        self.__extract_sitelinks(wd_item_json, wd_item_property_dict)\n        property_name_list, relation_property_name_list = WikiDataItem.__extract_claims(wd_item_json=wd_item_json,\n                                                                                        wd_item_property_dict=wd_item_property_dict)\n        if 'labels_en' in wd_item_property_dict:\n            wd_item_property_dict['name'] = wd_item_property_dict['labels_en']\n        return wd_item_property_dict, property_name_list, relation_property_name_list\n\n    @staticmethod\n    def __extract_metadata(entity_metadata):\n        metadata_dict = {}\n        metadata_dict['wd_item_id'] = entity_metadata['id']\n        metadata_dict['lastrevid'] = entity_metadata['lastrevid']\n        metadata_dict['modified'] = entity_metadata['modified']\n        return metadata_dict\n\n    @staticmethod\n    def __extract_sitelinks(wd_item_json, wd_item_property_dict):\n        if 'sitelinks' in wd_item_json.keys():\n            sitelinks = wd_item_json['sitelinks']\n            if not sitelinks:\n                return\n            for k, v in sitelinks.items():\n                k_title = \"site:\" + k\n                wd_item_property_dict[k_title] = v['url']\n\n    @staticmethod\n    def __extract_claims(wd_item_json, wd_item_property_dict):\n        claims_dict = wd_item_json['claims']\n        if not claims_dict:\n            return [], []\n        property_name_list = []\n        relation_property_name_list = []\n        for k, v in claims_dict.items():\n            property_name_list.append(k)\n            wd_item_property_dict[k] = WikiDataItem.__extract_claims_property_from_value_list(v)\n            if v and WikiDataItem.is_property_relation(v[0]):\n                relation_property_name_list.append(k)\n        return property_name_list, relation_property_name_list\n\n    @staticmethod\n    def __extract_claims_property_from_value_list(value_list):\n        extract_values = []\n        for value in value_list:\n            t = WikiDataItem.__extract_claims_property_item_from_value_list(value)\n            if t:\n                extract_values.append(t)\n        # return ',,'.join(extract_values)\n        return extract_values\n\n    @staticmethod\n    def is_property_relation(value_dict):\n        try:\n            mainsnak = value_dict['mainsnak']\n            if mainsnak['snaktype'] == 'novalue' or mainsnak['snaktype'] == 'somevalue':\n                return False\n            data_type = mainsnak['datatype']\n            if data_type == 'wikibase-item':\n                return True\n            else:\n                return False\n        except Exception as e:\n            traceback.print_exc()\n            return False\n\n    @staticmethod\n    def __extract_claims_property_item_from_value_list(value_dict):\n        mainsnak = value_dict['mainsnak']\n        if mainsnak['snaktype'] == 'novalue' or mainsnak['snaktype'] == 'somevalue':\n            return None\n        data_type = mainsnak['datatype']\n        value = ''\n\n        string_value_type = ['string', 'external-id', 'math', 'url', 'commonsMedia', 'geo-shape']\n\n        if data_type in string_value_type:\n            value = mainsnak['datavalue']['value']\n        if data_type == 'wikibase-item':\n            value = mainsnak['datavalue']['value']['id']\n        if data_type == 'wikibase-property':\n            value = 'P' + str(mainsnak['datavalue']['value']['numeric-id'])\n        if data_type == 'time':\n            value = mainsnak['datavalue']['value']['time']\n        if data_type == 'monolingualtext':\n            value = mainsnak['datavalue']['value']['text']\n        if data_type == 'quantity':\n            value = mainsnak['datavalue']['value']['amount']\n        if data_type == 'globe-coordinate':\n            value = str(mainsnak['datavalue']['value']['latitude']) + ',' + str(\n                mainsnak['datavalue']['value']['longitude'])\n\n        return value\n\n    def __extract_muilti_language_property(self, wd_item_json, key, wd_item_property_dict):\n        language_dict = wd_item_json[key]\n        if not language_dict:\n            return\n        for k, v in language_dict.items():\n            try:\n                full_property_name = key + '_' + k\n                wd_item_property_dict[full_property_name] = self.__merge_value_from_dict_list(v)\n            except Exception as error:\n                print(error)\n\n    @staticmethod\n    def extract_muilti_language_property_from_dict(wd_item_json, key, wd_item_property_dict):\n        language_dict = wd_item_json[key]\n        if not language_dict:\n            return\n        for k, v in language_dict.items():\n            try:\n                full_property_name = key + '_' + k\n                wd_item_property_dict[full_property_name] = v['value']\n            except Exception as error:\n                print(error)\n\n    def init_wikidata_item_from_json_string(self, data_json_string):\n        try:\n\n            dict_json = self.parse_illegal_json_string(data_json_string)\n\n            self.source_wd_dict_json = dict_json\n            self.wd_item_id = dict_json[\"id\"]\n            self.__parse()\n            self.is_init = True\n            return self\n        except Exception as error:\n            traceback.print_exc()\n            self.source_wd_dict_json = None\n            self.is_init = False\n            return self\n\n    def init_wikidata_item(self, wd_item_id):\n        try:\n            r = requests.get(\n                \"https://www.wikidata.org/wiki/Special:EntityData/{wd_item_id}.json\".format(wd_item_id=wd_item_id))\n            json_response = r.content.decode(encoding='utf-8')\n            dict_json = json.loads(json_response, encoding='utf-8')\n            self.source_wd_dict_json = dict_json[\"entities\"][wd_item_id]\n            self.wd_item_id = wd_item_id\n            self.__parse()\n            self.is_init = True\n            return self\n        except Exception as error:\n            traceback.print_exc()\n            self.source_wd_dict_json = None\n            self.is_init = False\n            return self\n\n    def init_wikidata_item_from_wikipedia_title(self, wikipedia_title):\n        try:\n            r = requests.get(\n                \"https://en.wikipedia.org/w/api.php?action=query&prop=pageprops&ppprop=wikibase_item&redirects=1&format=json&titles=\" + wikipedia_title)\n\n            json_response = r.content.decode(encoding='utf-8')\n            query_json = json.loads(json_response, encoding='utf-8')\n            wd_item_id = None\n            pages = query_json[\"query\"][\"pages\"]\n            for page_index, page in pages.items():\n                if page_index == \"-1\":\n                    break\n                wd_item_id = page[\"pageprops\"][\"wikibase_item\"]\n            self.wd_item_id = wd_item_id\n            if wd_item_id is None:\n                self.is_init = False\n                return self\n            return self.init_wikidata_item(wd_item_id=wd_item_id)\n\n        except Exception as error:\n            traceback.print_exc()\n            self.source_wd_dict_json = None\n            self.is_init = False\n            return self\n\n    def init_wikidata_item_from_wikipedia_url(self, wikipedia_url):\n        wikipedia_title = wikipedia_url.replace(\"https://en.wikipedia.org/wiki/\", \"\")\n        return self.init_wikidata_item_from_wikipedia_title(wikipedia_title)\n\n    def __parse_wikidata_item_to_dict(self):\n        property_dict, property_name_list, relation_property_name_list = self.__parse_wd_json_to_dict(\n            self.source_wd_dict_json)\n        metadata_dict = self.__extract_metadata(self.source_wd_dict_json)\n\n        return dict(property_dict, **metadata_dict), property_name_list, relation_property_name_list\n\n    def __parse(self):\n        data_dict, property_name_list, relation_property_name_list = self.__parse_wikidata_item_to_dict()\n        self.data_dict = data_dict\n        self.property_name_list = property_name_list\n        self.relation_property_name_list = relation_property_name_list\n\n    def get_wikidata_item_property_dict(self):\n        return self.data_dict\n\n    def get_relation_property_name_list(self):\n        return self.relation_property_name_list\n\n    def get_wikidata_item_property_name_list(self):\n        return self.property_name_list\n\n    def exist(self):\n        return self.is_init\n\n    def get_en_wiki_url(self):\n        pass\n\n    def get_en_wiki_title(self):\n        try:\n            if self.source_wd_dict_json:\n                if \"sitelinks\" in self.source_wd_dict_json.keys() and \"enwiki\" in self.source_wd_dict_json[\n                    'sitelinks'].keys():\n                    return self.source_wd_dict_json['sitelinks'][\"enwiki\"][\"title\"]\n        except Exception:\n            traceback.print_exc()\n        return None\n\n    @staticmethod\n    def parse_illegal_json_string(data_json_string):\n\n        if data_json_string == None or data_json_string == \"\":\n            return None\n\n        try:\n            try:\n                json_instance = json.loads(data_json_string, encoding='utf-8')\n                return json_instance\n            except Exception:\n                data_json_string = json.dumps(eval(data_json_string))\n\n                json_instance = json.loads(data_json_string, encoding='utf-8')\n                return json_instance\n        except Exception:\n            traceback.print_exc()\n            return None\n\n    @staticmethod\n    def is_valid_json_string(data_json_string):\n        try:\n            json_instance = WikiDataItem.parse_illegal_json_string(data_json_string)\n            if json_instance == None:\n                return False\n            if json_instance[\"claims\"] == {}:\n                return False\n            return True\n        except Exception:\n            traceback.print_exc()\n            return False\n","sub_path":"venv/Lib/site-packages/sekg/wiki/WikiDataItem.py","file_name":"WikiDataItem.py","file_ext":"py","file_size_in_byte":11106,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"564746556","text":"#!/usr/bin/env python3\n\n#\n# Python script that parses a yacpm.json and downloads necessary libraries into\n# yacpkgs/ directory.\n#\n\nfrom io import TextIOWrapper\nfrom typing import Any, Union, Tuple\nfrom copy import deepcopy\nimport json\nimport os\nimport re\nimport shutil\nimport subprocess\nimport sys\nimport urllib.error\nimport urllib.request\n\nYACPM_BRANCH = \"main\"\n\n# global variables (do not touch lines above [not including imports] or merge conflict will happen)\nDIR_ARG = sys.argv[1] if len(sys.argv) > 1 else None\nTOP_LEVEL_CMAKE_DIR = os.path.abspath(DIR_ARG or os.getcwd())\n\n# utility functions\n\ndef dict_try_get(value, key: str, return_val_instead: bool = False) -> Any:\n    return_val = value if return_val_instead else None\n    return value.get(key) if isinstance(value, dict) else return_val\n\ndef dict_get_set(dict_input: dict, key: str, set_value):\n    if key not in dict_input:\n        dict_input[key] = set_value\n    return dict_input[key]\n\ndef get_include_list(dictionary: dict):\n    array = dictionary.get(\"include\", [])\n    include_list = \"\"\n    for item in array:\n        include_list += f' \"{item}\"'\n    return include_list\n\ndef error(msg: str, print_wrapper: bool = True):\n    text = f\"==== YACPM ERROR: {msg}\" if print_wrapper else msg \n    print(text, file=sys.stderr)\n    exit(1)\n\ndef info(msg: str, print_wrapper: bool = True):\n    msg = msg.strip()\n    text = f\"==== {msg}\" if print_wrapper else msg\n    # normal printing doesn't update realtime with cmake\n    subprocess.run(f\"\\\"{sys.executable}\\\" -c \\\"print('''{text}''')\\\"\", shell=True)\n\ndef open_read_write(filename: str, parse_json: bool = False) -> Tuple[TextIOWrapper, Any]:\n    file = open(filename, \"r+\")\n    content = json.load(file) if parse_json else file.read()\n    file.seek(0)\n    return (file, content)\n\ndef write_json(data: dict, file: TextIOWrapper):\n    json.dump(data, file, indent=4)\n    file.truncate()\n    file.close()\n\ndef download_if_missing(path: str, outfile: str) -> bool:\n    if not os.path.exists(outfile):\n        if path.startswith(\"http\"):\n            urllib.request.urlretrieve(path, outfile)\n        else:\n            file_path = os.path.join(TOP_LEVEL_CMAKE_DIR, path)\n            shutil.copyfile(file_path, outfile)\n        return True\n    else:\n        return False\n\ndef exec_shell(command: str) -> str:\n    proc = subprocess.run(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n    if proc.returncode != 0:\n        error(proc.stderr.decode(\"utf-8\"), False)\n\n    stdout = proc.stdout.decode(\"utf-8\")\n    \n    if verbose:\n        info(f\"> {command}\", False)\n        if stdout: \n            info(stdout, False)\n\n    return stdout\n\n# Main functions\n\ndef parse_package_version(package_version: str, package_repository: str) -> str:\n    git_ref = package_version.replace(\"+\", \"\")\n    # get default branch if no version specifed\n    if git_ref == \"\":\n        result = exec_shell(f\"git remote show {package_repository}\")\n        git_ref = re.findall(\"(?<=HEAD branch: ).+\", result)[0]\n\n    # fetch minimal info from repo with filter and depth 1 \n    exec_shell(f\"git fetch --depth=1 --filter=blob:none origin {git_ref}\")\n    exec_shell(\"git sparse-checkout init\")\n    exec_shell(\"git checkout FETCH_HEAD\")\n\n    # freeze if not starting with +\n    if not package_version.startswith(\"+\"):\n        rev_name = exec_shell(\"git name-rev HEAD\").strip()\n        # ref-name/version is a branch\n        if not rev_name.endswith(\"undefined\"):\n            # get commit hash\n            package_version = exec_shell(\"git rev-parse HEAD\").strip()\n    # don't set default branch if it's ++\n    elif not package_version.startswith(\"++\"):\n        package_version = \"+\" + git_ref\n\n    return package_version\n\n# returns remote that was downloaded from (if actually did download)\ndef download_package_metadata(remotes: set, package_name: str) -> Union[str, None]:\n    for remote in remotes:\n        if remote == \"DEFAULT_REMOTE\":\n            remote = f\"https://github.com/Calbabreaker/yacpm/raw/{YACPM_BRANCH}/packages\"\n\n        package_path = f\"{remote}/{package_name}\"\n        try:\n            did_download = download_if_missing(f\"{package_path}/yacpkg.json\", \"yacpkg.json\")\n            did_download = download_if_missing(f\"{package_path}/CMakeLists.txt\", \"CMakeLists-downloaded.txt\")\n        # try next remote if fail to download\n        except (urllib.error.HTTPError, FileNotFoundError) as err:\n            if isinstance(err, FileNotFoundError) or err.code == 404:\n                continue\n            else:\n                raise\n\n        # else return successfully\n        return remote if did_download else None\n\n    error(f\"{package_name} was not found on {', '.join(remotes)}!\")\n\ndef generate_cmake_variables(package_info: Union[str, dict]) -> str:\n    cmake_variables = \"\"\n    if isinstance(package_info, dict):\n        # set cmake variables using CACHE FORCE to configure package\n        for variable, value in package_info.get(\"variables\", {}).items():\n            if isinstance(value, bool):\n                value = \"ON\" if value else \"OFF\"\n                type_str = \"BOOL\"\n            elif isinstance(value, str):\n                value = f'\"{value}\"'\n                type_str = \"STRING\"\n            else:\n                error(\"{variable} needs to be a string or boolean!\")\n\n            if variable == \"BUILD_SHARED_LIBS\":\n                cmake_variables += f\"set({variable} {value})\\n\"\n            else:\n                cmake_variables += f'set({variable} {value} CACHE {type_str} \"\" FORCE)\\n'\n    return cmake_variables\n\n# calc sparse checkout list and download the neccessery package sources\ndef download_package_files(yacpkg: dict, package_info: Union[dict, str], progress_print: str):\n    # get lists of includes from the yacpm.json package declaration and yacpkg.json package \n    # config and combines them\n    sparse_checkout_list = \"\"\n    sparse_checkout_list += get_include_list(yacpkg)\n    if isinstance(package_info, dict):\n        sparse_checkout_list += get_include_list(package_info)\n\n    if yacpkg.get(\"^sparse_checkout_list\") != sparse_checkout_list:\n        info(progress_print)\n        exec_shell(f\"git sparse-checkout set {sparse_checkout_list}\")\n        yacpkg[\"^sparse_checkout_list\"] = sparse_checkout_list\n\n# gets all packages inside a yacpm.json and put it in a combined package\n# dependencies dict to combine all the includes, variables, ect.\ndef get_package_dependencies(package_deps_combined: dict, remotes: set, name_to_dependent: dict, dependent_name: str):\n    package_yacpm = json.load(open(\"yacpm.json\"))\n\n    for package_name, package_info in package_yacpm[\"packages\"].items():\n        package_in_combined = package_deps_combined.get(package_name)\n        if not isinstance(package_in_combined, dict):\n            package_in_combined = {}\n            package_deps_combined[package_name] = package_in_combined\n\n        dependents = package_in_combined.get(\"dependents\", [])\n        if not isinstance(dependents, set):\n            package_in_combined[\"dependents\"] = set(dependents)\n            package_in_combined[\"dependents_left\"] = set(dependents) \n\n        if not package_in_combined.get(\"version\"):\n            package_in_combined[\"version\"] = dict_try_get(package_info, \"version\", True)\n\n        if isinstance(package_info, dict):\n            dict_get_set(package_in_combined, \"include\", []).extend(package_info.get(\"include\", []))\n            variables = dict_get_set(package_in_combined, \"variables\", {})\n            for key, value in package_info.get(\"variables\", {}).items():\n                variables[key] = value\n\n        package_in_combined[\"dependents\"].add(dependent_name)\n        package_in_combined[\"dependents_left\"].discard(dependent_name)\n\n        dict_get_set(name_to_dependent, package_name, []).append(dependent_name)\n\n    # add only unique remotes from yacpm.json\n    remotes |= set(package_yacpm.get(\"remotes\", []))\n\n# main loop that gets all package code\ndef get_packages(package_list: dict, remotes: set, package_deps_combined: dict, p_name_to_dependent: dict = None):\n    package_names = p_name_to_dependent or list(package_list.keys())\n    name_to_dependent = {}\n\n    for i, package_name in enumerate(package_names):\n        package_info = package_list[package_name]\n\n        # if haven't parsed all dependents config yet\n        dependents_left = dict_try_get(package_info, \"dependents_left\")\n        if dependents_left and len(dependents_left) != 0:\n            continue\n\n        progress_indicator = f\"[{i + 1}/{len(package_names)}]\"\n\n        output_dir = f\"yacpkgs/{package_name}\"\n        # make the package output dir (repository dir as well for later use)\n        os.makedirs(f\"{output_dir}/repository\", exist_ok=True)  \n        os.chdir(output_dir)\n\n        package_version = dict_try_get(package_info, \"version\", True)\n        package_repository = dict_try_get(package_info, \"repository\")\n        specified_cmake_file = dict_try_get(package_info, \"cmake\") \n\n        if specified_cmake_file != None:\n            download_if_missing(specified_cmake_file, \"CMakeLists-downloaded.txt\")\n\n        # if the user has specifed both the package repo and CMakeLists then we can\n        # just use that instead downloading the package metadata\n        if specified_cmake_file == None or package_repository == None:\n            remote_used = download_package_metadata(remotes, package_name)\n            if remote_used:\n                info(f\"{progress_indicator} Downloaded {package_name} package metadata from {remote_used}\")\n\n        if not os.path.exists(\"yacpkg.json\"):\n            open(\"yacpkg.json\", \"w\").write(\"{}\")\n\n        yacpkg_file, yacpkg = open_read_write(\"yacpkg.json\", True)\n\n        package_repository = package_repository or yacpkg[\"repository\"]\n        os.chdir(\"repository\")\n\n        # initialize git repository\n        if not os.path.exists(\".git\"):\n            exec_shell(\"git init\")\n            exec_shell(f\"git remote add origin {package_repository}\")\n            yacpkg[\"^current_version\"] = None\n\n        # all keys with ^ at the front was created by this script\n        if yacpkg.get(\"^current_version\") != package_version:\n            info(f\"{progress_indicator} Fetching {package_name}@{package_version} at {package_repository}\")\n\n            # freeze package versions that use commit hashes\n            package_version = parse_package_version(package_version, package_repository)\n\n            if isinstance(package_info, str):\n                package_list[package_name] = package_version\n            else:\n                package_info[\"version\"] = package_version\n\n            if package_name in package_deps_combined:\n                package_deps_combined[package_name][\"version\"] = package_version\n\n            yacpkg[\"^current_version\"] = package_version\n            yacpkg[\"^sparse_checkout_list\"] = \"\"\n\n        prepend_cmake = generate_cmake_variables(package_info)\n\n        cmake_lists_content = open(\"../CMakeLists-downloaded.txt\").read()\n        open(\"../CMakeLists.txt\", \"w\").write(prepend_cmake + cmake_lists_content)\n\n        download_print = f\"{progress_indicator} Downloading files for {package_name}\"\n        if p_name_to_dependent and package_name in p_name_to_dependent:\n            download_print += f\" (required by {', '.join(p_name_to_dependent[package_name])})\"\n        download_package_files(yacpkg, package_info, download_print)\n        write_json(yacpkg, yacpkg_file)\n\n        # run potential yacpm config inside the yacpkgs config\n        if \"yacpm\" in yacpkg:\n            json.dump(yacpkg[\"yacpm\"], open(\"yacpm.json\", \"w\"))\n            exec_shell(f\"\\\"{sys.executable}\\\" {__file__} {TOP_LEVEL_CMAKE_DIR}\")\n\n        if os.path.isfile(\"yacpm.json\"):\n            get_package_dependencies(package_deps_combined, remotes, name_to_dependent, package_name)\n\n        os.chdir(TOP_LEVEL_CMAKE_DIR)\n\n    # use package_dep_names since package_deps_combined is a combination of all\n    # iteration while package_dep_names contains package names only from this iteration\n    if name_to_dependent:\n        info(f\"Calculating dependencies: {', '.join(name_to_dependent.keys())}\")\n        get_packages(package_deps_combined, remotes, package_deps_combined, name_to_dependent)\n\ndef update_package_list_deps(dependency_packages: dict, package_list: dict, package_deps_combined: dict):\n    for package_name, package_info in package_deps_combined.items():\n        # remove missing dependents\n        dependents = package_info[\"dependents\"]\n        has_parsed_dep = isinstance(dependents, set)\n        if has_parsed_dep:\n            dependents.difference_update(package_info[\"dependents_left\"])\n\n        # if no package depends on this package move it back to normal package list\n        if len(dependents) == 0 or not has_parsed_dep:\n            package_list[package_name] = dependency_packages[package_name]\n            dependency_packages.pop(package_name)\n            continue\n\n        if package_name in package_list:\n            # move package from yacpm.packages to dependency packages list\n            pkg_list_pkg = package_list[package_name]\n            dependency_packages[package_name] = { \"version\": pkg_list_pkg } if isinstance(pkg_list_pkg, str) else pkg_list_pkg\n            package_list.pop(package_name)\n        elif not isinstance(dependency_packages.get(package_name), dict):\n            dependency_packages[package_name] = { \"version\": package_info[\"version\"] }\n\n        dependency_packages[package_name][\"dependents\"] = list(dependents)\n\nif __name__ == \"__main__\":\n    # load yacpm.json\n    yacpm_file, yacpm = open_read_write(\"yacpm.json\", True)\n    verbose = yacpm.get(\"verbose\")\n    \n    package_list = yacpm[\"packages\"]\n    if not isinstance(package_list, dict):\n        error(\"Expected yacpm.json to have a packages field that is an object!\")\n\n    if not os.path.isdir(\"yacpkgs\"):\n        os.mkdir(\"yacpkgs\")\n\n    all_package_names = list(package_list.keys())\n\n    # only do if is top level yacpm or if the top level yacpm.json doesn't exist\n    # in order to handle multiple packages using the same package\n    if TOP_LEVEL_CMAKE_DIR == os.getcwd() or not os.path.isfile(f\"{TOP_LEVEL_CMAKE_DIR}/yacpm.json\"):\n        remotes = set(yacpm.get(\"remotes\", [\"DEFAULT_REMOTE\"]))\n        dependency_packages = yacpm.get(\"dependency_packages\", {})\n        package_deps_combined = deepcopy(dependency_packages)\n        get_packages(package_list, remotes, package_deps_combined)\n\n        if package_deps_combined:\n            update_package_list_deps(dependency_packages, package_list, package_deps_combined)\n            all_package_names.extend(dependency_packages.keys())\n            if \"dependency_packages\" not in yacpm:\n                yacpm[\"dependency_packages\"] = dependency_packages\n\n        write_json(yacpm, yacpm_file)\n\n        # prune unused packages in yacpkgs\n        for directory in next(os.walk(\"yacpkgs\"))[1]:\n            if directory not in package_list and directory not in dependency_packages:\n                info(f\"Removing unused package {directory}\")\n                shutil.rmtree(f\"yacpkgs/{directory}\")\n\n    # write yacpkgs/packages.cmake\n    packages_cmake_output = f\"set(YACPM_PKGS {' '.join(all_package_names)})\\n\\n\"\n    for name in all_package_names:\n        packages_cmake_output += f\"if(NOT TARGET {name})\\n\"\n        packages_cmake_output += f\"    add_subdirectory(${{CMAKE_SOURCE_DIR}}/yacpkgs/{name} yacpkgs/{name})\\n\"\n        packages_cmake_output +=  \"endif()\\n\"\n    open(\"yacpkgs/packages.cmake\", \"w\").write(packages_cmake_output)\n","sub_path":"yacpm.py","file_name":"yacpm.py","file_ext":"py","file_size_in_byte":15497,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"207206827","text":"import math\nimport os\nimport random\nimport re\nimport sys\n\n\n\ndef hourglassSum(arr):\n    for column in arr[0]:\n        for row in column:\n            print(row)\n        \n\n    return 0\n\nif __name__ == '__main__':\n    arr = [\n        [1, 1, 1, 0, 0, 0],\n        [0, 1, 0, 0, 0, 0],\n        [1, 1, 1, 0, 0, 0],\n        [0, 0, 2, 4, 4, 0],\n        [0, 0, 0, 2, 0, 0],\n        [0, 0, 1, 2, 4, 0],\n    ]\n    result = hourglassSum(arr)\n    print(result)","sub_path":"Arrays/Hour Glass/HourGlass.py","file_name":"HourGlass.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"322298712","text":"\nfrom prompt_toolkit import PromptSession\nfrom prompt_toolkit.completion import WordCompleter\n\nfrom concurrent.futures import ThreadPoolExecutor\nimport logging\nimport os\nimport shlex\n\nfrom state import State\nfrom completer import ShellCompleter\nfrom handler import meta_handlers, task_handlers, load_handlers\nimport config\nimport constants\nimport mlogger\n\n# Configurations\nload_handlers()\n\ndef handle_command(command_with_args, state, logger):\n    command, *args = command_with_args\n    \n    if command in task_handlers:\n        try:\n            with ThreadPoolExecutor(max_workers=len(state.browsers), thread_name_prefix=\"thread\") as executor:\n                for browser in state.get_target_browser_names():\n                    executor.submit(task_handlers[command],\n                                    browser,\n                                    state.browsers[browser],\n                                    logger,\n                                    args)\n            logger.info(\"Finished.\")\n        except Exception as e:\n            logger.error(\"An error occured while executing {} in {} with arguments {}.\".format(command, state.browsers, args))\n            logger.debug(\"{}\".format(e))\n\n    elif command in meta_handlers:\n        meta_handlers[command](state, logger, args)\n    else:\n        logger.error(\"Command not found: {}\".format(command))\n    \n# Act as an interpreter until EOF.\ndef interpreter():\n    logger = mlogger.getLogger(__name__)\n    state = State(logger)\n    \n    command_completer = ShellCompleter(state)\n    psession = PromptSession(completer=command_completer)\n\n\n    if config.is_state_saving_enabled:\n        if os.path.exists(config.default_path_to_state_file):\n            logger.info(\"Restoring state ...\")\n            state.restore(config.default_path_to_state_file)\n    \n    while True:\n        try:\n            input_raw = psession.prompt(\"[{}]> \".format(state.current_target))\n            try:\n                tokens = list(shlex.shlex(input_raw, punctuation_chars=True))\n                colon_indices = [i for i in range(0, len(tokens)) if tokens[i] == \";\"]\n                for start_index, end_index in zip([0] + list(map(lambda x: x+1, colon_indices)), colon_indices + [len(tokens)]):\n                    command_with_args = [t[1:-1] if t.startswith('\"') and t.endswith('\"') else t\n                                         for t in tokens[start_index:end_index]]\n                    handle_command(command_with_args, state, logger)\n            except Exception as e:\n                logger.error(\"An error ocurred when handling commands.\")\n                logger.debug(\"{}\".format(e))\n            if not state.is_running:\n                break        \n        except KeyboardInterrupt:\n            continue\n        \n        except EOFError:\n            break\n        \n        except Exception as e:\n            logger.error(\"An unknown error occured :-(\")\n            logger.debug(\"{}\".format(e))\n            break\n\n    logger.info(\"Saving state...\")\n    state.save(config.default_path_to_state_file)\n    logger.info(\"Done. Disconnecting...\")\n    handle_command([\"disconnect\", \"all\"], state, logger)\n    logger.info(\"Done. Bye! :-)\")\n\n\nif __name__ == '__main__':\n    interpreter()\n","sub_path":"src/interpreter/interpreter.py","file_name":"interpreter.py","file_ext":"py","file_size_in_byte":3224,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"339649883","text":"import keras\nfrom keras.models import Sequential, load_model\nfrom keras.optimizers import Adam\nfrom keras.layers import Dense, Dropout, LSTM, Flatten, BatchNormalization, TimeDistributed, Activation\nfrom sklearn.model_selection import train_test_split\nimport tensorflow as tf\nimport numpy as np\nfrom log import log\n\nX = np.load(\"gesture_data/x.npy\")\ny = np.load(\"gesture_data/y.npy\")\n\nx_train, x_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 4)\n\nx_train = keras.utils.normalize(x_train, axis = 1)\nx_test = keras.utils.normalize(x_test, axis = 1)\n\nepochs = 200\nbatch_size = 67\n_dropout = 0.5\n_activation='relu'\n_optimizer='adam'\ninput_shape = x_train.shape[1], x_train.shape[2]\nmodel = Sequential()\nmodel.add(LSTM(128, input_shape=input_shape, return_sequences = True))\nmodel.add(Dropout(_dropout))                             \nmodel.add(BatchNormalization())\n\nmodel.add(LSTM(128, input_shape=input_shape, return_sequences = True))\nmodel.add(Dropout(_dropout))                             \nmodel.add(BatchNormalization())\n\nmodel.add(LSTM(64, input_shape=input_shape, return_sequences = True))\nmodel.add(Dropout(_dropout))                             \nmodel.add(BatchNormalization())\n\nmodel.add(LSTM(32, input_shape=input_shape, return_sequences = True))\nmodel.add(Dropout(_dropout))                             \nmodel.add(BatchNormalization())\n\nmodel.add(TimeDistributed(Dense(3))) \n\nmodel.add(Flatten())\nmodel.add(Dense(y_train.shape[1], activation = tf.nn.softmax))\nmodel.add(Activation(_activation))\n\nmodel.compile(optimizer=_optimizer,\n               loss='categorical_crossentropy',\n               metrics=['accuracy'])\nmodel.fit(x_train, y_train, epochs=epochs,batch_size=batch_size)\nmodel.summary()\nloss, acc = model.evaluate(x_test, y_test,\n                                batch_size=batch_size)\n\nlog('Loss: {:.3}'.format(loss))\nlog('Acc: {:.3}'.format(acc))\n\nmodel.save('gesture_data/pointing.h5')\n","sub_path":"vision/gesture/gesture_data/rnn_lstm_classifier.py","file_name":"rnn_lstm_classifier.py","file_ext":"py","file_size_in_byte":1939,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"602894267","text":"valor=input(\"Qual o valor da casa?\")\nsalario=input(\"Qual o valor do seu salário?\")\nanos=input(\"Em quantos anos planeja pagar?\")\nmeses=anos*12\nprestacaomensal=valor/meses\nmaximo=salario*0,3\nif prestacaomensal<maximo:\n    print('Empréstimo aprovado')\nelse:\n    print('Empréstimo não aprovado')","sub_path":"backup/user_138/ch26_2020_04_06_19_14_52_994532.py","file_name":"ch26_2020_04_06_19_14_52_994532.py","file_ext":"py","file_size_in_byte":295,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"232770345","text":"import networkx as nx\nimport json\n\n\nG = nx.read_gml('metabolicpos.gml',label='id')\n\nnodes = G.nodes()\nedges = G.edges()\n\n\nelements = []\nlen_nodes = len(nodes)\nlen_edges = len(edges)\n\nfor node_id in range(len_nodes):\n    node = nodes[node_id]\n    label = int(node['label'])\n    posX = int(node['graphics']['x'])\n    posY = int(node['graphics']['y'])\n    u = {'data':{'id':(\"%d\" %node_id),'weight':label,'posX':posX,'posY':posY}}\n    elements.append(u)\n\nedge_id = 0\nfor edge in edges:\n    source = edge[0]\n    target = edge[1]\n    u = {'data':{'id':(\"%d_%d_%d\" %(source,target,edge_id)),'source':(\"%d\" %source),'target':(\"%d\" %target)}}\n    edge_id +=1\n    elements.append(u)\n\nwith open('metabolicpos.json','w') as fp:\n    ele = json.dump(elements,fp)\nprint(\"ok\")","sub_path":"front/data/metabolic/a.py","file_name":"a.py","file_ext":"py","file_size_in_byte":761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"87448012","text":"import numpy as np\r\nimport pandas as pd\r\nfrom ete3 import Tree, TreeStyle\r\n\r\n\r\n\r\n\r\n\r\ndef build_dictionary(tree_sequence, filename='one_tree.csv'):\r\n\r\n    depths = np.array(tree_sequence, dtype=int)[:,2]\r\n    transitions = np.array(tree_sequence,dtype=int)[:,:2]\r\n    counts = np.max(np.bincount(np.array(depths)))\r\n        \r\n    dct = {}\r\n    for depth in list(set(depths)):\r\n        dct[depth] = []\r\n    for depth in list(set(depths)):\r\n        dct['{} confidents'.format(depth)] = []\r\n    for idx, depth in enumerate(depths):\r\n        dct[depth].append(transitions[idx])\r\n        dct['{} confidents'.format(depth)].append(tree_conf[5][idx])\r\n    for depth in list(set(depths)):\r\n        dct[depth] += [''] * (counts-len(dct[depth]))\r\n        dct['{} confidents'.format(depth)] += [''] * (counts-len(dct['{} confidents'.format(depth)]))\r\n        \r\n    df = pd.DataFrame.from_dict(dct)\r\n    df.to_csv(filename)\r\n    \r\n    return dct\r\n\r\none_tree = seqs[8]\r\nconfidences = tree_conf[8]\r\n\r\n\r\ndef build_tree(one_tree, confidences):\r\n    # one_tree - egy lista, amely a szekvenciasorozatokat tartalmazza, amiket lépésekben generál\r\n    # confidences - a generálási lépéshez tartozó konfidencia\r\n    \r\n    confidences = np.insert(confidences, 0, 1.0)\r\n    tree = Tree()\r\n    nodes = {'': [1.0, tree]}\r\n    for idx, seq in enumerate(one_tree):\r\n        parent = ''\r\n        for i in range(len(seq)): \r\n            if not str(seq[:i+1]) in tree:\r\n                \r\n                nodes[str(seq[:i+1])] = []\r\n                nodes[str(seq[:i+1])].append(confidences[idx] * nodes[parent][0]) \r\n                nodes[str(seq[:i+1])].append(nodes[parent][1].add_child(name=str(seq), dist=nodes[str(seq[:i+1])][0]))\r\n\r\n            else:\r\n                parent = str(seq[:i+1])\r\n    \r\n    ts = TreeStyle()\r\n    ts.show_leaf_name = True\r\n    ts.show_branch_length = True\r\n    ts.show_branch_support = True\r\n    print(tree)\r\n    \r\n    return tree\r\n\r\n\r\ndef find_frequency(one_tree, decoder_input_data):\r\n    \r\n    def rolling_window(a, size):\r\n        shape = a.shape[:-1] + (a.shape[-1] - size + 1, size)\r\n        strides = a.strides + (a. strides[-1],)\r\n        \r\n        return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)\r\n\r\n\r\n    occurances = []\r\n    for generated_sequence in one_tree:\r\n        \r\n        occurance = 0\r\n        for decoder_data in decoder_input_data:\r\n            if np.sum(np.all(rolling_window(decoder_data,len(generated_sequence)) == generated_sequence, axis=1)):\r\n                occurance += 1\r\n        occurances.append(occurance / len(decoder_input_data))\r\n        \r\n    return occurances\r\n\r\n\r\nprint('finished..')","sub_path":"seq2seq/evaluate.py","file_name":"evaluate.py","file_ext":"py","file_size_in_byte":2655,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"142265668","text":"#The football.csv file contains the results from the English Premier League. \n# The columns labeled ‘Goals’ and ‘Goals Allowed’ contain the total number of \n# goals scored for and against each team in that season (so Arsenal scored 79 goals \n# against opponents, and had 36 goals scored against them). Write a program to read the file, \n# then print the name of the team with the smallest difference in ‘for’ and ‘against’ goals.\n\n# The below skeleton is optional.  You can use it or you can write the script with an approach of your choice.\n\n# Team, Games, Wins, Losses, Draws, Goals, Goals Allowed, Points\n\nimport csv\n\nf = open('football.csv', 'r')\nread = list(csv.reader(f))[1:]\nfor i in read:\n    i[1:] = list(map(lambda x : int(x), i[1:]))\nfootball = {i[0] : i[1:] for i in read}\n\ndef get_min_score_difference(football):\n    diff = [ [k, abs(v[4]-v[5])] for k,v in list(football.items())]\n    return sorted(diff, key = lambda x : x[1])[0]\n\nprint(get_min_score_difference(football))","sub_path":"python/q8_parsing.py","file_name":"q8_parsing.py","file_ext":"py","file_size_in_byte":1002,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"313889275","text":"import torch\nfrom ..builder import PROCESSOR\nfrom .baseprocessor import BaseProcessor\nfrom ..builder import build_vocab, build_preprocessor\n\n\n@PROCESSOR.register_module()\nclass Processor(BaseProcessor):\n    \"\"\"Tokenizes a sentence and processes it.\n\n    Attributes:\n        tokenizer (function): Type of tokenizer to be used.\n    \"\"\"\n\n    def __init__(self, *args, **kwargs):\n        from imix.utils.third_party_libs import tokenize\n\n        self.tokenizer = tokenize\n\n    def __call__(self, item, *args, **kwargs):\n        return {'text': self.tokenizer(item['text'], *args, **kwargs)}\n\n\n@PROCESSOR.register_module()\nclass VocabProcessor(BaseProcessor):\n    \"\"\"Use VocabProcessor when you have vocab file and you want to process\n    words to indices. Expects UNK token as \"<unk>\" and pads sentences using\n    \"<pad>\" token. Config parameters can have ``preprocessor`` property which\n    is used to preprocess the item passed and ``max_length`` property which\n    points to maximum length of the sentence/tokens which can be convert to\n    indices. If the length is smaller, the sentence will be padded. Parameters\n    for \"vocab\" are necessary to be passed.\n\n    **Key**: vocab\n\n    Example Config::\n\n        task_attributes:\n            vqa:\n                vqa2:\n                    processors:\n                      text_processor:\n                        type: vocab\n                        params:\n                          max_length: 14\n                          vocab:\n                            type: intersected\n                            embedding_name: glove.6B.300d\n                            vocab_file: vocabs/vocabulary_100k.txt\n\n    Args:\n        config (DictConfig): node containing configuration parameters of\n                             the processor\n\n    Attributes:\n        vocab (Vocab): Vocab class object which is abstraction over the vocab\n                       file passed.\n    \"\"\"\n\n    MAX_LENGTH_DEFAULT = 50\n    PAD_TOKEN = '<pad>'\n    PAD_INDEX = 0\n\n    def __init__(self,\n                 vocab=dict(\n                     type='IntersectedVocab',\n                     vocab_file='textvqa/defaults/extras/vocabs/vocabulary_100k.txt',\n                     embedding_name='glove.6B.300d'),\n                 preprocessor=dict(type='SimpleSentenceProcessor'),\n                 *args,\n                 **kwargs):\n\n        # self.vocab = Vocab(*args, **config.vocab, **kwargs)\n        self.vocab = build_vocab(vocab)\n        self.max_length = self.MAX_LENGTH_DEFAULT\n        self.preprocessor = build_preprocessor(preprocessor)\n\n        # self._init_extras(config)\n\n    # def _init_extras(self, config, *args, **kwargs):\n    #     self.writer = registry.get(\"writer\")\n    #     self.preprocessor = None\n    #\n    #     if hasattr(config, \"max_length\"):\n    #         self.max_length = config.max_length\n    #     else:\n    #         warnings.warn(\n    #             \"No 'max_length' parameter in Processor's \"\n    #             \"configuration. Setting to {}.\".format(self.MAX_LENGTH_DEFAULT)\n    #         )\n    #         self.max_length = self.MAX_LENGTH_DEFAULT\n    #\n    #     if \"preprocessor\" in config:\n    #         self.preprocessor = Processor(config.preprocessor, *args, **kwargs)\n    #\n    #         if self.preprocessor is None:\n    #             raise ValueError(\n    #                 f\"No text processor named {config.preprocessor} is defined.\"\n    #             )\n\n    def __call__(self, item):\n        \"\"\"Call requires item to have either \"tokens\" attribute or either\n        \"text\" attribute. If \"text\" is present, it will tokenized using the\n        preprocessor.\n\n        Args:\n            item (Dict): Dict containing the \"text\" or \"tokens\".\n\n        Returns:\n            Dict: Dict containing indices in \"text\" key, \"tokens\" in \"tokens\"\n                  key and \"length\" of the string in \"length\" key.\n        \"\"\"\n        indices = None\n        if not isinstance(item, dict):\n            raise TypeError('Argument passed to the processor must be '\n                            \"a dict with either 'text' or 'tokens' as \"\n                            'keys')\n        if 'tokens' in item:\n            tokens = item['tokens']\n            indices = self._map_strings_to_indices(item['tokens'])\n        elif 'text' in item:\n            if self.preprocessor is None:\n                raise AssertionError('If tokens are not provided, a text ' 'processor must be defined in the config')\n\n            tokens = self.preprocessor({'text': item['text']})['text']\n            indices = self._map_strings_to_indices(tokens)\n        else:\n            raise AssertionError(\"A dict with either 'text' or 'tokens' keys \" 'must be passed to the processor')\n\n        tokens, length = self._pad_tokens(tokens)\n\n        return {'text': indices, 'tokens': tokens, 'length': length}\n\n    def _pad_tokens(self, tokens):\n        padded_tokens = [self.PAD_TOKEN] * self.max_length\n        token_length = min(len(tokens), self.max_length)\n        padded_tokens[:token_length] = tokens[:token_length]\n        token_length = torch.tensor(token_length, dtype=torch.long)\n        return padded_tokens, token_length\n\n    def get_pad_index(self):\n        \"\"\"Get index of padding <pad> token in vocabulary.\n\n        Returns:\n            int: index of the padding token.\n        \"\"\"\n        return self.vocab.get_pad_index()\n\n    def get_vocab_size(self):\n        \"\"\"Get size of the vocabulary.\n\n        Returns:\n            int: size of the vocabulary.\n        \"\"\"\n        return self.vocab.get_size()\n\n    def _map_strings_to_indices(self, tokens):\n        length = min(len(tokens), self.max_length)\n        tokens = tokens[:length]\n\n        output = torch.zeros(self.max_length, dtype=torch.long)\n        output.fill_(self.vocab.get_pad_index())\n\n        for idx, token in enumerate(tokens):\n            output[idx] = self.vocab.stoi[token]\n\n        return output\n","sub_path":"imix/data/vqadata/processor.py","file_name":"processor.py","file_ext":"py","file_size_in_byte":5883,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"476758633","text":"import argparse\nimport numpy as np\nimport threading\n\nfrom copy import deepcopy\nfrom os import system\n\nimport rospy\n\nimport cv2\n\nfrom cv2 import cv\nfrom cv_bridge import CvBridge\n\nclass image_test():\n    def __init__(self, image):\n        pass\n\n    def _show_image(self):\n        self.subLock.acquire(True)\n        local_image = deepcopy(self._np_image)\n        self.subLock.release()\n\n        for idx, points in enumerate(self._roi_points):\n            cv2.circle(local_image, (points[0], points[1]), 5, (255, 0, 0), 2)\n\n        cv2.polylines(local_image, np.int32([np.array(self._roi_points)]),\n                      1, (0, 255, 0), 2)\n\n        cv2.imshow(\"Connect Four RGB\", local_image)\n\n        cv.SetMouseCallback(\"Connect Four RGB\", self._on_mouse_click, 0)\n        cv.CreateTrackbar(\"Gain\", \"Connect Four RGB\", self._gain_slider,\n                          100, self._on_gain_slider)\n        cv.CreateTrackbar(\"Red Threshold\", \"Connect Four RGB\",\n                          self._red_thresh, 500, self._on_red_slider)\n        cv.CreateTrackbar(\"Yellow Threshold\", \"Connect Four RGB\",\n                          self._yellow_thresh, 500, self._on_yellow_slider)\n        cv.WaitKey(3)\n\n    def _on_gain_slider(self, pos):\n        self._gain_slider = pos\n        self._gain_set = True\n        self._slider_time = rospy.Time.now()\n\n    def _on_red_slider(self, pos):\n        self._red_thresh = pos\n\n    def _on_yellow_slider(self, pos):\n        self._yellow_thresh = pos\n\n    def _on_mouse_click(self, event, x, y, flags, param):\n        if event == cv.CV_EVENT_LBUTTONDOWN:\n            width = self.cv_image.shape[0]\n            height = self.cv_image.shape[1]\n            for idx, points in enumerate(self._roi_points):\n                if (x <= points[0] + 5 and x >= points[0] - 5\n                    and y <= points[1] + 5 and y >= points[1] - 5):\n                    self._roi_move = True\n                    self._point_selected = idx\n\n        elif event == cv.CV_EVENT_MOUSEMOVE and self._roi_move:\n            self._roi_points[self._point_selected] = [x, y]\n\n        elif event == cv.CV_EVENT_LBUTTONUP and self._roi_move:\n            self._roi_move = False\n","sub_path":"temp/open_cv_slider_example.py","file_name":"open_cv_slider_example.py","file_ext":"py","file_size_in_byte":2166,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"524989712","text":"from django.shortcuts import render\nfrom robust.checker import *\nfrom team.models import *\nfrom utilities.common import current_time, encode\nfrom django.http import HttpResponse\n\n\ndef push_msg(request):\n    data = Checker.request(request, ['user', 'content'])\n    user = Member.objects.get(name=data['user'])\n    msg = Message()\n    msg.user = user\n    msg.content = data['content']\n    msg.time = current_time()\n    msg.save()\n    return HttpResponse(encode({\"code\": 100, \"log\": \"Normal\", \"data\": None}))\n\n\ndef pull_msg(request):\n    #now = current_time()\n    msgs = Message.objects.all()\n    #later = lambda t: t >= now\n    dict_m = lambda m: {'id': m.user.name, 'time': m.time[0:10]+' '+m.time[11:16], 'content': m.content}\n    msg_q = [dict_m(m) for m in msgs]\n    msg_q.sort(key=lambda m: m['time'], reverse=True)\n    return HttpResponse(encode(msg_q))\n\n\ndef pull_bug(request):\n    bugs = Bug.objects.all()\n    dict_b = lambda b: {'src': b.source.name, 'desc': b.description,\n                        'reptr': b.reporter.name, 'checked': b.checked,\n                        'time': b.time, 'id': b.id}\n    bug_q = [dict_b(b) for b in bugs if not (b.checked and b.dealt)]\n    bug_q.sort(key=lambda b: b['time'], reverse=True)\n    return HttpResponse(encode(bug_q))\n\n\ndef report_bug(request):\n    data = Checker.request(request, ['src', 'desc', 'reptr'])\n    bug = Bug()\n    bug.source = Api.objects.get(name=data['src'])\n    bug.reporter = Member.objects.get(name=data['reptr'])\n    bug.description = data['desc']\n    bug.time = current_time()\n    bug.checked = True\n    bug.save()\n    return HttpResponse(encode({\"code\":100, \"log\": \"Normal\", \"data\": None}))\n\n\"\"\"\ndef check_bug(request):\n    data = Checker.request(request, ['id'])\n    bug = Bug.objects.get(id=data['id'])\n    bug.checked = True\n\"\"\"\n\n\ndef deal_bug(request):\n    data = Checker.request(request, ['id'])\n    bug = Bug.objects.get(id=data['id'])\n    bug.checked = True\n    bug.dealt = True\n    return HttpResponse(encode({\"code\": 100, \"log\": \"Normal\", \"data\": None}))\n\n\ndef update_api(request):\n    data = Checker.request(request, ['name', 'digest', 'url', 'response', 'request'])\n    api = Api()\n    api.request = data['request']\n    api.name = data['name']\n    api.digest = data['digest']\n    api.url = data['url']\n    api.response = data['response']\n    api.save()\n    return HttpResponse(encode({\"code\": 100, \"log\": \"Normal\", \"data\": None}))\n\n\ndef get_api(request):\n    data = Checker.request(request, ['name'])\n    api = Api.objects.get(name=data['name'])\n    return HttpResponse(encode({'url': api.url,\n                                'name': api.name,\n                                'digest': api.digest,\n                                'request': api.request,\n                                'response': api.response}))","sub_path":"team/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2794,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"142807629","text":"import logging\n\nfrom pajbot.managers import AdminLogManager\nfrom pajbot.models.command import Command\nfrom pajbot.modules import BaseModule\nfrom pajbot.modules import ModuleType\nfrom pajbot.modules.basic import BasicCommandsModule\n\nlog = logging.getLogger(__name__)\n\n\nclass AdminCommandsModule(BaseModule):\n    ID = __name__.split('.')[-1]\n    NAME = 'Basic admin commands'\n    DESCRIPTION = 'All miscellaneous admin commands'\n    CATEGORY = 'Feature'\n    ENABLED_DEFAULT = True\n    MODULE_TYPE = ModuleType.TYPE_ALWAYS_ENABLED\n    PARENT_MODULE = BasicCommandsModule\n\n    def whisper(self, **options):\n        message = options['message']\n        bot = options['bot']\n\n        if message:\n            msg_args = message.split(' ')\n            if len(msg_args) > 1:\n                username = msg_args[0]\n                rest = ' '.join(msg_args[1:])\n                bot.whisper(username, rest)\n\n    def level(self, **options):\n        message = options['message']\n        bot = options['bot']\n        source = options['source']\n\n        if message:\n            msg_args = message.split(' ')\n            if len(msg_args) > 1:\n                username = msg_args[0].lower()\n                new_level = int(msg_args[1])\n                if new_level >= source.level:\n                    bot.whisper(source.username, 'You cannot promote someone to the same or higher level as you ({0}).'.format(source.level))\n                    return False\n\n                # We create the user if the user didn't already exist in the database.\n                user = bot.users[username]\n\n                old_level = user.level\n                user.level = new_level\n\n                log_msg = '{}\\'s user level changed from {} to {}'.format(\n                        user.username_raw,\n                        old_level,\n                        new_level)\n\n                bot.whisper(source.username, log_msg)\n\n                AdminLogManager.add_entry('Userlevel edited', source, log_msg)\n\n                return True\n\n        bot.whisper(source.username, 'Usage: !level USERNAME NEW_LEVEL')\n        return False\n\n    def load_commands(self, **options):\n        self.commands['w'] = Command.raw_command(self.whisper,\n                level=2000,\n                description='Send a whisper from the bot')\n        self.commands['level'] = Command.raw_command(self.level,\n                level=1000,\n                description='Set a users level')\n","sub_path":"pajbot/modules/basic/admincommands.py","file_name":"admincommands.py","file_ext":"py","file_size_in_byte":2430,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"110146009","text":"#!/usr/bin/env python3\ntry:\n    import sys\n    import re\n    import json\n    import argparse\n    import time\n    import requests\n    import html2text\n    import hashlib\n    import difflib\n    import sqlalchemy\n    from sqlalchemy import Column, Integer, String, Table, MetaData\n    from sqlalchemy.ext.declarative import declarative_base\n    from sqlalchemy.orm import sessionmaker\nexcept ImportError:\n    print(\"\"\"Import failed make sure you have set up the virtual enviroment.\npython3 -m venv venv\nsource venv/bin/activate\npip install -r requirements.txt\"\"\")\n    exit(1)\n\ndef get_text(html):\n    \"\"\"\n    Input html.  Returns utf-8 markdown without links\n\n    requests.get().text will be used as the input data\n    html2text will be used to remove most of the changing parts of the response\n    links will be ignored since most large sites have dynamic links\n    if you want to closely monitor a basic site it is probably better to hash\n    requests.get().content and not bother stripping the html\n    \"\"\"\n    h = html2text.HTML2Text()\n    h.ignore_links = True\n    return h.handle(html)\n\ndef get_md5(html):\n    \"\"\"\n    Input html. Returns MD5 hash of the text.\n    \"\"\"\n    return hashlib.md5(get_text(html).encode('utf-8')).hexdigest()\n\ndef failed_connection(check, session):\n    current_time = time.time()\n    if not check.failed_since:\n        check.failed_since = current_time\n        session.commit()\n    if current_time - check.failed_since >= check.max_down_time:\n        print('Warning: Can\\'t connect to {}'.format(check.url))\n\n    return ''\n\ndef check_if_recovered(check, session):\n    if not check.failed_since:\n        return ''\n    check.failed_since = 0\n    session.commit()\n    last_run = check.run_after - check.check_frequency\n    if last_run - check.failed_since >= check.max_down_time:\n        print('Reastablished connection to {}'.format(check.url))\n\n    return ''\n\ndef run_checks():\n    \"\"\"Perform hash, string, difference and raw checks for all stored url's\"\"\"\n    for check in session.query(MD5Check).filter(MD5Check.run_after <\n                    time.time()).order_by(MD5Check.id):\n        check.run_after = time.time() + check.check_frequency\n        session.commit()\n        try:\n            url_content = requests.get(check.url, timeout=check.check_timeout)\n        except requests.exceptions.ConnectionError:\n            failed_connection(check, session)\n            continue\n\n        if url_content.status_code != 200:\n            failed_connection(check, session)\n            continue\n\n        check_if_recovered(check, session)\n        try:\n            new_hash = get_md5(url_content.text)\n        except:\n            print('Error: Failed to hash response from {}'.format(check.url))\n            continue\n\n        if new_hash != check.current_hash:\n            if new_hash == check.old_hash:\n                print('The md5 for {} has been reverted'.format(check.url))\n            else:\n                print('The md5 for {} has changed'.format(check.url))\n\n            check.old_hash = check.current_hash\n            check.current_hash = new_hash\n            session.commit()\n\n    for check in session.query(StringCheck).filter(StringCheck.run_after <\n                    time.time()).order_by(StringCheck.id):\n        check.run_after = time.time() + check.check_frequency\n        session.commit()\n        try:\n            url_content = requests.get(check.url, timeout=check.check_timeout)\n        except requests.exceptions.ConnectionError:\n            failed_connection(check, session)\n            continue\n\n        if url_content.status_code != 200:\n            failed_connection(check, session)\n            continue\n\n        check_if_recovered(check, session)\n        string_found = check.string_to_match in get_text(url_content.text)\n        if string_found != check.present:\n            if check.present:\n                print('{} is no longer present on {}'.format(\n                                                        check.string_to_match,\n                                                        check.url))\n                check.present = 0\n            else:\n                print('{} is now present on {}'.format(check.string_to_match,\n                                                    check.url))\n                check.present = 1\n\n            session.commit()\n\n    for check in session.query(DiffCheck).filter(DiffCheck.run_after <\n                    time.time()).order_by(DiffCheck.id):\n        check.run_after = time.time() + check.check_frequency\n        session.commit()\n        try:\n            url_content = requests.get(check.url, timeout=check.check_timeout)\n        except requests.exceptions.ConnectionError:\n            failed_connection(check, session)\n            continue\n\n        if url_content.status_code != 200:\n            failed_connection(check, session)\n            continue\n\n        check_if_recovered(check, session)\n        text = get_text(url_content.text)\n        if text != check.current_content:\n            for line in difflib.context_diff(check.current_content.split('\\n'),\n                            text.split('\\n'),\n                            fromfile='Old content for {}'.format(check.url),\n                            tofile='New content for {}'.format(check.url)):\n                print(line)\n            check.current_content = text\n            session.commit()\n\n    for check in session.query(RawCheck).filter(RawCheck.run_after <\n                    time.time()).order_by(RawCheck.id):\n        check.run_after = time.time() + check.check_frequency\n        session.commit()\n        try:\n            url_content = requests.get(check.url, timeout=check.check_timeout)\n        except requests.exceptions.ConnectionError:\n            failed_connection(check, session)\n            continue\n\n        if url_content.status_code != 200:\n            failed_connection(check, session)\n            continue\n\n        check_if_recovered(check, session)\n        try:\n            new_hash = hashlib.md5(url_content.text.encode('utf-8')).hexdigest()\n        except:\n            print('Error: Failed to hash response from {}'.format(check.url))\n            continue\n\n        if new_hash == check.current_hash:\n            continue\n\n        check.old_hash = check.current_hash\n        session.commit()\n        try:\n            m = re.search(check.expression, url_content.text, re.S)\n        except:\n            # I couldn't catch the sre_constants.error I'm looking for so...\n            print('Error: invalid regular expression')\n            continue\n\n        try:\n            capture_groups = m.groups()\n        except AttributeError:\n            print('Error: no matches for regular expression on {}'.format(url))\n            continue\n\n        try:\n            old_capture_groups = tuple(json.loads(check.capture_groups))\n        except:\n            print('Error: could not retreive data for raw check of {}'.format(\n                                                                    check.url))\n            continue\n\n        if capture_groups == old_capture_groups:\n            continue\n\n        print('RawCheck with expression {} changed for {}').format(\n                                                            check.expression,\n                                                            check.url)\n        for count, old_capture_group in enumerate(old_capture_groups):\n            if old_capture_group != capture_groups[count]:\n                print('{} has been changed to {}'.format(old_capture_group,\n                                                    capture_groups[count]))\n\n        check.capture_groups = json.dumps(capture_groups)\n        session.commit()\n\n    return ''\n\ndef validate_input(max_down_time, check_frequency, check_timeout):\n    \"\"\"\n    Check's integers are given and that check_timeout is positive.\n\n    Negative max_down_time and check_frequency values have no purpose but are\n    still a valid input.  The check would run each time the script is called and\n    alert if a connection failed, values of 0 will have the same effect.\n    \"\"\"\n    try:\n        max_down_time = int(max_down_time)\n    except ValueError:\n        print('Error: max_down_time {} given, must be an integer'.format(\n                                                                max_down_time))\n        exit(1)\n\n    try:\n        check_frequency = int(check_frequency)\n    except ValueError:\n        print('Error: check_frequency {} given, must be an integer'.format(\n                                                            check_frequency))\n        exit(1)\n\n    try:\n        check_timeout = int(check_timeout)\n    except ValueError:\n        print('Error: check_timeout {} given, must be an integer'.format(\n                                                                check_timeout))\n        exit(1)\n\n    if not check_timeout > 0:\n        print('Error: check-timeout {} given, must be greater than 0'.format(\n                                                                check_timeout))\n        exit(1)\n\n    return (max_down_time, check_frequency, check_timeout)\n\ndef add_md5(url, max_down_time, check_frequency, check_timeout):\n    \"\"\"\n    Add a database entry for a url to monitor the md5 hash of.  Returns message\n    relating to success.\n    \"\"\"\n    max_down_time, check_frequency, check_timeout = validate_input(\n        max_down_time, check_frequency, check_timeout)\n    try:\n        url_content = requests.get(url, timeout=check_timeout)\n    except requests.exceptions.ConnectionError:\n        return 'Error: Could not connect to chosen url {}'.format(url)\n    except requests.exceptions.MissingSchema as e:\n        return e\n    except requests.exceptions.InvalidSchema as e:\n        return e\n\n    if url_content.status_code != 200:\n        return 'Error: {} code from server'.format(url_content.status_code)\n\n    try:\n        current_hash = get_md5(url_content.text)\n    except:\n        return 'Error: Failed to hash response from {}'.format(url)\n    check = MD5Check(url=url,\n                current_hash=current_hash,\n                failed_since=0,\n                max_down_time=max_down_time,\n                run_after=0,\n                check_frequency=check_frequency,\n                check_timeout=check_timeout)\n    session.add(check)\n    try:\n        session.commit()\n    except sqlalchemy.exc.IntegrityError:\n        session.rollback()\n        return 'Error: An entry for {} is already in database'.format(url)\n    else:\n        return 'Added MD5 Check for {}'.format(url)\n\ndef add_string(url, string, max_down_time, check_frequency, check_timeout):\n    \"\"\"\n    Add a database entry for a url to monitor for a string.  Returns message\n    relating to success.\n    \"\"\"\n    max_down_time, check_frequency, check_timeout = validate_input(\n        max_down_time, check_frequency, check_timeout)\n    try:\n        url_content = requests.get(url, timeout=check_timeout)\n    except requests.exceptions.ConnectionError:\n        return 'Error: Could not connect to chosen url {}'.format(url)\n    except requests.exceptions.MissingSchema as e:\n        return e\n    except requests.exceptions.InvalidSchema as e:\n        return e\n\n    if url_content.status_code != 200:\n        return 'Error: {} code from server'.format(url_content.status_code)\n\n    string_exists = 0\n    if string in get_text(url_content.text):\n        string_exists = 1\n\n    check = StringCheck(url=url,\n                    string_to_match=string,\n                    present=string_exists,\n                    failed_since=0,\n                    max_down_time=max_down_time,\n                    run_after= 0,\n                    check_frequency=check_frequency,\n                    check_timeout=check_timeout)\n    session.add(check)\n    try:\n        session.commit()\n    except sqlalchemy.exc.IntegrityError:\n        session.rollback()\n        return 'Error: An entry for {} is already in database'.format(url)\n    else:\n        if string_exists:\n            print('{} is currently present, will alert if this changes'.format(\n                                                                    string))\n        else:\n            print('{} is currently not present, will alert if this changes'\n.format(string))\n\n        return 'Added String Check for {}'.format(url)\n\ndef add_diff(url, max_down_time, check_frequency, check_timeout):\n    \"\"\"\n    Add a database entry for a url to monitor for any text changes.\n    Returns message relating to success.\n    \"\"\"\n    max_down_time, check_frequency, check_timeout = validate_input(\n        max_down_time, check_frequency, check_timeout)\n    try:\n        url_content = requests.get(url, timeout=check_timeout)\n    except requests.exceptions.ConnectionError:\n        return 'Error: Could not connect to chosen url {}'.format(url)\n    except requests.exceptions.MissingSchema as e:\n        return e\n    except requests.exceptions.InvalidSchema as e:\n        return e\n\n    if url_content.status_code != 200:\n        return 'Error: {} code from server'.format(url_content.status_code)\n\n    check = DiffCheck(url=url,\n                    current_content=get_text(url_content.text),\n                    failed_since=0,\n                    max_down_time=max_down_time,\n                    run_after=0,\n                    check_frequency=check_frequency,\n                    check_timeout=check_timeout)\n    session.add(check)\n    try:\n        session.commit()\n    except sqlalchemy.exc.IntegrityError:\n        session.rollback()\n        return 'Error: An entry for {} is already in database'.format(url)\n    else:\n        return 'Added Diff Check for {}'.format(url)\n\ndef add_raw(url, expression, max_down_time, check_frequency, check_timeout):\n    \"\"\"\n    Add a database entry for a url to monitor for a change using regex.\n    Returns message relating to success.\n    \"\"\"\n    max_down_time, check_frequency, check_timeout = validate_input(\n        max_down_time, check_frequency, check_timeout)\n    try:\n        url_content = requests.get(url, timeout=check_timeout)\n    except requests.exceptions.ConnectionError:\n        return 'Error: Could not connect to chosen url {}'.format(url)\n    except requests.exceptions.MissingSchema as e:\n        return e\n    except requests.exceptions.InvalidSchema as e:\n        return e\n\n    if url_content.status_code != 200:\n        return 'Error: {} code from server'.format(url_content.status_code)\n\n    try:\n        # Not sure if I want to hash .text.encode('utf-8') or .content\n        current_hash = hashlib.md5(url_content.text.encode('utf-8')).hexdigest()\n    except:\n        return 'Error: Failed to hash response from {}'.format(url)\n\n    try:\n        m = re.search(expression, url_content.text, re.S)\n        # This regex is too expensive\n        #\n        # Maybe I can remove the multi line, it is making it harder to match\n        # the exact bit I am interested in\n        #\n        # Maybe the regex should be matched as many times as possible but\n        # encourage a simpler check\n        #\n        # I feel this currently is encouraging an expression like\n        # <static tags>Constant Title(\\w*)<tags>.*<tags>Title tens of lines\n        # further down the page(.*?)<tags>\n        # which is a pain to write and runs for way too long\n        #\n        # Also when do people care about the html, there should probably at\n        # least be an option to have it stripped out all it's doing is making\n        # a mess of my regex or putting .* and .*? everywhere\n    except:\n        # I couldn't catch the sre_constants.error I'm looking for so...\n        return 'Error: invalid regular expression'\n\n    try:\n        capture_groups = m.groups()\n    except AttributeError:\n        return 'Error: no matches for regular expression on {}'.format(url)\n\n    json_capture_groups = json.dumps(capture_groups)\n    check = RawCheck(url=url,\n                expression=expression,\n                current_hash=current_hash,\n                capture_groups=json_capture_groups,\n                failed_since=0,\n                max_down_time=max_down_time,\n                run_after=0,\n                check_frequency=check_frequency,\n                check_timeout=check_timeout)\n    session.add(check)\n    try:\n        session.commit()\n    except sqlalchemy.exc.IntegrityError:\n        session.rollback()\n        return 'Error: An entry for {} is already in database'.format(url)\n    else:\n        for count, capture_group in enumerate(capture_groups):\n            print('{} matched capture group {}, will alert if this changes'.format(\n                                                                    capture_group,\n                                                                    count))\n\n        return 'Added Raw Check for {}'.format(url)\n\ndef get_longest_md5():\n    longest_url = 3\n    longest_current_hash = 12\n    longest_old_hash = 8\n    longest_failed_since = 12\n    longest_max_down_time = 14\n    longest_run_after = 9\n    longest_check_frequency = 15\n    longest_check_timeout = 13\n    for check in session.query(MD5Check).order_by(MD5Check.id):\n        if len(str(check.url)) > longest_url:\n            longest_url = len(str(check.url))\n        if len(str(check.current_hash)) > longest_current_hash:\n            longest_current_hash = len(str(check.current_hash))\n        if len(str(check.old_hash)) > longest_old_hash:\n            longest_old_hash = len(str(check.old_hash))\n        if len(str(check.failed_since)) > longest_failed_since:\n            longest_failed_since = len(str(check.failed_since))\n        if len(str(check.max_down_time)) > longest_max_down_time:\n            longest_max_down_time = len(str(check.max_down_time))\n        if len(str(check.run_after)) > longest_run_after:\n            longest_run_after = len(str(check.run_after))\n        if len(str(check.check_frequency)) > longest_check_frequency:\n            longest_check_frequency = len(str(check.check_frequency))\n        if len(str(check.check_timeout)) > longest_check_timeout:\n            longest_check_timeout = len(str(check.check_timeout))\n\n    return (('url', longest_url),\n        ('current_hash', longest_current_hash),\n        ('old_hash', longest_old_hash),\n        ('failed_since', longest_failed_since),\n        ('max_down_time', longest_max_down_time),\n        ('run_after', longest_run_after),\n        ('check_frequency', longest_check_frequency),\n        ('check_timeout', longest_check_timeout))\n\ndef get_longest_string():\n    longest_url = 3\n    longest_string_to_match = 15\n    longest_present = 7\n    longest_failed_since = 12\n    longest_max_down_time = 14\n    longest_run_after = 9\n    longest_check_frequency = 15\n    longest_check_timeout = 13\n    for check in session.query(StringCheck).order_by(StringCheck.id):\n        if len(str(check.url)) > longest_url:\n            longest_url = len(str(check.url))\n        if len(str(check.string_to_match)) > longest_string_to_match:\n            longest_string_to_match = len(str(check.string_to_match))\n        if len(str(check.present)) > longest_present:\n            longest_present = len(str(check.present))\n        if len(str(check.failed_since)) > longest_failed_since:\n            longest_failed_since = len(str(check.failed_since))\n        if len(str(check.max_down_time)) > longest_max_down_time:\n            longest_max_down_time = len(str(check.max_down_time))\n        if len(str(check.run_after)) > longest_run_after:\n            longest_run_after = len(str(check.run_after))\n        if len(str(check.check_frequency)) > longest_check_frequency:\n            longest_check_frequency = len(str(check.check_frequency))\n        if len(str(check.check_timeout)) > longest_check_timeout:\n            longest_check_timeout = len(str(check.check_timeout))\n\n    return (('url', longest_url),\n        ('string_to_match', longest_string_to_match),\n        ('present', longest_present),\n        ('failed_since', longest_failed_since),\n        ('max_down_time', longest_max_down_time),\n        ('run_after', longest_run_after),\n        ('check_frequency', longest_check_frequency),\n        ('check_timeout', longest_check_timeout))\n\ndef get_longest_diff():\n    \"\"\"\n    Called by list_checks to check how much to pad the tables.\n    \"\"\"\n    longest_url = 3\n    longest_current_content = 15\n    longest_failed_since = 12\n    longest_max_down_time = 14\n    longest_run_after = 9\n    longest_check_frequency = 15\n    longest_check_timeout = 13\n    for check in session.query(DiffCheck).order_by(DiffCheck.id):\n        if len(str(check.url)) > longest_url:\n            longest_url = len(str(check.url))\n        # Not checking how long current_content is since it will be long and\n        # make the table look silly\n        #if len(str(check.current_content)) > longest_current_content:\n        #    longest_current_content = len(str(check.current_content))\n        if len(str(check.failed_since)) > longest_failed_since:\n            longest_failed_since = len(str(check.failed_since))\n        if len(str(check.max_down_time)) > longest_max_down_time:\n            longest_max_down_time = len(str(check.max_down_time))\n        if len(str(check.run_after)) > longest_run_after:\n            longest_run_after = len(str(check.run_after))\n        if len(str(check.check_frequency)) > longest_check_frequency:\n            longest_check_frequency = len(str(check.check_frequency))\n        if len(str(check.check_timeout)) > longest_check_timeout:\n            longest_check_timeout = len(str(check.check_timeout))\n\n    return (('url', longest_url),\n        ('current_content', longest_current_content),\n        ('failed_since', longest_failed_since),\n        ('max_down_time', longest_max_down_time),\n        ('run_after', longest_run_after),\n        ('check_frequency', longest_check_frequency),\n        ('check_timeout', longest_check_timeout))\n\ndef get_longest_raw():\n    longest_url = 3\n    longest_expression = 10\n    longest_current_hash = 12\n    longest_capture_groups = 14\n    longest_failed_since = 12\n    longest_max_down_time = 14\n    longest_run_after = 9\n    longest_check_frequency = 15\n    longest_check_timeout = 13\n    for check in session.query(RawCheck).order_by(RawCheck.id):\n        if len(str(check.url)) > longest_url:\n            longest_url = len(str(check.url))\n        if len(str(check.expression)) > longest_expression:\n            longest_expression = len(str(check.expression))\n        if len(str(check.current_hash)) > longest_current_hash:\n            longest_current_hash = len(str(check.current_hash))\n        if len(str(check.capture_groups)) > longest_capture_groups:\n            longest_capture_groups = len(str(check.capture_groups))\n        if len(str(check.failed_since)) > longest_failed_since:\n            longest_failed_since = len(str(check.failed_since))\n        if len(str(check.max_down_time)) > longest_max_down_time:\n            longest_max_down_time = len(str(check.max_down_time))\n        if len(str(check.run_after)) > longest_run_after:\n            longest_run_after = len(str(check.run_after))\n        if len(str(check.check_frequency)) > longest_check_frequency:\n            longest_check_frequency = len(str(check.check_frequency))\n        if len(str(check.check_timeout)) > longest_check_timeout:\n            longest_check_timeout = len(str(check.check_timeout))\n\n    return (('url', longest_url),\n        ('expression', longest_expression),\n        ('current_hash', longest_current_hash),\n        ('capture_groups', longest_capture_groups),\n        ('failed_since', longest_failed_since),\n        ('max_down_time', longest_max_down_time),\n        ('run_after', longest_run_after),\n        ('check_frequency', longest_check_frequency),\n        ('check_timeout', longest_check_timeout))\n\ndef list_checks():\n    \"\"\"\n    List all of the checks from the database in a table like format.\n    \"\"\"\n    table_skel = '|'\n    columns = []\n    arguments = []\n    for column, longest_entry in get_longest_md5():\n        table_skel += (' {{: <{}}} |'.format(longest_entry))\n        columns.append(column)\n        arguments.append('row.{}'.format(column))\n\n    print('{} Checks:'.format('MD5Check'))\n    print(table_skel.format(*columns))\n    for check in session.query(MD5Check).order_by(MD5Check.id):\n        print(table_skel.format(str(check.url),\n                        str(check.current_hash),\n                        str(check.old_hash),\n                        str(check.failed_since),\n                        str(check.max_down_time),\n                        str(check.run_after),\n                        str(check.check_frequency),\n                        str(check.check_timeout)))\n\n    table_skel = '|'\n    columns = []\n    arguments = []\n    for column, longest_entry in get_longest_string():\n        table_skel += (' {{: <{}}} |'.format(longest_entry))\n        columns.append(column)\n        arguments.append('row.{}'.format(column))\n\n    print('{} Checks:'.format('StringCheck'))\n    print(table_skel.format(*columns))\n    for check in session.query(StringCheck).order_by(StringCheck.id):\n        print(table_skel.format(str(check.url),\n                        str(check.string_to_match),\n                        str(check.present),\n                        str(check.failed_since),\n                        str(check.max_down_time),\n                        str(check.run_after),\n                        str(check.check_frequency),\n                        str(check.check_timeout)))\n\n    table_skel = '|'\n    columns = []\n    arguments = []\n    for column, longest_entry in get_longest_diff():\n        table_skel += (' {{: <{}}} |'.format(longest_entry))\n        columns.append(column)\n        arguments.append('row.{}'.format(column))\n\n    print('{} Checks:'.format('DiffCheck'))\n    print(table_skel.format(*columns))\n    for check in session.query(DiffCheck).order_by(DiffCheck.id):\n        print(table_skel.format(str(check.url),\n                            str(check.current_content),\n                            str(check.failed_since),\n                            str(check.max_down_time),\n                            str(check.run_after),\n                            str(check.check_frequency),\n                            str(check.check_timeout)))\n\n    table_skel = '|'\n    columns = []\n    arguments = []\n    for column, longest_entry in get_longest_raw():\n        table_skel += (' {{: <{}}} |'.format(longest_entry))\n        columns.append(column)\n        arguments.append('row.{}'.format(column))\n\n    print('{} Checks:'.format('RawCheck'))\n    print(table_skel.format(*columns))\n    for check in session.query(RawCheck).order_by(RawCheck.id):\n        print(table_skel.format(str(check.url),\n                            str(check.expression),\n                            str(check.current_hash),\n                            str(check.capture_groups),\n                            str(check.failed_since),\n                            str(check.max_down_time),\n                            str(check.run_after),\n                            str(check.check_frequency),\n                            str(check.check_timeout)))\n\n    return ''\n\ndef delete_check(check_type, url):\n    if check_type == 'md5':\n        check = session.query(MD5Check).filter(MD5Check.url == url)\n    elif check_type == 'string':\n        check = session.query(StringCheck).filter(StringCheck.url == url)\n    elif check_type == 'diff':\n        check = session.query(DiffCheck).filter(DiffCheck.url == url)\n    elif check_type == 'raw':\n        check = session.query(RawCheck).filter(RawCheck.url == url)\n    else:\n        return 'Chose either md5, string, diff or raw check'\n\n    if check.delete():\n        session.commit()\n        return '{} check for {} removed'.format(check_type, url)\n\n    return 'There is no {} check for {}'.format(check_type, url)\n\ndef import_from_file(import_file):\n    \"\"\"\n    Add's new database entrys from a file\n    \"\"\"\n    error_message = 'Import failed: {} is not formatted correctly'\n    with open(import_file, 'r') as f:\n        for line in f:\n            line = line.split('#', 1)[0].rstrip()\n            if not line:\n                continue\n            try:\n                check_type, data = line.split('|', 1)\n            except ValueError:\n                return error_message.format(line)\n\n            max_down_time = default_max_down_time\n            check_frequency = default_check_frequency\n            check_timeout = default_check_timeout\n            if check_type == 'md5':\n                # There are two accepted line formats:\n                # check_type|url|max_down_time|check_frequency|check_timeout\n                # and check_type|url\n                if '|' in data:\n                    try:\n                        url, max_down_time, check_frequency, check_timeout\\\n                        = data.split('|')\n                    except ValueError:\n                        return error_message.format(line)\n\n                else:\n                    url = data\n\n                print(add_md5(url, max_down_time, check_frequency,\n                        check_timeout))\n            elif check_type == 'string':\n                # There are two accepted line formats:\n                # check_type|url|string_to_check|max_down_time|check_frequency\n                # |check_timeout\n                # and check_type|url\n                try:\n                    string_to_check, data = data.split('|', 1)\n                except ValueError:\n                    return error_message.format(line)\n                if '|' in data:\n                    try:\n                        url, max_down_time, check_frequency, check_timeout\\\n                        = data.split('|')\n                    except ValueError:\n                        return error_message.format(line)\n\n                else:\n                    url = data\n\n                print(add_string(url, string_to_check, max_down_time,\n                        check_frequency, check_timeout))\n            elif check_type == 'diff':\n                # There are two accepted line formats:\n                # check_type|url|max_down_time|check_frequency|check_timeout\n                # and check_type|url\n                if '|' in data:\n                    try:\n                        url, max_down_time, check_frequency, check_timeout\\\n                        = data.split('|')\n                    except ValueError:\n                        return error_message.format(line)\n\n                else:\n                    url = data\n\n                print(add_diff(url, max_down_time, check_frequency,\n                        check_timeout))\n            elif check_type == 'raw':\n                try:\n                    expression, data = data.split('|', 1)\n                except ValueError:\n                    return error_message.format(line)\n                if '|' in data:\n                    try:\n                        url, max_down_time, check_frequency, check_timeout\\\n                        = data.split('|')\n                    except ValueError:\n                        return error_message.format(line)\n\n                else:\n                    url = data\n\n                print(add_raw(url, expression, max_down_time,\n                        check_frequency, check_timeout))\n            else:\n                return error_message.format(line)\n\n    return ''\n\n\nif __name__ == '__main__':\n    default_max_down_time = 86400\n    default_check_frequency = 3600\n    default_check_timeout = 30\n    default_database_location = 'web_checks.db'\n    parser = argparse.ArgumentParser()\n    parser.add_argument('-c', '--check', action='store_true',\n        help='Run checks against all monitored urls')\n    parser.add_argument('-l', '--list', action='store_true',\n        help='Maximum number of set string that can occur')\n    parser.add_argument('-d', '--delete', nargs=2,\n        help='The entry to delete id must be used')\n    parser.add_argument('-a', '--add', nargs='+',\n        help='The type of check to setup and what url to check against')\n    parser.add_argument('--max-down-time', type=int,\n        default=default_max_down_time,\n        help='Number of seconds a site can be down for before warning')\n    parser.add_argument('--check-frequency', type=int,\n        default=default_check_frequency,\n        help='Specify the number of seconds to check after')\n    parser.add_argument('--check-timeout', type=int,\n        default=default_check_timeout,\n        help='Specify the number of seconds to check_timeout after')\n    parser.add_argument('--database-location',\n        default=default_database_location,\n        help='Specify a database name and location')\n    parser.add_argument('--import-file',\n        help='Chose a file to populate the database from')\n    parser.allow_abbrev = False\n    args = parser.parse_args()\n\n    engine = sqlalchemy.create_engine('sqlite:///{}'.format(\n                                                    args.database_location))\n    Base = declarative_base()\n    metadata = MetaData()\n\n    class MD5Check(Base):\n        __tablename__ = 'md5s'\n        id = Column(Integer, primary_key=True)\n        url = Column(String, unique=True)\n        current_hash = Column(String)\n        old_hash = Column(String)\n        failed_since = Column(Integer)\n        max_down_time = Column(Integer)\n        run_after = Column(Integer)\n        check_frequency = Column(Integer)\n        check_timeout = Column(Integer)\n        def __repr__(self):\n            return '<url(url={}, current_hash={}, old_hash={},\\\nfailed_since={}, max_down_time={}, run_after={},\\\ncheck_frequency={}, check_timeout{})>'.format(\n                        self.url,\n                        self.current_hash,\n                        self.old_hash,\n                        self.failed_since,\n                        self.max_down_time,\n                        self.run_after,\n                        self.check_frequency,\n                        self.check_timeout)\n\n    class StringCheck(Base):\n        __tablename__ = 'strings'\n        id = Column(Integer, primary_key=True)\n        url = Column(String)\n        string_to_match = Column(String)\n        present = Column(Integer)\n        failed_since = Column(Integer)\n        max_down_time = Column(Integer)\n        run_after = Column(Integer)\n        check_frequency = Column(Integer)\n        check_timeout = Column(Integer)\n        def __repr__(self):\n            return '<url(url={}, string_to_match={}, present={},\\\nfailed_since={}, max_down_time={}, run_after={},\\\ncheck_frequency={}, check_timeout{})>'.format(\n                        self.url,\n                        self.string_to_match,\n                        self.present,\n                        self.failed_since,\n                        self.max_down_time,\n                        self.run_after,\n                        self.check_frequency,\n                        self.check_timeout)\n\n    class DiffCheck(Base):\n        __tablename__ = 'diffs'\n        id = Column(Integer, primary_key=True)\n        url = Column(String)\n        current_content = Column(String)\n        failed_since = Column(Integer)\n        max_down_time = Column(Integer)\n        run_after = Column(Integer)\n        check_frequency = Column(Integer)\n        check_timeout = Column(Integer)\n        def __repr__(self):\n            return '<url(url={}, current_content={}, failed_since=\\\n{}, max_down_time={}, run_after={},\\\ncheck_frequency={})>'.format(\n                            self.url,\n                            self.string_to_match,\n                            self.failed_since,\n                            self.max_down_time,\n                            self.run_after,\n                            self.check_frequency,\n                            self.check_timeout)\n\n    class RawCheck(Base):\n        __tablename__ = 'raws'\n        id = Column(Integer, primary_key=True)\n        url = Column(String)\n        expression = Column(String)\n        current_hash = Column(String)\n        capture_groups = Column(String)\n        failed_since = Column(Integer)\n        max_down_time = Column(Integer)\n        run_after = Column(Integer)\n        check_frequency = Column(Integer)\n        check_timeout = Column(Integer)\n        def __repr__(self):\n            return '<url(url={}, expression={}, current_hash={},\\\ncapture_groups={}, failed_since={}, max_down_time={}, run_after={},\\\ncheck_frequency={}, check_timeout{})>'.format(\n                        self.url,\n                        self.expression,\n                        self.current_hash,\n                        self.capture_groups,\n                        self.failed_since,\n                        self.max_down_time,\n                        self.run_after,\n                        self.check_frequency,\n                        self.check_timeout)\n\n    MD5Check.__table__\n    Table('md5s', metadata,\n            Column('id', Integer(), primary_key=True, nullable=False),\n            Column('url', String(), unique=True),\n            Column('current_hash', String()),\n            Column('old_hash', String()),\n            Column('failed_since', Integer()),\n            Column('max_down_time', Integer()),\n            Column('run_after', Integer()),\n            Column('check_frequency', Integer()),\n            Column('check_timeout', Integer()),   schema=None)\n\n    StringCheck.__table__\n    Table('strings', metadata,\n            Column('id', Integer(), primary_key=True, nullable=False),\n            Column('url', String(), unique=True),\n            Column('string_to_match', String()),\n            Column('present', Integer()),\n            Column('failed_since', Integer()),\n            Column('max_down_time', Integer()),\n            Column('run_after', Integer()),\n            Column('check_frequency', Integer()),\n            Column('check_timeout', Integer()),   schema=None)\n\n    DiffCheck.__table__\n    Table('diffs', metadata,\n            Column('id', Integer(), primary_key=True, nullable=False),\n            Column('url', String(), unique=True),\n            Column('current_content', String()),\n            Column('failed_since', Integer()),\n            Column('max_down_time', Integer()),\n            Column('run_after', Integer()),\n            Column('check_frequency', Integer()),\n            Column('check_timeout', Integer()),   schema=None)\n\n    RawCheck.__table__\n    Table('raws', metadata,\n            Column('id', Integer(), primary_key=True, nullable=False),\n            Column('url', String(), unique=True),\n            Column('expression', String()),\n            Column('current_hash', String()),\n            Column('capture_groups', String()),\n            Column('failed_since', Integer()),\n            Column('max_down_time', Integer()),\n            Column('run_after', Integer()),\n            Column('check_frequency', Integer()),\n            Column('check_timeout', Integer()),   schema=None)\n\n    try:\n        metadata.create_all(engine)\n    except sqlalchemy.exc.OperationalError:\n        print('Could not create or connect to database at {}'.format(\n                                                    args.database_location))\n        exit(1)\n\n    Session = sessionmaker(bind=engine)\n    session = Session()\n\n    if args.check:\n        run_checks()\n    elif args.list:\n        list_checks()\n    elif args.add:\n        if args.add[0] == 'md5':\n            if len(args.add) != 2:\n                print('call as -a \\'md5\\' \\'url-to-check\\'')\n                exit(1)\n\n            print(add_md5(args.add[1], args.max_down_time, args.check_frequency,\n                        args.check_timeout))\n        elif args.add[0] == 'string':\n            if len(args.add) != 3:\n                print('call as -a \\'string\\' string-to-check \\'url-to-check\\'')\n                exit(1)\n\n            print(add_string(args.add[2], args.add[1], args.max_down_time,\n                    args.check_frequency, args.check_timeout))\n        elif args.add[0] == 'diff':\n            if len(args.add) != 2:\n                print('call as -a \\'diff\\' \\'url-to-check\\'')\n                exit(1)\n\n            print(add_diff(args.add[1], args.max_down_time,\n                    args.check_frequency, args.check_timeout))\n        elif args.add[0] == 'raw':\n            if len(args.add) != 3:\n                print('call as -a \\'raw\\' \\'expression\\' \\'url-to-check\\'')\n                exit(1)\n\n            print(add_raw(args.add[2], args.add[1], args.max_down_time,\n                    args.check_frequency, args.check_timeout))\n        else:\n            print('Choose either md5, string, diff or raw.')\n\n    elif args.delete:\n        if len(args.delete) != 2:\n            print('call as -d \\'check_type\\' \\'url-to-remove\\'')\n            exit(1)\n\n        print(delete_check(args.delete[0], args.delete[1]))\n    elif args.import_file:\n        error = import_from_file(args.import_file)\n        if error:\n            print(error)\n            exit(1)\n    else:\n        print(\"\"\"\\\nArguments:\n  -h/--help\\t\\tShow the help message and exit\n  -c/--check\\t\\tRun checks against all monitored urls\n  -l/--list\\t\\tList stored checks from the database\n  -a/--add\\t\\tAdds a check to the database:\n  \\t\\t\\t\\t-a md5 [url]\n  \\t\\t\\t\\t-a string [string] [url]\n  \\t\\t\\t\\t-a diff [url]\n  \\t\\t\\t\\t-a raw [expression] [url]\n  -d/--delete\\t\\tDelete a check:\n  \\t\\t\\t\\t-d [check_type] [url]\n  --max-down-time\\t\\tNumber of seconds a site can be down for before warning\n  --check-frequency\\tNumber of seconds to wait between checks\n  --check-timeout\\t\\tNumber of seconds to check_timeout after\n  --database-location\\tSpecify a database name and location\n  --import-file\\t\\tSpecify a file to populate the database from\\\n  \"\"\")\n","sub_path":"web-check.py","file_name":"web-check.py","file_ext":"py","file_size_in_byte":41497,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"326157262","text":"#-*- coding:UTF-8 -*-\nimport requests\nimport smtplib\nimport ctypes\nimport os\nif __name__=='__main__':\n\turls = [\n\t\t'http://graduate.dufe.edu.cn/zsgz/sszs/'\n\t]\n\ti=1\n\thttpsession = requests.session()\n\twhile(True):\n\t\tfor url in urls:\n\t\t\tresponse =  httpsession.get(url)\n\t\t\tif('调剂' in str(response.text)):\n\t\t\t\tctypes.windll.user32.MessageBoxA(0,u\"有调剂消息\".encode('gb2312'),u' 信息'.encode('gb2312'),0)\n\t\t\telse:\n\t\t\t\tprint(\"第\",i,'次')\n\t\t\ti=i+1\n\n\t\t\t\t#os.system(r'C:\\Workflow\\GitHub\\python\\kaoyan\\test.bat')","sub_path":"kaoyan/tiaoji.py","file_name":"tiaoji.py","file_ext":"py","file_size_in_byte":515,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"651219150","text":"#! usr/bin/env python3\n\n# Original GHS algorithm\nimport csv\nimport random, math\nimport os\n\ndef objective_function(vector, problem_size):\n    sum = 0\n    for val in vector:\n        sum += val ** 2 - 10*math.cos(2*math.pi*val)\n    sum += 10*problem_size\n    return sum\n\ndef rand_in_bounds(minimum, maximum):\n    return minimum + ((maximum - minimum) * random.random())\n\ndef random_vector(search_space):\n    i = 0\n    limit = len(search_space)\n    random_vector = [0 for i in range(limit)]\n    \n    for i in range(limit):\n        random_vector[i] = rand_in_bounds(search_space[i][0], search_space[i][1])   \n    return random_vector\n\ndef create_random_harmony(search_space, problem_size):\n    harmony = {}\n    harmony[\"vector\"] = random_vector(search_space)\n    harmony[\"fitness\"] = objective_function(harmony[\"vector\"], problem_size)\n    return harmony\n\ndef initialize_harmony_memory(search_space, HMS, problem_size, factor=3):\n    memory = [create_random_harmony(search_space, problem_size) for i in range(HMS*factor)]\n    #memory = sorted(memory, key=itemgetter('fitness')) \n    memory = sorted(memory, key= lambda k: k[\"fitness\"])\n    return [memory[i] for i in range(HMS)]\n\ndef create_harmony(search_space, memory, best, HMCR, PARmin, PARmax, max_iter, gn):\n    limit = len(search_space)\n    vector = [0 for i in range(limit)]\n\n    PAR = PARmin + ((PARmax - PARmin)/max_iter)*gn\n\n    for i in range(limit):\n        if random.random() < HMCR:\n            value = memory[random.randint(0, len(memory)-1)][\"vector\"][i]\n            if random.random() < PAR:\n                value = best[\"vector\"][random.randint(0, limit-1)]\n            if value < search_space[i][0]:\n                value = search_space[i][0]\n            if value > search_space[i][1]:\n                value = search_space[i][1]\n\n            vector[i] = value\n        else:\n            vector[i] = rand_in_bounds(search_space[i][0], search_space[i][1])\n    \n    return {\"vector\": vector}\n\ndef search(search_space, max_iter, HMS, HMCR, PARmin, PARmax, problem_size, total):\n    memory = initialize_harmony_memory(search_space, HMS, problem_size)\n    best = memory[0]\n    for i in range(max_iter):\n        harm = create_harmony(search_space, memory, best, HMCR, PARmin, PARmax, max_iter, i)\n        harm[\"fitness\"] = objective_function(harm[\"vector\"], problem_size)\n        if harm[\"fitness\"] < best[\"fitness\"]:\n            best = harm\n        memory.append(harm)\n        memory = sorted(memory, key= lambda k: k[\"fitness\"])\n        del memory[-1]\n\n        # write fitness\n        if not os.path.exists(os.path.join(os.getcwd(), 'data', 'ghs_cont_ra')):\n            os.mkdir(os.path.join(os.getcwd(), 'data', 'ghs_cont_ra'))\n        path0 = os.path.join(os.getcwd(), 'data', 'ghs_cont_ra', 'fitness')\n        if not os.path.exists(path0):\n            os.mkdir(path0)\n        path = os.path.join(path0, str(total))\n        if not os.path.exists(path):\n            os.mkdir(path)\n        with open(os.path.join(path, 'fitness.csv'), 'a', newline='') as csvfile:\n                spamwriter = csv.writer(csvfile, delimiter=' ',\n                                        quotechar='|', quoting=csv.QUOTE_MINIMAL)\n                spamwriter.writerow([i,best['fitness']])\n\n    return best\n","sub_path":"1-continuous-opt-benchmark/modules/ghs_cont_ra.py","file_name":"ghs_cont_ra.py","file_ext":"py","file_size_in_byte":3243,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"170794084","text":"# Class Definition of Energy Conversion Technology\n# Version 1.2, Edward, 181004\n\n__version__ = 1.2\n\nif __name__ == '__main__':\n    print('!!! energy_conver_tech.py is being run by itself.\\n')\nelse:\n    print('!!! energy_conver_tech.py is imported from another file.\\n')\n\n\nclass EnergyConverTech:\n    \"\"\"Class Definition of Energy Conversion Technology\"\"\"\n    number_ect = 0\n\n    def __init__(self, identifier, name, output):\n        self.id = identifier\n        self.name = name\n        self.type = output  # if \"1\", Energy2Elec; if \"2\", Energy2Heat\n        EnergyConverTech.number_ect += 1\n\n    def print(self):\n        \"\"\"Print the common feature of energy conversion technology\"\"\"\n        type_text = [\"Energy to Elec\", \"Energy to Heat\", \"Energy to Elec and Heat\"]\n        print('Energy Conversion Technology: {}, type: {}, id: {},'\n              .format(self.name, type_text[self.type], self.id), end=\" \")\n\n    @classmethod\n    def print_number(cls):\n        print('Database have {} type(s) of energy conversion technology'.format(EnergyConverTech.number_ect))\n\n\nclass Energy2ElecConverter(EnergyConverTech):\n    \"\"\"Class Definition of Energy to Electricity Technology in Energy Conversion Technology\"\"\"\n    number = 0\n\n    def __init__(self, identifier, name, output, efficiency):\n        EnergyConverTech.__init__(self, identifier, name, output)\n        self.efficiency = efficiency\n        Energy2ElecConverter.number += 1\n        print('You have profiled a new energy to electricity technology. \\n')\n        Energy2ElecConverter.print_number()\n\n    def print(self):\n        \"\"\"Print the feature of energy to electricity conversion technology\"\"\"\n        EnergyConverTech.print(self)\n        print('efficiency: {}.\\n'.format(self.efficiency))\n\n    @classmethod\n    def print_number(cls):\n        print('Number of profiled energy to electricity converters is {}.'.format(Energy2ElecConverter.number))\n\n    @classmethod\n    def create_input(cls, identifier_ect):\n        print('You are profiling a new product, which can convert energy to electricity.')\n        return cls(identifier_ect,\n                   input('Name: '),\n                   int(1),\n                   int(input('Efficiency(%): '))\n                   )\n\n\nclass Energy2HeatConverter(EnergyConverTech):\n    \"\"\"Class Definition of Energy to Heat Technology in Energy Conversion Technology\"\"\"\n    number = 0\n\n    def __init__(self, identifier, name, output, efficiency):\n        EnergyConverTech.__init__(self, identifier, name, output)\n        self.efficiency = efficiency\n        Energy2HeatConverter.number += 1\n        print('You have profiled a new energy to heat tech. \\n'\n              'Number of profiled energy to heat technology is {}. \\n'.format(Energy2HeatConverter.number))\n\n    def print(self):\n        \"\"\"Print the feature of energy to heat conversion technology\"\"\"\n        EnergyConverTech.print(self)\n        print('efficiency: {}.\\n'.format(self.efficiency))\n\n    @classmethod\n    def create_input(cls, identifier_ect):\n        print('You are profiling a new product, which can convert energy to heat.')\n        return cls(identifier_ect,\n                   input('Name: '),\n                   int(2),\n                   int(input('Efficiency(%): '))\n                   )\n\n\nclass Energy2ElecHeatConverter(EnergyConverTech):\n    \"\"\"Class Definition of Energy to Electricity & Heat Technology in Energy Conversion Technology\"\"\"\n    number = 0\n\n    def __init__(self, identifier, name, output, efficiency_e2e, efficiency_e2h):\n        EnergyConverTech.__init__(self, identifier, name, output)\n        self.efficiency_e2e = efficiency_e2e\n        self.efficiency_e2h = efficiency_e2h\n        Energy2ElecHeatConverter.number += 1\n        print('You have profiled a new energy to electricity and heat technology. \\n'\n              'Number of profiled energy to electricity and heat technology is {}. \\n'\n              .format(Energy2ElecHeatConverter.number))\n\n    def print(self):\n        \"\"\"Print the feature of energy to electricity conversion technology\"\"\"\n        EnergyConverTech.print(self)\n        print('e2e efficiency: {}, e2h: efficiency {}. \\n'.format(self.efficiency_e2e, self.efficiency_e2h))\n\n    @classmethod\n    def create_input(cls, identifier_ect):\n        print('You are profiling a new product, which can convert energy to electricity and heat.')\n        return cls(identifier_ect,\n                   input('Name: '),\n                   int(3),\n                   int(input('Energy to Electricity Efficiency(%): ')),\n                   int(input('Energy to Heat Efficiency(%): '))\n                   )\n\n\n# Initialize default energy technology.\nidentifier_ect = 'eg_001'\neg_001 = Energy2ElecConverter(identifier_ect, 'Electric Generator 001', 1, 50)\nelectricGenerators = {'eg_001': eg_001}\nidentifier_ect = 'he_001'\nhe_001 = Energy2HeatConverter(identifier_ect, 'Heat Exchanger 001', 2, 50)\nheatExchangers = {'he_001': he_001}\nidentifier_ect = 'chp_001'\nchp_001 = Energy2ElecHeatConverter(identifier_ect, 'Combined Heat Power 001', 3, 20, 30)\ncombinedHeatPowers = {'chp_001': chp_001}\nidentifier_ect = 'exit'  # Initialize the value of identifier_ect\n\n\ndef check_identifier_ect(identifier_ect):\n    \"\"\"Check if the input identifier already exits.\"\"\"\n    operand_ect_m = 1\n    if identifier_ect == 'exit':\n        operand_ect_m = 0\n    else:\n        for all_identifier_ect in {**electricGenerators, **heatExchangers, **combinedHeatPowers}:\n            if identifier_ect == all_identifier_ect:\n                operand_ect_m = 2\n                break\n    return operand_ect_m\n\n\ndef profile_ect():\n    \"\"\"Input energy conversion technology from terminal.\"\"\"\n    operand_ect_i = 1\n    operand_ect_j = 1\n    while operand_ect_i <= 3:\n        identifier_ect = input('Do you want profile a new model for energy conversion technology? \\n'\n                               'Input ID first, or input \"exit\" to main menu: \\n')\n        operand_ect_m = check_identifier_ect(identifier_ect)\n        if operand_ect_m == 1:\n            while operand_ect_j != 0 and operand_ect_j <= 10:\n                operand_ect_n = input('Then, input number with following reference to profile '\n                                      'a certain type of technology:\\n'\n                                      'Or input \"exit\" to exit to main menu. \\n'\n                                      '1. Profile a product can converse energy to electricity. \\n'\n                                      ' 1.1 Electric Generator \\n'\n                                      '2. Profile a product can converse energy to heat. \\n'\n                                      ' 2.1 Heat Exchanger \\n'\n                                      '3. Profile a product can converse energy to electricity and heat. \\n'\n                                      ' 3.1 Combined Heat and Power Plant \\n')\n                if operand_ect_n == '1.1':\n                    electricGenerators[identifier_ect] = Energy2ElecConverter.create_input(identifier_ect)\n                    print('The energy to electricity converter profiled is a electric generator.\\n')\n                    electricGenerators[identifier_ect].print()\n                    for electricGenerator in electricGenerators.items():\n                        print(electricGenerator)\n                    break\n                elif operand_ect_n == '2.1':\n                    heatExchangers[identifier_ect] = Energy2HeatConverter.create_input(identifier_ect)\n                    print('You have profiled a new heat exchanger.\\n')\n                    heatExchangers[identifier_ect].print()\n                    break\n                elif operand_ect_n == '3.1':\n                    combinedHeatPowers[identifier_ect] = Energy2ElecHeatConverter.create_input(identifier_ect)\n                    print('You have profiled a new combine heat and power.\\n')\n                    combinedHeatPowers[identifier_ect].print()\n                    break\n                elif operand_ect_n == 'exit':\n                    operand_ect_i = 0\n                    break\n                else:\n                    print('\\n! Error, following the instruction below: \\n')\n        elif operand_ect_m == 2:\n            print('The ID you input is already used.')\n            energyConverTechs = {**electricGenerators, **heatExchangers, **combinedHeatPowers}\n            energyConverTechs[identifier_ect].print()\n            print('---------- Please input another identifier ---------- \\n')\n        elif operand_ect_m == 0:\n            print('---------- Return to Main Menu ---------- \\n')\n            break\n        operand_ect_i += 1\n    if operand_ect_m != 0:\n        print('---------- Too many operations. Return to Main Menu ---------- \\n')\n","sub_path":"energy_conver_tech.py","file_name":"energy_conver_tech.py","file_ext":"py","file_size_in_byte":8696,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"86152352","text":"# Time:  O(m * k), where m is string length, n is dictionary size, k is word length\n# Space: O(n)\n\n# 30\n# You are given a string, s, and a list of words, words,\n# that are all of the same length. Find all starting indices of substring(s)\n# in s that is a concatenation of each word in words exactly once and\n# without any intervening characters.\n#\n# For example, given:\n# s: \"barfoothefoobarman\"\n# words: [\"foo\", \"bar\"]\n#\n# You should return the indices: [0,9].\n# (order does not matter).\n\n\nimport collections\n\n# Sliding window solution:\n# since the length of all words is same and fixed, treat each word as a UNIT\n# we can convert the problem like: s = 'acxbccacy', find substrings containing ['a', 'b', 'c', 'c']\n# obvious we can use sliding window to solve it in O(len(s)).\n# Now each UNIT is a word, we should try starting from each char in a word in O(len(word[0])).\nclass Solution(object):\n    def findSubstring(self, s, words): # USE THIS: much better time complexity\n        \"\"\"\n        :type s: str\n        :type words: List[str]\n        :rtype: List[int]\n        \"\"\"\n        ans, cnt, wlen = [], len(words), len(words[0])\n        if len(s) < cnt * wlen:\n            return ans\n\n        expect = collections.Counter(words)\n        for i in range(wlen):               # Time: O(k), test i in 0,1,2,..k-1 where k is word length\n                                            # for each i, do sliding window with word treating as a UNIT\n            start, curCnt = i, 0\n            actual = collections.defaultdict(int)\n            for cur in range(i, len(s)-wlen+1, wlen): # Time:  O(m / k)\n                sub = s[cur : cur+wlen]               # Time:  O(k)\n                if sub not in expect:\n                    start, curCnt = cur + wlen, 0\n                    actual = collections.defaultdict(int)\n                else:\n                    actual[sub] += 1\n                    curCnt += 1\n                    while actual[sub] > expect[sub]:\n                        actual[s[start : start+wlen]] -= 1\n                        curCnt -= 1\n                        start += wlen\n                    if curCnt == cnt:\n                        ans.append(start)\n        return ans\n\n\n# Time:  O(m * n * k), where m is string length, n is dictionary size, k is word length\n# Space: O(n * k)\nclass Solution2(object):\n    def findSubstring(self, s, words):\n        \"\"\"\n        :type s: str\n        :type words: List[str]\n        :rtype: List[int]\n        \"\"\"\n        ans = []\n        cnt, wlen = len(words), len(words[0])\n        expect = collections.Counter(words)\n        for start in range(len(s) - cnt * wlen + 1):       # O(m)\n            actual = collections.defaultdict(int)\n            for i in range(cnt):                           # O(n)\n                sub = s[start+i*wlen : start+(i+1)*wlen]   # O(k)\n                if sub not in expect or actual[sub] + 1 > expect[sub]:\n                    break\n                actual[sub] += 1\n            else:    # finish iteration of cnt, no break\n                ans.append(start)\n        return ans\n\n\n    # similar to the above, just not that Pythonic\n    def findSubstring2(self, s, words):\n        ans, m, n, k = [], len(s), len(words), len(words[0])\n        if m < n*k:\n            return ans\n\n        expect = collections.defaultdict(int)\n        for i in words:\n            expect[i] += 1                            # Space: O(n * k)\n\n        for i in xrange(m+1-k*n):                     # Time: O(m)\n            actual, j = collections.defaultdict(int), 0\n            while j < n:                              # Time: O(n)\n                word = s[i+j*k : i+j*k+k]             # Time: O(k)\n                if word not in expect or actual[word] + 1 > expect[word]:\n                    break\n                actual[word] += 1\n                j += 1\n            if j == n:\n                ans.append(i)\n\n        return ans\n\nif __name__ == \"__main__\":\n    print(Solution().findSubstring(\"barfoothefoobarman\", [\"foo\", \"bar\"])) # [0, 9]\n","sub_path":"Python/substring-with-concatenation-of-all-words.py","file_name":"substring-with-concatenation-of-all-words.py","file_ext":"py","file_size_in_byte":3993,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"476857557","text":"import random\n\ncardfaces=[]\ntrap=[\"naval mine\",\"smokescreen\",\"sabotage\"]\nperk=[\"flak armor\",\"far sight\",\"aluminium hull\"]\noffense=[\"FMJ upgrade\",\"Rifling\",\"Advanced Rifling\",\"EMP upgrade\"]\ndefensive=[\"Reinforced hull\",\"Sonar\"]\nhelp=[\"Backup\",\"Extra fuel\",\"Extra fuel 2\",\"Rally\",\"Adrinaline Rush\",]\nspecial=[\"Repair\",\"Hack intel\",\"Jack sparrow\"]\ndeck=[]\nfor i in range(4):\n    cardfaces.append(help[i]+\" (help card)\")\n\nfor h in range(4):\n    cardfaces.append(offense[h]+\" (offense card)\")\n\nfor q in range(2):\n    cardfaces.append(defensive[q]+\" (defensive card)\")\n\nfor a in range(3):\n    cardfaces.append(special[a]+\" (special card)\")\n\nfor j in range(3):\n    cardfaces.append(perk[j]+ \" (Special perk card)\")\n\nfor k in range(3):\n    if trap==\"naval mine\":\n        cardfaces.append(trap[k]+\" offensive trap card\")\n    elif trap==\"smokescreen\" or \"sabotage\":\n        cardfaces.append(trap[k]+\" (Defensive trap card)\")\n    else:\n        cardfaces.append(trap[k]+\" (trap card)\")\n\n\n\n#print(cardfaces)\n\n\nfor l in range(19):\n    card =(cardfaces[l])\n    deck.append(card)\n\n\nrandom.shuffle(deck)\n\nfor m in range(19):\n    print(deck[m])\n","sub_path":"cards.py","file_name":"cards.py","file_ext":"py","file_size_in_byte":1127,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"181360343","text":"def Crypt(phrase, key, alpha):\n    res = \"\"\n    for i in range(len(phrase)) :\n        for j in range (len(alpha)):\n            if (phrase[i] == alpha[j]):\n                res += key[j]\n            #endif\n        #endfor\n    #endfor\n    return res\n#enddef\n\nkey = \"HYLUJPVREAKBNDOFSQZCWMGITX\"\nkeyP = [str(c) for c in str(key)]\n\nalpha = \"ABCDEFGHIJKLMNOPQRSTUVWXYZ\"\nalphaP = [str(c) for c in str(alpha)]\n\nphrase = input(\"Saisir une phrase : \")\n\nprint(Crypt(phrase.upper(),keyP, alphaP))","sub_path":"Cnam-Algo/Algo Simple/10.3.py","file_name":"10.3.py","file_ext":"py","file_size_in_byte":483,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"167502466","text":"from Descriptor.Atoms import *\r\n\r\n# (cal/mol-1, 300K)\r\n__Amino_acid_conformational_entropy = {\"LYS\": -189, \"ARG\": -188, \"GLN\": -173, \"MET\": -146, \"GLU\": -146,\r\n                                           \"ILE\": -76, \"LEU\": -71, \"ASN\": -103, \"THR\": -108, \"VAL\": -43,\r\n                                           \"TYR\": -113, \"SER\": -111,  \"HIS\": -95, \"ASP\": -78, \"CYS\": -85,\r\n                                           \"TRP\": -99, \"PHE\": -62, \"ALA\": -0, \"PRO\": -6, \"GLY\": 0}\r\n\r\n# read protein atoms and create class ResidueAtom for each atom\r\ndef get_pdb_atoms(pdbfile):\r\n    atoms = []\r\n    with open(pdbfile, 'r') as pdb:\r\n        lines = tuple(atom.strip() for atom in pdb.readlines()[0: -2])\r\n    for atom in lines:\r\n        identifier = atom[0: 6].strip()\r\n        if identifier == 'ATOM':\r\n            atom_type = atom[12: 16].strip()\r\n            residue = atom[17: 20].strip()\r\n            index = atom[22: 26].strip()\r\n            atoms.append(ResidueAtom(atom_type=atom_type,\r\n                                     coordinate=tuple(map(float, (atom[30: 38].strip(),\r\n                                                                  atom[38: 46].strip(),\r\n                                                                  atom[46: 54].strip()))),\r\n                                     index=index, residue=residue))\r\n    return tuple(atoms)\r\n\r\n# read ligand atoms and create class LigandAtom for each atom\r\ndef get_sdf_atoms(sdffile, single=True):\r\n    atom_types = []\r\n    atoms = []\r\n    if single:\r\n        with open(sdffile, 'r') as sdf:\r\n            lines = tuple(sdf.readlines())\r\n    else:\r\n        lines = sdffile\r\n    split_line = lines[3].strip().split()\r\n    natoms = int(split_line[0])\r\n    nbonds = int(split_line[1])\r\n    if natoms > len(lines):\r\n        count = len(split_line[0])\r\n        while natoms > len(lines):\r\n            natoms = int(split_line[0][:count])\r\n            count -= 1\r\n        nbonds = int(split_line[0][count + 1:])\r\n    atom_lines = lines[4: 4 + natoms]\r\n    property_lines = lines[5 + natoms + nbonds:]\r\n\r\n    for i, line in enumerate(property_lines):\r\n        index = i\r\n        while line == \">  <TYPE_INFO>\\n\":\r\n            next_line = property_lines[index + 1]\r\n            if next_line == \"\\n\":\r\n                break\r\n            atom_types.extend(next_line.strip().split())\r\n            index += 1\r\n\r\n    for i, atom in enumerate(atom_lines):\r\n        split_atom = atom.strip().split()\r\n        atom_type = atom_types[i]\r\n        coordinate = tuple(map(float, split_atom[0: 3]))\r\n        atoms.append(LigandAtom(atom_type=atom_type, coordinate=coordinate))\r\n\r\n    return tuple(atoms)\r\n\r\n\"\"\" function of lost water entropy and side chain conformational\r\nentropy calculation\"\"\"\r\ndef cal_lw_fs(ligand, protein):\r\n    ligand_protein_contacts = {}\r\n    total_flexible_entropy = 0\r\n    total_protein_water_loss = 0\r\n    total_ligand_water_loss = 0\r\n    deduct_water = 1\r\n    protein_contacted_water = {}\r\n    contacted_atom = set()\r\n    # obtain contacted atoms in protein     \r\n    # contacted atom distance is set to 3.5\r\n    for ligand_atom in ligand:\r\n        ligand_atom_coordinate = ligand_atom.get_coordinate()\r\n        ligand_protein_contacts[ligand_atom] = set()\r\n        for residue_atom in protein:\r\n            residue_atom_coordinate = residue_atom.get_coordinate()\r\n            dist = ligand_atom_coordinate.cal_dist(residue_atom_coordinate)\r\n            if dist > 3.5:\r\n                pass\r\n            else:\r\n                ligand_protein_contacts[ligand_atom].add(residue_atom)\r\n\r\n    for latm in ligand_protein_contacts:\r\n\r\n        # calculate ligand water loss\r\n        if len(ligand_protein_contacts[latm]) >= latm.get_water_count():\r\n            ligand_water_loss = latm.get_water_count()\r\n            total_ligand_water_loss += ligand_water_loss\r\n        else:\r\n            total_ligand_water_loss += len(ligand_protein_contacts[latm])\r\n\r\n        for atm in ligand_protein_contacts[latm]:\r\n            # calculate flexibility entropy\r\n            flexible_entropy = 0\r\n            index, residue = atm.get_residue()\r\n            if index not in contacted_atom:\r\n                contacted_atom.add(index)\r\n                flexible_entropy = __Amino_acid_conformational_entropy[residue]\r\n            total_flexible_entropy += flexible_entropy\r\n\r\n            # calculate protein water loss\r\n            if atm not in protein_contacted_water:\r\n                protein_contacted_water[atm] = atm.get_water_count()\r\n            original_water = protein_contacted_water[atm]\r\n            if original_water == 0:\r\n                pass\r\n            else:\r\n                left_water = original_water - deduct_water\r\n                total_protein_water_loss += deduct_water\r\n                protein_contacted_water.update({atm: left_water})\r\n    total_water_loss = total_ligand_water_loss + total_protein_water_loss\r\n    water_entropy = total_water_loss * 10 * 300 * 0.001\r\n    return water_entropy, total_flexible_entropy\r\n","sub_path":"SampleCode/Descriptor/calEntropy.py","file_name":"calEntropy.py","file_ext":"py","file_size_in_byte":4985,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"326819207","text":"import os\n#\nrootdir = \"F:\\\\work\\\\workspace\\\\workspace1.1\\\\file\\\\IT\\\\项目级\\\\TMS-周迭代\\\\3.0.31\\\\\"\nquery = \"SETTLE_DT\"\n\ndef walk_all_files(rootdir ,query):\n    print(os.walk(rootdir))\n    for parent,dirnames,filenames in os.walk(rootdir):\n        # for dirname in dirnames:\n        #     print(\"parent is :\"+parent)\n        #     print(\"dirname is:\"+dirname)\n        #     pass\n        ##print(filenames)\n        for filename in filenames:\n            #print(\"fileName:\"+filename)\n            is_file_contain_word(os.path.join(parent,filename),query)\n\ndef is_file_contain_word(file_,query_word):\n    #print(\"search file:\" + file_)\n    file_suffix = os.path.splitext(file_)[1]\n    if file_suffix != '.sql':\n        #print(\"file_suffix:\"+file_suffix)\n        return\n\n    if query_word in open(file_,encoding='UTF-8').read():\n        print (file_)\n        filecontext = open(file_,encoding='UTF-8').read()\n        lines = filecontext.split('\\n')\n        for line in lines:\n            if query_word in line:\n                print(line)\n\nwalk_all_files(rootdir,query.upper())\n\nprint(\"done\")","sub_path":"SearchFile2.py","file_name":"SearchFile2.py","file_ext":"py","file_size_in_byte":1091,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"53980861","text":"# class type Player that holds multiple functions\nclass Player(object):\n  \"\"\" Adds players and gives access to view the team \"\"\"\n# function that contains variables that is unique to the input, meaning each input has its own name, age, and amount of goals\n  def __init__(self, name, age, goals):\n    self.name = name\n    self.age = age\n    self.goals = goals\n# function that displays the player's name, age, and amount of goals\n  def getStats(self):\n    summary = \"Player: \" + self.name + \"\\n\"\n    summary = summary + \"Age: \" + str(self.age) + \"\\n\"\n    summary = summary + \"Goals: \" + str(self.goals) + \"\\n\"\n    return summary\n\n\n\n# execution starts here\n\n# empty list that is used to add the players beng inputed \nmyPlayers = []\n\n\n\nkeepRunning = True\n# while the code runs, the prompts below appear \nwhile keepRunning:\n  print(\"What would you like to do? Enter your choice and press 'enter'.\")\n  print(\"(1) Add players.\")\n  print(\"(2) View team.\")\n  print(\"(0) Exit.\")\n  # the user's input is labeled as \"response\" in order to use their responses for other functions\n  response = input()\n# user inputs \"0\", the code ends\n  if response == \"0\":\n    keepRunning = False\n# if user inputs \"1\", then they are asked to input the new player's name, age, and score. Then the player's information is added to the empty list containing the team\n  elif response == \"1\":\n    print(\"what is the player's name?\")\n    playerName = input()\n    print(\"What is the player's age?\")\n    playerAge = int(input())\n    print(\"How many goals have they scored?\")\n    playerGoals = int(input())\n    newPlayer = Player(playerName, playerAge, playerGoals)\n    myPlayers.append(newPlayer)\n# if the user inputs \"2\", the list is accessed and a summary of each player is displayed\n  elif response == \"2\":\n        for p in myPlayers:\n          print(p.getStats())\n          \n\n  \n  \n  \n  ","sub_path":"ps6/jose.py","file_name":"jose.py","file_ext":"py","file_size_in_byte":1852,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"21902137","text":"#!/usr/bin/env python3\n'''\nusage: hzip.py [-h] [-o OUTFILE] [-s SUFFIX] [-f] [-l LEVEL] [-a] infile\n\nCompress files using the hzlib module.\n\npositional arguments:\n  infile\n\noptional arguments:\n  -h, --help            show this help message and exit\n  -o OUTFILE, --outfile OUTFILE\n                        Name of output file\n  -s SUFFIX, --suffix SUFFIX\n                        Suffix to use instead of .hz\n  -f, --force           Force compression and overwrite output file if it\n                        exists\n  -l LEVEL, --level LEVEL\n                        Maximum levels of compression\n  -a, --alwayscompress  Compress to max level even if it would make output\n                        larger\n\nFormat of saved file is the following:\nThe string of bytes MAGIC from hzlib, followed by one byte containing the\ncompression level of the data, followed by the data.\n\nCompression level 0 is the raw input. The data used in compression level\nn+1 is the result of compressing the result provided by compression\nlevel n. Note that each level includes its codebook in its data, but does\nnot include the magic number.\n'''\nfrom hzlib import *\n\nDEFAULT_EXTENSION = '.hz'\nMAX_COMPRESSION_LEVEL = 255\nMIN_COMPRESSION_LEVEL = 0\n\n\ndef main():\n    import argparse\n    parser = argparse.ArgumentParser(\n        description='Compress files using the hzlib module.')\n##    parser.add_argument(\"infile\")\n    parser.add_argument(\"-o\", \"--outfile\", type=str, default=None,\n                        help='Name of output file')\n    parser.add_argument(\"-s\", \"--suffix\", type=str, default=DEFAULT_EXTENSION,\n                        help=('Suffix to use instead of ' +\n                              DEFAULT_EXTENSION))\n    parser.add_argument(\"-f\", \"--force\", action='store_true',\n                        help=('Force compression and overwrite output ' +\n                              'file if it exists'))\n    parser.add_argument(\"-l\", \"--level\", type=int,\n                        default=MAX_COMPRESSION_LEVEL,\n                        help='Maximum levels of compression')\n    parser.add_argument(\"-a\", \"--alwayscompress\", action='store_true',\n                        help=('Compress to max level even if it would ' +\n                              'make output larger'))\n    args = parser.parse_args()\n\n    \n    args.outfile = \"ziptest\"\n    args.infile = \"unziptest\"\n    args.level = 5\n    args.alwayscompress = True\n\n\n    # open files\n    level = args.level\n    with open(args.outfile + args.suffix, \"wb\") as outfile:\n        with open(args.infile, \"rb\") as data_file:\n            data = data_file.read()\n\n            # code it once for each level\n            for level in range(1, args.level+1):\n                codebook = build_canonical_codebook(\n                    build_codebook(make_huffman_tree(\n                        symbol_count(data))))\n                code = bytearray((byte for byte in join(pad(compress(\n                    data, codebook)), codebook)))\n\n                #  If you do not want to overwrite and\n                #  the code contains more bytes\n                #  use last data.\n                if not args.alwayscompress and len(data) <= len(code):\n                    level -= 1\n                    break\n                data = code\n\n            # If compression is 0 return it as is\n            if level == 0:\n                start = b\"\"\n\n            # Otherwise add the starting chars to it\n            else:\n                start = MAGIC + bytearray([level])\n            if type(data) != bytearray:\n                outfile.write(data)\n            else:\n                start = bytearray(start)\n                start.extend(data)\n                outfile.write(start)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"ex09/testings/hzip.py","file_name":"hzip.py","file_ext":"py","file_size_in_byte":3718,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"578750775","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Sep 18 13:07:35 2020\n\n@author: hemma\n\"\"\"\n\nimport random \nfrom funcs import *\n\nwith open('names/first_names.txt', 'r') as first, \\\n    open('names/last_names.txt', 'r') as last, \\\n    open('names/movies.txt', 'r') as movies:\n    # first = first.read().splitlines()\n    # last = last.read().splitlines()\n    f = first.read().splitlines()\n    l = last.read().splitlines()\n    m = movies.read().splitlines()\n    for i in range(2):\n        \n        a = random.randint(0, 20)\n        if a < 5:\n            movie = m[0]\n        elif a < 8:\n            movie = m[1]\n        elif a < 9:\n            movie = m[2]\n        elif a < 11:\n            movie = m[3]\n        elif a < 12:\n            movie = m[4]\n        elif a < 15:\n            movie = m[5]\n        else:\n            movie = m[6]\n        \n\n        text = \"\"\n        text = text + \"Name \" + random.choice(f) + \" \" + random.choice(l) + \"\\n\"\n        text = text + \"Customer number \" + \"\".join([str(r) for r in random.sample(range(10), 10)]) + \"\\n\"\n        text = text + \"Movie title \" + movie + \"\\n\"\n        text = text + \"Adult \" + str(random.randint(0, 5)) + \"\\n\"\n        \n        if movie==m[0]:\n            text = text + \"Child \" + str(random.randint(0, 5)) + \"\\n\"\n        else:\n            text = text + \"Child \" + str(0) + \"\\n\"\n        \n        text = text + \"Concession \" + str(random.randint(0, 3)) + \"\\n\"\n        \n        encrypt\n        text = encrypt(random.randint(1, 25), text)\n        \n        \n        with open(\"sample_data/file_\" + str(i) + \".txt\", \"w\") as file:\n            file.write(text)\n            \n        print(\"\")\n            \n        \n        \n        \n        \n        \n        \n    \n        \n        \n    ","sub_path":"assignment_2020_V2/supplemetary_files/Intro_to_Python_Assignment_2020/sample_data_generator.py","file_name":"sample_data_generator.py","file_ext":"py","file_size_in_byte":1747,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"613444904","text":"import openpyxl\nimport Functions\n\nEntry = openpyxl.load_workbook('BookForProgramDirector.xlsx')\nData = openpyxl.load_workbook('BookForPython.xlsx')\n\nDUTY_SCHEDULE_SHEET = Entry[\"Duty Schedule\"]\nFULL_DAY_SHEET = Entry[\"Full Day\"]\nHALF_DAY_SHEET = Entry[\"Half Day\"]\nSESSION_DATES_SHEET = Entry[\"Session Dates\"]\n\nB1_START = (SESSION_DATES_SHEET.cell(row=4, column=2)).value\nC_START = (SESSION_DATES_SHEET.cell(row=6, column=2)).value\n\nDUTY_SCHEDULE_NAME_COLUMN = 2\nDAY_OFF_DATES_COLUMN = 2\n\nEND_DATE = B1_START\n\n# Print A session day offs to full day and half day sheets\n\ndate_row_acc = 3\nwhile (FULL_DAY_SHEET.cell(row=date_row_acc, column=DAY_OFF_DATES_COLUMN)).value != END_DATE:\n\n    date_column_acc = date_row_acc + 1\n\n    name_row_acc = 3\n    half_day_off_slot_acc = 3\n    full_day_off_slot_acc = 3\n    while (DUTY_SCHEDULE_SHEET.cell(row=name_row_acc, column=DUTY_SCHEDULE_NAME_COLUMN)).value:\n\n        name = (DUTY_SCHEDULE_SHEET.cell(row=name_row_acc, column=DUTY_SCHEDULE_NAME_COLUMN)).value\n\n        if (DUTY_SCHEDULE_SHEET.cell(row=name_row_acc, column=date_column_acc)).value == \"F\":\n            FULL_DAY_SHEET.cell(row=date_row_acc, column=full_day_off_slot_acc, value=name)\n            full_day_off_slot_acc += 1\n\n        elif (DUTY_SCHEDULE_SHEET.cell(row=name_row_acc, column=date_column_acc)).value in [\"H\", \"T/H\"]:\n            HALF_DAY_SHEET.cell(row=date_row_acc, column=half_day_off_slot_acc, value=name)\n            half_day_off_slot_acc += 1\n\n        name_row_acc += 1\n\n    date_row_acc += 1\n\nEntry.save('BookForProgramDirector.xlsx')\n","sub_path":"FillDayOffSheets.py","file_name":"FillDayOffSheets.py","file_ext":"py","file_size_in_byte":1555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"156378429","text":"#!/usr/bin/env python\r\n# coding: utf-8\r\n\r\n\r\n\r\nfrom saenopy import FiniteBodyForces\r\n    \r\n# initialize the object\r\nM = FiniteBodyForces()\r\n\r\n\r\nfrom saenopy.materials import SemiAffineFiberMaterial\r\n\r\n# provide a material model\r\nmaterial = SemiAffineFiberMaterial(1645, 0.0008, 0.0075, 0.033)\r\nM.setMaterialModel(material)\r\n\r\n\r\nimport numpy as np\r\n\r\n# define the coordinates of the nodes of the mesh\r\n# the array has to have the shape N_v x 3\r\nR = np.array([[0., 0., 0.],  # 0\r\n              [0., 1., 0.],  # 1\r\n              [1., 1., 0.],  # 2\r\n              [1., 0., 0.],  # 3\r\n              [0., 0., 1.],  # 4\r\n              [1., 0., 1.],  # 5\r\n              [1., 1., 1.],  # 6\r\n              [0., 1., 1.]]) # 7\r\n\r\n# define the tetrahedra of the mesh\r\n# the array has to have the shape N_t x 4\r\n# every entry is an index referencing a verces in R (indices start with 0)\r\nT = np.array([[0, 1, 7, 2],\r\n              [0, 2, 5, 3],\r\n              [0, 4, 5, 7],\r\n              [2, 5, 6, 7],\r\n              [0, 7, 5, 2]])\r\n\r\n\r\n# provide the node data\r\nM.setNodes(R)\r\n# and the tetrahedron data\r\nM.setTetrahedra(T)\r\n\r\n\r\n# the displacements of the nodes which shall be fitted\r\n# during the solving\r\nU = np.array([[0   , 0, 0],  # 0\r\n              [0   , 0, 0],  # 1\r\n              [0.01, 0, 0],  # 2\r\n              [0.01, 0, 0],  # 3\r\n              [0   , 0, 0],  # 4\r\n              [0.01, 0, 0],  # 5\r\n              [0.01, 0, 0],  # 6\r\n              [0   , 0, 0]]) # 7\r\n\r\n# hand the displacements over to the class instance\r\nM.setTargetDisplacements(U)\r\n\r\n\r\n# relax the mesh and move the \"varible\" nodes\r\nM.regularize(stepper=0.1, alpha=0.001)\r\n\r\n\r\nresults = M.computeForceMoments(rmax=100e-6)\r\n\r\n# store the forces of the nodes\r\nM.storeF(\"F.dat\")\r\n# store the positions and the displacements\r\nM.storeRAndU(\"R.dat\", \"U.dat\")\r\n# store the center of each tetrahedron and a combined list with energies and volumina of the tetrahedrons\r\nM.storeEandV(\"RR.dat\", \"EV.dat\")\r\n\r\nM.U\r\n\r\nM.viewMesh(50, 1)\r\n\r\n","sub_path":"docs/regularization.py","file_name":"regularization.py","file_ext":"py","file_size_in_byte":1992,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"72812207","text":"from geopy.geocoders import Bing\nimport ssl\nimport numpy as np\nimport pandas as pd\nimport os\nimport datetime\nfrom dateutil.relativedelta import relativedelta\nimport urllib\nfrom flask import Flask, flash, redirect, render_template, request, session, abort, make_response, url_for\n\napp = Flask(__name__)\n\n# appened static content with version number to overcome caching\n\n\n@app.context_processor\ndef override_url_for():\n    return dict(url_for=dated_url_for)\n\n\ndef dated_url_for(endpoint, **values):\n    if endpoint == 'static':\n        filename = values.get('filename', None)\n        if filename:\n            file_path = os.path.join(app.root_path,\n                                     endpoint, filename)\n            values['q'] = int(os.stat(file_path).st_mtime)\n    return url_for(endpoint, **values)\n\n\n# need this to avoid ssl certificate error\nssl._create_default_https_context = ssl._create_unverified_context\n\n# dont need to update this because gov.uk returns whatever the most recently uploaded file is\nurl = 'https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/762070/Monthly_museums_and_galleries_October_2018.csv'\n# url = 'Monthly_museums_and_galleries_October_2018.csv'\ndata = pd.read_csv(url, encoding='latin1')\n\n# data checks\ndata.columns\ndata.museum.unique()\ndata.year.unique()\ndata.month.unique()\ndata.visits.unique()\ndata.dtypes\n\n\n\n# data.to_csv('data.csv', index=False)\n# data = pd.read_csv('data.csv')\n\n# convert column names to lower case\nmycols = data.columns.tolist()\nmycols = [x.lower().strip() for x in mycols]\ndata.columns = mycols\n\ndata['day'] = 1\ndata['date'] = pd.to_datetime(data[['month', 'year', 'day']])\n\n\n# remove any records for months after the current one\n# set current date\nmyfilename = urllib.request.urlopen(urllib.request.Request(url, method='HEAD')).info().get_filename()\nfilename_date = myfilename[46:-4].lower()\n\nif filename_date != '':\n    current_month_string = filename_date[:-5]\n    current_month = datetime.datetime.strptime(current_month_string, '%B').month\n    current_year = int(filename_date[-4:])\n    current_date = pd.datetime(current_year, current_month, 1)\n\n    nextMonth = current_date + relativedelta(months=1)\n    nextMonthLastYear = nextMonth - relativedelta(years=1)\n\n    new_month = current_date.strftime(\"%b %Y\")\n    old_month = nextMonthLastYear.strftime(\"%b %Y\")\n\n    data = data.loc[data.date <= current_date, ]\nelse:\n    current_date = pd.datetime(2019, 3, 1)\n    nextMonth = current_date + relativedelta(months=1)\n    nextMonthLastYear = nextMonth - relativedelta(years=1)\n    new_month = current_date.strftime(\"%b %Y\")\n    old_month = nextMonthLastYear.strftime(\"%b %Y\")\n\n# check for NaNs\ndata.isnull().any()\n\n# null_mus = data.loc[data.visits.isnull(), ].index.copy()\n# data.loc[null_mus, ]\n\n# check for duplicates\n# as of october 2018, there is an exact duplicate of ROYAL MUSEUMS GREENWICH Sep 2018, and dupes with missing visits value for NATURAL HISTORY MUSEUM (TRING) and TATE BRITAIN Jan 2018\ndata.shape\ndata.loc[data.duplicated(['museum', 'year', 'month'])]\n\n# remove duplicates\ndata = data.loc[~(data.duplicated(['museum', 'year', 'month']))]\n\n# delete previous records with missing values\n# data['visits'] = data['visits'].replace(np.nan, 0)\n\n# check values in visits column\ndata.visits.unique().size\ndata.visits.isnull().sum()\ndata[data['visits'] > 0].head()\n\n# clean up string numbers in visits column and convert to int\n# replace - with 0 and convert visit number strings to numeric - should fix this in CSV\ndata.loc[data.visits.isin(['-']), 'visits'] = '0'\ndata.loc[data.visits.isin([' -   ']), 'visits'] = '0'\n# data['visits'] = data['visits'].str.replace(',', '')\ndata['visits'] = data['visits'].fillna(0)\ndata.dtypes\n\ndata['visits'] = data['visits'].astype(int)\ndata.loc[data['visits'] == 0, 'visits'] = np.nan\n\ncurrent_museums = data.loc[(data['date'] == current_date) & ~(pd.isna(data['visits'])), 'museum'].copy()\nall_museums = data.loc[:, 'museum'].copy()\nnolongersponsored = all_museums[~all_museums.isin(current_museums)].unique()\n\n#locale.setlocale(locale.LC_NUMERIC, '')\n#data['visits'] = pd.DataFrame({'temp': data.visits}).applymap(atof)\n\n# museums_list = data.museum.unique().tolist()\n# years_list = data.year.unique().tolist()\nmuseums_list = data.museum.unique()\nyears_list = data.year.unique()\n\nraw_data = data.copy()\n\n# pivot data to create column for each series (museum)\ndata = data.pivot(index='date', columns='museum', values='visits')\ndata = data.reset_index()\n\n# for each museum create moving average column\nfor col in museums_list:\n    data[col + '_MA'] = data.rolling(window=12)[col].mean()\n\ndata['date'] = data['date'].dt.strftime('%Y-%m-%d')\ndata = data.transpose().reset_index()\ndata = data.to_dict(orient='split')['data']\n\nseasonal_data = {}\nfor mus in museums_list:\n    df = raw_data.copy()\n    df['date'] = df['date'].dt.strftime('%Y-%m-%d')\n    # data = data.transpose().reset_index()\n    df = df.loc[df['museum'] == mus]\n    df['day'] = 1\n    df['backupyear'] = df['year'].copy()\n    df['year'] = 2018\n    df['month'] = pd.to_datetime(df[['month', 'year', 'day']])\n    df['year'] = df['backupyear'].copy()\n    df['year'] = df['year'].apply(str)\n    df['month'] = df['month'].dt.strftime('%Y-%m-%d')\n    df = df.pivot(index='month',columns='year',values='visits')\n    df = df.reset_index()\n    df = df.transpose().reset_index()\n    df = df.to_dict(orient='split')['data']\n    seasonal_data[mus] = df.copy()\n\n\n# [x for x in museums_list if 'TOTAL' not in x]\nmuseums_list_individual = {\n    'BRITISH MUSEUM': ['WC1B 3DG', 51.518970, -0.126500],\n    'GEFFRYE MUSEUM': ['E2 8EA', 51.531556, -0.076271],\n    'HORNIMAN MUSEUM (Excluding visits to the Garden)': ['SE23 3PQ', 51.441131, -0.060762],\n    'IMPERIAL WAR MUSEUM LONDON': ['SE1 6HZ', 51.496008, -0.108353],\n    'HMS BELFAST (IMPERIAL WAR MUSEUM)': ['SE1 2JH', 51.506048, -0.081481],\n    'CHURCHILL WAR ROOMS (IMPERIAL WAR MUSEUM)': ['SW1A 2AQ', 51.501764, -0.129108],\n    'IMPERIAL WAR MUSEUM DUXFORD ': ['CB22 4QR', 52.094764, 0.128180],\n    'IMPERIAL WAR MUSEUM NORTH': ['M17 1TZ', 53.469713, -2.298734],\n    'NATIONAL GALLERY': ['WC2N 5DN', 51.509097, -0.127683],\n    '(NHM) SOUTH KENSINGTON': ['SW7 5BD', 51.496563, -0.176892],\n    '(NHM) TRING': ['HP23 6AP', 51.791524, -0.660652],\n    'ROYAL MUSEUMS GREENWICH ': ['SE10 9NF', 51.481154, -0.003746],\n    'NATIONAL MUSEUMS LIVERPOOL': ['L3 1DG', 53.406165, -2.995119],\n    'NATIONAL COAL MINING MUSEUM FOR ENGLAND': ['WF4 4RH', 53.643479, -1.619416],\n    'SCIENCE MUSEUM GROUP SOUTH KENSINGTON ': ['SW7 2DD', 51.497295, -0.176503],\n    'SCIENCE MUSEUM GROUP NATIONAL MEDIA MUSEUM': ['BD1 1NQ', 53.790557, -1.756460],\n    'SCIENCE MUSEUM GROUP NATIONAL RAILWAY MUSEUM': ['YO26 4XJ', 53.960767, -1.096551],\n    'SCIENCE MUSEUM GROUP  LOCOMOTION AT SHILDON': ['DL4 2RE'],\n    'SCIENCE MUSEUM GROUP MUSEUM OF SCIENCE AND INDUSTRY, MANCHESTER': ['M3 4FP'],\n    'SCIENCE MUSEUM GROUP SWINDON (WROUGHTON)': ['SN4 9LT'],\n    'NATIONAL PORTRAIT GALLERY': ['WC2H 0HE'],\n    '(RA) LEEDS': ['LS10 1LT'],\n    '(RA) FORT NELSON ': ['PO17 6AN'],\n    '(RA) WHITE TOWER (BASED AT THE TOWER OF LONDON) ': ['EC3N 4AB'],\n    \"SIR JOHN SOANE'S MUSEUM\": ['WC2A 3BP'],\n    'TATE BRITAIN ': ['SW1P 4RG'],\n    'TATE MODERN  ': ['SE1 9TG'],\n    'TATE LIVERPOOL': ['L3 4BB'],\n    'TATE ST IVES': ['TR26 1TG'],\n    '(V&A) SOUTH KENSINGTON': ['SW7 2RL'],\n    '(V&A) MUSEUM OF CHILDHOOD, BETHNAL GREEN': ['E2 9PA'],\n    '(V&A) BLYTHE HOUSE': ['W14 0QX'],\n    'WALLACE COLLECTION': ['W1U 3BN'],\n    '(T&W) ARBEIA': ['NE33 2BB'],\n    '(T&W) DISCOVERY': ['NE1 4JA'],\n    '(T&W) GREAT NORTH MUSEUM': ['NE2 4PT'],\n    '(T&W) LAING': ['NE1 8AG'],\n    '(T&W) WASHINGTON F PIT': ['NE37 1BN'],\n    '(T&W) SEGEDUNUM': ['NE28 6HR'],\n    '(T&W) SHIPLEY': ['NE8 4JB'],\n    '(T&W) SOUTH SHIELDS': ['NE33 2JA'],\n    '(T&W) HATTON GALLERY': ['NE1 7RU'],\n    '(T&W) STEPHENSON ': ['NE29 8DX'],\n    'MUSEUM OF LONDON': ['EC2Y 5HN'],\n    'MUSEUM IN DOCKLANDS': ['E14 4AL'],\n}\n\n# add lon lat to postcodes\ngeolocator = Bing(\n    api_key='As--8aijMhZO5UZPjONPEaePK5nn16TgjznsOZEYbDTEwgsaL3C364fnOIwzEg8N')\n\n# from geopy.geocoders import GoogleV3\n# geolocator = GoogleV3()\n\n# from geopy.geocoders import Nominatim\n# geolocator = Nominatim(user_agent=\"ithinkthiscanbeanything\")\n\n##api_key\n#location = geolocator.geocode(\"BRITISH MUSEUM\")\n#print((location.latitude, location.longitude))\n#geolocator.geocode(\"WC1B 3DG\").latitude\n#geolocator.geocode(\"YO26 4XJ\").latitude\n#geolocator.geocode(\"HORNIMAN MUSEUM (Excluding visits to the Garden)\").longitude\n\n# mus_locs = pd.DataFrame.from_dict(museums_list_individual, orient='index')\n# mus_locs['location'] = mus_locs[0].apply(geolocator.geocode)\n# mus_locs['lon'] = mus_locs['location'].apply(lambda x: x.longitude)\n# mus_locs['lat'] = mus_locs['location'].apply(lambda x: x.latitude)\n#mus_locs.to_csv('mus_locs.csv')\n# mus_locs = pd.read_csv('museum_locations.csv', index_col=0)\n\n\nmus_locs = pd.read_csv('museum_locations.csv')\nmus_locs_json = mus_locs.to_dict('records')\n\n\n# define number format function\ndef format_nums(num):\n    if num >= 999000:\n        fnum = str(round(num / 1000000, 1)) + 'M'\n    elif num >= 1000:\n        fnum = str(int(round(num / 1000, 0))) + 'k'\n    else:\n        fnum = str(int(num))\n    return(fnum)\n\n\n# kpi data\ndf = pd.read_csv('museum-dashboard-kpi2.csv')\ndf = df.set_index(['Museum', 'Year'])\ndf = df.stack().reset_index()\ndf.columns = ['Museum', 'Year', 'Indicator', 'Value']\nkpi_list = df.Indicator.unique()\nkpi_museums_list = df.Museum.unique()\n# kpi_list = df.Indicator.unique().tolist()\n# kpi_museums_list = df.Museum.unique().tolist()\n\nkpi_raw = df.copy()\n\nkpi_data = {}\nfor kpi in kpi_list:\n    df = kpi_raw.copy()\n    df = df.loc[df['Indicator'] == kpi]\n    df = df.pivot(index='Year',columns='Museum',values='Value')\n    df = df.reset_index()\n    df = df.transpose().reset_index()\n    df = df.to_dict(orient='split')['data']\n    kpi_data[kpi] = df.copy()\n\n\n\n# for j in indicator:\n#     j2 = j\n#     if j == 'Facilitated and self directed visits by visitors under 18 years old and in formal education':\n#         j2 = 'Formal Education'\n#     if j == 'Number of instances where visitors under 18 years old participated in on-site activites':\n#         j2 = 'On-site activities'\n\n# leaderboard df\n\n# summarise total visits in last 12 months\nmusy = raw_data.copy()\ntoday = max(musy.date)\n# only keep records from previous year\nmusy = musy.loc[(musy['date'] > (today - pd.DateOffset(years=1)))\n                & (musy['date'] <= today)]\nlast_year = musy.groupby(['museum']).sum()\nlast_year = last_year.reset_index()\nlast_year = last_year.loc[last_year['visits'] > 0]\nlast_year['visits_format'] = last_year['visits'].apply(format_nums)\n\n# IMPERIAL WAR MUSEUM (TOTAL) is no longer IMPERIAL WAR MUSEUM TOTAL\n# need to keep the museum names consistent otherwise wont be able to match to postcodes\n\nleaderboard_mus = ['BRITISH MUSEUM',\n                   'IMPERIAL WAR MUSEUM TOTAL',\n                   'NHM TOTAL',\n                   'SCIENCE MUSEUM GROUP TOTAL',\n                   '(RA) TOTAL',\n                   'TATE TOTAL',\n                   '(V&A) TOTAL',\n                   'TYNE & WEAR TOTAL',\n                   'HORNIMAN MUSEUM',\n                   'NATIONAL GALLERY',\n                   'NATIONAL PORTRAIT GALLERY']\n# leaderboard_df = last_year.loc[last_year['museum'].isin(leaderboard_mus)]\n# leaderboard_df = leaderboard_df.sort_values(by='visits', ascending=True)\n\nexcl_mus = ['TOTAL VISITOR FIGURES', 'TATE MODERN  ', '(NHM) SOUTH KENSINGTON', 'SCIENCE MUSEUM GROUP SOUTH KENSINGTON ',\n            '(V&A) SOUTH KENSINGTON', '(RA) WHITE TOWER (BASED AT THE TOWER OF LONDON) ', 'TATE BRITAIN ']\nleaderboard_df = last_year.sort_values(by='visits', ascending=False)\nleaderboard_df = leaderboard_df.loc[~last_year['museum'].isin(excl_mus)]\n# leaderboard_df = leaderboard_df.tail(10)\nleaderboard_df = leaderboard_df.transpose()\nleaderboard_df = leaderboard_df.reset_index()\nleaderboard_json = leaderboard_df.to_dict(orient='split')['data']\n\nprint('starting')\nprint(app.static_folder)\nprint(app.static_url_path)\nprint(app.template_folder)\n\n\n@app.route('/')\ndef index():\n    \"\"\"Render home page.\"\"\"\n    return 'hi there'  # we can render templates as usual\n\n@app.route('/data-tools/museum-visits')\ndef index_with_path():\n    \"\"\"Render home page.\"\"\"\n    print(app.static_folder)\n    print(app.static_url_path)\n    print(app.template_folder)\n    return render_template('index.html', leaderboard_data=leaderboard_json, map_data=mus_locs_json, ts_data=data, museums_list=museums_list, years_list=years_list, seasonal_data=seasonal_data, kpi_data=kpi_data, kpi_list=kpi_list, kpi_museums_list=kpi_museums_list, old_month=old_month, new_month=new_month, nolongersponsored=nolongersponsored)  # we can render templates as usual\n\n\nif __name__ == '__main__':\n    app.run(debug=True)\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":12874,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"414085489","text":"import tkinter as tk\r\nfrom tkinter import ttk\r\nimport arrow\r\nimport csv\r\nimport calendar\r\nimport math\r\n\r\nwin = tk.Tk()\r\nwin.title(\"Budgeting App\")\r\n\r\nTAX_PERSONAL_ALLOWANCE = 456\r\nTAX_BASE_LIMIT = 1327\r\nTAX_HIGHER_LIMIT = 5770\r\nNATIONAL_INSURANCE_ALLOWANCE = 324\r\nTAX_BASE_RATE = 0.2\r\nTAX_HIGHER_RATE = 0.4\r\nTAX_ADDITIONAL_RATE = 0.45\r\nNATIONAL_INSURANCE_RATE = 0.12\r\nDAYS_BETWEEN_PAY = 14\r\nSAVINGS = 200\r\n\r\nexp = []\r\nnames = []\r\ncosts = []\r\ndates = []\r\n\r\n\r\ndef radCall():\r\n    pass\r\n\r\n\r\ndef taxCalc():\r\n\r\n    money = int(pay.get())\r\n\r\n    if money >= TAX_HIGHER_LIMIT:\r\n        addTaxAmt = money - TAX_HIGHER_LIMIT\r\n        higTaxAmt = TAX_HIGHER_LIMIT - TAX_BASE_LIMIT\r\n        basTaxAmt = TAX_BASE_LIMIT - TAX_PERSONAL_ALLOWANCE\r\n        tax = math.floor((addTaxAmt * TAX_ADDITIONAL_RATE) + (higTaxAmt * TAX_HIGHER_RATE) + (basTaxAmt * TAX_BASE_RATE))\r\n    elif money >= TAX_BASE_LIMIT:\r\n        higTaxAmt = money - TAX_BASE_LIMIT\r\n        basTaxAmt = TAX_BASE_LIMIT - TAX_PERSONAL_ALLOWANCE\r\n        tax = math.floor((higTaxAmt * TAX_HIGHER_RATE) + (basTaxAmt * TAX_BASE_RATE))\r\n    elif money >= TAX_PERSONAL_ALLOWANCE:\r\n        tax = math.floor(((money - TAX_PERSONAL_ALLOWANCE) * TAX_BASE_RATE))\r\n    else:\r\n        pass\r\n\r\n    if money >= NATIONAL_INSURANCE_ALLOWANCE:\r\n        NI = math.floor((money - NATIONAL_INSURANCE_ALLOWANCE) * NATIONAL_INSURANCE_RATE)\r\n    else:\r\n        pass\r\n\r\n    money -= tax\r\n    exp.append(['Tax', str(tax), str(money)])\r\n    ttk.Label(win, text=\"Tax: £\" + str(tax) + \", £\" + str(money)).grid(column=0, row=8, columnspan=3)\r\n    money -= NI\r\n    exp.append(['National Insurance', str(NI), str(money)])\r\n    ttk.Label(win, text='National Insurance: £' + str(NI) + \", £\" + str(money)).grid(column=0, row=9, columnspan=3)\r\n\r\n    save(money)\r\n\r\n\r\ndef restBudget(m):\r\n\r\n    a = arrow.utcnow()\r\n    month = a.month\r\n    days = 0\r\n    rent = 60\r\n\r\n    with open('expenses.txt', 'r') as csvfile:\r\n        readers = csv.reader(csvfile, delimiter=\",\")\r\n        for row1 in readers:\r\n            names.append(row1[0])\r\n            costs.append(row1[1])\r\n            dates.append(row1[2])\r\n\r\n    rows = 10\r\n\r\n    if (month == '2') and (a.year // 4) == 0:\r\n        month = 'February2'\r\n    elif month == '2':\r\n        month = 'February1'\r\n    else:\r\n        month = calendar.month_name[month]\r\n    print(month)\r\n\r\n    with open('months.txt', 'r') as csvfile:\r\n        readers = csv.reader(csvfile, delimiter=\",\")\r\n        for row in readers:\r\n            if row[0] == month:\r\n                days = row[1]\r\n            else:\r\n                pass\r\n\r\n    x = 0\r\n    days = int(days)\r\n    nextpay = int(date.get()) + DAYS_BETWEEN_PAY\r\n\r\n    while x != len(names):\r\n        if int(date.get()) <= days:\r\n            if int(dates[x]) >= int(date.get()) and int(dates[x]) < nextpay:\r\n                rows += 1\r\n                m -= int(costs[x])\r\n                ttk.Label(win, text=names[x] + \": £\" + costs[x] + \", £\" + str(m)).grid(column=0, row=rows, columnspan=3)\r\n            else:\r\n                pass\r\n        else:\r\n            daysend = days - int(date.get())\r\n            payday = DAYS_BETWEEN_PAY - daysend\r\n            print(payday)\r\n            if dates[x] < nextpay:\r\n                rows += 1\r\n                m -= int(costs[x])\r\n                ttk.Label(win, text=names[x] + \": £\" + costs[x] + \", £\" + str(m)).grid(column=0, row=rows, columnspan=3)\r\n            else:\r\n                pass\r\n        x += 1\r\n\r\n    m -= rent\r\n    ttk.Label(win, text=\"Rent: £\" + str(rent) + \", £\" + str(m)).grid(column=0, row=rows + 1, columnspan=3)\r\n    ttk.Label(win, text=\"Remaining Money: £\" + str(m)).grid(column=0, row=7, columnspan=3)\r\n\r\n\r\ndef save(money):\r\n\r\n    if savings.get() == 1:\r\n        money -= SAVINGS\r\n        exp.append([\"Savings\", str(SAVINGS), str(money)])\r\n        print(exp)\r\n        restBudget(money)\r\n        ttk.Label(win, text=\"Savings: £\" + str(SAVINGS) + \", £\" + str(money)).grid(column=0, row=10, columnspan=3)\r\n    else:\r\n        restBudget(money)\r\n\r\n\r\n# adding a label for instructions\r\nttk.Label(win, text=\"Enter Date number of the Pay Day\").grid(column=0, row=0, columnspan=3)\r\n\r\n# adding another entry for the date\r\ndate = tk.StringVar()\r\ndateEntered = ttk.Entry(win, width=12, textvariable=date)\r\ndateEntered.grid(column=0, row=1, columnspan=3)\r\n\r\n# Adding a label for instructions\r\nttk.Label(win, text='Enter the amount to be paid before tax').grid(column=0, row=2, columnspan=3)\r\n\r\n# Adding a Textbox Entry Widget to enter the amount\r\npay = tk.StringVar()\r\nnameEntered = ttk.Entry(win, width=12, textvariable=pay)\r\nnameEntered.grid(column=0, row=3, columnspan=3)\r\n\r\n# Adding a check button for saving\r\nsavings = tk.IntVar()\r\ncheck1 = tk.Checkbutton(win, text='Saving', variable=savings).grid(column=0, row=4, columnspan=3)\r\n\r\n# Adding a button to workout the budget\r\nworking = ttk.Button(win, text=\"Workout the Budget\", command=taxCalc).grid(column=0, row=6, columnspan=3)\r\n\r\nwin.mainloop()\r\n","sub_path":"Budgeter/bugeterv2.py","file_name":"bugeterv2.py","file_ext":"py","file_size_in_byte":4963,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"231024146","text":"# The MIT License\n#\n#  Copyright 2016-2017 UB Dortmund <daten.ub@tu-dortmund.de>.\n#\n#  Permission is hereby granted, free of charge, to any person obtaining a copy\n#  of this software and associated documentation files (the \"Software\"), to deal\n#  in the Software without restriction, including without limitation the rights\n#  to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n#  copies of the Software, and to permit persons to whom the Software is\n#  furnished to do so, subject to the following conditions:\n#\n#  The above copyright notice and this permission notice shall be included in\n#  all copies or substantial portions of the Software.\n#\n#  THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n#  IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n#  FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n#  AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n#  LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n#  OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n#  THE SOFTWARE.\n\nimport babelfish\nimport bibtexparser\nfrom bibtexparser.bibdatabase import BibDatabase\nimport datetime\nimport requests\nimport simplejson as json\nimport uuid\n\ntry:\n    import local_secrets as secrets\nexcept ImportError:\n    import secrets\n\n\nORCID_PUBTYPES = {\n    'Monograph': 'BOOK',\n    'AudioBook': 'BOOK',\n    'Chapter': 'BOOK_CHAPTER',\n    'ChapterInLegalCommentary': 'BOOK_CHAPTER',\n    'ChapterInMonograph': 'BOOK_CHAPTER',\n    'review': 'BOOK_REVIEW',\n    'Thesis': 'DISSERTATION',\n    'Conference': 'CONFERENCE_PAPER',\n    'Collection': 'EDITED_BOOK',\n    'ArticleJournal': 'JOURNAL_ARTICLE',\n    'SpecialIssue': 'JOURNAL_ISSUE',\n    'InternetDocument': 'ONLINE_RESOURCE',\n    'ArticleNewspaper': 'NEWSPAPER_ARTICLE',\n    'Report': 'REPORT',\n    'ResearchData': 'DATA_SET',\n    'Patent': 'PATENT',\n    'Lecture': 'LECTURE_SPEECH',\n    'Standard': 'STANDARDS_AND_POLICY'\n}\n\nWTF_PUBTYPES = {\n    'OTHER': 'Other',\n    'REPORT': 'Report',\n    'JOURNAL_ARTICLE': 'ArticleJournal',\n    'DATA_SET': 'ResearchData',\n    'DISSERTATION': 'Thesis',\n    'EDITED_BOOK': 'Collection',\n    'BOOK_CHAPTER': 'Chapter'\n}\n\nBIBTEX_PUBTYPES = {\n    'Monograph': 'book',\n    'AudioBook': 'book',\n    'Chapter': 'inbook',\n    'ChapterInLegalCommentary': 'inbook',\n    'ChapterInMonograph': 'inbook',\n    'review': 'misc',\n    'Thesis': 'phdthesis',\n    'Conference': 'proceedings',\n    'Collection': 'book',\n    'ArticleJournal': 'article',\n    'SpecialIssue': 'misc',\n    'InternetDocument': 'misc',\n    'ArticleNewspaper': 'article',\n    'Report': 'unpublished',\n    'ResearchData': 'misc',\n    'Patent': 'misc',\n    'Lecture': 'misc',\n    'Standard': 'misc'\n}\n\n\ndef orcid2mms(orcid_id='', orcid_work_record=None):\n\n    mms_json = {}\n\n    if orcid_work_record and (orcid_work_record.get('visibility') == 'PUBLIC' or orcid_work_record.get('visibility') == 'LIMITED'):\n        mms_json['id'] = str(uuid.uuid4())\n\n        orcid_sync = {\n            'orcid_id': orcid_id,\n            'orcid_put_code': '',\n            'orcid_visibility': str(orcid_work_record.get('visibility'))\n        }\n        mms_json['orcid_sync'] = [orcid_sync]\n        for extid in orcid_work_record.get('external-ids').get('external-id'):\n            if extid.get('external-id-type') == 'doi':\n                # print('\\tadd scopus_id \"%s\"' % extid.get('external-id-value'))\n                mms_json['DOI'] = [extid.get('external-id-value')]\n            if extid.get('external-id-type') == 'eid':\n                # print('\\tadd scopus_id \"%s\"' % extid.get('external-id-value'))\n                mms_json['scopus_id'] = extid.get('external-id-value')\n            if extid.get('external-id-type') == 'wosuid':\n                # print('\\tadd wosuid \"%s\"' % extid.get('external-id-value'))\n                mms_json['WOSID'] = extid.get('external-id-value').replace('WOS:', '')\n            if extid.get('external-id-type') == 'pmid':\n                # print('\\tadd pmid \"%s\"' % extid.get('external-id-value'))\n                mms_json['PMID'] = extid.get('external-id-value')\n            if extid.get('external-id-type') == 'urn':\n                # print('\\tadd pmid \"%s\"' % extid.get('external-id-value'))\n                mms_json['uri'] = [extid.get('external-id-value')]\n\n        mms_json['title'] = orcid_work_record.get('title').get('title').get('value')\n        if orcid_work_record.get('title').get('subtitle'):\n            mms_json['subtitle'] = orcid_work_record.get('title').get('subtitle').get('value')\n\n        mms_json['pubtype'] = WTF_PUBTYPES.get(orcid_work_record.get('type'))\n        if not mms_json['pubtype']:\n            WTF_PUBTYPES.get('OTHER')\n\n        issued = orcid_work_record.get('publication-date').get('year').get('value')\n        if orcid_work_record.get('publication-date').get('month'):\n            issued += '-%s' % orcid_work_record.get('publication-date').get('month').get('value')\n            if orcid_work_record.get('publication-date').get('day'):\n                issued += '-%s' % orcid_work_record.get('publication-date').get('day').get('value')\n        mms_json['issued'] = issued\n\n        mms_json['editorial_status'] = 'new'\n        mms_json['note'] = 'added by ORCID synchronization'\n        mms_json['created'] = timestamp()\n        mms_json['changed'] = timestamp()\n        mms_json['owner'] = ['daten.ub@tu-dortmund.de']\n\n    return mms_json\n\n\ndef mms2orcid(affiliation='', mms_records=None):\n    orcid_records = []\n\n    # logging.info('wtf_records: %s' % wtf_records)\n    if mms_records is None:\n        mms_records = []\n\n    if len(mms_records) > 0:\n        for record in mms_records:\n\n            orcid_record = {}\n            db = BibDatabase()\n            db.entries = []\n            bibtex_entry = {}\n\n            # work type\n            orcid_type = ORCID_PUBTYPES.get(record.get('pubtype'))\n            if orcid_type is None:\n                orcid_type = 'OTHER'\n            orcid_record.setdefault('type', orcid_type)\n\n            bibtex_type = BIBTEX_PUBTYPES.get(record.get('pubtype'))\n            if bibtex_type is None:\n                bibtex_type = 'misc'\n            bibtex_entry.setdefault('ENTRYTYPE', bibtex_type)\n\n            external_ids = {}\n            external_id = []\n            # ids - record id (source-work-id)\n            ext_id = {}\n            ext_id.setdefault('external-id-type', 'source-work-id')\n            ext_id.setdefault('external-id-value', record.get('id'))\n            ext_id.setdefault('external-id-relationship', 'SELF')\n            if affiliation and affiliation in secrets.AFFILIATION_URL.keys():\n                ext_id.setdefault('external-id-url', '%s%s/%s' % (secrets.AFFILIATION_URL.get(affiliation), record.get('pubtype'), record.get('id')))\n            external_id.append(ext_id)\n            bibtex_entry.setdefault('ID', record.get('id'))\n\n            # ids - ISBN (isbn)\n            if record.get('ISBN'):\n                for isbn in record.get('ISBN'):\n                    if isbn:\n                        ext_id = {}\n                        ext_id.setdefault('external-id-type', 'isbn')\n                        ext_id.setdefault('external-id-value', isbn)\n                        ext_id.setdefault('external-id-relationship', 'SELF')\n                        external_id.append(ext_id)\n\n            # ids - ISSN (issn)\n            if record.get('ISSN'):\n                for issn in record.get('ISSN'):\n                    if issn:\n                        ext_id = {}\n                        ext_id.setdefault('external-id-type', 'issn')\n                        ext_id.setdefault('external-id-value', issn)\n                        ext_id.setdefault('external-id-relationship', 'SELF')\n                        external_id.append(ext_id)\n\n            # ids - ZDB (other-id)\n            if record.get('ZDBID'):\n                for zdbid in record.get('ZDBID'):\n                    if zdbid:\n                        ext_id = {}\n                        ext_id.setdefault('external-id-type', 'other-id')\n                        ext_id.setdefault('external-id-value', zdbid)\n                        ext_id.setdefault('external-id-url', 'http://ld.zdb-services.de/resource/%s' % zdbid)\n                        ext_id.setdefault('external-id-relationship', 'SELF')\n                        external_id.append(ext_id)\n\n            # ids - PMID (pmc)\n            if record.get('PMID'):\n                ext_id = {}\n                ext_id.setdefault('external-id-type', 'pmid')\n                ext_id.setdefault('external-id-value', record.get('PMID'))\n                ext_id.setdefault('external-id-url', 'http://www.ncbi.nlm.nih.gov/pubmed/%s' % record.get('PMID'))\n                ext_id.setdefault('external-id-relationship', 'SELF')\n                external_id.append(ext_id)\n\n            # ids - WOS-ID (wosuid)\n            if record.get('WOSID'):\n                ext_id = {}\n                ext_id.setdefault('external-id-type', 'doi')\n                ext_id.setdefault('external-id-value', record.get('WOSID'))\n                ext_id.setdefault('external-id-url', 'http://ws.isiknowledge.com/cps/openurl/service?url_ver=Z39.88-2004&rft_id=info:ut/%s' % record.get('WOSID'))\n                ext_id.setdefault('external-id-relationship', 'SELF')\n                external_id.append(ext_id)\n\n            # ids - doi\n            if record.get('DOI'):\n                for doi in record.get('DOI'):\n                    if doi:\n                        ext_id = {}\n                        ext_id.setdefault('external-id-type', 'doi')\n                        ext_id.setdefault('external-id-value', doi)\n                        ext_id.setdefault('external-id-url', 'https://doi.org/%s' % doi)\n                        ext_id.setdefault('external-id-relationship', 'SELF')\n                        external_id.append(ext_id)\n                bibtex_entry.setdefault('doi', record.get('DOI')[0])\n\n            if external_id:\n                external_ids.setdefault('external-id', external_id)\n\n            orcid_record.setdefault('external-ids', external_ids)\n\n            # titles\n            title = {}\n            title.setdefault('title', record.get('title'))\n            if record.get('subtitle'):\n                title.setdefault('subtitle', record.get('subtitle'))\n            orcid_record.setdefault('title', title)\n\n            title = record.get('title')\n            if record.get('subtitle'):\n                title += ': %s' % record.get('subtitle')\n            bibtex_entry.setdefault('title', title)\n\n            # issued\n            if record.get('issued'):\n                publication_date = {}\n                date_parts = []\n                for date_part in str(record.get('issued')).replace('[', '').replace(']', '').split('-'):\n                    date_parts.append(date_part)\n                publication_date.setdefault('year', int(date_parts[0]))\n                bibtex_entry.setdefault('year', date_parts[0])\n                if len(date_parts) > 1:\n                    publication_date.setdefault('month', int(date_parts[1]))\n                    bibtex_entry.setdefault('month', date_parts[1])\n                if len(date_parts) > 2:\n                    publication_date.setdefault('day', int(date_parts[2]))\n                    bibtex_entry.setdefault('day', date_parts[2])\n                orcid_record.setdefault('publication-date', publication_date)\n\n            # contributors\n            contributors = {}\n            contributor = []\n            author_str = ''\n            for author in record.get('person'):\n                if 'aut' in author.get('role'):\n                    con = {}\n                    con.setdefault('credit-name', author.get('name'))\n                    if author.get('orcid'):\n                        con.setdefault('contributor-orcid', {'uri': 'http://orcid.org/%s' % author.get('orcid')})\n                    contributor_attributes = {}\n                    contributor_attributes.setdefault('contributor-role', 'AUTHOR')\n                    con.setdefault('contributor-attributes', contributor_attributes)\n                    contributor.append(con)\n                    if author_str != '':\n                        author_str += ' and '\n                    author_str += author.get('name')\n            contributors.setdefault('contributor', contributor)\n            orcid_record.setdefault('contributors', contributors)\n\n            bibtex_entry.setdefault('author', author_str)\n\n            # language\n            if record.get('language') and record.get('language')[0] and record.get('language')[0] != 'None':\n                lang_code = ''\n                try:\n                    lang_code = str(babelfish.Language.fromalpha3b(record.get('language')[0]))\n                except babelfish.exceptions.LanguageReverseError:\n                    pass\n                if lang_code:\n                    orcid_record.setdefault('language-code', lang_code)\n\n            # is_part_of\n            hosts = []\n            if record.get('is_part_of'):\n                hosts = record.get('is_part_of')\n\n            for host in hosts:\n                if host.get('is_part_of') != '':\n                    try:\n                        response = requests.get('%s/%s/%s' % (secrets.MMS_API, 'work',\n                                                              host.get('is_part_of')),\n                                                headers={'Accept': 'application/json'},\n                                                )\n                        status = response.status_code\n                        if status == 200:\n                            myjson = json.loads(response.content.decode('utf8').get('wtf_json'))\n\n                            title = myjson.get('title')\n                            if myjson.get('subtitle'):\n                                title += ': %s' % myjson.get('subtitle')\n                            orcid_record.setdefault('journal-title', title)\n                            if bibtex_entry.get('ENTRYTYPE') == 'article':\n                                bibtex_entry.setdefault('journal', title)\n                            elif bibtex_entry.get('ENTRYTYPE') == 'inbook':\n                                bibtex_entry.setdefault('booktitle', title)\n                            elif bibtex_entry.get('ENTRYTYPE') == 'inproceedings':\n                                bibtex_entry.setdefault('booktitle', title)\n                            elif bibtex_entry.get('ENTRYTYPE') == 'incollection':\n                                bibtex_entry.setdefault('booktitle', title)\n                            else:\n                                bibtex_entry.setdefault('series', title)\n                    except AttributeError as e:\n                        print('ERROR: ' % e)\n                elif host.get('host_title'):\n                    orcid_record.setdefault('journal-title', host.get('host_title'))\n\n                if host.get('volume') != '':\n                    bibtex_entry.setdefault('volume', host.get('volume'))\n                else:\n                    bibtex_entry.setdefault('volume', '')\n\n            if bibtex_entry:\n                db.entries.append(bibtex_entry)\n\n            citation = {}\n            citation.setdefault('citation-type', 'BIBTEX')\n            citation.setdefault('citation-value', bibtexparser.dumps(db))\n            orcid_record.setdefault('citation', citation)\n\n            orcid_records.append(orcid_record)\n\n    return orcid_records\n\n\ndef timestamp():\n    date_string = str(datetime.datetime.now())[:-3]\n    if date_string.endswith('0'):\n        date_string = '%s1' % date_string[:-1]\n\n    return date_string\n","sub_path":"orcid_mms.py","file_name":"orcid_mms.py","file_ext":"py","file_size_in_byte":15698,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"291857365","text":"# Feestlunch\n\nAantalCroissantjes = 17\nAantalStokbroden = 2 \nAantalKorting = 3\n\nPrijsCroissantjes = 0.39\nPrijsStokbroden = 2.78\nPrijsKorting = -0.50\n\nTotaalprijsCroissantjes = AantalCroissantjes * PrijsCroissantjes\nTotaalprijsStokbroden = AantalStokbroden * PrijsStokbroden\nKorting = AantalKorting * PrijsKorting\n\ntotaalprijs = TotaalprijsCroissantjes + TotaalprijsStokbroden + Korting\n\nprint(\"De feestlucnh kost je bij de bakker €\" + str(totaalprijs) + \" voor de 17 croissantjes en de 2 stokboden als de 3 kortingsbonnen nog geldig zijn\")","sub_path":"Feestlunch.py","file_name":"Feestlunch.py","file_ext":"py","file_size_in_byte":540,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"632522357","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Aug 18 12:07:50 2018\n\n@author: yumi.zhang\n\"\"\"\n\nimport datetime\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\nfrom tabledef import *\n\nengine = create_engine('sqlite:///tutorial.db', echo = True)\n\nSession = sessionmaker(bind = engine)\nsession = Session()\n\n#manually add users information into the database\nuser = User(\"1\", \"1\")\nsession.add(user)\n\nuser2 = User(\"2\", \"2\")\nsession.add(user2)\n\nuser3 = User(\"3\", \"3\")\nsession.add(user3)\n\nsession.commit()\nsession.commit()","sub_path":"dummy.py","file_name":"dummy.py","file_ext":"py","file_size_in_byte":561,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"447424907","text":"import sys\nimport os\n\nimport csv\nfrom lxml import etree\n\n\ndef apply_translations(csv_path, xml_path, from_lang_key, to_lang_key):\n    # Reads csv\n    csv_file = open(csv_path, \"r\")\n    reader = csv.DictReader(csv_file, delimiter=\";\")\n\n    # Verifies both languages are present in read csv\n    if from_lang_key not in reader.fieldnames:\n        print(f\"'{from_lang_key}' not found in '{os.path.basename(csv_path)}'\")\n        return\n\n    if to_lang_key not in reader.fieldnames:\n        print(f\"'{to_lang_key}' not found in '{os.path.basename(csv_path)}'\")\n        return\n\n    # Parses xml\n    tree = etree.parse(xml_path)\n    context_list = tree.findall(\"context\")\n\n    for context in context_list:\n        if context.find(\"name\").text == \"CardsData\":\n            # Saves all message elements for faster lookup\n            messages = {}\n            for message in context.findall(\"message\"):\n                text = \"\\\\n\".join(message.find(\"source\").text.split(\"\\n\")).strip()\n                messages[text] = message\n            break\n\n    for line in reader:\n        # Skips empty lines\n        if line[from_lang_key] == \"\":\n            continue\n\n        from_text = line[from_lang_key].strip()\n        to_text = line[to_lang_key].strip()\n\n        message = messages.get(from_text)\n\n        if message is not None:\n            tr = message.find(\"translation\")\n            tr.text = to_text.replace(\"\\\\n\", \"\\n\")\n            # Marks current translation as done\n            tr.attrib.pop(\"type\")\n\n    tree.write(xml_path, encoding=\"utf-8\", pretty_print=True)\n\n\nif __name__ == \"__main__\":\n    if len(sys.argv) < 5:\n        print(\"Usage:\")\n        print(\"babel-tower.py <csv_file> <xml_file> <from> <to>\")\n        print(\"<from> and <to> must be the name of columns contained in <csv_file>\")\n        exit()\n\n    apply_translations(sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4])\n","sub_path":"babel-tower.py","file_name":"babel-tower.py","file_ext":"py","file_size_in_byte":1876,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"86285027","text":"from flask import Flask, jsonify, request\nfrom pymongo import MongoClient\nimport os\nimport base64\nfrom userScraper import user_scraping\nfrom evaluate import eval\nimport shutil\nfrom create_model import text_pre_process\n\nclient = MongoClient('localhost', 27017)\ndatabase = client.ReachOut\n\napp = Flask(__name__)\n\n@app.route('/')\ndef home_function():\n    return \"This is the home route request on a different one!\"\n\n@app.route('/register', methods = [\"POST\"])\ndef register_function():\n    try:\n        creds = request.get_json()\n        collection = database.register\n        collection.insert_one(creds)\n        return \"Done\"\n    except:\n        return \"Failed\"\n\n@app.route('/login', methods = [\"POST\"])\ndef login_function():\n    try:\n        log_creds = request.get_json()\n        search = {\"email\" : log_creds[\"email\"]}\n        collection = database.register\n        search_creds = collection.find_one(search)\n        if search_creds is not None:\n            if(log_creds[\"password\"] == search_creds[\"password\"]):\n                return search_creds[\"name\"]\n            else:\n                return \"NF\"\n        else:\n            return \"NF\"\n    except:\n        return \"NF\"\n\n@app.route('/analyze', methods = [\"POST\"])\ndef analyze_function():\n    try:\n        analyze_creds = request.get_json()\n        result = dict()\n        result[\"audio\"] = \"none\"\n        result[\"class\"] = \"none\"\n        path = \"data/\" + analyze_creds[\"email\"]\n        if(analyze_creds[\"audio\"] != \"\"):\n            audioString = analyze_creds[\"audio\"]\n            audioString = bytes(audioString, encoding=\"utf-8\")\n            if not os.path.exists(path):\n                os.makedirs(path)\n            file = path + \"/temp.wav\"\n            with open(file, \"wb+\") as f:\n                f.write(base64.decodebytes(audioString))\n            result[\"audio\"] = eval(file, 1)\n\n        print(analyze_creds[\"link\"])\n        if(analyze_creds[\"link\"] != \"\"):\n            user_scraping(analyze_creds[\"link\"], path)\n\n        file = path + \"/twitter_output.txt\"\n        print(analyze_creds[\"messages\"])\n        if(analyze_creds[\"messages\"] != \"\"):\n            if not os.path.exists(path):\n                os.makedirs(path)\n            lines = analyze_creds[\"messages\"].split(\";\")\n            with open(file, \"a+\") as f:\n                for i in lines:\n                    f.write(i)\n                    f.write(\"\\n\")\n        if os.path.exists(file):\n            result[\"class\"] = eval(file, 2)\n            result[\"class\"] = round(result[\"class\"], 2)\n        print(result)\n        result_creds = jsonify(result)\n        shutil.rmtree(path)\n        return result_creds\n    except Exception as e:\n        print(e)\n        return \"NF\"\n\n\nif __name__ == \"__main__\":\n    app.run(port=8000, debug=True, host=\"0.0.0.0\")\n","sub_path":"Flask Server/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":2769,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"190878832","text":"# -*- coding: utf-8 -*-\n\nimport sys\nimport re\ntarget_file = sys.argv[1]\nvocab_file = sys.argv[2]\nall_words = []\nvocab = []\nwith open(target_file,\"r\",encoding=\"utf-8\") as f:\n\tfor line in f:\n\t\tif line != \"\\n\":\n\t\t\tline = re.sub(\"\\n\",\"\",line)\n\t\t\tline = line.split(\" \")\n\t\t\tall_words.extend(line)\n\nall_words = set(all_words)\nwith open(vocab_file,\"r\",encoding=\"utf-8\") as g:\n\tfor line in g:\n\t\tline = re.sub(\"\\n\",\"\",line)\n\t\tvocab.append(line)\nvocab = set(vocab)\nintersection = all_words & vocab\ndifference = all_words - vocab\nprint(difference)\ncoverage = len(intersection)/len(all_words)\ntag = sys.argv[3]\n\nwith open(\"result_coverage.txt\",\"a\",encoding=\"utf-8\") as h:\n\tresult = tag +\": \"+str(coverage)+\"\\n\"\n\th.write(result)\n\n\n\n","sub_path":"check_info/check_coverage_for_NE.py","file_name":"check_coverage_for_NE.py","file_ext":"py","file_size_in_byte":718,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"569200068","text":"'''\nCreated on Aug 8, 2011\n\n@package: ally core\n@copyright: 2011 Sourcefabric o.p.s.\n@license: http://www.gnu.org/licenses/gpl-3.0.txt\n@author: Gabriel Nistor\n\nProvides the integration of the additional arguments into the main arguments.\n'''\n\nfrom ally.api.type import Input, typeFor\nfrom ally.core.spec.resources import Invoker, Path\nfrom ally.design.processor.attribute import requires, defines\nfrom ally.design.processor.context import Context\nfrom ally.design.processor.handler import HandlerProcessorProceed\n\n# --------------------------------------------------------------------\n\nclass RequestProvide(Context):\n    '''\n    The request context.\n    '''\n    # ---------------------------------------------------------------- Defined\n    arguments = defines(dict, doc='''\n    @rtype: dictionary{string, object}\n    The dictionary containing the arguments that will be passes to the invoker that provides the response object.\n    ''')\n    argumentsOfType = defines(dict, doc='''\n    @rtype: dictionary{Type, object}\n    A dictionary containing as a key the argument type, this dictionary needs to be populated by the \n    processors with any system values that might be used for invoking, the actual use of this arguments depends\n    ''')\n\n# --------------------------------------------------------------------\n\nclass ArgumentsPrepareHandler(HandlerProcessorProceed):\n    '''\n    Implementation for a processor that provides the integration of the additional arguments into the invoke arguments.\n    This processor will provide the argument by type.\n    '''\n\n    def process(self, request:RequestProvide, **keyargs):\n        '''\n        @see: HandlerProcessorProceed.process\n        \n        Provides the additional arguments by type to be populated.\n        '''\n        assert isinstance(request, RequestProvide), 'Invalid request %s' % request\n\n        request.argumentsOfType = {}\n        request.arguments = {}\n\n# --------------------------------------------------------------------\n\nclass Request(Context):\n    '''\n    The request context.\n    '''\n    # ---------------------------------------------------------------- Required\n    path = requires(Path)\n    invoker = requires(Invoker)\n    argumentsOfType = requires(dict)\n    arguments = requires(dict)\n\n# --------------------------------------------------------------------\n\nclass ArgumentsBuildHandler(HandlerProcessorProceed):\n    '''\n    Implementation for a processor that provides the integration of the additional arguments into the invoke arguments.\n    '''\n\n    def process(self, request:Request, **keyargs):\n        '''\n        @see: HandlerProcessorProceed.process\n        \n        Transpose the additional arguments into the main arguments.\n        '''\n        assert isinstance(request, Request), 'Invalid request %s' % request\n        if request.invoker is None: return  # If there is no invoker it means that no arguments need to be processed\n        assert isinstance(request.path, Path), 'Invalid request path %s' % request.path\n        assert isinstance(request.invoker, Invoker), 'Invalid request invoker %s' % request.invoker\n\n        if request.argumentsOfType:\n            for inp in request.invoker.inputs:\n                assert isinstance(inp, Input), 'Invalid input %s' % inp\n\n                if inp.name in request.arguments: continue\n\n                for argType, value in request.argumentsOfType.items():\n                    if typeFor(argType) == inp.type:\n                        if inp.name not in request.arguments: request.arguments[inp.name] = value\n                        break\n\n        request.arguments.update(request.path.toArguments(request.invoker))\n","sub_path":"components/ally-core/ally/core/impl/processor/arguments.py","file_name":"arguments.py","file_ext":"py","file_size_in_byte":3650,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"404850833","text":"from MarkdownTestCase import *\n\nclass XSSTestCase(MarkdownTestCase):\n\n\tdef test_preventImgSrcError(self):\n\t\tinputText = [\n\t\t\t'<img src=\"xss.png\" onerror=\"alert(\\\"xss\\\")\">',\n\t\t]\n\t\t\n\t\texpectedResult = [\n\t\t\t'<p>&lt;img src=&quot;xss.png&quot; onerror=&quot;alert(&quot;xss&quot;)&quot;&gt;</p>\\n'\n\t\t]\n\t\t\n\t\tfor i in range(0, len(inputText)):\n\t\t\tactualResult = parser.parse(inputText[i])\n\t\t\tself.assertEqual(expectedResult[i], actualResult)\n\t\n\tdef test_blockScriptTag(self):\n\t\tinputText = [\n\t\t\t'<script>alert(\"xss\");</script>',\n\t\t\t'<ScRiPt>alert(\"xss\");</ScRiPt>',\n\t\t]\n\t\t\n\t\texpectedResult = [\n\t\t\t'',\n\t\t\t'',\n\t\t]\n\t\t\n\t\tfor i in range(0, len(inputText)):\n\t\t\tactualResult = parser.parse(inputText[i])\n\t\t\tself.assertEqual(expectedResult[i], actualResult)\n\t\t\t\n\tdef test_rejectsRawHTML(self):\n\t\tinputText = [\n\t\t\t'<script>alert(\"xss\");</script>',\n\t\t\t'<p>xss</p>'\n\t\t]\n\t\t\n\t\texpectedResult = [\n\t\t\t'',\n\t\t\t'',\n\t\t]\n\t\t\n\t\tfor i in range(0, len(inputText)):\n\t\t\tactualResult = parser.parse(inputText[i])\n\t\t\tself.assertEqual(expectedResult[i], actualResult)\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t","sub_path":"markdown/tests/test_XSS.py","file_name":"test_XSS.py","file_ext":"py","file_size_in_byte":1047,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"299148472","text":"from queue import Queue\n\nimport scrapy\nfrom scrapy.crawler import CrawlerProcess\n\n\nclass Reddit(scrapy.Spider):\n    name = 'reddit'\n    queue = None\n\n    def parse(self, response):\n        for thing in response.css('div.thing'):\n            upvotes = int(thing.css('::attr(data-score)').extract_first())\n            if upvotes > 5000:\n                params = {\n                    'subreddit': response.request.url.rsplit('/', 2)[1],\n                    'title': thing.css('a.title::text').extract_first(),\n                    'votes': upvotes,\n                    'thread_link': thing.css('a.title::attr(href)').extract_first(),\n                    'comments_link': thing.css('ul.buttons a.comments::attr(href)').extract_first(),\n                }\n                if params['thread_link'] in params['comments_link']:\n                    params['thread_link'] = params['comments_link']\n                self.queue.put(params)\n\n        response.css(\"div.quote\")\n        for href in response.css('span.next-button a::attr(href)'):\n            yield response.follow(href, self.parse)\n\n\ndef crawler(subreddits):\n    \"\"\"\n        Scrapy reddit crawler.\n    \"\"\"\n    queue = Queue()\n    urls = ['https://old.reddit.com/r/' + subreddit for subreddit in subreddits.split(';')]\n\n    process = CrawlerProcess({'LOG_ENABLED': False})\n    spider = Reddit\n    spider.queue = queue\n    spider.start_urls = urls\n    process.crawl(spider)\n    process.start()\n\n    return queue\n\n\ndef format_thread(data):\n    for d in sorted(data, key=lambda x: x['votes'], reverse=True):\n        yield ('SubReddit: {subreddit} \\n'\n               'Title: {title} \\n'\n               'Votes: {votes} \\n'\n               'Thread link: {thread_link} \\n'\n               'Comments link: {comments_link} \\n'\n               ''.format(subreddit=d['subreddit'], title=d['title'],\n                         votes=d['votes'], thread_link=d['thread_link'],\n                         comments_link=d['comments_link']))\n","sub_path":"spider/subreddit.py","file_name":"subreddit.py","file_ext":"py","file_size_in_byte":1966,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"119453126","text":"\"\"\" Functions used for generating and interpreting typical filename patterns\nused in rockstar hlist analysis.\n\"\"\"\nimport string\nimport os\nimport fnmatch\nimport numpy as np\n\n\n__all__ = ('base_10_signed_int_to_base_n_signed_int', 'tree_subvol_substring_from_int')\n\n\ndef base_10_signed_int_to_base_n_signed_int(i, n):\n    digs = string.digits + string.letters\n\n    if i < 0:\n        sign = -1\n    elif i == 0:\n        return digs[0]\n    else:\n        sign = 1\n    i *= sign\n\n    digits = []\n    while i:\n        digits.append(digs[i % n])\n        i /= n\n\n    if sign < 0:\n        digits.append('-')\n\n    digits.reverse()\n\n    return int(''.join(digits))\n\n\ndef tree_subvol_substring_from_int(i, n):\n    \"\"\" From any non-negative integer i, and n subdivisions per dimension,\n    return the substring designating the corresponding subvolume.\n\n    For an explicit example, the following (i, n) pairs yield the values below:\n\n    (2, 5) --> '0_0_2'\n    (5, 5) --> '0_1_0'\n    (24, 5) --> '0_4_4'\n    (25, 5) --> '1_0_0'\n    \"\"\"\n    error_msg = (\"The `tree_subvol_substring_from_int` function \"\n        \"is only intended \\nto work with 3d subvolumes \"\n        \"with at most 10 subdivisions per dimension.\\n\"\n        \"You selected n = {0} subdivisions, for which there are \"\n        \"at most {1} different subvolumes, \\n\"\n        \"exceeding your request for file number i = {2}.\".format(n, n**3, i))\n\n    s = str(base_10_signed_int_to_base_n_signed_int(i, n))\n    if len(s) == 1:\n        return '0_0_'+s\n    elif len(s) == 2:\n        return '0_'+s[0]+'_'+s[1]\n    elif len(s) == 3:\n        return '_'.join(s)\n    else:\n        raise ValueError(error_msg)\n\n\ndef _binary_fname_from_structured_arr_column(arr, colname):\n    \"\"\" For column  ``colname`` of an input structured array ``arr``,\n    use the dtype to create a string that will be used as the basename\n    of the file storing a Numpy binary of the data for that column.\n    \"\"\"\n    msg = \"Column name ``{0}`` does not appear in input array\".format(colname)\n    assert colname in arr.dtype.names, msg\n\n    type_string = str(arr[colname].dtype.type.__name__)\n    return colname + '_data_' + type_string\n\n\ndef fname_generator(input_dirname, filepat):\n    \"\"\" Yield all the files in ``input_dirname`` with basenames matching\n    the specified file pattern.\n    \"\"\"\n    for path, dirlist, filelist in os.walk(input_dirname):\n        for name in fnmatch.filter(filelist, filepat):\n            yield os.path.join(path, name)\n","sub_path":"filename_utils.py","file_name":"filename_utils.py","file_ext":"py","file_size_in_byte":2464,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"396609035","text":"from Projetos.models import Materia, Usuario, Atividade, Gradeestudo, Horarioestudo\r\nfrom django.http import HttpResponse, Http404, HttpResponseRedirect\r\nfrom django.views import generic\r\nfrom django.shortcuts import render, redirect, get_object_or_404\r\nfrom .forms import atividades_form, horarios_form\r\n\r\ndef Principal(request):\r\n    usuario = Usuario.objects.get(id=1)\r\n\r\n    form = atividades_form(request.POST or None)\r\n\r\n    if form.is_valid():\r\n        m = form.cleaned_data\r\n        mm = Materia.objects.filter(nome = m[\"materia\"])\r\n        cc = m[\"conteudo\"]\r\n        Atividade(aluno = usuario, conteudo = cc, materia = mm[0]).save()\r\n\r\n    estudos = Horarioestudo.objects.filter(aluno = usuario).values_list('materias', 'horario')\r\n    materias = Materia.objects.filter(id__in=Gradeestudo.objects.filter(aluno=usuario).values_list('materias')).order_by('horario')\r\n    atividades = Atividade.objects.filter(aluno=usuario).values_list('materia', 'conteudo')\r\n    lista_atividades = []\r\n    sla = [] #vai guardar o nome da materia e o conteudo da atividade, respectivamente\r\n    lista_estudos = []\r\n    for e in estudos:\r\n        for m in materias:\r\n            if(e[0] == m.id):\r\n                    sla.append(m.nome)\r\n                    sla.append(e[1])\r\n                    lista_estudos.append(sla)\r\n                    sla = []\r\n\r\n    for x in atividades:\r\n        a = x[0]\r\n        c = Materia.objects.filter(id=a)\r\n        if (len(c)>0):\r\n            sla.append(c[0].nome)\r\n            sla.append(x[1])\r\n            lista_atividades.append(sla)\r\n            sla = []\r\n    return render(request, 'Projetos/principal.html', locals())\r\n\r\ndef horarios_auto(request):\r\n    usuario = Usuario.objects.get(id=1)\r\n    try:\r\n        choice = request.GET[\"escolha\"] # estudar ou revisar. retorna o id das opções cujo nome é \"escolha\"\r\n    except:\r\n        choice = \"\"\r\n    estudos = Horarioestudo.objects.filter(aluno = usuario)\r\n    materias = Materia.objects.filter(id__in=Gradeestudo.objects.filter(aluno=usuario).values_list('materias')).order_by('horario')\r\n    sla = ''\r\n    k = 0\r\n    materias_sem_h = Materia.objects.exclude(id__in=Horarioestudo.objects.filter(aluno=usuario).values_list('materias'))\r\n    if choice == \"estudar\":\r\n        for x in ['1','2','3','4','5','6']:\r\n            dia = Materia.objects.filter(id__in=Gradeestudo.objects.filter(aluno=usuario).values_list('materias'), horario__startswith = x).order_by('horario') # separando as matérias por dia\r\n            if len(dia) > 0 and dia[k] in materias_sem_h: #verifica se\r\n                print(\"X:\" + x + \"\\n\")\r\n                for y in [\"12\",\"34\",\"56\"]:\r\n                    if len(Horarioestudo.objects.filter(aluno=usuario, horario = x+\"M\"+y)) == 0: #verifica se ja não existe nada naquele horário\r\n                        q = Horarioestudo(aluno=usuario, materias=dia[k], horario = x+\"M\"+y) # criando novo horário de estudo\r\n                        q.save()\r\n                        k+=1\r\n                    if k>len(dia)-1:\r\n                        k = 0\r\n                        break\r\n    elif choice == \"revisar\":\r\n        for x in ['1','2','3','4','5','6']:\r\n            dia = Materia.objects.filter(id__in=Gradeestudo.objects.filter(aluno=usuario).values_list('materias'), horario__startswith = x).order_by('horario') # separando as matérias por dia\r\n            if len(dia) > 0 and dia[k] in materias_sem_h: #verifica se\r\n                for y in [\"12\",\"34\",\"56\"]:\r\n                    if len(Horarioestudo.objects.filter(aluno=usuario, horario = str(int(x)+1)+\"M\"+y)) == 0: #verifica se ja não existe nada naquele horário\r\n                        q = Horarioestudo(aluno=usuario, materias=dia[k], horario = str(int(x)+1)+\"M\"+y) # criando novo horário de estudo\r\n                        q.save()\r\n                        k+=1\r\n                    if k>len(dia)-1:\r\n                        k = 0\r\n                        break\r\n    else:\r\n        erro = \"Você não fez uma escolha\"\r\n    return Principal(request)\r\n\r\ndef horarios_manual(request):\r\n    usuario = Usuario.objects.get(id=1)\r\n    estudos = Horarioestudo.objects.filter(aluno = usuario).values_list('id', 'materias', 'horario').order_by('horario')\r\n    lista_estudos = []\r\n    sla = []\r\n    erro = \"\"\r\n    for e in estudos:\r\n        m = Materia.objects.filter(id=e[1])\r\n        sla.append(e[0])\r\n        sla.append(m[0].nome)\r\n        sla.append(e[2])\r\n        lista_estudos.append(sla)# sla = [id do horario de estudo, nome da materia que esta estudando, horario da materia]\r\n        sla=[]\r\n\r\n    try:\r\n        apagar = request.POST.getlist(\"apagar\")\r\n    except:\r\n        apagar = []\r\n        erro = \"Não deu certo\"\r\n    if len(apagar) > 0:\r\n        for x in apagar:\r\n            Horarioestudo.objects.filter(aluno=usuario, id=int(x)).delete()\r\n    form = horarios_form(request.POST or None)\r\n    \r\n    if form.is_valid():\r\n        m = form.cleaned_data\r\n        ida = m['id']\r\n        hh = m['horario']\r\n        if len(Horarioestudo.objects.filter(id=ida, aluno=usuario)) > 0: # editando o horario de uma materia\r\n            h = Horarioestudo.objects.get(id=ida, aluno = usuario)\r\n            if len(hh) != 4 and len(hh) != 9 and len(hh) != 0:\r\n                erro = \"o horario preenchido não tem 4 ou 9 caracteres\"\r\n            else:\r\n                if len(Materia.objects.filter(nome = m['materia'], id__in=Gradeestudo.objects.filter(aluno=usuario).values_list('materias'))) > 0: #se mandar nulo, o index range é menor que 0:\r\n                    mm = Materia.objects.filter(nome = m['materia'], id__in=Gradeestudo.objects.filter(aluno=usuario).values_list('materias')) # recebe a nova materia\r\n                else:\r\n                    mm = Materia.objects.filter(id=h.materias.id, id__in=Gradeestudo.objects.filter(aluno=usuario).values_list('materias')) # recebe a materia que ja estava lá\r\n\r\n                h.delete()\r\n                h.horario = hh\r\n                h.id = ida\r\n                h.save()\r\n\r\n        else: #criando um novo horario de estudo\r\n            # {'id': 1, 'horario': '1M12', 'materia': 'Inglês Técnico'}\r\n            if len(Horarioestudo.objects.filter(horario=hh, aluno = usuario)) > 0: # verifica se o horario de estudo está preenchido\r\n                erro = \"horario já preenchido\"\r\n            elif len(hh) != 4 and len(hh) != 9:\r\n                erro = \"o horario preenchido não tem 4 caracteres\"\r\n            else:\r\n                mm = Materia.objects.filter(nome = m['materia'], id__in=Gradeestudo.objects.filter(aluno=usuario).values_list('materias'))\r\n                if (len(mm) > 0):\r\n                    if (len(hh) == 4):\r\n                        if len(Horarioestudo.objects.filter(horario=hh, aluno = usuario)) > 0:\r\n                            erro = \"Horário já ocupado\"\r\n                        else:\r\n                            Horarioestudo(aluno = usuario, materias=mm[0], horario = hh).save()\r\n                    elif (len(hh) == 9):\r\n                        #str = 1M12 2M12\r\n                        a = hh[:4] #1M12\r\n                        b = hh[5:] #2M12\r\n                        if len(Horarioestudo.objects.filter(horario__contains=a, aluno = usuario)) > 0 or len(Horarioestudo.objects.filter(horario__contains=b, aluno = usuario)) > 0:\r\n                            erro = \"Horario ja ocupado\"\r\n                        else:\r\n                            Horarioestudo(aluno = usuario, materias=mm[0], horario = hh).save()\r\n                else:\r\n                    erro = \"Você não esta cadastrado nessa matéria\"\r\n    \r\n    return render(request, 'Projetos/horarios.html', locals())\r\n\r\ndef apagar_horarios(request):\r\n    usuario = Usuario.objects.get(id=1)\r\n    estudos = Horarioestudo.objects.filter(aluno = usuario)\r\n    materias = Materia.objects.filter(id__in=Gradeestudo.objects.filter(aluno=usuario).values_list('materias')).order_by('horario')\r\n    for x in estudos:\r\n        x.delete()\r\n    return Principal(request)\r\n\r\ndef editar_atividades(request):\r\n    lista_atividades = []\r\n    sla = []\r\n    form = atividades_form(request.POST or None)\r\n    usuario = Usuario.objects.get(id=1)\r\n    materias = Materia.objects.filter(id__in=Gradeestudo.objects.filter(aluno=usuario).values_list('materias')).order_by('horario')\r\n    atividades = Atividade.objects.filter(aluno=usuario).values_list('materia', 'conteudo', 'id')\r\n    erro = \"\"\r\n\r\n    try:\r\n        apagar = request.POST.getlist(\"apagar\")\r\n    except:\r\n        apagar = []\r\n        erro = \"Não deu certo\"\r\n    if len(apagar) > 0:\r\n        for x in apagar:\r\n            Atividade.objects.filter(aluno=usuario, id=int(x)).delete()\r\n    else:\r\n        try:\r\n            choice = request.POST[\"escolha\"] # estudar ou revisar. retorna o id das opções cujo nome é \"escolha\"\r\n            # Aqui foi usado post por causa no templat, que no form está descrito que o method é um post\r\n        except:\r\n            choice = \"\"\r\n\r\n        if choice == \"apagar_tudo\":\r\n            for x in Atividade.objects.filter(aluno=usuario):\r\n                x.delete()\r\n        elif form.is_valid():\r\n            m = form.cleaned_data\r\n            cc = m[\"conteudo\"]\r\n            mm = Materia.objects.filter(nome = m[\"materia\"], id__in=Gradeestudo.objects.filter(aluno=usuario).values_list('materias'))\r\n            if choice == \"apagar_materia\":\r\n                for x in Atividade.objects.filter(materia=mm[0],aluno=usuario):\r\n                    x.delete()\r\n            else:\r\n                if cc != \"\":\r\n                    Atividade(materia=mm[0], conteudo=cc, aluno = usuario).save()\r\n        \r\n    for x in atividades:\r\n        a = x[0]\r\n        c = Materia.objects.filter(id=a)\r\n        if (len(c)>0):\r\n            sla.append(c[0].nome) # matéria\r\n            sla.append(x[1]) #conteúdo\r\n            sla.append(str(x[2])); #id\r\n            lista_atividades.append(sla)\r\n            sla = []\r\n\r\n    return render(request, 'Projetos/atividades.html', locals())","sub_path":"Projetos/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":9969,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"247971102","text":"#!/usr/bin/env python3\nclass CycleGetter():\n    def __init__(self, max_time, lift: \"list or func\", start = 0, decrement = True):\n        \"\"\"\n        :param max_time: 移動回数\n        :param start: 初期条件\n        :param lift: 遷移の関数（リストの場合はlift[i]）\n        :return res: max_time 回進んだあとの場所\n                front: cycleまでの要素のリスト\n                cycle: cycle内の要素のリスト\n                end: cycle後の余った部分の要素のリスト\n                cnt: cycle回数\n        \"\"\"\n        self.max_time = max_time\n        if hasattr(lift, \"__getitem__\"):\n            LIFT = lift\n            if decrement:\n                LIFT = [None] + LIFT\n                max_time += 1\n            lift = lambda x: LIFT[x]\n        p = start\n        front, cycle, end = [], [], []\n        cnt = 0\n        visit = {p:0}\n        L, R = max_time, -1\n        P = [p]\n        for i in range(1, max_time):\n            p = lift(p)\n            if p in visit:\n                # (L, R) = (サイクルに入るまでに移動した回数, サイクルの終端に着くまでに移動した回数)\n                L, R = visit[p], i\n                period = R - L\n                break\n            visit[p] = i\n            P.append(p)\n        front = P[:L]\n        if L != max_time:\n            cycle, end = P[L : R], P[L : L + (max_time - L) % period]\n            cnt = (max_time - L) // period\n        self.front, self.cycle, self.end, self.cnt = front, cycle, end, cnt\n\n    def __call__(self):\n        return self.front, self.cycle, self.end, self.cnt # self.end[-1] if self.end else self.cycle[-1], \n    \n    def apply(self, time = None):\n        \"\"\"\n        :param time: 進む回数\n        :return: 進み終わったときの場所\n        \"\"\"\n        if time is None:\n            time = self.max_time\n        if time < len(self.front):\n            return self.front[time]\n        else:\n            time -= len(self.front)\n            if self.cycle:\n                time %= len(self.cycle)\n                return self.cycle[time]\n            else:\n                return self.end()\n    \n    def sum(self):\n        return sum(self.front) + sum(self.cycle) * self.cnt + sum(self.end)\n\nn, x, m = map(int, input().split())\nprint(CycleGetter(n, lambda a: pow(a, 2, m), x).sum())","sub_path":"Contest/ABC179/e/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2341,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"171094573","text":"import logging\n\nfrom typing import List\n\nlogger = logging.getLogger(__name__)\n\n\ndef construct_zkp(zero_knowledge_proof: List[dict], schema_id: str) -> list:\n    if zero_knowledge_proof == [{}]:\n        return []\n    req_preds = []\n    [\n        req_preds.append(\n            {\n                \"name\": item[\"name\"],\n                \"p_type\": item[\"p_type\"],\n                \"p_value\": item[\"p_value\"],\n                \"restrictions\": [{\"schema_id\": schema_id}],\n            }\n        )\n        for item in zero_knowledge_proof\n    ]\n    return req_preds\n\n\ndef construct_indy_proof_request(\n    name_proof_request: str, schema_id: str, attr_req, req_preds\n):\n    indy_proof_request = {\n        \"name\": name_proof_request,\n        \"version\": schema_id.split(\":\")[-1],\n        \"requested_attributes\": {\n            f\"0_{req_attr['name']}_uuid\": req_attr for req_attr in attr_req\n        },\n        \"requested_predicates\": {\n            f\"0_{req_pred['name']}_GE_uuid\": req_pred for req_pred in req_preds\n        },\n    }\n    return indy_proof_request\n","sub_path":"app/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1047,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"172870306","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Apr  7 18:43:00 2020\n\n@author: kerui\n\"\"\"\n\nimport os\nimport os.path\nimport glob\nimport fnmatch  # pattern matching\nimport numpy as np\nfrom numpy import linalg as LA\nfrom random import choice\nfrom PIL import Image\nimport torch\nimport torch.utils.data as data\nimport cv2\nfrom dataloaders import transforms\nfrom dataloaders.pose_estimator import get_pose_pnp\nimport skimage\nimport collections\n\ninput_options = ['d', 'rgb', 'rgbd', 'g', 'gd']\n\n\ndef load_calib():\n    \"\"\"\n    Temporarily hardcoding the calibration matrix using calib file from 2011_09_26\n    \"\"\"\n    calib = open(\"dataloaders/calib_cam_to_cam.txt\", \"r\")\n    lines = calib.readlines()\n    P_rect_line = lines[25]\n\n    Proj_str = P_rect_line.split(\":\")[1].split(\" \")[1:]\n    Proj = np.reshape(np.array([float(p) for p in Proj_str]),\n                      (3, 4)).astype(np.float32)\n    K = Proj[:3, :3]  # camera matrix\n\n    # note: we will take the center crop of the images during augmentation\n    # that changes the optical centers, but not focal lengths\n    K[0, 2] = K[\n        0,\n        2] - 13  # from width = 1242 to 1216, with a 13-pixel cut on both sides\n    K[1, 2] = K[\n        1,\n        2] - 11.5  # from width = 375 to 352, with a 11.5-pixel cut on both sides\n    return K\n\n\ndef get_paths_and_transform(split, args):\n    assert (args.use_d or args.use_rgb\n            or args.use_g), 'no proper input selected'\n            \n    if split == \"train\":\n        transform = train_transform\n        \n        # 1\n        glob_pc = os.path.join(\n            args.data_folder,\n            #'data_depth_velodyne/train/*_sync/proj_depth/velodyne_raw/image_0[2,3]/*.png'\n            #'audi_dataset/knn_pc/*.png'\n            'audi_dataset/knn_pc/*.png'\n        )\n        \n        # 2、用于道路分割的label\n        glob_road_label = os.path.join(\n            args.data_folder,\n            #'data_depth_annotated/train/*_sync/proj_depth/groundtruth/image_0[2,3]/*.png'\n            #'audi_dataset/road_label/*.png'\n            'audi_dataset/road_label/*.png'\n        )\n        \n        # 3、用于车道线分割的label\n        glob_lane_label = os.path.join(\n            args.data_folder,\n            #'data_depth_annotated/train/*_sync/proj_depth/groundtruth/image_0[2,3]/*.png'\n            #'audi_dataset/lane_label/*.png'\n            'audi_dataset/lane_label/*.png'\n        )\n        \n        # 4、\n        glob_rgb = os.path.join(\n                args.data_folder,\n                #'audi_dataset/train_image_2_lane/*.png'\n                'audi_dataset/train_image_2_lane/*.png'\n            )\n\n        def get_rgb_paths(p):\n            rgb_path = '../data/data_rgb/train/2011_09_26_drive_0001_sync/2011_09_26/2011_09_26_drive_0001_sync/image_02/data/'\n            \n            ps = p.split('\\\\')\n#            pnew = '/'.join([args.data_folder] + ['data_rgb'] + ps[-6:-4] +\n#                            ps[-2:-1] + ['data'] + ps[-1:])\n            \n            pnew = os.path.join(rgb_path, ps[-1])\n            \n            #print('-------------------1-------------------\\n')\n            return pnew\n    elif split == \"val\":\n        if args.val == \"select\":\n            transform = no_transform\n            \n            # 1\n            glob_pc = os.path.join(\n                args.data_folder,\n                #'data_depth_velodyne/train/*_sync/proj_depth/velodyne_raw/image_0[2,3]/*.png'\n                #'audi_dataset/test/knn_pc/*.png'\n                'audi_dataset/test/knn_pc/*.png'\n            )\n            \n            # 2、用于道路分割的label\n            glob_road_label = os.path.join(\n                args.data_folder,\n                #'data_depth_annotated/train/*_sync/proj_depth/groundtruth/image_0[2,3]/*.png'\n                #'audi_dataset/test/road_label/*.png'\n                'audi_dataset/test/road_label/*.png'\n            )\n            \n            # 3、用于车道线分割的label\n            glob_lane_label = os.path.join(\n                args.data_folder,\n                #'data_depth_annotated/train/*_sync/proj_depth/groundtruth/image_0[2,3]/*.png'\n                #'audi_dataset/test/lane_label/*.png'\n                'audi_dataset/test/lane_label/*.png'\n            )\n            \n            # 4、\n            glob_rgb = os.path.join(\n                    args.data_folder,\n                    #'audi_dataset/test/train_image_2_lane/*.png'\n                    'audi_dataset/test/train_image_2_lane/*.png'\n                )       \n    \n    # 测试, 输入为点云强度和rgb\n    elif split == 'test_road_lane_segmentation':\n        transform = no_transform\n        glob_pc = os.path.join(\n            args.data_folder,\n            'reflectance/*.png'\n        )\n        glob_road_label = None\n        glob_lane_label = None\n        glob_rgb = os.path.join(\n            args.data_folder,\n            \"train_image_2_lane/*.png\")\n\n    else:\n        raise ValueError(\"Unrecognized split \" + str(split))\n\n    if glob_lane_label is not None:\n        # train or val-full or val-select\n        # 点云：原始+强度+高度\n        paths_pc = sorted(glob.glob(glob_pc)) \n        paths_rgb = sorted(glob.glob(glob_rgb))\n        paths_road_label = sorted(glob.glob(glob_road_label))\n        paths_lane_label = sorted(glob.glob(glob_lane_label))\n    else:  \n        # test only has d or rgb\n        paths_rgb = sorted(glob.glob(glob_rgb))\n        paths_road_label = [None]*len(paths_rgb)\n        paths_lane_label = [None]*len(paths_rgb)\n        paths_pc = sorted(glob.glob(glob_pc))\n            \n\n    if len(paths_pc) == 0 and len(paths_rgb) == 0 and len(paths_lane_label) == 0 and len(paths_road_label) == 0:\n        raise (RuntimeError(\"Found 0 images under {}\".format(paths_lane_label)))\n    if len(paths_pc) == 0 and args.use_d:\n        raise (RuntimeError(\"Requested sparse depth but none was found\"))\n    if len(paths_rgb) == 0 and args.use_rgb:\n        raise (RuntimeError(\"Requested rgb images but none was found\"))\n    if len(paths_rgb) == 0 and args.use_g:\n        raise (RuntimeError(\"Requested gray images but no rgb was found\"))\n    if len(paths_rgb) != len(paths_pc) or len(paths_rgb) != len(paths_lane_label):\n        raise (RuntimeError(\"Produced different sizes for datasets\"))\n\n    paths = {\"rgb\": paths_rgb, \"pc\": paths_pc, \"lane_label\": paths_lane_label, \"road_label\": paths_road_label}\n    return paths, transform\n\n#oheight, owidth = 352, 1216\n\ndef rgb_read(filename, args):\n    assert os.path.exists(filename), \"file not found: {}\".format(filename)\n    img_file = Image.open(filename)\n    \n    #img_file = img_file.resize((400, 416))\n    #img_file = img_file.resize((args.image_height, args.image_width))\n    img_file = img_file.resize((args.image_width, args.image_height))\n    # 将读入的数据统一成该原始代码所使用的尺寸 oheight, owidth = 352, 1216\n    #img_file = img_file.resize((owidth, oheight))\n    \n    # rgb_png = np.array(img_file, dtype=float) / 255.0 # scale pixels to the range [0,1]\n    rgb_png = np.array(img_file, dtype='uint8')  # in the range [0,255]\n    img_file.close()\n    return rgb_png\n\n\ndef depth_read(filename, args):\n    # loads depth map D from png file\n    # and returns it as a numpy array,\n    # for details see readme.txt\n    assert os.path.exists(filename), \"file not found: {}\".format(filename)\n    img_file = Image.open(filename)\n    \n    #img_file = img_file.resize((400, 416))\n    #img_file = img_file.resize((args.image_height, args.image_width))\n    img_file = img_file.resize((args.image_width, args.image_height))\n    # 将读入的数据统一成该原始代码所使用的尺寸 oheight, owidth = 352, 1216\n    #img_file = img_file.resize((owidth, oheight))\n    \n    depth_png = np.array(img_file, dtype=int)\n    img_file.close()\n    # make sure we have a proper 16bit depth map here.. not 8bit!\n    assert np.max(depth_png) > 255, \\\n        \"np.max(depth_png)={}, path={}\".format(np.max(depth_png),filename)\n\n    depth = depth_png.astype(np.float) / 256.\n    # depth[depth_png == 0] = -1.\n    depth = np.expand_dims(depth, -1)\n    return depth\n\n\ndef label_read(filename, args):\n    # loads depth map D from png file\n    # and returns it as a numpy array,\n    # for details see readme.txt\n    assert os.path.exists(filename), \"file not found: {}\".format(filename)\n    img_file = Image.open(filename)\n    \n    #img_file = img_file.resize((400, 416))\n    #img_file = img_file.resize((args.image_height, args.image_width))\n    img_file = img_file.resize((args.image_width, args.image_height))\n    # 将读入的数据统一成该原始代码所使用的尺寸 oheight, owidth = 352, 1216\n    #img_file = img_file.resize((owidth, oheight))\n    \n    label_png = np.array(img_file, dtype=int)\n    img_file.close()\n    # make sure we have a proper 16bit depth map here.. not 8bit!\n    assert np.max(label_png) < 2, \\\n        \"np.max(label_png)={}, path={}\".format(np.max(label_png),filename)\n\n    label = label_png.astype(np.float)\n    # depth[depth_png == 0] = -1.\n    #label = np.expand_dims(label, -1)\n    return label\n\n\noheight, owidth = 352, 1216\n\n\ndef drop_depth_measurements(depth, prob_keep):\n    mask = np.random.binomial(1, prob_keep, depth.shape)\n    depth *= mask\n    return depth\n\ndef train_transform(rgb, sparse, target, segmentation_label, args):\n    # s = np.random.uniform(1.0, 1.5) # random scaling\n    # angle = np.random.uniform(-5.0, 5.0) # random rotation degrees\n    do_flip = np.random.uniform(0.0, 1.0) < 0.5  # random horizontal flip\n\n    transform_geometric = transforms.Compose([\n        # transforms.Rotate(angle),\n        # transforms.Resize(s),\n        # 将读入的数据统一改成原始代码所使用的尺寸 oheight, owidth = 352, 1216\n        #transforms.BottomCrop((oheight, owidth)),\n        #transforms.BottomCrop((args.image_height, args.image_width)),\n        #Resize((args.image_height, args.image_width)),\n        \n        transforms.HorizontalFlip(do_flip)\n    ])\n    if sparse is not None:\n        sparse = transform_geometric(sparse)\n    target = transform_geometric(target)\n    segmentation_label = transform_geometric(segmentation_label)\n    if rgb is not None:\n        brightness = np.random.uniform(max(0, 1 - args.jitter),\n                                       1 + args.jitter)\n        contrast = np.random.uniform(max(0, 1 - args.jitter), 1 + args.jitter)\n        saturation = np.random.uniform(max(0, 1 - args.jitter),\n                                       1 + args.jitter)\n        transform_rgb = transforms.Compose([\n            transforms.ColorJitter(brightness, contrast, saturation, 0),\n            transform_geometric\n        ])\n        rgb = transform_rgb(rgb)\n    # sparse = drop_depth_measurements(sparse, 0.9)\n\n    return rgb, sparse, target, segmentation_label\n\n\ndef val_transform(rgb, sparse, target, segmentation_label, args):\n    transform = transforms.Compose([\n        #transforms.BottomCrop((oheight, owidth)),\n        #transforms.BottomCrop((args.image_height, args.image_width)),\n        # 将读入的数据统一改成原始代码所使用的尺寸 oheight, owidth = 352, 1216\n        #transforms.BottomCrop((oheight, owidth)),\n        #Resize((args.image_height, args.image_width)),\n    ])\n    if rgb is not None:\n        rgb = transform(rgb)\n    if sparse is not None:\n        sparse = transform(sparse)\n    if target is not None:\n        target = transform(target)\n        \n    if segmentation_label is not None:\n        segmentation_label = transform(segmentation_label)\n    return rgb, sparse, target, segmentation_label\n\n\ndef no_transform(rgb, sparse, target, segmentation_label, args):\n    return rgb, sparse, target, segmentation_label\n\n\nto_tensor = transforms.ToTensor()\nto_float_tensor = lambda x: to_tensor(x).float()\n\n\ndef handle_gray(rgb, args):\n    if rgb is None:\n        return None, None\n    if not args.use_g:\n        return rgb, None\n    else:\n        img = np.array(Image.fromarray(rgb).convert('L'))\n        img = np.expand_dims(img, -1)\n        if not args.use_rgb:\n            rgb_ret = None\n        else:\n            rgb_ret = rgb\n        return rgb_ret, img\n\nclass KittiDepth(data.Dataset):\n    \"\"\"A data loader for the Kitti dataset\n    \"\"\"\n    def __init__(self, split, args):\n        self.args = args\n        self.split = split\n        paths, transform = get_paths_and_transform(split, args)\n        self.paths = paths\n        self.transform = transform\n        self.K = load_calib()\n        self.threshold_translation = 0.1\n\n    def __getraw__(self, index):\n        # rgb图像\n        rgb = rgb_read(self.paths['rgb'][index], self.args) if \\\n            (self.paths['rgb'][index] is not None and (self.args.use_rgb or self.args.use_g)) else None\n        \n        # 点云：原始+强度+高度\n        point_cloud = rgb_read(self.paths['pc'][index], self.args) if \\\n            (self.paths['pc'][index] is not None and self.args.use_d) else None\n            \n        # 车道标签\n        road_label = label_read(self.paths['road_label'][index], self.args) if \\\n            self.paths['road_label'][index] is not None else None\n            \n        # 车道线标签\n        lane_label = label_read(self.paths['lane_label'][index], self.args) if \\\n            self.paths['lane_label'][index] is not None else None\n        \n        return rgb, point_cloud, road_label, lane_label\n\n    def __getitem__(self, index):\n        rgb, point_cloud, road_label, lane_label = self.__getraw__(index)\n        rgb, point_cloud, road_label, lane_label = self.transform(rgb, point_cloud, road_label,lane_label\n                                                       ,self.args)\n\n        rgb, gray = handle_gray(rgb, self.args)\n        #gray = None\n        candidates = {\"rgb\":rgb, \"pc\":point_cloud, \"road_label\":road_label, \"lane_label\":lane_label ,\\\n            \"g\":gray}\n        items = {\n            key: to_float_tensor(val)\n            for key, val in candidates.items() if val is not None\n        }\n\n        return items\n\n    def __len__(self):\n        return len(self.paths['road_label'])\n","sub_path":"v3/dataloaders/completion_segmentation_loader_new_.py","file_name":"completion_segmentation_loader_new_.py","file_ext":"py","file_size_in_byte":14015,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"81940545","text":"\nfrom discord.ext import commands\nimport discord\nimport asyncio\nimport platform\nimport logging\n\n\nclass moderation():\n    def __init__(self, bot):\n        self.bot = bot\n\n    @commands.command(brief='Creates a guild invite')\n    @commands.has_permissions(create_instant_invite=True)\n    async def invite(self, ctx):\n        \"\"\"Used to create a guild invite in the current channel.\"\"\"\n        invite = await ctx.channel.create_invite()\n        await ctx.send(invite)\n\n    @commands.command(brief='Kicks the specified user')\n    @commands.has_permissions(kick_members=True)\n    async def kick(self, ctx, userName: discord.User):\n        \"\"\"Kicks the specified user from the guild!\"\"\"\n        await ctx.guild.kick(userName)\n\n    @commands.command(brief='Bans the specified user')\n    @commands.has_permissions(ban_members=True)\n    async def ban(self, ctx, userName: discord.User, *, stor=None):\n        \"\"\"Bans the specified user from the guild!\"\"\"\n        await ctx.guild.ban(userName, reason=stor, delete_message_days=1)\n\n    @commands.command(brief='Softbans the specified user')\n    @commands.has_permissions(ban_members=True)\n    async def softban(self, ctx, userName: discord.User, *, stor=None):\n        \"\"\"Softbans the specified user from the guild! Also deletes their messages from the last 24 hours!\"\"\"\n        await ctx.guild.ban(userName, reason=stor, delete_message_days=1)\n        await ctx.guild.unban(userName)\n        await ctx.send(embed=discord.Embed(title=f'```{userName.name} was softbanned from the guild! Their messages from the last 24 hours have been purged!```', color=0xc71a))\n\n    @commands.group(brief='Channel related commands')\n    @commands.has_permissions(manage_channels=True)\n    async def channel(self, ctx):\n        \"\"\"Usage is {prefix}channel >command<\\nChannel commands are as follows:\"\"\"\n        pass\n    @channel.command(name='create', brief='Creates a channel')\n    async def c1(self, ctx, channel):\n        \"\"\"Used to create a channel.\"\"\"\n        if channel in [I.name for I in ctx.guild.channels]:\n            output = 'That channel already exists!'\n        else:\n            channel = await ctx.guild.create_text_channel(channel)\n            output = f\"The channel '{channel}' has been created!\"\n        await ctx.send(embed=discord.Embed(title=output, color=0xc71a))\n    @channel.command(name='delete', brief='Deletes a channel')\n    async def c2(self, ctx, channel: discord.TextChannel):\n        \"\"\"Used to delete a channel!\"\"\"\n        if channel in [I for I in ctx.guild.channels]:\n            channel = await channel.delete()\n            output = f\"The channel '{channel}' has been deleted!\"\n        await ctx.send(embed=discord.Embed(title=output, color=0xc71a))\n\n    @commands.group(brief='Role related commands')\n    @commands.has_permissions(manage_roles=True)\n    async def role(self, ctx):\n        \"\"\"Usage is {prefix}role >command<\\nRole commands are as follows:\"\"\"\n        pass\n    @role.command(brief='Creates a role')\n    async def create(self, ctx, *,  crole: str):\n        \"\"\"Used to create a role. Role created will have all default permissions\"\"\"\n        if crole in [I.name for I in ctx.guild.roles]:\n            output = 'That role already exists!'\n        else:\n            role = await ctx.guild.create_role(name=crole)\n            output = f'{role} created successfully!'\n        await ctx.send(embed=discord.Embed(title=output, color=0xc71a))\n    @role.command(brief='Deletes a role')\n    async def delete(self, ctx, role: discord.Role):\n        \"\"\"Used to delete a role.\"\"\"\n        if role in ctx.guild.roles:\n            await role.delete()\n            output = f'{role} deleted successfully!'\n        else:\n            output = 'No such role exists!'\n        await ctx.send(embed=discord.Embed(title=output, color=0xc71a))\n    @role.command(brief='Adds a role to a user')\n    async def add(self, ctx, username:discord.Member, *, role: discord.Role):\n        \"\"\"Used to add a role to a user.\"\"\"\n        if role in username.roles:\n            output = 'The user specified already has that role!'\n        else:\n            await username.add_roles(role)\n            output = f'{role} added to {username}!'\n        await ctx.send(embed=discord.Embed(title=output, color=0xc71a))\n    @role.command(brief='Removes a role from a user')\n    async def remove(self, ctx, modrole, *, username:discord.Member):\n        \"\"\"Used to remove a role from a user.\"\"\"\n        role = discord.utils.get(ctx.guild.roles, name=f'{modrole}')\n        if role in username.roles:\n            await username.remove_roles(role)\n            output = f'{role} removed from {username}!'\n        else:\n            output = 'The user specified does not have that role!'\n        await ctx.send(embed=discord.Embed(title=output, color=0xc71a))\n\n    @commands.command(brief='Deletes a specified number of messages')\n    @commands.has_permissions(manage_messages=True)\n    async def purge(self, ctx, num=None):\n        \"\"\"Purges a specified number of messages from the current channel.\"\"\"\n        await ctx.message.delete()\n        username = ctx.message.mentions\n        try:\n            purge = int(num)\n        except (ValueError, TypeError):\n            purge = 100\n        if len(username) == 0:\n            if num == None:\n                deleted = await ctx.channel.purge(limit=100, check=lambda m: m.author == ctx.bot.user)\n            else:\n                deleted = await ctx.channel.purge(limit=int(purge))\n        else:\n            deleted = await ctx.channel.purge(limit=int(purge), check=lambda m: m.author == username[0])\n        await ctx.send(embed=discord.Embed(title=f'Deleted {len(deleted)} message(s)', color=0xc71a), delete_after=10)\n\n    @commands.command(brief='Changes a users nickname')\n    @commands.has_permissions(manage_nicknames=True)\n    async def setnick(self, ctx, userName: discord.Member, *, nick):\n        \"\"\"Changes the nickname of the specified user.\"\"\"\n        await userName.edit(nick=nick)\n        await ctx.send(embed=discord.Embed(title=f\"Changed {userName.name}'s nickname to {nick}\", color=0xc71a))\n\n    \n    @commands.command(brief='Sets/creates a muterole')\n    @commands.has_permissions(manage_roles=True)\n    async def setmute(self, ctx, role=None):\n        async with self.bot.pool.acquire() as conn:\n            prefix, muterole = await conn.fetchrow('SELECT prefix, muterole FROM guilds WHERE guild = ($1)', ctx.guild.id)\n            check = False\n            if muterole is 0:\n                for rle in ctx.guild.roles:\n                    if rle.name == role:\n                        role = rle\n                        output = f\"I will now use '{role}' when muting users!\"\n                        check = True\n                        break\n                if check is False:\n                    if role is None:\n                        role = await ctx.guild.create_role(name='Mute')\n                        output = \"A mute role has been created!\"\n                    else:\n                        role = await ctx.guild.create_role(name=role)\n                        output = f\"A new mute role has been created! I will now use '{role}' when muting users!\"\n                    for channel in ctx.guild.channels:\n                        await channel.set_permissions(role, send_messages=False)\n                role = role.id\n            else:\n                try:\n                    for role in ctx.guild.roles:\n                        if role.id is muterole:\n                            await role.delete()\n                            break\n                    output = f'Mute role has been deleted! You will need to set the mute role again using \"{prefix}setmute [role]\"'\n                except:\n                    pass\n                    output = f'I was unable to delete the mute role! The role will need to be deleted manually if you chooes to do so! You will need to set the mute role again using \"{prefix}setmute [role]\"'\n                role = 0\n            await conn.execute('UPDATE guilds SET muterole = ($1) WHERE guild = ($2)', role, ctx.guild.id)\n            await ctx.send(embed=discord.Embed(description=output, color=0x9013fe))\n\n\n    @commands.command(brief='Punishes a user and cleans up the channel')\n    @commands.has_permissions(manage_roles=True)\n    async def subdue(self, ctx, user: discord.Member):\n        \"\"\"Mutes the specified user and deletes their last 100 messages!\"\"\"\n        async with self.bot.pool.acquire() as conn:\n            prefix, muterole = await conn.fetchrow('SELECT prefix, muterole FROM guilds WHERE guild = ($1)', ctx.guild.id)\n        async with ctx.typing():\n            for _ in range(100):\n                try:   \n                    msg = await ctx.channel.history().get(author__name=user.name)  \n                    await msg.delete()\n                except:\n                    break\n        if muterole is not 0:\n            for role in ctx.guild.roles:\n                if role.id == muterole:\n                    await user.add_roles(role)\n            output = f'{user.mention} has been muted! Their last 100 messages have been deleted!'\n        else:\n            output = f'There is currently no mute role set! Use \"{prefix}setmute [role]\" to set a mute role! {user.mention} has had their last 100 messages deleted!'\n        await ctx.send(embed=discord.Embed(description=output, color=0x9013fe))\n\n\n    @commands.command(brief='Mutes a specified user')\n    @commands.has_permissions(manage_roles=True)\n    async def mute(self, ctx, user: discord.Member):\n        \"\"\"Mutes the specified user\"\"\"\n        async with self.bot.pool.acquire() as conn:\n            prefix, muterole = await conn.fetchrow('SELECT prefix, muterole FROM guilds WHERE guild = ($1)', ctx.guild.id)\n        if muterole is not 0:\n            for role in ctx.guild.roles:\n                if role.id == muterole:\n                    if role not in user.roles:\n                        await user.add_roles(role)\n                        output = f'{user.mention} has been muted!'\n                        break\n                    else:\n                        output = f'That user is already muted! Use \"{prefix}unmute [user]\" to unmute the user!'\n                        break\n        else:\n            output = f'There is currently no mute role set! Use \"{prefix}setmute [role]\" to set a mute role!'\n        try:\n            await ctx.send(embed=discord.Embed(description=output, color=0x9013fe))\n        except:\n            pass\n\n    \n    @commands.command(brief='Unmutes a specified user')\n    @commands.has_permissions(manage_roles=True)\n    async def unmute(self, ctx, user: discord.Member):\n        \"\"\"Unmutes the specified user\"\"\"\n        async with self.bot.pool.acquire() as conn:\n            prefix, muterole = await conn.fetchrow('SELECT prefix, muterole FROM guilds WHERE guild = ($1)', ctx.guild.id)\n        if muterole is not 0:\n            for role in ctx.guild.roles:\n                if role.id == muterole:\n                    if role in user.roles:\n                        await user.remove_roles(role)\n                        output = f'{user.mention} has been unmuted!'\n                        break\n                    else:\n                        output = f'That user is not currently muted! Use \"{prefix}mute [user]\" to mute the user!'\n        else:\n            output = f'There is currently no mute role set! Use \"{prefix}setmute [role]\" to set a mute role!'\n        try:\n            await ctx.send(embed=discord.Embed(description=output, color=0x9013fe))\n        except:\n            pass\n\n\n    @commands.command(brief='Causes the bot to leave the guild')\n    @commands.has_permissions(mange_guild=True)\n    async def exit(self, ctx):\n        \"\"\"Causes the bot to exit the guild! Must have permission MANAGE_GUILD\"\"\"\n        await ctx.send('Leaving...')\n        await self.bot.leave()\n\ndef setup(bot):\n    bot.add_cog(moderation(bot))\n","sub_path":"src/modules/moderation.py","file_name":"moderation.py","file_ext":"py","file_size_in_byte":11873,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"442876904","text":"\r\n# Author: tHaninG\r\n# Email: tHan.ninG0@outlook.com\r\n# Date:   2017-10-22 21:55:00\r\n# Last Modified time: 2017-11-06 14:19:26\r\n# -*- coding: utf-8 -*-\r\n\r\nprint('作者: tHaninG\\n邮箱: tHan.ninG@outlook.com\\n有任何问题请联系我. \\n载入中......')\r\n\r\nfrom os import system\r\nfrom tkinter import Tk\r\nfrom tkinter import filedialog\r\nfrom openpyxl import load_workbook\r\n\r\n# set time format Y-M-D-T\r\ntime_format = '%Y-%m-%d'\r\n\r\n# initialize Tk()\r\nroot = Tk()\r\nroot.withdraw()\r\n\r\n# get chart's file path\r\nsystem(\"cls\")\r\ninput('按 Enter 选择文件\\n')\r\nfpath = filedialog.askopenfilename()\r\nrecord = load_workbook(fpath)  # load xlsx\r\nsheet_name = record.get_sheet_names()  # get sheet names\r\n\r\n\r\nn_sheet = len(sheet_name)  # number of sheets\r\n\r\nif n_sheet == 1:\r\n    sheet = record.get_sheet_by_name(sheet_name[0])  # load sheet\r\nelse:\r\n    option = []\r\n    brace1 = ''\r\n    brace2 = ''\r\n    for i in range(1, n_sheet + 1):\r\n        option.append(chr(64 + i))\r\n        # left-justified\r\n        brace1 = brace1 + ' {}'.ljust(2 + len(sheet_name[i - 1]))\r\n        brace2 = brace2 + ' {}'\r\n    while(1):\r\n        print('\\n' + brace1[1:].format(*option) +\r\n              '\\n' + brace2[1:].format(*sheet_name))\r\n        chose_sheet = str.upper(input('输入字母选择表格:\\n'))\r\n        system(\"cls\")\r\n        if chose_sheet in option:\r\n            sheet = record.get_sheet_by_name(\r\n                sheet_name[ord(chose_sheet) - 65])  # lshee\r\n            break\r\n        else:\r\n            print('\\nERROR:Invalid Input\\n请重试')\r\n\r\n\r\n# Check all rows and update given ID's record\r\ndef update(ID, c_IDname, c_time, c_name, sheet):\r\n    \"\"\"\r\n    Update record of student ID\r\n\r\n    Arguments:\r\n    ID -- student ID ('2017222010032')\r\n    c_IDname -- the column name of ID ('B')\r\n    c_time -- the column name of times ('J')\r\n    c_name -- the column name of student name\r\n    sheet -- data sheet\r\n\r\n    Return:\r\n    0/1 -- whether update success or not\r\n    \"\"\"\r\n    n = 0\r\n    backup = []\r\n    produceID = None\r\n\r\n    # find the position of ID\r\n    for rowNum in range(2, sheet.max_row):\r\n        produceID = sheet[c_IDname + str(rowNum)].value\r\n        if str(produceID).find(ID) >= 0:\r\n            n = n + 1\r\n            # ensure the ID is unique\r\n            if n == 1:\r\n                sheet[c_time + str(rowNum)].value = chr(10003)  # check\r\n                backup = str(rowNum)\r\n            else:\r\n                sheet[c_time + backup].value = None\r\n                break\r\n\r\n    if n == 0:\r\n        print('\\nERROR:can\\'t find this ID!!! \\n请重试')\r\n        return 0\r\n\r\n    elif n == 1:\r\n        print('\\n%s 更新成功' %\r\n              sheet[c_name + backup].value)\r\n\r\n        # prevent wrong ID\r\n        while (1):\r\n            opt = input(\r\n                '\\n按 Enter 确认\\n(输入 C 取消)\\n')\r\n            if opt == 'C':\r\n                sheet[c_time + backup].value = None\r\n                print('\\n更新取消')\r\n                return 0\r\n            elif opt == '':\r\n                system(\"cls\")\r\n                return 1\r\n            else:\r\n                print('\\nERROR:Invalid Input\\n请重试')\r\n\r\n    else:\r\n        print('\\nERROR:ID is not unique!!! \\n按 Enter 重试')\r\n        return 0\r\n\r\n\r\n# Check all rows and update given ID's record and grade\r\ndef update_grade(ID, c_IDname, c_time, c_name, sheet):\r\n    \"\"\"\r\n    Update grade of student ID\r\n\r\n    Arguments:\r\n    ID -- student ID ('2017222010032')\r\n    c_IDname -- the column name of ID ('B')\r\n    c_time -- the column name of times ('J')\r\n    c_name -- the column name of student name\r\n    sheet -- data sheet\r\n\r\n    Return:\r\n    0/1 -- whether update success or not\r\n    \"\"\"\r\n    n = 0\r\n    backup = []\r\n    produceID = None\r\n\r\n    # find the position of ID\r\n    for rowNum in range(2, sheet.max_row):\r\n        produceID = sheet[c_IDname + str(rowNum)].value\r\n        if str(produceID).find(ID) >= 0:\r\n            n = n + 1\r\n            # ensure the ID is unique\r\n            if n == 1:\r\n                backup = str(rowNum)\r\n            else:\r\n                break\r\n    if n == 0:\r\n        print('\\nERROR:can\\'t find this ID!!! \\n请重试')\r\n        return 0\r\n\r\n    elif n == 1:\r\n        while (1):\r\n            print('\\n请输入 %s 的成绩:' %\r\n                  sheet[c_name + backup].value)\r\n            print('(输入 C 取消)')\r\n            grade_input = input()\r\n            try:\r\n                grade_bu = int(grade_input)\r\n                if grade_bu >= 0 and grade_bu <= 100:\r\n                    sheet[c_time + str(backup)].value = str(grade_bu)\r\n                    system(\"cls\")\r\n                    print('%s 更新成功\\n' %\r\n                          sheet[c_name + backup].value)\r\n                    return 1\r\n                else:\r\n                    print('\\nERROR:Invalid Input\\n请重试')\r\n            except:\r\n                if grade_input == 'C':\r\n                    print('\\n更新取消')\r\n                    return 0\r\n                else:\r\n                    print('\\nERROR:Invalid Input\\n请重试')\r\n\r\n    else:\r\n        print('\\nERROR:ID is not unique!!! \\n请重试')\r\n        return 0\r\n\r\n# Check all rows and update given ID's record and rank\r\n\r\n\r\ndef update_rank(ID, c_IDname, c_time, c_name, sheet):\r\n    \"\"\"\r\n    Update rank of student ID\r\n\r\n    Arguments:\r\n    ID -- student ID ('2017222010032')\r\n    c_IDname -- the column name of ID ('B')\r\n    c_time -- the column name of times ('J')\r\n    c_name -- the column name of student name\r\n    sheet -- data sheet\r\n\r\n    Return:\r\n    0/1 -- whether update success or not\r\n    \"\"\"\r\n    n = 0\r\n    backup = []\r\n    produceID = None\r\n\r\n    # find the position of ID\r\n    for rowNum in range(2, sheet.max_row):\r\n        produceID = sheet[c_IDname + str(rowNum)].value\r\n        if str(produceID).find(ID) >= 0:\r\n            n = n + 1\r\n            # ensure the ID is unique\r\n            if n == 1:\r\n                backup = str(rowNum)\r\n            else:\r\n                break\r\n    if n == 0:\r\n        print('\\nERROR:can\\'t find this ID!!! \\n请重试')\r\n        return 0\r\n\r\n    elif n == 1:\r\n        while (1):\r\n            print('\\n请输入 %s 的等级:' %\r\n                  sheet[c_name + backup].value)\r\n            print('(输入 CC 取消)')\r\n            rank_input = str.upper(input())\r\n\r\n            if 'ABCD+-'.find(rank_input) >= 0:\r\n                sheet[c_time + str(backup)].value = rank_input\r\n                system(\"cls\")\r\n                print('%s 更新成功\\n' %\r\n                      sheet[c_name + backup].value)\r\n                return 1\r\n            elif rank_input=='CC':\r\n                return 0\r\n            else:\r\n                print('\\nERROR:Invalid Input\\n请重试')\r\n\r\n    else:\r\n        print('\\nERROR:ID is not unique!!! \\n请重试')\r\n        return 0\r\n\r\n\r\n# chose record what\r\nwhile(1):\r\n    while(1):\r\n        rg = str.upper(input('A:成绩 B:等级(A,B,C) C:签到\\n选择任务:\\n'))\r\n        if 'ABC'.find(rg)>=0:\r\n            opt = input('\\n任务为 '+str(rg)+'\\n按 Enter 确定\\n(输入 \\'CC\\' 取消)\\n')\r\n            if opt=='':\r\n                system(\"cls\")\r\n                break\r\n            elif opt=='CC':\r\n                pass\r\n            else:\r\n                print('\\nERROR:Invalid Input\\n请重试')\r\n        else:\r\n            print('\\nERROR:Invalid Input\\n请重试')\r\n\r\n    # input index of ID,name,...\r\n    index_name = ['学号列号',\r\n                  '时间列号', '姓名列号']\r\n    index = ['','','']\r\n    num = -1\r\n    for i in index_name:\r\n        num = num + 1\r\n        while(1):\r\n            index[num]=str.upper(input('输入 ' + str(i) + ':\\n'))\r\n            while(1):\r\n                opt = input('\\n按 Enter 确定\\n(输入 \\'CC\\' 取消)\\n')\r\n                if opt=='' or opt=='CC':\r\n                    break\r\n                else:\r\n                    print('\\nERROR:Invalid Input\\n请重试')\r\n            if opt=='':\r\n                system(\"cls\")\r\n                break\r\n\r\n\r\n    # record check\r\n    if rg == 'C':\r\n        # update all data\r\n        k = 0\r\n        while(1):\r\n            # input student ID\r\n            ID = str(\r\n                input('请输入 学号:\\n(输入 stop 退出更新)\\n'))\r\n            if ID == 'stop':\r\n                system(\"cls\")\r\n                break\r\n\r\n            # update data\r\n            k = k + update(ID, index[0], index[1], index[2], sheet)\r\n        break\r\n\r\n    # record grade\r\n    elif rg == 'A':\r\n        # update all data\r\n        k = 0\r\n        while(1):\r\n            # input student ID\r\n            ID = str(\r\n                input('请输入 学号:\\n(输入 stop 退出更新)\\n'))\r\n            if ID == 'stop':\r\n                system(\"cls\")\r\n                break\r\n\r\n            # update data\r\n            k = k + update_grade(ID, index[0], index[1], index[2], sheet)\r\n        break\r\n\r\n    # record rank\r\n    elif rg == 'B':\r\n        # update all data\r\n        k = 0\r\n        while(1):\r\n            # input student ID\r\n            ID = str(\r\n                input('请输入 学号:\\n(输入 stop 退出更新)\\n'))\r\n            if ID == 'stop':\r\n                system(\"cls\")\r\n                break\r\n\r\n            # update data\r\n            k = k + update_rank(ID, index[0], index[1], index[2], sheet)\r\n        break\r\n\r\n    else:\r\n        print('\\nERROR:Invalid Input\\n请重试')\r\n\r\n\r\n# save updated recrodchart\r\nwhile(1):\r\n    sv = str.upper(input('是否保存? Y/N\\n'))\r\n    if sv == 'Y':\r\n        while(1):\r\n            cover = str.upper(input(\r\n                '\\n是否覆盖原文件? Y/N\\n(输入 \\'N\\' 将在原文件的路径保存一个备份)\\n'))\r\n            if cover == 'Y':\r\n                try:\r\n                    record.save(fpath)\r\n                except:\r\n                    record.save(fpath[:-5] + '(备份)' + '.xlsx')\r\n                    print('\\n无法覆盖原文件\\n已保存为备份')\r\n                break\r\n            elif cover == 'N':\r\n                record.save(fpath[:-5] + '(备份)' + '.xlsx')\r\n                break\r\n            else:\r\n                print('\\nERROR:Invalid Input\\n请重试')\r\n\r\n        system(\"cls\")\r\n        print('任务完成!!!' +\r\n              '\\n更新数据总数: ' + str(k))\r\n        input('\\n按 Enter 退出\\n')\r\n        break\r\n\r\n    elif sv == 'N':\r\n        system(\"cls\")\r\n        print('任务取消!!!')\r\n        input('\\n按 Enter 退出\\n')\r\n        break\r\n    else:\r\n        print('\\nERROR:Invalid Input\\n请重试\\n')\r\n","sub_path":"python/recordchart/recordchart2.0.py","file_name":"recordchart2.0.py","file_ext":"py","file_size_in_byte":10447,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"302608631","text":"import multiprocessing as mp\nimport re\nfrom random import choice\n\nimport dateparser\nimport requests\nfrom bs4 import BeautifulSoup\n\n\ndef _get_proxy():\n    html = requests.get('https://free-proxy-list.net/').text\n    soup = BeautifulSoup(html, 'lxml')\n    trs = soup.find('table', id='proxylisttable').find_all('tr')[1:100]\n\n    proxies = []\n\n    for tr in trs:\n\n        tds = tr.find_all('td')\n        if 'yes' in tds[6].text.strip():\n            ip = tds[0].text.strip()\n            port = tds[1].text.strip()\n            schema = 'https'\n            proxy = {'schema': schema, 'address': ip + ':' + port}\n            proxies.append(proxy)\n\n    return choice(proxies)\n\n\ndef _get_html(url: str):\n    p = _get_proxy()\n    proxy = {p['schema']: p['address']}\n    try:\n        r = requests.get(url, proxies=proxy, timeout=5)\n        r.encoding = 'cp1251'\n        if r.ok:\n            return r.text\n        return None\n    except (requests.ConnectTimeout, requests.ConnectionError):\n        _get_html(url)\n\n\ndef _get_datetime_seance(date_str: str):\n\n    date_str = date_str.replace('\\n', '')\n    date_str = ' '.join(date_str.split())\n    date_str = date_str.replace('начало в', 'в')\n\n    return dateparser.parse(date_str)\n\n\ndef get_seances(url: str, seance_list: list):\n\n    html = _get_html(url)\n    if not html:\n        return seance_list\n    # with open('index.html', 'rb') as f:  # for offline test\n    #     html = f.read()\n    soup = BeautifulSoup(html, 'lxml')\n    regex = re.compile('.*Repertoire.*')\n    seances = soup.find_all('div', {'class': regex})\n    for seance in seances:\n        event = (seance.find('div', class_='LongerName') or\n                 seance.find('div', class_='ShortName') or\n                 seance.find('div', class_='LongName'))\n        try:\n            event_dict = {\n                'event_name': event.text.strip().strip('\\n'),\n                'event_url': event.find('a')['href'],\n                'starts_at': _get_datetime_seance(seance.find('div', class_='DateTime').text),\n            }\n            seance_list.append(event_dict)\n        except Exception as e:\n            print(seance)\n            print(e)\n            continue\n\n\ndef _get_pages(url: str):\n\n    urls = [url]\n    next_page = 'https://www.bigteatr.com/cat/229/page/{}/'\n    for i in range(2, 20):\n        url = next_page.format(i)\n        text = _get_html(url)\n        if not text:\n            break\n        # with open('index.html', 'rb') as f:  # for offline test\n        #     text = f.read()\n        soup = BeautifulSoup(text, 'lxml')\n        check_page_exists = soup.find('div', class_='RepertoireLight') or soup.find('div', class_='RepertoireDark')\n\n        if not check_page_exists:\n            break\n\n        urls.append(url)\n\n    return urls\n\n\ndef main():\n\n    urls = _get_pages('https://www.bigteatr.com/afisha/')\n    pool = mp.Pool(processes=10)\n    manager = mp.Manager()\n    seance_list = manager.list()\n    [pool.apply_async(get_seances, args=[url, seance_list]) for url in urls]\n    pool.close()\n    pool.join()\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"bigteatr.py","file_name":"bigteatr.py","file_ext":"py","file_size_in_byte":3077,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"234847851","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\nimport sys, os, re, datetime\nimport getpass\nimport argparse\nimport cx_Oracle\nimport mysql.connector \nfrom mysql.connector import Error\nimport requests\nfrom configparser import ConfigParser\nimport multimodule\nfrom multiprocessing import Pool\nfrom acds import configuration\n\ndef main(args_temp):\n\taction =  (''.join(list(args_temp.keys())))\n\targs = {action: {'ip': None, 'hostname': None, 'sd': None, 'uplink': None, 'office': None, 'structura': None, 'classid': None, 'vendor': None, 'model': None, 'serial': None, 'status': None}}\n\tfor k in args_temp[action]:\n\t\tif k in args[action].keys():\n\t\t\targs[action].update ({\n\t\t\t\tk: args_temp[action][k],\n\t\t\t})\n\n\tuser = getattr(configuration, 'ASTU_USER')\n\ttry:\n\t\tastu_password = getattr(configuration, 'ASTU_PASS')\n\texcept:\n\t\tprint (f\"add ASTU_PASSWORN in configuration file\")\n\t\tsys.exit()\n\n\tos.environ['LINES'] = \"100\"\n\tos.environ['COLUMNS'] = \"80\"\n\n\tdate_now =  (datetime.datetime.now().strftime(\"%d.%m.%Y\"))\n\tnodeinfo = 'none'\n\tt = multimodule.FastModulAut()\n\n\tif args[action]['ip'] != None:\n\t\tip = args[action]['ip']\n\telse:\n\t\tprint ('Введите IP --ip')\n\t\tsys.exit()\n\n\thostname = args[action]['hostname']\n\tsd = args[action]['sd']\n\tuplink = args[action]['uplink']\n\toffice = args[action]['office']\n\tstructura = args[action]['structura']\n\tclassid = args[action]['classid']\n\tvendor = args[action]['vendor']\n\tmodel = args[action]['model']\n\tserial = args[action]['serial']\n\tstatus = args[action]['status']\n\n\toptions = {}\n\toptions[action] = {}\n\n\tt.sql_connect('connect')\n\tt.sql_update(f\"INSERT INTO guspk.logs (scr_name, DEVICEID, who, message) VALUES ('astu_data', '{ip}', '{user}', '{action}') \")\n\tif model == None and action == 'insert':\n\t\tif structura != None and vendor != None:\n\t\t\tquery = \"SELECT DEVICEMODELNAME, MODELID FROM guspk.host_model WHERE TYPE_ID LIKE '{}' AND VENDORID LIKE '{}'\".format(classid, vendor)\n\t\t\t# print (query)\n\t\t\tcursor = t.sql_select(query, 'full')\n\t\t\tprint ('Available models list')\n\t\t\tfor models in cursor:\n\t\t\t\tprint (\"{:25}{:5}\".format(models[0], models[1]))\n\t\t\tsys.exit()\n\t\telse:\n\t\t\tprint('Please select only --str and --vendor. And I show you available models list.')\n\t\t\tsys.exit()\n\n\tif action == 'update':\n\t\tuplink = None\n\t\tdevice_data = t.sql_select(f\"SELECT DEVICEID FROM guspk.host WHERE IPADDMGM like '{ip}' or NETWORKNAME like '{ip}'\", 'full')\n\t\tdeviceid = device_data[0][0]\n\t\tif uplink != None:\n\t\t\tquery = f\"SELECT NODEID, BUILDING, NODENAME FROM guspk.host_node WHERE NODEID LIKE '{uplink}'\"\n\t\telse:\n\t\t\tquery = f\"SELECT b.NODEID, b.BUILDING, b.NODENAME FROM guspk.host a, guspk.host_node b WHERE a.IPADDMGM LIKE '{ip}' AND a.NODEID = b.NODEID\"\n\t\toptions[action].update ({\n\t\t\t'id': deviceid,\n\t\t\t'deviceid': deviceid,\n\t\t})\n\n\tif action == 'insert':\n\t\tif uplink != None:\n\t\t\tquery = f\"SELECT NODEID, BUILDING, NODENAME FROM guspk.host_node WHERE NODEID LIKE '{uplink}'\"\n\t\telse:\n\t\t\tprint ('Input --uplink')\n\t\t\tsys.exit()\n\n\t# print (query)\n\tcursor = t.sql_select(query, 'full') \n\t# print (cursor)\n\tt.sql_connect('disconnect')\n\tfor (nodeid, nodebuilding, nodename) in cursor:\n\t\tnodeinfo = (nodeid, nodebuilding, nodename)\n\t# print (nodeinfo)\n\tif nodeinfo == 'none':\n\t\tprint ('Uplink not founded in astu')\n\t\tsys.exit()\n\toptions[action].update({\n\t\t'user':user, \n\t\t'password':astu_password, \n\t\t'ipmgm':ip, \n\t\t'networkname':hostname, \n\t\t'devicedescr':sd, \n\t\t'nodeid':nodeid, \n\t\t'nodename':nodename, \n\t\t'parentid':nodeid, \n\t\t'parenttype':nodeid, \n\t\t'nodebuilding':nodebuilding, \n\t\t'dateinstall':date_now, \n\t\t'datazip':date_now, \n\t\t'strukturalevelid':structura, \n\t\t'objectclassidid':classid, \n\t\t'objectclassid':classid, \n\t\t'vendorid':vendor, \n\t\t'objectmodelid':model, \n\t\t'serialnumber':serial, \n\t\t'devicestatus':status,\n\t\t'office':office,\n\t})\n\n\n\n\tif action == 'update':\n\t\tsave = update(options, action)\n\t\tif save == '':\n\t\t\t# save = 'OK'\n\t\t\tprint ('OK')\n\t\telse:\n\t\t\tprint (save)\n\tif action == 'insert':\n\t\tsave = insert(options, action)\n\t\t# print (save)\n\t\tif save == '':\n\t\t\t# save = 'OK'\n\t\t\tprint ('OK')\n\t\telse:\n\t\t\tprint (save)\n\n\treturn save\n\n\ndef update(options, action):\n\tlogpass = {}\n\tlogpass['user_login'] = options[action]['user']\n\tlogpass['user_pwd'] = options[action]['password']\n\t\n\tdevice_edit = { \n\t\t\"tree\" : \"mdevice\",\n\t}\n\tdevice_edit['id'] = options[action]['id']\n\n\tcheck_uniq = {}\n\tdevicefield_save = {}\n\tif options[action]['networkname'] != None:\n\t\t# check_uniq['networkname'] = options[action]['networkname']\n\t\tdevicefield_save['networkname'] = options[action]['networkname']\n\tif options[action]['deviceid'] != None:\n\t\tcheck_uniq['deviceid'] = options[action]['deviceid']\n\t\tdevicefield_save['deviceid'] = options[action]['deviceid']\n\tif options[action]['ipmgm'] != None:\n\t\tcheck_uniq['ipmgm'] = options[action]['ipmgm']\n\t\tdevicefield_save['ipmgm'] = options[action]['ipmgm']\n\tif \toptions[action]['devicedescr'] != None:\n\t\tcheck_uniq['devicedescr'] = options[action]['devicedescr']\n\t\tdevicefield_save['devicedescr'] = options[action]['devicedescr']\n\tif options[action]['objectclassidid'] != None:\n\t\tcheck_uniq['objectclassidid'] = options[action]['objectclassidid']\n\t\tdevicefield_save['objectclassidid'] = options[action]['objectclassidid']\n\tif options[action]['strukturalevelid'] != None:\n\t\tcheck_uniq['strukturalevelid'] = options[action]['strukturalevelid']\n\t\tdevicefield_save['strukturalevelid'] = options[action]['strukturalevelid']\n\tif options[action]['vendorid'] != None:\n\t\tcheck_uniq['vendorid'] = options[action]['vendorid']\n\t\tdevicefield_save['vendorid'] = options[action]['vendorid']\n\tif options[action]['objectmodelid'] != None:\n\t\tcheck_uniq['objectmodelid'] = options[action]['objectmodelid']\n\t\tdevicefield_save['objectmodelid'] = options[action]['objectmodelid']\n\tif options[action]['serialnumber'] != None:\n\t\tcheck_uniq['serialnumber'] = options[action]['serialnumber']\n\t\tdevicefield_save['serialnumber'] = options[action]['serialnumber']\n\tif options[action]['devicestatus'] != None:\n\t\tcheck_uniq['devicestatus'] = options[action]['devicestatus']\n\t\tdevicefield_save['devicestatus'] = options[action]['devicestatus']\n\tif options[action]['parentid'] != None:\n\t\tcheck_uniq['parentid'] = options[action]['parentid']\n\t\tdevicefield_save['parentid'] = options[action]['parentid']\n\t\tdevicefield_save['nodeid'] = options[action]['nodeid']\n\tif options[action]['parenttype'] != None:\n\t\tcheck_uniq['parenttype'] = options[action]['parenttype']\n\t\tdevicefield_save['parenttype'] = options[action]['parenttype']\n\tif options[action]['office'] != None:\n\t\tdevicefield_save['office'] = options[action]['office']\n\tcheck_uniq['user_login_saved'] = options[action]['user']\n\t\n\t\n\n\twith requests.Session() as s:\n\t\tauth = s.post('http://10.184.67.68/', data = logpass) #If auth ok, return '1' # , headers = headers\n\t\tcookie_phpid = {'PHPSESSID': requests.utils.dict_from_cookiejar(s.cookies)['PHPSESSID']}\n\t\tcheck_uniq['PHPSESSID'] = cookie_phpid['PHPSESSID']\n\t\tdevice_edit = s.post('http://10.184.67.68/device/edit', data = device_edit)\n\t\tdevicefield_save = s.post('http://10.184.67.68/device/device_field/save', data = devicefield_save)\n\t\tdevicefield_get = s.get('http://10.184.67.68/device/device_field/get')\n\t\tcheck_uniq = s.post('http://10.184.67.68/device/check_uniq', data = check_uniq)\n\t\tsave = s.post('http://10.184.67.68/device/save')\n\n\tresult = 'ok'\n\tif save.text:\n\t\tresult = save.text\n\treturn result\n\ndef insert(options, action):\n\tlogpass = {}\n\tlogpass['user_login'] = options[action]['user']\n\tlogpass['user_pwd'] = options[action]['password']\n\n\tdevice_edit = { \n\t\t\"parent\" : \"node\",\n\t\t\"tree\" : \"mdevice\",\n\t}\n\tdevice_edit['parentid'] = options[action]['nodeid']\n\n\tdevicefield_save_b = { \n\t\t\"deviceid\" : \"\",\n\t\t\"devicetype\" : \"device\",\n\t\t\"office\" : \"\",\n\t\t\"numinstall\" : \"\",\n\t\t\"sysname\" : \"\",\n\t\t\"ipmgmid\" : \"\",\n\t\t\"qosid\" : \"0\",\n\t\t\"forvlan\" : \"0\",\n\t\t\"foragg\" : \"0\",\n\t\t\"timeout\" : \"0\",\n\t\t\"port\" : \"\",\n\t\t\"mezhregion\" : \"0\",\n\t\t\"deviceelectricpoint\" : \"0\",\n\t\t\"avalmodeid\" : \"1\",\n\t\t\"meid\" : \"\",\n\t}\n\tdevicefield_save_b['parentid'] = options[action]['parentid']\n\tdevicefield_save_b['parenttype'] = options[action]['parenttype']\n\tdevicefield_save_b['nodeid'] = options[action]['nodeid']\n\tdevicefield_save_b['dateinstall'] = options[action]['dateinstall']\n\tdevicefield_save_b['networkname'] = options[action]['networkname']\n\tdevicefield_save_b['ipmgm'] = options[action]['ipmgm']\n\tdevicefield_save_b['devicedescr'] = options[action]['devicedescr']\n\tdevicefield_save_b['strukturalevelid'] = options[action]['strukturalevelid']\n\n\tfizdevice_edit = { \n\t\t\"tree\" : \"mdevice\",\n\t}\n\tvendor_load = {}\n\tvendor_load['objectclassid'] = options[action]['objectclassid']\n\t\n\tmodel_load = {}\n\tmodel_load['vendor'] = options[action]['vendorid']\n\tmodel_load['objectclassid'] = options[action]['objectclassid']\n\t\n\tdevicefield_save_a = {\n\t\t\"deviceid\" : \"\",\n\t\t\"devicetype\" : \"device\",\n\t\t\"zipregionid\" : \"\",\n\t\t\"zipnodeid\" : \"\",\n\t\t\"deviceinvnumber\" : \"\",\n\t\t\"kategoriyaid\" : \"4\",\n\t\t\"bssid\" : \"\",\n\t\t\"macadr\" : \"\",\n\t\t\"covertype\" : \"\",\n\t\t\"lastmileid\" : \"\",\n\t}\n\tdevicefield_save_a['parentid'] = options[action]['parentid']\n\tdevicefield_save_a['parenttype'] = options[action]['parenttype']\n\tdevicefield_save_a['objectclassidid'] = options[action]['objectclassidid']\n\tdevicefield_save_a['datazip'] = options[action]['datazip']\n\tdevicefield_save_a['vendorid'] = options[action]['vendorid']\n\tdevicefield_save_a['objectmodelid'] = options[action]['objectmodelid']\n\tdevicefield_save_a['serialnumber'] = options[action]['serialnumber']\n\tdevicefield_save_a['devicestatus'] = options[action]['devicestatus']\n\tdevicefield_save_a['office'] = options[action]['office']\n\n\tcheck_uniq = {\n\t\t\"cityname\" : \"null\",\n\t\t\"streetname\" : \"null\",\n\t\t\"nodeoffice\" : \"null\",\n\t\t\"region\" : \"null\",\n\t\t\"avalmodeid\" : \"1\",\t#24*7,\n\t\t\"forvlan\" : \"0\",\n\t\t\"foragg\" : \"0\",\n\t\t\"timeout\" : \"0\",\n\t\t\"treeinfo\" : \"null\",\n\t\t\"deviceid\" : \"false\",\n\t\t\"devicetype\" : \"device\",\n\t\t# \"office\" : \"false\",\n\t\t\"numinstall\" : \"false\",\n\t\t\"sysname\" : \"false\",\n\t\t\"ipmgmid\" : \"false\",\n\t\t\"qosid\" : \"false\",\n\t\t\"port\" : \"false\",\n\t\t\"mezhregion\" : \"0\",\n\t\t\"deviceelectricpoint\" : \"false\",\n\t\t\"meid\" : \"false\",\n\t\t\"user_login_saved\" : \"yuzhakov-da\",\t\t\t#login\n\t\t\"zipregionid\" : \"false\",\n\t\t\"zipnodeid\" : \"false\",\n\t\t\"deviceinvnumber\" : \"false\",\n\t\t\"kategoriyaid\" : \"4\",\n\t\t\"bssid\" : \"false\",\n\t\t\"macadr\" : \"false\",\n\t\t\"covertype\" : \"false\",\n\t\t\"lastmileid\" : \"false\",\n\t}\n\tcheck_uniq['nodename'] = options[action]['nodename']\n\tcheck_uniq['nodebuilding'] = options[action]['nodebuilding']\n\tcheck_uniq['nodeid'] = options[action]['nodeid']\n\tcheck_uniq['networkname'] = options[action]['networkname']\n\tcheck_uniq['dateinstall'] = options[action]['dateinstall']\n\tcheck_uniq['parentid'] = options[action]['parentid']\n\tcheck_uniq['parenttype'] = options[action]['parenttype']\n\tcheck_uniq['ipmgm'] = options[action]['ipmgm']\n\tcheck_uniq['devicedescr'] = options[action]['devicedescr']\n\tcheck_uniq['strukturalevelid'] = options[action]['strukturalevelid']\n\tcheck_uniq['objectclassidid'] = options[action]['objectclassidid']\n\tcheck_uniq['datazip'] = options[action]['datazip']\n\tcheck_uniq['vendorid'] = options[action]['vendorid']\n\tcheck_uniq['objectmodelid'] = options[action]['objectmodelid']\n\tcheck_uniq['serialnumber'] = options[action]['serialnumber']\n\tcheck_uniq['devicestatus'] = options[action]['devicestatus']\n\tcheck_uniq['office'] = options[action]['office']\n\n\twith requests.Session() as s:\n\t\tauth = s.post('http://10.184.67.68/', data = logpass) #If auth ok, return '1' # , headers = headers\n\t\tcookie_phpid = {'PHPSESSID': requests.utils.dict_from_cookiejar(s.cookies)['PHPSESSID']}\n\t\tcheck_uniq['PHPSESSID'] = cookie_phpid['PHPSESSID']\n\t\n\t\tdevice_edit = s.post('http://10.184.67.68/device/edit', data = device_edit) #, headers = header_devicelist\n\t\tdevicefield_save_b = s.post('http://10.184.67.68/device/device_field/save', data = devicefield_save_b) #, headers = headersssave\n\t\tfizdevice_edit = s.post('http://10.184.67.68/device/fizdevice/edit', data = fizdevice_edit)\n\t\tvendor_load = s.post('http://10.184.67.68/vendor/load', data = vendor_load)\n\t\tmodel_load = s.post('http://10.184.67.68/model/load', data = model_load)\n\t\tdevicefield_save_a = s.post('http://10.184.67.68/device/device_field/save', data = devicefield_save_a)\n\t\tdevicefield_get = s.get('http://10.184.67.68/device/device_field/get')\n\t\n\t\tcheck_uniq = s.post('http://10.184.67.68/device/check_uniq', data = check_uniq) #, headers = headers\n\t\tsave = s.post('http://10.184.67.68/device/save') #, headers = headersssave\n\n\tresult = 'ok'\n\tif save.text:\n\t\tresult = save.text\n\treturn result\n\n\n\ndef start():\n\tparser = createParser()\n\tnamespace = parser.parse_args()\n\targs = {}\n\targs[namespace.req] = {\n\t\t'action' : namespace.req,\n\t\t'ip' : namespace.ip,\n\t\t'hostname' : namespace.hostname,\n\t\t'sd' : namespace.sd,\n\t\t'uplink' : namespace.uplink,\n\t\t'office' : namespace.office,\n\t\t'structura' : namespace.str,\n\t\t'classid' : namespace.classid,\n\t\t'vendor' : namespace.vendor,\n\t\t'model' : namespace.model,\n\t\t'serial' : namespace.serial,\n\t\t'status' : namespace.status,\n\t}\n\tresult = main(args)\n\n\nif __name__ == \"__main__\":\n\tstart()","sub_path":"scripts/astu_data.py","file_name":"astu_data.py","file_ext":"py","file_size_in_byte":12991,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"411379447","text":"# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, val=0, left=None, right=None):\n#         self.val = val\n#         self.left = left\n#         self.right = right\n\n################################################################################\n##\n##     BFS\n##\n################################################################################\nfrom collections import deque\nclass Solution:\n    def longestConsecutive(self, root: Optional[TreeNode]) -> int:\n        queue = deque([(root,1)])\n        max_len = 0\n        while queue:\n            curr, path_len = queue.popleft()\n            max_len = max(max_len,path_len)\n            if curr.left:\n                if curr.left.val ==curr.val+1:\n                    left_path_len = path_len+1\n                else:\n                    left_path_len = 1\n                queue.append((curr.left,left_path_len))\n            if curr.right:\n                if curr.right.val ==curr.val+1:\n                    right_path_len = path_len+1\n                else:\n                    right_path_len = 1\n                queue.append((curr.right,right_path_len))\n        return max_len\n\n################################################################################\n##\n##     Recursion\n##\n################################################################################\nclass Solution:\n    def longestConsecutive(self, root: Optional[TreeNode]) -> int:\n        def helper(root):\n            nonlocal max_len\n            if not root:\n                return 0\n            left_len = helper(root.left)\n            right_len = helper(root.right)\n            if root.left and root.left.val == root.val+1:\n                left_len += 1\n            else:\n                left_len = 1\n            if root.right and root.right.val == root.val+1:\n                right_len += 1\n            else:\n                right_len = 1\n            max_len = max(max_len, left_len,right_len)\n            return max(left_len,right_len)\n        max_len =0\n        helper(root)\n        return max_len\n","sub_path":"Problem298_Binary_Tree_Longest_Consecutive_Sequence.py","file_name":"Problem298_Binary_Tree_Longest_Consecutive_Sequence.py","file_ext":"py","file_size_in_byte":2043,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"376824101","text":"import numpy as np\nfrom math import sin, cos\n\npoint = {'type':'point', 'color':(255,0,0), 'lw':1, 'body':(10,10)}\npoints = {'type':'points', 'color':(255,0,0), 'lw':1, 'body':[(10,10),(100,200)]}\nline = {'type':'line', 'color':(255,0,0), 'lw':1, 'style':'-', 'body':[(10,10),(100,200),(200,200)]}\nlines = {'type':'lines', 'color':(255,0,0), 'lw':1, 'style':'-', 'body':[[(10,10),(100,200),(200,200)],[(150,10),(50,250)]]}\npolygon = {'type':'polygon', 'color':(255,0,0), 'fcolor':(255,255,0), 'lw':1, 'style':'o', 'body':[(10,10),(100,200),(200,200)]}\npolygons = {'type':'polygons', 'color':(255,0,0), 'fcolor':(255,255,0,30), 'fill':False, 'lw':1, 'style':'o', 'body':[[(10,10),(100,200),(200,200)],[(150,10),(50,250),(288,0)]]}\ncircle = {'type':'circle', 'color':(255,0,0), 'fcolor':(255,255,0), 'fill':False, 'body':(100,100,50)}\ncircles = {'type':'circles', 'color':(255,0,0), 'fcolor':(255,255,0), 'fill':False, 'body':[(100,100,50),(300,300,100)]}\nellipse = {'type':'ellipse', 'color':(255,0,0), 'fcolor':(255,255,0), 'fill':False, 'body':(100,100,100,50,1)}\nellipses = {'type':'ellipses', 'color':(255,0,0), 'fcolor':(255,255,0), 'fill':False, 'body':[(100,100,100,50,1),(200,250,50,100,3.14)]}\nrectangle = {'type':'rectangle', 'color':(255,0,0), 'fcolor':(255,255,0), 'fill':True, 'body':(100,100,80,50)}\nrectangles = {'type':'rectangles', 'color':(255,0,0), 'fcolor':(255,255,0), 'fill':False, 'body':[(100,100,80,50),(200,200,80,100)]}\ntext = {'type':'text', 'color':(255,255,0), 'fcolor':(0,0,0), 'size':8, 'pt':True, 'body':(100,200,'id=0')}\ntexts = {'type':'texts', 'color':(255,255,0), 'fcolor':(0,0,0), 'size':8, 'pt':True, 'body':[(100,200,'id=0'),(180,250,'id=1')]}\n\nlayer = {'type':'layer', 'num':-1, 'clolor':(255,255,0), 'fcolor':(255,255,255), 'fill':False, \n\t\t\t'body':[point, points, line, lines, polygon, polygons, circle, circles, ellipse, ellipses, rectangle, rectangles, text, texts]}\n\t\t\nlayers = {'type':'layers', 'num':-1, 'clolor':(255,255,0), 'fcolor':(255,255,255), 'fill':False, \n\t'body':{1:points, 2:line, 3:layer}}\n\ndef plot(pts, dc, f, **key):\n\tpen, brush = dc.GetPen(), dc.GetBrush()\n\twidth, color = pen.GetWidth(), pen.GetColour()\n\tfcolor, style = brush.GetColour(), brush.GetStyle()\n\t\n\tif 'color' in pts: \n\t\tpen.SetColour(pts['color'])\n\tif 'fcolor' in pts:\n\t\tbrush.SetColour(pts['fcolor'])\n\tif 'lw' in pts:\n\t\tpen.SetWidth(pts['lw'])\n\tif 'fill' in pts:\n\t\tbrush.SetStyle((106,100)[pts['fill']])\n\n\tdc.SetPen(pen)\n\tdc.SetBrush(brush)\n\t\n\tif pts['type'] == 'point':\n\t\tpen.SetWidth(1)\n\t\tbrush.SetStyle(100)\n\t\tbrush.SetColour(pen.GetColour())\n\t\tdc.SetPen(pen)\n\t\tdc.SetBrush(brush)\n\t\tr = pts['r'] if 'r' in pts else 2\n\t\tx, y =  f(*pts['body'])\n\t\tdc.DrawEllipse (x-r,y-r,r*2,r*2)\n\t\tpen.SetWidth(pts['lw'] if 'lw' in pts else width)\n\t\tbrush.SetStyle((106,100)[pts['fill']] if 'fill' in pts else style)\n\t\tbrush.SetColour(pts['fc'] if 'fc' in pts else fcolor)\n\t\tdc.SetPen(pen)\n\t\tdc.SetBrush(brush)\n\telif pts['type'] in {'points','line','polygon'}:\n\t\tlst, plst = [], []\n\t\tr = pts['r'] if 'r' in pts else 2\n\t\tfor p in pts['body']:\n\t\t\tx, y =  f(*p)\n\t\t\tlst.append((x-r,y-r,r*2,r*2))\n\t\t\tplst.append((x,y))\n\t\tisline = 'style' in pts and '-' in pts['style']\n\t\tispoint = 'style' in pts and 'o' in pts['style']\n\t\tif pts['type'] == 'polygon':\n\t\t\tdc.DrawPolygon(plst)\n\t\t\n\t\tif isline or pts['type'] == 'line':\n\t\t\tdc.DrawLines(plst)\n\t\t\n\t\tif pts['type']=='points' or ispoint:\n\t\t\tpen.SetWidth(1)\n\t\t\tbrush.SetStyle(100)\n\t\t\tbrush.SetColour(pen.GetColour())\n\t\t\tdc.SetPen(pen)\n\t\t\tdc.SetBrush(brush)\n\t\t\tdc.DrawEllipseList(lst)\n\t\t\tpen.SetWidth(pts['lw'] if 'lw' in pts else width)\n\t\t\tbrush.SetStyle((106,100)[pts['fill']] if 'fill' in pts else style)\n\t\t\tbrush.SetColour(pts['fc'] if 'fc' in pts else fcolor)\n\t\t\tdc.SetPen(pen)\n\t\t\tdc.SetBrush(brush)\n\telif pts['type'] in {'lines','polygons'}:\n\t\tlst, plst = [], []\n\t\tr = pts['r'] if 'r' in pts else 2\n\t\tfor i in pts['body']:\n\t\t\tline = []\n\t\t\tfor p in i:\n\t\t\t\tx, y =  f(*p)\n\t\t\t\tlst.append((x-r,y-r,r*2,r*2))\n\t\t\t\tline.append((x,y))\n\t\t\tplst.append(line)\n\t\tisline = 'style' in pts and '-' in pts['style']\n\t\tispoint = 'style' in pts and 'o' in pts['style']\n\t\tif pts['type'] == 'polygons':\n\t\t\tdc.DrawPolygonList(plst)\n\t\t\n\t\tif isline or pts['type'] == 'lines':\n\t\t\tfor line in plst:\n\t\t\t\tdc.DrawLines(line)\n\t\t\n\t\tif pts['type']=='points' or ispoint:\n\t\t\tpen.SetWidth(1)\n\t\t\tbrush.SetStyle(100)\n\t\t\tbrush.SetColour(pen.GetColour())\n\t\t\tdc.SetPen(pen)\n\t\t\tdc.SetBrush(brush)\n\t\t\tdc.DrawEllipseList(lst)\n\t\t\tpen.SetWidth(pts['lw'] if 'lw' in pts else width)\n\t\t\tbrush.SetStyle((106,100)[pts['fill']] if 'fill' in pts else style)\n\t\t\tbrush.SetColour(pts['fc'] if 'fc' in pts else fcolor)\n\t\t\tdc.SetPen(pen)\n\t\t\tdc.SetBrush(brush)\n\n\tpen.SetWidth(width)\n\tpen.SetColour(color)\n\tbrush.SetColour(fcolor)\n\tbrush.SetStyle(style)\n\tdc.SetPen(pen)\n\tdc.SetBrush(brush)\n\ndef draw_circle(pts, dc, f, **key):\n\tpen, brush = dc.GetPen(), dc.GetBrush()\n\twidth, color = pen.GetWidth(), pen.GetColour()\n\tfcolor, style = brush.GetColour(), brush.GetStyle()\n\t\n\tif 'color' in pts: \n\t\tpen.SetColour(pts['color'])\n\tif 'fcolor' in pts:\n\t\tbrush.SetColour(pts['fcolor'])\n\tif 'lw' in pts:\n\t\tpen.SetWidth(pts['lw'])\n\tif 'fill' in pts:\n\t\tbrush.SetStyle((106,100)[pts['fill']])\n\n\tdc.SetPen(pen)\n\tdc.SetBrush(brush)\n\n\tif pts['type'] == 'circle':\n\t\tx, y ,r = pts['body']\n\t\tx, y =  f(x, y)\n\t\tdc.DrawCircle(x, y, r*key['k'])\n\tif pts['type'] == 'circles':\n\t\tlst = []\n\t\tfor x, y ,r in pts['body']:\n\t\t\tx, y =  f(x, y)\n\t\t\tr *= key['k']\n\t\t\tlst.append((x-r,y-r,r*2,r*2))\n\t\tdc.DrawEllipseList(lst)\n\n\tpen.SetWidth(width)\n\tpen.SetColour(color)\n\tbrush.SetColour(fcolor)\n\tbrush.SetStyle(style)\n\tdc.SetPen(pen)\n\tdc.SetBrush(brush)\n\ndef make_ellipse(l1, l2, ang):\n\tm = np.array([[l1*cos(-ang),-l2*sin(-ang)],\n\t\t\t\t [l1*sin(-ang),l2*cos(-ang)]])\n\ta = np.linspace(0, np.pi*2, 36)\n\txys = np.array((np.cos(a), np.sin(a)))\n\treturn np.dot(m, xys).T\n\ndef draw_ellipse(pts, dc, f, **key):\n\tpen, brush = dc.GetPen(), dc.GetBrush()\n\twidth, color = pen.GetWidth(), pen.GetColour()\n\tfcolor, style = brush.GetColour(), brush.GetStyle()\n\t\n\tif 'color' in pts: \n\t\tpen.SetColour(pts['color'])\n\tif 'fcolor' in pts:\n\t\tbrush.SetColour(pts['fcolor'])\n\tif 'lw' in pts:\n\t\tpen.SetWidth(pts['lw'])\n\tif 'fill' in pts:\n\t\tbrush.SetStyle((106,100)[pts['fill']])\n\n\tdc.SetPen(pen)\n\tdc.SetBrush(brush)\n\n\tif pts['type'] == 'ellipse':\n\t\tx, y ,l1, l2, a = pts['body']\n\t\telp = make_ellipse(l1,l2,a)\n\t\telp = elp*key['k']+f(x,y)\n\t\tdc.DrawPolygon(elp)\n\tif pts['type'] == 'ellipses':\n\t\tlst = []\n\t\tfor x, y, l1, l2, a in pts['body']:\n\t\t\telp = make_ellipse(l1,l2,a)\n\t\t\tlst.append(elp*key['k']+f(x,y))\n\t\tdc.DrawPolygonList(lst)\n\t\t\n\n\tpen.SetWidth(width)\n\tpen.SetColour(color)\n\tbrush.SetColour(fcolor)\n\tbrush.SetStyle(style)\n\tdc.SetPen(pen)\n\tdc.SetBrush(brush)\n\ndef draw_rectangle(pts, dc, f, **key):\n\tpen, brush = dc.GetPen(), dc.GetBrush()\n\twidth, color = pen.GetWidth(), pen.GetColour()\n\tfcolor, style = brush.GetColour(), brush.GetStyle()\n\t\n\tif 'color' in pts: \n\t\tpen.SetColour(pts['color'])\n\tif 'fcolor' in pts:\n\t\tbrush.SetColour(pts['fcolor'])\n\tif 'lw' in pts:\n\t\tpen.SetWidth(pts['lw'])\n\tif 'fill' in pts:\n\t\tbrush.SetStyle((106,100)[pts['fill']])\n\n\tdc.SetPen(pen)\n\tdc.SetBrush(brush)\n\n\tif pts['type'] == 'rectangle':\n\t\tx, y, w, h = pts['body']\n\t\tx, y = f(x, y)\n\t\tw, h = w*key['k'], h*key['k']\n\t\tdc.DrawRectangle(x-w/2, y-h/2, w, h)\n\tif pts['type'] == 'rectangles':\n\t\tlst = []\n\t\tfor x, y, w, h in pts['body']:\n\t\t\tx, y = f(x, y)\n\t\t\tw, h = w*key['k'], h*key['k']\n\t\t\tlst.append((x-w/2, y-h/2, w, h))\n\t\tdc.DrawRectangleList(lst)\n\n\tpen.SetWidth(width)\n\tpen.SetColour(color)\n\tbrush.SetColour(fcolor)\n\tbrush.SetStyle(style)\n\tdc.SetPen(pen)\n\tdc.SetBrush(brush)\n\ndef draw_text(pts, dc, f, **key):\n\tpen, brush, font = dc.GetPen(), dc.GetBrush(), dc.GetFont()\n\twidth, color = pen.GetWidth(), pen.GetColour()\n\tfcolor, style = brush.GetColour(), brush.GetStyle()\n\tsize  = font.GetPointSize()\n\ttcolor = dc.GetTextForeground()\n\tbcolor = dc.GetTextBackground()\n\t\n\tif 'color' in pts: \n\t\tpen.SetColour(pts['color'])\n\t\tdc.SetTextForeground(pts['color'])\n\tbrush.SetColour(pen.GetColour())\n\tbrush.SetStyle(100)\n\tif 'fcolor' in pts:\n\t\tprint('hahaha')\n\t\tdc.SetTextBackground(pts['fcolor'])\n\tif 'size' in pts:\n\t\tfont.SetPointSize(pts['size'])\n\n\tdc.SetPen(pen)\n\tdc.SetBrush(brush)\n\tdc.SetFont(font)\n\n\tif pts['type'] == 'text':\n\t\tx, y, text = pts['body']\n\t\tx, y = f(x, y)\n\t\tdc.DrawText(text, x+3, y+3)\n\t\tif not 'pt' in pts or pts['pt']:\n\t\t\tdc.DrawEllipse(x-2,y-2,4,4)\n\tif pts['type'] == 'texts':\n\t\ttlst, clst, elst = [], [], []\n\t\tfor x, y, text in pts['body']:\n\t\t\tx, y = f(x, y)\n\t\t\ttlst.append(text)\n\t\t\tclst.append((x+3, y+3))\n\t\t\telst.append((x-2, y-2, 4, 4))\n\t\tdc.DrawTextList(tlst, clst)\n\t\tif not 'pt' in pts or pts['pt']:\n\t\t\tdc.DrawEllipseList(elst)\n\n\tfont.SetPointSize(size)\n\tpen.SetColour(color)\n\tbrush.SetColour(fcolor)\n\tbrush.SetStyle(style)\n\tdc.SetPen(pen)\n\tdc.SetBrush(brush)\n\tdc.SetFont(font)\n\tdc.SetTextForeground(tcolor)\n\tdc.SetTextBackground(bcolor)\n\ndraw_dic = {'points':plot, 'point':plot, 'line':plot, 'polygon':plot, 'lines':plot, 'polygons':plot,\n\t\t\t'circle':draw_circle, 'circles':draw_circle, 'ellipse':draw_ellipse, 'ellipses':draw_ellipse,\n\t\t\t'rectangle':draw_rectangle, 'rectangles':draw_rectangle, 'text':draw_text, 'texts':draw_text}\n\ndef draw(obj, dc, f, **key): draw_dic[obj['type']](obj, dc, f, **key)\n\ndef draw_layer(pts, dc, f, **key):\n\tpen, brush = dc.GetPen(), dc.GetBrush()\n\twidth, color = pen.GetWidth(), pen.GetColour()\n\tfcolor, style = brush.GetColour(), brush.GetStyle()\n\t\n\tif 'color' in pts: \n\t\tpen.SetColour(pts['color'])\n\tif 'fcolor' in pts:\n\t\tbrush.SetColour(pts['fcolor'])\n\tif 'lw' in pts:\n\t\tpen.SetWidth(pts['lw'])\n\tif 'fill' in pts:\n\t\tbrush.SetStyle((106,100)[pts['fill']])\n\n\tdc.SetPen(pen)\n\tdc.SetBrush(brush)\n\n\tfor i in pts['body']:draw(i, dc, f, **key)\n\n\tpen.SetWidth(width)\n\tpen.SetColour(color)\n\tbrush.SetColour(fcolor)\n\tbrush.SetStyle(style)\n\tdc.SetPen(pen)\n\tdc.SetBrush(brush)\n\ndraw_dic['layer'] = draw_layer\n\ndef draw_layers(pts, dc, f, **key):\n\tpen, brush = dc.GetPen(), dc.GetBrush()\n\twidth, color = pen.GetWidth(), pen.GetColour()\n\tfcolor, style = brush.GetColour(), brush.GetStyle()\n\t\n\tif 'color' in pts: \n\t\tpen.SetColour(pts['color'])\n\tif 'fcolor' in pts:\n\t\tbrush.SetColour(pts['fcolor'])\n\tif 'lw' in pts:\n\t\tpen.SetWidth(pts['lw'])\n\tif 'fill' in pts:\n\t\tbrush.SetStyle((106,100)[pts['fill']])\n\n\tdc.SetPen(pen)\n\tdc.SetBrush(brush)\n\tif key['cur'] in pts['body']:\n\t\tdraw(pts['body'][key['cur']], dc, f, **key)\n\n\tpen.SetWidth(width)\n\tpen.SetColour(color)\n\tbrush.SetColour(fcolor)\n\tbrush.SetStyle(style)\n\tdc.SetPen(pen)\n\tdc.SetBrush(brush)\n\ndraw_dic['layers'] = draw_layers\n\ndefault_color = (255, 255, 0)\ndefault_face = (255, 255, 255)\ndefault_fill = True\ndefault_lw = 1\ndefault_tcolor = (255, 0, 0)\ndefault_tsize = 8\n\ndef drawmark(dc, f, body, **key):\n\tpen, brush, font = dc.GetPen(), dc.GetBrush(), dc.GetFont()\n\tpen.SetColour(default_color or (255,255,0))\n\tbrush.SetColour(default_face or (255,255,255))\n\tbrush.SetStyle((106,100)[default_fill or False])\n\tpen.SetWidth(default_lw or 1)\n\tdc.SetTextForeground(default_tcolor or (255,0,0))\n\tfont.SetPointSize(default_tsize or 8)\n\tdc.SetPen(pen); dc.SetBrush(brush); dc.SetFont(font);\n\tdraw(body, dc, f, **key)\n\nclass GeometryMark:\n\tdef __init__(self, body):\n\t\tself.body = body\n\n\tdef draw(self, dc, f, **key):\n\t\tdrawmark(dc, f, self.body, key)\n\nif __name__ == '__main__':\n\tpass\n\t# print(make_ellipse(0,0,2,1,0))\n","sub_path":"imagepy/ui/canvas/mark.py","file_name":"mark.py","file_ext":"py","file_size_in_byte":11319,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"71178","text":"import argparse,re,os\n\nclass Parser:\n    def __init__(self,desc):\n        # Adding arguments\n        self._parser = argparse.ArgumentParser(description=desc)\n        self._parser.add_argument('target', help='List one IP Addresses, URL or Hash to query or pass the filename of a file containing IP Addresses, URL or Hash to query each separated by a newline.')\n        self._parser.add_argument('-o', '--output', help='This option will output the results to a file.')\n        self._parser.add_argument('-w', '--web', help='This option will output the results to an HTML file.')\n        self._parser.add_argument('-c', '--csv', help='This option will output the results to a CSV file.')\n        self._parser.add_argument('-d', '--delay', type=int, default=2, help='This will change the delay to the inputted seconds. Default is 2.')\n        self._parser.add_argument('-s', '--source', help='This option will only run the target against a specific source engine to pull associated domains.  Options are defined in the name attribute of the site element in the XML configuration file')\n        self._parser.add_argument('--p', action=\"store_true\", help='This option tells the program to post information to sites that allow posting. By default the program will NOT post to sites that require a post.')\n        self.args = self._parser.parse_args()        \n\n    def hasHTMLOutFile(self):\n        if self.args.web:\n            return True\n        else:\n            return False\n    \n    @property    \n    def HTMLOutFile(self):\n        if self.hasHTMLOutFile():\n            return self.args.web\n        else:\n            return None\n        \n    def hasTextOutFile(self):\n        if self.args.output:\n            return True\n        else:\n            return False\n    \n    @property    \n    def TextOutFile(self):\n        if self.hasTextOutFile():\n            return self.args.output\n        else:\n            return None\n    \n    def hasCSVOutSet(self):\n        if self.args.csv:\n            return True\n        else:\n            return False\n    \n    @property\n    def CSVOutFile(self):\n        if self.hasCSVOutSet():\n            return self.args.csv\n        else:\n            return None    \n        \n    @property\n    def Delay(self):\n        return self.args.delay\n    \n    def print_help(self):\n        self._parser.print_help()\n    \n    def hasTarget(self):\n        if self.args.target == None:\n            return False\n        else:\n            return True\n    \n    def hasNoTarget(self):\n        return not(self.hasTarget())\n    \n    @property\n    def Target(self):\n        if self.hasNoTarget():\n            return None\n        else:\n            return self.args.target\n\n    def hasInputFile(self):\n        if os.path.exists(self.args.target) and os.path.isfile(self.args.target):\n            return True\n        else:\n            return False\n        \n    @property    \n    def Source(self):\n        if self.hasSource():\n            return self.args.source\n        else:\n            return None\n        \n    def hasSource(self):\n        if self.args.source:\n            return True\n        else:\n            return False\n    \n    def hasPost(self):\n        if self.args.p:\n            return True\n        else:\n            return False\n        \n    @property    \n    def InputFile(self):\n        if self.hasNoTarget():\n            return None\n        elif self.hasInputFile():\n            return self.Target\n        else:\n            return None\n            \nclass IPWrapper(object):\n    \n    @classmethod\n    def isIPorIPList(self,target):\n        #IP Address range using prefix syntax\n        ipRangePrefix = re.compile('\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\/\\d{1,2}', re.IGNORECASE)\n        ipRgeFind = re.findall(ipRangePrefix,target)\n        ipRangeDash = re.compile('\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}-\\d{1,3}', re.IGNORECASE)\n        ipRgeDashFind = re.findall(ipRangeDash,target)\n        ipAddress = re.compile('\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}', re.IGNORECASE)\n        ipFind = re.findall(ipAddress,target)\n        if ((ipRgeFind is None or len(ipRgeFind) == 0) and (ipRgeDashFind is None or len(ipRgeDashFind) == 0) and (ipFind is None and len(ipFind) == 0)):\n            return False\n        else:\n            return True\n    \n    @classmethod\n    def getTarget(self,target):\n        #IP Address range using prefix syntax\n        ipRangePrefix = re.compile('\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\/\\d{1,2}', re.IGNORECASE)\n        ipRgeFind = re.findall(ipRangePrefix,target)\n        ipRangeDash = re.compile('\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}-\\d{1,3}', re.IGNORECASE)\n        ipRgeDashFind = re.findall(ipRangeDash,target)\n        ipAddress = re.compile('\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}', re.IGNORECASE)\n        ipFind = re.findall(ipAddress,target)        \n        if ipRgeFind is not None and len(ipRgeFind) > 0:\n            #this can be used if we ever get bigger than a class C\n            #but truthfully we don't need to split the whole address\n            #since we'll only be using the last octet.\n            iplist = target[:target.index(\"/\")].split(\".\")\n            ipprefix=givenipprefix=target[target.index(\"/\")+1:]\n            #create a bytearry to hold the one byte\n            #this would be 4 bytes for IPv4 and gives us the capability to grow\n            #if we ever want to go larger than a class C\n            bytearr = bytearray(2)\n            bytearr[0] = int(iplist[3])\n            #prefix must be class C or larger\n            if int(givenipprefix) < 24:\n                ipprefix = 24\n            if int(givenipprefix) > 32 or int(givenipprefix) == 31:\n                ipprefix = 32\n                bytearr[1]=0\n            else:\n                bytearr[1]=pow(2,32-int(ipprefix))#-1\n            \n            if bytearr[0]>bytearr[1]:    \n                start=bytearr[0]\n                last=bytearr[0]^bytearr[1]\n            else:\n                start=bytearr[0]\n                last=bytearr[1]            \n            if start == last:\n                yield target[:target.rindex(\".\")+1]+str(start)\n            if start<last:\n                for lastoctet in range(start,last):\n                    yield target[:target.rindex(\".\")+1]+str(lastoctet)\n            else:\n                yield target[:target.rindex(\".\")+1]+str(start)\n        #IP Address range seperated with a dash       \n        elif ipRgeDashFind is not None and len(ipRgeDashFind) > 0:\n            iplist = target[:target.index(\"-\")].split(\".\")\n            iplast = target[target.index(\"-\")+1:]\n            if int(iplist[3])<int(iplast):\n                for lastoctet in range(int(iplist[3]),int(iplast)+1):\n                    yield target[:target.rindex(\".\")+1]+str(lastoctet)\n            else:\n                yield target[:target.rindex(\".\")+1]+str(iplist[3])\n        #it's just an IP address at this point\n        else:\n            yield target                \n","sub_path":"utilities.py","file_name":"utilities.py","file_ext":"py","file_size_in_byte":6878,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"186463255","text":"# It was proposed by Christian Goldbach that every odd composite\n# number can be written as the sum of a prime and twice a square.\n#\n# 9 = 7 + 2×1^2\n# 15 = 7 + 2×2^2\n# 21 = 3 + 2×3^2\n# 25 = 7 + 2×3^2\n# 27 = 19 + 2×2^2\n# 33 = 31 + 2×1^2\n#\n# It turns out that the conjecture was false.\n#\n# What is the smallest odd composite that cannot be written as the\n# sum of a prime and twice a square?\n\nfrom time import time\nimport sys\nsys.path.append(\"../Library\")\nfrom peresult import peresult\nfrom primefns import isprime, primesbelow\nfrom itertools import count\nfrom math import sqrt\n\ndef solve():\n    primes = primesbelow(33)\n    for odd in count(35, 2):\n        if isprime(odd):\n            primes.append(odd)\n            continue\n        for prime in primes:\n            if sqrt((odd - prime) // 2) % 1 == 0:\n                break\n        else:\n            return odd\n\nif __name__ == \"__main__\":\n    start = time()\n    peresult(46, solve(), time() - start)\n","sub_path":"Problems 1-100/p046_GoldbachsOtherConjecture.py","file_name":"p046_GoldbachsOtherConjecture.py","file_ext":"py","file_size_in_byte":958,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"182980636","text":"# encoding: utf-8\n\n\"\"\"\n# @Time    : 19/11/2019 9:14 上午\n# @Author  : Function\n# @FileName    : urls.py\n# @Software: PyCharm\n\n课程相关URL配置\n\"\"\"\nfrom django.conf.urls import url, include\nfrom django.urls import path\nfrom .views import CourseListView,CourseDetailView,CourseInfoView,CourseCommentView,AddCommentsView,VideoPlayView\n\nurlpatterns = [\n    # 课程列表页\n    path('list/', CourseListView.as_view(), name=\"course_list\"),\n\n    #  课程详情页面\n    url(r'^detail/(?P<course_id>\\d+)/$',CourseDetailView.as_view(),name=\"course_detail\"),\n\n    #  课程详情页面\n    url(r'^info/(?P<course_id>\\d+)/$', CourseInfoView.as_view(), name=\"course_info\"),\n\n    #  课程评论页面\n    url(r'^comment/(?P<course_id>\\d+)/$', CourseCommentView.as_view(), name=\"course_comment\"),\n\n    #  添加课程评论\n    url(r'^add_comment/$', AddCommentsView.as_view(), name=\"add_comment\"),\n\n    #  课程视频播放\n    url(r'^video/(?P<course_id>\\d+)/$', VideoPlayView.as_view(), name=\"course_video\"),\n\n    ]\n\n","sub_path":"apps/courses/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1019,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"518951881","text":"#!/usr/bin/env python3\n\n\nfrom abc import ABC\n\n\nclass Book(ABC):\n    def check_for_fine(self, ISBN, title, subject, publisher, language, number_of_pages):\n        self.__ISBN = ISBN\n        self.__title = title\n        self.__subject = subject\n        self.__publisher = publisher\n        self.__language = language\n        self.__number_of_pages = number_of_pages\n        self.__authors = []\n","sub_path":"library/book/book.py","file_name":"book.py","file_ext":"py","file_size_in_byte":392,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"607986753","text":"import datetime\n\nconfig = {\n    \"access\": \"public\",\n    \"help\": \".mariafuckingwhere || .mariafuckingwhere || Tells you where Maria fucking is\",\n    \"reversible\": False\n}\n\ndef command(self, user, channel, msg):\n    dt = datetime.date\n    times = {\"8\":dt(2013,1,6),\"9\":dt(2013,5,2),\"10\":dt(2013,8,31),\"11\":dt(2014,1,5),\"12\":dt(2014,5,18),\"13\":dt(2014,10,11)}\n    show = self.factory.resolve(\"Maria\", channel)\n    if show is None:\n        return\n    ep = int(show[\"current_ep\"]) + 1\n    if ep > 12:\n        self.msg(channel, \"Maria fucking here! http://www.nyaa.eu/?page=view&tid=418999\")\n    else:\n        when = times[str(ep)] - dt.today()\n        self.msg(channel, \"%s %d will be released in %d days\" % (show[\"series\"], ep, when.days))","sub_path":"commands/mariafuckingwhere.py","file_name":"mariafuckingwhere.py","file_ext":"py","file_size_in_byte":735,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"333258124","text":"# I take a dictionary as input\n# I also take as input a string of three words\n# I want to use the third word, the second and the third words, and all three\n\t# words to predict a possible fourth word.\n\n# Given third word. What is the most frequent next word? What is its probability?\n# I need to loop through all of the keys in a dictionary. I need to find the\n# key with the highest count_key and the value for the count_key.\n\ndef mostFrequentKey(dic):\n\tfrequentKey = \"thisflatoutdidntwork\"\n\tmaxCount= 0\n\ttotalCount = dic['count_key']\n\tfor key in dic.keys():\n\t\tif key != 'count_key':\n\t\t\tcurrentCount = dic[key]['count_key']\n\t\t\tif currentCount > maxCount:\n\t\t\t\tmaxCount = currentCount\n\t\t\t\tfrequentKey = key\n\tprobability = float(maxCount) / totalCount\n\treturn (frequentKey, probability)\n\n# given \"word1 word2 word3\" I will try to predict word4\n\ndef predictWord4(wordString, dic):\n\twordList = wordString.lower().split()\n\tif wordList[2] in dic:\n\t\tonePredict = mostFrequentKey(dic[wordList[2]])\n\tif wordList[1] in dic and wordList[2] in dic[wordList[1]]:\n\t\ttwoPredict = mostFrequentKey(dic[wordList[1]][wordList[2]])\n\tif wordList[0] in dic and wordList[1] in dic[wordList[0]] and wordList[2] in dic[wordList[0]][wordList[1]]:\n\t\tthreePredict = mostFrequentKey(dic[wordList[0]][wordList[1]][wordList[2]])\n\treturn (onePredict, twoPredict, threePredict)\n\n\n","sub_path":"MakePredictions.py","file_name":"MakePredictions.py","file_ext":"py","file_size_in_byte":1346,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"506361837","text":"import datajoint as dj\r\nimport re\r\nimport pathlib\r\n\r\nfrom datajoint_utilities.dj_data_copy import diagram_restriction, db_migration\r\n\r\nfrom .tables_for_bk import tables_for_bk\r\n\r\n\r\n# ---- reconstruct schema/table code for the selected tables ----\r\nschema_prefix_update_mapper = {\r\n    'prod_mlims_data': 'group_shared_topopaper_mlims',\r\n    'group_imaging_1b': 'group_shared_topopaper_main_imaging',\r\n    'user_horsto_imaging': 'group_shared_topopaper_horst_imaging',\r\n    'user_horsto_borderscore': 'group_shared_topopaper_borderscore'\r\n    }\r\n\r\n\r\ndef main():\r\n    all_tables = []\r\n    for schema_name, selected_table_names in tables_for_bk.items():\r\n        all_tables.extend([f'`{schema_name}`.`{dj.utils.from_camel_case(t).replace(\".\", \"__\")}`' for t in selected_table_names])\r\n\r\n    schemas_code, schemas_table_definition = diagram_restriction.generate_schemas_definition_code(\r\n        all_tables, schema_prefix_update_mapper,\r\n        verbose=True, save_dir=None)\r\n\r\n    # ---- replace blob@store with longblob ----\r\n    for k, code in schemas_code.items():\r\n        schemas_code[k] = re.sub('(blob@\\w+)\\s', 'longblob', code)\r\n\r\n    # ---- write code into .py files ----\r\n    save_dir = pathlib.Path('./clone_pipeline/pipeline_code').absolute()\r\n    save_dir.mkdir(exist_ok=True, parents=True)\r\n    for cloned_schema_name, schema_definition_str in schemas_code.items():\r\n        with open(save_dir / f'{cloned_schema_name}.py', 'wt') as f:\r\n            f.write(schema_definition_str)\r\n\r\n    # ---- execute the pipeline codes ----\r\n    # this is where you review the .py files, and run them manually once\r\n    # to get the cloned pipeline initiated\r\n    # (we can code this up, but this should be a manual step with careful inspection)\r\n\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n","sub_path":"clone_pipeline/generate_pipeline.py","file_name":"generate_pipeline.py","file_ext":"py","file_size_in_byte":1787,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"639250236","text":"import tensorflow as tf\nimport numpy as np\nfrom random import shuffle\n\ndef read_process(filname, sep=\",\"):\n    df = []\n    with tf.gfile.FastGFile(filname) as f:\n        for line in f.readlines() :\n            df.append([int(row.replace('\"','')) for row in line.split(sep)[1:3]])\n    return df\n\ndef getData(datas):\n    datas = np.array(datas)\n    users = np.unique(datas[:][:,0])\n    books = np.unique(datas[:][:,1])\n    usersId2Index = dict()\n    booksId2Index = dict()\n    usersIndex2Id = dict()\n    booksIndex2Id = dict()\n    for user in users :\n        index = len(usersIndex2Id)\n        usersIndex2Id[index]=user\n        usersId2Index[user]=index\n    for book in books:\n        index = len(booksIndex2Id)\n        booksIndex2Id[index] = book\n        booksId2Index[book] = index\n    ds = []\n    for data in datas :\n        ds.append([usersId2Index[data[0]],booksId2Index[data[1]],1])\n    return np.array(ds),usersId2Index,booksId2Index,usersIndex2Id,booksIndex2Id\n\ndef getDataSet(datas, tranrate):\n    rows = len(datas)\n    split_index = int(rows * tranrate)\n    indexs = np.random.permutation(rows)\n    result = np.array([datas[i] for i in indexs])\n    train = result[:split_index]\n    test = result[split_index:]\n    return train, test\n\n\nclass DataSet(object):\n    def __init__(self,datas,usersLen,booksLen):\n        df = np.zeros([usersLen, booksLen])\n        for data in datas:\n            df[data[0],data[1]] = data[2]\n        self._df = df\n        self._usersLen=usersLen\n        self._booksLen=booksLen\n\n    @property\n    def df(self):\n        return self._df\n\n    @property\n    def usersLen(self):\n        return self._usersLen\n\n    @property\n    def booksLen(self):\n        return self._booksLen\n\n\n# datas = read_process(\"/Users/liuyang/ml/data/huabenshelf/huaben-user-shelf.csv\")\n# print(datas[:10])\n# dataset = DataSet([[1,2],[3,4],[2,1],[1,2]])\n# print(dataset.df)\n# shuffle(dataset.df)\n# print(dataset.df)","sub_path":"huaben/bookrecommend/data_set.py","file_name":"data_set.py","file_ext":"py","file_size_in_byte":1921,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"175690909","text":"from datetime import datetime\n\n\nclass WMSLayerSummary():\n\n    def __init__(self, title=\"\", name=\"\"):\n        self.name = name\n        self.title = title\n        self.layers = []\n        self.referenceSystems = []\n\n    @staticmethod\n    def fromElement(element):\n        wmsls = WMSLayerSummary()\n        for child in element:\n            tag = child.tag.lower()\n            if tag == \"name\":\n                wmsls.name = child.text\n            elif tag == \"title\":\n                wmsls.title = child.text\n            elif tag == \"srs\":\n                wmsls.referenceSystems.append(child.text)\n            elif tag == \"layer\":\n                wmsls.layers.append(WMSLayerSummary.fromElement(child))\n        return wmsls\n\n\nclass WMSServerSummary(WMSLayerSummary):\n\n    def __init__(self, title=\"\", name=\"\"):\n        WMSLayerSummary.__init__(self, title, name)\n        self.timestamp = datetime.now(None)\n\n    @staticmethod\n    def fromElement(element):\n        wmsss = WMSServerSummary()\n        for child in element:\n            tag = child.tag.lower()\n            if tag == \"name\":\n                wmsss.name = child.text\n            elif tag == \"title\":\n                wmsss.title = child.text\n            elif tag == \"layer\":\n                wmsss.layers.append(WMSLayerSummary.fromElement(child))\n        return wmsss\n","sub_path":"ymac/gis/qgis/data/wms/wmsserver.py","file_name":"wmsserver.py","file_ext":"py","file_size_in_byte":1324,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"534904816","text":"import asyncio\nimport httpx\nimport time\n\n\nasync def get_book_name(index: int, isbn: int):\n    async with httpx.AsyncClient() as client:\n        response = await client.get(f\"https://www.googleapis.com/books/v1/volumes?q=isbn:{isbn}\")\n        response_dict = response.json()\n        title = response_dict[\"items\"][0][\"volumeInfo\"][\"title\"]\n        print(f\"{index}: {title}\")\n\n\nasync def main():\n    isbn_list = [\n        9780007355143,\n        9780008108298,\n        9780547249643,\n        9781405882583,\n        9780316095860,\n        9780930289232,\n        9780486415871,\n    ]\n\n    task_list = []\n    for index, isbn in enumerate(isbn_list):\n        task_list.append(get_book_name(index, isbn))\n    await asyncio.gather(*task_list)\n\n\n\nif __name__ == \"__main__\":\n    start_time = time.monotonic()\n    asyncio.run(main())\n    print(f\"Time Taken:{time.monotonic() - start_time}\")\n","sub_path":"async/isbn.py","file_name":"isbn.py","file_ext":"py","file_size_in_byte":879,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"571950827","text":"# -*- coding: utf-8 -*-\n'''\nManage dingtalk work flow process.\nDocument available at\nhttps://ding-doc.dingtalk.com/doc#/serverapi2/ca8r99\nand\nhttps://ding-doc.dingtalk.com/doc#/serverapi2/civf9v\n'''\nimport time\nfrom ..util.api import check_none_params, client\nfrom ..util.conf import get_config\nfrom .models import convert_to_dict,\\\n                    FormComponent,\\\n                    FormComponentProp,\\\n                    ProcessInstanceApprover\n\n\ndef process_save(agent_id=None,\n                 process_code=None,\n                 name='',\n                 description='',\n                 form_component_list=None):\n    if agent_id is None:\n        agent_id = get_config().get('agent_id')\n    request = {\n        \"agentid\": agent_id,\n        \"process_code\": process_code,\n        \"name\": name,\n        \"description\": description,\n        \"fake_mode\": True\n    }\n    request['form_component_list'] = convert_to_dict(form_component_list)\n    resp = client.call('POST', '/topapi/process/save',\n                       json={\"saveProcessRequest\": request})\n    return resp.json()\n\n\ndef process_delete(agent_id=None,\n                   process_code=None):\n    if not process_code:\n        raise Exception(\"Must provide a process_code.\")\n    if agent_id is None:\n        agent_id = get_config().get('agent_id')\n    data = {\n        \"agentid\": agent_id,\n        \"process_code\": process_code\n    }\n    resp = client.call('POST', '/topapi/process/delete',\n                       json=data)\n    return resp.json()\n\n\ndef process_workrecord_create(agent_id=None,\n                              process_code=None,\n                              originator_user_id=None,\n                              title=None,\n                              form_component_values=None,\n                              url=None):\n    if not (process_code and originator_user_id and url):\n        raise Exception(\"Must provide a process_code.\")\n    if agent_id is None:\n        agent_id = get_config().get('agent_id')\n    request = {\n        \"agentid\": agent_id,\n        \"process_code\": process_code,\n        \"originator_user_id\": originator_user_id,\n        \"title\": title,\n        \"form_component_values\": form_component_values,\n        \"url\": url\n    }\n    resp = client.call('POST', '/topapi/process/workrecord/create',\n                       json={\"request\": request})\n    return resp.json()\n\n\ndef process_workrecord_update(agent_id=None,\n                              process_instance_id=None,\n                              status=None,\n                              result=None):\n    if not (process_instance_id and status):\n        raise Exception(\"Must provide a process_instance_id and status.\")\n    if not status in ('COMPLETED', 'TERMINATED'):\n        raise Exception(\"Illegal status.\")\n    if agent_id is None:\n        agent_id = get_config().get('agent_id')\n    request = {\n        \"agentid\": agent_id,\n        \"process_instance_id\": process_instance_id,\n        \"status\": status,\n        \"result\": result\n    }\n    resp = client.call('POST', '/topapi/process/workrecord/update',\n                       json={\"request\": request})\n    return resp.json()\n\n\ndef process_workrecord_task_create(agent_id=None,\n                                   process_instance_id=None,\n                                   activity_id=None,\n                                   tasks=None):\n    if not (process_instance_id and status):\n        raise Exception(\"Must provide a process_instance_id and status.\")\n    if agent_id is None:\n        agent_id = get_config().get('agent_id')\n    if tasks is None:\n        tasks = []\n    request = {\n        \"agentid\": agent_id,\n        \"process_instance_id\": process_instance_id,\n        \"activity_id\": activity_id,\n        \"tasks\": tasks\n    }\n    resp = client.call('POST', '/topapi/process/workrecord/task/create',\n                       json={\"request\": request})\n    return resp.json()\n\n\ndef process_workrecord_task_update(agent_id=None,\n                                   process_instance_id=None,\n                                   tasks=None):\n    if not process_instance_id:\n        raise Exception(\"Must provide the process_instance_id.\")\n    if agent_id is None:\n        agent_id = get_config().get('agent_id')\n    request = {\n        \"agentid\": agent_id,\n        \"process_instance_id\": process_instance_id,\n        \"tasks\": tasks\n    }\n    resp = client.call('POST', '/topapi/process/workrecord/task/update',\n                       json={\"request\": request})\n    return resp.json()\n \n\ndef process_workrecord_taskgroup_cancel(agent_id=None,\n                                        process_instance_id=None,\n                                        activity_id=None):\n    if not (process_instance_id and activity_id):\n        raise Exception(\"Must provide the process_instance_id and activity_id.\")\n    if agent_id is None:\n        agent_id = get_config().get('agent_id')\n    request = {\n        \"agentid\": agent_id,\n        \"process_instance_id\": process_instance_id,\n        \"activity_id\": activity_id\n    }\n    resp = client.call('POST', '/topapi/process/workrecord/taskgroup/cancel',\n                       json={\"request\": request})\n    return resp.json()\n\n\ndef processinstance_create(agent_id=None,\n                           process_code=None,\n                           originator_user_id=None,\n                           dept_id=None,\n                           approvers=None,\n                           approvers_v2=None,\n                           cc_list=None,\n                           cc_position=None,\n                           form_component_values=None):\n    check_none_params(process_code=process_code,\n                      originator_user_id=originator_user_id,\n                      dept_id=dept_id,\n                      form_component_values=form_component_values)\n    if agent_id is None:\n        agent_id = get_config().get('agent_id')\n    data = {\n        'agent_id': agent_id,\n        'process_code': process_code,\n        'originator_user_id': originator_user_id,\n        'dept_id': dept_id,\n        'approvers': approvers,\n        'approvers_v2': convert_to_dict(approvers_v2),\n        'cc_list': cc_list,\n        'cc_position': cc_position,\n        'form_component_values': convert_to_dict(form_component_values)\n    }\n    resp = client.call('POST', '/topapi/processinstance/create',\n                       json=data)\n    return resp.json()\n\n\ndef processinstnce_listids(process_code=None,\n                           start_time=None,\n                           end_time=None,\n                           size=20,\n                           cursor=0,\n                           userid_list=None):\n    check_none_params(process_code=process_code,\n                      start_time=start_time)\n    data = {\n        \"process_code\": process_code,\n        \"start_time\": start_time,\n        \"end_time\": end_time,\n        \"size\": size,\n        \"cursor\": cursor,\n        \"userid_list\": userid_list\n    }\n    resp = client.call('POST',\n                       '/topapi/processinstance/listids',\n                       json=data)\n    return resp.json()\n\n\ndef processinstance_get(process_instance_id):\n    resp = client.call('POST', '/topapi/processinstance/get',\n                       json={'process_instance_id': process_instance_id})\n    return resp.json()\n\n\ndef process_gettodonum(userid):\n    data = {\"userid\": userid}\n    resp = client.call('POST',\n                       '/topapi/process/gettodonum',\n                       json=data)\n    return resp.json()\n\n\ndef process_listbyuserid(userid=None,\n                         offset=0,\n                         size=100):\n    check_none_params(userid=userid)\n    data = {\n        \"userid\": userid,\n        \"offset\": offset,\n        \"size\": size\n    }\n    resp = client.call('POST',\n                       '/topapi/process/listbyuserid',\n                       json=data)\n    return resp.json()\n\n\ndef processinstance_cspace_info(user_id=None):\n    check_none_params(user_id=user_id)\n    data = {'user_id': user_id}\n    resp = client.call('POST', '/topapi/processinstance/cspace/info',\n                       json=data)\n    return resp.json()\n","sub_path":"dingle/dingtalk/process.py","file_name":"process.py","file_ext":"py","file_size_in_byte":8127,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"135692495","text":"# -*- coding: utf-8 -*-\r\n\r\nimport modules.var as v\r\nimport numpy as np\r\nfrom scipy.integrate import ode\r\n\r\n\r\ndef update():\r\n\r\n    # calculate water depth as difference between hydraulic head and surface\r\n    depoN = np.zeros((v.nNode, v.nClsMat), dtype=np.float64)\r\n\r\n    for node in range(v.nNode):\r\n        totalDepo = 0.0\r\n        aix = v.wDepth[node]-v.wMin\r\n        for mat in range(v.nTotalMat):\r\n            totalDepo = totalDepo+v.depo[node][mat]\r\n        if totalDepo <= 0.0 or aix <= 0.0:\r\n            continue\r\n        elif totalDepo > aix:\r\n            x = aix/totalDepo\r\n            for mat in range(v.nTotalMat):\r\n                if mat < v.nClsMat:\r\n                    depoN[node][mat] = v.depo[node][mat]*x\r\n                    # s'ha d'actualitzar conClast\r\n                    retorn = v.depo[node][mat]-depoN[node][mat]\r\n                    v.conClast[node][mat] = v.conClast[node][mat]+retorn\r\n                    v.depo[node][mat] = depoN[node][mat]\r\n                else:\r\n                    v.depo[node][mat] = v.depo[node][mat]*x\r\n\r\n        for mat in range(v.nTotalMat):\r\n            # reduce surface\r\n            v.surface[node] = v.surface[node]+v.depo[node][mat]\r\n            # update thickness of clastic sed deposited (grid array)\r\n            v.grid[node][v.jti-1][mat] = v.grid[node][v.jti-1][mat]+v.depo[node][mat]\r\n\r\n    v.depo = np.zeros((v.nNode, v.nTotalMat), dtype=np.float64)\r\n\r\n\r\ndef dn_dt(t, y, eF, iM, cboP, imp):\r\n    ans = 0.0\r\n    eq = []\r\n    for i in range(v.nCboMat):\r\n        for j in range(v.nCboMat):\r\n            ans += iM[i][j]*y[i]*y[j]\r\n        eq.append(eF[i]*y[i]+ans)\r\n    for i in range(v.nCboMat):\r\n        if imp[i] != 0.0:\r\n            eq.append(cboP[i]*y[i]/imp[i])\r\n        else:\r\n            eq.append(cboP[i]*y[i]*imp[i])\r\n    return eq\r\n\r\n\r\ndef carbonate():\r\n    ti = 0.0\r\n    tf = v.dt\r\n    iM = v.interMatrix\r\n    # backend = 'vode'\r\n    backend = 'dopri5'\r\n    # backend = 'dop853'\r\n\r\n    # calculate growthrate of species as function of environmental parameters\r\n    for node in range(v.nNode):\r\n        if v.wDepth[node] > v.wMin:\r\n            eF = np.zeros(v.nCboMat, dtype=np.float64)\r\n            invmp = np.zeros(v.nCboMat, dtype=np.float64)\r\n            for i in range(v.nCboMat):\r\n                v.factorWD[node][v.jti-1][i] = np.interp(v.wDepth[node], v.deptFactorX[i][:], v.deptFactorY[i][:])\r\n                v.factorVL[node][v.jti-1][i] = np.interp(v.veloNode[node]/365.25, v.flowFactorX[i][:], v.flowFactorY[i][:])\r\n                v.factorSL[node][v.jti-1][i] = np.interp(v.slopeNode[v.jti-1][node], v.slopeFactorX[i][:],\r\n                                                         v.slopeFactorY[i][:])\r\n                v.factorNU[node][v.jti-1][i] = 1.0\r\n\r\n                # factorNU=np.interp(v.nutriConc[node],v.nutrFactorX[i][:],v.nutrFactorY[i][:])\r\n                for j in range(v.nClsMat):\r\n                    v.factorCL[node][v.jti-1][i][j] = np.interp(v.conClast[node][j], v.clsFactorX[i][j][:],\r\n                                                                v.clsFactorY[i][j][:])\r\n                if v.factorType == 2:\r\n                    fcl = np.amin(v.factorCL)\r\n                    v.envFactor[node][v.jti-1][i] = min([v.factorWD[node][v.jti-1][i], v.factorVL[node][v.jti-1][i],\r\n                                                         v.factorSL[node][v.jti-1][i], v.factorNU[node][v.jti-1][i],\r\n                                                         fcl])\r\n                elif v.factorType == 1:\r\n                    fcl = 1.0\r\n                    for j in range(v.nClsMat):\r\n                        fcl = fcl*v.factorCL[node][i][j]\r\n                    v.envFactor[node][v.jti-1][i] = v.factorWD[node][v.jti-1][i]*v.factorVL[node][v.jti-1][i]\\\r\n                                                    * v.factorSL[node][v.jti-1][i]*v.factorNU[node][v.jti-1][i]*fcl\r\n                else:\r\n                    v.envFactor[node][v.jti-1][i] = 1.0\r\n\r\n                eF[i] = v.birth[i]*v.envFactor[node][v.jti-1][i]\r\n\r\n                if v.specPop[node][v.jti-1][i] < v.specPopMin[i]:\r\n                    v.specPop[node][v.jti-1][i] = v.specPopMin[i]\r\n\r\n                ixx = 0.0\r\n                for j in range(v.nCboMat):\r\n                    if i != j:\r\n                        ixx += v.interMatrix[i][j]\r\n                if ixx != 0.0 and eF[i] > 0.0:\r\n                    ainv = np.linalg.inv(v.interMatrix)\r\n                    invmp = np.dot(ainv, -eF)\r\n                elif ixx == 0.0 and eF[i] > 0.0:\r\n                    invmp[i] = -eF[i]/v.interMatrix[i][i]\r\n                else:\r\n                    invmp[i] = 0.0\r\n            if any(invmp) > 0.0:\r\n                y0 = np.zeros((2*v.nCboMat), dtype=np.float64)\r\n                for ix in range(v.nCboMat):\r\n                    y0[ix] = v.specPop[node][v.jti-1][ix]\r\n                    y0[v.nCboMat+ix] = 0.0\r\n                solver = ode(dn_dt)\r\n                solver.set_integrator(backend)\r\n                solver.set_initial_value(y0, ti)\r\n                solver.set_f_params(eF, iM, v.cboProd, invmp)\r\n                solt = []\r\n                soly = []\r\n                while solver.t < tf:\r\n                    solver.integrate(tf, step=True)\r\n                    solt.append(solver.t)\r\n                    soly.append(solver.y)\r\n                # print soly\r\n                solyp = np.asarray(soly)\r\n                # if (solyp.shape[0])>1:\r\n                #     print solyp\r\n                #     print solt\r\n                #     plt.plot(solt, solyp[:,0])\r\n                #     plt.plot(solt, solyp[:,1])\r\n                #     plt.show()\r\n                #     quit()\r\n                for i in range(v.nCboMat):\r\n                    v.specPop[node][v.jti-1][i] = solyp[solyp.shape[0]-1][i]\r\n                    if solyp[solyp.shape[0]-1][v.nCboMat+i] > 0.0:\r\n                        v.depo[node][v.nClsMat+i] = solyp[solyp.shape[0]-1][v.nCboMat+i]\r\n            else:\r\n                for i in range(v.nCboMat):\r\n                    v.specPop[node][v.jti-1][i] = 0.0\r\n        else:\r\n            for i in range(v.nCboMat):\r\n                v.specPop[node][v.jti-1][i] = 0.0\r\n\r\n\r\ndef clastic():\r\n    # It calculates the clastic sedimentation\r\n    for mat in range(v.nClsMat):\r\n        for node in range(v.nNode):\r\n            # check if velocity is below critical value for sedim type\r\n            # velo comes in m/y,  vCritic im m/day, set velo to m/day\r\n\r\n            if v.veloNode[node]/365.25 <= v.vCritic[mat] and (v.wDepth[node]-v.head[node]) > v.wMin and v.conClast[node][mat] > 0.0:\r\n\r\n                # clastmass is suspended mass (in m thickness of sediment)\r\n                clastMass = (v.conClast[node][mat]*v.wDepth[node])/v.density[mat]\r\n                # calculate settle velocity (comes in m/day)\r\n                factor = (v.vCritic[mat]-(v.veloNode[node]/365.25))/v.vCritic[mat]\r\n                if factor > 1:\r\n                    factor = 1.0\r\n                if factor < 0:\r\n                    factor = 0.0\r\n                # calculate settle distance for dt\r\n                sediPortion = v.setClast[mat]*365.25*factor*v.dt/v.wDepth[node]\r\n                if sediPortion > 1.0:\r\n                    sediPortion = 1.0\r\n                # calculate mass deposited in dt\r\n                v.depo[node][mat] = clastMass*sediPortion\r\n                # calculate new concentration after deposition\r\n                clastMass = clastMass-v.depo[node][mat]\r\n                v.conClast[node][mat] = (v.density[mat]*clastMass)/(v.wDepth[node]-v.depo[node][mat])\r\n","sub_path":"modules/sedimentation.py","file_name":"sedimentation.py","file_ext":"py","file_size_in_byte":7562,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"98327558","text":"\n#!/usr/bin/env python\n\n\nimport smbus            #import SMBus module of I2C\nimport time\nimport array as arr\n\nimport statistics as st\nimport math as m\n\n\nPWR_MGMT_1   = 0x6B\nSMPLRT_DIV   = 0x19\nCONFIG       = 0x1A\nGYRO_CONFIG  = 0x1B\nINT_ENABLE   = 0x38\nACCEL_XOUT_H = 0x3B\nACCEL_YOUT_H = 0x3D\nACCEL_ZOUT_H = 0x3F\nGYRO_XOUT_H  = 0x43\nGYRO_YOUT_H  = 0x45\nGYRO_ZOUT_H  = 0x47\n\npi = 3.1415926\nto_deg = 180/pi\nto_rad = pi/180\n\ngyro_rate_adjust = 1 # not used\nacc_rate_adjust = 1  # not used\n\nalpha=0.95\nacc_ref = 16834.0\ngyro_ref = 131.0\n\ngyro_angle=[0,0,0]\nprev_acc = [0,0,0]\ncombined_angle = [0,0,0]\n\ngyro_offset_x,gyro_offset_y,gyro_offset_z=0,0,0\n\n\ndef offset_calculation():\n    gyro_x_check,gyro_y_check,gyro_z_check = [],[],[]\n    global gyro_offset_x,gyro_offset_y,gyro_offset_z\n    \n    #looping to find no. of samples\n    for i in range(1000):\n        gyro_x_check.append(read_raw_data(GYRO_XOUT_H))\n        gyro_y_check.append(read_raw_data(GYRO_YOUT_H))\n        gyro_z_check.append(read_raw_data(GYRO_ZOUT_H))\n    \n    #offset calculation through mean of 1000 samples\n    gyro_offset_x =st.mean(gyro_x_check)\n    gyro_offset_y =st.mean(gyro_y_check)\n    gyro_offset_z =st.mean(gyro_z_check)\n\n\ndef MPU_Init():\n    #write to sample rate register\n    bus.write_byte_data(Device_Address, SMPLRT_DIV, 7)\n    \n    #Write to power management register\n    bus.write_byte_data(Device_Address, PWR_MGMT_1, 1)\n    \n    #Write to Configuration register\n    bus.write_byte_data(Device_Address, CONFIG, 0)\n    \n    #Write to Gyro configuration register\n    bus.write_byte_data(Device_Address, GYRO_CONFIG, 0)\n    \n    #Write to interrupt enable register\n    bus.write_byte_data(Device_Address, INT_ENABLE, 1)\n\n\ndef read_raw_data(addr):\n    #Accelero and Gyro value are 16-bit\n    high = bus.read_byte_data(Device_Address, addr)\n    low = bus.read_byte_data(Device_Address, addr+1)\n\n    #concatenate higher and lower value\n    value = ((high << 8) | low)\n    \n    #to get signed value from mpu6050\n    if(value > 32768):\n            value = value - 65536\n    return value\n\n\ndef euler_to_quaternion(yaw, pitch, roll):\n    qx = m.sin(roll/2) * m.cos(pitch/2) * m.cos(yaw/2) - m.cos(roll/2) * m.sin(pitch/2) * m.sin(yaw/2)\n    qy = m.cos(roll/2) * m.sin(pitch/2) * m.cos(yaw/2) + m.sin(roll/2) * m.cos(pitch/2) * m.sin(yaw/2)\n    qz = m.cos(roll/2) * m.cos(pitch/2) * m.sin(yaw/2) - m.sin(roll/2) * m.sin(pitch/2) * m.cos(yaw/2)\n    qw = m.cos(roll/2) * m.cos(pitch/2) * m.cos(yaw/2) + m.sin(roll/2) * m.sin(pitch/2) * m.sin(yaw/2)\n\n    return qx, qy, qz, qw\n\n\ndef read_raw_values():\n    global gyro_offset_x,gyro_offset_y,gyro_offset_z\n    #read accelerometer raw value\n    acc_x_raw = read_raw_data(ACCEL_XOUT_H)\n    acc_y_raw = read_raw_data(ACCEL_YOUT_H)\n    acc_z_raw = read_raw_data(ACCEL_ZOUT_H)\n    #read accelerometer raw value\n    gyro_x=read_raw_data(GYRO_XOUT_H) - gyro_offset_x\n    gyro_y=read_raw_data(GYRO_YOUT_H) - gyro_offset_y\n    gyro_z=read_raw_data(GYRO_ZOUT_H) - gyro_offset_z\n\n    return [acc_x_raw, acc_y_raw, acc_z_raw], [gyro_x, gyro_y, gyro_z]\n\n\ndef normalize_acc_data(acc_raw):\n    global acc_ref\n    #normalize acc values\n    a_norm_x = acc_raw[0]/acc_ref\n    a_norm_y = acc_raw[1]/acc_ref\n    a_norm_z = acc_raw[2]/acc_ref\n    \n    return [a_norm_x, a_norm_y, a_norm_z]\n\n\ndef normalize_gyro_data(gyro_raw):\n    global gyro_ref\n    #normalize gyro values\n    g_norm_x = gyro_raw[0]/gyro_ref\n    g_norm_y = gyro_raw[1]/gyro_ref\n    g_norm_z = gyro_raw[2]/gyro_ref\n\n    return [g_norm_x, g_norm_y, g_norm_z]\n\n\ndef get_angle_gyro(gyro_angle, gyro, dt):\n    #determine angle due to gyro effect\n    gyro_angle_x = gyro_angle[0] + gyro[0]*dt\n    gyro_angle_y = gyro_angle[1] + gyro[1]*dt\n    gyro_angle_z = gyro_angle[2] + gyro[2]*dt\n    \n    return [gyro_angle_x,gyro_angle_y,gyro_angle_z]\n\n\ndef get_median(acc_x, acc_y, acc_z):\n    a_x=st.median(acc_x)\n    a_y=st.median(acc_y)\n    a_z=st.median(acc_z)\n    \n    return [a_x, a_y, a_z]\n\n\ndef get_angle_acc(acc):\n    try:\n        #acc_angle_x = m.atan2(acc[1], acc[2]) * to_deg\n        #acc_angle_y = m.atan2((-1*acc[0]), m.sqrt(acc[1]**2 + acc[2]**2)) * to_deg\n        #acc_angle_z = m.acos(acc[2] / m.sqrt(acc[0]**2 + acc[1]**2 + acc[2]**2)) * to_deg\n        acc_angle_x = m.asin(acc[1] / m.sqrt(acc[0]**2 + acc[1]**2 + acc[2]**2)) * to_deg\n        acc_angle_y = m.asin(-acc[0] / m.sqrt(acc[0]**2 + acc[1]**2 + acc[2]**2)) * to_deg\n        acc_angle_z = m.acos(acc[2] / m.sqrt(acc[0]**2 + acc[1]**2 + acc[2]**2)) * to_deg\n    except Exception as e:\n        pass\n    \n    return [acc_angle_x, acc_angle_y, acc_angle_z]\n\n\ndef get_alpha(prev_acc, acc):\n    # Complementary filter is applied on basis of rate of accelrometer measurement\n    alpha_x = abs((prev_acc[0] - acc[0]))\n    alpha_y = abs((prev_acc[1] - acc[1]))\n    alpha_z = abs((prev_acc[2] - acc[2]))\n    alpha = max(alpha_x, alpha_y, alpha_z)\n    #alpha = alpha * 10\n    if alpha > 1:\n        alpha = 1\n        \n    return alpha\n\n\ndef get_combined_angle(acc_angle, gyro_angle, alpha):\n    print(alpha)\n    combined_angle_x = alpha*gyro_angle[0] + (1-alpha)*acc_angle[0]\n    combined_angle_y = alpha*gyro_angle[1] + (1-alpha)*acc_angle[1]\n    combined_angle_z = gyro_angle[2]\n    \n    return [combined_angle_x, combined_angle_y, combined_angle_z]\n\n\ndef get_velocity(vel_prev, acc_norm, dt):\n    # accc in m/s2\n    acc_actual = [acc_norm[0] * 9.8, acc_norm[1] * 9.8, acc_norm[2] * 9.8]\n    vel_x = vel_prev[0] + acc_actual[0]*dt\n    vel_y = vel_prev[1] + acc_actual[1]*dt\n    vel_z = vel_prev[2] + acc_actual[2]*dt\n    \n    return [vel_x, vel_y, vel_z]\n\n\ndef get_position(prev_position, prev_velocity, dt):\n    # accc in m/s2\n    pos_x = prev_position[0] + prev_velocity[0]*dt\n    pos_y = prev_position[1] + prev_velocity[1]*dt\n    pos_z = prev_position[2] + prev_velocity[2]*dt\n    \n    return [pos_x, pos_y, pos_z]\n\n\n\ndef gravity_compensation(acc_norm, combined_angle):\n    x_acc_linear = acc_norm[0] + m.sin(combined_angle[1] * to_rad);      #16384 height latitude compensation\n    y_acc_linear = acc_norm[1] - m.sin(combined_angle[0] * to_rad);      #16384\n    z_acc_linear = acc_norm[2] - m.cos(combined_angle[2] * to_rad);      #16384\n    \n    return [x_acc_linear, y_acc_linear, z_acc_linear]\n\n\nprev_velocity = [0,0,0]\nprev_velocity_raw = [0,0,0]\nprev_position = [0,0,0]\nacc_accumulation_x = []\nacc_accumulation_y = []\nacc_accumulation_z = []\ncount = 0\nno_of_acc_samples = 5\ndef get_data(dt):\n    global gyro_angle, prev_acc, prev_velocity, prev_position, combined_angle\n    global alpha, prev_velocity_raw, count, acc_accumulation, no_of_acc_samples\n    \n    gyro_angle = combined_angle\n    acc_raw, gyro_raw = read_raw_values()\n    \n    gyro_norm = normalize_gyro_data(gyro_raw)\n    acc_norm = normalize_acc_data(acc_raw)\n    \n    count = count+1\n    acc_accumulation_x.append(acc_raw[0])\n    acc_accumulation_y.append(acc_raw[1])\n    acc_accumulation_z.append(acc_raw[2])\n    \n    if count >= no_of_acc_samples:    \n        acc_median_x = st.median(acc_accumulation_x)\n        acc_median_y = st.median(acc_accumulation_y)\n        acc_median_z = st.median(acc_accumulation_z)\n        \n        acc_median = [acc_median_x, acc_median_y, acc_median_z]\n        acc_norm = normalize_acc_data(acc_median)\n        count = 0\n        acc_accumulation_x.clear()\n        acc_accumulation_y.clear()\n        acc_accumulation_z.clear()\n        \n        gyro_angle = get_angle_gyro(gyro_angle, gyro_norm, dt)\n    else:\n        return\n    \n    acc_angle = get_angle_acc(acc_norm)\n    print(\"Accelerometer Angle:\")\n    print(\"\\tx: \",acc_angle[0])\n    print(\"\\ty: \",acc_angle[1])\n    print(\"\\tz: \",acc_angle[2])\n\n    gyro_angle = get_angle_gyro(gyro_angle, gyro_norm, dt)\n    #print(\"gyro_angle\",gyro_angle)\n    print(\"Gyroscope Angle:\")\n    print(\"\\tx: \",gyro_angle[0])\n    print(\"\\ty: \",gyro_angle[1])\n    print(\"\\tz: \",gyro_angle[2])\n\n    velocity_raw = get_velocity(prev_velocity_raw, acc_norm, dt)\n\n    #alpha = get_alpha(prev_acc, acc_norm)\n    #alpha = get_alpha(prev_velocity_raw, velocity_raw)\n    print(\"Alpha: \",alpha)\n    \n    combined_angle = get_combined_angle(acc_angle, gyro_angle, alpha)\n    #print(\"Combined angle: \",combined_angle)\n    \n    angle_for_compensation = [combined_angle[0], combined_angle[1], acc_angle[2]]\n    #print(angle_for_compensation)\n    print(\"Angle for Compensation:\")\n    print(\"\\tx: \",angle_for_compensation[0])\n    print(\"\\ty: \",angle_for_compensation[1])\n    print(\"\\tz: \",angle_for_compensation[2])\n\n    acc_norm_compensated = gravity_compensation(acc_norm, angle_for_compensation)\n    #print(\"Raw\",acc_norm)\n    #print(\"Compensated\",acc_norm_compensated)\n    print(\"Accelerometer Norm:\")\n    print(\"\\tx: \",acc_norm[0])\n    print(\"\\ty: \",acc_norm[1])\n    print(\"\\tz: \",acc_norm[2])\n\n    print(\"Compensated Accelerometer Normal:\")\n    print(\"\\tx: \",acc_norm_compensated[0])\n    print(\"\\ty: \",acc_norm_compensated[1])\n    print(\"\\tz: \",acc_norm_compensated[2])\n    \n    velocity = get_velocity(prev_velocity, acc_norm_compensated, dt)\n    #print(\"Velocity: \",velocity)\n    print(\"Velocity:\")\n    print(\"\\tx: \",velocity[0])\n    print(\"\\ty: \",velocity[1])\n    print(\"\\tz: \",velocity[2])\n    \n    position = get_position(prev_position, prev_velocity, dt)\n    #print(\"Position: \",position)\n    print(\"Position from origin:\")\n    print(\"\\tx: \",position[0])\n    print(\"\\ty: \",position[1])\n    print(\"\\tz: \",position[2])\n    print(\"\")\n\n    prev_velocity_raw = velocity_raw     \n    prev_acc = acc_norm\n    prev_velocity = velocity\n    prev_position = position\n    \n    #combined_angle_rad = list_to_radian(combined_angle)\n    \n    x, y, z, w = euler_to_quaternion(combined_angle[0]*to_rad, combined_angle[1]*to_rad, combined_angle[2]*to_rad)\n\n\n\ndef direct_acc_angle():\n    acc_raw, gyro_raw = read_raw_values()\n    acc_norm = normalize_acc_data(acc_raw)\n    acc_angle = get_angle_acc(acc_norm)\n    acc_angle[2] = 0\n    \n    return acc_angle\n\nbus = smbus.SMBus(1)    # or bus = smbus.SMBus(0) for older version boards\nDevice_Address = 0x68   # MPU6050 device address\n\nif __name__==\"__main__\":\n    MPU_Init()\n    offset_calculation()\n    print (\" Reading Data of Gyroscope and Accelerometer\")\n    t1 = time.time()\n    combined_angle = direct_acc_angle()\n    \n    while True:\n        try:\n            dt=time.time()-t1\n            t1=time.time()\n            \n            get_data(dt)\n            #time.sleep(0.1)\n        except Exception as e:\n            print(e)\n            #pass\n\n\n","sub_path":"imu_temp_sampling_acc_noob.py","file_name":"imu_temp_sampling_acc_noob.py","file_ext":"py","file_size_in_byte":10430,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"370707924","text":"# coding = \"utf-8\"\n\n# 爬取所有歌曲的链接信息，写文件代码在获得数据后注销，防止误操作\n\nfrom selenium import webdriver\nimport time\n\nsongFilePath = \"F://RescueMrZhou/Corpus/songSum.txt\"\n\nif __name__==\"__main__\":\n    # 启动浏览器\n    browser = webdriver.Chrome()\n    # 打开要抓取的歌手页面\n    browser.get('http://www.kuwo.cn/artist/content?name=周杰伦')\n    # 点击歌曲按钮，获取歌曲列表\n    songButton = browser.find_element_by_xpath('//div[@class=\"tab\"]//li[@id=\"tab_music\"]//span')\n    songButton.click()\n    time.sleep(3)\n    print(\"songButton\",songButton)\n    # songFile = open(songFilePath,\"w\",encoding=\"utf-8\")\n    pageNum=1\n    while(pageNum<65):\n        songList = browser.find_elements_by_xpath('//div[@id=\"song\"]//ul[@class=\"listMusic\"]//li//div[@class=\"name\"]/a')\n        for i in songList:\n            print(i.text,i.get_attribute(\"href\"))\n            # songFile.write(i.text)\n            # songFile.write(\":\")\n            # songFile.write(i.get_attribute(\"href\"))\n            # songFile.write(\"\\n\")\n        # 找到下页链接\n        next = browser.find_element_by_xpath('//div[@class=\"page\"]//a[@class=\"next\"]')\n        browser.execute_script(\"arguments[0].scrollIntoView()\", next)\n        next.click()\n        time.sleep(10)\n\n    # songFile.close()\n    browser.close()\n\n","sub_path":"data_crawling/crawlLyricSum.py","file_name":"crawlLyricSum.py","file_ext":"py","file_size_in_byte":1346,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"189217478","text":"\"\"\"Contains serializers for models.\"\"\"\n\nfrom django.conf import settings\nfrom django.core.exceptions import ValidationError\nfrom django.utils import timezone\nfrom rest_framework import serializers\nfrom .models import Game, Player, User\n\n\nclass UserReadOnlySerializer(serializers.ModelSerializer):\n    \"\"\"A serializer for a user.\"\"\"\n\n    class Meta:\n        model = User\n        fields = [\"username\", \"date_joined\", \"last_login\", \"is_staff\"]\n\n\nclass PlayerSerializer(serializers.ModelSerializer):\n    \"\"\"A serializer for a player.\"\"\"\n\n    user = serializers.SlugRelatedField(\n        queryset=User.objects.all(),\n        slug_field=\"username\",\n        required=False,\n        allow_null=True,\n    )\n\n    class Meta:\n        model = Player\n        fields = [\"id\", \"name\", \"user\"]\n\n\nclass GameSerializer(serializers.ModelSerializer):\n    \"\"\"A serializer for a game.\"\"\"\n\n    datetime_played = serializers.DateTimeField(\n        default=lambda: timezone.localtime().strftime(\n            settings.REST_FRAMEWORK[\"DATETIME_FORMAT\"]\n        ),\n        initial=lambda: timezone.localtime().strftime(\n            settings.REST_FRAMEWORK[\"DATETIME_FORMAT\"]\n        ),\n    )\n    winner = serializers.SlugRelatedField(\n        queryset=Player.objects.all(), slug_field=\"name\"\n    )\n    loser = serializers.SlugRelatedField(\n        queryset=Player.objects.all(), slug_field=\"name\"\n    )\n    winner_score = serializers.IntegerField(min_value=0, initial=8)\n    loser_score = serializers.IntegerField(min_value=0, initial=0)\n    submitted_by = serializers.SlugRelatedField(\n        queryset=User.objects.all(), slug_field=\"username\"\n    )\n\n    class Meta:\n        model = Game\n        fields = [\n            \"id\",\n            \"datetime_played\",\n            \"winner\",\n            \"loser\",\n            \"winner_score\",\n            \"loser_score\",\n            \"submitted_by\",\n        ]\n\n    def validate(self, attrs):\n        \"\"\"Call model's clean method.\"\"\"\n        attrs = super().validate(attrs)\n\n        try:\n            Game(**attrs).clean()\n        except ValidationError as e:\n            raise serializers.ValidationError(str(e))\n\n        return attrs\n","sub_path":"backend/api/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":2140,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"184506644","text":"town = input().lower()\nsales = float(input())\n\ncommissions = {}\nhas_errors = False\n\nif sales >= 0 and sales <= 500:\n    commissions = {\n        \"sofia\": 0.05,\n        \"varna\": 0.045,\n        \"plovdiv\": 0.055\n    }\nelif sales > 500 and sales <= 1000:\n    commissions = {\n        \"sofia\": 0.07,\n        \"varna\": 0.075,\n        \"plovdiv\": 0.08\n    }\nelif sales > 1000 and sales <= 10_000:\n    commissions = {\n        \"sofia\": 0.08,\n        \"varna\": 0.10,\n        \"plovdiv\": 0.12\n    }\nelif sales > 10_000:\n    commissions = {\n        \"sofia\": 0.12,\n        \"varna\": 0.13,\n        \"plovdiv\": 0.145\n    }\n\nif not town in commissions:\n    has_errors = True\n\nif has_errors:\n    print(\"error\")\nelse:\n    commission_percent = commissions[town]\n    earned = commission_percent * sales\n    print(\"{0:.2f}\".format(earned))\n","sub_path":"PythonBasics/04. ComplexConditionalStatements/08.TradeCommissions.py","file_name":"08.TradeCommissions.py","file_ext":"py","file_size_in_byte":811,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"379459967","text":"from azure.storage.blob import BlobServiceClient,ContentSettings\r\nimport requests\r\nimport json\r\nimport os\r\nimport re\r\nfrom datetime import datetime\r\nimport uuid\r\nimport azure.functions as func\r\n\r\nclass EndpointsClient:\r\n  # Azure Public\r\n  url = \"https://www.microsoft.com/en-us/download/confirmation.aspx?id=56519\"\r\n  # Azure Government\r\n  #url = \"https://www.microsoft.com/en-us/download/confirmation.aspx?id=57063\"\r\n  # Azure China\r\n  #url = \"https://www.microsoft.com/en-us/download/confirmation.aspx?id=57062\"\r\n  # Azure Germany\r\n  #url = \"https://www.microsoft.com/en-us/download/confirmation.aspx?id=57064\"\r\n  RE_PATTERN = 'https:\\\\/\\\\/download\\\\.microsoft\\\\.com\\\\/download\\\\/[a-zA-Z0-9\\\\/\\\\-\\\\_\\\\.]+'\r\n\r\n  def __init__(self, storage_connection_string, storage_container_name, working_path):\r\n    service_client = BlobServiceClient.from_connection_string(storage_connection_string)\r\n    self.client = service_client.get_container_client(storage_container_name)\r\n    self.uuid = str(uuid.uuid4())\r\n    self.container_name = storage_container_name\r\n    self.main_page = 'main.html'\r\n    self.out_path = 'artifacts'\r\n    self.artifacts_path = working_path + '/' + self.out_path\r\n    self.main_page_path = working_path + '/' + self.main_page\r\n    if not os.path.exists(self.artifacts_path):\r\n      os.mkdir(self.artifacts_path)\r\n  def get_service_endpoints(self):\r\n    '''\r\n    Get Azure Service endpoint IP addresses\r\n    '''\r\n    regex = re.compile(EndpointsClient.RE_PATTERN)\r\n    r = requests.get(EndpointsClient.url)\r\n    self.article_text = r.text\r\n    m = regex.findall(self.article_text)\r\n    r = requests.get(m[0], stream=True)\r\n    response = r.raw\r\n    self.service_tags = json.load(response)\r\n  def export_locally(self,prepend_value=''):\r\n    '''\r\n    Store obtained data locally\r\n    '''\r\n    for key in self.service_tags['values']:\r\n        with open(f\"{self.artifacts_path}/{prepend_value}{key['id']}.txt\", 'w') as out_file:\r\n            for item in key['properties']['addressPrefixes']:\r\n                out_file.write(\"%s\\n\" % item)\r\n  def new_main_page(self):\r\n    '''\r\n    Generate main webpage\r\n    '''\r\n    artifacts_files = os.listdir(self.artifacts_path)\r\n    artifacts_files.sort(reverse=True)\r\n    main_page_content = '<html>\\n<head>\\n</head>\\n<body>\\n Generated date:<br>' + str(datetime.now()) + '<br><br>Generated list:<br>'\r\n    for item in artifacts_files:\r\n      print(item)\r\n      main_page_content = main_page_content + '<a href=\"' + self.out_path + '/' + item + '\" download>' + item + '</href>\\n <br>'\r\n    main_page_content += '</body></html>'\r\n    with open(self.main_page_path,'w') as out_file:\r\n        out_file.write(\"%s\" % main_page_content)\r\n  def upload_main_page(self):\r\n    '''\r\n    Upload main webpage\r\n    '''\r\n    self.upload_file(self.main_page_path,self.main_page)\r\n  def upload(self, source, dest):\r\n    '''\r\n    Upload a file or directory to a path inside the container\r\n    '''\r\n    if (os.path.isdir(source)):\r\n      self.upload_dir(source, dest)\r\n    else:\r\n      self.upload_file(source, dest)\r\n  def upload_file(self, source, dest):\r\n    '''\r\n    Upload a single file to a path inside the container\r\n    '''\r\n    content_settings=ContentSettings(content_type='text/html')\r\n    print(f'Uploading {source} to {dest}')\r\n    with open(source, 'rb') as data:\r\n      self.client.upload_blob(name=dest, data=data, content_settings=content_settings, overwrite=True)\r\n  def upload_dir(self, source='', dest=''):\r\n    '''\r\n    Upload a directory to a path inside the container\r\n    '''\r\n    if not source:\r\n        source = self.artifacts_path\r\n    prefix = '' if dest == '' else dest + '/'\r\n    prefix += os.path.basename(source) + '/'\r\n    for root, dirs, files in os.walk(source):\r\n      for name in files:\r\n        dir_part = os.path.relpath(root, source)\r\n        dir_part = '' if dir_part == '.' else dir_part + '/'\r\n        file_path = os.path.join(root, name)\r\n        blob_path = prefix + dir_part + name\r\n        self.upload_file(file_path, blob_path)\r\n  def download(self, source, dest):\r\n    '''\r\n    Download a file or directory to a path on the local filesystem\r\n    '''\r\n    if not dest:\r\n      raise Exception('A destination must be provided')\r\n    blobs = self.ls_files(source, recursive=True)\r\n    if blobs:\r\n      # if source is a directory, dest must also be a directory\r\n      if not source == '' and not source.endswith('/'):\r\n        source += '/'\r\n      if not dest.endswith('/'):\r\n        dest += '/'\r\n      # append the directory name from source to the destination\r\n      dest += os.path.basename(os.path.normpath(source)) + '/'\r\n      blobs = [source + blob for blob in blobs]\r\n      for blob in blobs:\r\n        blob_dest = dest + os.path.relpath(blob, source)\r\n        self.download_file(blob, blob_dest)\r\n    else:\r\n      self.download_file(source, dest)\r\n  def download_file(self, source, dest):\r\n    '''\r\n    Download a single file to a path on the local filesystem\r\n    '''\r\n    # dest is a directory if ending with '/' or '.', otherwise it's a file\r\n    if dest.endswith('.'):\r\n      dest += '/'\r\n    blob_dest = dest + os.path.basename(source) if dest.endswith('/') else dest\r\n    print(f'Downloading {source} to {blob_dest}')\r\n    os.makedirs(os.path.dirname(blob_dest), exist_ok=True)\r\n    bc = self.client.get_blob_client(blob=source)\r\n    with open(blob_dest, 'wb') as file:\r\n      data = bc.download_blob()\r\n      file.write(data.readall())\r\n  def ls_files(self, path, recursive=False):\r\n    '''\r\n    List files under a path, optionally recursively\r\n    '''\r\n    if not path == '' and not path.endswith('/'):\r\n      path += '/'\r\n    blob_iter = self.client.list_blobs(name_starts_with=path)\r\n    files = []\r\n    for blob in blob_iter:\r\n      relative_path = os.path.relpath(blob.name, path)\r\n      if recursive or not '/' in relative_path:\r\n        files.append(relative_path)\r\n    return files\r\n\r\ndef main(mytimer: func.TimerRequest) -> None:\r\n  client = EndpointsClient(storage_connection_string=os.environ['AzureWebJobsStorage'], storage_container_name='$web',working_path='/tmp')\r\n  client.get_service_endpoints()\r\n  client.export_locally()\r\n  client.upload_dir()\r\n  client.new_main_page()\r\n  client.upload_main_page()\r\n","sub_path":"TimerTrigger/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":6213,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"221008606","text":"import os\nimport sys\nimport re\nimport glob\nimport logging\nimport subprocess\nimport numpy as np\nfrom itertools import groupby\nfrom collections import namedtuple\nfrom nest.parsers.fastq import Fastq\n\nclass Identifier:\n    \"\"\" The Identifier class contains a set of modules that recognize the\n    type of fastq file from the fastq the fastq header. The class is initialzed\n    by passing a record to the constructor\"\"\"\n\n    def __init__(self, record):\n        \"\"\"Initialize the Identifier class from a fastq record\"\"\"\n        self.rec = record\n\n\n    def isIlluminaOld(self):\n        \"\"\"Identify fastq headers in the following format\n         @HWUSI-EAS100R:6:73:941:1973#0/1\"\"\"\n        header_regex = re.compile('@\\w+-?\\w+:\\d+:\\d+:\\d+:\\d+#\\d*')\n        match = re.fullmatch(header_regex, self.rec.header)\n        if match != None:\n            return(True)\n        else:\n            return(False)\n\n\n    def isIlluminaNew(self):\n        \"\"\"Idetify fastq headers in the following format\n        #@D00468:24:H8ELMADXX:1:1101:1470:2237 1:N:0:2\"\"\"\n        header_regex = re.compile('@\\w+-?\\w+:\\d+:\\w+-?\\w+:\\d+:\\d+:\\d+:\\d+\\s\\d:\\w+:\\w+:\\w*')\n        match = re.fullmatch(header_regex, self.rec.header)\n        if match != None:\n            return(True)\n        else:\n            return(False)\n\n\n    def isSraOld(self):\n        \"\"\"Identify fastq headers in the following format\n        #@SRR037455.1 HWI-E4_6_30ACL:4:1:0:29 length=35\n        #@SRR902931.1 HWI-ST1384:61:D1DJ4ACXX:8:1101:1240:2015 length=50\"\"\"\n        header_regex = re.compile('@\\w+\\.?\\w+ \\w+-?\\w+:\\d+:\\d+:\\d+:\\d+ length=\\d+')\n        match = re.fullmatch(header_regex, self.rec.header)\n        if match != None:\n            return(True)\n        else:\n            return(False)\n\n    def isSraNew(self):\n        \"\"\"Identify fastq headers in the following format\n        #@SRR1873770.5 DH1DQQN1:437:HACT2ADXX:1:2204:8270:58140 length=150\"\"\"\n        header_regex = re.compile('@\\w+\\.?\\w+ \\w+-?\\w+:\\d+:\\w+:\\d+:\\d+:\\d+:\\d+ length=\\d+')\n        match = re.fullmatch(header_regex, self.rec.header)\n        if match != None:\n            return(True)\n        else:\n            return(False)\n\n    def isENA(self):\n        \"\"\"Identify fastq headers in the following format\n        # ERR161234.14 14 length=100\"\"\"\n        header_regex = re.compile('@[\\w\\.]+ \\d+ length=\\d+')\n        match = re.fullmatch(header_regex, self.rec.header)\n        if match != None:\n            return(True)\n        else:\n            return(False)\n\n    def isPacbio(self):\n        \"\"\"Identify fastq headers in the following format\n        #@m160113_152755_42135_c100906712550000001823199104291667_s1_p0/15/7044_26271\"\"\"\n        header_regex = re.compile('@\\w+/\\d+/\\d+_\\d+')\n        match = re.fullmatch(header_regex, self.rec.header)\n        if match != None:\n            return(True)\n        else:\n            return(False)\n\n    def isFastq(self):\n        \"\"\"Identify fastq with any other header format\n        @\\w+\"\"\"\n        header_regex = re.compile('@.+')\n        match = re.fullmatch(header_regex, self.rec.header)\n        if match != None:\n            return(True)\n        else:\n            return(False)\n\n\nclass Metrics:\n    \"\"\"The Metrics class calculates read length and quality metrics for a given\n    fastq file. The class is initialized with the following inputs:\n      1. fastq (str): Path to the fastq file\"\"\"\n\n    def __init__(self, fastq):\n        self.fastq = fastq\n\n    def avgReadLen(self):\n        \"\"\"Given a fastq file, calculate average read length\"\"\"\n        fastq_reader = Fastq(self.fastq, './', 'phred33')\n        total_length = 0\n        total_reads = 0\n        for lines in fastq_reader.read():\n            total_length += len(lines.seq)\n            total_reads += 1\n            if total_reads >= 100:\n                break\n\n        avg_length = total_length/float(total_reads)\n        return(avg_length)\n\nclass Prepper:\n    \"\"\"Prepper class create the config dictionary for a given study. The class\n    constructor takes the following input parameters:\n      1. input_path (str) : Path to input directory or sra accession list\n      2. sra_path (str) : Path to fastq-dump executable \"\"\"\n\n    def __init__(self, input_path, sra_path):\n        self.input_path = os.path.abspath(input_path)\n        self.sra_path = sra_path\n        self.logger = logging.getLogger('NeST.prepInputs')\n\n    def downloadSRA(self):\n        \"\"\"Give a SRA accession list, download all the associated fastq files\"\"\"\n        out_dir = os.path.dirname(self.input_path)\n        sra_list = open(self.input_path)\n        for accessions in sra_list:\n            self.logger.debug('Downloading : {0}'.format(accessions))\n            accessions = accessions.strip()\n            fqd_cmd = [self.sra_path, '--gzip', '--split-3', '-O', out_dir,\n                        '-A', accessions]\n            fqd_run = subprocess.Popen(fqd_cmd, shell=False,\n                                    stdout=subprocess.PIPE,\n                                    stderr=subprocess.PIPE)\n            fqd_run.wait()\n            if fqd_run.returncode != 0:\n                self.logger.error('Could not download {0}'.format(accessions))\n                self.logger.error(' '.join(fqd_cmd))\n            else:\n                self.logger.info('Downladed complete: {0}'.format(accessions))\n        return(out_dir)\n\n    def getFastqPaths(self):\n        \"\"\"Given a directory path, extract all the fastq file names\"\"\"\n        filenames = list()\n        for subdir, dirname, files in os.walk(self.input_path):\n            for filename in files:\n                if ('.fastq' in filename or '.fastq.gz' in filename or\n                    'fq' in filename or 'fq.gz' in filename):\n                    filepath = subdir + os.sep + filename\n                    filenames.append(filepath)\n        return(filenames)\n\n    def getReadPresence(self, file_name, minimum=1):\n        \"\"\"Given a fastq file, check for the presence of a minimum number of\n        reads\"\"\"\n        reader = Fastq(file_name, None, 'phred33')\n        read_number = 0\n        for rec in reader.read():\n            read_number += 1\n            if read_number >= minimum:\n                return(True)\n            else:\n                return(False)\n\n    def parseMaRS(self, file_name):\n        \"\"\"Given a fastq file, check is the file name follows the MaRS regex:\n        <Year>{2}<Country>{2}<Site>{2}<Day of treatment>{2}<Treatment>{1}\n        <SampleID>{4}<Genus and species>{2}<Strain type>{1}<Markers>{3}<Rep>{1}\n        If the regex is found, the module decodes the sample information and\n        returns a namedtuple for the sample\"\"\"\n\n        mars_regex = ('(?P<Year>[0-9x]{2})(?P<Country>[A-Zx]{2})'\n                      '(?P<Site>[A-Zx]{2})(?P<DT>[0-9]{2})'\n                      '(?P<Treatment>[A-Kx]{1})(?P<SID>[0-9]{4})'\n                      '(?P<GS>[a-zA-Z]{2})(?P<ST>[BFPTSx]{1})'\n                      '(?P<Markers>[0-9]{3})(?P<Rep>[0-9]{1})')\n        mars_groups = re.match(mars_regex, file_name)\n        sample_info  = namedtuple('Sample', ['Year', 'Country', 'Site',\n            'TreatmentDay', 'Treatment', 'ID', 'Genus', 'Type', 'Markers',\n            'Replicate'])\n\n        if not mars_groups:\n            marker_list = np.array(['PfK13', 'PfCRT', 'PfMDR', 'MT', 'CytB',\n                'PfDHPS', 'PfDHFR'])\n            sample = sample_info(np.nan, 'xx', 'xx', 'xx', 'xx', 'xxxx', 'xx',\n                'x', marker_list, 1)\n            return(sample)\n        if mars_groups.group('Year') == 'xx':\n            year = np.nan\n        else:\n            year = int('20' + mars_groups.group('Year'))\n        country = mars_groups.group('Country')\n        site = mars_groups.group('Site')\n        treatDate = mars_groups.group('DT')\n        treatment = mars_groups.group('Treatment')\n        sampleID = mars_groups.group('SID')\n        genusSpecies = mars_groups.group('GS')\n        type_dict = {'B': 'Blood', 'F': 'Filter blood spots', 'P': 'Plasma',\n            'T': 'Tissue', 'S': 'Stool', 'x': 'Unknown'}\n        type = type_dict[mars_groups.group('ST')]\n        marker_list = np.array(['PfK13', 'PfCRT', 'PfMDR', 'MT', 'CytB',\n            'PfDHPS', 'PfDHFR'])\n        marker_found = list()\n        markers = int(mars_groups.group('Markers'))\n        while len(marker_found) < 8:\n            markers, rem = divmod(markers, 2)\n            marker_found.append(rem)\n\n        marker_found = np.array(marker_found[::-1][1:])\n        marker_list = np.extract(marker_found, marker_list)\n        rep = int(mars_groups.group('Rep'))\n        sample = sample_info(year, country, site, treatDate, treatment,\n            sampleID, genusSpecies, type, marker_list, rep)\n        return(sample)\n\n    def prepInputs(self):\n        \"\"\"Given a input directory path or sra accession list, iterate through\n        the list and create a sample record of reach file. Each sample record is\n        added to a dictionary with the sample name as the key and sample record\n        as the value\"\"\"\n        if os.path.isfile(self.input_path):\n            self.logger.info('Found SRA accession list,'\n                            'Will download files from SRA')\n            self.input_path = self.downloadSRA()\n        files = self.getFastqPaths()\n        experiment = dict()\n        for fastq in files:\n            reader = Fastq(fastq, './', 'phred33')\n            Sample = namedtuple('Sample', ['sample', 'libname', 'library',\n            'files', 'prep', 'paired', 'year', 'country', 'site',\n            'treatmentDay', 'treatment', 'iD', 'genus', 'type', 'markers',\n            'replicate'])\n            readPresence = self.getReadPresence(fastq)\n            if not readPresence:\n                self.logger.warning('Sample doesn\\'t contain minimum number of required reads; skipping sample : {0}'.format(fastq))\n                continue\n            rec = next(reader.read())\n            identifier = Identifier(rec)\n            metric = Metrics(fastq)\n            isIllOld =  identifier.isIlluminaOld()\n            isIllNew =  identifier.isIlluminaNew()\n            isSraOld = identifier.isSraOld()\n            isSraNew = identifier.isSraNew()\n            isPac = identifier.isPacbio()\n            isENA = identifier.isENA()\n            isFastq = identifier.isFastq()\n            seqType = ''\n            libType = ''\n            sample_regex = re.compile('_r1|_r2|_?l001|_?l002|_?l003|_?l004|_R1|_R2|_L001|_?L002|_L003|_L004|_1|_2') #|L001|L002|L003|L004')\n            sample = sample_regex.split(os.path.basename(fastq))[0]\n            sample_info = self.parseMaRS(sample)\n            year = sample_info.Year\n            country = sample_info.Country\n            site = sample_info.Site\n            td = sample_info.TreatmentDay\n            treatment = sample_info.Treatment\n            sid = sample_info.ID\n            gs = sample_info.Genus\n            stype = sample_info.Type\n            markers = sample_info.Markers\n            replicate = sample_info.Replicate\n            if isIllOld:\n                paired_regex = re.compile('@\\w+-?\\w+:\\d+:\\d+:\\d+:\\d+#\\d')\n                lib = re.findall(paired_regex, rec.header)[0]\n                paired = False\n                seqType = 'Illumina'\n                #if metric.avgReadLen(): removing unnecessary call for the time being ##01/24/19\n                libType = 'Short'\n            elif isIllNew:\n                paired_regex = re.compile('@\\w+-?\\w+:\\d+:\\w+-?\\w+:\\d+:\\d+:\\d+:\\d+\\s')\n                lib = re.findall(paired_regex, rec.header)[0]\n                paired = False\n                seqType = 'Illumina'\n                #if metric.avgReadLen():\n                libType = 'Short'\n            elif isSraOld:\n                paired_regex = re.compile('@\\w+\\.?\\w+ \\w+-?\\w+:\\d+:\\d+:\\d+:\\d+ length=\\d+')\n                lib = re.findall(paired_regex, rec.header)[0]\n                paired = False\n                seqType = 'Illumina'\n                #if metric.avgReadLen():\n                libType = 'Short'\n            elif isSraNew:\n                paired_regex = re.compile('@\\w+\\.?\\w+ \\w+-?\\w+:\\d+:\\w+:\\d+:\\d+:\\d+:\\d+ length=\\d+')\n                lib = re.findall(paired_regex, rec.header)[0]\n                paired = False\n                seqType = 'Illumina'\n                #if metric.avgReadLen():\n                libType = 'Short'\n            elif isENA:\n                paired_regex = re.compile('@[\\w\\.]+ \\d+ length=\\d+')\n                lib = re.findall(paired_regex, rec.header)[0]\n                paired = False\n                seqType = 'Illumina'\n                #if metric.avgReadLen():\n                libType = 'Short'\n            elif isPac:\n                lib_regex = re.compile('@\\w+_\\d+_\\d+_\\w+')\n                lib = re.findall(lib_regex, rec.header)[0]\n                paired = False\n                seqType = 'Pacbio'\n                #if metric.avgReadLen():\n                libType = 'Long'\n            elif isFastq:\n                lib_regex = re.compile('@.+')\n                lib = re.findall(lib_regex, rec.header)[0]\n                paired = False\n                seqType = 'Unknown'\n                #if metric.avgReadLen():\n                libType = 'Short'\n            else:\n                self.logger.warning('Read from {0} with header : {1} does not follow any defined fastq header format.Please correct it'.format(fastq, rec.header))\n            try:\n                paired = True\n                experiment[sample] = Sample(sample, lib, seqType,\n                    [experiment[sample].files[0],fastq], libType, paired,\n                    year, country, site, td, treatment, sid, gs, stype, markers,\n                    replicate)\n            except (KeyError, AttributeError):\n                experiment[sample] = Sample(sample, lib, seqType, [fastq],\n                libType, paired, year, country, site, td, treatment, sid, gs,\n                stype, markers, replicate)\n        self.logger.info('A total of {0} libraries were identified from the given folder {1}'.format(len(experiment), self.input_path))\n        self.logger.debug('The following libraries were detected in the given folder : {0}'.format(self.input_path))\n        for sample, values in experiment.items():\n            self.logger.debug('Sample : {0}; Library: {1} ; Sequence type: {2} ; Files: {3} ; Library type: {4} ; Paired: {5}'.format(\n                    values.sample, values.libname, values.library, ''.join(values.files), values.prep, values.paired))\n        for samples, info in experiment.items():\n            if not info.paired:\n                self.logger.warning('NeST does not currently support single end runs; skipping sample : {0}'.format(samples))\n                experiment.pop(samples)\n        return(experiment)\n\nif __name__ == '__main__':\n    path = os.path.abspath(sys.argv[1])\n    prepper = Prepper(path)\n    experiment = prepper.prepInputs()\n    rone = list()\n    rtwo = list()\n    for study in experiment:\n        for files in experiment[study].files:\n            if re.findall('.*_R1.*', files):\n                rone.append(files)\n            else:\n                rtwo.append(files)\n    print(rone)\n    print(rtwo)\n","sub_path":"nest/prepinputs.py","file_name":"prepinputs.py","file_ext":"py","file_size_in_byte":15062,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"481068993","text":"import matplotlib.pyplot as plt\nplt.switch_backend('agg')\nimport matplotlib.image as mpimg\nimport matplotlib.animation as animation\nimport math\nfrom template_config import *\n\n# SPECIFY CONFIG FILE HERE\nfrom config.paper_config import *\n\nfig = plt.figure() # (figsize=(9,7))\n\nif do_densanim :\n\tfileprefix = densanim_direction\n\twritename = densanim_direction + '_dens_'\nelif do_bernoulli :\n\tfileprefix = 'bern'\n\twritename = 'bern_'\n\ndef animate(i):\n\tplt.clf()\n\tdataset = i * frameskip + startingset\n\tfile = fileprefix + '_snap_' + simname + '_ds' + str(dataset) + '.png'\n\timg = mpimg.imread( framepath + file )\n\timgplot = plt.imshow(img)\n\tplt.axis('off')\n\t# plt.tight_layout()\n\tprint('mergeanim: Added file ' + file)\n\treturn imgplot\n\nanim = animation.FuncAnimation(fig, animate, frames = nframes, interval = period, repeat = False)\nsaveas = writepath + writename + simname + '.mp4'\nanim.save( saveas, dpi=500 )\nprint('mergeanim: Saved animation ' + saveas)\n","sub_path":"mergeanim.py","file_name":"mergeanim.py","file_ext":"py","file_size_in_byte":955,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"97974976","text":"'''\r\n相机：0\r\n估计相对位置由无人机相机高度、云台角度和像素点坐标偏移量计算 *通过微调云台角��使目标中心点坐标基本保持在图像的中心位置\r\n融合位置信息是由无人机三维坐标(GPS)和估计相对位置简单加权融合得到\r\n无人机基本到达降落平台正上方时只根据像素点坐标来调整\r\nCamera:0\r\nThe estimated relative position is calculated by the height of the UAV camera, PTZ angles and the pixel coordinate offsets *By fine-tuning the angles of the PTZ, the coordinate of the target center point is basically kept at the center of the image\r\nThe fusion position information is obtained by simple weighted fusion of the UAV three-dimensional coordinate (GPS) and the estimated relative position\r\nWhen the UAV basically reaches the landing platform, it is only adjusted according to the pixel coordinate\r\n'''\r\nimport airsim\r\nimport time\r\nimport math\r\nimport pprint\r\nimport os\r\nimport tempfile\r\nimport numpy as np\r\nimport cv2\r\nimport cv2.aruco as aruco\r\nimport xlwt\r\n\r\nwb = xlwt.Workbook(encoding='utf-8')\r\nws = wb.add_sheet('data')\r\nws.write(0, 0, label = 'UAV_position_x_val')\r\nws.write(0, 1, label = 'UAV_position_y_val')\r\nws.write(0, 2, label = 'UAV_position_z_val')\r\nws.write(0, 3, label = 'estimate_x')\r\nws.write(0, 4, label = 'estimate_y')\r\nws.write(0, 5, label = 'fusion_x')\r\nws.write(0, 6, label = 'fusion_y')\r\nws.write(0, 7, label = 'camera_yaw')\r\nws.write(0, 8, label = 'camera_pitch')\r\nws.write(0, 9, label = 'vx')\r\nws.write(0, 10, label = 'vy')\r\nws.write(0, 11, label = 'vz')\r\n\r\n#连接到Airsim模拟器\r\n#connect to Airsim simulator\r\nclient = airsim.MultirotorClient()\r\nclient.confirmConnection()\r\nclient.enableApiControl(True)\r\nclient.armDisarm(True)\r\n\r\n#相机图片大小\r\n#camera picture size\r\nimage_width = 1080\r\nimage_height = 720\r\n\r\n#设定水平最大速度、最大降落速度、安全降落高度\r\n#set the horizontal maximum speed, the maximum landing speed and the safe landing height\r\nvmax = 5\r\nvzmax = 2\r\nh = 3\r\n\r\n#起飞，飞到目标点附近某位置\r\n#take off and fly to a position near the target point\r\nairsim.wait_key('Press any key to takeoff')\r\nclient.takeoffAsync().join()\r\nprint(\"already flying...\")\r\nclient.hoverAsync().join()\r\nclient.moveToPositionAsync(-15, 12, -16, 2).join()\r\ntime.sleep(10)\r\n\r\n#获取无人机当前位置\r\n#get current location of the UAV\r\nUAV_position_x_val = client.getMultirotorState().kinematics_estimated.position.x_val\r\nUAV_position_y_val = client.getMultirotorState().kinematics_estimated.position.y_val\r\nUAV_position_z_val = -client.getMultirotorState().kinematics_estimated.position.z_val\r\nif UAV_position_x_val > 0:\r\n    sgn_x = 1\r\nelse:\r\n    sgn_x = -1\r\nif UAV_position_y_val > 0:\r\n    sgn_y = 1\r\nelse:\r\n    sgn_y = -1\r\n\r\n#无人机与相机高度不同，读取相机高度\r\n#the height of the UAV and the camera is different, read the height of the camera\r\ncamara_height = -client.simGetCameraInfo(0).pose.position.z_val\r\n\r\n#计算相机初始角度，转动相机对准目标点\r\n#calculate the initial angles of the camera, rotate the camera to aim at the target point\r\nx = UAV_position_x_val\r\ny = UAV_position_y_val\r\nz = camara_height\r\ncamera_yaw = math.asin(-y/math.sqrt(x*x + y*y))\r\ncamera_pitch = math.asin(-z/math.sqrt(x*x + y*y + z*z))\r\n#print(camera_yaw)\r\n#print(camera_pitch)\r\nclient.simSetCameraOrientation(0, airsim.to_quaternion(camera_pitch, 0, camera_yaw))\r\n\r\n#计算和比较最小水平移动时间t1和最小降落时间t2，决定降落方案，设定初始速度\r\n#calculate and compare the minimum horizontal moving time t1 and the minimum landing time t2, determine the landing scheme, and set the initial speeds\r\nt1 = math.sqrt(x*x + y*y)/vmax\r\nt2 = UAV_position_z_val/vzmax\r\nif t1 > t2:\r\n    vx = -x/math.sqrt(x*x + y*y)*vmax\r\n    vy = -y/math.sqrt(x*x + y*y)*vmax\r\n    vz = (UAV_position_z_val - h)/t1\r\nelse:\r\n    vx = -x/math.sqrt(x*x + y*y)*(t1/t2)*vmax\r\n    vy = -y/math.sqrt(x*x + y*y)*(t1/t2)*vmax\r\n    vz = vzmax\r\n\r\n\r\n#设定执行周期dt\r\n#set the execution cycle dt\r\ndt = 0.5\r\n\r\n#GPS信号标志位\r\n#the GPS signal marker\r\nGPS_signal = 1\r\n\r\n#GPS信息与视觉信息权重\r\n#the weights of the GPS information and the visual information\r\nGPS_weight = 0.9\r\nvisual_weight = 0.1\r\n\r\n#PID参数\r\n#the PID parameters\r\nKp = 1\r\nKi = 0\r\nKd = 0\r\n\r\nrow = 1\r\n\t\r\n#降落\r\n#landing\r\nwhile UAV_position_z_val > h:\r\n    client.moveByVelocityAsync(vx, vy, vz, dt, airsim.DrivetrainType.MaxDegreeOfFreedom, airsim.YawMode(False, 0)).join\r\n    time.sleep(10)\r\n    #if abs(UAV_position_x_val) > 0.5 and abs(UAV_position_y_val) > 0.5:\r\n    #    time.sleep(10)\r\n    #else:\r\n\t#    time.sleep(0.5)\r\n\t\r\n\t#保存相机图片\r\n\t#save the camera images\r\n    responses = client.simGetImages([\r\n        airsim.ImageRequest(\"0\", airsim.ImageType.Scene),\r\n        airsim.ImageRequest(\"3\", airsim.ImageType.Scene), \r\n        airsim.ImageRequest(\"4\", airsim.ImageType.Scene)]) \r\n    print('Retrieved images: %d' % len(responses))\r\n\t\r\n    tmp_dir = os.path.join(tempfile.gettempdir(), \"airsim_drone\")\r\n    print (\"Saving images to %s\" % tmp_dir)\r\n    try:\r\n        os.makedirs(tmp_dir)\r\n    except OSError:\r\n        if not os.path.isdir(tmp_dir):\r\n            raise\r\n\t\r\n    for idx, response in enumerate(responses):\r\n\r\n        filename = os.path.join(tmp_dir, str(idx))\r\n    \r\n        if response.pixels_as_float:\r\n            print(\"Type %d, size %d\" % (response.image_type, len(response.image_data_float)))\r\n            airsim.write_pfm(os.path.normpath(filename + '.pfm'), airsim.get_pfm_array(response))\r\n        elif response.compress:\r\n            print(\"Type %d, size %d\" % (response.image_type, len(response.image_data_uint8)))\r\n            airsim.write_file(os.path.normpath(filename + '.png'), response.image_data_uint8)\r\n        else:\r\n            print(\"Type %d, size %d\" % (response.image_type, len(response.image_data_uint8)))\r\n            img1d = np.fromstring(response.image_data_uint8, dtype=np.uint8) \r\n            img_rgb = img1d.reshape(response.height, response.width, 3)\r\n            cv2.imwrite(os.path.normpath(filename + '.png'), img_rgb)\r\n\t\r\n\t#识别ArUco\r\n\t#detect the ArUco code\r\n    if abs(UAV_position_x_val) > 1 or abs(UAV_position_y_val) > 1:\r\n        img = cv2.imread('../Appdata/Local/Temp/airsim_drone/0.png')\r\n    else:\r\n\t    img = cv2.imread('../Appdata/Local/Temp/airsim_drone/1.png')\r\n    aruco_dict = cv2.aruco_Dictionary.get(aruco.DICT_6X6_250)\r\n    parameters = cv2.aruco_DetectorParameters.create()\r\n    corners, ids, rejectedImgPoints = cv2.aruco.detectMarkers(img, aruco_dict, parameters=parameters)\r\n    \r\n\t#计算目标坐标偏移量\r\n\t#calculate the target coordinate offsets\r\n    x1 = corners[0][0][0][0]\r\n    y1 = corners[0][0][0][1]\r\n    x2 = corners[0][0][1][0]\r\n    y2 = corners[0][0][1][1]\r\n    x3 = corners[0][0][2][0]\r\n    y3 = corners[0][0][2][1]\r\n    x4 = corners[0][0][3][0]\r\n    y4 = corners[0][0][3][1]\r\n    center_x = (x1 + x2 + x3 + x4)/4\r\n    center_y = (y1 + y2 + y3 + y4)/4\r\n    print(center_x,center_y)\r\n    dx = center_x - image_width/2\r\n    dy = center_y - image_height/2\r\n\t\r\n\t#粗调\r\n\t#coarse adjustment\r\n    while ((abs(UAV_position_x_val) > 1 or abs(UAV_position_y_val) > 1) and (abs(dx) > 2 or abs(dy) > 2)):\r\n\t    #根据偏移调整相机角度\r\n\t\t#adjust the camera angles according to the offsets\r\n        if dx > 0:\r\n            camera_yaw = camera_yaw + 0.001\r\n        else:\r\n            camera_yaw = camera_yaw - 0.001\r\n        if dy > 0:\r\n            camera_pitch = camera_pitch - 0.002\r\n        else:\r\n            camera_pitch = camera_pitch + 0.002\r\n        \r\n        #重新调整相机\r\n\t\t#readjust the camera\r\n        client.simSetCameraOrientation(0, airsim.to_quaternion(camera_pitch, 0, camera_yaw))\r\n\t\t\r\n\t\t#保存相机图片\r\n\t\t#save the camera images\r\n        responses = client.simGetImages([\r\n            airsim.ImageRequest(\"0\", airsim.ImageType.Scene),\r\n            airsim.ImageRequest(\"3\", airsim.ImageType.Scene), \r\n            airsim.ImageRequest(\"4\", airsim.ImageType.Scene)]) \r\n        print('Retrieved images: %d' % len(responses))\r\n\t\r\n        tmp_dir = os.path.join(tempfile.gettempdir(), \"airsim_drone\")\r\n        print (\"Saving images to %s\" % tmp_dir)\r\n        try:\r\n            os.makedirs(tmp_dir)\r\n        except OSError:\r\n            if not os.path.isdir(tmp_dir):\r\n                raise\r\n\t\r\n        for idx, response in enumerate(responses):\r\n\r\n            filename = os.path.join(tmp_dir, str(idx))\r\n    \r\n            if response.pixels_as_float:\r\n                print(\"Type %d, size %d\" % (response.image_type, len(response.image_data_float)))\r\n                airsim.write_pfm(os.path.normpath(filename + '.pfm'), airsim.get_pfm_array(response))\r\n            elif response.compress:\r\n                print(\"Type %d, size %d\" % (response.image_type, len(response.image_data_uint8)))\r\n                airsim.write_file(os.path.normpath(filename + '.png'), response.image_data_uint8)\r\n            else:\r\n                print(\"Type %d, size %d\" % (response.image_type, len(response.image_data_uint8)))\r\n                img1d = np.fromstring(response.image_data_uint8, dtype=np.uint8) \r\n                img_rgb = img1d.reshape(response.height, response.width, 3)\r\n                cv2.imwrite(os.path.normpath(filename + '.png'), img_rgb)\r\n        \r\n        #识别ArUco\r\n\t\t#detect the ArUco code\r\n        img = cv2.imread('../Appdata/Local/Temp/airsim_drone/0.png')\r\n        aruco_dict = cv2.aruco_Dictionary.get(aruco.DICT_6X6_250)\r\n        parameters = cv2.aruco_DetectorParameters.create()\r\n        corners, ids, rejectedImgPoints = cv2.aruco.detectMarkers(img, aruco_dict, parameters=parameters)\r\n    \r\n\t    #计算目标坐标偏移量\r\n\t\t#calculate the target coordinate offsets\r\n        x1 = corners[0][0][0][0]\r\n        y1 = corners[0][0][0][1]\r\n        x2 = corners[0][0][1][0]\r\n        y2 = corners[0][0][1][1]\r\n        x3 = corners[0][0][2][0]\r\n        y3 = corners[0][0][2][1]\r\n        x4 = corners[0][0][3][0]\r\n        y4 = corners[0][0][3][1]\r\n        center_x = (x1 + x2 + x3 + x4)/4\r\n        center_y = (y1 + y2 + y3 + y4)/4\r\n        print(center_x,center_y)\r\n        dx = center_x - image_width/2\r\n        dy = center_y - image_height/2\t\r\n\t\r\n\t#细调\r\n\t#fine adjustment\r\n    while ((abs(UAV_position_x_val) > 1 or abs(UAV_position_y_val) > 1) and (abs(dx) > 1 or abs(dy) > 1)):\r\n\t    #根据偏移调整相机角度\r\n\t\t#adjust the camera angles according to the offsets\r\n        if dx > 0:\r\n            camera_yaw = camera_yaw + 0.0005\r\n        else:\r\n            camera_yaw = camera_yaw - 0.0005\r\n        if dy > 0:\r\n            camera_pitch = camera_pitch - 0.001\r\n        else:\r\n            camera_pitch = camera_pitch + 0.001\r\n        \r\n        #重新调整相机\r\n\t\t#readjust the camera\r\n        client.simSetCameraOrientation(0, airsim.to_quaternion(camera_pitch, 0, camera_yaw))\r\n\t\t\r\n\t\t#保存相机图片\r\n\t\t#save the camera images\r\n        responses = client.simGetImages([\r\n            airsim.ImageRequest(\"0\", airsim.ImageType.Scene),\r\n            airsim.ImageRequest(\"3\", airsim.ImageType.Scene), \r\n            airsim.ImageRequest(\"4\", airsim.ImageType.Scene)]) \r\n        print('Retrieved images: %d' % len(responses))\r\n\t\r\n        tmp_dir = os.path.join(tempfile.gettempdir(), \"airsim_drone\")\r\n        print (\"Saving images to %s\" % tmp_dir)\r\n        try:\r\n            os.makedirs(tmp_dir)\r\n        except OSError:\r\n            if not os.path.isdir(tmp_dir):\r\n                raise\r\n\t\r\n        for idx, response in enumerate(responses):\r\n\r\n            filename = os.path.join(tmp_dir, str(idx))\r\n    \r\n            if response.pixels_as_float:\r\n                print(\"Type %d, size %d\" % (response.image_type, len(response.image_data_float)))\r\n                airsim.write_pfm(os.path.normpath(filename + '.pfm'), airsim.get_pfm_array(response))\r\n            elif response.compress:\r\n                print(\"Type %d, size %d\" % (response.image_type, len(response.image_data_uint8)))\r\n                airsim.write_file(os.path.normpath(filename + '.png'), response.image_data_uint8)\r\n            else:\r\n                print(\"Type %d, size %d\" % (response.image_type, len(response.image_data_uint8)))\r\n                img1d = np.fromstring(response.image_data_uint8, dtype=np.uint8) \r\n                img_rgb = img1d.reshape(response.height, response.width, 3)\r\n                cv2.imwrite(os.path.normpath(filename + '.png'), img_rgb)\r\n        \r\n        #识别ArUco\r\n\t\t#detect the ArUco code\r\n        img = cv2.imread('../Appdata/Local/Temp/airsim_drone/0.png')\r\n        aruco_dict = cv2.aruco_Dictionary.get(aruco.DICT_6X6_250)\r\n        parameters = cv2.aruco_DetectorParameters.create()\r\n        corners, ids, rejectedImgPoints = cv2.aruco.detectMarkers(img, aruco_dict, parameters=parameters)\r\n    \r\n\t    #计算目标坐标偏移量\r\n\t\t#calculate the target coordinate offsets\r\n        x1 = corners[0][0][0][0]\r\n        y1 = corners[0][0][0][1]\r\n        x2 = corners[0][0][1][0]\r\n        y2 = corners[0][0][1][1]\r\n        x3 = corners[0][0][2][0]\r\n        y3 = corners[0][0][2][1]\r\n        x4 = corners[0][0][3][0]\r\n        y4 = corners[0][0][3][1]\r\n        center_x = (x1 + x2 + x3 + x4)/4\r\n        center_y = (y1 + y2 + y3 + y4)/4\r\n        print(center_x,center_y)\r\n        dx = center_x - image_width/2\r\n        dy = center_y - image_height/2\t\t\r\n\t\t\r\n\r\n\t#更新无人机当前位置\r\n\t#update current location of the UAV\r\n    UAV_position_x_val = client.getMultirotorState().kinematics_estimated.position.x_val\r\n    UAV_position_y_val = client.getMultirotorState().kinematics_estimated.position.y_val\r\n    UAV_position_z_val = -client.getMultirotorState().kinematics_estimated.position.z_val\r\n    ws.write(row, 0, label = UAV_position_x_val)\r\n    ws.write(row, 1, label = UAV_position_y_val)\r\n    ws.write(row, 2, label = UAV_position_z_val)\r\n\t\r\n\t#更新相机高度\r\n\t#update the camera height\r\n    camara_height = -client.simGetCameraInfo(0).pose.position.z_val\r\n\t\r\n    #推算相对位置,计算期望角度\r\n\t#calculate the relative position and the expected angles\r\n    estimate_s = camara_height*math.tan(math.pi/2-abs(camera_pitch))\r\n    estimate_x = -estimate_s*math.cos(camera_yaw)*(1-dx*2/image_height)\r\n    estimate_y = -estimate_s*math.sin(camera_yaw)*(1-dy*2/image_width)\r\n    #print(UAV_position_x_val,UAV_position_y_val)\r\n    #print(estimate_x,estimate_y)\r\n    #estimate_yaw = math.asin(-estimate_y/math.sqrt(estimate_y*estimate_y + camara_height*camara_height))\r\n    #estimate_pitch = math.asin(-estimate_x/math.sqrt(estimate_x*estimate_x + estimate_y*estimate_y + camara_height*camara_height))\r\n    ws.write(row, 3, label = estimate_x)\r\n    ws.write(row, 4, label = estimate_y)\r\n\t\r\n    #GPS信息与视觉位置信息做融合，更新相机角度\r\n\t#the GPS information and the visual position information are integrated to update the camera angles\r\n    x = ((visual_weight*estimate_x) + (GPS_weight*UAV_position_x_val*GPS_signal))/(visual_weight + GPS_weight*GPS_signal)\r\n    y = ((visual_weight*estimate_y) + (GPS_weight*UAV_position_y_val*GPS_signal))/(visual_weight + GPS_weight*GPS_signal)\r\n    z = camara_height\r\n    print(UAV_position_x_val,UAV_position_y_val,UAV_position_z_val)\r\n    print(estimate_s,estimate_x,estimate_y)\r\n    print(x,y)\r\n    ws.write(row, 5, label = x)\r\n    ws.write(row, 6, label = y)\r\n    ws.write(row, 7, label = camera_yaw)\r\n    ws.write(row, 8, label = math.pi/2 + camera_pitch)\r\n    '''\r\n\t#更新无人机速度（仿真）\r\n\t#update the UAV speeds (simulation)\r\n    t1 = math.sqrt(x*x + y*y)/vmax\r\n    t2 = UAV_position_z_val/vzmax\r\n    if abs(UAV_position_x_val) > 0.5 and abs(UAV_position_y_val) > 0.5:\r\n        vx = -x/math.sqrt(x*x + y*y)*vmax\r\n        vy = -y/math.sqrt(x*x + y*y)*vmax\r\n    else:\r\n        vx = -x/math.sqrt(x*x + y*y)*vmax*(t1/t2)\r\n        vy = -y/math.sqrt(x*x + y*y)*vmax*(t1/t2)\r\n    if t1 > t2:\r\n        vz = (UAV_position_z_val - h)/t1\r\n    else:\r\n        vz = vzmax\r\n    print(vx,vy,vz)\r\n    ws.write(row, 9, label = vx)\r\n    ws.write(row, 10, label = vy)\r\n    ws.write(row, 11, label = vz)\r\n\t'''\r\n    #更新无人机速度（实验）\r\n\t#update the UAV speeds (experiment)\r\n    t1 = math.sqrt(x*x + y*y)/vmax\r\n    t2 = UAV_position_z_val/vzmax\r\n    if abs(UAV_position_x_val) > 1 or abs(UAV_position_y_val) > 1:\r\n        vx = -x/math.sqrt(x*x + y*y)*vmax\r\n        vy = -y/math.sqrt(x*x + y*y)*vmax\r\n    else:\r\n        vx = -dy*0.05\r\n        vy = dx*0.05\r\n    if t1 > t2:\r\n        vz = (UAV_position_z_val - h)/t1\r\n    else:\r\n        vz = vzmax\r\n    print(vx,vy,vz)\r\n    ws.write(row, 9, label = vx)\r\n    ws.write(row, 10, label = vy)\r\n    ws.write(row, 11, label = vz)\r\n\t\r\n    row = row+1\r\n\r\n\t\t\r\nwb.save('data.xls')\t\r\nclient.landAsync().join()\r\ntime.sleep(5)\r\n\r\n\r\nclient.armDisarm(False)\r\nclient.enableApiControl(False)\r\n","sub_path":"landing/autoland_v2.py","file_name":"autoland_v2.py","file_ext":"py","file_size_in_byte":16818,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"607518656","text":"#!/usr/bin/python\n\n# [Icarus] settings.py\n#\n# Mike Bonnington <mjbonnington@gmail.com>\n# (c) 2015-2019\n#\n# Modular settings editor dialog.\n# Provides a skeleton dialog which can be extended with load-in panels, each\n# with their own UI file and helper module (if required).\n\n\nimport os\nimport sys\n\nfrom Qt import QtCompat, QtCore, QtWidgets\n\n# Import custom modules\nimport ui_template as UI\n\nfrom shared import verbose\n\n# ----------------------------------------------------------------------------\n# Configuration\n# ----------------------------------------------------------------------------\n\ncfg = {}\n\n# Set window title and object names\ncfg['window_title'] = \"Settings\"\ncfg['window_object'] = \"settingsUI\"\n\n# Set the UI and the stylesheet\ncfg['ui_file'] = 'settings.ui'\ncfg['stylesheet'] = 'style.qss'  # Set to None to use the parent app's stylesheet\n\n# Other options\ncfg['store_window_geometry'] = True\n\n# ----------------------------------------------------------------------------\n# Main dialog class\n# ----------------------------------------------------------------------------\n\nclass SettingsDialog(QtWidgets.QDialog, UI.TemplateUI):\n\t\"\"\" Settings editor dialog class.\n\t\"\"\"\n\tdef __init__(self, parent=None):\n\t\tsuper(SettingsDialog, self).__init__(parent)\n\t\tself.parent = parent\n\n\t\tself.setupUI(**cfg)\n\n\t\t# Set window flags\n\t\tself.setWindowFlags(QtCore.Qt.Dialog)\n\t\tself.setWindowIcon(self.iconSet('icon_settings.png', tintNormal=False))\n\n\t\t# Set other Qt attributes\n\t\tself.setAttribute(QtCore.Qt.WA_DeleteOnClose, True)\n\n\t\t# Connect signals & slots\n\t\tself.ui.categories_listWidget.currentItemChanged.connect(lambda current: self.openProperties(current.text()))\n\t\t# self.ui.categories_listWidget.itemActivated.connect(lambda item: self.openProperties(current.text()))\n\n\t\tself.ui.settings_buttonBox.button(QtWidgets.QDialogButtonBox.Reset).clicked.connect(self.removeOverrides)\n\t\tself.ui.settings_buttonBox.button(QtWidgets.QDialogButtonBox.Save).clicked.connect(self.saveAllAndClose)\n\t\tself.ui.settings_buttonBox.button(QtWidgets.QDialogButtonBox.Cancel).clicked.connect(self.reject)\n\n\n\tdef display(self, \n\t\tsettings_type=\"Generic\",\n\t\tself_name=\"\", \n\t\tcategory_list=[], \n\t\tstart_panel=None, \n\t\tprefs_file=None, \n\t\tinherit=None, \n\t\tautofill=False):\n\t\t\"\"\" Display the dialog.\n\n\t\t\t'settings_type' is the name given to the settings dialog.\n\t\t\t'self_name' e.g. the name of the job or shot.\n\t\t\t'category_list' is a list of categories, should correspond to a\n\t\t\tpage of properties defined by a .ui file.\n\t\t\t'start_panel' if set will jump straight to the named panel.\n\t\t\t'prefs_file' is the path to the file storing the settings. Can be\n\t\t\teither an XML or a JSON file.\n\t\t\t'inherit' whether to inherit any values. This should be in the\n\t\t\tform of a path just like the 'prefs_file' argument.\n\t\t\t'autofill' when true, attempt to fill some fields automatically.\n\t\t\"\"\"\n\t\tif start_panel:\n\t\t\tverbose.debug(\"Start Panel: \" + start_panel)\n\t\t\tself.currentCategory = start_panel\n\t\telse:\n\t\t\tself.currentCategory = \"\"\n\n\t\tself.settings_type = settings_type\n\t\tself.self_name = self_name\n\t\tself.category_list = category_list\n\t\tself.prefs_file = prefs_file\n\t\tself.inherit = inherit\n\t\tself.autofill = autofill\n\n\t\tself.reset()\n\n\t\t# Set window title\n\t\tif self_name != \"\":\n\t\t\tself.setWindowTitle(\n\t\t\t\t\"%s %s: %s\" % (settings_type, cfg['window_title'], self_name))\n\t\telse:\n\t\t\tself.setWindowTitle(\n\t\t\t\t\"%s %s\" % (settings_type, cfg['window_title']))\n\n\t\treturn self.exec_()\n\n\n\tdef reset(self):\n\t\t\"\"\" Initialise or reset by reloading data.\n\t\t\"\"\"\n\t\t# self.ui.categories_listWidget.blockSignals(True)\n\n\t\t# Instantiate preferences data class(es)\n\t\tif self.prefs_file is not None:\n\t\t\tself.prefs = self.createPrefs(self.prefs_file)\n\t\t\tif self.inherit:\n\t\t\t\tself.prefs_inherited = self.createPrefs(self.inherit)\n\t\t\telse:\n\t\t\t\tself.prefs_inherited = None\n\n\t\t# Populate categories\n\t\tif self.category_list:\n\t\t\tself.ui.categories_listWidget.clear()\n\n\t\t\tfor cat in self.category_list:\n\t\t\t\tself.ui.categories_listWidget.addItem(cat)\n\n\t\t\t# Set the maximum size of the list widget\n\t\t\tself.ui.categories_listWidget.setMaximumWidth(self.ui.categories_listWidget.sizeHintForColumn(0)*2)\n\n\t\t\t# Select the first item & show the appropriate settings panel\n\t\t\tif self.currentCategory == \"\":\n\t\t\t\tcurrentItem = self.ui.categories_listWidget.item(0)\n\t\t\telse:\n\t\t\t\tcurrentItem = self.ui.categories_listWidget.findItems(self.currentCategory, QtCore.Qt.MatchExactly)[0]\n\n\t\t\tcurrentItem.setSelected(True)\n\t\t\t# self.openProperties(currentItem.text())\n\n\t\t\t# Hide category list if there's only one item\n\t\t\tif len(self.category_list) <= 1:\n\t\t\t\t# self.ui.categories_listWidget.hide()  # doesn't work\n\t\t\t\tself.ui.categories_listWidget.setMaximumWidth(0)\n\n\t\t# self.ui.categories_listWidget.blockSignals(False)\n\n\n\tdef openProperties(self, category, storeProperties=True):\n\t\t\"\"\" Open properties panel for selected settings category. Loads UI\n\t\t\tfile and sets up widgets.\n\t\t\"\"\"\n\t\tself.currentCategory = category\n\n\t\t# Show panel & load values into form widgets\n\t\tif self.loadPanel(category):\n\t\t\tif (self.inherit is not None) \\\n\t\t\tand self.ui.settings_frame.property('inheritable'):\n\t\t\t\tverbose.print_(\"Category: %s (values inheritable)\" % category)\n\t\t\t\tself.setupWidgets(\n\t\t\t\t\tself.ui.settings_frame, \n\t\t\t\t\tforceCategory=category, \n\t\t\t\t\tinherit=self.prefs_inherited, \n\t\t\t\t\tstoreProperties=False)\n\t\t\telse:\n\t\t\t\tverbose.print_(\"Category: %s\" % category)\n\t\t\t\tself.setupWidgets(\n\t\t\t\t\tself.ui.settings_frame, \n\t\t\t\t\tforceCategory=category, \n\t\t\t\t\tstoreProperties=storeProperties)\n\n\n\tdef loadPanel(self, category):\n\t\t\"\"\" Load the panel UI (and helper module if required).\n\t\t\tThe exec function is called here to avoid the error: 'unqualified\n\t\t\texec is not allowed in function because it contains a nested\n\t\t\tfunction with free variables' with Python 2.x.\n\t\t\"\"\"\n\t\tui_file = \"settings_%s.ui\" % category\n\t\thelper_module = 'settings_%s' % category\n\t\tpanel_ui_loaded = False\n\t\thelper_module_loaded = False\n\n\t\t# Create new frame to hold properties UI & load into frame\n\t\tself.ui.settings_frame.close()\n\t\ttry:\n\t\t\tuifile = os.path.join(os.environ['IC_FORMSDIR'], ui_file)\n\t\t\tself.ui.settings_frame = QtCompat.loadUi(uifile)\n\t\t\tself.ui.settings_verticalLayout.addWidget(self.ui.settings_frame)\n\t\t\tpanel_ui_loaded = True\n\t\texcept FileNotFoundError:\n\t\t\tmessage = \"Could not open '%s' properties panel UI. \" % category\n\t\t\tverbose.error(message)\n\n\t\t# Load helper module\n\t\ttry:\n\t\t\texec_str = 'from . import %s as sh; helper = sh.helper(self, self.ui.settings_frame)' % helper_module\n\t\t\t# print(exec_str)\n\t\t\texec(exec_str)\n\t\t\thelper_module_loaded = True\n\t\texcept ImportError:\n\t\t\tmessage = \"Could not import '%s' module. \" % helper_module\n\t\t\tverbose.debug(message)\n\n\t\tif panel_ui_loaded: # and helper_module_loaded:\n\t\t\treturn True\n\t\telse:\n\t\t\treturn False\n\n\n\tdef removeOverrides(self):\n\t\t\"\"\" Remove stored values and instead inherit defaults for widgets on\n\t\t\tthe current panel.\n\t\t\"\"\"\n\t\tfor widget in self.ui.settings_frame.findChildren(QtWidgets.QWidget):\n\t\t\tattr = widget.property('xmlTag')\n\t\t\tif attr:\n\t\t\t\tself.prefs.remove_attr(self.currentCategory, attr)\n\n\t\tself.openProperties(self.currentCategory, storeProperties=False)\n\n\n\tdef saveAllAndClose(self):\n\t\t\"\"\" Save settings and close the dialog.\n\t\t\"\"\"\n\t\t# Store the values from widgets on all pages\n\t\tfor category in self.category_list:\n\t\t\tself.openProperties(category, storeProperties=True)\n\n\t\t# There's a bug where all property panel widgets become visible if a\n\t\t# save fails. As a quick dodgy workaround we exit so we don't see it\n\t\t# happen.\n\t\tif self.save():\n\t\t\tself.accept()\n\t\telse:\n\t\t\t# self.close()\n\t\t\tself.reject()\n\n\n\tdef keyPressEvent(self, event):\n\t\t\"\"\" Override function to prevent Enter / Esc keypresses triggering\n\t\t\tOK / Cancel buttons.\n\t\t\"\"\"\n\t\tif event.key() == QtCore.Qt.Key_Return \\\n\t\tor event.key() == QtCore.Qt.Key_Enter:\n\t\t\treturn\n\n\n\tdef hideEvent(self, event):\n\t\t\"\"\" Event handler for when window is hidden.\n\t\t\"\"\"\n\t\tself.storeWindow()  # Store window geometry\n\n\n\t# def closeEvent(self, event):\n\t# \t\"\"\" Event handler for when window is closed.\n\t# \t\"\"\"\n\t# \t#self.save()  # Save settings\n\t# \t#self.storeWindow()  # Store window geometry\n","sub_path":"tools/settings/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":8121,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"603159756","text":"\"\"\"\n    Autograde Lesson 8 assignment\n\n\"\"\"\n\nimport csv\nimport os\nfrom pathlib import Path\nimport pytest\nfrom loguru import logger\nimport inventory as l\n\n\ndef test_add_furniture():\n    my_path = Path('test.txt')\n    logger.debug('If the test.txt file already exists, remove it.')\n    if my_path.is_file():\n        os.remove(my_path)\n    logger.debug('Make sure the file is gone.')\n    assert not my_path.is_file()\n    logger.debug('Test file creation on first addition')\n    l.add_furniture(\n        'test.txt',\n        'Jim',\n        '001',\n        'spaceship',\n        2000\n    )\n    assert my_path.is_file()\n    logger.debug('Test subsequent additions.')\n    l.add_furniture(\n        'test.txt',\n        'Naomi',\n        '002',\n        'antimatter',\n        12000\n    )\n    l.add_furniture(\n        'test.txt',\n        'Amos',\n        '003',\n        'wrench',\n        11\n    )\n    test_data = []\n    with open(my_path) as f:\n        reader = csv.reader(f)\n        for line in reader:\n            test_data.append(line)\n    assert test_data[0] == ['Jim', '001', 'spaceship', '2000']\n    assert test_data[1] == ['Naomi', '002', 'antimatter', '12000']\n    assert test_data[2] == ['Amos', '003', 'wrench', '11']\n\n    logger.debug('Cleaning up test.txt')\n    if my_path.is_file():\n        os.remove(my_path)\n\ndef test_single_customer():\n    my_path = Path('test.txt')\n    test_result = [\n        ['Alex', 'LR01', 'Small lamp', '7.50'],\n        ['Alex', 'LR02', 'Television', '28.00'],\n        ['Alex', 'BR07', 'LED lamp', '5.50'],\n        ['Alex', 'KT08', 'Basic refrigerator', '40.00']\n    ]\n    logger.debug('If test.txt exists, delete it.')\n    if my_path.is_file():\n        os.remove(my_path)\n    test_func = l.single_customer('Alex', 'test.txt')\n    test_func('../data/test_items.csv')\n    test_data = []\n    with open(my_path) as f:\n        reader = csv.reader(f)\n        for line in reader:\n            test_data.append(line)\n    assert test_data == test_result\n","sub_path":"students/rachel_s/lesson08/assignment/tests/test_inventory.py","file_name":"test_inventory.py","file_ext":"py","file_size_in_byte":1966,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"255746015","text":"\nimport os\nimport json\nimport logging\nfrom os.path import join\n\nimport pandas as pd\n\n\n__author__ = 'dh8835'\n__email__ = 'dasha.herrmannova@open.ac.uk'\n\n\nclass ResultsParser(object):\n\n    def __init__(self, output_dir: str) -> None:\n        \"\"\"Initialize class.\n        \n        :param output_dir: directory for storing results\n        :type output_dir: str\n        :return: none\n        :rtype: None\n        \"\"\"\n        self._logger = logging.getLogger(__name__)\n        self._output_dir = output_dir\n\n    def parse_results(self) -> None:\n        \"\"\"Parse all results files and put all results in one CSV file.\n        \n        :return: None\n        :rtype: None\n        \"\"\"     \n        experiment_dir = join(self._output_dir, 'experiments')\n        all_results = []\n        self._logger.info('Parsing all results in %s', experiment_dir)\n        for dir_name in os.listdir(experiment_dir):\n            dir_path = join(experiment_dir, dir_name)\n            if os.path.isdir(dir_path):\n                res_path = join(dir_path, 'results.json')\n                cfg_path = join(dir_path, 'model_config.json')\n                if os.path.exists(res_path) and os.path.exists(cfg_path):\n                    with open(res_path) as fp:\n                        results = json.load(fp)\n                    with open(cfg_path) as fp:\n                        cfg = json.load(fp)\n                        results['name'] = cfg['name']\n                        results['description'] = cfg['description']\n                        results['dir'] = dir_name\n                    all_results.append(results)\n        self._logger.info('Found %d experiments', len(all_results))\n        output_file = join(self._output_dir, 'results.csv')\n        self._logger.info('Storing all results in %s', output_file)\n        pd.DataFrame(all_results).to_csv(output_file)\n","sub_path":"WP3/Task3.3/src/evaluation/results_parser.py","file_name":"results_parser.py","file_ext":"py","file_size_in_byte":1836,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"563522334","text":"# 接雨水\n# 双指针，高度取决于 左右两边比较矮的那个\ndef trap(height):\n    \"\"\"\n    :type height: List[int]\n    :rtype: int\n    \"\"\"\n    left = 0\n    right = len(height)-1\n    left_max = 0\n    right_max = 0\n    ans = 0\n    while left < right:\n        if height[left] < height[right]:\n            if height[left] >= left_max:\n                left_max = height[left]\n            else:\n                ans = ans + left_max - height[left]\n            left = left + 1\n        else:\n            if height[right] >= right_max:\n                right_max = height[right]\n            else:\n                ans = ans + right_max - height[right]\n            right = right - 1\n\n    return ans\n\n\nif __name__ == \"__main__\":\n    print(trap([0, 1, 0, 2, 1, 0, 1, 3, 2, 1, 2, 1]))\n","sub_path":"Q42.py","file_name":"Q42.py","file_ext":"py","file_size_in_byte":781,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"127163154","text":"#encoding=utf-8\n\nfrom django.contrib import admin\nfrom django.forms.extras import widgets\n\nfrom django.utils.translation import ugettext as _\n\nfrom mptt.admin import MPTTModelAdmin\nfrom mptt.fields import TreeForeignKey, TreeManyToManyField\n#from mptt.forms import MPTTAdminForm, TreeNodeChoiceField\n\nfrom General.models import *  # Tree, Human, Adress, Region, Concept, Type, Being_Type\n\n#class CustomMPTTModelAdmin(MPTTModelAdmin):\n    # speficfy pixel amount for this ModelAdmin only:\n    #fields = ['name']\n#    mptt_level_indent = 20\n#    mptt_indent_field = \"name\"\n\n\nfrom itertools import chain\nfrom django import forms\nfrom django.conf import settings\nfrom django.contrib.admin import widgets\nfrom django.utils.encoding import smart_unicode, force_unicode\nfrom django.utils.safestring import mark_safe\nfrom django.utils.html import escape, conditional_escape\n\n\n\nclass ProjectAdmin(MPTTModelAdmin): # admin.ModelAdmin):\n  #class Media:\n  #  js = ('mselect-to-mcheckbox.js', 'jquery-ui-1.10.2.custom.js',)\n  #  css = {\n  #    'all': ('css/mselect-to-mcheckbox.css',)\n  #  }\n\n  fieldsets = (\n    (None, {\n      'fields':(('name', 'nickname'), ('website', 'socialweb'), ('being_type', 'parent'), ('email', 'email2', 'telephone'))\n    }),\n    (_(u\"Membres de referencia\"), {\n      'classes': ('collapse',),\n      'fields':('ref_members',),\n      #'filter_horizontal': ('members', 'ref_members',)\n    }),\n    (_(u\"Adreçes\"), {\n      'classes': ('collapse',),\n      'fields': ('addresses',)\n    }),\n    (_(\"Arts\"), {\n      'classes': ('collapse',),\n      'fields': ('jobs',)\n    }),\n    (_(u\"Dates inici/fi\"), {\n      'classes': ('collapse',),\n      'fields': (('birth_date', 'dead_date'),)\n    })\n  )\n  filter_horizontal = ('ref_members', 'addresses', 'jobs')\n\n\n\nclass PersonAdmin(admin.ModelAdmin):\n\n  #class Media:\n  #  js = ('mselect-to-mcheckbox.js', 'jquery-ui-1.10.2.custom.js',)\n  #  css = {\n  #    'all': ('css/mselect-to-mcheckbox.css',)\n  #  }\n\n\n  fieldsets = (\n    (None, {\n      'fields':(('name', 'surnames', 'id_card'), ('nickname', 'nickname2'), ('email', 'email2'), ('website', 'telephone'))\n    }),\n    (_(u\"Adreçes\"), {\n      'classes': ('collapse',),\n      'fields': ('addresses',)\n    }),\n    (_(\"Arts\"), {\n      'classes': ('collapse',),\n      'fields': ('jobs',)\n    }),\n    (_(u\"Projectes\"), {\n      'classes': ('collapse',),\n      'fields': ('projects',)\n    }),\n    (_(u\"Dates naixement/mort\"), {\n      'classes': ('collapse',),\n      'fields': (('birth_date', 'dead_date'),)\n    })\n  )\n  filter_horizontal = ('addresses', 'jobs', 'projects',)\n\n\nclass CompanyAdmin(admin.ModelAdmin): # admin.ModelAdmin):\n  #class Media:\n  #  js = ('mselect-to-mcheckbox.js', 'jquery-ui-1.10.2.custom.js',)\n  #  css = {\n  #    'all': ('css/mselect-to-mcheckbox.css',)\n  #  }\n\n  fieldsets = (\n    (None, {\n      'fields':(('name', 'nickname'), ('legal_name', 'vat_number'), ('being_type', 'website'), ('email', 'telephone'))\n    }),\n    (_(u\"Membres de referencia\"), {\n      'classes': ('collapse',),\n      'fields':('ref_members',),\n      #'filter_horizontal': ('members', 'ref_members',)\n    }),\n    (_(u\"Adreçes\"), {\n      'classes': ('collapse',),\n      'fields': ('addresses',)\n    }),\n    (_(\"Arts\"), {\n      'classes': ('collapse',),\n      'fields': ('jobs',)\n    }),\n    (_(u\"Dates inici/fi\"), {\n      'classes': ('collapse',),\n      'fields': (('birth_date', 'dead_date'),)\n    })\n  )\n  filter_horizontal = ('ref_members', 'addresses', 'jobs')\n\n\n\n\n# Register your models here.\n\n#admin.site.register(Tree)\n\n#admin.site.register(Being)\n#admin.site.register(Being_Type, MPTTModelAdmin) # Comment this line after creating 'Human', then 'Person', 'Project' and 'Company' under Human, inside Being_Types.\n#admin.site.register(Human)\nadmin.site.register(Person, PersonAdmin)\n\nadmin.site.register(Project, ProjectAdmin)\nadmin.site.register(Project_Type, MPTTModelAdmin)\n\nadmin.site.register(Company, CompanyAdmin)\nadmin.site.register(Company_Type, MPTTModelAdmin)\n\n\n#admin.site.register(Art, MPTTModelAdmin) # Comment this line after creating 'Relation' and 'Job' inside Arts.\nadmin.site.register(Relation, MPTTModelAdmin)\nadmin.site.register(Job, MPTTModelAdmin)\n\n\n#admin.site.register(Artwork)\n#admin.site.register(Artwork_Type, MPTTModelAdmin) # Comment this line after creating 'Unit', 'Record', 'Material' and 'Nonmaterial' inside Artwork_Types\nadmin.site.register(Unit)\nadmin.site.register(Unit_Type, MPTTModelAdmin)\nadmin.site.register(UnitRatio)\n\nadmin.site.register(Nonmaterial)\nadmin.site.register(Nonmaterial_Type, MPTTModelAdmin)\n\nadmin.site.register(Material)\nadmin.site.register(Material_Type, MPTTModelAdmin)\n\nadmin.site.register(Record)\nadmin.site.register(Record_Type, MPTTModelAdmin)\nadmin.site.register(AccountCes)\nadmin.site.register(AccountBank)\n\n\n#admin.site.register(Space)\n#admin.site.register(Space_Type, MPTTModelAdmin) # Comment this line after creating 'Address' and 'Region' inside Space_Types\nadmin.site.register(Address)\nadmin.site.register(Address_Type, MPTTModelAdmin)\n\nadmin.site.register(Region, MPTTModelAdmin)\nadmin.site.register(Region_Type, MPTTModelAdmin)\n\nadmin.site.register(Concept, MPTTModelAdmin)\nadmin.site.register(Type, MPTTModelAdmin) # Comment this line whenever you don't need to edit the main whole Types tree\n","sub_path":"General/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":5274,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"463672793","text":"from airfoil_NUFT import *\nimport time\nimport csv\n\n# Get list of airfoils to process\nairfoil_list = []\nwith open(\"CaseList.txt\") as list_file:\n\treader = csv.reader(list_file, delimiter = \",\")\n\tline = 0\n\tfor row in reader:\n\t\tif line == 0:\n\t\t\tline += 1\n\t\telif not len(row) == 0: \n\t\t\tif airfoil_list.count(row[0]) == 0:\n\t\t\t\tairfoil_list.append(row[0])\n\nprint('Will process {} airfoils'.format(len(airfoil_list)))\n\t\n\t\n\n\n# Convert all airfoils in airfoil-data\n \ni=0\nstart=time.time()\nfor airfoil in airfoil_list:\n\tif not os.path.exists('data/'+airfoil+'/seligdatfile'):\n\t\tprint('WARNING: Selig .dat file for '+airfoil+' not found. Airfoil not converted and saved.')\n\telse:                   \n\t\t# Create save file name\n\t\tsave_file='processed_data/'+airfoil+'.pt'\n\t\tif not os.path.exists(save_file):\n\t\t\tairfoil_phys(airfoil, res=(224,224), save_name=save_file, device=\"cpu\", grad=False)\n\t\ti=i+1\n\n\t\tif (i+1)%100==0:\n\t\t\tend=time.time()\n\t\t\tprint(str(i+1)+' airfoils processed! Time elapsed: '+str(end-start))\n\t\t\tstart=time.time()\n                    \nprint('Processing complete! Processed {} airfoils'.format(i))\n","sub_path":"process_airfoil_shapes_from_csv.py","file_name":"process_airfoil_shapes_from_csv.py","file_ext":"py","file_size_in_byte":1103,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"497681844","text":"from __future__ import print_function\nimport sys\nimport os\nsys.path.append(os.path.abspath(\"C:\\\\Users\\\\genia\\\\Documents\\\\Source\\\\Repos\\\\vs_drl_bootcamp1\"))\nsys.path.append(os.path.abspath(\"C:\\\\Program Files (x86)\\\\Microsoft Visual Studio\\\\Shared\\Anaconda3_64\\\\Lib\\\\site-packages\"))\n\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass Net(nn.Module):\n\n    def __init__(self, n_states, n_actions, n_hidden=64):\n        super(Net, self).__init__()\n\n        n_hidden_1 = n_hidden\n        n_hidden_2 = n_hidden\n        #n_hidden_3 = 64\n        self.fc1 = nn.Linear(n_states, n_hidden_1)\n        self.fc2 = nn.Linear(n_hidden_1, n_hidden_2)\n        #self.fc3 = nn.Linear(n_hidden_2, n_hidden_3)\n        self.outmu1 = nn.Linear(n_hidden_2, n_actions)\n        torch.nn.init.kaiming_uniform_(self.fc2.weight.data)\n        torch.nn.init.kaiming_uniform_(self.fc1.weight.data)\n        torch.nn.init.kaiming_uniform_(self.outmu1.weight.data)\n        self.outmu1.weight.data.mul_(0.01)\n\n        self.logstd = nn.Parameter(torch.add(torch.zeros(n_actions), 0))\n\n    def forward(self, input):\n        x = F.tanh(self.fc1(input))\n        x = F.tanh(self.fc2(x))\n        #x = F.tanh(self.fc3(x))\n        mu = F.tanh(self.outmu1(x))*1\n        logstd = self.logstd.expand_as(mu)\n        std = torch.exp(logstd)\n\n        return mu, std\n\n    def num_flat_features(self, x):\n        size = x.size()[1:]  # all dimensions except the batch dimension\n        num_features = 1\n        for s in size:\n            num_features *= s\n        return num_features\n","sub_path":"Box2dEnv/BackupNewer/Curious_net_actor_cont_2.py","file_name":"Curious_net_actor_cont_2.py","file_ext":"py","file_size_in_byte":1556,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"603921514","text":"from rest_framework import status\nfrom rest_framework import viewsets\nfrom rest_framework.decorators import action\nfrom rest_framework.response import Response\n\nfrom apps.shared.serializers import DeleteShareSerializer\nfrom apps.shared.utils import custom_retrieve\nfrom .models import Building, BuildingOwners\nfrom .serializers import (\n    BuildingRetrieveSerializer, BuildingBaseSerializer,\n    CreateUpdateShareSerializer\n)\n\n\nclass BuildingViewSet(viewsets.ModelViewSet):\n    \"\"\"\n    ViewSet to handle all Building CRUD actions.\n    Shareholders is accessible via Retrieve action.\n    Also here is 2 custom actions for creating, updating, deleting shares.\n    \"\"\"\n    queryset = Building.objects.filter()\n\n    def get_serializer_class(self):\n        if self.action == 'retrieve':\n            return BuildingRetrieveSerializer\n        return BuildingBaseSerializer\n\n    def retrieve(self, request, *args, **kwargs):\n        response = super().retrieve(request, *args, **kwargs)\n        custom_retrieve(kwargs, request, response)\n        return response\n\n    @action(detail=False, name='update', methods=['post', 'put'])\n    def update_share(self, request, *args, **kwargs):\n        \"\"\"\n        Custom action to handle shares create and update actions\n        \"\"\"\n        serializer = CreateUpdateShareSerializer(data=request.data)\n        valid = serializer.is_valid()\n        if valid:\n            serializer.save()\n        # solution to make update and create in single action endpoint\n        elif 'non_field_errors' in serializer.errors:\n            if serializer.errors.get('non_field_errors')[0] == (\n                    'The fields shareholder, shared_object must make a unique set.'):\n                BuildingOwners.objects.filter(\n                    shared_object=serializer.initial_data.get('shared_object'),\n                    shareholder=serializer.initial_data.get('shareholder'))\\\n                    .update(share=serializer.initial_data.get('share'))\n        else:\n            serializer.is_valid(raise_exception=True)\n        headers = self.get_success_headers(serializer.validated_data)\n        return Response(serializer.data, status=status.HTTP_200_OK,\n                        headers=headers)\n\n    @action(detail=False, name='delete', methods=['delete'])\n    def delete_share(self, request, *args, **kwargs):\n        \"\"\"\n        Custom action to handle shares delete action\n        \"\"\"\n        serializer = DeleteShareSerializer(data=request.data)\n        serializer.is_valid(raise_exception=True)\n        deleted = BuildingOwners.objects.filter(\n            shared_object=serializer.initial_data.get('shared_object'),\n            shareholder=serializer.initial_data.get('shareholder')).delete()\n        if deleted[0]:\n            return Response({\"success\": \"Successfully deleted\"},\n                            status=status.HTTP_204_NO_CONTENT)\n        elif not deleted[0]:\n            return Response({\"error\": \"No such share found\"},\n                            status=status.HTTP_404_NOT_FOUND)\n","sub_path":"apps/building/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3025,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"100489829","text":"# Copyright 2020, Google, Inc.\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#    http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport datetime\nimport time\nimport uuid\n\nfrom google.api_core import exceptions\n\nimport pytest\n\nimport create_cluster\nimport create_realm\nimport delete_cluster\nimport delete_realm\nimport get_cluster\nimport get_realm\nimport list_clusters\nimport list_realms\nimport update_cluster\nimport update_realm\n\nPROJECT_ID = \"python-docs-samples-tests\"\nREALM_LOCATION = \"global\"\nCLUSTER_ID = \"my-cluster\"\nGKE_CLUSTER_NAME = \"projects/gcgs-client-lib-samples/locations/us-central1/clusters/gke-shared-default\"\n\n\n# The format of realm ID. This is used in the unit tests and cleanup below.\nrealm_id_format = 'test-realm-{}-{}'\n\n\n@pytest.fixture(scope=\"session\", autouse=True)\ndef clean_up_old_realms():\n    all_realms = list_realms.list_realms(PROJECT_ID, REALM_LOCATION)\n    for realm in all_realms:\n        realm_name = realm.name\n        realm_id = realm_name[realm_name.rfind('/') + 1: len(realm_name)]\n        if realm_id.find('test-realm-') == 0:\n            time_str = realm_id[realm_id.rfind('-') + 1: len(realm_id)]\n            test_date = datetime.datetime.utcfromtimestamp(int(time_str))\n            now_date = datetime.datetime.utcfromtimestamp(int(time.time()))\n            difftime = now_date - test_date\n\n            # *NOTE* Restrict to realms used in the tests older than 2 days\n            #        to prevent thrashing in the case of async tests\n            if (difftime.days > 2):\n                print(f\"Cleaning up old realm {realm_id} and its clusters, difftime: {difftime}\")\n                clean_up_realm_and_clusters(realm_id)\n\n\n@pytest.fixture(scope=\"function\")\ndef test_realm():\n    realm_id = realm_id_format.format(uuid.uuid4().hex, int(time.time()))\n\n    print(f\"Creating realm {realm_id} in location {REALM_LOCATION} in project {PROJECT_ID}\")\n    create_realm.create_realm(PROJECT_ID, REALM_LOCATION, realm_id)\n\n    yield realm_id\n\n    print(f\"Cleaning up realm {realm_id} in teardown\")\n    clean_up_realm(realm_id)\n\n\ndef clean_up_realm(realm_id):\n    # Delete the realm if it still exists.\n    print(f\"Deleting realm: {realm_id}\")\n    try:\n        delete_realm.delete_realm(PROJECT_ID, REALM_LOCATION, realm_id)\n    except exceptions.NotFound:  # Ignore the non-existent realm\n        return\n\n\n@pytest.fixture(scope=\"function\")\ndef test_realm_with_cluster():\n    realm_id = realm_id_format.format(uuid.uuid4().hex, int(time.time()))\n\n    print(f\"Creating realm {realm_id} in location {REALM_LOCATION} in project {PROJECT_ID}\")\n    create_realm.create_realm(PROJECT_ID, REALM_LOCATION, realm_id)\n\n    print(f\"Creating cluster {CLUSTER_ID} in realm {realm_id} in project {PROJECT_ID}\")\n    create_cluster.create_cluster(PROJECT_ID, REALM_LOCATION, realm_id, CLUSTER_ID, GKE_CLUSTER_NAME)\n\n    yield realm_id\n\n    print(f\"Cleaning up realm {realm_id} in teardown\")\n    clean_up_realm_and_clusters(realm_id)\n\n\ndef clean_up_realm_and_clusters(realm_id):\n    # Delete the realm and the game server clusters in the realm.\n    try:\n        get_realm.get_realm(PROJECT_ID, REALM_LOCATION, realm_id)\n    except exceptions.NotFound:  # Ignore the non-existent realm\n        return\n\n    clusters = list_clusters.list_clusters(PROJECT_ID, REALM_LOCATION, realm_id)\n    for cluster in clusters:\n        cluster_id = cluster.name.rsplit('/', 1)[-1]\n        print(f\"Deleting cluster {cluster_id} in realm {realm_id}\")\n        try:\n            delete_cluster.delete_cluster(PROJECT_ID, REALM_LOCATION, realm_id, cluster_id)\n        except exceptions.NotFound:  # Ignore the non-existent cluster\n            return\n\n    print(f\"Deleting realm: {realm_id}\")\n    try:\n        delete_realm.delete_realm(PROJECT_ID, REALM_LOCATION, realm_id)\n    except exceptions.NotFound:  # Ignore the non-existent realm\n        return\n\n\ndef test_create_realm(test_realm):\n    print(f\"Created realm {test_realm} in project {PROJECT_ID}\")\n\n\ndef test_get_realm(test_realm):\n    realm = get_realm.get_realm(PROJECT_ID, REALM_LOCATION, test_realm)\n    assert realm.name == f\"projects/{PROJECT_ID}/locations/{REALM_LOCATION}/realms/{test_realm}\"\n\n\ndef test_list_realms(test_realm):\n    realms = list_realms.list_realms(PROJECT_ID, REALM_LOCATION)\n\n    realm_name_list = []\n    for realm in realms:\n        realm_name_list.append(realm.name)\n\n    realm_name = f\"projects/{PROJECT_ID}/locations/{REALM_LOCATION}/realms/{test_realm}\"\n    assert realm_name in realm_name_list\n\n\ndef test_update_realm(test_realm):\n    update_realm.update_realm(PROJECT_ID, REALM_LOCATION, test_realm)\n    realm = get_realm.get_realm(PROJECT_ID, REALM_LOCATION, test_realm)\n    assert realm.labels == {\"label-key-1\": \"label-value-1\", \"label-key-2\": \"label-value-2\"}\n\n\ndef test_delete_realm(test_realm):\n    delete_realm.delete_realm(PROJECT_ID, REALM_LOCATION, test_realm)\n    with pytest.raises(exceptions.NotFound):\n        get_realm.get_realm(PROJECT_ID, REALM_LOCATION, test_realm)\n\n\ndef test_create_cluster(test_realm_with_cluster):\n    print(f\"Created cluster {CLUSTER_ID} in realm {test_realm_with_cluster} in project {PROJECT_ID}\")\n\n\ndef test_get_cluster(test_realm_with_cluster):\n    cluster = get_cluster.get_cluster(PROJECT_ID, REALM_LOCATION, test_realm_with_cluster, CLUSTER_ID)\n    assert cluster.name == f\"projects/{PROJECT_ID}/locations/{REALM_LOCATION}/realms/{test_realm_with_cluster}/gameServerClusters/{CLUSTER_ID}\"\n\n\ndef test_list_clusters(test_realm_with_cluster):\n    clusters = list_clusters.list_clusters(PROJECT_ID, REALM_LOCATION, test_realm_with_cluster)\n\n    cluster_name_list = []\n    for cluster in clusters:\n        cluster_name_list.append(cluster.name)\n\n    cluster_name = f\"projects/{PROJECT_ID}/locations/{REALM_LOCATION}/realms/{test_realm_with_cluster}/gameServerClusters/{CLUSTER_ID}\"\n    assert cluster_name in cluster_name_list\n\n\ndef test_update_cluster(test_realm_with_cluster):\n    update_cluster.update_cluster(PROJECT_ID, REALM_LOCATION, test_realm_with_cluster, CLUSTER_ID)\n    cluster = get_cluster.get_cluster(PROJECT_ID, REALM_LOCATION, test_realm_with_cluster, CLUSTER_ID)\n    assert cluster.labels == {\"label-key-1\": \"label-value-1\", \"label-key-2\": \"label-value-2\"}\n\n\ndef test_delete_cluster(test_realm_with_cluster):\n    delete_cluster.delete_cluster(PROJECT_ID, REALM_LOCATION, test_realm_with_cluster, CLUSTER_ID)\n    with pytest.raises(exceptions.NotFound):\n        get_cluster.get_cluster(PROJECT_ID, REALM_LOCATION, test_realm_with_cluster, CLUSTER_ID)\n","sub_path":"samples/snippets/realm_and_cluster_test.py","file_name":"realm_and_cluster_test.py","file_ext":"py","file_size_in_byte":6938,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"172123578","text":"import unittest\r\nimport itertools\r\n\r\n\r\n\r\n#Question 1\r\n\r\ndef memoize(func):\r\n    cache = {}\r\n    def f(*args):\r\n        if args not in cache:\r\n            v = func(*args)\r\n            cache[args] = v\r\n        else:\r\n            v = cache[args]\r\n        return v\r\n    return f\r\n\r\n@memoize\r\ndef sum2(x, y):\r\n    print('calling sum2')\r\n    return x + y\r\n\r\n\r\n#Question 2\r\n\r\nclass Rational:\r\n    max = None\r\n    \r\n    @staticmethod\r\n    def gcd(x,y):\r\n        while y!=0:\r\n            (x,y) = (y,x%y)\r\n        return x\r\n    \r\n    def __init__(self, numerator, denominator=1):\r\n        if (denominator<0):\r\n            self.numerator = numerator*-1\r\n            self.denominator = denominator*-1\r\n        else:\r\n            self.numerator = numerator\r\n            self.denominator = denominator\r\n        if Rational.max is None or self > Rational.max:\r\n            Rational.max=self\r\n            \r\n\r\n              \r\n    def __add__(self, other):\r\n        mult1 = other.denominator\r\n        mult2 = self.denominator\r\n        num1 = self.numerator*mult1\r\n        num2 = other.numerator*mult2\r\n        return Rational(num1+num2,mult1*mult2)\r\n    \r\n    def __sub__(self,other):\r\n        return self+Rational(other.numerator*-1,other.denominator)\r\n    \r\n    def __mul__(self,other):\r\n        return Rational(self.numerator*other.numerator,self.denominator*other.denominator)\r\n    \r\n    def __truediv__(self,other):\r\n        return self*Rational(other.denominator,other.numerator)\r\n\r\n    def __gt__(self,other):\r\n        temp1 = self.numerator * other.denominator\r\n        temp2 = self.denominator * other.numerator\r\n        return temp1 > temp2\r\n    \r\n    def __ge__(self,other):  \r\n        temp1 = self.numerator * other.denominator\r\n        temp2 = self.denominator * other.numerator\r\n        return temp1 >= temp2\r\n    \r\n    def __lt__(self,other):\r\n        return other > self\r\n\r\n    \r\n    def __le__(self,other):\r\n        return other >= self\r\n\r\n    \r\n    def __eq__(self,other):\r\n        return self.numerator*other.denominator == self.denominator*other.numerator\r\n\r\n    \r\n    def __ne__(self,other):\r\n        return self.numerator*other.denominator!=self.denominator*other.numerator\r\n\r\n    def __str__(self):\r\n        gcd = Rational.gcd(self.numerator,self.denominator)\r\n        newNum = self.numerator//gcd\r\n        newDen = self.denominator//gcd\r\n        return \"%s/%s\" % (newNum,newDen)\r\n    \r\n    def __repr__(self):\r\n        gcd = Rational.gcd(self.numerator,self.denominator)\r\n        newNum = self.numerator//gcd\r\n        newDen = self.denominator//gcd\r\n        return \"Rational(%r,%r)\" % (newNum,newDen)\r\n\r\n    \r\n#Question 3\r\n    \r\ndef myZip(iterable1, iterable2, fill=None):\r\n    it1 = iter(iterable1)\r\n    it2 = iter(iterable2)\r\n    elem1 = next(it1,fill)\r\n    elem2 = next(it2,fill)\r\n    while elem1 is not fill or elem2 is not fill:\r\n        result = []\r\n        if elem1 is not None and elem2 is not None:\r\n             result.append(elem1)\r\n             result.append(elem2)\r\n             yield tuple(result)   \r\n        elem1 = next(it1,fill)\r\n        elem2 = next(it2,fill)\r\n             \r\n\r\n#Question 4\r\n        \r\nclass MyZip:\r\n\r\n    def __init__(self, iterable1,iterable2,fill=None):\r\n        self.iterable1=iterable1\r\n        self.iterable2=iterable2\r\n        self.fill=fill\r\n\r\n    def __iter__(self):\r\n        \r\n        class Iterator:\r\n            \r\n            def __init__(self,iterable1,iterable2,fill):\r\n                self.it1 = iter(iterable1)\r\n                self.it2 = iter(iterable2)\r\n                self.fill=fill\r\n                \r\n            def __iter__(self):\r\n                return self\r\n            \r\n            def __next__(self):\r\n                elem1 = next(self.it1,self.fill)\r\n                elem2 = next(self.it2,self.fill)\r\n                result = [] \r\n                if  elem1 is not self.fill or elem2 is not self.fill:\r\n                     if elem1 is not None and elem2 is not None:\r\n                         result.append(elem1)\r\n                         result.append(elem2)\r\n                         return tuple(result)\r\n                     else:\r\n                         raise StopIteration\r\n\r\n                else:\r\n                    raise StopIteration\r\n                                               \r\n        return Iterator(self.iterable1,self.iterable2,self.fill)\r\n\r\n    \r\n#Question 5\r\n    \r\ndef anagram(word):\r\n        list = []\r\n        fin = open('words.txt')\r\n        permutations_of_word = [''.join(permutation) for permutation in itertools.permutations(word)]\r\n        for line in fin:\r\n            line = line.strip()\r\n            if line in permutations_of_word:\r\n                list.append(line)\r\n        fin.close()\r\n        return sorted(list)          \r\n\r\n#Question 6                \r\nclass VariableDescriptor:\r\n\r\n\r\n    def __set__(self,instance,value):\r\n        instance._value=value\r\n        instance._history.append(value)\r\n        instance.wCount +=1\r\n\r\n    def __get__(self,instance,owner):\r\n        instance.rCount+= 1\r\n        return instance._value\r\n\r\nclass Variable:\r\n\r\n    def __init__(self,value):\r\n        self.wCount=0\r\n        self.rCount=0\r\n        self._history = []\r\n        self.value=value\r\n\r\n    def readCounter(self):\r\n        return self.rCount\r\n\r\n    def writeCounter(self):\r\n        return self.wCount\r\n\r\n    def history(self):\r\n        return self._history\r\n\r\n    value = VariableDescriptor()\r\n\r\n#  TESTS\r\n\r\nclass TestExercise2(unittest.TestCase):\r\n\r\n    def test_decorator(self):\r\n        print(sum2(1,3))\r\n        print(sum2(1,3))\r\n        print(sum2(1,4))\r\n\r\n    def test_rational(self):\r\n        half = Rational(5,10)\r\n        third = Rational(1,3)\r\n        self.assertEqual(repr(half),\"Rational(1,2)\")\r\n        self.assertEqual(str(half),\"1/2\")\r\n        self.assertEqual(half < third, False)\r\n        self.assertEqual(half <= third, False)\r\n        self.assertEqual(half > third, True)\r\n        self.assertEqual(half >= third, True)\r\n        self.assertEqual(half != third, True)\r\n        self.assertEqual(half == third, False)\r\n        half2 = Rational(10,20)\r\n        self.assertEqual(half == half2, True)\r\n        self.assertEqual(half+third, Rational(5,6))\r\n        self.assertEqual(half-third, Rational(1,6))\r\n        self.assertEqual(half*third, Rational(1,6))\r\n        self.assertEqual(half/third, Rational(3,2))\r\n\r\n\r\n    def test_myZip(self):\r\n        g = (3*i for i in range(5))\r\n        mzip = myZip(g, [\"a\", \"b\", \"c\"], fill=\"bye\")\r\n        self.assertEqual(mzip.__next__(), (0, 'a'))\r\n        self.assertEqual(mzip.__next__(), (3, 'b'))\r\n\r\n\r\n    def test_MyZip(self):\r\n        try1 = MyZip([1,2],[\"a\",\"b\",\"c\"],\"bye\")\r\n        it1 = iter(try1)\r\n        self.assertEqual(it1.__next__(),(1,\"a\"))\r\n        self.assertEqual(it1.__next__(),(2,\"b\"))\r\n        self.assertEqual(it1.__next__(),(\"bye\",\"c\"))\r\n        try2 = MyZip([1,2],[\"a\",\"b\",\"c\"])\r\n        it2 = iter(try2)\r\n        self.assertEqual(it2.__next__(),(1,\"a\"))\r\n        self.assertEqual(it2.__next__(),(2,\"b\"))        \r\n\r\n\r\n    def test_descriptor(self):\r\n        v = Variable(8)\r\n        self.assertEqual(v.history(),[8])\r\n        v.value=3\r\n        self.assertEqual(v.writeCounter(),2)\r\n        self.assertEqual(v.readCounter(),0)\r\n        self.assertEqual(v.value,3)\r\n        self.assertEqual(v.readCounter(),1)\r\n\r\n    def test_anagram(self):\r\n        self.assertEqual(anagram('restful'),['fluster', 'fluters', 'restful'])\r\n        self.assertEqual(anagram('rrao'),['orra', 'roar'])\r\n        \r\n        \r\n\r\n        \r\n        \r\n        \r\n\r\n\r\nunittest.main()   \r\n    \r\n                    \r\n                \r\n                \r\n                \r\n                \r\n                \r\n            \r\n\r\n","sub_path":"ex2 .py","file_name":"ex2 .py","file_ext":"py","file_size_in_byte":7675,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"269945081","text":"\"\"\"\r\nИмя проекта: №59\r\nНомер версии: 1.0\r\nИмя файла: practicum-1(№59).py\r\nАвтор: 2020 © Н.Д.Кислицын, Челябинск\r\nЛицензия использования:  CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/deed.ru)\r\nДата создания: 18/12/2020\r\nДата последней модификации: 18/12/2020\r\nОписание: Решение задач № 59 практикума № 1\r\nописание: Заданы M строк, которые вводятся с клавиатуры. Подсчитать количество пробелов в каждой из строк.\r\n#версия Python: 3.6\r\n\"\"\"\r\n\r\n\"\"\"\r\nОригинал публикации: Практикум №1 - набиваем руку (Бизнес-информатика 2020)\r\n\"\"\"\r\n\r\nimport re\r\nM = 2\r\n\r\nlist_strings = []\r\nfor i in range(0, M):\r\n    print(\"Введите строку:\", end=' ')\r\n    list_strings.append(input())\r\n\r\nfor string in list_strings:\r\n    count_spaces = len(re.findall(r'\\s', string))\r\n    print(\"В строке \\\"%s\\\" %s пробелов\" % (string, count_spaces))\r\n","sub_path":"№59.py","file_name":"№59.py","file_ext":"py","file_size_in_byte":1158,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"55535721","text":"#!/usr/bin/env python3\n\nfrom sys import argv\n\n'''Quick script to explain the addition of binary numbers'''\n\ndef _convert_to_binary_list(s):\n    out = []\n    for i in range(len(s)):\n        value = int(s[i])\n        if 0 <= value <= 1:\n            out.append(int(s[i]))\n        else:\n            raise ValueError('Invalid binary number!')\n    return out\n\ndef add_binary_strings(a,b,debug=False):\n    ans = []\n    digits = max(len(a),len(b))\n    a = _convert_to_binary_list(a.zfill(digits))\n    b = _convert_to_binary_list(b.zfill(digits))\n    carry = 0\n    for i in range(digits-1,-1,-1):\n        column_value = a[i] + b[i] + carry\n        if column_value == 0:\n            value = 0\n            carry = 0\n        elif column_value == 1:\n            value = 1\n            carry = 0\n        elif column_value == 2:\n            value = 0\n            carry = 1\n        elif column_value == 3:\n            value = 1\n            carry = 1\n        ans.insert(0,value)\n        if debug:\n            print('a[{0}] = {1}, b[{0}] = {2}, value = {4}, carry = {3}'.format(\n                i,a[i],b[i],carry,value))\n    if debug:\n        print('{:>36s} = {}'.format('carry',carry))\n    if carry:\n        ans.insert(0,1)\n    return ''.join([str(digit) for digit in ans])\n\n\nif __name__ == '__main__':\n    assert isinstance(add_binary_strings('1010','1101'),str), 'Answer must by of type string'\n    assert add_binary_strings('1010','1101') == '10111', 'Binary add gave incorrect answer'\n\n    a = argv[1]\n    b = argv[2]\n    pad = max(len(a),len(b)) + 1\n    try:\n        ans = add_binary_strings(a,b,True)\n        print('    A = {{:>{:d}s}}'.format(pad).format(a))\n        print('    B = {{:>{:d}s}}'.format(pad).format(b))\n        print('(A+B) = {{:>{:d}s}}'.format(pad).format(ans))\n    except ValueError as e:\n        print(e)\n","sub_path":"binaryAdd.py","file_name":"binaryAdd.py","file_ext":"py","file_size_in_byte":1813,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"93637269","text":"import os\nfrom datetime import datetime\nfrom pytz import timezone\nfrom django.db.models import Q\n\nfrom django.contrib.auth.admin import User\n\nfrom getperson.models import Timeput\nfrom getperson.myfunc import ids_list, datetimeToWeek\nfrom lish import settings\n\n\ntemp_url = \"/media/templates\"\n\n\ndef tp_to_enabletp(tps, u):\n    u_and_fr_u_ids = [i.id_friend for i in u.userfriends_set.all()]\n    u_and_fr_u_ids.append(u.id)\n    u_and_fr_us = User.objects.filter(id__in=u_and_fr_u_ids)\n    uet_ids = [i.tp_id for i in u.userenabletp_set.all()]\n\n    tp_objs = tps.filter(Q(public=\"2\") | (Q(public=\"1\") & Q(user__in=u_and_fr_us))\\\n            | Q(public=\"3\") & (Q(id__in=uet_ids) | Q(user=u)))\n\n    pub_response = tp_to_response(u, tp_objs)\n\n    response = pub_response\n    return response\n\n\n# APIのいたる所\ndef tp_to_response(u_obj, tp):\n    p_obj = u_obj.profile\n    pin_events = ids_list(p_obj.pin_event)\n    res_events = ids_list(p_obj.reserve_event)\n\n    response = []\n    fav_state = False\n    res_state = False\n\n    for i in tp:\n        is_self = False\n        if i.id in pin_events:\n            fav_state = True\n        if i.id in res_events:\n            res_state = True\n        if i.user.id == u_obj.id:\n            is_self = True\n\n        if not i.begin_time == None:\n            if not i.finish_time == None:\n                # 世界標準時刻使用\n                i.begin_time = i.begin_time.astimezone(timezone('Asia/Tokyo'))\n                i.finish_time = i.finish_time.astimezone(timezone('Asia/Tokyo'))\n                # begin_time を投稿の時間表示形式に変換して渡す\n                begin_dotw = \"({0})\".format(datetimeToWeek(i.begin_time))\n                begin_datetime = i.begin_time.strftime(\"%m月%d日\")\n                begin_time = i.begin_time.strftime(\"%H:%M\")\n                finish_dotw = \"({0})\".format(datetimeToWeek(i.finish_time))\n                finish_datetime = i.finish_time.strftime(\"%m月%d日\")\n                finish_time = i.finish_time.strftime(\"%H:%M\")\n            else:\n                # 世界標準時刻使用\n                i.begin_time = i.begin_time.astimezone(timezone('Asia/Tokyo'))\n                # begin_time を投稿の時間表示形式に変換して渡す\n                begin_dotw = \"({0})\".format(datetimeToWeek(i.begin_time))\n                begin_datetime = i.begin_time.strftime(\"%m月%d日\")\n                begin_time = i.begin_time.strftime(\"%H:%M\")\n                finish_dotw = \"\"\n                finish_datetime = \"終了時間未定\"\n                finish_time = \"\"\n        elif i.finish_time == None:\n            begin_dotw = \"\"\n            begin_datetime = \"開始時間未定\"\n            begin_time = \"\"\n    \n            finish_dotw = \"\"\n            finish_datetime = \"終了時間未定\"\n            finish_time = \"\"\n        else:\n            i.finish_time = i.finish_time.astimezone(timezone('Asia/Tokyo'))\n            begin_dotw = \"\"\n            begin_datetime = \"開始時間未定\"\n            begin_time = \"\"\n            finish_dotw = \"({0})\".format(datetimeToWeek(i.finish_time))\n            finish_datetime = i.finish_time.strftime(\"%m月%d日\")\n            finish_time = i.finish_time.strftime(\"%H:%M\")\n\n        # templatesからの画像か、ユーザ投稿の画像を使うか\n        if i.img:\n            tp_img = i.img.url\n        else:\n            tp_img = os.path.join(temp_url, \"{0}.png\".format(i.temp_flag))\n\n        response.append({\n            \"userId\": i.user.id,\n            \"username\": i.user.profile.disp_name,\n            \"profileImg\": i.user.profile.profile_img.url,\n            \"tpImg\": tp_img,\n            \"begin_time\": begin_time,\n            \"begin_datetime\": begin_datetime,\n            \"begin_dotw\": begin_dotw,\n            \"finish_time\": finish_time,\n            \"finish_datetime\": finish_datetime,\n            \"finish_dotw\": finish_dotw,\n            \"title\": i.title,\n            \"memo\": i.memo,\n            \"location\": i.place,\n            \"tpId\":i.id,\n            \"fav_state\": fav_state,\n            \"res_state\": res_state,\n            \"is_self\": is_self,\n        })\n        fav_state = False\n        res_state = False\n    return response\n","sub_path":"getperson/myfuncs/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":4177,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"314119756","text":"from ftw.solr.interfaces import ISolrSearch\nfrom opengever.base.solr.contentlisting import OGSolrContentListing  # noqa\nfrom opengever.base.solr.contentlisting import OGSolrContentListingObject  # noqa\nfrom opengever.base.solr.contentlisting import OGSolrDocument\nfrom zope.component import getUtility\n\n\ndef solr_doc_from_uuid(uuid, fields):\n    solr = getUtility(ISolrSearch)\n    resp = solr.search(filters=(\"UID:{}\".format(uuid)), fl=fields)\n    if not resp.docs:\n        return None\n    return OGSolrDocument(resp.docs[0])\n\n\ndef batched_solr_results(**kwargs):\n    \"\"\"Returns all Solr results in batches.\n    \"\"\"\n    solr = getUtility(ISolrSearch)\n    last_batch = False\n    start = 0\n    rows = kwargs.get('rows', 1000)\n    while not last_batch:\n        resp = solr.search(start=start, **kwargs)\n        yield resp.docs\n        if start + len(resp.docs) >= resp.num_found or not resp.docs:\n            last_batch = True\n        start += rows\n","sub_path":"opengever/base/solr/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":946,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"505078940","text":"# Define your item pipelines here\n#\n# Don't forget to add your pipeline to the ITEM_PIPELINES setting\n# See: https://docs.scrapy.org/en/latest/topics/item-pipeline.html\n\n\n\n\nfrom datetime import datetime\nfrom jieba import analyse\n\nimport pymysql\n\n\n\n\nclass NewschinaPipeline(object):\n     def __init__(self):\n         self.conn = pymysql.connect(\n             host='.......',\n             port=3306,\n             database='news_China',\n             user='....',\n             password='.....',\n             charset='utf8'\n         )\n         # 实例一个游标\n         self.cursor = self.conn.cursor()\n\n     def process_item(self, item, spider):\n         sql = \"\"\"\n               insert into ChinaNews(ID, 标题, 关键词, 正文, 数据来源,报道时间，抓取时间)\n                values (%s, %s, %s, %s, %s,%s, %s);\"\"\"\n\n         values = [\n             item['title'],\n             item['keywords'],\n             item['content'],\n             item['source'],\n#             item['reported_time']\n             item['time']\n         ]\n#\n         self.cursor.execute(sql, values)\n         self.conn.commit()\n\n         return item\n\n     def close_spider(self, spider):\n         self.cursor.close()\n         self.conn.close()","sub_path":"mySpider/mySpider/pipelines.py","file_name":"pipelines.py","file_ext":"py","file_size_in_byte":1229,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"162848537","text":"import unittest\nfrom apple import *\n\nclass ValidateAppleClass(unittest.TestCase):\n    \"\"\"Tests for `apple.py`.\"\"\"\n\n    def test_apple_diameter(self):\n        \"\"\"When you create an Apple, it will have a diameter greater than 0\"\"\"\n        apple = Apple()\n        self.assertGreater(apple.diameter, 0)\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"apple_spec.py","file_name":"apple_spec.py","file_ext":"py","file_size_in_byte":347,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"205558593","text":"from django.shortcuts import get_object_or_404, render\r\nfrom django.http import HttpResponseRedirect\r\nfrom django.core.urlresolvers import reverse\r\n\r\nfrom .models import Choice\r\n\r\n\r\n# Create your views here.\r\nfrom .models import Question\r\n\r\n\r\ndef index(request):\r\n    latest_question_list = Question.objects.order_by('-pub_date')[:5]\r\n    context = {'latest_question_list': latest_question_list}\r\n    return render(request, 'polls/index.html', context)\r\n\r\n\r\ndef detail(request, poll_id):\r\n    context = get_object_or_404(Question, pk=poll_id)\r\n    return render(request, 'polls/detail.html', {'question': context})\r\n\r\n\r\ndef results(request, poll_id):\r\n    poll = get_object_or_404(Question, pk=poll_id)\r\n    return render(request, 'polls/results.html', {'poll': poll})\r\n\r\n\r\ndef vote(request, poll_id):\r\n    p = get_object_or_404(Question, pk=poll_id)\r\n    try:\r\n        selected_choice = p.choice_set.get(pk=request.POST['choice'])\r\n    except (KeyError, Choice.DoesNotExist):\r\n        # Redisplay the question voting form.\r\n        return render(request, 'polls/detail.html', {\r\n            'question': p,\r\n            'error_message': \"You didn't select a choice.\",\r\n        })\r\n    else:\r\n        selected_choice.votes += 1\r\n        selected_choice.save()\r\n        return HttpResponseRedirect(reverse('polls:results', args=(p.id,)))\r\n\r\n","sub_path":"mysite/polls/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1339,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"201287140","text":"\n\n#\n# READ a CSV em python\n#\n#\nimport csv \nimport sys\n \nf = open('/home/jadson/Documentos/temp_files/test.csv', 'rt')\nreader = csv.reader(f, delimiter=';')\nfor row in reader:\n\tprint ('row:')\n\tprint (row)\n\tprint ('--- coluns ---')\n\tfor col in row:\n\t\tprint (col)\n\n\tprint ('--- read especific column number ---')\t\n\tcolnum = 0\t\n\tfor col in row:\n\t\tprint ('reading colum '+str(colnum)+': '+row[colnum])\n\t\tcolnum += 1\t\n \nf.close()","sub_path":"python/read_csv.py","file_name":"read_csv.py","file_ext":"py","file_size_in_byte":423,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"342044175","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\n# log_format ui_short '$remote_addr $remote_user $http_x_real_ip [$time_local] \"$request\" '\n#                     '$status $body_bytes_sent \"$http_referer\" '\n#                     '\"$http_user_agent\" \"$http_x_forwarded_for\" \"$http_X_REQUEST_ID\" \"$http_X_RB_USER\" '\n#                     '$request_time';\n\nimport argparse\nfrom datetime import datetime\nimport gzip\nimport json\nimport logging\nimport os\nimport re\nimport sys\n\nconfig = {\n    \"REPORT_SIZE\": 1000,\n    \"REPORT_DIR\": \"./reports\",\n    \"LOG_DIR\": \"./log\",\n    \"SELF_LOG_FILE\": \"./tmp/log_analyzer.log\",\n    \"TS_FILE\": \"./tmp/log_analyzer.ts\",\n    \"REPORT_TEMPLATE\": \"./reports/report.html\"\n}\n\n\ndef merge_configs(config, config_file):\n\n    try:\n        if os.stat(config_file).st_size == 0:\n            return config\n        with open(config_file) as f:\n            conf_from_file = json.load(f)\n    except:\n        logging.error(\"Error reading config file\")\n        sys.exit(1)\n\n    for key in conf_from_file.keys():\n        config[key] = conf_from_file[key]\n\n    return config\n\n\ndef choose_log_file(log_dir, ts_file):\n    LOG_SAMPLE = \"nginx-access-ui.log-\"\n    comp = re.compile(r'%s[0-9]{8}' % LOG_SAMPLE)\n    max_date = datetime.strptime(\"19010101\", \"%Y%m%d\")\n\n    for f in os.listdir(log_dir):\n        tmp = comp.match(f)\n        if tmp is not None:\n            date = datetime.strptime(tmp.string[len(LOG_SAMPLE):len(LOG_SAMPLE)+8], \"%Y%m%d\")\n            max_date = date if date > max_date else max_date\n\n    if max_date == datetime.strptime(\"19010101\", \"%Y%m%d\"):\n        logging.info(\"Nothing to parse: exiting\")\n        return None, None\n\n    if LOG_SAMPLE + max_date.strftime(\"%Y%m%d\") + \".gz\" in os.listdir(log_dir):\n        log_file = LOG_SAMPLE + max_date.strftime(\"%Y%m%d\") + \".gz\"\n    else:\n        log_file = LOG_SAMPLE + max_date.strftime(\"%Y%m%d\")\n\n    mtime = os.path.getmtime(log_dir + \"/\" + log_file)\n\n    try:\n        with open(ts_file) as f:\n            ts = f.readline()\n            ts_mtime = float(ts)\n    except:\n        return log_file, max_date.strftime(\"%Y%m%d\")\n\n    if ts_mtime > mtime:\n        logging.info(\"Nothing to parse: exiting\")\n        return None, None\n    else:\n        return log_file, max_date.strftime(\"%Y%m%d\")\n\n\ndef parse_log(log_file, log_dir, report_size):\n\n    def parse_line(line):\n\n        try:\n            tmp_line = line.split('] \"')[1]\n            '''\n            # tmp_line =  GET /api/v2/internal/banner/24288647/info HTTP/1.1\"\n             200 351 \"-\" \"-\" \"-\" \"1498697423-2539198130-4708-9752780\"\n             \"89f7f1be37d\" 0.072\n            '''\n            request_time = float(tmp_line.split('\" ')[-1])\n            request = tmp_line.split('\" ')[0]\n            url = str(request.split(' ')[1])\n\n            return url, request_time\n\n        except:\n            return None, None\n\n    def calc_stats(numbers):\n        numbers.sort()\n        count = len(numbers)\n        time_sum = 0\n        for n in numbers:\n            time_sum += n\n        time_avg = time_sum*1.0/count\n        time_max = numbers[-1]\n        time_med = numbers[int(count/2)]\n\n        return count, \\\n               round(time_sum, 3), \\\n               round(time_avg, 3), \\\n               round(time_max, 3), \\\n               round(time_med, 3)\n\n    try:\n        log_path = log_dir+'/'+log_file\n        if log_path.endswith(\".gz\"):\n            f = gzip.open(log_path, 'rt')\n        else:\n            f = open(log_path)\n    except:\n        logging.error(\"Error opening log file\")\n        sys.exit(1)\n\n    ERROR_LEVEL = 0.1  # acceptable error level during log parsing\n    total_requests = 0\n    total_time = 0\n    lines_processed = 0\n    errors = 0\n    raw_data = {}\n\n    for line in f:\n        url, time = parse_line(line)\n        if url is None or time is None:\n            errors += 1\n            if lines_processed > 100 and \\\n               errors*1.0/lines_processed > ERROR_LEVEL:  # UGLY, just for PEP8\n                logging.error(\"Too much errors, during parsing log file\")\n                sys.exit(1)\n        else:\n            total_requests += 1\n            total_time += time\n            if url not in raw_data.keys():\n                raw_data[url] = []\n            raw_data[url].append(time)\n    f.close()\n\n    statistics = []\n    time_sums = []\n\n    for k in raw_data.keys():\n        d = {}\n        d['url'] = k\n        d['count'], d['time_sum'], d['time_avg'], d['time_max'], d['time_med'] = calc_stats(raw_data[k])\n        d['count_perc'] = round(d['count']*100/total_requests, 3)\n        d['time_perc'] = round(d['time_sum']*100/total_requests, 3)\n        time_sums.append(d['time_sum'])\n        statistics.append(d)\n    if len(statistics) < report_size:\n        return statistics\n    else:\n        time_sums.sort(reverse=True)\n        timesum_border = time_sums[report_size]\n\n        result = []\n        for s in statistics:\n            if s['time_sum'] >= timesum_border:\n                result.append(s)\n        return result\n\n\ndef create_report(report_dir, template, date, statistics):\n\n    try:\n        with open(template) as f:\n            html = f.read()\n    except:\n        logging.error(\"Error reading report template file\")\n        sys.exit(1)\n\n    html = html.replace('$table_json', str(statistics))\n    outfile_path = report_dir + \"/report-\" + date + \".html\"\n\n    try:\n        with open(outfile_path, \"w\") as f:\n            f.write(html)\n    except:\n        logging.error(\"Error writing report file\")\n        sys.exit(1)\n\n    return outfile_path\n\n\ndef update_ts(ts_file, time):\n    try:\n        with open(ts_file, \"w\") as f:\n            f.write(str(time))\n    except:\n        logging.error(\"Error writing timestamp to file\")\n        sys.exit(1)\n\n\ndef main():\n    '''\n    1. Read config file\n        1.1 if fail - exit with error\n        1.2 else - merge with config var\n    2. Get log file\n        2.1 if fail - exit\n        2.2 else - pass to parser\n    3. Parse file, count statistics, including error stat\n        3.1 if error level > acceptable - exit\n    4. Generate html report\n    5. Update/create ts-file\n    '''\n\n    parser = argparse.ArgumentParser(description='Log Analyzer')\n    parser.add_argument('-c', '--config',\n                        dest='config',\n                        default=\"./config/log_analyzer.conf\",\n                        required=False,\n                        help='Path to config file')\n    args = parser.parse_args()\n\n    conf = merge_configs(config, args.config)\n\n    if \"SELF_LOG_FILE\" not in conf.keys():\n        conf[\"SELF_LOG_FILE\"] = None\n    else:\n        try:\n            f = open(conf[\"SELF_LOG_FILE\"], 'r')\n            f.close()\n        except IOError:\n            f = open(conf[\"SELF_LOG_FILE\"], 'w')\n            f.close()\n\n    logging.basicConfig(format='[%(asctime)s] %(levelname).1s %(message)s',\n                        datefmt='%Y.%m.%d %H:%m:%S',\n                        filename=conf[\"SELF_LOG_FILE\"])\n\n    log_file, date = choose_log_file(conf[\"LOG_DIR\"], conf[\"TS_FILE\"])\n\n    if log_file is None:\n        return 0\n\n    statistics = parse_log(log_file, conf[\"LOG_DIR\"], conf[\"REPORT_SIZE\"])\n\n    report_file = create_report(conf[\"REPORT_DIR\"],\n                                conf[\"REPORT_TEMPLATE\"],\n                                date,\n                                statistics)\n\n    mtime = os.path.getmtime(report_file)\n    update_ts(conf[\"TS_FILE\"], mtime)\n\n    logging.info(\"Report file: %s succcessfully created\" % report_file)\n    return 0\n\nif __name__ == \"__main__\":\n\n    logging.basicConfig(format='[%(asctime)s] %(levelname).1s %(message)s',\n                        datefmt='%Y.%m.%d %H:%m:%S',\n                        filename=config[\"SELF_LOG_FILE\"])\n\n    try:\n        main()\n    except:\n        logging.exception(\"Unexpected exception occured\")\n        sys.exit(1)\n","sub_path":"hw1/log_analyzer/log_analyzer.py","file_name":"log_analyzer.py","file_ext":"py","file_size_in_byte":7802,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"303423148","text":"from ml import NLOneNeuron, NLTwoLayer, NLThreeLayer\nfrom ml import case1\nimport numpy as np\n\n\ndef main():\n    training_inputs = np.array([[0, 0, 1],\n                                [1, 1, 1],\n                                [1, 0, 1],\n                                [0, 1, 1]])\n    training_outputs = np.array([[0, 1, 1, 0]]).T\n    my_nl_one = NLOneNeuron(training_inputs=training_inputs, training_outputs=training_outputs)\n    new_inputs = np.array([0, 0, 1])  # new situation\n    my_nl_one.calculate(input_values=new_inputs)\n\n    training_inputs = np.array([[0, 0, 1],\n                                [1, 1, 1],\n                                [1, 0, 1],\n                                [0, 0, 1]])\n    training_outputs = np.array([[0, 1, 1, 0]]).T\n\n    my_nl_two = NLTwoLayer(training_inputs=training_inputs, training_outputs=training_outputs, hidden_neurons=4)\n    my_nl_two.calculate(input_values=new_inputs)\n\n    my_nl_three = NLThreeLayer(training_inputs=training_inputs, training_outputs=training_outputs,\n                               first_layer_neurons=4, second_layer_neurons=4)\n    my_nl_three.calculate(input_values=new_inputs)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1185,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"428901156","text":"# 使用python 操作mongodb\n# 因为mongodb 是面向对象编程,所以很友好\nimport pymongo\n# api 文档地址 https://pymongo.readthedocs.io/en/stable/api/pymongo/collection.html\n# create to mongoclinet\n# 1:链接数据库\nclient=pymongo.MongoClient(host='192.168.5.147',port=27017)\n#2:创建数据库/指定数据库\nmydb=client['lktbz']\n\n# 判断创建的数据库是否存在\n# fordbs=client.list_database_names()\n# if 'lktbz' in fordbs:\n#     print(\"数据存在\")\n\n#指定集合\ncollection=mydb.students\n#准备插入数据\nstudent={\n    'id': '20170101',\n    'name': 'Jordan',\n    'age': 20,\n    'gender': 'male'\n}\n\n# result=collection.insert(student)\n# print(result)\n\n# 查询\nresult=collection.find_one({'id':201702023})\nprint(result)\n\n# 多次插入\nstudent_list=[]\n\nfor i in range(1,10):\n        student2 = {\n         'id': '20170202'+str(i),\n         'name': 'Mike',\n         'age': 21+i,\n         'gender': 'male'\n        }\n        student_list.append(student2)\n\n    # result=collection.insert_many(student_list)\n    # print(result)\n\n    #查询全部\n    # result=collection.find()\n    # for x in result:\n    #     print(x['id'])\n\n\n# 查询特定的字段\n# try:\n#     result=collection.find({'age',20})\n#     for res in result:\n#       print(res)\n# except:\n#     print(\"没有找到合适的数据\")\n\n# 查询大于20的\n# try:\n#     myquery = {\"age\":{\"$gt\":22}}\n#     result=collection.find(myquery)\n#     for res in result:\n#       print(res)\n# except:\n#     print(\"没有找到合适的数据\")\n\n","sub_path":"01improve/16mangodb学习.py","file_name":"16mangodb学习.py","file_ext":"py","file_size_in_byte":1520,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"273141264","text":"import sys\nimport os\n\nfrom collections import Counter\nfrom collections import defaultdict\n\n\ndef get_pics_per_tags(pics):\n    \"\"\"\n    Format the dict of pics to a dict with tags as keys and set of Picture as values\n    \"\"\"\n    pictures = pics.values()\n    pictures_per_tags = defaultdict(set)\n\n    for pic in pictures:\n        for tag in pic.tags:\n            pictures_per_tags[tag].add(pic.id)\n\n    # From default dict to dict\n    pictures_per_tags = dict(pictures_per_tags)\n\n    return pictures_per_tags\n\n\nif __name__ == '__main__':\n\n    from config import INPUT_DIRECTORY\n    from config import COUNTER_DIRECTORY\n\n    from InputReader import InputReader\n\n\n    def write_counter(counter, filename):\n        with open(filename, 'w') as f:\n            f.write('%s %s\\n' % (len(counter), sum(counter.values())))\n            for tag, count in counter.most_common():\n                f.write('%s %s\\n' % (tag, count))\n\n\n    # Get input file from command line arg list\n    input_file = sys.argv[1]\n    file_path = os.path.join(INPUT_DIRECTORY, input_file)\n    assert os.path.exists(file_path)\n\n    # Get objects from input file\n    inputReader = InputReader(file_path)\n\n    counter = Counter()\n    for i, pic in inputReader.photos.items():\n        current_counter = Counter(pic.tags)\n        counter.update(current_counter)\n\n    # Save result to output file\n    output_file = os.path.splitext(input_file)[0] + '.count'\n    out_path = os.path.join(COUNTER_DIRECTORY, output_file)\n\n    write_counter(counter, out_path)\n","sub_path":"count.py","file_name":"count.py","file_ext":"py","file_size_in_byte":1511,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"217281968","text":"def solution(rows, columns, queries):\n    field = [[(c + 1) + r * columns for c in range(columns)] for r in range(rows)]\n    answer = []\n\n    for query in queries:\n        y1, x1, y2, x2 = map(lambda x: x - 1, query)\n        min_val = 0xffffff\n        idx_list = []\n        val_list = []\n        for a in range(x1, x2 + 1):\n            idx_list.append((y1, a))\n            val_list.append(field[y1][a])\n            if min_val > field[y1][a]:\n                min_val = field[y1][a]\n        for b in range(y1 + 1, y2 + 1):\n            idx_list.append((b, x2))\n            val_list.append(field[b][x2])\n            if min_val > field[b][x2]:\n                min_val = field[b][x2]\n        for c in range(x2 - 1, x1 - 1, -1):\n            idx_list.append((y2, c))\n            val_list.append(field[y2][c])\n            if min_val > field[y2][c]:\n                min_val = field[y2][c]\n        for d in range(y2 - 1, y1, -1):\n            idx_list.append((d, x1))\n            val_list.append(field[d][x1])\n            if min_val > field[d][x1]:\n                min_val = field[d][x1]\n\n        for i in range(len(idx_list)):\n            c_y, c_x = idx_list[i]\n            field[c_y][c_x] = val_list[i - 1]\n\n        answer.append(min_val)\n\n    return answer","sub_path":"정준현/12_프로그래머스_1/행렬 테두리 회전하기.py","file_name":"행렬 테두리 회전하기.py","file_ext":"py","file_size_in_byte":1247,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"267178035","text":"from pwn import *\r\n\r\np = process('./pwn4')\r\ne = ELF('./pwn4')\r\n\r\nsystem = e.plt['system']\r\nbinsh = 0x0804a038\r\n\r\npayload = 'A' * 32\r\n\r\npayload += p32(system)\r\npayload += 'AAAA'\r\npayload += p32(binsh)\r\npayload += '\\n'\r\n\r\np.send(payload)\r\n\r\np.interactive()\r\n","sub_path":"tamu_pwn4exp.py","file_name":"tamu_pwn4exp.py","file_ext":"py","file_size_in_byte":256,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"258218342","text":"# Copyright (c) 2014-present PlatformIO <contact@platformio.org>\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#    http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom platformio.package.exception import UnknownPackageError\nfrom platformio.package.meta import PackageSpec\n\n\nclass PlatformPackagesMixin(object):\n    def get_package_spec(self, name, version=None):\n        return PackageSpec(\n            owner=self.packages[name].get(\"owner\"),\n            name=name,\n            requirements=version or self.packages[name].get(\"version\"),\n        )\n\n    def get_package(self, name, spec=None):\n        if not name:\n            return None\n        return self.pm.get_package(spec or self.get_package_spec(name))\n\n    def get_package_dir(self, name):\n        pkg = self.get_package(name)\n        return pkg.path if pkg else None\n\n    def get_package_version(self, name):\n        pkg = self.get_package(name)\n        return str(pkg.metadata.version) if pkg else None\n\n    def get_installed_packages(self, with_optional=False):\n        result = []\n        for name, options in self.packages.items():\n            versions = [options.get(\"version\")]\n            if with_optional:\n                versions.extend(options.get(\"optionalVersions\", []))\n            for version in versions:\n                if not version:\n                    continue\n                pkg = self.get_package(name, self.get_package_spec(name, version))\n                if pkg:\n                    result.append(pkg)\n        return result\n\n    def dump_used_packages(self):\n        result = []\n        for name, options in self.packages.items():\n            if options.get(\"optional\"):\n                continue\n            pkg = self.get_package(name)\n            if not pkg or not pkg.metadata:\n                continue\n            item = {\"name\": pkg.metadata.name, \"version\": str(pkg.metadata.version)}\n            if pkg.metadata.spec.external:\n                item[\"src_url\"] = pkg.metadata.spec.url\n            result.append(item)\n        return result\n\n    def autoinstall_runtime_packages(self):\n        for name, options in self.packages.items():\n            if options.get(\"optional\", False):\n                continue\n            if self.get_package(name):\n                continue\n            self.pm.install(self.get_package_spec(name))\n        return True\n\n    def install_packages(  # pylint: disable=too-many-arguments\n        self,\n        with_packages=None,\n        without_packages=None,\n        skip_default_package=False,\n        silent=False,\n        force=False,\n    ):\n        with_packages = set(self._find_pkg_names(with_packages or []))\n        without_packages = set(self._find_pkg_names(without_packages or []))\n\n        upkgs = with_packages | without_packages\n        ppkgs = set(self.packages)\n        if not upkgs.issubset(ppkgs):\n            raise UnknownPackageError(\", \".join(upkgs - ppkgs))\n\n        for name, options in self.packages.items():\n            if name in without_packages:\n                continue\n            if name in with_packages or not (\n                skip_default_package or options.get(\"optional\", False)\n            ):\n                self.pm.install(self.get_package_spec(name), silent=silent, force=force)\n\n        return True\n\n    def _find_pkg_names(self, candidates):\n        result = []\n        for candidate in candidates:\n            found = False\n\n            # lookup by package types\n            for _name, _opts in self.packages.items():\n                if _opts.get(\"type\") == candidate:\n                    result.append(_name)\n                    found = True\n\n            if (\n                self.frameworks\n                and candidate.startswith(\"framework-\")\n                and candidate[10:] in self.frameworks\n            ):\n                result.append(self.frameworks[candidate[10:]][\"package\"])\n                found = True\n\n            if not found:\n                result.append(candidate)\n\n        return result\n\n    def update_packages(self, only_check=False):\n        for pkg in self.get_installed_packages():\n            self.pm.update(\n                pkg,\n                to_spec=self.get_package_spec(pkg.metadata.name),\n                only_check=only_check,\n                show_incompatible=False,\n            )\n\n    def are_outdated_packages(self):\n        for pkg in self.get_installed_packages():\n            if self.pm.outdated(\n                pkg, self.get_package_spec(pkg.metadata.name)\n            ).is_outdated(allow_incompatible=False):\n                return True\n        return False\n","sub_path":"platformio/platform/_packages.py","file_name":"_packages.py","file_ext":"py","file_size_in_byte":5014,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"93911911","text":"# -*- coding: utf-8 -*-\n\n\nimport os\nimport sys\nimport simplejson as json\nfrom jinja2 import Environment, FileSystemLoader\n\n\ndef write_file(rendered, filename):\n    with open(filename, 'wb') as f:\n        f.write(rendered)\n\n\ndef gen_spider(kwargs):\n    # validate parameters\n    assert 'class_name' in kwargs\n    assert 'spider_name' in kwargs\n    assert 'domains' in kwargs\n    assert 'start_urls' in kwargs\n    assert 'rules' in kwargs\n    assert 'fields' in kwargs\n    assert 'producer_name' in kwargs\n    assert 'brand_name' in kwargs\n    assert not kwargs['domains'] is None\n    assert not kwargs['domains'] == []\n    assert not kwargs['start_urls'] is None\n    assert not kwargs['start_urls'] == []\n    assert not kwargs['rules'] is None\n    assert not kwargs['rules'] == []\n    assert not kwargs['fields'] is None\n    assert not kwargs['fields'] == []\n\n    env = Environment(\n        autoescape=False,\n        loader=FileSystemLoader(\n            os.path.join(os.path.dirname(__file__), 'templates')))\n\n    tmpl = env.get_template('spider.html')\n    rendered = tmpl.render(**kwargs)\n    write_file(rendered, ''.join([kwargs['spider_name'], '.py']))\n\n\nif __name__ == '__main__':\n    conf_files = sys.argv[1:]\n    confs = list()\n\n    for conf_file in conf_files:\n        with open(conf_file, 'r') as conf:\n            con_str = conf.read()\n            confs.append(json.loads(con_str))\n\n    for conf in confs:\n        gen_spider(conf)\n","sub_path":"spider_gen.py","file_name":"spider_gen.py","file_ext":"py","file_size_in_byte":1439,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"529288627","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar 12 13:55:06 2019\n\n@author: Nout\n\"\"\"\nimport numpy as np\ndef Flightdata():\n    #### RETURNS ALL ANGLE DATA IN RADIANS ####\n    import csv\n    with open('flightdata.txt', 'rt') as f:\n      reader = csv.reader(f, delimiter=',', skipinitialspace=True)\n      lineData = list()\n      cols = next(reader)\n      for col in cols:\n        # Create a list in lineData for each column of data.\n        lineData.append(list())\n    \n      for line in reader:\n        for i in range(0, len(lineData)):\n          # Copy the data from the line into the correct columns.\n          lineData[i].append(line[i])\n    \n      data = dict()\n      for i in range(0, len(cols)):\n        # Create each key in the dict with the data in its column.\n        data[cols[i]] = lineData[i]\n        \n        \n    # store all parameters in lists \n    AOA_deg = np.array(data[\"AOA_deg\"])\n    elevator_dte_deg = np.array(data[\"elevator_dte_deg\"])\n    column_fe_N = np.array(data[\"column_fe_N\"]) \n    FF_L = np.array(data[\"FF_L_lbs\"])\n    FF_R = np.array(data[\"FF_R_lbs\"])\n    aileron_def_deg = np.array(data[\"aileron_def_deg\"])\n    elevator_def_deg = np.array(data[\"elevator_def_deg\"])\n    rudder_def_deg = np.array(data[\"rudder_def_deg\"])\n    roll_angle_deg = np.array(data[\"roll_angle_deg\"])\n    pitch_angle_deg = data[\"pitch_angle_deg\"]\n    stat_airtemp_C = np.array(data[\"static_airtemp_C\"])\n    pressure_altitude = np.array(data[\"pressure_altitude_ft\"])\n    mach = np.array(data[\"mach_number\"])\n    cas_airspeed = np.array(data[\"cas_airspeed_kts\"])\n    tas_airspeed_kts = np.array(data[\"tas_airspeed_kts\"])\n    time_sec = data[\"time_sec\"]\n    roll_rate_deg_s = np.array(data[\"roll_rate\"])\n    pitch_rate_deg_s = np.array(data[\"pitch_rate\"])\n    yaw_rate_deg_s = np.array(data[\"yaw_rate\"])\n    delta_e_deg = np.array(data[\"delta_e\"])\n    delta_a_deg = np.array(data[\"delta_a\"])\n    delta_r_deg = np.array(data[\"delta_r\"])\n    F_used_L_lbs = np.array(data[\"F_used_L_lbs\"])\n    F_used_R_lbs = np.array(data[\"F_used_R_lbs\"])\n\n    \n    #### MAKE DATA INTO FLOATS AND CONVERSE THEM TO SI UNITS & RADIANS     \n    AOA = []\n    elevator_dte = []\n    Fe_N = []\n    FF_L_lbs = []\n    FF_R_lbs = []\n    aileron_def = []\n    elevator_def = []\n    rudder_def = []\n    roll_angle = []\n    pitch_angle =[]\n    static_airtemp_C = []\n    pressure_altitude_ft = []\n    mach_number = []\n    cas_airspeed_kts = []\n    Vtrue =[]\n    time = []\n    roll_rate = []\n    pitch_rate = []\n    yaw_rate = []\n    delta_e = []\n    delta_a = []\n    delta_r = []\n    F_used  = []\n    \n    ### Convert strings to floats and put them in an list to transform thes as an array###\n    for i in range(len(AOA_deg)):        \n        AOA.append(float(AOA_deg[i])*np.pi/180)                         #[rad]\n        elevator_dte.append(float(elevator_dte_deg[i])*np.pi/180.)      #[rad]\n        Fe_N.append(float(column_fe_N[i]))                              #[N]\n        FF_L_lbs.append(float(FF_L[i]))                                 #[lbs] not converted as this will be done in floyds program\n        FF_R_lbs.append(float(FF_R[i]))                                 #[lbs] not converted as this will be done in floyds program\n        aileron_def.append(float(aileron_def_deg[i])*np.pi/180.)        #[rad]\n        elevator_def.append(float(elevator_def_deg[i])*np.pi/180.)      #[rad]\n        rudder_def.append(float(rudder_def_deg[i])*np.pi/180.)          #[rad]\n        roll_angle.append(float(roll_angle_deg[i])*np.pi/180.)          #[rad]\n        pitch_angle.append(float(pitch_angle_deg[i])*np.pi/180.)        #[rad]\n        static_airtemp_C.append(float(stat_airtemp_C[i]))               #[degree C]\n        pressure_altitude_ft.append(float(pressure_altitude[i]))        #[ft]not converted to [m] as this will be done in floyd's program\n        mach_number.append(float(mach[i]))                              #[-]\n        cas_airspeed_kts.append(float(cas_airspeed[i]))                 #[kts]not converted to [m/s] as this will be done in floyd's program\n        Vtrue.append(float(tas_airspeed_kts[i])*0.51444444444)          #[m/s]\n        time.append(float(time_sec[i]))                                 #[sec]\n        roll_rate.append(float(roll_rate_deg_s[i])*np.pi/180.)          #[rad/s]\n        pitch_rate.append(float(pitch_rate_deg_s[i])*np.pi/180.)        #[rad/s]\n        yaw_rate.append(float(yaw_rate_deg_s[i])*np.pi/180.)            #[rad/s]\n        delta_e.append(float(delta_e_deg[i])*np.pi/180.)                #[rad]\n        delta_a.append(float(delta_a_deg[i])*np.pi/180.)                #[rad]\n        delta_r.append(float(delta_r_deg[i])*np.pi/180.)                #[rad]\n        F_used.append((float(F_used_L_lbs[i]) + float(F_used_R_lbs[i]))*0.45359237)      #[kg]\n     \n        \n    AOA = np.array(AOA)\n    elevator_dte =np.array(elevator_dte)\n    Fe_N = np.array(Fe_N)\n    FF_L_lbs = np.array(FF_L_lbs)\n    FF_R_lbs = np.array(FF_R_lbs)\n    aileron_def = np.array(aileron_def)\n    elevator_def = np.array(elevator_def)\n    rudder_def = np.array(rudder_def)\n    roll_angle = np.array(roll_angle)\n    pitch_angle =np.array(pitch_angle)\n    static_airtemp_C = np.array(static_airtemp_C)\n    pressure_altitude_ft = np.array(pressure_altitude_ft)\n    mach_number = np.array(mach_number)\n    cas_airspeed_kts = np.array(cas_airspeed_kts)\n    Vtrue = np.array(Vtrue)\n    time = np.array(time)\n    roll_rate = np.array(roll_rate)\n    pitch_rate = np.array(pitch_rate)\n    yaw_rate = np.array(yaw_rate)\n    delta_e = np.array(delta_e)\n    delta_a = np.array(delta_a)\n    delta_r = np.array(delta_r)\n    F_used = np.array(F_used)\n    \n    #### RETURNS ALL DATA IN SI UNITS AND RADIANS Except for few parameters ####\n    return AOA,elevator_dte,Fe_N,FF_L_lbs, FF_R_lbs,aileron_def,elevator_def,rudder_def,roll_angle,pitch_angle,static_airtemp_C,pressure_altitude_ft,mach_number,cas_airspeed_kts,Vtrue,time,roll_rate,pitch_rate,yaw_rate,delta_e,delta_a,delta_r, F_used\n\n\n\n\n\n\n\n\n\n    \n\n","sub_path":"SVV_FlightDynamics_Python/SVV_FlightdataImporter.py","file_name":"SVV_FlightdataImporter.py","file_ext":"py","file_size_in_byte":5973,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"587352238","text":"'''\nObserver pattern lab\n\"Lab: Watching a File Change Size\"\n\nFrom Aaron Maxwell's awesome class \"Python: Beyond Basics\"\n'''\n\nimport os\nimport time\n\nclass FileWatcher(object):\n    def __init__(self, path_of_file_to_watch):\n        self.path = path_of_file_to_watch\n        self.observers_for_channel = {\n            'increase': set(),\n            'decrease': set(),\n            }\n        self._last_size = 0\n\n    def register(self, channel, observer):\n        self.observers_for_channel[channel].add(observer)\n    def unregister(self, channel, observer):\n        self.observers_for_channel[channel].discard(observer)\n\n    def check_forever(self):\n        while True:\n            self.check_file()\n            time.sleep(0.1)\n\n    def check_file(self):\n        size = os.stat(self.path).st_size\n        if size != self._last_size:\n            if size > self._last_size:\n                channel = 'increase'\n            else:\n                channel = 'decrease'\n            self._last_size = size\n            self.dispatch(channel, size)\n\n    def dispatch(self, channel, size):\n        for observer in self.observers_for_channel[channel]:\n            observer.update(size)\n\nclass FileObserver(object):\n    def __init__(self, name):\n        self.name = name\n    def update(self, size):\n        print('{} noticed that the file is now {} bytes'.format(self.name, size))\n\nbob = FileObserver('Bob')\njohn = FileObserver('John')\nstacy = FileObserver('Stacy')\n\nwatcher = FileWatcher('data/watched.txt')\nwatcher.register('increase', bob)\nwatcher.register('decrease', john)\nwatcher.register('increase', stacy)\nwatcher.register('decrease', stacy)\n\nwatcher.check_forever()\n\n","sub_path":"myapps/serving/watcher/watcher.py","file_name":"watcher.py","file_ext":"py","file_size_in_byte":1660,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"306908025","text":"year = 2008\ntransmission = 'automatic'\nstate = 'OK'\ncolor = 'silver'\nmiles = 85000\nmpg = 22\naccident = 'no'\n\n# Criterion a\nnot (state=='IL' or state=='MN' or state=='WI')\n# Criterion b\n(miles < 80000) and (mpg > 24)\n# Criterion c\n(color == 'silver' or color == 'white') and (transmission == 'automatic')\n# Criterion d\naccident == 'no' and year >= 2010\n","sub_path":"LogicalOp/UsedCar.py","file_name":"UsedCar.py","file_ext":"py","file_size_in_byte":352,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"318251077","text":"# -*- coding: utf-8 -*-\n##############################################################################\n#\n#    OpenERP, Open Source Management Solution\n#    Copyright (C) 2014-Today BrowseInfo (<http://www.browseinfo.in>).\n#\n#    This program is free software: you can redistribute it and/or modify\n#    it under the terms of the GNU Affero General Public License as\n#    published by the Free Software Foundation, either version 3 of the\n#    License, or (at your option) any later version.\n#\n#    This program is distributed in the hope that it will be useful,\n#    but WITHOUT ANY WARRANTY; without even the implied warranty of\n#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n#    GNU Affero General Public License for more details.\n#\n#    You should have received a copy of the GNU Affero General Public License\n#    along with this program.  If not, see <http://www.gnu.org/licenses/>.\n#\n##############################################################################\n\nimport time\nfrom openerp.osv import osv\nfrom openerp.tools.translate import _\nfrom openerp.report import report_sxw\nfrom openerp.tools import ustr\nfrom openerp.tools.amount_to_text_en import amount_to_text\nimport util\nfrom datetime import datetime\n#from num2words import num2words\nfrom openerp.tools import amount_to_text_en\nfrom openerp.modules.module import get_module_resource\n\n\nclass invoice_custom(report_sxw.rml_parse):\n\n\n    def __init__(self, cr, uid, name, context=None):\n        super(invoice_custom, self).__init__(cr, uid, name, context=context)\n        self.init_bal_sum = 0.0\n        self.ucluler=[\"\",\"bin\",\"milyon\",\"milyar\",\"trilyon\",\"katrilyon\",\"kentilyon\",\n             \"sekstilyon\",\"oktilyon\",\"nonilyon\",\"desilyon\"]\n        self.localcontext.update({\n\n            'time': time,\n            'convert': self.convert,\n            'get_date':self.get_date,\n            })\n\n\n    def get_date(self,date):\n        if date:\n            date_list = date.split(' ')\n            date1 = datetime.strptime(date_list[0], '%Y-%m-%d')\n            date1 = date1.strftime('%d-%b-%y')\n            return date1\n\n    def convert(self, sayi, currency):\n        sayi =  str(sayi).split('.')\n        translation1 = self.return_number(float(sayi[0]))\n        if sayi[1] == '00':\n            translation2 = ustr('sıfır‏')\n        else:\n            translation2 = self.return_number(float(sayi[1]))\n        translation = translation1 + translation2\n        if currency.name == 'EUR':\n            translation = translation1 + 'AVRO' + translation2 + 'SENT'\n        if currency.name == 'USD':\n            translation = translation1 + 'DOLAR' + translation2 + 'SENT'\n        if currency.name == 'TRY':\n            translation = translation1 + 'TL' + translation2 + 'KR'\n        return translation\n\n    def ucluyuVer(self,sayi):\n        birler = [\"\",\"bir\",\"iki\",ustr(\"üç\"),ustr(\"dört\"),\"bes\",\"alti\",\"yedi\",\"sekiz\",\"dokuz\"]\n        onlar = [\"\",\"on\",\"yirmi\",\"otuz\",\"kirk\",\"elli\",ustr(\"altmış‏\"),\"yetmis\",\"seksen\",\"doksan\"]\n        yuzler = [i+ustr(\"yüz\") for i in birler]\n        yuzler[1] = ustr(\"yüz\")\n\n        basamaklar = [birler,onlar,yuzler]\n\n        sayi = sayi[::-1]\n        yazi,bs = [],0\n        for i in sayi:\n            rakam = sayi[bs]\n            bs += 1\n            if rakam != \"0\":\n                yazi.append(basamaklar[bs-1][int(rakam)])\n        return \"\".join(reversed(yazi))\n\n    def return_number(self,sayi):\n        sayi = '{:,}'.format(int(sayi))\n        haneler = reversed(sayi.split(\",\"))\n        uclus,sonuc = 0,[]\n        for hane in haneler:\n            uclu = self.ucluyuVer(hane)\n            if uclu != \"\":\n                sonuc.append(uclu+\"\"+ self.ucluler[uclus])\n            uclus+=1\n        son = \"\".join(reversed(sonuc))\n        if son.startswith('birbin'):\n            son = son[3:]\n        return(son.strip())\n\nclass test_report_template_id(osv.AbstractModel):\n    _name = 'report.custom_invoice_report.invoice_report_template_id'\n    _inherit = 'report.abstract_report'\n    _template = 'custom_invoice_report.invoice_report_template_id'\n    _wrapped_report_class = invoice_custom\n\n# vim:expandtab:smartindent:tabstop=4:softtabstop=4:shiftwidth=4:\n","sub_path":"custom_invoice_report/report/invoice_report_custom.py","file_name":"invoice_report_custom.py","file_ext":"py","file_size_in_byte":4176,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"519371324","text":"import argparse\n\nparser = argparse.ArgumentParser(description=\"Make prediction object detection on input images\")\nrequiredArgs = parser.add_argument_group(\"required argument\")\nrequiredArgs.add_argument(\"--input\", dest=\"input\", type=str,\n                   help=\"path to image for prediciton\",\n                   required = True)\nargs = parser.parse_args()\ninput = args.input\n\nprint(input)","sub_path":".ipynb_checkpoints/predict-checkpoint.py","file_name":"predict-checkpoint.py","file_ext":"py","file_size_in_byte":388,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"219334348","text":"# 1.write a python program to get a string which is n(non-negative integer) copies of a given string.\r\ns = input(\"enter a string:\")\r\nnew_string = \"\"\r\nn = int(input(\"enter the number of times you want to copy:\"))\r\nfor i in range(n):\r\n    new_string = new_string + s + \" \"\r\nprint(new_string)\r\n\r\n# 2.write a python program to get the n copies of the first 2 characters of a given string.Return the n copies of the whole string if the length is less than 2.\r\ndef string_copies(string,n):\r\n    new_string_2 = \" \"\r\n    if len(string) < 2:\r\n        for i in range(n):\r\n            new_string_2 = new_string_2 + string\r\n    else:\r\n        string2 = string[0] + string[1]\r\n        for x in range(n):\r\n            new_string_2 = new_string_2 + string2\r\n    print(new_string_2)\r\nstring_copies(\"s\",2)\r\nstring_copies(\"sweety\",2)\r\n\r\n# 3.write a python program to test whether a passed letter is a vowel or not.\r\ndef check_vowels(letter):\r\n    all_vowels = \"aeiouAEIOU\"\r\n    return letter in all_vowels\r\nprint(check_vowels(\"F\"))\r\n\r\n# 4.write a python program to check whether a specified value is contained in a group of values\r\ndef list_check(number):\r\n    list_ = [1, 5, 8, 3]\r\n    return number in list_\r\nprint(list_check(9))\r\n\r\n# 5.write a python program to create a histogram from a given list of integers\r\ndef histogram(items):\r\n    for n in items:\r\n        output = \"\"\r\n        while n > 0:\r\n            output += \"@\"\r\n            n = n-1\r\n        print(output)\r\nhistogram([1,3,5,7])\r\n# 6.write a python program to concatenate all elements in a list into a string and return it.\r\ndef str_concatenate(words):\r\n    new_string_ = \"\"\r\n    for word in words:\r\n        new_string_ += str(word)\r\n    print(new_string_)\r\nstr_concatenate([1,2,3,4])\r\n\r\n# 7.write a python program to print all even numbers from a given numbers list in the same order and stop the printing if any numbers that come after 237 in the sequence.\r\nthe_list = [386,462,47,418,907,344,236,375,823,566,597,978,328,615,953,345,399,162,758,219,918,237,412,566,248,333,73]\r\ndef check_even(numbers):\r\n    for i in numbers:\r\n        if i == 237:\r\n            break\r\n        elif i%2 == 0:\r\n            print(i)\r\ncheck_even(the_list)\r\n# 8.write a python program to print out a set containing all the colors from color_list_1 which are not present in color_list_2.\r\ncolor_list_1 = set(['white','Black','Red'])\r\ncolor_list_2 = set(['Red','Green'])\r\nprint(color_list_1.difference(color_list_2))\r\n\r\n# 9.write a python program that will accept the base and height of a triangle and compute the area.\r\nbase = float(input(\"enter the base:\"))\r\nheight = float(input(\"enter the height:\"))\r\narea = 0.5*(base * height)\r\nprint(\"area of the triangle is:\", area)\r\n# 10.write a python program to compute the greatest common divisor(GCD) of two positive integers\r\ndef compute_gcd(w,z):\r\n    if w>z:\r\n        smaller = z\r\n    else:\r\n        smaller = w\r\n    for p in range(1,smaller+1):\r\n        if((w%p == 0) and (z%p == 0)):\r\n            hcf = p\r\n    return hcf\r\nprint(\"the HCF is:\",compute_gcd(10,15))\r\n# or\r\nimport math\r\nprint(\"the HCF is: \",end = \"\")\r\nprint(math.gcd(60,48))","sub_path":"Basics_of_Python_3.py","file_name":"Basics_of_Python_3.py","file_ext":"py","file_size_in_byte":3111,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"17803386","text":"import requests\nfrom dataclasses import dataclass, asdict\nimport logging\nimport time\n\ntry:\n    from weather_station.anemometer import Anemometer\n    from weather_station.temp_probe import take_spot_reading\nexcept ImportError:\n    from anemometer import Anemometer\n    from temp_probe import take_spot_reading\n\nURL = \"http://gotwind.live/data\"\n\nlogger = logging.getLogger(__name__)\n\n\n@dataclass(frozen=True)\nclass Payload:\n    windspeed: float\n    temperature: float\n    humidity: float\n\n    def post(self):\n        try:\n            payload = asdict(self)\n            res = requests.post(URL, json=payload)\n            if res.status_code == 200:\n                logger.info(f\"posted {payload}\")\n            else:\n                logger.error(f\"something went wrong. {res.status_code}\")\n        except Exception:\n            logger.error(\"something went very wrong\")\n\n\ndef poll(sleep: int):\n    anemometer = Anemometer()\n    while True:\n        anemometer.get_reading()\n        temp, humidity = take_spot_reading()\n        payload = Payload(\n            windspeed=anemometer.wind_speed_kn,\n            temperature=temp,\n            humidity=humidity,\n        )\n        payload.post()\n        time.sleep(sleep)\n","sub_path":"weather_station/data_handler.py","file_name":"data_handler.py","file_ext":"py","file_size_in_byte":1208,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"120706889","text":"class Enemy:\n    def __init__(self, name, hp, atk, defence):\n        self.name = name\n        self.hp = hp\n        self.atk = atk\n        self.defence = defence\n\n    def __str__(self):\n        return \"名字:%s, hp:%s, atk:%s, defence:%s\" % (self.name, self.hp, self.atk, self.defence)\n\n\nlist01 = [\n    Enemy(\"毛玉\", 0, 15, 10),\n    Enemy(\"小妖精\", 120, 20, 5),\n    Enemy(\"大妖精\", 400, 30, 20),\n    Enemy(\"灭霸\", 5000, 100, 200)\n]\n\n\ndef fun01():\n    list02 = []\n    for item in list01:\n        list02.append((item.name))\n    return list02\n\n\nre = fun01()\nprint(re)\n\n\ndef fun02():\n    list02 = []\n    for item in list01:\n        list02.append((item.name, item.hp))\n    return list02\n\n\nre = fun02()\nprint(re)\nprint(\"----------------------------\")\n\n\ndef select(list_target, func_handle):\n    list_temp = []\n    for item in list_target:\n        list_temp.append(func_handle(item))\n    return list_temp\n\n\ndef func_handle(item):\n    return item.name, item.hp\n\n\nre = select(list01, func_handle)\nprint(re)\nprint('-------------------------')\n\nre = select(list01, lambda item: (item.name, item.atk))\nprint(re)\nprint('-------------------------')\n\nfrom list_helper import *\n\nre = ListHelper.select(list01, lambda item: (item.name, item.atk))\nfor item in re:\n    print(item)\n","sub_path":"part_01_python_base/python_pro/day18/exercise02.py","file_name":"exercise02.py","file_ext":"py","file_size_in_byte":1272,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"159163897","text":"'''\r\n----------------- AUTOMATIZAR BUSCA DE ERRO DE MULTIPOLIGONOS ----------------------\r\n\r\nAutor: Samuel  Amico\r\nData: 20/01/2020\r\nProjeto: NetMapBR \r\nObjetivo: Encontrar Falsos Poligonos e detectar o nome das cidades com este problema \r\n\r\n------------------------------------------------------------------------------------\r\n\r\n'''\r\n\r\nimport json\r\nimport glob\r\n\r\nLista_json = glob.glob('*.json')\r\n\r\n# Para cada Estado descobrir cidades erradas:\r\n\r\nfor estado in Lista_json:\r\n    numero_estado = estado.split('-')[1]\r\n\r\n    # Lendo e carregando arquivo JSON em variavel data\r\n    json_data = open(estado).read()\r\n    data = json.loads(json_data)\r\n\r\n    # Variaveis: contador de cidades e dicionario de cidades erradas\r\n    cnt = 0\r\n    cidades_erradas = {}\r\n\r\n    # interar para cada cidade existente\r\n    for json_cidades in data[\"features\"]:\r\n        cnt += 1\r\n        # Vetor que contem as coordenadas, pode ser apenas uma LineString ou multiplos LinesStrings\r\n        vector =(json_cidades[\"geometry\"][\"coordinates\"])\r\n        # Verifica se existe apenas um poligono\r\n        if(len(vector) > 1):\r\n            # Dicionario que recebe o nome da cidade e quantos poligos ele possui:\r\n            cidade_errada = json_cidades[\"properties\"][\"name\"]\r\n            poligonos = len(vector)\r\n            cidades_erradas[str(cidade_errada)] = poligonos\r\n\r\n    print( (\" Estado = {} , Cidades-Totais = {} , Cidades erradas e seus respectivos poligonos: {}\").format(numero_estado,cnt,cidades_erradas) ) \r\n\r\n\r\n","sub_path":"Verificador.py","file_name":"Verificador.py","file_ext":"py","file_size_in_byte":1502,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"429598895","text":"from tkinter import *\r\nroot = Tk()\r\ndef key (event):\r\n    print(\"Pressed\",repr(event.char))\r\ndef pushb(event):\r\n    frame.focus_set()\r\n    print(\"Clicked at\",event.x,event.y)\r\nframe = Frame(root,width = 200, height = 200)\r\nframe.bind(\"<Key>\",key)\r\nframe.bind(\"<Button>\",pushb)\r\nframe.pack()\r\nroot.mainloop()\r\n","sub_path":"onClick.py","file_name":"onClick.py","file_ext":"py","file_size_in_byte":309,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"50919640","text":"import copy\n\nfrom rastervision.core.data.vector_source.label_maker.filter import (\n    create_filter)\n\n\nclass ClassInference():\n    \"\"\"Infers missing class_ids from GeoJSON features.\"\"\"\n\n    def __init__(self,\n                 default_class_id,\n                 class_config=None,\n                 class_id_to_filter=None):\n        self.class_config = class_config\n        self.class_id_to_filter = class_id_to_filter\n        self.default_class_id = default_class_id\n\n        if self.class_id_to_filter is not None:\n            self.class_id_to_filter = {}\n            for class_id, filter_exp in class_id_to_filter.items():\n                self.class_id_to_filter[int(class_id)] = create_filter(\n                    filter_exp)\n\n    def infer_class_id(self, feature):\n        \"\"\"Infer the class_id for a GeoJSON feature.\n\n        Args:\n            feature: (dict) GeoJSON feature\n\n        Rules:\n            1) If class_id is in feature['properties'], use it.\n            2) If class_name or label are in feature['properties'] and in class_config,\n                use corresponding class_id.\n            3) If class_id_to_filter is set and filter is true when applied to feature,\n                use corresponding class_id.\n            4) Otherwise, return the default_class_id\n        \"\"\"\n        class_id = feature.get('properties', {}).get('class_id')\n        if class_id is not None:\n            return class_id\n\n        if self.class_config is not None:\n            class_name = feature.get('properties', {}).get('class_name')\n            if class_name in self.class_config.names:\n                return self.class_config.names.index(class_name)\n\n            label = feature.get('properties', {}).get('label')\n            if label in self.class_config.names:\n                return self.class_config.names.index(label)\n\n        if self.class_id_to_filter is not None:\n            for class_id, filter_fn in self.class_id_to_filter.items():\n                if filter_fn(feature):\n                    return class_id\n\n        return self.default_class_id\n\n    def transform_geojson(self, geojson):\n        \"\"\"Transform GeoJSON by appending class_ids and removing features with no class.\n\n        For each feature in geojson, the class_id is inferred and is set into\n        feature['properties']. If the class_id is None (because none of the rules apply\n        and the default_class_id is None), the feature is dropped.\n        \"\"\"\n        new_features = []\n        for feature in geojson['features']:\n            class_id = self.infer_class_id(feature)\n            if class_id is not None:\n                feature = copy.deepcopy(feature)\n                properties = feature.get('properties', {})\n                properties['class_id'] = class_id\n                feature['properties'] = properties\n                new_features.append(feature)\n        new_geojson = {'type': 'FeatureCollection', 'features': new_features}\n        return new_geojson\n","sub_path":"rastervision_core/rastervision/core/data/vector_source/class_inference.py","file_name":"class_inference.py","file_ext":"py","file_size_in_byte":2955,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"15259785","text":"# ====================================================================#\n# File Name: snake_game.py\n# Author: Przemyslaw Gawron\n# Purpose: The main file for the snake game\n# ====================================================================#\nimport pygame\nimport sys\nimport colors\nimport fonts\nfrom board import Board\nfrom snake import Snake\nimport random\nfrom apple import Apple\nfrom side_panel import SidePanel\n\n\nclass SnakeGame:\n    _WINDOW_WIDTH = 1000\n    _WINDOW_HEIGHT = 600\n    _FPS = 15\n    _clock = None\n    _game_display = None\n    _score = 0\n    _APPLE_DELTA = 4\n\n    def __init__(self):\n        pygame.init()\n        self._game_display = pygame.display.set_mode((self._WINDOW_WIDTH, self._WINDOW_HEIGHT))\n        pygame.display.set_caption('Snake by Przemyslaw Gawron')\n        self._clock = pygame.time.Clock()\n\n    def start_game(self):\n        game_exit = False\n        self._display_start_screen()\n\n        while not game_exit:  # Loops through the game screen and the game over screen\n            self._display_game_screen()\n            game_exit = self._display_game_over_screen()\n\n    def _display_game_over_screen(self):\n        self._game_display.fill(colors.BLACK)\n\n        while True:\n            for event in pygame.event.get():\n                self._check_if_exit(event)\n\n                if event.type == pygame.KEYDOWN:\n                    if event.key == pygame.K_KP_ENTER or event.key == pygame.K_RETURN:\n                        return False\n\n            self._game_display.fill(colors.BLACK)\n            self._display_message(\"Game Over\",\n                                  colors.GREEN,\n                                  x_displace=0,\n                                  y_displace=0,\n                                  size=\"LARGE\")\n            self._display_message(\"Press Enter to play again or Q to quit...\",\n                                  colors.WHITE, x_displace=0,\n                                  y_displace=30,\n                                  size=\"MEDIUM\")\n            pygame.display.update()\n            self._clock.tick(self._FPS)\n\n    def _is_game_over(self):\n        snake_coords = self._snake.get_snake_coords()\n        snake_head = self._snake.get_head()\n\n        # Snake hit a boundary\n        if snake_head['x'] == -1 or snake_head['x'] >= self._board.get_cell_width() or snake_head['y'] == -1 or snake_head['y'] >= self._board.get_cell_height():\n            return True\n\n        # Snake ate a body part\n        for body in snake_coords[1:]:\n            if snake_head['x'] == body['x'] and snake_head['y'] == body['y']:\n                return True\n\n    def _is_eaten(self):\n        snake_head = self._snake.get_head()\n        apple_coord = self._apple.get_apple()\n\n        # Snake ate an apple\n        if snake_head['x'] == apple_coord['x'] and snake_head['y'] == apple_coord['y']:\n            return True\n        return False\n\n    def _validate_apple_spawn(self):\n        for body in self._snake.get_snake_coords(): # Makes sure that the apple didn't spawn inside the snake\n            if body['x'] == self._apple.get_apple()['x'] and body['y'] == self._apple.get_apple()['y']:\n                return False\n        return True\n\n    def _display_game_screen(self):\n        self._game_display.fill(colors.BLACK)\n        self._score = 0\n\n        self._board = Board(self._game_display)\n        self._side_panel = SidePanel(self._game_display)\n        self._snake = Snake(self._board.get_cell_width(), self._board.get_cell_height())\n        self._apple = Apple(self._board.get_cell_width(), self._board.get_cell_height())\n\n        #direction = random.choice(list(Snake.DIRECTIONS.keys()))\n        direction = Snake.DIRECTIONS[\"UP\"]\n        valid_apple = False\n        while not valid_apple:\n            apple = self._apple.new_apple()\n            valid_apple = self._validate_apple_spawn()\n\n        while True:\n            for event in pygame.event.get():\n                self._check_if_exit(event)\n\n                if event.type == pygame.KEYDOWN:\n                    if (event.key == pygame.K_a or event.key == pygame.K_LEFT) and direction != Snake.DIRECTIONS[\"RIGHT\"]:\n                        direction = Snake.DIRECTIONS[\"LEFT\"]\n                    elif (event.key == pygame.K_d or event.key == pygame.K_RIGHT) and direction != Snake.DIRECTIONS[\"LEFT\"]:\n                        direction = Snake.DIRECTIONS[\"RIGHT\"]\n                    elif (event.key == pygame.K_w or event.key == pygame.K_UP) and direction != Snake.DIRECTIONS[\"DOWN\"]:\n                        direction = Snake.DIRECTIONS[\"UP\"]\n                    elif (event.key == pygame.K_s or event.key == pygame.K_DOWN) and direction != Snake.DIRECTIONS[\"UP\"]:\n                        direction = Snake.DIRECTIONS[\"DOWN\"]\n\n            if self._is_eaten():\n                valid_apple = False\n                while not valid_apple:\n                    apple = self._apple.new_apple()\n                    valid_apple = self._validate_apple_spawn()\n                self._snake.grow()\n                self._score += 1\n            elif self._apple.get_seconds() > self._APPLE_DELTA:\n                valid_apple = False\n                while not valid_apple:\n                    apple = self._apple.new_apple()\n                    valid_apple = self._validate_apple_spawn()\n\n            self._game_display.fill(colors.BLACK)\n            pygame.draw.line(self._game_display, colors.WHITE,\n                             (self._WINDOW_WIDTH - self._side_panel.get_panel_width() + 2, 0),\n                             (self._WINDOW_WIDTH - self._side_panel.get_panel_width() + 2,\n                              self._side_panel.get_panel_height()))\n\n            self._snake.snake_move(direction)\n            self._board.draw_board()\n            self._side_panel.display_panel(self._score, self._APPLE_DELTA - self._apple.get_seconds() + 1)\n            self._board.draw_snake(self._snake.get_snake_coords())\n            self._board.draw_apple(apple)\n\n            if self._is_game_over():\n                return\n\n            pygame.display.update()\n            self._clock.tick(self._FPS)\n\n    def _end_game(self):\n        pygame.quit()\n        sys.exit()\n\n    def _message_objects(self, msg, color, size):\n        if size == \"SMALL\":\n            msg_surface = fonts.SMALL_FONT.render(msg, True, color)\n            return msg_surface, msg_surface.get_rect()\n        elif size == \"MEDIUM\":\n            msg_surface = fonts.MEDIUM_FONT.render(msg, True, color)\n            return msg_surface, msg_surface.get_rect()\n        elif size == \"LARGE\":\n            msg_surface = fonts.LARGE_FONT.render(msg, True, color)\n            return msg_surface, msg_surface.get_rect()\n\n    def _display_message(self, msg, color, x_displace=0, y_displace=0, size=\"SMALL\"):\n        msg_surf, msg_rect = self._message_objects(msg, color, size)\n        msg_rect.center = (self._WINDOW_WIDTH / 2) + x_displace, (self._WINDOW_HEIGHT / 2) + y_displace\n        self._game_display.blit(msg_surf, msg_rect)\n\n    def _check_if_exit(self, event):\n        if event.type == pygame.QUIT:  # Quit the program if the user hits the red X\n            self._end_game()\n        elif event.type == pygame.KEYUP or event.type == pygame.KEYDOWN:  # Quit the game if the user presses escape\n            if event.key == pygame.K_q:\n                self._end_game()\n\n    def _display_start_screen(self):\n        while True:\n            for event in pygame.event.get():\n                self._check_if_exit(event)\n\n                if event.type == pygame.KEYDOWN:\n                    if event.key == pygame.K_KP_ENTER or event.key == pygame.K_RETURN:\n                        return\n\n            self._game_display.fill(colors.BLACK)\n            self._display_message(\"Snake by: Przemyslaw Gawron\",\n                                  colors.GREEN,\n                                  x_displace=0,\n                                  y_displace=0,\n                                  size=\"LARGE\")\n            self._display_message(\"Press Enter to start or Q to quit...\",\n                                  colors.WHITE, x_displace=0,\n                                  y_displace=30,\n                                  size=\"MEDIUM\")\n            pygame.display.update()\n            self._clock.tick(self._FPS)\n\nif __name__ == \"__main__\":\n    SnakeGame().start_game()\n","sub_path":"snake_game.py","file_name":"snake_game.py","file_ext":"py","file_size_in_byte":8305,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"276631799","text":"# ===========================================\n# @Time    : 2018/12/13 12:15\n# @Author  : antony\n# @Email   : 502202879@qq.com\n# @File    : Customer_Lifetime_Value.py\n# @Software: PyCharm\n# ===========================================\n\nimport sys\nsys.path.append('../DBbase')\nfrom db_func import *\n\n# allorder_create_sqls = \"\"\"CREATE TABLE `bimodels`.`bm_user_alloilorders` (\n#   `batch_date` date NOT NULL COMMENT '跑批日期',\n#    order_type tinyint(4) unsigned NOT NULL COMMENT '交易类型,10及以上-取saas的订单类型, 小于10-管控获取的sjly',\n#   `order_code` varchar(36) NOT NULL DEFAULT '0' COMMENT '订单父ID',\n#   `uid` int(11) NOT NULL COMMENT '用户id',\n#   `stid` int(11) NOT NULL COMMENT '油站id',\n#   `pay_time` int(11) DEFAULT NULL COMMENT '结账时间',\n#   `goods_amount` int(11) NOT NULL COMMENT '原始金额, 单位:分',\n#   `order_amount` int(11) NOT NULL COMMENT '应付金额, 单位:分',\n#   `pay_money` int(11) NOT NULL COMMENT '实付金额, 单位:分',\n#   `coupon_fee` int(11) NOT NULL COMMENT '券抵扣, 单位:分',\n#   `credit_fee` int(11) NOT NULL COMMENT '积分抵扣, 单位:分',\n#   `oil_id` int(11) NOT NULL COMMENT '油号id',\n#   `liters` double(10,5) NOT NULL COMMENT '升数',\n#   KEY `IDX_uid` (`uid`),\n#   KEY `IDX_BATCH_DATE` (`batch_date`),\n#   KEY `Idx_group_id` (`stid`),\n#   KEY `IDX_pay_type` (`order_type`)\n# ) ENGINE=InnoDB DEFAULT CHARSET=utf8 COMMENT='油品订单全表'\n# /*!50100 PARTITION BY HASH (uid)\n# PARTITIONS 128 */;\n# \"\"\"\n\n# clv_factor_disorder_type_ = \"\"\"CREATE TABLE `bimodels`.`bm_user_stid_clv_factor` (\n#   `uid` int(11) NOT NULL COMMENT '用户id',\n#   `stid` int(11) NOT NULL COMMENT '油站id',\n#   order_type tinyint(4) unsigned NOT NULL COMMENT '交易类型,10及以上-取saas的订单类型, 小于10-管控获取的sjly',\n#   `first_time` int(11) DEFAULT NULL COMMENT '用户的第一次油品结账时间',\n#   `last_time` int(11) DEFAULT NULL COMMENT '用户最近一次油品结账时间',\n#   cnt_pay int(11) DEFAULT 0 COMMENT '用户油品结账次数',\n#   sum_goods_money int(11) DEFAULT 0 COMMENT '总的商品原金额, 单位:分',\n#   sum_pay_money int(11) DEFAULT 0 COMMENT '总的实付金额, 单位:分',\n#   sum_coupon_fee int(11) DEFAULT 0 COMMENT '总的券优惠金额, 单位:分',\n#   sum_credit_fee int(11) DEFAULT 0 COMMENT '总的积分优惠金额, 单位:分',\n#   cnt_coupon int(11) DEFAULT 0 COMMENT '总的券优惠使用次数',\n#   cnt_credit int(11) DEFAULT 0 COMMENT '总的积分优惠使用次数',\n#   sum_liters double(14,3) DEFAULT 0 COMMENT '总的升数',\n#   mwa_delta_seconds double(14,3) DEFAULT NULL COMMENT '移动平滑-平均加油间隔秒数',\n#   mwa_pay_money double(11,4) DEFAULT 0 COMMENT '移动平滑-平均实付金额',\n#   mwa_liters double(11,4) DEFAULT 0 COMMENT '移动平滑-平均加油升数',\n#   mwa_paytoc double(9,3) DEFAULT 0 COMMENT '移动平滑-平均加油时(0-24),加油时间点偏向',\n#   `update_times` int(11) DEFAULT 0 COMMENT '这条记录的更新次数',\n#   `create_time` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP,\n#   `update_time` timestamp NOT NULL DEFAULT '0000-00-00 00:00:00' ON UPDATE CURRENT_TIMESTAMP,\n#   `obligate` text COMMENT '其他预留',\n#   PRIMARY KEY (`uid`, `stid`, `order_type`),\n#   KEY `IDX_order_type` (`order_type`),\n#   KEY `IDX_uid_stid` (`uid`, `stid`),\n#   KEY `IDX_uid` (`uid`),\n#   KEY `IDX_first_time` (`first_time`),\n#   KEY `IDX_last_time` (`last_time`)\n# ) ENGINE=InnoDB DEFAULT CHARSET=utf8 COMMENT='用户clv因素表-分油站'\n# \"\"\"\n#\n# clv_factor_ = \"\"\"CREATE TABLE profile.`pr_user_stid_clv_factor` (\n#   `uid` int(11) NOT NULL COMMENT '用户id',\n#   `stid` int(11) NOT NULL COMMENT '油站id',\n#   `first_time` int(11) DEFAULT NULL COMMENT '用户的第一次油品结账时间',\n#   `last_time` int(11) DEFAULT NULL COMMENT '用户最近一次油品结账时间',\n#   `cnt_pay` int(11) DEFAULT '0' COMMENT '用户油品结账次数',\n#   `sum_goods_money` int(11) DEFAULT '0' COMMENT '总的商品原金额, 单位:分',\n#   `sum_pay_money` int(11) DEFAULT '0' COMMENT '总的实付金额, 单位:分',\n#   `sum_coupon_fee` int(11) DEFAULT '0' COMMENT '总的券优惠金额, 单位:分',\n#   `sum_credit_fee` int(11) DEFAULT '0' COMMENT '总的积分优惠金额, 单位:分',\n#   `cnt_coupon` int(11) DEFAULT '0' COMMENT '总的券优惠使用次数',\n#   `cnt_credit` int(11) DEFAULT '0' COMMENT '总的积分优惠使用次数',\n#   `sum_liters` double(14,3) DEFAULT '0.000' COMMENT '总的升数',\n#   `mwa_delta_seconds` double(14,3) DEFAULT NULL COMMENT '移动平滑-平均加油间隔秒数',\n#   `mwa_pay_money` double(11,4) DEFAULT '0.0000' COMMENT '移动平滑-平均实付金额',\n#   `mwa_liters` double(11,4) DEFAULT '0.0000' COMMENT '移动平滑-平均加油升数',\n#   `mwa_paytoc` double(9,3) DEFAULT '0.000' COMMENT '移动平滑-平均加油时(0-24),加油时间点偏向',\n#   `update_times` int(11) DEFAULT '0' COMMENT '这条记录的更新次数',\n#   `create_time` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP,\n#   `update_time` timestamp NOT NULL DEFAULT '0000-00-00 00:00:00' ON UPDATE CURRENT_TIMESTAMP,\n#   `obligate` text COMMENT '其他预留',\n#   PRIMARY KEY (`uid`,`stid`),\n#   KEY `IDX_uid` (`uid`),\n#   KEY `IDX_first_time` (`first_time`),\n#   KEY `IDX_last_time` (`last_time`)\n# ) ENGINE=InnoDB DEFAULT CHARSET=utf8 COMMENT='用户clv因素表-分油站';\n# \"\"\"\n\n# create_bm_mid_user_stid_clv_ = \"\"\"CREATE TABLE `bimodels`.`bm_mid_user_stid_clv_` (\n#   `uid` int(11) NOT NULL COMMENT '用户id',\n#   `stid` int(11) NOT NULL COMMENT '油站id',\n#   clv1 double(14,3) DEFAULT 0 COMMENT '计算的clv1',\n#   clv2 double(14,3) DEFAULT 0 COMMENT '计算的clv2',\n#   clv3 double(14,3) DEFAULT 0 COMMENT '计算的clv3',\n#   clv4 double(14,3) DEFAULT 0 COMMENT '计算的clv4',\n#   clv5 double(14,3) DEFAULT 0 COMMENT '计算的clv5',\n#   `create_time` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP,\n#   `update_time` timestamp NOT NULL DEFAULT '0000-00-00 00:00:00' ON UPDATE CURRENT_TIMESTAMP,\n#   `obligate` text COMMENT '其他预留',\n#   PRIMARY KEY (`uid`, `stid`),\n#   KEY `IDX_uid` (`uid`),\n#   KEY `IDX_update_time` (`update_time`)\n# ) ENGINE=InnoDB DEFAULT CHARSET=utf8 COMMENT='用户clv计算中间表-分油站';\n# \"\"\"\n\n# create_pr_merchant_tags = \"\"\"CREATE TABLE `profile`.`pr_merchant_tags` (\n#   `batch_date` date NOT NULL COMMENT '跑批日期',\n#   `merchant_type` tinyint(4) unsigned NOT NULL COMMENT '商户类型(0.平台,1.油站,2.集团)',\n#   `merchant_id` int(11) unsigned NOT NULL COMMENT '商户id(包括油站和集团)',\n#   `tar_id` int(6) NOT NULL DEFAULT '0' COMMENT '指标id',\n#   `spr_id` int(6) NOT NULL DEFAULT '0' COMMENT '展开(子指标)指标id',\n#   `value_c` int(13) DEFAULT NULL COMMENT '对应指标取值1(int类型)_一般表示聚合了多少条数据',\n#   `value_e` double(20,6) DEFAULT NULL COMMENT '对应指标取值_一般是实际需求的值',\n#   `value_ob` double(20,6) DEFAULT NULL COMMENT '对应指标取值2_预留',\n#   `create_time` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP,\n#   `update_time` timestamp NOT NULL DEFAULT '0000-00-00 00:00:00' ON UPDATE CURRENT_TIMESTAMP,\n#   `end_time` int DEFAULT NULL COMMENT '失效时间',\n#   `obligate` text DEFAULT NULL COMMENT '其他预留',\n#   PRIMARY KEY (`batch_date`,`merchant_type`,`merchant_id`,`tar_id`,`spr_id`),\n#   KEY `T_sID` (`tar_id`,`spr_id`),\n#   KEY `IDX_batch` (`batch_date`),\n#   KEY `MER_taID` (`merchant_type`,`merchant_id`,`tar_id`),\n#   KEY `MERID` (`merchant_type`,`merchant_id`)\n# ) ENGINE=InnoDB DEFAULT CHARSET=utf8 COMMENT='标签: 集团';\n# \"\"\"\n\nallorders_insert_sql = \"\"\"\ndelete from bimodels.bm_user_alloilorders where batch_date = %(do_day)s;\ninsert into bimodels.bm_user_alloilorders\nselect \n  O.batch_date,order_type,O.order_code,user_id uid,merchant_id stid,\n  pay_time,goods_amount,order_amount,pay_money,OI.coupon_fee,OI.credit_fee,goods_id oil_id,goods_number liters\nfrom ods.ods_wei_order_info O\nleft join ods.ods_wei_order_items OI \nON O.id = OI.order_id\nwhere is_suborder = 1 and goods_type = 10 and user_id > 0\nand order_status in (1160,2010) \nand O.batch_date = %(do_day)s ;\ninsert into bimodels.bm_user_alloilorders\nselect \n  batch_date,sjly order_type,jyid order_code,uid,stid,\n  jzsj pay_time,je*100 goods_amount,zhje*100 order_amount,(zhje-dkje)*100 pay_money,dkje*100 coupon_fee,\n  dxje*100 credit_fee,oil_id,sl liters\nfrom dw.dw_bpos_order_by_user \nwhere sjly = 1 and lx='Y'\nand batch_date = %(do_day)s \"\"\"\n\nallorders_get_by_day = '''\nselect \n   uid,stid,order_type,pay_time,goods_amount,order_amount,pay_money,coupon_fee,credit_fee,liters,\n   cast(from_unixtime(pay_time, '%%H')  as unsigned)+cast(from_unixtime(pay_time, '%%i')  as unsigned)/60 paytoc\nfrom bimodels.bm_user_alloilorders\nwhere batch_date = %(do_day)s\norder by pay_time; '''\n\nclv_factor_get_by_day = '''\nselect \n  O.uid,O.stid,O.order_type,first_time,last_time,cnt_pay,sum_goods_money,sum_pay_money,sum_coupon_fee,sum_credit_fee\n  ,cnt_coupon,cnt_credit,sum_liters,mwa_delta_seconds,mwa_pay_money,mwa_liters,mwa_paytoc,update_times\nfrom bimodels.bm_user_alloilorders O\ninner join %(clv_table)s CLVF\non O.uid = CLVF.uid and O.stid = CLVF.stid and O.order_type = CLVF.order_type\nwhere batch_date = %(do_day)s\ngroup by O.uid,O.stid;\n'''\n\nallorder_clv_fac_get_by_day = \"\"\"\nselect \n   O.uid,O.stid,O.order_type,pay_time,goods_amount,order_amount,pay_money,coupon_fee,credit_fee,liters,\n   cast(from_unixtime(pay_time, '%%H')  as unsigned)+cast(from_unixtime(pay_time, '%%i')  as unsigned)/60 paytoc,\n   first_time,last_time,cnt_pay,sum_goods_money,sum_pay_money,sum_coupon_fee,sum_credit_fee,\n   cnt_coupon,cnt_credit,sum_liters,mwa_delta_seconds,mwa_pay_money,mwa_liters,mwa_paytoc,update_times\nfrom bimodels.bm_user_alloilorders O\nleft join %(clv_table)s CLVF\non O.uid = CLVF.uid and O.stid = CLVF.stid and O.order_type = CLVF.order_type\nwhere O.batch_date = %(do_day)s\norder by pay_time; \n\"\"\"\n\n# factor_get_by_uid = 'select * from %(clv_table)s where uid = %(uid)d and stid = %(stid)d'\n\n# 暴力更新一张中间表, 由于clv对计算时间敏感，后续的油站/集团clv计算���此为快照\nto_mid_sql = \"\"\"insert into `bimodels`.`bm_mid_user_stid_clv_` (uid, stid, clv1)\n    select uid,stid,\n       sum(10*365*86400*2/((last_time-first_time)/cnt_pay+mwa_delta_seconds) * sum_liters/cnt_pay * \n         pow(2,-1.5*pow(10,-7)*(UNIX_TIMESTAMP(CURDATE())-last_time))) clv1\n    from %(clv_table)s\n    where cnt_pay > 1\n    group by uid,stid\nON DUPLICATE KEY UPDATE clv1=VALUES(clv1);\"\"\"\n\n# 快照保存表\nsnapshot_sql = \"create table\"\n\ndfsql_choice = {'mid_clv1': \"\"\"\nselect 0 merchant_type,0 merchant_id, count(1) value_c,sum(clv1) value_e,STD(clv1) value_ob\nfrom `bimodels`.`bm_mid_user_stid_clv_`\nunion all \nselect 1 merchant_type,stid merchant_id, count(1) value_c,sum(clv1) value_e,STD(clv1) value_ob\nfrom `bimodels`.`bm_mid_user_stid_clv_`\ngroup by stid\nunion all \nselect 2 merchant_type,group_id merchant_id, count(1) value_c,sum(clv1) value_e,STD(clv1) value_ob\nfrom `bimodels`.`bm_mid_user_stid_clv_` M\ninner join ods.ods_wei_group_stations gs\non gs.stid = M.stid and gs.isvalid = 1\ngroup by group_id;\n\"\"\"}\n\n# 'alloil_order_table': 'bimodels.bm_user_alloilorders'}\npara = {'clv_table': 'bimodels.bm_user_stid_clv_factor', 'do_day': day_forpast(-1)}\ntags_table = 'profile.pr_merchant_tags'\nkey_cols = ['uid', 'stid', 'order_type']\norders_cols = ['pay_time', 'goods_amount', 'order_amount', 'pay_money', 'coupon_fee', 'credit_fee', 'liters', 'paytoc']\nclv_cols = ['first_time', 'last_time', 'cnt_pay', 'sum_goods_money', 'sum_pay_money',\n            'sum_coupon_fee', 'sum_credit_fee', 'cnt_coupon', 'cnt_credit', 'sum_liters',\n            'mwa_delta_seconds', 'mwa_pay_money', 'mwa_liters', 'mwa_paytoc', 'update_times']\n# sum_cols = ['cnt_pay', 'cnt_coupon', 'cnt_credit', 'sum_pay_money', 'sum_coupon_fee', 'sum_credit_fee', 'sum_liters']\n# # 进行移动平滑的列\n# mwa_cols = ['mwa_pay_money', 'mwa_liters', 'mwa_paytoc', 'mwa_delta_seconds']\npay_columns = ['pay_money', 'coupon_fee', 'credit_fee', 'liters', ]\n\n\ndef insert_allorders():\n    dw = my_(config.MYSQL_BI_RW_ENV)\n    sql_li = (allorders_insert_sql % para).split(';')\n    print(day_forpast(0, ss='%Y-%m-%d %H:%M:%S'), para['do_day'], [dw.sql_engine(x) for x in sql_li],)\n\n\ndef k_avg(v1, v2, k=0.5):\n    if v1 is None or ~(v1 > 0 or v1 <= 0):\n        return v2\n    return (1 - k) * v1 + k * v2\n\n\ndef circle_avg(v1, v2, k=0.5, clock=24):\n    # 类似clock, 在首尾相连的情况下求均值。如：23点和03点的均值是01点\n    half_c = .5 * clock\n    if abs(v1 - v2) < half_c:\n        return k_avg(v1, v2, k)\n    else:\n        if v1 < v2:\n            v1 += clock\n        else:\n            v2 += clock\n        resu = k_avg(v1, v2, k)\n        return resu if resu < 24 else resu - 24\n\n\ndef count_biger_than_n(ser, n=0):\n    return len([1 for x in ser if x > n])\n\n\ndef do_clv_factor():\n\n    dw = my_(config.MYSQL_BI_RW_ENV)\n    # print(day_forpast(0, ss='%Y-%m-%d %H:%M:%S'), end='--> ')\n    # df = dw.to_dataframe(allorders_get_by_day % para)\n    # print(len(df), day_forpast(0, ss='%Y-%m-%d %H:%M:%S'), end='--> ')\n    # dfr_before = dw.to_dataframe(clv_factor_get_by_day % para)\n    # print(len(dfr_before), day_forpast(0, ss='%Y-%m-%d %H:%M:%S'))\n\n    print(day_forpast(0, ss='%Y-%m-%d %H:%M:%S'), end='--> ')\n    df_clv = dw.to_dataframe(allorder_clv_fac_get_by_day % para)\n    print(len(df_clv), day_forpast(0, ss='%Y-%m-%d %H:%M:%S'), end='--> ')\n    df_new = df_clv.loc[df_clv.cnt_pay.map(pd.isna), key_cols + orders_cols]\n    df_before = df_clv.query('cnt_pay>0')\n    dfr_before = df_before.drop_duplicates(key_cols)[key_cols + clv_cols].reset_index(drop=True)\n    dfr_inx = dfr_before[key_cols].reset_index()\n    df_before = pd.merge(df_before[key_cols + orders_cols], dfr_inx)\n    print(len(df_before), len(dfr_before), day_forpast(0, ss='%Y-%m-%d %H:%M:%S'))\n\n    t = Timer_(0)\n\n    for ind in df_before.index:\n        row_di = df_before.loc[ind, :].to_dict()\n    # for ind, row in df_before.iterrows():\n    #     row_di = row.to_dict()\n    #     # inx = dfr_before.query('uid==%(uid)d & stid==%(stid)d & order_type==%(order_type)d' % row_di).index\n    #     inx = row['index']\n        inx = row_di['index']\n        if inx >= 0:\n            t.tic()\n            dfr_p = dfr_before.loc[inx, :]\n            row_di['delta_seconds'] = row_di['pay_time'] - dfr_p['last_time']  # delta_seconds\n            if row_di['delta_seconds'] <= 0:\n                continue\n            dfr_before.loc[inx, 'last_time'] = row_di['pay_time']  # 5s\n            dfr_before.loc[inx, 'cnt_pay'] += 1  # 10s\n            dfr_before.loc[inx, 'sum_goods_money'] += row_di['goods_amount']  # 5s\n            t.toc()\n            for x in pay_columns:\n                dfr_before.loc[inx, 'sum_' + x] += row_di[x]\n            dfr_before.loc[inx, 'cnt_coupon'] += 1 if row_di['coupon_fee'] > 0 else 0\n            dfr_before.loc[inx, 'cnt_credit'] += 1 if row_di['credit_fee'] > 0 else 0\n            for x in ['delta_seconds', 'pay_money', 'liters']:\n                dfr_before.loc[inx, 'mwa_' + x] = k_avg(dfr_p['mwa_' + x], row_di[x], k=0.4)\n            dfr_before.loc[inx, 'mwa_paytoc'] = circle_avg(dfr_p['mwa_paytoc'], row_di['paytoc'])\n            dfr_before.loc[inx, 'update_times'] += 1\n        else:\n            print('this is not supposed to happen')\n\n    dfg_new = df_new.groupby(key_cols)\n    dfr_new = dfg_new['pay_time'].min().reset_index().rename(columns={'pay_time': 'first_time'})\n    dfr_new.insert(0, 'last_time', dfg_new['pay_time'].agg(max).reset_index()['pay_time'])\n    dfr_new.insert(0, 'cnt_pay', dfg_new['pay_time'].agg(len).reset_index()['pay_time'])\n    dfr_new.insert(0, 'sum_goods_money', dfg_new['goods_amount'].agg(sum).reset_index()['goods_amount'])\n    for x in pay_columns:\n        dfr_new.insert(0, 'sum_' + x, dfg_new[x].agg(sum).reset_index()[x])\n    dfr_new.insert(0, 'cnt_coupon', dfg_new['coupon_fee'].agg(count_biger_than_n).reset_index()['coupon_fee'])\n    dfr_new.insert(0, 'cnt_credit', dfg_new['credit_fee'].agg(count_biger_than_n).reset_index()['credit_fee'])\n    for x in ['pay_money', 'liters', 'paytoc']:\n        dfr_new.insert(0, 'mwa_' + x, dfg_new[x].agg(sum).reset_index()[x])\n\n    # for i, row in df_new.iterrows():\n    #     row_di = row.to_dict()\n    #     row_di['last_time'] = row_di['first_time'] = row_di.pop('pay_time')\n    #     row_di['cnt_pay'] = 1\n    #     row_di['sum_goods_money'] = row_di.pop('goods_amount')\n    #     row_di.pop('order_amount')\n    #     for x in pay_columns:\n    #         row_di['sum_' + x] = row_di[x]\n    #     row_di['cnt_coupon'] = 1 if row_di.pop('coupon_fee') > 0 else 0\n    #     row_di['cnt_credit'] = 1 if row_di.pop('credit_fee') > 0 else 0\n    #     for x in ['pay_money', 'liters', 'paytoc']:\n    #         row_di['mwa_' + x] = row_di.pop(x)\n    #     row_di['update_times'] = 1\n    #     dfr_before = dfr_before.append(row_di, ignore_index=True)\n    #     # dfr_before.loc[len(dfr_before)] = row_di\n\n    # for i, row in df.iterrows():\n    #     row_di = row.to_dict()\n    #     inx = dfr_before.query('uid==%(uid)d & stid==%(stid)d & order_type==%(order_type)d' % row_di).index\n    #     if len(inx) == 1:\n    #         dfr_p = dfr_before.loc[inx, :]\n    #         row_di['delta_seconds'] = row_di['pay_time'] - dfr_p['last_time']  # delta_seconds\n    #         if row_di['delta_seconds'] <= 0:\n    #             continue\n    #         dfr_before.loc[inx, 'last_time'] = row_di['pay_time']\n    #         dfr_before.loc[inx, 'cnt_pay'] += 1\n    #         dfr_before.loc[inx, 'sum_goods_money'] += row_di['goods_amount']\n    #         for x in pay_columns:\n    #             dfr_before.loc[inx, 'sum_' + x] += row_di[x]\n    #         dfr_before.loc[inx, 'cnt_coupon'] += 1 if row_di['coupon_fee'] > 0 else 0\n    #         dfr_before.loc[inx, 'cnt_credit'] += 1 if row_di['credit_fee'] > 0 else 0\n    #         # print(dfr_before.loc[inx, 'mwa_delta_seconds'], row_di['delta_seconds'])\n    #         for x in ['delta_seconds', 'pay_money', 'liters']:\n    #             dfr_before.loc[inx, 'mwa_' + x] = k_avg(dfr_p['mwa_' + x], row_di[x], k=0.4)\n    #         # print(dfr_before.loc[inx, 'mwa_delta_seconds'])\n    #         dfr_before.loc[inx, 'mwa_paytoc'] = circle_avg(dfr_p['mwa_paytoc'], row_di['paytoc'])\n    #     else:\n    #         row_di['last_time'] = row_di['first_time'] = row_di.pop('pay_time')\n    #         row_di['cnt_pay'] = 1\n    #         row_di['sum_goods_money'] = row_di.pop('goods_amount')\n    #         row_di.pop('order_amount')\n    #         for x in pay_columns:\n    #             row_di['sum_' + x] = row_di[x]\n    #         row_di['cnt_coupon'] = 1 if row_di.pop('coupon_fee') > 0 else 0\n    #         row_di['cnt_credit'] = 1 if row_di.pop('credit_fee') > 0 else 0\n    #         for x in ['pay_money', 'liters', 'paytoc']:\n    #             row_di['mwa_' + x] = row_di.pop(x)\n    #         row_di['update_times'] = 1\n    #         dfr_before = dfr_before.append(row_di, ignore_index=True)\n    print(sum(t.tictoc))\n    print(day_forpast(0, ss='%Y-%m-%d %H:%M:%S'), len(dfr_before), end='==> ')\n    print(dw.df_upd_tosql(dfr_before, 2000, para['clv_table']), day_forpast(0, ss='%Y-%m-%d %H:%M:%S'), end='==> ')\n    print(dw.df_upd_tosql(dfr_new, 2000, para['clv_table']), day_forpast(0, ss='%Y-%m-%d %H:%M:%S'))\n    print('finish mid_table::', dw.sql_engine(to_mid_sql % para), day_forpast(0, ss='%Y-%m-%d %H:%M:%S'))\n\n    # dics = []\n    # while len(df):\n    #     row_di = df.iloc[0][['uid', 'stid']].to_dict()\n    #     inx = df.query('uid==%(uid)d & stid==%(stid)d' % row_di).index\n    #     dfp = df.loc[inx, :]\n    #     row_di['first_time'] = dfp.pay_time.min()\n    #     row_di['last_time'] = dfp.pay_time.max()\n    #     row_di['update_times'] = 0\n    #     row_di['cnt_pay'] = len(dfp)\n    #     for x in ['pay_money', 'coupon_fee', 'credit_fee', 'liters', ]:\n    #         row_di['sum_' + x] = dfp[x].sum()\n    #     row_di['cnt_coupon'] = len(dfp.query('coupon_fee > 0'))\n    #     row_di['cnt_credit'] = len(dfp.query('credit_fee > 0'))\n    #     row_di['mwa_delta_seconds'] = dfp.pay_time.diff().fillna(0).mean()\n    #     row_di['mwa_pay_money'] = dfp['pay_money'].mean()\n    #     row_di['mwa_liters'] = dfp['liters'].mean()\n    #     row_di['mwa_paytoc'] = dfp.iloc[0]['paytoc']\n    #     if row_di['cnt_pay'] > 1:\n    #         for i in range(1, row_di['cnt_pay']):\n    #             row_di['mwa_paytoc'] = circle_avg(row_di['mwa_paytoc'], dfp.iloc[i]['paytoc'])\n    #     df_before = dw.to_dataframe(factor_get_by_uid % row_di)\n    #     if len(df_before):\n    #         delta_seconds = row_di['first_time'] - df_before.iloc[0]['last_time']\n    #         # print('before:::', row_di['uid'], row_di['stid'], row_di['first_time'],\n    #         #  df_before.iloc[0]['last_time'], delta_seconds)\n    #         if delta_seconds > 0:\n    #             # print('update:', )\n    #             row_di['first_time'] = df_before.iloc[0]['first_time']\n    #             row_di['mwa_delta_seconds'] = .5 * row_di['mwa_delta_seconds'] + .5 * delta_seconds\n    #             row_di['update_times'] = df_before.iloc[0]['update_times'] + 1\n    #             for x in sum_cols:\n    #                 row_di[x] += df_before.iloc[0][x]\n    #             for x in mwa_cols:\n    #                 row_di[x] = df_before.iloc[0][x] * 0.7 + row_di[x] * 0.3\n    #             dics.append(row_di)\n    #             # dw.di_upd_tosql(row_di, '%(clv_table)s')\n    #         # else:\n    #         #     print('this is not supposed to happen')\n    #     else:\n    #         dics.append(row_di)\n    #         # dw.di_upd_tosql(row_di, '%(clv_table)s')\n    #     df.drop(inx, inplace=True)\n    # # res = dw.df_replace_tosql(pd.DataFrame(dics), '%(clv_table)s')\n    # print(day_forpast(0, ss='%Y-%m-%d %H:%M:%S'), dw.dics_upd_tosql(dics, '%(clv_table)s'))\n\n\ndef do_snapshot(snap_name):\n    dw = my_(config.MYSQL_BI_RW_ENV)\n    return dw.sql_engine(\"create table %s as select * from %s \" % (snap_name + '_shot_%(do_day)s' % para, snap_name))\n\n\ndef drop_snapshot(snap_name):\n    dw = my_(config.MYSQL_BI_RW_ENV)\n    return dw.sql_engine(\"drop table if exists %s \" % snap_name)\n\n\ndef do_clv_merchant_tags(tar_id=25):\n    dw = my_(config.MYSQL_BI_RW_ENV)\n    dfr = dw.to_dataframe(dfsql_choice['mid_clv1'])\n    dfr.insert(0, 'tar_id', tar_id)\n    dfr.insert(0, 'batch_date', para['do_day'])\n    print(dw.df_upd_tosql(dfr, batch=2000, table=tags_table), day_forpast(0, ss='%Y-%m-%d %H:%M:%S'))\n\n\ndef cons_(dayago1, dayago4):\n    para.update({'do_day': dayago1})\n    insert_allorders()\n    do_clv_factor()\n    # 每天处理clv_factor完之后, 进行快照, 只对最关键的clv_table进行快照\n    print('snapshot:: ', do_snapshot(para['clv_table']), day_forpast(0, ss='%Y-%m-%d %H:%M:%S'))\n    do_clv_merchant_tags()\n    # 删除4天前的快照,\n    para.update({'do_day': dayago4})\n    if para['do_day'][-2:] > '01':  # 每月1号的快照暂不删除,保证3天出错也可以从月初恢复\n        table_name = '%(clv_table)s_shot_%(do_day)s' % para\n        print('drop ::', table_name, drop_snapshot(table_name))\n\n\nif __name__ == \"__main__\":\n    do_day_li = [(day_forpast(d), day_forpast(d-3)) for d in range(-3, 0)]\n    for d1, d2 in do_day_li:\n        cons_(d1, d2)\n\n    # dw = my_(config.MYSQL_BI_RW_ENV)\n    # for i in range(-1456, -1100):\n    #     para.update({'do_day': day_forpast(i)})\n    #     dfr_before = dw.to_dataframe(clv_factor_get_by_day % para)\n    #     if len(dfr_before):\n    #         break\n    #     insert_allorders(i)\n    #     do_clv_factor()\n","sub_path":"Profile_upl/Customer_Lifetime_Value.py","file_name":"Customer_Lifetime_Value.py","file_ext":"py","file_size_in_byte":23650,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"59547412","text":"import tensorflow as tf\n\nclass NP_CNN_Rand(object):\n    \"\"\"\n    A CNN which leverages both lexical and program structure information to learn unified features\n    from natural language and source code in programming language for automatically locating the\n    potential buggy source code according to bug report.\n    \"\"\"\n    def __init__(\n            self, n_vocab_size, n_sequence_length, n_filter_sizes, n_num_filters,\n            p_vocab_size, p_statement_length, p_statement_filter_size, p_statement_filters,\n            p_semantics_filter_sizes, p_semantics_filters, embedding_size,\n            l2_reg_lambda=0.0, add_class_weights=False, class_weights=None):\n\n        # Placeholders for input, output and dropout\n        self.input_n = tf.placeholder(tf.int32, [None, n_sequence_length], name=\"input_n\")\n        self.input_p = tf.placeholder(tf.int32, [None, None, p_statement_length], name=\"input_p\")\n        self.input_y = tf.placeholder(tf.float32, [None, 1], name=\"input_y\")\n        self.dropout_keep_prob = tf.placeholder(tf.float32, name=\"dropout_keep_prob\")\n\n        # Keeping track of l2 regularization loss (optional)\n        l2_loss = tf.constant(0.0)\n\n        # Convolution for natural text\n        # Embedding layer\n        with tf.name_scope(\"n_embedding\"):\n            self.n_W = tf.Variable(\n                tf.random_uniform([n_vocab_size, embedding_size], -1.0, 1.0),\n                name=\"n_W\")\n            self.n_embedded_chars = tf.nn.embedding_lookup(self.n_W, self.input_n) # shape (batch_size, n_sentence_length, embedding_size)\n            self.n_embedded_chars_expanded = tf.expand_dims(self.n_embedded_chars, -1) # shape (batch_size, n_sentence_length, embedding_size, 1)\n\n        # Create a convolution + maxpool layer for each filter size\n        n_pooled_outputs = []\n        for i, filter_size in enumerate(n_filter_sizes):\n            with tf.name_scope(\"n_conv_maxpool_%s\" % filter_size):\n                # Convolution Layer\n                filter_shape = [filter_size, embedding_size, 1, n_num_filters]\n                n_W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name=\"n_W\")\n                n_b = tf.Variable(tf.constant(0.1, shape=[n_num_filters]), name=\"n_b\")\n                n_conv = tf.nn.conv2d(\n                    self.n_embedded_chars_expanded,\n                    n_W,\n                    strides=[1, 1, 1, 1],\n                    padding=\"VALID\",\n                    name=\"n_conv\") # shape (batch_size, n_sentence_length - fiter_size + 1, 1 fiters)\n                # Apply nonlinearity\n                n_h = tf.nn.relu(tf.nn.bias_add(n_conv, n_b), name=\"n_relu\") # shape (batch_size, n_sentence_length - fiter_size + 1, 1 fiters)\n                # Maxpooling over the outputs\n                n_pooled = tf.nn.max_pool(\n                    n_h,\n                    ksize=[1, n_sequence_length - filter_size + 1, 1, 1],\n                    strides=[1, 1, 1, 1],\n                    padding='VALID',\n                    name=\"n_pool\") # shape (batch_size, 1, 1, fiters)\n                n_pooled_outputs.append(n_pooled)\n        # Combine all the natural pooled features\n        n_num_filters_total = n_num_filters * len(n_filter_sizes)\n        self.n_h_pool = tf.concat(n_pooled_outputs, 3) # shape (batch_size, 1, 1, fiters * len(n_filter_sizes))\n        self.n_h_pool_flat = tf.reshape(self.n_h_pool, [-1, n_num_filters_total]) # shape (batch_size, filters * num_fiters)\n\n        # Convolution for program\n        with tf.name_scope(\"p_embedding\"):\n            self.p_W = tf.Variable(\n                tf.random_uniform([p_vocab_size, embedding_size], -1.0, 1.0),\n                name=\"p_W\"\n            )\n            self.p_embedding_chars = tf.nn.embedding_lookup(self.p_W, self.input_p)  # shape (batch_size, code_length, statement_length, embedding_size)\n            self.p_embedded_chars_expanded = tf.expand_dims(self.p_embedding_chars, -1) # shape (batch_size, code_length, statement_length, embedding_size, 1)\n\n        # The first convolutional and pooling layer: represent the semantics of a statement based on the tokens within the statement\n        with tf.name_scope(\"p_statement_conv_maxpool\"):\n            # Convolution Layer\n            p_statement_filter_shape = [1, p_statement_filter_size, embedding_size, 1, p_statement_filters]\n            p_statement_W = tf.Variable(tf.truncated_normal(p_statement_filter_shape, stddev=0.1), name=\"p_statement_W\")\n            p_statement_b = tf.Variable(tf.constant(0.1, shape=[p_statement_filters]), name=\"p_statement_b\")\n            p_statement_conv = tf.nn.conv3d(\n                self.p_embedded_chars_expanded,\n                p_statement_W,\n                strides=[1, 1, 1, 1, 1],\n                padding=\"VALID\",\n                name=\"p_statement_conv\")  # shape (batch_size, code_length, statement_length-d+1, 1, p_statement_filters)\n            # Apply nonlinearity\n            p_statement_h = tf.nn.relu(tf.nn.bias_add(p_statement_conv, p_statement_b), name=\"p_statement_relu\")\n            # Maxpooling over the outputs\n            p_statement_pooled = tf.nn.max_pool3d(\n                p_statement_h,\n                ksize=[1, 1, p_statement_length - p_statement_filter_size + 1, 1, 1],\n                strides=[1, 1, 1, 1, 1],\n                padding='VALID',\n                name=\"p_statement_pool\") # shape (batch_size, code_length, 1, 1, p_statement_filters)\n            self.p_squeezed_statement_pooled = tf.squeeze(p_statement_pooled, axis=[2, 3]) # shape (batch_size, code_length, p_statement_filters)\n            self.p_statement_pooled_expanded = tf.expand_dims(self.p_squeezed_statement_pooled, -1) # shape (batch_size, code_length, p_statement_filters, 1)\n\n        # The second convolutional and pooling layer: model the semantics conveyed by the interactions between statements\n        p_semantics_pooled_outputs = []\n        for i, p_semantics_filter_size in enumerate(p_semantics_filter_sizes):\n            with tf.name_scope(\"p_semantics_conv_maxpool_%s\" % p_semantics_filter_size):\n                # Convolution Layer\n                p_semantics_filter_shape = [p_semantics_filter_size, p_statement_filters, 1, p_semantics_filters]\n                p_semantics_W = tf.Variable(tf.truncated_normal(p_semantics_filter_shape, stddev=0.1), name=\"p_semantics_W\")\n                p_semantics_b = tf.Variable(tf.constant(0.1, shape=[p_semantics_filters]), name=\"p_semantics_b\")\n                p_semantics_conv = tf.nn.conv2d(\n                    self.p_statement_pooled_expanded,\n                    p_semantics_W,\n                    strides=[1, 1, 1, 1],\n                    padding=\"VALID\",\n                    name=\"p_semantics_conv\") # shape (batch_size, code_length, 1, p_semantics_filters)\n                # Apply nonlinearity\n                p_semantics_h = tf.nn.relu(tf.nn.bias_add(p_semantics_conv, p_semantics_b), name=\"p_semantics_relu\")\n                # Maxpooling over the outputs\n                p_semantics_pooled = tf.nn.max_pool(\n                    p_semantics_h,\n                    ksize=[1, 100000, 1, 1],\n                    strides=[1, 1, 1, 1],\n                    padding='VALID',\n                    name=\"p_semantics_pooled\") # shape (batch_size, 1, 1, p_semantics_filters)\n                p_semantics_pooled_outputs.append(p_semantics_pooled)\n        # Combine all the pooled features\n        p_num_filters_total = p_semantics_filters * len(p_semantics_filter_sizes)\n        self.p_h_pool = tf.concat(p_semantics_pooled_outputs, 3) # shape (batch_size, 1, 1, p_semantics_filters * len(p_filter_sizes))\n        self.p_h_pool_flat = tf.reshape(self.p_h_pool, [-1, p_num_filters_total]) # shape (batch_size, p_semantics_filters * len(p_filter_sizes))\n\n        # Cross-language Feature Fusion Layers\n        with tf.name_scope(\"fusion\"):\n            np_fusion_num_filters = n_num_filters_total + p_num_filters_total\n            self.np_h_fusion = tf.concat([self.n_h_pool_flat, self.p_h_pool_flat], 1) # shape (batch_size, n_h_pool_flat.shape[1] + p_h_pool_flat.shape[1])\n\n        # Add dropout\n        with tf.name_scope(\"dropout\"):\n            self.np_h_drop = tf.nn.dropout(self.np_h_fusion, self.dropout_keep_prob)\n\n        # Final (unnormalized) scores and predictions\n        with tf.name_scope(\"output\"):\n            W = tf.get_variable(\n                \"W\",\n                shape=[np_fusion_num_filters, 2],\n                initializer=tf.contrib.layers.xavier_initializer())\n            b = tf.Variable(tf.constant(0.1, shape=[2]), name=\"b\")\n            l2_loss += tf.nn.l2_loss(W)\n            l2_loss += tf.nn.l2_loss(b)\n            self.logits = tf.nn.xw_plus_b(self.np_h_drop, W, b, name=\"logits\")\n            self.scores = tf.nn.softmax(self.logits)\n            self.predictions = tf.argmax(self.logits, 1, name=\"predictions\")\n\n            # Calculate mean cross-entropy loss\n            with tf.name_scope(\"loss\"):\n                if add_class_weights:\n                    # classes_weights = tf.constant([0.57, 0.43])\n                    # losses = tf.nn.weighted_cross_entropy_with_logits(logits=self.scores, targets=self.input_y,\n                    #                                                   pos_weight=classes_weights)\n                    if class_weights is None:\n                        weights = tf.reduce_sum(self.input_y, axis=1)\n                    else:\n                        weights = tf.reduce_sum(class_weights * self.input_y, axis=1)\n                    unweighted_losses = tf.nn.softmax_cross_entropy_with_logits(labels=self.input_y,\n                                                                                logits=self.logits)\n                    weighted_losses = unweighted_losses * weights\n                    losses = tf.reduce_mean(weighted_losses)\n                else:\n                    losses = tf.nn.softmax_cross_entropy_with_logits(logits=self.scores, labels=self.input_y)\n                self.loss = tf.reduce_mean(losses) + l2_reg_lambda * l2_loss\n\n            with tf.name_scope(\"evaluate_measures\"):\n                # Accuracy\n                with tf.name_scope(\"accuracy\"):\n                    self.labels = tf.argmax(self.input_y, 1)\n                    correct_predictions = tf.equal(self.predictions, self.labels)\n                    self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, \"float\"), name=\"accuracy\")\n\n                p_cond = tf.equal(self.labels, 0)  # get positive answer position, return a list of True and False, the p position corresponds to True\n                n_cond = tf.equal(self.labels, 1)  # get negative answer position\n                p_prediction = tf.boolean_mask(self.predictions, p_cond)\n                p_label = tf.boolean_mask(self.labels, p_cond)\n                self.tp = tf.reduce_sum(tf.cast(tf.equal(p_prediction, p_label), \"float\"), name=\"tp\")\n                self.fp = tf.subtract(tf.reduce_sum(tf.cast(tf.equal(self.predictions, 0), \"float\")), self.tp)\n                n_prediction = tf.boolean_mask(self.predictions, n_cond)\n                n_label = tf.boolean_mask(self.labels, n_cond)\n                self.tn = tf.reduce_sum(tf.cast(tf.equal(n_prediction, n_label), \"int64\"), name=\"tn\")\n                self.fn = tf.subtract(tf.reduce_sum(self.predictions), self.tn)\n                # cast tp, fp, tn, fn to float64\n                self.tp = tf.cast(self.tp, tf.float64)\n                self.fp = tf.cast(self.fp, tf.float64)\n                self.tn = tf.cast(self.tn, tf.float64)\n                self.fn = tf.cast(self.fn, tf.float64)\n                # Precision\n                self.precision = tf.div(self.tp, tf.add(self.tp, self.fp), name=\"precision\")\n                # Recall\n                self.recall = tf.div(self.tp, tf.add(self.tp, self.fn), name=\"recall\")\n                # F1-score\n                self.product = tf.multiply(tf.cast(tf.constant(2.0), tf.float64),\n                                           tf.multiply(self.precision, self.recall))\n                self.f1_score = tf.div(self.product, tf.add(self.precision, self.recall), name=\"f1_score\")\n\n\n\n","sub_path":"np_cnn.py","file_name":"np_cnn.py","file_ext":"py","file_size_in_byte":12068,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"250714509","text":"import json\nimport requests\nimport yaml\nimport os\nfrom requests import HTTPError\n\n\ndef load_config():\n    test_filename = os.path.join(os.path.dirname(__file__), 'config.yaml')\n    print(test_filename)\n    with open(test_filename, mode='r') as yaml_file:\n        config = yaml.safe_load(yaml_file)\n    return config\n\n\ndef main(config):\n\n    base_url = config['url']\n    api = config['api']\n    auth = config['auth']\n\n    headers = {'content-type': 'application/json'}\n    auth_token = ''\n    api_res=None\n    try:\n        res = requests.post(f'{base_url}{auth[\"endpoint\"]}', data=json.dumps(auth[\"credential\"]), headers=headers)\n        res.raise_for_status()\n        resp = res.json()\n        auth_token = f'JWT {resp[\"access_token\"]}'\n    except HTTPError as e:\n        print(e)\n\n    headers[\"Authorization\"] = auth_token\n\n    dates = api['date']\n    for date in dates:\n        try:\n            api_res = requests.get(f'{base_url}{api[\"endpoint\"]}', data=json.dumps({\"date\": date}), headers=headers)\n            api_res.raise_for_status()\n        except HTTPError as e:\n            print(e)\n        current_directory = os.getcwd()\n        data_folder = os.path.join(current_directory, 'data')\n        if not os.path.exists(data_folder):\n            os.makedirs(data_folder)\n\n        path_to_file = os.path.join(data_folder, f'{date}.json')\n        with open(path_to_file, mode='w+') as date_file:\n            json.dump(api_res.json(), date_file)\n\n\nif __name__ == '__main__':\n    config = load_config()\n    main(config)\n","sub_path":"Lesson7/taks2/script/readDataFromApi.py","file_name":"readDataFromApi.py","file_ext":"py","file_size_in_byte":1521,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"50556403","text":"# must sync with attribute.h and ctype_util.h\n\nfrom ctypes import *\n\n# typedef\n\nBOOLEAN = c_char\n\n# _MINI_TASK_NODE\n\nclass MINI_TASK_NODE(Structure):\n    _fields_ = [(\"vm_id\",          c_int),\n                (\"rt_type\",        c_uint, 8),\n                (\"data_ptr\",       c_ulonglong),\n\n                (\"node_id\",        c_longlong),\n                (\"IsSwap\",         BOOLEAN),\n                (\"IsHighPriority\", BOOLEAN)\n               ]\n\nSTOP_HANDLER = -100  # assign to vm_id\n\nclass PARAM_BLOCK_INFO(Structure):\n    _fields_ = [\n                   (\"param_local_offset\", c_uint64),\n                   (\"param_block_size\", c_uint64)\n               ]\n\nclass RESERVED_AREA(Union):\n    _fields_ = [\n                   (\"string\", c_char * 64),\n                   (\"uint64\", c_uint64 * 8),\n                   (\"uint8\",  c_uint8 * 64),\n                   (\"pb_info\", PARAM_BLOCK_INFO)\n               ]\nclass COMMAND_BASE(Structure):\n    _fields_ = [\n                   (\"api_id\", c_uint8),\n                   (\"vm_id\",  c_uint8),\n                   (\"command_type\", c_uint64),\n                   (\"thread_id\", c_int64),\n                   (\"flags\", c_int, 8),\n                   (\"command_id\", c_uint64),\n                   (\"command_size\", c_size_t),\n                   (\"data_region\", c_void_p),\n                   (\"region_size\", c_size_t),\n                   (\"reserved_area\", RESERVED_AREA)\n               ]\n\n\n","sub_path":"worker/py/structs.py","file_name":"structs.py","file_ext":"py","file_size_in_byte":1416,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"465495152","text":"from django.shortcuts import render, get_object_or_404, redirect\nfrom django.http import HttpResponse, Http404\nfrom django.http import JsonResponse\nfrom django.core.serializers.json import DjangoJSONEncoder\nfrom .parsers import Parser, JsonParser\nfrom .models import Experiment, ExperimentData, Template, Fields, Comment\nfrom .models import Project, Tag\nfrom io import TextIOWrapper\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth import get_user\nimport json\nimport csv\nfrom .forms import FileUpload, ExperimentForm, ExperimentDataForm, ProjectForm\nfrom io import StringIO\nfrom django.views.generic import ListView\nfrom .mixins import CompanyObjectsMixin, ExpFilterMixin\n\n\n@login_required\ndef index(request):\n    \"\"\"\n    Index should be the main landing page for the application. It will show\n    all of the available data to the researcher, and allow them to link to\n    other resources, such as uploading and analysis.\n    \"\"\"\n\n    company = request.user.company\n\n    latest = Experiment.objects.filter(company=company).order_by('-id').values_list('metadata', flat=True)[:10]\n\n    return render(request, 'app/index.html', {'latest': latest})\n\n\n@login_required\ndef upload(request):\n    \"\"\"\n    View for uploading data through form or upload file.\n    Will accept any file format supported by the parser.\n    The form itself sends experiment data and metadata as JSON.\n\n    \"\"\"\n    company = request.user.company\n\n    if request.method == \"POST\":\n        exp_form = ExperimentForm(request.POST, prefix='exp', company=company)\n        file_form = FileUpload(request.POST, request.FILES, prefix='file')\n        exp_data = ExperimentDataForm(request.POST, prefix='exp_data', company=company)\n\n        print(\"request\", request)\n\n        if exp_form.is_valid() and (file_form.is_valid() or exp_data.is_valid()):\n            print(\"exp_form cleaned_data\", exp_form.cleaned_data)\n\n\n            # get experiment object and add missing attributes.\n            # still missing metadata.\n\n            experiment = exp_form.save(commit=False)\n            experiment.company = company\n            experiment.user = request.user\n\n            # if it was a file upload, parse and populate form with data for\n            # confirmation.\n            if file_form.is_valid():\n                parser = Parser(buffer=TextIOWrapper(\n                    request.FILES['file-upload_file'],\n                    encoding=request.encoding\n                    ))\n                parser.get_parser().create_objects(experiment)\n\n            # if it was a form upload\n            elif exp_data.is_valid():\n                data = exp_data.cleaned_data.get(\"json\")\n                parser = JsonParser(buffer=StringIO(data))\n                parser.create_objects(experiment)\n\n            exp_form.save_m2m()\n\n            return redirect(\"/app/upload/success/\" + str(experiment.id))\n\n    if request.method == \"GET\":\n        exp_form = ExperimentForm(prefix='exp', company=company)\n        file_form = FileUpload(prefix='file')\n        exp_data = ExperimentDataForm(prefix='exp_data', company=company)\n\n    # to handle errors, the context declaration is here.\n    context = {\n        'exp_form': exp_form,\n        'file_form': file_form,\n        'exp_data': exp_data\n    }\n\n    return render(request, 'app/upload.html', context)\n\n\n@login_required\ndef get_template(request):\n    \"\"\"\n    Responds to ajax request for getting fields in template.\n    \"\"\"\n    if request.method == 'GET':\n\n        template = get_object_or_404(\n            Template,\n            company=request.user.company,\n            name=request.GET.get('name'))\n\n        metadata = list(template.metadata.values_list('name', 'data_type'))\n        fields = list(template.fields.values_list('name', 'data_type'))\n\n        context = {\n            \"metadata\": metadata,\n            \"fields\": fields,\n        }\n\n        return JsonResponse(context)\n\n    raise Http404\n\n\n# Response for successful upload.\n@login_required\ndef upload_success(request, exp_id):\n    get_object_or_404(Experiment, id=exp_id)\n    return render(request, 'app/upload_success.html', {'exp_id': exp_id})\n\n\nclass ExperimentListView(ExpFilterMixin, ListView):\n    \"\"\"\n    View for Experiment list\n    \"\"\"\n    model = Experiment\n    template_name = 'app/experiments_page.html'\n\n@login_required\ndef experiment(request, exp_id):\n    company = request.user.company\n    user = get_user(request)\n\n    this_experiment = get_object_or_404(Experiment, company=company,\n        id=exp_id)\n\n    metadata = json.dumps(this_experiment.metadata)\n\n    comments = Comment.objects.filter(experiment = this_experiment).order_by('id')\n    return render(request,\"app/experiment.html\", {\"this_experiment\": this_experiment, \"usr\": user, \"metadata\": metadata, \"comments\": comments})\n\n\n@login_required\ndef experiment_json(request, exp_id):\n    company = request.user.company\n    data = ExperimentData.objects.filter(company=company, experiment=exp_id)\n\n    if not data.exists():\n        raise Http404(\"Experiment does not exist.\")\n\n    newval = {}\n    newval = {k: v.experimentData for k, v in enumerate(data)}\n    return JsonResponse(newval)\n\n@login_required\ndef experimentrm(request, exp_id):\n    company = request.user.company\n    data = Experiment.objects.filter(company=company, id=exp_id)\n    if not data.exists():\n        raise Http404(\"Experiment does not exist.\")\n\n    res = data.delete()\n    return JsonResponse({\"result\": res[0]>0})\n\n\n@login_required\ndef get_csv(request, exp_id, header=0):\n    company = request.user.company\n    response = HttpResponse(content_type='text/csv')\n    response['Content-Disposition'] = 'attachment; filename=\"'+exp_id+'.csv\"'\n    vals = ExperimentData.objects.filter(company=company, experiment=exp_id)\n    newdata = []\n    [newdata.append(json.loads(i.experimentData)) for i in vals]\n    fieldnames = []\n    [fieldnames.append(k) for k in newdata[0]]\n    writer = csv.DictWriter(response, fieldnames)\n    writer.writeheader()\n    [writer.writerow(i) for i in newdata]\n    return response\n\n\ndef analysis_page(request):\n    company = request.user.company\n    all_tags = Tag.objects.filter(company=company)\n    all_groups = Project.objects.filter(company=company)\n    return render(request, \"app/analysis.html\", {\"usr\":get_user(request), \"tags\":all_tags, \"groups\":all_groups})\n\n\nclass ProjectListView(CompanyObjectsMixin, ListView):\n    model = Project\n    template_name = 'app/project_list.html'\n\n\ndef create_project(request):\n    \"\"\"\n    View for creating new projects.\n    \"\"\"\n    if request.method == \"POST\":\n        company = request.user.company\n        project_form = ProjectForm(request.POST)\n\n        if project_form.is_valid():\n            project = project_form.save(commit=False)\n            project.company = company\n            project.save()\n\n            return redirect(\"/app/projects\")\n\n    if request.method == \"GET\":\n        project_form = ProjectForm()\n\n    context = {\"form\": project_form}\n\n    return render(request, 'app/create_project.html', context)\n\n\ndef project_page(request, p_id):\n    \"\"\"\n    View for displaying the details of a project.\n    \"\"\"\n    company = request.user.company\n\n    try:\n        project = Project.objects.get(company=company, id=p_id)\n    except Project.DoesNotExist:\n        raise Http404(\"Project not found\")\n\n    experiments = Experiment.objects.filter(company=company, project=p_id)\n\n    context = {\n        \"experiments\": experiments,\n        \"project\": project\n    }\n\n    return render(request, \"app/view_project.html\", context)\n\n\n@login_required\ndef create_tag(request):\n    if request.method == \"POST\":\n        tag = request.POST.get(\"tag\")\n        Tag.objects.create(name=tag, company=request.user.company)\n\n        return HttpResponse(status=201)\n\n    raise Http404\n","sub_path":"app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7773,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"448748011","text":"# -*- coding: utf-8 -*-\nimport time\n\nimport scrapy\n\nfrom githubletters.db import ProjectItem\nfrom githubletters.items import GithubLettersItem\nfrom githubletters.utils import next_date, get_date, strip, get_now_date\n\n\nclass LettersSpider(scrapy.Spider):\n    name = 'letters'\n    init_date = '2014-10-8'\n    now_date = get_now_date()\n    allowed_domains = ['www.open-open.com']\n    url_format = 'http://www.open-open.com/github/view/github{}.html'\n\n    def start_requests(self):\n        projects = ProjectItem.select().order_by(ProjectItem.id.desc()).limit(1)\n        date = projects[0].date if len(projects) == 1 else self.init_date\n        url = self.url_format.format(next_date(date))\n        yield scrapy.Request(url=url, callback=self.parse)\n\n    def parse(self, response):\n        rows = response.css('.rowlink tr')\n        for row in rows:\n            item = GithubLettersItem()\n            item['language'] = strip(row.css('.table-language::text').extract_first())\n            item['star'] = strip(row.css('.table-change::text').extract_first())\n            item['title'] = strip(row.css('.table-name a::text').extract_first())\n            item['description'] = strip(row.css('.table-description::text').extract_first())\n            item['link'] = strip(row.css('.table-name a::attr(\"href\")').extract_first())\n            item['created_at'] = int(time.time())\n            item['date'] = get_date(response.url)\n            yield item\n        new_date = next_date(get_date(response.url))\n        if new_date <= self.now_date:\n            new_url = self.url_format.format(new_date)\n            yield scrapy.Request(url=new_url, callback=self.parse)\n","sub_path":"githubletters/spiders/letters.py","file_name":"letters.py","file_ext":"py","file_size_in_byte":1653,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"27998580","text":"from pymongo import MongoClient\nfrom dateutil import parser\nfrom datetime import datetime, timedelta\n\nconn = MongoClient(\"mongodb://127.0.0.1:27017/\")\ndb = conn.get_database(\"finance\")\n\ndef make_coll(category):\n    collections = db.get_collection(category)\n    return collections\n\ndef upsert_DB(info, category):\n    coll_category = make_coll(category)\n    for i in info:\n        coll_category.replace_one({\"Date\":i[\"Date\"]}, i , upsert=True)\n\ndef get_DB(dateStr, category):\n    coll_category = make_coll(category)\n    dateISO = parser.parse(dateStr)\n    obj_train = coll_category.aggregate([{\"$match\":{\"Date\":{\"$lt\":dateISO}}},\n                                {\"$project\":{\"_id\":0, \"Date\":1, \"High\":1, \"Low\":1, \"Open\":1, \"Close\":1}},\n                                {\"$sort\":{\"Date\":1}}])\n\n    obj_test = coll_category.aggregate([{\"$match\":{\"Date\":{\"$gte\":dateISO}}},\n                                {\"$project\":{\"_id\":0, \"Date\":1, \"High\":1, \"Low\":1, \"Open\":1, \"Close\":1}},\n                                {\"$sort\":{\"Date\":1}}])\n    return (list(obj_train), list(obj_test))\n\ndef upsert_DB_holly(dateStr, category):\n    coll_category = make_coll(category)\n    dateISO = parser.parse(dateStr)\n    start_obj = coll_category.aggregate([{\"$sort\":{\"Date\":1}},\n                                {\"$limit\":1}\n                                ])\n\n    end_obj = coll_category.aggregate([{\"$sort\":{\"Date\":-1}},\n                                {\"$limit\":1}\n                                ])\n    list_data = list(start_obj)\n    tmp_date = list_data[0][\"Date\"]\n    tmp_value = list_data[0][\"values\"]\n    to_date = list(end_obj)[0][\"Date\"]\n    to_date = datetime.strptime(to_date, \"%Y-%m-%d\").date()\n   \n    for i in range(1,1000):\n        date_obj = datetime.strptime(tmp_date, \"%Y-%m-%d\")\n        if date_obj.date() == to_date:\n            break\n        \n        res = coll_category.find_one({\"Date\":f\"{tmp_date}\"})\n        if res != None:\n            target_obj = date_obj + timedelta(days=1)\n            next_day = datetime.strftime(target_obj, '%Y-%m-%d')\n            tmp_date = next_day\n            tmp_value = res[\"values\"]\n            \n        else:\n            target_obj = date_obj + timedelta(days=1)\n            next_day = datetime.strftime(target_obj, '%Y-%m-%d')\n\n            tmp_dict = {\"Date\":tmp_date, \"values\": tmp_value, \"etc\" : \"HollyDay\"}\n            \n            print(tmp_dict)\n            \n            coll_category.replace_one({\"Date\":tmp_date}, tmp_dict , upsert=True)\n            \n\n\n\"\"\" \n##############################################################################\n\n    INSERT DATA\n    upsert --> prevent overlap\n    \n    upsert_kospi  => insert all KOSPI data\n    upsert_nasdaq => insert all NASDAQ data\n    \n    (after predict)\n    upsert_kospi_pred  => insert predicted KOSPI data\n    upsert_nasdaq_pred => insert predicted NASDAQ data\n\n##############################################################################\n\"\"\"\n\n\"\"\"\n##############################################################################\ndef upsert_kospi(info):\n    for i in info:\n        coll_kospi.replace_one({\"Date\":i[\"Date\"]}, i , upsert=True)\n\ndef upsert_nasdaq(info):\n    for i in info:\n        coll_nasdaq.replace_one({\"Date\":i['Date']}, i, upsert=True)\n\ndef upsert_kospi_pred(info):\n    for i in info:\n        coll_kospi_pred.replace_one({\"Date\":i[\"Date\"]}, i , upsert=True)\n\ndef upsert_nasdaq_pred(info):\n    for i in info:\n        coll_nasdaq_pred.replace_one({\"Date\":i['Date']}, i, upsert=True)\n\n##############################################################################\n###############################################################################\n    \n    GET DATA FROM MONGO\n    (to make csv file)\n\n    get_kospi  => get all KOSPI data from MONGO except {_id} and sorted by Date\n    get_nasdaq =>    ''   NASDAQ  ''\n\n    get_kospi_cond  => get specific period KOSPI data from MONGO except {_id} and sorted by Date\n    get_nasdaq_cond => get specific period NASDAQ                     ''\n\n\ndef get_kospi():\n    obj = coll_kospi.aggregate([{\"$project\":{\"_id\":0, \"Date\":1, \"High\":1, \"Low\":1, \"Open\":1, \"Close\":1}},\n                                {\"$sort\":{\"Date\":1}}])\n    return list(obj)\n\ndef get_nasdaq():\n    obj = coll_nasdaq.aggregate([{\"$project\":{\"_id\":0, \"Date\":1, \"High\":1, \"Low\":1, \"Open\":1, \"Close\":1}},\n                                {\"$sort\":{\"Date\":1}}])\n    return list(obj)\n\ndef get_kospi_cond():\n    dateStr = \"2021-04-01T00:00.000Z\"\n    dateISO = dateutil.parser.parse(dateStr)\n    obj = coll_kospi.aggregate([{\"$match\":{\"Date\":{\"$lt\":dateISO}}},\n                                {\"$project\":{\"_id\":0, \"Date\":1, \"High\":1, \"Low\":1, \"Open\":1, \"Close\":1}},\n                                {\"$sort\":{\"Date\":1}}])\n    return list(obj)\n\ndef get_nasdaq_cond():\n    dateStr = '2021-04-01T00:00:00.000Z'\n    dateISO = dateutil.parser.parse(dateStr)\n    obj = coll_nasdaq.aggregate([{\"$match\":{\"Date\":{\"$lt\":dateISO}}},\n                                {\"$project\":{\"_id\":0, \"Date\":1, \"High\":1, \"Low\":1, \"Open\":1, \"Close\":1}},\n                                {\"$sort\":{\"Date\":1}}])\n    return list(obj)\n\n\n###############################################################################\n\"\"\"\n","sub_path":"Python/insert_data/mongo.py","file_name":"mongo.py","file_ext":"py","file_size_in_byte":5197,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"14175331","text":"#\n# -*- coding: utf-8 -*-\n#\n# Copyright (c) 2007 Jared Crapo\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in\n# all copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n# THE SOFTWARE.\n#\n\nimport unittest.mock as mock\n\nimport pytest\n\nimport tomcatmanager as tm\n\n\ndef get_itm(tms):\n    \"\"\"\n    Using this as a fixture with capsys breaks capsys. So we use a function.\n    \"\"\"\n    itm = tm.InteractiveTomcatManager()\n    args = 'connect {url} {user} {password}'.format(**tms)\n    itm.onecmd_plus_hooks(args)\n    return itm\n\n\n###\n#\n# list\n#\n###\n@pytest.fixture()\ndef mock_apps(mocker):\n    return mocker.patch('tomcatmanager.models.TomcatManagerResponse.result', create=True,\n                        new_callable=mock.PropertyMock)\n\ndef parse_apps(lines):\n    \"\"\"helper function to turn colon seperated text into objects\"\"\"\n    apps = []\n    for line in lines.splitlines():\n        app = tm.models.TomcatApplication()\n        app.parse(line)\n        apps.append(app)\n    return apps\n\ndef test_list_process_apps_empty():\n    lines = ''\n    itm = tm.InteractiveTomcatManager()\n    args = itm.parse_args(itm.list_parser, '')\n    apps = parse_apps(lines)\n    apps = itm._list_process_apps(apps, args)\n    assert isinstance(apps, list)\n    assert apps == []\n\nLIST_ARGV_BAD = [\n    '-raw',\n    '--raw -b tate',\n    '--by=version',\n    '-s loading',\n    '-sl',\n    '-bs',\n]\n@pytest.mark.parametrize('argv', LIST_ARGV_BAD)\ndef test_list_parse_args_failure(argv):\n    itm = tm.InteractiveTomcatManager()\n    with pytest.raises(SystemExit):\n        itm.parse_args(itm.list_parser, argv)\n    assert itm.exit_code == itm.exit_codes.usage\n\n@pytest.mark.parametrize('raw', ['', '-r', '--raw'])\n@pytest.mark.parametrize(\n    'state',\n    ['', '-s running', '-s stopped', '--state=running', '--state=stopped'],\n)\n@pytest.mark.parametrize(\n    'sort',\n    ['', '-b state', '-b path', '--by=state', '--by=path'],\n)\ndef test_list_parse_args(raw, state, sort):\n    itm = tm.InteractiveTomcatManager()\n    argv = '{} {} {}'.format(raw, state, sort)\n    itm.parse_args(itm.list_parser, argv)\n    assert itm.exit_code == itm.exit_codes.success\n\ndef test_list_sort_by_state(tomcat_manager_server, mock_apps, capsys):\n    raw_apps = \"\"\"/shiny:stopped:0:shiny##v2.0.6\n/:running:0:ROOT\n/shiny:running:15:shiny##v2.0.7\n/host-manager:running:0:/usr/share/tomcat8-admin/host-manager\n/manager:running:0:/usr/share/tomcat8-admin/manager\n\"\"\"\n    expected = \"\"\"/:running:0:ROOT\n/host-manager:running:0:/usr/share/tomcat8-admin/host-manager\n/manager:running:0:/usr/share/tomcat8-admin/manager\n/shiny:running:15:shiny##v2.0.7\n/shiny:stopped:0:shiny##v2.0.6\n\"\"\"\n    mock_apps.return_value = raw_apps\n    interactive_tomcat = get_itm(tomcat_manager_server)\n    interactive_tomcat.onecmd_plus_hooks('list --raw -b state')\n    out, _ = capsys.readouterr()\n    assert out == expected\n\ndef test_list_sort_by_path(tomcat_manager_server, mock_apps, capsys):\n    raw_apps = \"\"\"/:running:0:ROOT\n/shiny:stopped:0:shiny##v2.0.6\n/shiny:running:15:shiny##v2.0.7\n/host-manager:running:0:/usr/share/tomcat8-admin/host-manager\n/manager:running:0:/usr/share/tomcat8-admin/manager\n\"\"\"\n    expected = \"\"\"/:running:0:ROOT\n/host-manager:running:0:/usr/share/tomcat8-admin/host-manager\n/manager:running:0:/usr/share/tomcat8-admin/manager\n/shiny:stopped:0:shiny##v2.0.6\n/shiny:running:15:shiny##v2.0.7\n\"\"\"\n    mock_apps.return_value = raw_apps\n    interactive_tomcat = get_itm(tomcat_manager_server)\n    interactive_tomcat.onecmd_plus_hooks('list --raw -b path')\n    out, _ = capsys.readouterr()\n    assert out == expected\n\ndef test_list_state_running(tomcat_manager_server, mock_apps, capsys):\n    raw_apps = \"\"\"/:running:0:ROOT\n/shiny:stopped:17:shiny##v2.0.6\n/shiny:running:15:shiny##v2.0.7\n/host-manager:stopped:0:/usr/share/tomcat8-admin/host-manager\n/manager:running:0:/usr/share/tomcat8-admin/manager\n\"\"\"\n    expected = \"\"\"/:running:0:ROOT\n/manager:running:0:/usr/share/tomcat8-admin/manager\n/shiny:running:15:shiny##v2.0.7\n\"\"\"\n    mock_apps.return_value = raw_apps\n    interactive_tomcat = get_itm(tomcat_manager_server)\n    interactive_tomcat.onecmd_plus_hooks('list --raw -s running')\n    out, _ = capsys.readouterr()\n    assert out == expected\n","sub_path":"tests/test_interactive_apps.py","file_name":"test_interactive_apps.py","file_ext":"py","file_size_in_byte":5123,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"308169131","text":"\"\"\"Drawing forests in a loop.\"\"\"\n\n__author__ = \"730481343\"\n\n# The string constant for the pine tree emoji\nTREE: str = '\\U0001F332'\n\ndepth: int = int(input(\"Depth: \"))\ncounter: int = 0\n\ntree_image: str = \"\"\n\nif depth > 0: \n    while counter < depth: \n        if counter != depth:\n            tree_image = tree_image + TREE + \" \"\n            counter = counter + 1\n            print(tree_image)\n        else: \n            beat_string = tree_image + TREE\n            counter = counter + 1\n            print(tree_image)","sub_path":"exercises/ex03/happy_trees.py","file_name":"happy_trees.py","file_ext":"py","file_size_in_byte":514,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"33806673","text":"import os\nimport sys\n\nsys.path.append(os.path.join(os.path.dirname(__file__), '../common/'))\nsys.path.append(os.path.join(os.path.dirname(__file__), '../'))\n\nfrom common import CommonUtils\n\n__VERSION__ = \"0.0.0\"\n__NAME__ = \"MediaLib\"\n__APP_NAME__ = str.format(f\"{__NAME__} {__VERSION__}\")\n\nlogger = CommonUtils.get_logger(__APP_NAME__)\n\n\n__DB_PATH__ = os.path.join(os.path.dirname(__file__), \"resource/db\")\n__DB_NAME__ = \"media_lib.sqlite\"\n__DB_TEMPLATE__ = \"media_lib_template.sqlite\"\n\ndb_path = os.path.join(__DB_PATH__, __DB_NAME__)\ndb_template_path = os.path.join(__DB_PATH__, __DB_TEMPLATE__)\n","sub_path":"MediaLib/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":598,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"256351188","text":"# -*- coding: utf-8 -*-\n\nfrom __future__ import print_function\nimport traceback\n\nfrom controller.base_command import Command\nfrom client.hippo_serving_service import HippoServingService\nfrom entity.column_enum import HippoColumn, CliBeanColumn\n\nfrom entity.request_entity import HippoInstanceRequest\n\n\nclass StartCommand(Command):\n    def __init__(self, api_url):\n        super(StartCommand, self).__init__()\n        self.hippoServingService = HippoServingService(api_url)\n\n    def verify_args(self, **kwargs):\n\n        interval = kwargs.get(CliBeanColumn.INTERVAL.value)\n\n        if interval != None:\n            interval = interval * 1000\n            kwargs[CliBeanColumn.INTERVAL.value] = interval\n\n        return kwargs\n\n    def execute(self, **kwargs):\n\n        try:\n            inputs = self.verify_args(**kwargs)\n            hippo_id = inputs.get(CliBeanColumn.ID.value)\n            interval = inputs.get(CliBeanColumn.INTERVAL.value)\n            # call http\n            request_entity = HippoInstanceRequest(\n                id=hippo_id, interval=interval)\n            is_success, resp = self.hippoServingService.start_service(\n                request_entity)\n            if not is_success:\n                raise Exception('message: {}\\n reason: {}'.format(resp.get('message'),resp.get('reason')))\n            output_dict = self.refactor_result(resp)\n            self.output(output_dict)\n\n        except Exception as e:\n            self.logger.error('Start {} service failed'.format(hippo_id))\n            self.logger.error(e.message)\n            self.logger.debug(traceback.format_exc())\n","sub_path":"cli/app/controller/start_cmd.py","file_name":"start_cmd.py","file_ext":"py","file_size_in_byte":1597,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"305964532","text":"# -*- coding: utf-8 -*-\r\nimport ctn\r\nimport block\r\n\r\n# 玩家状态容器\r\nclass cStateContainer(ctn.cContainerBase, block.cBlock):\r\n\tdef __init__(self, iOwnerId):\r\n\t\tblock.cBlock.__init__(self, '状态数据块', iOwnerId)\r\n\t\tctn.cContainerBase.__init__(self, iOwnerId)\r\n\t\tself.setIsStm(sql.STATE_INSERT)\r\n\t\tself.setDlStm(sql.STATE_DELETE)\r\n\t\tself.setUdStm(sql.STATE_UPDATE)\r\n\t\tself.setSlStm(sql.STATE_SELECT)\r\n\r\n\tdef _createAndLoadItem(self, iIndex, uData):\r\n\t\tif isinstance(uData, tuple):\r\n\t\t\tiNo, dData = uData\r\n\t\telse:\r\n\t\t\tiNo, dData = uData, {}\r\n\t\tobj = state.createAndLoad(iNo, dData)\r\n\t\tif not obj.isValid():\r\n\t\t\treturn None\r\n\t\treturn obj\r\n\r\n\tdef _newItem(self, key):\r\n\t\treturn state.new(key)\r\n\r\n\tdef setup(self, obj, isLogin=False):\r\n\t\twho = self.getOwnerObj()\r\n\t\tif who and hasattr(obj, \"setup\"):\r\n\t\t\tobj.setup(who)\r\n\r\n\tdef cancelSetup(self, obj):\r\n\t\twho = self.getOwnerObj()\r\n\t\tif who and hasattr(obj, \"cancelSetup\"):\r\n\t\t\tobj.cancelSetup(who)\r\n\r\n\t@property\r\n\tdef endPoint(self):\r\n\t\timport mainService\r\n\t\treturn mainService.getEndPointByRoleId(self.ownerId)\r\n\r\n\tdef _rpcAddItem(self, stateObj):\r\n\t\tself.endPoint.rpcStateInfo(stateObj.getMsg())\r\n\r\n\tdef _rpcRemoveItem(self, stateObj):\r\n\t\tself.endPoint.rpcStateInfoDel(stateObj.no)\r\n\r\n\tdef _rpcRefresh(self):\r\n\t\tstateInfos = []\r\n\t\tfor stateObj in self.getAllValues():\r\n\t\t\tstateInfos.append(stateObj.getMsg())\r\n\t\tstateInfoAll = state_pb2.stateInfoAll()\r\n\t\tstateInfoAll.stateInfoList.extend(stateInfos)\r\n\t\tself.endPoint.rpcStateInfoAll(stateInfoAll)\r\n\r\n\tdef _dirtyEventHandler(self):\r\n\t\tfactoryConcrete.stateFtr.schedule2tail4save(self.ownerId)\r\n\r\n\t# 更新状态值并刷新给客户端\r\n\tdef updateItemByKey(self, iKey):\r\n\t\tobj = self.getItem(iKey)\r\n\t\tif not obj:\r\n\t\t\treturn\r\n\t\tself.endPoint.rpcStateInfo(obj.getMsg())\r\n\r\nimport factoryConcrete\r\nimport sql\r\nimport state\r\nimport state_pb2\r\n","sub_path":"logic/block/blockState.py","file_name":"blockState.py","file_ext":"py","file_size_in_byte":1848,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"116572430","text":"\"\"\"\n/*******************************************************************************\n * @author  Joseph Kamel\n * @email   josephekamel@gmail.com\n * @date    28/11/2018\n * @version 2.0\n *\n * SCA (Secure Cooperative Autonomous systems)\n * Copyright (c) 2013, 2018 Institut de Recherche Technologique SystemX\n * All rights reserved.\n *******************************************************************************/\n\"\"\"\nimport os\nimport numpy as np\nfrom numpy import array\nimport math\nimport itertools\nimport operator\n\nclass MlArrayStorage:\n\n\tdef __init__(self, *args, **kwargs):\n\t\tself.id_index = []\n\t\tself.id_time = []\n\t\tself.id_bsm = []\n\t\tself.id_array_x = []\n\t\tself.id_array_y = []\n\n\tdef add_bsm(self, id, time, cur_bsm, batch_size):\n\t\ttry:\n\t\t\tindex = self.id_index.index(id)\n\t\t\tcur_array = self.get_bsm_array(cur_bsm,self.id_bsm[index])\n\t\texcept ValueError:\n\t\t\tcur_array = self.get_bsm_array(cur_bsm,cur_bsm)\n\n\t\tself.add_array(id,time,cur_array,cur_bsm,batch_size)\n\n\tdef add_array(self, id, time, cur_array,cur_bsm, batch_size):\n\t\tindex = 0\n\t\ttry:\n\t\t\tindex = self.id_index.index(id)\n\t\t\tself.id_time[index].append(time)\n\t\t\tself.id_bsm[index] = cur_bsm\n\t\t\tself.id_array_x[index].append(cur_array[0])\n\t\t\tself.id_array_y[index].append(cur_array[1])\n\n\t\t\tif len(self.id_time[index]) > batch_size:\n\t\t\t\tself.id_time[index] = self.id_time[index][-batch_size:]\n\t\t\t\tself.id_array_x[index] = self.id_array_x[index][-batch_size:]\n\t\t\t\tself.id_array_y[index] = self.id_array_y[index][-batch_size:]\n\t\t\tself.bubblesort(index)\n\n\t\texcept ValueError:\n\t\t\tself.id_index.append(id)\n\t\t\tappend_time = []\n\t\t\tappend_time.append(time)\n\t\t\tself.id_time.append(append_time)\n\t\t\tself.id_bsm.append(cur_bsm)\n\t\t\tappend_array_x = []\n\t\t\tappend_array_x.append(cur_array[0])\n\t\t\tself.id_array_x.append(append_array_x)\n\t\t\tappend_array_y = []\n\t\t\tappend_array_y.append(cur_array[1])\n\t\t\tself.id_array_y.append(append_array_y)\n\n\t\t#print  'local: ' + str(index) + ' ' + str(id) + ' ' + str(len(self.id_index))\n\t\t#print  'id_index: ' + str(id_index.shape)\n\n\tdef bubblesort(self, index):\n\t\tfor iter_num in range(len(self.id_time[index])-2,-1,-1):\n\t\t\tfor idx in range(iter_num):\n\t\t\t\tif self.id_time[index][idx]>self.id_time[index][idx+1]:\n\t\t\t\t\ttemp = self.id_time[index][idx]\n\t\t\t\t\tself.id_time[index][idx] = self.id_time[index][idx+1]\n\t\t\t\t\tself.id_time[index][idx+1] = temp\n\n\t\t\t\t\ttemp2 = self.id_array_x[index][idx]\n\t\t\t\t\tself.id_array_x[index][idx] = self.id_array_x[index][idx+1]\n\t\t\t\t\tself.id_array_x[index][idx+1] = temp2\n\n\t\t\t\t\ttemp3 = self.id_array_y[index][idx]\n\t\t\t\t\tself.id_array_y[index][idx] = self.id_array_y[index][idx+1]\n\t\t\t\t\tself.id_array_y[index][idx+1] = temp3\n\n\t\t\t\t\tos._exit(0)\n\n\n\tdef get_bsm_array(self,bsmNew,bsmOld):\n\t\trP = bsmNew['BsmPrint']['BsmCheck']['rP']\n\t\tpP = bsmNew['BsmPrint']['BsmCheck']['pP']\n\t\tsP = bsmNew['BsmPrint']['BsmCheck']['sP']\n\t\tpC = bsmNew['BsmPrint']['BsmCheck']['pC']\n\t\tsC = bsmNew['BsmPrint']['BsmCheck']['sC']\n\t\tpsC = bsmNew['BsmPrint']['BsmCheck']['psC']\n\t\tpsmC = bsmNew['BsmPrint']['BsmCheck']['psmC']\n\t\tphC = bsmNew['BsmPrint']['BsmCheck']['phC']\n\t\tsA = bsmNew['BsmPrint']['BsmCheck']['sA']\n\t\tbF = bsmNew['BsmPrint']['BsmCheck']['bF']\n\t\tkPACS = bsmNew['BsmPrint']['BsmCheck']['kPACS']\n\t\tkPCS = bsmNew['BsmPrint']['BsmCheck']['kPCS']\n\t\tkPSCP = bsmNew['BsmPrint']['BsmCheck']['kPSCP']\n\t\tkPSCS = bsmNew['BsmPrint']['BsmCheck']['kPSCS']\n\t\tkPSCSP = bsmNew['BsmPrint']['BsmCheck']['kPSCSP']\n\t\tkPSCSS = bsmNew['BsmPrint']['BsmCheck']['kPSCSS']\n\t\tkSCC = bsmNew['BsmPrint']['BsmCheck']['kSCC']\n\t\tinT = 1\n\t\tfor x in bsmNew['BsmPrint']['BsmCheck']['inT']:\n\t\t\tif inT>x['uVal']:\n\t\t\t\tinT = x['uVal']\n\n\t\tDeltaPos = self.get_distance(bsmNew['BsmPrint']['BSMs'][0]['Pos'], bsmOld['BsmPrint']['BSMs'][0]['Pos'])\n\t\tPosConfidence = self.get_speed(bsmNew['BsmPrint']['BSMs'][0]['PosConfidence'])\n\t\tSpeed = self.get_speed(bsmNew['BsmPrint']['BSMs'][0]['Speed'])\n\t\tDeltaSpeed = self.get_speed(bsmNew['BsmPrint']['BSMs'][0]['Speed']) - self.get_speed(bsmOld['BsmPrint']['BSMs'][0]['Speed'])\n\t\tSpeedConfidence = self.get_speed(bsmNew['BsmPrint']['BSMs'][0]['SpeedConfidence'])\n\t\tAccel = self.get_speed(bsmNew['BsmPrint']['BSMs'][0]['Accel'])\n\t\tDeltaAccel = self.get_speed(bsmNew['BsmPrint']['BSMs'][0]['Accel']) - self.get_speed(bsmOld['BsmPrint']['BSMs'][0]['Accel'])\n\t\tAccelConfidence = self.get_speed(bsmNew['BsmPrint']['BSMs'][0]['AccelConfidence'])\n\t\tDeltaHeading = self.get_angle_delta(bsmNew['BsmPrint']['BSMs'][0]['Heading'], bsmOld['BsmPrint']['BSMs'][0]['Heading'])\n\t\tHeadingConfidence = self.get_speed(bsmNew['BsmPrint']['BSMs'][0]['HeadingConfidence'])\n\t\tDeltaTime = bsmNew['BsmPrint']['Metadata']['generationTime'] - bsmOld['BsmPrint']['Metadata']['generationTime']\n\t\t\n\t\t#simulated HCA, please request for real function\n\t\tCC = self.get_CC(pC,sC,psC,psmC,phC)\n\t\tOBC = self.get_OBC(inT)\n\t\tRBC = self.get_RBC(rP,pP,sP,sA)\n\t\tBBC = self.get_BBC(bF)        \n        \n\t\tif DeltaTime<0:\n\t\t\tprint('Error')\n\n\t\tlabel = bsmNew['BsmPrint']['Metadata']['mbType']\n\n\t\t#label = 0\n\t\tif(label == 'Genuine'):\n\t\t\tnumLabel = 0.0\n\t\telse:\n\t\t\tnumLabel = 1.0\n\n\t\tvelAry1 = array([1.0-rP,1.0-pP,1.0-sP,1.0-pC,1.0-sC,1.0-psC,1.0-psmC,1.0-phC,1.0-sA,1.0-kPACS,1.0-kPCS,1.0-kPSCP,1.0-kPSCS,1.0-kPSCSP,1.0-kPSCSS,1.0-kSCC,1.0-inT])\n\t\tzeros = np.count_nonzero(velAry1 == 1.0)\n\t\tvelAry2 = [CC,OBC,RBC,BBC,DeltaPos, PosConfidence, DeltaSpeed, SpeedConfidence,DeltaAccel,AccelConfidence, DeltaHeading, HeadingConfidence, DeltaTime]\n\t\tvelAry = velAry1.tolist() + velAry2\n\t\tvaluesArray = np.asarray(velAry)\n\t\ttargetArray = array([numLabel])\n\t\treturnArray = array([valuesArray,targetArray])\n\t\t#print valuesArray\n\t\t#print \"returnArray: \" + str(returnArray)\n\t\t#returnArray = returnArray.astype(np.float)\n\t\treturn returnArray\n\n\tdef get_array(self, id):\n\t\tindex = self.id_index.index(id)\n\t\treturn array([self.id_array_x[index][-1],self.id_array_y[index][-1]])\n\n\tdef get_array_MLP(self, id, batch_size):\n\t\tindex = self.id_index.index(id)\n\t\tlist_X = self.id_array_x[index][-batch_size:]\n\n\t\tapp_ary = array([[len(list_X)]])\n\t\tfor i in range(0,len(list_X)-1):\n\t\t\tapp_ary = np.append(app_ary.tolist(),[[len(list_X)]],axis=0)\n\t\tlist_X = np.append(list_X,app_ary,axis=1)\n\t\t\n\t\t#ret_array = [sum(x) for x in zip(*list_X)]\n\t\tret_array = np.append(list_X[:,:11].sum(axis=0),list_X[-1,11:])\n\t\tlist_Y = self.id_array_y[index][-batch_size:]\n\t\treturn array([array(ret_array),list_Y[-1]])\n\n\tdef get_array_lstm(self, id, batch_size):\n\t\tindex = self.id_index.index(id)\n\t\tlist_X = self.id_array_x[index][-batch_size:]\n\t\tif len(list_X)<batch_size:\n\t\t\tlist_X_Ret = np.zeros(len(list_X[-1]))\n\t\t\tfor i in range(0,batch_size-len(list_X) - 1):\n\t\t\t\tlist_X_Ret = np.vstack((list_X_Ret,np.zeros(len(list_X[-1]))))\n\t\t\tlist_X_Ret = np.vstack((list_X_Ret,list_X))\n\t\t\tlist_X = list_X_Ret\n\n\t\t#nx , ny = np.array(list_X).shape\n\t\t#list_X = np.array(list_X).reshape((nx*ny))\n\n\t\tlist_Y = self.id_array_y[index][-batch_size:]\n\t\tret_array = array([list_X,list_Y[-1]])\n\t\t#print ret_array\n\t\treturn ret_array\n\n\tdef get_latest_array(self,id): \n\t\ttry:\n\t\t\tindex = self.id_index.index(id)\n\t\t\treturn self.id_array_x[index][-1]\n\t\texcept ValueError:\n\t\t\treturn 0\n\n\tdef get_latest_time(self,id): \n\t\ttry:\n\t\t\tindex = self.id_index.index(id)\n\t\t\treturn self.id_time[index][-1]\n\t\texcept ValueError:\n\t\t\treturn 0\n\n\tdef get_distance(self,p1,p2):\n\t\tdist = (((p1[0]-p2[0])**2 + (p1[1]-p2[1])**2)**.5) \n        \n\t\treturn dist\n\n\tdef get_CC(self,p1,p2,p3,p4,p5):\n\t\tCC = round(1.0 - ((1.0-p1)**2 + (1.0-p2)**2 +(1.0-p3)**2+(1.0-p4)**2+(1.0-p5)**2)**.5) \n\t\treturn CC\n    \n\tdef get_OBC(self,p1):\n\t\tOBC = round(p1) \n\t\treturn OBC\n    \n\tdef get_RBC(self,p1,p2,p3,p4):\n\t\tRRC = round(1.0 - ((1.0-p1)**2 + (1.0-p2)**2 +(1.0-p3)**2+(1.0-p4)**2)**.5)\n\t\treturn RRC\n    \n\tdef get_BBC(self,p1):\n\t\tBBC = round(p1)\n\t\treturn BBC    \n    \n\tdef get_speed(self,spd):\n\t\tspeed = math.sqrt((spd[0]**2) + (spd[1]**2))\n\t\treturn speed\n\n\tdef get_angle_delta(self,angl1,angl2):\n\t\tdeltaX = angl2[0] - angl1[0];\n\t\tdeltaY = angl2[1] - angl1[1];\n\t\trad = math.atan2(deltaY, deltaX);\n\t\tdeg = rad * (180 / math.pi)\n\t\treturn deg\n","sub_path":"Local-Detection/MLArrayStorage.py","file_name":"MLArrayStorage.py","file_ext":"py","file_size_in_byte":7973,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"488839776","text":"import logging\n\nimport common\n\n\nLOGGER = logging.getLogger(__name__)\nLOGGER.setLevel(logging.INFO)\n\n\ndef lambda_handler(event, context):\n    ddb_table = common.get_ddb_table()\n    box_client, _ = common.get_box_client()\n    root_folder = box_client.folder(common.BOX_FOLDER_ID)\n\n    LOGGER.info(\"Checking files in Box\")\n    shared_file_ids = set()\n    count = 0\n    for file in common.iterate_files(root_folder):\n        count += 1\n        if common.is_box_file_public(file):\n            shared_file_ids.add(file.id)\n            common.put_file_item(ddb_table, file)\n        else:\n            common.delete_file_item(ddb_table, file)\n    LOGGER.info(\"Processed %s files\", count)\n\n    LOGGER.info(\"Checking items in DynamoDB\")\n    count = 0\n    scan_response = ddb_table.scan()\n    delete_keys = set()\n    while True:\n        for item in scan_response[\"Items\"]:\n            count += 1\n            if item[\"box_file_id\"] not in shared_file_ids:\n                delete_keys.add(item[\"filename\"])\n\n        # If the data returned by a scan would exceed 1MB, DynamoDB will begin paging.\n        # The LastEvaluatedKey field is the placeholder used to request the next page.\n        if scan_response.get(\"LastEvaluatedKey\"):\n            scan_response = ddb_table.scan(\n                ExclusiveStartKey=scan_response[\"LastEvaluatedKey\"]\n            )\n        else:\n            break\n\n    for key in delete_keys:\n        ddb_table.delete_item(Key={\"filename\": key})\n    LOGGER.info(\"Processed %s items\", count)\n","sub_path":"redirector/sync.py","file_name":"sync.py","file_ext":"py","file_size_in_byte":1503,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"262695382","text":"import os\nfrom setuptools import setup, find_packages\nfrom distutils.util import convert_path\n\n\n# Directory of script\nroot_dir = os.path.dirname(__file__)\n\n\n# Get package version without importing it\nversion_ns = dict()\nwith open(convert_path('pycyt/version.py')) as fobj:\n\texec(fobj.read(), version_ns)\nversion = version_ns['__version__']\n\n\n# Dynamic download URL based off current version - git tag should match\ndownload_url = (\n\t'https://github.com/jlumpe/pycyt/archive/{}.tar.gz'\n\t.format(version)\n)\n\n\n# Read readme file for long description\nwith open(os.path.join(root_dir, 'README.md')) as fobj:\n\treadme_contents = fobj.read()\n\n\nsetup(\n\tname='pycyt',\n\tversion=version,\n\tdescription='Python package for the analysis of flow cytometry data',\n\tlong_description=readme_contents,\n\turl='https://github.com/jlumpe/pycyt',\n\tauthor='Jared Lumpe',\n\tauthor_email='mjlumpe@gmail.com',\n\tclassifiers=[\n\t\t'Development Status :: 2 - Pre-Alpha',\n\t\t'Intended Audience :: Developers',\n\t\t'Intended Audience :: Science/Research',\n\t\t'Operating System :: OS Independent',\n\t\t'Programming Language :: Python :: 3',\n\t\t'Programming Language :: Python :: 3.5',\n\t\t'Programming Language :: Python :: 3.6',\n\t\t'Topic :: Scientific/Engineering',\n\t\t'Topic :: Scientific/Engineering :: Bio-Informatics',\n\t],\n\tpackages=find_packages(),\n\tinstall_requires=[\n\t\t'numpy>=1.11',\n\t],\n)\n","sub_path":"pypi_install_script/pycyt-0.0.1.tar/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1349,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"541361261","text":"__author__ = 'newtonis'\n\nimport threading\nfrom source.data import *\nfrom source.gui.text import Text\nfrom source.gui.input import Input\nfrom source.gui.window import Window\nfrom source.gui.button import *\nfrom source.database import serverQ\n\nclass ConfigWindow(Window):\n    def __init__(self):\n        Window.__init__(self,\"Client sync configuration\",(43, 76, 111),(117, 108, 163),0,0,400,170,(255,255,255),255)\n\n        self.AddInput()\n        self.AddAcceptButtons()\n\n    def AddInput(self):\n        interpolationConstantText = Text(fonts.BebasNeue.c20,\"Interpolation constant:\",(255,255,255))\n        interpolationConstantText.y = 60 - interpolationConstantText.surface.get_size()[1]/2\n        interpolationConstantText.x = self.width/4 - interpolationConstantText.surface.get_size()[0]/2\n        self.AddElement(interpolationConstantText,\"textIC\")\n\n        interpolationConstantInput = Input()\n        interpolationConstantInput.AllowNumbers()\n        interpolationConstantInput.SetSize(100,30)\n        interpolationConstantInput.y = 60 - interpolationConstantInput.size[1]/2\n        interpolationConstantInput.x = self.width/4*3 - interpolationConstantInput.size[0]/2\n        interpolationConstantInput.text = str(config.interpolation_constant)\n        self.AddElement(interpolationConstantInput,\"IConstant\")\n\n        thresholdText = Text(fonts.BebasNeue.c20,\"Threshold correction:\",(255,255,255))\n        thresholdText.y = 100 - thresholdText.surface.get_size()[1]/2\n        thresholdText.x = self.width/4 - thresholdText.surface.get_size()[0]/2\n        self.AddElement(thresholdText,\"textTH\")\n\n        thresholdInput = Input()\n        thresholdInput.AllowNumbers()\n        thresholdInput.SetSize(100,30)\n        thresholdInput.y = 100 - thresholdInput.size[1]/2\n        thresholdInput.x = self.width/4*3 - thresholdInput.size[0]/2\n        thresholdInput.text = str(config.threshold)\n        self.AddElement(thresholdInput,\"TH\")\n    def AddAcceptButtons(self):\n        acceptButton = AcceptButton(\"ACCEPT\",130)\n        acceptButton.y = 130\n        acceptButton.x = self.width/4 - acceptButton.imageA.get_size()[0]/2\n        self.AddElement(acceptButton,\"accept\")\n\n        cancelButton = RejectButton(\"CANCEL\",130)\n        cancelButton.y = 130\n        cancelButton.x = self.width/4*3 - cancelButton.imageA.get_size()[0]/2\n        self.AddElement(cancelButton,\"cancel\")\n    def GetIC(self):\n        return self.references[\"IConstant\"].text\n    def GetTH(self):\n        return self.references[\"TH\"].text\n    def IsCorrect(self,value):\n        try:\n            nvalue = float(value)\n        except:\n            return False\n        if nvalue > 0.0 and nvalue < 5.0:\n            return True\n    def ExtraLogicUpdate(self):\n        if self.ButtonCheck(\"accept\"):\n            self.Send()\n        if self.ButtonCheck(\"cancel\"):\n            self.Kill()\n        self.parent.AddUpdateRect(self.x,self.y,self.width,self.height+30)\n    def Send(self):\n        self.Kill()\n        if not self.IsCorrect(self.GetIC()) or not self.IsCorrect(self.GetTH()):\n           self.Fail()\n           return\n        nIC = float(self.GetIC())\n        nTH = float(self.GetTH())\n        config.interpolation_constant = nIC\n        config.threshold = nTH\n        threadDB = threading.Thread(target=self.UpdateDB)\n        threadDB.start()\n    def UpdateDB(self):\n        serverQ.UpdateConfig(config.threshold,config.interpolation_constant)\n\n    def Fail(self):\n        textFail = Text(fonts.BebasNeue.c17,\"Are you crazy?!?! try again\",(255,255,255))\n        textFail.y = 25\n        textFail.x = self.width/2 - textFail.surface.get_size()[0]/2\n        self.AddElement(textFail,\"fail\")\n\n        self.references[\"IConstant\"].SetBackgroundColor(200,0,0)\n        self.references[\"TH\"].SetBackgroundColor(200,0,0)","sub_path":"head_soccer_06_2/source/network_game/ping/config_window.py","file_name":"config_window.py","file_ext":"py","file_size_in_byte":3781,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"203694175","text":"import timeit;\n\nimport numpy as np\nimport scipy.io.wavfile as wav\nfrom tqdm import tqdm\n\nprogram_start_time = timeit.default_timer()\nimport pdb\nimport python_speech_features\n\nfrom phoneme_set import phoneme_set_39_list\nimport fixDataset.transform as transform\n\n\nnbPhonemes = 39\nphoneme_set_list = phoneme_set_39_list  # import list of phonemes,\n# convert to dictionary with number mappings (see phoneme_set.py)\nvalues = [i for i in range(0, len(phoneme_set_list))]\nphoneme_classes = dict(zip(phoneme_set_list, values))\n\n\n## Functions ##\ndef get_total_duration(file):\n    \"\"\"Get the length of the phoneme file, i.e. the 'time stamp' of the last phoneme\"\"\"\n    for line in reversed(list(open(file))):\n        [_, val, _] = line.split()\n        return int(val)\n\n\ndef create_mfcc(method, filename, type=2):\n    \"\"\"Perform standard preprocessing, as described by Alex Graves (2012)\n\thttp://www.cs.toronto.edu/~graves/preprint.pdf\n\tOutput consists of 12 MFCC and 1 energy, as well as the first derivative of these.\n\t[1 energy, 12 MFCC, 1 diff(energy), 12 diff(MFCC)\n\n\tmethod is a dummy input!!\"\"\"\n\n    (rate, sample) = wav.read(filename)\n\n    mfcc = python_speech_features.mfcc(sample, rate, winlen=0.025, winstep=0.01, numcep=13, nfilt=26,\n                                       preemph=0.97, appendEnergy=True)\n    out = mfcc\n    if type > 13:\n        derivative = np.zeros(mfcc.shape)\n        for i in range(1, mfcc.shape[0] - 1):\n            derivative[i, :] = mfcc[i + 1, :] - mfcc[i - 1, :]\n\n        mfcc_derivative = np.concatenate((mfcc, derivative), axis=1)\n        out = mfcc_derivative\n        if type > 26:\n            derivative2 = np.zeros(derivative.shape)\n            for i in range(1, derivative.shape[0] - 1):\n                derivative2[i, :] = derivative[i + 1, :] - derivative[i - 1, :]\n\n            out = np.concatenate((mfcc, derivative, derivative2), axis=1)\n            if type > 39:\n                derivative3 = np.zeros(derivative2.shape)\n                for i in range(1, derivative2.shape[0] - 1):\n                    derivative3[i, :] = derivative2[i + 1, :] - derivative2[i - 1, :]\n\n                out = np.concatenate((mfcc, derivative, derivative2, derivative3), axis=1)\n\n    return out, out.shape[0]\n\n\ndef calc_norm_param(X):\n    \"\"\"Assumes X to be a list of arrays (of differing sizes)\"\"\"\n    total_len = 0\n    mean_val = np.zeros(X[0].shape[1])\n    std_val = np.zeros(X[0].shape[1])\n    for obs in X:\n        obs_len = obs.shape[0]\n        mean_val += np.mean(obs, axis=0) * obs_len\n        std_val += np.std(obs, axis=0) * obs_len\n        total_len += obs_len\n\n    mean_val /= total_len\n    std_val /= total_len\n\n    return mean_val, std_val, total_len\n\n\ndef normalize(X, mean_val, std_val):\n    for i in range(len(X)):\n        X[i] = (X[i] - mean_val) / std_val\n    return X\n\n\ndef set_type(X, type):\n    for i in range(len(X)):\n        X[i] = X[i].astype(type)\n    return X\n\n\ndef preprocess_dataset(source_path, nbMFCCs=39, logger=None, debug=None, verbose=False):\n    \"\"\"Preprocess data, ignoring compressed files and files starting with 'SA'\"\"\"\n    X = []\n    y = []\n    valid_frames = []\n\n    # source_path is the root dir of all the wav/phn files\n    wav_files = transform.loadWavs(source_path)\n    label_files = transform.loadPhns(source_path)\n\n    logger.debug(\"Found %d WAV files\" % len(wav_files))\n    logger.debug(\"Found %d PHN files\" % len(label_files))\n    assert len(wav_files) == len(label_files)\n    assert len(wav_files) != 0\n\n    processed = 0\n    for i in tqdm(range(len(wav_files))):\n        phn_name = str(label_files[i])\n        wav_name = str(wav_files[i])\n\n        if (wav_name.startswith(\"SA\")):  #specific for TIMIT: these files contain strong dialects; don't use them\n            continue\n\n        # Get MFCC of the WAV\n        X_val, total_frames = create_mfcc('DUMMY', wav_name, nbMFCCs)  # get 3 levels: 0th, 1st and 2nd derivative (=> 3*13 = 39 coefficients)\n        total_frames = int(total_frames)\n\n        X.append(X_val)\n\n\n        # Get phonemes and valid frame numbers out of .phn files\n        total_duration = get_total_duration(phn_name)\n        fr = open(phn_name)\n\n        # some .PHN files don't start at 0. Set default phoneme to silence (expected at the end of phoneme_set_list)\n        y_vals = np.zeros(total_frames) + phoneme_classes[phoneme_set_list[-1]]\n        valid_frames_vals = []\n\n        for line in fr:\n            [start_time, end_time, phoneme] = line.rstrip('\\n').split()\n            start_time = int(start_time)\n            end_time = int(end_time)\n            start_ind = int(np.round(start_time / (total_duration / total_frames)))\n            end_ind = int(np.round(end_time / (total_duration / total_frames)))\n\n            valid_ind = int( (start_ind + end_ind)/2)\n            valid_frames_vals.append(valid_ind)\n\n            phoneme_num = phoneme_classes[phoneme]\n            # check that phoneme is found in dict\n            if (phoneme_num == -1):\n                logger.error(\"In file: %s, phoneme not found: %s\", phn_name, phoneme)\n                pdb.set_trace()\n            y_vals[start_ind:end_ind] = phoneme_num\n\n            if verbose:\n                logger.debug('%s', (total_frames / float(total_duration)))\n                logger.debug('TIME  start: %s end: %s, phoneme: %s, class: %s', start_time, end_time, phoneme, phoneme_num)\n                logger.debug('FRAME start: %s end: %s, phoneme: %s, class: %s', start_ind, end_ind, phoneme, phoneme_num)\n        fr.close()\n\n        # append the target array to our y\n        y.append(y_vals.astype('int32'))\n\n        # append the valid_frames array to our valid_frames\n        valid_frames_vals = np.array(valid_frames_vals)\n        valid_frames.append(valid_frames_vals.astype('int32'))\n\n\n        if verbose:\n            logger.debug('(%s) create_target_vector: %s', i, phn_name[:-4])\n            logger.debug('type(X_val): \\t\\t %s', type(X_val))\n            logger.debug('X_val.shape: \\t\\t %s', X_val.shape)\n            logger.debug('type(X_val[0][0]):\\t %s', type(X_val[0][0]))\n\n            logger.debug('type(y_val): \\t\\t %s', type(y_vals))\n            logger.debug('y_val.shape: \\t\\t %s', y_vals.shape)\n            logger.debug('type(y_val[0]):\\t %s', type(y_vals[0]))\n            logger.debug('y_val: \\t\\t %s', (y_vals))\n\n        processed += 1\n        if debug != None and processed >= debug:\n            break\n\n    return X, y, valid_frames\n\n\ndef preprocess_unlabeled_dataset(source_path, nbMFCCs=39,verbose=False, logger=None): # TODO\n    wav_files = transform.loadWavs(source_path)\n    logger.debug(\"Found %d WAV files\" % len(wav_files))\n    assert len(wav_files) != 0\n\n    X = []\n    for i in tqdm(range(len(wav_files))):\n        wav_name = str(wav_files[i])\n        X_val, total_frames = create_mfcc('DUMMY', wav_name, nbMFCCs)\n        X.append(X_val)\n\n        if verbose:\n            logger.debug('type(X_val): \\t\\t %s', type(X_val))\n            logger.debug('X_val.shape: \\t\\t %s', X_val.shape)\n            logger.debug('type(X_val[0][0]):\\t %s', type(X_val[0][0]))\n    return X","sub_path":"code/audioSR/preprocessWavs.py","file_name":"preprocessWavs.py","file_ext":"py","file_size_in_byte":7050,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"643436525","text":"# Copyright 2016 Google Inc. All Rights Reserved.\r\n#\r\n# Licensed under the Apache License, Version 2.0 (the \"License\");\r\n# you may not use this file except in compliance with the License.\r\n# You may obtain a copy of the License at\r\n#\r\n#     http://www.apache.org/licenses/LICENSE-2.0\r\n#\r\n# Unless required by applicable law or agreed to in writing, software\r\n# distributed under the License is distributed on an \"AS IS\" BASIS,\r\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\r\n# See the License for the specific language governing permissions and\r\n# limitations under the License.\r\n# ==============================================================================\r\n\r\nimport os\r\nimport math\r\nimport random\r\nimport sys\r\n\r\nimport tensorflow as tf\r\n\r\nimport cv2\r\nimport numpy as np\r\nfrom tqdm import tqdm\r\n\r\n#import matplotlib.pyplot as plt\r\n\r\nclass ImageHelper(object):\r\n    \"\"\"\r\n    Helper class that provides TensorFlow image coding utilities.\r\n    \r\n    Args: \r\n        height: Prefered height of an output image. \r\n                Default is 15 pixel.\r\n        width:  Prefered width of an output image. \r\n                Default is 15 pixel.\r\n\r\n    Methods:\r\n        cv_read_img_with_abs_path(self, abs_file_name)\r\n        cv_bgra2rgb(self, images)\r\n        cv_bgr2rgb(self, image)\r\n        \r\n    \"\"\"\r\n    def __init__(self, height=15, width=15, verbose=False):\r\n        self._height = height\r\n        self._width = width\r\n        self._channels = 3\r\n        #self._image_data = None\r\n        self._verbose_img = verbose\r\n\r\n    def cv_read_img_with_abs_path(self, abs_file_name):\r\n        image = cv2.imread(abs_file_name, cv2.IMREAD_UNCHANGED)\r\n        if self._verbose_img==True: print(\"1. Number of channels: \", image.shape); sys.stdout.flush()\r\n        print(\"Image shape: \", image.shape)\r\n\r\n        if image.shape[2] > self._channels: # if image is in RGBD format\r\n            image = self.cv_bgra2rgb(image)\r\n        elif image.shape[2] == self._channels:\r\n            image = self.cv_bgr2rgb(image)\r\n        \r\n        if image.shape[0]!=15 and image.shape[1]!=15: # default image size, 15X15\r\n            image = cv2.resize(image, dsize=(self._width, self._height), interpolation = cv2.INTER_AREA)\r\n\r\n        if self._verbose_img==True: print(\"2. Number of channels: \", image.shape); sys.stdout.flush()\r\n        \r\n        return image.astype(np.float32)\r\n\r\n    def cv_bgra2rgb(self, image):\r\n        return cv2.cvtColor(image, cv2.COLOR_BGRA2RGB)\r\n\r\n    def cv_bgr2rgb(self, image):\r\n        return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\r\n\r\n\r\nclass TFRecord_Helper(ImageHelper):\r\n    \"\"\"\r\n    Helper class which converts image to TFRecord format with features including label, encoded, height, width, format of the image.\r\n    \r\n    Args:\r\n        height:  height of desired output image\r\n        width: width of desired output image\r\n        seed: random seed to shuffle images\r\n\r\n    Methods:\r\n        _get_dataset_filename(self, tfrecord_dir, split_name, shard_id, dataset_name, num_shards)\r\n        _get_filenames_and_classes(self, dataset_dir)\r\n        _int64_feature(self, value)\r\n        _bytes_feature(self, value)\r\n        convert_to_tfrecord(self, tf_record_root_dir, dataset_name, num_shards, validation_ratio)\r\n    \"\"\"\r\n    def __init__(self, height=15, width=15, seed=0, verbose=False):\r\n        ImageHelper.__init__(self, height, width, False)\r\n        self._random_seed = seed\r\n        self._verbose_tfr = verbose\r\n        #self.get_next_in_interators = []\r\n\r\n    def _get_dataset_filename(self, tfrecord_dir, split_name, shard_id, dataset_name, num_shards):\r\n        assert split_name in ['train', 'valid']\r\n        \r\n        output_filename = dataset_name + '_%s_%05d-of-%05d.tfrecord' % (\r\n            split_name, shard_id, num_shards)\r\n        return os.path.join(tfrecord_dir, output_filename)\r\n\r\n    def _get_filenames_and_classes(self, dataset_dir):\r\n        \"\"\"Returns a list of filenames and inferred class names.\r\n        Args:\r\n        dataset_dir: A directory containing a set of subdirectories representing\r\n            class names. Each subdirectory should contain PNG or JPG encoded images.\r\n        Returns:\r\n        A list of image file paths, relative to `dataset_dir` and the list of\r\n        subdirectories, representing class names.\r\n        \"\"\"\r\n        image_root = os.path.join(dataset_dir, 'images')\r\n        directories = []\r\n        class_names = []\r\n        for filename in os.listdir(image_root):\r\n            path = os.path.join(image_root, filename)\r\n            if os.path.isdir(path):\r\n                directories.append(path)\r\n                class_names.append(filename)\r\n\r\n        photo_filenames = []\r\n        for directory in directories:\r\n            for filename in os.listdir(directory):\r\n                path = os.path.join(directory, filename)\r\n                photo_filenames.append(path)\r\n\r\n        return photo_filenames, sorted(class_names)\r\n\r\n    def _int64_feature(self, value):\r\n      return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))\r\n    \r\n    def _bytes_feature(self, value):\r\n      return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))\r\n\r\n    def convert_to_tfrecord(self, tf_record_root_dir, dataset_name, num_shards, validation_ratio):\r\n        #print(self._get_dataset_filename(root_image_dir, 'train', 2, 'face', 5))\r\n        #print(self._get_filenames_and_classes(root_image_dir))\r\n\r\n        def _seperate_train_and_validation_set():\r\n            \"\"\" Inner function of covert_to_tfrecord \"\"\"\r\n            filenames, class_names = self._get_filenames_and_classes(tf_record_root_dir)\r\n            class_names_to_ids = dict(zip(class_names, range(len(class_names))))\r\n            if self._verbose_tfr==True: print(class_names_to_ids); sys.stdout.flush()\r\n\r\n            # Cacluate number of validation proportional to validation_ratio\r\n            num_validation = int(len(filenames) * validation_ratio)\r\n\r\n            # Divide into train and validation:\r\n            random.seed(self._random_seed)\r\n            random.shuffle(filenames)\r\n            training_filenames = filenames[num_validation:]\r\n            validation_filenames = filenames[:num_validation]\r\n\r\n            return training_filenames, validation_filenames, class_names_to_ids\r\n\r\n        train_filenames, valid_filenames, class_names_to_ids = _seperate_train_and_validation_set()\r\n        if self._verbose_tfr==True: print(\"Number of training-set: {0}, Number of validation-set: {1}\".format(len(train_filenames), len(valid_filenames))); sys.stdout.flush()\r\n        \r\n        for filenames, train_or_valid in tqdm(zip((train_filenames, valid_filenames), ('train', 'valid'))):\r\n            num_per_shard = int(math.ceil(len(filenames) / float(num_shards)))\r\n            if self._verbose_tfr==True: print(\"Number per shard: \", num_per_shard); sys.stdout.flush()\r\n\r\n            for shard_id in tqdm(range(num_shards)):\r\n                output_filename = self._get_dataset_filename(tf_record_root_dir, train_or_valid, shard_id, dataset_name, num_shards)\r\n                if self._verbose_tfr==True: print(output_filename); sys.stdout.flush()\r\n\r\n                with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:\r\n                    start_ndx = shard_id * num_per_shard\r\n                    end_ndx = min((shard_id + 1) * num_per_shard, len(filenames))\r\n                    for i in range(start_ndx, end_ndx):\r\n                        #sys.stdout.write('\\r>> Converting [%s] image %d/%d shard %d' % (train_or_valid, i + 1, len(filenames), shard_id))\r\n                        #print('Converting [{0}] image {1}/{2} shard {3}'.format(train_or_valid, i + 1, len(filenames), shard_id))\r\n                        #sys.stdout.flush()\r\n\r\n                        # Read the filename:\r\n                        if self._verbose_tfr==True: print(filenames[i]); sys.stdout.flush()\r\n                        #image_data_cv = self.cv_read_img_with_abs_path(filenames[i])\r\n                        #image_data_binary = tf.compat.as_bytes(image_data_cv.tostring())\r\n                        # Read image data in terms of bytes\r\n                        with tf.gfile.FastGFile(filenames[i], 'rb') as fid:\r\n                            image_data_binary = fid.read()\r\n                        #image_data_binary = image_data_cv.tostring()\r\n                        #height, width = image_reader.read_image_dims(sess, image_data)\r\n                        \r\n                        class_name = os.path.basename(os.path.dirname(filenames[i]))\r\n                        \r\n                        # Get a class ids\r\n                        class_id = class_names_to_ids[class_name]\r\n                        if self._verbose_tfr==True: print(class_id); sys.stdout.flush()\r\n\r\n                        # Create a feature\r\n                        feature = {'image/label': self._int64_feature(np.int64(class_id)),\r\n                            'image/encoded': self._bytes_feature(image_data_binary),\r\n                            'image/height': self._int64_feature(np.int64(self._height)),\r\n                            'image/width': self._int64_feature(np.int64(self._width)),\r\n                            'image/channel': self._int64_feature(self._channels),\r\n                            'image/name': self._bytes_feature(os.path.basename(filenames[i])[:-4].encode('utf-8'))}\r\n\r\n                        # Create an Example protocol buffer\r\n                        example = tf.train.Example(features=tf.train.Features(feature=feature))\r\n\r\n                        # Serialize to string and write on the file \r\n                        tfrecord_writer.write(example.SerializeToString())\r\n                        '''\r\n                        example = dataset_utils.image_to_tfexample(\r\n                            image_data, b'jpg', height, width, class_id)\r\n                        tfrecord_writer.write(example.SerializeToString())\r\n                        '''\r\n        print('\\n')\r\n\r\n\r\n    def convert_from_tfrecord(self, tf_record_root_dir, _epoch_num, _batch_size):\r\n        # Create a feature\r\n        feature = {'image/label': tf.FixedLenFeature([], tf.int64),\r\n            'image/encoded': tf.FixedLenFeature([], tf.string),\r\n            'image/height': tf.FixedLenFeature([], tf.int64),\r\n            'image/width': tf.FixedLenFeature([], tf.int64),\r\n            'image/channel': tf.FixedLenFeature([], tf.int64),\r\n            'image/name': tf.FixedLenFeature([], tf.string)}\r\n\r\n        def _get_list():\r\n            \"\"\"\r\n            Neted function of conver_from_tfrecord.\r\n            Returns:\r\n                filenames: list of file names which have tfrecord file format\r\n            \"\"\"\r\n\r\n            \"\"\"\r\n            filenames = []\r\n            for tfrecord_filename in os.listdir(tf_record_root_dir):\r\n                 if tfrecord_filename.endswith('.tfrecord'):\r\n                     filenames.append(tfrecord_filename)\r\n            \"\"\"\r\n            os.chdir(tf_record_root_dir) # Change working directory\r\n            filenames = [os.path.abspath(tfrecord_filename) # Return a list of abolute file name for tfrecords\r\n                for tfrecord_filename in os.listdir(tf_record_root_dir) \r\n                    if tfrecord_filename.endswith('.tfrecord')] \r\n\r\n            return filenames\r\n\r\n        tfrecord_filenames = _get_list() #get tfrecord's absolute file names\r\n        #if self._verbose_tfr==True: [print(tfrecord_filename) for tfrecord_filename in tfrecord_filenames]; sys.stdout.flush()\r\n\r\n \r\n        def _extract_from_tfrecord(_tfrecord_filenames):\r\n            \"\"\"\r\n            Load tfrecord file on memory\r\n            \r\n            Returns:\r\n                tfrecord iterator generator\r\n            \"\"\"\r\n            # Extract the data record\r\n            datum_record = tf.parse_single_example(_tfrecord_filenames, feature)\r\n\r\n            image = tf.image.decode_image(datum_record['image/encoded'])\r\n            image_shape = tf.stack([datum_record['image/height'], datum_record['image/height'], datum_record['image/channel']])\r\n            image_label = datum_record['image/label']\r\n            image_name = datum_record['image/name']\r\n            return image_name, image_shape, image_label, image\r\n\r\n        for train_or_valid in ('train', 'valid'):\r\n            #tfrecord_dataset = tf.data.TFRecordDataset([i for i in tfrecord_filenames if train_or_valid in i])\r\n\r\n            for tfrecord_file in [i for i in tfrecord_filenames if train_or_valid in i]:\r\n                #tfrecord_dataset = tf.data.TFRecordDataset([\"/home/shared-data/SJC_Dev/Projects/SJC_Git/Face-Detector/SJC-Face-Data/face_train_00000-of-00006.tfrecord\"])\r\n                tfrecord_dataset = tf.data.TFRecordDataset([tfrecord_file])\r\n\r\n                #if self._verbose_tfr==True: [print(i) for i in tfrecord_filenames if train_or_valid in i]; sys.stdout.flush()\r\n                \r\n                tfrecord_dataset = tfrecord_dataset.map(_extract_from_tfrecord)\r\n\r\n                #tfrecord_dataset = tfrecord_dataset.repeat(_epoch_num).batch(_batch_size)\r\n                \r\n                tfrecord_iterator = tfrecord_dataset.make_one_shot_iterator()\r\n                get_next_in_interator = tfrecord_iterator.get_next()\r\n\r\n                \"\"\" # Example of how to use tfrecord_iterator inside this function; convert_from_tfrecord(self, tf_record_root_dir)\r\n                with tf.Session() as sess:\r\n                    sess.run(tf.global_variables_initializer())\r\n\r\n                    try:\r\n                        # Keep extracting data till TFRecord is exhausted\r\n                        while True:\r\n                            image_data = sess.run(get_next_in_interator)\r\n\r\n                            print(\"Extracted image name: \", image_data[0]); sys.stdout.flush()\r\n\r\n                    except:\r\n                        print(\"End of an {0}\".format(os.path.basename(tfrecord_file)))\r\n                        pass\r\n                \"\"\"\r\n\r\n                #get_next_in_interators.append(get_next_in_interator)\r\n                print(\"[[[[[[[[[[[[ {0} ]]]]]]]]]]]]\".format(os.path.basename(tfrecord_file)))\r\n                yield get_next_in_interator\r\n\r\n\r\n    def convert_from_tfrecord_with_tf_dataset(self, tf_record_root_dir, _batch_size, _phase=\"test\"):\r\n        \"\"\" Convert tfrecord files to batch dataset using tensorflow Dataset\r\n        \r\n        Args:\r\n            pahse: String type, \"train\", \"valid\", \"test. Default is \"test\"\r\n\r\n        Returns:\r\n            iterator: iterator of tensorflow Dataset. Currently, make_one_shot_iterator is being used.\r\n            Future work: update iterator to initializable, reinitializable, and feeadable.\r\n        \"\"\"\r\n\r\n        if _phase == \"train\": filenames = os.path.join(tf_record_root_dir, \"*train*.tfrecord\")\r\n        elif _phase == \"valid\": filenames = os.path.join(tf_record_root_dir, \"*valid*.tfrecord\")\r\n        elif _phase == \"test\": filenames = os.path.join(tf_record_root_dir, \"*test*.tfrecord\")\r\n        \r\n        tf_dataset_files = tf.data.Dataset.list_files(filenames, shuffle=True)\r\n\r\n        # parallel fetch tfrecords dataset using the file list in parallel\r\n        tf_dataset = tf_dataset_files.apply(tf.data.experimental.parallel_interleave(lambda filename: tf.data.TFRecordDataset(filename), cycle_length=os.cpu_count()))\r\n\r\n        def _extract_from_tfrecord(_tfrecord_filenames, resize=[]):\r\n            \"\"\"\r\n            Load tfrecord file on memory\r\n            \r\n            Returns:\r\n                tfrecord iterator generator\r\n            \"\"\"\r\n\r\n            feature = {'image/label': tf.FixedLenFeature([], tf.int64), 'image/encoded': tf.FixedLenFeature([], tf.string),\r\n                        'image/height': tf.FixedLenFeature([], tf.int64), 'image/width': tf.FixedLenFeature([], tf.int64),\r\n                        'image/channel': tf.FixedLenFeature([], tf.int64), 'image/name': tf.FixedLenFeature([], tf.string)}\r\n\r\n            # Extract the data record\r\n            datum_record = tf.parse_single_example(_tfrecord_filenames, feature)\r\n\r\n            jpeg_image = datum_record['image/encoded']\r\n            image = tf.image.decode_jpeg(jpeg_image)\r\n            image = tf.cast(image, tf.float32)\r\n            #image_shape = tf.stack([datum_record['image/height'], datum_record['image/height'], datum_record['image/channel']])\r\n            image_label = datum_record['image/label']\r\n            #image_name = datum_record['image/name']\r\n\r\n            assert len(resize) > 1 # Assertion error if image height and width are not defined\r\n            #image = tf.image.resize_images(image, resize)#, method=tf.image.ResizeMethod.AREA, align_corners=True)\r\n            image = tf.image.resize_images(image, resize, method=tf.image.ResizeMethod.AREA, align_corners=True)\r\n            #print(\"===========================> \", image)\r\n            image = image - tf.cast(tf.stack((0, 0, 0)), tf.float32) # to specify the dimension of the tfrecord image. If not using this, it will give an error in makeing model phase\r\n\r\n            image = image * (1./255) # Normalization\r\n\r\n            #return image_name, image_shape, image_label, image\r\n            #return {\"image\": image}, {\"class_idx\": image_label}\r\n            return {\"image\": image, \"labels\": image_label}\r\n\r\n        # map the parse  function to each example individually in threads*2 parallel calls\r\n        tf_dataset = tf_dataset.map(map_func=lambda example: _extract_from_tfrecord(example, resize=[self._height, self._width]), num_parallel_calls=os.cpu_count())\r\n        \r\n        #All the individually processed examples are then batched and ready for processing\r\n        tf_dataset = tf_dataset.batch(batch_size=_batch_size)\r\n        #tf_dataset = tf_dataset.repeat(_epoch_num).batch(batch_size=_batch_size)\r\n\r\n        # Load\r\n        tf_dataset = tf_dataset.prefetch(buffer_size=_batch_size)\r\n        #print(\"\\n====> \", dataset)\r\n\r\n        #Input Function\r\n        #iterator = tf_dataset.make_one_shot_iterator()\r\n        iterator = tf_dataset.make_initializable_iterator()\r\n\r\n        return iterator\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    \r\n    select = 2\r\n    #image = image_helper.cv_read_img_with_abs_path(\"/home/shared-data/Personal_Dev/Machine-Learning/TensorFlow/slim/face-recognition/dataset/images/370/370-11.jpg\")\r\n\r\n    if select == 0:\r\n        image_helper = TFRecord_Helper(height=224, width=224, verbose=False)\r\n        image_helper.convert_to_tfrecord(\r\n            #'/home/shared-data/Personal_Dev/Machine-Learning/TensorFlow/slim/face-recognition/dataset/',\r\n            '/home/shared-data/SJC_Dev/Projects/SJC_Git/Face-Detector/SJC-Face-Data/',\r\n            'face', 6, 0.3)\r\n    \r\n    elif select == 1:\r\n        image_helper = TFRecord_Helper(height=224, width=224, verbose=True)\r\n\r\n        # Example of how to use tfrecord_iterator generator yielded by convert_from_tfrecord(self, tf_record_root_dir)\r\n        get_next_in_interators = image_helper.convert_from_tfrecord('/home/shared-data/SJC_Dev/Projects/SJC_Git/Face-Detector/SJC-Face-Data/', 2, 10)\r\n\r\n        with tf.Session() as sess:\r\n            sess.run(tf.global_variables_initializer())\r\n\r\n            # Keep extracting data till TFRecord is exhausted\r\n            for id, get_next_in_interator in enumerate(get_next_in_interators):\r\n                print(\"<<<<<<<<<<<< ITERATOR {0} >>>>>>>>>\".format(id))\r\n                try:\r\n                    while True:\r\n                    #print(get_next_in_interator)\r\n                        image_data = sess.run(get_next_in_interator)\r\n\r\n                        #print(\"Extracted image name: \", np.shape(image_data[3])); sys.stdout.flush()\r\n                        #print(\"Extracted image name: \", image_data[2]); sys.stdout.flush()\r\n                        print(\"Extracted image name: \", np.shape(image_data)); sys.stdout.flush()\r\n                        break\r\n\r\n                except:\r\n                    #print(\"End of an {0}\".format(os.path.basename(tfrecord_file)))\r\n                    print(\"ERROR\")\r\n                    pass\r\n                break\r\n\r\n    elif select == 2:\r\n        \r\n        image_helper = TFRecord_Helper(height=224, width=224, verbose=False)\r\n        train_iterator = image_helper.convert_from_tfrecord_with_tf_dataset('/home/shared-data/SJC_Dev/Projects/SJC_Git/Face-Detector/SJC-Face-Data/', 10, \"train\")\r\n        valid_iterator = image_helper.convert_from_tfrecord_with_tf_dataset('/home/shared-data/SJC_Dev/Projects/SJC_Git/Face-Detector/SJC-Face-Data/', 10, \"valid\")\r\n        \"\"\"\r\n        placeholder_X = tf.placeholder(tf.float32, shape = [None, 28, 28, 1])\r\n        placeholder_y = tf.placeholder(tf.int32, shape = [None])\r\n\r\n        # Create separate Datasets for training, validation and testing\r\n        train_dataset = tf.data.Dataset.from_tensor_slices((placeholder_X, placeholder_y))\r\n        def map_fn(x, y):\r\n            # Do transformations here\r\n            return x, y\r\n        train_dataset = train_dataset.batch(10).map(lambda x, y: map_fn(x, y))\r\n\r\n        print(train_dataset.output_types, train_dataset.output_shapes)\r\n        \"\"\"\r\n        handle = tf.placeholder(tf.string, shape=[])\r\n        iterator = tf.data.Iterator.from_string_handle(\r\n            handle, (tf.float32, tf.int64), ([None, 224, 224, 3], [None]))\r\n        next_element = iterator.get_next()\r\n\r\n        with tf.Session() as sess:\r\n            # Return handles that can be feed as the iterator in sess.run\r\n            training_handle = sess.run(train_iterator.string_handle())\r\n            validation_handle = sess.run(valid_iterator.string_handle())\r\n\r\n            for _ in range(3):\r\n                try:\r\n                    count = 0\r\n                    sess.run(train_iterator.initializer)\r\n                    while True:\r\n                        a = sess.run(next_element, feed_dict={handle: training_handle})\r\n                        count += 1\r\n                except tf.errors.OutOfRangeError:\r\n                    print(count)\r\n                    pass\r\n\r\n                try:\r\n                    count = 0\r\n                    sess.run(valid_iterator.initializer)\r\n                    while True:\r\n                        a = sess.run(next_element, feed_dict={handle: validation_handle})\r\n                        count += 1\r\n                except tf.errors.OutOfRangeError:\r\n                    print(count)\r\n                    print(\"\\n\")\r\n                    pass\r\n\r\n    elif select == 3:\r\n        image_helper = TFRecord_Helper(height=224, width=224, verbose=False)\r\n        input_fn = image_helper.convert_from_tfrecord_with_tf_dataset('/home/shared-data/SJC_Dev/Projects/SJC_Git/Face-Detector/SJC-Face-Data/', 0, 20, \"train\")\r\n\r\n        with tf.Session() as sess:\r\n            sess.run(tf.global_variables_initializer())\r\n\r\n            try:\r\n                while True:\r\n                #print(get_next_in_interator)\r\n                    image_data = sess.run(input_fn)\r\n\r\n                    #print(\"Extracted image name: \", np.shape(image_data[3])); sys.stdout.flush()\r\n                    #print(\"Extracted image name: \", image_data[2]); sys.stdout.flush()\r\n                    print(\"Extracted image name: \", np.shape(image_data[0]['image'])); sys.stdout.flush()\r\n                    break\r\n\r\n            except:\r\n                #print(\"End of an {0}\".format(os.path.basename(tfrecord_file)))\r\n                print(\"ERROR\")\r\n                pass","sub_path":"TensorFlow/Common_helper/tfrecorder_helper.py","file_name":"tfrecorder_helper.py","file_ext":"py","file_size_in_byte":23371,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"616812374","text":"import sys\r\n\r\nT = int(input())\r\nN,J = map(int,input().split())\r\n\r\nprint(\"Case\",\"#1:\")\r\nfor bit in range(1 << (N-2)):\r\n\tvs = [0]*11\r\n\tv = (1 << (N-1)) + (bit<<1) + 1\r\n\tfor base in range(2,10+1):\r\n\t\tvt = v;val = 0;d = 0\r\n\t\twhile(vt != 0) :\r\n\t\t\tval += (vt%2) * (base ** d)\r\n\t\t\tvt //= 2\r\n\t\t\td += 1\r\n\t\tvs[base] = val;\r\n\t# cerr << vs << endl;\r\n\tres = [-1]*11\r\n\tok = True\r\n\tfor base in range(2,10+1):\r\n\t\tfor p in range(2,200000):\r\n\t\t\tif(p*p > vs[base]):\r\n\t\t\t\tbreak\r\n\t\t\tif(vs[base] % p == 0):\r\n\t\t\t\tres[base] = p\r\n\t\t\t\tbreak\r\n\t\tif(res[base] == -1):\r\n\t\t\tok = False; break\r\n\tif(not ok) :\r\n\t\tcontinue\r\n\r\n\tprint(vs[10],end=\"\")\r\n\tfor base in range(2,10+1):\r\n\t\tprint(\"\",res[base],end=\"\")\r\n\tprint()\r\n\tJ-= 1\r\n\tif J==0 :\r\n\t\tbreak\r\n","sub_path":"codes/CodeJamCrawler/16_0_3/shimomire/C.py","file_name":"C.py","file_ext":"py","file_size_in_byte":712,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"217441797","text":"__author__ = 'SEOKHO'\n\nfrom gensim.models import Word2Vec\nimport readData\nfrom nltk import word_tokenize\nimport numpy as np\nfrom scipy.spatial.distance import cosine\nimport sklearn\n\ndef train():\n    train, test = readData.read(\"SICK.txt\")\n    sentences = []\n    for trainData in train:\n        sentences.append(word_tokenize(trainData[0]))\n        sentences.append(word_tokenize(trainData[1]))\n    model = Word2Vec(sentences, size=100, window=5, min_count=5, workers=8)\n    for i in range(10):\n        model.train(sentences)\n    model.save(\"vectors.bin\")\n\ndef testPooling():\n    mat = np.array([[0.5, 0.6], [0.3, 0.4]])\n    print(dynamicPooling(mat, 8))\n\n\n\ndef simMatrix(model, sentence1, sentence2):\n        tokens1 = word_tokenize(sentence1)\n        tokens2 = word_tokenize(sentence2)\n        mat = np.zeros((len(tokens1), len(tokens2)))\n        for index1, token1 in enumerate(tokens1):\n            for index2, token2 in enumerate(tokens2):\n                vec1 = model[token1] if token1 in model else np.zeros((len(model['the'])))\n                vec2 = model[token2] if token2 in model else np.zeros((len(model['the'])))\n                mat[index1][index2] = cosine(vec1, vec2)\n        return mat\n\ndef dynamicPooling(matrix, finalDim):\n        finalMatrix = np.zeros((finalDim, finalDim))\n        for i in range(finalDim):\n            for j in range(finalDim):\n                compressionArea = []\n                for a in range(int(float(i) / finalDim * matrix.shape[0]), int(float(i + 1) / finalDim * matrix.shape[0])):\n                    for b in range(int(float(j) / finalDim * matrix.shape[1]), int(float(j + 1) / finalDim * matrix.shape[1])):\n                        compressionArea.append(matrix[a][b])\n                if len(compressionArea) == 0:\n                    finalMatrix[i][j] = matrix[int(float(i) / finalDim * matrix.shape[0])][int(float(j) / finalDim * matrix.shape[1])]\n                else:\n                    finalMatrix[i][j] = min(compressionArea)\n        return finalMatrix\n\nif __name__ == \"__main__\":\n    #train()\n    #test()\n    testPooling()\n\n","sub_path":"wordVectors.py","file_name":"wordVectors.py","file_ext":"py","file_size_in_byte":2079,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"597457684","text":"#Take two lists, say for example these two:\n\n#  a = [1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]\n#  b = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]\n#and write a program that returns a list that contains only the elements that are common between the lists \n#(without duplicates). Make sure your program works on two lists of different sizes.\n\n#Extras:\n\n#Randomly generate two lists to test this\n#Write this in one line of Python (don’t worry if you can’t figure this out at this point - we’ll get to it soon)\n\na = [1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]\nb = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]\n\nans = []\n\n#print([x for x in a if x in b])\n\nfor x in a:\t\n\tif x in b and x not in ans:\n\t\tans.append(x)\n\nprint([y for y in ans])","sub_path":"PracticePython_5_ListOverlap.py","file_name":"PracticePython_5_ListOverlap.py","file_ext":"py","file_size_in_byte":727,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"41125840","text":"import tornado.ioloop\nimport tornado.web\nimport MWAR_config as CONFIG\n\n\nclass Controller(tornado.web.RequestHandler):\n    def get(self):\n        self.write(CONFIG.config[\"rootHTML\"])\n\n\nclass Application(tornado.web.Application):\n    def __init__(self):\n        handlers = [\n            (r\"/\", Controller),\n        ]\n        settings = {\n            \"debug\": True\n        }\n        tornado.web.Application.__init__(self, handlers, **settings)\n\n\nif __name__ == \"__main__\":\n    app = Application()\n    app.listen(8888)\n    print(\"launching App\")\n    tornado.ioloop.IOLoop.current().start()\n","sub_path":"core/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":587,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"230062107","text":"from django.forms import ModelForm\nfrom core.models import Profissional\n\n\n\nclass formProfissional(ModelForm):\n    class Meta:\n        model = Profissional\n        fields = [ 'nome', 'sexo', 'nascimento', 'cpf', 'profissao', 'especialidade',\n                   'referencia', 'contato1', 'contato2', 'email',\n                   'login', 'senha'\n                   ]\n","sub_path":"profissionais/form.py","file_name":"form.py","file_ext":"py","file_size_in_byte":364,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"628545567","text":"#!/usr/bin/env python3\n\n# convert-static.py\n#\n# This program reads the static html and js files and converts them into\n# minified strings defined in a C header file.\n#\n# ----------------------------------------------------------------------------\n# \"THE BEER-WARE LICENSE\" (Revision 42):\n# <xythobuz@xythobuz.de> & <ghost-ghost@web.de> wrote this file.  As long as\n# you retain this notice you can do whatever you want with this stuff. If we\n# meet some day, and you think this stuff is worth it, you can buy us a beer\n# in return.                                   Thomas Buck & Christian Högerle\n# ----------------------------------------------------------------------------\n\nimport binascii\n\ndef fileToString(filename, js = False):\n    f = open(filename, \"r\")\n    sf = \"\"\n    for s in f.readlines():\n        st = s\n        if js == True:\n            ss = s.split(\"//\", 1)\n            if ss[0][-1:] == ':':\n                st = ss[0] + '//' + ss[1]\n            else:\n                st = ss[0]\n        sf += st\n    return sf\n\ndef minify(text, js = False):\n    text = text.replace(\"\\r\", \"\")\n    text = text.replace(\"\\t\", \"\")\n    text = text.replace(\"  \", \" \")\n    if js == True:\n        text = text.replace(\"\\\\\", \"\\\\\\\\\")\n        text = text.replace(\"\\n\", \"\\\\n\")\n    else:\n        text = text.replace(\"\\n\", \" \")\n    text = text.replace(\"  \", \" \")\n    text = text.replace(\"  \", \" \")\n    text = text.replace(\"  \", \" \")\n    text = text.replace(\"  \", \" \")\n    text = text.replace(\"  \", \" \")\n    text = text.replace(\"> <\", \"><\")\n    if js == True:\n        text = text.replace(\"\\\\n \", \"\\\\n\")\n        text = text.replace(\"\\\\n\\\\n\", \"\\\\n\")\n    if (text[-1:] == ' '):\n        text = text[:-1]\n    if (text[-2:] == '\\\\n'):\n        text = text[:-2]\n    if (text[0] == ' '):\n        text = text[1:]\n    if (text[0] == '\\\\') and (text[1] == 'n'):\n        text = text[2:]\n    return text\n\ndef getBinaryFile(filename, id):\n    f = open(filename, \"rb\")\n    s = \"const static unsigned char \" + id + \"[] PROGMEM = {\\n\";\n    i = 0\n    c = 0\n    while True:\n        d = f.read(1)\n        if not d:\n            break\n        if i == 0:\n            s += \"    \"\n        s += \"0x\" + binascii.hexlify(d).decode(\"utf-8\") + \", \"\n        i += 1\n        c += 1\n        if i >= 8:\n            i = 0\n            s += \"\\n\"\n    if i == 0:\n        s = s[:-3]\n    else:\n        s = s[:-2]\n    s += \"\\n\"\n    s += \"};\"\n    s = \"const static unsigned int \" + id + \"Size = \" + str(c) + \";\\n\" + \"const static char faviconMimeType[] PROGMEM = \\\"image/x-icon\\\";\\n\" + s;\n    return s\n\ndef getAsDefine(name, text):\n    return \"#define \" + name + \" \\\"\" + text.replace(\"\\\"\", \"\\\\\\\"\") + \"\\\"\"\n\nprint(\"Preparing static.h output file...\")\nf = open(\"static.h\", \"w\")\nf.write(\"// !!DO NOT EDIT, AUTO-GENERATED FILE!!\\n\")\nf.write(\"// Use convert-static.py to recreate this.\\n\")\nf.write(\"\\n\")\nf.write(\"#ifndef __STATIC_H__\\n\")\nf.write(\"#define __STATIC_H__\\n\")\nf.write(\"\\n\")\n\nprint(\"Processing template.html...\")\ntemplate = fileToString(\"template.html\")\ntemplate = minify(template)\ntemplates = template.split(\" /* %% INSERT_CLIENT_LIST_HERE %% */ \")\n\nf.write(getAsDefine(\"HTML_BEGIN\", templates[0]) + \"\\n\")\nf.write(getAsDefine(\"HTML_END\", templates[1]) + \"\\n\")\n\nprint(\"Processing client-script.js...\")\njs = fileToString(\"client-script.js\", True)\njs = minify(js, True)\nf.write(getAsDefine(\"JS_FILE\", js) + \"\\n\")\n\nprint(\"Processing client-style.css...\")\ncss = fileToString(\"client-style.css\", True)\ncss = minify(css)\nf.write(getAsDefine(\"CSS_FILE\", css) + \"\\n\")\nf.write(\"\\n\")\n\nprint(\"Processing favicon.ico...\")\nf.write(getBinaryFile(\"favicon.ico\", \"favicon\") + \"\\n\")\n\nprint(\"Done!\")\nf.write(\"\\n\")\nf.write(\"#endif // __STATIC_H__\\n\")\nf.write(\"\\n\")\nf.close()\n\n","sub_path":"convert-static.py","file_name":"convert-static.py","file_ext":"py","file_size_in_byte":3701,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"23988718","text":"import numpy as np\nfrom gym.utils import seeding\nfrom alr_envs.mujoco.gym_table_tennis.utils.util import read_yaml, read_json\nfrom pathlib import Path\n\n\ndef ball_initialize(random=False, scale=False, context_range=None, scale_value=None):\n    if random:\n        if scale:\n            # if scale_value is None:\n            scale_value = context_scale_initialize(context_range)\n            v_x, v_y, v_z = [2.5, 2, 0.5] * scale_value\n            dx = 1\n            dy = 0\n            dz = 0.05\n        else:\n            seed = None\n            np_random, seed = seeding.np_random(seed)\n            dx = np_random.uniform(-0.1, 0.1)\n            dy = np_random.uniform(-0.1, 0.1)\n            dz = np_random.uniform(-0.1, 0.1)\n\n            v_x = np_random.uniform(1.7, 1.8)\n            v_y = np_random.uniform(0.7, 0.8)\n            v_z = np_random.uniform(0.1, 0.2)\n        # print(dx, dy, dz, v_x, v_y, v_z)\n    # else:\n    #     dx = -0.1\n    #     dy = 0.05\n    #     dz = 0.05\n    #     v_x = 1.5\n    #     v_y = 0.7\n    #     v_z = 0.06\n    # initial_x = -0.6 + dx\n    # initial_y = -0.3 + dy\n    # initial_z = 0.8 + dz\n    else:\n        if scale:\n            v_x, v_y, v_z = [2.5, 2, 0.5] * scale_value\n        else:\n            v_x = 2.5\n            v_y = 2\n            v_z = 0.5\n        dx = 1\n        dy = 0\n        dz = 0.05\n\n    initial_x = 0 + dx\n    initial_y = -0.2 + dy\n    initial_z = 0.3 + dz\n    # print(\"initial ball state: \", initial_x, initial_y, initial_z, v_x, v_y, v_z)\n    initial_ball_state = np.array([initial_x, initial_y, initial_z, v_x, v_y, v_z])\n    return initial_ball_state\n\n\ndef context_scale_initialize(range):\n    \"\"\"\n\n    Returns:\n\n    \"\"\"\n    low, high = range\n    scale = np.random.uniform(low, high, 1)\n    return scale\n\n\ndef config_handle_generation(config_file_path):\n    \"\"\"Generate config handle for multiprocessing\n\n    Args:\n        config_file_path:\n\n    Returns:\n\n    \"\"\"\n    cfg_fname = Path(config_file_path)\n    # .json and .yml file\n    if cfg_fname.suffix == \".json\":\n        config = read_json(cfg_fname)\n    elif cfg_fname.suffix == \".yml\":\n        config = read_yaml(cfg_fname)\n\n    return config\n","sub_path":"modified_envs/mujoco/gym_table_tennis/utils/experiment.py","file_name":"experiment.py","file_ext":"py","file_size_in_byte":2149,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"198884528","text":"# Make Udacity datasets NVIDIA friendly\nimport os, pandas, sys, tqdm\n\nPATH = sys.argv[1]\n\n# Read a CSV file\ndata = pandas.read_csv(PATH+'/interpolated.csv')\ncentered = data[(data['frame_id'] == 'center_camera')].reset_index(drop=True)\n\nwith open(PATH + '/data.txt', 'w') as out:\n    for i in tqdm.tqdm(range(len(centered['frame_id']))):\n        out.write(str(centered['filename'][i]) + ' ' + str(centered['angle'][i]) + '\\n')\n    out.close()\n","sub_path":"util/preprocess.py","file_name":"preprocess.py","file_ext":"py","file_size_in_byte":442,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"600183427","text":"#! /usr/bin/env python\n\nimport os\nimport sys\n\nsys.path.append(os.path.abspath(os.path.join(__file__, os.path.pardir)))\n\nimport argparse\n\nimport pingu\nfrom pingu.corpus import io\n\nimport text_cleaning\n\n\ndef clean_transcriptions(corpus):\n    for utterance in corpus.utterances.values():\n        transcription = utterance.label_lists['transcription'][0].value\n        cleaned = text_cleaning.clean_sentence(transcription)\n        utterance.label_lists['transcription'][0].value = cleaned\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(description='Prepare data for training.')\n    parser.add_argument('tuda_path', type=str)\n    parser.add_argument('target_path', type=str)\n\n    args = parser.parse_args()\n\n    tuda_corpus = pingu.Corpus.load(args.tuda_path, reader='tuda')\n    clean_transcriptions(tuda_corpus)\n\n    deepspeech_writer = io.MozillaDeepSpeechWriter(transcription_label_list_idx='transcription')\n    deepspeech_writer.save(tuda_corpus, args.target_path)\n","sub_path":"prepare_data_tuda.py","file_name":"prepare_data_tuda.py","file_ext":"py","file_size_in_byte":983,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"492355997","text":"import unittest\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions\nfrom common.GetMakesModelsLinks import GetMakesModelsLinks\nimport time\n\nclass challenge7(unittest.TestCase):\n\n    def setUp(self):\n        self.driver = webdriver.Chrome('../chromedriver')\n\n    def tearDown(self):\n        self.driver.close()\n\n    def test_challenge7(self):\n        self.driver.get(\"https://www.copart.com/\")\n        self.assertIn(\"Copart\", self.driver.title)\n        self.driver.implicitly_wait(10)\n        s = GetMakesModelsLinks(self.driver)\n        ListLinkarray = s.getlinks()\n        print(len(ListLinkarray))\n        i = 0\n        while i < len(ListLinkarray):\n            print(ListLinkarray[i][1])\n            try:\n                self.driver.get(ListLinkarray[i][1])\n                WebDriverWait(self.driver, 60).until(\n                    expected_conditions.visibility_of_element_located((By.XPATH, '//img[@alt=\"Copart\"]')))\n            except TimeoutError:\n                print(\"ERROR: {}'s link ({}) timed out\".format(ListLinkarray[i][0], ListLinkarray[i][1]))\n            i += 1\n            time.sleep(2)\nif __name__ == '__main__':\n    unittest.main()","sub_path":"challenge7/challenge7.py","file_name":"challenge7.py","file_ext":"py","file_size_in_byte":1338,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"257748187","text":"# coding: utf-8\n# Universidade Federal de Campina Grande\n# Ciência da Computação\n# Programação 1 - 2018.2\n# Aluno = Daniel Gomes de Lima\n# Matrícula = 118210357\n# Unidade 10: Número de disciplinas\n\ndef frequencia(ele, seq):\n\tcont = 0\n\tfor i in range(len(seq)):\n\t\tif ele == seq[i]:\n\t\t\tcont += 1\n\treturn cont\n\ndef numero_disciplinas(grade, horario, l):\n    soma = 0\n    lista = []\n    l2 = []\n    for k in grade.keys():\n        if grade[k] == [] and k not in l:\n            lista.append(k)\n\n        elif grade[k] != []:\n            control = 0\n            for e in grade[k]:\n                if e in l:\n                    control += 1\n            if control == len(grade[k]):\n                lista.append(k)\n\n    for e in lista:\n        l2.append(horario[e])\n    \n    for e in range(len(l2)):\n        if frequencia(l2[e], l2) >= 2:\n            soma += 0.5\n        else:\n            soma += 1\n    soma = int(soma)\n    \n    return soma","sub_path":"unidade10/numero_disciplinas/a.py","file_name":"a.py","file_ext":"py","file_size_in_byte":938,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"379575115","text":"import matplotlib\nfrom matplotlib import pylab as plt\nimport nibabel as nib\nfrom nibabel import nifti1\nfrom nibabel.viewers import OrthoSlicer3D\nimport os\n# from scipy import misc\nimport imageio\nimport numpy as np\n\ndef uint16to8(bands, lower_percent=0.001, higher_percent=99.999):\n    out = np.zeros_like(bands,dtype = np.uint8)\n    n = bands.shape[0]\n    for i in range(n):\n        a = 0 # np.min(band)\n        b = 255 # np.max(band)\n        c = np.percentile(bands[i, :], lower_percent)\n        d = np.percentile(bands[i, :], higher_percent)\n        t = a + (bands[i, :] - c) * (b - a) / (d - c)\n        t[t<a] = a\n        t[t>b] = b\n        out[i, :] = t\n    return out\ndef nii2jpg(filename, out_dir, out_filename, step=1):\n    fn = filename\n    step = step\n    outdir = out_dir\n    outfilename = out_filename\n    img = nib.load(fn)\n    # print(img)\n    width, height, queue = img.dataobj.shape\n    num = 1\n    for i in range(0, queue, step):\n        img_arr = img.dataobj[:, :, i]\n\n        img8=uint16to8(img_arr.T)\n        imageio.imsave(os.path.join(outdir, outfilename + str(num) + '.jpg'),img8)\n\n        num += 1\n    # plt.show()\n\n\nif __name__ == '__main__':\n    fn = \"120-huan.nii.gz\"\n    nii2jpg(filename=fn, step=30, out_dir=\"120-all\", out_filename=\"120-all-T\")\n","sub_path":"nifti-test/nii2jpg.py","file_name":"nii2jpg.py","file_ext":"py","file_size_in_byte":1273,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"18006241","text":"import math\n\ndef start():\n\tprint(\"Give ? if it is unknown. \")\n\tcircum_input = raw_input(\"What is the circumference of the circle? \")\n\tradius_input = raw_input(\"What is the radius of the circle? \")\n\n\tif circum_input == \"?\":\n\t\tradius_input = int(radius_input)\n\t\tcalc_circum(radius_input)\n\telif radius_input == \"?\":\n\t\tcircum_input = int(circum_input)\n\t\tcalc_radius(circum_input)\n\telse:\n\t\tradius_input = int(radius_input)\n\t\tcalc_area(radius_input)\n\ndef calc_circum(radius):\n\tcircumference = 2*math.pi*radius\n\tprint(\"The circumference of the circle is \" + str(circumference))\n\tcalc_area(radius)\n\ndef calc_radius(circumference):\n\tradius = (circumference)/(2*math.pi)\n\tprint(\"The radius of the circle is \" + str(radius))\n\tcalc_area(radius)\n\ndef calc_area(radius):\n\tarea = math.pi*radius*radius\n\tprint(\"The area of the circle is \" + str(area))\n\tprint(\"\")\n\nstart()","sub_path":"circles.py","file_name":"circles.py","file_ext":"py","file_size_in_byte":855,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"276147104","text":"# -*- coding: utf-8 -*-\n\nfrom  source.NSE import  downloadHistory\nfrom  talibs.Enrich import enrich\nfrom  model.Svm import SvmModel\nfrom  model.ModelUtil import process\nfrom model.DataLoader import DataLoader\nimport ConfigReader\nimport os\n\nenrichUri = []\nconfig = ConfigReader.readConfigFile('config.ini')\nyIndexArray  = [int(x) for x  in config['NSE_DATA']['Y_INDEX'].split(',')]\ninputUri = downloadHistory(config['NSE_DATA']['dumpPath'])\nprint (\"Ta-lib process started \")\nenrichUri=enrich(inputUri,config)\nprint (\"Ta-lib process completed \")\nfor uri in enrichUri :\n    try :\n        if os.path.exists(uri) :\n            print (\"start process \", uri)\n            process(SvmModel() ,DataLoader(uri),config['NSE_DATA']['DailyJob_result_path'],\n                    int(config['NSE_DATA']['RMSE_MAX']),yIndexArray)\n            print (\"completed process \", uri)\n        else :\n            print(\"Error while process file. File Not found \",uri)\n    except :\n        print (\" Error While process {}\".format(uri))\n\n\n   \n   \n   \n","sub_path":"Kubera/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1022,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"593965105","text":"def fav(n):\r\n    a = []\r\n    sum = 0\r\n    while n != 0:\r\n        a.append(n % 10)\r\n        n = int(n / 10)\r\n    for i in a:\r\n        sum += i\r\n    if sum % a[0] == 0:\r\n        print(\"Yes\")\r\n    else:\r\n        print(\"No\")\r\n\r\n\r\nn = input().strip()\r\nfav(int(n))\r\n\r\n","sub_path":"Lab3/Lab4Q.py","file_name":"Lab4Q.py","file_ext":"py","file_size_in_byte":262,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"588151677","text":"from serpent.machine_learning.reinforcement_learning.agent import Agent\n\nfrom serpent.game_frame import GameFrame\nfrom serpent.game_frame_buffer import GameFrameBuffer\n\nfrom serpent.enums import InputControlTypes\n\nfrom serpent.utilities import SerpentError\n\nimport os\n\nimport numpy as np\n\ntry:\n    from tensorforce.agents import PPOAgent as TFPPOAgent  # Currently matching the API of tensorforce 0.4.2\nexcept ImportError:\n    raise SerpentError(\"Setup has not been been performed for the ML module. Please run 'serpent setup ml'\")\n\n\nclass PPOAgent(Agent):\n\n    def __init__(\n        self,\n        name,\n        game_inputs=None,\n        callbacks=None,\n        input_shape=None,\n        input_type=None,\n        use_tensorboard=False,\n        tensorforce_kwargs=None\n    ):\n        super().__init__(name, game_inputs=game_inputs, callbacks=callbacks)\n\n        if input_shape is None or not isinstance(input_shape, tuple):\n            raise SerpentError(\"'input_shape' should be a tuple...\")\n\n        if input_type is None or input_type not in [\"bool\", \"int\", \"float\"]:\n            raise SerpentError(\"'input_type' should be one of bool|int|float...\")\n\n        states = {\"type\": input_type, \"shape\": input_shape}\n        actions = self._generate_actions_spec()\n\n        summary_spec = None\n\n        if use_tensorboard:\n            summary_spec = {\n                \"directory\": \"./tensorboard/\",\n                \"steps\": 50,\n                \"labels\": [\n                    \"configuration\",\n                    \"gradients_scalar\",\n                    \"regularization\",\n                    \"inputs\",\n                    \"losses\",\n                    \"variables\"\n                ]\n            }\n\n        default_network = [\n            {\"type\": \"conv2d\", \"size\": 32, \"window\": 8, \"stride\": 4},\n            {\"type\": \"conv2d\", \"size\": 64, \"window\": 4, \"stride\": 2},\n            {\"type\": \"conv2d\", \"size\": 64, \"window\": 3, \"stride\": 1},\n            {\"type\": \"flatten\"},\n            {\"type\": \"dense\", \"size\": 512}\n        ]\n\n        agent_kwargs = dict(\n            batched_observe=True,\n            batching_capacity=100,\n            update_mode={\n                \"unit\": \"episodes\",\n                \"batch_size\": 5,\n                \"frequency\": 5\n            },\n            memory={\n                \"type\": \"latest\",\n                \"include_next_states\": False,\n                \"capacity\": 5000\n            },\n            network=default_network,\n            saver=None,\n            summarizer=summary_spec,\n            variable_noise=None,\n            states_preprocessing=None,\n            actions_exploration=None,\n            reward_preprocessing=None,\n            discount=0.99,\n            distributions=None,\n            entropy_regularization=0.01,\n            baseline_mode=None,\n            baseline=None,\n            baseline_optimizer=None,\n            gae_lambda=None,\n            likelihood_ratio_clipping=None,\n            step_optimizer=dict(\n                type=\"adam\",\n                learning_rate=1e-2\n            ),\n            subsampling_fraction=0.1,\n            optimization_steps=50\n        )\n\n        if isinstance(tensorforce_kwargs, dict):\n            for key, value in tensorforce_kwargs.items():\n                if key in agent_kwargs:\n                    agent_kwargs[key] = value\n\n        self.agent = TFPPOAgent(\n            states=states,\n            actions=actions,\n            **agent_kwargs\n        )\n\n        try:\n            self.restore_model()\n        except Exception:\n            pass\n\n        self.agent.reset()\n\n    def generate_actions(self, state, **kwargs):\n        if isinstance(state, GameFrame):\n            self.current_state = state.frame\n        elif isinstance(state, GameFrameBuffer):\n            self.current_state = np.stack(\n                [game_frame.frame for game_frame in state.frames],\n                axis=2\n            )\n        else:\n            self.current_state = state\n\n        agent_actions = self.agent.act(self.current_state)\n        actions = list()\n\n        for index, game_inputs_item in enumerate(self.game_inputs):\n            if game_inputs_item[\"control_type\"] == InputControlTypes.DISCRETE:\n                label = self.game_inputs_mappings[index][agent_actions[game_inputs_item[\"name\"]]]\n                action = game_inputs_item[\"inputs\"][label]\n\n                actions.append((label, action, None))\n            elif game_inputs_item[\"control_type\"] == InputControlTypes.CONTINUOUS:\n                label = game_inputs_item[\"name\"]\n                action = game_inputs_item[\"inputs\"][\"events\"]\n\n                if isinstance(agent_actions[label], np.ndarray):\n                    input_value = [float(f) for f in agent_actions[label]]\n                else:\n                    input_value = float(agent_actions[label])\n\n                actions.append((label, action, input_value))\n\n        for action in actions:\n            self.analytics_client.track(\n                event_key=\"AGENT_ACTION\",\n                data={\n                    \"label\": action[0],\n                    \"action\": [str(a) for a in action[1]],\n                    \"input_value\": action[2]\n                }\n            )\n\n        return actions\n\n    def observe(self, reward=0, terminal=False, episode=0, **kwargs):\n        if self.current_state is None:\n            return None\n\n        if self.callbacks.get(\"before_observe\") is not None:\n            self.callbacks[\"before_observe\"]()\n\n        will_update = not episode % self.agent.update_mode[\"frequency\"] and terminal\n\n        if will_update:\n            if self.callbacks.get(\"before_update\") is not None:\n                self.callbacks[\"before_update\"]()\n\n            self.agent.observe(terminal, reward)\n            self.save_model()\n\n            if self.callbacks.get(\"after_update\") is not None:\n                self.callbacks[\"after_update\"]()\n        else:\n            self.agent.observe(terminal, reward)\n\n        self.current_state = None\n\n        self.current_reward = reward\n        self.cumulative_reward += reward\n\n        print(self.cumulative_reward)\n\n        self.analytics_client.track(event_key=\"REWARD\", data={\"reward\": self.current_reward, \"total_reward\": self.cumulative_reward})\n\n        if terminal:\n            self.analytics_client.track(event_key=\"TOTAL_REWARD\", data={\"reward\": self.cumulative_reward})\n            self.agent.reset()\n\n        if self.callbacks.get(\"after_observe\") is not None:\n            self.callbacks[\"after_observe\"]()\n\n    def save_model(self):\n        self.agent.save_model(directory=os.path.join(os.getcwd(), \"datasets\", self.name, self.name), append_timestep=False)\n\n    def restore_model(self):\n        self.agent.restore_model(directory=os.path.join(os.getcwd(), \"datasets\", self.name))\n\n    def _generate_actions_spec(self):\n        actions_spec = dict()\n\n        for game_inputs_item in self.game_inputs:\n            if game_inputs_item[\"control_type\"] == InputControlTypes.DISCRETE:\n                actions_spec[game_inputs_item[\"name\"]] = dict(type=\"int\", num_actions=len(game_inputs_item[\"inputs\"]))\n            elif game_inputs_item[\"control_type\"] == InputControlTypes.CONTINUOUS:\n                size = 1\n\n                if \"size\" in game_inputs_item[\"inputs\"]:\n                    size = game_inputs_item[\"inputs\"][\"size\"]\n\n                actions_spec[game_inputs_item[\"name\"]] = dict(\n                    type=\"float\",\n                    min_value=game_inputs_item[\"inputs\"][\"minimum\"],\n                    max_value=game_inputs_item[\"inputs\"][\"maximum\"]\n                )\n\n                if size > 1:\n                    actions_spec[game_inputs_item[\"name\"]][\"shape\"] = size\n\n        return actions_spec\n","sub_path":"serpent/machine_learning/reinforcement_learning/agents/ppo_agent.py","file_name":"ppo_agent.py","file_ext":"py","file_size_in_byte":7689,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"520172039","text":"'''\nInput: a List of integers\nReturns: a List of integers\n'''\n\n\ndef product_of_all_other_numbers(arr):\n    total = 1\n    new_arr = []\n    # remove first number and save it\n    last_num = arr.pop(0)\n    for i in range(len(arr)):\n        # get multiplied total of all numbers left\n        total = total * arr[i]\n    # append that total to new array\n    new_arr.append(total)\n    # return new_arr\n    for i in range(0, len(arr)):\n        arr.append(last_num)\n        print('append: ', last_num)\n        last_num = arr.pop(0)\n        print('pop: ', last_num)\n        total = 1\n        for i in range(len(arr)):\n            total = total * arr[i]\n        new_arr.append(total)\n    return new_arr\n\n\nif __name__ == '__main__':\n    # Use the main function to test your implementation\n    # arr = [1, 2, 3, 4, 5]\n    arr = [2, 6, 9, 8, 2, 2, 9, 10, 7, 4, 7, 1, 9, 5, 9, 1, 8, 1, 8, 6, 2, 6, 4, 8,\n           9, 5, 4, 9, 10, 3, 9, 1, 9, 2, 6, 8, 5, 5, 4, 7, 7, 5, 8, 1, 6, 5, 1, 7, 7, 8]\n\n    print(\n        f\"Output of product_of_all_other_numbers: {product_of_all_other_numbers(arr)}\")\n","sub_path":"product_of_all_other_numbers/product_of_all_other_numbers.py","file_name":"product_of_all_other_numbers.py","file_ext":"py","file_size_in_byte":1078,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"353321164","text":"def lonelyinteger(a):\n    a = a.split('\\n')\n    a = a[1]\n    a = a.split(' ')\n    numbers = {}\n    for i in a:\n        if i in numbers:\n            numbers[i] += 1\n        else:\n            numbers[i] = 1\n    for k, v in numbers.items():\n        if v == 1:\n            answer = int(k)\n    print(answer)\n    return answer","sub_path":"LonelyInteger.py","file_name":"LonelyInteger.py","file_ext":"py","file_size_in_byte":320,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"261539766","text":"from spot import Spot\nfrom vehicle import Vehicle\nfrom enums import Size, Color\nfrom collections import deque\n\n\nclass ParkingLot:\n    def __init__(self, _id, zipcode, num_small, num_medium, num_large, num_xlarge):\n        self.___id = _id\n        self.__zipcode = zipcode\n        self.__small_slots = deque([Spot('S'+str(i), Size.SMALL) for i in range(num_small)])\n        self.__medium_slots = deque([Spot('M'+str(i), Size.MEDIUM) for i in range(num_medium)])\n        self.__large_slots = deque([Spot('L'+str(i), Size.LARGE) for i in range(num_large)])\n        self.__xlarge_slots = deque([Spot('XL'+str(i), Size.XLARGE) for i in range(num_xlarge)])\n        self.assigned_slots = {}\n\n    def switcher(self, type):\n        mapping = {\n            Size.SMALL: self.__small_slots,\n            Size.MEDIUM: self.__medium_slots,\n            Size.LARGE: self.__large_slots,\n            Size.XLARGE: self.__xlarge_slots\n        }\n        return mapping.get(type, None)\n\n    def assign(self, vehicle: Vehicle) -> Spot:\n        vehicle_type = vehicle.get_size()\n        free_slots = self.switcher(vehicle_type)\n\n        if free_slots:\n            assigned_slot = free_slots.popleft()\n            self.assigned_slots[vehicle.get_license()] = assigned_slot\n            return assigned_slot.get_spot_id()\n        else:\n            print('Invalid Vehicle Size')\n\n    def free(self, vehicle: Vehicle):\n        if vehicle.get_license() not in self.assigned_slots:\n            print('Vehicle not parked')\n            return None\n        assigned_slot = self.assigned_slots[vehicle.get_license()]\n        slots = self.switcher(vehicle.get_size())\n        slots.appendleft(assigned_slot)\n        del self.assigned_slots[vehicle.get_license()]\n        return assigned_slot\n\n    def is_full(self) -> bool:\n        small = len(self.__small_slots) == 0\n        medium = len(self.__small_slots) == 0\n        large = len(self.__large_slots) == 0\n        xlarge = len(self.__xlarge_slots) == 0\n        return small and medium and large and xlarge\n\n    def print_parking_lot(self):\n\n        print('small slots', len(self.__small_slots))\n        print('medium slots', len(self.__medium_slots))\n        print('large slots', len(self.__large_slots))\n        print('X-large slots', len(self.__xlarge_slots))\n","sub_path":"parking_lot.py","file_name":"parking_lot.py","file_ext":"py","file_size_in_byte":2279,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"110318720","text":"from read_input import Read_input\n\nimport tensorflow as tf\n\nclass CNN_CelebA(object) :\n\n\tdef __init__(self, path_check_point, summaries_dir) :\n\t\tself.path_check_point = path_check_point\n\t\tself.summaries_dir = summaries_dir\n\n\tdef weight_variable(self, shape):\n\t\tinitial = tf.truncated_normal(shape, stddev=0.1)\n\t\treturn tf.Variable(initial)\n\n\tdef bias_variable(self, shape):\n\t\tinitial = tf.constant(0.2, shape=shape)\n\t\treturn tf.Variable(initial)\n\n\tdef conv2d(self, x, W):\n\t\treturn tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')\n\n\tdef max_pool_2x2(self, x):\n\t\treturn tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n\n\tdef define_cnn(self, x) :\n\t\t#self.saver = tf.train.Saver() # to save the model\n\t\t\n\t\t# Reshape the image\n\t\tx_image = tf.reshape(x, [-1, 64, 64, 3])\n\n\t\t#--------1\n\t\tW_conv1 = self.weight_variable([5, 5, 3, 8])\n\t\tb_conv1 = self.bias_variable([8])\n\n\t\th_conv1 = tf.nn.relu(self.conv2d(x_image, W_conv1) + b_conv1)\n\t\th_pool1 = self.max_pool_2x2(h_conv1) # 32 * 32 * 8\n\t\t#--------1\n\n\t\t#---------2\n\t\tW_conv2 = self.weight_variable([5, 5, 8, 32])\n\t\tb_conv2 = self.bias_variable([32])\n\n\t\th_conv2 = tf.nn.relu(self.conv2d(h_pool1, W_conv2) + b_conv2)\n\t\th_pool2 = self.max_pool_2x2(h_conv2) # 16 * 16 * 32 \n\t\t#---------2\n\n\t\t#---------3\n\t\tW_conv3 = self.weight_variable([7, 7, 32, 64])\n\t\tb_conv3 = self.bias_variable([64])\n\n\t\th_conv3 = tf.nn.relu(self.conv2d(h_pool2, W_conv3) + b_conv3)\n\t\th_pool3 = self.max_pool_2x2(h_conv3) # 8 * 8 * 64 --- \n\t\t#---------3\n\n\t\t#---------4 fc\n\t\tW_fc1 = self.weight_variable([8 * 8 * 64, 512])\n\t\tb_fc1 = self.bias_variable([512])\n\n\t\th_pool3_flat = tf.reshape(h_pool3, [-1, 8 * 8 * 64])\n\t\th_fc1 = tf.nn.relu(tf.matmul(h_pool3_flat, W_fc1) + b_fc1)\n\t\t#--------4 fc\n\n\t\t#--------5 fc last\n\t\tkeep_prob = tf.placeholder(tf.float32)\n\t\th_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)\n\n\t\tW_fc2 = self.weight_variable([512, 2])\n\t\tb_fc2 = self.bias_variable([2])\n\n\t\ty_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2\n\t\t#-------5 fc last\n\n\t\tself.saver = tf.train.Saver() # to save the model\n\n\t\treturn y_conv, keep_prob\n\n\tdef loss(self) :\n\n\t\tx = tf.placeholder(tf.float32, [None, 64, 64, 3])\n\t\ty_ = tf.placeholder(tf.float32, [None, 2])\n\n\t\ty_conv, keep_prob = self.define_cnn(x)\n\n\t\tcross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = y_conv, labels = y_))\n\t\ttrain_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)\n\t\ttf.summary.scalar('cross_entropy', cross_entropy)\n\n\t\tcorrect_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))\n\t\taccuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n\t\ttf.summary.scalar('accuracy', accuracy)\n\n\t\tmerged = tf.summary.merge_all()\n\n\t\treturn x, y_, keep_prob, y_conv, train_step, accuracy, merged\n\n\tdef save_model(self, sess):\n\t\tsave_path = self.saver.save(sess, self.path_check_point)\n\t\tprint(\"Model saved in file: %s\" % save_path)\n\n\t#def get_image_labels():\n\t\t\n\n\tdef train_model(self, path, image_dir, batch_size, num_epochs, shuffle, test_size, distortion_range):\n\t\tread_object = Read_input(path, image_dir, batch_size, num_epochs, shuffle, test_size, distortion_range)\n\t\timage_batch = read_object.image_batch\n\t\tlabel_batch = read_object.label_batch\n\t\timage_test = read_object.image_test\n\t\tlabel_test = read_object.label_test\n\n\t\twith tf.Session() as sess:\n\t\t\ttrain_writer = tf.summary.FileWriter(self.summaries_dir + '/train', sess.graph)\n\t\t\ttest_writer = tf.summary.FileWriter(self.summaries_dir + '/test')\n\n\t\t\tx, y_, keep_prob, y_conv, train_step, accuracy, merged = self.loss()\n\t\t\tsess.run(tf.global_variables_initializer())\n\t\t\tsess.run([\n\t\t\t\ttf.local_variables_initializer(),\n\t\t\t\ttf.global_variables_initializer(),\n\t\t\t\t])\n\n\t\t\tcoord = tf.train.Coordinator()\n\t\t\tthreads = tf.train.start_queue_runners(sess=sess, coord=coord)\n\n\t\t\t#try:\n\t\t\t# in most cases coord.should_stop() will return True\n\t\t\t# when there are no more samples to read\n\t\t\t# if num_epochs=0 then it will run for ever\n\t\t\ti = 0\n\t\t\ttry :\n\t\t\t\twhile not coord.should_stop():\n\t\t\t\t\t# will start reading, working data from input queue\n\t\t\t\t\t# and \"fetch\" the results of the computation graph\n\t\t\t\t\t# into raw_images and raw_labels\n\t\t\t\t\tprint(i)\n\t\t\t\t\traw_images, raw_labels = sess.run([image_batch, label_batch])\n\n\t\t\t\t\t#y_conv = def_cnn()\n\t\t\t\t\t#x, y_, keep_prob, y_conv, train_step, accuracy = self.loss()\n\n\t\t\t\t\t#train_step.run(feed_dict={x: raw_images, y_: raw_labels, keep_prob: 0.5})\n\t\t\t\t\t\n\t\t\t\t\tsummary, _ = sess.run([merged, train_step], feed_dict={x: raw_images, y_: raw_labels, keep_prob: 0.5})\n\t\t\t\t\ttrain_writer.add_summary(summary, i)\n\n\t\t\t\t\tif i % 10 == 0:\n\t\t\t\t\t\traw_images_test, raw_labels_test = sess.run([image_test, label_test])\n\t\t\t\t\t\t#pred = y_conv.eval(feed_dict={x:raw_images_test, keep_prob: 1.0})\n\t\t\t\t\t\t#index = pred.argmax(1)#max(enumerate(pred), key=operator.itemgetter(1))\n\t\t\t\t\t\t#index_y = raw_labels_test.argmax(1)#max(enumerate(raw_labels), key=operator.itemgetter(1))\n\t\t\t\t\t\t#acc = sum(index == index_y)/(float(len(index_y)))\n\t\t\t\t\t\t#print(acc, y_conv.eval(feed_dict={x: raw_images_test, keep_prob : 1.0}))\n\t\t\t\t\t\t#print(raw_labels)\n\n\t\t\t\t\t\tsummary, acc = sess.run([merged, accuracy], feed_dict={x: raw_images_test, y_: raw_labels_test, keep_prob: 1.0})\n\t\t\t\t\t\tprint(\"The tensorflow accuracy {}\".format(acc))\n\t\t\t\t\t\t#summary = sess.run(merged, feed_dict=feed_dict(False))\n\t\t\t\t\t\ttest_writer.add_summary(summary, i)\n\t\t\t\t\t#else :\n\t\t\t\t\t\t#summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True))\n\t\t\t\t\t\t#train_writer.add_summary(summary, i)\n\n\t\t\t\t\tif i % 100 == 0:\n\t\t\t\t\t\t# Save the variables to disk.\n\t\t\t\t\t\tself.save_model(sess)\n\t\t\t\t\t\t\n\t\t\t\t\ti = i + 1\n\n\t\t\t\t\t#print(cross_entropy)\n\t\t\t\t\t#print raw_labels\n\t\t\t\t\t#finally:\n\t\t\t\tcoord.request_stop()\n\t\t\t\tcoord.join(threads)\n\t\t\texcept Exception as e:\n\t\t\t\tprint(e)\n\t\t\t\tprint(\"Done Training\")\n\t\t\tfinally:\n\t\t\t\ttrain_writer.close()\n\t\t\t\ttest_writer.close()\n","sub_path":"cnn_celeba.py","file_name":"cnn_celeba.py","file_ext":"py","file_size_in_byte":5818,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"36944845","text":"# this is a script for running with the exp_lab (LIME) \n\nimport laboritory_class\nimport support_classes\nimport experiment_classes\nimport logging\nreload(laboritory_class)\nreload(support_classes)\nreload(experiment_classes)\n\nfrom numpy import pi, random, arange, size\nfrom time import time,sleep\nimport sys\nimport qt\n\n#first, create a 'lab'\nlab = laboritory_class.Laboratory()\ntr_set = {}\nscale_factor = -1.0\n\n#local funcs (where should this go?)\ndef update_mag_field(field):\n    if lab.MAG.get_field_setpoint() is not lab.MAG.get_persistent_field():\n        logging.warning(\"Magnet persistent field is not equal to magnet field\")\n        lab.MAG.set_field_setpoint(lab.MAG.get_persistent_field())\n        \n    lab.MAG.to_setpoint()\n    \n    while lab.MAG.get_mode2() is not 0:\n        qt.msleep(1)\n        \n    lab.MAG.set_switch_heater(1)\n    lab.MAG.set_field_setpoint(field)\n    \n    while lab.MAG.get_mode2() is not 0:\n        qt.msleep(1)\n        \n    lab.MAG.hold()\n    lab.MAG.set_switch_heater(0)\n    lab.MAG.to_zero()\n    print(\"Magnet set to persistent mode at %f\" %field)\ndef set_scale(s):\n    global scale_factor\n    scale_factor = s\ndef get_scale():\n    global scale_factor\n    return (scale_factor) #Should be a float\n    \ndef vG3_set(v):\n    vGr.set_value(v)\n    vGl.set_value(v)\n    vGm.set_value(v)\nlab.vG3_set = vG3_set  \ndef vG3_get():\n    return vGm.get_value()\nlab.vG3_get = vG3_get\ndef vG2_set(v):\n    vGr.set_value(v)\n    vGl.set_value(v)\nlab.vG2_set = vG2_set\ndef vG2_get():\n    return vGr.get_value()\nlab.vG2_get = vG2_get\ndef vG2ML_set(v):\n    vGm.set_value(v)\n    vGl.set_value(v)\nlab.vG2ML_set = vG2ML_set\ndef vG2ML_get():\n    return vGl.get_value()\nlab.vG2ML_get = vG2ML_get\ndef vG2SR_set(v):\n    vGs.set_value(v)\n    vGr.set_value(v)\nlab.vG2SR_set = vG2SR_set\ndef vG2SR_get():\n    return vGr.get_value()\nlab.vG2SR_get = vG2SR_get\n\ndef get_deltaval():\n    val = iD.get_last_value()\n    val = iD.read_value() - val\n    return val\n    \ndef make_safe():\n    vS.set_value(0.0)\n    vGl.set_value(0.0)\n    vGr.set_value(0.0)\n    vGm.set_value(0.0)\n    vGs.set_value(0.0)\n    # AWG.set_ch1_state('OFF')\n    # AWG.set_ch2_state('OFF')\nlab.make_safe = make_safe\n\n#timing\nlab.t0 = time()\ndef set_count(n):\n    if n==0:\n        lab.t0 = time()\ndef my_time():\n    return time()-lab.t0\n\nlab.META = qt.instruments.create('meta1', 'x_Metadata')\nlab.add_instrument(lab.META)\n#adjust basic settings\nlab.META.set_user(\"Sam Hile & the Vipers\")\nlab.META.set_device(\"VIP002, viper_triple_dot, nov run\")\nlab.META.set_dip(\"NML RF2 starting 24/11/15\")\nlab.META.set_notes(\"SIM928 x5 with dividers: src 50x; Gl,Gr 1x; Gm,Gs 10x + 50x for pulses (AC still labelled L & R); Drn to femto 10^8, jfet, filt-variable, and K2000\")\n\n    \n#DC sources\nlab.YOKO_S = qt.instruments.create('YOKO_source', 'x_Yokogawa_7651', address='GPIB::5', reset=False, step=.05, delay=0, upperlim=1, lowerlim=-1)\n# lab.YOKO_D = qt.instruments.create('YOKO_drain', 'x_Yokogawa_7651', address='GPIB::8', reset=False, step=.05, delay=0, upperlim=1, lowerlim=-1)\nlab.add_instrument(lab.YOKO_S)\nlab.SIM_L = qt.instruments.create('SIM_left', 'x_SR_SIM928', address='GPIB::3', port=3, reset=False, step=.1, delay=0, lowerlim=-2.0, upperlim=2.0)\nlab.SIM_R = qt.instruments.create('SIM_right', 'x_SR_SIM928', address='GPIB::3', port=4, reset=False, step=.01, delay=0, lowerlim=-1.20, upperlim=1.20)\nlab.SIM_M = qt.instruments.create('SIM_mid', 'x_SR_SIM928', address='GPIB::3', port=2, reset=False, step=.1, delay=0, lowerlim=-2.0, upperlim=2.0)\nlab.SIM_G = qt.instruments.create('SIM_gset', 'x_SR_SIM928', address='GPIB::3', port=1, reset=False,  step=.01, delay=0, lowerlim=-1.2, upperlim=1.2)\nlab.add_instrument(lab.SIM_L)\nlab.add_instrument(lab.SIM_R)\nlab.add_instrument(lab.SIM_M)\nlab.add_instrument(lab.SIM_G)\n\n#DC measure\nlab.MET = qt.instruments.create('current_meter', 'x_Keithley_2001', address='GPIB::16', reset=False)\nlab.add_instrument(lab.MET)\nlab.FILT = qt.instruments.create('filt1', 'x_SR_SIM965',address='GPIB::3', port=8, reset=False)\nlab.add_instrument(lab.FILT)\n#settings\nlab.MET.set_averaging(0)\nlab.MET.set_trigger_continuous(0)\nlab.MET.set_nplc(1)\nlab.MET.set_range(10)\nlab.MET.set_autorange(0)\n\n#ESR tools\nlab.MAG = qt.instruments.create('magnet', 'x_OxfordInstruments_IPS120', address='GPIB::25')\nlab.add_instrument(lab.MAG)\n#lab.MWG   = qt.instruments.create('ESR_Source', 'x_Agilent_E8267D', address='GPIB::11',reset = False)\n#lab.add_instrument(lab.MWG)\n\n#PULSING\nNLP = qt.instruments.create('nlp', 'g_N_level_pulse', number = 6, reset=False)\nlab.add_instrument(NLP)\n# NLP_CP = qt.instruments.create('nlp_cp', 'g_NLP_cp_move', number = get_num_pulses(), parts = get_num_parts(), reset=False)\n# lab.add_instrument(NLP_CP)\n# OSC = qt.instruments.create('osc', 'x_Agilent_3024A', address='GPIB::10', reset=False)\n# lab.add_instrument(OSC)\n# AWG = qt.instruments.create('awg', 'g_Tektronix_AFG3022', address='GPIB::12', reset=False)\n# lab.add_instrument(AWG)\n\n# --------- Variables & channels\n#Gate voltage variables\nvS = support_classes.Variable('Voltage Source', lab.YOKO_S.set_voltage,  lab.YOKO_S.get_voltage, units='V', gain=0.02)\nvGl = support_classes.Variable('Voltage Gate Left', lab.SIM_L.set_voltage,  lab.SIM_L.get_voltage, units='V', gain=1.0)\nvGm = support_classes.Variable('Voltage Gate Mid', lab.SIM_M.set_voltage,  lab.SIM_M.get_voltage, units='V', gain=1.0)\nvGr = support_classes.Variable('Voltage Gate Right', lab.SIM_R.set_voltage,  lab.SIM_R.get_voltage, units='V', gain=1.0)\nvGset = support_classes.Variable('Voltage Gate SET', lab.SIM_G.set_voltage,  lab.SIM_G.get_voltage, units='V', gain=1.0)\nvGs = vGset #helpful proxy\nlab.add_variable(vS)\nlab.add_variable(vGl)\nlab.add_variable(vGm)\nlab.add_variable(vGr)\nlab.add_variable(vGset)\n#combos\nvG3 = support_classes.Variable('Voltage Gates Triple L M R', lab.vG3_set, lab.vG3_get, units='V', gain=1)\nvG2 = support_classes.Variable('Voltage Gates L and R', lab.vG2_set, lab.vG2_get, units='V', gain=1)\nvG2ML = support_classes.Variable('Voltage Gates M and L', lab.vG2ML_set, lab.vG2ML_get, units='V', gain=1)\nvG2SR = support_classes.Variable('Voltage Gates S and R', lab.vG2SR_set, lab.vG2SR_get, units='V', gain=1)\nlab.add_variable(vG3)\nlab.add_variable(vG2ML)\nlab.add_variable(vG2SR)\n\n#ESR Variables\nBz = support_classes.Variable('Magnetic Field', update_mag_field, lab.MAG.get_persistent_field, units='T')\nlab.add_variable(Bz)\n# ESR_Fr = support_classes.Variable('ESR Frequency', lab.MWG.set_frequency, lab.MWG.get_frequency, units='Hz')\n# ESR_Pw = support_classes.Variable('ESR Power', lab.MWG.set_power, lab.MWG.get_power, units='dB')\n# ESR_Dl = support_classes.Variable('ESR Delay', lab.MWG.set_delay, lab.MWG.get_delay, units='s')\n# ESR_Wd = support_classes.Variable('ESR Width', lab.MWG.set_duration, lab.MWG.get_duration, units='s')\n# lab.add_variable(ESR_Fr)\n# lab.add_variable(ESR_Pw)\n# lab.add_variable(ESR_Dl)\n# lab.add_variable(ESR_Wd)\n\n#AWG and OSC Variables\n# T = support_classes.Variable('Time', support_classes.set_void_func, support_classes.get_void_func, units='S')\n# F = support_classes.Variable('Frequency', AWG.set_ch1_frequency, AWG.get_ch1_frequency, units='Hz')#ch2 linked on frontpanel\n# Al =  support_classes.Variable('Gate Pulse Amplitude left', AWG.set_ch1_amplitude, AWG.get_ch1_amplitude, units='V', gain=0.02)\n# Ar =  support_classes.Variable('Gate Pulse Amplitude right', AWG.set_ch2_amplitude, AWG.get_ch2_amplitude, units='V', gain=0.02)\n# Ol =  support_classes.Variable('Gate Pulse Offset left', AWG.set_ch1_offset, AWG.get_ch1_offset, units='V', gain=1)\n# Or =  support_classes.Variable('Gate Pulse Offset right', AWG.set_ch2_offset, AWG.get_ch2_offset, units='V', gain=1)\n# SCL = support_classes.Variable('Gate Pulse scale factor', set_scale, get_scale, units='1')\nBW = support_classes.Variable('Bandwidth', lab.FILT.set_frequency, lab.FILT.get_frequency, units='Hz') \n# lab.add_variable(T)\n# lab.add_variable(F)\n# lab.add_variable(Al)\n# lab.add_variable(Ar)\n# lab.add_variable(Ol)\n# lab.add_variable(Or)\n# lab.add_variable(SCL)\nlab.add_variable(BW)\n\n#Pulsing variables\n# PV1 = support_classes.Variable('Pulse Voltage 1', NLP.set_level1, NLP.get_level1, units='V')\n# PV2 = support_classes.Variable('Pulse Voltage 2', NLP.set_level2, NLP.get_level2, units='V')\n# PV3 = support_classes.Variable('Pulse Voltage 3', NLP.set_level3, NLP.get_level3, units='V')\n# PD1 = support_classes.Variable('Pulse Duration 1', NLP.set_duration1, NLP.get_duration1, units='s')\n# PD2 = support_classes.Variable('Pulse Duration 2', NLP.set_duration2, NLP.get_duration2, units='s')\n# PD3 = support_classes.Variable('Pulse Duration 3', NLP.set_duration3, NLP.get_duration3, units='s')\n# lab.add_variable(PV1)\n# lab.add_variable(PV2)\n# lab.add_variable(PV3)\n# lab.add_variable(PD1)\n# lab.add_variable(PD2)\n# lab.add_variable(PD3)\n\n# PV4 = support_classes.Variable('Pulse Voltage 4', NLP.set_level4, NLP.get_level4, units='V')\n# PV5 = support_classes.Variable('Pulse Voltage 5', NLP.set_level5, NLP.get_level5, units='V')\n# PD4 = support_classes.Variable('Pulse Duration 4', NLP.set_duration4, NLP.get_duration4, units='s')\n# PD5 = support_classes.Variable('Pulse Duration 5', NLP.set_duration5, NLP.get_duration5, units='s')\n# lab.add_variable(PV4)\n# lab.add_variable(PV5)\n# lab.add_variable(PD4)\n# lab.add_variable(PD5)\n\n# PV6 = support_classes.Variable('Pulse Voltage 6', NLP.set_level6, NLP.get_level6, units='V')\n# PD6 = support_classes.Variable('Pulse Duration 6', NLP.set_duration6, NLP.get_duration6, units='s')\n# lab.add_variable(PD6)\n# lab.add_variable(PV6)\n# PV7 = support_classes.Variable('Pulse Voltage 7', NLP.set_level7, NLP.get_level7, units='V')\n# PV8 = support_classes.Variable('Pulse Voltage 8', NLP.set_level8, NLP.get_level8, units='V')\n# PD7 = support_classes.Variable('Pulse Duration 7', NLP.set_duration7, NLP.get_duration7, units='s')\n# PD8 = support_classes.Variable('Pulse Duration 8', NLP.set_duration8, NLP.get_duration8, units='s')\n# lab.add_variable(PV7)\n# lab.add_variable(PV8)\n# lab.add_variable(PD7)\n# lab.add_variable(PD8)\n\n# create channels\niD = support_classes.Channel('Drain Current', lab.MET.get_readval, units='pA', gain=(1e-8)*(1e12))\ndiD = support_classes.Channel('delta Drain Current', get_deltaval, units='pA', gain=1)\nTi = support_classes.Channel('Real Time', my_time, units='s')\nX = support_classes.Channel('Scope Signal', support_classes.get_void_func, units='AU', gain=1)\nuP = support_classes.Channel('Spin Up Fraction', support_classes.get_void_func, units='1')\nlab.add_channel(iD)\nlab.add_channel(diD)\nlab.add_channel(Ti)\nlab.add_channel(X)\nlab.add_channel(uP)\n    \nPANEL = qt.instruments.create('panel', 'x_Panel', lab=lab, safety=lab.make_safe)\n\n#Set magnet to remote and unlocked\nlab.MAG.set_remote_status(3)\nlab.MAG.get_remote_status()\n#benchview\nOSC.set_waveform_format('BYTE')\n   \nlab.list_variables()\nlab.list_channels()\nlab.time_channels()\n\n#################scan funcs###########################\ndef GSweep(sweep_var1, sweep_vec1, file_name,**kwargs):\n    print('Running: %s sweep' %sweep_var1.name)\n    PANEL.get_all()\n    \n    d_G1 = lab.create_data([sweep_var1],[iD],name = file_name)\n    lab.add_data(d_G1)\n    p_G1 = lab.create_plot2D(d_G1,sweep_var1,iD, autoupdates=True)\n    lab.add_plot(p_G1)\n    # p_G2 = lab.create_plot2D(d_G1,sweep_var1,diD, autoupdates=True)\n    # lab.add_plot(p_G2)\n\n    m_G1 = experiment_classes.Map_Experiment(channels=[iD], variables=[sweep_var1], sweeps=[sweep_vec1], data=d_G1, plots=[p_G1], plot_updates=True, **kwargs)\n    lab.add_experiment(m_G1)\n    m_G1.run()\nlab.GSweep = GSweep\n    \ndef GPeng(counts, control_var, control_init, control_step, sig_chan, sig_floor, deriv_chan, deriv_peak, file_name, **kwargs):\n    print('Running: compensation %s ' %control_var.name)\n    PANEL.get_all()\n       \n    d_G1 = lab.create_data([control_var],[T, sig_chan, deriv_chan],name = file_name)\n    lab.add_data(d_G1)\n    p_G1 = lab.create_plot2D(d_G1, T, control_var, autoupdates=True, maxpoints = counts)\n    lab.add_plot(p_G1)\n    p_G2 = lab.create_plot2D(d_G1, T, sig_chan, autoupdates=True, maxpoints = counts)\n    lab.add_plot(p_G2)\n    p_G3 = lab.create_plot2D(d_G1, T, deriv_chan, autoupdates=True, maxpoints = counts)\n    lab.add_plot(p_G3)\n\n    m_G1 = experiment_classes.Feedback_Experiment(channels=[sig_chan, deriv_chan],#T is left out here\n                                variables=[control_var], sweeps=[[control_init,control_init,1]], \n                                data=d_G1, plots=[p_G1, p_G2, p_G3], counts = counts,\n                                signal=0, signal_floor=sig_floor,\n                                derivative=1, derivative_peak=deriv_peak,\n                                control=0, control_maxstep=control_step, **kwargs)\n    lab.add_experiment(m_G1)\n    m_G1.run()\nlab.GPeng = GPeng\n    \ndef GGMap(sweep_var1, sweep_vec1, sweep_var2, sweep_vec2, file_name, swap_plot_axes=False, **kwargs):\n    print('Running: %s vs. %s' % (sweep_var1.name, sweep_var2.name))\n    \n    # AWG.set_ch1_state('OFF')\n    # AWG.set_ch2_state('OFF')\n    \n    PANEL.get_all()\n    d_G1_G2 = lab.create_data([sweep_var1,sweep_var2],[iD,Ti],name = file_name)\n    lab.add_data(d_G1_G2)\n    p_G1 = lab.create_plot2D(d_G1_G2,sweep_var2,iD, autoupdates=False)\n    if swap_plot_axes:\n        p_G1_G2 = lab.create_plot3D(d_G1_G2,sweep_var2,sweep_var1,iD, autoupdates=False)\n    else:\n        p_G1_G2 = lab.create_plot3D(d_G1_G2,sweep_var1,sweep_var2,iD, autoupdates=False)\n    # pd_G1 = lab.create_plot2D(d_G1_G2,sweep_var2,diD, autoupdates=False)\n    pt_G1_G2 = lab.create_plot3D(d_G1_G2,Ti,sweep_var2,iD, autoupdates=False)#realtime plot\n    lab.add_plot(p_G1)\n    lab.add_plot(p_G1_G2)\n    # lab.add_plot(pd_G1)\n    lab.add_plot(pt_G1_G2)\n    kwargs['point_pause_time'] = kwargs.get('point_pause_time', 0.0)\n    kwargs['line_pause_time'] = kwargs.get('line_pause_time', 1.0)\n    kwargs['initial_pause_time'] = kwargs.get('initial_pause_time', 5.0)\n    m_G1_G2 = experiment_classes.Map_Experiment(channels=[iD,Ti], variables=[sweep_var1,sweep_var2], sweeps=[sweep_vec1,sweep_vec2], data=d_G1_G2, plots=[p_G1,p_G1_G2,pt_G1_G2], **kwargs)\n    lab.add_experiment(m_G1_G2)\n    m_G1_G2.run()\nlab.GGMap = GGMap\n\n# def GGMapPulse(sweep_var1, sweep_vec1, sweep_var2, sweep_vec2, file_name, pulse_freq, swap_plot_axes=False, **kwargs):\n    # print('Running: %s vs. %s' % (sweep_var1.name, sweep_var2.name))\n    \n    # AWG.set_ch1_state('OFF')\n    # AWG.set_ch2_state('ON')\n    # AWG.set_ch2_shape('SQU')\n    # Or.set_value(0)\n    # F.set_value(pulse_freq)\n    \n    # PANEL.get_all()\n    # d_G1_G2 = lab.create_data([sweep_var1,sweep_var2],[iD,Ti],name = file_name)\n    # lab.add_data(d_G1_G2)\n    # p_G1 = lab.create_plot2D(d_G1_G2,sweep_var2,iD, autoupdates=False)\n    # if swap_plot_axes:\n        # p_G1_G2 = lab.create_plot3D(d_G1_G2,sweep_var2,sweep_var1,iD, autoupdates=False)\n    # else:\n        # p_G1_G2 = lab.create_plot3D(d_G1_G2,sweep_var1,sweep_var2,iD, autoupdates=False)\n    # pd_G1 = lab.create_plot2D(d_G1_G2,sweep_var2,diD, autoupdates=False)\n    # pt_G1_G2 = lab.create_plot3D(d_G1_G2,Ti,sweep_var2,iD, autoupdates=False)#realtime plot\n    # lab.add_plot(p_G1)\n    # lab.add_plot(p_G1_G2)\n    # lab.add_plot(pd_G1)\n    # lab.add_plot(pt_G1_G2)\n    # kwargs['point_pause_time'] = 0.0\n    # kwargs['line_pause_time'] = 1.0\n    # kwargs['initial_pause_time'] = 5.0\n    # m_G1_G2 = experiment_classes.Map_Experiment(channels=[iD,Ti], variables=[sweep_var1,sweep_var2], sweeps=[sweep_vec1,sweep_vec2], data=d_G1_G2, plots=[p_G1,p_G1_G2,pt_G1_G2], **kwargs)\n    # lab.add_experiment(m_G1_G2)\n    # m_G1_G2.run()\n# lab.GGMapPulse = GGMapPulse\n\n\n# def RampMap(sweep_var, sweep_vec, step_time = 1.0/1000.0, averages = 10, frequency = 66, file_name='RampMap', ch1=True, swap_plot_axes=False):\n    # print 'Running RampMap \\nSaved files called %s_%s' %(file_name, sweep_var.name)\n    # PANEL.get_all()\n    \n    # tr_set = {}\n    # if ch1: \n        # AWG.set_ch1_state('ON')\n    # else:\n        # AWG.set_ch1_state('OFF')\n\n    # AWG.set_ch2_state('ON')\n    # F.set_value(frequency)\n    \n    # DataRamp = lab.create_data([sweep_var,T],[X], name = file_name)\n    # lab.add_data(DataRamp)\n    # P_2D = lab.create_plot2D(DataRamp,T,X)\n    # if swap_plot_axes:\n        # P_3D = lab.create_plot3D(DataRamp,T,sweep_var,X)\n    # else:\n        # P_3D = lab.create_plot3D(DataRamp,sweep_var,T,X)\n    # lab.add_plot(P_2D)\n    # lab.add_plot(P_3D)\n    # tr_set['step_time'] = step_time\n    # tr_set['cycle_time'] = 1/F.get_value()\n    # tr_set['cycle_num'] = 1\n    # tr_set['averages'] = averages\n    # tr_set['rep_num'] = 1\n    # tr_set['channel_units'] = 'V'\n    # tr_set['channel_label'] = 'Signal'\n    # tr_set['variable_units'] = 'V'\n    # tr_set['variable_label'] = 'VoltageGL_mod'\n    # tr_set['gain'] = 1\n    \n\n    # READ = support_classes.AverageTrace('Readout Trace', awg_setup_ramp, awg_check, osc_setup_avgs, osc_trigger, osc_collect, **tr_set)\n    # lab.add_trace(READ)\n    # Trace_data = experiment_classes.Trace_Experiment(trace=READ, variable=sweep_var, sweep=sweep_vec, data=DataRamp, plots=[P_2D,P_3D])\n    # lab.add_experiment(Trace_data)\n    # Trace_data.run()\n# lab.RampMap = RampMap\n\n\n    \n# def MultiLP(sweep_var, sweep_vec, cyc_num, rep_num, step_time, file_name='PulseMap',len=None):\n    # print 'Running %d-level pulse with %s sweep \\nSaved files called %s' %(3, sweep_var.name, file_name)\n    # PANEL.get_all()\n    \n    # tr_set = {}\n    # AWG.set_ch1_state('ON')\n    # AWG.set_ch2_state('ON')\n \n    # Data_pulse = lab.create_data([sweep_var,T],[X], name = file_name)\n    # lab.add_data(Data_pulse)\n    # P_2D = lab.create_plot2D(Data_pulse,T,X)\n    # P_3D = lab.create_plot3D(Data_pulse,sweep_var,T,X)\n    # lab.add_plot(P_2D)\n    # lab.add_plot(P_3D)\n    # tr_set['step_time'] = step_time\n    # tr_set['cycle_time'] = NLP.get_period()\n    # if len:\n        # tr_set['cycle_time'] = len\n    # tr_set['cycle_num'] = cyc_num\n    # tr_set['rep_num'] = rep_num\n    # tr_set['channel_units'] = 'V'\n    # tr_set['channel_label'] = 'Voltage'\n    # tr_set['variable_units'] = 'S'\n    # tr_set['variable_label'] = 'Time'\n    # tr_set['variable_units'] = 'S'\n    # tr_set['variable_label'] = 'Time'\n    # tr_set['osc_offset'] = 0.05 #Not sure whether this is actually needed. \n    # tr_set['gain'] = 1\n    # tr_set['norm_avg'] = False\n    # tr_set['trace_maps'] = True\n    \n\n    # READ = support_classes.Trace('Readout Trace', awg_setup_pulse, awg_check, osc_setup, osc_trigger, osc_collect, **tr_set)\n    # lab.add_trace(READ)\n    # Trace_pulse = experiment_classes.Trace_Experiment(trace=READ, variable=sweep_var, sweep=sweep_vec, data=Data_pulse, plots=[P_2D,P_3D])\n    # lab.add_experiment(Trace_pulse)\n    # Trace_pulse.run()\n# lab.MultiLP = MultiLP\n# lab.SpinTail = MultiLP\n\n# def MultiLP_avg(sweep_var, sweep_vec, averages, step_time, file_name='PulseMap',len=None):\n    # print 'Running %d-level pulse with %s sweep \\nSaved files called %s' %(3, sweep_var.name, file_name)\n    # PANEL.get_all()\n    \n    # tr_set = {}\n    # AWG.set_ch1_state('ON')\n    # AWG.set_ch2_state('ON')\n \n    # Data_pulse = lab.create_data([sweep_var,T],[X], name = file_name)\n    # lab.add_data(Data_pulse)\n    # P_2D = lab.create_plot2D(Data_pulse,T,X)\n    # P_3D = lab.create_plot3D(Data_pulse,sweep_var,T,X)\n    # lab.add_plot(P_2D)\n    # lab.add_plot(P_3D)\n    # tr_set['step_time'] = step_time\n    # tr_set['cycle_time'] = NLP.get_period()\n    # if len:\n        # tr_set['cycle_time'] = len\n    # tr_set['cycle_num'] = 1\n    # tr_set['rep_num'] = 1\n    # tr_set['channel_units'] = 'V'\n    # tr_set['channel_label'] = 'Voltage'\n    # tr_set['variable_units'] = 'S'\n    # tr_set['variable_label'] = 'Time'\n    # tr_set['variable_units'] = 'S'\n    # tr_set['variable_label'] = 'Time'\n    # tr_set['osc_offset'] = 0.05 #Not sure whether this is actually needed. \n    # tr_set['gain'] = 1\n    # tr_set['averages'] = averages\n    # tr_set['norm_avg'] = False\n    # tr_set['trace_maps'] = False\n    \n\n    # READ = support_classes.AverageTrace('Readout Trace', awg_setup_pulse, awg_check, osc_setup_avgs, osc_trigger, osc_collect, **tr_set)\n    # lab.add_trace(READ)\n    # Trace_pulse = experiment_classes.Trace_Experiment(trace=READ, variable=sweep_var, sweep=sweep_vec, data=Data_pulse, plots=[P_2D,P_3D])\n    # lab.add_experiment(Trace_pulse)\n    # Trace_pulse.run()\n# lab.MultiLP = MultiLP\n# lab.SpinTail = MultiLP\n\n\n# def MultiLPRO(sweep_var, sweep_vec, cyc_num, rep_num, step_time, file_name='PulseMap',len=None,ro_time=[0,0.0025],ro_thresh=[0.02]):\n    # print 'Running %d-level pulse with %s sweep \\nSaved files called %s' %(3, sweep_var.name, file_name)\n    # PANEL.get_all()\n    \n    # tr_set = {}\n    # AWG.set_ch1_state('ON')\n    # AWG.set_ch2_state('ON')\n \n    # Data_pulse = lab.create_data([sweep_var,T],[X,uP], name = file_name)\n    # lab.add_data(Data_pulse)\n    \n    # P_2D = lab.create_plot2D(Data_pulse,T,X)\n    # P_3D = lab.create_plot3D(Data_pulse,sweep_var,T,X)\n    # lab.add_plot(P_2D)\n    # lab.add_plot(P_3D)\n    \n    # P_up = lab.create_plot2D(Data_pulse,sweep_var,uP,maxtraces=20,style='points',pointtype=7)#draw filled circles\n    # lab.add_plot(P_up)\n    \n    \n    # tr_set['step_time'] = step_time\n    # tr_set['cycle_time'] = NLP.get_period()\n    # if len:\n        # tr_set['cycle_time'] = len\n    # tr_set['cycle_num'] = cyc_num\n    # tr_set['rep_num'] = rep_num\n    # tr_set['channel_units'] = 'V'\n    # tr_set['channel_label'] = 'Voltage'\n    # tr_set['variable_units'] = 'S'\n    # tr_set['variable_label'] = 'Time'\n    # tr_set['variable_units'] = 'S'\n    # tr_set['variable_label'] = 'Time'\n    # tr_set['osc_offset'] = 0.05 #Not sure whether this is actually needed. \n    # tr_set['gain'] = 1\n    # tr_set['norm_avg'] = False\n    # tr_set['trace_maps'] = True\n    # tr_set['read_bounds'] = ro_time\n    # tr_set['read_threshold'] = ro_thresh\n    \n\n    # READ = support_classes.Trace('Readout Trace', awg_setup_pulse, awg_check, osc_setup, osc_trigger, osc_collect, **tr_set)\n    # lab.add_trace(READ)\n    # Trace_pulse = experiment_classes.Trace_Experiment(trace=READ, variable=sweep_var, channels = [X,uP], sweep=sweep_vec, data=Data_pulse, plots=[P_up,P_2D,P_3D])\n    # lab.add_experiment(Trace_pulse) # Trace_Stats_Experiment(channels = [X,uP], trace=READ, variables=[sweep_vars[-1],T], sweeps=sweep_vecs, data=Data_pulse, plots=[P_up,P_2D,P_3D])\n    # Trace_pulse.run()\n# lab.MultiLPRO = MultiLPRO\n# lab.T1Time = MultiLPRO\n\n##############trace funcs########################\n# def awg_setup_ramp(w):\n\n    # AWG.syncphase()\n    # AWG.set_ch2_phase(180)\n    # AWG.set_ch1_shape('RAMP')\n    # AWG.set_ch2_shape('RAMP')\n    # Ol.set_value(0)\n    # Or.set_value(0)\n\n    # pass\n    \n# def awg_setup_pulse(w):\n    # #send ch1\n    # AWG.set_ch1_shape('USER1')\n    # AWG.set_ch1_phase(0)\n    # AWG.waveform_send(NLP.get_bytearray(NLP.get_wave(points=10000)))\n    # AWG.waveform_copy('EMEM','USER1')\n    # Al.set_value(NLP.get_amplitude())\n    # Ol.set_value(NLP.get_offset())\n   \n    # # flip & scale for ch2\n    # NLP.set_amplitude(NLP.get_amplitude()*SCL.get_value())\n    # NLP.set_offset(NLP.get_offset()*SCL.get_value())\n    \n    # # NLP.set_amplitude(NLP.get_amplitude()/SCL_SET_M.get_value())\n    # # NLP.set_offset(NLP.get_offset()/SCL_SET_M.get_value())\n    \n    # # send ch2\n    # AWG.set_ch2_shape('USER2')\n    # AWG.set_ch2_phase(0)\n    # AWG.waveform_send(NLP.get_bytearray(NLP.get_wave(points=10000)))\n    # AWG.waveform_copy('EMEM','USER2')\n    # Ar.set_value(NLP.get_amplitude())\n    # Or.set_value(NLP.get_offset())\n    \n    # # flip & scale back for ch1\n    # NLP.set_amplitude(NLP.get_amplitude()/SCL.get_value())\n    # NLP.set_offset(NLP.get_offset()/SCL.get_value())\n    \n    # # NLP.set_amplitude(NLP.get_amplitude()*SCL_SET_M.get_value())\n    # # NLP.set_offset(NLP.get_offset()*SCL_SET_M.get_value())\n    \n    # # NLP.set_ramp_duration(0.002)############ NEW #############\n    \n    # F.set_value(NLP.get_frequency())\n    # pass   \n\ndef awg_check():\n    #not implemented\n    return 1\n\ndef osc_setup(tr=None):\n    if tr is not None:\n        # tr.set_cycle_time(NLP.get_period())\n        OSC.stop()\n        OSC.set_waveform_source('CHAN1')\n        OSC.set_timebase_origin('LEFT')\n        OSC.set_acquire_type('NORM')\n        OSC.set_waveform_points_mode('MAX')\n        OSC.set_timebase_range(tr.get_rep_time())\n        OSC.set_waveform_points(tr.get_rep_length())\n    \ndef osc_setup_avgs(tr=None):\n    if tr is not None:\n        tr.set_cycle_time(1.0/F.get_value())\n        OSC.stop()\n        OSC.set_waveform_source('CHAN1')\n        OSC.set_timebase_origin('LEFT')\n        OSC.set_acquire_type('AVER')\n        OSC.set_acquire_averages(tr.get_averages())\n        OSC.set_waveform_points_mode('MAX')\n        OSC.set_timebase_range(tr.get_rep_time())\n        OSC.set_waveform_points(tr.get_rep_length())\n    \ndef osc_trigger():\n    OSC.digitize(0.1)#wait time before querying completion\n\ndef osc_collect():\n    w = OSC.record()\n    return OSC.byt_wav_to_array(w)\n","sub_path":"bilby/aff_scripts/vip002/vip2_setup_dc_nov.py","file_name":"vip2_setup_dc_nov.py","file_ext":"py","file_size_in_byte":24959,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"343923051","text":"# In all the exercises that follow, insert your code directly in this file\n# where you find a comment saying \"Answer to Question [...]\"\n#\n# IMPORTANT NOTES: \n#\n# 1) THIS IS A PROGRAMMING COURSE. :-) THIS MEANS THAT THE ANSWERS TO THESE QUESTIONS\n# ARE MEANT TO BE DONE IN CODE AS MUCH AS POSSIBLE. E.G., WHEN A QUESTION SAYS \"COUNT\n# THE NUMBER OF TIMES THAT [...]\" YOU SHOULD INTERPRET THIS AS \"WRITE CODE THAT COUNTS\n# THE NUMBER OF TIMES THAT [...]\". USE YOUR BEST JUDGMENT AND YOU WILL DO FINE. :-)\n#\n# 2) WHEN A QUESTION PROVIDES A VALUE FOR YOU TO USE IN COMPUTATIONS, FIRST STORE IT IN\n# A VARIABLE AND WRITE CODE THAT MAKES USE OF THAT VARIABLE.\n#\n# 3) WHENEVER A QUESTION DOES NOT SPECIFY A PARTICULAR WAY TO DO SOMETHING,\n#    THEN YOU CAN DO IT *ANY* WAY. :-)\n\n\"\"\"\n\nIntro\n\nIn this assignment you will explore the Star Wars API. This is an \nopen and free-to-use API that provides all information you could \npossibly need about... Star Wars!\n\nYou can read about the project on the page\n\nhttp://swapi.co/about\n\nand access the technical documentation for the API on the page\n\nhttp://swapi.co/documentation\n\nAs mentioned in class this week, using an API requires that you get\nthe relevant information from its documentation. This API has great\ndocumentation, so be sure to check it carefully. The documentation\ncontains all the details you need to answer the questions below.\n\n\nNote 1: you are not meant to use the Python bindings provided on\nthe site. Instead, use the two modules we used in class (requests and\njson) to answer all questions below.\n\n\nNote 2: if at any point you get an error message ending with a line that reads\n\nrequests.exceptions.SSLError:  (...)\n\nthen pass the additional argument verify=False in _all_ calls to requests.get(), eg:\n    \n    r = requests.get(url, verify=False)\n\nor if you want to pass the dictionary 'parameters' to the server:\n    \n    r = requests.get(url, params=parameters, verify=False)\n    \n\"\"\"\n\n\"\"\"\n\nQuestion 1\n\n\nYou can access information about 10 planets in Star Wars by sending a\nget request to\n\nhttp://swapi.co/api/planets/\n\n1.1) Using a for loop, print out the names of all 10 planets returned\nby that call.\n\n\n\n1.2) Write a function called get_planet_population that takes as an\nargument a string called 'planet_name'. Your function should then\nreturn (note: *not* print):\n\n   - the population of that planet *as a number*, if that planet is\n   among the 10 planets listed in the data returned by an API call to\n   http://swapi.co/api/planets/ and the API lists its population; or\n   \n   - the special Python value None, if that planet is among the 10\n   planets listed in the data returned by an API call to\n   http://swapi.co/api/planets/ and the API tells you the population\n   is 'unknown'.\n   \n   - the string \"unknown planet\", if that planet is not among the 10\n   planets listed in the data returned by an API call to\n   http://swapi.co/api/planets/.\n\n   \nNotes for Question 1.2: \n    \n    1) Your function should be case-insensitive in the way it handles\n    planet_name and matches it against the planets available through\n    that API call.\n    \n    2) The planet API supports a 'search' feature which you are not\n    meant to use here. \n    \n    3) Please note None (no quotes) is a special Python value and\n    different from the string 'None'. In the second scenario described\n    above, your function should return the special Python value.\n\n    \n1.3) Print the names of all planets, from among the 10 planets\nobtained by sending a request to http://swapi.co/api/planets/, that\nhave a population less than or equal to 30,000,000 and whose climate\ndescription includes the word 'temperate'.\n\nNotes for Question 1.3:\n    \n    1) Please note that some planets have a population that is listed\n    as 'unknown' and that string cannot be converted into a number. In\n    your if statement you will have to handle that case (population ==\n    'unknown') separately, otherwise you will get an error when you\n    try to convert the population into a number. Obviously, planets\n    with a 'unknown' population should not be listed by your code.\n    \n    2) Please note that the 'climate' for a planet often is a\n    description containing multiple words. You merely have to check if\n    'temperate' is found in that string.\n\n\"\"\"\n\n# Answer to Question 1.1\nimport requests\nurl = \"https://swapi.co/api/planets/?format=json\"\nr = requests.get(url)\nr_json = r.json()\nresults = r_json['results']\nnames = list()\nfor i in results:\n    names.append(i['name'])\nprint(names)\n# Answer to Question 1.2\n\n\n# This function gives the population of the planet\n# @param:   planet_name A String specifying the name of the planet\n# @return:  if 'unknown',return None\n#           if not in the 10 planets returned by the API, return \"unknown planet\"\n#           else, return the integer value of the population.   \ndef get_planet_population(planet_name):\n    for j in results:\n        if j['name'].upper() == planet_name.upper():\n            if j['population'] == 'unknown':\n                return None\n            else:\n                return int(j['population'])\n    return \"unknown planet\"\n#print(get_planet_population('hoth'))\n#print(get_planet_population('naboo'))\n    \n\n# Answer to Question 1.3\n    \n\n# This function gives the list of names of planets of population under a certain amount\n# @param:   bound The amount under which the population of the planet must be\n#           climate The String that the climate of the planet must include\n# @return:  list of names of accepted planets\n    \ndef printPlanet(climate,bound):\n    output = list()\n    for j in results:\n        if type(get_planet_population(j['name'])) == int :\n            if climate in j['climate'] and get_planet_population(j['name']) <= bound:\n                output.append(j['name'])\n        else:\n            continue\n    return output\n\nprint(printPlanet('temperate',30000000))\n\"\"\"\n\nQuestion 2\n\nIn the previous question, you were only asked to use the 10 planets\nreturned by a first call to the API at http://swapi.co/api/planets/.\nHowever, this API lists information about many more planets. What\nhappened was that SWAPI (both for planets as well as for any other\ntype of information) only serves 10 results at a time and then expects\nyour code to send additional requests for the rest of the data, which\nwill be served in \"batches\" of 10 results at a time. This is very\ncommon behavior \"in the real world\", since it avoids needlessly\nwasting bandwidth.\n\nIn this question you will use a while loop to issue requests for\ninformation about all starships in Star Wars. The API to use is\nlocated at\n\nhttp://swapi.co/api/starships/\n\nNote that the data you get back is a dictionary that contains a\nkey called 'next'. The value for that key is the URL to which you\nshould send the next request using requests.get() to fetch the\nadditional batch of information about the following 10 starships. \n\n2.1) Retrieve information about *all* starships available via this API\nand store it in a list called 'starships'.\n\nHint: As mentioned above, the typical way to fetch all results from an\nAPI is to use a while loop that will retrieve a batch of 10 results,\nadd them to a list (or similar data structure) and then send another\nrequest for more results *if the value for the key 'next' in the\ndictionary in the previous response contained a URL*. When you\nretrieve the final batch of results and no more results are available,\nthe server will send you a dictionary that will probably still contain\nresults you need to add to the list but the value for key 'next' will\nbe None (rather than a URL). Therefore, one common strategy is to have\nyour while loop end when the value for key 'next' == None.\n\n\n\n2.2) Print out the name of the fastest starship Star Wars. As\nindicated in the documentation, speed is given by the MGLT (Maximum\nnumber of Megalights) attribute of a starship.  \n\n\"\"\"\n\n# Answer to Question 2.1\nimport requests\n# Answer to Question 1.2\n\n\n\n# This function gives the dictionary fo all starship information\n# @param: \n# @return:  dictionary containing all starship information.\n#           The keys are ships' names and the values are all its information,excluding the name\ndef get_starships():\n    url = \"https://swapi.co/api/starships/?format=json\"\n    starships = dict()\n    while url != None:\n        r = requests.get(url)\n        r_json = r.json()\n        results = r_json['results']\n        for i in results:\n            name = i['name']\n            del i['name']\n            starships[name] = i \n        url = r_json['next']     \n    return starships\n\n\n# This function shows the starship information\n# @param:\n# @return:\ndef seeShipNames():    \n    print(list(get_starships().keys()))\nseeShipNames() \n\n\n# Answer to Question 2.2\n\n\n# This function gets the dictionary of all speeds and their corresponding names\n# @param:\n# @return: dictionary of all speeds and their corresponding starship names\ndef getShipBySpeed():\n    starships = get_starships()\n    starships_by_speed = dict()\n    for i in starships.keys():\n        speed = starships[i]['MGLT']\n        try:\n            speed = int(speed)\n        except:\n            continue\n        if speed not in starships_by_speed.keys():\n            starships_by_speed[speed] = [i]\n        else:\n            starships_by_speed[speed].append(i)\n    return starships_by_speed\n\n\ndef recursiveFindFastest(listOfSpeed):\n    if len(listOfSpeed) == 1:\n        return listOfSpeed[0]\n    else:\n        halfLength = int(len(listOfSpeed)/2)\n        lower = recursiveFindFastest(listOfSpeed[:halfLength])\n        upper = recursiveFindFastest(listOfSpeed[halfLength:])\n        if lower < upper:\n            return upper\n        else:\n            return lower\n\n\nspeed = getShipBySpeed()\nfastest = speed[recursiveFindFastest(list(speed.keys()))]\nfastestString = ''\nfor i in fastest:\n    fastestString += i\n    fastestString += \";\"\nprint(\"Fastest ship is:\",fastestString,sep = ' ')\n\n","sub_path":"HW4/hw9_harry_zhang_recursive.py","file_name":"hw9_harry_zhang_recursive.py","file_ext":"py","file_size_in_byte":9930,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"444763420","text":"import requests\nfrom datetime import *\nfrom question.data2sentence.util import BraveSql\nimport json\nimport socket\n\nuser_dict = dict()\n\n\ndef get_json(input_json):\n    bravesql = BraveSql()\n    select_sql = \"SELECT subject FROM logdb.scenario WHERE (cid = '{cid}' and datetime > '{date}');\".format(\n        cid=input_json['clientId'], date=date.today().isoformat()\n    )\n    _subjects = [row['subject'] for row in bravesql.select(sql_text=select_sql)]\n\n    hour = datetime.now().hour\n    weekday = datetime.now().weekday()\n\n    if 5 <= hour < 12 and 'wake' not in _subjects:\n        chosen = 'wake'\n        question = 'common_sleep_start'\n    elif hour == 8 and 'breakfast' not in _subjects:\n        chosen = 'breakfast'\n        question = 'food_start'\n    elif hour == 12 and 'lunch' not in _subjects:\n        chosen = 'lunch'\n        question = 'food_start'\n    elif hour == 18 and 'dinner' not in _subjects:\n        chosen = 'dinner'\n        question = 'food_start'\n    elif hour == 10 and (weekday == 0 or weekday == 2) and 'medical' not in _subjects:\n        chosen = 'medical'\n        question = 'medical_treatment_start'\n    elif hour == 14 and 'exercise' not in _subjects:\n        chosen = 'exercise'\n        question = 'exercise_start'\n    else:\n        return\n\n    data = {\n        'userID': \"cB2vNbjGOKOxPSm0VIl2YKniXhC2\",\n        'question': question,\n        'botID': \"BB-b10-20191120155218-cVK1y4yxeo\",\n        'lang': 'ko',\n        'clientId': input_json['clientId']\n    }\n\n    if 'dasomInfo' in input_json:\n        data['dasomInfo'] = input_json['dasomInfo']\n\n    res = requests.post(\n        url=\"http://104.155.207.154:8080/Sarah/external/chat\",\n        data=json.dumps(data, ensure_ascii=False).encode('utf-8'),\n        headers={'Content-Type': 'application/json; charset=utf-8'}\n    )\n\n    if res:\n        if socket.gethostbyname(socket.gethostname()) != '172.31.20.2':\n            sql_text = \"INSERT INTO logdb.scenario (cid, subject, datetime) \" \\\n                       \"VALUES ('{cid}', '{subject}', '{datetime}');\".format(\n                            cid=input_json['clientId'],\n                            subject=chosen,\n                            datetime=datetime.now().isoformat()\n                        )\n            bravesql.insert(sql_text=sql_text)\n\n        bravesql.close()\n\n        return {\n            'domain': 'smartfriend',\n            'clientId': input_json['clientId'],\n            'appId': 'dasom2',\n            'hint': res.json()['data']['message'],\n            'is_scenario': 'y',\n            'data': {\n                'serviceName': 'smartfriend',\n                'actionName': 'scenario'\n            }\n        }\n    else:\n        return\n\n\nif __name__ == '__main__':\n    print(get_json(\n        input_json={'clientId': 'brave'}\n    ))\n","sub_path":"question/scenario.py","file_name":"scenario.py","file_ext":"py","file_size_in_byte":2781,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"256320964","text":"# Import the dependencies\nimport numpy as np\nimport pandas as pd\nimport pickle\nimport matplotlib.pyplot as plt\nimport re\n\nfrom transformers import pipeline\nfrom nltk import *\nfrom nltk.corpus import stopwords\nfrom keras.layers import Dense, Dropout\nfrom keras.models import Sequential, load_model\nfrom keras.callbacks import EarlyStopping, ModelCheckpoint\nfrom keras.utils import to_categorical\nfrom keras.optimizers import SGD\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.metrics import precision_recall_fscore_support\n\n# fix random seed for reproducibility\nnp.random.seed(2)\n\n# Loading the dataset\n# pandas read_csv function to read csv dataset\ndf = pd.read_csv('./Data Collection + Data/Datasets/LinkedIn_ProfileDataset_Text_Role_200.csv')\n\n# get info from pandas about this csv dataset\ndf.info()\n\n# load nltk stopwords\nstop = stopwords.words('english')\n\n\n# Text preprocessing\n# function to clean text from any punctuation, symbols and weird characters\ndef clean_up(text):\n    text = text.replace(' ', ' ')\n    text = text.replace('–', '-')\n    text = text.replace('\\n', '')\n    text = re.sub(r'[IVXLCDM]+\\.', '', text)\n    text = re.sub(r'\\[[^]]*\\]', '', text)\n    text = re.sub(r'\\((.*?)\\)', '', text)\n    text = re.sub(r'\\{(.*?)\\}', '', text)\n    text = re.sub(r'\\b[A-Z]{2,}\\b', '', text)\n    text = text.replace(' ,', ',')\n    text = text.replace(' .', '.')\n    text = text.replace(' ;', ';')\n    text = re.sub(r'[^A-Za-z0-9äÄöÖüÜß\\s\\.\\-\\!\\?\\:\\;\\,]', '', text)\n    text = re.sub(r' +', ' ', text)\n    text = text.replace('ä', 'ae')\n    text = text.replace('ö', 'oe')\n    text = text.replace('ü', 'ue')\n    text = text.replace('Ä', 'Ae')\n    text = text.replace('Ö', 'Oe')\n    text = text.replace('Ü', 'Ue')\n    text = text.replace('ß', 'ss')\n    text = text.replace('See endorsements', '')\n    text = re.sub(r' +', ' ', text)\n    text = re.sub('[0-9]+', '', text)\n    return text\n\n\n# a regex syntax for removing all nltk stopwords in the text\nstopword_regex = r'\\b(?:{})\\b'.format('|'.join(stop))\n\n# using df.apply to apply regex syntax to dataframe column\ndf['Input'] = df['Input'].str.replace(stopword_regex, '')\n\n# using df.apply to apply function to dataframe column\ndf['Role'] = df['Role'].apply(clean_up, 1)\ndf['Input'] = df['Input'].apply(clean_up, 1)\n\n# using df.value_counts() from pandas to calculate amount of rows of data to every value of 'Role'\ndf.Role.value_counts()\n\n# convert dataframe to numpy array\ndataset = df.to_numpy()\n\n# The dataset contains first column as labels (X) and second column as text input (Y)\n# split into output (X) and input (y) variables\nX = dataset[:, :-1]\nY = dataset[:, -1]\n\n\n# function for splitting long sentences (huggingface summarization api doesn't take long input of text)\ndef split_sentences(text):\n    words = text.split()\n\n    length = len(words)\n\n    print('Text original length is ' + str(length) + ' words')\n\n    mid_index = length // 2\n\n    if mid_index > 512:\n\n        print('The original middle index of text is ' + str(mid_index))\n\n        while mid_index > 512:\n            print('Shortening text')\n            mid_index = mid_index // 2\n            print('The middle index of text is now ' + str(mid_index))\n            length = mid_index * 2\n            print('The length of text is now ' + str(length))\n\n            if mid_index in range(512, 1024):\n                print('The middle index exceeds range')\n                length = mid_index\n                print('The length of text is now ' + str(length))\n                mid_index = mid_index // 2\n                print('The middle index of text is now ' + str(mid_index))\n\n        first_half = words[:mid_index]\n\n        second_half = words[mid_index:length]\n\n        sent_1 = ' '.join(first_half)\n\n        sent_2 = ' '.join(second_half)\n\n        sentences = [sent_1, sent_2]\n\n        print('Splitting sentences completed.')\n\n    else:\n        first_half = words[:mid_index]\n\n        second_half = words[mid_index:]\n\n        sent_1 = ' '.join(first_half)\n\n        sent_2 = ' '.join(second_half)\n\n        sentences = [sent_1, sent_2]\n\n        print('Splitting sentences completed.')\n\n    return sentences\n\n\n# using pipeline API for summarization task\nsummarization = pipeline(\"summarization\")\n\n\n# function to iterate a list and summarize elements in a list and append them to a new list\ndef summarize(text):\n    summarized_text = []\n    i = 0\n    while i < len(text):\n        sentences = text[i]\n        summary_text = summarization(sentences, min_length=3, max_length=500)\n        summarized_text.append(summary_text)\n        i += 1\n\n    new_list = []\n\n    i = 0\n    while i < len(summarized_text):\n        for d in summarized_text[i]:\n            new_list.append(d['summary_text'])\n        i += 1\n\n    new_text = ' '.join(map(str, new_list))\n\n    print('Summary process finished')\n\n    return new_text\n\n\n# text summarizing pipeline containing text splitting and text summarization functions\ndef text_summarize_pipeline(text):\n    new_text = []\n    i = 0\n    while i < len(text):\n        print('Processing text number : ' + str(i))\n        split_text = split_sentences(text[i])\n        summary = summarize(split_text)\n        new_text.append(summary)\n        print('Finished processing text number : ' + str(i))\n        i += 1\n\n    print('Text has been summarized')\n\n    return new_text\n\n\n# summarizing text requires function to store the summarized text in memory\n# therefore not all the text in the list gets summarized\n# Y dataset will be spliced to only summarize a part of the list\n\n# splicing Y dataset from element 1401 to element 2002\n# Y_data = Y[1401:2002]\n\n# feeding spliced array into text summarizing pipeline function\n# Y_split = text_summarize_pipeline(Y_data)\n\n# splicing Y dataset from element 0 to element 1401\n# Y_1 = Y[0:1401]\n\n# concatenate or join both spliced arrays into one\n# Y_new = np.concatenate((Y_1, Y_split))\n\n# Feature Extraction part\n# Convert a collection of text documents to a matrix of token counts\n# This implementation produces a sparse representation of the counts using scipy.sparse.csr_matrix.\n# we are trying to produce a bag-of-words representation which will be displayed as a sparse matrix\n# containing term frequency depending on ngram range we've set\nmatrix = CountVectorizer(analyzer='word',\n                         token_pattern=r'\\b[a-zA-Z]{3,}\\b',\n                         ngram_range=(1, 2), min_df=2, vocabulary=pickle.load(open(\"feature.pkl\", \"rb\")))\n\n# Uncomment if want to use summarized text for bag-of-words representation\n# y = matrix.fit_transform(Y_new).toarray()\n\n# We've already load the pre-defined vocabulary from the pickle file\n# Now we can use Y dataset without summarizing the text (memory efficient)\ny = matrix.transform(Y).toarray()\n\n# Save/Dump vocabulary in a pickle file to use later during prediction\n# A Mapping (e.g., a dict) where keys are terms and values are indices in the feature matrix, or an iterable over terms\npickle.dump(matrix.vocabulary_, open(\"feature.pkl\", \"wb\"))\n\n# Setting ratio for train, test and validation data\ntrain_ratio = 0.60\nvalidation_ratio = 0.20\ntest_ratio = 0.20\n\n# train is now 60% of the entire data set\n# the _junk suffix means that we drop that variable completely\nx_train, x_test, y_train, y_test = train_test_split(X, y, test_size=1 - train_ratio, random_state=1)\n\n# test is now 10% of the initial data set\n# validation is now 15% of the initial data set\nx_val, x_test, y_val, y_test = train_test_split(x_test, y_test, test_size=test_ratio / (test_ratio + validation_ratio),\n                                                random_state=1)\n\n\n# Categorical encoding using sklearn's Label Encoder function\n# Encode target labels with value between 0 and n_classes-1\ndef prepare_targets(x_train, x_test, x_val):\n    le = LabelEncoder()\n    le.fit(x_train)\n    x_train = le.transform(x_train)\n    x_test = le.transform(x_test)\n    x_val = le.transform(x_val)\n    le_name_mapping = dict(zip(le.classes_, le.transform(le.classes_)))\n    x_train_enc = to_categorical(x_train)\n    x_test_enc = to_categorical(x_test)\n    x_val_enc = to_categorical(x_val)\n    return x_train_enc, x_test_enc, x_val_enc, le_name_mapping\n\n\n# prepare output data\nx_train_enc, x_test_enc, x_val_enc, le_name_mapping = prepare_targets(x_train, x_test, x_val)\n\ninput_dim = y_train.shape[1]  # Number of features\n\n# Number of features as reference when during prediction (same input size)\nprint(input_dim)\n\n# initialize Sequential model Keras\nmodel = Sequential()\n\nmodel.add(Dense(65, input_dim=input_dim, activation='relu'))\n\nmodel.add(Dropout(0.5))\n\nmodel.add(Dense(10, activation='softmax'))\n\nfile_path = \"./model.hdf5\"\n\nckpt = ModelCheckpoint(file_path, monitor='val_loss', verbose=1, save_best_only=True, mode='min')\n\nearly = EarlyStopping(monitor=\"val_loss\", mode=\"min\", patience=5)\n\nepochs = 64\nlearning_rate = 0.1\ndecay_rate = learning_rate / epochs\nmomentum = 0.8\nsgd = SGD(lr=learning_rate, momentum=momentum, decay=decay_rate, nesterov=False)\n\nmodel.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n\nmodel.summary()\n\n# fitting the model and hyper-parameters\nhistory_0 = model.fit(y_train, x_train_enc, epochs=epochs, batch_size=40, validation_data=(y_val, x_val_enc),\n                      callbacks=[ckpt, early], verbose=1)\n\n# plot history\nplt.plot(history_0.history['loss'], label='training loss')\nplt.plot(history_0.history['val_loss'], label='validation loss')\nplt.legend()\nplt.show()\n\n# plot history\nplt.plot(history_0.history['accuracy'], label='training accuracy')\nplt.plot(history_0.history['val_accuracy'], label='validation accuracy')\nplt.legend()\nplt.show()\n\n# returns a compiled model\n# identical to the previous one\nModel = load_model('Models/model.hdf5')\n\n\n# Testing part using the testing data\ni = 0\npredicted = []\nfor x_t in y_test:\n    prediction = np.argmax(Model.predict(np.array([x_t])), axis=-1)\n\n    Categories = {'Data Scientist': 0,\n                  'Database Administrator': 1,\n                  'Java Developer': 2,\n                  'Network Administrator': 3,\n                  'Project Manager': 4,\n                  'Python Developer': 5,\n                  'Security Analyst': 6,\n                  'Software Developer': 7,\n                  'Systems Administrator': 8,\n                  'Web Developer': 9}\n\n    predicted_label = list(Categories.keys())[list(Categories.values()).index(prediction[0])]\n    predicted.append(predicted_label)\n    print(\"File ->\", y_test[i], \"Actual label: \" + x_test[i], \"Predicted label: \" + predicted_label)\n    i += 1\n\n# Print model evaluation using sklearn's metrics for model evaluation part\n# Compute precision, recall, F-measure and support for each class.\nprint(precision_recall_fscore_support(x_test, predicted, average='micro'))\n","sub_path":"model-train.py","file_name":"model-train.py","file_ext":"py","file_size_in_byte":10892,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"13532614","text":"'''\nReescreva a função leiaInt() que fizemos no desafio 104, incluindo agora a possibilidade da\ndigitação de um número de tipo inválido. Aproveite e crie também uma função leiaFloat() com a mesma funcionalidade.\nSe o usuário não digitar entra o valor 0.\n'''\n\nfrom ex111a.utilidadesCeV import dado\n\ninteiro = dado.inputInt()\nreal = dado.inputFloat()\n\nprint(f\"Número inteiro: {inteiro}\\nNúmero real: {real}\")\n","sub_path":"ex113 Receber Int e Float sem erros.py","file_name":"ex113 Receber Int e Float sem erros.py","file_ext":"py","file_size_in_byte":420,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"358955773","text":"\"\"\"\nТесты главной страницы приложения\n\"\"\"\nimport pytest\nfrom .pages.main_page import MainPage\nfrom .pages.login_page import LoginPage\nfrom .pages.basket_page import BasketPage\n\n# Ссылка на главную страницу приложения\nBASE_LINK = \"http://selenium1py.pythonanywhere.com/\"\n\n\n@pytest.mark.login_guest\nclass TestLoginFromMainPage():\n    \"\"\"\n    Класс с тестами входа пользователя\n    \"\"\"\n\n    def setup_method(self):\n        \"\"\"\n        Задаем ссылку для каждого метода\n        \"\"\"\n        self.link = BASE_LINK\n\n    def test_guest_can_go_to_login_page(self, browser):\n        \"\"\"\n        Гость может перейти на страницу регистрации\n        \"\"\"\n        # инициализируем Page Object, передаем в констурктор экземлпяр\n        # драйвера и url\n        page = MainPage(browser, self.link)\n        # открываем страницу\n        page.open()\n        # выполняем метод страницы - переходим на страницу логина\n        page.go_to_login_page()\n        # Делаем проверки на странице логина\n        login_page = LoginPage(browser, browser.current_url)\n        login_page.should_be_login_page()\n\n    def test_guest_should_see_login_link(self, browser):\n        \"\"\"\n        Гость может видеть ссылку на страницу регистрации\n        \"\"\"\n        page = MainPage(browser, self.link)\n        page.open()\n        page.should_be_login_link()\n\n\ndef test_guest_cant_see_product_in_basket_opened_from_main_page(browser):\n    \"\"\"\n    Гость открывает главную страницу\n    Переходит в корзину по кнопке в шапке сайта\n    Ожидаем, что в корзине нет товаров\n    Ожидаем, что есть текст о том что корзина пуста\n    \"\"\"\n    page = MainPage(browser, BASE_LINK)\n    page.open()\n    page.is_basket_link_present()\n    page.go_to_basket_page()\n    basket_page = BasketPage(browser, browser.current_url)\n    basket_page.is_basket_empty()\n    basket_page.there_is_a_message_that_the_basket_is_empty()\n","sub_path":"test_main_page.py","file_name":"test_main_page.py","file_ext":"py","file_size_in_byte":2336,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"437194697","text":"import tkinter as tk\nimport sys\n\n#This is the root\nroot = tk.Tk()\n\ndef quit():\n    sys.exit()\n\nlabel = tk.Label(root, text = \"Press to quit\")\nlabel.pack()\n\nbutton = tk.Button(root, text = \"Quit\", command = quit)\nbutton.pack()\n\nroot.mainloop()","sub_path":"Test/tktest.py","file_name":"tktest.py","file_ext":"py","file_size_in_byte":242,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"309456987","text":"from django.urls import path, re_path\nfrom core import views\n\n# These are preliminary URL patterns. You will have to change the views. calls to the correct method names.\nurlpatterns = [\n    re_path(r'^$', views.home, name = 'home'),\n    # Reviews for the \"about\" section assume that FAQ and contact won't be on the same page.\n\n#     re_path(r'^about/$', views.about, name='about'),\n#     re_path(r'^contact/$', views.contact, name='contact'),\n     re_path(r'^faq/$', views.faq, name='faq'),\n# \n#     # Tea Catalog URLs. This assumes each sort (popularity, type, origin) will need its own URL.\n#     # I do not know what the difference between tea catalog and tea listings is.\n\n     re_path(r'^teas/catalog/(?P<tea_name_slug>[\\w\\-]+)/$', views.specific_tea, name='specific_tea'),\n     re_path(r'^teas/catalog/$', views.show_catalog, name='catalog'),\n     re_path(r'^teas/popular/$', views.most_popular, name='popular'),\n    re_path(r'^teas/type/$', views.type, name='type'),\n    re_path(r'^teas/origin/$', views.origin, name='origin'),\n\n\n# \n#     # Specific review pages have an id slug that is passed on to the view function.\n#     re_path(r'^reviews/$', views.reviews, name='reviews'),\n#     re_path(r'^reviews/(?P<review_id_slug>[\\w\\-]+)/$', views.show_review, name='show_review'),\n# \n#     # Generic registration and login pages.\n#     re_path(r'^register/$', views.register, name='register'),\n     re_path(r'^login/$', views.user_login, name='login'),\n#     re_path(r'^logout/$', views.user_logout, name='logout'),\n# \n#     # Account pages. These will only show information if user is logged in.\n#     # Unsure how much is being shown on a single page (do wee need separate URLs for dashboard\n#     # and account details? Dashboard and saved teas?\n\n      re_path(r'^account/$', views.show_account, name='show_account'),\n\n#     re_path(r'^account/settings/$', views.settings, name='settings'),\n#     re_path(r'^account/reviews/$', views.user_reviews, name='user_reviews'),\n#     re_path(r'^account/saved_teas/$', views.saved_teas, name='saved_teas'),\n# \n#     # Search URLs. Unsure whether search will be its own page or just a bar on the home page.\n#     # Again, unsure what the difference between Tea catalog and tea listings is.\n     re_path(r'^search/$', views.search, name='search'),\n]","sub_path":"core/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2294,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"254814468","text":"import csv\nfrom dotenv import load_dotenv\nimport json\nimport os\nimport pdb\nimport requests\nimport datetime\ndef parse_response(response_text):\n    # response_text can be either a raw JSON string or an already-converted dictionary\n    if isinstance(response_text, str): # if not yet converted, then:\n        response_text = json.loads(response_text) # convert string to dictionary\n\n    results = []\n\n    time_series_daily = response_text[\"Time Series (Daily)\"] #> a nested dictionary\n    for trading_date in time_series_daily: # FYI: can loop through a dictionary's top-level keys/attributes\n        #pdb.set_trace()\n\n        prices = time_series_daily[trading_date] #> {'1. open': '101.0924', '2. high': '101.9500', '3. low': '100.5400', '4. close': '101.6300', '5. volume': '22165128'}\n        result = {\n            \"date\": trading_date,\n            \"open\": prices[\"1. open\"],\n            \"high\": prices[\"2. high\"],\n            \"low\": prices[\"3. low\"],\n            \"close\": prices[\"4. close\"],\n            \"volume\": prices[\"5. volume\"]\n        }\n        results.append(result)\n    return results\n\ndef write_prices_to_file(prices=[], filename=\"db/prices.csv\"):\n    csv_filepath = os.path.join(os.path.dirname(__file__), \"..\", filename)\n    with open(csv_filepath, \"w\") as csv_file:\n        writer = csv.DictWriter(csv_file, fieldnames=[\"timestamp\", \"open\", \"high\", \"low\", \"close\", \"volume\"])\n        writer.writeheader()\n        for d in prices:\n            row = {\n                \"timestamp\": d[\"date\"], # change attribute name to match project requirements\n                \"open\": d[\"open\"],\n                \"high\": d[\"high\"],\n                \"low\": d[\"low\"],\n                \"close\": d[\"close\"],\n                \"volume\": d[\"volume\"]\n            }\n            writer.writerow(row)\n\n\n\n\n\nif __name__ == '__main__': # only execute if file invoked from the command-line, not when imported into other files, like tests\n\n    load_dotenv() # loads environment variables set in a \".env\" file, including the value of the ALPHAVANTAGE_API_KEY variable\n\n    api_key = os.environ.get(\"ALPHAVANTAGE_API_KEY\") or \"OOPS. Please set an environment variable named 'ALPHAVANTAGE_API_KEY'.\"\n\n    # CAPTURE USER INPUTS (SYMBOL)\n\n    symbol = input(\"Please input a stock symbol (e.g. 'NFLX'):\") # input(\"Please input a stock symbol (e.g. 'NFLX'): \")\n\n    # VALIDATE SYMBOL AND PREVENT UNECESSARY REQUESTS\n\n    try:\n        float(symbol)\n        quit(\"CHECK YOUR SYMBOL. EXPECTING A NON-NUMERIC SYMBOL\")\n    except ValueError as e:\n        pass\n\n    request_url = f\"https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol={symbol}&apikey={api_key}\"\n\n    # ISSUE \"GET\" REQUEST\n    print(\"ISSUING A REQUEST\")\n    response = requests.get(request_url)\n\n\n    # VALIDATE RESPONSE AND HANDLE ERRORS\n\n\n\n    if \"Error Message\" in response.text:\n        print(\"REQUEST ERROR< PLEASE TRY AGAIN> CHEC YOUR STOCK SYMBOL>\")\n        quit (\"Stopping the program\")\n\n    # PARSE RESPONSE (AS LONG AS THERE ARE NO ERRORS)\n\n    daily_prices = parse_response(response.text)\n\n    # WRITE TO CSV\n\n    write_prices_to_file(prices=daily_prices, filename=\"db/\" +symbol+ \"prices.csv\")\n\n    # PERFORM CALCULATIONS\n    print(\"-------------------------\")\n    print(\"Ticker symbol:\" + symbol)\n    print(\"-------------------------\")\n        #PRINT date and time when the program was executed\n    print(\"Run at: \", datetime.datetime.now().strftime(\"%-I:%M%p on %b %dth, %Y\"))\n    print(\"----------------------------------\")\n    #PRINT date when the data was last refreshed\n    refreshed_date = parse_response(response.text)\n    latest_refeshed_date = refreshed_date[0][\"date\"]\n    print(\"Latest data refreshed on: \" + latest_refeshed_date)\n\n    latest_closing_price = daily_prices[0][\"close\"]\n    latest_closing_price = float(latest_closing_price)\n    latest_closing_price_usd = \"${0:,.2f}\".format(latest_closing_price)\n    print(\"Recent closing price: \" + latest_closing_price_usd)\n    # PRODUCE FINAL RECOMMENDATION\n    recent_high_price = []\n    for h in daily_prices:#print(daily_prices)\n        high_price =  h[\"high\"]\n        recent_high_price.append(high_price)\n    recent_average_high_price = float(max(recent_high_price))\n    recent_average_high_price_usd = \"${0:,.2f}\".format(recent_average_high_price)\n    print(\"Recent average high price: \" + recent_average_high_price_usd)\n\n    recent_low_price = []\n    for l in daily_prices:\n        low_price =  l[\"low\"]\n        recent_low_price.append(low_price)\n    recent_average_low_price = float(max(recent_low_price))\n    recent_average_low_price_usd = \"${0:,.2f}\".format(recent_average_low_price)\n    print(\"Recent average low price: \" + recent_average_low_price_usd)\n\n    if recent_average_low_price*0.8 > latest_closing_price:\n        print(\"Buy it\")\n        print(\"---------------------------------------\")\n    elif recent_average_high_price < latest_closing_price*0.8:\n        print(\"Sell it\")\n        print(\"---------------------------------------\")\n    else:\n        print(\"Hold it\")\n        print(\"---------------------------------------\")\n    \n","sub_path":"app/robo_adviser.py","file_name":"robo_adviser.py","file_ext":"py","file_size_in_byte":5069,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"17742746","text":"from priorityqueue import PriorityQueue\nfrom graph import Graph,G2\n\ndef UCS(start, goal, came_from={}, G=G2):\n    '''一致代价搜索：优先访问当前代价最低的节点'''\n    '''基本BFS即每一步cost都为1的特殊UCS'''\n    print(f'搜索过程为:')\n    frontier = PriorityQueue()\n    cost_so_far = {}                #目前为止的花费\n    frontier.enqueue(start, 0)      #start加入待查找队列\n    cost_so_far[start] = 0          #cost_so_far 为某节点截至当前查找的节点的最短cost\n    came_from[start] = None\n    while not frontier.is_empty():\n        v = frontier.dequeue()\n        print(f'{v}', end='->')\n        if v == goal:\n            print('end')\n            return goal\n        else:\n            for w in G.adjacentEdges(v):\n                new_cost = cost_so_far[v] + G.cost(v,w)        #new_cost = 前面的cost + 当前节点的cost\n                if cost_so_far.get(w) == None or new_cost < cost_so_far[w]:  #==None为start节点\n                    cost_so_far[w] = new_cost\n                    priority = new_cost\n                    frontier.enqueue(w, priority)\n                    came_from[w] = v #该节点为cost最小节点\n    return None\n\ndef main():\n    '''找到目标节点'''\n    came_from = {}\n    start = '1'\n    goal = '12'\n    found = UCS(start, goal, came_from)\n    print('start:', start)\n    print('goal:', goal)\n    #print(came_from)\n    path = []               #存储路径\n    while found:\n        path.append(found)\n        found = came_from.get(found)\n        #print(found)\n    print('->'.join(path[::-1]),end='->')     #逆序输出path\n    print('end')\n\nif __name__ == \"__main__\":\n    main()","sub_path":"人工智能/第6周/code/UCS(BFS).py","file_name":"UCS(BFS).py","file_ext":"py","file_size_in_byte":1675,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"329160604","text":"inp1 = input()\r\ninp2 = input()\r\n\r\nif len(inp1) != len(inp2):\r\n    print(\"impossible\")\r\n    quit()\r\n\r\nlinp1 = [0]*300\r\nlinp2 = [0]*300\r\n\r\nfor x in inp1:\r\n    linp1[ord(x)] += 1\r\n\r\n\r\nfor x in inp2:\r\n    linp2[ord(x)] += 1\r\n\r\nif linp1 == linp2:\r\n    print(\"possible\")\r\n\r\nelse:\r\n    print(\"impossible\")\r\n","sub_path":"confused.py","file_name":"confused.py","file_ext":"py","file_size_in_byte":300,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"258844070","text":"#socket服务端\nimport socket\nimport threading\nserver = socket.socket()\n#绑定到0.0.0.0:8000端口上\nserver.bind(('0.0.0.0', 8000))\nserver.listen()\n\ndef handle_sock(sock, addr):\n    while True:\n        # recv方法是阻塞的\n        tmp_data = sock.recv(1024)\n        print(tmp_data.decode(\"utf8\"))\n        response_template = '''HTTP/1.1 200 OK\nContent-Type: application/json\n\n<html>\n  <head>\n    <title>Build A Web Server</title>\n  </head>\n  <body>\n    Hello World, this is a very simple HTML document.\n  </body>\n</html>\n\n'''\n        sock.send(response_template.encode(\"utf8\"))\n        sock.close()\n        break\n\n#获取客户端连接并启动线程去处理\nwhile True:\n    # 阻塞等待连接\n    sock, addr = server.accept()\n\n    #启动一个线程去处理新的用户连接\n    client_thread = threading.Thread(target=handle_sock, args=(sock, addr))\n    client_thread.start()\n","sub_path":"python_spider/socket_test/http_server.py","file_name":"http_server.py","file_ext":"py","file_size_in_byte":893,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"182497965","text":"from Helpers import helper as hlp\nfrom Helpers import test_class\nimport re\n\nclass Solution(test_class.test_class):\n\n    # left = i * 2\n    # right = i * 2 + 1\n    # parent = i // 2\n    def setUp(self):\n        super().setUp()\n\n    def heapSort(self, arr):\n        self.build_heap(arr)\n        self.sort_heap(arr)\n        return arr\n\n    def build_heap(self, arr):\n        for i in range(len(arr)):\n            while i > 0:\n                p = i // 2  # parent\n                if arr[i] >= arr[p]:\n                    arr[i], arr[p] = arr[p], arr[i]\n                i = p\n        return arr\n\n    def sort_heap2(self, arr, k = -1):\n        n = len(arr)\n        if k == -1:\n            k = n\n\n        while k > 0:\n            # swap first with last\n            arr[0], arr[n-1] = arr[n-1], arr[0]\n            # array is smaller by 1 (last item is deleted)\n            n -= 1\n            i = 0\n\n            while i * 2 + 1 < n:\n                l = i * 2 + 1 # left child\n                r = i * 2 + 2 # right child\n                c = l if (arr[l] > arr[r]) or (r > n-1) else r\n\n                if arr[c] >= arr[i]:\n                    arr[i], arr[c] = arr[c], arr[i]\n                    i += c\n                else:\n                    break\n            k -= 1\n\n    def heapSort(self, arr, n = -1):\n        n = len(arr)\n\n        for i in range(n - 1, 0, -1):\n\n            # swap value of first indexed\n            # with last indexed\n            arr[0], arr[i] = arr[i], arr[0]\n\n            # maintaining heap property\n            # after each swapping\n            j, index = 0, 0\n\n            while True:\n                index = 2 * j + 1\n\n                # if left child is smaller than\n                # right child point index variable\n                # to right child\n                if (index < (i - 1) and\n                        arr[index] < arr[index + 1]):\n                    index += 1\n\n                # if parent is smaller than child\n                # then swapping parent with child\n                # having higher value\n                if index < i and arr[j] < arr[index]:\n                    arr[j], arr[index] = arr[index], arr[j]\n\n                j = index\n                if index >= i:\n                    break\n\n        return arr\n\n\n    def get_kth(self, arr, k):\n        self.build_heap(arr)\n        self.sort_heap(arr, k)\n        return arr[len(arr) - k]\n\n    def test_1(self):\n        self.assertEqual([40,30,20,15,10], self.build_heap([10,20,15,30,40]))\n\n    def test_2(self):\n        self.assertEqual([6,5,4,2,1,3], self.build_heap([3,2,1,5,6,4]))\n\n    def test_3(self):\n        self.assertEqual([6,5,4,1,2,3,0], self.build_heap([5,2,4,1,3,6,0]))\n\n    def test_1_1(self):\n        self.assertEqual([10,15,20,30,40], self.heapSort([10,20,15,30,40]))\n\n    def test_3_1(self):\n        self.assertEqual([0,1,2,3,4,5,6], self.heapSort([5,2,4,1,3,6,0]))\n\n    def test_3_2(self):\n        self.assertEqual(3, self.get_kth([5,2,4,1,3,6,0], 4))\n\n    def test_4(self):\n        self.assertEqual([5], self.heapSort([3,2,3,1,2,4,5,5,6,7,7,8,2,3,1,1,1,10,11,5,6,2,4,7,8,5,6]))\n\n    def test_5(self):\n        self.assertEqual([1,1,1,1,2,2,2,3,3,3,4,5,7], self.heapSort([3,2,3,1,2,4,5,7,2,3,1,1,1]))","sub_path":"General/Heap_sort_mine.py","file_name":"Heap_sort_mine.py","file_ext":"py","file_size_in_byte":3207,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"299105355","text":"import os\n\nfrom conans.client.cache.remote_registry import Remote\nfrom conans.errors import ConanException, PackageNotFoundException, RecipeNotFoundException\nfrom conans.errors import NotFoundException\nfrom conans.model.ref import ConanFileReference, PackageReference, check_valid_ref\nfrom conans.paths import SYSTEM_REQS, rm_conandir\nfrom conans.search.search import filter_outdated, search_packages, search_recipes\nfrom conans.util.log import logger\n\n\nclass DiskRemover(object):\n\n    def _remove(self, path, ref, msg=\"\"):\n        try:\n            logger.debug(\"REMOVE: folder %s\" % path)\n            rm_conandir(path)\n        except OSError:\n            error_msg = \"Folder busy (open or some file open): %s\" % path\n            raise ConanException(\"%s: Unable to remove %s\\n\\t%s\" % (repr(ref), msg, error_msg))\n\n    def _remove_file(self, path, ref, msg=\"\"):\n        try:\n            logger.debug(\"REMOVE: file %s\" % path)\n            if os.path.exists(path):\n                os.remove(path)\n        except OSError:\n            error_msg = \"File busy (open): %s\" % path\n            raise ConanException(\"Unable to remove %s %s\\n\\t%s\" % (repr(ref), msg, error_msg))\n\n    def remove_recipe(self, package_layout, output):\n        self.remove_src(package_layout)\n        self._remove(package_layout.export(), package_layout.ref, \"export folder\")\n        self._remove(package_layout.export_sources(), package_layout.ref, \"export_source folder\")\n        for f in package_layout.conanfile_lock_files(output=output):\n            try:\n                os.remove(f)\n            except OSError:\n                pass\n\n    def remove(self, package_layout, output):\n        self.remove_recipe(package_layout, output=output)\n        self.remove_builds(package_layout)\n        self.remove_packages(package_layout)\n        self._remove(package_layout.base_folder(), package_layout.ref)\n\n    def remove_src(self, package_layout):\n        self._remove(package_layout.source(), package_layout.ref, \"src folder\")\n\n    def remove_builds(self, package_layout, ids=None):\n        if not ids:\n            path = package_layout.builds()\n            for build in package_layout.conan_builds():\n                self._remove(os.path.join(path, build), package_layout.ref,\n                             \"build folder:%s\" % build)\n            self._remove(path, package_layout.ref, \"builds\")\n        else:\n            for id_ in ids:\n                # Removal build IDs should be those of the build_id if present\n                pkg_path = package_layout.build(PackageReference(package_layout.ref, id_))\n                self._remove(pkg_path, package_layout.ref, \"package:%s\" % id_)\n\n    def remove_packages(self, package_layout, ids_filter=None):\n        if not ids_filter:  # Remove all\n            path = package_layout.packages()\n            # Necessary for short_paths removal\n            for package in package_layout.conan_packages():\n                self._remove(os.path.join(path, package), package_layout.ref,\n                             \"package folder:%s\" % package)\n            self._remove(path, package_layout.ref, \"packages\")\n            self._remove_file(package_layout.system_reqs(), package_layout.ref, SYSTEM_REQS)\n        else:\n            for id_ in ids_filter:  # remove just the specified packages\n                pref = PackageReference(package_layout.ref, id_)\n                if not package_layout.package_exists(pref):\n                    raise PackageNotFoundException(pref)\n                pkg_folder = package_layout.package(pref)\n                self._remove(pkg_folder, package_layout.ref, \"package:%s\" % id_)\n                self._remove_file(pkg_folder + \".dirty\", package_layout.ref, \"dirty flag\")\n                self._remove_file(package_layout.system_reqs_package(pref), package_layout.ref,\n                                  \"%s/%s\" % (id_, SYSTEM_REQS))\n\n\nclass ConanRemover(object):\n    \"\"\" Class responsible for removing locally/remotely conans, package folders, etc. \"\"\"\n\n    def __init__(self, cache, remote_manager, user_io, remotes):\n        self._user_io = user_io\n        self._cache = cache\n        self._remote_manager = remote_manager\n        self._remotes = remotes\n\n    def _remote_remove(self, ref, package_ids, remote):\n        assert(isinstance(remote, Remote))\n        if package_ids is None:\n            result = self._remote_manager.remove(ref, remote)\n            return result\n        else:\n            tmp = self._remote_manager.remove_packages(ref, package_ids, remote)\n            return tmp\n\n    @staticmethod\n    def _message_removing_editable(ref):\n        return \"Package '{r}' is installed as editable, remove it first using \" \\\n               \"command 'conan editable remove {r}'\".format(r=ref)\n\n    def _local_remove(self, ref, src, build_ids, package_ids):\n        if self._cache.installed_as_editable(ref):\n            self._user_io.out.warn(self._message_removing_editable(ref))\n            return\n\n        # Get the package layout using 'short_paths=False', remover will make use of the\n        #  function 'rm_conandir' which already takes care of the linked folder.\n        package_layout = self._cache.package_layout(ref, short_paths=False)\n\n        package_layout.remove_package_locks()  # Make sure to clean the locks too\n        remover = DiskRemover()\n        if src:\n            remover.remove_src(package_layout)\n        if build_ids is not None:\n            remover.remove_builds(package_layout, build_ids)\n\n        if package_ids is not None:\n            remover.remove_packages(package_layout, package_ids)\n            with package_layout.update_metadata() as metadata:\n                for package_id in package_ids:\n                    metadata.clear_package(package_id)\n\n        if not src and build_ids is None and package_ids is None:\n            remover.remove(package_layout, output=self._user_io.out)\n\n    def remove(self, pattern, remote_name, src=None, build_ids=None, package_ids_filter=None,\n               force=False, packages_query=None, outdated=False):\n        \"\"\" Remove local/remote conans, package folders, etc.\n        @param src: Remove src folder\n        @param pattern: it could be OpenCV* or OpenCV or a ConanFileReference\n        @param build_ids: Lists with ids or empty for all. (Its a filter)\n        @param package_ids_filter: Lists with ids or empty for all. (Its a filter)\n        @param force: if True, it will be deleted without requesting anything\n        @param packages_query: Only if src is a reference. Query settings and options\n        \"\"\"\n\n        if remote_name and (build_ids is not None or src):\n            raise ConanException(\"Remotes don't have 'build' or 'src' folder, just packages\")\n\n        try:\n            input_ref = ConanFileReference.loads(pattern)\n        except (ConanException, TypeError):\n            input_ref = None\n\n        if not input_ref and packages_query is not None:\n            raise ConanException(\"query parameter only allowed with a valid recipe \"\n                                 \"reference as the search pattern.\")\n\n        if input_ref and package_ids_filter and not input_ref.revision:\n            for package_id in package_ids_filter:\n                if \"#\" in package_id:\n                    raise ConanException(\"Specify a recipe revision if you specify a package \"\n                                         \"revision\")\n\n        if remote_name:\n            remote = self._remotes[remote_name]\n            if input_ref:\n                if not self._cache.config.revisions_enabled and input_ref.revision:\n                    raise ConanException(\"Revisions not enabled in the client, cannot remove \"\n                                         \"revisions in the server\")\n                refs = [input_ref]\n            else:\n                refs = self._remote_manager.search_recipes(remote, pattern)\n        else:\n            if input_ref and check_valid_ref(input_ref, allow_pattern=False):\n                refs = []\n                if self._cache.installed_as_editable(input_ref):\n                    raise ConanException(self._message_removing_editable(input_ref))\n                if not self._cache.package_layout(input_ref).recipe_exists():\n                    raise RecipeNotFoundException(input_ref,\n                                                  print_rev=self._cache.config.revisions_enabled)\n                refs.append(input_ref)\n            else:\n                refs = search_recipes(self._cache, pattern)\n                if not refs:\n                    self._user_io.out.warn(\"No package recipe matches '%s'\" % str(pattern))\n                    return\n\n        if input_ref and not input_ref.revision:\n            # Ignore revisions for deleting if the input was not with a revision\n            # (Removing all the recipe revisions from a reference)\n            refs = [r.copy_clear_rev() for r in refs]\n\n        deleted_refs = []\n        for ref in refs:\n            assert isinstance(ref, ConanFileReference)\n            package_layout = self._cache.package_layout(ref)\n            package_ids = package_ids_filter\n            if packages_query or outdated:\n                # search packages\n                if remote_name:\n                    packages = self._remote_manager.search_packages(remote, ref, packages_query)\n                else:\n                    packages = search_packages(package_layout, packages_query)\n                if outdated:\n                    if remote_name:\n                        manifest, ref = self._remote_manager.get_recipe_manifest(ref, remote)\n                        recipe_hash = manifest.summary_hash\n                    else:\n                        recipe_hash = package_layout.recipe_manifest().summary_hash\n                    packages = filter_outdated(packages, recipe_hash)\n                if package_ids_filter:\n                    package_ids = [p for p in packages if p in package_ids_filter]\n                else:\n                    package_ids = list(packages.keys())\n                if not package_ids:\n                    self._user_io.out.warn(\"No matching packages to remove for %s\"\n                                           % ref.full_repr())\n                    continue\n\n            if self._ask_permission(ref, src, build_ids, package_ids, force):\n                try:\n                    if remote_name:\n                        self._remote_remove(ref, package_ids, remote)\n                    else:\n                        self._local_remove(ref, src, build_ids, package_ids)\n                except NotFoundException:\n                    # If we didn't specify a pattern but a concrete ref, fail if there is no\n                    # ref to remove\n                    if input_ref:\n                        raise\n                else:\n                    deleted_refs.append(ref)\n\n        if not remote_name:\n            self._cache.delete_empty_dirs(deleted_refs)\n\n    def _ask_permission(self, ref, src, build_ids, package_ids_filter, force):\n        def stringlist(alist):\n            return \", \".join(['\"%s\"' % p for p in alist])\n\n        if force:\n            return True\n        aux_str = []\n        if src:\n            aux_str.append(\" src folder\")\n        if build_ids is not None:\n            if build_ids:\n                aux_str.append(\" %s builds\" % stringlist(build_ids))\n            else:\n                aux_str.append(\" all builds\")\n        if package_ids_filter is not None:\n            if package_ids_filter:\n                aux_str.append(\" %s packages\" % stringlist(package_ids_filter))\n            else:  # All packages to remove, no filter\n                aux_str.append(\" all packages\")\n        return self._user_io.request_boolean(\"Are you sure you want to delete%s from '%s'\"\n                                             % (\", \".join(aux_str), str(ref)))\n","sub_path":"conans/client/remover.py","file_name":"remover.py","file_ext":"py","file_size_in_byte":11795,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"9824905","text":"\n\nfrom xai.brain.wordbase.nouns._bermuda import _BERMUDA\n\n#calss header\nclass _BERMUDAS(_BERMUDA, ):\n\tdef __init__(self,): \n\t\t_BERMUDA.__init__(self)\n\t\tself.name = \"BERMUDAS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"bermuda\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_bermudas.py","file_name":"_bermudas.py","file_ext":"py","file_size_in_byte":245,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"161474929","text":"from collections import defaultdict\nimport re\nimport spacy\nimport en_core_web_sm\nnlp=en_core_web_sm.load()\nimport argparse\n\n\n\ndef prepare_src_trg(m2file, outprefix, copy_source=False, cleanup=False):\n    def spacytok1word(w):\n        return ' '.join([tok.text for tok in list(nlp(w))])\n\n    def do_cleanup(s):\n        if cleanup == False:\n            return s\n        s = s.replace(\"`\", \"'\").replace(\"''\",'\"')\n        s = s.replace(\"' ve \",\"'ve \").replace(\"' t \", \"'t \").replace(\"' t \",\"'t \").replace(\"' d \",\"'d \")\n        s = s.replace(\"' m \",\"'m \").replace(\"' ll \",\"'ll \").replace(\"' re \",\"'re \")\n        s = s.replace(\"'. . .\",\"...\")\n        s = s.replace(\"n 't\",\" n't\")\n        s = re.sub(r'(\\d+) %',r'\\1%', s)\n        s = ' '.join([spacytok1word(tok) if '-' in tok else tok for tok in s.split() ])\n        return s\n\n\n    words = []\n    sid = eid = 0\n    prev_sid = prev_eid = -1\n    pos = 0\n    output_src_path=outprefix+'.src'\n    output_tgt_path=outprefix+'.trg'\n    print(\"src: {} , trg: {}\".format(output_src_path,output_tgt_path))\n    skip_list = [\"UNK\",\"Um\",\"noop\"]\n    with open(m2file) as fin, open(output_src_path, 'w') as output_src_file, open(output_tgt_path, 'w') as output_tgt_file:\n        for line in fin:\n            line = line.strip()\n            if line.startswith('S'):\n                line = line[2:]\n                source = line\n                annots_set = defaultdict(list)\n                targets = dict()\n            elif line.startswith('A'):\n                line = line[2:]\n                # add annotations to dictionary\n                annot = line.split(\"|||\")\n                ann_id = annot[-1]\n                annots_set[ann_id].append(annot)\n                # initialize corrected sents with source sentence preceded by <S>\n                swords = source.split()\n                target = ['<S>'] + swords[:]\n                targets[ann_id] = target\n            else:\n                if annots_set:\n                    for ann_id, annots in annots_set.items():\n                        for annot in annots:\n                            sid, eid = annot[0].split()\n                            sid = int(sid) + 1; eid = int(eid) + 1;\n                            error_type = annot[1]\n                            if error_type in skip_list:\n                                continue\n                            for idx in range(sid, eid):\n                                targets[ann_id][idx] = \"\"\n                            if sid == eid:\n                                if sid == 0: continue   # Originally index was -1, indicating no op\n                                if sid != prev_sid or eid != prev_eid:\n                                    pos = len(targets[ann_id][sid-1].split())\n                                    cur_words = targets[ann_id][sid-1].split()\n                                    cur_words.insert(pos, annot[2])\n                                    pos += len(annot[2].split())\n                                    targets[ann_id][sid-1] = \" \".join(cur_words)\n                            else:\n                                targets[ann_id][sid] = annot[2]\n                            pos = 0\n                            prev_sid = sid\n                            prev_eid = eid\n                        target_sentence = ' '.join([word for word in targets[ann_id] if word != \"\"])\n                        assert target_sentence.startswith('<S>'), '(' + target_sentence + ')'\n                        target_sentence = target_sentence[4:]\n                        output_src_file.write(do_cleanup(source) + '\\n')\n                        output_tgt_file.write(do_cleanup(target_sentence) + '\\n')\n                        prev_sid = -1\n                        prev_eid = -1\n                        pos = 0\n                else:\n                    if copy_source:\n                        output_src_file.write(do_cleanup(source) + '\\n')\n                        output_tgt_file.write(do_cleanup(source) + '\\n')\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument('-m2', required=True, help='path to input lang-8 m2 file')\nparser.add_argument('-out', required=True, help='output prefix')\nparser.add_argument('--cleanup', action='store_true')\nparser.add_argument('--copy-unchanged-source', action='store_true')\nargs = parser.parse_args()\n\nprepare_src_trg(args.m2, args.out, copy_source=args.copy_unchanged_source, cleanup=args.cleanup)","sub_path":"beast19/scripts/m2_to_parallel_with_spacy_tok_rules.py","file_name":"m2_to_parallel_with_spacy_tok_rules.py","file_ext":"py","file_size_in_byte":4387,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"243160242","text":"import datetime\nimport numpy as np\nfrom UserInput import UserInput\n\n\nclass Intention():\n\n    def __init__(self):\n        \"\"\"Initial intention object\"\"\"\n        self.name = None\n        self.category = None\n        self.sequenceType = None\n        self.estimatedTime = None\n        self.isRecurring = None\n        self.howOften = None\n        self.ickyness = None\n        self.importance = None\n        self.urgency = None\n        self.dueDate = None\n        self.sessionRejects = 0\n        self.sessionCommits = 0\n        self.overallRejects = 0\n        self.overallCommits = 0\n        self.consecutiveRejects = 0\n        self.consecutiveCommits = 0\n        self.key = None\n        self.commitOrReject = None\n        # TODO: implement (estimatedTime), (howOften), (dueDate)\n\n\n    def getAttributesFromList(self, intentionList, intentionKey):\n        \"\"\" get parameters of intention that has been picked by probab algorithm from intention list\"\"\"\n\n        # get intention list\n        intentionItemFromList = intentionList.iloc[intentionKey]\n\n        # get attributes\n        self.name = intentionItemFromList[0]\n        self.category = intentionItemFromList[1]\n        self.sequenceType = intentionItemFromList[2]\n        self.estimatedTime = intentionItemFromList[3]\n        self.isRecurring = self.isTrue(intentionItemFromList[4])\n        self.howOften = intentionItemFromList[5]\n\n        self.ickyness = intentionItemFromList[6]\n        self.importance = intentionItemFromList[7]\n        self.urgency = intentionItemFromList[8]\n        self.dueDate = intentionItemFromList[9]\n\n        self.sessionRejects = intentionItemFromList[10]\n        self.sessionCommits = intentionItemFromList[11]\n        self.overallRejects = intentionItemFromList[12]\n        self.overallCommits = intentionItemFromList[13]\n        self.consecutiveRejects = intentionItemFromList[14]\n        self.consecutiveCommits = intentionItemFromList[15]\n        self.key = intentionItemFromList[16]\n\n\n    def getCommitOrReject(self):\n        input = UserInput()\n        input.setCommitOrReject()\n        self.commitOrReject = input.commitOrReject\n\n    def getAttributesFromInput(self):\n\n        # create new intention object from user input\n        input = UserInput()\n        input.newUserIntentionInput()\n\n        # get attributes\n        self.name = input.name\n        self.category = input.category\n        self.sequenceType = input.sequenceType\n        self.ickyness = input.ickyness\n        self.importance = input.importance\n        self.urgency = input.urgency\n        self.isRecurring = input.isRecurring\n\n        self.sessionRejects = 0\n        self.sessionCommits = 0\n        self.overallRejects = 0\n        self.overallCommits = 0\n        self.consecutiveRejects = 0\n        self.consecutiveCommits = 0\n\n\n    def putThingsIntoArray(self):\n        dataArray = []\n        dataArray.append(self.name)\n        dataArray.append(self.category)\n        dataArray.append(self.sequenceType)\n        dataArray.append(self.estimatedTime)\n        dataArray.append(self.isRecurring)\n        dataArray.append(self.howOften)\n        dataArray.append(self.ickyness)\n        dataArray.append(self.importance)\n        dataArray.append(self.urgency)\n        dataArray.append(self.dueDate)\n        dataArray.append(self.sessionRejects)\n        dataArray.append(self.sessionCommits)\n        dataArray.append(self.overallRejects)\n        dataArray.append(self.overallCommits)\n        dataArray.append(self.consecutiveRejects)\n        dataArray.append(self.consecutiveCommits)\n        dataArray.append(self.key)\n        return dataArray\n\n    def setKey(self):\n        key = \"\"\n        if len(str(self.name)):\n            for chars in range(0, 3):\n                key += (str(self.name[chars]))\n        else:\n            key += str(\"abc\")\n        key = str(key) + str(datetime.datetime.utcnow().year)  \\\n            + str(datetime.datetime.utcnow().month) + str(datetime.datetime.utcnow().day) \\\n            + str(datetime.datetime.utcnow().hour) + str(datetime.datetime.utcnow().minute) \\\n            + str(datetime.datetime.utcnow().second)\n        return key\n\n\n    def isTrue(self, string):\n        assert isinstance(string, str) or isinstance(string, np.bool_) or isinstance(string, bool)\n        possibleTrues = [True, 'True', 'true', 'yes']\n        if string in possibleTrues:\n            return True\n        else:\n            return False\n\n\n\n\n\n\n\n","sub_path":"early_prototype/whatAbout/Intention.py","file_name":"Intention.py","file_ext":"py","file_size_in_byte":4411,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"185247542","text":"from typing import List\n\nimport numba as nb\nimport numpy as np\n\nLOOKUP_TABLE = np.array([\n    1, 1, 2, 6, 24, 120, 720, 5040, 40320,\n    362880, 3628800, 39916800, 479001600,\n    6227020800, 87178291200, 1307674368000,\n    20922789888000, 355687428096000, 6402373705728000,\n    121645100408832000, 2432902008176640000], dtype='int64')\n\n\n@nb.njit\ndef fast_factorial(n):\n    \"\"\"Fast factorial is computed using a simple lookup table\"\"\"\n    if n > 20:\n        raise ValueError\n    return LOOKUP_TABLE[n]\n\n\n@nb.njit(nb.int32(nb.int32, nb.int32))\ndef binom(n, k):\n    return fast_factorial(n) / (fast_factorial(k) * fast_factorial(n - k))\n\n\n@nb.njit(nb.types.Tuple((nb.int32[:, :],\n                         nb.int32[:]))(nb.int32[:],\n                                       nb.int32[:],\n                                       nb.int64, nb.int64))\ndef cproduct(sizes: np.ndarray, current_tuple: np.ndarray, start_idx: int, end_idx: int):\n    assert len(sizes) >= 2\n    assert start_idx < end_idx\n\n    tuples = np.zeros((end_idx - start_idx, len(sizes)), dtype=np.int32)\n    tuple_idx = 0\n    current_tuple = current_tuple.copy()\n    while tuple_idx < end_idx - start_idx:\n        tuples[tuple_idx] = current_tuple\n        current_tuple[0] += 1\n        if current_tuple[0] == sizes[0]:\n            current_tuple[0] = 0\n            current_tuple[1] += 1\n            for i in range(1, len(sizes) - 1):\n                if current_tuple[i] == sizes[i]:\n                    current_tuple[i + 1] += 1\n                    current_tuple[i] = 0\n                else:\n                    break\n        tuple_idx += 1\n    return tuples, current_tuple\n\n\ndef chunked_cartesian_product(sizes: List[int], chunk_size: int):\n    prod = np.prod(sizes)\n\n    # putting the largest number at the front to more efficiently make use\n    # of the cproduct numba function\n    sizes = np.array(sizes, dtype=np.int32)\n    sorted_idx = np.argsort(sizes)[::-1]\n    sizes = sizes[sorted_idx]\n    if chunk_size > prod:\n        chunk_bounds = (np.array([0, prod])).astype(np.int64)\n    else:\n        num_chunks = np.maximum(np.ceil(prod / chunk_size), 2).astype(np.int32)\n        chunk_bounds = (np.arange(num_chunks + 1) * chunk_size).astype(np.int64)\n        chunk_bounds[-1] = prod\n    current_tuple = np.zeros(len(sizes), dtype=np.int32)\n    for start_idx, end_idx in zip(chunk_bounds[:-1], chunk_bounds[1:]):\n        tuples, current_tuple = cproduct(sizes, current_tuple, start_idx, end_idx)\n        # re-arrange columns to match the original order of the sizes list\n        # before yielding\n        yield tuples[:, np.argsort(sorted_idx)]\n\n\ndef cartesian_product(sizes: List[int]):\n    return next(chunked_cartesian_product(sizes, np.prod(sizes)))\n\n\n","sub_path":"pygamma_agreement/numba_utils.py","file_name":"numba_utils.py","file_ext":"py","file_size_in_byte":2717,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"500927013","text":"# coding:utf-8\nimport sys\nimport time\nimport commands\nimport os\nimport copy\n\nfrom watchmen.common import enums\nfrom watchmen.common.fmevent import FmEvent\nfrom watchmen.producer.eventsender import EventSende\n\n\ndef main():\n    SCRIPTS_DIR = \"/var/cmcc-la/scripts\"\n    DATA_DIR = \"/var/cmcc-la/data\"\n    vm_last_status_file = \"%s/vm_last_status.txt\" % (DATA_DIR)\n    region_status, region = commands.getstatusoutput(\n        \"grep \\\"region_name\\\" /etc/watchmen/watchmen-producer.conf | cut -d\\\"=\\\" -f2\")\n\n    # if no such source, the nova list will report error that must provide a user name or user id\n    # commands.getstatusoutput(\"source /var/cmcc-la/data/openrc\")\n    commands.getstatusoutput(\"source /root/openrc\")\n\n    if os.access(\"{}\".format(vm_last_status_file), os.F_OK) == False:\n        # create the vm status file\n        commands.getstatusoutput(\"nova list --all 2>/dev/null | grep ^\\\"|\\\" | grep -v ID | while read line;   do    vm_uuid=`echo \\\"$line\\\" | awk -F\\\"|\\\" '{print $2}' | tr -d ' '`;    vm_name=`echo \\\"$line\\\" | awk -F\\\"|\\\" '{print $3}' | tr -d ' ' `;    vm_last_status=`echo \\\"$line\\\" | awk -F\\\"|\\\" '{print $5}' | tr -d ' ' `;    echo \\\"$vm_name,$vm_uuid,$vm_last_status\\\" >> $%s;   done\"%(vm_last_status_file))\n\n    while True:\n        commands.getstatusoutput(\"cp -p ${%s} ${%s}.bak\"%(vm_last_status_file,vm_last_status_file))\n        nova_list_status, nova_list =commands.getstatusoutput(\"nova list --all 2>/dev/null | grep ^\\\"|\\\" | grep -v ID\")\n        if nova_list_status !=0:\n            continue\n            nova_list_per=nova_list.split(\"\\n\")\n            for i in nova_list_per:\n                b = i.split(\"|\")\n                vm_uuid = b[1]\n                vm_uuid_split = vm_uuid.split(\" \")\n                vm_uuid_rel = vm_uuid_split[1]\n                vm_name = b[2]\n                vm_name_split = vm_name.split(\" \")\n                vm_name_rel = vm_name_split[1]\n                vm_curr_status = b[4]\n                vm_curr_status_split = vm_curr_status.split(\" \")\n                vm_curr_status_rel = vm_curr_status_split[1]\n                #vm_last_status_file = vm_name_rel + ',' + vm_uuid_rel + ',' + vm_last_status_rel\n                tenant_id =b[3]\n                tenant_id_split = tenant_id.split(\" \")\n                tenant_id_rel = tenant_id_split[1]\n                vm_last_status = commands.getstatusoutput(\"cat ${%s} | grep %s | awk -F, '{print $3}'\"%(vm_last_status_file,vm_uuid_rel))\n                status_changed = \"\"\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"vm_monitor_old.py","file_name":"vm_monitor_old.py","file_ext":"py","file_size_in_byte":2517,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"384894140","text":"# --------------------------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License. See License.txt in the project root for license information.\n# --------------------------------------------------------------------------------------------\nfrom typing import Optional, Dict, Any\n\nfrom uamqp.constants import TransportType\n\n\nclass Configuration(object):  # pylint:disable=too-many-instance-attributes\n    def __init__(self, **kwargs):\n        self.user_agent = kwargs.get(\"user_agent\")  # type: Optional[str]\n        self.retry_total = kwargs.get(\"retry_total\", 3)  # type: int\n        self.retry_backoff_factor = kwargs.get(\"retry_backoff_factor\", 0.8)  # type: float\n        self.retry_backoff_max = kwargs.get(\"retry_backoff_max\", 120)  # type: int\n        self.logging_enable = kwargs.get(\"logging_enable\", False)  # type: bool\n        self.http_proxy = kwargs.get(\"http_proxy\")  # type: Optional[Dict[str, Any]]\n        self.transport_type = (\n            TransportType.AmqpOverWebsocket\n            if self.http_proxy\n            else kwargs.get(\"transport_type\", TransportType.Amqp)\n        )\n        self.auth_timeout = kwargs.get(\"auth_timeout\", 60)  # type: int\n        self.encoding = kwargs.get(\"encoding\", \"UTF-8\")\n        self.auto_reconnect = kwargs.get(\"auto_reconnect\", True)\n        self.idle_timeout = kwargs.get(\"idle_timeout\", None)\n        prefetch = kwargs.get(\"prefetch\", 0)\n        if int(prefetch) < 0 or int(prefetch) > 50000:\n            raise ValueError(\"Prefetch must be an integer between 0 and 50000 inclusive.\")\n        self.prefetch = prefetch + 1\n","sub_path":"sdk/servicebus/azure-servicebus/azure/servicebus/_common/_configuration.py","file_name":"_configuration.py","file_ext":"py","file_size_in_byte":1686,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"286657092","text":"import glob\nimport os\nfrom discord_bot import bot\n\n\ndef main():\n    files = list(filter(os.path.isfile, glob.glob(\"*.jpg\")))\n    files.sort(key=lambda x: os.path.getctime(x))\n    for file in files:\n        bot(\"\", \"discord.json\", file)\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"replay_stats.py","file_name":"replay_stats.py","file_ext":"py","file_size_in_byte":276,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"82385386","text":"from django.shortcuts import render\nfrom django.views import generic\nfrom django.conf import settings\nimport os\nfrom django.core.files.storage import FileSystemStorage\nfrom . import stem, indeces, tokenizer\n\n\n# Create your views here.\n\ndef index(request):\n\n    return render(request, \"search/index.html\", {})\n\ndef results(request):\n    s = request.GET['q']\n    file = open(os.path.join(settings.MEDIA_ROOT, 'file1.txt'), 'r').read()\n    token = tokenizer.tokens(file, s)\n    \n    context = {\n        's':s,\n        'file':file,\n        'token':token,\n    }\n    return render(request, \"search/results.html\", context)\n\n\ndef upload(request):\n    if request.method == 'POST' and request.FILES['myfile']:\n        myfile = request.FILES['myfile']\n        fs = FileSystemStorage()\n        filename = fs.save(myfile.name, myfile)\n        uploaded_file_url = fs.url(filename)\n        return render(request, 'search/simple_upload.html', {\n            'uploaded_file_url': uploaded_file_url\n        })\n    return render(request, 'search/simple_upload.html')\n\n\ndef model_form_upload(request):\n    if request.method == 'POST':\n        form = DocumentForm(request.POST, request.FILES)\n        if form.is_valid():\n            form.save()\n            return redirect('home')\n    else:\n        form = DocumentForm()\n    return render(request, 'search/model_form_upload.html', {\n        'form': form\n    })\n","sub_path":"Desktop/WorkSpace/search_engine/search/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1389,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"628814128","text":"import  json\n# Program to show various ways to read and\n# write data in a file.\ndict = {\"USERNAME\":\"NIRBHAY\"}\nfile1 = open(\"myfile.txt\", \"w\")\nfile1.write(str(dict))\nfile1.close()  # to change file access modes\n\nfile1 = open(\"myfile.txt\", \"r+\")\nstring_value = file1.read()\ndict = eval(string_value)\nif \"USERNAME\" in dict:\n    print (dict[\"USERNAME\"])\n","sub_path":"AllFinalStuff/Google Assistant/fileHandling.py","file_name":"fileHandling.py","file_ext":"py","file_size_in_byte":350,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"126080939","text":"from heapq import heapify,heappop,heappush\nclass Node():\n    def __init__ (self,weight,info=None,left_child=None,right_child=None):\n        self.info = info\n        self.weight = weight\n        self.left_child = left_child\n        self.right_child = right_child\n\n\nclass HuffmanCode():\n    def __init__(self):\n        self.heap = []\n        self.entries_added = 0\n        self.root_node = None\n    \n    def add_symbol(self,weight,symbol_info=None):\n        symbol_node = Node(weight,symbol_info)\n        self.entries_added +=1\n        heappush(self.heap,(weight,self.entries_added,symbol_node))\n    \n    def huffman_encode(self):\n\n        while len(self.heap) > 1:\n            node1 = heappop(self.heap)[2]\n            node2 = heappop(self.heap)[2]\n            merged_node = self.__merge_nodes(node1, node2)\n            self.push_to_heap(merged_node)\n        self.root_node = heappop(self.heap)[2]\n        return self.root_node\n\n    def push_to_heap(self, merged_node):\n        self.entries_added+=1\n        heappush(self.heap,(merged_node.weight,self.entries_added,merged_node))\n\n    def __merge_nodes(self, node1, node2):\n        left_child = node1 if node1.weight >= node2.weight else node2\n        right_child = node1 if node1.weight < node2.weight else node2\n        new_weight = node1.weight + node2.weight\n        merged_node = Node(weight=new_weight,left_child=left_child,right_child=right_child)\n        return merged_node\n    \n    def create_encode_map_string(self):\n        encoding_map = {}\n        self.__traverse_tree(self.root_node,\"\",encoding_map)\n        return encoding_map\n\n    def __traverse_tree(self,current_node,prefix,encoding_map):\n        if current_node.left_child is None and current_node.right_child is None:\n            encoding_map[(current_node.info,current_node.weight)] = prefix\n        else:\n            self.__traverse_tree(current_node.left_child,prefix + \"0\",encoding_map)\n            self.__traverse_tree(current_node.right_child,prefix + \"1\",encoding_map)\n\n\n            \n\n\n                \n","sub_path":"greedy_algorithms_mst_dynamic_programming/week3/huffman/huffman.py","file_name":"huffman.py","file_ext":"py","file_size_in_byte":2029,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"560905379","text":"from django import forms\n\nfrom members.models import QGroup\n\n\nclass ContributionForm(forms.Form):\n    student = forms.DecimalField(decimal_places=2,\n                                 min_value=0,\n                                 help_text=\"Hoogte van de contributie voor studenten.\")\n    non_student = forms.DecimalField(decimal_places=2,\n                                     min_value=0,\n                                     label=\"Burger\",\n                                     help_text=\"Hoogte van de contributie voor burgers.\")\n    administration_fee = forms.DecimalField(decimal_places=2,\n                                            min_value=0,\n                                            initial=6,\n                                            label=\"Administratiekosten\",\n                                            help_text=\"Wordt alleen toegepast bij personen zonder SEPA machtiging.\")\n\n\nclass ContributionExceptionForm(forms.Form):\n    group = forms.ModelChoiceField(queryset=QGroup.objects.all())\n    student = forms.DecimalField(decimal_places=2,\n                                 min_value=0)\n    non_student = forms.DecimalField(decimal_places=2,\n                                     min_value=0,\n                                     label=\"Burger\")\n\n\nclass BaseContributionExceptionFormSet(forms.BaseFormSet):\n    def clean(self):\n        \"\"\"Checks that the groups are unique.\"\"\"\n        if any(self.errors):\n            # Don't bother validating the formset unless each form is valid on its own\n            return\n        groups = []\n        for form in self.forms:\n            group = form.cleaned_data.get(\"group\")\n            if not group:\n                continue  # Form might be empty\n            if group in groups:\n                raise forms.ValidationError(\"Groups need to be distinct.\")\n            groups.append(group)\n\n\nContributionExceptionFormSet = forms.formset_factory(ContributionExceptionForm,\n                                                     formset=BaseContributionExceptionFormSet,\n                                                     extra=0)\n","sub_path":"pennotools/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":2085,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"202426289","text":"from argparse import ArgumentParser\n\nclass InputHandler(ArgumentParser):\n    def __init__(self):\n        super().__init__()\n\n        self.add_argument('--world_size', help='Specify size of the world between 10-50.',\n                            type=int, default=30)\n        self.add_argument('--live_density', help='Specify proportion of alive cells at the beggining between 0-1',\n                            type=float, default=0.5)\n        self.add_argument('--rounds_number', help='Specify for how many rounds the game will run',\n                            type=int, default=1000)\n        self.add_argument('--run_speed', help='Specify how many roudns per second will be computed 4-20',\n                            type=int, default=5)\n\n        self.args = self.parse_args()\n\n        if self.args.world_size not in range(10, 51):\n            self.error(\"World size has to be between 10-50\")\n        if self.args.live_density < 0 or self.args.live_density > 1:\n            self.error(\"Life density has to be between 0-1\")\n        if self.args.run_speed not in range(4, 31):\n            self.error(\"Run speed has to be between 4-30\")\n","sub_path":"inputs.py","file_name":"inputs.py","file_ext":"py","file_size_in_byte":1136,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"174183651","text":"\"\"\"\nAuthor:kingsley\nVersion:0.0.1\nRequirements:股票数据行情\n\"\"\"\nimport pandas as pd\nimport tushare as ts\n\nif __name__ == '__main__':\n    df = ts.get_k_data(\"300104\")\n    df.index = pd.to_datetime(df.date)\n    df.drop('date', inplace=True, axis=1)\n    days = [5, 15, 50]\n    for ma in days:\n        column_name = \"MA{}\".format(ma)\n        df[column_name] = pd.Series.rolling(df.close, ma)\n\n    # 计算浮动比例\n    df[\"pchange\"] = df.close.pct_change()\n    # 计算浮动点数\n    df[\"change\"] = df.close.diff()\n\n    df[[\"close\", \"MA5\", \"MA15\", \"MA50\"]].plot(figsiz=(10, 18))","sub_path":"test/demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"78212248","text":"import wx\nimport wx.lib.mixins.listctrl\nfrom os import stat\nfrom os import listdir\nfrom os import rename\nfrom os.path import isfile, join\n\n\nclass AutoWidthListCtrl(wx.ListCtrl, wx.lib.mixins.listctrl.ListCtrlAutoWidthMixin):\n    def __init__(self, parent, *args, **kwargs):\n        wx.ListCtrl.__init__(self, parent, wx.ID_ANY, style=wx.LC_REPORT)\n        wx.lib.mixins.listctrl.ListCtrlAutoWidthMixin.__init__(self)\n\nclass Interface(wx.Frame):\n    def __init__(self, *args, **kwargs):\n        super(Interface, self).__init__(*args, **kwargs)\n        self.initUi()\n\n    def initUi(self):\n        panel_main = wx.Panel(parent=self)\n\n        box_horizontal_dialogs = wx.BoxSizer(orient=wx.HORIZONTAL)\n        box_horizontal_buttons = wx.BoxSizer(orient=wx.HORIZONTAL)\n        box_vertical_buttons = wx.BoxSizer(orient=wx.VERTICAL)\n        box_vertical_main = wx.BoxSizer(orient=wx.VERTICAL)\n\n        flexgrid_dialogs = wx.FlexGridSizer(2, 2, 5, 5)\n\n        btn_run = wx.Button(parent=panel_main, id=wx.ID_ANY, label=\"Run\")\n        btn_dialog_source = wx.Button(parent=panel_main, id=wx.ID_ANY, label=\"Source folder\", pos=(10, 10))\n        btn_dialog_target = wx.Button(parent=panel_main, id=wx.ID_ANY, label=\"Target folder\", pos=(10, 10))\n\n        chk_filter = wx.CheckBox(parent=panel_main, id=wx.ID_ANY, label=\"Show only different named files?\")\n        chk_filter.Bind(event=wx.EVT_CHECKBOX, handler=self.onChkFilter)\n        self.chk_rename = wx.CheckBox(parent=panel_main, id=wx.ID_ANY, label=\"Rename different named files?\")\n        self.chk_rename.Bind(event=wx.EVT_CHECKBOX, handler=self.onChkRename)\n\n        self.chk_rename_state = False\n        self.chk_filter_state = False\n        self.chk_rename.Enable(False)\n\n        box_horizontal_buttons.Add(window=btn_run, proportion=1, flag=wx.EXPAND | wx.TOP, border=4)\n        box_vertical_buttons.Add(window=chk_filter, proportion=1, flag=wx.TOP)\n        box_vertical_buttons.Add(window=self.chk_rename, proportion=1, flag=wx.TOP)\n\n        box_horizontal_buttons.Add(sizer=box_vertical_buttons, proportion=1, flag=wx.TOP | wx.LEFT, border=5)\n\n        self.Bind(event=wx.EVT_BUTTON,\n                  handler=self.onRun,\n                  id=btn_run.GetId())\n\n        font = wx.Font(13, wx.DEFAULT, wx.NORMAL, wx.NORMAL)\n\n        self.st_source = wx.StaticText(parent=panel_main, label=\"Source folder...\", style=wx.ALIGN_LEFT)\n        self.st_source.SetFont(font=font)\n        self.st_target = wx.StaticText(parent=panel_main, label=\"Target folder...\", style=wx.ALIGN_LEFT)\n        self.st_target.SetFont(font=font)\n\n        flexgrid_dialogs.AddMany([(btn_dialog_source, 0, wx.EXPAND), self.st_source,\n                                  (btn_dialog_target, 0, wx.EXPAND), self.st_target])\n\n        self.Bind(event=wx.EVT_BUTTON, handler=self.onDirSource, id=btn_dialog_source.GetId())\n        self.Bind(event=wx.EVT_BUTTON, handler=self.onDirTarget, id=btn_dialog_target.GetId())\n\n        box_horizontal_dialogs.Add(sizer=flexgrid_dialogs, proportion=1, flag=wx.ALL | wx.EXPAND, border=5)\n\n        self.list = AutoWidthListCtrl(parent=panel_main)\n        self.list.InsertColumn(col=0, heading=\"Source\", width=180)\n        self.list.InsertColumn(col=1, heading=\"Target\", width=180)\n\n        box_vertical_main.Add(sizer=box_horizontal_dialogs, proportion=0, flag=wx.EXPAND | wx.RIGHT, border=5)\n        box_vertical_main.Add(window=self.list, proportion=1, flag=wx.ALL | wx.EXPAND, border=5)\n        box_vertical_main.Add(sizer=box_horizontal_buttons, proportion=0, flag=wx.LEFT | wx.RIGHT | wx.BOTTOM | wx.EXPAND, border=5)\n\n        panel_main.SetSizer(sizer=box_vertical_main)\n\n        self.SetSize((400, 400))\n        self.SetTitle(title=\"File renamer\")\n        self.Center()\n\n    def onRun(self, event):\n        try:\n            self.rename_files_in_folder(folder_source=self.folder_dlg_source,\n                                        folder_target=self.folder_dlg_target)\n        except:\n            wx.MessageBox(message=\"Please select source and target folders\", caption=\"Error\")\n\n    def onChkFilter(self, event):\n        self.chk_filter_state = event.GetEventObject().GetValue()\n        if self.chk_filter_state:\n            self.chk_rename.Enable(True)\n        else:\n            self.chk_rename.SetValue(False)\n            self.chk_rename.Enable(False)\n\n    def onChkRename(self, event):\n        self.chk_rename_state = event.GetEventObject().GetValue()\n\n    def onDirSource(self, event):\n        \"\"\"\n        Show the DirDialog and print the user's choice to stdout\n        \"\"\"\n        dlg = wx.DirDialog(self, \"Choose a directory:\",\n                           style=wx.DD_DEFAULT_STYLE)\n        if dlg.ShowModal() == wx.ID_OK:\n            self.folder_dlg_source = dlg.GetPath()\n            self.st_source.Label = self.folder_dlg_source\n            self.st_source.ToolTip = self.folder_dlg_source\n        dlg.Destroy()\n\n    def onDirTarget(self, event):\n        \"\"\"\n        Show the DirDialog and print the user's choice to stdout\n        \"\"\"\n        dlg = wx.DirDialog(self, \"Choose a directory:\",\n                           style=wx.DD_DEFAULT_STYLE)\n        if dlg.ShowModal() == wx.ID_OK:\n            self.folder_dlg_target = dlg.GetPath()\n            self.st_target.Label = self.folder_dlg_target\n            self.st_target.ToolTip = self.folder_dlg_target\n        dlg.Destroy()\n\n    def rename_files_in_folder(self, folder_source, folder_target):\n        \"\"\"\n        :param folder_source: str\n        :param folder_target: str\n        :return: None\n        \"\"\"\n        # source_dict = {source file names: source file sizes}\n        source_dict = {k: v for k, v in [(f, stat(join(folder_source, f)).st_size) for f in listdir(folder_source) if isfile(join(folder_source, f))]}\n        # target_list = [target file names: target file sizes]\n        target_list = [(f, stat(join(folder_target, f)).st_size) for f in\n                       listdir(folder_target) if isfile(join(folder_target, f))]\n        self.list.DeleteAllItems()\n        self.state_same_size_found = False\n        self.state_same_name_found = False\n        for file_name_size in target_list:\n            target_file_name = file_name_size[0]\n            target_file_size = file_name_size[1]\n            if target_file_size in source_dict.values():\n                self.state_same_size_found = True\n                for source_file_name, source_file_size in source_dict.items():\n                    idx = 0\n                    if source_file_size == target_file_size:\n                        if self.chk_filter_state:\n                            if source_file_name != target_file_name:\n                                self.state_same_name_found = True\n                                print(source_file_name, \">>>>\", target_file_name)\n                                index = self.list.InsertItem(index=idx, label=source_file_name)\n                                self.list.SetItem(index=index, column=1, label=target_file_name)\n\n                                idx += 1\n\n                                if self.chk_rename_state and self.chk_rename.Enabled:\n                                    rename(join(folder_target, target_file_name), join(folder_target, source_file_name))\n                                    print(\"Dosya ismi başarıyla değiştirilmiştir.\")\n\n                        else:\n                            index = self.list.InsertItem(index=idx, label=source_file_name)\n                            self.list.SetItem(index=index, column=1, label=target_file_name)\n\n                            idx += 1\n                            print(source_file_name, \">>>>\", target_file_name)\n\n        if not self.state_same_size_found:\n            wx.MessageBox(message=\"There are no same sized files\", caption=\"Error\")\n\n        if not self.state_same_name_found and self.state_same_size_found:\n            wx.MessageBox(message=\"These file names are identical\", caption=\"Error\")\n\n\nif __name__ == '__main__':\n    app = wx.App()\n    my_interface = Interface(None)\n    my_interface.Show()\n    app.MainLoop()\n\n","sub_path":"Python3/Genel Deneme/File renamer by file size_Wx.py","file_name":"File renamer by file size_Wx.py","file_ext":"py","file_size_in_byte":8043,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"173036642","text":"import time\ndef wela():\n    vantee = time.localtime()\n    d1 = time.strftime(\"%d %B %Y, %H:%M:%S\", vantee)\n    print(d1)\nNA = []\nSC = []\nTT = []\nHI = []\nnum = int(input(\"Plese Enter Num of Compettitor : \"))\nfor i in range(num):\n    name = input(\"Plese Enter Compettitor Name : \")\n    pts = float(input(\"Enter PTS Score : \"))\n    tim = float(input(\"Enter Time : \"))\n    NA.append(name)\n    SC.append(pts)\n    TT.append(tim)\n    HI.append(pts/tim)\nfor i in range(num):\n    j = i\n    for j in range(num):\n        if HI[i] > HI[j]:\n            z,x,c,v = HI[i],SC[i],TT[i],NA[i]\n            HI[i],SC[i],TT[i],NA[i] = HI[j],SC[j],TT[j],NA[j]\n            HI[j],SC[j],TT[j],NA[j] = z,x,c,v\nvantee = time.localtime()\na = time.strftime(\"%A\", vantee)\nb = time.strftime(\"%Y\", vantee)\nprint(\"Shortgun \"+a+\"Training\",b,\"\\nCondition : 1\")\nwela()\nprint(\"_\"*100)\nprint(\"{0:-<6}{1:-<6}{2:-<8}{3:-<17}{4:-<12}{5:-<15}{6}\".format(\"NO.\",\"PTS\",\"TIME\",\"COMPETITOR#Name\",\"HIT FACTOR\",\"STATE POINTS\",\"STATE PERCENT\"))\nprint(\"_\"*100)\nfor i in range(num):\n    PO = (HI[i]/HI[0])*50\n    PE = (PO/(HI[0]/HI[0]*50))*100\n    print(\"{0:_<6}{1:_<6}{2:_<8}{3:_<17}{4:_<12}{5:_<15}{6}\".format(i+1,int(SC[i]),\"%.2f\"%TT[i],NA[i],\"%.4f\"%HI[i],\"%.4f\"%PO,\"%.2f\"%PE))\n","sub_path":"Ex 4.3.py","file_name":"Ex 4.3.py","file_ext":"py","file_size_in_byte":1227,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"125626222","text":"# -*- coding: utf-8 -*-\nimport rainbox\n\n\ndef create_test_db():\n    import sqlite3\n    db = sqlite3.connect(\":memory:\")\n    queries = [\n        \"create table image(id int primary key, title text, author int)\",\n        \"create table user(id int primary key, name text)\",\n        \"create table tag(id int primary key, name text)\",\n        \"create table image_tag(image int, tag int)\",\n        \"insert into user values(1, 'castaneai')\",\n        \"insert into image values(1, 'アリスちゃん', 1)\",\n        \"insert into tag values(1, '金髪')\",\n        \"insert into tag values(2, 'きんいろモザイク')\",\n        \"insert into image_tag values(1, 1)\",\n        \"insert into image_tag values(1, 2)\"\n    ]\n    for query in queries:\n        db.execute(query)\n    return db\n\n\ndef test_exist_user():\n    u\"\"\"ユーザーの存在を確認する\"\"\"\n    with create_test_db() as db:\n        rows = db.execute(\"select * from user where id = 1\").fetchall()\n        assert len(rows) == 1\n        assert rows[0] == (1, u\"castaneai\")\n\n\ndef test_exist_image():\n    u\"\"\"画像の存在を確認する\"\"\"\n    with create_test_db() as db:\n        rows = db.execute(\"select * from image where id = 1\").fetchall()\n        assert len(rows) == 1\n        assert rows[0] == (1, u'アリスちゃん', 1)\n\n\ndef test_search_tags():\n    with create_test_db() as db:\n        rainbox_db = rainbox.RainboxDB(db)\n        tags = rainbox_db.search_tags([u'金髪'])\n        assert tags == (u'金髪',)\n\n","sub_path":"src/test/test_db.py","file_name":"test_db.py","file_ext":"py","file_size_in_byte":1474,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"439038790","text":"from __future__ import unicode_literals\n\nimport shutil\nfrom contextlib import contextmanager\nfrom pathlib import Path\nfrom tempfile import mkdtemp\n\nTEMPLATE_PATH = Path(__file__).parent.parent / \"template\"\n\n\n@contextmanager\ndef temp_dir():\n    tmp_dir = mkdtemp()\n    try:\n        yield Path(tmp_dir)\n    finally:\n        shutil.rmtree(tmp_dir)\n\n\ndef get_entity_short_name(entity_name):\n    # snips/musicArtist -> musicartist\n    return entity_name[6:].lower()\n\n\ndef get_supported_entities(language):\n    if language not in {\"fr\", \"en\"}:\n        return set()\n    return {\n        \"snips/musicAlbum\",\n        \"snips/musicArtist\",\n        \"snips/musicTrack\"\n    }\n","sub_path":"cli/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":662,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"506679559","text":"# Zero Matrix: Write an algorithm such that if an element in an MxN matrix is 0, its entire row and\n# column are set to 0.\n# Input:\n# [\n#   [0,1,2,0],\n#   [3,4,5,2],\n#   [1,3,1,5]\n# ]\n# Output:\n# [\n#   [0,0,0,0],\n#   [0,4,5,0],\n#   [0,3,1,0]\n# ]\n\nclass Solution(object):\n    def setZeroes(self, matrix):\n        \"\"\"\n        :type matrix: List[List[int]]\n        :rtype: None Do not return anything, modify matrix in-place instead.\n        \"\"\"\n        rows = len(matrix)\n        cols = len(matrix[0])\n\n        r, c = set(), set()\n\n        for x in range(rows):\n            for y in range(cols):\n                if matrix[x][y] == 0:\n                    r.add(x)\n                    c.add(y)\n\n        for x in range(rows):\n            for y in range(cols):\n                if x in r or y in c:\n                    matrix[x][y] = 0\n        return matrix\n\n\nobj = Solution()\nprint(obj.setZeroes([[2, 1, 2, 0], [3, 4, 5, 2], [1, 3, 1, 5]]))\n","sub_path":"zeroMatrix.py","file_name":"zeroMatrix.py","file_ext":"py","file_size_in_byte":935,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"477072741","text":"from tkinter import *\nfrom checkingResult import checkingResult\nimport time\n\n\ndef destroyElement(arr):\n    for elem in arr:\n        elem.destroy()\n\n\ndef resultFunc(values=None, userValues=None):\n    if values is not None:\n        with open('result.txt', 'w') as file_w:\n            for elem in values:\n                file_w.write(elem[2][0] + '\\n')\n        open('userResult.txt', 'w').close()\n    elif userValues is not None:\n        with open('userResult.txt', 'a') as file:\n            file.write(userValues + '\\n')\n\n\ndef testProccess(master, values, login, test_time, amout, counter):\n    master.geometry(\"500x500\")\n    v = StringVar(master, \"1\")\n    label_0 = Label(master, text=values[counter][0][0] + \" \" + values[counter][1][0], width=20, font=(\"bold\", 20))\n    label_0.place(x=120, y=53)\n\n    radio1 = Radiobutton(master, text=values[counter][1][1], variable=v,\n                         value=\"A\", indicator=0,\n                         background=\"light blue\",\n                         command=lambda: testController(values, master, login, test_time, amout, v, counter,\n                                                        arrElement, time.time()))\n\n    radio2 = Radiobutton(master, text=values[counter][1][2], variable=v,\n                         value=\"B\", indicator=0,\n                         background=\"light blue\",\n                         command=lambda: testController(values, master, login, test_time, amout, v, counter,\n                                                        arrElement, time.time()))\n    radio3 = Radiobutton(master, text=values[counter][1][3], variable=v,\n                         value=\"C\", indicator=0,\n                         background=\"light blue\",\n                         command=lambda: testController(values, master, login, test_time, amout, v, counter,\n                                                        arrElement, time.time()))\n    radio1.place(x=100, y=130)\n    radio2.place(x=100, y=180)\n    radio3.place(x=100, y=230)\n    arrElement = [label_0, radio1, radio2, radio3]\n    counter += 1\n\n\ndef testController(values, root, login, test_time, amout, v=None, counter=0, arrElement=None, timer=None):\n    global start_time\n    if v is not None:\n        if v.get() == 'A':\n            result = 'A'\n        elif v.get() == 'B':\n            result = 'B'\n        else:\n            result = 'C'\n        resultFunc(None, result)\n    elif counter == 0:\n        start_time = time.time()\n        resultFunc(values)\n\n    if arrElement is not None: destroyElement(arrElement)\n\n    if timer is not None:\n        if (test_time - ((timer / 60) - start_time)) < 0:\n            checkingResult(root)\n\n    if counter < int(amout):\n        testProccess(root, values, login, test_time, amout, counter)\n    else:\n        checkingResult(root)\n","sub_path":"testingProcess.py","file_name":"testingProcess.py","file_ext":"py","file_size_in_byte":2778,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"81875505","text":"import tensorflow as tf\nimport pickle\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom keras.preprocessing.image import ImageDataGenerator\n\ncifar10_dataset_folder_path = 'cifar-10-batches-py'\ncost_history=[]\nbatch_cost_history=[]\nfinal_cost_history=[]\naccuracy_history=[]\nbatch_accuracy_history=[]\nfinal_accuracy_history=[]\ndef variable_summaries(var,name):\n  \"\"\"Attach a lot of summaries to a Tensor (for TensorBoard visualization).\"\"\"\n  with tf.name_scope('summaries_'+name):\n    mean = tf.reduce_mean(var)\n    tf.summary.scalar('mean_'+name, mean)\n    tf.summary.scalar(name+'_value',var)\n    tf.summary.histogram('histogram_'+name, var)\n    \ndef load_label_names():\n    return ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n\ndef load_cfar10_batch(cifar10_dataset_folder_path, batch_id):\n    with open(cifar10_dataset_folder_path + '/data_batch_' + str(batch_id), mode='rb') as file:\n        # note the encoding type is 'latin1'\n        batch = pickle.load(file, encoding='latin1')\n        \n    features = batch['data'].reshape((len(batch['data']), 3, 32, 32)).transpose(0, 2, 3, 1)\n    labels = batch['labels']\n    return features, labels\n\ndef normalize(x):\n    min_val = np.min(x)\n    max_val = np.max(x)\n    x = (x-min_val) / (max_val-min_val)\n    return x\n\ndef one_hot_encode(x):\n    encoded = np.zeros((len(x), 10))\n    for idx, val in enumerate(x):\n        encoded[idx][val] = 1\n    return encoded\n\ndef data_aug(features,labels,augment_size=5000): \n    image_generator = ImageDataGenerator(rotation_range=90,horizontal_flip=True,vertical_flip=True)\n    train_size=features.shape[0]\n#   get transformed images\n    randidx = np.random.randint(train_size, size=augment_size)\n\n    labels=np.array(labels)\n    x_augmented = features[randidx].copy()\n    y_augmented = labels[randidx].copy()\n    x_augmented = image_generator.flow(x_augmented, np.zeros(augment_size),\n                                    batch_size=augment_size, shuffle=False).next()[0]\n#   append augmented data to trainset\n    trainx = np.concatenate((features, x_augmented))\n    trainy= np.concatenate((labels, y_augmented)) \n    return trainx,trainy\n\ndef _preprocess_and_save(normalize, one_hot_encode,data_aug,features, labels, filename):\n    features,labels=data_aug(features,labels)\n    features = normalize(features)\n    labels = one_hot_encode(labels)\n\n    pickle.dump((features, labels), open(filename, 'wb'))\n    \ndef minibatchoptimization(cifar10_dataset_folder_path, normalize, one_hot_encode,data_aug):\n    n_batches = 5\n    valid_features = []\n    valid_labels = []\n\n    for batch_i in range(1, n_batches + 1):\n        features, labels = load_cfar10_batch(cifar10_dataset_folder_path, batch_i)\n        \n        # find index to be the point as validation data in the whole dataset of the batch (10%)\n        index_of_validation = int(len(features) * 0.1)\n\n        # preprocess the 90% of the whole dataset of the batch\n        # - normalize the features\n        # - one_hot_encode the lables\n        # - save in a new file named, \"preprocess_batch_\" + batch_number\n        # - each file for each batch\n        _preprocess_and_save(normalize, one_hot_encode,data_aug,\n                             features[:-index_of_validation], labels[:-index_of_validation], \n                             'preprocess_batch_' + str(batch_i) + '.p')\n\n        # unlike the training dataset, validation dataset will be added through all batch dataset\n        # - take 10% of the whold dataset of the batch\n        # - add them into a list of\n        #   - valid_features\n        #   - valid_labels\n        valid_features.extend(features[-index_of_validation:])\n        valid_labels.extend(labels[-index_of_validation:])\n\n    # preprocess the all stacked validation dataset\n    _preprocess_and_save(normalize, one_hot_encode,data_aug,\n                         np.array(valid_features), np.array(valid_labels),\n                         'preprocess_validation.p')\n\n    # load the test dataset\n    with open(cifar10_dataset_folder_path + '/test_batch', mode='rb') as file:\n        batch = pickle.load(file, encoding='latin1')\n\n    # preprocess the testing data\n    test_features = batch['data'].reshape((len(batch['data']), 3, 32, 32)).transpose(0, 2, 3, 1)\n    test_labels = batch['labels']\n\n    # Preprocess and Save all testing data\n    _preprocess_and_save(normalize, one_hot_encode,data_aug,\n                         np.array(test_features), np.array(test_labels),\n                         'preprocess_testing.p')\n    \nminibatchoptimization(cifar10_dataset_folder_path, normalize, one_hot_encode,data_aug)\nvalid_features, valid_labels = pickle.load(open('preprocess_validation.p', mode='rb'))\ntest_features, test_labels = pickle.load(open('preprocess_testing.p', mode='rb'))\n\ntf.reset_default_graph()\n\n# Inputs\nx = tf.placeholder(tf.float32, shape=(None, 32, 32, 3), name='input_x')\ny =  tf.placeholder(tf.float32, shape=(None, 10), name='output_y')\nkeep_prob = tf.placeholder(tf.float32, name='keep_prob')\n\ndef conv_net(x, keep_prob):\n    conv1_filter = tf.Variable(tf.truncated_normal(shape=[3, 3, 3, 64], mean=0, stddev=0.08))\n\n    \n    conv1 = tf.nn.conv2d(x, conv1_filter, strides=[1,1,1,1], padding='SAME')\n    conv1 = tf.nn.relu(conv1)\n    conv1_pool = tf.nn.max_pool(conv1, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')\n#    conv1_bn = tf.layers.batch_normalization(conv1_pool)\n    variable_summaries(conv1,'conv1')\n    variable_summaries(conv1_pool,'conv1_pool')\n#    variable_summaries(conv1,'conv1_bn')\n    \n\n    \n    flat = tf.contrib.layers.flatten(conv1)  \n    full1 = tf.contrib.layers.fully_connected(inputs=flat, num_outputs=128, activation_fn=tf.nn.relu)\n    full1 = tf.nn.dropout(full1, keep_prob)\n#    full1 = tf.layers.batch_normalization(full1)\n    variable_summaries(full1,'full1')\n    \n\n\n    out = tf.contrib.layers.fully_connected(inputs=full1, num_outputs=10, activation_fn=None)\n    return out\n\nepochs = 20\nepoch_1=np.arange(0,epochs)\nbatch_size = 128\nkeep_probability= 0.7\nlearning_rate = 0.001\n\nlogits = conv_net(x, keep_prob)\nmodel = tf.identity(logits, name='logits') # Name logits Tensor, so that can be loaded from disk after training\n\n# Loss and Optimizer\nmy_normal_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels = y))\nreg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)\nreg_constant = 0.01  # Choose an appropriate one.\nloss = my_normal_loss + reg_constant * sum(reg_losses)\n\ntf.summary.scalar('loss', loss)\n\noptimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)\n\n# Accuracy\ncorrect_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))\naccuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')\ntf.summary.scalar('accuracy', accuracy)\n\n\n#tf.summary.histogram('accuary',accuracy)\n#variable_summaries(loss,'loss')\n#variable_summaries(accuracy, 'accuracy')\nglobal_step = tf.Variable(0, dtype=tf.int32, trainable=False)\n\ndef train_neural_network(session, optimizer, keep_probability, feature_batch, label_batch):\n    session.run(optimizer, \n                feed_dict={\n                    x: feature_batch,\n                    y: label_batch,\n                    keep_prob: keep_probability\n                })\ndef print_stats(session, feature_batch, label_batch, cost, accuracy):\n    loss = session.run(cost, \n                    feed_dict={\n                        x: feature_batch,\n                        y: label_batch,\n                        keep_prob: 1.\n                    })\n    cost_history.append(loss)\n    \n    valid_acc = session.run(accuracy,feed_dict={x: valid_features,y: valid_labels,keep_prob: 1.})\n    accuracy_history.append(valid_acc)\n    print('Loss: {:>10.4f} Validation Accuracy: {:.6f}'.format(loss, valid_acc))\n    return cost_history,accuracy_history\n#   \n    \ndef batch_features_labels(features, labels, batch_size):\n    \"\"\"\n    Split features and labels into batches\n    \"\"\"\n    for start in range(0, len(features), batch_size):\n        end = min(start + batch_size, len(features))\n        yield features[start:end], labels[start:end]\n\ndef load_preprocess_training_batch(batch_id, batch_size):\n    \"\"\"\n    Load the Preprocessed Training data and return them in batches of <batch_size> or less\n    \"\"\"\n    filename = 'preprocess_batch_' + str(batch_id) + '.p'\n    features, labels = pickle.load(open(filename, mode='rb'))\n\n    # Return the training data in batches of size <batch_size> or less\n    return batch_features_labels(features, labels, batch_size)\n\n\nsave_model_path = r'C:\\Users\\manna\\Documents\\GitHub\\CIFAR10-img-classification-tensorflow\\graph_EX1'\n\nprint('Training...')\nloss_over_time = np.zeros(epochs)\nwith tf.Session() as sess:\n    merged_summary_op = tf.summary.merge_all()\n    summary_writer = tf.summary.FileWriter(r\"C:\\Users\\manna\\Documents\\GitHub\\CIFAR10-img-classification-tensorflow\\graph_EX1\", sess.graph)\n    \n    # Initializing the variables\n    sess.run(tf.global_variables_initializer())\n    \n    # Training cycle\n    for epoch in range(epochs):\n        # Loop over all batches\n        n_batches = 5\n        for batch_i in range(1, n_batches + 1):\n            for batch_features, batch_labels in load_preprocess_training_batch(batch_i, batch_size):\n                train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)\n                \n            print('Epoch {:>2}, CIFAR-10 Batch {}:  '.format(epoch + 1, batch_i), end='')\n            cost_history,accuracy_history=print_stats(sess, batch_features, batch_labels, loss, accuracy)\n            batch_cost_history.append(cost_history)\n            batch_accuracy_history.append(accuracy_history)\n        final_cost_history.append(np.mean(batch_cost_history))\n        final_accuracy_history.append(np.mean(batch_accuracy_history))\n    fig, axs = plt.subplots(1, 1, figsize=(5, 5))\n    fig.suptitle(\"loss vs epoch\")\n#    axs.plot(time_train,rmse_train,label='rmse_train')\n    axs.plot(epoch_1,final_cost_history,label='loss',color='red')\n    plt.legend()\n    plt.show()\n    \n    fig, axs = plt.subplots(1, 1, figsize=(5, 5))\n    fig.suptitle(\"accuracy vs epoch\")\n#    axs.plot(time_train,rmse_train,label='rmse_train')\n    axs.plot(epoch_1,accuracy_history,label='validation_accuracy',color='red')\n    plt.legend()\n    plt.show()\n    print (\"Testing Accuracy:\", sess.run(accuracy, feed_dict={x: test_features,y:test_labels,keep_prob: 1.}))        \n    # Save Model\n    saver = tf.train.Saver()\n    save_path = saver.save(sess, save_model_path)","sub_path":"lab7/DDA_CIFAR10_CNN_EX1.py","file_name":"DDA_CIFAR10_CNN_EX1.py","file_ext":"py","file_size_in_byte":10527,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"298995504","text":"def frequency_sort(items):\n    freq_items = {}  # example [4, 6, 2, 2, 6, 2, 4, 4, 4]\n    for i in items:\n        if i in freq_items:\n            freq_items[i] += 1\n        else:\n            freq_items.update({i: 1})\n    # freq_items -> <class 'dict'>: {4: 4, 6: 2, 2: 3}\n    sorted_keys = sorted(freq_items, key=freq_items.get, reverse=True)\n    # sorted_keys -> <class 'list'>: [4, 2, 6]\n    res = []\n    for j in sorted_keys:\n        res += [j]*freq_items[j] # append [value] count = freq_items[j]\n    # res -> <class 'list'>: [4, 4, 4, 4, 2, 2, 2, 6, 6]\n    return res\n\n\nif __name__ == '__main__':\n    print(\"Example:\")\n    print(frequency_sort([4, 6, 2, 2, 6, 4, 4, 4]))\n\n    # These \"asserts\" are used for self-checking and not for an auto-testing\n    assert list(frequency_sort([4, 6, 2, 2, 6, 4, 4, 4])) == [4, 4, 4, 4, 6, 6, 2, 2]\n    assert list(frequency_sort(['bob', 'bob', 'carl', 'alex', 'bob'])) == ['bob', 'bob', 'bob', 'carl', 'alex']\n    assert list(frequency_sort([17, 99, 42])) == [17, 99, 42]\n    assert list(frequency_sort([])) == []\n    assert list(frequency_sort([1])) == [1]\n    print(\"Coding complete? Click 'Check' to earn cool rewards!\")","sub_path":"frequency_sort.py","file_name":"frequency_sort.py","file_ext":"py","file_size_in_byte":1165,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"540655593","text":"#!/usr/bin/python\n\nimport argparse\n\n# + nargs expects 1+ arguments\n\nparser = argparse.ArgumentParser()\nparser.add_argument('num', type=int, nargs='+')\nargs = parser.parse_args()\n\nprint(f\"The sum of values is {sum(args.num)}\")","sub_path":"stdlib/argparse/oneplus.py","file_name":"oneplus.py","file_ext":"py","file_size_in_byte":225,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"120988411","text":"#NEEDS PYTHON2 NOT 3\nfrom flask import Flask #pip install flask\nfrom flask import jsonify\nfrom flask import request\nimport folder_asset\nimport s3_rest_handler\nimport platform\nimport dyanmodb_handler\n\napp = Flask(__name__)\napp.json_encoder = folder_asset.FolderAssetJSONEncoder\n\n@app.route('/')\ndef base_page_handler():\n    return \"api:\" \\\n           \"\\n\" \\\n           \"GET [base_url]/everything\\n\" \\\n           \"returns every asset in s3\" \\\n           \"\\n\\n\" \\\n           \"GET [base_url]/filter?=[unix_timestamp]\\n\" \\\n           \"returns every asset uploaded after [unix_timestamp] argument\" \\\n           \"\\n\\n\" \\\n           \"GET [base_url]/timeline \\n\" \\\n           \"returns the mission timeline\"\n\n@app.route('/everything')\ndef everything_page_handler():\n    output = s3_rest_handler.retrieve_assets()\n    return jsonify(output)\n\n@app.route('/filter')\ndef filter_page_handler():\n    s3_data = s3_rest_handler.retrieve_assets()\n    s3_filtered_data = {}\n    last_update_timestamp = request.args.get('last_update', default=0, type=long)\n    for folder in s3_data:\n        asset_list = s3_data[folder]\n        for idx, asset in enumerate(asset_list):\n            if asset.upload_time > last_update_timestamp:\n                s3_filtered_data[folder] = asset_list[idx:]\n                break\n    return jsonify(s3_filtered_data)\n\n@app.route('/timeline')\ndef timeline():\n    return dyanmodb_handler.getTimelineJSON()\n\ndef main():\n    if platform.system() == \"Linux\":\n        app.run(host='0.0.0.0', port=5000, debug=True)\n        # If the system is a windows /!\\ Change  /!\\ the   /!\\ Port\n    elif platform.system() == \"Windows\":\n        app.run(host='0.0.0.0', port=50000, debug=True)\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1722,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"166704258","text":"from __future__ import print_function\nfrom googleapiclient.discovery import build\n\n# server to server authentication SERVICE_ACCOUNT_FILE = 'client_id_server.json'\n# server to server authenSERVICE_ACCOUNT_FILE = 'client_id_server.json'tication\n\nfrom google.oauth2 import service_account\n\n# If modifying these scopes, delete the file token.pickle.\nSCOPES = ['https://www.googleapis.com/auth/drive.readonly']\nSERVICE_ACCOUNT_FILE = 'client_id_server.json'\n\n\ndef main():\n    # Start input form user\n    print('Enter file id:')\n    file_id = input()\n    # End of input\n\n    # File IDs:                                Restrictions:  Comments:\n    # 1IPDyDzqnCR2VQf1liPh-j_5eUEjQmz9ABnFNAYPZ4sI  public         35\n    # 1h3iBLl5_u4W0w4ScH7NKxxMP2t23LE8dwlXz7geRjRA  public         33\n    # 1XzgO8nJccx-L9Bnsmecn-n-u0L46OyL7gAkRh0EKzLI  public         5\n\n    # test file 1: 18qODvBsWxVJyp7G3ui5_77rl53D-NEIPunxWRBa882Q\n    # test file 2: 1iLJ6tDs14ACwqsqHskB3YcM1yb1C7xprrsulq0QQbBk\n\n    \"\"\"Shows basic usage of the Drive v3 API.\n    Prints the names and ids of the first 10 files the user has access to.\n    \"\"\"\n    creds = service_account.Credentials.from_service_account_file(\n        SERVICE_ACCOUNT_FILE, scopes=SCOPES)\n\n    service_comments = build('drive', 'v3', credentials=creds)\n    service_replies = build('drive', 'v3', credentials=creds)\n\n\n    # Call the Drive v3 API\n    results_comments_list = service_comments.comments().list(fileId=file_id,\n                                                             fields='comments, nextPageToken',\n                                                             includeDeleted='false',\n                                                             # startModifiedTime='2018-09-01T00:00:00Z',\n                                                             pageSize=100).execute()\n    received_comments = results_comments_list.get('comments',    [])\n    received_comments.reverse()\n    total_comments = 0\n    total_replies = 0\n\n    if not received_comments:\n\n        print('No comments found.')\n    else:\n        for comment in received_comments:\n            comment_created_datetime = comment['createdTime']\n            comment_author = comment['author']\n            comment_id = comment['id']\n            comment_display_name = comment_author['displayName']\n            comment_content = comment['content']\n            total_comments = total_comments + 1\n\n            print(comment_created_datetime,  ' ', comment_display_name, ' ', comment_content)\n\n            results_replies_list = service_replies.replies().list(fileId=file_id,\n                                                                  commentId=comment_id,\n                                                                  fields='replies, nextPageToken',\n                                                                  includeDeleted='false',\n                                                                  pageSize=100).execute()\n            received_replies = results_replies_list.get('replies', [])\n            if not received_replies:\n                print('No replies found.')\n            else:\n                print(' Replies:')\n                for reply in received_replies:\n                    reply_created_time = reply['createdTime']\n                    reply_author = reply['author']\n                    reply_display_name = reply_author['displayName']\n                    reply_content = reply['content']\n                    total_replies = total_replies + 1\n\n                    print(' ', reply_created_time, ' ', reply_display_name, ' ', reply_content)\n                print()\n                print(' Replies for this comment: ', len(received_replies))\n                print()\n        print()\n        print('Total comments: ', total_comments)\n        print('Total replies: ', total_replies)\n        print()\n        # print('Comments type: ', ' ', type(received_comments))\n        # print('Replies type: ', ' ', type(received_comments))\n        # print(type(items))\n        # print(items[0])\n        # print(json.dumps(received_replies)\n        # print(json.dumps(received_replies)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"comments_with_replies.py","file_name":"comments_with_replies.py","file_ext":"py","file_size_in_byte":4147,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"46800372","text":"\"\"\"\nTesting UrlValidator\n\nThere are no comments for each testcase because the name\nof each testcase is very descriptive\n\"\"\"\n\nfrom django.test import TestCase\nfrom sectionproject.urlutils.urlvalidator.urlvalidator import UrlValidator\n\nclass UrlValidatorTest(TestCase):\n    # setup urlvalidator\n    def setUp(self):\n        self.urlValidator = UrlValidator()\n    \n    # wiki example validation, expect true\n    def test_wikiexample(self):\n        urls = ['http://en.wikipedia.org/wiki/Unit_testing#Unit_testing_limitations',\\\n                'http://en.wikipedia.org/wiki/Unit_testing#Language-']\n        \n        for url in urls:\n            self.assertTrue(self.urlValidator.validate(url))\n    \n    # check param input\n    def test_illegalinput(self):\n        with self.assertRaises(AssertionError) as err:\n            self.urlValidator.validate(None)\n            \n        with self.assertRaises(AssertionError) as err:\n            self.urlValidator.validate(123)\n            \n    # check empty string\n    def test_emptystring(self):\n        self.assertFalse(self.urlValidator.validate(''))\n    \n    # scheme\n    def test_correct_incorrect_scheme(self):\n        correct_list = ['http://www.google.com', \\\n                        'ftp://www.google.com', \\\n                        'www.google.com']    \n        for url in correct_list:\n            self.assertTrue(self.urlValidator.validate(url), 'fail at url = ' + url)\n            \n        incorrect_list = ['htp://www.google.com', \\\n                        '://www.google.com', \\\n                        'http:/www.google.com', \\\n                        'http//www.google.com']\n        for url in incorrect_list:\n            self.assertFalse(self.urlValidator.validate(url), 'fail at url = ' + url)\n            \n    # uname pword      \n    def test_correct_incorrect_usernamepassword(self):\n        correct_list = ['http://hunlan:password@www.google.com', \\\n                        'http://www.google.com']\n        for url in correct_list:\n            self.assertTrue(self.urlValidator.validate(url), 'fail at url = ' + url)\n            \n        incorrect_list = ['http://hunlan@www.google.com', \\\n                          'http://hunlan:pass:word@www.google.com', \\\n                          'http://@www.google.com', \\\n                          'http://@@www.google.com']\n        for url in incorrect_list:\n            self.assertFalse(self.urlValidator.validate(url), 'fail at url = ' + url)\n    \n    # dname\n    def test_correct_incorrect_domainname(self):\n        correct_list = ['http://google.com', \\\n                        'http://cs.washington.edu/path', \\\n                        'http://555.com']\n        for url in correct_list:\n            self.assertTrue(self.urlValidator.validate(url), 'fail at url = ' + url)\n            \n        incorrect_list = ['http://.com', \\\n                          'http://localhost:80', \\\n                          'http://cs.wash.ing.ton.edu', \\\n                          'http://www.n*a.com', \\\n                          'http://www.google.cam',\\\n                          'http://',\\\n                          '']\n        for url in incorrect_list:\n            self.assertFalse(self.urlValidator.validate(url), 'fail at url = ' + url)\n            \n    # ipv4\n    def test_correct_incorrect_ipv4(self):\n        correct_list = ['http://127.0.0.1', \\\n                        'http://127.0.0.1:8000/path']\n        for url in correct_list:\n            self.assertTrue(self.urlValidator.validate(url), 'fail at url = ' + url)\n            \n        incorrect_list = ['http://127', \\\n                          'http://...', \\\n                          'http://256.0.0.1', \\\n                          'http://a.a.a.a',\\\n                          'http://1.2.3',\\\n                          'http://1.2.3.4.5']\n        for url in incorrect_list:\n            self.assertFalse(self.urlValidator.validate(url), 'fail at url = ' + url)\n            \n    # port\n    def test_correct_incorrect_port(self):\n        correct_list = ['http://127.0.0.1:8000/url', \\\n                        'http://www.google.com:80/', \\\n                        'http://www.google.com/']\n        for url in correct_list:\n            self.assertTrue(self.urlValidator.validate(url), 'fail at url = ' + url)  \n            \n        incorrect_list = ['http://127.0.0.1:65536', \\\n                          'http://www.google.com:port0', \\\n                          'http://www.google.com:']\n        for url in incorrect_list:\n            self.assertFalse(self.urlValidator.validate(url)) \n            \n    # path\n    def test_correct_incorrect_path(self):\n        correct_list = ['http://127.0.0.1:8000/url//nba/videos///', \\\n                        'http://127.0.0.1:8000/%2b', \\\n                        'http://www.google.com:80/', \\\n                        'http://www.google.com']\n        for url in correct_list:\n            self.assertTrue(self.urlValidator.validate(url), 'fail at url = ' + url)  \n            \n        incorrect_list = ['http://www.google.com:80/something@nba.com', \\\n                          'http://www.google.com/images%', \\\n                          'http://www.google.com/%2x/']\n        for url in incorrect_list:\n            self.assertFalse(self.urlValidator.validate(url)) \n            \n    # query\n    def test_correct_incorrect_query(self):\n        correct_list = ['http://127.0.0.1:8000/url//nba/videos///?nba=cool', \\\n                        'http://127.0.0.1:8000/%2b?key=val1&key2=val2;key3=3#frag', \\\n                        'http://www.google.com:80/path/', \\\n                        'http://www.google.com:80/path/?', \\\n                        'http://www.google.com:80/path?']\n        for url in correct_list:\n            self.assertTrue(self.urlValidator.validate(url), 'fail at url = ' + url)  \n            \n        incorrect_list = ['http://www.google.com?nba', \\\n                          'http://www.google.com/??', \\\n                          'http://www.google.com/?cmm = cmm', \\\n                          'http://www.google.com/?key==value', \\\n                          'http://www.google.com/?1a=1b']\n        for url in incorrect_list:\n            self.assertFalse(self.urlValidator.validate(url)) \n            \n    # fragment\n    def test_correct_incorrect_fragment(self):\n        correct_list = ['http://127.0.0.1:8000/url//nba/videos///?nba=cool#fragment', \\\n                        'http://127.0.0.1:8000/%2b?key=val1&key2=val2;key3=3#_-_', \\\n                        'http://127.0.0.1:8000/%2b?key=val1&key2=val2;key3=3#', \\\n                        'http://www.google.com:80/path/']\n        for url in correct_list:\n            self.assertTrue(self.urlValidator.validate(url), 'fail at url = ' + url)  \n            \n        incorrect_list = ['http://www.google.com?nba#wrong fragment', \\\n                          'http://www.google.com/##']\n        for url in incorrect_list:\n            self.assertFalse(self.urlValidator.validate(url)) \n\n'''\nTest scheme part of url\n'''\nclass SchemeTest(TestCase):\n    def setUp(self):\n        self.urlValidator = UrlValidator()\n        \n    # input: correct url\n    # expected: non-None\n    def test_basicCorrectSituation(self):\n        valid_scheme_url = ['http://www.google.com', \\\n                            'https://www.google.com', \\\n                            'ftp://www.google.com']\n        expected = ['www.google.com', \\\n                    'www.google.com', \\\n                    'www.google.com']\n        for i in range(0, len(expected)):\n            ret = self.urlValidator._isSchemeNameValid(valid_scheme_url[i])\n            self.assertEqual(expected[i], ret)\n    \n    # input: incorrect url\n    # expected: original url\n    def test_wrongSchemeType(self):\n        invalid_scheme_url = ['htp://www.google.com', \\\n                              'www://www.google.com', \\\n                              'ftp:/www.google.com', \\\n                              'http//www.google.com']\n        for url in invalid_scheme_url:\n            ret = self.urlValidator._isSchemeNameValid(url)\n            self.assertEqual(url, ret)\n        \n   # input: no scheme url\n    # expected: original url  \n    def test_inputWithoutSchemeType(self):\n        no_scheme_url = ['', 'www.google.com']\n        for url in no_scheme_url:\n            ret = self.urlValidator._isSchemeNameValid(url)\n            self.assertEqual(url, ret)\n\n'''\nTest username password part of url\n'''\nclass UserPasswordTest(TestCase):\n    def setUp(self):\n        self.urlValidator = UrlValidator()\n    # input: correct username/password\n    # expected: non-None\n    def test_basicCorrectSituation(self):\n        urls = ['hunlan:password@gmail.com']\n        expected = ['gmail.com']\n        for i in range(0, len(expected)):\n            ret = self.urlValidator._isUserPasswordValid(urls[i])\n            self.assertEqual(expected[i], ret)\n            \n    # input: incorrect username/password\n    # expected: None\n    def test_illegalUsernamePassword(self):\n        urls = ['hunlan@gmail.com',\\\n                ':password@gmail.com',\\\n                'hunlan:pass:word@gmail.com',\\\n                'hunlan:****@gmail.com',\\\n                '@gmail.com']\n        for url in urls:\n            ret = self.urlValidator._isUserPasswordValid(url)\n            self.assertEqual(None, ret, 'wrong val at url: ' + url)\n    \n    # input: no username and password\n    # expected: non-None\n    def test_noUsernamePassword(self):\n        urls = ['www.gmail.com']\n        expected = ['www.gmail.com']\n        for i in range(0, len(expected)):\n            ret = self.urlValidator._isUserPasswordValid(urls[i])\n            self.assertEqual(expected[i], ret)      \n        \n'''\nTest domainname part of url\n'''\nclass DomainNameTest(TestCase):\n    def setUp(self):\n        self.urlValidator = UrlValidator()\n        \n    # input: correct input\n    # expected: non-None\n    def test_basicCorrectSituation(self):\n        urls = ['www.google.com/images',\\\n                'google.com',\\\n                'wiki.org/info',\\\n                'cs.washington.edu/cse403']\n        expected = ['/images',\\\n                    '',\\\n                    '/info',\\\n                    '/cse403']\n        for i in range(0, len(expected)):\n            ret = self.urlValidator._isDomainNameValid(urls[i])\n            self.assertEqual(expected[i], ret)\n            \n    # input: incorrect input\n    # expected: None\n    def test_illegalDomainName(self):\n        urls = ['google/images',\\\n                'www.google.google.com',\\\n                'www.g**gle.com',\\\n                'cs.washington.ed/cse403',\\\n                '']\n        for url in urls:\n            ret = self.urlValidator._isDomainNameValid(url)\n            self.assertEqual(None, ret, 'wrong val at url: ' + url)\n            \n'''\nTest IPv4 part of url\n'''\nclass IPv4Test(TestCase):\n    def setUp(self):\n        self.urlValidator = UrlValidator()\n        \n        \n    # input: correct input\n    # expected: non-None\n    def test_basicCorrectSituation(self):\n        urls = ['127.0.0.1:8000/urls',\\\n                '127.0.0.1']\n        expected = [':8000/urls',\\\n                    '']\n        for i in range(0, len(expected)):\n            ret = self.urlValidator._checkAndRemoveIPv4(urls[i])\n            self.assertEqual(expected[i], ret)\n            \n    # input: incorrect input\n    # expected: None\n    def test_illegalIPv4(self):\n        urls = ['127.0.1.',\\\n                '127.0.1', \\\n                '0.0.0.256',\\\n                '0.0.0.-1',\\\n                'a.b.c.d',\\\n                '127.0.0.0.1',\\\n                '123',\\\n                '']\n        for url in urls:\n            ret = self.urlValidator._checkAndRemoveIPv4(url)\n            self.assertEqual(None, ret, 'wrong val at url: ' + url)\n\n'''\nTest Port part of url\n'''\nclass PortTest(TestCase):\n    def setUp(self):\n        self.urlValidator = UrlValidator()\n        \n        \n    # input: correct input\n    # expected: non-None\n    def test_basicCorrectSituation(self):\n        urls = [':8000/urls',\\\n                ':65535',\\\n                ':0000000000000000000000000000000000000000000000065535',\\\n                '']\n        expected = ['/urls',\\\n                    '']\n        for i in range(0, len(expected)):\n            ret = self.urlValidator._isPortNumberValid(urls[i])\n            self.assertEqual(expected[i], ret)\n            \n    # input: incorrect input\n    # expected: None\n    def test_illegalPort(self):\n        urls = ['::',\\\n                ':abc', \\\n                ':-1',\\\n                ':',\\\n                ':65536',\\\n                ':065536']\n        for url in urls:\n            ret = self.urlValidator._isPortNumberValid(url)\n            self.assertEqual(None, ret, 'wrong val at url: ' + url)\n            \n    # input: no port\n    # expected: original url \n    def test_noPort(self):\n        urls = ['/url/paths',\\\n                '']\n        for url in urls:\n            ret = self.urlValidator._isPortNumberValid(url)\n            self.assertEqual(url, ret, 'wrong val at url: ' + url)\n            \n            \n'''\nTest Path part of url\n'''\nclass PathTest(TestCase):\n    def setUp(self):\n        self.urlValidator = UrlValidator()\n        \n        \n    # input: correct input\n    # expected: non-None\n    def test_basicCorrectSituation(self):\n        urls = ['/urls?q=a',\\\n                '//urls//nba/com',\\\n                '/change%2bmy%2bmood',\\\n                '/change_my-mood',\\\n                '/../',\\\n                '/./',\\\n                '']\n        expected = ['?q=a',\\\n                    '',\\\n                    '',\\\n                    '',\\\n                    '',\\\n                    '']\n        for i in range(0, len(expected)):\n            ret = self.urlValidator._isPathValid(urls[i])\n            self.assertEqual(expected[i], ret)\n            \n    # input: incorrect input\n    # expected: None\n    def test_illegalPath(self):\n        urls = ['/change*my*mood',\\\n                '/change%xxmymood', \\\n                '/change%', \\\n                '/change./', \\\n                '/ha ha did this slip the test?']\n        for url in urls:\n            ret = self.urlValidator._isPathValid(url)\n            self.assertEqual(None, ret, 'wrong val at url: ' + url)\n            \n    # input: no path\n    # expected: original url \n    def test_noPath(self):\n        urls = ['?q=a',\\\n                '']\n        for url in urls:\n            ret = self.urlValidator._isPathValid(url)\n            self.assertEqual(url, ret, 'wrong val at url: ' + url)          \n            \n'''\nTest Query part of url\n'''\nclass QueryTest(TestCase):\n    def setUp(self):\n        self.urlValidator = UrlValidator()\n        \n        \n    # input: correct input\n    # expected: non-None\n    def test_basicCorrectSituation(self):\n        urls = ['?q=a#fragment',\\\n                '?a=1&b=2;c=3',\\\n                '?',\\\n                '?#fragment']\n        expected = ['#fragment',\\\n                    '',\\\n                    '',\\\n                    '#fragment']\n        for i in range(0, len(expected)):\n            ret = self.urlValidator._isQueryValid(urls[i])\n            self.assertEqual(expected[i], ret, 'wrong val at url: ' + urls[i])\n            \n    # input: incorrect input\n    # expected: None\n    def test_illegalPath(self):\n        urls = ['?a', \\\n                '?1=2', \\\n                '?a=*', \\\n                '?a=1,b=2']\n        for url in urls:\n            ret = self.urlValidator._isQueryValid(url)\n            self.assertEqual(None, ret, 'wrong val at url: ' + url)\n            \n    # input: no Query\n    # expected: original url \n    def test_noQuery(self):\n        urls = ['#fragment',\\\n                '']\n        for url in urls:\n            ret = self.urlValidator._isQueryValid(url)\n            self.assertEqual(url, ret, 'wrong val at url: ' + url)     \n            \n            \n'''\nTest Query part of url\n'''\nclass FragmentTest(TestCase):\n    def setUp(self):\n        self.urlValidator = UrlValidator()\n        \n        \n    # input: correct input\n    # expected: non-None\n    def test_basicCorrectSituation(self):\n        urls = ['#fragment',\\\n                '#_-_',\\\n                '#']\n        expected = ['',\\\n                    '',\\\n                    '']\n        for i in range(0, len(expected)):\n            ret = self.urlValidator._isFragmentValid(urls[i])\n            self.assertEqual(expected[i], ret, 'wrong val at url: ' + urls[i])\n            \n    # input: incorrect input\n    # expected: None\n    def test_illegalPath(self):\n        urls = ['##',\\\n                '#omg this is a fragment?', \\\n                '#www.google.com']\n        for url in urls:\n            ret = self.urlValidator._isFragmentValid(url)\n            self.assertEqual(None, ret, 'wrong val at url: ' + url)\n            \n    # input: no Fragment\n    # expected: original url \n    def test_noFragment(self):\n        urls = ['']\n        for url in urls:\n            ret = self.urlValidator._isFragmentValid(url)\n            self.assertEqual(url, ret, 'wrong val at url: ' + url)   \n            \n            ","sub_path":"sectionproject/urlutils/urlvalidator/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":17067,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"615906492","text":"import os\nimport traceback\nfrom concurrent.futures import ThreadPoolExecutor\n\nroot_path = os.path.dirname(os.path.realpath(__file__))\n\nprofile_path = os.path.join(root_path, 'profiles')\n\nif not os.path.isdir(profile_path):\n    os.mkdir(profile_path)\n\nexisting_profiles = os.listdir(profile_path)\nfailed_profiles = []\n\nexecutor = ThreadPoolExecutor(max_workers=5)\n\n\ndef get_insta_loader():\n    try:\n        os.system('pip3 install instaloader')\n    except Exception as err:\n        print('There was a problem while setting up instaloader. Is python3 added to the path? '\n              'you can get more info below..')\n        traceback.print_tb(err.__traceback__)\n\n\ndef refresh_profile(name):\n    print('Updating {}'.format(name))\n    try:\n        os.chdir(profile_path)\n        os.system('instaloader --fast-update {}'.format(profile))\n        os.chdir(root_path)\n    except Exception as err:\n        print('Unable to run refresh on profile \"{}\"'.format(profile))\n        traceback.print_tb(err.__traceback__)\n        os.chdir(root_path)\n        failed_profiles.append(profile)\n    print('{} Update completed'.format(name))\n\n\ndef download_profile(name):\n    print('Downloading {}'.format(name))\n    try:\n        os.chdir(profile_path)\n        os.system('instaloader -profile {}'.format(profile))\n        os.chdir(root_path)\n    except Exception as err:\n        print('Unable to run download on profile \"{}\"'.format(profile))\n        traceback.print_tb(err.__traceback__)\n        os.chdir(root_path)\n        failed_profiles.append(profile)\n    print('{} Download completed'.format(name))\n\n\n# Press the green button in the gutter to run the script.\nif __name__ == '__main__':\n    get_insta_loader()\n    with open('profiles.txt', 'r') as profiles:\n        for profile in profiles:\n            if profile.strip() in existing_profiles:\n                executor.submit(refresh_profile, profile.strip())\n            else:\n                executor.submit(download_profile, profile.strip())\n    executor.shutdown(wait=True)\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2015,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"112641744","text":"\"\"\"\nPlot multidimensional uniform time series.\n\"\"\"\n\nfrom __future__ import division\n\nfrom itertools import izip\n\nimport numpy as np\n\nfrom .. import _data_obj as _dta\nfrom .._names import INTERPOLATE_CHANNELS\nfrom . import _base\nfrom ._base import _EelFigure\n\n\nclass _plt_im(object):\n\n    _aspect = 'auto'\n\n    def __init__(self, ax, ndvar, overlay, cmaps, vlims, contours, extent,\n                 interpolation, mask=None):\n        self.ax = ax\n        im_kwa = _base.find_im_args(ndvar, overlay, vlims, cmaps)\n        self._meas = meas = ndvar.info.get('meas', _base.default_meas)\n        self._contours = contours.get(meas, None)\n        self._data = data = self._data_from_ndvar(ndvar)\n        self._extent = extent\n        self._mask = mask\n\n        if im_kwa is not None:\n            self.im = ax.imshow(data, origin='lower', aspect=self._aspect,\n                                extent=extent, interpolation=interpolation,\n                                **im_kwa)\n            self._cmap = im_kwa['cmap']\n            if mask is not None:\n                self.im.set_clip_path(mask)\n        else:\n            self.im = None\n\n        # draw flexible parts\n        self._contour_h = None\n        self._draw_contours()\n\n    def _data_from_ndvar(self, ndvar):\n        raise NotImplementedError\n\n    def _draw_contours(self):\n        if self._contour_h:\n            for c in self._contour_h.collections:\n                c.remove()\n            self._contour_h = None\n\n        if self._contours:\n            h = self.ax.contour(self._data, aspect=self._aspect, origin='lower',\n                                extent=self._extent, **self._contours)\n            if self._mask is not None:\n                for c in h.collections:\n                    c.set_clip_path(self._mask)\n            self._contour_h = h\n\n    def add_contour(self, meas, level, color):\n        if self._meas == meas:\n            self._contours[level] = color\n            self._draw_contours()\n\n    def get_kwargs(self):\n        \"Get the arguments required for plotting the im\"\n        if self.im:\n            vmin, vmax = self.im.get_clim()\n            args = dict(cmap=self._cmap, vmin=vmin, vmax=vmax)\n        else:\n            args = {}\n        return args\n\n    def set_cmap(self, cmap, meas=None):\n        if (self.im is not None) and (meas is None or meas == self._meas):\n            self.im.set_cmap(cmap)\n            self._cmap = cmap\n\n    def set_data(self, ndvar):\n        data = self._data_from_ndvar(ndvar)\n        if self.im is not None:\n            self.im.set_data(data)\n        self._data = data\n        self._draw_contours()\n\n    def set_vlim(self, vmax, meas, vmin):\n        if self.im is None:\n            return\n        elif (meas is not None) and (self._meas != meas):\n            return\n\n        if vmax is None:\n            _, vmax = self.im.get_clim()\n\n        vmin, vmax = _base.fix_vlim_for_cmap(vmin, vmax, self._cmap)\n        self.im.set_clim(vmin, vmax)\n\n\nclass _plt_im_array(_plt_im):\n\n    def __init__(self, ax, ndvar, overlay, dims=('time', 'sensor'),\n                 extent=None, interpolation=None, vlims={}, cmaps={},\n                 contours={}):\n        self._dims = dims\n        _plt_im.__init__(self, ax, ndvar, overlay, cmaps, vlims, contours,\n                         extent, interpolation)\n\n    def _data_from_ndvar(self, ndvar):\n        data = ndvar.get_data(self._dims)\n        if data.ndim > 2:\n            assert data.shape[0] == 1\n            data = data[0]\n        return data\n\n\nclass _ax_im_array(object):\n    def __init__(self, ax, layers, x='time', title=None, interpolation=None,\n                 vlims={}, cmaps={}, contours={}):\n        \"\"\"\n        plots segment data as im\n\n        define a colorspace by supplying one of those kwargs: ``colorspace`` OR\n        ``p`` OR ``vmax``\n\n        \"\"\"\n        self.ax = ax\n        self.data = layers\n        self.layers = []\n        epoch = layers[0]\n\n        xdim = epoch.get_dim(x)\n        if epoch.ndim == 2:\n            xdim_i = epoch.dimnames.index(x)\n            ydim_i = {1:0, 0:1}[xdim_i]\n            y = epoch.dimnames[ydim_i]\n        else:\n            err = (\"Need 2 dimensions, got %i\" % epoch.ndim)\n            raise ValueError(err)\n\n        ydim = epoch.get_dim(y)\n        if not ydim.adjacent:\n            ydim = _dta.Var(np.arange(len(ydim)), y)\n\n        # plot\n        overlay = False\n        extent = (xdim[0], xdim[-1], ydim[0], ydim[-1])\n        for l in layers:\n            p = _plt_im_array(ax, l, overlay, (y, x), extent, interpolation,\n                              vlims, cmaps, contours)\n            self.layers.append(p)\n            overlay = True\n\n        # title\n        if title is None:\n            title = _base.str2tex(epoch.name)\n        ax.set_title(title)\n\n    def add_contour(self, meas, level, color):\n        for l in self.layers:\n            l.add_contour(meas, level, color)\n\n    def set_cmap(self, cmap, meas=None):\n        \"\"\"Change the colormap in the array plot\n\n        Parameters\n        ----------\n        cmap : str | colormap\n            New colormap.\n        meas : None | str\n            Measurement to which to apply the colormap. With None, it is\n            applied to all.\n        \"\"\"\n        for l in self.layers:\n            l.set_cmap(cmap, meas)\n\n    def set_data(self, layers):\n        self.data = layers\n        for l, p in zip(layers, self.layers):\n            p.set_data(l)\n\n    def set_vlim(self, vmax=None, meas=None, vmin=None):\n        for l in self.layers:\n            l.set_vlim(vmax, meas, vmin)\n\n\nclass Array(_EelFigure):\n    \"\"\"Plot UTS data to a rectangular grid.\n\n    Parameters\n    ----------\n    epochs : NDVar\n        If data has only 1 dimension, the x-axis defines epochs.\n    Xax : None | categorial\n        Create a separate plot for each cell in this model.\n    xlabel, ylabel : bool | str\n        Labels for x- and y-axis; the default is determined from the data.\n    xticklabels : bool\n        Add tick-labels to the x-axis (default True).\n    ds : None | Dataset\n        If a Dataset is provided, ``epochs`` and ``Xax`` can be specified\n        as strings.\n    x : str\n        Dimension to plot on the x axis (default 'time').\n    vmax : scalar\n        Upper limits for the colormap.\n    vmin : scalar\n        Lower limit for the colormap.\n    tight : bool\n        Use matplotlib's tight_layout to expand all axes to fill the figure\n        (default True)\n    title : None | string\n        Figure title.\n    \"\"\"\n    def __init__(self, epochs, Xax=None, xlabel=True, ylabel=True,\n                 xticklabels=True, ds=None, x='time', vmax=None, vmin=None,\n                 *args, **kwargs):\n        epochs, (xdim, ydim) = _base.unpack_epochs_arg(epochs, (x, None), Xax, ds)\n\n        nax = len(epochs)\n        _EelFigure.__init__(self, \"Array Plot\", nax, 4, 2, *args, **kwargs)\n\n        self.plots = []\n        vlims = _base.find_fig_vlims(epochs, vmax, vmin)\n        for i, ax, layers in zip(xrange(nax), self._axes, epochs):\n            p = _ax_im_array(ax, layers, x, vlims=vlims)\n            self.plots.append(p)\n\n        self._configure_xaxis_dim(xdim, xlabel, xticklabels)\n        self._configure_yaxis_dim(ydim, ylabel)\n        self._show()\n\n    def add_contour(self, meas, level, color='k'):\n        \"\"\"Add a contour line\n\n        Parameters\n        ----------\n        meas : str\n            The measurement for which to add a contour line.\n        level : scalar\n            The value at which to draw the contour.\n        color : matplotlib color\n            The color of the contour line.\n        \"\"\"\n        for p in self.plots:\n            p.add_contour(meas, level, color)\n        self.draw()\n\n    def set_cmap(self, cmap, meas=None):\n        \"\"\"Change the colormap in the array plots\n\n        Parameters\n        ----------\n        cmap : str | colormap\n            New colormap.\n        meas : None | str\n            Measurement to which to apply the colormap. With None, it is\n            applied to all.\n        \"\"\"\n        for p in self.plots:\n            p.set_cmap(cmap, meas)\n        self.draw()\n\n    def set_vlim(self, vmax=None, meas=None, vmin=None):\n        for p in self.plots:\n            p.set_vlim(vmax, meas, vmin)\n        self.draw()\n\n\nclass _plt_utsnd:\n\n    def __init__(self, ax, epoch, linedim, sensors=None, *args, **kwargs):\n        \"\"\"\n        uts plot for a single epoch\n\n        Parameters\n        ----------\n        ax : matplotlib axes\n            Target axes.\n        epoch : NDVar (sensor by time)\n            Epoch to plot.\n        sensors : None | True | numpy index\n            The sensors to plot (None or True -> all sensors).\n        others :\n            Matplotlib plot() arguments.\n        \"\"\"\n        if sensors is not None and sensors is not True:\n            epoch = epoch.sub(sensor=sensors)\n\n        self._dims = (linedim, 'time')\n        kwargs['label'] = epoch.name\n        self.lines = ax.plot(epoch.time.x, epoch.get_data(('time', linedim)),\n                             *args, **kwargs)\n\n        for y, kwa in _base.find_uts_hlines(epoch):\n            ax.axhline(y, **kwa)\n\n        self.epoch = epoch\n        self._sensors = sensors\n\n    def remove(self):\n        while self.lines:\n            self.lines.pop().remove()\n\n    def set_ydata(self, epoch):\n        if self._sensors:\n            epoch = epoch.sub(sensor=self._sensors)\n        for line, y in izip(self.lines, epoch.get_data(self._dims)):\n            line.set_ydata(y)\n\n\nclass _ax_butterfly(object):\n\n    def __init__(self, ax, layers, linedim, sensors=None, title='{name}',\n                 color=None, vlims={}):\n        \"\"\"\n        Parameters\n        ----------\n        vmin, vmax: None | scalar\n            Y axis limits.\n        \"\"\"\n        self.ax = ax\n        self.data = layers\n        self.layers = []\n        self._xvalues = []  # values on the x axis\n        self._meas = None\n\n        vmin, vmax = _base.find_uts_ax_vlim(layers, vlims)\n\n        name = ''\n        overlay = False\n        for l in layers:\n            uts_args = _base.find_uts_args(l, overlay, color)\n            if uts_args is None:\n                continue\n            overlay = True\n\n            # plot\n            h = _plt_utsnd(ax, l, linedim, sensors, **uts_args)\n            self.layers.append(h)\n            if not name:\n                name = getattr(l, 'name', '')\n\n            self._xvalues = np.union1d(self._xvalues, l.time.x)\n\n        # axes decoration\n        ax.set_xlim(min(l.epoch.time[0] for l in self.layers),\n                    max(l.epoch.time[-1] for l in self.layers))\n\n    #    ax.yaxis.set_offset_position('right')\n        ax.yaxis.offsetText.set_va('top')\n\n        if isinstance(title, str):\n            ax.set_title(title.format(name=name))\n\n        self.set_vlim(vmax, vmin)\n\n    def set_vlim(self, vmax=None, vmin=None):\n        if vmin is None and vmax is not None:\n            vmin = -vmax\n        self.ax.set_ylim(vmin, vmax)\n        vmin, vmax = self.ax.get_ylim()\n        self.vmin = vmin\n        self.vmax = vmax\n\n\nclass Butterfly(_EelFigure):\n    \"\"\"Butterfly plot for NDVars\n\n    Parameters\n    ----------\n    epochs : (list of) NDVar\n        Data to plot.\n    Xax : None | categorial\n        Create a separate plot for each cell in this model.\n    sensors: None or list of sensor IDs\n        sensors to plot (``None`` = all)\n    axtitle : str | None\n        Title to plot for axes. Default is the NDVar names.\n    xlabel : str | None\n        X-axis labels. By default the label is inferred from the data.\n    ylabel : str | None\n        Y-axis labels. By default the label is inferred from the data.\n    xticklabels : bool\n        Add tick-labels to the x-axis (default True).\n    color : matplotlib color\n        default (``None``): use segment color if available, otherwise\n        black; ``True``: alternate colors (mpl default)\n    ds : None | Dataset\n        If a Dataset is provided, ``epochs`` and ``Xax`` can be specified\n        as strings.\n    tight : bool\n        Use matplotlib's tight_layout to expand all axes to fill the figure\n        (default True)\n    title : None | string\n        Figure title.\n    \"\"\"\n    def __init__(self, epochs, Xax=None, sensors=None, axtitle='{name}',\n                 xlabel=True, ylabel=True, xticklabels=True, color=None,\n                 ds=None, *args, **kwargs):\n        epochs, (xdim, linedim) = _base.unpack_epochs_arg(epochs, ('time', None),\n                                                          Xax, ds)\n        _EelFigure.__init__(self, 'Butterfly Plot', len(epochs), 4, 2, *args,\n                            **kwargs)\n        self._configure_xaxis_dim(xdim, xlabel, xticklabels)\n        self._configure_yaxis(epochs[0][0], ylabel)\n\n        self.plots = []\n        vlims = _base.find_fig_vlims(epochs)\n        for ax, layers in zip(self._axes, epochs):\n            h = _ax_butterfly(ax, layers, linedim, sensors, axtitle, color,\n                              vlims)\n            self.plots.append(h)\n\n        self._show()\n\n    def set_vlim(self, vmax=None, vmin=None):\n        for p in self.plots:\n            p.set_vlim(vmax, vmin)\n        self.draw()\n\n\nclass _ax_bfly_epoch:\n    def __init__(self, ax, epoch, mark=None, state=True, color='k', lw=0.2,\n                 mcolor='r', mlw=0.8, antialiased=True, vlims={}):\n        \"\"\"Specific plot for showing a single sensor by time epoch\n\n        Parameters\n        ----------\n        ax : mpl Axes\n            Plot target axes.\n        epoch : NDVar\n            Sensor by time epoch.\n        mark : dict {int: mpl color}\n            Channel: color dict of channels with custom color.\n        color : mpl color\n            Color for unmarked traces.\n        lw : scalar\n            Sensor trace plot Line width (default 0.5).\n        mlw : scalar\n            Marked sensor plot line width (default 1).\n        \"\"\"\n        self.lines = _plt_utsnd(ax, epoch, 'sensor', color=color, lw=lw,\n                                antialiased=antialiased)\n\n        self.ax = ax\n        self.epoch = epoch\n        self._state_h = []\n        self._ylim = _base.find_uts_ax_vlim([epoch], vlims)\n        self._update_ax_lim()\n        self._styles = {None: {'color': color, 'lw': lw, 'ls': '-',\n                               'zorder': 2},\n                        'mark': {'color': mcolor, 'lw': mlw, 'ls': '-',\n                                 'zorder': 10},\n                        INTERPOLATE_CHANNELS: {'color': 'b', 'lw': 1.2,\n                                               'ls': ':', 'zorder': 6}}\n        self._marked = {'mark': set(), INTERPOLATE_CHANNELS: set()}\n        if mark:\n            self.set_marked('mark', mark)\n\n        # create initial plots\n        self.set_state(state)\n\n    def set_data(self, epoch):\n        self.lines.set_ydata(epoch)\n\n    def _update_ax_lim(self):\n        self.ax.set_xlim(self.epoch.time[0], self.epoch.time[-1])\n        ylim = self._ylim\n        if ylim:\n            if np.isscalar(ylim):\n                self.ax.set_ylim(-ylim, ylim)\n            else:\n                y_min, y_max = ylim\n                self.ax.set_ylim(y_min, y_max)\n\n    def set_marked(self, kind, sensors):\n        \"\"\"Set the channels which should be marked for a specific style\n\n        Parameters\n        ----------\n        kind : str\n            The style.\n        sensors : collection of int\n            Channel index for the channels to mark as ``kind``.\n        \"\"\"\n        old = self._marked[kind]\n        new = self._marked[kind] = set(sensors)\n        if not old and not new:\n            return\n        # mark new channels\n        for i in new.difference(old):\n            self.lines.lines[i].update(self._styles[kind])\n        # find channels to unmark\n        old.difference_update(new)\n        if not old:\n            return\n        # possible alternate style\n        if kind == 'mark':\n            other_kind = INTERPOLATE_CHANNELS\n        else:\n            other_kind = 'mark'\n        # reset old channels\n        for i in old:\n            if i in self._marked[other_kind]:\n                self.lines.lines[i].update(self._styles[other_kind])\n            else:\n                self.lines.lines[i].update(self._styles[None])\n\n    def set_state(self, state):\n        \"Set the state (True=accept / False=reject)\"\n        if state:\n            while self._state_h:\n                h = self._state_h.pop()\n                h.remove()\n        else:\n            if not self._state_h:\n                h1 = self.ax.plot([0, 1], [0, 1], color='r', linewidth=1,\n                                  transform=self.ax.transAxes)\n                h2 = self.ax.plot([0, 1], [1, 0], color='r', linewidth=1,\n                                  transform=self.ax.transAxes)\n                self._state_h.extend(h1 + h2)\n\n    def set_ylim(self, ylim):\n        self._ylim = ylim\n        self._update_ax_lim()\n","sub_path":"eelbrain/plot/_utsnd.py","file_name":"_utsnd.py","file_ext":"py","file_size_in_byte":16789,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"206758967","text":"\"\"\"\nSupport for deCONZ binary sensor.\n\nFor more details about this component, please refer to the documentation at\nhttps://home-assistant.io/components/binary_sensor.deconz/\n\"\"\"\nfrom homeassistant.components.binary_sensor import BinarySensorDevice\nfrom homeassistant.components.deconz.const import (\n    ATTR_DARK, ATTR_ON, CONF_ALLOW_CLIP_SENSOR, DOMAIN as DATA_DECONZ,\n    DECONZ_DOMAIN)\nfrom homeassistant.const import ATTR_BATTERY_LEVEL\nfrom homeassistant.core import callback\nfrom homeassistant.helpers.device_registry import CONNECTION_ZIGBEE\nfrom homeassistant.helpers.dispatcher import async_dispatcher_connect\n\nDEPENDENCIES = ['deconz']\n\n\nasync def async_setup_platform(hass, config, async_add_entities,\n                               discovery_info=None):\n    \"\"\"Old way of setting up deCONZ binary sensors.\"\"\"\n    pass\n\n\nasync def async_setup_entry(hass, config_entry, async_add_entities):\n    \"\"\"Set up the deCONZ binary sensor.\"\"\"\n    @callback\n    def async_add_sensor(sensors):\n        \"\"\"Add binary sensor from deCONZ.\"\"\"\n        from pydeconz.sensor import DECONZ_BINARY_SENSOR\n        entities = []\n        allow_clip_sensor = config_entry.data.get(CONF_ALLOW_CLIP_SENSOR, True)\n        for sensor in sensors:\n            if sensor.type in DECONZ_BINARY_SENSOR and \\\n               not (not allow_clip_sensor and sensor.type.startswith('CLIP')):\n                entities.append(DeconzBinarySensor(sensor))\n        async_add_entities(entities, True)\n\n    hass.data[DATA_DECONZ].listeners.append(\n        async_dispatcher_connect(hass, 'deconz_new_sensor', async_add_sensor))\n\n    async_add_sensor(hass.data[DATA_DECONZ].api.sensors.values())\n\n\nclass DeconzBinarySensor(BinarySensorDevice):\n    \"\"\"Representation of a binary sensor.\"\"\"\n\n    def __init__(self, sensor):\n        \"\"\"Set up sensor and add update callback to get data from websocket.\"\"\"\n        self._sensor = sensor\n\n    async def async_added_to_hass(self):\n        \"\"\"Subscribe sensors events.\"\"\"\n        self._sensor.register_async_callback(self.async_update_callback)\n        self.hass.data[DATA_DECONZ].deconz_ids[self.entity_id] = \\\n            self._sensor.deconz_id\n\n    async def async_will_remove_from_hass(self) -> None:\n        \"\"\"Disconnect sensor object when removed.\"\"\"\n        self._sensor.remove_callback(self.async_update_callback)\n        self._sensor = None\n\n    @callback\n    def async_update_callback(self, reason):\n        \"\"\"Update the sensor's state.\n\n        If reason is that state is updated,\n        or reachable has changed or battery has changed.\n        \"\"\"\n        if reason['state'] or \\\n           'reachable' in reason['attr'] or \\\n           'battery' in reason['attr'] or \\\n           'on' in reason['attr']:\n            self.async_schedule_update_ha_state()\n\n    @property\n    def is_on(self):\n        \"\"\"Return true if sensor is on.\"\"\"\n        return self._sensor.is_tripped\n\n    @property\n    def name(self):\n        \"\"\"Return the name of the sensor.\"\"\"\n        return self._sensor.name\n\n    @property\n    def unique_id(self):\n        \"\"\"Return a unique identifier for this sensor.\"\"\"\n        return self._sensor.uniqueid\n\n    @property\n    def device_class(self):\n        \"\"\"Return the class of the sensor.\"\"\"\n        return self._sensor.sensor_class\n\n    @property\n    def icon(self):\n        \"\"\"Return the icon to use in the frontend.\"\"\"\n        return self._sensor.sensor_icon\n\n    @property\n    def available(self):\n        \"\"\"Return True if sensor is available.\"\"\"\n        return self._sensor.reachable\n\n    @property\n    def should_poll(self):\n        \"\"\"No polling needed.\"\"\"\n        return False\n\n    @property\n    def device_state_attributes(self):\n        \"\"\"Return the state attributes of the sensor.\"\"\"\n        from pydeconz.sensor import PRESENCE\n        attr = {}\n        if self._sensor.battery:\n            attr[ATTR_BATTERY_LEVEL] = self._sensor.battery\n        if self._sensor.on is not None:\n            attr[ATTR_ON] = self._sensor.on\n        if self._sensor.type in PRESENCE and self._sensor.dark is not None:\n            attr[ATTR_DARK] = self._sensor.dark\n        return attr\n\n    @property\n    def device_info(self):\n        \"\"\"Return a device description for device registry.\"\"\"\n        if (self._sensor.uniqueid is None or\n                self._sensor.uniqueid.count(':') != 7):\n            return None\n        serial = self._sensor.uniqueid.split('-', 1)[0]\n        bridgeid = self.hass.data[DATA_DECONZ].api.config.bridgeid\n        return {\n            'connections': {(CONNECTION_ZIGBEE, serial)},\n            'identifiers': {(DECONZ_DOMAIN, serial)},\n            'manufacturer': self._sensor.manufacturer,\n            'model': self._sensor.modelid,\n            'name': self._sensor.name,\n            'sw_version': self._sensor.swversion,\n            'via_hub': (DECONZ_DOMAIN, bridgeid),\n        }\n","sub_path":"homeassistant/components/binary_sensor/deconz.py","file_name":"deconz.py","file_ext":"py","file_size_in_byte":4848,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"420575231","text":"\ndef solution(li):\n    for i in range(0, len(li)-1):\n        for j in range(i+1, len(li)):\n            check = li[:]\n            check.remove(li[i])\n            check.remove(li[j])\n            if sum(check) == 100:\n                return check\n\nli = []\n\nfor i in range(9):\n    temp = int(input())\n    li.append(temp)\n\nres = solution(li)\nres.sort()\nfor elem in res:\n    print(elem)","sub_path":"sourcecode/backjoon_2309.py","file_name":"backjoon_2309.py","file_ext":"py","file_size_in_byte":380,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"310096144","text":"kilometers = float(input('Hoeveel kilometer moet u rijzen?'))\nleeftijd = int(input('Wat is uw leeftijd?'))\nweekendrit = False\n\nwhile True:\n    answer = input('Is het een weekendrit? (ja/nee)')\n    if answer in ('ja', 'nee'):\n        break\n    print('Verkeerd antwoord')\nif answer == 'ja':\n    weekendrit = True\nelif answer == 'nee':\n    weekendrit = False\nelse:\n    print('Verkeerd antwoord')\n\nprijsonder50km = 0.80\nprijsboven50km = 0.60\nstandaardprijs = 15\n\n\ndef standaardprijs(afstandKM):\n\n    totaleprijs = 0\n\n    if afstandKM > 0 < 50:\n        totaleprijs = afstandKM*prijsonder50km\n\n    elif afstandKM > 0 < 50:\n        totaleprijs = afstandKM*prijsboven50km+standaardprijs\n\n    elif afstandKM < 0:\n        totaleprijs = 0\n\n    return totaleprijs\n\n\ndef ritprijs(leeftijd, weekendrit, afstandKM):\n    totaleprijs = standaardprijs(afstandKM)\n\n    if weekendrit == False and leeftijd < 12 or leeftijd > 62:\n        totaleprijs = (totaleprijs * 30) / 100\n        print('De prijs van deze rit is €', totaleprijs)\n    elif weekendrit == True and leeftijd < 12 or leeftijd > 62:\n        totaleprijs = (totaleprijs * 35) / 100\n        print('De prijs van deze rit is €', totaleprijs)\n    elif weekendrit == False and leeftijd > 12 or leeftijd < 62:\n        print('De prijs van deze rit is €', totaleprijs)\n    elif weekendrit == True and leeftijd > 12 or leeftijd < 62:\n        totaleprijs = (totaleprijs * 40) / 100\n        print('De prijs van deze rit is €', totaleprijs)\n\nritprijs(leeftijd, weekendrit, kilometers)","sub_path":"NS app/ns.py","file_name":"ns.py","file_ext":"py","file_size_in_byte":1522,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"547898022","text":"#!/usr/bin/python\n# -*- coding:utf-8 -*- \n'''\n作者:jia.zhou@aliyun.com\n创建时间:2019-11-09 上午10:58\n'''\nimport datetime\nimport getopt\n# !/usr/bin/python\n# -*- coding:utf-8 -*-\nimport os\nimport sys\n\nimport MySQLdb\nimport xlwt\n\n###############################################################################\nMSSQL_HOST = '192.168.5.105'\nMSSQL_PORT = 3306\nMSSQL_USER = 'root'\nMSSQL_PSWD = 'zunjiazichan123'\nMSSQL_DB = 'jcbms'\nMSSQL_CHARSET = 'utf8'\nMSSQL_CONN = None\nISINCODE_A = {}\nISINCODE_HK = {}\nISINCODE_US = {}\nISINCODE = {}\n\n\n###############################################################################\ndef initMssqlConnection():\n    global MSSQL_CONN\n    MSSQL_CONN = MySQLdb.connect(\n        host=MSSQL_HOST,\n        port=MSSQL_PORT,\n        user=MSSQL_USER,\n        passwd=MSSQL_PSWD,\n        db=MSSQL_DB,\n        charset=MSSQL_CHARSET)\n\n\ndef initIsInCode():\n    con = MySQLdb.connect(host=\"192.168.5.105\", user=\"root\", passwd=\"zunjiazichan123\", db=\"jy_uts\", charset='utf8')\n    cursor = con.cursor()\n    sql_hk = '''\n        SELECT\n            CODE as code,\n            IFNULL(ISIN,\"\") AS isin_code\n        FROM\n            hk_secumain\n        GROUP BY\n            CODE,\n            ISIN\n    '''\n    cursor.execute(sql_hk)\n    retHK = cursor.fetchall()\n    for i in retHK: ISINCODE_HK[i[0]] = i[1]\n\n    sql_a = '''\n        SELECT\n            CODE as code,\n            IFNULL(ISIN,\"\") AS isin_code\n        FROM\n            secumain\n        GROUP BY\n            CODE,\n            ISIN\n    '''\n    cursor.execute(sql_a)\n    retA = cursor.fetchall()\n    for i in retA: ISINCODE_A[i[0]] = i[1]\n    sql_us = '''\n        SELECT\n            a. CODE as code,\n            b.isin as isin_code\n        FROM\n            mv_us_cl a\n        LEFT JOIN sym_isin_v1 b ON a.fsym_id_s = b.fsym_id\n        ORDER BY\n            a. CODE\n    '''\n    cursor.execute(sql_us)\n    retUS = cursor.fetchall()\n    for i in retUS:\n        ISINCODE_US[i[0]] = i[1]\n    global ISINCODE\n    ISINCODE = {\"CNY\": ISINCODE_A, \"HKD\": ISINCODE_HK, \"USD\": ISINCODE_US}\n\n\ndef select_Mssql(cursor, sqlStr, isListNotDict=True):\n    cursor.execute(sqlStr)\n    head = [col[0] for col in cursor.description]\n    if isListNotDict:\n        results = cursor.fetchall()\n        results = (tuple(head),) + results\n        # results.insert(0, head)\n    else:\n        results = [dict(zip(head, data)) for data in cursor.fetchall()]\n    return results\n\n\ndef saveToFile(wbk, allLine, filename, mode, encoding, skipFirstLine, Sheet):\n    '''\n    mode: a(append,附加), w(清空文件内容然后写入)\n    encoding = 'utf_8'或'utf8'等.\n    skipFirstLine = True\n    '''\n    assert type(allLine) is tuple\n    if 0 < len(allLine):\n        assert type(allLine[0]) in (list, tuple)\n        if skipFirstLine:\n            allLine = allLine[1:]\n    dirName = os.path.dirname(filename)\n    if dirName and (not os.path.exists(dirName)):\n        os.makedirs(dirName)\n\n    # 新建一个名为Sheet1的excel sheet。此处的cell_overwrite_ok =True是为了能对同一个单元格重复操作。\n    sheet = wbk.add_sheet(Sheet, cell_overwrite_ok=True)\n\n    for i in range(len(allLine)):\n        # 对allLine的每个子元素作遍历，\n        for j in range(len(allLine[i])):\n            # 将每一行的每个元素按行号i,列号j,写入到excel中。\n            tmp = allLine[i][j]\n            if isinstance(tmp, datetime.date):\n                tmp = str(tmp)\n            elif isinstance(tmp,bytes):\n                tmp = str(tmp, encoding=\"utf-8\")\n            try:\n                sheet.write(i, j, tmp)\n            except Exception as e:\n                print(e)\n    wbk.save(file_name)\n\n\ndef bos_DailyTrades(wbk, file_path, from_date, to_date, user):\n    '''DailyTrades'''\n    sqlStr = '''\n        SELECT\n            RIGHT (trade_id, 8) AS 'Ref.No',\n            process_date AS 'Tran.Date',\n            a.settle_date AS 'Settle Date',\n            CASE\n        WHEN buy_sell = 1 THEN\n            'Buy'\n        WHEN buy_sell = 2 THEN\n            'Sell'\n        END AS 'Type',\n         CONCAT(\n            a.product_id,\n            ':',\n            CASE\n        WHEN market_id = 1 THEN\n            'HK'\n        WHEN market_id = 2 THEN\n            'US'\n        WHEN market_id = 4 THEN\n            'SHA'\n        WHEN market_id = 8 THEN\n            'SZA'\n        END\n        ) AS 'Product',\n         b.external_code AS 'Isin Code',\n         a.product_description AS 'Name',\n         avg_price AS 'Avg.Price',\n         quantity AS 'Quantity',\n         trade_amount AS 'Trade Amount',\n         charges AS 'Mkt Charges',\n         commission AS 'Commission',\n         net_amount AS 'Net Amount'\n        FROM\n            (\n                SELECT\n                    *\n                FROM\n                    account_trade\n                WHERE\n                    account_id = '{user}'\n                AND process_date >= '{from_date}'\n                AND process_date <= '{to_date}'\n                AND STATUS = 'Confirmed'\n                ORDER BY\n                    trade_id ASC\n            ) a\n        LEFT JOIN product b ON a.product_id = b.product_id\n        AND a.market_id = b.list_market_id\n    '''.format(from_date=from_date, to_date=to_date, user = user)\n\n    allLine = select_Mssql(MSSQL_CONN.cursor(), sqlStr, True)\n    allLineT = ()\n    marketCcy = {\"SHA\": ISINCODE_A, \"SZA\": ISINCODE_A, \"HK\": ISINCODE_HK, \"US\": ISINCODE_US}\n    for i, j in enumerate(allLine):\n        if i == 0:\n            allLineT = allLineT + (j,)\n        else:\n            codeMarket = j[4].split(\":\")\n            code = codeMarket[0]\n            market = codeMarket[1]\n            isinCode = marketCcy[market][code]\n            allLineT = allLineT + (\n            (j[0], j[1], j[2], j[3], j[4], isinCode, j[6], j[7], j[8], j[9], j[10], j[11], j[12]),)\n    saveToFile(wbk, allLineT, file_path, 'w', 'utf8', False, 'Transactions')\n\n\ndef bos_PortfolioSummary(wbk, file_path, to_date, user):\n    '''Portfolio Summary'''\n    sqlStr = '''\n        SELECT\n            CASE\n        WHEN a.currency = 2 THEN\n            'HKD'\n        WHEN a.currency = 4 THEN\n            'USD'\n        WHEN a.currency = 1 THEN\n            'CNY'\n        END AS Ccy,\n         settle_balance AS 'Today Balance',\n         unsettled_amount AS 'Pendong Settlement',\n         balance_avail AS 'Net Balance',\n         market_value AS Equity,\n         0 AS Bonds,\n         0 AS funds,\n         0 AS Others,\n         market_value AS 'Total Investment',\n         b.exchange_rate AS 'RATE',\n         (balance_avail + market_value) * b.exchange_rate AS 'Protfolio Value HKD'\n        FROM\n            (\n                SELECT\n                    *\n                FROM\n                    account_balance\n                WHERE\n                    account_id = '{user}'\n                AND process_date = '{to_date}'\n            ) a\n        LEFT JOIN currency_history b ON a.process_date = b.process_date\n        AND a.currency = b.currency;\n    '''.format(user=user, to_date=to_date)\n    allLine = select_Mssql(MSSQL_CONN.cursor(), sqlStr, True)\n    saveToFile(wbk, allLine, file_path, 'w', 'utf8', False, 'Portfolio Summary')\n\n\ndef bos_Position(wbk, file_path, to_date, user):\n    '''Position'''\n    sqlStr = '''\n        SELECT\n            b.product_id as 'Code',\n            b.external_code as 'Isin Code' ,\n            CASE\n        WHEN a.currency = 2 THEN\n            'HKD'\n        WHEN a.currency = 4 THEN\n            'USD'\n        WHEN a.currency = 1 THEN\n            'CNY'\n        END AS Ccy,\n         b.product_description as 'Name',\n         net_quantity as 'Today Balance',\n         unsettled_quantity as 'Pending Settle',\n         nominee_quantity as 'Net Balance',\n         closing_price as 'Closing Price',\n         market_value as 'Market Value',\n         0 as 'Margin',\n         0 as 'Marginable Value'\n        FROM\n            (\n                SELECT\n                    *\n                FROM\n                    account_position\n                WHERE\n                    process_date = '{to_date}'\n                AND account_id = '{user}'\n                AND net_quantity > 0\n            ) a\n        LEFT JOIN product b ON a.product_id = b.product_id\n        AND a.market_id = b.list_market_id\n        ORDER BY\n            Ccy,\n            a.product_id ASC\n    '''.format(to_date=to_date, user=user)\n\n    allLine = select_Mssql(MSSQL_CONN.cursor(), sqlStr, True)\n    allLineT = ()\n    for i, j in enumerate(allLine):\n        if i == 0:\n            allLineT = allLineT + (j,)\n        else:\n            ccy = j[2]\n            code = j[0]\n            isinCode = ISINCODE[ccy][code]\n            allLineT = allLineT + ((j[0], isinCode, j[2], j[3], j[4], j[5], j[6], j[7], j[8], j[9], j[10]),)\n    saveToFile(wbk, allLineT, file_path, 'a', 'utf8', False, 'Position')\n\n\ndef bos_corpaction(wbk, file_path, from_date, to_date, user):\n    '''CA'''\n    sqlStr = '''\n        SELECT\n            c.product_id AS 'Code',\n            c.product_description AS 'Name',\n            b.bc_quantity AS 'Book Close Qty',\n            a.description AS 'Desscription',\n            CASE\n        WHEN b.dividend_currency = 2 THEN\n            'HKD'\n        WHEN b.dividend_currency = 4 THEN\n            'USD'\n        WHEN b.dividend_currency = 1 THEN\n            'CNY'\n        END AS 'Dividend Ccy',\n         b.dividend_amount AS 'Dividend Amount',\n         b.dividend_quantity AS 'Dividend/Bonus Qty',\n         a.pay_date AS 'Estimated Payable Date'\n        FROM\n            (\n                SELECT\n                    event_id,\n                    product_id,\n                    market_id,\n                    description,\n                    pay_date\n                FROM\n                    corp_action\n                WHERE\n                    posted_date >= '{from_date}'\n                AND posted_date <= '{to_date}'\n            ) a\n        INNER JOIN (\n            SELECT\n                *\n            FROM\n                corp_action_detail\n            WHERE\n                account_id = '{user}'\n        ) b ON a.event_id = b.event_id\n        LEFT JOIN product c ON a.product_id = c.product_id\n        AND a.market_id = c.list_market_id\n    '''.format(from_date=from_date, to_date=to_date, user=user)\n    # sqlStr = sqlStr.format(from_date, to_date)\n    allLine = select_Mssql(MSSQL_CONN.cursor(), sqlStr, True)\n    # allLine.insert(0, [\"Code\", \"Name\", \"Book Close Qty\", \"Desscription\", \"Dividend Ccy\", \"Dividend Amount\",\"Dividend/Bonus Qty\",\"Estimated Payable Date\"])\n    saveToFile(wbk, allLine, file_path, 'a', 'utf8', False, 'CorpAction')\n\n\ndef query_begin_balance(ms_cur, buss_date, user, ccy):\n    sql = '''\n        SELECT\n        process_date,\n        trade_balance\n        FROM\n            account_balance\n        WHERE\n            account_id = '{user}'\n        AND currency = {ccy}\n        AND process_date = (\n            SELECT\n                process_date\n            FROM\n                account_balance\n            WHERE\n                process_date < '{buss_date}'\n            AND account_id = '{user}'\n            ORDER BY\n                process_date DESC\n            LIMIT 1)\n    '''.format(ccy=ccy, buss_date=buss_date, user=user)\n    allLine = ((\"Ref.No\", \"Settle Date\", \"Tran.Date\", \"Type\", \"Descriptioin\", \"Quantity\", \"Amount\", \"Balance(T)\"),)\n    ms_cur.execute(sql)\n    result = ms_cur.fetchall()\n    for rs in result:\n        currency = \"CNY\"\n        if ccy == 2:\n            currency = \"HKD\"\n        elif ccy == 4:\n            currency = \"USD\"\n        allLine = allLine + ((\"\", buss_date, \"\", \"B/F\", \"\", \"\", currency, rs[1]),)\n    return allLine\n\n\ndef query_momvement_detail(allLine, balance_left, ms_cur, from_date, to_date, user, ccy):\n    if ccy == 1:\n        market_id_str = \"market_id in (4,8)\"\n    elif ccy == 2:\n        market_id_str = \"market_id = 1\"\n    elif ccy == 4:\n        market_id_str = \"market_id = 2\"\n    sql = '''   \n        SELECT\n            RIGHT(trade_id, 8) AS 'Ref.No',\n            a.settle_date AS 'Settle Date',\n            process_date AS 'Tran.Date',\n            'Trade' AS Type,\n            CONCAT(\n    \n                IF (buy_sell = 1, 'Buy', 'Sell'),\n                \" \",\n                CONCAT(\n                    a.product_id,\n                    ':',\n                    CASE\n                WHEN market_id = 1 THEN\n                    'HK'\n                WHEN market_id = 2 THEN\n                    'US'\n                WHEN market_id = 4 THEN\n                    'SHA'\n                WHEN market_id = 8 THEN\n                    'SZA'\n                END,\n                \" \",\n                a.product_description,\n                \"@\",\n                avg_price\n                )\n            ) AS 'Description',\n    \n        IF (\n            buy_sell = 1,\n            quantity,\n            - quantity\n        ) AS 'Quantity',\n    \n    IF (\n        buy_sell = 1 ,- net_amount,\n        net_amount\n    ) AS 'Amount'\n    FROM\n        (\n            SELECT\n                *\n            FROM\n                account_trade\n            WHERE\n                process_date >= '{from_date}'\n            AND process_date <= '{to_date}'\n            AND account_id = '{user}'\n            AND {market_id_str}\n            AND STATUS = 'Confirmed'\n            ORDER BY\n                trade_id ASC\n        ) a\n    LEFT JOIN product b ON a.product_id = b.product_id\n    AND a.market_id = b.list_market_id\n    UNION ALL\n        (\n            SELECT\n                RIGHT(concat('000000', id), 8) AS 'Ref.No',\n                process_date AS 'Settle Date',\n                process_date AS 'Tran.Date',\n    \n            IF (\n                purpose = 1,\n                'Deposit',\n                'Withdraw'\n            ) AS Type,\n            remark AS Description,\n            0 AS Quantity,\n    \n        IF (purpose = 1, amount ,- amount) AS Amount\n        FROM\n            cash_flow\n        WHERE\n            process_date >= '{from_date}'\n        AND process_date <= '{to_date}'\n        AND account_id = '{user}'\n        AND currency = {ccy}\n        AND (\n            remark NOT LIKE '%自动货币兑换%'\n            AND remark NOT LIKE 'in;%'\n            AND remark NOT LIKE 'out;%'\n            AND remark NOT LIKE 'conversion;%'\n        )\n        AND STATUS = 'Confirmed'\n        ORDER BY\n            id ASC\n        )\n    UNION ALL\n        (\n            SELECT\n                RIGHT(concat('000000', id), 8) AS 'Ref.No',\n                settle_date AS 'Settle Date',\n                process_date AS 'Tran.Date',\n    \n            IF (\n                purpose = 1,\n                'Deposit',\n                'Withdraw'\n            ) AS Type,\n            concat(\n                remark,\n                \" \",\n                product_id,\n                \":\",\n                CASE\n            WHEN market_id = 1 THEN\n                'HK'\n            WHEN market_id = 2 THEN\n                'US'\n            WHEN market_id = 4 THEN\n                'SHA'\n            WHEN market_id = 8 THEN\n                'SZA'\n            END,\n            \"@\",\n            avg_price\n            ) AS Description,\n            quantity AS 'Quantity',\n    \n        IF (\n            purpose = 1,\n            quantity * avg_price ,- quantity * avg_price\n        ) AS Amount\n        FROM\n            product_flow\n        WHERE\n            process_date >= '{from_date}'\n        AND process_date <= '{to_date}'\n        AND account_id = '{user}'\n        AND {market_id_str}\n        AND (\n            remark LIKE '%转入%'\n            OR remark LIKE '%转出%'\n            OR remark LIKE '%轉入%'\n            OR remark LIKE '%轉出%'\n        )\n        AND STATUS = 'Confirmed'\n        ORDER BY\n            id ASC\n        )\n    '''.format(from_date=from_date, to_date=to_date, market_id_str=market_id_str, ccy=ccy, user=user)\n    ms_cur.execute(sql)\n    result = ms_cur.fetchall()\n    for rs in result:\n        balance_left = balance_left + rs[6]\n        allLine = allLine + ((rs[0], rs[1], rs[2], rs[3], rs[4], rs[5], rs[6], balance_left),)\n    return allLine\n\n\ndef export_account_movement_by_ccy(wbk, file_path, ms_cur, from_date, to_date, user, ccy, Sheet):\n    # file_name = os.path.join(file_path,\"account_movement_%s.csv\"%(ccy))\n\n    # 查询承上余额\n    allLine = query_begin_balance(ms_cur, from_date, user, ccy)\n    allLine = query_momvement_detail(allLine, allLine[1][7], ms_cur, from_date, to_date, user, ccy)\n    saveToFile(wbk, allLine, file_path, 'a', 'utf8', False, Sheet)\n    # saveToCsv(allLine,file_name,'w', 'ansi', False)\n\n\ndef export_account_movement(wbk, file_path, ms_cur, from_date, to_date, user):\n    export_account_movement_by_ccy(wbk, file_path, ms_cur, from_date, to_date, user, 1, 'account movement CNY')\n    export_account_movement_by_ccy(wbk, file_path, ms_cur, from_date, to_date, user, 2, 'account movement HKD')\n    export_account_movement_by_ccy(wbk, file_path, ms_cur, from_date, to_date, user, 4, 'account movement USD')\n\n\ndef WriteToFile(file_path, ms_cur, from_date, to_date, user):\n    # 实例化一个Workbook()对象(即excel文件)\n    wbk = xlwt.Workbook()\n\n    bos_PortfolioSummary(wbk, file_path, to_date, user)\n    bos_Position(wbk, file_path, to_date, user)\n    bos_DailyTrades(wbk, file_name, from_date, to_date, user)\n    export_account_movement(wbk, file_path, ms_cur, from_date, to_date, user)\n    bos_corpaction(wbk, file_path, from_date, to_date, user)\n    # 以传递的name+当前日期作为excel名称保存。\n    wbk.save(file_path)\n    print(\"export success\")\n\n\ndef calc_args():\n    file_path = None\n    from_date = None\n    to_date = None\n\n    user = None\n    print(sys.argv)\n    opts, args = getopt.getopt(sys.argv[1:], 'd:f:t:u:')\n    for op, value in opts:\n        if op == '-d':\n            file_path = value\n        if op == '-f':\n            from_date = value\n        if op == '-t':\n            to_date = value\n        if op == '-u':\n            user = value\n    if file_path is None or from_date is None or to_date is None:\n        raise Exception('need file_path(-d) and from_date(-f) and to_date(-t)')\n    return file_path, from_date, to_date, user\n\n\nif __name__ == \"__main__\":\n\n    initMssqlConnection()\n    initIsInCode()\n    ms_cur = MSSQL_CONN.cursor()\n    file_path, from_date, to_date, user = calc_args()\n    if from_date == to_date:\n        Monthly = from_date[0:8]\n    else:\n        Monthly = from_date[0:6]\n    file_name = os.path.join(file_path, '{}_monthly_statement_{}.xls'.format(user, Monthly))\n    print(file_name)\n    WriteToFile(file_name, ms_cur, from_date, to_date, user)\n","sub_path":"junior/BussinessMonthlyBill.py","file_name":"BussinessMonthlyBill.py","file_ext":"py","file_size_in_byte":18502,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"584953285","text":"import cv2\n\ndef show_webcam(mirror=False):\n    cam=cv2.VideoCapture(0)\n    while True:\n        return_value,img=cam.read()\n        if mirror:\n            img=cv2.flip(img,1)\n            cv2.imshow('Webcam',img)\n            if cv2.waitKey(1)==27:\n                break\n    cv2.destroyAllWindows()\n\n    def main():\n        show_webcam(mirror=True)\n\n        if __name__=='__main__':\n            main()\n","sub_path":"barcode/barcode.py","file_name":"barcode.py","file_ext":"py","file_size_in_byte":399,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"228170777","text":"import discord\nfrom discord.ext import commands\nimport random\nimport os\nimport asyncio\nfrom datetime import datetime\nfrom datetime import timedelta\nimport sys\nimport json\nfrom DBInterface import *\n\nclass Cards(commands.Cog):\n    def __init__(self, mog):\n        self.mog = mog\n\n\n    @commands.command()\n    async def collect(self, ctx):\n        with open(r\".\\\\jenova\\\\cells\\\\users.json\", \"r\") as userlist:\n            users = json.load(userlist)\n        with open(r\".\\\\jenova\\\\cells\\\\settings.json\", \"r\") as settingsjson:\n            settings = json.load(settingsjson)\n            user_id = str(ctx.author.id)\n        if int(ctx.channel.id) == settings['lastCardSpawnedAtCHANNEL']:\n            if settings['lastCardSpawnedAtMSG'] == None:\n                return\n            card_message = int(settings['lastCardSpawnedAtMSG'])\n            msg = await ctx.channel.fetch_message(card_message)\n            await msg.delete()\n            settings['lastCardSpawnedAtMSG'] = None\n            settings['lastCardSpawnedAtCHANNEL'] = None\n            if 'Collection' not in users[user_id]:\n                users[user_id]['Collection'] = {}\n            \n            card_name = settings['cardID']\n            card_image = settings['lastCardImage']\n            if card_name not in users[user_id]['Collection']:\n                users[user_id]['Collection'].update( {card_name : 1})\n            else:\n                users[user_id]['Collection'][card_name] += 1\n            \n            settings['cardID'] = None\n            settings['lastCardImage'] = None\n            with open(r\".\\\\jenova\\\\cells\\\\settings.json\", \"w\") as settingsjson:\n                json.dump(settings, settingsjson, indent=4)\n            updateUsers(users)\n            ##\\u200B\n            embed = discord.Embed(title=f\"\",\n                                  colour=discord.Colour(0xf3e24f),\n                                  description=f\"**{ctx.author.name}**\"\n                                               \" collected a **{card_name}** card!\")\n            embed.set_author(name=\"Card Collected\", icon_url=f\"{card_image}\")\n            await ctx.send(embed=embed) \n            \n        else:\n            await ctx.send(f\"**<a:smallkupo:648982442362929173>\"\n                            \" {random.choice(moogles)} said:**\\n>\"\n                            \" There are no cards to collect in here.\") \n\ndef setup(mog):\n    mog.add_cog(Cards(mog))\n    print('Cards loaded.')\n","sub_path":"jenova/Cards.py","file_name":"Cards.py","file_ext":"py","file_size_in_byte":2427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"513642456","text":"'''\n25. Reverse Nodes in k-Group\nDescriptionHintsSubmissionsDiscussSolution\nGiven a linked list, reverse the nodes of a linked list k at a time and return its modified list.\n\nk is a positive integer and is less than or equal to the length of the linked list. If the number of nodes is not a multiple of k then left-out nodes in the end should remain as it is.\n\nExample:\n\nGiven this linked list: 1->2->3->4->5\n\nFor k = 2, you should return: 2->1->4->3->5\n\nFor k = 3, you should return: 3->2->1->4->5\n\nNote:\n\nOnly constant extra memory is allowed.\nYou may not alter the values in the list's nodes, only nodes itself may be changed.\n\n'''\n\n# Definition for singly-linked list.\n# class ListNode(object):\n#     def __init__(self, x):\n#         self.val = x\n#         self.next = None\n\nclass Solution(object):\n    def reverseKGroup(self, head, k):\n        \"\"\"\n        :type head: ListNode\n        :type k: int\n        :rtype: ListNode\n        \"\"\"\n        ans = ListNode(0)\n        ans.next = head\n        head = ans\n        curr = head\n        tail = ListNode(0)\n        while head:\n            for i in range(k):\n                curr = curr.next\n                if curr == None: return ans.next\n            curr = head.next\n            for i in range(k-1):\n                a = head.next\n                b = curr.next\n                head.next = b\n                curr.next = b.next\n                b.next = a\n                                \n            head = curr\n                ","sub_path":"solved/25_reverse_nodes_in_K_Group.py","file_name":"25_reverse_nodes_in_K_Group.py","file_ext":"py","file_size_in_byte":1476,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"17106411","text":"# -*- coding: utf-8 -*-\n\n'''\nCopyright (c) 2019 Colin Curtain\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE.\n\nAuthor: Colin Curtain (ccbogel)\nhttps://github.com/ccbogel/QualCoder\nhttps://qualcoder.wordpress.com/\n\nmight need: sudo pip install pdfminer.six\n'''\n\nimport logging\nimport datetime\nimport os\nimport platform\nimport sys\nfrom shutil import copyfile\nimport subprocess\nimport traceback\nimport zipfile\n\nfrom PyQt5 import QtCore, QtGui, QtWidgets\n\npdfminer_installed = True\ntry:\n    from pdfminer.pdfpage import PDFPage\n    from pdfminer.pdfparser import PDFParser\n    from pdfminer.pdfdocument import PDFDocument\n    from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter\n    from pdfminer.converter import PDFPageAggregator\n    from pdfminer.layout import LAParams, LTTextBox, LTTextLine\nexcept:  # ModuleNotFoundError\n    pdfminer_installed = False\n    text = \"For Linux run the following on the terminal: sudo pip install pdfminer.six\\n\"\n    text += \"For Windows run the following in the command prmpt: pip install pdfminer.six\"\n    QtWidgets.QMessageBox.critical(None, _('pdfminer is not installed.'), _(text))\n\nimport ebooklib\nfrom ebooklib import epub\n\nfrom add_item_name import DialogAddItemName\nfrom confirm_delete import DialogConfirmDelete\nfrom docx import opendocx, getdocumenttext\nfrom GUI.ui_dialog_attribute_type import Ui_Dialog_attribute_type\nfrom GUI.ui_dialog_manage_files import Ui_Dialog_manage_files\nfrom GUI.ui_dialog_memo import Ui_Dialog_memo  # for manually creating a new file\nfrom html_parser import *\nfrom memo import DialogMemo\nfrom view_image import DialogViewImage\nfrom view_av import DialogViewAV\n\n\npath = os.path.abspath(os.path.dirname(__file__))\nlogger = logging.getLogger(__name__)\n\n\ndef exception_handler(exception_type, value, tb_obj):\n    \"\"\" Global exception handler useful in GUIs.\n    tb_obj: exception.__traceback__ \"\"\"\n    tb = '\\n'.join(traceback.format_tb(tb_obj))\n    text = 'Traceback (most recent call last):\\n' + tb + '\\n' + exception_type.__name__ + ': ' + str(value)\n    print(text)\n    logger.error(_(\"Uncaught exception: \") + text)\n    QtWidgets.QMessageBox.critical(None, _('Uncaught Exception'), text)\n\n\nclass DialogManageFiles(QtWidgets.QDialog):\n    \"\"\" View, import, export, rename and delete text files. \"\"\"\n\n    source = []\n    app = None\n    text_dialog = None\n    header_labels = []\n    NAME_COLUMN = 0\n    MEMO_COLUMN = 1\n    DATE_COLUMN = 2\n    ID_COLUMN = 3\n    default_import_directory = os.path.expanduser(\"~\")\n    attribute_names = []  # list of dictionary name:value for additem dialog\n    parent_textEdit = None\n    dialogList = []\n    order_by = \"\"\n\n    def __init__(self, app, parent_textEdit):\n\n        sys.excepthook = exception_handler\n        self.app = app\n        self.parent_textEdit = parent_textEdit\n        self.dialogList = []\n        self.attributes = []\n        QtWidgets.QDialog.__init__(self)\n        self.ui = Ui_Dialog_manage_files()\n        self.ui.setupUi(self)\n        font = 'font: ' + str(self.app.settings['fontsize']) + 'pt '\n        font += '\"' + self.app.settings['font'] + '\";'\n        self.setStyleSheet(font)\n        self.ui.tableWidget.itemChanged.connect(self.cell_modified)\n        self.ui.pushButton_create.clicked.connect(self.create)\n        self.ui.pushButton_view.clicked.connect(self.view)\n        self.ui.pushButton_delete.clicked.connect(self.delete)\n        self.ui.pushButton_import.clicked.connect(self.import_files)\n        self.ui.pushButton_export.clicked.connect(self.export)\n        self.ui.pushButton_add_attribute.clicked.connect(self.add_attribute)\n        self.ui.tableWidget.cellClicked.connect(self.cell_selected)\n        self.ui.tableWidget.setContextMenuPolicy(QtCore.Qt.CustomContextMenu)\n        self.ui.tableWidget.customContextMenuRequested.connect(self.table_menu)\n        self.order_by = \"\"\n        self.load_file_data()\n\n    def table_menu(self, position):\n        \"\"\" Context menu for displaying table rows in differing order \"\"\"\n\n        row = self.ui.tableWidget.currentRow()\n        col = self.ui.tableWidget.currentColumn()\n        text = None\n        try:\n            text = str(self.ui.tableWidget.item(row, col).text())\n            # some blanks cells contain None and some contain blank strings\n            if text == \"\":\n                text = None\n        except:\n            pass\n        #print(self.row, self.col, self.cellValue)\n        # action cannot be None otherwise may default to one of the actionsbelow depending on column clicked\n        menu = QtWidgets.QMenu()\n        action_view = menu.addAction(_(\"View\"))\n        action_alphabetic = 1\n        action_date = 1\n        action_type = 1\n        action_by_value = 1\n        if col < 4:\n            action_alphabetic = menu.addAction(_(\"Alphabetic order\"))\n            action_date = menu.addAction(_(\"Date order\"))\n            action_type = menu.addAction(_(\"File type order\"))\n        if col > 3:\n            action_by_value = menu.addAction(_(\"Show this value\"))\n        action_export = menu.addAction(_(\"Export\"))\n        action_delete = menu.addAction(_(\"Delete\"))\n        action = menu.exec_(self.ui.tableWidget.mapToGlobal(position))\n\n        if action == action_view:\n            self.view()\n        if action == action_export:\n            self.export()\n        if action== action_delete:\n            self.delete()\n        if action == action_alphabetic:\n            self.order_by = \"\"\n            self.load_file_data()\n        if action == action_date:\n            self.order_by = \"date\"\n            self.load_file_data()\n            self.fill_table()\n        if action == action_type:\n            self.order_by = \"filetype\"\n            self.load_file_data()\n        if action == action_by_value:\n            # Hide rows that do not match this value, text can be None type\n            # Cell items can be None or exist with ''\n            for r in range(0, self.ui.tableWidget.rowCount()):\n                item = self.ui.tableWidget.item(r, col)\n                # items can be None or appear to be None when item text == ''\n                if text is None and (item is not None and len(item.text()) > 0):\n                    self.ui.tableWidget.setRowHidden(r, True)\n                if text is not None and (item is None or item.text().find(text) == -1):\n                    self.ui.tableWidget.setRowHidden(r, True)\n\n    def load_file_data(self):\n        \"\"\" Documents images and audio contain the filetype suffix.\n        No suffix implies the 'file' was imported from a survey question.\n        This also fills out the table header lables with file attribute names.\n        Files with the '.transcribed' suffix mean they are associated with audio and\n        video files.\n        \"\"\"\n\n        self.source = []\n        cur = self.app.conn.cursor()\n        # default alphabetic order\n        sql = \"select name, id, fulltext, mediapath, memo, owner, date from source order by upper(name)\"\n        if self.order_by == \"date\":\n            sql = \"select name, id, fulltext, mediapath, memo, owner, date from source order by date, upper(name)\"\n        if self.order_by == \"filetype\":\n            sql = \"select name, id, fulltext, mediapath, memo, owner, date from source order by mediapath\"\n        cur.execute(sql)\n        result = cur.fetchall()\n        for row in result:\n            self.source.append({'name': row[0], 'id': row[1], 'fulltext': row[2],\n            'mediapath': row[3], 'memo': row[4], 'owner': row[5], 'date': row[6]})\n        # attributes\n        self.header_labels = [_(\"Name\"), _(\"Memo\"), _(\"Date\"), _(\"Id\")]\n        sql = \"select name from attribute_type where caseOrFile='file'\"\n        cur.execute(sql)\n        result = cur.fetchall()\n        self.attribute_names = []\n        for n in result:\n            self.header_labels.append(n[0])\n            self.attribute_names.append({'name': n[0]})\n        sql = \"select attribute.name, value, id from attribute join attribute_type on \\\n        attribute_type.name=attribute.name where attribute_type.caseOrFile='file'\"\n        cur.execute(sql)\n        result = cur.fetchall()\n        self.attributes = []\n        for row in result:\n            self.attributes.append(row)\n        self.fill_table()\n\n    def add_attribute(self):\n        \"\"\" When add button pressed, opens the addItem dialog to get new attribute text.\n        Then get the attribute type through a dialog.\n        AddItem dialog checks for duplicate attribute name.\n        New attribute is added to the model and database. \"\"\"\n\n        check_names = self.attribute_names + [{'name': 'name'}, {'name':'memo'}, {'name':'id'}, {'name':'date'}]\n        ui = DialogAddItemName(check_names, _(\"New attribute name\"))\n        ui.exec_()\n        name = ui.get_new_name()\n        if name is None or name == \"\":\n            return\n        Dialog_type = QtWidgets.QDialog()\n        ui = Ui_Dialog_attribute_type()\n        ui.setupUi(Dialog_type)\n        ok = Dialog_type.exec_()\n        valuetype = \"character\"\n        if ok and ui.radioButton_numeric.isChecked():\n            valuetype = \"numeric\"\n        self.attribute_names.append({'name': name})\n        # update attribute_type list and database\n        now_date = str(datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"))\n        cur = self.app.conn.cursor()\n        cur.execute(\"insert into attribute_type (name,date,owner,memo,caseOrFile, valuetype) values(?,?,?,?,?,?)\"\n            ,(name, now_date, self.app.settings['codername'], \"\", 'file', valuetype))\n        self.app.conn.commit()\n        sql = \"select id from source\"\n        cur.execute(sql)\n        ids = cur.fetchall()\n        for id_ in ids:\n            sql = \"insert into attribute (name, value, id, attr_type, date, owner) values (?,?,?,?,?,?)\"\n            cur.execute(sql, (name, \"\", id_[0], 'file', now_date, self.app.settings['codername']))\n        self.app.conn.commit()\n        self.load_file_data()\n        self.fill_table()\n        self.parent_textEdit.append(_(\"Attribute added to files: \") + name + \", \" + _(\"type\") + \": \" + valuetype)\n\n    def cell_selected(self):\n        \"\"\" When the table widget memo cell is selected display the memo.\n        Update memo text, or delete memo by clearing text.\n        If a new memo also show in table widget by displaying YES in the memo column. \"\"\"\n\n        x = self.ui.tableWidget.currentRow()\n        y = self.ui.tableWidget.currentColumn()\n\n        if y == self.MEMO_COLUMN:\n            name =self.source[x]['name'].lower()\n            if name[-5:] == \".jpeg\" or name[-4:] in ('.jpg', '.png', '.gif'):\n                ui = DialogMemo(self.app, _(\"Memo for file \") + self.source[x]['name'],\n                self.source[x]['memo'])\n                ui.exec_()\n                self.source[x]['memo'] = ui.memo\n                cur = self.app.conn.cursor()\n                cur.execute('update source set memo=? where id=?', (ui.memo, self.source[x]['id']))\n                self.app.conn.commit()\n            else:\n                ui = DialogMemo(self.app, _(\"Memo for file \") + self.source[x]['name'],\n                self.source[x]['memo'])\n                ui.exec_()\n                self.source[x]['memo'] = ui.memo\n                cur = self.app.conn.cursor()\n                cur.execute('update source set memo=? where id=?', (ui.memo, self.source[x]['id']))\n                self.app.conn.commit()\n            if self.source[x]['memo'] == \"\":\n                self.ui.tableWidget.setItem(x, self.MEMO_COLUMN, QtWidgets.QTableWidgetItem())\n            else:\n                self.ui.tableWidget.setItem(x, self.MEMO_COLUMN, QtWidgets.QTableWidgetItem(\"Yes\"))\n\n    def cell_modified(self):\n        \"\"\" Attribute values can be changed.\n        Filenames cannot be changed. \"\"\"\n\n        x = self.ui.tableWidget.currentRow()\n        y = self.ui.tableWidget.currentColumn()\n        '''if y == self.NAME_COLUMN:\n            new_text = str(self.ui.tableWidget.item(x, y).text()).strip()\n\n            # check that no other source file has this text and this is is not empty\n            update = True\n            if new_text == \"\":\n                update = False\n            for c in self.source:\n                if c['name'] == new_text:\n                    update = False\n            # .transcribed suffix is not to be used on a media file\n            if new_text[-12:] == \".transcribed\" and self.source[x]['mediapath'] is not None:\n                update = False\n            # Need to preserve names of a/v files and their\n            # dependent transcribed files: filename.type.transcribed\n            if update:\n                if self.source[x]['mediapath'] is not None and self.source[x]['mediapath'][:2] in ('/a', '/v'):\n                    msg = _(\"If there is an associated '.transcribed' file please rename \")\n                    msg += _(\"it to match the media file plus '.transcribed'\")\n                    QtWidgets.QMessageBox.warning(None, \"Media name\", msg)\n                if self.source[x]['name'][-12:] == \".transcribed\":\n                    msg = _(\"If there is an associated media file please rename \")\n                    msg += _(\"it to match the media file before the '.transcribed' suffix\")\n                    QtWidgets.QMessageBox.warning(None, _(\"Media name\"), msg)\n                # update source list and database\n                self.source[x]['name'] = new_text\n                cur = self.app.conn.cursor()\n                cur.execute(\"update source set name=? where id=?\", (new_text, self.source[x]['id']))\n                self.app.conn.commit()\n            else:  # put the original text in the cell\n                self.ui.tableWidget.item(x, y).setText(self.source[x]['name'])'''\n        # update attribute value\n        if y > self.ID_COLUMN:\n            value = str(self.ui.tableWidget.item(x, y).text()).strip()\n            attribute_name = self.header_labels[y]\n            cur = self.app.conn.cursor()\n            cur.execute(\"update attribute set value=? where id=? and name=? and attr_type='file'\",\n            (value, self.source[x]['id'], attribute_name))\n            self.app.conn.commit()\n            #logger.debug(\"updating: \" + attribute_name + \" , \" + value)\n            self.ui.tableWidget.resizeColumnsToContents()\n\n    def is_caselinked_or_coded_or_annotated(self, fid):\n        \"\"\" Check for text linked to case, coded or annotated text.\n        param: fid   the text file id\n        return: True or False\n        \"\"\"\n\n        cur = self.app.conn.cursor()\n        sql = \"select pos0,pos1 from case_text where fid=?\"\n        cur.execute(sql, [fid, ])\n        case_linked = cur.fetchall()\n        sql = \"select pos0,pos1 from annotation where fid=?\"\n        cur.execute(sql, [fid, ])\n        annote_linked = cur.fetchall()\n        sql = \"select pos0,pos1 from code_text where fid=?\"\n        cur.execute(sql, [fid, ])\n        code_linked = cur.fetchall()\n        if case_linked != [] or annote_linked != [] or code_linked != []:\n            return True\n        return False\n\n    def highlight(self, fid, textEdit):\n        \"\"\" Add coding and annotation highlights. \"\"\"\n        cur = self.app.conn.cursor()\n        sql = \"select pos0,pos1 from annotation where fid=? union all select pos0,pos1 from code_text where fid=?\"\n        cur.execute(sql, [fid, fid])\n        annoted_coded = cur.fetchall()\n        format_ = QtGui.QTextCharFormat()\n        format_.setFontFamily(self.app.settings['font'])\n        format_.setFontPointSize(self.app.settings['fontsize'])\n\n        # remove formatting\n        cursor = textEdit.textCursor()\n        cursor.setPosition(0, QtGui.QTextCursor.MoveAnchor)\n        cursor.setPosition(len(textEdit.toPlainText()), QtGui.QTextCursor.KeepAnchor)\n        cursor.setCharFormat(format_)\n        # add formatting\n        for item in annoted_coded:\n            cursor.setPosition(int(item[0]), QtGui.QTextCursor.MoveAnchor)\n            cursor.setPosition(int(item[1]), QtGui.QTextCursor.KeepAnchor)\n            format_.setFontUnderline(True)\n            format_.setUnderlineColor(QtCore.Qt.red)\n            cursor.setCharFormat(format_)\n\n    def view(self):\n        \"\"\" View and edit text file contents.\n        Alternatively view an image or other media. \"\"\"\n\n        x = self.ui.tableWidget.currentRow()\n        if self.source[x]['mediapath'] is not None:\n            if self.source[x]['mediapath'][:8] == \"/images/\":\n                self.view_image(x)\n                return\n            if self.source[x]['mediapath'][:7] == \"/video/\":\n                self.view_av(x)\n                return\n            if self.source[x]['mediapath'][:7] == \"/audio/\":\n                self.view_av(x)\n                return\n\n        restricted = self.is_caselinked_or_coded_or_annotated(self.source[x]['id'])\n        # cannot easily edit file text of there are linked cases, codes or annotations\n        self.text_dialog = QtWidgets.QDialog()\n        self.text_ui = Ui_Dialog_memo()\n        self.text_ui.setupUi(self.text_dialog)\n        self.text_ui.textEdit.setReadOnly(restricted)\n        if restricted:\n            self.text_ui.textEdit.setContextMenuPolicy(QtCore.Qt.CustomContextMenu)\n            self.text_ui.textEdit.customContextMenuRequested.connect(self.textEdit_menu)\n        self.text_ui.textEdit.setFontPointSize(self.app.settings['fontsize'])\n        self.text_ui.textEdit.setPlainText(self.source[x]['fulltext'])\n        self.highlight(self.source[x]['id'], self.text_ui.textEdit)\n\n        title = _(\"View file: \") + self.source[x]['name'] + \" (ID:\" + str(self.source[x]['id']) + \") \"\n        if restricted:\n            title += \"RESTRICTED EDIT\"\n        self.text_dialog.setWindowTitle(title)\n        self.text_dialog.exec_()\n        text = self.text_ui.textEdit.toPlainText()\n        if text == self.source[x]['fulltext']:\n            return\n\n        self.source[x]['fulltext'] = text\n        cur = self.app.conn.cursor()\n        cur.execute(\"update source set fulltext=? where id=?\", (text, self.source[x]['id']))\n        self.app.conn.commit()\n\n    def textEdit_menu(self, position):\n        \"\"\" Context menu for selection of small sections of text to be edited.\n        The section of text must be only non-annotated and non-coded or\n        only annotated or coded. \"\"\"\n\n        x = self.ui.tableWidget.currentRow()\n        menu = QtWidgets.QMenu()\n        ActionItemEdit = menu.addAction(_(\"Edit text maximum 20 characters\"))\n        action = menu.exec_(self.text_ui.textEdit.mapToGlobal(position))\n        text_cursor = self.text_ui.textEdit.textCursor()\n        if text_cursor.position() == 0 and text_cursor.selectionEnd() == 0:\n            msg = _(\"Select a section of text, maximum 20 characters.\\nThe selection must be either all underlined or all not-underlined.\")\n            QtWidgets.QMessageBox.warning(None, _('No text selected'), msg, QtWidgets.QMessageBox.Ok)\n            return\n        result = self.crossover_check(x, text_cursor)\n        if result['crossover']:\n            return\n\n        if action == ActionItemEdit:\n            self.restricted_edit_text(x, text_cursor)\n            # reload text\n            self.text_ui.textEdit.setPlainText(self.source[x]['fulltext'])\n            self.highlight(self.source[x]['id'], self.text_ui.textEdit)\n\n    def crossover_check(self, x, text_cursor):\n        \"\"\" Check text selection for codes and annotations that cross over with non-coded\n        and non-annotated sections. User can only select coded or non-coded text, this makes\n        updating changes much simpler.\n\n        param: x the current table row\n        param: text_cursor  - the document cursor\n        return: dictionary of crossover indication and of whether selection os entirely coded annotated or neither \"\"\"\n\n        response = {\"crossover\": True, \"coded_section\":[], \"annoted_section\":[], \"cased_section\":[]}\n        msg = _(\"Please select text that does not have a combination of coded and uncoded text.\")\n        msg += _(\" Nor a combination of annotated and un-annotated text.\\n\")\n        selstart = text_cursor.selectionStart()\n        selend = text_cursor.selectionEnd()\n        msg += _(\"Selection start: \") + str(selstart) + _(\" Selection end: \") + str(selend) + \"\\n\"\n        cur = self.app.conn.cursor()\n        sql = \"select pos0,pos1 from code_text where fid=? and \"\n        sql += \"((pos0>? and pos0<?)  or (pos1>? and pos1<?)) \"\n        cur.execute(sql, [self.source[x]['id'], selstart, selend, selstart, selend])\n        code_crossover = cur.fetchall()\n        if code_crossover != []:\n            msg += _(\"Code crossover: \") + str(code_crossover)\n            QtWidgets.QMessageBox.warning(None, _('Codes cross over text'), msg, QtWidgets.QMessageBox.Ok)\n            return response\n        # find if the selected text is coded\n        sql = \"select pos0,pos1 from code_text where fid=? and ?>=pos0 and ?<=pos1\"\n        cur.execute(sql, [self.source[x]['id'], selstart, selend])\n        response['coded_section'] = cur.fetchall()\n        sql = \"select pos0,pos1 from annotation where fid=? and \"\n        sql += \"((pos0>? and pos0<?) or (pos1>? and pos1<?))\"\n        cur.execute(sql, [self.source[x]['id'], selstart, selend, selstart, selend])\n        annote_crossover = cur.fetchall()\n        if annote_crossover != []:\n            msg += _(\"Annotation crossover: \") + str(annote_crossover)\n            QtWidgets.QMessageBox.warning(None, _('Annotations cross over text'), msg, QtWidgets.QMessageBox.Ok)\n            return response\n        # find if the selected text is annotated\n        sql = \"select pos0,pos1 from annotation where fid=? and ?>=pos0 and ?<=pos1\"\n        cur.execute(sql, [self.source[x]['id'], selstart, selend])\n        response['annoted_section'] = cur.fetchall()\n        response['crossover'] = False\n        # find if the selected text is assigned to case\n        sql = \"select pos0,pos1, id from case_text where fid=? and ?>=pos0 and ?<=pos1\"\n        cur.execute(sql, [self.source[x]['id'], selstart, selend])\n        response['cased_section'] = cur.fetchall()\n        response['crossover'] = False\n        return response\n\n    def restricted_edit_text(self, x, text_cursor):\n        \"\"\" Restricted edit of small sections of text. selected text can be replaced.\n        Mainly used for fixing spelling mistakes.\n        original text is here: self.source[x]['fulltext']\n\n        param: x the current table row\n        param: text_cursor  - the document cursor\n        \"\"\"\n\n        txt = text_cursor.selectedText()\n        selstart = text_cursor.selectionStart()\n        selend = text_cursor.selectionEnd()\n\n        if len(txt) > 20:\n            msg = _(\"Can only edit small selections of text, up to 20 characters in length.\") + \"\\n\"\n            msg += _(\"You selected \" + str(len(txt)) + _(\" characters\"))\n            QtWidgets.QMessageBox.warning(None, _('Too much text selected'), msg, QtWidgets.QMessageBox.Ok)\n            return\n\n        edit_dialog = QtWidgets.QDialog()\n        edit_ui = Ui_Dialog_memo()\n        edit_ui.setupUi(edit_dialog)\n        edit_dialog.resize(400, 60)\n        edit_dialog.setWindowTitle(_(\"Edit text: start\") +str(selstart) + _(\" end:\") + str(selend))\n        edit_ui.textEdit.setFontPointSize(self.app.settings['fontsize'])\n        edit_ui.textEdit.setPlainText(txt)\n        edit_dialog.exec_()\n        new_text = edit_ui.textEdit.toPlainText()\n\n        # split original text and fix\n        #original_text = self.source[x]['fulltext']\n        before = self.source[x]['fulltext'][0:text_cursor.selectionStart()]\n        after = self.source[x]['fulltext'][text_cursor.selectionEnd():len(self.source[x]['fulltext'])]\n        fulltext = before + new_text + after\n\n        # update database with the new fulltext\n        self.source[x]['fulltext'] = fulltext\n        cur = self.app.conn.cursor()\n        sql = \"update source set fulltext=? where id=?\"\n        cur.execute(sql, [fulltext, self.source[x]['id']])\n        self.app.conn.commit()\n        length_diff = len(new_text) - len(txt)\n        if length_diff == 0:\n            return\n        \"\"\" UPDATE CODES//CASES/ANNOTATIONS located after the selected text\n        Update database for codings, annotations and case linkages.\n        Find affected codings annotations and case linkages.\n        All codes, annotations and case linkages that occur after this text selection can be easily updated\n        by adding the length diff to the pos0 and pos1 fields. \"\"\"\n        cur = self.app.conn.cursor()\n        # find cases after this text section\n        sql = \"select id, pos0,pos1 from case_text where fid=? and pos1>? \"\n        sql += \"and not(?>=pos0 and ?<=pos1)\"\n        cur.execute(sql, [self.source[x]['id'], selend, selstart, selend])\n        post_case_linked = cur.fetchall()\n        # find annotations after this text selection\n        sql = \"select anid,pos0,pos1 from annotation where fid=? and pos1>? \"\n        sql += \"and not(?>=pos0 and ?<=pos1)\"\n        cur.execute(sql, [self.source[x]['id'], selend, selstart, selend])\n        post_annote_linked = cur.fetchall()\n        # find codes after this text selection section\n        sql = \"select pos0,pos1 from code_text where fid=? and pos1>? \"\n        sql += \"and not(?>=pos0 and ?<=pos1)\"\n        cur.execute(sql, [self.source[x]['id'], selend, selstart, selend])\n        post_code_linked = cur.fetchall()\n        txt = text_cursor.selectedText()\n        #print(\"cursor selstart\", text_cursor.selectionStart())\n        #print(\"cursor selend\", text_cursor.selectionEnd())\n        #print(\"length_diff\", length_diff, \"\\n\")\n\n        for i in post_case_linked:\n            #print(i)\n            #print(i[0], i[1] + length_diff, i[2] + length_diff)\n            #print(\"lengths \", len(original_text), i[2] - i[1])\n            sql = \"update case_text set pos0=?, pos1=? where id=?\"\n            cur.execute(sql, [i[1] + length_diff, i[2] + length_diff, i[0]])\n        for i in post_annote_linked:\n            sql = \"update annotation set pos0=?, pos1=? where anid=?\"\n            cur.execute(sql, [i[1] + length_diff, i[2] + length_diff, i[0]])\n        for i in post_code_linked:\n            sql = \"update code_text set pos0=?,pos1=? where fid=? and pos0=? and pos1=?\"\n            cur.execute(sql, [i[0] + length_diff, i[1] + length_diff, self.source[x]['id'], i[0], i[1]])\n        self.app.conn.commit()\n\n        # UPDATE THE CODED AND/OR ANNOTATED SECTION\n        # The crossover dictionary contains annotations and codes for this section\n        # need to extend or reduce the code or annotation length\n        # the coded text stored in code_text also need to be updated\n        crossovers = self.crossover_check(x, text_cursor)\n        # Codes in this selection\n        for i in crossovers['coded_section']:\n            #print(\"selected text coded: \", i)\n            sql = \"update code_text set seltext=?,pos1=? where fid=? and pos0=? and pos1=?\"\n            newtext = fulltext[i[0]:i[1] + length_diff]\n            cur.execute(sql, [newtext, i[1] + length_diff, self.source[x]['id'], i[0], i[1]])\n        # Annotations in this selection\n        for i in crossovers['annoted_section']:\n            #print(\"selected text annoted: \", i)\n            sql = \"update annotation set pos1=? where fid=? and pos0=? and pos1=?\"\n            cur.execute(sql, [i[1] + length_diff, self.source[x]['id'], i[0], i[1]])\n        for i in crossovers['cased_section']:\n            #print(\"selected text as case: \", i)\n            sql = \"update case_text set pos1=? where fid=? and pos0=? and pos1=?\"\n            cur.execute(sql, [i[1] + length_diff, self.source[x]['id'], i[0], i[1]])\n        self.app.conn.commit()\n\n    def view_av(self, x):\n        \"\"\" View an audio or video file. Edit the memo. Edit the transcribed file.\n        Added try block in case VLC bindings do not work.\n        Uses a non-modal dialog.\n        \"\"\"\n\n        try:\n            ui = DialogViewAV(self.app, self.source[x])\n            #ui.exec_()  # this dialog does not display well on Windows 10 so trying .show()\n            self.dialogList.append(ui)\n            ui.show()\n            # try and update file data here\n            self.load_file_data()\n            if self.source[x]['memo'] == \"\":\n                self.ui.tableWidget.setItem(x, self.MEMO_COLUMN, QtWidgets.QTableWidgetItem())\n            else:\n                self.ui.tableWidget.setItem(x, self.MEMO_COLUMN, QtWidgets.QTableWidgetItem(\"Yes\"))\n        except Exception as e:\n            logger.debug(e)\n            print(e)\n            QtWidgets.QMessageBox.warning(None, 'view av error', str(e), QtWidgets.QMessageBox.Ok)\n            return\n\n    def view_image(self, x):\n        \"\"\" View an image file and edit the image memo. \"\"\"\n\n        ui = DialogViewImage(self.app, self.source[x])\n        ui.exec_()\n        memo = ui.ui.textEdit.toPlainText()\n        if self.source[x]['memo'] != memo:\n            self.source[x]['memo'] = memo\n            cur = self.app.conn.cursor()\n            cur.execute('update source set memo=? where id=?', (self.source[x]['memo'],\n                self.source[x]['id']))\n            self.app.conn.commit()\n        if self.source[x]['memo'] == \"\":\n            self.ui.tableWidget.setItem(x, self.MEMO_COLUMN, QtWidgets.QTableWidgetItem())\n        else:\n            self.ui.tableWidget.setItem(x, self.MEMO_COLUMN, QtWidgets.QTableWidgetItem(\"Yes\"))\n\n    def create(self):\n        \"\"\" Create a new text file by entering text into the dialog.\n        Implements the QtDesigner memo dialog. \"\"\"\n        name, ok = QtWidgets.QInputDialog.getText(self, _('New File'), _('Enter the file name:'))\n        if not ok:\n            return\n        if name is None or name == \"\":\n            QtWidgets.QMessageBox.warning(None, _('Warning'),\n                _(\"No filename was selected\"), QtWidgets.QMessageBox.Ok)\n            return\n        # check for non-unique filename\n        if any(d['name'] == name for d in self.source):\n            QtWidgets.QMessageBox.warning(None, _('Warning'),\n                _(\"Filename in use\"), QtWidgets.QMessageBox.Ok)\n            return\n\n        ui = DialogMemo(self.app, _(\"Creating a new file: \") + name)\n        ui.exec_()\n        filetext = ui.memo\n        # update database\n        entry = {'name': name, 'id': -1, 'fulltext': filetext, 'memo': \"\",\n        'owner': self.app.settings['codername'], 'date': datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"),\n        'mediapath': None}\n        cur = self.app.conn.cursor()\n        cur.execute(\"insert into source(name,fulltext,mediapath,memo,owner,date) values(?,?,?,?,?,?)\",\n            (entry['name'], entry['fulltext'], entry['mediapath'], entry['memo'], entry['owner'], entry['date']))\n        self.app.conn.commit()\n        cur.execute(\"select last_insert_rowid()\")\n        id_ = cur.fetchone()[0]\n        entry['id'] = id_\n        self.parent_textEdit.append(_(\"File created: \") + entry['name'])\n        self.source.append(entry)\n        self.fill_table()\n\n    def import_files(self):\n        \"\"\" Import files and store into relevant directories (documents, images, ?audio?).\n        Convert documents to plain text and store this in data.qda\n        Can import from plain text files, also import from html, odt and docx\n        Note importing from html, odt and docx all formatting is lost.\n        Imports images as jpg, jpeg, png which are stored in an images directory.\n        Imports audio as mp3, wav which are stored in an audio directory\n        Imports video as mp4, mov, ogg, wmv which are stored in a video directory\n        \"\"\"\n\n        imports, ok = QtWidgets.QFileDialog.getOpenFileNames(None, _('Open file'),\n            self.default_import_directory)\n        if not ok or imports == []:\n            return\n        known_file_type = False\n        nameSplit = imports[0].split(\"/\")\n        temp_filename = nameSplit[-1]\n        self.default_import_directory = imports[0][0:-len(temp_filename)]\n        for f in imports:\n            # Added process events, in case many large files are imported, which leaves the FileDialog open and covering the screen.\n            QtWidgets.QApplication.processEvents()\n            filename = f.split(\"/\")[-1]\n            destination = self.app.project_path\n            if f.split('.')[-1].lower() in ('docx', 'odt', 'txt', 'htm', 'html', 'epub'):\n                destination += \"/documents/\" + filename\n                copyfile(f, destination)\n                self.load_file_text(f)\n                known_file_type = True\n            if f.split('.')[-1].lower() in ('pdf'):\n                if pdfminer_installed is False:\n                    text = \"For Linux run the following on the terminal: sudo pip install pdfminer.six\\n\"\n                    text += \"For Windows run the following in the command prmpt: pip install pdfminer.six\"\n                    QtWidgets.QMessageBox.critical(None, _('pdfminer is not installed.'), _(text))\n                    return\n                destination += \"/documents/\" + filename\n                # remove encryption from pdf if possible, for Linux\n                if platform.system() == \"Linux\":\n                    process = subprocess.Popen([\"qpdf\", \"--decrypt\", f, destination],\n                        stdout=subprocess.PIPE)\n                    process.wait()\n                    self.load_file_text(destination)\n                else:\n                    #TODO qpdf decrypt not implemented for windows, OSX\n                    QtWidgets.QMessageBox.warning(None, _('If import error occurs'),\n                    _(\"Sometimes pdfs are encrypted, download and decrypt using qpdf before trying to load the pdf\") + \":\\n\" + f)\n                    copyfile(f, destination)\n                    self.load_file_text(destination)\n                known_file_type = True\n            if f.split('.')[-1].lower() in ('jpg', 'jpeg', 'png'):\n                destination += \"/images/\" + filename\n                copyfile(f, destination)\n                self.load_media_reference(\"/images/\" + filename)\n                known_file_type = True\n            if f.split('.')[-1].lower() in ('wav', 'mp3'):\n                destination += \"/audio/\" + filename\n                copyfile(f, destination)\n                self.load_media_reference(\"/audio/\" + filename)\n                known_file_type = True\n            if f.split('.')[-1].lower() in ('mkv', 'mov', 'mp4', 'ogg', 'wmv'):\n                destination += \"/video/\" + filename\n                copyfile(f, destination)\n                self.load_media_reference(\"/video/\" + filename)\n                known_file_type = True\n            if not known_file_type:\n                QtWidgets.QMessageBox.warning(None, _('Unknown file type'),\n                    _(\"Unknown file type for import\") + \":\\n\" + f)\n        self.load_file_data()\n        self.fill_table()\n\n    def load_media_reference(self, mediapath):\n        \"\"\" Load media reference information for audio video images. \"\"\"\n\n        # check for duplicated filename and update model, widget and database\n        name_split = mediapath.split(\"/\")\n        filename = name_split[-1]\n        if any(d['name'] == filename for d in self.source):\n            QtWidgets.QMessageBox.warning(None, _('Duplicate file'), _(\"Duplicate filename.\\nFile not imported\"))\n            return\n        entry = {'name': filename, 'id': -1, 'fulltext': None, 'memo': \"\", 'mediapath': mediapath,\n        'owner': self.app.settings['codername'], 'date': datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")}\n        cur = self.app.conn.cursor()\n        cur.execute(\"insert into source(name,memo,owner,date, mediapath, fulltext) values(?,?,?,?,?,?)\",\n            (entry['name'], entry['memo'], entry['owner'], entry['date'], entry['mediapath'], entry['fulltext']))\n        self.app.conn.commit()\n        cur.execute(\"select last_insert_rowid()\")\n        id_ = cur.fetchone()[0]\n        entry['id'] = id_\n        self.parent_textEdit.append(entry['name'] + _(\" imported.\"))\n        self.source.append(entry)\n\n        # Create an empty transcription file for audio and video\n        if mediapath[:6] in(\"/audio\", \"/video\"):\n            entry = {'name': filename + \".transcribed\", 'id': -1, 'fulltext': \"\", 'mediapath': None, 'memo': \"\",\n            'owner': self.app.settings['codername'], 'date': datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")}\n            cur = self.app.conn.cursor()\n            cur.execute(\"insert into source(name,fulltext,mediapath,memo,owner,date) values(?,?,?,?,?,?)\",\n                (entry['name'],  entry['fulltext'], entry['mediapath'], entry['memo'], entry['owner'], entry['date']))\n            self.app.conn.commit()\n            cur.execute(\"select last_insert_rowid()\")\n            id_ = cur.fetchone()[0]\n            entry['id'] = id_\n            self.parent_textEdit.append(entry['name'] + _(\" imported.\"))\n            self.source.append(entry)\n\n    def load_file_text(self, import_file):\n        \"\"\" Import from file types of odt, docx pdf, epub, txt, html, htm.\n        \"\"\"\n\n        text = \"\"\n\n        # Import from odt\n        if import_file[-4:].lower() == \".odt\":\n            text = self.convert_odt_to_text(import_file)\n        # Import from docx\n        if import_file[-5:].lower() == \".docx\":\n            #text = convert(importFile)  # uses docx_to_html\n            document = opendocx(import_file)\n            list_ = getdocumenttext(document)\n            text = \"\\n\".join(list_)\n        # Import from epub\n        if import_file[-5:].lower() == \".epub\":\n            book = epub.read_epub(import_file)\n            for d in book.get_items_of_type(ebooklib.ITEM_DOCUMENT):\n                try:\n                    #print(d.get_content())\n                    bytes_ = d.get_body_content()\n                    string = bytes_.decode('utf-8')\n                    text += html_to_text(string) + \"\\n\"\n                except TypeError as e:\n                    logger.debug(\"ebooklib get_body_content error \" + str(e))\n        # import PDF\n        if import_file[-4:].lower() == '.pdf':\n            fp = open(import_file, 'rb')  # read binary mode\n            parser = PDFParser(fp)\n            doc = PDFDocument(parser=parser)\n            parser.set_document(doc)\n            # potential error with encrypted PDF\n            rsrcmgr = PDFResourceManager()\n            laparams = LAParams()\n            laparams.char_margin = 1.0\n            laparams.word_margin = 1.0\n            device = PDFPageAggregator(rsrcmgr, laparams=laparams)\n            interpreter = PDFPageInterpreter(rsrcmgr, device)\n            for page in PDFPage.create_pages(doc):\n                interpreter.process_page(page)\n                layout = device.get_result()\n                for lt_obj in layout:\n                    if isinstance(lt_obj, LTTextBox) or isinstance(lt_obj, LTTextLine):\n                        text += lt_obj.get_text()\n        # import from html\n        if import_file[-5:].lower() == \".html\" or import_file[-4:].lower() == \".htm\":\n            importErrors = 0\n            with open(import_file, \"r\") as sourcefile:\n                fileText = \"\"\n                while 1:\n                    line = sourcefile.readline()\n                    if not line:\n                        break\n                    fileText += line\n                text = html_to_text(fileText)\n                QtWidgets.QMessageBox.warning(None, _('Warning'), str(importErrors) + _(\" lines not imported\"))\n        # Try importing as a plain text file.\n        if text == \"\":\n            import_errors = 0\n            try:\n                with open(import_file, \"r\") as sourcefile:\n                    while 1:\n                        line = sourcefile.readline()\n                        if not line:\n                            break\n                        try:\n                            text += line\n                        except Exception as e:\n                            #logger.debug(\"Importing plain text file, line ignored: \" + str(e))\n                            import_errors += 1\n                    if text[0:6] == \"\\ufeff\":  # associated with notepad files\n                        text = text[6:]\n            except Exception as e:\n                QtWidgets.QMessageBox.warning(None, _('Warning'),\n                    _(\"Cannot import \") + str(import_file) + \"\\n\" + str(e))\n                return\n            if import_errors > 0:\n                QtWidgets.QMessageBox.warning(None, _('Warning'),\n                    str(import_errors) + _(\" lines not imported\"))\n                logger.warning(import_file + \": \" + str(import_errors) + _(\" lines not imported\"))\n        # import of text file did not work\n        if text == \"\":\n            QtWidgets.QMessageBox.warning(None, _('Warning'),\n                _(\"Cannot import \") + str(import_file) + \"\\n\" + str(e))\n            return\n        # Final checks: check for duplicated filename and update model, widget and database\n        nameSplit = import_file.split(\"/\")\n        filename = nameSplit[-1]\n        if any(d['name'] == filename for d in self.source):\n            QtWidgets.QMessageBox.warning(None, _('Duplicate file'),\n                _(\"Duplicate filename.\\nFile not imported\"))\n            return\n        entry = {'name': filename, 'id': -1, 'fulltext': text, 'mediapath': None, 'memo': \"\",\n        'owner': self.app.settings['codername'], 'date': datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")}\n        cur = self.app.conn.cursor()\n        #logger.debug(\"type fulltext: \" + str(type(entry['fulltext'])))\n        cur.execute(\"insert into source(name,fulltext,mediapath,memo,owner,date) values(?,?,?,?,?,?)\",\n            (entry['name'],  entry['fulltext'], entry['mediapath'], entry['memo'], entry['owner'], entry['date']))\n        self.app.conn.commit()\n        cur.execute(\"select last_insert_rowid()\")\n        id_ = cur.fetchone()[0]\n        entry['id'] = id_\n        self.parent_textEdit.append(entry['name'] + _(\" imported.\"))\n        self.source.append(entry)\n\n    def convert_odt_to_text(self, import_file):\n        \"\"\" Convert odt to very rough equivalent with headings, list items and tables for\n        html display in qTextEdits. \"\"\"\n\n        odt_file = zipfile.ZipFile(import_file)\n        data = str(odt_file.read('content.xml'))  # bytes class to string\n        #https://stackoverflow.com/questions/18488734/python3-unescaping-non-ascii-characters\n        data = str(bytes([ord(char) for char in data.encode(\"utf_8\").decode(\"unicode_escape\")]), \"utf_8\")\n        data_start = data.find(\"</text:sequence-decls>\")\n        data_end = data.find(\"</office:text>\")\n        if data_start == -1 or data_end == -1:\n            logger.warning(\"ODT IMPORT ERROR\")\n            return \"\"\n        data = data[data_start + 22: data_end]\n        #print(data)\n        data = data.replace('<text:h', '\\n<text:h')\n        data = data.replace('</text:h>', '\\n\\n')\n        data = data.replace('</text:list-item>', '\\n')\n        data = data.replace('</text:span>', '')\n        data = data.replace('</text:p>', '\\n')\n        data = data.replace('</text:a>', ' ')\n        data = data.replace('</text:list>', '')\n        data = data.replace('<text:list-item>', '')\n        data = data.replace('<table:table table:name=', '\\n=== TABLE ===\\n<table:table table:name=')\n        data = data.replace('</table:table>', '=== END TABLE ===\\n')\n        data = data.replace('</table:table-cell>', '\\n')\n        data = data.replace('</table:table-row>', '')\n        data = data.replace('<draw:image', '\\n=== IMG ===<draw:image')\n        data = data.replace('</draw:frame>', '\\n')\n\n        text = \"\"\n        tagged = False\n        for i in range(0, len(data)):\n            if data[i: i + 6] == \"<text:\" or data[i: i + 7] == \"<table:\" or data[i: i + 6] == \"<draw:\":\n                tagged = True\n            if not tagged:\n                text += data[i]\n            if data[i] == \">\":\n                tagged = False\n        return text\n\n    def export(self):\n        \"\"\" Export files to selected directory.\n        If an imported file was from a docx, odt, pdf, html, epub then export the original file\n        and also export the plain text version.\n        \"\"\"\n\n        options = QtWidgets.QFileDialog.DontResolveSymlinks | QtWidgets.QFileDialog.ShowDirsOnly\n        directory = QtWidgets.QFileDialog.getExistingDirectory(None,\n            _(\"Select directory to save file\"), os.getenv('HOME'), options)\n        if directory == \"\":\n            return\n        x = self.ui.tableWidget.currentRow()\n        filename = self.source[x]['name']\n        msg = directory + \"/\" + filename\n        filename_txt = None\n        if len(filename) > 5 and (filename[-5:] == \".html\" or filename[-5:] == \".docx\" or filename[-5:] == \".epub\"):\n            filename_txt = filename[0:len(filename) - 5] + \".txt\"\n        if len(filename) > 4 and (filename[-4:] == \".htm\" or filename[-4:] == \".odt\" or filename[-4] == \".txt\"):\n            filename_txt = filename[0:len(filename) - 4] + \".txt\"\n        # Below is for transcribed files and for user created text files within QualCoder\n        if self.source[x]['mediapath'] is None and filename_txt is None:\n            filename_txt = filename + \".txt\"\n            msg = \"\"  # added to below\n        if filename_txt is not None:\n            filename_txt = directory + \"/\" + filename_txt\n            #logger.info(_(\"Exporting to \") + filename)\n            filedata = self.source[x]['fulltext']\n            f = open(filename_txt, 'w')\n            f.write(filedata)\n            f.close()\n        # export audio, video, picture files\n        if self.source[x]['mediapath'] is not None:\n            file_path = self.app.project_path + self.source[x]['mediapath']\n            destination = directory + \"/\" + filename\n            try:\n                copyfile(file_path, destination)\n            except FileNotFoundError:\n                pass\n        # export pdf, docx, odt, epub, html files if located in documents directory\n        if self.source[x]['mediapath'] is None:\n            file_path = self.app.project_path + \"/documents/\" + self.source[x]['name']\n            destination = directory + \"/\" + self.source[x]['name']\n            try:\n                copyfile(file_path, destination)\n            except FileNotFoundError:\n                pass\n        if filename_txt is not None:\n            msg += \"\\n\" + directory + \"/\" + filename_txt\n        QtWidgets.QMessageBox.information(None, _(\"File Exported\"), msg)\n        self.parent_textEdit.append(filename + _(\" exported to \") + msg)\n\n    def delete(self):\n        \"\"\" Delete file from database and update model and widget.\n        Also, delete files from sub-directories. \"\"\"\n\n        x = self.ui.tableWidget.currentRow()\n        file_id = self.source[x]['id']\n        ui = DialogConfirmDelete(self.source[x]['name'])\n        ok = ui.exec_()\n\n        if not ok:\n            return\n        cur = self.app.conn.cursor()\n        # delete text source\n        if self.source[x]['mediapath'] is None:\n            try:\n                os.remove(self.app.project_path + \"/documents/\" + self.source[x]['name'])\n            except Exception as e:\n                logger.warning(_(\"Deleting file error: \") + str(e))\n            cur.execute(\"delete from source where id = ?\", [file_id])\n            cur.execute(\"delete from code_text where fid = ?\", [file_id])\n            cur.execute(\"delete from annotation where fid = ?\", [file_id])\n            cur.execute(\"delete from case_text where fid = ?\", [file_id])\n            sql = \"delete from attribute where attr_type in (select attribute_type.name from \"\n            sql += \"attribute_type where id=? and attribute_type.caseOrFile='file')\"\n            cur.execute(sql, [file_id])\n            self.app.conn.commit()\n        # delete image audio video source\n        if self.source[x]['mediapath'] is not None:\n            # Remove avid links in code_text\n            sql = \"select avid from code_av where id=?\"\n            cur.execute(sql, (file_id, ))\n            avids = cur.fetchall()\n            sql = \"update code_text set avid=null where avid=?\"\n            for avid in avids:\n                cur.execute(sql, (avid[0], ))\n            self.app.conn.commit()\n            # Remove folder file, database stored coded sections and source details\n            filepath = self.app.project_path + self.source[x]['mediapath']\n            try:\n                os.remove(filepath)\n            except Exception as e:\n                logger.warning(_(\"Deleting file error: \") + str(e))\n            cur.execute(\"delete from source where id = ?\", [file_id])\n            cur.execute(\"delete from code_image where id = ?\", [file_id])\n            cur.execute(\"delete from code_av where id = ?\", [file_id])\n            sql = \"delete from attribute where attr_type in (select attribute_type.name from \"\n            sql += \"attribute_type where id=? and attribute_type.caseOrFile='file')\"\n            cur.execute(sql, [file_id])\n            self.app.conn.commit()\n        self.parent_textEdit.append(_(\"Deleted: \") + self.source[x]['name'])\n        for item in self.source:\n            if item['id'] == file_id:\n                self.source.remove(item)\n        self.load_file_data()\n        self.fill_table()\n\n    def get_icon(self, name):\n        ''' Get icon to put in table. Helper method for fill_table\n\n         parameter: filename\n\n         return: QIcon '''\n\n        icon_text = QtGui.QIcon(\"GUI/text.png\")\n        icon_play = QtGui.QIcon(\"GUI/play.png\")\n        icon_picture = QtGui.QIcon(\"GUI/picture.png\")\n        icon_sound = QtGui.QIcon(\"GUI/sound.png\")\n        suffix = name[-4:].lower()\n        if suffix in (\".png\", \".jpg\", \"jpeg\"):\n            return icon_picture\n        if suffix in (\".mp4\", \".mov\", \".avi\", \".mkv\"):\n            return icon_play\n        if suffix in (\".mp3\", \".wav\", \".ogg\"):\n            return icon_sound\n        return icon_text\n\n    def fill_table(self):\n        \"\"\" Reload the file data and Fill the table widget with file data. \"\"\"\n\n        self.ui.tableWidget.setColumnCount(len(self.header_labels))\n        self.ui.tableWidget.setHorizontalHeaderLabels(self.header_labels)\n        self.ui.tableWidget.setSelectionMode(QtWidgets.QAbstractItemView.SingleSelection)\n        rows = self.ui.tableWidget.rowCount()\n        for r in range(0, rows):\n            self.ui.tableWidget.removeRow(0)\n\n        for row, data in enumerate(self.source):\n            self.ui.tableWidget.insertRow(row)\n            icon = self.get_icon(data['name'])\n            name_item = QtWidgets.QTableWidgetItem(data['name'])\n            name_item.setIcon(icon)\n            # having un-editable file names helps with assigning icons\n            name_item.setFlags(name_item.flags() ^ QtCore.Qt.ItemIsEditable)\n            self.ui.tableWidget.setItem(row, self.NAME_COLUMN, name_item)\n            date_item = QtWidgets.QTableWidgetItem(data['date'])\n            date_item.setFlags(date_item.flags() ^ QtCore.Qt.ItemIsEditable)\n            self.ui.tableWidget.setItem(row, self.DATE_COLUMN, date_item)\n            memoitem = data['memo']\n            if memoitem != None and memoitem != \"\":\n                self.ui.tableWidget.setItem(row, self.MEMO_COLUMN, QtWidgets.QTableWidgetItem(\"Yes\"))\n            fid = data['id']\n            if fid is None:\n                fid = \"\"\n            iditem = QtWidgets.QTableWidgetItem(str(fid))\n            iditem.setFlags(iditem.flags() ^ QtCore.Qt.ItemIsEditable)\n            self.ui.tableWidget.setItem(row, self.ID_COLUMN, iditem)\n            # Add the attribute values\n            for a in self.attributes:\n                for col, header in enumerate(self.header_labels):\n                    #print(fid, a[2], a[0], header)\n                    #print(type(fid), type(a[2]), type(a[0]), type(header))\n                    if fid == a[2] and a[0] == header:\n                        #print(\"found\", a)\n                        self.ui.tableWidget.setItem(row, col, QtWidgets.QTableWidgetItem(str(a[1])))\n        self.ui.tableWidget.resizeColumnsToContents()\n        self.ui.tableWidget.resizeRowsToContents()\n        self.ui.tableWidget.hideColumn(self.ID_COLUMN)\n        if self.app.settings['showids'] == 'True':\n            self.ui.tableWidget.showColumn(self.ID_COLUMN)\n        self.ui.tableWidget.verticalHeader().setVisible(False)\n\n\nif __name__ == \"__main__\":\n    app = QtWidgets.QApplication(sys.argv)\n    ui = Ui_dialog_manage_files()\n    ui.show()\n    sys.exit(app.exec_())\n\n","sub_path":"qualcoder/manage_files.py","file_name":"manage_files.py","file_ext":"py","file_size_in_byte":53200,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"379019430","text":"import collections\n\nimport matplotlib\nimport pandas as pd\nimport pytest\n\nfrom poptimizer import config\nfrom poptimizer.ml import plots\n\nML_PARAMS = {\n    \"data\": (\n        (\"Label\", {\"days\": 58}),\n        (\"STD\", {\"days\": 195}),\n        (\"Ticker\", {}),\n        (\"Mom12m\", {\"days\": 282}),\n        (\"DivYield\", {\"days\": 332, \"periods\": 2}),\n        (\"Mom1m\", {\"days\": 21}),\n    ),\n    \"model\": {\n        \"bagging_temperature\": 0.9388504407881838,\n        \"depth\": 5,\n        \"l2_leaf_reg\": 3.2947929042414654,\n        \"learning_rate\": 0.07663371920281654,\n        \"one_hot_max_size\": 100,\n        \"random_strength\": 0.9261064363697566,\n        \"ignored_features\": [1],\n    },\n}\n\nTICKERS = (\"BANEP\", \"DSKY\", \"LKOH\", \"MOEX\", \"NKNCP\")\nDATE = pd.Timestamp(\"2019-01-03\")\n\ntest_data = [(2, 2), (3, 3), (4, 4), (5, 6), (6, 6), (7, 8), (9, 9), (10, 12)]\n\n\n@pytest.mark.parametrize(\"n_plots,size\", test_data)\ndef test_axs_iter(n_plots, size):\n    rez = plots.axs_iter(n_plots)\n    assert isinstance(rez, collections.Iterator)\n    rez_list = list(rez)\n    assert len(rez_list) == size\n    # noinspection PyUnresolvedReferences\n    assert isinstance(rez_list[0], matplotlib.axes.Axes)\n\n\n@pytest.fixture(autouse=True)\ndef patch_params_and_show(monkeypatch):\n    monkeypatch.setattr(config, \"ML_PARAMS\", ML_PARAMS)\n    monkeypatch.setattr(plots.plt, \"show\", lambda: None)\n\n\ndef test_partial_dependence_curve(monkeypatch):\n    monkeypatch.setattr(plots, \"QUANTILE\", [0.3, 0.7])\n    result = plots.partial_dependence_curve(TICKERS, DATE)\n    assert len(result) == 5\n    assert len(result[0]) == 2\n","sub_path":"poptimizer/ml/tests/test_plots.py","file_name":"test_plots.py","file_ext":"py","file_size_in_byte":1580,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"134807881","text":"from typing import Optional, Union, TYPE_CHECKING\n\nimport cbor2\n\nfrom cose import CoseMessage\nfrom cose.messages import cosemessage, signcommon\nfrom cose.keys.ec2 import EC2\nfrom cose.keys.okp import OKP\n\nif TYPE_CHECKING:\n    from cose.attributes.algorithms import CoseAlgorithms, CoseEllipticCurves\n\n\n@cosemessage.CoseMessage.record_cbor_tag(18)\nclass Sign1Message(cosemessage.CoseMessage, signcommon.SignCommon):\n    context = \"Signature1\"\n    cbor_tag = 18\n\n    @classmethod\n    def from_cose_obj(cls, cose_obj) -> 'Sign1Message':\n        msg = super().from_cose_obj(cose_obj)\n        msg.signature = cose_obj.pop(0)\n        return msg\n\n    def __init__(self,\n                 phdr: Optional[dict] = None,\n                 uhdr: Optional[dict] = None,\n                 payload: bytes = b'',\n                 external_aad: bytes = b''):\n\n        super().__init__(phdr, uhdr, payload, external_aad)\n        self.signature = b''\n\n    @property\n    def signature(self):\n        return self._signature\n\n    @signature.setter\n    def signature(self, value):\n        if not isinstance(value, bytes):\n            TypeError(\"Signature must be of type 'bytes'\")\n\n        self._signature = value\n\n    @property\n    def _sig_structure(self):\n        \"\"\"\n        Create the sig_structure that needs to be signed\n        :return: to_be_signed\n        \"\"\"\n        sig_structure = [self.context]\n        sig_structure = self._base_structure(sig_structure)\n\n        sig_structure.append(self.payload)\n\n        return cbor2.dumps(sig_structure)\n\n    def encode(self,\n               private_key: Union[EC2, OKP],\n               alg: Optional['CoseAlgorithms'] = None,\n               curve: Optional['CoseEllipticCurves'] = None,\n               tagged: bool = True,\n               sign: bool = True) -> bytes:\n        \"\"\" Encodes the message into a CBOR array with or without a CBOR tag. \"\"\"\n\n        if sign:\n            message = [self.encode_phdr(),\n                       self.encode_uhdr(),\n                       self.payload,\n                       self.compute_signature(alg=alg, private_key=private_key, curve=curve)]\n        else:\n            message = [self.encode_phdr(), self.encode_uhdr(), self.payload]\n\n        if tagged:\n            res = cbor2.dumps(cbor2.CBORTag(self.cbor_tag, message), default=self._special_cbor_encoder)\n        else:\n            res = cbor2.dumps(message, default=self._special_cbor_encoder)\n\n        return res\n\n    def __repr__(self) -> str:\n        return f'<COSE_Sign1: [{self._phdr}, {self._uhdr}, {CoseMessage._truncate(self._payload)}, ' \\\n               f'{CoseMessage._truncate(self._signature)}]>'\n","sub_path":"cose/messages/sign1message.py","file_name":"sign1message.py","file_ext":"py","file_size_in_byte":2632,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"54158784","text":"#!/usr/bin/env python3\n\"\"\"\nAuthor : vpaz\nDate   : 2019-03-01\nPurpose: Rock the Casbah\n\"\"\"\n\nimport argparse\nimport sys\nimport os\nimport csv\n\n# --------------------------------------------------\ndef get_args():\n    \"\"\"get command-line arguments\"\"\"\n    parser = argparse.ArgumentParser(\n        description='Annotate BLAST output',\n        formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n   \n   # parser.add_argument(\n    #    'input', nargs='+', type=str)\n\n    parser.add_argument(\n        'FILE', metavar='FILE', help='BLAST output (-outfmt 6)')\n\n    parser.add_argument(\n        '-a',\n        '--annotations',\n        help='Annotation file',\n        metavar='FILE',\n        type=str,\n        default='')\n\n    parser.add_argument(\n        '-o',\n        '--outfile',\n        help='Output file',\n        metavar='FILE',\n        type=str,\n        default='')\n\n    return parser.parse_args()\n\n\n# --------------------------------------------------\ndef warn(msg):\n    \"\"\"Print a message to STDERR\"\"\"\n    print(msg, file=sys.stderr)\n\n\n# --------------------------------------------------\ndef die(msg):\n    \"\"\"warn() and exit with error\"\"\"\n    warn(msg)\n    sys.exit(1)\n\n\n# --------------------------------------------------\ndef main():\n    \"\"\"Make a jazz noise here\"\"\"\n    args = get_args()\n    annot = args.annotations\n    tab_file = args.FILE\n    out_file = args.outfile\n\n    \n   # tab_file = file_name[0] \n    if not os.path.isfile(tab_file):\n        die('\"{}\" is not a file'.format(tab_file))\n   \n    if not os.path.isfile(annot):\n        die('\"{}\" is not a file'.format(annot)) \n   \n    \n\n    with open(annot, 'r') as csvfile:\n        reader = csv.reader(csvfile, delimiter ='\\t')\n        for row_csv in reader:\n            genus = row_csv[6][0:]\n            species = row_csv[7][0:]\n    \n    with open(tab_file, 'r') as tabfile:\n        readers = csv.reader(tabfile, delimiter = '\\t')\n        print('seq_id\\t')\n        for rows in readers:\n           # row = list(rows)\n            seq_id = rows[1][0:]\n            pident = rows[2][0:] \n            print(seq_id)\n            print(pident)\n    if out_file:\n        out = open('out.txt', 'w')\n        out.writelines(seq_id, pident, genus, species)\n        out.close()\n # print(','.join(seq_id, pident, genus, species)\n           #outfile.write('\\t',join(seq_id, pident)\n        \n\n# --------------------------------------------------\nif __name__ == '__main__':\n    main()\n","sub_path":"assignments/07-csv/blastomatic.py","file_name":"blastomatic.py","file_ext":"py","file_size_in_byte":2425,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"45339768","text":"a=0\r\nb=0\r\nc=0\r\nd=0\r\ndef collatz(n):\r\n\tif n%2 == 0:\r\n\t\tn/=2\r\n\telse:\r\n\t\tn = 3*n+1\r\n\treturn n\r\n\r\nfor i in range(2,1000001):\r\n\tb = collatz(i)\r\n\twhile b!=1:\r\n\t\tb = collatz(b)\r\n\t\ta+=1\r\n\t\tif a > c:\r\n\t\t\tc = a\r\n\t\t\td = i\r\n\ta = 0\r\n\tb = 0\r\nprint(\"Finished, number = \", d, \"Number of iterations = \", c)","sub_path":"PE_P14.py","file_name":"PE_P14.py","file_ext":"py","file_size_in_byte":289,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"209072463","text":"\nimport com.ihsan.foundation.pobjecthelper as phelper\nimport time, sys, os\n\ndef formSetDataEx(UIDefList, Parameter):\n    config = UIDefList.config\n    helper = phelper.PObjectHelper(config)\n    keyclass=Parameter.FirstRecord.key\n\n    rep=keyclass.replace(\"CompanionProduct\",\"CustomerPersonal\").replace(\"PRODUCTID\",\"CUSTOMERID\")\n    idcomp=rep.split('=')\n    idcus = helper.GetObject('Companion',idcomp[2])\n    idnasabah='%s=%d' % (idcomp[0],idcus.CustomerId)\n    #raise '',keyclass+' -- '+idnasabah\n    #raise 'test', idcus.LCustomer.CustomerName\n    UIDefList.SetData('uipart', idnasabah)\n    uip = UIDefList.uipart.Dataset.GetRecord(0)\n    uip.cid = idcomp[2]\n    \n    if idcus.ParentId not in [0,'',None]:\n      parent = helper.GetObject('Companion', idcus.ParentId)\n      uip = UIDefList.uipart.Dataset.GetRecord(0)\n      uip.SetFieldByName('LSupervisor.LCompanion.CompanionId',parent.CompanionId)\n      uip.SetFieldByName('LSupervisor.LCompanion.LCustomer.CustomerName', parent.LCustomer.CustomerName)\n\n\ndef Simpan(config, parameter, returnpacket):\n    recParam = parameter.uipart.GetRecord(0)\n    config.BeginTransaction()\n    try:\n        helper = phelper.PObjectHelper(config)\n        key=recParam.key.split('=')[-1]\n        simpanCustomer = helper.GetObject('CustomerPersonal',key )\n        simpanCustomer.CustomerName=recParam.CustomerName\n        simpanCustomer.PhoneNumber=recParam.PhoneNumber\n        simpanCustomer.AddressStreet=recParam.AddressStreet\n        simpanCustomer.AddressKelurahan=recParam.AddressKelurahan\n        simpanCustomer.AddressKecamatan=recParam.AddressKecamatan\n        simpanCustomer.AddressCity=recParam.AddressCity\n        simpanCustomer.IdPropinsi=recParam.GetFieldByName('LPropinsi.IdPropinsi')\n        simpanCustomer.IdNegara=recParam.GetFieldByName('LNegara.IdNegara')\n        simpanCustomer.AddressPostalCode=recParam.AddressPostalCode\n        simpanCustomer.CustomerStatus=recParam.CustomerStatus\n        simpanCustomer.BirthDate=recParam.BirthDate\n        simpanCustomer.BirthPlace=recParam.BirthPlace\n        simpanCustomer.FormalEducation=recParam.FormalEducation\n        simpanCustomer.Gender=recParam.Gender\n        simpanCustomer.IdentityNumber=recParam.IdentityNumber\n        simpanCustomer.IdentityType=recParam.IdentityType\n        simpanCustomer.Marital=recParam.Marital\n        simpanCustomer.Religion=recParam.Religion\n        simpanCustomer.TotalFamily=recParam.TotalFamily\n        simpanCustomer.WorkSector=recParam.WorkSector\n        simpanPendampingLKMS = helper.GetObject('Companion', recParam.cid)\n        simpanPendampingLKMS.ParentId =  recParam.GetFieldByName('LSupervisor.CompanionId')\n\n        config.Commit()\n    except:\n        config.Rollback()\n        raise\n\n","sub_path":"dialogs/Beasiswa/fEditPendampingBeasiswa_data.py","file_name":"fEditPendampingBeasiswa_data.py","file_ext":"py","file_size_in_byte":2731,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"587834395","text":"#!/usr/bin/env python\n# -*- encoding: utf8 -*-\n\nimport unittest\nimport filecmp\nimport os\nfrom SourceScanner import SourceScanner\nfrom SourceWriter import SourceWriter\nfrom StringTableGenerator import StringTableGenerator\nfrom LineProcessor import LineProcessor\n\nclass SourceScanner_Test(unittest.TestCase):\n    def setUp(self):\n        self.__stg = StringTableGenerator(1001)\n        self.__processor = LineProcessor()\n        self.__scanner = SourceScanner(self.__stg, self.__processor)\n\n    def test_processLine(self):\n        teststr = u'a = \"Hello, \" & \"世界\" & \"人たち\"'\n        writer = SourceWriter('c:/test.txt')\n        retline = self.__scanner.processLine(teststr, writer)\n        self.assertEqual(retline, u'a = \"Hello, \" & LoadResString(1001) & LoadResString(1002)')\n\n    def test_scan_1(self):\n        fname = 'test/vbtest/vbtest_with_load/Form1.frm'\n        writer = SourceWriter(fname)\n        self.__scanner.scan(fname, writer)\n        writer.write(True)\n        try:\n            result = 'test/vbtest/results/vbtest_with_load/Form1.frm'\n            self.assertTrue(filecmp.cmp(result, fname+ '.test'))\n        except:\n            raise\n        finally:\n           os.remove(fname+'.test')\n\n    def test_scan_2(self):\n        fname = 'test/vbtest/vbtest_without_load/Form1.frm'\n        writer = SourceWriter(fname)\n        self.__scanner.scan(fname, writer)\n        writer.write(True)\n        try:\n            result = 'test/vbtest/results/vbtest_without_load/Form1.frm'\n            self.assertTrue(filecmp.cmp(result, fname+ '.test'))\n        except:\n            raise\n        finally:\n           os.remove(fname+'.test')\n        \n\n\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"SourceScanner_Test.py","file_name":"SourceScanner_Test.py","file_ext":"py","file_size_in_byte":1703,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"602762884","text":"import numpy as np\r\nfrom pprint import pprint\r\n\r\nasteroid_map = list(map(str.strip, open('input.txt', 'r').readlines()))\r\n\r\n# Part 1\r\n\r\nmax_vision = 0\r\nfor y0 in range(len(asteroid_map)):\r\n    for x0 in range(len(asteroid_map[y0])):\r\n    \r\n        if asteroid_map[y0][x0] == '#': \r\n            vision = set()\r\n\r\n            for y1 in range(len(asteroid_map)):\r\n                for x1 in range(len(asteroid_map[y1])):\r\n                    if y1 == y0 and x1 == x0:\r\n                        continue\r\n                    if asteroid_map[y1][x1] == '#':\r\n                        vision.add(np.arctan2(y0-y1,x0-x1))\r\n            current_vision = len(vision)\r\n            if current_vision > max_vision:\r\n                max_vision = current_vision\r\n                best_coords = (x0, y0)\r\nprint('Part1: Coords={}, Number Of Visible Asteroids={}'.format(best_coords, max_vision)) # (26,36) 347\r\n\r\n# Part 2\r\n\r\n(x0, y0) = best_coords\r\ncurrent_asteroid = 0\r\nasteroid_coords = {}\r\nfor y in range(len(asteroid_map)):\r\n    for x in range(len(asteroid_map[y])):\r\n        if y == y0 and x == x0:\r\n            continue\r\n        if asteroid_map[y][x] == '#':\r\n            phi = np.arctan2(y0-y,x0-x)\r\n            if phi not in asteroid_coords:\r\n                asteroid_coords[phi] = [(x,y)]\r\n            else:\r\n                asteroid_coords[phi].append((x,y))\r\n\r\n# Re-Order keys so that the first is the closest positive to 0\r\nsorted_keys = sorted(asteroid_coords.keys())\r\nfor i in range(len(sorted_keys)):\r\n    if sorted_keys[i] >= 1.56:\r\n        start = i\r\n        break\r\nsorted_keys = sorted_keys[start:] + sorted_keys[:start]\r\n\r\nasteroids_destroyed = 0\r\nN_coords = len(sorted_keys)\r\nwhile asteroids_destroyed < 200:\r\n    coords_destroyed = asteroid_coords[sorted_keys[asteroids_destroyed]].pop()\r\n    asteroids_destroyed += 1\r\n\r\nprint('Part2: {}'.format(coords_destroyed[0]*100 + coords_destroyed[1]))\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"D10/D10.py","file_name":"D10.py","file_ext":"py","file_size_in_byte":1905,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"119075829","text":"#!/usr/bin/env python\n\nUPDATE_LAST = 'update-last'\n\n\nclass History(object):\n\n    # ---------- initialize ----------\n    \n    def __init__(self, model, attributesToBackup, maxSize, onUndoOrRedoListener=None):\n        self.model = model\n        self.setAttributesToBackup( attributesToBackup )\n        self.maxSize = maxSize\n        self.onLoadListener = onUndoOrRedoListener\n        self.clear()\n\n    def setAttributesToBackup(self, attributesToBackup):\n        '''attributesToBackup: an iterable of attribute specifications.\n        each specification is a list/tuple of up to three elements:\n            0: the name of the attribute\n            1: a function to copy the value of the attribute\n            2: a flag whether corresponding value of the last history entry shall be updated.\n               this can be useful to save a cursor.\n            '''\n        self.attributesToBackup = list()\n        for attrSpec in attributesToBackup:\n            if not isinstance(attrSpec, (tuple, list)) or len(attrSpec) == 1:\n                self.attributesToBackup.append((attrSpec, None, None))\n            elif len(attrSpec) == 2:\n                self.attributesToBackup.append((attrSpec[0], attrSpec[1], None))\n            elif len(attrSpec) == 3:\n                self.attributesToBackup.append(attrSpec)\n            else:\n                assert False\n\n    def clear(self):\n        self.history = list()\n        self.historyIndex = -1\n\n\n    # ---------- getters ----------\n\n    def __len__(self):\n        return len(self.history)\n\n\n    # ---------- save ----------\n    \n    def makeBackup(self):\n        self.historyIndex += 1\n        while self.historyIndex < len(self.history):\n            del self.history[-1]\n        \n        if len(self.history) > 0:\n            prior = self.history[-1]\n        else:\n            prior = None\n        d = dict()\n        for attr, copy, updateLast in self.attributesToBackup:\n            val = getattr(self.model, attr)\n            if copy != None:\n                val = copy(val)\n            d[attr] = val\n            if updateLast == UPDATE_LAST and prior != None:\n                prior[attr] = val\n                \n        self.history.append(d)\n\n        while len(self.history) > self.maxSize:\n            del self.history[0]\n            self.historyIndex -= 1\n\n\n    # ---------- load ----------\n    \n    def undo(self):\n        if self.historyIndex <= 0:\n            return False\n\n        self.historyIndex -= 1\n        self._load()\n        return True\n\n    def redo(self):\n        if self.historyIndex + 1 >= len(self.history):\n            return False\n\n        self.historyIndex += 1\n        self._load()\n        return True\n\n    def _load(self):\n        d = self.history[self.historyIndex]\n        for attr, copy, updateLast in self.attributesToBackup:\n            val = d[attr]\n            if copy != None:\n                val = copy(val)\n            setattr(self.model, attr, val)\n        if self.onLoadListener != None:\n            self.onLoadListener()\n\n\n\nif __name__=='__main__':\n\n    class Model(object):\n\n        ATTRIBUTES_TO_BACKUP = (\n            # attribute name, copy function\n            ('x', None),\n        )\n\n        def __init__(self):\n            self.x = 0\n            self.history = History(self, self.ATTRIBUTES_TO_BACKUP, 30)\n            self.history.makeBackup()\n\n        def increment(self):\n            self.x += 1\n            self.history.makeBackup()\n\n    N = 3\n\n    m = Model()\n    for i in range(N):\n        m.increment()\n    assert m.x == N\n    \n    assert m.history.undo()\n    assert m.x == N - 1\n    assert m.history.redo()\n    assert m.x == N\n    assert not m.history.redo()\n\n    for i in range(N):\n        assert m.history.undo()\n        assert m.x == N - 1 - i\n    assert not m.history.undo()\n    assert m.x == 0\n    assert len(m.history) == N + 1\n\n    print(\"tests successful\")\n","sub_path":"py/model_history.py","file_name":"model_history.py","file_ext":"py","file_size_in_byte":3858,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"159295928","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Dec 27 15:24:48 2018\n\n@author: tom verguts\nboltzmann machine for implementing logical rules\nhere, the input pattern is clamped in the free phase \n(and everything in the fixed phase)\nworks more robustly than direct approach in ch10_logical_rules.py\n\"\"\"\n#%% initialize\nimport numpy as np\nimport time\nnp.set_printoptions(suppress = True, precision = 2)\nn_train_trials = 1000\nbeta = 0.05\ne = 0.001 # small (non-zero) number\nT = 1 # temperature\nN = 4 # number of units in the network\nw = np.zeros((N, N))\npatterns = np.array(   [[1,0,0,0], [1,1,0,0], [1,0,1,0], [1,0,0,1],\n                        [1,1,1,0], [1,1,0,1], [1,0,1,1], [1,1,1,1]] )\nprob_fixed = np.array([  1,       1,       1,       e,\n                         e,       e,       e,       1]) # the AND rule\n#prob_fixed = np.array([  1,       e,       e,       e,\n#                         e,       1,       1,       1]) # the OR rule\n#prob_fixed = np.array([  e,       1,       1,       1,\n#                         e,       e,       e,       1]) # the XOR ruleprob_fixed = prob_fixed/np.sum(prob_fixed) # at pattern level\nprob_fixed = prob_fixed/np.sum(prob_fixed) # at pattern level\nprob_free = np.ndarray(2**(N-1))      # probability distribution when only part is clamped (and the rest free)\np_fixed = np.ndarray((N, N))   # at feature (and thus matrix) level\np_free  = np.ndarray((N, N))  \nt1 = time.time()\n# check if i and j are jointly active in the required distribution (based on prob)\nfor row in range(N):\n    for column in range(N):\n        som = 0\n        for p_index in range(len(patterns)):\n            if (patterns[p_index][row]==1) and (patterns[p_index][column]==1) :\n                som += prob_fixed[p_index]\n        p_fixed[row, column] = som\nprint(p_fixed) \n       \n#%% main loop\nx = np.ndarray(N)\nfor loop in range(n_train_trials):\n    x[0] = 1\n    x[1] = np.random.choice([0, 1]) # random clamp\n    x[2] = np.random.choice([0, 1]) # random clamp    \n    for p_index in range(len(patterns)):\n        prob_free[p_index]  = (patterns[p_index][0]==x[0])*\\\n                              (patterns[p_index][1]==x[1])*\\\n                              (patterns[p_index][2]==x[2])*\\\n             np.exp( (1/T)*np.matmul(np.matmul(patterns[p_index],w),patterns[p_index]) )\n         \n    prob_free = prob_free/np.sum(prob_free)      \n    for row in range(N):\n        for column in range(N):\n            som = 0\n            for p_index in range(len(patterns)):\n                if (patterns[p_index][row]==1) and (patterns[p_index][column]==1) :\n                    som += prob_free[p_index]\n            p_free[row, column] = som\n    w_previous = np.copy(w)\n    w = w + beta*(p_fixed - p_free)\n    dev = np.sum((w-w_previous)**2, axis = (0,1))\n    #print(dev)\nprint(w)\nprint(prob_free)\nt2 = time.time()\n\n#%% test the network\nprint(\"simulation took {:.2f} sec\".format(t2-t1))\nprint(\"test phase:\")\ntest_patterns = [[1, 0, 0], [1, 1, 0], [1, 0, 1], [1, 1, 1]]\nfor test_index in range(len(test_patterns)):\n    for p_index in range(len(patterns)):\n        prob_free[p_index]  = np.all(patterns[p_index][0:3]==test_patterns[test_index])*\\\n                              np.exp( (1/T)*np.matmul(np.matmul(patterns[p_index],w),patterns[p_index]) )\n    prob_free = prob_free/np.sum(prob_free)\n    print(prob_free)      ","sub_path":"code by chapter/Chapter 10/ch10_logical_rules_2.py","file_name":"ch10_logical_rules_2.py","file_ext":"py","file_size_in_byte":3342,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"384848385","text":"from django.conf.urls import url, include\nfrom . import views\n\nurlpatterns = [\n    url(r'^$', views.home),\n    url(r'^new_student/$', views.new_student),\n    url(r'^new_professor/$', views.new_professor),\n    url(r'^project_type/$', views.project_type),\n    url(r'^new_news/$', views.new_news),\n    url(r'^change_project_status/$', views.change_project_status),\n]\n","sub_path":"adminweb/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":364,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"78217348","text":"def input_data(n):\r\n    arr=[]\r\n    for i in range(n):\r\n        x = int(input(f\"Input item[{i}]: \"))\r\n        arr.append()\r\n    return arr\r\n\r\ndef input_list(n):\r\n    arr = []\r\n    i = 0\r\n    while True:\r\n        x = input(f\"Input item[{i}]: \")\r\n        try:\r\n            arr.append(int(x))\r\n            i += 1\r\n            if i >= n:\r\n                break\r\n        except:\r\n            print(\"Chưa nhập đúng số nguyên. Nhập lại!\")\r\n    return arr\r\ndef so_le(n):\r\n    n=input_list(n)\r\n    result = []\r\n    for i in n:\r\n        if(i%2!=0):\r\n            result.append(i)\r\n        return result\r\nn=int(input(\"Nhap: \"))\r\nprint(input_list(n))\r\nprint(so_le(n))","sub_path":"Buoi5/bai5.py","file_name":"bai5.py","file_ext":"py","file_size_in_byte":666,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"621340942","text":"from colormath.color_objects import sRGBColor, LabColor\nfrom colormath.color_conversions import convert_color\nfrom colormath.color_diff import delta_e_cie2000\n\nimport math\ncolor1=(255,0,0)\ncolor2=(0,0,255)\n\n\n\nred_diff = math.pow((color1[0] - color2[0]), 2)\ngreen_diff = math.pow((color1[1] - color2[1]), 2)\nblue_diff = math.pow((color1[2] - color2[2]), 2)\nprint(math.sqrt(red_diff + green_diff + blue_diff))\n\n# define colors as RGB objects\ncolor1_rgb = sRGBColor(255, 0, 0, True);\ncolor2_rgb = sRGBColor(0, 0, 255, True);\n# convert the colors to LabColor\ncolor1_lab = convert_color(color1_rgb, LabColor);\ncolor2_lab = convert_color(color2_rgb, LabColor);\n# get the distance\ndelta_e = delta_e_cie2000(color1_lab, color2_lab);\nprint(f'The difference is {delta_e}.')","sub_path":"labs/lab7/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":763,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"242484186","text":"from typing import Dict, List, Union\nfrom uuid import UUID, uuid4\n\nimport pytest\n\nfrom pydantic import UUID1, BaseModel, conint, errors\nfrom pydantic.error_wrappers import ValidationError, flatten_errors, get_exc_type\n\n\n@pytest.mark.parametrize(\n    'result,expected',\n    (\n        (\n            'errors',\n            [\n                {'loc': ('a',), 'msg': 'value is not a valid integer', 'type': 'type_error.integer'},\n                {'loc': ('b', 'x'), 'msg': 'field required', 'type': 'value_error.missing'},\n                {'loc': ('b', 'z'), 'msg': 'field required', 'type': 'value_error.missing'},\n                {'loc': ('c', 0, 'x'), 'msg': 'value is not a valid integer', 'type': 'type_error.integer'},\n                {'loc': ('d',), 'msg': 'value is not a valid integer', 'type': 'type_error.integer'},\n                {'loc': ('d',), 'msg': 'value is not a valid uuid', 'type': 'type_error.uuid'},\n                {'loc': ('e', '__key__'), 'msg': 'value is not a valid integer', 'type': 'type_error.integer'},\n                {'loc': ('f', 0), 'msg': 'value is not a valid integer', 'type': 'type_error.integer'},\n                {'loc': ('f', 0), 'msg': 'none is not an allowed value', 'type': 'type_error.none.not_allowed'},\n                {\n                    'loc': ('g',),\n                    'msg': 'uuid version 1 expected',\n                    'type': 'value_error.uuid.version',\n                    'ctx': {'required_version': 1},\n                },\n                {\n                    'loc': ('h',),\n                    'msg': 'yet another error message template 42',\n                    'type': 'value_error.number.not_gt',\n                    'ctx': {'limit_value': 42},\n                },\n            ],\n        ),\n        (\n            'json',\n            \"\"\"\\\n[\n  {\n    \"loc\": [\n      \"a\"\n    ],\n    \"msg\": \"value is not a valid integer\",\n    \"type\": \"type_error.integer\"\n  },\n  {\n    \"loc\": [\n      \"b\",\n      \"x\"\n    ],\n    \"msg\": \"field required\",\n    \"type\": \"value_error.missing\"\n  },\n  {\n    \"loc\": [\n      \"b\",\n      \"z\"\n    ],\n    \"msg\": \"field required\",\n    \"type\": \"value_error.missing\"\n  },\n  {\n    \"loc\": [\n      \"c\",\n      0,\n      \"x\"\n    ],\n    \"msg\": \"value is not a valid integer\",\n    \"type\": \"type_error.integer\"\n  },\n  {\n    \"loc\": [\n      \"d\"\n    ],\n    \"msg\": \"value is not a valid integer\",\n    \"type\": \"type_error.integer\"\n  },\n  {\n    \"loc\": [\n      \"d\"\n    ],\n    \"msg\": \"value is not a valid uuid\",\n    \"type\": \"type_error.uuid\"\n  },\n  {\n    \"loc\": [\n      \"e\",\n      \"__key__\"\n    ],\n    \"msg\": \"value is not a valid integer\",\n    \"type\": \"type_error.integer\"\n  },\n  {\n    \"loc\": [\n      \"f\",\n      0\n    ],\n    \"msg\": \"value is not a valid integer\",\n    \"type\": \"type_error.integer\"\n  },\n  {\n    \"loc\": [\n      \"f\",\n      0\n    ],\n    \"msg\": \"none is not an allowed value\",\n    \"type\": \"type_error.none.not_allowed\"\n  },\n  {\n    \"loc\": [\n      \"g\"\n    ],\n    \"msg\": \"uuid version 1 expected\",\n    \"type\": \"value_error.uuid.version\",\n    \"ctx\": {\n      \"required_version\": 1\n    }\n  },\n  {\n    \"loc\": [\n      \"h\"\n    ],\n    \"msg\": \"yet another error message template 42\",\n    \"type\": \"value_error.number.not_gt\",\n    \"ctx\": {\n      \"limit_value\": 42\n    }\n  }\n]\"\"\",\n        ),\n        (\n            '__str__',\n            \"\"\"\\\n11 validation errors for Model\na\n  value is not a valid integer (type=type_error.integer)\nb -> x\n  field required (type=value_error.missing)\nb -> z\n  field required (type=value_error.missing)\nc -> 0 -> x\n  value is not a valid integer (type=type_error.integer)\nd\n  value is not a valid integer (type=type_error.integer)\nd\n  value is not a valid uuid (type=type_error.uuid)\ne -> __key__\n  value is not a valid integer (type=type_error.integer)\nf -> 0\n  value is not a valid integer (type=type_error.integer)\nf -> 0\n  none is not an allowed value (type=type_error.none.not_allowed)\ng\n  uuid version 1 expected (type=value_error.uuid.version; required_version=1)\nh\n  yet another error message template 42 (type=value_error.number.not_gt; limit_value=42)\"\"\",\n        ),\n    ),\n)\ndef test_validation_error(result, expected):\n    class SubModel(BaseModel):\n        x: int\n        y: int\n        z: str\n\n    class Model(BaseModel):\n        a: int\n        b: SubModel\n        c: List[SubModel]\n        d: Union[int, UUID]\n        e: Dict[int, str]\n        f: List[Union[int, str]]\n        g: UUID1\n        h: conint(gt=42)\n\n        class Config:\n            error_msg_templates = {'value_error.number.not_gt': 'yet another error message template {limit_value}'}\n\n    with pytest.raises(ValidationError) as exc_info:\n        Model.parse_obj(\n            {\n                'a': 'not_int',\n                'b': {'y': 42},\n                'c': [{'x': 'not_int', 'y': 42, 'z': 'string'}],\n                'd': 'string',\n                'e': {'not_int': 'string'},\n                'f': [None],\n                'g': uuid4(),\n                'h': 21,\n            }\n        )\n\n    assert getattr(exc_info.value, result)() == expected\n\n\ndef test_errors_unknown_error_object():\n    with pytest.raises(RuntimeError):\n        list(flatten_errors([object]))\n\n\n@pytest.mark.parametrize(\n    'exc,type_',\n    (\n        (TypeError(), 'type_error'),\n        (ValueError(), 'value_error'),\n        (AssertionError(), 'assertion_error'),\n        (errors.DecimalIsNotFiniteError(), 'value_error.decimal.not_finite'),\n    ),\n)\ndef test_get_exc_type(exc, type_):\n    if isinstance(type_, str):\n        assert get_exc_type(type(exc)) == type_\n    else:\n        with pytest.raises(type_) as exc_info:\n            get_exc_type(type(exc))\n        assert isinstance(exc_info.value, type_)\n\n\ndef test_single_error():\n    class Model(BaseModel):\n        x: int\n\n    with pytest.raises(ValidationError) as exc_info:\n        Model(x='x')\n\n    expected = \"\"\"\\\n1 validation error for Model\nx\n  value is not a valid integer (type=type_error.integer)\"\"\"\n    assert str(exc_info.value) == expected\n    assert str(exc_info.value) == expected  # to check lru cache doesn't break anything\n\n    with pytest.raises(ValidationError) as exc_info:\n        Model()\n\n    assert (\n        str(exc_info.value)\n        == \"\"\"\\\n1 validation error for Model\nx\n  field required (type=value_error.missing)\"\"\"\n    )\n\n\ndef test_nested_error():\n    class NestedModel3(BaseModel):\n        x: str\n\n    class NestedModel2(BaseModel):\n        data2: List[NestedModel3]\n\n    class NestedModel1(BaseModel):\n        data1: List[NestedModel2]\n\n    with pytest.raises(ValidationError) as exc_info:\n        NestedModel1(data1=[{'data2': [{'y': 1}]}])\n\n    expected = [{'loc': ('data1', 0, 'data2', 0, 'x'), 'msg': 'field required', 'type': 'value_error.missing'}]\n\n    assert exc_info.value.errors() == expected\n\n\ndef test_validate_assignment_error():\n    class Model(BaseModel):\n        x: int\n\n        class Config:\n            validate_assignment = True\n\n    model = Model(x=1)\n    with pytest.raises(ValidationError) as exc_info:\n        model.x = 'a'\n    assert (\n        str(exc_info.value)\n        == '1 validation error for Model\\nx\\n  value is not a valid integer (type=type_error.integer)'\n    )\n","sub_path":"tests/test_error_wrappers.py","file_name":"test_error_wrappers.py","file_ext":"py","file_size_in_byte":7124,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"6839272","text":"# Advent of Code 2017\n# Day 6, Part 2\n# @geekygirlsarah\n\n# Files to run through\n# input.txt being the input the puzzle provides\ninputFile = \"input.txt\"\n# inputFile = \"testinput.txt\"\n\n# Variables\nnumbers = []\nold_combos = dict()\nlength = 0\nnum_steps = 0\n\n# To make checking lists a lot simpler\ndef listToString(the_list):\n    return \" \".join(str(elem) for elem in the_list)\n\n# Process file\nwith open(inputFile) as f:\n    while True:\n        contents = f.readline(-1)\n        if not contents:\n            # print \"End of file\"\n            break\n        # print (\"Contents: \", contents)\n\n        numbers = contents.split()\n        numbers = map(int, numbers)\n\nlength = len(numbers)\n\nprint(\"length: \" + str(length))\nprint(\"list: \")\nprint(numbers)\n\nwhile True:\n    num_steps += 1\n    print(\"Step: \" + str(num_steps))\n\n    # add combo to tracking list\n    old_combos[listToString(numbers)] = num_steps\n\n    # Find largest position\n    max_num = max(numbers)\n    position = numbers.index(max_num)\n    print(\"   Found max number: \" + str(max_num))\n    print(\"   Found at index: \" + str(position))\n\n    # Redistribute blocks\n    numbers[position] = 0\n    print(\"   Before redistribution: \")\n    print(numbers)\n    for i in range (0, max_num):\n        # increment position\n        position = (position + 1) % length\n        # Add one to the position's number\n        numbers[position] += 1\n    print(\"   After redistribution: \")\n    print(numbers)\n\n    # See if this combo exists\n    string = listToString(numbers)\n    if string in old_combos:\n        print(\"\\n\\nNumber of steps: \" + str(num_steps))\n        step_found = num_steps - old_combos[string] + 1\n        print(\"\\nStep found at: \" + str(step_found))\n        break\n","sub_path":"day06/day06part2.py","file_name":"day06part2.py","file_ext":"py","file_size_in_byte":1713,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"538449451","text":"import unittest\nfrom ..inspector import NameInspector, MatchupInspector\nfrom .. import Replay\nimport platform\n\nclass InspectorTestCase(unittest.TestCase):\n    def setUp(self):\n        self.replay = None\n        if platform.system() is 'Windows':\n            self.replay = Replay('SampleReplays\\\\Sample 1.SC2Replay')\n        else:\n            self.replay = Replay('SampleReplays/Sample 1.SC2Replay')\n\n        self.namer = NameInspector()\n        self.matchupper = MatchupInspector()\n\n    def test_name_inspector(self):\n        names = self.namer.inspect(self.replay)\n        self.assertTrue(names == ['[IxGeu] goblin', ' Clem'])\n\n    def test_matchup_inspector(self):\n        matchup = self.matchupper.inspect(self.replay)\n        self.assertTrue('Protoss vs Terran' == matchup[0])","sub_path":"reps/Tests/inspector_tests.py","file_name":"inspector_tests.py","file_ext":"py","file_size_in_byte":780,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"101196732","text":"# coding:utf-8\n# 現在、SageMakerではTensorFlowの実行はPython 2.7のみのサポートとなっているので注意\n\nimport os\nimport tensorflow as tf\nfrom tensorflow.python.estimator.model_fn import ModeKeys as Mode\n\nEXAMPLE_SPEC = {\n    'image': tf.FixedLenFeature([28 * 28], tf.float32),\n    'label': tf.FixedLenFeature([]       , tf.int64),\n}\n\ndef train_input_fn(data_dir, params):\n    tfrecord = os.path.join(data_dir, 'test.tfr')\n    return _input_fn(tfrecord, params, training=True)\n    \ndef eval_input_fn(data_dir, params):\n    tfrecord = os.path.join(data_dir, 'test.tfr')\n    return _input_fn(tfrecord, params, training=False)\n\ndef _parse_proto(proto):\n    parsed = tf.parse_single_example(proto, EXAMPLE_SPEC)\n    image = parsed['image']\n    label = tf.one_hot(parsed['label'], 10)\n    return image, label\n\ndef _input_fn(tfrecord, params, training):\n    dataset = (tf.data.TFRecordDataset(tfrecord, compression_type=tf.python_io.TFRecordCompressionType.GZIP)\n        .map(_parse_proto)\n        .shuffle(1000))\n    # 学習のときは無限に生成する\n    dataset = dataset.repeat() if training else dataset\n    images, labels = (dataset\n        .batch(params.get('batch_size', 512))\n        .make_one_shot_iterator()\n        .get_next())\n    return {'images': images}, labels\n\ndef serving_input_fn(hparams):\n    features = {\n        'images': tf.placeholder(tf.float32, [None, 784])\n    }\n    return tf.estimator.export.build_raw_serving_input_receiver_fn(features)()\n\ndef model_fn(features, labels, mode, params):    \n    x = features['images']\n    \n    W = tf.get_variable('W', [784,10])\n    b = tf.get_variable('b', [10])\n    \n    y = tf.nn.softmax(tf.matmul(x, W) + b)\n    y_indices = tf.argmax(input=y, axis=1) # 予測したラベルのインデクス\n    \n    predictions = {\n        'classes': y_indices,\n        'probabilities': y,\n    }\n    export_outputs = {\n        'predictions': tf.estimator.export.PredictOutput(predictions),\n    }\n    \n    if mode == tf.estimator.ModeKeys.PREDICT:\n        return tf.estimator.EstimatorSpec(\n            mode=mode,\n            predictions=predictions,\n            export_outputs=export_outputs)\n\n    learning_rate = params.get('learning_rate', 0.5)\n    global_step = tf.train.get_or_create_global_step()\n    \n    cross_entropy = tf.reduce_mean(\n        -tf.reduce_sum(labels * tf.log(y), reduction_indices=[1]))\n    \n    optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n    fit = optimizer.minimize(cross_entropy, global_step)\n\n    return tf.estimator.EstimatorSpec(\n        mode=mode,\n        predictions=predictions,\n        loss=cross_entropy,\n        train_op=fit,\n        export_outputs=export_outputs)\n","sub_path":"code/mnist.py","file_name":"mnist.py","file_ext":"py","file_size_in_byte":2694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"153068302","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Apr  9 13:15:07 2018\n\n@author: chingandywu\n\"\"\"\n\nimport pickle \nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\n# Global Vararables\n\nmu = 0        \nsigma = 0.01\nlamda = 1\nd = 3072\nK = 10        \nN = 10000     \n\n\ndef LoadBatch(file):\n    with open(file, 'rb') as fo:\n        dict = pickle.load(fo, encoding= 'bytes')\n    \n    X = dict[b'data'].T / 255\n    y = dict[b'labels']# list of labels as int\n    Y = np.zeros((10, X.shape[1]))\n    for i in range(len(y)):\n        Y[y[i], i ] = 1;   \n    return X, Y, y\n    \ndef params_init(mu, sigma, K, d):\n    # initialize the parameters of the model w and b\n    np.random.seed(400)\n    W = np.random.normal(mu, sigma, (K, d))\n    b = np.random.normal(mu, sigma, (K, 1))\n    return W, b\n\n\ndef EvaluateClassifier(X, W, b):\n    s = np.dot(W, X) + b\n    return np.exp(s) / np.sum(np.exp(s), axis=0)\n\ndef ComputeCost(X, Y, W, b, lamda):\n    data_size = X.shape[1]\n    l_loss = np.diag(-np.log(np.dot(Y.T,EvaluateClassifier(X, W, b))))\n    l_cross_sum = np.sum(l_loss)\n    \n    regularization = 2*lamda * np.sum(W**2)\n    \n    cost = l_cross_sum/data_size + regularization\n    return  cost, l_cross_sum\n\ndef ComputeAccuracy(X, y, W, b):\n    \n    pred_class = np.argmax(EvaluateClassifier(X,W,b), axis = 0)\n    error_amount = np.count_nonzero(np.abs(pred_class - y))\n    error = error_amount/ X.shape[1]\n    \n    return 1 - error \n\n    #eval_out = np.abs(pred - np.argmax(y, axis = 0))\n    #error = np.count_nonzero(eval_out) / X.shape[1]\n    #return 1 - error\n\n#========================================================================\ndef ComputeGradients(X, Y, P, W, lamda):\n    # Compute the gradients analytically\n    \n    grad_W = np.zeros((K, d))\n    grad_b = np.zeros((K, 1))\n    diag_p = np.zeros((K, K))\n    \n    for i in range(X.shape[1]):                \n            y = np.copy(Y[:, i])\n            y = y.reshape(K,1)\n            y_T = y.T\n            \n            p = np.copy(P[:, i])\n            p = p.reshape(K, 1)\n            p_T = p.T\n            \n            np.fill_diagonal(diag_p, p)\n            x = np.copy(X[:, i])\n            x = x.reshape(d, 1)\n            g = -(np.dot(y_T, (diag_p - np.dot(p, p_T)))) / np.dot(y_T, p)\n            grad_b += g.T\n            grad_W += np.dot(g.T, x.T)\n            \n    agrad_W = grad_W / X.shape[1] + 2 * lamda * W\n    agrad_b = grad_b / X.shape[1]               \n    return agrad_W, agrad_b\n    \n#========================================================================\ndef ComputeGradsNumSlow(X, Y, W, b, lamda, h = 1e-6):\n    # translate the MATLAB code to Python\n    # compute the gradients numerically\n    no = W.shape[0] \n    \n    ngrad_W = np.zeros(W.shape)\n    ngrad_b = np.zeros((no,1))\n    \n    for i in range(len(b)):\n        b_try = np.copy(b)\n        b_try[i] = b_try[i] - h\n        c1 = ComputeCost(X,Y,W, b_try, lamda)[0]\n        b_try = np.copy(b)\n        b_try[i] = b_try[i] + h\n        c2 = ComputeCost(X,Y,W, b_try, lamda)[0]\n        ngrad_b[i] = (c2 - c1) / (2*h)\n        \n        \n        \n    for i in range(len(W)):\n        for j in range(len(W[0])):\n            W_try = np.copy(W)\n            W_try[i][j] -= h\n            c1 = ComputeCost(X, Y, W_try, b, lamda)[0]\n            W_try = np.copy(W)\n            W_try[i][j] += h\n            c2 = ComputeCost(X, Y, W_try, b, lamda)[0]\n            ngrad_W[i][j] = (c2-c1) / (2*h)\n            \n    return ngrad_W, ngrad_b\n\n#========================================================================\ndef CheckGrad(agrad_W, agrad_b, ngrad_W, ngrad_b):\n    \n    Wdiff = agrad_W - ngrad_W\n    bdiff = agrad_b  - ngrad_b\n    Wabs_sum = np.abs(agrad_W) + np.abs(ngrad_W)\n    babs_sum = np.abs(agrad_b)  + np.abs(ngrad_b)\n    eps = 0.0001\n    \n    Wratio = np.sum(np.abs(Wdiff))/max(eps, Wabs_sum.sum())\n    bratio = np.sum(np.abs(bdiff))/ max(eps, babs_sum.sum())\n    print(Wratio)\n    if Wratio > 1e-6  or bratio > 1e-6 :\n        print('Wrong gradients')\n    else:\n        print('safe!')\n\n#========================================================================\nclass GD_params:\n    def __init__(self, n_batch, eta, n_epochs):\n        self.n_batch = n_batch\n        self.eta = eta\n        self.n_epochs = n_epochs\n\n    def __str__(self):\n        return \"GD parameters: \" + \"n_batch \" + str(self.n_batch)  + \" eta \"+ str(self.eta) + \" n_epochs \"  + str(self.n_epochs)\n\n\n\n# =============================================================================\n                \ndef MiniBatchGD(X, Y,y_tr,X_valid, Y_valid,y_valid,X_test,Y_test, y_test, GDparams, W, b, lamda):\n    \n    cost_list = []\n    l_cross_sum_list = []\n    \n    vcost_list = []\n    vl_cross_sum_list = []\n    \n    acc_list = []\n  \n    \n    for epoch in range(GDparams.n_epochs):  \n        print('epoch:', epoch)\n        \n        for j in range(0, N // GDparams.n_batch):\n                 # set of mini-batches for 1 epoch\n                 j_start = j * GDparams.n_batch\n                 j_end = (j+1) * GDparams.n_batch\n                 X_batch = X[:, j_start : j_end]\n                 Y_batch = Y[:, j_start : j_end]\n                 J_grad_W, J_grad_b =ComputeGradients(X_batch, Y_batch, EvaluateClassifier(X_batch, W, b), W, lamda)\n                 W -= GDparams.eta * J_grad_W\n                 b -= GDparams.eta * J_grad_b\n                \n        # compute cost function, loss function for training data \n        cost , l_cross = ComputeCost(X, Y, W, b, lamda)\n        cost_list.append(cost)\n        l_cross_sum_list.append(l_cross.sum()/X.shape[1])\n        \n        # compute cost function, loss function for validation data \n        vcost , vl_cross = ComputeCost(X_valid, Y_valid, W, b, lamda)\n        vcost_list.append(vcost)\n        vl_cross_sum_list.append(vl_cross.sum()/X.shape[1])\n        \n        \n        # compute accuracy using test data\n        acc_list.append(ComputeAccuracy(X_test, y_test, W, b)) \n        \n    acc_max = np.max(acc_list)\n        \n    return W, b, cost_list, l_cross_sum_list, vcost_list, vl_cross_sum_list, acc_max\n    \ndef GraphPlot(cost_train, cost_valid, loss_train, loss_valid, GDparams):\n    fig0 = plt.figure(figsize=(20,10))\n    fig0.add_subplot(1,2,1)\n    l1 = plt.plot(cost_train,'g', label = 'Training cost')\n    l2 = plt.plot(cost_valid,'r', label = 'Validation cost')\n    plt.legend(loc = 'upper right')\n    plt.ylabel('cost')\n    plt.xlabel('epochs')\n    plt.title('Cost function')\n\n    fig0.add_subplot(1,2,2)\n    l3 = plt.plot(loss_train,'g', label = 'Total Training loss')\n    l4 = plt.plot(loss_valid,'r', label = 'Total Validation loss')\n    plt.legend(loc = 'upper right')\n    plt.ylabel('loss')\n    plt.xlabel('epochs')\n    plt.title('Loss')\n    plt.savefig(\"/Users/chingandywu/chinganwu/KTH/Y1P4/Deep Learning/Assignment1/fig/graphs_l=\" + str(lamda) + \"_b=\" + str(GDparams.n_batch) + \"_eta=\"+ str(GDparams.eta) + \"_ep=\" + str(GDparams.n_epochs) + \".png\", bbox_inches='tight')\n    \n# =========================================================================\ndef WeightsVisual(W, GDparams):\n    # visualize the weight matrix W\n    \n    images = []\n    for img in W:\n        raw_img=np.rot90(img.reshape(3, 32, 32).T, -1)\n        image = ((raw_img-np.min(raw_img))/(np.max(raw_img)-np.min(raw_img)))\n        images.append(image)\n    fig1 = plt.figure(figsize = (20, 5))\n    \n    for idx in range(len(W)):\n        ax = fig1.add_subplot(1, 10, idx+1) \n        ax.set_title('Class %s'%(idx+1))\n        ax.imshow(images[idx])\n        ax.axis('off')\n    plt.savefig(\"/Users/chingandywu/chinganwu/KTH/Y1P4/Deep Learning/Assignment1/fig/weights_l=\" + str(lamda) + \"_b=\" + str(GDparams.n_batch) + \"_eta=\" + str(GDparams.eta) + \"_ep=\" + str(GDparams.n_epochs) + \".png\", bbox_inches='tight')                        \n\n\n\n #========================================================================    \ndef main():\n    \n    print('Loading data')\n    \n    X_train, Y_train ,y_train= LoadBatch('/Users/chingandywu/chinganwu/KTH/Y1P4/Deep Learning/Assignment1/dataset/cifar-10-batches-py/data_batch_1')\n    X_valid, Y_valid ,y_valid = LoadBatch('/Users/chingandywu/chinganwu/KTH/Y1P4/Deep Learning/Assignment1/dataset/cifar-10-batches-py/data_batch_2')\n    X_test, Y_test ,y_test = LoadBatch('/Users/chingandywu/chinganwu/KTH/Y1P4/Deep Learning/Assignment1/dataset/cifar-10-batches-py/test_batch')\n    W,b = params_init(mu, sigma, K, d)\n    \n# =============================================================================\n#     # check gradient computations!\n#     agrad_W, agrad_b =  ComputeGradients(X_train[:,:100], Y_train[:,:100], EvaluateClassifier(X_train[:,:100], W, b), W, lamda)\n#     ngrad_W, ngrad_b =  ComputeGradsNumSlow(X_train[:,:100], Y_train[:,:100], W, b, lamda, h = 1e-6)\n#     CheckGrad(agrad_W, agrad_b, ngrad_W, ngrad_b)\n# =============================================================================\n    \n    \n    GDparams= GD_params(n_batch = 100, eta = 0.01 , n_epochs = 40)\n    W_train, b_train,cost_list_train, l_cross_sl_train, cost_list_valid, l_cross_sl_valid, acc  = MiniBatchGD(X_train, Y_train,y_train, X_valid, Y_valid, y_valid,X_test,Y_test,y_test ,GDparams, W, b, lamda)\n\n    print(\"The final accuracy: \", acc)\n    \n    GraphPlot(cost_list_train, cost_list_valid, l_cross_sl_train, l_cross_sl_valid, GDparams)\n    WeightsVisual(W_train, GDparams )\n    \nif __name__ == \"__main__\":\n    main()","sub_path":"Assignment1/Assignment1.py","file_name":"Assignment1.py","file_ext":"py","file_size_in_byte":9352,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"209169795","text":"#!/usr/bin/env python3\n\"\"\"\nBasic Python CKY parser implementation.\n\nNote that the root symbol is defined at\nthe top of the file.\n\"\"\"\nimport sys, string, nltk\nimport collections\nimport argparse\n\n# -------------------------------------------\n# Root Symbol Definition\n# -------------------------------------------\nROOT_SYMBOL = 'TOP'\n\n\ndef pprint(tree):\n    \"\"\"\n    Prints a cleanly indented version of the parse tree.\n    :param tree:\n    :return:\n    \"\"\"\n    out_str = ''\n    indent_depth = 0\n    indent_span = '  '\n    open_brackets = tree.split('(')\n    open_count = 0\n    for open_bracket in open_brackets:\n        if open_count > 0:\n            ct = 0\n            while ct < indent_depth:\n                out_str += indent_span\n                ct += 1\n            out_str += '('\n        if open_bracket.find(')') >= 0:\n            closes = open_bracket.split(')')\n            close_count = 0\n            while close_count < len(closes)-1:\n                out_str += closes[close_count] + ')'\n                indent_depth -= 1\n                close_count += 1\n            out_str += '\\n'\n        else:\n            out_str += open_bracket+'\\n'\n        if open_count > 0:\n            indent_depth += 1\n\n        open_count += 1\n\n    print(out_str+'\\n')\n\n\nclass Rule(object):\n    \"\"\"\n    Representation of a rule A -> B ... C\n    \"\"\"\n    def __init__(self, head, symbols):\n        self.head = head\n        self.symbols = symbols\n        self._key = head, symbols\n\n    def __eq__(self, other):\n        return self._key == other._key\n\n    def __hash__(self):\n        return hash(self._key)\n\n\ndef get_grammar(grammar_data):\n    \"\"\"\n    Build a grammar from strings like \"X -> Y Z | b\"\n    :type grammar_data: str\n    :rtype: set[Rule]\n    \"\"\"\n    grammar = set()\n    for line in grammar_data.splitlines():\n        head, symbols_str = line.split(' -> ')\n        for symbols_str in symbols_str.split(' | '):\n            symbols = tuple(symbols_str.split())\n            grammar.add(Rule(head, symbols))\n    return grammar\n\n\ndef cky_table(grammar: set, words):\n    \"\"\"\n    Given a grammar and the input words, build both the\n    nonterminal table and backpointer table.\n    :type grammar: set[rule]\n    \"\"\"\n    nonterminal_table = collections.defaultdict(set)\n    backpointer_table = collections.defaultdict(lambda: {})\n    for j, word in enumerate(words):\n        j += 1\n        # find all rules for the current word\n        for rule in grammar:\n            if rule.symbols == (word,):\n                nonterminal_table[j - 1, j].add(rule.head)\n                backpointer_table[j - 1, j][rule.head] = []\n                back = None, None, None\n                backpointer_table[j - 1, j][rule.head].append(back)\n\n        # for each span of words ending at the current word,\n        # find all splits that could have formed that span\n        for i in range(j - 2, -1, -1):\n            for k in range(i + 1, j):\n                # if the two constituents identified by this\n                # split can be combined, add the combination\n                # to the table\n                for rule in grammar:\n                    if len(rule.symbols) == 2:\n                        l_sym, r_sym = rule.symbols\n                        if l_sym in nonterminal_table[i, k]:\n                            if r_sym in nonterminal_table[k, j]:\n                                nonterminal_table[i, j].add(rule.head)\n                                back = k, l_sym, r_sym\n                                if rule.head not in backpointer_table[i, j]:\n                                    backpointer_table[i, j][rule.head] = []\n                                if back not in backpointer_table[i, j][rule.head]:\n                                    backpointer_table[i, j][rule.head].append(back)\n\n\n        \n    # return tree\n    return get_tree(0, len(words), ROOT_SYMBOL, backpointer_table, words)\n\ndef get_tree(row, col, symbol, backpointer_table, words) -> list:\n    \"\"\"\n    Helper function for converting backpointers to a tree\n    :param row:\n    :param col:\n    :param symbol:\n    :return:\n    \"\"\"\n    mid, head1, head2 = backpointer_table[row, col][symbol][0]\n    if mid is head1 is head2 is None:\n        return ['(%s %s)' % (symbol, words[row])]\n    trees = []\n    for mid, head1, head2 in backpointer_table[row, col][symbol]:\n        trees_left = get_tree(row, mid, head1, backpointer_table, words)\n        trees_right = get_tree(mid, col, head2, backpointer_table, words)\n        for tree_l in trees_left:\n            for tree_r in trees_right:\n                trees.append('(%s %s %s)' % (symbol, tree_l, tree_r))\n\n    return(trees)\n\ndef main():\n    p = argparse.ArgumentParser()\n    p.add_argument('grammarfile', help='Path to the CFG file to load.')\n    p.add_argument('sentfile', help='Path to the sentences to parse.')\n\n    args = p.parse_args()\n\n    grammar_f = open(args.grammarfile,'r')\n    grammar_text = ''.join(grammar_f.readlines())\n\n    grammar_cnf = get_grammar(grammar_text.replace(\"'\",''))\n\n    with open(args.sentfile, 'r') as sent_f:\n\n        for sent in sent_f.readlines():\n            sent = sent[:-1]\n            words = nltk.word_tokenize(sent)\n            if len(words) > 0:\n                trees = cky_table(grammar_cnf, words)\n\n                # Print the sentence and number of parses.\n                print(sent + ' ['+str(len(trees))+' parse(s)]\\n')\n\n                # Prettyprint the resulting parses.\n                for tree in trees:\n                    pprint(tree)\n            else:\n                print(sent)\n\nmain()\n","sub_path":"hw4 - OOV (Extra-Credit)/examples/tools/example_cky.py","file_name":"example_cky.py","file_ext":"py","file_size_in_byte":5532,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"496170739","text":"\nimport json, os\n\n# This should eventually be changed once the prefix is customizable\nPLANUTILS_PREFIX = os.path.join(os.path.expanduser('~'), '.planutils')\n\nSETTINGS_FILE = os.path.join(PLANUTILS_PREFIX, 'settings.json')\n\ndef load():\n    with open(SETTINGS_FILE, 'r') as f:\n        settings = json.loads(f.read())\n    return settings\n\ndef save(s):\n    with open(SETTINGS_FILE, 'w') as f:\n        f.write(json.dumps(s))\n","sub_path":"planutils/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":420,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"402920975","text":"\"\"\"\nStandard setup.py for easy install\n\nFor development do\n# python setup.py develop\n\"\"\"\nfrom setuptools import setup, find_packages\n\npackages = find_packages('src')\nversion = \"0.1\"\n\nsetup(\n    name=\"admin\",\n    version=version,\n    zip_safe=False,\n    packages=packages,\n    package_dir={'': 'src'},\n    include_package_data=True,\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":334,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"11271571","text":"from django.db import models\nfrom django.conf import settings\nfrom django.contrib.auth.models import User\nimport datetime, random\nimport shutil\nimport os\nfrom PIL import Image, ImageDraw\nimport textwrap\nimport pyimgur\n\nF_TITULO = settings.F_TITULO\nF_FALTAM = settings.F_FALTAM\nF_TEXTO = settings.F_TEXTO\nF_WRAP = settings.F_WRAP\nfont_title = settings.FONT_TITLE\nfont_text = settings.FONT_TEXT\nfont_titulo = settings.FONT_TITULO\nfont_faltam = settings.FONT_FALTAM\nfont_descricao = settings.FONT_DESCRICAO\n\n\nclass Esc(models.Model):\n    titulo = models.CharField(max_length=30)\n    faltam = models.CharField(max_length=30)\n    descricao = models.CharField(max_length=200)\n\n\n    def __unicode__(self):\n        return self.titulo\n\n\ndef img_default():\n    return str(hash(datetime.datetime.now())) + '.jpg'\n\n\nclass EscImg(models.Model):\n    criado_em = models.DateTimeField(auto_now_add=True)\n    esc = models.ForeignKey('Esc')\n    img_id = models.CharField(max_length=50, default=img_default)\n    votos = models.IntegerField(default=0)\n\n\n    def autonumber(self):\n        prefixo = 'NO.' + str(random.randint(1,24))\n        self.esc.titulo = prefixo + ' ' + self.esc.titulo\n\n\n    def prepare(self):\n        base = os.path.join(settings.BASE_DIR, 'escenario_template.jpg')\n        alvo = os.path.join(settings.BASE_DIR, 'tempfiles', self.img_id)\n        shutil.copy(base, alvo)\n        return alvo\n\n\n    def draw(self, alvo):\n        img = Image.open(alvo)\n        linhas = textwrap.wrap(self.esc.descricao, width=F_WRAP)\n        y_text = 90\n        draw = ImageDraw.Draw(img)\n        draw.text((20,10), self.esc.titulo.upper(), (255,255,255), font=font_titulo)\n        draw.text((80,47), self.esc.faltam.upper(), (150,255,0), font=font_faltam)\n        for linha in linhas:\n            h = font_descricao.getsize(linha)[1]\n            draw.text((15, y_text), linha, (255,255,255), font=font_descricao)\n            y_text += h + 2\n        draw = ImageDraw.Draw(img)\n        img.save(alvo)\n\n\n    def upload(self, alvo):\n        IMGUR_API = os.environ.get('IMGUR_API', None)\n        if not IMGUR_API:\n            raise Exception('IMGUR API missing')\n        else:\n            im = pyimgur.Imgur(IMGUR_API)\n            uploaded_image = im.upload_image(alvo, title=self.esc.titulo)\n            os.remove(alvo)\n            self.img_id = uploaded_image.link\n            self.save()\n\n\n    def gostei(self):\n        self.votos += 1\n        return self.votos \n\n\n    def __unicode__(self):\n        return self.esc.titulo\n\nclass MicroblogPost(models.Model):\n    text = models.TextField()\n    author = models.ForeignKey(User)\n    created_at = models.DateTimeField(auto_now_add=True)\n    updated_at = models.DateTimeField(auto_now=True)\n    fixed = models.BooleanField(default=False)\n\n    def save(self, *args, **kwargs):\n        if self.fixed:\n            others = MicroblogPost.objects.exclude(id=self.id)\n            for other in others:\n                other.fixed = False\n                other.save()\n        super(MicroblogPost, self).save(*args, **kwargs)\n","sub_path":"generator/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3044,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"365999536","text":"def check_text(text=''):\n\n    if text == '':  # if not text:  <- it's the same\n        return 'Enter the text.'\n\n    black_list = ['next', 'continue']\n    for word in black_list:\n        if word in text:\n            raise Exception('Text is not valid.')\n\n    return 'Text is valid.'\n","sub_path":"lessno_48/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":283,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"200162779","text":"import pyodbc\nfrom db_connect import *\n\nclass DBCustomers(DBConnection):\n\n    def __init__(self):\n        super().__init__()\n        self.table = 'Customers'\n\n\n    def read_allcustomers(self):  # return iterable object!\n        query = 'SELECT * FROM Customers'\n        data = self.sql_query(query)\n        return data\n\n    def set_customerid(self):\n        id = int(input('Select a Customer ID: '))\n        return id\n\n    def read_customerid(self, id):\n        query = f\"SELECT * FROM Customers WHERE CustomerID = {id}\"\n        result = self.sql_query(query)\n        return result.fetchone()\n\n    def print_allcustomers(self):\n        query = 'SELECT * FROM Customers'\n        data = self.sql_query(query)\n        while True:\n            record = data.fetchone()\n            if record is None:\n                break\n            print(f\"{record.CustomerID} - {record.FirstName} - {record.LastName} - {record.Address}\")\n\n\n\n\n\n# while True:\n#     record = all_customers.fetchone()\n#     if record is None:\n#         break\n#     print(record)    # prints all IDs\n#\n# customer = table_customers.print_id(2)\n# print(customer) # prints specific ID\n","sub_path":"db_customers.py","file_name":"db_customers.py","file_ext":"py","file_size_in_byte":1141,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"239511602","text":"import argparse\nfrom subprocess import Popen\nfrom p4utils.utils.topology import Topology\nimport time\nimport sys\n\n# prev_list_normal=[]\n# current_list_normal=[]\ndef main():\n\tprev_list=[]\n\tparser = argparse.ArgumentParser()\n\tparser.add_argument('--duration', nargs='?', type=int, default=40, help='Duration of traffic')\n\targs = parser.parse_args()\n\tduration= args.duration\n\tpast_list= []\n\titeration_number=0\n\n\tf= open('normal_ks.txt','w')\n\tstart_time= time.time()\n\ttry:\n\t\twhile True:\n\t\t\ttime.sleep(5)\n\t\t\tp1=(Popen(\"simple_switch_CLI --thrift-port 9090 < commands1.txt >out1.txt\",shell=True))\n\t\t\tp1.wait()\n\n\t\t\tp2=(Popen(\"simple_switch_CLI --thrift-port 9090 < commands2.txt >out2.txt\",shell=True))\n\t\t\tp2.wait()\n\n\t\t\tmyfile1=open(\"out1.txt\",\"r\")\n\t\t\tcount1=0\n\t\t\tline1 = myfile1.readlines()\n\t\t\t# print(line)\n\t\t\tline_to_read1= line1[3]\n\t\t\trequired_array1= line_to_read1[21:-1]\n\t\t\tmy_reg1 = required_array1.split(\", \")\n\t\t\t# my_reg1=[]\n\t\t\t# for val in temp1:\n\t\t\t# \tmy_reg1.append(val)\n\t\t\tfor i in range(0,len(my_reg1)):\n\t\t\t\tmy_reg1[i]= int(my_reg1[i])\n\t\t\tmyfile1.close()\n\n\t\t\tmyfile2=open(\"out2.txt\",\"r\")\n\t\t\tcount2=0\n\t\t\tline2 = myfile2.readlines()\n\t\t\t# print(line)\n\t\t\tline_to_read2= line2[3]\n\t\t\trequired_array2= line_to_read2[22:-1]\n\t\t\tmy_reg2 = required_array2.split(\", \")\n\t\t\t# my_reg2=[]\n\t\t\t# for val in temp2:\n\t\t\t# \tmy_reg2.append(val)\n\t\t\tfor i in range(0,len(my_reg2)):\n\t\t\t\tmy_reg2[i]=int(my_reg2[i])\n\t\t\tmyfile2.close()\n\n\t\t\tmy_reg3=[]\n\t\t\tfor i,j in zip(range(len(my_reg1)),range(len(my_reg2))):\n\t\t\t\tmy_reg3.append([my_reg1[i],my_reg2[j]])\n\n\t\t\t# print(my_reg3)\n\n\t\t\t# if iteration_number==0:\n\t\t\t# \ta=2\n\t\t\t# else:\n\t\t\tif iteration_number!=0:\n\t\t\t\t#calculate current window distribution.\n\t\t\t\tcurr_window=[]\n\t\t\t\tfor i,j,k in zip(my_reg3,prev_list,past_list):\n\t\t\t\t\ttemp_count= i[1]-j[1]\n\t\t\t\t\tcurrent_count= 0.2*temp_count + 0.8*k[1]\n\t\t\t\t\tcurr_window.append([i[0],current_count])\n\n\t\t\t\tfor item in curr_window:\n\t\t\t\t\t# to_write1= str(item[0])\n\t\t\t\t\t# to_write2= str(item[1])\n\t\t\t\t\t# f.write(\"%s,\" %to_write1)\n\t\t\t\t\t# f.write(\"%s,\" %to_write2)\n\t\t\t\t\tf.write(str(str(item[0]) + ','))\n\t\t\t\t\tf.write(str(str(item[1]) + ','))\n\n\t\t\t#perform ks test ((o-e)^2)/e\n\t\t\t#write in a file the expected valuess.\n\t\t\tf.write(\"\\n\")\n\t\t\tprev_list=my_reg3\n\t\t\tif iteration_number==0:\n\t\t\t\tpast_list= my_reg3\n\t\t\telse:\n\t\t\t\tpast_list= curr_window\n\t\t\titeration_number=iteration_number+1\n\tfinally:\n\t\tpass\nif __name__ == \"__main__\":\n\tmain()\n\t# for i in my_reg3:\n\t# \tif i[1]>0:\n\t# \t\tprint(i)\n\t# print(\"done\")\n","sub_path":"netcache-latest/src/p4/enormal_ks.py","file_name":"enormal_ks.py","file_ext":"py","file_size_in_byte":2455,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"427557671","text":"# from enum import Enum\nimport re\nfrom skrutil.skr_logger import skr_log_warning\nfrom skr_cpp_builder.cpp_variable import VarType\n\n\n_JAVA_BR = '\\n\\n'\n_JAVA_SPACE = '    '\n\n\ndef function_space(count):\n    space = ''\n    for i in range(1, count + 1):\n        space += _JAVA_SPACE\n    return space\n\n\n# class VarType(Enum):\n#     java_bool = 1\n#     java_int = 2\n#     java_string = 3\n#     java_enum = 4\n#     java_string_array = 5\n#     java_time = 6\n#\n#     def __init__(self, value):\n#         self.enum_class_name = ''\n#\n#     def set_enum_class_name(self, enum_class_name):\n#         if enum_class_name is None:\n#             enum_class_name = ''\n#\n#         self.enum_class_name = enum_class_name\n#\n#         # check input\n#         if self.value == 4 and enum_class_name == '':\n#             skr_log_warning('Enum value should declare its enum class name via \"enum\"')\n#\n#     def to_getter_setter_string(self):\n#         if self.value == 1:\n#             return 'boolean'\n#         elif self.value == 2:\n#             return 'int'\n#         elif self.value == 3:\n#             return 'String'\n#         elif self.value == 4:\n#             return self.java_enum_type_string()\n#         elif self.value == 5:\n#             return 'List<String>'\n#         elif self.value == 6:\n#             return 'long'\n#\n#     @classmethod\n#     def type_from_string(cls, var_type_string):\n#         if var_type_string == 'bool':\n#             return 1\n#         elif var_type_string == 'int':\n#             return 2\n#         elif var_type_string == 'string':\n#             return 3\n#         elif var_type_string == 'enum':\n#             return 4\n#         elif var_type_string == '[string]':\n#             return 5\n#         elif var_type_string == 'time':\n#             return 6\n#\n#     def java_enum_type_string(self):\n#         if self.value != 4 and self.enum_class_name is None or self.enum_class_name == '':\n#             return ''\n#         enum_paths = re.split('\\.', self.enum_class_name)\n#         java_enum = enum_paths[len(enum_paths) - 1]\n#         return java_enum\n\n\nclass JavaVariable:\n    def __init__(self, name, var_type_string):\n        var_type = VarType.instance_from_string(var_type_string)\n\n        var_name_str = ''\n        get_set_name = ''\n        name_splits = re.split('_', name)\n        index = 1\n        for name_split in name_splits:\n            if index == 1:\n                var_name_str += name_split\n            else:\n                var_name_str += name_split.capitalize()\n            get_set_name += name_split.capitalize()\n            index += 1\n\n        self.name_str = var_name_str\n        self.get_set_name_str = get_set_name\n        self.var_type = var_type\n        self.java_enum = ''\n\n    def set_enum_class_name(self, enum_class_name):\n        self.var_type.set_enum_class_name(enum_class_name)\n        self.java_enum = self.var_type.java_enum_type_string()\n\n    def getter(self):\n        function_str = ''\n        if self.var_type == VarType.cpp_bool:\n            function_str += function_space(1) + 'public {0} is{1}() {{\\n'\\\n                .format(self.var_type.to_java_getter_setter_string(), self.get_set_name_str)\n            function_str += function_space(2) + 'return nativeIs{0}(mNativeHandler);\\n'.format(self.get_set_name_str)\n            function_str += function_space(1) + '}'\n        elif self.var_type == VarType.cpp_string_array:\n            function_str += self.to_get_string_array_string()\n        elif self.var_type == VarType.cpp_enum:\n            function_str += function_space(1) + 'public {0} get{1}() {{\\n'.format(self.java_enum, self.get_set_name_str)\n            function_str += function_space(2) + 'return {0}.get{0}ByValue(nativeGet{1}(mNativeHandler));\\n'\\\n                .format(self.java_enum, self.get_set_name_str)\n            function_str += function_space(1) + '}'\n        else:\n            function_str += function_space(1) + 'public {0} get{1}() {{\\n'.\\\n                format(self.var_type.to_java_getter_setter_string(), self.get_set_name_str)\n            function_str += function_space(2) + 'return nativeGet{0}(mNativeHandler);\\n'.format(self.get_set_name_str)\n            function_str += function_space(1) + '}'\n        return function_str\n\n    def setter(self):\n        if self.var_type == VarType.cpp_string_array:\n            return self.to_set_string_array_string()\n        elif self.var_type == VarType.cpp_enum:\n            return function_space(1) + 'public void set{0}({2} {1}) {{ nativeSet{0}(mNativeHandler, {1}.getValue()); }}'.format(self.get_set_name_str,\n                                                                                                                               self.name_str,\n                                                                                                                               self.java_enum)\n        else:\n            return function_space(1) + 'public void set{0}({2} {1}) {{ nativeSet{0}(mNativeHandler, {1}); }}'.format(self.get_set_name_str,\n                                                                                                                     self.name_str,\n                                                                                                                     self.var_type.to_getter_setter_string())\n\n    def native_getter(self):\n        if self.var_type == VarType.cpp_bool:\n            return function_space(1) + 'private native {0} nativeIs{1}(long handler);'.format(self.var_type.to_java_getter_setter_string(), self.get_set_name_str)\n        elif self.var_type == VarType.cpp_string_array:\n            return function_space(1) + 'private native String[] nativeGet{0}(long handler);'.format(self.get_set_name_str)\n        elif self.var_type == VarType.cpp_enum:\n            return function_space(1) + 'private native int nativeGet{0}(long handler);'.format(self.get_set_name_str)\n        else:\n            return function_space(1) + 'private native {0} nativeGet{1}(long handler);'.format(self.var_type.to_java_getter_setter_string(),\n                                                                                               self.get_set_name_str)\n\n    def native_setter(self):\n        if self.var_type == VarType.cpp_string_array:\n            return function_space(1) + 'private native void nativeSet{0}(long handler, String[] {1});'.format(self.get_set_name_str,\n                                                                                                             self.name_str)\n        elif self.var_type == VarType.cpp_enum:\n            return function_space(1) + 'private native void nativeSet{0}(long handler, {1} {2});'.format(self.get_set_name_str,\n                                                                                                        'int',\n                                                                                                        self.name_str)\n        else:\n            return function_space(1) + 'private native void nativeSet{0}(long handler, {1} {2});'.format(self.get_set_name_str,\n                                                                                                        self.var_type.to_getter_setter_string(),\n                                                                                                        self.name_str)\n\n    def to_get_string_array_string(self):\n        function = function_space(1) + 'public List<String> get{0}() {{\\n'.format(self.get_set_name_str)\n        function += function_space(2) + 'String[] strs = nativeGet{0}(mNativeHandler);\\n'.format(self.get_set_name_str)\n        function += function_space(2) + 'if (strs == null) {\\n'\n        function += function_space(3) + 'return new ArrayList<String>();\\n'\n        function += function_space(2) + '}\\n'\n        function += function_space(2) + 'List<String> list = new ArrayList<String>(Arrays.asList(strs));\\n'\n        function += function_space(2) + 'return list;\\n'\n        function += function_space(1) + \"}\"\n        return function\n\n    def to_set_string_array_string(self):\n        function = function_space(1) + 'public void set{0}(List<String> strs) {{\\n'.format(self.get_set_name_str)\n        function += function_space(2) + 'String[] {0} = new String[strs.size()];\\n'.format(self.name_str)\n        function += function_space(2) + 'strs.toArray({0});\\n'.format(self.name_str)\n        function += function_space(2) + 'nativeSet{0}(mNativeHandler, {1});\\n'.format(self.get_set_name_str, self.name_str)\n        function += function_space(1) + '}\\n'\n        return function\n","sub_path":"skr_java_builder/java_variable.py","file_name":"java_variable.py","file_ext":"py","file_size_in_byte":8517,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"64044979","text":"class Students:\n    School_Name = \"Srinivas\"\n\n    def __init__(self, m1, m2, m3):\n        self.m1 = m1\n        self.m2 = m2\n        self.m3 = m3\n\n    def avg(self):\n        return (self.m1+self.m2+self.m3)/3\n\n    @classmethod\n    def getSchool(cls):\n        return cls.School_Name\n\n    @staticmethod\n    def info():\n        print(\"This is static method\")\n\n\ns1 = Students(45, 55, 60)\ns2 = Students(98, 55, 78)\n\nprint(s2.avg())\nprint(s1.m2)\n# print(Students.info())\nStudents.info()\nprint(Students.getSchool())\n","sub_path":"basics-oops/methods.py","file_name":"methods.py","file_ext":"py","file_size_in_byte":508,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"16349841","text":"\"\"\"\nhttps://leetcode.com/problems/maximum-depth-of-binary-tree/\nLC104 Maximum Depth of Binary Tree\nEasy\n\n\nGiven a binary tree, find its maximum depth.\nThe maximum depth is the number of nodes along the\nlongest path from the root node down to the farthest leaf node.\n\nNote: A leaf is a node with no children.\n\"\"\"\n\nfrom A02_TreeNode import *\n\n\nclass Solution_A:\n    def maxDepth(self, root: TreeNode) -> int:\n        \"\"\"\n        Recursive compare left and right, inherit root level by + 1\n        \"\"\"\n        if not root:\n            return 0\n        else:\n            return 1 + max(self.maxDepth(root.left), self.maxDepth(root.right))\n\n\nif __name__ == \"__main__\":\n    testCase = Solution_A()\n\n    T0 = None\n    assert testCase.maxDepth(T0) == 0, \"Edge 0\"\n\n    T1 = TreeNode([1])\n    assert testCase.maxDepth(T1) == 1, \"Edge 1\"\n\n    T2 = genTree([\n        3,\n        9, 20,\n        None, None, 15, None\n    ])\n    assert testCase.maxDepth(T2) == 3, \"Example 1\"\n\n    print(\"All passed\")\n\n","sub_path":"LeetCode/LC104_maximum_depth_of_binary_tree.py","file_name":"LC104_maximum_depth_of_binary_tree.py","file_ext":"py","file_size_in_byte":986,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"652805944","text":"# Author - Pdt\n# 2019-01-03 13:08:17\nfrom random import shuffle\n\nSUITE = 'H D S C'.split()\nRANKS = '2 3 4 5 6 7 8 9 10 J Q K A'.split()\n\nclass Deck:\n\n    \n\n    def __init__(self):\n        self.deck = self.createDeck()\n\n    def createDeck(self):\n        return [s+r for s in SUITE for r in RANKS]\n    \n    def shuffle(self):\n        #We will shuffle it two times\n        shuffle(self.deck)\n        return shuffle(self.deck)\n    \n    def __len__(self):\n        return len(self.deck);\n\n\n\nclass Hand:\n\n    def __init__(self, cards):\n        self.cards = cards;\n    \n\n\n\nclass Player:\n\n\n    def __init__(self, name, hand=None):\n        self.name = name\n        self.hand = hand\n\n        \n    def playTopCard(self):\n        card = self.hand.pop(0)\n        return card\n    \n\n\n        \n\n\nprint(\"Welcome to war\")\nmydeck = Deck()\n\nplayer1 = Player(input(\"Hi player1 please enter your name?\"))\nplayer2 = Player(input(\"Hi player2 please enter your name?\"))\n\nprint(mydeck.deck)\nprint(mydeck.shuffle())\n\nplayer1.hand = mydeck.deck[0::2]\nplayer2.hand = mydeck.deck[1::2]\n\ninplay = []\nwarcount = 0\ndef play(p1top, p2top):\n    rank1 = p1top[1:]\n    rank2 = p2top[1:]\n    global inplay\n    global warcount\n    print(\"Ranks P1: {} P2: {}\".format(rank1, rank2))\n    if rank1 == rank2:\n        print('We have a war!')\n        inplay.append(p1top)\n        inplay.append(p2top)\n        p1top = player1.playTopCard()\n        p2top = player2.playTopCard()\n        warcount += 1\n        play(p1top, p2top)\n    elif RANKS.index(rank1) > RANKS.index(rank2):\n        print(\"Player one has the higher card!\")\n        player1.hand.append(p1top)\n        player1.hand.append(p2top)\n        if len(inplay) > 0:\n            for card in inplay:\n                player1.hand.append(card)\n            inplay = []\n    else:\n        print(\"Player two has the higher card!\")\n        player2.hand.append(p2top)\n        player2.hand.append(p1top)\n        if len(inplay) > 0:\n            for card in inplay:\n                player2.hand.append(card)\n            inplay = []\n\ntotal_rounds = 0\n\nwhile len(player1.hand) > 0 or len(player2.hand) > 0:\n    p1top = player1.playTopCard()\n    p2top = player2.playTopCard()\n    play(p1top, p2top)\n    total_rounds += 1\n    if len(player1.hand) == 51:\n        print(\"{} is the winner after {} rounds with {} wars\".format(player1.name, total_rounds, warcount))\n        break;\n    elif len(player2.hand) == 51:\n        print(\"{} is the winner after {} rounds with {} wars\".format(player2.name, total_rounds, warcount))\n        break;\n\n\n\n\n\n    ","sub_path":"python_django_fullstack/backend/python/oop_project.py","file_name":"oop_project.py","file_ext":"py","file_size_in_byte":2536,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"576934810","text":"import DatabaseConnection\nimport Printer\nimport Plotter\nimport Supplement\nimport datetime\nimport math\nfrom datetime import datetime, timedelta\nimport time\n\n# Count all Tuple in a Table\n# Return the Number of Tuple\n\ndef countTuple(tablename):\n    query = \"SELECT COUNT(*) FROM \" + tablename + \"\"\n    records = DatabaseConnection.databaseQuery(query)\n    return records[0][0]\n\n# TEMPORAL analysis !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n\n# Input tablename, name of the Table that should be analysed\n# Input y1 Year where it should start\n# Input m1 Month where it should start\n# Input d1 Day where it should start\n# Input y2 Year where it should end\n# Input m2 Month where it should end\n# Input d2 Day where it should end\n# return Dictionary with Results\n\ndef analysisTimestamp(tablename, y1, m1, d1, y2, m2, d2):\n\n    # the date where it starts\n    date = datetime(y1, m1, d1)\n\n    # Dic with the Result of the Hour analysis\n    dic = timestampAnalysisByHour(tablename, y1, m1, d1, y2, m2, d2)\n\n    # new Dic used to make a Date analysis\n    dic2 = {}\n\n    # counts to 24 so you dont have to go through the whole Dictionary\n    counter = 0\n    while date < datetime(y2, m2, d2):\n        dic2[date] = 0\n        for x, y in dic.items():\n\n            # if the date is the Day without hours of the dic time\n            if date == datetime(x.year, x.month, x.day) and counter < 25:\n                counter = counter + 1\n                # calculate new Value\n                newValue = dic2.get(date) + dic.get(x)[0]\n                # update Value\n                dic2[date] = newValue\n\n        date = date + timedelta(days=1)\n        counter = 0\n    # Prints the results in CSV\n    Printer.printerTimestampDay(dic2)\n    # Plots the Result\n    Plotter.barChart(dic2, False, \"Timestampanalyse\", \"Summe der Einträge\", \"Datum\", \"Menge der Tupel\", 30, 20)\n    Plotter.barChart(dic2, False, \"Timestampanalyse\", \"Summe der Einträge\", \"Datum\", \"Menge der Tupel\", 50, 40)\n    Plotter.barChart(dic2, False, \"Timestampanalyse\", \"Summe der Einträge\", \"Datum\", \"Menge der Tupel\", 20, 10)\n    return dic2\n\n\n# USED BY analysisTimestamp\n# Input tablename, name of the Table that should be analysed\n# Input y1 Year where it should start\n# Input m1 Month where it should start\n# Input d1 Day where it should start\n# Input y2 Year where it should end\n# Input m2 Month where it should end\n# Input d2 Day where it should end\n# return Dictionary with Results\n\ndef timestampAnalysisByHour(tablename, y1, m1, d1, y2, m2, d2):\n    date = datetime(y1, m1, d1)\n\n    dic = {}\n    while(date < datetime(y2, m2, d2)):\n        t1 = time.clock()\n\n        nextdate = date + timedelta(hours=1)\n        # creates query\n        query = \"SELECT COUNT(*) FROM \" + tablename + \" WHERE timestamp >= '\" + str(date) + \"' AND timestamp < '\" + str(nextdate) + \"'\"\n\n        # Execute query\n        records = DatabaseConnection.databaseQuery(query)\n\n        # add date and number of Tuple to the dictionary\n        dic[date] = [records[0][0], date.weekday(), date.hour]\n        t2 = time.clock()\n        print(\"\" + str(date) + \":  \" + str(t2 - t1))\n        date = nextdate\n\n    # Print the results as csv\n    Printer.printerTimestampHour(dic)\n    # Plotting the results\n    Plotter.lineChar(dic, \"Stunden\", \"Anzahl der Eintäge\", \"Vergleich von Wochentagen\", \"Wochentage\", 30, 20)\n    Plotter.lineChar(dic, \"Stunden\", \"Anzahl der Eintäge\", \"Vergleich von Wochentagen\", \"Wochentage\", 25, 15)\n    Plotter.lineChar(dic, \"Stunden\", \"Anzahl der Eintäge\", \"Vergleich von Wochentagen\", \"Wochentage\", 50, 40)\n    return dic\n\n\n# Calculate the average Number of Tuple per Hour and the relative Deviation\n# return: average Number of Tuple per Hour\n# return: dic with relative Deviation per Hour\ndef relativeHourDeviation():\n    average = 0\n    dic = {}\n    # read CSV\n    list = Supplement.csvReader(\"TimestampHour.csv\")\n\n    # fill Dictionary with Hours\n    for i in range(0,24):\n        dic[i] = 0\n\n    # Calculate the average\n    for entry in list:\n        average = average + int(entry[1])\n    average = average / 24\n\n    # Get the absolute Values per Hour\n    for entry in list:\n        hour = int(entry[0][11] + entry[0][12])\n        value = dic.get(hour)\n        dic[hour] = value + int(entry[1])\n\n    for key, value in dic.items():\n        #Calculate Deviation\n        dic[key] = (value - average) / average\n\n    return dic, average\n\n# Get all Timesstamp, which are out of Range\ndef timestampOutOfRange(y1, m1, d1, y2, m2, d2):\n    date1 = \"'{0}-{1}-{2}'\".format(y1, m1, d1)\n    date2 = \"'{0}-{1}-{2}'\".format(y2, m2, d2)\n    query = \"SELECT obsid, timestamp FROM blt_obs WHERE timestamp < {0} AND timestamp > {1}\".format(date1, date2)\n    records = DatabaseConnection.databaseQuery(query)\n    if not records:\n        return []\n    else:\n        list = []\n        for i in range(len(records)):\n            list.append(records[i][0])\n        return list\n\n\n# SPATIAL analysis !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n\n# Gives an Overview of the Spartial Analysis\n# Input tablename\n# Input columnnae\n# return dic with the Link and the Number of Tuples of this Link\ndef overviewSpatialAnalysis(tablename, columnname):\n    # Dictionary\n    dic = {}\n    # Query and Excute\n    query = \"SELECT \" + columnname + \", COUNT(*) FROM \" + tablename + \" GROUP BY \" + columnname\n    records = DatabaseConnection.databaseQuery(query)\n    # Save the results in the Dictionary\n    for i in range(len(records)):\n        dic[records[i][0]] = records[1]\n    # Print an File with the results\n    Printer.printerOverviewSpatialAnalysis(dic)\n    return dic\n\n\n# Shows which links werent supervised\n# Input dic with linkids as key of the overviewSpatialAnalysis\n# return an Dictionary with all the Links without any Observations\ndef linkidsExits(dic, tablename, columnname):\n    dicLink = dic\n    # Query and execute to get all Links that exist\n    query = \"SELECT \" + columnname + \" FROM \" + tablename + \"\"\n    records = DatabaseConnection.databaseQuery(query)\n    for i in range(len(records)):\n        try:\n            # Delete all Links that were observated\n            del dicLink[records[i][0]]\n        except:\n            print(\"Keinen Link gelöscht\")\n            continue\n    # return dic with all Links without Observations\n    return dicLink\n\n\n# Get the average number of Tuple of every Link every Day\n# Input startdate\n# Input enddate\n# Input table with the Observations\n# Input table with the Links\n# return average\ndef linksPerDayAverage(startdate, enddate, tablename, tablename2):\n    date = startdate\n\n    dic = {}\n    # Get the number of Links at all dates\n    while date < enddate:\n        nextdate = date + timedelta(days=1)\n        query = \"SELECT COUNT(*) FROM \" + tablename +\" WHERE timestamp >= '\" + str(date) +\\\n                \"' AND timestamp < '\" + str(nextdate) + \"'\"\n        records = DatabaseConnection.databaseQuery(query)\n        dic[date] = records[0][0]\n        date = nextdate\n\n    # Get the number of links\n    query = \"SELECT COUNT(*) FROM \" + tablename2 + \"\"\n    records = DatabaseConnection.databaseQuery(query)\n\n    # Divide all Number of links every Day with the number of links that exists\n    for x, y in dic.items():\n        dic[x] = y / records[0][0]\n\n    sum = 0\n    # get the sum of all average per day\n    for x, y in dic.items():\n        sum = sum + y\n\n    return sum / len(dic)\n\n# Attribute Speed!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n\ndef traveltimeOverview(tablename, columnname):\n    max = 0;\n    min = 0;\n\n    query = \"SELECT MAX(\" + columnname + \"), MIN(\" + columnname + \") FROM \" + tablename\n    records = DatabaseConnection.databaseQuery(query)\n\n    max = records[0][0]\n    min = records[0][1]\n    print(\"MIN {0}, MAX {1}\".format(min, max))\n\n\n\n    times = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60]\n    vorheriges = 0\n    dic = {}\n    for i in times:\n        if (i == 0):\n            continue\n        else:\n            query = \"SELECT COUNT(obsid) FROM blt_obs WHERE travel_time >= {0} AND travel_time < {1}\".format(times[vorheriges], i)\n            print(query)\n            records = DatabaseConnection.databaseQuery(query)\n            dic[i] = records[0][0]\n            vorheriges = vorheriges + 1\n\n    return min, max, dic\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"Project/AnalyseCompleteness.py","file_name":"AnalyseCompleteness.py","file_ext":"py","file_size_in_byte":8426,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"390955655","text":"# -*- coding: utf-8 -*-\nimport scrapy\nfrom scrapy import Request\n\nbase_headers = {\n    \"Host\": \"wenku.baidu.com\",\n    \"Referer\": \"https://wenku.baidu.com/\",\n    \"Connection\": \"keep-alive\",\n    \"Upgrade-Insecure-Requests\": \"1\",\n    \"Accept-Encoding\": \"gzip, deflate, br\",\n    \"Accept-Language\": \"zh-CN,zh;q=0.9\",\n    \"Sec-Fetch-Site\": \"same-origin\",\n    \"Sec-Fetch-User\": \"?1\",\n    \"Sec-Fetch-Mode\": \"navigate\",\n    \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) \"\n                  \"Chrome/78.0.3904.108 Safari/537.36\",\n    \"Accept\": \"text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,\"\n              \"application/signed-exchange;v=b3;q=0.9\",\n}\n\n\nclass WenkuSpider(scrapy.Spider):\n    name = 'wenku'\n    allowed_domains = ['wenku.baidu.com']\n    start_urls = ['http://wenku.baidu.com/']\n    search_url = 'https://wenku.baidu.com/search?word={word}org=0&fd=0&lm=0&od=0&pn={pn}'\n\n    def start_requests(self):\n        keywords = ['牛顿']\n        for keyword in keywords:\n            url = self.search_url.format(word=keyword, pn=10)\n            print(url)\n            yield Request(url, callback=self.parse, dont_filter=True)\n\n    def parse(self, response):\n        dls = response.xpath(\"//div[@class='search-result']/dl\")\n        for dl in dls:\n            title = dl.xpath(\"./dt[@class='logFirstClickTime mb6 clearfix']/p[@class='fl']/a/@title\").extract_first()\n            tag = ''.join(dl.xpath(\"./dd[@class='tag-tips']/a[@class='log-xsend']//text()\").extract())\n            summary = ''.join(dl.xpath(\"./dd[@class='clearfix']/div[@class='summary-box fl']/p[@class='summary lh21']//text()\").extract())\n            detail_info = ''.join(dl.xpath(\"./dd[@class='clearfix']/div[@class='summary-box fl']//div[@class='detail-info']//text()[not]\").extract())\n            detail_info = detail_info.replace('\\n', '')\n            print(title)\n            print(tag)\n            print(summary)\n            print(detail_info)\n            print(\"==\" * 30)\n\n","sub_path":"spider_niu_dun/niu_dun/spiders/wenku.py","file_name":"wenku.py","file_ext":"py","file_size_in_byte":2033,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"21727251","text":"# \"\"\"\n# This is the interface that allows for creating nested lists.\n# You should not implement it, or speculate about its implementation\n\n# \"\"\"\n\n#class NestedInteger(object):\n\n#    def isInteger(self):\n\n#        \"\"\"\n#        @return True if this NestedInteger holds a single integer, rather than a nested list.\n#        :rtype bool\n#        \"\"\"\n\n#\n\n#    def getInteger(self):\n\n#        \"\"\"\n#        @return the single integer that this NestedInteger holds, if it holds a single integer\n#        Return None if this NestedInteger holds a nested list\n#        :rtype int\n#        \"\"\"\n\n#\n#    def getList(self):\n#        \"\"\"\n#        @return the nested list that this NestedInteger holds, if it holds a nested list\n#        Return None if this NestedInteger holds a single integer\n#        :rtype List[NestedInteger]\n#        \"\"\"\n\n\nclass Solution(object):\n\n    def depthSumInverse(self, nestedList):\n\n        \"\"\"\n        :type nestedList: List[NestedInteger]\n        :rtype: int\n        \"\"\"\n\n        maxDepth = self.dfsDeep(nestedList)\n\n        return self.depthSumHelper(nestedList, maxDepth)\n\n\n    def dfsDeep(self, nestedList):\n\n        if len(nestedList) == 1 and nestedList[0].isInteger():\n\n            return 1\n\n        deep = 0    \n\n        for nInt in nestedList:\n\n            if nInt.isInteger():\n\n                deep = max(deep, 1)\n\n            else:\n\n                deep = max(self.dfsDeep(nInt.getList()) + 1, deep)\n\n        return deep\n\n        \n    def depthSumHelper(self, nestedList, maxDepth): \n\n        sum = 0;\n\n        for item in nestedList:\n\n            if item.isInteger():\n\n                sum = sum + item.getInteger() * maxDepth;\n\n            else:\n\n                sum = sum + self.depthSumHelper(item.getList(), maxDepth - 1);\n                \n        return sum;\n\n","sub_path":"nested_list_weight_sum_II.py","file_name":"nested_list_weight_sum_II.py","file_ext":"py","file_size_in_byte":1793,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"153532623","text":"import os\nimport zipfile\nfrom xml.etree.ElementTree import Element, SubElement\nfrom xml.etree import ElementTree\nfrom xml.dom import minidom\nfrom datetime import datetime\nfrom . import log\n\ndef createMef(uuid, metadataFilename, mefFilename, thumbnailFilename):\n    z = zipfile.ZipFile(mefFilename, \"w\")    \n    z.write(metadataFilename, os.path.join(uuid, \"metadata\", os.path.basename(metadataFilename)))\n    z.write(thumbnailFilename, os.path.join(uuid, \"public\", os.path.basename(thumbnailFilename)))\n    info = getInfoXmlContent(uuid, thumbnailFilename)\n    z.writestr(os.path.join(uuid, \"info.xml\"), info)\n    z.close()\n    log.logInfo(\"MEF file written to %s\" % mefFilename)\n\ndef _addSubElement(parent, tag, value=None, attrib=None):\n    sub = SubElement(parent, tag, attrib=attrib or {})\n    if value is not None:\n        sub.text = value\n    return sub\n\ndef getInfoXmlContent(uuid, thumbnailFilename):\n    root = Element(\"info\", {\"version\": \"1.1\"})\n    general = _addSubElement(root, \"general\")\n    d = datetime.now().isoformat()\n    _addSubElement(general, \"changeDate\", d)\n    _addSubElement(general, \"createDate\", d)\n    _addSubElement(general, \"schema\", \"iso19139\")\n    _addSubElement(general, \"format\", \"full\")\n    _addSubElement(general, \"uuid\", uuid)\n    _addSubElement(general, \"siteName\", \"GeoCatBridge\")\n    _addSubElement(general, \"isTemplate\", \"n\")\n    _addSubElement(root, \"categories\")\n    _addSubElement(root, \"privileges\")\n    public = _addSubElement(root, \"public\")\n    _addSubElement(public, \"file\", attrib = {\"name\": os.path.basename(thumbnailFilename), \"changeDate\": d})\n    _addSubElement(root, \"private\")    \n    xmlstring = ElementTree.tostring(root, encoding='UTF-8', method='xml').decode()\n    dom = minidom.parseString(xmlstring)    \n    return dom.toprettyxml(indent=\"  \")","sub_path":"bridgecommon/meftools.py","file_name":"meftools.py","file_ext":"py","file_size_in_byte":1806,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"240384387","text":"import os\nimport copy\nimport operator\nimport numpy as np\nimport pygame\nfrom collections import deque\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.optimizers import RMSprop\nfrom ple import PLE\nfrom ple.games.snake import Snake\nfrom pygame.constants import K_a, K_s, K_w, K_d\n\nos.putenv('SDL_VIDEODRIVER', 'fbcon')\nos.environ[\"SDL_VIDEODRIVER\"] = \"dummy\"\npygame.display.init()\n\nEPISODE = 10000\n\ndef process_state(state):\n    return np.array([state.values()])\n\nclass DQNAgent:\n    def __init__(self, env):\n        self.env = env\n        self.memory= deque(maxlen = 10000)\n        self.gamma = 0.95\n        self.epsilon = 1\n        self.epsilon_decay = 0.99\n        self.epsilon_min = 0.05\n        self.learning_rate = 0.005\n        self._build_model()\n\n    def _build_model(self):\n        model = Sequential()\n        model.add(Dense(128, input_dim = 5, activation='tanh', init='he_uniform'))\n        model.add(Dense(128, activation='tanh', init='he_uniform'))\n        model.add(Dense(128, activation='tanh', init='he_uniform'))\n        model.add(Dense(5, activation='linear', init='he_uniform'))\n\n        model.compile(loss='mse',\n                      optimizer=RMSprop(lr=self.learning_rate))\n\n        self.model = model\n\n    def remember(self, state, action, reward, next_state):\n        self.memory.append((state, action, reward, next_state))\n\n    def act(self, state):\n        if np.random.rand() <= self.epsilon:\n            return np.random.choice([K_a, None, K_w, K_s, K_d])\n        act_values = self.model.predict(state)\n        return  [K_a, None, K_w, K_s, K_d][np.argmax(act_values[0])]\n\n    def replay(self, batch_size):\n        batchs = min(batch_size, len(self.memory))\n        batchs = np.random.choice(len(self.memory), batchs, replace= False)\n        for i in batchs:\n            state, action, reward, next_state = self.memory[i]\n            target = reward + self.gamma * \\\n                                    np.amax(self.model.predict(next_state)[0])\n            target_f = self.model.predict(state)\n            target_f[0][action] = target\n            self.model.fit(state, target_f, nb_epoch=1, verbose=0)\n            if self.epsilon > self.epsilon_min:\n               self.epsilon *= self.epsilon_decay\n\n    def load(self, name):\n        self.model.load_weights(name)\n\n    def save(self, name):\n        self.model.save_weights(name)\n\n\nif __name__ == \"__main__\":\n\n    game = Snake(width=256, height=256)\n    env = PLE(game, display_screen= True, fps = 10,  state_preprocessor=process_state)\n    agent = DQNAgent(env)\n    agent.load('./save/snake.h5')\n    env.init()\n\n    for e in range(EPISODE):\n        env.reset_game()\n        score = 0\n        state = game.getGameState()\n\n\n        state = sorted(state.items(), key=operator.itemgetter(0))\n        for i in range(len(state)):\n            state[i] = state[i][1]\n        state[2] = len(state[2])\n        state = np.array([state])\n        for time_t in range(5000):\n            action = agent.act(state)\n\n            reward = env.act(action)\n            score += reward\n\n            next_state = game.getGameState()\n            next_state = sorted(next_state.items(), key=operator.itemgetter(0))\n            for i in range(len(next_state)):\n                next_state[i] = next_state[i][1]\n            next_state[2] = len(next_state[2])\n            #print (next_state[2])\n            next_state = np.array([next_state])\n\n            action = [K_a, None, K_w, K_s, K_d].index(action)\n\n            agent.remember(state, action, reward, next_state)\n            state = copy.deepcopy(next_state)\n\n            if env.game_over():\n                print(\"episode: {}/{}, score: {}, memory size: {}, e: {}\"\n                      .format(e, EPISODE, score,\n                              len(agent.memory), agent.epsilon))\n                break\n\n            if e % 100 == 0:\n                agent.save(\"./save/snake.h5\")\n\n            agent.replay(16)\n\n","sub_path":"deep-q-learning/snake_DQN.py","file_name":"snake_DQN.py","file_ext":"py","file_size_in_byte":3947,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"120566441","text":"# 파이썬 ≥3.5 필수\nimport sys\nassert sys.version_info >= (3, 5)\n\n# 사이킷런 ≥0.20 필수\nimport sklearn\nassert sklearn.__version__ >= \"0.20\"\n\n# 공통 모듈 임포트\nimport numpy as np\nimport os\n\n# 노트북 실행 결과를 동일하게 유지하기 위해\nnp.random.seed(42)\n\n# 깔끔한 그래프 출력을 위해\n# %matplotlib inline\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nmpl.rc('axes', labelsize=14)\nmpl.rc('xtick', labelsize=12)\nmpl.rc('ytick', labelsize=12)\n\n# 그림을 저장할 위치\nPROJECT_ROOT_DIR = \".\"\nCHAPTER_ID = \"classification\"\nIMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\nos.makedirs(IMAGES_PATH, exist_ok=True)\n\ndef save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n    print(\"그림 저장:\", fig_id)\n    if tight_layout:\n        plt.tight_layout()\n    plt.savefig(path, format=fig_extension, dpi=resolution)\n\nfrom sklearn.datasets import fetch_openml\nmnist = fetch_openml('mnist_784', version=1)\nprint(mnist.keys())\n\nX, y = mnist[\"data\"], mnist[\"target\"]\nsome_digit = X[0]\nsome_digit_image = some_digit.reshape(28, 28)\nplt.imshow(some_digit_image, cmap=mpl.cm.binary)\nplt.axis(\"off\")\n\nsave_fig(\"some_digit_plot\")\nplt.show()\ny = y.astype(np.uint8)\n\ndef plot_digit(data):\n    image = data.reshape(28, 28)\n    plt.imshow(image, cmap = mpl.cm.binary,\n               interpolation=\"nearest\")\n    plt.axis(\"off\")\n\n# 숫자 그림을 위한 추가 함수\ndef plot_digits(instances, images_per_row=10, **options):\n    size = 28\n    images_per_row = min(len(instances), images_per_row)\n    images = [instance.reshape(size,size) for instance in instances]\n    n_rows = (len(instances) - 1) // images_per_row + 1\n    row_images = []\n    n_empty = n_rows * images_per_row - len(instances)\n    images.append(np.zeros((size, size * n_empty)))\n    for row in range(n_rows):\n        rimages = images[row * images_per_row : (row + 1) * images_per_row]\n        row_images.append(np.concatenate(rimages, axis=1))\n    image = np.concatenate(row_images, axis=0)\n    plt.imshow(image, cmap = mpl.cm.binary, **options)\n    plt.axis(\"off\")\n\nplt.figure(figsize=(9,9))\nexample_images = X[:100]\nplot_digits(example_images, images_per_row=10)\nsave_fig(\"more_digits_plot\")\nplt.show()\n\nX_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]\n\n# 이진 분류기\ny_train_5 = (y_train == 5)\ny_test_5 = (y_test == 5)\n\n# 노트: max_iter와 tol 같은 일부 매개변수는 사이킷런 다음 버전에서 기본값이 바뀝니다. \n# 버전이 업데이트되더라도 결과가 바뀌지 않도록 아예 나중에 바뀔 기본값을 사용해 명시적으로 지정하겠습니다. \n# 번거로움을 피하기 위해 책에는 따로 표시하지 않았습니다.\n\nfrom sklearn.linear_model import SGDClassifier\n\nsgd_clf = SGDClassifier(max_iter=1000, tol=1e-3, random_state=42)\nsgd_clf.fit(X_train, y_train_5)\n\nsgd_clf.predict([some_digit])\n\n# 3개의 그룹으로 나누어 \nfrom sklearn.model_selection import cross_val_score\ncross_val_score(sgd_clf, X_train, y_train_5, cv=3, scoring=\"accuracy\")\n\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.base import clone\n\n# shuffle=False가 기본값이기 때문에 random_state를 삭제하던지 shuffle=True로 지정하라는 경고가 발생합니다.\n# 0.24버전부터는 에러가 발생할 예정이므로 향후 버전을 위해 shuffle=True을 지정합니다.\nskfolds = StratifiedKFold(n_splits=3, random_state=42, shuffle=True)\n# cross_val_score(sgd_clf, X_train, y_train_5, cv=skfolds)\n\nfor train_index, test_index in skfolds.split(X_train, y_train_5):\n    clone_clf = clone(sgd_clf)\n    X_train_folds = X_train[train_index]\n    y_train_folds = y_train_5[train_index]\n    X_test_fold = X_train[test_index]\n    y_test_fold = y_train_5[test_index]\n\n    clone_clf.fit(X_train_folds, y_train_folds)\n    y_pred = clone_clf.predict(X_test_fold)\n    n_correct = sum(y_pred == y_test_fold)\n    print(n_correct / len(y_pred))\n\nfrom sklearn.base import BaseEstimator\nclass Never5Classifier(BaseEstimator):\n    def fit(self, X, y=None):\n        pass\n    def predict(self, X):\n        return np.zeros((len(X), 1), dtype=bool)\n\nnever_5_clf = Never5Classifier()\ncross_val_score(never_5_clf, X_train, y_train_5, cv=3, scoring=\"accuracy\")\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"handson-ml2/ch03.py","file_name":"ch03.py","file_ext":"py","file_size_in_byte":4383,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"131657010","text":"# -*- coding: utf-8 -*-\n\nfrom django.test import TestCase\nfrom channel.models import Channel\n\n\nclass ChannelTest(TestCase):\n\n    def setUp(self):\n        self.channel_obj = Channel(name='channel full name')\n        self.assertEqual(None, self.channel_obj.save())\n\n    def test(self):\n        self.assertEquals(\n                self.channel_obj.testSpeak(),\n                'channel full name:channel-full-name'\n                )\n","sub_path":"work-at-olist/channel/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":429,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"355065085","text":"import torch\nfrom torchvision import transforms\nimport numpy as np\nfrom PIL import Image, ImageDraw\nfrom utils import tools\nimport nets\nimport time\nimport os\n\nDEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n\nclass Detector:\n    def __init__(self, p_net_param=r\"./save_params/p_net.pth\",\n                 r_net_param=r\"./save_params/r_net.pth\",\n                 o_net_param=\"./save_params/o_net.pth\",\n                 isCuda=False):\n        self.isCuda = isCuda\n        self.p_net = nets.PNet().to(DEVICE)\n        self.r_net = nets.RNet().to(DEVICE)\n        self.o_net = nets.ONet().to(DEVICE)\n\n        self.p_net.load_state_dict(torch.load(p_net_param))\n        self.r_net.load_state_dict(torch.load(r_net_param))\n        self.o_net.load_state_dict(torch.load(o_net_param))\n        self.p_net.eval()\n        self.r_net.eval()\n        self.o_net.eval()\n        self.__image_transform = transforms.Compose([\n            transforms.ToTensor(),\n            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])  # 对于图像这个是有经验值的\n        ])\n\n    def detect(self, image):\n        start_time = time.time()\n        p_net_boxes = self.__p_net_detect(image)\n        if p_net_boxes.shape[0] == 0:\n            return np.array([])\n        end_time = time.time()\n        t_p_net = end_time - start_time\n        # return p_net_boxes\n\n        start_time = time.time()\n        r_net_boxes = self.__ro_net_detect(image, p_net_boxes, face_size=24)\n        if r_net_boxes.shape[0] == 0:\n            return np.array([])\n        end_time = time.time()\n        t_r_net = end_time - start_time\n        # return r_net_boxes\n\n        start_time = time.time()\n        o_net_boxes = self.__ro_net_detect(image, r_net_boxes, face_size=48)\n        if o_net_boxes.shape[0] == 0:\n            return np.array([])\n        end_time = time.time()\n        t_o_net = end_time - start_time\n        total_time = t_p_net + t_r_net + t_o_net\n        print(f\"[time]  total:{total_time} p_net:{t_p_net} r_net:{t_r_net} o_net:{t_o_net}\")\n        return o_net_boxes\n\n    def __p_net_detect(self, image):\n        bboxes = []\n\n        scale = 1\n        w, h = image.size\n        min_side_len = min(w, h)\n        while min_side_len >= 12:\n            img_data = self.__image_transform(image)\n            out_cls, out_offset = self.p_net(img_data.to(DEVICE).unsqueeze(0))\n            out_cls, out_offset = out_cls[0][0].cpu().detach(), out_offset[0].cpu().detach()\n            idxs = torch.nonzero(torch.gt(out_cls, 0.6))  # out_cls > 0.6  idxy[idx, idy]\n            boxes = self.__box(idxs, out_offset, out_cls, scale)\n            bboxes.extend(boxes)\n            scale *= 0.709\n            _w = int(w * scale)\n            _h = int(h * scale)\n            # 图像金字塔\n            image = image.resize((_w, _h))\n            min_side_len = min(_w, _h)  # 这里是跳出循环的条件\n\n        return tools.nms(np.array(bboxes), 0.3, False)\n\n    def __box(self, start_index, offset, cls, scale, stride=2, side_len=12):\n        _x1 = ((start_index[:, 1]).to(torch.float32) * stride) / scale\n        _y1 = ((start_index[:, 0]).to(torch.float32) * stride) / scale\n        _x2 = ((start_index[:, 1]).to(torch.float32) * stride + side_len) / scale\n        _y2 = ((start_index[:, 0]).to(torch.float32) * stride + side_len) / scale\n\n        ow, oh = side_len / scale, side_len / scale\n        _offset = offset[:, start_index[:, 0], start_index[:, 1]]\n        x1 = _x1 + ow * _offset[0, :]\n        y1 = _y1 + oh * _offset[1, :]\n        x2 = _x2 + ow * _offset[2, :]\n        y2 = _y2 + oh * _offset[3, :]\n        cls_ = cls[start_index[:, 0], start_index[:, 1]]\n        bboxes = torch.stack([x1, y1, x2, y2, cls_], dim=1)\n        return np.array(bboxes)\n\n    def __ro_net_detect(self, image, net_boxes, face_size):\n        print(face_size)\n        _img_dataset = []\n        _net_boxes = tools.convert_to_square(net_boxes)\n        for _box in _net_boxes:  # _pnet_boxes [N, 5]\n            _x1 = int(_box[0])\n            _y1 = int(_box[1])\n            _x2 = int(_box[2])\n            _y2 = int(_box[3])\n            img = image.crop((_x1, _y1, _x2, _y2))\n            img = img.resize((face_size, face_size))\n            img_data = self.__image_transform(img)\n            _img_dataset.append(img_data)  # [N, 3, 24, 24] (array([...], array([...]))\n        img_dataset = torch.stack(_img_dataset)  # 有元组转换成张量\n        img_dataset = img_dataset.to(DEVICE)\n        if face_size == 24:\n            out_cls, out_offset = self.r_net(img_dataset)  # [N, 1] [N, 4]\n        elif face_size == 48:\n            out_cls, out_offset = self.o_net(img_dataset)\n        else:\n            raise Exception(\"face_size not in [24, 48]!\")\n        out_cls = out_cls.cpu().detach().numpy()  # [N. 1]\n        out_offset = out_offset.cpu().detach().numpy()  # [N, 4]\n\n        idxs, _ = np.where(out_cls > 0.3)  # [N,]\n        _boxes = _net_boxes[idxs]  # [N, 5]\n        _x1 = _boxes[:, 0]\n        _y1 = _boxes[:, 1]\n        _x2 = _boxes[:, 2]\n        _y2 = _boxes[:, 3]\n\n        ow, oh = _x2 - _x1, _y2 - _y1\n\n        x1 = _x1 + ow * out_offset[idxs][:, 0]  # [N, 1]\n        y1 = _y1 + oh * out_offset[idxs][:, 1]  # [N, 1]\n        x2 = _x2 + ow * out_offset[idxs][:, 2]  # [N, 1]\n        y2 = _y2 + oh * out_offset[idxs][:, 3]  # [N, 1]\n        cls = out_cls[idxs][:, 0]  # [N, 1]\n\n        boxes = np.stack([x1, y1, x2, y2, cls], axis=1)\n        if face_size == 24:\n            return tools.nms(np.array(boxes), 0.3, False)\n        else:\n            return tools.nms(np.array(boxes), 0.3, True)\n\n\ndef show_single_image(image_file):\n    with Image.open(image_file) as im:\n        detector = Detector(\n                 p_net_param=r\"./save_params/p_net.pth\",\n                 r_net_param=r\"./save_params/r_net.pth\",\n                 o_net_param=\"./save_params/o_net.pth\")\n        boxes = detector.detect(im)\n        # print(boxes.shape)\n        imDraw = ImageDraw.Draw(im)\n        for box in boxes:\n            x1 = int(box[0])\n            y1 = int(box[1])\n            x2 = int(box[2])\n            y2 = int(box[3])\n            # print(box[4])\n            imDraw.rectangle((x1, y1, x2, y2), outline='red', width=3)\n        y = time.time()\n        # print(y - x)\n        im.show()\n\n","sub_path":"5.mtcnn/mtcnn_qjx/detector_lzy.py","file_name":"detector_lzy.py","file_ext":"py","file_size_in_byte":6276,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"407494577","text":"class Tree:\n    def __init__(self, root):\n        self.root = root\n\n    def add(self, subtree):\n        self.root.children.append(subtree)\n\nclass Node:\n    def __init__(self, value, children=None):\n        self.value = value\n        self.children = children if children is not None else []\n\n# Implementuj funkci, která pro libovolný strom na vstupu vrátí počet jeho uzlů.\ndef count_nodes(tree):\n    count = 0\n    if isinstance(tree, Tree):\n        if tree.root is None:\n            return count\n        else:\n            if tree.root.children == []:\n                return 1\n            count += count_nodes(tree.root)\n    elif isinstance(tree, Node):\n        if tree.children == []:\n            return 1\n        else:\n            count += count_nodes(tree.children) + 1\n    else:\n        for node in tree:\n            if node.children == []:\n                count += 1\n            else:\n                count += count_nodes(node.children) + 1\n    return count\n\n# Testy:\nprint(count_nodes(Tree(None)))  # 0\nprint(count_nodes(Tree(Node(10))))  # 1\nprint(count_nodes(Tree(Node(10, Node(22, Node(321))))))  # 3\nprint(count_nodes(Tree(Node(5, [Node(6), Node(17)]))))  #3\nprint(count_nodes(Tree(Node(5, [Node(4, Node(7)), Node(17), Node(321), Node(3213, Node(4))]))))  # 7\nprint(count_nodes(Tree(Node(5, [Node(4, Node(7, [Node(8, Node(0, Node(3))), Node(9)])), Node(17), Node(321), Node(3213, Node(4))]))))  # 11\n","sub_path":"KSI_19-20/trees.py","file_name":"trees.py","file_ext":"py","file_size_in_byte":1414,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"617783700","text":"from rest_framework import serializers\nfrom circle.models import *\nfrom django.contrib.auth.models import User\nfrom rest_framework.pagination import PaginationSerializer\nfrom rest_framework import pagination\n\n\"\"\"\nAcademy serializer\n\"\"\"\n\nclass AcademySerializer(serializers.ModelSerializer):\n#    users = UserProfileSerializer()\n    class Meta:\n        model = Academy\n        fields = ('id','name','description')\n\"\"\"\nSchool serializer\n\"\"\"\nclass SchoolSerialzier(serializers.ModelSerializer):\n    academies = AcademySerializer()\n    class Meta:\n        model = School\n        fields = ('id','name','academies')\n\"\"\"\nUser serializer\n\"\"\"        \nclass UserSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = User\n        fields = ('id','username','email','password')\n        \n\nclass UserProfileSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = UserProfile\n\n\nclass UserProfileMainSerializer(serializers.ModelSerializer):\n    avatar  = serializers.Field(source='user_profile.avatar')\n    class Meta:\n        model = User\n        fields = ('id','username','email','avatar')\n        \n\"\"\"\nGroup serializer\n\"\"\"\nclass GroupMainSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = Group;\n        fields = ('id','name','avatar','total')\n\nclass UserDetailSerializer(serializers.ModelSerializer):\n    user_profile = UserProfileSerializer()\n    groups = GroupMainSerializer()\n    class Meta:\n        model = User\n        fields = ('id','username','email','user_profile','groups')\n\n\nclass GroupDetailSerializer(serializers.ModelSerializer):\n    founder = UserProfileMainSerializer()\n    members = UserProfileMainSerializer(many=True)\n    class Meta:\n        model = Group\n\nclass PaginatedGroupSerializer(pagination.PaginationSerializer):\n    class Meta:\n        object_serializer_class = GroupMainSerializer\n\"\"\"\nMessage serialzier\n\"\"\"\nclass MessageSerializer(serializers.ModelSerializer):\n    user = UserProfileMainSerializer()\n    group = GroupMainSerializer()\n    \n    class Meta:\n        model = Message\n        \nclass MessageMainSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = Message\n        fields = ('id','content','location','media')\n\nclass PaginatedMessageSerializer(pagination.PaginationSerializer):\n    class Meta:\n        object_serializer_class = MessageMainSerializer\n\n\n\n\"\"\"\nComment serializer\n\"\"\"        \nclass CommentMainSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = Comment\n        fields = ('id','content','location')\n\n\nclass CommentDetailSerializer(serializers.ModelSerializer):\n    user = UserProfileMainSerializer()\n    reply = CommentMainSerializer()\n    class Meta:\n        model = Comment\n\nclass PaginatedCommentSerializer(pagination.PaginationSerializer):\n    class Meta:\n        object_serializer_class = CommentMainSerializer\n","sub_path":"0/circle/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":2852,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"109606213","text":"#!/usr/bin/env python3\nimport argparse\nimport sys\n\n\ndef parse_arguments():\n    parser = argparse.ArgumentParser(description='')\n\n    parser.add_argument('--qrels', type=argparse.FileType('r'), required=True)\n\n    parser.add_argument(\n        '-i',\n        '--input',\n        type=argparse.FileType('r'),\n        default=sys.stdin,\n        help='default stdin')\n\n    parser.add_argument(\n        '-o',\n        '--output',\n        type=argparse.FileType('w'),\n        default=sys.stdout,\n        help='default stdout')\n\n    return parser.parse_args()\n\n\ndef main():\n    args = parse_arguments()\n\n    qrels = {}\n    for line in args.qrels:\n        splits = line.split()\n        if len(splits) == 2:\n            qno, dno, rel = *splits, 1\n        elif len(splits) == 3:\n            qno, dno, rel = splits\n        elif len(splits) == 4:\n            # Trec format\n            qno, _, dno, rel = splits\n        qrels.setdefault(qno, {})[dno] = int(rel)\n\n    for line in args.input:\n        delimeter = ' ' if ' ' in line else '\\t'\n        splits = line.split()\n        if len(splits) == 2:\n            qno, dno = splits\n        elif len(splits) == 3:\n            qno, dno, _ = splits\n        elif len(splits) == 6:\n            qno, _, dno, *_ = splits\n        else:\n            assert False, f'Unknown format {len(splits)} fields found'\n        rel = qrels.get(qno, {}).get(dno, 0)\n\n        args.output.write(line.rstrip('\\n') + f'{delimeter}{rel}\\n')\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"scripts/label.py","file_name":"label.py","file_ext":"py","file_size_in_byte":1484,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"302173761","text":"# encoding: utf-8\nfrom __future__ import unicode_literals\n\nfrom django.db import models\nfrom django.utils import timezone\nfrom django.utils.translation import ugettext as _\nfrom django.utils.encoding import python_2_unicode_compatible\n\n\n@python_2_unicode_compatible\nclass AbstractBase(models.Model):\n    \"\"\"Абстрактная модель для категорий и записей.\"\"\"\n\n    title = models.CharField(\n        _(\"Заголовок\"), max_length=255,\n        help_text=_(\"Полное название записи\"))\n    slug = models.SlugField(\n        _(\"Имя для урл\"), unique=True,\n        help_text=_(\"Уникальный урл состоящий из латинских символов\"))\n    is_published = models.BooleanField(\n        _(\"Опубликовано\"), default=True,\n        help_text=_(\"Только опубликованные записи выводятся на сайте\"))\n    created = models.DateTimeField(\n        _(\"Дата создания\"), auto_now_add=True,\n        help_text=_(\"Дата создания записи\"))\n    edited = models.DateTimeField(\n        _(\"Дата изменения\"), default=timezone.now,\n        help_text=_(\"Дата последнего изменения записи\"))\n    # SEO поля\n    meta_title = models.CharField(\n        _(\"Мета заголовок\"), max_length=75, null=True, blank=True,\n        help_text=_(\"Заголовок страницы для поисковых роботов\"))\n    meta_description = models.TextField(\n        _(\"Мета описание\"), max_length=125, null=True, blank=True,\n        help_text=_(\"Короткое описание страницы для поисковых роботов\"))\n\n    def __str__(self):\n        return self.title\n\n    class Meta:\n        abstract = True\n","sub_path":"zxt_blog/models/abstract.py","file_name":"abstract.py","file_ext":"py","file_size_in_byte":1839,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"95342420","text":"\"\"\"\n    Module that defines the `NetworkGroup` object and methods to read, write and manipulate it\n\"\"\"\n\nfrom collections.abc import Collection\nfrom itertools import product\nfrom typing import Any, Dict, Iterator, List, Union\n\nimport networkx as nx\nimport numpy as np\nimport pandas as pd\nfrom scipy.stats import pearsonr, chi2\nimport simplejson\n\nfrom .network import Network\n\nDType = List[Dict[str, Any]]\n\n\nclass NetworkGroup(Collection):\n    \"\"\"\n    Class that represents a group of network objects\n    These network objects are intended to be visualized together\n\n    Parameters\n    ----------\n    networks : List[Network]\n        The collection of networks to be grouped\n        key = context-id, value = Network\n\n    Attributes\n    ----------\n    graph : Union[nx.MultiGraph, nx.MultiDiGraph]\n        The networkx multi-graph representation of the network\n    nodes: DType\n        The list of nodes in the network group\n    links: DType\n        The list of links in the network group\n    contexts: DType\n        The list of all contexts in the network group\n    \"\"\"\n\n    def __init__(self, networks: List[Network]) -> None:\n        self.nodeid_map: Dict[int, Dict[str, str]] = dict()\n        self._networks = networks\n        if not networks or [n for n in networks if not isinstance(n, Network)]:\n            raise ValueError(\n                \"The networks parameter must be a list of one or more networks\"\n            )\n        self.graph = self._combine_networks(networks)\n\n    def __contains__(self, key) -> bool:\n        if key in range(len(self)):\n            return True\n        return False\n\n    def __len__(self) -> int:\n        return len(self._networks)\n\n    def __iter__(self) -> Iterator:\n        return iter(self._networks)\n\n    def __repr__(self) -> str:\n        n_nodes = len(self.nodes)\n        n_links = len(self.links)\n        n_contexts = len(self.contexts)\n        return f\"<NetworkGroup contexts={n_contexts} nodes={n_nodes} links={n_links}>\"\n\n    def __add__(self, other: \"NetworkGroup\") -> \"NetworkGroup\":\n        \"\"\"Combine two `NetworkGroup` objects and return a new `NetworkGroup` object\n        The new `NetworkGroup` contains nodes and edges from both the input objects\n        \"\"\"\n        networks = [*self._networks, *other._networks]\n        return NetworkGroup(networks)\n\n    def _combine_nodes(self, all_nodes: Dict[int, DType]) -> DType:\n        \"\"\" Combine nodes of individual networks into a single list \"\"\"\n        nodes: DType = []\n        node_hash: Dict[int, int] = dict()  # taxid => nodes.index\n        if len(all_nodes) == 1:\n            return all_nodes[0]\n        for cid, network_nodes in all_nodes.items():\n            self.nodeid_map[cid] = dict()\n            for node in network_nodes:\n                if node[\"taxid\"] not in node_hash:\n                    id_ = len(nodes)\n                    id_old = node[\"id\"]\n                    id_new = f\"id{id_}\"\n                    nodes.append(\n                        {**node, **{\"id\": id_new, \"children\": [], \"abundance\": None}}\n                    )\n                    node_hash[node[\"taxid\"]] = id_\n                    self.nodeid_map[cid][id_old] = id_new\n                else:\n                    id_old = node[\"id\"]\n                    ind = node_hash[node[\"taxid\"]]\n                    id_new = nodes[ind][\"id\"]\n                    self.nodeid_map[cid][id_old] = id_new\n        return nodes\n\n    def _combine_links(self, all_links: Dict[int, DType]) -> DType:\n        \"\"\" Combine links of individual networks into a single list \"\"\"\n        links = []\n        if len(all_links) == 1:\n            for link in all_links[0]:\n                links.append({**link, \"context_index\": 0})\n            return links\n        for cid, network_links in all_links.items():\n            for link in network_links:\n                source, target = link[\"source\"], link[\"target\"]\n                new_source = self.nodeid_map[cid][source]\n                new_target = self.nodeid_map[cid][target]\n                links.append(\n                    {\n                        **link,\n                        **{\n                            \"source\": new_source,\n                            \"target\": new_target,\n                            \"context_index\": cid,\n                        },\n                    }\n                )\n        return links\n\n    def _combine_networks(\n        self, networks: List[Network]\n    ) -> Union[nx.MultiGraph, nx.MultiDiGraph]:\n        \"\"\"\n        Combine networks into a network group\n\n        Parameters\n        ----------\n        networks : List[Network]\n            The list of networks to be grouped\n\n        Returns\n        -------\n        Union[nx.MultiGraph, nx.MultiDiGraph]\n            The networkx graph of the network\n        \"\"\"\n        nodes_dict = dict()\n        links_dict = dict()\n        contexts = []\n        for cid, network in enumerate(networks):\n            nodes_dict[cid] = network.nodes\n            links_dict[cid] = network.links\n            contexts.append(network.metadata)\n        merged_nodes = self._combine_nodes(nodes_dict)\n        merged_links = self._combine_links(links_dict)\n        if all([n.graph.is_directed() for n in networks]):\n            graph = nx.MultiDiGraph(contexts=contexts)\n        else:\n            graph = nx.MultiGraph(contexts=contexts)\n        for node in merged_nodes:\n            graph.add_node(node[\"id\"], **node)\n        for link in merged_links:\n            graph.add_edge(link[\"source\"], link[\"target\"], **link)\n        return graph\n\n    @property\n    def nodes(self) -> DType:\n        \"\"\" The list of nodes in the `NetworkGroup` and their corresponding properties \"\"\"\n        return [data for _, data in self.graph.nodes(data=True)]\n\n    @property\n    def links(self) -> DType:\n        \"\"\" The list of links in the `NetworkGroup` and their corresponding properties \"\"\"\n        return [data for _, _, data in self.graph.edges(data=True)]\n\n    @property\n    def contexts(self) -> DType:\n        \"\"\" The contexts for the group of networks \"\"\"\n        return self.graph.graph[\"contexts\"]\n\n    def get_adjacency_vectors(self, key: str) -> List[pd.Series]:\n        \"\"\"\n        Returns the adjacency matrix for each context as a `pd.Series`\n\n        Parameters\n        ----------\n        key : str\n            The `edge` property to be used to contruct the vectors\n\n        Returns\n        -------\n        List[pd.Series]:\n            The list of adjacency vectors\n        \"\"\"\n        ids = list(self.nodes)\n        size = len(ids) * len(ids)\n        index = [f\"{id1}-{id2}\" for id1, id2 in product(ids, repeat=2)]\n        adj_vector_list: List[pd.Series] = [\n            pd.Series(np.zeros((size), dtype=float), index=index)\n        ]\n        graph = self.graph\n        for source, target, data in graph.edges(data=True):\n            cid = data[\"cid\"]\n            id_ = f\"{source}-{target}\"\n            adj_vector_list[cid][id_] = data[key]\n        return adj_vector_list\n\n    def update_thresholds(\n        self, interaction_threshold: float = 0.3, pvalue_threshold: float = 0.05\n    ) -> None:\n        \"\"\"Update the thresholds on the networks\n\n        Parameters\n        ----------\n        interaction_threshold : float, optional\n            The value to which the interactions (absolute value) are to be thresholded\n            To disable thresholding based on interaction value then pass in 0.0\n            Default value is 0.3\n        pvalue_threshold : float, optional\n            This is the `alpha` value for pvalue cutoff\n            Default value is 0.05\n        \"\"\"\n        for network in self._networks:\n            network.interaction_threshold = interaction_threshold\n            network.pvalue_threshold = pvalue_threshold\n\n    def filter_links(self, pvalue_filter: bool, interaction_filter: bool) -> DType:\n        \"\"\"\n        The links of the networks after applying filtering\n\n        Parameters\n        ----------\n        pvalue_filter : bool\n            If True will use `pvalue_threshold` for filtering\n        interaction_filter : bool\n            If True will use `interaction_threshold` for filtering\n\n        Returns\n        -------\n        DType\n            The list of links in the network after applying thresholds\n        \"\"\"\n        filtered_links_dict = dict()\n        for cid, network in enumerate(self._networks):\n            filtered_links_dict[cid] = network.filter_links(\n                pvalue_filter=pvalue_filter, interaction_filter=interaction_filter\n            )\n        merged_filtered_links = self._combine_links(filtered_links_dict)\n        return merged_filtered_links\n\n    def json(\n        self, pvalue_filter: bool = False, interaction_filter: bool = False\n    ) -> str:\n        \"\"\"\n        Returns the network as a `JSON` string\n\n        Parameters\n        ----------\n        pvalue_filter : bool\n            If True will use `pvalue_threshold` for filtering\n            Default  value is False\n        interaction_filter : bool\n            If True will use `interaction_threshold` for filtering\n            Default  value is False\n\n        Returns\n        -------\n        str\n            The `JSON` string representation of the network\n        \"\"\"\n        nodes = self.nodes\n        links = self.filter_links(\n            pvalue_filter=pvalue_filter, interaction_filter=interaction_filter\n        )\n        contexts = self.contexts\n        network = {\"contexts\": contexts, \"nodes\": nodes, \"links\": links}\n        return simplejson.dumps(network, indent=2, sort_keys=True, ignore_nan=True)\n\n    def write(\n        self, fpath: str, pvalue_filter: bool = False, interaction_filter: bool = False\n    ) -> None:\n        \"\"\"\n        Write network to file as JSON\n\n        Parameters\n        ----------\n        fpath : str\n            The path to the `JSON` file\n        pvalue_filter : bool\n            If True will use `pvalue_threshold` for filtering\n            Default  value is False\n        interaction_filter : bool\n            If True will use `interaction_threshold` for filtering\n            Default  value is False\n        \"\"\"\n        with open(fpath, \"w\") as fid:\n            fid.write(\n                self.json(\n                    pvalue_filter=pvalue_filter, interaction_filter=interaction_filter\n                )\n            )\n\n    @classmethod\n    def load_json(cls, fpath: str) -> \"NetworkGroup\":\n        \"\"\"\n        Create a `NetworkGroup` object from network `JSON` file\n\n        Parameters\n        ----------\n        fpath : str\n            The path to the network `JSON` file\n\n        Returns\n        -------\n        NetworkGroup\n            The instance of the `NetworkGroup` class\n        \"\"\"\n        with open(fpath, \"r\") as fid:\n            raw_data = simplejson.load(fid)\n        n_networks = len(raw_data[\"contexts\"])\n        all_node_dict = {n[\"id\"]: n for n in raw_data[\"nodes\"]}\n        data_dict: Dict[int, dict] = {\n            n: {\"nodes\": [], \"links\": [], \"metadata\": {}} for n in range(n_networks)\n        }\n        unique_node_dict: Dict[int, dict] = {n: set() for n in range(n_networks)}\n        for cid in range(n_networks):\n            data_dict[cid][\"metadata\"] = {**raw_data[\"contexts\"][cid]}\n        for link in raw_data[\"links\"]:\n            link_cid = link[\"context_index\"]\n            source = all_node_dict[link[\"source\"]]\n            source_name = link[\"source\"]\n            target = all_node_dict[link[\"target\"]]\n            target_name = link[\"target\"]\n            data_dict[link_cid][\"links\"].append(link)\n            if source_name not in unique_node_dict[link_cid]:\n                data_dict[link_cid][\"nodes\"].append(source)\n                unique_node_dict[link_cid].add(source_name)\n            if target_name not in unique_node_dict[link_cid]:\n                data_dict[link_cid][\"nodes\"].append(target)\n                unique_node_dict[link_cid].add(target_name)\n        networks: List[Network] = []\n        for cid in range(n_networks):\n            metadata = data_dict[cid][\"metadata\"]\n            nodes = data_dict[cid][\"nodes\"]\n            links = data_dict[cid][\"links\"]\n            network_raw_data = {**metadata, \"nodes\": nodes, \"links\": links}\n            networks.append(Network.load_json(raw_data=network_raw_data))\n        return cls(networks)\n\n    def combine_pvalues(self, cids: List[str]) -> pd.Series:\n        \"\"\"\n        Combine pvalues of links in the `cids` using Brown's p-value merging method\n\n        Parameters:\n        -----------\n        cids : List[str]\n            The list of context ids that are to be used in the merger\n\n        Returns\n        -------\n        pvalues_combined\n            The `pd.Series` containing the combined pvalues\n        \"\"\"\n        pvalue_vectors = self.get_adjacency_vectors(\"pvalue\")\n        weight_vectors = self.get_adjacency_vectors(\"weight\")\n        pvalue_df: pd.DataFrame = pd.concat(\n            [pvalue_vectors[i] for i in cids], join=\"outer\"\n        )\n        weight_df: pd.DataFrame = pd.concat(\n            [weight_vectors[i] for i in cids], join=\"outer\"\n        )\n        # E[psi] = 2 * k\n        k = pvalue_df.shape[1]\n        expected_value = 2 * k\n        # Var[psi] = 4*k + 2 * sum{i<j} (3.263 * corr_ij + 0.710 * corr_ij^2 + 0.027 * corr_ij^3)\n        variance = 4 * k\n        for i in range(1, k):\n            for j in range(0, i - 1):\n                x_i = weight_df.iloc[:, i].values\n                x_j = weight_df.iloc[:, j].values\n                corr_ij, _ = pearsonr(x_i, x_j)\n                cov_ij_approx = (\n                    3.263 * corr_ij + 0.710 * (corr_ij ** 2) + 0.027 * (corr_ij ** 3)\n                )\n                variance += 2 * cov_ij_approx\n        # df = 2 * E[psi]^2 / var[psi]\n        degrees_of_freedom = 2 * (expected_value ** 2) / variance\n        # c = var[psi] / (2 * E[psi])\n        correction_factor = variance / (2 * expected_value)\n        link_ids = list(pvalue_df.index)\n        pvalues_combined = pd.Series(data=np.zeros(len(link_ids)), index=link_ids)\n        for row_id in list(pvalue_df.index):\n            pvalues = pvalue_df[row_id, :].values\n            chi_square = -2.0 * np.log(pvalues).sum() / correction_factor\n            pvalues_combined[row_id] = chi2.sf(chi_square, df=degrees_of_freedom)\n        return pvalues_combined\n","sub_path":"micone/main/network_group.py","file_name":"network_group.py","file_ext":"py","file_size_in_byte":14236,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"98144417","text":"#Kleiner werdende 6Ecke\r\n\r\nfrom turtle import *\r\ncolormode = 1.0\r\nspeed(100)\r\nseite = 100\r\n\r\nfor i in range(20):\r\n    pencolor(1-i/20,i/20,i/20)\r\n    pensize(5-i/4)\r\n    for k in range(6):\r\n        fd(seite)\r\n        lt(60)\r\n    pu()\r\n    lt(45)\r\n    fd(15)\r\n    rt(45)\r\n    pd()\r\n    seite = seite-5\r\n    \r\nht()\r\nmainloop()\r\n","sub_path":"Quellen/python/1 Arbeiten mit Turtle/Ü1 Turtle Lösungen/A4d_6Ecke.py","file_name":"A4d_6Ecke.py","file_ext":"py","file_size_in_byte":326,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"223720539","text":"import os\nimport sys\nimport time\nimport argparse\nimport numpy as np\nfrom scipy.misc import imshow\n\nimport torch\nimport torchvision\nimport torchvision.transforms as transforms\nimport torchvision.models as models\nfrom torch import nn\nfrom torch import autograd\nfrom torch import optim\nfrom torch.optim.lr_scheduler import StepLR\nfrom torch.nn import functional as F\n\nimport ops\nimport plot\nimport utils\nimport generators\nimport discriminators\nfrom vgg import vgg19, vgg19_bn, VGGextraction\n\n\ndef load_args():\n\n    parser = argparse.ArgumentParser(description='recover-gan')\n    parser.add_argument('-d', '--downsample', default=4, type=int, help='')\n    parser.add_argument('-l', '--gp', default=10, type=int, help='grad penalty')\n    parser.add_argument('-b', '--batch_size', default=4, type=int)\n    parser.add_argument('-e', '--epochs', default=200000, type=int)\n    parser.add_argument('-t', '--task', default='SRGAN', type=str)\n    parser.add_argument('-c', '--crop_size', default=96, type=int)\n    parser.add_argument('--dim', default=64)\n    parser.add_argument('--dataset', default='mnist')\n    parser.add_argument('--batchnorm', default=True)\n    parser.add_argument('--resumeG', default=False)\n    parser.add_argument('--resumeD', default=False)\n    args = parser.parse_args()\n    return args\n\n\ndef load_models(args):\n\n    crop = args.crop_size\n    downsize = crop // args.downsample\n    shape = (3, crop, crop)\n    if args.dataset == 'cifar10':\n        netG = generators.genResNet(args).cuda()\n        netD = discriminators.SRdiscriminatorCIFAR(args).cuda()\n        vgg = vgg19_bn(pretrained=True).cuda()\n        netL = VGGextraction(vgg)\n\n    elif args.dataset == 'imagenet':\n        netG = generators.SRResNet(args, shape).cuda(0)\n        netD = discriminators.SRdiscriminator(args, shape).cuda(0)\n        netD = None\n        vgg = vgg19_bn(pretrained=True).cuda(1)\n        netL = VGGextraction(vgg).cuda(1)\n    \n    elif args.dataset == 'raise':\n        netG = generators.SRResNet(args, shape).cuda(0)\n        netD = discriminators.SRdiscriminator(args, shape).cuda(0)\n        vgg = models.vgg19(pretrained=True).cuda(1)\n        netL = VGGextraction(vgg).cuda(1)\n\n    print (netG, netD, netL)\n    return (netG, netD, netL)\n\n\ndef train():\n    args = load_args()\n    np.set_printoptions(precision=4)\n    train_gen, dev_gen, test_gen = utils.dataset_iterator(args)\n    torch.manual_seed(1)\n    netG, netD, netL = load_models(args)\n\n    optimizerG = optim.Adam(netG.parameters(), lr=10e-5)\n    optimizerD = optim.Adam(netD.parameters(), lr=10e-4)\n    scheduler = StepLR(optimizerG, step_size=500000, gamma=0.1)\n    vgg_scale = 0.006  # scales perceptual loss to be on order of MSE loss\n    loss_ratio = 0.001 # balancing ratio on content vs GAN\n    mse_criterion = nn.MSELoss()\n    one = torch.FloatTensor([1]).cuda(0)\n    mone = (one * -1).cuda(0)\n\n    gen = utils.inf_train_gen(train_gen)\n\n   \n    \"\"\" Pretrain SRResNet with MSE only \"\"\"\n    if args.task == 'SRResNet':\n        \n        \"\"\" Attempt to resume generator from checkpoint \"\"\"\n        if args.resumeG is True:\n            print (\"loading generator from weights\")\n            state = torch.load('./SRResNet.pt')\n            netG.load_state_dict(state)\n\n        for iter in range(1, 1000000):\n            start_time = time.time()\n            scheduler.step()\n            _data_hr = next(gen)\n            real_data_lr, real_data_hr = utils.scale_data(args, _data_hr)\n            real_data_lr = real_data_lr.cuda(0)\n            real_data_hr = real_data_hr.cuda(0)\n            real_data_hr_v = autograd.Variable(real_data_hr)\n            real_data_lr_v = autograd.Variable(real_data_lr)\n            \n            fake_hr = netG(real_data_lr_v)\n\n            netG.zero_grad()\n            content_loss = mse_criterion(fake_hr, real_data_hr_v)\n            \"\"\" \n            # something is weird here\n            if (iter > 1000) and (content_loss.cpu().data.numpy()[0] > .4):\n                print(\"\\nweird thing happened, check for saved logs\\n\")\n                np.save(\"weird_fake.npy\", fake_hr.cpu().data.numpy())\n                np.save(\"weird_real.npy\", real_data_hr_v.cpu().data.numpy())\n                continue\n            \"\"\"\n            psnr = ops.psnr(args, content_loss)\n            content_loss.backward()\n            optimizerG.step()\n\n            save_dir = './plots/'+args.dataset\n            plot.plot('content_loss_(mse)', content_loss.data.cpu().numpy())\n            plot.plot('psnr', np.array(psnr))\n            data = (real_data_lr, real_data_hr, fake_hr)\n            if iter % 100 == 99:\n                utils.generate_sr_image(iter, netG, save_dir, args, data)\n            if (iter < 5) or (iter % 100 == 99):\n                plot.flush()\n            plot.tick()\n            if iter % 100000 == 0:\n                torch.save(netG.state_dict(), './SRResNet_{}.pt'.format(iter))\n\n    elif args.task == 'SRGAN':\n        \"\"\" Attempt to resume generator from checkpoint \"\"\"\n        if args.resumeD:\n            print (\"loading generator\")\n            state = torch.load('./SRGAN_D.pt')\n            netD.load_state_dict(state)\n        if args.resumeG:\n            print (\"loading discriminator\")\n            state = torch.load('./SRResNet.pt')\n            netG.load_state_dict(state)\n\n        for p in netL.parameters():  # reset requires_grad\n            p.requires_grad = False  # they are set to False below in netG update\n        \n        \"\"\" train SRGAN \"\"\"\n\n        for iteration in range(1, args.epochs):\n            start_time = time.time()\n            \"\"\" Update D network \"\"\"\n            for p in netD.parameters():  # reset requires_grad\n                p.requires_grad = True  # they are set to False below in netG update\n            \n            for i in range(1):\n                _data_hr = next(gen)\n                real_data_lr, real_data_hr = utils.scale_data(args, _data_hr)\n                real_data_lr = real_data_lr.cuda(0)\n                real_data_hr = real_data_hr.cuda(0)\n                real_data_hr_v = autograd.Variable(real_data_hr)\n                real_data_lr_v = autograd.Variable(real_data_lr)\n                fake_hr = netG(real_data_lr_v)\n                \"\"\" trying DCGAN first \"\"\"\n                netD.zero_grad()\n                D_real = netD(real_data_hr_v)\n                D_fake = netD(fake_hr)\n                D_fake_loss = torch.log(1. - D_fake + 1e-6)\n                D_real_loss = torch.log(D_real + 1e-6)\n                D_cost = (-(D_fake_loss + D_real_loss)).mean()\n                D_cost.backward(retain_graph=True)\n                \"\"\"\n                D_real = D_real.mean()\n                D_real.backward(mone)\n                D_fake = D_fake.mean()\n                D_fake.backward(one)\n                # train with gradient penalty \n                gradient_penalty = ops.calc_gradient_penalty(args,\n                        netD, real_data_hr_v.data, fake_hr.data)\n                gradient_penalty.backward()\n                D_cost = D_fake - D_real + gradient_penalty\n                Wasserstein_D = D_real - D_fake\n                \"\"\"\n                optimizerD.step()\n\n            \"\"\" Train Generator \"\"\"\n            for p in netD.parameters():  # reset requires_grad\n                p.requires_grad = False  # they are set to False below in netG update\n            netG.zero_grad()\n            \"\"\"\n            _data_hr = next(gen)\n            real_data_lr, real_data_hr = utils.scale_data(args, _data_hr)\n            real_data_lr = real_data_lr.cuda(0)\n            real_data_hr = real_data_hr.cuda(0)\n            real_data_hr_v = autograd.Variable(real_data_hr)\n            real_data_lr_v = autograd.Variable(real_data_lr)\n            fake_hr = netG(real_data_lr_v)\n            \"\"\"\n            real_gpu1 = autograd.Variable(real_data_hr.cuda(1), requires_grad=False)\n            fake_gpu1 = autograd.Variable(fake_hr.cpu().data.cuda(1), requires_grad=False)\n\n            vgg_real = netL(real_gpu1)\n            vgg_fake = netL(fake_gpu1)\n            # p1_loss = mse_criterion(fake_gpu1, real_gpu1)\n            p2_loss = vgg_scale * mse_criterion(vgg_fake, vgg_real)\n            \"\"\" \n            test_loss = vgg_scale * ((vgg_fake - vgg_real).pow(2).sum(3).mean())\n            if (iteration % 20) == 0:\n                print (\"test: \", test_loss.cpu().data.numpy()[0])\n                print (\"mse: \", p2_loss.cpu().data.numpy()[0])\n            \"\"\"\n            #print (\"mse loss: \", p1_loss.cpu().data.numpy())\n            #print (\"vgg loss: \", p2_loss.cpu().data.numpy())\n            # perceptual_loss =  (p1_loss + p2_loss).cuda(0)\n            perceptual_loss = p2_loss.cuda(0)\n            # perceptual_loss.backward(one)\n            \"\"\" Try DCGAN first \"\"\"\n            adv_loss = (-torch.log(netD(fake_hr) + 1e-6)).mean()\n            #G = netD(fake_hr)\n            #G = G.mean()\n            # G.backward(mone)\n            #adv_loss = -G\n            #perceptual_loss = vgg_scale * ((diff.pow(2)).sum(3).mean())  \n            total_loss = perceptual_loss + (loss_ratio * adv_loss)\n            total_loss.backward()\n            optimizerG.step()\n\n            psnr = ops.psnr(args, perceptual_loss)\n            if iteration % 100 == 0:\n                save_dir = './plots/'+args.dataset\n                content_loss = np.around(float(perceptual_loss.cpu().data.numpy()[0]), 5)\n                adversarial_loss = np.around(float(adv_loss.cpu().data.numpy()[0]), 5)\n                disc_cost = np.around(float(D_cost.cpu().data.numpy()[0]), 5)\n                psnr = np.around(float(np.array(psnr)), 5)\n                print(\"iter {}\\tcontent loss: {}\\tadv loss: {}\\tdisc cost: {}\\tpsnr: {}\"\n                        .format(iteration, content_loss, adversarial_loss, disc_cost, psnr)\n                )\n                data = (real_data_lr, real_data_hr, fake_hr)\n                utils.generate_sr_image(iteration, netG, save_dir, args, data)\n            \"\"\"\n            #plot.plot('w1 distance', Wasserstein_D.cpu().data.numpy())\n            if (iteration < 5) or (iteration % 20 == 19):\n                plot.flush()\n            \"\"\"\n            plot.tick()\n            if (iteration % 5000) == 0:\n                torch.save(netD.state_dict(), 'SRGAN_D.pt')\n                torch.save(netG.state_dict(), 'SRGAN_G.pt')\n\nif __name__ == '__main__':\n    train()\n","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":10285,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"612494663","text":"import os\nimport csv\ntotal_months = 0\ntotal_amount = 0\np_l = []\nmonths = []\navg_change = []\ntotal_change = 0\ncsvpath = os.path.join(\"BootCamp_Data\",\"USC-LA-DATA-PT-08-2019-U-C\",\"03-Python\",\"Homework\",\"Instructions\",\"PyBank\",\"Resources\",\"budget_data.csv\")\nwith open(csvpath, newline=\"\") as csvfile:\n    csv_reader = csv.reader(csvfile, delimiter =\",\")\n    csv_header = next(csv_reader)\n    for row in csv_reader:\n        total_months = total_months + 1\n        total_amount = total_amount + int(row[1])\n        p_l.append(row[1])\n        months.append(row[0])\n    for  i in range(1, len(p_l)):\n        avg_change.append(int(p_l[i])  - int(p_l[i - 1]))\n    for i in range(len(avg_change)):\n        total_change = total_change  + avg_change[i]\n    avg = total_change / len(avg_change)\n    maxElem = max(avg_change)\n    minElem = min(avg_change)\n    print(\"Financial Analysis\")\n    print(\".....................................\")\n    print(\"Total Months: \" +str(total_months))\n    print(\"Total: \"+ \"$\"+ str(total_amount) )\n    print(\"Average Change: \"+ \"$\" + str(avg_change.index(maxElem)))\n    print(\"Greatest Increase in Profits: \"+ months[avg_change.index(maxElem) + 1] + \" \" + \"(\" + \"$\"+ str(maxElem) + \")\")\n    print(\"Greatest Decrease in Profits: \" + months[avg_change.index(minElem) + 1] + \" \" + \"($\" + str(minElem) + \")\")\n\ncsvPath = os.path.join(\"PyBank.txt\")\nwith open(csvPath,'w',newline = \"\") as csvfile:\n    csv_writer = csv.writer(csvfile, delimiter = \" \")\n    csv_writer.writerow([\"Financial Analysis\"])\n    csv_writer.writerow([\".....................................\"])\n    csv_writer.writerow([\"Total Months: \" +str(total_months)])\n    csv_writer.writerow([\"Total: \"+ \"$\"+ str(total_amount)])\n    csv_writer.writerow([\"Average Change: \"+ \"$\" + str(avg_change.index(maxElem))] )\n    csv_writer.writerow([\"Greatest Increase in Profits: \"+ months[avg_change.index(maxElem) + 1] + \" \" + \"($\"+ str(maxElem) + \")\"])\n    csv_writer.writerow([\"Greatest Decrease in Profits: \" + months[avg_change.index(minElem) + 1] + \" \"  + \"($\" + str(minElem) + \")\"])  ","sub_path":"PyBank/PyBank.py","file_name":"PyBank.py","file_ext":"py","file_size_in_byte":2057,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"33382211","text":"import requests\nfrom bs4 import BeautifulSoup\n\ns = raw_input('What do you want to search for? ')\n\nurl = \"https://google.com/search?q=%22\"+s+\"%22&num=100&start=0&biw=1374&bih=737\"\nsource_code = requests.get(url)\nplain_text = source_code.content\nsoup = BeautifulSoup(plain_text, \"html.parser\")\nlinks = soup.find_all('h3', { \"class\" : \"r\" })\nlink_list = []\n\nfor l in links:\n\n    for anchor in l.find_all('a', href=True):\n        a = anchor['href'].strip('/url?q=')\n        a = a.split('&')\n        link_list.append(a[0])\n\nwith open(\"scrape_results.html\", \"w\") as file:\n    file.write('<html><head></head><body><ul>')\n    for line in link_list:\n        file.write('<li><a href=\"'+line+'\" target=\"_blank\">'+line+'</a></li>')\n    file.write('</ul></body></html>')\n","sub_path":"scrape.py","file_name":"scrape.py","file_ext":"py","file_size_in_byte":758,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"102418527","text":"from __future__ import unicode_literals\nfrom django.db import models\nfrom convert import alpha\nfrom django.db.models import Count\nfrom django.core.exceptions import ObjectDoesNotExist\n\n\nschema_version = 16\n\n\nscatterplot_query = \"\"\"\\\nSELECT\n  x.id\n  ,3600 * (x.ra - r.wm_ra) as ra_dist_arcsec\n  ,3600 * (x.decl - r.wm_decl) as decl_dist_arcsec\n  ,3600 * x.ra_err\n  ,3600 * x.decl_err\nFROM assocxtrsource a\n  ,extractedsource x\n  ,runningcatalog r\n  ,image im1\nWHERE a.runcat = r.id\nAND a.xtrsrc = x.id\nAND x.image = im1.id\nAND im1.dataset = %s\n\"\"\"\n\n\nclass Assoccatsource(models.Model):\n    id = models.IntegerField(primary_key=True)\n    xtrsrc = models.ForeignKey('Extractedsource', db_column='xtrsrc',\n                               related_name='assoccatsources')\n    catsrc = models.ForeignKey('Catalogedsource', db_column='catsrc',\n                               related_name='assoccatsources')\n    type = models.IntegerField()\n    distance_arcsec = models.FloatField()\n    r = models.FloatField()\n    loglr = models.FloatField()\n\n    class Meta:\n        managed = False\n        db_table = 'assoccatsource'\n\n\nclass Assocskyrgn(models.Model):\n    id = models.IntegerField(primary_key=True)\n    runcat = models.ForeignKey('Runningcatalog', db_column='runcat',\n                               related_name='assocskyrgns')\n    skyrgn = models.ForeignKey('Skyregion', db_column='skyrgn',\n                               related_name='assocskyrgns')\n    distance_deg = models.FloatField(null=True)\n\n    class Meta:\n        managed = False\n        db_table = 'assocskyrgn'\n\n\nclass Assocxtrsource(models.Model):\n    id = models.IntegerField(primary_key=True)\n    runcat = models.ForeignKey('Runningcatalog', db_column='runcat',\n                               related_name='Assocxtrsources')\n    xtrsrc = models.ForeignKey('Extractedsource', db_column='xtrsrc',\n                               related_name='asocxtrsources')\n    type = models.IntegerField()\n    distance_arcsec = models.FloatField()\n    r = models.FloatField()\n    loglr = models.FloatField(null=True)\n\n    class Meta:\n        managed = False\n        db_table = 'assocxtrsource'\n\n\nclass Catalog(models.Model):\n    id = models.IntegerField(primary_key=True)\n    name = models.CharField(max_length=50)\n    fullname = models.CharField(max_length=250)\n\n    class Meta:\n        managed = False\n        db_table = 'catalog'\n\n\nclass Catalogedsource(models.Model):\n    id = models.IntegerField(primary_key=True)\n    catalog = models.ForeignKey(Catalog, db_column='catalog',\n                                related_name='catalogedsources')\n    orig_catsrcid = models.IntegerField()\n    catsrcname = models.CharField(max_length=120)\n    tau = models.IntegerField()\n    band = models.ForeignKey('Frequencyband', db_column='band',\n                             related_name='catalogedsources')\n    stokes = models.IntegerField()\n    freq_eff = models.FloatField()\n    zone = models.IntegerField()\n    ra = models.FloatField()\n    decl = models.FloatField()\n    ra_err = models.FloatField()\n    decl_err = models.FloatField()\n    x = models.FloatField()\n    y = models.FloatField()\n    z = models.FloatField()\n    margin = models.BooleanField()\n    det_sigma = models.FloatField()\n    src_type = models.CharField(max_length=1)\n    fit_probl = models.CharField(max_length=2)\n    pa = models.FloatField()\n    pa_err = models.FloatField()\n    major = models.FloatField()\n    major_err = models.FloatField()\n    minor = models.FloatField()\n    minor_err = models.FloatField()\n    avg_f_peak = models.FloatField()\n    avg_f_peak_err = models.FloatField()\n    avg_f_int = models.FloatField()\n    avg_f_int_err = models.FloatField()\n    frame = models.CharField(max_length=20)\n\n    class Meta:\n        managed = False\n        db_table = 'catalogedsource'\n\n\nclass Classification(models.Model):\n    id = models.IntegerField(primary_key=True)\n    transient_id = models.IntegerField()\n    classification = models.CharField(max_length=256)\n    weight = models.FloatField()\n\n    class Meta:\n        managed = False\n        db_table = 'classification'\n\n\nclass Dataset(models.Model):\n    id = models.IntegerField(primary_key=True)\n    rerun = models.IntegerField()\n    type = models.IntegerField()\n    process_start_ts = models.DateTimeField()\n    process_end_ts = models.DateTimeField()\n    detection_threshold = models.FloatField(null=True)\n    analysis_threshold = models.FloatField(null=True)\n    assoc_radius = models.FloatField(null=True)\n    backsize_x = models.IntegerField(null=True)\n    backsize_y = models.IntegerField(null=True)\n    margin_width = models.FloatField(null=True)\n    description = models.CharField(max_length=100)\n    node = models.IntegerField()\n    nodes = models.IntegerField()\n\n    class Meta:\n        managed = False\n        db_table = 'dataset'\n\n    def __unicode__(self):\n        return self.description\n\n    def transients(self):\n        return Transient.objects.using(self._state.db).\\\n            filter(runcat__dataset=self)\n\n    def extractedsources(self):\n        return Extractedsource.objects.using(self._state.db).\\\n            filter(image__dataset=self)\n\n    def scatterplot(self):\n        return Extractedsource.objects.raw(scatterplot_query,\n                                           params=[self.id]).\\\n                                                  using(self._state.db)\n\n    def rejected_images(self):\n        return Image.objects.using(self._state.db).filter(dataset=self).\\\n            annotate(num_rejections=Count('rejections')).\\\n            filter(num_rejections__gt=0)\n\n\nclass Extractedsource(models.Model):\n    id = models.IntegerField(primary_key=True)\n    image = models.ForeignKey('Image', db_column='image',\n                              related_name='extractedsources')\n    zone = models.IntegerField()\n    ra = models.FloatField()\n    decl = models.FloatField()\n    uncertainty_ew = models.FloatField()\n    uncertainty_ns = models.FloatField()\n    ra_err = models.FloatField()\n    decl_err = models.FloatField()\n    ra_fit_err = models.FloatField()\n    decl_fit_err = models.FloatField()\n    ew_sys_err = models.FloatField()\n    ns_sys_err = models.FloatField()\n    x = models.FloatField()\n    y = models.FloatField()\n    z = models.FloatField()\n    racosdecl = models.FloatField()\n    margin = models.BooleanField()\n    det_sigma = models.FloatField()\n    semimajor = models.FloatField()\n    semiminor = models.FloatField()\n    pa = models.FloatField()\n    f_peak = models.FloatField()\n    f_peak_err = models.FloatField()\n    f_int = models.FloatField()\n    f_int_err = models.FloatField()\n    extract_type = models.IntegerField()\n    node = models.IntegerField()\n    nodes = models.IntegerField()\n\n    class Meta:\n        managed = False\n        db_table = 'extractedsource'\n\n    def __unicode__(self):\n        return str(self.id)\n\n    def runningcatalogs(self):\n        assocs = Assocxtrsource.objects.using(self._state.db).\\\n            filter(xtrsrc=self.id)\n        return [a.runcat for a in assocs]\n\n\nclass Frequencyband(models.Model):\n    id = models.IntegerField(primary_key=True)\n    freq_central = models.FloatField(null=True)\n    freq_low = models.FloatField(null=True)\n    freq_high = models.FloatField(null=True)\n\n    class Meta:\n        managed = False\n        db_table = 'frequencyband'\n\n    def __unicode__(self):\n        if self.freq_central:\n            return \"%s MHz\" % int(self.freq_central / 10e5)\n        else:\n            return \"0 MHz\"\n\n\nclass Image(models.Model):\n    id = models.IntegerField(primary_key=True)\n    dataset = models.ForeignKey(Dataset, db_column='dataset',\n                                related_name='images')\n    tau = models.IntegerField(null=True)\n    band = models.ForeignKey(Frequencyband, db_column='band',\n                             related_name='images')\n    stokes = models.IntegerField()\n    tau_time = models.FloatField(null=True)\n    freq_eff = models.FloatField()\n    freq_bw = models.FloatField()\n    taustart_ts = models.DateTimeField()\n    skyrgn = models.ForeignKey('Skyregion', db_column='skyrgn',\n                               related_name='images')\n    rb_smaj = models.FloatField()\n    rb_smin = models.FloatField()\n    rb_pa = models.FloatField()\n    deltax = models.FloatField()\n    deltay = models.FloatField()\n    fwhm_arcsec = models.FloatField(null=True)\n    fov_degrees = models.FloatField(null=True)\n    url = models.CharField(max_length=1024)\n    node = models.IntegerField()\n    nodes = models.IntegerField()\n\n    class Meta:\n        managed = False\n        db_table = 'image'\n\n    def filename(self):\n        return self.url.split('/')[-1]\n\n    def get_next_by_taustart_ts_only(self):\n        \"\"\"\n        returns next image, limited by dataset, stokes and frequency,\n        sorted by time.\n        \"\"\"\n        qs = Image.objects.using(self._state.db).\\\n            filter(dataset=self.dataset,\n                   stokes=self.stokes,\n                   freq_eff=self.freq_eff,\n                   skyrgn=self.skyrgn).\\\n            order_by(\"taustart_ts\")\n        l = list(qs.values_list('id', flat=True))\n        index = l.index(self.id)\n        try:\n            id = l[index+1]\n        except IndexError:\n            raise ObjectDoesNotExist\n        return Image.objects.using(self._state.db).get(id=id)\n\n\nclass Monitoringlist(models.Model):\n    id = models.IntegerField(primary_key=True)\n    runcat = models.ForeignKey('Runningcatalog', db_column='runcat',\n                               related_name='monitoringlists')\n    ra = models.FloatField()\n    decl = models.FloatField()\n    dataset = models.ForeignKey(Dataset, db_column='dataset',\n                                related_name='monitoringlists')\n    userentry = models.BooleanField()\n\n    class Meta:\n        managed = False\n        db_table = 'monitoringlist'\n\n\nclass Node(models.Model):\n    id = models.IntegerField(primary_key=True)\n    node = models.IntegerField()\n    zone = models.IntegerField()\n    zone_min = models.IntegerField()\n    zone_max = models.IntegerField()\n    zone_min_incl = models.BooleanField()\n    zone_max_incl = models.BooleanField()\n    zoneheight = models.FloatField()\n    nodes = models.IntegerField()\n\n    class Meta:\n        managed = False\n        db_table = 'node'\n\n\nclass Rejection(models.Model):\n    id = models.IntegerField(primary_key=True)\n    image = models.ForeignKey(Image, db_column='image',\n                              related_name='rejections')\n    rejectreason = models.ForeignKey('Rejectreason', db_column='rejectreason')\n    comment = models.CharField(max_length=512)\n\n    class Meta:\n        managed = False\n        db_table = 'rejection'\n\n    def __unicode__(self):\n        return \"%s: %s\" % (self.rejectreason, self.comment)\n\n\nclass Rejectreason(models.Model):\n    id = models.IntegerField(primary_key=True)\n    description = models.CharField(max_length=512)\n\n    class Meta:\n        managed = False\n        db_table = 'rejectreason'\n\n    def __unicode__(self):\n        return \"%s\" % (self.description)\n\n\nclass Runningcatalog(models.Model):\n    id = models.IntegerField(primary_key=True)\n\n    # we don't create a reverse mapping here, since this is only the first\n    # extracted source\n    xtrsrc = models.ForeignKey(Extractedsource, db_column='xtrsrc',\n                               related_name='+')\n    dataset = models.ForeignKey(Dataset, db_column='dataset',\n                                related_name='runningcatalogs')\n    datapoints = models.IntegerField()\n    zone = models.IntegerField()\n    wm_ra = models.FloatField()\n    wm_decl = models.FloatField()\n    wm_uncertainty_ew = models.FloatField()\n    wm_uncertainty_ns = models.FloatField()\n    avg_ra_err = models.FloatField()\n    avg_decl_err = models.FloatField()\n    avg_wra = models.FloatField()\n    avg_wdecl = models.FloatField()\n    avg_weight_ra = models.FloatField()\n    avg_weight_decl = models.FloatField()\n    x = models.FloatField()\n    y = models.FloatField()\n    z = models.FloatField()\n    margin = models.BooleanField()\n    inactive = models.BooleanField()\n\n    class Meta:\n        managed = False\n        db_table = 'runningcatalog'\n\n    def __unicode__(self):\n        return \"%s\" % (self.id)\n\n    def lightcurve(self):\n        assocs = Assocxtrsource.objects.using(self._state.db).filter(runcat=self.id)\n        related = ['image', 'image__band']\n        return Extractedsource.objects.using(self._state.db).filter(asocxtrsources__in=assocs).prefetch_related(*related)\n\n    @property\n    def ra_err(self):\n        return alpha(self.wm_uncertainty_ew, self.wm_decl)\n\n\nclass RunningcatalogFlux(models.Model):\n    id = models.IntegerField(primary_key=True)\n    runcat = models.ForeignKey(Runningcatalog, db_column='runcat')\n    band = models.ForeignKey(Frequencyband, db_column='band')\n    stokes = models.IntegerField()\n    f_datapoints = models.IntegerField()\n    resolution = models.FloatField(null=True)\n    avg_f_peak = models.FloatField()\n    avg_f_peak_sq = models.FloatField()\n    avg_f_peak_weight = models.FloatField()\n    avg_weighted_f_peak = models.FloatField()\n    avg_weighted_f_peak_sq = models.FloatField()\n    avg_f_int = models.FloatField()\n    avg_f_int_sq = models.FloatField()\n    avg_f_int_weight = models.FloatField()\n    avg_weighted_f_int = models.FloatField()\n    avg_weighted_f_int_sq = models.FloatField()\n\n    class Meta:\n        managed = False\n        db_table = 'runningcatalog_flux'\n\n\nclass Skyregion(models.Model):\n    id = models.IntegerField(primary_key=True)\n    dataset = models.ForeignKey(Dataset, db_column='dataset',\n                                related_name='skyregions')\n    centre_ra = models.FloatField()\n    centre_decl = models.FloatField()\n    xtr_radius = models.FloatField()\n    x = models.FloatField()\n    y = models.FloatField()\n    z = models.FloatField()\n\n    def __unicode__(self):\n        return \"%s, %s\" % (self.centre_ra, self.centre_decl)\n\n    class Meta:\n        managed = False\n        db_table = 'skyregion'\n\n\nclass Temprunningcatalog(models.Model):\n    id = models.IntegerField(primary_key=True)\n    runcat = models.ForeignKey(Runningcatalog, db_column='runcat',\n                               related_name='Temprunningcatalogs')\n    xtrsrc = models.ForeignKey(Extractedsource, db_column='xtrsrc',\n                               related_name='Temprunningcatalogs')\n    distance_arcsec = models.FloatField()\n    r = models.FloatField()\n    dataset = models.ForeignKey(Dataset, db_column='dataset',\n                                related_name='Temprunningcatalogs')\n    band = models.ForeignKey(Frequencyband, db_column='band',\n                             related_name='Temprunningcatalogs')\n    stokes = models.IntegerField()\n    datapoints = models.IntegerField()\n    zone = models.IntegerField()\n    wm_ra = models.FloatField()\n    wm_decl = models.FloatField()\n    wm_uncertainty_ew = models.FloatField()\n    wm_uncertainty_ns = models.FloatField()\n    avg_ra_err = models.FloatField()\n    avg_decl_err = models.FloatField()\n    avg_wra = models.FloatField()\n    avg_wdecl = models.FloatField()\n    avg_weight_ra = models.FloatField()\n    avg_weight_decl = models.FloatField()\n    x = models.FloatField()\n    y = models.FloatField()\n    z = models.FloatField()\n    margin = models.BooleanField()\n    inactive = models.BooleanField()\n    beam_semimaj = models.FloatField()\n    beam_semimin = models.FloatField()\n    beam_pa = models.FloatField()\n    f_datapoints = models.IntegerField()\n    avg_f_peak = models.FloatField()\n    avg_f_peak_sq = models.FloatField()\n    avg_f_peak_weight = models.FloatField()\n    avg_weighted_f_peak = models.FloatField()\n    avg_weighted_f_peak_sq = models.FloatField()\n    avg_f_int = models.FloatField()\n    avg_f_int_sq = models.FloatField()\n    avg_f_int_weight = models.FloatField()\n    avg_weighted_f_int = models.FloatField()\n    avg_weighted_f_int_sq = models.FloatField()\n\n    class Meta:\n        managed = False\n        db_table = 'temprunningcatalog'\n\n\nclass Transient(models.Model):\n    id = models.IntegerField(primary_key=True)\n    runcat = models.ForeignKey(Runningcatalog, db_column='runcat',\n                               related_name='transients')\n    band = models.ForeignKey(Frequencyband, db_column='band',\n                             related_name='transients')\n    siglevel = models.FloatField()\n    v_int = models.FloatField()\n    eta_int = models.FloatField()\n    detection_level = models.FloatField()\n    trigger_xtrsrc = models.ForeignKey(Extractedsource,\n                                       db_column='trigger_xtrsrc',\n                                       related_name='transients')\n    status = models.IntegerField()\n    t_start = models.DateTimeField(null=True)\n\n    class Meta:\n        managed = False\n        db_table = 'transient'\n\n    def __unicode__(self):\n        return str(self.id)\n\n    def lightcurve(self):\n        assocs = Assocxtrsource.objects.using(self._state.db).\\\n            filter(runcat=self.runcat)\n        related = ['image', 'image__band']\n        return Extractedsource.objects.using(self._state.db).\\\n            filter(asocxtrsources__in=assocs).prefetch_related(*related)\n\n\nclass Version(models.Model):\n    name = models.CharField(max_length=12, primary_key=True)\n    value = models.IntegerField()\n\n    class Meta:\n        managed = False\n        db_table = 'version'\n","sub_path":"banana/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":17306,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"79369309","text":"import AAshe.utils.config\nimport AAshe.utils.request\nimport AAshe.utils.ratelimit\nimport AAshe.sqlite\n\nimport AAshe.lolstatus.lolstatus\n\nimport asyncio\nimport aiohttp\nimport sqlite3\nimport time\nimport json\nimport copy\n\nimport typing\n\n\nclass Translation(AAshe.sqlite.SQLiteSubClass):\n\t__slots__ = (\n\t\t\"locale\",  # type: str\n\t\t\"content\",  # type: str\n\t\t\"updated_at\"  # type: str\n\t)\n\n\tdef __repr__(self):\n\t\treturn f\"<{self.locale}:{self.updated_at}>\"\n\n\nclass Message(AAshe.sqlite.SQLiteSubClass):\n\t__slots__ = (\n\t\t\"severity\",  # type: str\n\t\t\"author\",  # type: str\n\t\t\"created_at\",  # type: str\n\t\t\"translations\",  # type: typing.Union[Translation]\n\t\t\"updated_at\",  # type: str\n\t\t\"content\",  # type: str\n\t\t\"id\"  # type: str\n\t)\n\n\tdef __repr__(self):\n\t\treturn f\"<{self.id}:{self.updated_at}>\"\n\n\nclass Incident(AAshe.sqlite.SQLiteSubClass):\n\t__slots__ = (\n\t\t\"active\",  # type:\tbool\n\t\t\"created_at\",  # type:\tstr\n\t\t\"id\",  # type: int\n\t\t\"updates\",  # type:\ttyping.Union[Message]\n\t)\n\n\tdef __repr__(self):\n\t\treturn f\"<{self.id}:{self.active}>\"\n\n\nclass Service(AAshe.sqlite.SQLiteSubClass):\n\t__slots__ = (\n\t\t\"status\",  # type: str\n\t\t\"incidents\",  # type: typing.Union[Incident]\n\t\t\"name\",  # type: str\n\t\t\"slug\",  # type: str\n\t)\n\n\tdef __repr__(self):\n\t\treturn f\"<{self.name}:{self.slug}>\"\n\n\nclass ShardStatus(AAshe.sqlite.SQLite):\n\t\"\"\"\n\tRepresent a Shard retrieved from the Riot API.\n\t\"\"\"\n\n\t__table_name__ = \"aashe_shard\"\n\t__request_cooldown__ = 0\n\t__loudness__ = 4\n\n\t__slots__ = (\n\t\t\"name\",  # type: str\n\t\t\"region_tag\",  # type: str\n\t\t\"hostname\",  # type: str\n\t\t\"services\",  # type: typing.List[Service]\n\t\t\"slug\",  # type: str\n\t\t\"locales\",  # type: typing.List[str]\n\t\t\"region\",\n\n\t\t\"time\",  # type: float\n\t)\n\t\n\t__null__ = []\n\t__null_key__ = []\n\t__integer__ = []\n\t__integer_key__ = []\n\t__real__ = [\"time\"]\n\t__real_key__ = []\n\t__text__ = [\n\t\t\"name\",\n\t\t\"region_tag\",\n\t\t\"hostname\",\n\t\t\"slug\",\n\t\t\n\t\t\"services\",\n\t\t\"locales\"\n\t]\n\t__text_key__ = [\n\t\t\"region\"\n\t]\n\t__blob__ = []\n\t__blob_key__ = []\n\n\tdef __init__(self, **kwargs):\n\t\tfor k in self.__class__.__slots__:\n\t\t\tsetattr(self, k, kwargs.get(k, None))\n\n\tdef __repr__(self):\n\t\treturn f\"<{self.region}:{self.region_tag}:{self.hostname}>\"\n\n\t@classmethod\n\tasync def get_shardstatus(\n\t\t\tcls,\n\t\t\tregion: str,\n\t\t\taiosession: aiohttp.ClientSession) -> typing.Union['ShardStatus', None]:\n\t\t\"\"\"\n\t\t\tGets a match by it's id.\n\n\t\t\tArgs:\n\t\t\t\tregion: str\n\t\t\t\t\tThe region searched on.\n\t\t\t\taiosession: :class: `aiohttp.ClientSession`\n\t\t\t\t\tThe aiosession used for the async search.\n\n\t\t\tReturns:\n\t\t\t\tShardStatus:\n\t\t\t\t\tIf found, it returns a type Match.\n\t\t\t\tNone:\n\t\t\t\t\tElse if nothing is returned.\n\n\t\t\tRaises:\n\t\t\t\tBadRequest:\n\t\t\t\t\tThe request cannot be fulfilled due to bad syntax.\n\t\t\t\tUnauthorized:\n\t\t\t\t\tError code response for missing or invalid API key.\n\t\t\t\tForbidden:\n\t\t\t\t\tThe request was a legal request, but the server is\n\t\t\t\t\trefusing to respond to it. Unlike a 401 Unauthorized\n\t\t\t\t\tresponse, authenticating will make no difference.\n\t\t\t\tDataNotFound:\n\t\t\t\t\tThe requested resource could not be found but may\n\t\t\t\t\tbe available again in the future. Subsequent requests\n\t\t\t\t\tby the client are permissible.\n\t\t\t\tMethodNotAllowed:\n\t\t\t\t\tA request was made of a resource using a request\n\t\t\t\t\tmethod not supported by that resource.\n\t\t\t\tUnsupportedMediaType:\n\t\t\t\t\tThe request entity has a media type which the\n\t\t\t\t\tserver does not support.\n\t\t\t\tRateLimitExceeded:\n\t\t\t\t\tThe user has sent too many requests in a given amount\n\t\t\t\t\tof time.\n\t\t\t\tInternalServerError:\n\t\t\t\t\tThe general catch-all error when the server-side\n\t\t\t\t\tthrows an exception.\n\t\t\t\tBadGateway:\n\t\t\t\t\tThe server was acting as a gateway or proxy and\n\t\t\t\t\treceived an invalid response from the upstream server.\n\t\t\t\tServiceUnavailable:\n\t\t\t\t\tThe server is currently unavailable (because it is\n\t\t\t\t\toverloaded or down for maintenance).\n\t\t\t\tGatewayTimeout:\n\t\t\t\t\tThe server was acting as a gateway or proxy and did\n\t\t\t\t\tnot receive a timely response from the upstream server.\n\n\n\t\t\t>>> AAshe.utils.ratelimit.RateLimit.__loudness__ = 0\n\t\t\t>>> ShardStatus.__loudness__ = 0\n\t\t\t>>> conn = sqlite3.connect(\"database.db\")\n\t\t\t>>> ShardStatus.init_database(c=conn.cursor(), conn=conn)\n\t\t\t>>> AAshe.utils.config.Config.initiate(api_key=\"RGAPI-498880b9-d3e9-4f45-98e6-b5f66721e28b\", conn=conn)\n\n\t\t\t>>> AAshe.utils.config.run_async(ShardStatus.get_shardstatus, region=\"euw1\")\n\t\t\t<euw1:eu:prod.euw1.lol.riotgames.com>\n\t\t\"\"\"\n\t\tshard = None\n\t\tdata = cls.read_all_data(region_tag=region, order_by=[cls.desc(\"time\")])\n\n\t\tif data:\n\t\t\tif time.time() - data[0].time < cls.__request_cooldown__:\n\t\t\t\tshard = data[0]\n\t\t\t\tshard.services = [Service(**kwargs) for kwargs in json.loads(shard.services)]\n\n\t\t\t\tcls.safe_print(\n\t\t\t\t\t\"Found shard still active in cache.\",\n\t\t\t\t\ttitle=cls.__name__,\n\t\t\t\t\tcolour=AAshe.sqlite.BColors.OKGREEN,\n\t\t\t\t\tlevel=4)\n\t\t\telse:\n\t\t\t\tfor d in data:\n\t\t\t\t\td.del_data(commit=False)\n\t\t\t\tcls.commit()\n\n\t\tif shard:\n\t\t\treturn shard\n\n\t\turl = \"https://{}.api.riotgames.com\" + \\\n\t\t\tf\"/lol/status/v3/shard-data?api_key={AAshe.utils.config.Config.get_api_key()}\"\n\n\t\tif url:\n\t\t\tcls.safe_print(\n\t\t\t\tf\"Making a webrequest({url}) to shard-data\",\n\t\t\t\ttitle=cls.__name__,\n\t\t\t\tcolour=AAshe.sqlite.BColors.OKGREEN,\n\t\t\t\tlevel=4)\n\n\t\t\tresp_data = await AAshe.lolstatus.lolstatus.LolStatusEndpoint.request_lolstatus(\n\t\t\t\taiosession=aiosession,\n\t\t\t\turl=url,\n\t\t\t\tregion=region,\n\t\t\t\theaders={},\n\t\t\t\t_cls=cls)\n\n\t\t\tkwargs = json.loads(resp_data.decode())\n\t\t\tkwargs[\"region\"] = region\n\t\t\tkwargs[\"time\"] = time.time()\n\t\t\tkwargs[\"services\"] = [Service(**kwargs) for kwargs in kwargs[\"services\"]]\n\n\t\t\tshard = cls(**kwargs)\n\t\t\tshard.write_data()\n\n\t\treturn shard\n\n\nif __name__ == \"__main__\":\n\t#import doctest\n\n\t#doctest.testmod()\n\tAAshe.utils.ratelimit.RateLimit.__loudness__ = 4\n\tShardStatus.__loudness__ = 4\n\n\tconn = sqlite3.connect(\"database.db\")\n\tShardStatus.init_database(c=conn.cursor(), conn=conn)\n\tAAshe.utils.config.Config.initiate(api_key=\"RGAPI-498880b9-d3e9-4f45-98e6-b5f66721e28b\", conn=conn)\n\n\tprint(AAshe.utils.config.run_async(ShardStatus.get_shardstatus, region=\"euw1\"))\n","sub_path":"lolstatus/sharddata.py","file_name":"sharddata.py","file_ext":"py","file_size_in_byte":6009,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"276096106","text":"#!/usr/bin/env python\n#/****************************************************************************\n# FroboMind cmd_vel_converter.py\n# Copyright (c) 2011-2013, author Leon Bonde Larsen <leon@bondelarsen.dk>\n# All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions are met:\n#    * Redistributions of source code must retain the above copyright\n#      notice, this list of conditions and the following disclaimer.\n#    * Redistributions in binary form must reproduce the above copyright\n#      notice, this list of conditions and the following disclaimer in the\n#      documentation and/or other materials provided with the distribution.\n#    * Neither the name FroboMind nor the\n#      names of its contributors may be used to endorse or promote products\n#      derived from this software without specific prior written permission.\n#\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND\n# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\n# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n# DISCLAIMED. IN NO EVENT SHALL <COPYRIGHT HOLDER> BE LIABLE FOR ANY\n# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES\n# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\n# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND\n# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\n# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n#****************************************************************************/\nimport rospy\nfrom geometry_msgs.msg import TwistStamped,Twist\nfrom std_msgs.msg import Bool\n\nclass CmdVelConverter():\n    \"\"\"\n        Converter for using FroboMind with stage. \n        Takes TwistStamped message from /fmSignals/cmd_vel and parses as Twist message on /cmd_vel\n    \"\"\"\n    def __init__(self):\n        # Init node\n        self.twist_pub = rospy.Publisher(\"/cmd_vel\", Twist)\n        self.twist_sub = rospy.Subscriber(\"/fmSignals/cmd_vel\", TwistStamped, self.onTwist )\n        self.deadman_sub = rospy.Subscriber(\"/fmSignals/deadman\", Bool, self.onDeadman )\n        self.twist = Twist()\n        self.deadman = False\n        \n \n    def onTwist(self,msg):\n        if self.deadman :\n            self.twist = msg.twist\n        else:\n            self.twist.linear.x = 0\n            self.twist.angular.z = 0        \n        self.twist_pub.publish(self.twist)\n        \n    def onDeadman(self,msg):\n        self.deadman = msg.data\n\n\nif __name__ == '__main__':\n    rospy.init_node('cmd_vel_converter')\n    node = CmdVelConverter()\n    rospy.spin()\n    \n\n\n\n    ","sub_path":"fmExecutors/platform/message_conversion/twist_stamped_to_twist/scripts/cmd_vel_converter.py","file_name":"cmd_vel_converter.py","file_ext":"py","file_size_in_byte":2840,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"291415049","text":"#Input: two strings of text of equal length\n#Output: similarity score s based on a +1/scoring scheme\n\ndef scoring(seq1, seq2):\n    s = 0\n    for i in range(len(seq1)):\n        if seq1[i] == seq2[i]:\n            s += 1\n        else:\n            s += 0\n    return s\n\n\nprint(scoring('ALASVLIRLITRLYP','ASAVHLNRLITRLYP'))\n\n","sub_path":"scripts/basic_scoring_scheme_2.py","file_name":"basic_scoring_scheme_2.py","file_ext":"py","file_size_in_byte":319,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"513069717","text":"from flask import Flask\r\nimport socket\r\nimport json\r\n\r\napp = Flask(__name__)\r\n\r\n@app.route('/')\r\ndef hello_world():\r\n    return 'Hello, World!'\r\n\r\n@app.route('/api/ip')\r\ndef get_ip():\r\n    h_name = socket.gethostname()\r\n    IP_addres = socket.gethostbyname(h_name)\r\n    return json.dumps({\r\n        \"host_name\": h_name,\r\n        \"ip\": IP_addres\r\n    })\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n    app.run(debug=True, host='0.0.0.0')","sub_path":"images/old/flask-server/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"321383467","text":"# Copyright 2014-2020 Scalyr Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#   http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom __future__ import unicode_literals\nfrom __future__ import print_function\nfrom __future__ import absolute_import\n\nif False:\n    from typing import Optional\n\nimport re\nimport threading\nimport time\nfrom io import open\nimport collections\n\nimport six\n\n\nclass LogReader(threading.Thread):\n    \"\"\"Reader that allows to read file in separate thread and read and wait for new lines\"\"\"\n\n    def __init__(self, file_path):\n        super(LogReader, self).__init__()\n        self.daemon = True\n        self._file = open(six.text_type(file_path), \"r\")\n        self._stop_event = threading.Event()\n        self._lines_lock = threading.Lock()\n        self._start_cv = threading.Condition()\n        self._has_data = False\n        self._lines = collections.deque(maxlen=1000)\n        self._wait_for_line_cv = threading.Condition()\n\n        self._has_new_line = False\n\n    def handle_new_line(self, line):\n        with self._lines_lock:\n            self._lines.append(line)\n        with self._wait_for_line_cv:\n            self._has_new_line = True\n            self._wait_for_line_cv.notify()\n\n    def run(self):\n        def get_line():\n            while not self._stop_event.is_set():\n                _line = self._file.readline()\n                if _line:\n                    return _line\n                time.sleep(0.1)\n\n        get_line()\n        with self._start_cv:\n            self._has_data = True\n            self._start_cv.notify()\n        self._file.seek(0)\n        while not self._stop_event.is_set():\n            line = get_line()\n            self.handle_new_line(line)\n\n    def start(self, wait_for_data=False):\n        super(LogReader, self).start()\n        if not wait_for_data:\n            return\n        with self._start_cv:\n            while not self._has_data:\n                self._start_cv.wait(0.1)\n        return\n\n    @property\n    def lines(self):\n        with self._lines_lock:\n            return list(self._lines)\n\n    def wait_for_new_line(self, timeout=10):\n        start_time = time.time()\n        timeout_time = start_time + timeout\n\n        with self._wait_for_line_cv:\n            while not self._has_new_line:\n                if time.time() >= timeout_time:\n                    raise ValueError(\n                        \"timeout of %s seconds reached while waiting for metrics\"\n                        % (timeout)\n                    )\n\n                self._wait_for_line_cv.wait(0.1)\n            self._has_new_line = False\n            with self._lines_lock:\n                return self._lines[-1]\n\n\nclass LogMetricReader(LogReader):\n    \"\"\"Subclass that allows to read and wait for particular metric.\"\"\"\n\n    LINE_PATTERN = None  # type: Optional[six.text_type]\n\n    def __init__(self, file_path):\n        super(LogMetricReader, self).__init__(file_path)\n        self._current_metrics = dict()\n\n    def handle_new_line(self, line):\n        super(LogMetricReader, self).handle_new_line(line)\n        name, value = self._parse_line(line)\n        with self._lines_lock:\n            self._current_metrics[name] = value\n\n    def _parse_line(self, line):\n        m = re.match(type(self).LINE_PATTERN, line)\n        metric_name = m.group(\"metric_name\")\n        metric_value_string = m.group(\"metric_value\")\n\n        if metric_value_string.isdigit():\n            metric_value = int(metric_value_string)\n        else:\n            try:\n                metric_value = float(metric_value_string)\n            except ValueError:\n                metric_value = metric_value_string\n\n        return metric_name, metric_value\n\n    def get_metrics(self, names, timeout=10):\n        def get():\n            _metrics = dict()\n            for name in names:\n                metric_value = self._current_metrics.get(name)\n                if metric_value is None:\n                    return None\n                _metrics[name] = metric_value\n            return _metrics\n\n        start_time = time.time()\n        timeout_time = start_time + timeout\n\n        while not self._stop_event.is_set():\n            with self._lines_lock:\n                metrics = get()\n            if metrics is not None:\n                return metrics\n\n            if time.time() >= timeout_time:\n                raise ValueError(\n                    \"timeout of %s seconds reached while waiting for metrics\"\n                    % (timeout)\n                )\n\n            time.sleep(0.1)\n\n    def get_metric(self, name):\n        metrics = self.get_metrics([name])\n        return metrics[name]\n\n    def wait_for_metrics_change(self, metric_names, previous_values, predicate):\n        while not self._stop_event.is_set():\n            current_values = self.get_metrics(metric_names)\n\n            predicate_results = [\n                predicate(current_values[name], previous_values[name])\n                for name in metric_names\n            ]\n\n            if all(predicate_results):\n                return\n            time.sleep(0.1)\n\n    def wait_for_metrics_increase(self, metric_names, previous_values):\n        self.wait_for_metrics_change(\n            metric_names, previous_values, lambda cur, prev: cur > prev,\n        )\n\n    def wait_for_metrics_increment(self, metric_names, previous_values):\n        self.wait_for_metrics_change(\n            metric_names, previous_values, lambda cur, prev: prev + 1 == cur\n        )\n","sub_path":"tests/utils/log_reader.py","file_name":"log_reader.py","file_ext":"py","file_size_in_byte":5877,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"237858525","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\nProject Euler Problem 326\n=======================\n\nLet a[n] be a sequence recursively defined by:\n\n[Image: 326_formula1.gif]\n\nSo the first 10 elements of a[n] are: 1,1,0,3,0,3,5,4,1,9.\n\nLet f(N,M) represent the number of pairs (p,q) such that:\n\n[Image: 326_formula2.gif]\n\nIt can be seen that f(10,10)=4 with the pairs (3,3), (5,5), (7,9) and\n(9,10). You are also given that f(10^4,10^3)=97158.\n\nFind f(10^12,10^6).\n\n\"\"\"\n\n\ndef main():\n    return \"unimplemented\"\n\n\nif __name__ == \"__main__\":\n    import ntpath\n    import time\n    from common.shared_functions import verify_solution\n\n    problem_number = int(ntpath.basename(__file__).replace(\"euler\", \"\").replace(\".py\", \"\"))\n    print(\"Retrieving my answer to Euler Problem {0} ...\".format(problem_number))\n\n    ts = time.time()\n    my_answer = main()\n    te = time.time()\n\n    print(\"My answer: {1}\".format(problem_number, my_answer))\n\n    verification_type = verify_solution(problem_number, my_answer)\n    print(\"Verification: {0}\".format(verification_type.name))\n    print(\"Took {0} seconds.\".format(te - ts))\n","sub_path":"project-euler/solvers/euler326.py","file_name":"euler326.py","file_ext":"py","file_size_in_byte":1111,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"551354561","text":"from selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.by import By\nfrom selenium.common.exceptions import TimeoutException\nimport time\n\n\nclass ItemDetailCrawler:\n    def __init__(self, driver):\n        self.driver = driver\n\n    def get_attributes(self):\n        brand = self.get_brand()\n        cid = self.get_cateid()\n        volume = self.get_volume()\n        if brand and cid and volume:\n            return brand, cid, volume\n        else:\n            return False\n\n    def get_brand(self):\n        try:\n            brand = WebDriverWait(self.driver, 5).until(\n                EC.presence_of_element_located(\n                    (By.XPATH, \"//ul[@class='attributes-list']/*[contains(text(), '品牌')]\"))).get_attribute('title')\n\n        except TimeoutException:\n            if not self.check_item_exist():\n                return False\n            if self.check404():\n                self.driver.refresh()\n                time.sleep(3)\n                self.get_brand()\n            else:\n                return False\n        else:\n            return brand\n\n    def get_cateid(self):\n        return self.driver.execute_script('return g_config;')['idata']['item']['cid']\n\n    # text是'30天内已售出7804件，其中交易成功5980件',我们要找到5980\n    def get_volume(self):\n        volume_text = WebDriverWait(self.driver, 5).until(\n            EC.presence_of_element_located(\n                (By.XPATH, \"//div[@class='tb-sell-counter']/a\"))).get_attribute('title')\n\n        pre_pos = volume_text.find('交易成功')\n        after_pos = volume_text[pre_pos:].find('件')\n        try:\n            volume = int(volume_text[pre_pos:][4:after_pos])\n        except ValueError:\n            return False\n        else:\n            return volume\n\n    def check_item_exist(self):\n        try:\n            WebDriverWait(self.driver, 5).until(\n                EC.presence_of_element_located(\n                    (By.XPATH, \"//div[@id='error-notice']//*[contains(text(), '宝贝不存在')]\")))\n            return False\n        except TimeoutException:\n            return True\n\n    def check404(self):\n        try:\n            WebDriverWait(self.driver, 5).until(\n                EC.presence_of_element_located(\n                    (By.XPATH, \"//body//*[contains(text(), '404 Not Found')]\")))\n            return True\n        except TimeoutException:\n            return False\n","sub_path":"Pages/taobao/itemdetail.py","file_name":"itemdetail.py","file_ext":"py","file_size_in_byte":2473,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"497888273","text":"\n# -*- coding:utf-8 -*-\n\n# 函数作为返回值\n\n# 实现一个可变参数的求和\ndef calc_sum(*args) :\n    ax = 0\n    for n in args :\n        ax = ax + n\n    return ax\n\n# 但是,如果不需要立刻求和,而是在后面的代码中,根据需要再计算怎么办?可以不返回求和的结果,而是返回求和的函数:\ndef lazy_sum(*args) :\n    def sum() :\n        ax = 0\n        for n in args :\n            ax = ax + n\n        return ax\n    return sum\n\n# 当我们调研lazy_sum()时,返回的并不是求和结果,而是求和函数\nf = lazy_sum(1,3,5,7,9)\nf # <funciton lazy_sum.<locals>.sum at 0x101c6ed90>\n# 调研函数f时,才真正计算求和的结果:\nf() # 25\n\n\"\"\"\n在这个例子中，我们在函数lazy_sum中又定义了函数sum，并且，内部函数sum可以引用外部函数lazy_sum的参数和局部变量，当lazy_sum返回函数sum时，相关参数和变量都保存在返回的函数中，这种称为“闭包（Closure）”的程序结构拥有极大的威力。\n\n请再注意一点，当我们调用lazy_sum()时，每次调用都会返回一个新的函数，即使传入相同的参数：\n\"\"\"\nf1 = lazy_sum(1,3,5,7,9)\nf2 = lazy_sum(1,3,5,7,9)\nf1 == f2 # False\n# f1()和f2()的调研结果互不影响\n\n# 闭包\n'''\n注意到返回的函数在其定义内部引用了局部变量args，所以，当一个函数返回了一个函数后，其内部的局部变量还被新函数引用，所以，闭包用起来简单，实现起来可不容易。\n\n另一个需要注意的问题是，返回的函数并没有立刻执行，而是直到调用了f()才执行。我们来看一个例子：\n'''\ndef count() :\n    fs = []\n    for i in range(4) :\n        def f() :\n            return i*i\n        fs.append(f)\n    return fs\nf1,f2,f3 = count()\n# 在上面的例子中,每次循环,都创建了一个新的函数,然后,把创建的3个函数都返回了。\n# 你可能认为调研f1(),f2()和f3()结果应该是1,4,9,但实际结果是:\nf1() # 9\nf2() # 9\nf3() # 9","sub_path":"Old/src/com/basic/return_function.py","file_name":"return_function.py","file_ext":"py","file_size_in_byte":1997,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"472101816","text":"'''\n    The Filer class\n'''\n\nimport py_midicsv\nimport pickle\nimport os\n\nclass Filer():\n\n    def __init__(self, fromFile, toFile):\n        '''\n            Initiates the Filer class.\n\n            Attributes:\n                fromFile: file to be deleted\n                toFile: file to which MIDI should be saved\n\n        '''\n\n        self.fromFile = fromFile\n        self.toFile = toFile\n    \n\n    def get_fromFile(self):\n        '''Returns fromFile'''\n\n        return self.fromFile\n    \n\n    def get_toFile(self):\n        '''Returns toFile'''\n\n        return self.toFile\n\n\n    def generatedToNums(self, genText):\n        '''Converts generated text to 2d-array of notes with note information. Returns the 2d-array'''\n\n        tagless = self.cutTags(genText)\n        splitted = self.splitNotes(tagless)\n        num_notes, last_note_end= self.reprToNum(splitted)\n        return num_notes, last_note_end\n    \n\n    def finishFinalMidi(self, num_notes_in_key, last_note_end):\n        '''Takes 2d-array with adjusted note values and saves as MIDI file'''\n\n        messages = self.doubleNoteMessages(num_notes_in_key)\n        sorted_messages = self.mergeSortOnMessages(messages)\n        on_off_messages = self.convToMidiMessages(sorted_messages)\n        CSV_midi = self.addNecessaryMessages(on_off_messages, last_note_end)\n        parsed_midi = self.createMidiFromCSV(CSV_midi)\n        self.saveMidiAsFile(parsed_midi)\n\n\n    def cutTags(self, generated):\n        '''Removes tags and identifies returns a full solo'''\n\n        gen_no_start_tags = generated[4:]\n        end_of_solo = gen_no_start_tags.find(' ###')\n        tagless = gen_no_start_tags[:end_of_solo]\n        return tagless\n\n\n    def splitNotes(self, generated):\n        '''Splits text into 2d-array of notes with relevant data and returns it'''\n\n        splitted_partly = generated.split()\n        fully_split = []\n        for note in splitted_partly:\n            fully_split.append(note.split('!'))\n        return fully_split\n    \n\n    def reprToNum(self, generated):\n        '''Converts representative compressed values to integer values and return 2d-array'''\n\n        num_notes = []\n        for note in generated:\n            try:\n                new_note = []\n                new_note.append(self.HextridecToDec(note[0]) * 6)\n                new_note.append(str(ord(note[1])-26))\n                new_note.append(self.HextridecToDec(note[2]) * 6)\n                num_notes.append(new_note)\n            except:\n                continue\n        end_track_on = num_notes[-1][-1] * 6\n        return num_notes, end_track_on\n\n\n    def HextridecToDec(self, hexatridecimal):\n        '''Converts hexatridecimal value to integer value'''\n\n        hexatridecimal = '0' + hexatridecimal\n        i = int(hexatridecimal, 36)\n        return i\n\n\n    def doubleNoteMessages(self, num_notes):\n        '''Splits note data into two lists - one for on-message and one for off-message. Returns the list.'''\n\n        messages = []\n        for bundle in num_notes:\n            note = bundle[1]\n            start = bundle[0]\n            end = bundle[2]\n            if start == end:\n                continue\n            message_start = [0, start, note]\n            messages.append(message_start)\n            message_end = [1, end, note]\n            messages.append(message_end)\n        return messages\n\n\n    def mergeSortOnMessages(self, messages):\n        '''Returns a sorted list of messages based on tick value'''\n\n        if len(messages) >1: \n            middle = len(messages)//2 #Finding the mid of the messagesay \n            L = messages[:middle] # Dividing the messagesay elements  \n            R = messages[middle:] # into 2 halves \n            self.mergeSortOnMessages(L) # Sorting the first half \n            self.mergeSortOnMessages(R) # Sorting the second half \n            i = 0\n            j = 0\n            k = 0\n            # Copy data to temp messagesays L[] and R[] \n            while i < len(L) and j < len(R): \n                if L[i][1] < R[j][1]: \n                    messages[k] = L[i] \n                    i+=1\n                else: \n                    messages[k] = R[j] \n                    j+=1\n                k+=1\n            # Checking if any element was left \n            while i < len(L): \n                messages[k] = L[i] \n                i+=1\n                k+=1           \n            while j < len(R): \n                messages[k] = R[j] \n                j+=1\n                k+=1 \n        return messages\n\n\n    def convToMidiMessages(self, messages):\n        '''Converts each list to a midi message'''\n\n        on_off_messages = []\n        for message in messages:\n            if message[0] == 0:\n                midi_message = ', Note_on_c, 0, '\n                velocity = ', 95\\n'\n            else:\n                midi_message = ', Note_off_c, 0, '\n                velocity = ', 0\\n'\n            full_mess = '2, ' + str(message[1]) + midi_message + str(message[2]) + velocity\n            on_off_messages.append(full_mess)\n        return on_off_messages\n\n\n    def addNecessaryMessages(self, messages, last_note_end):\n        '''Returns list of messages with added header, settings information, end_of_track and end_of_file messages'''\n\n        full_CSV_midi = []\n        beginning = self.getBeginning()\n        for message in beginning:\n            full_CSV_midi.append(message)\n        for message in messages:\n            full_CSV_midi.append(message)\n        end_track = self.findNextFullBar(last_note_end)\n        end_track_message = '2, {}, End_track\\n'.format(end_track)\n        full_CSV_midi.append(end_track_message)\n        full_CSV_midi.append('0, 0, End_of_file\\n')\n        return full_CSV_midi\n        \n\n    def findNextFullBar(self, last_note_end):\n        '''Returns the next full bar on which the track and file will end'''\n\n        end = last_note_end\n        if end % 3840 == 0:\n            return end\n        else:\n            neg_error = end % 3840\n            error = 3840 - neg_error\n            nextFullBar = end + error\n            return nextFullBar    \n    \n    def getBeginning(self):\n        '''Returns header and file settings information messages'''\n\n        with open('BeginningGenMidi.txt', 'r') as the_file:\n            beginning = the_file.readlines()\n        return beginning\n\n\n    def createMidiFromCSV(self, new_csv_List):\n        '''Returns the converted parsed MIDI from list messages'''\n\n        midi_object = py_midicsv.csv_to_midi(new_csv_List)\n        print('Midi object created from text')\n        return midi_object\n\n\n    def saveMidiAsFile(self, midi_object):\n        '''Save parsed MIDI to disk'''\n\n        with open(self.get_toFile(), \"wb\") as midi_file:\n            midi_writer = py_midicsv.FileWriter(midi_file)\n            midi_writer.write(midi_object)\n            print('Midi file saved')\n\n\n    def removeFile(self):\n        '''Removes file'''\n        os.remove(self.get_fromFile())\n        print('File deleted')\n","sub_path":"User/Modules/UserFileHandler.py","file_name":"UserFileHandler.py","file_ext":"py","file_size_in_byte":6943,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"572110767","text":"\"\"\"\nStudent's name: Chattipoom Sirimul\nid: 623040132-7\n\"\"\"\n\nif __name__ == '__main__':\n    n = int(input(\"Enter a number of values:\"))\n    res = 0\n    cnt = 1\n    while cnt <= n:\n        val = int(input(\"value:\"))\n        res += val\n        cnt += 1\n\n    print(\"Summation =\", res)\n","sub_path":"Lab/Computer Programming/EP1/P10.py","file_name":"P10.py","file_ext":"py","file_size_in_byte":281,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"382617509","text":"# timetable_manage.py\n\nfrom GlobalFunc import ensureNum, ensureDate\nfrom FilePathList import TIMETABAL_FILE, SEMESTER_FILE\nimport Module.Timetable.lesson_manage\n\n# 选择课程表中的时间，返回字符串列表：[单双周代号，日期代号，上课时间代号]\ndef time_choice():\n    time_list = []\n    # 获取单双周输入\n    print('请选择课程的上课频率：')\n    print('1. 每周')\n    print('2. 单周')\n    print('3. 双周')\n    odd_even = input()\n    # 检测输入合法性\n    odd_even = ensureNum(odd_even, '请输入正确的上课频率代号：', 3)\n    if odd_even == '0':\n        return []\n    time_list.append(odd_even)\n    # 获取星期几输入\n    print('请选择上课日期（数字1-7表示星期一至星期日）：')\n    week_day = input()\n    # 检测数据合法性\n    week_day = ensureNum(week_day, '请输入数字1-7：', 7)\n    if week_day == '0':\n        return []\n    time_list.append(week_day)\n    # 获取上课时间输入\n    print('请选择开始上课时间：')\n    print('1. 8:00')\n    print('2. 10:00')\n    print('3. 13:30')\n    print('4. 15:30')\n    print('5. 19:00')\n    clock = input()\n    # 检测数据合法性\n    clock = ensureNum(clock, '请选择正确的上课时间：', 5)\n    if clock == '0':\n        return []\n    time_list.append(clock)\n    return time_list\n\n# 设置课程\ndef timetable_set():\n    # 读入文件\n    lesson_list = Module.Timetable.lesson_manage.lesson_in()\n    if lesson_list == []:\n        input('没有课程，按回车返回')\n        return\n    # 获取课程序号输入\n    print('请选择所要设置的课程：')\n    Module.Timetable.lesson_manage.printLesson(lesson_list)\n    lesson_num = input()\n    # 检测输入合法性\n    lesson_len = len(lesson_list)\n    lesson_num = ensureNum(lesson_num, '请输入正确的课程代号：', lesson_len)\n    if lesson_num == '0':\n        return\n    # 获取上课时间输入\n    time_list = time_choice()\n    if time_list == []:\n        return\n    # 写入文件\n    timetable_file = open(TIMETABAL_FILE, 'a', encoding='UTF-8')\n    for i in range(3):\n        timetable_file.write(time_list[i] + ',')\n    timetable_file.write(lesson_list[int(lesson_num)  - 1])\n    timetable_file.close()\n\n# 清除课程表内容\ndef timetable_clear():\n    # 获取输入\n    time_list = time_choice()\n    if time_list == []:\n        return\n    # 获取课程表记录\n    try:\n        timetable_file = open(TIMETABAL_FILE, 'r', encoding='UTF-8')\n        record_list = timetable_file.readlines()\n        timetable_file.close()\n    except:\n        return\n    # 查找课程表记录并删除\n    clear_time = ''\n    for t in time_list:\n        clear_time += t\n        clear_time += ','\n    for i in range(len(record_list)):\n        if record_list[i][:6] == clear_time:\n            del record_list[i]\n            # 重新写入文件\n            timetable_file = open(TIMETABAL_FILE, 'w', encoding='UTF-8')\n            for record in record_list:\n                timetable_file.write(record)\n            timetable_file.close()\n            break\n\n# 设置学期开始时间\ndef semester_set():\n    # 获取输入\n    print('请输入学期开始的日期，格式为”年/月/日”：')\n    start = input()\n    # 检测输入合法性\n    start = ensureDate(start)\n    if start == '0':\n        return\n    # 写入文件\n    semester_file = open(SEMESTER_FILE, 'w', encoding='UTF-8')\n    semester_file.write(start)\n    semester_file.close()","sub_path":"Code/Module/Timetable/timetable_manage.py","file_name":"timetable_manage.py","file_ext":"py","file_size_in_byte":3478,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"80391349","text":"import matplotlib.pyplot as plt\nimport math\n\n\n\n\n\ndef build_plot(x, y, n, list_plot, database):\n    if math.ceil(n / 2) == 1:\n        col = math.ceil(n / 2)\n        row = n\n    else:\n        col = 2\n        row = math.ceil(n / 2)\n\n    fig, axs = plt.subplots(nrows=row, ncols=col, figsize=(20, 10))\n    fig.suptitle('Lab 1')\n    count = 0\n    for i in range(0, row):\n        if n - i * 2 != 1 and n != 2:\n            m = 2\n        else:\n            m = 1\n        for j in range(0, m):\n\n            if list_plot[count] == \"line\" :\n\n                if n == 2:\n                    line(database, axs[i], x[i])\n                elif n == 1:\n                    line(database, axs, x[i])\n                else:\n                    line(database, axs[i, j], x[count])\n\n            elif list_plot[count] == \"pie\":\n                if n == 2:\n                    pie(database, axs[i], x[i])\n                elif n == 1:\n                    pie(database, axs, x[i])\n                else:\n                    pie(database, axs[i, j], x[count])\n\n            elif list_plot[count] == \"bar\":\n                if n == 2:\n                    bar(database, axs[i], y[i], x[i])\n                elif n == 1:\n                    bar(database, axs, y[i], x[i])\n                else:\n                    bar(database, axs[i, j], y[count], x[count])\n\n            elif list_plot[count] == \"scatter\":\n                if n == 2:\n                    scatter(database, axs[i], y[i], x[i])\n                elif n == 1:\n                    scatter(database, axs, y[i], x[i])\n                else:\n                    scatter(database, axs[i, j], y[count], x[count])\n\n            elif list_plot[count] == \"box\":\n                if n == 2:\n                    box(database, axs[i], x[i])\n                elif n == 1:\n                    box(database, axs, x[i])\n                else:\n                    box(database, axs[i, j], x[count])\n\n            elif list_plot[count] == \"hist\":\n                if n == 2:\n                    hist(database, axs[i], x[i])\n                elif n == 1:\n                    hist(database, axs, x[i])\n                else:\n                    hist(database, axs[i, j], x[count])\n\n            count += 1\n    plt.show()\n\n\ndef line(database, axs, x):\n    axs.plot(database.groupby([x]).count())\n    axs.set(xlabel=x, ylabel=\"Count\")\n    axs.xaxis.set_tick_params(rotation=90)\n\n\ndef pie(database, axs, x):\n    database.groupby([x]).size().plot.pie(ax=axs)\n    axs.set(xlabel=x, ylabel=\"\")\n\n\ndef bar(database, axs, y, x):\n    if y == 'none':\n        axs.bar(database[x].unique(), database[x].value_counts(),\n                label=database[x].name)\n        axs.set(xlabel=x, ylabel=\"Count\")\n    else:\n        axs.bar(database[x], database[y])\n        axs.set(xlabel=x, ylabel=y)\n    axs.xaxis.set_tick_params(rotation=90)\n\n\ndef scatter(database, axs, y, x):\n    if y == 'none':\n        axs.scatter(database[x].unique(), database[x].value_counts(),\n                    label=database[x].name)\n        axs.set(xlabel=x, ylabel=\"Count\")\n    else:\n        axs.scatter(database[x], database[y])\n        axs.set(xlabel=x, ylabel=y)\n    axs.xaxis.set_tick_params(rotation=90)\n\n\ndef box(database, axs, x):\n    axs.boxplot(database[x])\n    axs.set(ylabel=x)\n\n\ndef hist(database, axs, x):\n    axs.hist(database[x], edgecolor='black')\n    axs.set(xlabel=x, ylabel=\"Count\")\n    axs.xaxis.set_tick_params(rotation=90)\n\n\n\n","sub_path":"lab_1/kn308_anastasiia_hrynyshyn_plot.py","file_name":"kn308_anastasiia_hrynyshyn_plot.py","file_ext":"py","file_size_in_byte":3401,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"529454745","text":"import string\r\n\r\ndef loadFile():\r\n    inFile = open('julyTemps.txt')\r\n    high = []\r\n    low = []\r\n    for line in inFile:\r\n        fields = line.split()\r\n        if len(fields) != 3 or 'Boston' == fields[0] or 'Day' == fields[0]:\r\n            continue\r\n        else:\r\n            high.append(int(fields[1]))\r\n            low.append(int(fields[2]))\r\n    return (low, high)\r\n\r\ndef parseLine(line):\r\n    tempSet = string.split(line)[1:3]\r\n    if not len(tempSet) or not tempSet[0].isdigit():\r\n        return False\r\n    return tempSet\r\n\r\ndef loadMonthTemp(filePath):\r\n    days = []\r\n    fileHandler = open(filePath, 'r', 0);\r\n    for line in fileHandler:\r\n        line = parseLine(line)\r\n        if line:\r\n            days.append(line)\r\n    return days\r\n\r\nloadMonthTemp('w1/julyTemps.txt')\r\n","sub_path":"w1/l1-p2.py","file_name":"l1-p2.py","file_ext":"py","file_size_in_byte":788,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"169727033","text":"a=[\n     [2],\n    [3,4],\n   [6,5,7],\n  [4,1,8,3]\n]\nclass Solution:\n    def minimumTotal1(self, triangle) -> int: # DFS + 回溯\n        ans = []\n        def dfs(x, y, selectedSoFar, ans):\n            if len(selectedSoFar) == len(triangle):\n                ans.append(selectedSoFar[:])\n                return\n            for delta_x, delta_y in [(1,0),(1,1)]:\n                cur_x = x + delta_x\n                cur_y = y + delta_y\n                selectedSoFar.append(triangle[cur_x][cur_y])\n                dfs(cur_x, cur_y, selectedSoFar[:], ans)\n                selectedSoFar.pop()\n        dfs(0, 0, [triangle[0][0]], ans)\n        return min(map(sum, ans))\n\n\n    def minimumTotal2(self, triangle) -> int:\n        levels = len(triangle)\n        dp = [elem for elem in triangle[- 1]] # dp记录当前层，以各节点为根的子树的最短路径\n        for level in range(levels-2, -1, -1): # 从倒数第2层开始\n            for i in range(len(triangle[level])):\n                dp[i] = min(dp[i], dp[i + 1]) + triangle[level][i]\n        return dp[0]\n\nprint(Solution().minimumTotal2(a))","sub_path":"动态规划dp/120. 三角形最小路径和.py","file_name":"120. 三角形最小路径和.py","file_ext":"py","file_size_in_byte":1094,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"593389612","text":"# -*- coding: utf-8 -*-\n\"\"\"\nSpyder Editor\nAuthor: A Gibson\nDate: April 2016\nWrite RGB to Edison Pins\n\n\"\"\"\n\n#import mraa library for controlling pins\nimport mraa\n\n# define pin numbers (there will be three LEDs with each R,G,B wired to same pin)\nredLedPin = 5\ngreenLedPin = 6\nblueLedPin = 9\n\n# define leds as pwm\nredLed = mraa.Pwm(redLedPin)\ngreenLed = mraa.Pwm(greenLedPin)\nblueLed = mraa.Pwm(blueLedPin)\n\n# set led pins to output\nredLed.enable(True)\ngreenLed.enable(True)\nblueLed.enable(True)\n\n#start loop\n#read in csv file\n#default data type = float\nwhile True:\n    f = open(\"sentiment.txt\", 'r')\n    value = float(f.read())\n    #print sentiment value to console\n    print(value)   \n    \n#check for tweet sentiment value\n    #if bad sentiment\n    if value < 0.3:\n        redLed.write(0)\n        greenLed.write(1)\n        blueLed.write(1)\n    #neutral sentiment\n    elif value >= 0.3 and value < 0.6:\n        redLed.write(1)\n        greenLed.write(0)\n        blueLed.write(1)  \n    #good sentiment\n    elif value >= 0.6 and value <= 1:\n        redLed.write(1)\n        greenLed.write(1)\n        blueLed.write(0)\n        #no sentiment read\n        #no sentiment read\n    elif value < 0 or value > 1:\n        #print (\"bad value\")\n        redLed.write(0)\n        greenLed.write(0)\n        blueLed.write(0)\n\n\n","sub_path":"configure-RGB-by-score.py","file_name":"configure-RGB-by-score.py","file_ext":"py","file_size_in_byte":1304,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"395792943","text":"from django.db import models\nfrom accounts.models import Company, Student\n\n\n# Create your models here.\n\n\nclass JobPosting(models.Model):\n    JOBTYPE = [('INTERNSHIP', 'internship'), ('PLACEMENT', 'placement')]\n    company = models.ForeignKey(Company, on_delete=models.CASCADE)\n    title = models.CharField(max_length=100, null=False)\n    job_type = models.CharField(max_length=30, choices=JOBTYPE, null=False)\n    date_posted = models.DateTimeField(auto_now=True)\n    deadline_date = models.DateField(null=False)\n    skills = models.TextField(null=False)\n    description = models.TextField(null=True)\n    industry = models.CharField(max_length=100, null=False)\n    branch_restriction = models.BooleanField(default=False)\n    branches = models.CharField(max_length=200, blank=True)\n    applicants = models.ManyToManyField(Student)\n\n    def __str__(self):\n        return self.title\n","sub_path":"jobs/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":880,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"281937249","text":"#The implementation of this class was derived from\n#http://nicky.vanforeest.com/misc/fitEllipse/fitEllipse.html\n\nimport numpy as np\nfrom numpy.linalg import eig, inv\nimport math\n\nclass Ellipse(object):\n\n    def __init__(self):\n        self.angle = None\n        self.a = None\n        self.b = None\n        self.box = None\n        self.parameters = None\n        self.error = False\n\n    def fitToData(self, data):\n        '''\n        param data: numpy array where [:,0] is x and [:,1] is y\n        '''\n        x = data[:, 0][:, np.newaxis]\n        y = data[:, 1][:, np.newaxis]\n        D = np.hstack((x*x, x*y, y*y, x, y, np.ones_like(x)))\n        S = np.dot(D.T, D)\n        C = np.zeros([6, 6])\n        C[0, 2] = C[2, 0] = 2; C[1, 1] = -1\n        E, V = eig(np.dot(inv(S), C))\n        n = np.argmax(np.abs(E))\n        self.parameters = V[:, n]\n\n        axes = self.ellipse_axis_length()\n        self.a = axes[0]\n        self.b = axes[1]\n        self.angle = self.ellipse_angle_of_rotation()\n\n        if not self.a or not self.b or self.parameters == None or np.iscomplexobj(self.parameters) or \\\n           math.isnan(self.a) or math.isnan(self.b) or math.isnan(self.ellipse_center()[0]) or \\\n           np.iscomplex(self.ellipse_center()[0]) or np.iscomplex(self.a) or np.iscomplex(self.b) or \\\n           np.iscomplexobj(self.angle):\n            self.a = 0\n            self.b = 0\n            self.parameters = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n            self.angle = 0\n            self.error = True\n\n    def ellipse_center(self):\n        a = self.parameters\n        b, c, d, f, g, a = a[1]/2, a[2], a[3]/2, a[4]/2, a[5], a[0]\n        num = b*b-a*c\n        if num != 0:\n            x0=(c*d-b*f)/num\n            y0=(a*f-b*d)/num\n            return np.array([x0, y0])\n        else:\n            return np.array([0, 0])\n\n    def ellipse_angle_of_rotation(self):\n        a = self.parameters\n        b, c, d, f, g, a = a[1]/2, a[2], a[3]/2, a[4]/2, a[5], a[0]\n\n        return 0.5*np.arctan(2*b/(a-c))\n\n    def ellipse_axis_length(self):\n        a = self.parameters\n        b, c, d, f, g, a = a[1]/2, a[2], a[3]/2, a[4]/2, a[5], a[0]\n        up = 2*(a*f*f+c*d*d+g*b*b-2*b*d*f-a*c*g)\n        down1 = (b*b-a*c)*((c-a)*np.sqrt(1+4*b*b/((a-c)*(a-c)))-(c+a))\n        down2 = (b*b-a*c)*((a-c)*np.sqrt(1+4*b*b/((a-c)*(a-c)))-(c+a))\n\n        if down1 == 0 or down2 == 0 or up/down1 < 0 or up/down2 < 0:\n            res1 = 0\n            res2 = 0\n            self.error = 0\n        else:\n            res1 = np.sqrt(up/down1)\n            res2 = np.sqrt(up/down2)\n\n        return np.array([res1, res2])\n\n    def ellipse_area(self):\n        return np.pi * self.a * self.b\n\n    def drawOntoLayer(self, img):\n        R = np.arange(0, 2*np.pi, 0.01)\n        center = self.ellipse_center()\n        phi = self.ellipse_angle_of_rotation()\n\n        a, b = (self.a, self.b)\n        xx = center[0] + a*np.cos(R)*np.cos(phi) - b*np.sin(R)*np.sin(phi)\n        yy = center[1] + a*np.cos(R)*np.sin(phi) + b*np.sin(R)*np.cos(phi)\n        img.drawPoints([(xx[i], yy[i]) for i in xrange(len(xx))], sz=2)\n\n    def getMask(self, img):\n        mask = np.zeros((img.width, img.height), dtype=np.bool)\n\n        R = np.arange(0, 2*np.pi, 0.01)\n        center = self.ellipse_center()\n        phi = self.ellipse_angle_of_rotation()\n        axes = self.ellipse_axis_length()\n\n        a, b = axes\n        xx = center[0] + a*np.cos(R)*np.cos(phi) - b*np.sin(R)*np.sin(phi)\n        yy = center[1] + a*np.cos(R)*np.sin(phi) + b*np.sin(R)*np.cos(phi)\n\n        mask[[(xx[i], yy[i]) for i in xrange(len(xx))]] = True\n\n        return\n\n\n","sub_path":"Ellipse.py","file_name":"Ellipse.py","file_ext":"py","file_size_in_byte":3581,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"266371812","text":"import logging\nimport os\nimport filecmp\nimport difflib\n\nfrom django.core.management import BaseCommand\n\n\nlogger = logging.getLogger('app_migration')\nlogger.setLevel('DEBUG')\n\nRELATIVE_YAML_V1 = '../../static/app_manager/json/v1'\nRELATIVE_YAML_V2 = '../../static/app_manager/json/v2'\n\nRELATIVE_DIFF_STORAGE_YAML = '../../tests/data/v2_diffs/yaml'\n\n\ndef get_diff_filename(filename):\n    return \"{}.diff.txt\".format(filename)\n\n\ndef get_diff(file_v1, file_v2):\n    with open(file_v1, \"r\") as fv1:\n        with open(file_v2, \"r\") as fv2:\n            data_v1 = fv1.readlines()\n            data_v2 = fv2.readlines()\n            return list(difflib.unified_diff(data_v1, data_v2))\n\n\nclass Command(BaseCommand):\n    help = '''\n    Computes diffs of\n    '''\n\n    def handle(self, *args, **options):\n        base_dir = os.path.dirname(os.path.realpath(__file__))\n\n        yaml_v1_dir = os.path.join(base_dir, RELATIVE_YAML_V1)\n        yaml_v2_dir = os.path.join(base_dir, RELATIVE_YAML_V2)\n        yaml_diff_dir = os.path.join(base_dir, RELATIVE_DIFF_STORAGE_YAML)\n\n        common_yaml = filecmp.dircmp(yaml_v1_dir, yaml_v2_dir).common_files\n\n        print(\"Computing YAML diffs...\")\n        for yaml_file in common_yaml:\n            yaml_file_v1 = os.path.join(yaml_v1_dir, yaml_file)\n            yaml_file_v2 = os.path.join(yaml_v2_dir, yaml_file)\n            diff_file = os.path.join(yaml_diff_dir, get_diff_filename(yaml_file))\n            print(\" >> Computing diff for {}\".format(yaml_file))\n            with open(diff_file, \"w\") as df:\n                df.writelines(get_diff(yaml_file_v1, yaml_file_v2))\n","sub_path":"corehq/apps/app_manager/management/commands/build_app_manager_v2_diffs.py","file_name":"build_app_manager_v2_diffs.py","file_ext":"py","file_size_in_byte":1599,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"637195938","text":"# -*- coding: utf-8 -*-\n# pylint: disable=bad-continuation\n\"\"\"Copyright ©  2018 Justin Stout <justin@jstout.us>.\"\"\"\nimport os\nimport platform\nimport shutil\n\nfrom pathlib import Path\n\nfrom invoke import task\n\n\ndef _get_dirs(root):\n    dirs = []\n\n    for child in Path(root).iterdir():\n        if child.is_dir():\n            dirs.append(child)\n            dirs.extend(_get_dirs(child))\n\n    return dirs\n\n\ndef _get_fpm_depends():\n    deps = []\n\n    with open('src/fpm/dependencies.txt', 'r') as fd_in:\n        for line in fd_in:\n            if line:\n                deps.append(\n                    '--depends {}'.format(line.strip())\n                    )\n\n    return deps\n\n\ndef _get_fpm_scripts():\n    scripts = []\n\n    before_install_sh = Path('src/fpm/scripts/before_install.sh')\n    after_install_sh = Path('src/fpm/scripts/after_install.sh')\n\n    if before_install_sh.is_file():\n        scripts.append(\n            '--before-install {}'.format(before_install_sh)\n            )\n\n    if after_install_sh.is_file():\n        scripts.append(\n            '--after-install {}'.format(after_install_sh)\n            )\n\n    return scripts\n\n\ndef _get_version(ctx):\n    version_file = Path(ctx.ppa.apk.version_file)\n\n    with version_file.open() as fd_in:\n        ver_str = fd_in.readline().strip()\n\n    version = ver_str[:ver_str.rfind('.')]\n    increment = ver_str[ver_str.rfind('.') + 1:]\n\n    return (version, increment)\n\n\ndef _prune_fpm_build_dir():\n    \"\"\"Delete all .keep files and empty directories.\"\"\"\n    for _file in Path('build/fpm').glob('**/.keep'):\n        _file.unlink()\n\n    dirs = _get_dirs('build/fpm')\n    dirs.sort(reverse=True)\n\n    for _dir in dirs:\n        try:\n            _dir.rmdir()\n\n        except OSError:\n            # dir not empty\n            pass\n\n\n@task\ndef fpm(ctx):\n    \"\"\"Create package using FPM.\"\"\"\n    os.makedirs('dist')\n\n    shutil.copytree('src/fpm/root', 'build/fpm')\n\n    if Path('build/bin/').is_dir():\n        ctx.run('cp build/bin/* build/fpm/usr/local/bin')\n\n    _prune_fpm_build_dir()\n\n    version, increment = _get_version(ctx)\n\n    options = [\n        '--name {}'.format(ctx.fpm.name),\n        '--architecture {}'.format(platform.machine()),\n        '--chdir build/fpm',\n        '--package dist',\n        '--input-type dir',\n        '--output-type apk',\n        '--version {}'.format(version),\n        '--iteration r{}'.format(increment)\n        ]\n\n    options.extend(_get_fpm_depends())\n    options.extend(_get_fpm_scripts())\n\n    cmd = 'fpm {}'.format(' '.join(options))\n\n    ctx.run(cmd)\n","sub_path":"src/js_invoke/js_invoke/package.py","file_name":"package.py","file_ext":"py","file_size_in_byte":2536,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"337787247","text":"from socket import *\n\nsockfd = socket(AF_INET,SOCK_DGRAM)\n\n\n# 消息收发\nwhile True:\n    data1 = input('客户端：')\n    if not data1:\n        print('退出')\n        break\n    sockfd.sendto(data1.encode(),('127.0.0.1',9999))\n    data,addr = sockfd.recvfrom(1024)\n    print('服务器消息：',data.decode())\nsockfd.close()","sub_path":"03-pythonNet/2019-1-9/udp_client.py","file_name":"udp_client.py","file_ext":"py","file_size_in_byte":328,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"549485910","text":"#MenuTitle: Make Web Variable\n# -*- coding: utf-8 -*-\nimport vanilla\nimport re\n\n__doc__=\"\"\"\nPrepares a file for web use, calculating an AVAR table and assigning USWeightClass values.\n\"\"\"\n\n\ncssdict = {\n\t\"Thin\": 100,\n\t\"Hair\": 100,\n\t\"ExtraLight\": 200,\n\t\"UltraLight\": 200,\n\t\"Light\": 300,\n\t\"Regular\": 400,\n\t\"Normal\": 400,\n\t\"Medium\": 500,\n\t\"SemiBold\": 600,\n\t\"DemiBold\": 600,\n\t\"Bold\": 700,\n\t\"ExtraBold\": 800,\n\t\"UltraBold\": 800,\n\t\"Black\": 900,\n\t\"Heavy\": 900,\n}\n\nfont = Glyphs.font\n\n# create a list which indexes all masters by weight\nweightlist = sorted({font.masters[i].weightValue for i in range(len(font.masters))})\n\naxisMinimum = weightlist[0] # lightest stem weight\naxisRange = weightlist[-1] - axisMinimum # axis range\n\n# set USWeightClass values for masters based on naming\nfor master in font.masters:\n\tif \"Thin\" in master.name or \"Hair\" in master.name:\n\t\tmaster.weightValue = 100\n\tif \"Light\" in master.name:\n\t\tmaster.weightValue = 300\n\tif \"ExtraLight\" in master.name or \"UltraLight\" in master.name:\n\t\tmaster.weightValue = 200\n\tif \"Regular\" in master.name or \"Normal\" in master.name:\n\t\tmaster.weightValue = 400\n\tif \"Medium\" in master.name:\n\t\tmaster.weightValue = 500\n\tif \"Bold\" in master.name:\n\t\tmaster.weightValue = 700\n\tif \"SemiBold\" in master.name or \"DemiBold\" in master.name:\n\t\tmaster.weightValue = 600\n\tif \"ExtraBold\" in master.name or \"UltraBold\" in master.name:\n\t\tmaster.weightValue = 800\n\tif \"Black\" in master.name or \"Heavy\" in master.name:\n\t\tmaster.weightValue = 900\n\ncssweightlist = sorted({font.masters[i].weightValue for i in range(len(font.masters))})\n\ncssMinimum = cssweightlist[0] # lightest USWeightClass value\ncssRange = cssweightlist[-1] - cssMinimum # USWeightClass range\n\n# inputWeight is old stem weight\ndef convertWeight(inputWeight):\n\toutputWeight = ((inputWeight - axisMinimum)/axisRange)*cssRange + cssMinimum\n\treturn int(outputWeight) # calculate reference USWeightClass weight for old stem weight\n\n# read only number from a string\ndef int_from_string(string):\n\tchars = [char for char in string]\t\t\n\tdigits = []\t\t\n\tfor char in chars:\n\t\tif char.isdigit():\n\t\t\tdigits.append(char)\t\t\n\tnumber=\"\".join(digits)\n\treturn int(number)\n\n# check whether layer is intermediate layer (has numbers)\ndef hasNumbers(layerName):\n\treturn any(char.isdigit() for char in layerName)\n\n# recalculate values in intermediate layers\nfor glyph in font.glyphs:\n\tfor layer in glyph.layers:\n\t\tif hasNumbers(layer.name):\n\t\t\tnewWeight = str(convertWeight(int_from_string(layer.name)))\n\t\t\tlayer.name = re.sub(r'\\d+', newWeight,layer.name)\n\n# calculate AVAR table and write to custom parameters\nfont.customParameters[\"Axis Mappings\"] = {\"wght\" : {l*100+cssMinimum:convertWeight(sorted({instance.weightValue for instance in font.instances})[l]) for l in range(int(cssRange/100+1))}}\n\n# set USWeightClass values for instances based on weight assignment\nfor instance in font.instances:\n\tinstance.weightValue = cssdict[instance.weight]\n\n# Rename font family\nif \"Variable\" not in font.familyName:\n\tfont.familyName = font.familyName + \" Variable\"\n","sub_path":"MakeWebVariable.py","file_name":"MakeWebVariable.py","file_ext":"py","file_size_in_byte":3031,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"383533417","text":"# -*- coding: utf-8 -*-\r\n\r\n### ------------------ 筛选条件 ------------------ ###\r\n# 当前价位高于前一段时间最低价的最大比例\r\nfluct_max = 0.15\r\n\r\n# 前一段时间的起始计算时间\r\ndate_start = '2017-01-01'\r\n\r\n# 年度每股收益最小值\r\neps_year_min = -0.05\r\n\r\n# 收益业绩筛选年份\r\nreport_years = [2015, 2016, 2017]\r\n\r\n# 总股本-亿元\r\ntotals_min = 0.7\r\ntotals_max = 2.5\r\n\r\n# 当前价格\r\ncur_price_min = 1.0\r\ncur_price_max = 30.0\r\n\r\n# 市盈率\r\npe_min = 0.0\r\npe_max = 200.0\r\n\r\n# 上市时间\r\ntimeToMarket_min = 20100101\r\ntimeToMarket_max = 20200901\r\n\r\n# 股东人数\r\nholders_min = 1\r\nholders_max = 100000\r\n\r\n# 每股收益\r\nesp_min = -99999999.0\r\nesp_max = 99999999.0\r\n\r\n# 股票正常交易天数最小值\r\ntrade_day_min = 30\r\n### ----------------------------------------------------- ###\r\n\r\n\r\n\r\n\r\n# 导入依赖库\r\nimport tushare as ts\r\nimport pandas as pd\r\nimport time\r\nimport os\r\n\r\n\r\nprint('\\n*************** 参数列表 *************** \\n')\r\nprint('\\n当前价位高于前一段时间最低价的最大比例:\\n{:.2f}'.format(fluct_max))\r\nprint('\\n前一段时间的起始计算时间:\\n{}'.format(date_start))\r\nprint('\\n年度每股收益最小值:\\n{}'.format(eps_year_min))\r\nprint('\\n收益业绩筛选年份:\\n{}'.format(report_years))\r\nprint('\\n总股本-亿元:\\n{:.2f} - {:.2f}'.format(totals_min, totals_max))\r\nprint('\\n当前价格:\\n{:.2f} - {:.2f}'.format(cur_price_min, cur_price_max))\r\nprint('\\n市盈率:\\n{:.2f} - {:.2f}'.format(pe_min, pe_max))\r\nprint('\\n上市时间起始点:\\n{}'.format(timeToMarket_min))\r\nprint('\\n股东人数:\\n{} - {}'.format(holders_min, holders_max))\r\nprint('\\n股票正常交易天数最小值:\\n{}\\n'.format(trade_day_min))\r\n\r\n\r\nprint('\\n------- 确认以上参数后, 按回车开始 -------\\n')\r\nos.system('pause')\r\n\r\n# 导入基本面数据\r\nprint('\\n### 加载基本面数据...')\r\ndf_basic = ts.get_stock_basics()\r\n\r\n### --------- 开始筛选 --------- ###\r\nprint('\\n### 进行基本面筛选...')\r\n#ix = (df_basic.totals > totals_min) & (df_basic.totals < totals_max) & (df_basic.pe > pe_min) & (df_basic.pe < pe_max)\r\ndf_res = df_basic[(df_basic.totals >= totals_min) & (df_basic.totals <= totals_max) &\r\n                  (df_basic.pe >= pe_min) & (df_basic.pe <= pe_max) &\r\n                  (df_basic.timeToMarket >= timeToMarket_min) & (df_basic.timeToMarket <= timeToMarket_max) &\r\n                  (df_basic.holders >= holders_min) & (df_basic.holders <= holders_max) &\r\n                  (df_basic.esp >= esp_min) & (df_basic.esp <= esp_max)\r\n                  ]\r\n\r\nprint('\\n### 进行业绩筛选...')\r\ndrop_indices = []\r\nreports = []\r\n#rep_2014_4 = ts.get_report_data(2014, 4)  # 同步业绩数据\r\n#rep_2014_4.to_csv('./data/rep_2014_4.csv', encoding='utf-8') # 保存业绩数据\r\nfor year in report_years:\r\n    filename = 'data/report_' + str(year) + '_4.csv'\r\n    report_one_year = pd.read_csv(filename, encoding='utf-8', dtype=str)\r\n    reports.append(report_one_year)\r\nfor code in df_res.index:\r\n    for report_one_year in reports:\r\n        eps_year = report_one_year[report_one_year.code == code].eps\r\n        if not eps_year.empty:\r\n            eps_year = float(eps_year.values[0])\r\n            if eps_year != 'nan':\r\n                if eps_year < eps_year_min:\r\n                    drop_indices.append(code)\r\ndf_res = df_res.drop(drop_indices)\r\n\r\n### 筛选当前价位不高于前一段时间的最低价\r\nprint('\\n### 进行相对低价位筛选...')\r\ndate_end = time.strftime('%Y-%m-%d', time.localtime())\r\nprint('走势计算时间终点: ', date_end)\r\ndrop_indices = []\r\niter = 0.0\r\nfor code in df_res.index:\r\n    iter += 1\r\n    one_stock_hist = ts.get_hist_data(code=code, start=date_start, end=date_end)\r\n    if type(one_stock_hist) is not pd.DataFrame:\r\n        drop_indices.append(code)\r\n        continue\r\n    if len(one_stock_hist) < trade_day_min:\r\n        drop_indices.append(code)\r\n        continue\r\n    if one_stock_hist.close[0] > cur_price_max:\r\n        drop_indices.append(code)\r\n        continue\r\n    lowest_price = min(one_stock_hist.low)\r\n    current_price = one_stock_hist.close[0]\r\n    if ((current_price - lowest_price) / lowest_price > fluct_max):    # 当前价位是否较低\r\n        drop_indices.append(code)\r\n    if iter % 10 == 0:\r\n        print('\\r正在计算: {:5.1f}%'.format(100*float(iter)/len(df_res)), end='')\r\nprint('\\r正在计算: {:5.1f}%'.format(100), end='')\r\ndf_res = df_res.drop(drop_indices)\r\n\r\n# 导出最终结果\r\ndf_res.to_excel('./股票筛选结果.xlsx')\r\n\r\nprint('\\n\\n--- 完成! ---\\n')\r\n","sub_path":"stock_filter.py","file_name":"stock_filter.py","file_ext":"py","file_size_in_byte":4569,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"153451402","text":"from picamera.array import PiRGBArray\nimport numpy as np\nfrom picamera import PiCamera\nimport time\nimport cv2\nimport time\nimport picamera\nimport picamera.array\nimport PID\n\ndef main_meth(client):\n\t# initialize the camera and grab a reference to the raw camera capture\n\tcamera = PiCamera()\n\tcamera.resolution = (640, 480)\n\tcamera.framerate = 90\n\trawCapture = PiRGBArray(camera, size=(640, 480))\n\tlatestReading = 0\n\n\t# allow the camera to warmup\n\ttime.sleep(0.1)\n\n\t# capture frames from the camera\n\tfor frame in camera.capture_continuous(rawCapture, format=\"bgr\", use_video_port=True):\n\t\t# grab the raw NumPy array representing the image, then initialize the timestamp\n\t\t# and occupied/unoccupied text\n\t\t# At this point the image is available as stream.array\n\t\timg = frame.array\n\t\t# capture frames from the camera\n\t\t# grab the raw NumPy array representing the image, then initialize the timestamp\n\t\t# and occupied/unoccupied text\n\t\theight, width, channels = img.shape \n\t\tcropped_img = img[height - int(height/8): height, 0:width]\t#convert the image to gray scale\n\t\tgray = cv2.cvtColor(cropped_img, cv2.COLOR_BGR2GRAY)\n\t\t#blur the image. Aka average the image\n\t\tblur = cv2.GaussianBlur(gray,(5,5),50)\n\t\t#create a binary inverse threshold of the image\n\t\tret,thresh = cv2.threshold(blur,40,255,cv2.THRESH_BINARY_INV)\n\t\tmask = cv2.erode(thresh, None, iterations=2)\n\t\tmask = cv2.dilate(mask, None, iterations=2)\n\t\t#find the contours of the image\n\t\timage, contours, _ = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\t\tif len(contours) > 0:\n\t\t\tc = max(contours, key=cv2.contourArea)\n\t\t\tM = cv2.moments(c)\n\t\t\tcx = int(M['m10']/M['m00'])\n\t\t\tcy = int(M['m01']/M['m00'])\t\t# If standard devitation of x coordinates is high then we have encountered a T intersection. Return Encountered Intersection\n\t\t\tif np.std(c[:,0][:, 0]) > 150:\n\t\t\t\tlatest_reading = \"Encountered T Intersection\"\n\t\t\t# Else if standard deviation is not high but the average is far to one side we have encountered a sharp turn. Return which way the sharp turn is\n\t\t\telif (width/2 - cx) > 225:\n\t\t\t\tlatest_reading = \"Sharp Turn Left\"\n\t\t\telif (width/2 - cx) < -225:\n\t\t\t\tlatest_reading = \"Sharp Turn Right\"\n\t\t\t\t# Else return pixel offset\n\t\t\telse:\n\t\t\t\tlatest_reading = (width/2 - cx)\n\t\t\t\t# show the frame\n\t\t\tPID.main_meth(width/2 - cx)\n\t\trawCapture.truncate(0)\n\t\t# if the `q` key was pressed, break from the loop","sub_path":"image_processing.py","file_name":"image_processing.py","file_ext":"py","file_size_in_byte":2387,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"625106110","text":"#!/usr/bin/env python\n# -- coding:utf-8 -*-\nimport codecs\nimport Morph\n\n\ndef get_morph(path):\n    output = []\n    with codecs.open(path, 'r') as fin:\n        c_list = ''.join(line for line in fin)\n    mo_list = (sentence.strip() for sentence in c_list.split('EOS\\n'))\n    for sentence in mo_list:\n        if not sentence:\n            output.append([''])\n            continue\n        output.append([Morph.Morph(word)\n                       for word in sentence.split('\\n')\n                       if word[0] != '*' and word[0] != '　'])\n    return output\n\n\ndef main():\n    morph = get_morph('neko.txt.cabocha')\n    for w in morph[2]:\n        print(w.surface, w.base, w.pos, w.pos1)\n\nif __name__ == '__main__':\n    main()\n","sub_path":"chapter5/k40.py","file_name":"k40.py","file_ext":"py","file_size_in_byte":720,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"295203617","text":"from transformers import *\nimport torch\nimport numpy as np\nfrom torch.utils.data import DataLoader,Dataset\nfrom sklearn.model_selection import train_test_split\nimport time\nfrom torch.nn import functional as F\nfrom sklearn.metrics import roc_auc_score,f1_score,accuracy_score\nimport os\nimport pickle\nfrom model import AlbertClassfier\nimport pandas as pd\nfrom tqdm import tqdm\nfrom pytorch_toolbelt import losses as L\nimport random\nimport math\nfrom utils import LabelSmoothSoftmaxCE\nrandom.seed(2020)\n#\nclass DataGen(Dataset):\n    def __init__(self, data, label):\n        self.data = data\n        self.label = label\n\n    def __len__(self):\n        return len(self.data)\n\n    def __getitem__(self, index):\n        return np.array(self.data[index]), np.array(self.label[index])\n#\nclass DataInfer(Dataset):\n    def __init__(self, data):\n        self.data = data\n\n    def __len__(self):\n        return len(self.data)\n\n    def __getitem__(self, index):\n        return np.array(self.data[index])\n#\ndef warm_up(warm_up_epochs=2):\n    # warm_up_with_cosine_lr\n    warm_up_with_cosine_lr = lambda epoch: (epoch + 1) / warm_up_epochs if epoch < warm_up_epochs \\\n        else 0.5 * (math.cos((epoch - warm_up_epochs) / (max_num_epochs - warm_up_epochs) * math.pi) + 1)\n    return warm_up_with_cosine_lr\nif __name__=='__main__':\n    decode_dic={0: '文化休闲',\n                 1: '医疗卫生',\n                 2: '经济管理',\n                 3: '教育科技',\n                 4: '城乡建设',\n                 5: '工业',\n                 6: '民政社区',\n                 7: '交通运输',\n                 8: '生态环境',\n                 9: '政法监察',\n                 10: '农业畜牧业',\n                 11: '文秘行政',\n                 12: '劳动人事',\n                 13: '资源能源',\n                 14: '信息产业',\n                 15: '旅游服务',\n                 16: '商业贸易',\n                 17: '气象水文测绘地震地理',\n                 18: '财税金融',\n                 19: '外交外事'}\n    pretrained = 'bert-base-chinese'\n    tokenizer = BertTokenizer.from_pretrained(pretrained)\n    model = BertModel.from_pretrained(pretrained)\n    config = BertConfig.from_pretrained(pretrained)\n    #\n    albertBertClassifier = AlbertClassfier(model, config, 20)\n    device = torch.device(\"cuda\") if torch.cuda.is_available() else 'cpu'\n    albertBertClassifier = albertBertClassifier.to(device)\n    #\n    train_batch_size=128\n    test_batch_size=128\n    max_num_epochs = 11\n    max_iters=50000\n    val_size=0.01\n    print_interval=50\n    save_interval=2\n    sumit_dir=\"submit_title/\"\n    save_ckpt='ckpt_title/'\n    if not os.path.exists(save_ckpt):\n        os.mkdir(save_ckpt)\n    if not os.path.exists(sumit_dir):\n        os.mkdir(sumit_dir)\n    n_gpu=2\n    if n_gpu>1:\n        albertBertClassifier=torch.nn.DataParallel(albertBertClassifier)\n    #\n    #weights_class = torch.FloatTensor([0.1, 0.9])\n    criterion = torch.nn.CrossEntropyLoss()\n    use_multi_loss=True\n    if use_multi_loss:\n        criterion = L.JointLoss(first=torch.nn.CrossEntropyLoss().cuda(), second=LabelSmoothSoftmaxCE().cuda(),\n                                first_weight=0.5, second_weight=0.5)\n    #optimizer = torch.optim.SGD(albertBertClassifier.parameters(), lr=0.001, momentum=0.9, weight_decay=1e-4)\n    # warm_up_with_cosine_lr\n    optimizer = AdamW(albertBertClassifier.parameters(),lr= 2e-5)\n    #\n    scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=warm_up(warm_up_epochs=3))\n    print('download model finished ...............')\n    #\n    file_path = \"dataset/train_data.pkl\"\n    with open(file_path, 'rb') as fr:\n        train_data = pickle.load(fr)\n    X_train, X_test, y_train, y_test = train_test_split(train_data['data'], train_data['label'], test_size=val_size, shuffle=True,random_state=2020)\n    print(len(X_train), len(X_test), len(y_train), len(y_test), len(X_train[0]))\n    print('download raw-data finished ...........')\n    #\n    train_dataset = DataGen(X_train, y_train)\n    test_dataset = DataGen(X_test, y_test)\n    train_dataloader = DataLoader(train_dataset, shuffle=True,batch_size=train_batch_size)\n    test_dataloader = DataLoader(test_dataset, batch_size=train_batch_size)\n    #\n    # inference dataset\n    file_path_infer = \"dataset/testA_data.pkl\"\n    with open(file_path_infer, 'rb') as fr:\n        infer_df = pickle.load(fr)\n    infer_data = infer_df['data']\n    sessionIds = infer_df['name']\n    infer_dataset = DataInfer(infer_data)\n    infer_dataloader = DataLoader(infer_dataset, shuffle=False, batch_size=128)\n    #\n    total_iters=len(train_dataloader)\n    current_iter=0\n    for epoch in range(1,max_num_epochs):\n        loss_sum = 0.0\n        accu = 0\n        albertBertClassifier.train()\n        begin_time=time.time()\n        for step, (token_ids, label) in enumerate(train_dataloader):\n            current_iter+=1\n            token_ids = token_ids.to(device)\n            label = label.to(device)\n            out = albertBertClassifier(token_ids)\n            loss = criterion(out, label)\n            optimizer.zero_grad()\n            loss.backward()  # 反向传播\n            optimizer.step()  # 梯度更新\n            scheduler.step(epoch + step / total_iters)\n            loss_sum += loss.cpu().data.numpy()\n            accu += (out.argmax(1) == label).sum().cpu().data.numpy()\n            if step%print_interval==0 and step!=0:\n                spend_time=time.time()-begin_time\n                print(\"epoch{},[{}/{}],train_loss:{:.4},lr:{:.4},ETA:{}min\".format(epoch,step,total_iters,loss,optimizer.param_groups[-1]['lr'],spend_time/ step * total_iters // 60 - spend_time // 60))\n        #\n        scheduler.step()\n        test_loss_sum = 0.0\n        test_acc = 0\n        albertBertClassifier.eval()\n        out_all=[]\n        out_pre=[]\n        label_all=[]\n        for step, (token_ids, label) in enumerate(test_dataloader):\n            token_ids = token_ids.to(device)\n            label = label.to(device)\n            with torch.no_grad():\n                out = albertBertClassifier(token_ids)\n                loss = criterion(out, label)\n                test_loss_sum += loss.cpu().data.numpy()\n                #out_all+=out.cpu().numpy()[:,1].tolist()\n                out_pre+=torch.max(out.data,1)[1].cpu().numpy().tolist()\n                label_all+=label.cpu().numpy().tolist()\n                test_acc += (out.argmax(1) == label).sum().cpu().data.numpy()\n        #test_auc=roc_auc_score(label_all,out_all)\n        #f1_val=f1_score(label_all,out_pre)\n        acc_val=accuracy_score(label_all,out_pre)\n        print(\"epoch{},train_loss:{},val_loss:{},acc_val:{}\".format (\n        epoch, loss_sum / len(train_dataloader), test_loss_sum / len(test_dataloader),acc_val))\n\n        if epoch%save_interval==0:\n            torch.save(albertBertClassifier.state_dict(), os.path.join(save_ckpt, str(epoch) + '_' + str(int(acc_val*1000))+ '_state_dict.pth'))\n\n        #inference\n        print(\"infer .....\")\n        out_list = []\n        for _, token_ids in enumerate(infer_dataloader):\n            token_ids = token_ids.to(device)\n            with torch.no_grad():\n                out = albertBertClassifier(token_ids)\n                out_list += torch.max(out.data,1)[1].cpu().numpy().tolist()\n\n        out_list = [decode_dic[i] for i in out_list]\n        submit = {'filename': sessionIds, 'label': out_list}\n        sub_df = pd.DataFrame(submit)\n        sub_df.to_csv(os.path.join(sumit_dir,'epoch_'+str(epoch) + '_' + str(int(acc_val*1000))+'.csv'), index=False)\n        #","sub_path":"dc竞赛/one-city编程大赛/bert/code/warmup_train.py","file_name":"warmup_train.py","file_ext":"py","file_size_in_byte":7586,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"317420774","text":"# -*- coding: utf-8 -*-\n\nimport os\n\ntry:\n    import urlparse\nexcept ImportError:\n    import urllib.parse as urlparse\n\n\n# Register database schemes in URLs.\nurlparse.uses_netloc.append(\"redis\")\n\nDEFAULT_ENV = \"REDIS_URL\"\n\n\ndef config(env=DEFAULT_ENV, default=None, **overrides):\n    \"\"\"Returns configured REDIS dictionary from REDIS_URL.\"\"\"\n\n    config = {}\n\n    s = os.environ.get(env, default)\n\n    if s:\n        config = parse(s)\n\n    overrides = dict([(k.upper(), v) for k, v in overrides.items()])\n\n    config.update(overrides)\n\n    return config\n\n\ndef parse(url):\n    \"\"\"Parses a database URL.\"\"\"\n\n    config = {}\n\n    url = urlparse.urlparse(url)\n\n    netloc = url.netloc\n    if \"@\" in netloc:\n        netloc = netloc.rsplit(\"@\", 1)[1]\n    if \":\" in netloc:\n        netloc = netloc.split(\":\", 1)[0]\n    hostname = netloc or ''\n    if url.scheme == 'unix':\n        hostname = url.path\n        db = (urlparse.parse_qs(url.query).get('db', []) + [''])[0]\n        port = None\n    else:\n        # Remove query strings.\n        db = url.path[1:]\n        db = db.split('?', 2)[0]\n        port = int(url.port or 6379)\n\n    # Update with environment configuration.\n    config.update({\n        \"DB\": int(db or 0),\n        \"PASSWORD\": url.password or None,\n        \"HOST\": hostname or \"localhost\",\n        \"PORT\": port,\n    })\n\n    return config\n","sub_path":"dj_redis_url.py","file_name":"dj_redis_url.py","file_ext":"py","file_size_in_byte":1341,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"171937392","text":"from django.db.transaction import atomic\nfrom rest_framework import views\nfrom rest_framework.permissions import IsAuthenticated\nfrom rest_framework.response import Response\n\nfrom authentication.services import PermissionsService\nfrom gitrello.exceptions import PermissionDeniedException\nfrom gitrello.handlers import retry_on_transaction_serialization_error\nfrom gitrello.schema import gitrello_schema\nfrom tickets.api.serializers import (\n    CreateCategorySerializer, CreateTicketSerializer, CreateTicketAssignmentSerializer, CreateCommentSerializer,\n    UpdateTicketSerializer, CreateCategoryResponseSerializer, CreateTicketAssignmentResponseSerializer,\n    CreateCommentResponseSerializer, CreateTicketResponseSerializer, UpdateTicketResponseSerializer,\n)\nfrom tickets.services import CategoryService, CommentService, TicketAssignmentService, TicketService\n\n\nclass CategoriesView(views.APIView):\n    permission_classes = (IsAuthenticated, )\n\n    @retry_on_transaction_serialization_error\n    @atomic\n    @gitrello_schema(query_serializer=CreateCategorySerializer, responses={201: CreateCategoryResponseSerializer})\n    def post(self, request, *args, **kwargs):\n        serializer = CreateCategorySerializer(data=request.data)\n        serializer.is_valid(raise_exception=True)\n\n        permissions = PermissionsService.get_board_permissions(\n            board_id=serializer.validated_data['board_id'],\n            user_id=request.user.id,\n        )\n        if not permissions.can_read:\n            raise PermissionDeniedException\n\n        category = CategoryService.create_category(**serializer.validated_data)\n        response_serializer = CreateCategoryResponseSerializer(instance=category)\n        return Response(\n            status=201,\n            data=response_serializer.data,\n        )\n\n\nclass TicketsView(views.APIView):\n    permission_classes = (IsAuthenticated, )\n\n    @retry_on_transaction_serialization_error\n    @atomic\n    @gitrello_schema(query_serializer=CreateTicketSerializer, responses={201: CreateTicketResponseSerializer})\n    def post(self, request, *args, **kwargs):\n        serializer = CreateTicketSerializer(data=request.data)\n        serializer.is_valid(raise_exception=True)\n\n        permissions = PermissionsService.get_category_permissions(\n            category_id=serializer.validated_data['category_id'],\n            user_id=request.user.id,\n        )\n        if not permissions.can_mutate:\n            raise PermissionDeniedException\n\n        ticket = TicketService.create_ticket(**serializer.validated_data)\n        response_serializer = CreateTicketResponseSerializer(instance=ticket)\n        return Response(\n            status=201,\n            data=response_serializer.data,\n        )\n\n\nclass TicketView(views.APIView):\n    permission_classes = (IsAuthenticated, )\n\n    @retry_on_transaction_serialization_error\n    @atomic\n    @gitrello_schema(query_serializer=UpdateTicketSerializer, responses={201: UpdateTicketResponseSerializer})\n    def patch(self, request, *args, **kwargs):\n        serializer = UpdateTicketSerializer(data=request.data)\n        serializer.is_valid(raise_exception=True)\n\n        permissions = PermissionsService.get_ticket_permissions(ticket_id=kwargs['id'], user_id=request.user.id)\n        if not permissions.can_mutate:\n            raise PermissionDeniedException\n\n        ticket = TicketService.update_ticket(ticket_id=kwargs['id'], validated_data=serializer.validated_data)\n        response_serializer = UpdateTicketResponseSerializer(instance=ticket)\n        return Response(\n            status=200,\n            data=response_serializer.data,\n        )\n\n    # TODO add tests\n    @retry_on_transaction_serialization_error\n    @atomic\n    @gitrello_schema(responses={204: ''})\n    def delete(self, request, *args, **kwargs):\n        permissions = PermissionsService.get_ticket_permissions(ticket_id=kwargs['id'], user_id=request.user.id)\n        if not permissions.can_delete:\n            raise PermissionDeniedException\n\n        TicketService.delete_ticket(ticket_id=kwargs['id'])\n        return Response(status=204)\n\n\nclass TicketAssignmentsView(views.APIView):\n    permission_classes = (IsAuthenticated, )\n\n    @retry_on_transaction_serialization_error\n    @atomic\n    @gitrello_schema(\n        query_serializer=CreateTicketAssignmentSerializer,\n        responses={201: CreateTicketAssignmentResponseSerializer},\n    )\n    def post(self, request, *args, **kwargs):\n        serializer = CreateTicketAssignmentSerializer(data=request.data)\n        serializer.is_valid(raise_exception=True)\n\n        permissions = PermissionsService.get_ticket_permissions(\n            ticket_id=serializer.validated_data['ticket_id'],\n            user_id=request.user.id,\n        )\n        if not permissions.can_mutate:\n            raise PermissionDeniedException\n\n        ticket_assignment = TicketAssignmentService.create_ticket_assignment(**serializer.validated_data)\n        response_serializer = CreateTicketAssignmentResponseSerializer(instance=ticket_assignment)\n        return Response(\n            status=201,\n            data=response_serializer.data,\n        )\n\n\nclass TicketAssignmentView(views.APIView):\n    permission_classes = (IsAuthenticated, )\n\n    @retry_on_transaction_serialization_error\n    @atomic\n    @gitrello_schema(responses={204: ''})\n    def delete(self, request, *args, **kwargs):\n        service = TicketAssignmentService()\n\n        permissions = PermissionsService.get_ticket_assignment_permissions(\n            ticket_assignment_id=kwargs['id'],\n            user_id=request.user.id,\n        )\n        if not permissions.can_delete:\n            raise PermissionDeniedException\n\n        service.delete_ticket_assignment(ticket_assignment_id=kwargs['id'])\n        return Response(status=204)\n\n\nclass CommentsView(views.APIView):\n    permission_classes = (IsAuthenticated, )\n\n    @retry_on_transaction_serialization_error\n    @atomic\n    @gitrello_schema(query_serializer=CreateCommentSerializer, responses={201: CreateCommentResponseSerializer})\n    def post(self, request, *args, **kwargs):\n        serializer = CreateCommentSerializer(data=request.data)\n        serializer.is_valid(raise_exception=True)\n\n        permissions = PermissionsService.get_ticket_permissions(\n            ticket_id=serializer.validated_data['ticket_id'],\n            user_id=request.user.id,\n        )\n        if not permissions.can_mutate:\n            raise PermissionDeniedException\n\n        comment = CommentService.create_comment(user_id=request.user.id, **serializer.validated_data)\n        response_serializer = CreateCommentResponseSerializer(instance=comment)\n        return Response(\n            status=201,\n            data=response_serializer.data,\n        )\n","sub_path":"gitrello/tickets/api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6736,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"287518065","text":"from PyQt5.QtCore import QUrl,pyqtProperty,pyqtSignal,pyqtSlot\nfrom PyQt5.QtGui import QGuiApplication\nfrom PyQt5.QtQml import QQmlApplicationEngine, qmlRegisterType, QQmlComponent\nfrom PyQt5.QtQuick import QQuickItem\nimport sys\nclass Widget(QQuickItem):\n    _name = \"MonNom\"\n    def __init_(self,parent=None) :\n        super().__init__(parent)\n        self._name = \"\"\n        print(\"New widget ha,dle create with name : \"+str(self._nom))\n\n    nameChanged = pyqtSignal()\n\n    @pyqtProperty('QInt', notify=nameChanged)\n    def name(self):\n        return self._name\n\n    @name.setter\n    def name(self,new_name):\n        self._name = new_name\n        self.nameChanged.emit()\n\n    @pyqtSlot(str,name=\"change\")\n    def change(self,value):\n        print(\"Catched : \"+str(value))\n        print(self.name)\n        self.name = \"Changement de nom \" + value\n        print(self._name)\n\n\nqmlRegisterType(Widget, 'Mywidget', 1, 0, 'Widget')\napp = QGuiApplication(sys.argv)\nengine = QQmlApplicationEngine()\nengine.load('test.qml')\nsys.exit(app.exec_())\n","sub_path":"packages/teste_qml_package.py","file_name":"teste_qml_package.py","file_ext":"py","file_size_in_byte":1039,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"477564618","text":"#-*- coding: utf-8 -*-\n\n# Copyright 2008-2012 Calculate Ltd. http://www.calculate-linux.org\n#\n#  Licensed under the Apache License, Version 2.0 (the \"License\");\n#  you may not use this file except in compliance with the License.\n#  You may obtain a copy of the License at\n#\n#      http://www.apache.org/licenses/LICENSE-2.0\n#\n#  Unless required by applicable law or agreed to in writing, software\n#  distributed under the License is distributed on an \"AS IS\" BASIS,\n#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#  See the License for the specific language governing permissions and\n#  limitations under the License.\n\nimport re\nimport sys\n\nimport sys\nimport re\nfrom os import path\nfrom files import listDirectory, readFile\nfrom common import getTupleVersion\n\nfrom calculate.lib.cl_lang import setLocalTranslate\nsetLocalTranslate('cl_lib3',sys.modules[__name__])\n\nreVerSplit = re.compile(r\"^(?:.*/var/db/pkg/)?(?:(\\w+-\\w+)/)?(.*?)-(([^-]+?)\"\n                         \"(?:-(r\\d+))?)(?:.(tbz2))?$\",re.S)\ndef reVerSplitToPV(x):\n    \"\"\"Convert match from reVerSplit to PV hash\"\"\"\n    if type(x) == str:\n        x = reVerSplit.search(x)\n    if x:\n        match = x.groups()\n        return {'CATEGORY':match[0] or \"\",\n                'PN':match[1],\n                'PF':\"%s-%s\"%(match[1],match[2]),\n                'P':\"%s-%s\"%(match[1],match[3]),\n                'PV':match[3],\n                'PR':match[4] or \"r0\",\n                'PVR':match[2]}\n    return {'PN':\"\",\n            'PF':\"\",\n            'P':\"\",\n            'PV':\"\",\n            'PR':\"\",\n            'PVR':\"\"}.copy()\n\ndef getPkgUses(fullpkg):\n    \"\"\"Get USE and IUSE from package\"\"\"\n    category,slash,pkg = fullpkg.partition('/')\n    pkgCategory = '/var/db/pkg/{0}'.format(category)\n    packages = filter(lambda x:x['PN'] == pkg,\n               map(reVerSplitToPV,\n               filter(lambda x:x,\n               map(lambda x:reVerSplit.search(x),\n               listDirectory(pkgCategory)))))\n    if not packages:\n        return None\n    usePath = path.join(pkgCategory,packages[-1]['PF'],\"USE\")\n    iusePath = path.join(pkgCategory,packages[-1]['PF'],\"IUSE\")\n    iuse = readFile(iusePath).strip().split()\n    use = readFile(usePath).strip().split()\n    return (map(lambda x:x[1:] if x.startswith(\"+\") else x,\n            filter(lambda x:x,\n            use)),\n            map(lambda x:x[1:] if x.startswith(\"+\") else x,\n            filter(lambda x:x,\n            iuse)))\n\ndef isPkgInstalled(pkg,prefix='/',sortByVersion=False):\n    \"\"\"Check is package installed\"\"\"\n    pkgDir = path.join(prefix,'var/db/pkg')\n    if \"/\" in pkg:\n        category,op,pkg = pkg.partition('/')\n        res = map(lambda x:x.update({'CATEGORY':category}) or x,\n                   filter(lambda x:x['PN'] == pkg,\n                   map(reVerSplitToPV,\n                   listDirectory(path.join(pkgDir,category)))))\n        if len(res)>1 and sortByVersion:\n            return sorted(res,key=lambda x:getTupleVersion(x['PVR']))\n        else:\n            return res\n    else:\n        return filter(lambda x: filter(lambda y:y['PN'] == pkg,\n                                map(reVerSplitToPV,\n                                listDirectory(x))),\n               listDirectory(pkgDir,fullPath=True))\n\ndef getPkgSlot(pkg,prefix='/'):\n    \"\"\"Get package slot\"\"\"\n    pkgs = isPkgInstalled(pkg,prefix)\n    pkgDir = path.join(prefix,'var/db/pkg')\n    return map(lambda x:readFile(path.join(pkgDir,x['CATEGORY'],\n                                           x['PF'],\"SLOT\")).strip(),\n           pkgs)\n\ndef getPkgActiveUses(fullpkg):\n    \"\"\"Get active uses from package\"\"\"\n    res = getPkgUses(fullpkg)\n    if not res:\n        return None\n    return list(set(res[0]) & set(res[1]))\n\ndef getSquashList():\n    \"\"\"Get supprted squashfs compressions method\"\"\"\n    wantMethod = set([\"lzo\",\"lzma\",\"xz\",\"gzip\"])\n    usesSquashFs = getPkgActiveUses(\"sys-fs/squashfs-tools\")\n    if not usesSquashFs:\n        return [\"gzip\"]\n    else:\n        pkgInfo = isPkgInstalled('sys-fs/squashfs-tools')\n        if pkgInfo and pkgInfo[0]['PV']:\n            pkgVer = getTupleVersion(pkgInfo[0]['PV'])\n            gzipVer = getTupleVersion('4.2')\n            if pkgVer >= gzipVer:\n                usesSquashFs.append('gzip')\n        return map(lambda x:{\"lzma\":\"xz\"}.get(x,x),\n               list(set(usesSquashFs) & wantMethod))\n\ndef searchProfile(dirpath,configname):\n    \"\"\"Search profile\"\"\"\n    paths = []\n    def search(dirpath):\n        parentpath = path.join(dirpath,\"parent\")\n        if path.exists(parentpath):\n            for line in open(parentpath,'r'):\n                search(path.join(dirpath,line.strip()))\n        fullconfig = path.join(dirpath,configname)\n        if path.exists(fullconfig):\n            paths.append(fullconfig)\n    search(dirpath)\n    return paths\n","sub_path":"calculate/lib/utils/portage.py","file_name":"portage.py","file_ext":"py","file_size_in_byte":4813,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"192646953","text":"from django.urls import path\nfrom django.conf.urls import url\n\nfrom . import views\n\nurlpatterns = [\n    path('login', views.login),\n    path('register', views.register),\n    path('create', views.create),\n    path('query/<int:food_start>', views.query),\n    path('product/<int:food_id>', views.product),\n    path('delete', views.delete),\n    path('update', views.update),\n    path('search', views.search),\n    path('random', views.ran),\n    url(r'^$', views.index, name='index')\n]","sub_path":"django_server/server/api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":479,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"439409652","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport random\nimport math\nimport experiment_testbed\n\n\ndef compute_l(epsilon, delta):\n    return int((4 / (epsilon * epsilon)) * (math.log(3 / delta)))\n\n\ndef run_mea(means, epsilon, delta):\n    means_list = means.copy()\n    x_axis = 0  # variable to store the total number of steps in the algorithm\n    rewards_list = list()  # average rewards list\n    epsilon = epsilon / 4  # epsilon for first run of algorithm\n    delta = delta / 2  # delta for first run of algorithm\n    arms_l = len(means_list[0])  # initial number of arms\n    optimum_arms = np.argmax(means_list, 1)  # list of best arm in each bandit\n    optimum_selection_count = list()\n    optimum_arms_l = optimum_arms\n    n_bandits = len(means_list)  # number of bandits\n\n    means_true_l = means_list\n\n    while arms_l != 1:  # loop till we get best arm\n        optimum_arm_selected = 0\n        computed_l = compute_l(epsilon, delta)  # get the number of time steps\n        rewards_l = np.zeros((n_bandits, arms_l))  # list to store total reward for each arm in each bandit\n\n        for i in range(0, computed_l):\n            print('arms: ' + str(arms_l) + ',' + str(i))\n            rewards = np.random.normal(means_true_l, 1)  # sample reward for each arm\n            rewards_l = rewards_l + rewards\n            x_axis += 1\n            rewards_list.append(np.mean(rewards))\n\n        bandit_rewards_avg = rewards_l / computed_l  # average reward for each arm\n        medians = np.median(bandit_rewards_avg, 1)  # median in each bandit\n\n        # new list to store arms with reward more than median\n        if arms_l % 2 == 0:\n            new_means_list = np.zeros((n_bandits, int(arms_l - arms_l / 2)))\n        else:\n            new_means_list = np.zeros((n_bandits, int(arms_l - arms_l / 2) + 1))\n\n        for j in range(0, n_bandits):\n            k1 = 0\n            for k in range(0, arms_l):\n                if bandit_rewards_avg[j][k] >= medians[j]:\n                    new_means_list[j][k1] = means_true_l[j][k]  # add arm in new list if average reward more than median\n                    if k == optimum_arms_l[j]:  # check if arm we added is the best arm for that bandit\n                        optimum_arm_selected += 1\n                    k1 += 1\n\n        means_true_l = new_means_list  # update old true means list with new list\n        if arms_l % 2 == 0:\n            arms_l = int(arms_l / 2)  # update number of arms\n        else:\n            arms_l = int(arms_l / 2) + 1  # update number of arms\n        optimum_arms_l = np.argmax(means_true_l, 1)  # get best arms in new means list\n        optimum_selection_count.append(optimum_arm_selected * 100 / n_bandits)\n        epsilon = (3 / 4) * epsilon  # epsilon for next round\n        delta = delta / 2  # delta for next round\n\n    return means_true_l, rewards_list, optimum_selection_count, x_axis\n\n\nbandits = 2000\narms = 1000\nmeans_list, times_selected = experiment_testbed.initialize(bandits, arms)\ncurr_epsilon = 1\ncurr_delta = 0.1\n\nfig_rewards = plt.figure().add_subplot(111)  # graph of average reward\nfig_optimum = plt.figure().add_subplot(111)  # graph of % optimum arm selection\n\nbest_arms, rewards, optimum_list, x_axis = run_mea(means_list, curr_epsilon, curr_delta)  # get the results\nfig_rewards.plot(range(x_axis), rewards, label='MEA' + r'$\\epsilon$ = ' + str(curr_epsilon) + ', '\n                                               + r'$\\delta$ = ' + str(curr_delta))\nfig_optimum.plot(range(1, len(optimum_list) + 1), optimum_list, label='MEA: ' + r'$\\epsilon$ = ' + str(curr_epsilon) + ', '\n                                                                      + r'$\\delta$ = ' + str(curr_delta))\n\nfig_rewards.set_xlabel('Steps')\nfig_rewards.set_ylabel('Average Reward')\nfig_rewards.title.set_text('Average Reward vs Steps')\nfig_rewards.legend(loc='lower right')\n\nfig_optimum.set_xlabel('Steps')\nfig_optimum.set_ylabel(r'$\\%$ Optimum Action')\nfig_optimum.title.set_text(r'$\\%$ Optimum Action vs Steps')\nfig_optimum.legend(loc='lower right')\nplt.show()","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":4036,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"152970383","text":"import socket\nfrom random import randint\nfrom ast import literal_eval as l_e\nfrom time import sleep\n\n# The socket Instance\ns = socket.socket(socket.AF_INET, socket.SOCK_STREAM, 0)\n\n# Local Machine Name\nhost = socket.gethostbyname(socket.gethostname())\n\n# Choose port & bind it\nport = 3010\ns.bind((host, port))\n\nprint (\"host ip:port:\\t\", host, port)\n\n# Listen for client connections. The number\n# it specifies the number of unaccepted connections that the system\n# will allow before refusing newconnections.\ns.listen(5)\n\nwhile True:\n\t# Establish a connection with the client\n\tcli, addr = s.accept()\n\n\t# Yay!\n\tprint (\"Got connection from\", addr)\n\n\t# Initial Prompt\n\t\"\"\"\n\t\tType of Service:\n\t\t\t0 - deduplication + [___]\n\t\t\t1 - intersection + [1___]\n\t\"\"\"\n\tprompt = int(cli.recv(1024).decode(\"utf-8\"))\n\n\tif prompt == 0:\n\t\tClient_List = cli.recv(1024).decode(\"utf-8\")\n\t\tList = l_e(Client_List)\n\t\tListDup = [i for i in List]\n\n\t\tprint (\"clientList (unsorted):\\t\", List)\n\n\t\tlen_list = 0\n\n\t\tfor element in List:\n\t\t\tlen_list += 1\n\n\t\tserverList = [randint(1, 100) for i in range(len_list)]\n\n\t\tList.sort()\n\n\t\tListDup = set(ListDup)\n\t\tserverList = set(ListDup)\n\n\t\tcommonNumbers = []\n\n\t\tfor i in ListDup:\n\t\t\tif i in serverList:\n\t\t\t\tcommonNumbers.append(i)\n\n\t\tcli.send(bytes(str(List).encode(\"utf-8\")))\n\t\tsleep(0.25)\n\n\t\tprint (\"serverList:\\t\", list(serverList))\n\t\tprint (\"clientList (sorted):\\t\", List)\n\t\tprint (\"commonList:\\t\", commonNumbers)\n\n\t\tcli.send(bytes(str(commonNumbers).encode(\"utf-8\")))\n\telse:\n\t\tClient_List = cli.recv(1024).decode(\"utf-8\")\n\t\tList = l_e(Client_List)\n\n\t\tprint (\"clientList (unsorted):\\t\", List)\n\n\t\tlen_list = 0\n\n\t\tfor element in List:\n\t\t\tlen_list += 1\n\n\t\tserverList = [randint(1, 100) for i in range(len_list)]\n\n\t\tprint (\"serverList:\\t\", serverList)\n\n\t\tList = set(List)\n\t\tserverList = set(List)\n\n\t\tintersectedList = list(List.intersection(serverList))\n\n\t\tprint (\"intersectedList\", intersectedList)\n\n\t\tcli.send(bytes(str(intersectedList).encode(\"utf-8\")))\n\n\tcli.close()\n","sub_path":"2-Jan-22/Task-2/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1980,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"510972495","text":"#!/usr/bin/env python3\nfrom flask import Flask, render_template\nfrom flask_socketio import SocketIO\nimport pty\nimport os\nimport subprocess\nimport select\n\napp = Flask(\n    __name__, template_folder=\".\", static_folder=\".\", static_url_path=\"\")\napp.config[\"SECRET_KEY\"] = \"secret!\"\napp.config[\"fd\"] = None\napp.config[\"child_pid\"] = None\nsocketio = SocketIO(app)\n\n\ndef read_and_forward_pty_output():\n    max_read_bytes = 1024 * 20\n    while True:\n        socketio.sleep(0.01)\n        if app.config[\"fd\"]:\n            timeout_sec = 0\n            (data_ready, _, _) = select.select([app.config[\"fd\"]], [], [],\n                                               timeout_sec)\n            if data_ready:\n                output = os.read(app.config[\"fd\"], max_read_bytes).decode()\n                socketio.emit(\n                    \"pty-output\", {\"output\": output}, namespace=\"/pty\")\n\n\n@app.route(\"/\")\ndef index():\n    return render_template(\"index.html\")\n\n\n@socketio.on(\"pty-input\", namespace=\"/pty\")\ndef pty_input(data):\n    \"\"\"write to the child pty\"\"\"\n    if app.config[\"fd\"]:\n        os.write(app.config[\"fd\"], data[\"input\"].encode())\n\n\n@socketio.on(\"connect\", namespace=\"/pty\")\ndef connect():\n    \"\"\"new client connected\"\"\"\n\n    if app.config[\"child_pid\"]:\n        # already started child process, don't start another\n        return\n\n    # create child process attached to a pty we can read from and write to\n    (child_pid, fd) = pty.fork()\n    if child_pid == 0:\n        # this is the child process fork.\n        # anything printed here will show up in the pty, including the output\n        # of this subprocess\n        subprocess.run([\"bash\"])\n    else:\n        # this is the parent process fork.\n        # store child fd and pid\n        app.config[\"fd\"] = fd\n        app.config[\"child_pid\"] = child_pid\n        print(\"child pid is\", child_pid)\n        print(\"starting background task to continously read and forward pty \"\n              \"output to client\")\n        socketio.start_background_task(target=read_and_forward_pty_output)\n        print(\"task started\")\n\n\ndef main(debug=False, port=5000):\n    print(f\"serving on http://127.0.0.1:{port}\")\n    socketio.run(app, debug=debug, port=port)\n\n\nif __name__ == \"__main__\":\n    main(debug=True)\n","sub_path":"pyxtermjs/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2237,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"158057585","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\n\"\"\"this module should contain some procedures that are based\non working the files and directory, and generally to things\nunrelated with doing a site\"\"\"\n\nimport os\nimport shutil\nimport datetime\nimport collections\nimport random\nimport string\nimport fnmatch\nimport textwrap\n\nimport sgconf\nfrom sgglobals import *\n\nlistarepl = collections.OrderedDict()  # list of replacing items in replace.conf\n\n\ndef clearfinal():\n\t\"\"\"if a directory doesn't exist on start, it must not be also in destination\"\"\"\n\tdir1 = sgconf.cfgget(\"dirstart\")\n\tdir2 = sgconf.cfgget(\"dirfinal\")\n\n\t#first, I clear directories that don't exist anymore\n\tfor root, subf, files in os.walk(dir2, topdown=False):\n\t\tfor fn in subf:\n\t\t\tchkdir = os.path.join(root, fn)\n\t\t\texdir = chkdir.replace(dir2, dir1)\n\t\t\tif not os.path.exists(exdir):\n\t\t\t\ttry:\n\t\t\t\t\tshowmsg(\"Clearing directory \" + fn, 0)\n\t\t\t\t\tshutil.rmtree(chkdir)\n\t\t\t\texcept:\n\t\t\t\t\tpass\n\n\t# then, I delete files: two ways. html & php are composed so others are only copied\n\tfor root, subf, files in os.walk(dir2):\n\t\tfor fn in files:\n\t\t\tchkfile = os.path.join(root, fn)\n\t\t\tchkext = os.path.splitext(chkfile)[1]\n\n\t\t\tif chkext == \".html\" or chkext == \".php\":\n\t\t\t\toldfile = str(os.path.splitext(chkfile)[0].replace(dir2, dir1)) + sgconf.cfgget(\"processingext\")\n\t\t\t\tif not os.path.exists(oldfile):\n\t\t\t\t\tos.remove(chkfile)\n\t\t\telse:\n\t\t\t\toldfile = chkfile.replace(dir2, dir1)\n\t\t\t\tif not os.path.exists(oldfile):\n\t\t\t\t\tos.remove(chkfile)\n\n\ndef clearstart():\n\t\"\"\" clear some kinds of files if they don't have to exists anymore \"\"\"\n\n\tlistimagedirs = sgconf.cfgget(\"dirimages\").split(\"|\")\n\n\tfor e in listimagedirs:\n\t\tfor root, subf, files in os.walk(os.path.join(sgconf.cfgget(\"dirstart\"), e)):\n\t\t\tfor fn in files:\n\t\t\t\tbasefile = os.path.splitext(fn)[0]          # base name\n\t\t\t\textfile = os.path.splitext(fn)[1]           # extension\n\t\t\t\tchkfile = os.path.join(root, basefile)      # all path except extension\n\t\t\t\tif extfile == sgconf.cfgget(\"processingext\"):\n\t\t\t\t\tif not os.path.exists(chkfile + sgconf.cfgget(\"processingext\")):\n\t\t\t\t\t\tshowmsg(\" removing \" + chkfile + sgconf.cfgget(\"processingext\"), 0)\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tos.remove(chkfile + sgconf.cfgget(\"processingext\"))\n\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\tpass\n\n\ndef checkpermission(fname, what):\n\t\"\"\"\n\tcheck file permissions\n\t:param fname: the name of the file\n\t:param what: request of the permission\n\t:return: True or False, if a situation is reached\n\t\"\"\"\n\tif what == \"write\":\n\t\treturn os.access(fname, os.W_OK)\n\tif what == \"read\":\n\t\treturn os.access(fname, os.R_OK)\n\n\ndef conformitynamecheck():\n\t\"\"\"\n\t:return: none\n\t\"\"\"\n\n\tspaths = []\n\t# two folders are checked, main and newposts, then i advise for empty values\n\tif sgconf.cfgget(\"dirstart\") == \"\":\n\t\tshowmsg(\"You've to set the start directory before checkfilenames\", 99)\n\t\treturn\n\telse:\n\t\tspaths.append(sgconf.cfgget(\"dirstart\"))\n\tif sgconf.cfgget(\"dirnewposts\") != \"\":\n\t\tspaths.append(sgconf.cfgget(\"dirnewposts\"))\n\n\tfor sdir in spaths:\n\t\tfor root, subFolders, files in os.walk(sdir):\n\t\t\tfor fn in files:\n\t\t\t\tnewfile = fn.lower()\n\t\t\t\tnewfile = newfile.replace(\"'\", \"_\")\n\t\t\t\tnewfile = newfile.replace(\" \", \"-\")\n\t\t\t\tnewfile = newfile.replace(\"..\", \".\")\n\n\t\t\t\tif fn != newfile:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tshowmsg(\" renaming \" + os.path.join(root, fn), 0)\n\t\t\t\t\t\tos.rename(os.path.join(root, fn), os.path.join(root, newfile))\n\t\t\t\t\texcept:\n\t\t\t\t\t\tshowmsg(\" error renaming \" + os.path.join(root, fn), 0)\n\n\ndef cuttext(text, mode):\n\t\"\"\" cut the text passed to function\n\t:param text: text to be cutted\n\t:param mode: the way\n\t:return:\n\t\"\"\"\n\n\tif mode == \"summary\":\n\t\tmaxdim = sgconf.cfgget(\"postsummarylength\")\n\t\treturn textwrap.shorten(sterilizetemplates(text), width=maxdim, placeholder=\"\")\n\ndef dir_clean(nomedir):\n\t\"\"\"clean directory  tree recursively removing empty dirs\n\t:param nomedir: the name of directory\n\t:return: none\n\t\"\"\"\n\n\tif nomedir.find(sgconf.cfgget(\"dirstart\")) < 0:\n\t\treturn\n\n\tfor dirpath, dirnames, filenames in os.walk(nomedir, topdown=False):\n\t\ttry:\n\t\t\tos.rmdir(dirpath)\n\t\texcept:\n\t\t\tpass\n\n\ndef file_search_replace(txt):\n\t\"\"\" replace some user defined variables\n\n\t:param txt:\n\t:return:\n\t\"\"\"\n\tglobal listarepl\n\n\tif not listarepl:\n\t\treplfile = os.path.join(sgconf.cfgget(\"dirstart\"), \"site\", \"replace.conf\")\n\t\tif os.path.exists(replfile):\n\t\t\trepconst = \"(01234567890123456789)\"\n\t\t\tshowmsg(\" replacing contents in \" + replfile, 0)\n\t\t\tf = open(replfile)\n\t\t\tlinee = f.read().splitlines()\n\t\t\tfor l in linee:\n\t\t\t\tif len(l) > 3 and l.find(\":\") > 1:\n\t\t\t\t\tif l.find(\"\\:\") >= 0:\n\t\t\t\t\t\tl = l.replace(\"\\:\", repconst)\n\t\t\t\t\ttry:\n\t\t\t\t\t\ta = l[:l.find(\":\"):].replace(repconst, \":\")\n\t\t\t\t\t\tb = l[l.find(\":\") + 1:].replace(repconst, \":\")\n\t\t\t\t\t\tlistarepl[a] = b\n\t\t\t\t\texcept:\n\t\t\t\t\t\tpass\n\t\t\tf.close()\n\t\telse:\n\t\t\tlistarepl[\"zzz\"] = \"zzz\"\n\n\tfor key, value in listarepl.items():\n\t\ttxt = txt.replace(key, value)\n\n\treturn txt\n\n\ndef findreplace(sdir, sfind, sreplace, filepattern, subst):\n\t\"\"\"\n\t:param sdir: directory of the site\n\t:param sfind: string to find\n\t:param sreplace: replacing string, empty if for finding only\n\t:param filepattern: default is *.md\n\t:param subst: if it is False, is a dry run\n\t:return:\n\t\"\"\"\n\tif filepattern == \"\":\n\t\tfilepattern = \"*.md\"\n\n\tfor path, dirs, files in os.walk(os.path.abspath(sdir)):\n\t\tfor filename in fnmatch.filter(files, filepattern):\n\t\t\tfilepath = os.path.join(path, filename)\n\t\t\twith open(filepath) as f:\n\t\t\t\ts = f.read()\n\n\t\t\tif s.find(sfind) >= 0:\n\t\t\t\tif subst:\n\t\t\t\t\ts = s.replace(sfind, sreplace)\n\t\t\t\t\twith open(filepath, \"w\") as f:\n\t\t\t\t\t\tf.write(s)\n\t\t\t\telse:\n\t\t\t\t\tshowmsg(\"Found: \" + filepath[len(sdir):] , 1)\n\n\ndef file_trash(filecont):\n\t\"\"\" remove all files containings this in name\n\t:param filecont: files that contains this will be deleted\n\t:return: none\n\t\"\"\"\n\n\tsdir = sgconf.cfgget(\"dirstart\")\n\tfor root, subdirs, files in os.walk(sdir):\n\t\tfor fl in files:\n\t\t\tif fl.find(filecont) >= 0:\n\t\t\t\tshowmsg(\"Removing \" + os.path.join(root, fl), 0)\n\t\t\t\ttry:\n\t\t\t\t\tos.remove(os.path.join(root, fl))\n\t\t\t\texcept:\n\t\t\t\t\tpass\n\n\ndef file_write(nomefile, testo, modo):\n\t\"\"\" open a file and write on it\n\n\t:param nomefile: file name\n\t:param testo: text\n\t:param modo: can be w or a, write or append\n\t:return: none\n\t\"\"\"\n\ttry:\n\t\twith open(nomefile, modo) as myfile:\n\t\t\tmyfile.write(testo)\n\texcept:\n\t\tshowmsg(\"Error deleting \" + nomefile, 9)\n\n\ndef file_read(nomefile):\n\t\"\"\"open a file and read it\n\n\t:param nomefile: text file name to be read\n\t:return: content of the file\n\t\"\"\"\n\tif os.path.exists(nomefile):\n\t\t# noinspection PyArgumentEqualDefault\n\t\twith open(nomefile, \"r\") as myfile:\n\t\t\tv = myfile.read()\n\t\treturn v\n\telse:\n\t\treturn \"\"\n\n\ndef fad(nomefile):\n\t\"\"\" Filename As Description\n\t\treturn the base file name cleared \"\"\"\n\tres = os.path.basename(os.path.splitext(nomefile)[0])\n\tres = res.replace(\"-\", \" \")\n\tres = res.replace(\"_\", \"'\")\n\n\treturn res.title()\n\n\ndef fwe(nomefile):\n\t\"\"\" Filename Without Extension\n\t\treturn the file name without extension: used to short routines \"\"\"\n\treturn os.path.splitext(nomefile)[0]\n\n\ndef getuniqueid(nf, modo):\n\t\"\"\" get an unique random string. For file id, it's simply an unique id. For 'salt', if you think to secure relatively the process\n\t\tyou can set in action file the string, or put in it the value 'random' or simply nothing, to make prefix always changing. A consideration\n\t\tis that you need to upload always the hashed password files. This is not generally a problem, due they are often small files, but\n\t\tthink to transfer mode due security, to passwords length, to simple words, etc etc\n\t:param nf: file name if useful,\n\t:param modo: the kind of id\n\t:return:\n\t\"\"\"\n\tif modo == \"fileid\":\n\t\tv = ''.join(random.choice(string.ascii_lowercase) for i in range(12))\n\t\tv += \"-\" + os.path.basename(os.path.splitext(nf)[0])\n\telif modo == \"salt\":\n\t\tif sgconf.cfgget(\"saltprefix\") == \"\":\n\t\t\tv = ''.join(random.choice(string.ascii_lowercase) for i in range(2))\n\t\telse:\n\t\t\tv = sgconf.cfgget(\"saltprefix\")\n\treturn v\n\n\ndef lastmod(nomefile, modo):\n\t\"\"\"\n\t:param nomefile: file name to be checked\n\t:param modo: 0=yyyy-mm-dd hh:mm  1=yyyymmdd\n\t:return: the last modification time\n\t\"\"\"\n\tv = \"\"\n\ttry:\n\t\tif os.path.exists(nomefile):\n\t\t\ttmp = os.path.getmtime(nomefile)\n\t\t\ttmp2 = datetime.datetime.fromtimestamp(tmp)\n\t\t\tif modo == 0:\n\t\t\t\tv = str(tmp2)[:16]\n\t\t\tif modo == 1:\n\t\t\t\tv = str(tmp2)[:10].replace(\"-\", \"\")\n\texcept:\n\t\tpass\n\n\treturn v\n\n\ndef lnkc(linkname):\n\t\"\"\" replace slash in windows systems: in Linux and Mac, I get paths and resolve them\n\t\tin Windows I have a backslash, different from the needs of the page. Due a lot\n\t\tof links are coming from file names, using Windows make path a pain for a prog\n\t\tbased on directories\n\n\t:param linkname: the contents of link\n\t:return: link corrected\n\t\"\"\"\n\n\treturn linkname.replace(os.sep, \"/\")\n\n\ndef removehtml(s):\n\t\"\"\"as function name says :-) \"\"\"\n\n\ttag = False\n\tquote = False\n\tout = \"\"\n\n\tfor c in s:\n\t\tif c == '<' and not quote:\n\t\t\ttag = True\n\t\telif c == '>' and not quote:\n\t\t\ttag = False\n\t\telif (c == '\"' or c == \"'\") and tag:\n\t\t\tquote = not quote\n\t\telif not tag:\n\t\t\tout = out + c\n\n\treturn out\n\n\ndef sterilizetemplates(s):\n\n\ttesto = s.replace(\"{\", \"[\").replace(\"}\", \"]\")\n\treturn testo\n\n\ndef filedatemod(nf):\n\t\"\"\" update the modification time of a file: these are standalone functions, due\n\t\tprobably I will have to adjust for use on Windows\n\n\t:param nf: name of the file to be updated\n\t:return: none\n\t\"\"\"\n\t#TODO check if Windows implementation is working\n\ttry:\n\t\tos.utime(nf, None)  # Set access/modified times to now\n\texcept OSError:\n\t\tshowmsg(\"Can't touch \" + nf, 1)\n\n\ndef filelink(oldfile, newlink):\n\t\"\"\" link a file to a position by sym link (equivalent to ln -s)\n\n\t:param oldfile: file to be linked\n\t:param newlink: new position\n\t:return: none\n\t\"\"\"\n\n\t#TODO check if Windows implementation is working\n\ttry:\n\t\tos.symlink(oldfile, newlink)\n\texcept:\n\t\tshowmsg(\"Error linking \" + oldfile, 1)\n\n\ndef showmsg(message, modo):\n\t\"\"\"\n\t:param message: message to be showed\n\t:param modo: the way message is showed 0=show, 9=log 99=future use\n\t:return: link corrected\n\t\"\"\"\n\n\tif sgconf.cfgget(\"hideprogressmessages\") == \"no\" and modo != 1:\n\t\treturn\n\n\tif message == \"\":\n\t\treturn\n\n\tif modo == 0:           #normal\n\t\tprint(message)\n\telif modo == 1:         #error\n\t\tprint(message)\n\telif modo == 9:         #log file\n\t\tfile_write(sgconf.cfgget(\"logfile\"), message + \"\\n\", \"a\")\n\telif modo == 99:        #debug\n\t\tprint(\"..................\\n.. \" + message + \"\\n..................\")\n\nif __name__ == \"__main__\":\n\tshowmsg(ERROR_LAUNCHED_SCRIPT, 0)\n\n","sub_path":"sgutils.py","file_name":"sgutils.py","file_ext":"py","file_size_in_byte":10439,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"257741025","text":"import pygame as pg\n#from ship import Ship\nclass Settings():\n\t\"\"\"A class to store all settings for game\"\"\"\n\t\n\tdef __init__(self):\n\t\t\"\"\"Initialize the class\"\"\"\n\t\tself.windowCaption = 'Interstellar'\n\t\tself.screenWidth = 450 \n\t\tself.screenHeight = 650\n\t\tself.bgColor = (0, 0, 0)\n\t\tself.bg = pg.image.load(\"gfx/settings/title_.jpg\")\n\t\tself.bg = pg.transform.scale(self.bg, (450,650))\n\t\t#\n\t\t#self.screen = pg.display.set_mode((self.screenWidth, self.screenHeight))\n\t\t#\n\t\tself.image_left = pg.image.load('gfx/settings/LEFT_.png')\n\t\tself.image_left = pg.transform.scale(self.image_left, (35,56))\n\t\t#self.rect = self.image_left.get_rect()\n\t\t#self.screenRect = screen.get_rect()\n\n\t\tself.image_right = pg.image.load('gfx/settings/RIGHT_.png')\n\t\tself.image_right = pg.transform.scale(self.image_right, (35,56))\n\t\t#self.rect = self.image_rihgt.get_rect()\n\t\t#self.image_center = Ship.image\t\n\t\tself.image_center = pg.image.load('gfx/settings/player_.png')\n\t\tself.image_center = pg.transform.scale(self.image_center, (35,56))\n\n\t\tself.image_boss = pg.image.load('gfx/settings/player.png')\n\t\tself.image_boss = pg.transform.scale(self.image_boss, (35,56))\n\t\t\n\t\tself.image_bombing = pg.image.load('gfx/settings/explosion1.png')\n\t\t#self.image_bombing = pg.image.load('gfx/sBullet.png')\n\t\tself.image_bombing = pg.transform.scale(self.image_bombing, (56,56))\n\n\t\tself.image_sBullet = pg.image.load('gfx/settings/sBullet.png')\n\t\tself.image_sBullet = pg.transform.scale(self.image_sBullet, (16,23))\n#alien.py\n#eBullet.py\n\t\tself.image_alien = pg.image.load('gfx/settings/alien_bullet.bmp')\n\t\t\n\t\t#self.intro =\t\n\t\t#Ships speed\n\t\tself.shipLimit = 5 \n\n\t\t#Bullet settings\n\t\tself.bulletWidth = 10\n\t\tself.bulletHeight = 20\n\t\tself.bulletColor = (60, 60, 60)\n\n\t\t#Alien settings\n\n\t\t#How quickly the game speeds up\n\t\tself.speedUp = 1.1\n\t\tself.scoreSpeedUp = 1.5\n\n\t\tself.initDynamicSettings()\n\tdef blitme_left(abc):\n\t\tscreen.blit(abc, self.rect)\n\t\t\n\tdef initDynamicSettings(self):\n\t\tself.shipSpeed = 1.5\n\t\tself.bulletSpeed = 3\n\t\tself.alienSpeed = 1.2\n\t\tself.fleetDropSpeed = 5\n\t\tself.fleetDir = 1\n\t\tself.alienPoints = 50\n\n\tdef increaseSpeed(self):\n\t\t\"\"\"Increase the speed settings\"\"\"\n\t\t#self.shipSpeed *= self.speedUp\n\t\t#self.bulletSpeed *= self.speedUp\n\t\tif self.alienSpeed <= 1.5:\n\t\t\tself.alienSpeed *= self.speedUp\n\t\t\tself.fleetDropSpeed *= self.speedUp\n\t\tself.alienPoints = int(self.alienPoints * self.scoreSpeedUp)\n","sub_path":"settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":2382,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"267329790","text":"from datetime import datetime\nfrom schema import Schema,And,Use,Optional,Or\n\n# Types\nDomainName = Schema(\n  And(str,len,Use(str.lower))\n)\n\nS3Object = Schema({\n  'bucket': And(str,len),\n  'key': And(str,len)\n})\n\nS3Result = Schema({\n  'data': S3Object\n})\n\nExceptionResult = Schema({\n  'exception': And(str,len)\n})\n\n# API\nWhoisQuery = Schema({\n  'domainname': DomainName\n})\n\nWhoisResult = Or(S3Result, ExceptionResult)\nDigResult = Or(S3Result, ExceptionResult)","sub_path":"app/casper-search/app/util/contracts.py","file_name":"contracts.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"653817051","text":"from discord.ext import commands\nimport io, os\nimport textwrap\nimport traceback\nfrom contextlib import redirect_stdout\nfrom git import Repo\nfrom datetime import datetime\n\ndef cleanup_code(content):\n    \"\"\"Automatically removes code blocks from the code.\"\"\"\n    if content.startswith('```') and content.endswith('```'):\n        return '\\n'.join(content.split('\\n')[1:-1])\n    return content.strip('` \\n')\n\nclass OwnerCog(commands.Cog, command_attrs=dict(hidden=True), name=\"Owner\"):\n    \"\"\"Commands meant for the owner only.\"\"\"\n    def __init__(self, bot):\n        self.bot = bot\n        self._last_result = None\n\n    @commands.command(name='pull')\n    @commands.is_owner()\n    async def git_update(self, ctx):\n        \"\"\"Pulls the bot from GitHub.\"\"\"\n        now = datetime.now()\n        message = \"\"\n        repo = Repo(path=os.getcwd())\n        o = repo.remotes.origin\n        for fetch_info in o.pull():\n            message = message + f\"\\n Updated '{fetch_info.ref}' To '{fetch_info.commit}'\"\n        later = datetime.now()\n        difference = (later - now).total_seconds()\n        await ctx.send(f\"Operation completed succesfully in {difference}s. Output: ```prolog\\n{message}\\n```\")\n\n    @commands.command()\n    @commands.is_owner()\n    async def shutdown(self, ctx):\n        \"\"\"Shuts the bot down.\"\"\"\n        await ctx.send(\"Goodbye!\")\n        await self.bot.logout()\n\n    @commands.command(name='load')\n    @commands.is_owner()\n    async def cogload(self, ctx, *, cog: str):\n        \"\"\"Command which Loads a Module.\n        Remember to use dot path. e.g: cogs.owner\"\"\"\n\n        try:\n            self.bot.load_extension(cog)\n        except Exception as e:\n            await ctx.send(f'**`ERROR:`** {type(e).__name__} - {e}')\n        else:\n            await ctx.send('**`SUCCESS`**')\n\n    @commands.command(name='unload')\n    @commands.is_owner()\n    async def cogunload(self, ctx, *, cog: str):\n        \"\"\"Command which Unloads a Module.\n        Remember to use dot path. e.g: cogs.owner\"\"\"\n\n        try:\n            self.bot.unload_extension(cog)\n        except Exception as e:\n            await ctx.send(f'**`ERROR:`** {type(e).__name__} - {e}')\n        else:\n            await ctx.send('**`SUCCESS`**')\n\n    @commands.command(name='reload')\n    @commands.is_owner()\n    async def cogreload(self, ctx, *, cog: str):\n        \"\"\"Command which Reloads a Module.\n        Remember to use dot path. e.g: cogs.owner\"\"\"\n\n        try:\n            self.bot.unload_extension(cog)\n            self.bot.load_extension(cog)\n        except Exception as e:\n            await ctx.send(f'**`ERROR:`** {type(e).__name__} - {e}')\n        else:\n            await ctx.send('**`SUCCESS`**')\n\n    @commands.command(name='eval')\n    @commands.is_owner()\n    async def _eval(self, ctx, *, body: str):\n        \"\"\"Evaluates a code\"\"\"\n\n        env = {\n            'bot': self.bot,\n            'ctx': ctx,\n            'channel': ctx.channel,\n            'author': ctx.author,\n            'guild': ctx.guild,\n            'message': ctx.message,\n            '_': self._last_result\n        }\n\n        env.update(globals())\n\n        body = cleanup_code(body)\n        stdout = io.StringIO()\n\n        to_compile = f'async def func():\\n{textwrap.indent(body, \"  \")}'\n\n        try:\n            exec(to_compile, env)\n        except Exception as e:\n            return await ctx.send(f'```py\\n{e.__class__.__name__}: {e}\\n```')\n\n        func = env['func']\n        try:\n            with redirect_stdout(stdout):\n                ret = await func()\n        except Exception as e:\n            value = stdout.getvalue()\n            await ctx.send(f'```py\\n{value}{traceback.format_exc()}\\n```')\n        else:\n            value = stdout.getvalue()\n            try:\n                await ctx.message.add_reaction('\\u2705')\n            except Exception as e:\n                pass\n\n            if ret is None:\n                if value:\n                    await ctx.send(f'```py\\n{value}\\n```')\n            else:\n                self._last_result = ret\n                await ctx.send(f'```py\\n{value}{ret}\\n```')\n\n\ndef setup(bot):\n    bot.add_cog(OwnerCog(bot))","sub_path":"cogs/owner.py","file_name":"owner.py","file_ext":"py","file_size_in_byte":4121,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"401712800","text":"import grp\nimport pwd\nfrom functools import lru_cache\n\n\n@lru_cache(maxsize=None)\ndef uid2user(uid, default=None):\n    try:\n        return pwd.getpwuid(uid).pw_name\n    except KeyError:\n        return default\n\n\n@lru_cache(maxsize=None)\ndef user2uid(user, default=None):\n    try:\n        return user and pwd.getpwnam(user).pw_uid\n    except KeyError:\n        return default\n\n\n@lru_cache(maxsize=None)\ndef gid2group(gid, default=None):\n    try:\n        return grp.getgrgid(gid).gr_name\n    except KeyError:\n        return default\n\n\n@lru_cache(maxsize=None)\ndef group2gid(group, default=None):\n    try:\n        return group and grp.getgrnam(group).gr_gid\n    except KeyError:\n        return default\n\n\ndef posix_acl_use_stored_uid_gid(acl):\n    \"\"\"Replace the user/group field with the stored uid/gid\n    \"\"\"\n    from .parseformat import safe_decode, safe_encode\n    entries = []\n    for entry in safe_decode(acl).split('\\n'):\n        if entry:\n            fields = entry.split(':')\n            if len(fields) == 4:\n                entries.append(':'.join([fields[0], fields[3], fields[2]]))\n            else:\n                entries.append(entry)\n    return safe_encode('\\n'.join(entries))\n","sub_path":"src/borg/helpers/usergroup.py","file_name":"usergroup.py","file_ext":"py","file_size_in_byte":1186,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"109049889","text":"import requests\nfrom concurrent.futures import ThreadPoolExecutor\nfrom tqdm import tqdm\n\n# example of api with page param => https://api.api.com?page=10\n\nurl = \"https://api.api.com\"\n\ndef make_api_call(page):\n    try:\n        response = requests.get(f'{url}?page={page}')\n        return response.json()['data']\n    except:\n        return []\n    \n# if we have 100 api calls we need to do,\n# we make a list of [1,2,...100]. each item\n# in the list will get passed to `make_api_call()`\ntotal_pages = 100\npages = list(range(1, total_pages+1))\n\n\nwith ThreadPoolExecutor(max_workers=20) as ex:\n    # use the \"executor\" and map the list of parameters (`pages`)\n    # to the function `make_api_call()`\n    results = ex.map(make_api_call, pages)\n    \n    # same as above but with a progress bar\n    # results = list(tqdm(ex.map(download, urls), total=len(urls)))\n\n# then do something with `results`","sub_path":"programming/other/multithreaded_api_calls.py","file_name":"multithreaded_api_calls.py","file_ext":"py","file_size_in_byte":888,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"110248926","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[184]:\n\n\nimport os\nimport os.path as path\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport nibabel as nib\nimport pandas as pd\nimport glob\n\n\n# In[185]:\n\n\ndef print_progressbar(total, i):\n    \"\"\"\n    total : total iteration number.\n    i : iteration count, starting from 0.\n    \"\"\"\n    import math\n    step = 25 / total\n\n    # Print the progress bar\n    print('\\r' + f'Progress: '\n        f\"[{'=' * int((i+1) * step) + ' ' * (25 - int((i+1) * step))}]\"\n        f\"({math.floor((i+1) * 100 / (total))} %) ({i+1}/{total})\",\n        end='')\n    if (i+1) == total: print(\"\")\n\n\n# ## image path\n\n# In[186]:\n\n\nIMAGE_PATH = 'segment_image_file'\nfile_paths = glob.glob(path.join(IMAGE_PATH,'*nii.gz'))\n\n\n# In[187]:\n\n\nfile_paths\n\n\n# ## image  load\n\n# In[188]:\n\n\nseg_images = [nib.load(path) for path in file_paths]\n\n\n# In[189]:\n\n\n## image shape example X x Y x Z\n\nseg_images[0].shape\n\n\n# ## volume calculate\n\n# In[190]:\n\n\ndf = pd.DataFrame(columns=['img_name','voxel_count', 'Volume_mm^3', 'Volume_cc'])\n\nfor i in range(0,len(seg_images)):\n    \n    img = file_paths[i].split(\"/\")[1]\n    \n    img_header = seg_images[i].header\n    img_header_zooms = img_header.get_zooms()\n    \n    np_images = seg_images[i].get_data()\n    np_images.shape  ## 512(x) x 512(y) x 50(z)images\n    \n    ## count number of voxel\n\n    num_vox = np.sum(np_images > 0)\n    \n\n    ## 3D image has 3 dimension, so distinguish 2D from the calculation.\n\n    if len(img_header_zooms)==3 :\n        volume_mm = img_header_zooms[0]*img_header_zooms[1]*img_header_zooms[2]*num_vox\n    else :\n        volume_mm = img_header_zooms[0]*img_header_zooms[1]**num_vox\n    \n    ## calculate cc\n\n    volume_cc = volume_mm*0.001\n\n\n    ## save to dataframe\n\n    df = df.append({\n        'img_name' : img,\n        'voxel_count' : num_vox,\n        'Volume_mm^3' : volume_mm,\n        'Volume_cc' : volume_cc\n    }, ignore_index=True)\n    print_progressbar(len(seg_images), i)\n\n\n# ## dataframe_result\n\n# In[191]:\n\n\nos.mkdir(\"result\")\n\ndf.to_csv(\"./result/result.csv\")\n\n\n# In[ ]:\n\n\n\n\n","sub_path":"volume_calculator.py","file_name":"volume_calculator.py","file_ext":"py","file_size_in_byte":2071,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"50863975","text":"# Keypirinha: a fast launcher for Windows (keypirinha.com)\r\n\r\nimport keypirinha_api\r\nimport keypirinha as kp\r\nimport keypirinha_util as kpu\r\nimport keypirinha_wintypes as kpwt\r\nimport globex\r\nimport filefilter\r\n\r\nfrom collections import namedtuple, OrderedDict\r\nimport ctypes as ct\r\nimport natsort\r\nimport os\r\nimport re\r\nimport string\r\nimport time\r\nimport traceback\r\nimport winreg\r\n\r\nTEMPLATE_TAG_SEP = (\"{\", \"}\")\r\nTEMPLATE_TAG_REGEX = re.compile(\r\n    r\"(?:\" +\r\n        # {{tag}} form\r\n        r\"(?P<escaped_literal_tag>\" +\r\n            re.escape(TEMPLATE_TAG_SEP[0]) +\r\n            r\"(?P<literal_tag>\" +\r\n                re.escape(TEMPLATE_TAG_SEP[0]) +\r\n                r\"(?P<literal_tag_name>[a-z0-9_]+)\" +\r\n                re.escape(TEMPLATE_TAG_SEP[1]) +\r\n            r\")\" +\r\n            re.escape(TEMPLATE_TAG_SEP[1]) +\r\n        r\")\" +\r\n        r\"|\" +\r\n        # {tag} form\r\n        r\"(?P<tag>\" +\r\n            re.escape(TEMPLATE_TAG_SEP[0]) +\r\n            r\"(?P<tag_name>[a-z0-9_]+)\" +\r\n            re.escape(TEMPLATE_TAG_SEP[1]) +\r\n        r\")\" +\r\n    r\")\")\r\n\r\nScanProfile = namedtuple(\"ScanProfile\", (\r\n    \"label\", \"paths\", \"max_depth\",\r\n    \"include_hidden\", \"include_dirs\", \"include_files\",\r\n    \"filters\", \"filters_default\",\r\n    \"trim_extensions\",\r\n    \"file_item_label\", \"file_item_desc\",\r\n    \"dir_item_label\", \"dir_item_desc\",\r\n    \"callback\", \"open_with\"))\r\n\r\nclass LazyItemLabelFormatter:\r\n    def __init__(self, entry, profile, plugin):\r\n        self._entry = entry\r\n        self._profile = profile\r\n        self._plugin = plugin\r\n\r\n    @classmethod\r\n    def list_invalid_tags(cls, template):\r\n        invalid_tags = []\r\n        for rem in TEMPLATE_TAG_REGEX.finditer(template):\r\n            pos = rem.start()\r\n            if rem.group(\"escaped_literal_tag\"):\r\n                continue # skip {{name}} form\r\n            if not cls.has_tag(rem.group(\"tag_name\")):\r\n                invalid_tags.append(rem.group(\"tag\"))\r\n        return invalid_tags\r\n\r\n    def format(self, template, fallback=\"\"):\r\n        label = \"\"\r\n        if template:\r\n            copy_start = 0\r\n            for rem in TEMPLATE_TAG_REGEX.finditer(template):\r\n                idx = rem.start()\r\n\r\n                # get tag's value\r\n                if rem.group(\"escaped_literal_tag\"):\r\n                    # insert {{name}} form as {name}\r\n                    value = rem.group(\"literal_tag\")\r\n                else:\r\n                    attr = rem.group(\"tag_name\")\r\n                    try:\r\n                        value = self.get_tag_value(attr)\r\n                    except AttributeError:\r\n                        self._plugin.warn(\r\n                            \"Invalid tag {}{}{} found in profile {}.\".format(\r\n                            TEMPLATE_TAG_SEP[0], rem.group(\"tag\"),\r\n                            TEMPLATE_TAG_SEP[1],\r\n                            self._profile.label))\r\n                        continue\r\n\r\n                # copy the preceding content before inserting tag's value\r\n                if copy_start < idx:\r\n                    label += template[copy_start:idx]\r\n                copy_start = rem.end()\r\n\r\n                label += value\r\n\r\n            if copy_start < len(template):\r\n                label += template[copy_start:]\r\n            label = label.strip()\r\n\r\n        return label if label else fallback\r\n\r\n    @classmethod\r\n    def has_tag(cls, tag_name):\r\n        return hasattr(cls, \"_make_\" + tag_name)\r\n\r\n    def get_tag_value(self, tag_name):\r\n        if not self.has_tag(tag_name):\r\n            raise AttributeError(tag_name)\r\n\r\n        # try to get the cached version first\r\n        try:\r\n            return getattr(self, tag_name)\r\n        except AttributeError:\r\n            pass\r\n\r\n        # otherwise, lazy-init then cache it\r\n        try:\r\n            meth = getattr(self, \"_make_\" + tag_name)\r\n        except AttributeError:\r\n            raise AttributeError(tag_name)\r\n        value = meth()\r\n        setattr(self, tag_name, value)\r\n\r\n        return value\r\n\r\n    def _make_package(self):\r\n        return self._plugin.package_full_name()\r\n\r\n    def _make_profile(self):\r\n        return self._profile.label\r\n\r\n    def _make_name(self):\r\n        return self._entry.name\r\n\r\n    def _make_clean_name(self):\r\n        # note: trim_extensions is already normalized\r\n        norm_name = os.path.normcase(self._entry.name)\r\n        for ext in self._profile.trim_extensions:\r\n            if norm_name.endswith(ext):\r\n                return self._entry.name[0:-len(ext)]\r\n        return self._entry.name\r\n\r\n    def _make_title(self):\r\n        title, ext = os.path.splitext(self._entry.name)\r\n        while ext:\r\n            title, ext = os.path.splitext(title)\r\n        return title\r\n\r\n    def _make_titlex(self):\r\n        return os.path.splitext(self._entry.name)[0]\r\n\r\n    def _make_ext(self):\r\n        return os.path.splitext(self._entry.name)[1]\r\n\r\n    def _make_exts(self):\r\n        exts = \"\"\r\n        title, ext = os.path.splitext(self._entry.name)\r\n        while 1:\r\n            if not ext:\r\n                break\r\n            exts += ext\r\n            title, ext = os.path.splitext(title)\r\n        return exts\r\n\r\n    def _make_drive(self):\r\n        drive = os.path.splitdrive(self._entry.path)[0]\r\n        if drive:\r\n            drive += os.sep # splitdrive() returns \"C:\", we want \"C:\\\"\r\n        return drive\r\n\r\n    def _make_dir(self):\r\n        return os.path.dirname(self._entry.path)\r\n\r\n    def _make_dir1(self):\r\n        dirname = os.path.dirname(self._entry.path)\r\n        return os.path.basename(dirname)\r\n\r\n    def _make_dir2(self):\r\n        dirname = os.path.dirname(self._entry.path)\r\n        dirname = os.path.dirname(dirname)\r\n        return os.path.basename(dirname)\r\n\r\n    def _make_dir3(self):\r\n        dirname = os.path.dirname(self._entry.path)\r\n        dirname = os.path.dirname(dirname)\r\n        dirname = os.path.dirname(dirname)\r\n        return os.path.basename(dirname)\r\n\r\n    def _make_2dirs(self):\r\n        return os.path.join(self._make_dir2(), self._make_dir1())\r\n\r\n    def _make_3dirs(self):\r\n        return os.path.join(self._make_dir3(), self._make_dir2(),\r\n                            self._make_dir1())\r\n\r\ndef default_scan_callback(entry, profile, plugin):\r\n    if not profile.include_hidden and entry.is_hidden():\r\n        return None\r\n    if not profile.include_dirs and entry.is_dir():\r\n        return None\r\n    if not profile.include_files and not entry.is_dir():\r\n        return None\r\n\r\n    include = profile.filters_default\r\n    for filter in profile.filters:\r\n        if filter.match(entry):\r\n            include = filter.inclusive\r\n            break\r\n    if not include:\r\n        return None\r\n\r\n    if entry.is_dir():\r\n        item_label_tmpl = profile.dir_item_label\r\n        item_desc_tmpl = profile.dir_item_desc\r\n    else:\r\n        item_label_tmpl = profile.file_item_label\r\n        item_desc_tmpl = profile.file_item_desc\r\n\r\n    if profile.open_with:\r\n        open_with = profile.open_with.replace('{}', entry.path)\r\n    else:\r\n        open_with = None\r\n\r\n    formatter = LazyItemLabelFormatter(entry, profile, plugin)\r\n    item_label = formatter.format(item_label_tmpl, fallback=entry.name)\r\n    item_desc = formatter.format(item_desc_tmpl, fallback=\"\")\r\n\r\n    return plugin.create_item(\r\n        category=kp.ItemCategory.FILE,\r\n        label=item_label,\r\n        short_desc=item_desc, # path is displayed on GUI if desc is empty\r\n        target=entry.path,\r\n        args_hint=kp.ItemArgsHint.ACCEPTED,\r\n        hit_hint=kp.ItemHitHint.KEEPALL,\r\n        data_bag=open_with)\r\n\r\ntry:\r\n    from FilesCatalog import filescatalog_user_callbacks\r\n\r\n    # monkey-patch user's module so it easily gets access to some tools\r\n    filescatalog_user_callbacks._filefilter = filefilter\r\n    filescatalog_user_callbacks._globex = globex\r\n    filescatalog_user_callbacks._TEMPLATE_TAG_SEP = TEMPLATE_TAG_SEP\r\n    filescatalog_user_callbacks._TEMPLATE_TAG_REGEX = TEMPLATE_TAG_REGEX\r\n    filescatalog_user_callbacks._LazyItemLabelFormatter = LazyItemLabelFormatter\r\n    filescatalog_user_callbacks._default_scan_callback = default_scan_callback\r\n\r\n    # notify user's module that we are done\r\n    try:\r\n        func = getattr(filescatalog_user_callbacks, \"on_imported\")\r\n        try:\r\n            func()\r\n        except:\r\n            traceback.print_exc()\r\n    except AttributeError:\r\n        pass\r\nexcept ImportError:\r\n    traceback.print_exc()\r\n    filescatalog_user_callbacks = None\r\n\r\nclass FilesCatalog(kp.Plugin):\r\n    \"\"\"A plugin to catalog items from the file system\"\"\"\r\n\r\n    CONFIG_SECTION_MAIN = \"main\"\r\n    CONFIG_SECTION_PATH_NAV = \"path_navigation\"\r\n    CONFIG_SECTION_HOME = \"path_navigation/home\"\r\n    CONFIG_SECTION_BROWSING = \"browsing\"\r\n    CONFIG_SECTION_PROFILE = \"profile/\"\r\n\r\n    MAX_PROFILE_INHERITANCE_DEPTH = 5\r\n\r\n    DEFAULT_CATALOG_LIMIT = 100_000\r\n    DEFAULT_CONFIG_DEBUG = False\r\n    DEFAULT_PATH_NAV_ENABLED = True\r\n    DEFAULT_SHOW_RECENTS = True\r\n    DEFAULT_SHOW_NETWORK_FILES = False\r\n    DEFAULT_SHOW_DIRS_FIRST = True\r\n    DEFAULT_SHOW_HIDDEN_FILES = False\r\n    DEFAULT_SHOW_SYSTEM_FILES = False\r\n    DEFAULT_FOLLOW_SHELL_LINKS = True\r\n    DEFAULT_HOME_TRIGGER = \"~\"\r\n    DEFAULT_ITEM_LABEL = \"{clean_name}\"\r\n\r\n    catalog_limit = DEFAULT_CATALOG_LIMIT\r\n    config_debug = DEFAULT_CONFIG_DEBUG\r\n    path_nav_enabled = DEFAULT_PATH_NAV_ENABLED\r\n    show_recents = DEFAULT_SHOW_RECENTS\r\n    show_network_files = DEFAULT_SHOW_NETWORK_FILES\r\n    show_dirs_first = DEFAULT_SHOW_DIRS_FIRST\r\n    show_hidden_files = DEFAULT_SHOW_HIDDEN_FILES\r\n    show_system_files = DEFAULT_SHOW_SYSTEM_FILES\r\n    follow_shell_links = DEFAULT_FOLLOW_SHELL_LINKS\r\n    home_trigger = DEFAULT_HOME_TRIGGER\r\n    home = []\r\n    profiles = OrderedDict()\r\n\r\n    def __init__(self):\r\n        super().__init__()\r\n\r\n    def on_start(self):\r\n        self._read_config()\r\n\r\n    def on_catalog(self):\r\n        start = time.perf_counter()\r\n        catalog = []\r\n        scanned_profiles = OrderedDict()\r\n\r\n        if self.profiles:\r\n            self.info(\"Cataloging {} profile{}...\".format(\r\n                      len(self.profiles), \"s\"[len(self.profiles)==1:]))\r\n\r\n        for profile_name, profile in self.profiles.items():\r\n            items_count = 0\r\n            sub_start = time.perf_counter()\r\n\r\n            for pattern in profile.paths:\r\n                # we call splitdrive() because a UNC may have a '?' char in it\r\n                # like in \\\\?\\C:\\dir\\file\r\n                has_magic = globex.has_magic(os.path.splitdrive(pattern)[1])\r\n\r\n                # if this is not a pattern, warn the user in case path is not\r\n                # found/readable\r\n                if not has_magic:\r\n                    is_dir = os.path.isdir(pattern)\r\n                    if not is_dir and not os.path.exists(pattern):\r\n                        self.warn(\"Path not found in profile {}: {}\".format(\r\n                                  profile.label, pattern))\r\n                        continue\r\n\r\n                    # If path points to a directory, assume only its direct\r\n                    # content must be scanned.  Also, globex.iglobex() would\r\n                    # return *pattern* as-is if no wildcard were appended.\r\n                    if is_dir:\r\n                        pattern = os.path.join(pattern, \"*\")\r\n\r\n                for entry in globex.iglobex(\r\n                        pattern,\r\n                        recursivity=profile.max_depth,\r\n                        include_hidden=profile.include_hidden):\r\n                    item = profile.callback(entry, profile, self)\r\n                    if item and isinstance(item, keypirinha_api.CatalogItem):\r\n                        catalog.append(item)\r\n                        items_count += 1\r\n                        if len(catalog) >= self.catalog_limit:\r\n                            self.warn((\r\n                                'Stopping scan of profile \"{}\" due to ' +\r\n                                'catalog_limit reached ({} items).').format(\r\n                                profile.label, self.catalog_limit))\r\n                            break\r\n\r\n            scanned_profiles[profile.label] = (items_count,\r\n                                               time.perf_counter() - sub_start)\r\n\r\n        if self.profiles:\r\n            for label, (count, elapsed) in scanned_profiles.items():\r\n                self.info(\r\n                    \"Profile {}: found {} item{} in {:.1f} seconds\".format(\r\n                    label, count, \"s\"[count==1:], elapsed))\r\n\r\n        sub_start = time.perf_counter()\r\n        self.set_catalog(catalog)\r\n\r\n        if self.profiles:\r\n            if catalog:\r\n                self.info(\"Cataloged {} item{} in {:.1f} seconds\".format(\r\n                          len(catalog), \"s\"[len(catalog)==1:],\r\n                          time.perf_counter() - sub_start))\r\n            self.info(\r\n                \"Total: {} item{} found and cataloged in {:.1f} seconds\".format(\r\n                len(catalog), \"s\"[len(catalog)==1:],\r\n                time.perf_counter() - start))\r\n\r\n    def on_suggest(self, user_input, items_chain):\r\n        orig_user_input = user_input\r\n        if len(user_input) > 0:\r\n            user_input = self._safe_normpath(user_input)\r\n\r\n        # initial search, home dir(s)\r\n        if (self.path_nav_enabled and\r\n                not items_chain and\r\n                len(self.home_trigger) > 0 and\r\n                orig_user_input.lower().startswith(self.home_trigger.lower())):\r\n            suggestions, match_method, sort_method = self._home_suggestions(\r\n                orig_user_input[len(self.home_trigger):])\r\n            self.set_suggestions(suggestions, match_method, sort_method)\r\n\r\n        # initial search, \\ or \\\\\r\n        elif (self.path_nav_enabled and\r\n                not items_chain and\r\n                ((user_input == os.sep) or\r\n                (user_input == os.sep * 2))):\r\n            suggestions = self._drives_suggestions()\r\n            match_method = kp.Match.ANY\r\n            sort_method = kp.Sort.LABEL_ASC\r\n            if self.show_recents:\r\n                self._insert_recents(suggestions, match_method, sort_method)\r\n            self.set_suggestions(suggestions, match_method, sort_method)\r\n\r\n        # initial search, user_input format is like \"X:\"\r\n        elif (self.path_nav_enabled and\r\n                not items_chain and (\r\n                len(user_input) == 2 and\r\n                user_input[1] == \":\" and\r\n                user_input[0].upper() in string.ascii_uppercase)):\r\n            suggestions, match_method, sort_method = self._browse_dir(user_input + os.sep)\r\n            if self.show_recents:\r\n                self._insert_recents(suggestions, match_method, sort_method, user_input)\r\n            self.set_suggestions(suggestions, match_method, sort_method)\r\n\r\n        # initial search, user_input is an absolute path, or a UNC\r\n        # reminder:\r\n        #   os.path.isabs(\"\\\\\\\\server\") == True\r\n        #   os.path.ismount(\"\\\\\\\\server\") == False\r\n        #\r\n        #   os.path.isabs(\"\\\\\\\\server\\\\\") == False\r\n        #   os.path.ismount(\"\\\\\\\\server\\\\\") == True  (1)\r\n        #\r\n        #   os.path.isabs(\"\\\\\\\\server\\\\share\") == False\r\n        #   os.path.ismount(\"\\\\\\\\server\\\\share\") == True\r\n        #\r\n        #   os.path.isabs(\"\\\\\\\\server\\\\share\\\\file\") == True\r\n        #   os.path.ismount(\"\\\\\\\\server\\\\share\\\\file\") == False\r\n        #\r\n        #   os.path.isabs(\"::\") == False (OK)\r\n        #   os.path.ismount(\"::\") == True (WRONG)\r\n        elif (self.path_nav_enabled and\r\n                not items_chain and\r\n                not user_input.startswith(\"::\") and (\r\n                os.path.isabs(os.path.expandvars(user_input)) or\r\n                os.path.ismount(os.path.expandvars(user_input).rstrip(os.sep)))): # rstrip() to avoid (1)\r\n            user_input = os.path.expandvars(user_input)\r\n            if user_input.endswith(os.sep):\r\n                # path is expected to be a directory\r\n                suggestions, match_method, sort_method = self._browse_dir(user_input)\r\n                if self.show_recents:\r\n                    self._insert_recents(suggestions, match_method, sort_method, user_input)\r\n                self.set_suggestions(suggestions, match_method, sort_method)\r\n            elif os.path.isdir(user_input):\r\n                # path is a directory\r\n                suggestions, match_method, sort_method = self._browse_dir(user_input, check_base_dir=False)\r\n                if self.show_recents:\r\n                    self._insert_recents(suggestions, match_method, sort_method, user_input)\r\n                self.set_suggestions(suggestions, match_method, sort_method)\r\n            elif os.path.exists(user_input):\r\n                # user_input is an item of the filesystem\r\n                self.set_suggestions(\r\n                    [self.create_item(\r\n                        category=kp.ItemCategory.FILE,\r\n                        label=os.path.basename(user_input),\r\n                        short_desc=\"\",\r\n                        target=user_input,\r\n                        args_hint=kp.ItemArgsHint.ACCEPTED,\r\n                        hit_hint=kp.ItemHitHint.KEEPALL,\r\n                        loop_on_suggest=True)],\r\n                    kp.Match.ANY,\r\n                    kp.Sort.NONE)\r\n            else:\r\n                # here, user_input is:\r\n                # * the beginning of a UNC path\r\n                # * expected to be a directory suffixed by search terms\r\n                base_dir, search_terms = os.path.split(user_input)\r\n                is_incomplete_unc = not base_dir.strip(os.sep)\r\n\r\n                if is_incomplete_unc:\r\n                    suggestions = []\r\n                    match_method = kp.Match.ANY\r\n                    sort_method = kp.Sort.NONE\r\n                else:\r\n                    suggestions, match_method, sort_method = self._browse_dir(\r\n                        base_dir, search_terms=search_terms, store_score=True)\r\n                    if len(search_terms) > 0:\r\n                        # Because of the self.home_trigger prefix, the\r\n                        # user_input cannot be matched against files names.\r\n                        # So we have to sort the suggestions by ourselves.\r\n                        self._sort_matched_suggestions(suggestions)\r\n                        match_method = kp.Match.ANY\r\n                        sort_method = kp.Sort.NONE\r\n\r\n                if self.show_recents:\r\n                    self._insert_recents(suggestions, match_method, sort_method,\r\n                                         user_input)\r\n\r\n                self.set_suggestions(suggestions, match_method, sort_method)\r\n\r\n        # current item is a FILE\r\n        elif items_chain and items_chain[-1].category() == kp.ItemCategory.FILE:\r\n            current_item = items_chain[-1]\r\n\r\n            # check file's attributes\r\n            if os.path.isdir(current_item.target()):\r\n                dir_target = current_item.target()\r\n                exists = True\r\n            elif os.path.exists(current_item.target()):\r\n                dir_target = None\r\n                exists = True\r\n                # check if file is a link pointing to a directory, in which case\r\n                # we want to browse it\r\n                if self.follow_shell_links and os.path.splitext(\r\n                        current_item.target())[1].lower() == \".lnk\":\r\n                    try:\r\n                        link_props = kpu.read_link(current_item.target())\r\n                        if os.path.isdir(link_props['target']):\r\n                            dir_target = link_props['target']\r\n                    except:\r\n                        pass\r\n            else:\r\n                dir_target = None\r\n                exists = False\r\n\r\n            if dir_target is not None:\r\n                suggestions, match_method, sort_method = self._browse_dir(\r\n                                    dir_target, check_base_dir=False,\r\n                                    search_terms=os.path.expandvars(user_input))\r\n                self.set_suggestions(suggestions, match_method, sort_method)\r\n            elif exists:\r\n                clone = current_item.clone()\r\n                clone.set_args(orig_user_input)\r\n                clone.set_loop_on_suggest(False)\r\n                self.set_suggestions([clone], kp.Match.ANY, kp.Sort.NONE)\r\n            else:\r\n                self.set_suggestions([self.create_error_item(\r\n                    label=orig_user_input,\r\n                    short_desc=\"File/Dir not found: \" + current_item.target())])\r\n\r\n    def on_execute(self, item, action):\r\n        if item.data_bag():\r\n            # open with a custom command\r\n            parts = re.findall(r'(?=\\s|\\A)\\s*([^\" ]+|\"[^\"]*\")(?=\\s|\\Z)', item.data_bag(), re.U)\r\n            if len(parts) > 0:\r\n                command, *params = parts\r\n                kpu.shell_execute(command, params)\r\n            else:\r\n                self.info(\"Cannot open item with: '%s', parsed as %s\" % (item.data_bag(), parts))\r\n        else:\r\n            kpu.execute_default_action(self, item, action)\r\n\r\n    def on_events(self, flags):\r\n        if flags & kp.Events.PACKCONFIG:\r\n            if self._read_config():\r\n                self.on_catalog()\r\n\r\n    def _read_config(self):\r\n        config_changed = False\r\n        settings = self.load_settings()\r\n\r\n        # main\r\n        self.config_debug = settings.get_bool(\r\n            \"debug\", section=self.CONFIG_SECTION_MAIN,\r\n            fallback=self.DEFAULT_CONFIG_DEBUG)\r\n        catalog_limit = settings.get_int(\r\n            \"catalog_limit\", section=self.CONFIG_SECTION_MAIN,\r\n            fallback=self.DEFAULT_CATALOG_LIMIT, min=5_000, max=300_000)\r\n        if catalog_limit != self.catalog_limit:\r\n            self.catalog_limit = catalog_limit\r\n            config_changed = True\r\n\r\n        # path_navigation\r\n        self.path_nav_enabled = settings.get_bool(\r\n            \"enabled\", section=self.CONFIG_SECTION_PATH_NAV,\r\n            fallback=self.DEFAULT_PATH_NAV_ENABLED)\r\n        self.show_recents = settings.get_bool(\r\n            \"show_recents\", section=self.CONFIG_SECTION_PATH_NAV,\r\n            fallback=self.DEFAULT_SHOW_RECENTS)\r\n        self.show_network_files = settings.get_bool(\r\n            \"show_network_files\", section=self.CONFIG_SECTION_PATH_NAV,\r\n            fallback=self.DEFAULT_SHOW_NETWORK_FILES)\r\n\r\n        # path_navigation/home\r\n        self.home_trigger = settings.get_stripped(\r\n            \"trigger\", section=self.CONFIG_SECTION_HOME,\r\n            fallback=self.DEFAULT_HOME_TRIGGER)\r\n        self.home = []\r\n        if len(self.home_trigger) > 0:\r\n            home_value_lines = settings.get_multiline(\r\n                \"home\", section=self.CONFIG_SECTION_HOME,\r\n                fallback=[])\r\n\r\n            # apply default \"home\" value if needed\r\n            if not home_value_lines:\r\n                try:\r\n                    home_value_lines = [\r\n                        kpwt.get_known_folder_path(kpwt.FOLDERID.Profile)]\r\n                except OSError as exc:\r\n                    self.warn(str(exc))\r\n                    home_value_lines = []\r\n\r\n            for idx in range(len(home_value_lines)):\r\n                home_dir = home_value_lines[idx].replace(\"/\", os.sep)\r\n\r\n                # If home_dir is prefixed by a \"::{guid}\" sequence\r\n                if home_dir.startswith('::') and len(home_dir) >= 38:\r\n                    (guid, tail) = (home_dir[2:].split(os.sep, maxsplit=1) + [None] * 2)[:2]\r\n                    try:\r\n                        kf_path = kpu.shell_known_folder_path(guid)\r\n                        if tail is not None:\r\n                            self.home.append(os.path.join(kf_path, tail))\r\n                        else:\r\n                            self.home.append(kf_path)\r\n                    except OSError:\r\n                        self.warn(\"Failed to get path of known folder from setting \\\"{}\\\"\".format(home_dir))\r\n                        continue\r\n\r\n                # Otherwise, home_dir is assumed to be a valid path to a\r\n                # directory. In order to be as flexible as possible, we must\r\n                # not assume it already exists.\r\n                else:\r\n                    self.home.append(self._safe_normpath(home_dir))\r\n\r\n        # browsing\r\n        self.show_dirs_first = settings.get_bool(\r\n            \"show_dirs_first\", section=self.CONFIG_SECTION_BROWSING,\r\n            fallback=self.DEFAULT_SHOW_DIRS_FIRST)\r\n        self.show_hidden_files = settings.get_bool(\r\n            \"show_hidden_files\", section=self.CONFIG_SECTION_BROWSING,\r\n            fallback=self.DEFAULT_SHOW_HIDDEN_FILES)\r\n        self.show_system_files = settings.get_bool(\r\n            \"show_system_files\", section=self.CONFIG_SECTION_BROWSING,\r\n            fallback=self.DEFAULT_SHOW_SYSTEM_FILES)\r\n        self.follow_shell_links = settings.get_bool(\r\n            \"follow_shell_links\", section=self.CONFIG_SECTION_BROWSING,\r\n            fallback=self.DEFAULT_FOLLOW_SHELL_LINKS)\r\n\r\n        # profiles\r\n        profiles_map = OrderedDict() # profile name -> profile label\r\n        profiles_def = OrderedDict() # profile name -> settings dict\r\n\r\n        old_profiles = self.profiles\r\n        self.profiles = OrderedDict()\r\n\r\n        # read profiles names and validate them\r\n        # note: ini section names in Keypirinha are case-sensitive, we want\r\n        # profile names to be case-insensitive\r\n        for section_name in settings.sections():\r\n            if not section_name.lower().startswith(self.CONFIG_SECTION_PROFILE):\r\n                continue\r\n\r\n            profile_label = section_name[len(self.CONFIG_SECTION_PROFILE):]\r\n            profile_label = profile_label.strip()\r\n            profile_name = profile_label.lower()\r\n\r\n            if not profile_name:\r\n                self.warn('Ignoring empty profile name (section \"{}\").'.format(\r\n                    section_name))\r\n                continue\r\n\r\n            forbidden_chars = \":;,/|\\\\\"\r\n            if any(c in forbidden_chars for c in profile_label):\r\n                self.warn((\r\n                    'Forbidden character(s) found in profile name \"{}\". ' +\r\n                    'Forbidden characters list \"{}\"').format(\r\n                    profile_label, forbidden_chars))\r\n                continue\r\n\r\n            if profile_name in profiles_map:\r\n                self.warn('Ignoring \"{}\" defined twice.'.format(\r\n                    section_name))\r\n                continue\r\n\r\n            profiles_map[profile_name] = section_name\r\n            profiles_def[profile_name] = {}\r\n\r\n            profiles_def[profile_name]['label'] = profile_label\r\n\r\n            profiles_def[profile_name]['inherit'] = settings.get_stripped(\r\n                \"inherit\", section=section_name, fallback=\"\").strip().lower()\r\n            if not profiles_def[profile_name]['inherit']:\r\n                profiles_def[profile_name]['inherit'] = None\r\n\r\n        # read and check profiles inheritance\r\n        for profile_name, section_name in profiles_map.items():\r\n            inheritance = [profile_name]\r\n            current_profile = profile_name\r\n            while 1:\r\n                if not profiles_def[current_profile]['inherit']:\r\n                    break\r\n\r\n                if profiles_def[current_profile]['inherit'] not in profiles_map:\r\n                    self.err((\r\n                        'Unknown profile to inherit from found in \"{}\". ' +\r\n                        'Please reconfigure.').format(\r\n                        section_name))\r\n                    return None\r\n\r\n                if profiles_def[current_profile]['inherit'] in inheritance:\r\n                    self.err((\r\n                        'Infinite inheritance loop detected in \"{}\". ' +\r\n                        'Please reconfigure.').format(\r\n                        section_name))\r\n                    return None\r\n\r\n                inheritance.append(profiles_def[current_profile]['inherit'])\r\n\r\n                if len(inheritance) > self.MAX_PROFILE_INHERITANCE_DEPTH:\r\n                    self.err((\r\n                        'Too deep inheritance depth detected in \"{}\". ' +\r\n                        'Please reconfigure.').format(\r\n                        section_name))\r\n                    return None\r\n\r\n                # go up in the inheritance lineage\r\n                current_profile = profiles_def[current_profile]['inherit']\r\n\r\n            profiles_def[profile_name]['inherit'] = tuple(inheritance[1:])\r\n\r\n        # read profiles settings\r\n        for profile_name, section_name in profiles_map.items():\r\n            profdef = profiles_def[profile_name]\r\n\r\n            # activated\r\n            profdef['enabled'] = self._read_profile_setting(\r\n                profiles_map, profiles_def, settings,\r\n                profile_name, \"get_bool\", \"activate\", fallback=False)\r\n\r\n            # paths\r\n            profdef['paths'] = self._read_profile_setting(\r\n                profiles_map, profiles_def, settings,\r\n                profile_name, \"get_multiline\", \"paths\", fallback=[],\r\n                keep_empty_lines=False)\r\n            if not profdef['paths'] and profdef['enabled']:\r\n                profdef['enabled'] = False\r\n                self.warn(\r\n                    'Deactivate \"{}\" because its paths value is empty'.format(\r\n                        section_name))\r\n            if profdef['paths']:\r\n                paths = profdef['paths']\r\n                profdef['paths'] = []\r\n\r\n                for p in paths:\r\n                    p = os.path.normpath(p)\r\n                    if not os.path.splitdrive(p)[0]:\r\n                        self.warn((\r\n                            'Skipping non-absolute path specified in \"{}\": ' +\r\n                            '{}').format(section_name, p))\r\n                    else:\r\n                        profdef['paths'].append(p)\r\n\r\n                profdef['paths'] = tuple(profdef['paths'])\r\n\r\n            # max_depth\r\n            profdef['max_depth'] = self._read_profile_setting(\r\n                profiles_map, profiles_def, settings,\r\n                profile_name, \"get_int\", \"max_depth\", fallback=None)\r\n            if profdef['max_depth'] is None:\r\n                profdef['max_depth'] = -1\r\n            elif profdef['max_depth'] < -1:\r\n                profdef['max_depth'] = -1\r\n\r\n            # include_hidden\r\n            profdef['include_hidden'] = self._read_profile_setting(\r\n                profiles_map, profiles_def, settings,\r\n                profile_name, \"get_bool\", \"include_hidden\", fallback=False)\r\n\r\n            # include_dirs\r\n            profdef['include_dirs'] = self._read_profile_setting(\r\n                profiles_map, profiles_def, settings,\r\n                profile_name, \"get_bool\", \"include_dirs\", fallback=False)\r\n\r\n            # include_files\r\n            profdef['include_files'] = self._read_profile_setting(\r\n                profiles_map, profiles_def, settings,\r\n                profile_name, \"get_bool\", \"include_files\", fallback=True)\r\n\r\n            # file_item_label, file_item_desc, dir_item_label and dir_item_desc\r\n            # CAUTION: order of the keys in the loop matters to apply defaults\r\n            for key in (\"file_item_label\", \"file_item_desc\",\r\n                        \"dir_item_label\", \"dir_item_desc\"):\r\n                profdef[key] = self._read_profile_setting(\r\n                    profiles_map, profiles_def, settings,\r\n                    profile_name, \"get_stripped\", key, fallback=None)\r\n\r\n                if profdef[key] and \"{\" not in profdef[key]:\r\n                    self.warn((\r\n                        '{} value of \"{}\" does not contain any placeholder. ' +\r\n                        'Falling back to default').format(key, section_name))\r\n                    profdef[key] = None\r\n\r\n                # apply defaults\r\n                if not profdef[key]:\r\n                    if key == \"file_item_label\":\r\n                        profdef[key] = \"{clean_name}\"\r\n\r\n                        # DEBUG\r\n                        #tags = (\r\n                        #    \"{package}\", \"{profile}\",\r\n                        #    \"{clean_name}\", \"{name}\", \"{title}\", \"{titlex}\",\r\n                        #    \"{ext}\", \"{exts}\",\r\n                        #    \"{drive}\", \"{dir}\", \"{dir1}\", \"{dir2}\", \"{dir3}\",\r\n                        #    \"{2dirs}\", \"{3dirs}\")\r\n                        #profdef[key] = \"{{ {} }} {{} \" # to test parser\r\n                        #profdef[key] += \" \".join(\r\n                        #    [\"{\" + t + \"}[\" + t + \"]\" for t in tags])\r\n                    elif key.startswith(\"dir_\"):\r\n                        from_key = \"file_\" + key[len(\"dir_\"):]\r\n                        profdef[key] = profdef[from_key]\r\n                    else:\r\n                        profdef[key] = None\r\n\r\n                if profdef[key]:\r\n                    invalid_tags = \\\r\n                        LazyItemLabelFormatter.list_invalid_tags(profdef[key])\r\n                    if invalid_tags:\r\n                        self.warn((\r\n                            \"Invalid tag(s) found in {}: {}. \" +\r\n                            \"Falling back to default\").format(\r\n                            section_name, \", \".join(invalid_tags)))\r\n                        profdef[key] = None\r\n\r\n            # filters\r\n            filters = self._read_profile_setting(\r\n                profiles_map, profiles_def, settings,\r\n                profile_name, \"get_multiline\", \"filters\", fallback=[],\r\n                keep_empty_lines=False)\r\n            profdef['filters'] = []\r\n            for expression in filters:\r\n                if not expression:\r\n                    continue\r\n                try:\r\n                    profdef['filters'].append(\r\n                        filefilter.create_filter(expression))\r\n                except ValueError as exc:\r\n                    self.warn((\r\n                        'Ignoring invalid filter \"{}\" from \"{}\". ' +\r\n                        'Error: {}').format(expression, section_name, str(exc)))\r\n                    continue\r\n\r\n            # filters - define the default filtering behavior\r\n            # We stick to the following rules for that:\r\n            # * if *filters* is empty, items are INCLUDED\r\n            # * if *filters* contains only negative filters, non-matching items\r\n            #   are INCLUDED\r\n            # * otherwise, default behavior is to EXCLUDE non-matching items\r\n            profdef['filters_default'] = True # if empty or has negative\r\n            if profdef['filters']:\r\n                for flt in profdef['filters']:\r\n                    if flt.inclusive:\r\n                        profdef['filters_default'] = False\r\n                        break\r\n\r\n            # filters - cleanup according to the rules stated above\r\n            # * this allows to optimize (speed) filtering\r\n            # * if filters list is not empty and contains mixed filters, we\r\n            #   can remove all the trailing negative filters if any\r\n            if not profdef['filters_default']: # if \"non-empty and mixed filters\"\r\n                count = 0\r\n                while profdef['filters'] and not profdef['filters'][-1].inclusive:\r\n                    profdef['filters'].pop()\r\n                    count += 1\r\n                if count:\r\n                    self.warn((\r\n                        'Ignoring trailing negative filter(s) from \"{}\". ' +\r\n                        'Read the inline documentation of the \"filters\" ' +\r\n                        'setting for more info').format(section_name))\r\n\r\n            profdef['filters'] = tuple(profdef['filters']) # memory usage\r\n\r\n            # trim_extensions\r\n            # note: we do not use PATHEXT as a default because user may need to\r\n            # differentiate between an exe file and a script with the same name\r\n            # for example.\r\n            trim_extensions = self._read_profile_setting(\r\n                profiles_map, profiles_def, settings,\r\n                profile_name, \"get_stripped\", \"trim_extensions\",\r\n                fallback=\".lnk\") # + os.environ.get(\"PATHEXT\", \"\"))\r\n            trim_extensions = trim_extensions.strip()\r\n            trim_extensions = os.path.normcase(trim_extensions)\r\n            if os.sep in trim_extensions:\r\n                profdef['trim_extensions'] = ()\r\n                self.warn(\r\n                    'Ignoring incorrect trim_extensions in \"{}\"'.format(\r\n                        section_name))\r\n            else:\r\n                profdef['trim_extensions'] = frozenset(\r\n                    filter(None, re.split(r'[\\s\\;]+', trim_extensions)))\r\n\r\n            # open_with\r\n            profdef['open_with'] = self._read_profile_setting(\r\n                profiles_map, profiles_def, settings,\r\n                profile_name, \"get_stripped\", \"open_with\",\r\n                fallback=\"\").strip()\r\n            if not profdef['open_with']:\r\n                profdef['open_with'] = None\r\n\r\n            # python_callback\r\n            profdef['callback'] = self._read_profile_setting(\r\n                profiles_map, profiles_def, settings,\r\n                profile_name, \"get_stripped\", \"python_callback\",\r\n                fallback=\"\").strip()\r\n            if not profdef['callback']:\r\n                profdef['callback'] = None\r\n            else:\r\n                try:\r\n                    profdef['callback'] = getattr(filescatalog_user_callbacks,\r\n                                                  profdef['callback'])\r\n                    if not callable(profdef['callback']):\r\n                        profdef['callback'] = None\r\n                        self.warn((\r\n                            'Ignoring incorrect python_callback in \"{}\". ' +\r\n                            'Not a callable.').format(section_name))\r\n                except AttributeError:\r\n                    profdef['callback'] = None\r\n                    self.warn(\r\n                        'Ignoring incorrect python_callback in \"{}\"'.format(\r\n                        section_name))\r\n            if not profdef['callback']:\r\n                profdef['callback'] = default_scan_callback\r\n\r\n        # create profile objects...\r\n        # ... now that we have roughly validated the settings\r\n        self.profiles = OrderedDict()\r\n        for profile_name, profile_def in profiles_def.items():\r\n            if profile_def['enabled']:\r\n                del profile_def['enabled']\r\n                del profile_def['inherit']\r\n                self.profiles[profile_name] = ScanProfile(**profile_def)\r\n\r\n        # print profiles settings\r\n        if self.config_debug and self.profiles:\r\n            self._print_profiles()\r\n\r\n        if not config_changed and self.profiles != old_profiles:\r\n            config_changed = True\r\n\r\n        return config_changed\r\n\r\n    def _read_profile_setting(self, profiles_map, profiles_def, settings,\r\n                              profile_name, meth, key, *, fallback=None,\r\n                              **kwargs):\r\n        meth = getattr(settings, meth)\r\n\r\n        if key in (\"activate\", \"inherit\"): # non-inheritable settings\r\n            profile_names = (profile_name, )\r\n        else:\r\n            profile_names = (profile_name, ) + \\\r\n                            profiles_def[profile_name]['inherit']\r\n\r\n        for sub_profile_name in profile_names:\r\n            sub_profile_section = profiles_map[sub_profile_name]\r\n            if settings.has(key, section=sub_profile_section):\r\n                return meth(key=key, section=sub_profile_section,\r\n                            fallback=fallback, **kwargs)\r\n\r\n        return fallback\r\n\r\n    def _print_profiles(self):\r\n        indent = 3\r\n        max_key_len = len(max((\"internal_name\", ) + ScanProfile._fields,\r\n                          key=len))\r\n\r\n        def _sprint_pair(name, value=None):\r\n            out = \" \" * indent\r\n\r\n            if name is not None:\r\n                out += name\r\n\r\n            if value is not None:\r\n                padding = max_key_len - (len(name) if name else 0)\r\n                if padding < 0:\r\n                    padding = 0\r\n                out += \" \" * (padding + 1)\r\n                if isinstance(value, str):\r\n                    out += value\r\n                else:\r\n                    out += str(value)\r\n\r\n            return out\r\n\r\n        log = \"Activated profiles ({}):\\n\".format(len(self.profiles))\r\n        for name, profile in self.profiles.items():\r\n            log += '{} Profile \"{}\":\\n'.format(\"*\" * (indent - 1),\r\n                                               profile.label)\r\n            log += _sprint_pair(\"internal_name\", '\"{}\"'.format(name)) + \"\\n\"\r\n\r\n            profile_dict = profile._asdict()\r\n            for key in (\"include_hidden\", \"include_dirs\", \"include_files\",\r\n                        \"max_depth\", \"trim_extensions\",\r\n                        \"file_item_label\", \"file_item_desc\",\r\n                        \"dir_item_label\", \"dir_item_desc\",\r\n                        \"callback\"):\r\n                value = profile_dict[key]\r\n                if value is None:\r\n                    value = \"<none>\"\r\n                elif isinstance(value, bool):\r\n                    value = \"no\" if not value else \"yes\"\r\n                elif isinstance(value, int):\r\n                    pass\r\n                elif isinstance(value, str):\r\n                    value = '\"{}\"'.format(value)\r\n                elif isinstance(value, (list, tuple, set, frozenset)):\r\n                    value = [repr(v) for v in value]\r\n                    value = \"(\" + \", \".join(value) + \")\"\r\n                elif callable(value):\r\n                    try:\r\n                        value = value.__name__\r\n                    except AttributeError:\r\n                        value = repr(value)\r\n                else:\r\n                    value = str(value)\r\n                log += _sprint_pair(key, value) + \"\\n\"\r\n\r\n            for key in (\"paths\", \"filters\"):\r\n                profile_attr = profile_dict[key]\r\n                if not profile_attr:\r\n                    log += _sprint_pair(key, \"<none>\") + \"\\n\"\r\n                else:\r\n                    log += _sprint_pair(key, profile_attr[0]) + \"\\n\"\r\n                    for idx in range(1, len(profile_attr)):\r\n                        log += _sprint_pair(None, profile_attr[idx]) + \"\\n\"\r\n\r\n            log +=  \"\\n\"\r\n\r\n        self.info(log)\r\n\r\n    def _safe_normpath(self, path):\r\n        # If the given path is not a complete UNC yet, os.path.normpath() will\r\n        # reduce the trailing \\\\ prefix to \\. Here, we want to preserve the\r\n        # meaning of the prefix specified by the user.\r\n        #\r\n        # Examples:\r\n        #   os.path.normpath(\"//server/share\") == \"\\\\\\\\server\\\\share\"\r\n        #   os.path.normpath(\"\\\\\\\\server\") == \"\\\\server\"\r\n        #   os.path.normpath(\"\\\\\\\\\\\\server\\\\share\") == \"\\\\server\\\\share\" (WRONG)\r\n        prefix_seps = 0\r\n        for idx in range(min(2, len(path))):\r\n            if path[idx] not in (os.sep, os.altsep):\r\n                break\r\n            prefix_seps += 1\r\n\r\n        path = os.path.normpath(path)\r\n        if prefix_seps > 1:\r\n            path = (os.sep * 2) + path.lstrip(os.sep)\r\n\r\n        return path\r\n\r\n    def _drives_suggestions(self):\r\n        suggestions = []\r\n        for drv_letter in kpwt.get_logical_drives():\r\n            drv_path = drv_letter + \":\" + os.sep\r\n            drv_type = kpwt.kernel32.GetDriveTypeW(drv_path)\r\n            if drv_type == kpwt.DRIVE_NO_ROOT_DIR:\r\n                continue\r\n            elif drv_type == kpwt.DRIVE_REMOTE:\r\n                if not self.show_network_files:\r\n                    continue\r\n            else:\r\n                # try to open the drive to see if it's ready\r\n                with kpwt.ScopedSysErrorMode():\r\n                    disk_bytes = kpwt.ULARGE_INTEGER(0)\r\n                    if kpwt.kernel32.GetDiskFreeSpaceExW(\r\n                            drv_path, None, ct.byref(disk_bytes), None):\r\n                        suggestions.append(self.create_item(\r\n                            category=kp.ItemCategory.FILE,\r\n                            label=drv_path,\r\n                            short_desc=\"\",\r\n                            target=drv_path,\r\n                            args_hint=kp.ItemArgsHint.ACCEPTED,\r\n                            hit_hint=kp.ItemHitHint.KEEPALL,\r\n                            loop_on_suggest=True))\r\n        return suggestions\r\n\r\n    def _home_suggestions(self, search_terms):\r\n        # suggest only existing home dirs\r\n        existing_home_dirs = []\r\n        for home_dir in self.home:\r\n            if os.path.isdir(home_dir):\r\n                existing_home_dirs.append(home_dir)\r\n\r\n        # If only one home dir remains, directly browse its content and filter\r\n        # the results using search_terms, if any.\r\n        if len(existing_home_dirs) == 1:\r\n            suggestions, match_method, sort_method = self._browse_dir(\r\n                                    existing_home_dirs[0], check_base_dir=False,\r\n                                    search_terms=search_terms, store_score=True)\r\n            if len(search_terms) > 0:\r\n                # Because of the self.home_trigger prefix, the user_input cannot\r\n                # be matched against files names. So we have to sort the\r\n                # suggestions by ourselves.\r\n                self._sort_matched_suggestions(suggestions)\r\n                return suggestions, kp.Match.ANY, kp.Sort.NONE\r\n            else:\r\n                return suggestions, match_method, sort_method\r\n\r\n        # Otherwise, we must first offer the list of available \"home\"\r\n        # directories. Here again, we filter them by using search_terms if\r\n        # needed.\r\n        else:\r\n            suggestions = []\r\n            for home_dir in existing_home_dirs:\r\n                match_score = None\r\n                if len(search_terms) > 0:\r\n                    match_score = kpu.fuzzy_score(search_terms,\r\n                                                  os.path.basename(home_dir))\r\n                    if not match_score:\r\n                        continue\r\n                    match_score = str(match_score)\r\n                suggestions.append(self.create_item(\r\n                    category=kp.ItemCategory.FILE,\r\n                    label=os.path.basename(home_dir),\r\n                    short_desc=\"\",\r\n                    target=home_dir,\r\n                    args_hint=kp.ItemArgsHint.ACCEPTED,\r\n                    hit_hint=kp.ItemHitHint.KEEPALL,\r\n                    loop_on_suggest=True,\r\n                    data_bag=match_score))\r\n\r\n            if len(search_terms) > 0:\r\n                # Because of the self.home_trigger prefix, the user_input cannot be\r\n                # matched against files names. So we have to sort the suggestions by\r\n                # ourselves.\r\n                self._sort_matched_suggestions(suggestions)\r\n\r\n            return suggestions, kp.Match.ANY, kp.Sort.NONE\r\n\r\n    def _sort_matched_suggestions(self, suggestions):\r\n        # sort suggestion using the score that is stored in the data_bag member,\r\n        # then clear it to avoid polluting the Catalog and the History\r\n        def _sortkey(item):\r\n            score = item.data_bag()\r\n            item.set_data_bag(\"\")\r\n            return score\r\n        suggestions.sort(key=_sortkey, reverse=True)\r\n\r\n    def _browse_dir(self, base_dir, check_base_dir=True, search_terms=\"\", store_score=False):\r\n        base_dir = self._safe_normpath(base_dir)\r\n        browse = kpu.browse_directory(self,\r\n                                    base_dir,\r\n                                    check_base_dir=check_base_dir,\r\n                                    search_terms=search_terms,\r\n                                    store_score=store_score,\r\n                                    show_dirs_first=self.show_dirs_first,\r\n                                    show_hidden_files=self.show_hidden_files,\r\n                                    show_system_files=self.show_system_files)\r\n\r\n        if os.path.samefile(base_dir, browse[0][0].target()):\r\n            browse[0][0].set_loop_on_suggest(False)\r\n\r\n        return browse\r\n\r\n    def _insert_recents(self, suggestions, match_method=kp.Match.ANY,\r\n                        sort_method=kp.Sort.NONE, search_terms=\"\"):\r\n        recents = []\r\n        if search_terms:\r\n            search_terms = os.path.normcase(self._safe_normpath(search_terms))\r\n\r\n        try:\r\n            with winreg.OpenKey(\r\n                    winreg.HKEY_CURRENT_USER,\r\n                    \"Software\\\\Microsoft\\\\Windows\\\\CurrentVersion\\\\Explorer\\\\TypedPaths\") as hkey:\r\n                url_idx = 0\r\n                while True:\r\n                    (name, value, typ) = winreg.EnumValue(hkey, url_idx)\r\n                    url_idx += 1\r\n                    if typ == winreg.REG_SZ and name.lower().startswith(\"url\"):\r\n                        # we must check that value is a path since value can\r\n                        # also be the name of a known folder like \"Computer\"\r\n                        value = self._safe_normpath(value)\r\n                        if not (os.path.isabs(value) or os.path.ismount(value)):\r\n                            continue\r\n\r\n                        if search_terms and not os.path.normcase(value).startswith(search_terms):\r\n                            continue\r\n\r\n                        if match_method == kp.Match.FUZZY:\r\n                            score = str(kpu.fuzzy_score(search_terms, value))\r\n                        else:\r\n                            score = None\r\n\r\n                        recents.append(self.create_item(\r\n                            category=kp.ItemCategory.FILE,\r\n                            label=value,\r\n                            short_desc=\"\",\r\n                            target=value,\r\n                            args_hint=kp.ItemArgsHint.ACCEPTED,\r\n                            hit_hint=kp.ItemHitHint.KEEPALL,\r\n                            loop_on_suggest=True,\r\n                            data_bag=score))\r\n        except OSError as exc:\r\n            pass\r\n\r\n        if sort_method == kp.Sort.NONE:\r\n\r\n            def _find_same_item(target, lst):\r\n                target = self._safe_normpath(target)\r\n                for idx in range(len(lst)):\r\n                    if target == self._safe_normpath(lst[idx].target()):\r\n                        return idx\r\n                return None\r\n\r\n            # remove duplicates by ourselves so Keypirinha does not try to merge\r\n            # them, which may alter the desired positioning of items\r\n            for recent_item in recents:\r\n                match_idx = _find_same_item(recent_item.target(), suggestions)\r\n                if match_idx is not None:\r\n                    suggestions.pop(match_idx)\r\n\r\n            recents = natsort.natsorted(recents, key=lambda x: x.label(),\r\n                alg=natsort.ns.PATH | natsort.ns.LOCALE | natsort.ns.IGNORECASE)\r\n\r\n            # prepend the recents list to the suggestions\r\n            # but always keep the \".\" item at the top\r\n            if len(suggestions) > 0 and suggestions[0].label() == \".\":\r\n                suggestions[1:0] = recents\r\n            else:\r\n                suggestions[:0] = recents\r\n\r\n        else:\r\n            # note: Keypirinha will take care of removing duplicates\r\n            suggestions += recents\r\n","sub_path":"FilesCatalog/filescatalog.py","file_name":"filescatalog.py","file_ext":"py","file_size_in_byte":51226,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"51163466","text":"# -*- coding: utf-8 -*-\nimport json\nimport os\nfrom zope.interface import Interface\n\nDOTCLOUD = \"DOTCLOUD\"\nHEROKU = \"HEROKU\"\nSTRIDER = \"STRIDER\"\n\nDOTCLOUD_FILE_PATH = '/home/dotcloud/environment.json'\n\nclass IPaaSEnv(Interface):\n    pass\n\nclass NoEnv(object):\n    \"\"\" No PaaS found \"\"\"\n    def __init__(self, settings):\n        self.PAAS_NAME = \"none\"\n        self._settings = settings\n\n    def get_mysql_url(self):\n        return self._settings.get('sqlalchemy.url')\n\n    def get_postgresql_url(self):\n        return self._settings.get('sqlalchemy.url')\n\n    def get_mongodb_url(self):\n        return self._settings.get('mongodb.url')\n\n    def get_redis_url(self):\n        return self._settings.get('redis.url')\n\nclass DotCloudEnv(object):\n    \"\"\"DotCloud environment for Pyramid. See http://docs.dotcloud.com/firststeps/platform-overview/ \"\"\"\n    def __init__(self, path=DOTCLOUD_FILE_PATH, env=None):\n        self.PAAS_NAME = \"dotcloud\"\n        if os.path.isfile(path):\n            with open(path) as f:\n                env = json.load(f)\n                for item in env:\n                    setattr(self, item, env[item])\n                self.env = env\n\n        if env:\n            self.env = env\n\n    def lookup(self, key):\n        for k, val in self.env.iteritems():\n            if k.startswith(\"DOTCLOUD\") and k.endswith(key):\n                return val\n\n    def get_mysql_url(self):\n        return self.lookup(\"MYSQL_URL\");\n\n    def get_postgresql_url(self):\n        # underscore prevents us from confusing with a MYSQL_URL\n        url = self.lookup(\"_SQL_URL\")\n        if url:\n            url = url.replace('pgsql:', 'postgresql:') + \"/template1\"\n        return url\n\n    def get_mongodb_url(self):\n        return self.lookup(\"MONGODB_URL\")\n\n    def get_redis_url(self):\n        return self.lookup(\"REDIS_URL\")\n\n    def get_solr_url(self):\n        # XXX not sure how to implement this for DotCloud yet:\n        # http://docs.dotcloud.com/services/solr/\n        pass\n\n\nclass HerokuEnv(object):\n    \"\"\" Heroku environment for Pyramid. See https://addons.heroku.com \"\"\"\n\n    def __init__(self, env=None):\n        self.PAAS_NAME = \"heroku\"\n        if not env:\n            self.env = os.environ\n        else:\n            self.env = env\n        for item in self.env:\n            setattr(self, item, self.env[item])\n\n    def get_mysql_url(self):\n        # ClearDB and Xeround options for MySQL\n        if 'CLEARDB_DATABASE_URL' in self.env:\n            return self.env['CLEARDB_DATABASE_URL']\n        if 'XEROUND_DATABASE_INTERNAL_URL' in self.env:\n            return self.env['XEROUND_DATABASE_INTERNAL_URL']\n\n    def get_postgresql_url(self):\n        if 'DATABASE_URL' in self.env:\n            return self.env['DATABASE_URL']\n\n    def get_mongodb_url(self):\n        # MongoHQ and MongoLab options for MongoDB\n        if 'MONGOHQ_URL' in self.env:\n            return self.env['MONGOHQ_URL']\n        if 'MONGOLAB_URI' in self.env:\n            return self.env['MONGOLAB_URI']\n\n    def get_redis_url(self):\n        # Redis To Go and Open Redis options for Redis\n        if 'REDISTOGO_URL' in self.env:\n            return self.env['REDISTOGO_URL']\n        if 'OPENREDIS_URL' in self.env:\n            return self.env['OPENREDIS_URL']\n\n    def get_solr_url(self):\n        # Web Solr option for Solr\n        if 'WEBSOLR_URL' in self.env:\n            return self.env['WEBSOLR_URL']\n\ndef get_paas_env(config):\n    return config.registry.queryUtility(IPaaSEnv)\n\n\ndef get_paas_env_from_request(request):\n    \"\"\"Obtain a PaaSEnv object previously registered via\n    ``config.include('pyramid_paas')``\n    \"\"\"\n    return request.registry.queryUtility(IPaaSEnv)\n\ndef detect_paas(dotcloud_env_path=DOTCLOUD_FILE_PATH, environ=os.environ):\n    if os.path.isfile(dotcloud_env_path):\n        return DOTCLOUD\n    try:\n        environ['PORT']\n        return HEROKU\n    except KeyError:\n        pass\n    try:\n        paas = environ['PAAS_NAME']\n        if paas == \"STRIDER\":\n            return STRIDER\n    except KeyError:\n        pass\n\n    # We don't think this is running on a PaaS\n    return None\n\n\ndef includeme(config):\n    paas = detect_paas()\n    if paas == DOTCLOUD:\n        config.registry.registerUtility(DotCloudEnv(), IPaaSEnv)\n    # Strider exports same vars as Heroku so can just re-use that impl.\n    elif paas in (HEROKU, STRIDER):\n        config.registry.registerUtility(HerokuEnv(), IPaaSEnv)\n    else:\n        config.registry.registerUtility(NoEnv(config.get_settings()), IPaaSEnv)\n\n\n    config.add_directive('get_paas_env', get_paas_env)\n    config.set_request_property(\n        get_paas_env_from_request,\n        'paas_env',\n        reify=True\n        )\n    config.include('.panel')\n","sub_path":"pyramid_paas/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":4684,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"261310645","text":"\"\"\"***************************  TITLE  ****************************\"\"\"\n\"\"\"1536  Find First and Last Position of Element in Sorted Array.py\"\"\"\n\n\n\n\"\"\"***************************  DESCRIPTION  ****************************\"\"\"\n\"\"\"\nGiven an array of integers nums sorted in ascending order, find the starting and ending position of a given target value.\n\"\"\"\n\n\n\n\"\"\"***************************  EXAMPLES  ****************************\"\"\"\n\"\"\"\nnums\n\"\"\"\n\n\n\n\"\"\"***************************  CODE  ****************************\"\"\"\nclass Solution:\n    def searchRange(self, nums, target):\n        if not nums: return [-1, -1]\n        start, end = 0, len(nums)-1\n        first, second = -1, -1    \n\n        while start + 1 < end:\n            mid = start + (end- start) // 2\n            if nums[mid] >= target:\n                end = mid\n            else:\n                start = mid\n        \n        if nums[start] == target:\n            first = start\n        elif nums[end] == target:\n            first = end\n            \n        # doing binary search the second time\n        # this time, we are doing something different\n        # we are moving the start to mid more often\n        \n        start, end = 0, len(nums)-1\n        while start + 1 < end:\n            mid = start + (end - start) // 2\n            if nums[mid] > target:\n                end = mid\n            else:\n                start = mid\n                \n        \n        if nums[end] == target:\n            second = end\n        elif nums[start] == target:\n            second = start\n            \n        return [first, second]\n            \n            \n","sub_path":"lintcode/1536  Find First and Last Position of Element in Sorted Array.py","file_name":"1536  Find First and Last Position of Element in Sorted Array.py","file_ext":"py","file_size_in_byte":1600,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"627363448","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Sep 28 10:11:08 2019\n\n@author: Alejandro\n\"\"\"\n\n# -*- coding: utf-8 -*-\nfrom PyQt5.QtWidgets import (QMainWindow, QApplication, QLabel, QLineEdit, QDialog,\n       QPushButton, QSizePolicy,QVBoxLayout, QWidget)\nfrom matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas\nfrom matplotlib.figure import Figure\nimport matplotlib.pyplot as plt\n\nimport pandas as pd\nimport numpy as np\nimport os\nglobal usuario, clave\nimport Filtros_EEG as filtros\n\n#Se crea la funcion principal donde se ingresa el nombre del archiuvo y el canal\nclass MainWindow(QMainWindow):\n    \n    def __init__(self, *args, **kwargs):\n        super(MainWindow, self).__init__(*args, **kwargs)\n        self.setWindowTitle(\"nombre del archivo de señales EEG\")\n        self.setFixedSize(400, 400)\n        self.Label = QLabel(self)\n        self.Label.setText('Ingrese nombre del archivo de señales EEG')\n        self.Label.resize(700,20)\n        self.Label.move(100, 50)\n        self.archivo=QLineEdit(self)\n        self.archivo.resize(250,20)\n        self.archivo.move(100,100)\n        self.Label1 = QLabel(self)\n        self.Label1.setText('Ingrese Canal')\n        self.Label1.move(100, 150)\n        self.canal =QLineEdit(self)\n        self.canal.move(100, 200)\n        self.button = QPushButton('Aceptar', self)\n        self.button.move(150,250)\n        self.button.clicked.connect(self.ingresar)\n        \n   \n#Se crea un archivo csv con el nombre de la señal y la clave\n    def ingresar(self):\n        self.archivo1=self.archivo.text()\n        self.canal1=self.canal.text()\n        self.nuevoregistro=pd.DataFrame({\"senal\":[self.archivo1],\"canal\":[self.canal1]})\n        self.nuevoregistro.to_csv(\"Senales.csv\")\n        funcion=Procesamiento(self)  # Dirige a la clase Procesamiento\n        funcion.show() \n        \n\n    def onclick_ECG2(self):\n        ECG=PlotECG(self)    # Dirige a la clase PlotECG\n        ECG.show()           # Muestra la grafica de la señal filtrada sin épocas\n    \n    def onclick_EEG2(self):\n        EEG=PlotEEG(self)    # Dirige a la clase PlotEEG\n        EEG.show()        # Muestra la grafica del periodograma de la señal original\n    \n    def onclick_EMG2(self):\n        EMG=PlotEMG(self)    # Dirige a la clase PlotEMG\n        EMG.show()          # Muestra la gráfica del hisograma de la señal filtrada con épocas\n        \n    def onclick_senal_respiratoria2(self):\n        senal=Plotsenal_respiratoria(self) # Dirige a la clase Plotsenal_respiratoria\n        senal.show()            # Muestra la grafica de la señal sin épocas filtradas\n \n    def onclick_grafica_periodograma_resultante(self):\n        senal=Plot_periodograma_resultante(self) # Dirige a la clase Plot_periodograma_resultante\n        senal.show() #MUestra la gráfica del periodograma de la señal resultante\n\n#Se define la ventana en donde estarán los botones para graficar los elementos dados\nclass Procesamiento(QMainWindow): \n    \n     def __init__(self, *args, **kwargs):\n        super(Procesamiento, self).__init__(*args, **kwargs)\n        self.setWindowTitle(\"Graficos\")\n        self.setFixedSize(680,300 )\n        self.button3 = QPushButton('Graficar señal filtrada mediante umbrales', self)\n        self.button3.move(200,10)\n        self.button3.resize(250,30)\n        self.button4 = QPushButton('Graficar periodograma señal original', self)\n        self.button4.move(200,40)\n        self.button4.resize(250,30)\n        self.button5 = QPushButton('Graficar histograma de señal con filtros', self)\n        self.button5.move(200,70)\n        self.button5.resize(250,30)\n        self.button6 = QPushButton('Eliminación ruido con pasa altas y bajas', self)\n        self.button6.move(200,100)\n        self.button6.resize(250,30)\n        self.button7 = QPushButton('Graficar periodograma señal filtrada', self)\n        self.button7.move(200,130)\n        self.button7.resize(250,30)\n        self.button3.clicked.connect(self.Graficar_canal_filtrado)\n        self.button4.clicked.connect(self.Graficar_periodograma)\n        self.button5.clicked.connect(self.Graficar_histograma)\n        self.button6.clicked.connect(self.Graficar_canal)\n        self.button7.clicked.connect(self.Graficar_periodograma_resultante)\n#Funcion que retrona a la clase principal donde despues se va hacia la funcion para graficar la señal filtrada sin épocas con ruido   \n     def Graficar_canal_filtrado(self):\n        MainWindow.onclick_ECG2(self)\n#Funcion que retrona a la clase principal donde despues se va hacia la funcion para graficar el periodograma de la señal original\n     def Graficar_periodograma(self):\n         MainWindow.onclick_EEG2(self)\n#Funcion que retrona a la clase principal donde despues se va hacia la funcion para graficar el histograma de la señal filtrada conépocas\n     def Graficar_histograma(self):\n         MainWindow.onclick_EMG2(self)\n#Funcion que retrona a la clase principal donde despues se va hacia la funcion para graficar la señal filtrada con épocas\n     def Graficar_canal(self):\n         MainWindow.onclick_senal_respiratoria2(self)\n#Funcion que retrona a la clase principal donde despues se va hacia la funcion para graficar el periodograma de la señal filtrada sin épocas\n     def Graficar_periodograma_resultante(self):\n         MainWindow.onclick_grafica_periodograma_resultante(self)\n    \n        \n        \nclass PlotECG(QMainWindow):\n    \n    \n     def __init__(self,  parent=None):\n        super(PlotECG, self).__init__(parent)\n        datos=pd.read_csv('Senales.csv')\n        canal=int(datos['canal'][0])\n        archivo=datos['senal'][0]\n        senal_filtrada_pasabajas1, senal_resultante, histograma,f,pot,f2,pot2,cota_inferior,cota_superior,f_pasa_bajas, f_pasa_altas, tiempo, tiempo1 =filtros.matrices_archivos(canal,archivo)\n        self.setWindowTitle(\"Gráfica filtrada\")\n        self.setFixedSize(800,600)\n        self.layout = QVBoxLayout()\n        self.setLayout(self.layout)\n        self.fig = Figure((140.0, 6.0), dpi=70, facecolor=\"#F6F4F2\")\n        self.Label=QLabel(self)\n        self.Label.setText('Umbral Superior:  '+str(cota_superior)+\" mV\")\n        self.Label.move(50,450)\n        self.Label.resize(250,30)\n        self.Label1=QLabel(self)\n        self.Label1.setText('Umbral Inferior:  '+str(cota_inferior)+\" mV\")\n        self.Label1.move(500,450)\n        self.Label1.resize(250,30)\n        \n        \n        m = PlotCanvasECG(self)\n        m.move(0,0)\n\n#Se configura la ventana donde se va a graficar la señal EEG\nclass PlotEEG(QMainWindow):\n    \n    \n     def __init__(self,  parent=None):\n        super(PlotEEG, self).__init__(parent)\n        datos=pd.read_csv('Senales.csv')\n        canal=int(datos['canal'][0])\n        archivo=datos['senal'][0]\n        senal_filtrada_pasabajas1, senal_resultante, histograma,f,pot,f2,pot2,cota_inferior,cota_superior,f_pasa_bajas, f_pasa_altas, tiempo, tiempo1 =filtros.matrices_archivos(canal,archivo)\n        self.setWindowTitle(\"Periodograma señal original\")\n        self.setFixedSize(800,600)\n        self.layout = QVBoxLayout()\n        self.setLayout(self.layout)\n        self.fig = Figure((130.0, 6.0), dpi=70, facecolor=\"#F6F4F2\")\n        self.Label=QLabel(self)\n        self.Label.setText('Frecuencia de corte filtro pasa-bajas:  '+str(f_pasa_bajas)+\"Hz\")\n        self.Label.move(50,450)\n        self.Label.resize(250,30)\n        self.Label1=QLabel(self)\n        self.Label1.setText('Frecuencia de corte filtro pasa-altas:  '+str(f_pasa_altas)+\"Hz\")\n        self.Label1.move(500,450)\n        self.Label1.resize(250,30)\n        \n        m = PlotCanvasEEG(self)\n        m.move(0,0)\n\n#Se configura la ventana donde se va a graficar la señal EMG        \nclass PlotEMG(QMainWindow):\n    \n    def __init__(self,  parent=None):\n        super(PlotEMG, self).__init__(parent)\n        self.setWindowTitle(\"Histograma\")\n        self.setFixedSize(800,600)\n        self.layout = QVBoxLayout()\n        self.setLayout(self.layout)\n        self.fig = Figure((130.0, 6.0), dpi=70, facecolor=\"#F6F4F2\")\n        \n        m = PlotCanvasEMG(self)\n        m.move(0,0)\n \n#Se configura la ventana donde se va a graficar la señal Señal_respiratoria     \nclass Plotsenal_respiratoria(QMainWindow):\n    \n     def __init__(self,  parent=None):\n        super(Plotsenal_respiratoria, self).__init__(parent)\n        self.setWindowTitle(\"Eliminación ruido ambiental\")\n        self.setFixedSize(800,600)\n        self.layout = QVBoxLayout()\n        self.setLayout(self.layout)\n        self.fig = Figure((130.0, 6.0), dpi=70, facecolor=\"#F6F4F2\")\n        \n        m = PlotCanvasSenal_respiratoria(self)\n        m.move(0,0)\n \nclass Plot_periodograma_resultante(QMainWindow):\n    \n    \n     def __init__(self,  parent=None):\n        super(Plot_periodograma_resultante, self).__init__(parent)\n        datos=pd.read_csv('Senales.csv')\n        canal=int(datos['canal'][0])\n        archivo=datos['senal'][0]\n        senal_filtrada_pasabajas1, senal_resultante, histograma,f,pot,f2,pot2,cota_inferior,cota_superior,f_pasa_bajas, f_pasa_altas, tiempo, tiempo1 =filtros.matrices_archivos(canal,archivo)\n        self.setWindowTitle(\"Periodograma señal filtrada\")\n        self.setFixedSize(800,600)\n        self.layout = QVBoxLayout()\n        self.setLayout(self.layout)\n        self.fig = Figure((140.0, 6.0), dpi=70, facecolor=\"#F6F4F2\")\n        \n        \n        m = PlotCanvasECG_periodograma_resultante(self)\n        m.move(0,0)\n\n    \n#Esta clase realiza el plot sobre la ventana ya configurada      \nclass PlotCanvasECG(FigureCanvas):\n \n    def __init__(self, parent=None, width=7, height=4, dpi=100):\n        fig = Figure(figsize=(width, height), dpi=dpi)\n        self.axes = fig.add_subplot(111)\n \n        FigureCanvas.__init__(self, fig)\n        self.setParent(parent)\n \n        FigureCanvas.setSizePolicy(self,\n                QSizePolicy.Expanding,\n                QSizePolicy.Expanding)\n        FigureCanvas.updateGeometry(self)\n        self.plot()\n    \n#Se define la función que genera la grafica\n    def plot(self):\n        datos=pd.read_csv('Senales.csv')\n        canal=int(datos['canal'][0])\n        archivo=datos['senal'][0]\n        senal_filtrada_pasabajas1, senal_resultante, histograma,f,pot,f2,pot2,cota_inferior,cota_superior,f_pasa_bajas, f_pasa_altas, tiempo, tiempo1 =filtros.matrices_archivos(canal,archivo)\n        ax = self.figure.add_subplot(111)\n        ax.plot(tiempo,senal_resultante)\n        ax.set_xlabel('Segundos')\n        ax.set_ylabel('mV')\n        ax.grid()\n        ax.set_title(\"Señal:\"+ archivo + \"  Canal:\" +str(canal))\n        self.draw()   #Se muestra el ECG sobre la ventana\n \n#Esta clase realiza el plot sobre la ventana ya configurada         \nclass PlotCanvasEEG(FigureCanvas):\n \n    def __init__(self, parent=None, width=7, height=4, dpi=100):\n        fig = Figure(figsize=(width, height), dpi=dpi)\n        self.axes = fig.add_subplot(111)\n \n        FigureCanvas.__init__(self, fig)\n        self.setParent(parent)\n \n        FigureCanvas.setSizePolicy(self,\n                QSizePolicy.Expanding,\n                QSizePolicy.Expanding)\n        FigureCanvas.updateGeometry(self)\n        self.plot()\n    \n#Se define la función que genera la grafica\n    def plot(self):\n        datos=pd.read_csv('Senales.csv')\n        canal=int(datos['canal'][0])\n        archivo=datos['senal'][0]\n        senal_filtrada_pasabajas1, senal_resultante, histograma,f,pot,f2,pot2,cota_inferior,cota_superior,f_pasa_bajas, f_pasa_altas, tiempo, tiempo1=filtros.matrices_archivos(canal,archivo)\n        ax = self.figure.add_subplot(111)\n        ax.plot(f,pot)\n        ax.set_xlabel('Frecuencia')\n        ax.set_ylabel('Potencia')\n        ax.grid()\n        ax.set_title(\"Señal:\"+ archivo + \"  Canal:\" +str(canal))\n        self.draw()   #Se muestra el ECG sobre la ventana\n\n#Esta clase realiza el plot sobre la ventana ya configurada \nclass PlotCanvasEMG(FigureCanvas):\n \n    def __init__(self, parent=None, width=7, height=4, dpi=100):\n        fig = Figure(figsize=(width, height), dpi=dpi)\n        self.axes = fig.add_subplot(111)\n \n        FigureCanvas.__init__(self, fig)\n        self.setParent(parent)\n \n        FigureCanvas.setSizePolicy(self,\n                QSizePolicy.Expanding,\n                QSizePolicy.Expanding)\n        FigureCanvas.updateGeometry(self)\n        self.plot()\n\n#Se define la función que genera la grafica\n    def plot(self):\n        datos=pd.read_csv('Senales.csv')\n        canal=int(datos['canal'][0])\n        archivo=datos['senal'][0]\n        senal_filtrada_pasabajas1, senal_resultante, histograma,f,pot,f2,pot2,cota_inferior,cota_superior,f_pasa_bajas, f_pasa_altas, tiempo,tiempo1=filtros.matrices_archivos(canal,archivo)\n        ax = self.figure.add_subplot(111)\n        ax.hist(senal_resultante)\n        ax.grid()\n        ax.set_title(\"Señal:\"+ archivo + \"  Canal:\" +str(canal))\n        self.draw()   #Se muestra el ECG sobre la ventanatana\n        \n\n#Esta clase realiza el plot sobre la ventana ya configurada \nclass PlotCanvasSenal_respiratoria(FigureCanvas):\n    \n     def __init__(self, parent=None, width=7, height=4, dpi=100):\n        fig = Figure(figsize=(width, height), dpi=dpi)\n        self.axes = fig.add_subplot(111)\n \n        FigureCanvas.__init__(self, fig)\n        self.setParent(parent)\n \n        FigureCanvas.setSizePolicy(self,\n                QSizePolicy.Expanding,\n                QSizePolicy.Expanding)\n        FigureCanvas.updateGeometry(self)\n        self.plot()\n\n#Se define la función que genera la grafica final\n     def plot(self):\n          \n        datos=pd.read_csv('Senales.csv')\n        canal=int(datos['canal'][0])\n        archivo=datos['senal'][0]\n        senal_filtrada_pasabajas1, senal_resultante, histograma,f,pot,f2,pot2,cota_inferior,cota_superior,f_pasa_bajas, f_pasa_altas, tiempo,tiempo1=filtros.matrices_archivos(canal,archivo)\n        ax = self.figure.add_subplot(111)\n        ax.plot(tiempo1,senal_filtrada_pasabajas1)\n        ax.set_xlabel('Segundos')\n        ax.set_ylabel('mV')\n        ax.grid()\n        ax.set_title(\"Señal:\"+ archivo + \"  Canal:\" +str(canal))\n        self.draw()   #Se muestra la grafica sobre la ventana\n\nclass PlotCanvasECG_periodograma_resultante(FigureCanvas):\n \n    def __init__(self, parent=None, width=7, height=4, dpi=100):\n        fig = Figure(figsize=(width, height), dpi=dpi)\n        self.axes = fig.add_subplot(111)\n \n        FigureCanvas.__init__(self, fig)\n        self.setParent(parent)\n \n        FigureCanvas.setSizePolicy(self,\n                QSizePolicy.Expanding,\n                QSizePolicy.Expanding)\n        FigureCanvas.updateGeometry(self)\n        self.plot()\n    \n#Se define la función que genera la grafica final\n    def plot(self):\n        datos=pd.read_csv('Senales.csv')\n        canal=int(datos['canal'][0])\n        archivo=datos['senal'][0]\n        senal_filtrada_pasabajas1, senal_resultante, histograma,f,pot,f2,pot2,cota_inferior,cota_superior,f_pasa_bajas, f_pasa_altas, tiempo, tiempo1 =filtros.matrices_archivos(canal,archivo)\n        ax = self.figure.add_subplot(111)\n        ax.plot(f2,pot2)\n        ax.set_xlabel('Hz')\n        ax.set_ylabel('mV')\n        ax.grid()\n        ax.set_title(\"Señal:\"+ archivo + \"  Canal:\" +str(canal))\n        self.draw()   #Se muestra sobre la ventana\n        \nif __name__ == \"__main__\":  \n    app = QApplication([])\n    window = MainWindow()\n    window.show()\n    app.exec_()","sub_path":"Interfaz_segundo.py","file_name":"Interfaz_segundo.py","file_ext":"py","file_size_in_byte":15387,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"210448263","text":"class Node:\n    \"\"\"\n    双向链表节点\n    \"\"\"\n\n    def __init__(self, key=None, val=None, freq=0):\n        self.key = key\n        self.value = val\n        self.pre = None\n        self.next = None\n        self.freq = freq\n\n\nclass DLinkedList:\n    \"\"\"\n    双向链表和一些列所需要的操作\n    \"\"\"\n\n    def __init__(self):\n        self.head = Node()\n        self.tail = Node()\n        self.head.next = self.tail\n        self.tail.pre = self.head\n\n    def insert_first(self, node):\n        node.next = self.head.next\n        node.pre = self.head\n        self.head.next.pre = node\n        self.head.next = node\n\n    def delete(self, node):\n        if self.head.next == self.tail:\n            return\n        node.pre.next = node.next\n        node.next.pre = node.pre\n\n    def get_last(self):\n        if self.head.next == self.tail:\n            return None\n        return self.tail.pre\n\n    def is_empty(self):\n        return self.head.next == self.tail\n\n\nclass LFUCache:\n    def __init__(self, capacity: int):\n        \"\"\"\n        key_map: {key: Node} - 字典\n        freq_map: {freq: LinkedList} -字典\n        \"\"\"\n        self.capacity = capacity\n        self.min_freq = 0\n        self.freq_map = dict()\n        self.key_map = dict()\n\n    def get(self, key: int) -> int:\n        \"\"\"\n        获得元素\n        - 如果存在 就返回应对val，更新访问元素访问频率\n        - 如果不在，返回 -1\n        \"\"\"\n        if key not in self.key_map:\n            return -1\n        node = self.key_map[key]\n        self._increase(node)\n        return node.value\n\n    def put(self, key: int, value: int) -> None:\n        \"\"\"\n        插入指定key:value\n        如果key存在则更新value和频率\n        如果key不存在，计划插入新元素\n            - 如果已满，则删除频率最低的元素，\n            - 插入新元素\n        \"\"\"\n        if key in self.key_map:\n            node = self.key_map[key]\n            node.value = value\n            self._increase(node)\n        else:\n            if self.capacity == 0:\n                return\n            if len(self.key_map) == self.capacity:\n                self._remove_min_freq_node()\n            node = Node(key, value, 1)\n            self._increase(node, True)\n            self.key_map[key] = node\n\n    def _increase(self, node, is_new_node=False):\n        \"\"\"\n        更新节点访问频率(freq)\n        \"\"\"\n        if is_new_node:\n            self.min_freq = 1\n            self._insert_first(node)\n        else:\n            self._delete(node)\n            node.freq += 1\n            self._insert_first(node)\n            if self.min_freq not in self.freq_map:\n                self.min_freq += 1\n\n    def _insert_first(self, node):\n        \"\"\"\n        根据节点的频率，插入对应的LinkedList中，没有则创建\n        \"\"\"\n        if node.freq not in self.freq_map:\n            self.freq_map[node.freq] = DLinkedList()\n        d_linked_list = self.freq_map[node.freq]\n        d_linked_list.insert_first(node)\n\n    def _delete(self, node):\n        if node.freq not in self.freq_map:\n            return\n        d_linked_list = self.freq_map[node.freq]\n        freq = node.freq\n        d_linked_list.delete(node)\n        # 列表为空时，删除freq_map上的索引\n        if d_linked_list.is_empty():\n            del self.freq_map[freq]\n        return node.key\n\n    def _remove_min_freq_node(self):\n        \"\"\"\n        删除频率最低的元素\n        找到freq_map[min_freq]中最后一个节点删除，同时在key_map也删除\n        如果链表为空，则删除此链表\n        \"\"\"\n        d_linked_list = self.freq_map[self.min_freq]\n        node = d_linked_list.get_last()\n        d_linked_list.delete(node)\n        del self.key_map[node.key]\n        # 列表为空时，删除freq_map上的索引\n        if d_linked_list.is_empty():\n            del self.freq_map[node.freq]\n\n\nif __name__ == '__main__':\n    lfu = LFUCache(2)\n    print(lfu.put(2, 2))\n    print(lfu.put(1, 1))\n    print(lfu.get(2))\n    print(lfu.get(1))\n    print(lfu.get(2))\n    print(lfu.put(3, 3))\n    print(lfu.put(4, 4))\n    print(lfu.get(3))\n    print(lfu.get(2))\n    print(lfu.get(1))\n    print(lfu.get(4))\n","sub_path":"leetcode/460.LFU 缓存/460.LFU 缓存.py","file_name":"460.LFU 缓存.py","file_ext":"py","file_size_in_byte":4208,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"48279540","text":"\"\"\"\n    This program will train a particular dataset using a pre-trained model. User can input which pretrained model to use,\n    number of epochs, learning rate, and number of hidden layers.\n    The program will print the accuracy, training loss, and validation loss.\n    \n    Pre-trained models: vgg16, vgg19, vgg11, vgg13\n    \n    Command Line:\n    python train.py data_dir --arch vgg16 --epochs 5\n    \n\"\"\"\n\n\n#Import modules\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport torch\nimport argparse\nimport torch.nn as nn\nfrom torch.autograd import Variable\nimport torch.optim as optim\nimport torch.nn.functional as F\nfrom collections import OrderedDict\n\nimport torchvision.transforms as transforms\nimport torchvision.datasets as datasets\nimport torchvision.models as models\nfrom PIL import Image\nimport json\n\n\n#define Command Line Arguments using the arparse module\ndef get_args():\n    parser = argparse.ArgumentParser(description='Input your arguments.')\n\n    parser.add_argument('data_dir', type=str,\n                    help='data directory containing training and testing dataset') \n    \n    parser.add_argument('--arch', type=str, default=\"vgg16\",\n                    help='provide model architecture from torchvision.models e.g. vgg16, alexnet etc.')\n                        \n    parser.add_argument('--hidden_units',type=list, default=4096,\n                    help='customise the classifier network by providing list of hidden layers, default is 4096')\n    \n    parser.add_argument('--learning_rate', type=float, default=0.001,\n                    help='provide learning rate of the optimizer, default is 0.001')\n    \n    parser.add_argument('--dropout', action = 'store', type=float, default = 0.05,\n                    help ='provide dropout for training the model, default is 0.05.')\n    \n    parser.add_argument('--epochs', type=int, default=1,\n                    help='provide number of epochs to train the model, default is 1')\n    \n    parser.add_argument('--output', type=int, default=102,\n                    help='enter output size')\n    \n    parser.add_argument('--save_dir', type=str, default='checkpoint.pth',\n                    help='directory for saving the checkpoint file')\n    \n    parser.add_argument('--gpu', default=True,\n                    help='use GPU or CPU to train model: True = GPU, False = CPU')\n    \n    args = parser.parse_args() \n                        \n    return args\n    \n#Load and preprocess the image dataset. Use torchvision/ ImageFolder to load the data. Using the image datasets and the trainforms, define the dataloaders\ndef load_data():\n                        \n    data_dir = 'flowers'\n    train_dir = data_dir + '/train'\n    valid_dir = data_dir + '/valid'\n    test_dir = data_dir + '/test'\n    \n    train_transforms = transforms.Compose([transforms.RandomRotation(20),\n                                                               transforms.RandomResizedCrop(224),\n                                                               transforms.RandomHorizontalFlip(),\n                                                               transforms.ToTensor(),\n                                                               transforms.Normalize(mean=[0.485, 0.456, 0.406], \n                                                                                    std=[0.229, 0.224, 0.225])])\n    valid_transforms = transforms.Compose([transforms.Resize(256),\n                                                               transforms.CenterCrop(224),\n                                                               transforms.ToTensor(),\n                                                               transforms.Normalize(mean=[0.485, 0.456, 0.406], \n                                                                                    std=[0.229, 0.224, 0.225])])\n\n    test_transforms = transforms.Compose([transforms.Resize(256),\n                                                              transforms.CenterCrop(224),\n                                                              transforms.ToTensor(),\n                                                              transforms.Normalize(mean=[0.485, 0.456, 0.406], \n                                                                                   std=[0.229, 0.224, 0.225])])\n\n\n    train_dataset = datasets.ImageFolder(train_dir, transform=train_transforms)\n    valid_dataset = datasets.ImageFolder(valid_dir, transform=valid_transforms)\n    test_dataset = datasets.ImageFolder(test_dir, transform=test_transforms)\n\n    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)\n    valid_loader = torch.utils.data.DataLoader(valid_dataset, batch_size=64, shuffle=True)\n    test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=64, shuffle=True)\n    \n    return train_dataset, valid_dataset, test_dataset, train_loader, valid_loader, test_loader\n\n\n\ndef build_model(arch, hidden_units, learning_rate):\n    model =  getattr(models,arch)(pretrained=True)\n    in_features = model.classifier[0].in_features\n\n    for param in model.parameters():\n        param.requires_grad = False\n        \n    \n    classifier = nn.Sequential(OrderedDict([\n                          ('fc1', nn.Linear(in_features, hidden_units, bias=True)),\n                          ('relu1', nn.ReLU()),\n                          ('dropout1', nn.Dropout(p=0.5)),\n                          ('fc2', nn.Linear(hidden_units, 102, bias=True)),\n                          ('output', nn.LogSoftmax(dim=1))\n                          ]))\n    model.classifier = classifier\n    \n    device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n    device\n    \n    model.to(device);\n    \n    criterion = nn.NLLLoss()\n    optimizer = optim.Adam(model.classifier.parameters(), lr=learning_rate)\n    \n    return model, criterion, optimizer\n\n\ndef validation(model, valid_loader, criterion, device):\n    valid_loss = 0\n    accuracy = 0\n    \n    for ii, (inputs, labels) in enumerate(valid_loader):\n        inputs, labels = inputs.to(device), labels.to(device)\n        output = model.forward(inputs)\n        valid_loss += criterion(output, labels).item()\n        ps = torch.exp(output)\n        equality = (labels.data == ps.max(dim=1)[1])\n        accuracy += equality.type(torch.FloatTensor).mean()\n        \n    \n    return valid_loss, accuracy\n                        \n    \ndef train_model(model, train_dataset, train_loader, valid_loader, test_loader, optimizer, criterion, epochs, device):\n    model.train()\n    \n    print(\"Training process starts .....\\n\")\n   \n    \n     # Define deep learning method\n    print_every = 30 # Prints every 30 images out of batch of 64 images\n    steps = 0\n\n    for e in range(epochs):\n        running_loss = 0\n        \n        for ii, (inputs, labels) in enumerate(train_loader):\n            steps += 1\n            inputs, labels = inputs.to(device), labels.to(device)\n            optimizer.zero_grad()\n            outputs = model.forward(inputs)\n            loss = criterion(outputs, labels)\n            loss.backward()\n            optimizer.step()\n            running_loss += loss.item()\n            \n            if steps % print_every == 0:\n                model.eval()\n                with torch.no_grad():\n                    valid_loss, accuracy = validation(model, valid_loader, criterion, device)\n                print(\"Epoch: {}/{} | \".format(e+1, epochs),\n                  \"Training Loss: {:.4f} | \".format(running_loss/print_every),\n                  \"Validation Loss: {:.4f} | \".format(valid_loss/len(valid_loader)),\n                  \"Validation Accuracy: {:.4f}\".format(accuracy/len(valid_loader)))\n                running_loss = 0\n                model.train()\n    \n    model.class_to_idx = train_dataset.class_to_idx\n    \n    print(\"\\nTraining process has ended.\")\n    print(\"Test is starting...\")\n\n\n#test the accuracy of the model on test dataset\n    test_correct = 0\n    test_total = 0\n    \n    with torch.no_grad():\n        model.eval()\n    \n    for data in test_loader:\n        images, labels = data\n        images, labels = images.to(device), labels.to(device)\n        outputs = model(images)\n        _, predicted = torch.max(outputs.data, 1)\n        test_total += labels.size(0)\n        test_correct += (predicted == labels).sum().item()\n    \n    \n    \n    model.class_to_idx = train_dataset.class_to_idx\n                                           \n    \n    print('Test accuracy ({0:d} validation images): {1:.1%}'.format(test_total, test_correct / test_total))\n    \n    return model\n\ndef save_checkpoint(model, save_dir, optimizer, epochs, arch, hidden_units):\n    checkpoint = {\n        'arch': arch,\n        'output_size': 102,\n        'hidden_units': hidden_units,\n        'epoch': epochs,\n        'state_dict': model.state_dict(),\n        'class_to_idx': model.class_to_idx,\n        'optimizer': optimizer.state_dict()}\n\n    torch.save(checkpoint, save_dir)\n\n    \n    return checkpoint\n    \n    \n\ndef main():\n    #load the category names                    \n    with open('cat_to_name.json', 'r') as f:\n        flower_names = json.load(f)\n                        \n    args = get_args()# get user arguments\n    \n   # print arguments\n\n    print('Data directory: ' + args.data_dir)\n    print('Architecture: ' + args.arch)\n    print('Hidden units: ' + str(args.hidden_units))\n    print('Epochs:' +str(args.epochs))\n    print('Learning rate:' +str(args.learning_rate))\n    print('Save directory: ' + args.save_dir)\n    \n    device = torch.device('cuda' if torch.cuda.is_available() and args.gpu==True else 'cpu') \n    print(\"Device:\", device)\n    \n    #load the datasets, transforms and loaders\n    train_dataset, valid_dataset, test_dataset, train_loader, valid_loader, test_loader = load_data()\n \n    #get the model                    \n    model, criterion, optimizer = build_model(args.arch, args.hidden_units, args.learning_rate)\n    \n    #train the model\n    \n    model = train_model(model, train_dataset, train_loader, valid_loader, test_loader, optimizer, criterion, args.epochs, device)\n    \n    \n    #save the model\n    checkpoint = save_checkpoint(model, args.save_dir, optimizer, args.epochs, args.arch, args.hidden_units)\n    \n    print('Checkpoint has been saved')\n    \n\n     \nif __name__ == '__main__':\n    main()                       \n                        \n                        \n    \n    ","sub_path":"train2-8.py","file_name":"train2-8.py","file_ext":"py","file_size_in_byte":10387,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"355427628","text":"from collections import deque\n\nclass TreeNode(object):\n    def __init__(self, val=0, left=None, right=None):\n        self.val = val\n        self.left = left\n        self.right = right\nclass Solution(object):\n    # Iteration\n    # Time complexity O(N)\n    # Space complexity O(N)\n    def levelOrder(self, root):\n        levels = []\n        if not root:\n            return levels\n        level = 0\n        queue = deque([root,])\n        while queue:\n            levels.append([])\n            for i in range(len(queue)):\n                popped = queue.popleft()\n                levels[level].append(popped)\n                if popped.left:\n                    queue.append(popped.left)\n                if popped.right:\n                    queue.append(popped.right)\n            level += 1\n        return levels","sub_path":"practice_problems/Trees_and_Graghs/Binary Tree Level Order Traversal.py","file_name":"Binary Tree Level Order Traversal.py","file_ext":"py","file_size_in_byte":806,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"180155034","text":"# coding=utf-8\nimport pymysql.cursors\nimport requests\nimport random\nimport time\n\n\ndef haha(log):\n\turl = \"http://127.0.0.1:8000/test/?\"\n\tp = []\n\tfor k in log.keys():\n\t\tp.append(\"{}={}\".format(k,log[k])) \n\trequests.get(url=url+\"&\".join(p))\n\ndb = pymysql.connect(\"47.100.97.152\",\"root\",\"123456\",\"AirConditioner\" )\ncursor = db.cursor()\ncursor.execute(\"delete from Logger_runlog;\")\ndb.commit()\n\nprint(\"hello\")\n\nimport pandas as pd\ndata = pd.read_csv(\"data.csv\",sep='\\t',encoding='gb2312').values\nmaps = {'roomid':0,'temperature':1,'windspeed':2,'status':3,'logtype':4,'flag':5}\nfor row in data:\n\tlog = {}\n\tfor one in maps.keys():\n\t\tlog[one] = row[maps[one]]\n\tif log['flag'] != \"request_off\":\n\t\tt = random.randint(10,20)\n\t\tprint(t)\n\t\ttime.sleep(t)\n\thaha(log)\n\n","sub_path":"help/write.py","file_name":"write.py","file_ext":"py","file_size_in_byte":754,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"251317079","text":"# -*- coding: utf-8 -*-\n\n###########################################\n# TASKS RELACIONADAS AO SETUP DO AMBIENTE #\n###########################################\n\nfrom fabric.contrib.files import exists\n\nfrom fabric.api import task\nfrom fabric.api import run\nfrom fabric.api import sudo\n\nfrom .settings import env\nfrom .settings import setup_env\n\nfrom .managers import static\nfrom .managers import project\nfrom .managers import virtualenv\n\nfrom .constants import PIP\nfrom .constants import EXISTS_DB\nfrom .constants import CREATE_DB\nfrom .constants import CREATE_ROLE\n\nfrom .utils import info\nfrom .utils import warning\nfrom .utils import success\n\n\n@task\ndef install():\n\n    '''\n        Task para instalar os pacotes necesários no sistema.\n    '''\n\n    info(u'Iniciando a instalação das dependências do sistema')\n    sudo('apt-get -y install nodejs-legacy')\n    sudo('apt-get -y install python-pip')\n    sudo('apt-get -y install python-dev')\n    sudo('apt-get -y install libpq-dev')\n    sudo('apt-get -y install postgresql')\n    sudo('apt-get -y install postgresql-contrib')\n    sudo('apt-get -y install git')\n    sudo('apt-get -y install python-psycopg2')\n    sudo('apt-get -y install virtualenv')\n    sudo('apt-get -y install virtualenvwrapper')\n    sudo('apt-get -y install nodejs')\n    sudo('apt-get -y install npm')\n    success(u'Dependencias instaladas com sucesso!')\n\n\n@task\ndef setup_virtualenv():\n    info(u'Verificando virtualenv')\n    if not exists(env.VENV_PATH):\n        prompt = input('\\nVIRTUALENV nao existe: {PROJECT_NAME}\\\n            \\ngostaria de criar? (yes/no) '.format(**env))\n\n        if prompt.lower() != 'yes':\n            warning(u'Cancelando!')\n            return False\n\n        run('source /usr/local/bin/virtualenvwrapper.sh')\n        run('mkvirtualenv {PROJECT_NAME} -p python3'.format(**env))\n        success(u'Virtualenv criado com sucesso!')\n\n\n@task\ndef setup_db():\n    info(u'Configurando o banco de dados')\n    if env.DB_NAME:\n        if not sudo(EXISTS_DB.format(**env)):\n            sudo(CREATE_ROLE.format(**env))\n            sudo(CREATE_DB.format(**env))\n\n\n@task\ndef setup_grunt():\n    info(u'Instalando o GRUNT')\n    with static():\n        sudo('npm install -g grunt-cli')\n        sudo('npm install')\n        success(u'GRUNT instalado com sucesso!')\n\n\n@task\ndef make_grunt():\n    info(u'Executando o comando \"grunt\"')\n    with static():\n        run('grunt')\n        success(u'comando \"grunt\" executado com sucesso!')\n\n\n@task\ndef requirements():\n    with project(), virtualenv():\n        info(u'Instalando as dependencias')\n        run(PIP.format(' -r requirements.txt'))\n        success(u'Dependencias instaladas com sucesso')\n\n\n@task\ndef setup():\n    info(u'Iniciando a configuração do ambiente de desenvolvimento.')\n    requirements()\n    setup_env()\n    make_grunt()\n    success(u'Configurações iniciais finalizadas com sucesso!')\n\n\n@task\ndef init():\n    install()\n    setup_db()\n    setup_virtualenv()\n    setup_grunt()\n    setup()\n","sub_path":"fabfile/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":2980,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"160401016","text":"import logging\n\nfrom raven.contrib.django.models import client\n\n\ndef alert(message: str, **kwargs) -> None:\n    \"\"\"\n    Example:\n\n        alert(\"webhook '{name}' response status={status}\",\n              name=subscription.name, status=webhook_status,\n              user=subscription.user.pk, retry=self.retries,\n              data=webhook_data)\n\n    \"\"\"\n    tags = {k: v for k, v in kwargs.items() if f'{{{k}}}' in message}\n    extra = {k: v for k, v in kwargs.items() if f'{{{k}}}' not in message}\n    text = message.format(**kwargs)\n    client.captureMessage(\n        message=text, extra=extra, tags=tags,\n        level=logging.ERROR)\n","sub_path":"core/monitoring.py","file_name":"monitoring.py","file_ext":"py","file_size_in_byte":636,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"183587759","text":"#######################################################\n# All things averaging\n#######################################################\n\nimport numpy as np\n\n# Vertically average the given field over all depths.\n\n# Arguments:\n# data: 3D (depth x lat x lon) or 4D (time x depth x lat x lon, needs time_dependent=True) array of data to average\n# grid: Grid object\n\n# Optional keyword arguments:\n# gtype: as in function Grid.get_lon_lat\n# time_dependent: as in function apply_mask\n\n# Output: array of dimension lat x lon (if time_dependent=False) or time x lat x lon (if time_dependent=True)\n\ndef vertical_average (data, grid, gtype='t', time_dependent=False):\n\n    # Choose the correct integrand of depth\n    if gtype == 'w':\n        dz = grid.dz_t\n    else:\n        dz = grid.dz\n    # Make it 3D\n    dz = np.tile(np.expand_dims(np.expand_dims(dz,1),2), (1, grid.ny, grid.nx))\n    # Get the correct hFac\n    hfac = grid.get_hfac(gtype=gtype)\n    if time_dependent:\n        # There's also a time dimension\n        num_time = data.shape[0]\n        dz = np.tile(dz, (num_time, 1, 1, 1))\n        hfac = np.tile(hfac, (num_time, 1, 1, 1))\n    # Vertically average    \n    return np.sum(data*dz*hfac, axis=-3)/np.sum(dz*hfac, axis=-3)\n\n    \n        \n","sub_path":"averaging.py","file_name":"averaging.py","file_ext":"py","file_size_in_byte":1240,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"516649013","text":"# *-* coding:UTF-8  *-*\n\nimport requests\ndptStation='武汉'\narrStation='深圳'\ndate='2017-12-15'\nurl=\"https://train.qunar.com/dict/open/s2s.do?dptStation=武汉&arrStation=长沙&date=2017-12-25&type=normal&user=neibu&source=site&start=1&num=500\"\n\nr=requests.get(url)\nd=list(r.json()['data']['s2sBeanList'])\nn=1\nfor i in d:\n    print(n,i['trainNo'],i['dptStationName'],i['arrStationName'],i['dptTime'],i['arrTime'],i['extraBeanMap']['interval'],i['seats'].get('无座'))\n    n += 1\n\n\n","sub_path":"qunaer/queryTickets.py","file_name":"queryTickets.py","file_ext":"py","file_size_in_byte":486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"615360568","text":"# -*- coding:utf-8 -*-\n'''\n@Project:newProject\n@File:RunTestCase.py\n@IDE:PyCharm\n@Author:lvy\n@Time:2020-08-04 14:59:49\n'''\n\nimport sys\nimport pytest\n\nfrom config.conf import ROOT_Dir, HTML_NAME\n\n# from util.sendMailForReprot import SendMailWithReport\n\n\ndef main():\n    if ROOT_Dir not in sys.path:\n        sys.path.append(ROOT_Dir)\n    # 执行用例\n    args = ['--reruns', '1', '--html=' + './report/' + HTML_NAME]\n    pytest.main(args)\n    # 发送邮件 这里我屏蔽了 自己添加自己的邮箱信息\n    # SendMailWithReport.send_mail(\n    #     smtpServer, fromUser, fromPassWord,\n    #     toUser, subject, contents,\n    #     htmlName)\n\n\nif __name__ == '__main__':\n    main()","sub_path":"RunTestCase.py","file_name":"RunTestCase.py","file_ext":"py","file_size_in_byte":689,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"530309308","text":"import argparse\nimport hail\nimport logging\nimport pprint\n\nfrom hail_scripts.v01.utils.computed_fields import get_expr_for_orig_alt_alleles_set\nfrom hail_scripts.v01.utils.hail_utils import create_hail_context\nfrom hail_scripts.v01.utils.vds_utils import write_vds, run_vep\n\nlogging.basicConfig(format='%(asctime)s %(levelname)-8s %(message)s')\nlogger = logging.getLogger()\nlogger.setLevel(logging.INFO)\n\np = argparse.ArgumentParser()\np.add_argument(\"--genome-version\", help=\"Genome build: 37 or 38\", choices=[\"37\", \"38\"], required=True)\np.add_argument(\"--block-size\", help=\"batch size - how many variants to pass to VEP for each VEP run\", type=int, default=500)\np.add_argument('--subset', const=\"X:31097677-33339441\", nargs='?',\n               help=\"subset to this chrom:start-end range. Intended for testing.\")\np.add_argument(\"input_file\", help=\"input vcf or vds\")\np.add_argument(\"output_vds\", nargs=\"?\", help=\"output vds\")\nargs = p.parse_args()\n\ninput_path = args.input_file.rstrip(\"/\")\nprint(\"Input File: %s\" % (input_path, ))\nif not args.output_vds:\n    output_vds_prefix = input_path.replace(\".vcf\", \"\").replace(\".vds\", \"\").replace(\".bgz\", \"\").replace(\".gz\", \"\").replace(\".vep\", \"\")\n    args.output_vds = output_vds_prefix + \".vep.vds\"\n\nprint(\"Output VDS: %s\" % (args.output_vds, ))\n\nhc = create_hail_context()\nif input_path.endswith(\".vds\"):\n    vds = hc.read(input_path)\nelif input_path.endswith(\".vcf\") or input_path.endswith(\"gz\"):\n    vds = hc.import_vcf(input_path, force_bgz=True, min_partitions=10000)\n\n    # ensure that va.wasSplit and va.aIndex are defined before calling split_multi() since split_multi() doesn't define these if all variants are bi-allelic\n    vds = vds.annotate_variants_expr('va.wasSplit=false, va.aIndex=1')\nelse:\n    p.error(\"Invalid input file: %s\" % input_path)\n\nif vds.num_partitions() < 50:\n    print(\"Repartitioning\")\n    vds = vds.repartition(10000)\n\nvds = vds.annotate_variants_expr(\"va.originalAltAlleles=%s\" % get_expr_for_orig_alt_alleles_set()) # save alt alleles before calling split_multi\nvds = vds.split_multi()\n\n#vds = vds.filter_alleles('v.altAlleles[aIndex-1].isStar()', keep=False)\nfilter_interval = \"1-MT\"\nif args.subset:\n    filter_interval = args.subset\n\nlogger.info(\"\\n==> set filter interval to: %s\" % (filter_interval, ))\nvds = vds.filter_intervals(hail.Interval.parse(filter_interval))\n\nsummary = vds.summarize()\npprint.pprint(summary)\nif summary.variants == 0:\n    p.error(\"0 variants in VDS. Make sure chromosome names don't contain 'chr'\")\n\nvds = run_vep(vds, genome_version=args.genome_version, block_size=args.block_size)\n\nwrite_vds(vds, args.output_vds)\n\npprint.pprint(vds.variant_schema)\n","sub_path":"hail_scripts/v01/run_vep.py","file_name":"run_vep.py","file_ext":"py","file_size_in_byte":2657,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"113676225","text":"#formattings\na=\"super\"\nb=\"iron\"\n#print('%s and %s are the part'%(a,b))\n\n\n#new\n#a=input(\"entre name\\n\")\n#b=float(input(\"enter marks\\n\"))\n#print('this is {} and {:.3f} are the part'.format(a,b))\n\n\n\n\n#string strpingss\na='somthine'+60*'-'+'digitallync'+50*'*'+'some'\nprint(a)\nb=a.strip('some')\nprint(b)","sub_path":"format.py","file_name":"format.py","file_ext":"py","file_size_in_byte":298,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"132987209","text":"# -*- encoding:utf-8 -*-\nfrom time import sleep\n\nfrom appium import webdriver\nfrom appium.webdriver.webdriver import WebDriver\nfrom selenium.webdriver import DesiredCapabilities\n\n__auth__ = 'bida'\n\ndef wechat_retur(driver):\n    driver.switch_to.context('NATIVE_APP')\n    # eles = driver.find_element_by_android_uiautomator('new UiSelector().resourceId(\"com.tencent.mm:id/js\")')\n    driver.find_element_by_android_uiautomator('new UiSelector().resourceId(\"com.tencent.mm:id/js\")').click()\n    driver.switch_to.context('WEBVIEW_com.tencent.mm:appbrand0')\n\n\nif __name__ == '__main__':\n    desired_caps = {\n        'platformName': 'Android',\n        # 'deviceName': 'huawei-lld_a10',\n        'deviceName': 'huawei-duk_al20',\n        'platformVersion': '7.0',\n        # 'platformVersion': '8.0',\n        'fastReset': 'false',\n        'appPackage': 'com.tencent.mm',\n        'appActivity': '.ui.LauncherUI',\n        'fullReset': 'False',\n        'noReset': 'True',\n        'unicodeKeyboard': 'False',\n        'resetKeyboard': 'False',\n        'chromeOptions': {\n            'androidProcess': 'com.tencent.mm:appbrand0'\n        }\n    }\n\n    driver = WebDriver('http://localhost:4723/wd/hub', desired_capabilities=desired_caps)\n    driver.implicitly_wait(30)\n    driver.find_element_by_android_uiautomator('new UiSelector().text(\"发现\")').click()\n    driver.find_element_by_android_uiautomator('new UiSelector().text(\"小程序\")').click()\n    driver.find_element_by_android_uiautomator('new UiSelector().text(\"京东购物\")').click()\n    # driver.find_element_by_android_uiautomator('new UiSelector().text(\"宜家会员活动\")').click()\n    print(driver.contexts)\n    driver.switch_to.context('WEBVIEW_com.tencent.mm:appbrand0')\n\n    main_handler = ''\n    for _ in range(10):\n        handles = driver.window_handles\n        find = False\n        for handler in handles:\n            driver.switch_to.window(handler)\n            if driver.find_elements_by_xpath(\"//wx-view[text()='定制频道']\"):\n                main_handler = handler\n                driver.find_element_by_xpath(\"//wx-view[text()='定制频道']\").click()\n                find = True\n                break\n        if find:\n            break\n        else:\n            sleep(1)\n\n    channel_handler = ''\n    for _ in range(10):\n        handles = driver.window_handles\n        find = False\n        for handler in handles:\n            driver.switch_to.window(handler)\n            if driver.find_elements_by_xpath(\"//wx-view[text()='保存']\"):\n                channel_handler = handler\n                # driver.find_element_by_xpath(\"//wx-view[text()='保存']\").click()\n                find = True\n                break\n        if find:\n            break\n        else:\n            sleep(1)\n\n    wechat_retur(driver)\n\n    coupon_handler = ''\n    for _ in range(10):\n        handles = driver.window_handles\n        find = False\n        for handler in handles:\n            driver.switch_to.window(handler)\n            if driver.find_elements_by_xpath(\"//wx-view[text()='领优惠券']\"):\n                coupon_handler = handler\n                driver.find_element_by_xpath(\"//wx-view[text()='领优惠券']\").click()\n                find = True\n                break\n        if find:\n            break\n        else:\n            sleep(1)\n\n\n","sub_path":"py3Example/appiumWechat.py","file_name":"appiumWechat.py","file_ext":"py","file_size_in_byte":3299,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"101538399","text":"\"\"\"Module containing handlers for source control management systems.\"\"\"\n\nimport json\nimport re\nimport urllib3\n\nfrom toolkit import config\nfrom toolkit.utils import classproperty\n\nurllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\n\n\nclass StatusError(Exception):\n\n    def __init__(self, status: int):\n        \"\"\"Initialize custom exception.\"\"\"\n        msg = \"Response status returned: `%d`\" % status\n\n        super(StatusError, self).__init__(msg)\n\n\nclass GitHubHandler(object):\n    \"\"\"\n    The handler manages GitHub repository, strips its source directory\n    and handles access to GitHub API making it easy to interact with\n    the repository.\n\n    :param url: str, url of any GitHub repository or blob\n    \"\"\"\n\n    __URL_BASE_PATTERN = u\"http[s]://github.com/([\\w-]+)/([\\w-]+[.]*[\\w-]*)\"\n    __API_URL = u\"https://api.github.com/repos/{user}/{project}/languages\"\n    __DEFAULT_PROPERTIES = ('user', 'project', 'repository')\n\n    def __init__(self, url: str = None):\n        self._src_url = self.strip_src_url(url)\n        self._user, self._project = self.get_user_project(self._src_url)\n        self._http = urllib3.PoolManager()\n\n        # prototyped variables\n        self._languages = None\n\n    # noinspection PyMethodParameters\n    @classproperty\n    def pattern(cls):\n        return cls.__URL_BASE_PATTERN\n\n    # noinspection PyMethodParameters\n    @classproperty\n    def default_properties(cls):\n        return cls.__DEFAULT_PROPERTIES\n\n    @property\n    def repository(self):\n        return self._src_url\n\n    @property\n    def user(self):\n        return self._user\n\n    @property\n    def project(self):\n        return self._project\n\n    @property\n    def languages(self):\n        if not self._languages:\n            # query API only the first time\n            self._languages = self.get_languages()\n        return self._languages\n\n    def strip_src_url(self, url: str) -> str:\n        \"\"\"Strips the source url from a given url.\n\n        :param url: str, url to be stripped\n\n        :returns: str, source url\n        :raises: ValueError if url does not match handler's pattern\n        \"\"\"\n        strip_url = re.search(self.__URL_BASE_PATTERN, url)\n\n        if not strip_url:\n            raise ValueError(\"url `%s` does not match handler's base pattern\" % url)\n\n        # noinspection PyUnresolvedReferences\n        return strip_url[0]\n\n    @staticmethod\n    def get_user_project(src_url: str) -> tuple:\n        \"\"\"Splits the source url and extracts username and project name.\n\n        :param src_url: url to the source repository of a project\n\n        :returns: tuple (username, project)\n        \"\"\"\n        # TODO: improve the splitting with regex .. this way it misses some projects\n        gh_user, gh_project = src_url.rsplit('/', 2)[-2:]\n\n        return gh_user, gh_project\n\n    def get_languages(self) -> dict:\n        \"\"\"\n        Query GitHub API languages used for the given user/project.\n\n        Note: GitHub will most likely require OAUTH_TOKEN specified,\n        provide your token via environment variable OAUTH_TOKEN.\n\n        :returns: dict, {\"str\"language: \"int\"bytes_of_code} or None\n        :raises: HTPPError on wrong response status\n        \"\"\"\n        request_url = self.__API_URL.format(user=self._user,\n                                            project=self._project)\n        response = self._http.request('GET', request_url,\n                                      headers=config.HEADERS)\n\n        # Handle limits and wrong responses\n        if response.status > 205:\n            raise StatusError(status=response.status)\n\n        return json.loads(response.data)\n","sub_path":"cves/toolkit/src/toolkit/preprocessing/handlers.py","file_name":"handlers.py","file_ext":"py","file_size_in_byte":3616,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"309471437","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom rest_framework import viewsets, status\nfrom rest_framework.decorators import action\nfrom rest_framework.exceptions import ValidationError\nfrom rest_framework.response import Response\n\nfrom projects.serializers import ProjectSerializer\nfrom organisations.serializers import OrganisationSerializer\nfrom users.serializers import UserFullSerializer, InviteSerializer\n\n\nclass OrganisationViewSet(viewsets.ModelViewSet):\n    serializer_class = OrganisationSerializer\n\n    def get_queryset(self):\n        return self.request.user.organisations.all()\n\n    def create(self, request):\n        \"\"\"\n        Override create method to add new organisation to authenticated user\n        \"\"\"\n        user = request.user\n        serializer = OrganisationSerializer(data=request.data)\n        if serializer.is_valid():\n            org = serializer.save()\n            user.organisations.add(org)\n\n            return Response(serializer.data, status.HTTP_201_CREATED)\n        else:\n            return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n    @action(detail=True)\n    def projects(self, request, pk):\n        organisation = self.get_object()\n        projects = organisation.projects.all()\n        return Response(ProjectSerializer(projects, many=True).data)\n\n    @action(detail=True)\n    def users(self, request, pk):\n        organisation = self.get_object()\n        users = organisation.users.all()\n        return Response(UserFullSerializer(users, many=True).data)\n\n    @action(detail=True, methods=[\"POST\"])\n    def invite(self, request, pk):\n        organisation = self.get_object()\n\n        if \"emails\" not in request.data:\n            raise ValidationError({\"detail\": \"List of emails must be provided\"})\n\n        if \"frontend_base_url\" not in request.data:\n            raise ValidationError({\"detail\": \"Must provide base url\"})\n\n        invites = []\n\n        for email in request.data[\"emails\"]:\n            invites.append({\"email\": email, \"frontend_base_url\": request.data[\"frontend_base_url\"],\n                            \"organisation\": organisation.id, \"invited_by\": self.request.user.id})\n\n        invites_serializer = InviteSerializer(data=invites, many=True)\n\n        if invites_serializer.is_valid():\n            invites_serializer.save()\n        else:\n            raise ValidationError(invites_serializer.errors)\n\n        return Response(status=status.HTTP_201_CREATED)\n","sub_path":"src/organisations/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2462,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"261013412","text":"#  Cloudera Airflow Provider\n#  (C) Cloudera, Inc. 2021-2021\n#  All rights reserved.\n#  Applicable Open Source License: Apache License Version 2.0\n#\n#  NOTE: Cloudera open source products are modular software products\n#  made up of hundreds of individual components, each of which was\n#  individually copyrighted.  Each Cloudera open source product is a\n#  collective work under U.S. Copyright Law. Your license to use the\n#  collective work is as provided in your written agreement with\n#  Cloudera.  Used apart from the collective work, this file is\n#  licensed for your use pursuant to the open source license\n#  identified above.\n#\n#  This code is provided to you pursuant a written agreement with\n#  (i) Cloudera, Inc. or (ii) a third-party authorized to distribute\n#  this code. If you do not have a written agreement with Cloudera nor\n#  with an authorized and properly licensed third party, you do not\n#  have any rights to access nor to use this code.\n#\n#  Absent a written agreement with Cloudera, Inc. (“Cloudera”) to the\n#  contrary, A) CLOUDERA PROVIDES THIS CODE TO YOU WITHOUT WARRANTIES OF ANY\n#  KIND; (B) CLOUDERA DISCLAIMS ANY AND ALL EXPRESS AND IMPLIED\n#  WARRANTIES WITH RESPECT TO THIS CODE, INCLUDING BUT NOT LIMITED TO\n#  IMPLIED WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY AND\n#  FITNESS FOR A PARTICULAR PURPOSE; (C) CLOUDERA IS NOT LIABLE TO YOU,\n#  AND WILL NOT DEFEND, INDEMNIFY, NOR HOLD YOU HARMLESS FOR ANY CLAIMS\n#  ARISING FROM OR RELATED TO THE CODE; AND (D)WITH RESPECT TO YOUR EXERCISE\n#  OF ANY RIGHTS GRANTED TO YOU FOR THE CODE, CLOUDERA IS NOT LIABLE FOR ANY\n#  DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, PUNITIVE OR\n#  CONSEQUENTIAL DAMAGES INCLUDING, BUT NOT LIMITED TO, DAMAGES\n#  RELATED TO LOST REVENUE, LOST PROFITS, LOSS OF INCOME, LOSS OF\n#  BUSINESS ADVANTAGE OR UNAVAILABILITY, OR LOSS OR CORRUPTION OF\n#  DATA.\n\n\n\"\"\"\nImplementation of the CDP API signature specification, V1\n\"\"\"\nfrom base64 import b64decode, urlsafe_b64encode\nfrom collections import OrderedDict\nimport json\n\nfrom urllib.parse import urlparse\n\n\nfrom pure25519 import eddsa\n\n\n\ndef create_canonical_request_string(method,\n                                    uri,\n                                    headers,\n                                    auth_method):\n    \"\"\"\n    Create a canonical request string from aspects of the request.\n    \"\"\"\n    headers_of_interest = []\n    for header_name in ['content-type', 'x-altus-date']:\n        found = False\n        for key in headers:\n            key_lc = key.lower()\n            if headers[key] is not None and key_lc == header_name:\n                headers_of_interest.append(headers[key].strip())\n                found = True\n        if not found:\n            headers_of_interest.append('')\n\n    # Our signature verification with treat a query with no = as part of the\n    # path, so we do as well. It appears to be a behavior left to the server\n    # implementation, and python and our java servlet implementation disagree.\n    uri_components = urlparse(uri)\n    path = uri_components.path\n    if not path:\n        path = '/'\n    if uri_components.query and '=' not in uri_components.query:\n        path += '?' + uri_components.query\n\n    canonical_string = method.upper() + '\\n'\n    canonical_string += '\\n'.join(headers_of_interest) + '\\n'\n    canonical_string += path + '\\n'\n    canonical_string += auth_method\n\n    return canonical_string\n\n\ndef create_signature_string(canonical_string,\n                            private_key):\n    \"\"\"\n    Create the string form of the digital signature of the canonical request\n    string.\n    \"\"\"\n    seed = b64decode(private_key)\n    if len(seed) != 32:\n        raise Exception('Not an Ed25519 private key!')\n    public_key = eddsa.publickey(seed)\n    signature = eddsa.signature(canonical_string.encode('utf-8'), seed,\n                                public_key)\n    return urlsafe_b64encode(signature).strip().decode('utf-8')\n\n\ndef create_encoded_authn_params_string(access_key,\n                                       auth_method):\n    \"\"\"\n    Create the base 64 encoded string of authentication parameters.\n    \"\"\"\n    auth_params = OrderedDict()\n    auth_params['access_key_id'] = access_key\n    auth_params['auth_method'] = auth_method\n    encoded_json = json.dumps(auth_params).encode('utf-8')\n    return urlsafe_b64encode(encoded_json).strip()\n\n\ndef create_signature_header(encoded_authn_params,\n                            signature):\n    \"\"\"\n    Combine the encoded authentication parameters string and signature string\n    into the signature header value.\n    \"\"\"\n    return \"%s.%s\" % (encoded_authn_params.decode('utf-8'), signature)\n\n\ndef make_signature_header(method,\n                          uri,\n                          headers,\n                          access_key,\n                          private_key):\n    \"\"\"\n    Generates the value to be used for the x-altus-auth header in the service\n    call.\n    \"\"\"\n    if len(private_key) != 44:\n        raise Exception(\"Only ed25519v1 keys are supported!\")\n\n    auth_method = 'ed25519v1'\n\n    canonical_string = create_canonical_request_string(method, uri, headers,\n                                                       auth_method)\n    signature = create_signature_string(canonical_string, private_key)\n    encoded_authn_params = create_encoded_authn_params_string(access_key,\n                                                              auth_method)\n    signature_header = create_signature_header(encoded_authn_params, signature)\n    return signature_header\n","sub_path":"cloudera_airflow_provider/cloudera/cdp/security/cdp_requests/cdpv1sign.py","file_name":"cdpv1sign.py","file_ext":"py","file_size_in_byte":5555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"327811644","text":"# Copyright (c) 2008 Alon Swartz <alon@turnkeylinux.org> - all rights reserved\n# Modified and adapted by Romain Forlot.\n# Copyright (c) 2014 Romain Forlot <romain.forlot@syleps.fr> - all rights reserved\n\nimport re\nimport os\nimport executil\nimport ifutil\nimport ipaddr\n\nclass Error(Exception):\n    pass\n\ndef path(filename):\n    '''\n    Try to find file in some default dir\n    Return abs path it finds.\n    '''\n    for dir in (\"/etc\", \"conf\", \"/etc/bootconsole\"):\n        path = os.path.join(dir, filename)\n        if os.path.exists(path):\n            return path\n\n    raise Error('could not find configuration file: %s' % path)\n\nclass Conf:\n    def __init__(self, conf_file, sep=None, merge=False):\n        self.param = []\n        self.sep = sep\n        self.merge = merge\n        self.conf_file = path(conf_file)\n        self._load_conf()\n\n    @staticmethod\n    def is_conf_already_configured(conf_file):\n        fh = open (conf_file, 'r')\n        for line in fh:\n            if \"# Syleps configuration\" in line or \"# SYLEPS CONFCONSOLE\" in line:\n                return True\n                \n        return False\n\n    def _load_conf(self):\n        if not os.path.exists(self.conf_file):\n            return\n\n        for line in file(self.conf_file).readlines():\n            line = line.strip()\n\n            if not line or line.startswith(\"#\"):\n                continue\n\n            self.param.append(line)\n\n    def del_param(self, key):\n        for elt in self.param:\n            if elt.startswith(key):\n                self.param.remove(elt)\n        for elt in self.param:\n            if elt.startswith(key):\n                self.param.remove(elt)\n\n    def get_param(self, key, bare=False):\n        ret = []\n        for elt in self.param:\n            if elt.split()[0] == key:\n                if bare == False:\n                    try:\n                        ret.append(elt.split(self.sep, 1)[1])\n                    except IndexError:\n                        ret.append('')\n                else:\n                    ret.append(elt)\n\n        if len(ret) == 1:\n            ret = ret[0]\n            \n        return ret\n\n    # Set a parameter at a given position or a the end by default.\n    # That does not replace an existing parameter but add a new one.\n    def set_param(self, key, val, index=None):\n        if index == None :\n            try:\n                line = key + self.sep + val\n            except TypeError:\n                line = key + ' ' + val\n            self.param.append(line)\n        else:\n            try:\n                self.param.insert(index, key + self.sep + val)\n            except TypeError:\n                self.param.insert(index, key + ' ' + val)\n\n    def change_param(self, key, val):\n        self.del_param(key)\n        self.set_param(key, val)\n\n    def write_conf(self):\n        try:\n            fh = open(self.conf_file, 'w')\n            fh.write(\"# Syleps configuration\\n\")\n            fh.write(\"# Don't modifiy this part !\\n\")\n            for elt in self.param:\n                fh.write(elt + \"\\n\")\n            fh.write(\"# End Syleps\\n\")\n            fh.close()\n        except:\n            return \"Something goes wrong when attempting to write file.\"\n\n    def set_hosts(self, ip, hostname, aliases, peer_hostname, peer_aliases, peer_ip):\n        # Set domain name if not provided\n        if not '.' in hostname:\n            hostname = hostname + \".sydel.univers\"\n        if not '.' in peer_hostname:\n            peer_hostname = peer_hostname + \".sydel.univers\"\n\n        ifutil.NetworkSettings().set_hostname(hostname)\n\n        all_aliases = aliases+peer_aliases\n        original_content = []\n        is_custom = False\n        fh = open(self.conf_file, 'r')\n        for line in fh.readlines():\n            for pattern in all_aliases:\n                    if re.search(pattern, line) is not None:\n                        continue\n            if line.startswith('# End Syleps'):\n                is_custom = False\n                continue\n            if is_custom == True:\n                continue\n            if line.startswith('# Syleps configuration'):\n                is_custom = True\n                continue\n            original_content.append(line)\n\n        fh.close()\n\n        fh = open(self.conf_file, 'w')\n        fh.writelines(original_content)\n        fh.close()\n\n        shortname = ''\n        peer_shortname = ''\n        if not hostname.split('.',1)[0] == hostname:\n            shortname = hostname.split('.', 1)[0] + '\\t'\n        if not peer_hostname.split('.', 1)[0] == peer_hostname:\n            peer_shortname = peer_hostname.split('.', 1)[0] + '\\t'\n\n        fh = open(self.conf_file, 'w')\n        fh.writelines(original_content)\n        fh.write(\"# Syleps configuration\\n\")\n        fh.write(\"# Don't modify this part !\\n\")\n        fh.write(\"# @IP\\tFQDN\\tShortname\\tOptionals aliases\\tMandatory component specification\\n\")\n        fh.write(ip + \"\\t\" + hostname + \"\\t\" + shortname + '\\t'.join(aliases) +\"# LOCAL\\n\")\n        fh.write(peer_ip + \"\\t\" + peer_hostname + \"\\t\" + peer_shortname + '\\t'.join(peer_aliases)+\"# PARTNER\\n\")\n        fh.write(\"# End Syleps hosts\\n\")\n        fh.close()\n\n    def get_host(self, host):\n        for line in self.param:\n            if host in line:\n                if '#' in line:\n                    elt, comment = line.split('#')\n                    elt = elt.strip()\n                    comment = comment.strip()\n                else:\n                    elt = line.strip()\n                    comment = ''\n                v = elt.split()[1:]\n                aliases = v[2:-1]\n                return { 'hostname': v[0],\n                        'ip' : elt.split()[0],\n                        'aliases': ','.join(aliases),\n                        'comment' : comment,\n                }\n        return { 'hostname': '',\n                 'ip': '',\n                 'aliases': '',\n                 'comment': '',\n                }","sub_path":"bootconsole/conf.py","file_name":"conf.py","file_ext":"py","file_size_in_byte":5908,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"46426550","text":"import requests\nfrom vibes.models import *\nfrom weeklyvibes import slack_utils\nimport urllib\nimport unidecode\n\nclass LastFMInfoGetter:\n\n    @classmethod\n    def get_artist_bio(cls, artist):\n        artist_name = unidecode.unidecode(artist.name)\n        f = {\n            'method': 'artist.getinfo',\n            'artist': artist.name.encode('utf-8'),\n            'api_key': 'f6b7fc447ef19d732008e34523371338',\n            'format': 'json'\n            }\n        search_url = \"http://ws.audioscrobbler.com/2.0?\" + urllib.urlencode(f)\n        r = requests.get(search_url)\n        if r.status_code is 200:\n            json = r.json()\n            if json.has_key('artist'):\n                artist_bio = json['artist']['bio']['content']\n                if len(artist_bio) > 0:\n                    artist.bio = artist_bio\n                    artist.save()\n                    return True\n        else:\n            slack_utils.send_error_slack_message('Last fm returned status code: %d' % (r.status_code))\n\n        return False\n","sub_path":"weeklyvibes/vibes/weekly_setup/lastfm_info_getter.py","file_name":"lastfm_info_getter.py","file_ext":"py","file_size_in_byte":1019,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"168700190","text":"\"\"\"\n      Loop over a set of dT/dz lapse rates and save the TOA radiances\n      Plot the brightness temperature difference between channel 1 (weak absorption) and \n      channel 7 (strong absoption) \n\"\"\"\nimport pdb\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom a301lib.radiation import Blambda, planckInvert\nplt.style.use('ggplot')\n#\n# OrderedDict keeps keys in order\n#\nfrom collections import OrderedDict\n\nkm2m = 1.e3  #convert km to meters\nm2km = 1.e-3\nmum2m = 1.e-6  #convert microns to meters\n\ndef hydrostat(T_surf, p_surf, dT_dz, delta_z, num_levels):\n    \"\"\"\n    build a hydrostatic atmosphere by integrating the hydrostatic equation from the surface,\n    using num_layers=num_levels-1 of constant thickness delta_z\n\n    Parameters\n    ----------\n\n    T_surf:  float\n          surface temperature  (K)\n\n    p_surf: float \n          surface pressure (Pa)\n\n    dT_dz: float\n          constant rate of temperature change with height in K/m\n\n    delta_z: float\n          layer thickness in m\n\n    num_levels: float\n          number of levels in the atmosphere\n\n    Returns\n    -------\n\n        out: tuple\n          tuple of numpy arrays: Temp (K) , press (Pa), rho (kg/m^3), height (m)\n    \"\"\"\n    Rd = 287.  #J/kg/K  -- gas constant for dry air\n    g = 9.8  #m/s^2\n    Temp = np.empty([num_levels])\n    press = np.empty_like(Temp)\n    rho = np.empty_like(Temp)\n    height = np.empty_like(Temp)\n    #\n    # layer 0 sits directly above the surface, so start\n    # with pressure, temp of air equal to ground temp, press\n    # and get density from equaiton of state\n    #\n    press[0] = p_surf\n    Temp[0] = T_surf\n    rho[0] = p_surf / (Rd * T_surf)\n    height[0] = 0\n    num_layers = num_levels - 1\n    #now march up the atmosphere a layer at a time\n    for i in range(num_layers):\n        delP = -rho[i] * g * delta_z\n        height[i + 1] = height[i] + delta_z\n        Temp[i + 1] = Temp[i] + dT_dz * delta_z\n        press[i + 1] = press[i] + delP\n        rho[i + 1] = press[i + 1] / (Rd * Temp[i + 1])\n    out= (Temp, press, rho, height)\n    return out\n\n\ndef find_tau(r_gas, k_lambda, rho, height):\n    \"\"\"\n    calculate the optical depth profile given an atmospheric rho, height profile\n\n    Parameters\n    ----------\n\n    r_gas:  float\n        gas mixing ratio (kg/kg)\n\n    k_lambda: float \n        mass absorption coefficient (kg/m^2)\n\n    rho: float vector \n        air density profile with height (kg/m^3)\n\n    height: \n       layer heights (m)\n\n    Returns\n    -------\n\n    tau:  float vector\n       vetical optical depth from the surface, same shape as rho\n    \"\"\"\n    tau = np.empty_like(rho)\n    tau[0] = 0\n    num_levels = len(rho)\n    num_layers = num_levels - 1\n    for index in range(num_layers):\n        delta_z = height[index + 1] - height[index]\n        delta_tau = r_gas * rho[index] * k_lambda * delta_z\n        tau[index + 1] = tau[index] + delta_tau\n    return tau\n\n\ndef top_radiance(tau, Temp, height, T_surf, wavel, k_lambda):\n    \"\"\"\n     Return the top of atmosphere\n     radiance at wavelength wavel using Stull equation 8.3\n\n     Parameters\n     ----------\n\n       tau: vector \n            level optical depths\n\n       Temp: vector \n           level temperatures (K)\n\n       height: vector \n            level heights (m)\n\n       T_surf: float\n            temperature of black surface (K)\n\n       wavel: float\n            wavelength (m)\n\n       k_lambda: float\n            mass absorption coefficient (m^2/kg)\n\n     Returns\n     -------\n\n       top_rad: radiance at top of atmosphere (W/m^2/micron/sr)\n    \"\"\"\n    sfc_rad = Blambda(wavel, T_surf)\n    up_rad = sfc_rad\n    print(\"-\" * 60)\n    print(\"wavelength: %8.2f microns\" % (wavel * 1.e6))\n    print(\"surface radiation: %8.2f W/m^2/micron/sr\" % (up_rad * 1.e-6))\n    print(\"total tau: %8.2f\" % tau[-1])\n    print(\"-\" * 60)\n    tot_levs = len(tau)\n    for index in np.arange(1, tot_levs):\n        upper_lev = index\n        lower_lev = index - 1\n        del_tau = tau[upper_lev] - tau[lower_lev]\n        trans = np.exp(-del_tau)\n        emiss = 1 - trans\n        layer_rad = emiss * Blambda(wavel, Temp[lower_lev])\n        #\n        # find the radiance at the next level\n        #\n        up_rad = trans * up_rad + layer_rad\n    return up_rad\n\n\nif __name__ == \"__main__\":\n    #\n    # \n    #\n    r_gas = 0.01  #kg/kg\n    T_surf = 300  #K\n    p_surf = 100.e3  #Pa\n    dT_dz = np.arange(-9.e-3, -4.e-3, 0.5e-3)\n    delta_z = 25000 / 7\n    num_levels = 7\n    #\n    # try to duplicate weighting functions for WH fig. 4.33\n    #\n    wavenums = np.linspace(666, 766, 7)\n    wavelengths = (1 / wavenums) * 1.e4  #microns\n    #\n    # we want most absorbing channel at 15 microns, so reverse\n    # the order of the wavelengths so that 15 microns is\n    # at the end\n    #\n    rad_profs = []\n    bright_profs = []\n    soundings=[]\n    for the_lapse_rate in dT_dz:\n        print(\"looping: \")\n        Temp, press, rho, height = hydrostat(T_surf, p_surf, the_lapse_rate,\n                                             delta_z, num_levels)\n        #\n        # I played around with the magnitude of k_lambda until the weighting functions\n        # peaked at a range of heights\n        #\n        #k_lambda=np.array([ 0.1  ,  0.08 ,  0.06 ,  0.05 ,  0.03 ,  0.015,  0.01 ])\n        k_lambda=np.array([ 0.175 , 0.15 ,  0.125 , 0.1  ,  0.075,  0.05 ,  0.025])\n        soundings.append((Temp,height,the_lapse_rate))\n        wavel_k_tup = zip(wavelengths, k_lambda)\n        rad_dict = OrderedDict()\n        bright_dict = OrderedDict()\n        for wavel, k_lambda in wavel_k_tup:\n            tau = find_tau(r_gas, k_lambda, rho, height)\n            #convert wavel to meters\n            rad_value = top_radiance(tau, Temp, height, T_surf, wavel * 1.e-6,\n                                     k_lambda)\n            rad_dict[wavel] = rad_value\n            bright_dict[wavel] = planckInvert(wavel * mum2m, rad_value)\n        rad_profs.append(rad_dict)\n        bright_profs.append(bright_dict)\n\n    plt.close('all')\n    fig0, axis0 = plt.subplots(1, 1)\n    for Temp,height,the_dT_dz in soundings:\n        axis0.plot(Temp, height*m2km, label='{:4.1f} (K/km)'.format(the_dT_dz * km2m))\n    axis0.set(xlabel = 'temperature (K)',\n              ylabel= 'height (km)',\n              title = 'temperature profiles for different dT/dz')\n    axis0.legend(loc='lower center', bbox_to_anchor=(0.3, 0.05),\n                 ncol=2)\n    fig0.savefig('soundings.png')\n    \n    fig1, axis1 = plt.subplots(1, 1)\n    for index, the_profile in enumerate(rad_profs):\n        wavelengths = list(the_profile.keys())\n        radiances = np.array(list(the_profile.values()))\n        radiances = radiances / radiances.mean()\n        #\n        # convert dT_dz to K/km\n        #\n        axis1.plot(wavelengths, radiances, label='{:4.1f} (K/km)'.format(dT_dz[index] * km2m))\n    axis1.set_title(\n        'normalized radiances at top of atmosphere for {} values of dT/dz (K/km)'.\n        format(len(dT_dz)))\n    axis1.set_ylabel('normalized radiances (no units)')\n    axis1.set_xlabel('wavelength (microns)')\n    axis1.legend(loc='best')\n    fig1.savefig('normalized_radiances.png')\n\n    fig2, axis2 = plt.subplots(1, 1)\n    for index, the_profile in enumerate(bright_profs):\n        wavelengths = list(the_profile.keys())\n        brights = np.array(list(the_profile.values()))\n        #\n        # convert dT_dz to K/km\n        #\n        axis2.plot(wavelengths, brights, label='{:4.1f} (K/km)'.format(dT_dz[index] * 1.e3))\n\n    axis2.set_title(\n        'brightness temperatures at top of atmosphere for {} values of dT/dz (K/km)'.\n        format(len(dT_dz)))\n    axis2.set_ylabel('Brightness temperature (K)')\n    axis2.set_xlabel('wavelength (microns)')\n    axis2.legend(loc='best')\n    fig2.savefig('brightness.png')\n\n    fig3, axis3 = plt.subplots(1, 1)\n    lapse_rate = []\n    diff_list = []\n    for index, the_profile in enumerate(bright_profs):\n        brights = np.array(list(the_profile.values()))\n        #\n        # dT_dz in K/km\n        #\n        lapse_rate.append(dT_dz[index] * 1.e3)\n        diff_list.append(brights[0] - brights[-1])\n\n    axis3.plot(lapse_rate, diff_list)\n\n    axis3.set_title(\n        '15 $\\mu m$ - 13 $\\mu m$ brightness temperature difference (K)')\n    axis3.set_ylabel('Brightness temperature differnce (K)')\n    axis3.set_xlabel('lapse rate (K/km)')\n    fig3.savefig('temp_diff.png')\n\n    fig4, axis4 = plt.subplots(1, 1)\n    lapse_rate = []\n    diff_list = []\n    for index, the_profile in enumerate(bright_profs):\n        brights = np.array(list(the_profile.values()))\n        #\n        # dT_dz in K/km\n        #\n        lapse_rate.append(dT_dz[index] * 1.e3)\n        diff_list.append(2. * (brights[0] - brights[-1]) /\n                         (brights[0] + brights[-1]))\n\n    axis4.plot(lapse_rate, diff_list)\n\n    axis4.set_title('$(T_{15} - T_{13})/ ( \\overline{T_{bright}}  )$')\n    axis4.set_ylabel('normalized brightness temperature differnce (no units)')\n    axis4.set_xlabel('lapse rate (K/km)')\n    fig4.savefig('normalized_temp_diff.png')\n\n    plt.show()\n","sub_path":"a301examples/diff_lapse_toa.py","file_name":"diff_lapse_toa.py","file_ext":"py","file_size_in_byte":9034,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"403325101","text":"import base64\nimport os\nfrom time import time, localtime, strftime, strptime, mktime\n\n\n# 时间戳转换成日期\ndef timestamp_to_date(timestamp):\n    # 转换为其他日期格式,如:\"%Y-%m-%d %H:%M:%S\"\n    timeArray = localtime(timestamp)  # 30/12/2020 21:05:19\n    otherStyleTime = strftime(\"%Y-%m-%d %H:%M:%S\", timeArray)\n    return otherStyleTime\n\n# 把时间转换成秒数\ndef str_to_timestamp(str_time=None, format='%Y-%m-%d %H:%M:%S'):\n    if str_time:\n        time_tuple = strptime(str_time, format)  # 把格式化好的时间转换成元祖\n        result = mktime(time_tuple)  # 把时间元祖转换成时间戳\n        return int(result)\n    return int(time())\n\n# base64数据转换成图片 返回图片保存的路径\ndef base64ToPicture(dataList, title,path='static/imgs/imgInBlog'):\n    #去掉文件名中的特殊符号\n    for i in '/ \\:*\"<>|?.':\n        title=title.replace(i,'')\n    title=title+str(time())\n\n    date = timestamp_to_date(time()).split(' ')[0].replace('-', '')\n    fileNameList = []\n    for data in dataList:\n        imgdata = base64.b64decode(data)\n        fileName = title + str(dataList.index(data)) + '.png'\n        path = f'{path}/{date}/'\n        if not os.path.exists(path):\n            os.mkdir(path)\n        f= open(path + fileName, 'wb')\n        f.write(imgdata)\n        f.close()\n\n        fileNameList.append(f'{path}' + fileName)\n    return fileNameList","sub_path":"myTool/tools.py","file_name":"tools.py","file_ext":"py","file_size_in_byte":1406,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"422853819","text":"#!/usr/bin/env python3\n\n# Create a Letsencrypt certificate(https: // letsencrypt.org) and store it in an azure key vault.\n\nimport json\nimport subprocess\nimport os\nimport sys\nimport argparse\nimport base64\nimport re\nimport logging\nimport string\n\nfrom time import gmtime, strftime, strptime\nfrom datetime import datetime, timedelta\n\nfrom python_modules import azure_account\n\nlogging.basicConfig(stream=sys.stdout, level=logging.INFO)\n\nparser = argparse.ArgumentParser(\n    description='Script to create LetsEncrypt SSL certificates and store in Azure Key Vault')\n\nparser.add_argument(\"-z\", \"--zone\", help=\"DNS Zone\")\nparser.add_argument(\"-n\", \"--hostname\", help=\"Hostname\")\nparser.add_argument(\"-g\", \"--resource-group\", help=\"Azure Resource Group for the DNS zone\")\nparser.add_argument(\"-s\", \"--subscription-id\", help=\"Azure Subscription ID\")\nparser.add_argument(\"-c\", \"--certbot\",\n                    help=\"Certbot configuration directory set during 'certbot register'. User must have write permissions.\")\nparser.add_argument(\n    \"-v\", \"--vault\", help=\"Azure Key Vault to store certificate in\")\nparser.add_argument(\n    \"-t\", \"--test-environment\", help=\"test mode - uses Letsencrypt staging environment\",action='store_true')\nparser.add_argument(\n    \"-e\", \"--ignore-expiry\", help=\"Ignore the expiry date check to perform an early renewal\",action='store_true')\nparser.add_argument(\n    \"-a\", \"--application-gateway\", help=\"Create a certificate for an Application Gateway.\",action='store_true')\nparser.add_argument(\n    \"-o\", \"--certificate-only\", help=\"Create a certificate but don't store in vault.\",action='store_true')\nparser.add_argument(\n    \"-x\", \"--extra-host\", help=\"Create an additional host on the certificate.\")\nparser.add_argument(\n        \"-y\", \"--extra-zone\", help=\"Add the zone or the additional host on the certificate.\")\n\nargs = parser.parse_args()\n\nhostname = args.hostname\nextra_host = args.extra_host\n\nif args.test_environment:\n    hostname = \"test-\" + hostname\n    if extra_host:\n        extra_host = \"test-\" + extra_host\n\n#    args.ignore_expiry = True\n\nfqdn = hostname + '.' + args.zone\n\n\ndef get_zone_details(resource_group, zone):\n\n    find_dns_records = subprocess.run(\n        [\"az\", \"network\", \"dns\", \"record-set\", \"soa\", \"show\",\n         \"-g\", resource_group, \"-z\", zone\n         ],\n        stdout=subprocess.PIPE,\n        check=True\n    ).stdout.decode()\n\n    if find_dns_records:\n        dns_records = json.loads(find_dns_records)\n\n        key = \"fqdn\"\n\n        for key, zone in dns_records.items():\n            return True\n        else:\n            return False\n    else:\n        return False\n\n\ndef create_certificate(dns_names, fqdn, resource_group, certbot_location):\n\n    path_to_hook_scripts = azure_account.get_git_root() + '/tools/letsencrypt'\n\n    manual_auth_hook = \"python3 {path_to_scripts}/authenticator.py '{dns_names}' {resource_group}\".format(\n        dns_names= json.dumps(dns_names), resource_group=resource_group, path_to_scripts=path_to_hook_scripts)\n\n    manual_cleanup_hook = \"python3 {path_to_scripts}/cleanup.py '{dns_names}' {resource_group}\".format(\n        dns_names= json.dumps(dns_names), resource_group=resource_group, path_to_scripts=path_to_hook_scripts)\n\n    logging.info(\"Creating certificate\")\n\n    common_name = '.'.join(dns_names[\"common_name\"])\n\n    domains_to_renew = []\n\n    domains_to_renew.extend([\"-d\", common_name])\n\n    if \"additional_name\" in dns_names:\n        additional_name = '.'.join(dns_names[\"additional_name\"])\n        domains_to_renew.extend([\"-d\", additional_name])\n\n    cmd = [\"certbot\", \"certonly\", \"--manual\",\n           \"--email\", \"noms-studio-webops@digital.justice.gov.uk\",\n           \"--preferred-challenges\", \"dns\",\n           \"--manual-auth-hook\", manual_auth_hook,\n           \"--manual-cleanup-hook\", manual_cleanup_hook,\n           \"--manual-public-ip-logging-ok\",\n           \"--config-dir\", certbot_location,\n           \"--work-dir\", certbot_location,\n           \"--logs-dir\", certbot_location,\n           \"--force-renewal\",\n           \"--agree-tos\",\n           \"--non-interactive\"\n           ]\n\n    cmd.extend(domains_to_renew)\n\n    if args.test_environment:\n        cmd.append('--staging')\n        logging.info(\"Using staging environment\")\n\n    try:\n        certificate = subprocess.run(\n            cmd,\n            stdout=subprocess.PIPE,\n            check=True\n        ).stdout.decode()\n\n    except subprocess.CalledProcessError:\n        sys.exit(\"There was an error creating the certificate\")\n\n    saved_cert = re.search(\"saved at:\\n(.+?)/fullchain\\.pem\", certificate)\n\n    path_to_cert = saved_cert.group(1).strip()\n\n    logging.info(\"The certificate is saved at \" + path_to_cert)\n\n    if os.path.exists(path_to_cert):\n        logging.info(\"Certificate created\")\n        return path_to_cert\n    else:\n        logging.error(\"There was an error creating the certificate\")\n        return False\n\n\ndef create_pkcs12(saved_cert,vault):\n\n    path_to_cert = saved_cert + \"/fullchain.pem\"\n    path_to_key = saved_cert + \"/privkey.pem\"\n    export_path = saved_cert + \"/pkcs.p12\"\n\n    set_passphrase = \"pass:\"\n\n    if args.application_gateway:\n        password = azure_account.create_password()\n        set_passphrase = \"pass:\" + password\n\n        store_password(password,vault)\n\n    subprocess.run(\n        [\"openssl\", \"pkcs12\", \"-export\",\n         \"-inkey\", path_to_key,\n         \"-in\", path_to_cert,\n         \"-out\", export_path,\n         \"-passout\", set_passphrase\n         ],\n        stdout=subprocess.PIPE,\n        check=True\n    ).stdout.decode()\n\n    if os.path.exists(export_path):\n        return export_path\n    else:\n        sys.exit(\"There was a problem creating the certificate\")\n\n\ndef store_certificate(vault, fqdn, certbot_location,saved_cert):\n\n    name = fqdn.replace(\".\", \"DOT\")\n\n    if args.application_gateway:\n        name = \"appgw-ssl-certificate\"\n        if args.test_environment:\n            name = \"test-\" + name\n\n    cert_file = create_pkcs12(saved_cert,vault)\n\n    cert_dates = get_local_certificate_expiry(saved_cert)\n\n    open_pkcs12 = open(cert_file, 'rb').read()\n    cert_encoded = base64.encodestring(open_pkcs12)\n\n    try:\n        set_secret = subprocess.run(\n            [\"az\", \"keyvault\", \"secret\", \"set\",\n             \"--file\", cert_file,\n             \"--encoding\", \"base64\",\n             \"--name\", name,\n             \"--vault-name\", vault\n             ],\n            stdout=subprocess.PIPE,\n            check=True\n        )\n\n    except subprocess.CalledProcessError:\n        sys.exit(\"There was an error saving to the key vault\")\n\n    if set_secret.returncode == 0:\n        set_secret_attributes = subprocess.run(\n            [\"az\", \"keyvault\", \"secret\", \"set-attributes\",\n             \"--name\", name,\n             \"--content-type\", \"application/x-pkcs12\",\n             \"--expires\", cert_dates[\"end\"],\n             \"--not-before\", cert_dates[\"start\"],\n             \"--vault-name\", vault\n             ],\n            stdout=subprocess.PIPE,\n            check=True\n        )\n\n        if set_secret_attributes.returncode == 0:\n            logging.info(\"Certificate successfully stored to vault.\")\n            return True\n        else:\n            sys.exit(\"Could not set secret attributes in key vault.\")\n    else:\n        sys.exit(\"Could not set secret in key vault.\")\n\ndef store_password(password,vault):\n\n    name = \"appgw-ssl-certificate-password\"\n\n    if args.test_environment:\n        name = \"test-\" + name\n\n    try:\n        set_secret = subprocess.run(\n            [\"az\", \"keyvault\", \"secret\", \"set\",\n             \"--value\", password,\n             \"--encoding\", \"base64\",\n             \"--name\", name,\n             \"--vault-name\", vault\n             ],\n            stdout=subprocess.PIPE,\n            check=True\n        )\n\n    except subprocess.CalledProcessError:\n        sys.exit(\"There was an error saving the passowrd to the vault\")\n\ndef get_local_certificate_expiry(saved_cert):\n\n    cert_dates = {}\n\n    cert_expiry_data = subprocess.run(\n        [\"openssl\", \"x509\",\n         \"-startdate\",\n         \"-enddate\",\n         \"-noout\",\n         \"-in\", saved_cert + \"/fullchain.pem\",\n         \"-inform\", \"pem\"\n         ],\n        stdout=subprocess.PIPE,\n        check=True\n    ).stdout.decode()\n\n    cert_expiry = cert_expiry_data.splitlines(True)\n\n    return get_certificate_dates(cert_expiry)\n\n\ndef get_remote_certificate_expiry(fqdn):\n\n    #    Use subprocess to run the shell command 'echo \"Q\" | openssl s_client -servername NAME -connect HOST:PORT 2>/dev/null | openssl x509 -noout -dates'\n\n    echo = subprocess.run(\n        [\"echo\", \"Q\"],\n        stdout=subprocess.PIPE,\n        check=True\n    )\n\n    try:\n        openssl_sclient = subprocess.run(\n            [\"openssl\", \"s_client\",\n             \"-servername\", fqdn,\n             \"-connect\", fqdn + \":443\"\n             ],\n            stdout=subprocess.PIPE,\n            stderr=subprocess.DEVNULL,\n            input=echo.stdout,\n            check=True\n        )\n    except:\n        logging.info(\"Could not connect to host\")\n        return False\n\n    try:\n        openssl_dates = subprocess.run(\n            [\"openssl\", \"x509\",\n             \"-startdate\",\n             \"-enddate\",\n             \"-noout\"\n             ],\n            stdout=subprocess.PIPE,\n            input=openssl_sclient.stdout,\n            check=True\n        ).stdout.decode()\n    except:\n       logging.info(\"Could not read certificate\")\n       return False\n\n    cert_expiry = openssl_dates.splitlines(True)\n\n    return get_certificate_dates(cert_expiry)\n\n\ndef get_certificate_dates(openssl_response):\n\n    cert_dates = {}\n\n    for certdate in openssl_response:\n\n        if certdate.find(\"notBefore\") != -1:\n            start_end = \"start\"\n\n        elif certdate.find(\"notAfter\") != -1:\n            start_end = \"end\"\n        else:\n            sys.exit(\"The certificate appears to have missing dates\")\n\n        certdate = certdate.rstrip()\n        certdate = re.sub(r\"not(After|Before)=\", '', certdate)\n\n        expiry_date_from_string = strptime(\n            certdate, \"%b %d %H:%M:%S %Y %Z\")\n\n        formatted_expiry_date = strftime(\n            \"%Y-%m-%dT%H:%M:%SZ\", expiry_date_from_string)\n\n        cert_dates[start_end] = formatted_expiry_date\n\n    return cert_dates\n\n\ndef check_dns_name_exits(host, zone, resource_group):\n    # az network dns record-set cname show -n notm-deva -g webops -z hmpps.dsd.io\n\n    cmd = [\"az\", \"network\", \"dns\", \"record-set\",\n     \"cname\", \"show\",\n     \"-n\", host,\n     \"-z\", zone,\n     \"-g\", resource_group\n     ]\n\n    logging.info(\"Trying CNAME record\")\n    cname = subprocess.run(\n        cmd,\n        stdout=subprocess.PIPE,\n        check=True\n    ).stdout.decode()\n\n    if cname:\n        return True\n    else:\n        cmd[4] = \"a\"\n        logging.info(\"Trying A record\")\n        a_record = subprocess.run(\n            cmd,\n            stdout=subprocess.PIPE,\n            check=True\n        ).stdout.decode()\n\n    if a_record:\n        return True\n    else:\n        return False\n\ndef certificate_renewal_due(fqdn):\n\n    remote_expiry = get_remote_certificate_expiry(fqdn)\n\n    if remote_expiry:\n        cert_end_date = datetime.strptime(\n            remote_expiry['end'], \"%Y-%m-%dT%H:%M:%SZ\")\n\n        # Adjust the renewal date to when the first letsencrypt email reminder is sent.\n        adjusted_renewal_date = cert_end_date - timedelta(days=21)\n\n        present_date = datetime.now()\n\n        if adjusted_renewal_date > present_date:\n            logging.info(\"Certificate does not need renewing until %s\" %\n                         (cert_end_date.strftime('%d %b %Y')))\n            sys.exit()\n    else:\n        logging.info(\"Couldn't check certificate.\")\n\n\n\nazure_account.azure_set_subscription(args.subscription_id)\n\nif not get_zone_details(args.resource_group, args.zone):\n    sys.exit(\"Failed to find existing zone \" + args.zone)\n\nif check_dns_name_exits(args.hostname, args.zone, args.resource_group):\n    if not args.ignore_expiry:\n        logging.info(\"Check expiry date\")\n        certificate_renewal_due(fqdn)\nelse:\n    logging.info(\"A record or CNAME doesn't exist. No expiry date to check.\")\n\ndns_names = {\n   \"common_name\": [hostname,args.zone]\n   }\nif args.extra_zone:\n   dns_names.update({\n   \"additional_name\" : [extra_host,args.extra_zone]\n   })\n\nsaved_cert = create_certificate(dns_names, fqdn, args.resource_group, args.certbot)\n\nif not args.certificate_only:\n\n    store_certificate(args.vault, fqdn, args.certbot, saved_cert)\n\n\nlogging.info(\"Certificate update complete.\")\n","sub_path":"tools/azure-letsencrypt-cli-auth.py","file_name":"azure-letsencrypt-cli-auth.py","file_ext":"py","file_size_in_byte":12488,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"27141667","text":"import sys\n\nfrom PyQt5.QtCore import Qt\nfrom PyQt5.QtWidgets import QApplication, QCheckBox, QHBoxLayout, QLabel, QLineEdit, QPushButton, QTextEdit, \\\n    QVBoxLayout, QWidget\n\nimport ngram\nfrom histogram import Histogram\n\n\nclass Window(QWidget):\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n\n        self.ngram = None\n        self.ngram_depth = 0\n        self.histogram = None\n\n        alignment = Qt.AlignLeft\n\n        # Left side\n        lbl_depth = QLabel('Depth:')\n        self.le_depth = QLineEdit('3')\n        depth_hbox = QHBoxLayout()\n        depth_hbox.addWidget(lbl_depth, alignment=alignment)\n        depth_hbox.addWidget(self.le_depth, alignment=alignment)\n\n        self.chb_is_words = QCheckBox('Words')\n        self.chb_is_words.setChecked(True)\n        btn_create = QPushButton('Go')\n        btn_create.clicked.connect(self.create_ngram)\n        btn_show_image = QPushButton('Show Image')\n        btn_show_image.clicked.connect(self.show_image)\n        chb_btn_hbox = QHBoxLayout()\n        chb_btn_hbox.addWidget(self.chb_is_words, alignment=alignment)\n        chb_btn_hbox.addWidget(btn_create, alignment=alignment)\n        chb_btn_hbox.addWidget(btn_show_image, alignment=alignment)\n\n        self.input_text = QTextEdit('mirjan has a ball bram has a laptop')\n\n        left_vbox = QVBoxLayout()\n        left_vbox.addLayout(depth_hbox)\n        left_vbox.addLayout(chb_btn_hbox)\n        left_vbox.addWidget(self.input_text, alignment=alignment)\n\n        # Right side\n        lbl_predict_count = QLabel('Predict:')\n        self.le_predict_count = QLineEdit('1')\n        predict_hbox = QHBoxLayout()\n        predict_hbox.addWidget(lbl_predict_count, alignment=alignment)\n        predict_hbox.addWidget(self.le_predict_count, alignment=alignment)\n\n        btn_generate = QPushButton('Generate')\n        btn_generate.clicked.connect(self.generate)\n        btn_clear = QPushButton('Clear')\n        btn_clear.clicked.connect(self.clear)\n        btn_hbox = QHBoxLayout()\n        btn_hbox.addWidget(btn_generate, alignment=alignment)\n        btn_hbox.addWidget(btn_clear, alignment=alignment)\n\n        self.output_text = QTextEdit()\n\n        right_vbox = QVBoxLayout()\n        right_vbox.addLayout(predict_hbox)\n        right_vbox.addLayout(btn_hbox)\n        right_vbox.addWidget(self.output_text, alignment=alignment)\n\n        main_hbox = QHBoxLayout()\n        main_hbox.addLayout(left_vbox)\n        main_hbox.addLayout(right_vbox)\n\n        self.setLayout(main_hbox)\n        self.show()\n\n    def create_ngram(self):\n        self.ngram_depth = int(self.le_depth.text())\n        self.ngram = ngram.create_ngram(\n            self.input_text.toPlainText(),\n            self.chb_is_words.isChecked(),\n            self.ngram_depth)\n        self.histogram = Histogram(self.ngram_depth, 20)\n\n    def show_image(self):\n        if self.ngram:\n            ngram.to_image(self.ngram, True)\n\n    def generate(self):\n        if self.ngram:\n            for _ in range(int(self.le_predict_count.text())):\n                if self.chb_is_words.isChecked():\n                    words = self.output_text.toPlainText().split()\n                else:\n                    words = list(self.output_text.toPlainText())\n                next_word = self.predict(words)\n                print(next_word)\n                print(self.histogram)\n                if self.chb_is_words.isChecked():\n                    self.output_text.insertPlainText(' ' + next_word)\n                else:\n                    self.output_text.insertPlainText(next_word)\n\n    def predict(self, words):\n        index = -self.ngram_depth + 1\n        predict_words = words[index:]\n        print(predict_words)\n        next_word = self.ngram.predict_histogram(predict_words, self.histogram)\n        if not next_word:\n            return self.predict(words[:index])\n        return next_word\n\n    def clear(self):\n        self.output_text.clear()\n\n\nif __name__ == '__main__':\n    app = QApplication([])\n    w = Window()\n    sys.exit(app.exec_())\n","sub_path":"gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":4032,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"647747051","text":"\"\"\"This module counts the number of lines\nin standard input.\nInput: A string from the system's standard input\nOutput: A string with the total number of lines\"\"\"\nimport sys\n\ncount=0\n\nfor line in sys.stdin:\n\tcount+=1\n\n\nprint(\"We found\",count,\"lines in the command line\")\n\n\n","sub_path":"count-lines.py","file_name":"count-lines.py","file_ext":"py","file_size_in_byte":271,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"561794799","text":"import csv\nimport os\nimport json\nfrom collections import Counter\n\nimport numpy as np\n\nimport irsg_core.data_pull as dp\nfrom config import get_config_path\n\nwith open(get_config_path()) as f:\n    cfg_data = json.load(f)\n    csv_path = cfg_data['file_paths']['csv_path']\n\n\ndef get_obj_attr_counts(if_data):\n    \"\"\"Get counts of each object and attribute in the supplied\n    ImageFetchData object.\"\"\"\n    obj_count = Counter()\n    attr_count = Counter()\n    for image in if_data.vg_data:\n        for triple in image.annotations.unary_triples:\n            attr_name = triple.object\n            attr_count[attr_name] += 1\n        for obj in image.annotations.objects:\n            obj_name = np.array(obj.names).reshape(-1)[0]\n            obj_count[obj_name] += 1\n    return obj_count, attr_count\n\n\ndef write_obj_attr_counts(obj_count, attr_count, train_objs,\n                          train_attrs, path):\n    \"\"\"Record object and attribute counts in CSV format.\"\"\"\n    obj_path = os.path.join(path, 'obj_counts.csv')\n    attr_path = os.path.join(path, 'attr_counts.csv')\n    for csv_path, count, train_vals in ((obj_path, obj_count, train_objs),\n                                        (attr_path, attr_count, train_attrs)):\n        with open(csv_path, 'w') as f:\n            csv_writer = csv.writer(f)\n            csv_writer.writerow(('name', 'count', 'is_counted'))\n            for obj_attr, count in count.most_common():\n                csv_writer.writerow((obj_attr, count, obj_attr in train_vals))\n\n\ndef count_to_threshold_list(count, thresh):\n    \"\"\"Convert a Counter object into a thresholded list of items.\"\"\"\n    return [obj for obj, obj_count in count.most_common()\n            if obj_count >= thresh]\n\n\nif __name__ == '__main__':\n    if_data_test = dp.get_ifdata(use_csv=True)\n    if_data_train = dp.get_ifdata(use_csv=True, split='train')\n    test_obj_count, test_attr_count = get_obj_attr_counts(if_data_test)\n    train_obj_count, train_attr_count = get_obj_attr_counts(if_data_train)\n    train_objs = count_to_threshold_list(train_obj_count, 50)\n    train_attrs = count_to_threshold_list(train_attr_count, 50)\n    write_obj_attr_counts(test_obj_count, test_attr_count, train_objs,\n                          train_attrs, csv_path)\n","sub_path":"code/tools/list_attributes.py","file_name":"list_attributes.py","file_ext":"py","file_size_in_byte":2238,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"148293926","text":"#Problem 2:https://leetcode.com/problems/lowest-common-ancestor-of-a-binary-search-tree/\n#Test Cases passed on Leetcode \n#Time Complexity:    O(logn) since we are rejecting half of the nodes in the BST\n#if it would have been a skewed tree then it would be O(n)\n#Space Complexity:   O(n)-recursive stack\n\n# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, x):\n#         self.val = x\n#         self.left = None\n#         self.right = None\n\n\nclass Solution:\n    def lowestCommonAncestor(self, root: 'TreeNode', p: 'TreeNode', q: 'TreeNode') -> 'TreeNode':\n        while root:\n        \t#check if p and q lies in left of root, then we'll find ancestor at left\n            if root.val>p.val and root.val>q.val:\n                return self.lowestCommonAncestor(root.left,p,q)\n            #check if p and q lies in right of root, then we'll find ancestor at right\n            elif root.val<p.val and root.val<q.val:\n                return self.lowestCommonAncestor(root.right,p,q)\n            #otherwise there could be 2 cases:\n            # Case 1: when one is greater than root and other is less than root , that is one is in right and other is in left\n            # Case 2: when root is one of p or q , then we need to return root\n            else:\n                return root","sub_path":"Problem2_LCA_BST.py","file_name":"Problem2_LCA_BST.py","file_ext":"py","file_size_in_byte":1300,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"70742002","text":"# -*- coding: utf-8 -*-\n\nfrom PyQt4 import QtCore, QtGui\n\ntry:\n    _fromUtf8 = QtCore.QString.fromUtf8\nexcept AttributeError:\n    _fromUtf8 = lambda s: s\n\nclass Ui_StreamErosionIndexDialog(object):\n    def setupUi(self, StreamErosionIndexguic):\n        StreamErosionIndexguic.setObjectName(_fromUtf8(\"StreamErosionIndex Dialog\"))\n        StreamErosionIndexguic.setWindowTitle(QtGui.QApplication.translate(\"StreamErosionIndexguic\", \"Stream Erosion Index\", None, QtGui.QApplication.UnicodeUTF8))        \n        StreamErosionIndexguic.setGeometry(QtCore.QRect(400,100,800,600))\n\n        self.verticalLayout = QtGui.QVBoxLayout(StreamErosionIndexguic)\n        self.verticalLayout.setObjectName(_fromUtf8(\"verticalLayout\"))\n\n        self.scrollArea = QtGui.QScrollArea(StreamErosionIndexguic)\n        self.scrollArea.setWidgetResizable(True)\n        self.scrollAreaWidgetContents = QtGui.QWidget(self.scrollArea)\n        self.scrollAreaWidgetContents.setGeometry(QtCore.QRect(0,0,500,500))\n        self.scrollArea.setWidget(self.scrollAreaWidgetContents)\n        '''\n        self.groupBox = QtGui.QGroupBox(StreamErosionIndexguic)\n        self.groupBox.setTitle(QtGui.QApplication.translate(\"StreamErosionIndexguic\", \"Regional climate data\", None, QtGui.QApplication.UnicodeUTF8))\n        self.groupBox.setObjectName(_fromUtf8(\"groupBox\"))\n        '''\n\n        fontBold = QtGui.QFont()\n        fontBold.setBold(True)\n        fontBigHeader = QtGui.QFont()\n        fontBigHeader.setBold(True)\n        fontBigHeader.setPointSize(14)\n\n        self.gridLayout = QtGui.QGridLayout(self.scrollAreaWidgetContents)\n        self.gridLayout.setObjectName(_fromUtf8(\"gridLayout\"))\n\n        self.bigheader = QtGui.QLabel(self.scrollAreaWidgetContents)\n        self.bigheader.setText(QtGui.QApplication.translate(\"StreamErosionIndexguic\", \"Climate data\", None, QtGui.QApplication.UnicodeUTF8))\n        self.bigheader.setObjectName(_fromUtf8(\"label\"))\n        self.bigheader.setFont(fontBigHeader)\n        self.gridLayout.addWidget(self.bigheader, 0, 0, 1, 1)  \n        \n        self.header = QtGui.QLabel(self.scrollAreaWidgetContents)\n        self.header.setText(QtGui.QApplication.translate(\"StreamErosionIndexguic\", \"Rainfall series\", None, QtGui.QApplication.UnicodeUTF8))\n        self.header.setObjectName(_fromUtf8(\"label\"))\n        self.gridLayout.addWidget(self.header, 1, 0, 1, 1)  \n\n        self.label = QtGui.QLabel(self.scrollAreaWidgetContents)\n        self.label.setText(QtGui.QApplication.translate(\"StreamErosionIndexguic\", \"      Select Csv Rain \", None, QtGui.QApplication.UnicodeUTF8))\n        self.label.setObjectName(_fromUtf8(\"label\"))\n        self.gridLayout.addWidget(self.label, 2, 0, 1, 1)\n        self.le_r = QtGui.QLineEdit(self.scrollAreaWidgetContents)\n        self.le_r.setObjectName(_fromUtf8(\"le_r\"))\n        self.gridLayout.addWidget(self.le_r, 2, 2, 1, 1)\n        self.pb_r = QtGui.QPushButton(self.scrollAreaWidgetContents)\n        self.pb_r.setText(QtGui.QApplication.translate(\"StreamErosionIndex_GUI\", \"Import File\", None, QtGui.QApplication.UnicodeUTF8))\n        self.pb_r.setObjectName(_fromUtf8(\"pb_r\"))\n        self.gridLayout.addWidget(self.pb_r, 2, 3, 1, 1)\n        \n        self.header2 = QtGui.QLabel(self.scrollAreaWidgetContents)\n        self.header2.setText(QtGui.QApplication.translate(\"StreamErosionIndexguic\", \"PET series\", None, QtGui.QApplication.UnicodeUTF8))\n        self.header2.setObjectName(_fromUtf8(\"label\"))\n        self.gridLayout.addWidget(self.header2, 3, 0, 1, 1)  \n\n        self.label2 = QtGui.QLabel(self.scrollAreaWidgetContents)\n        self.label2.setText(QtGui.QApplication.translate(\"StreamErosionIndexguic\", \"      Select ET File \", None, QtGui.QApplication.UnicodeUTF8))\n        self.label2.setObjectName(_fromUtf8(\"label2\"))\n        self.gridLayout.addWidget(self.label2, 4, 0, 1, 1)\n        self.le_r2 = QtGui.QLineEdit(self.scrollAreaWidgetContents)\n        self.le_r2.setObjectName(_fromUtf8(\"le_r2\"))\n        self.gridLayout.addWidget(self.le_r2, 4, 2, 1, 1)\n        self.pb_r2 = QtGui.QPushButton(self.scrollAreaWidgetContents)\n        self.pb_r2.setText(QtGui.QApplication.translate(\"StreamErosionIndex_GUI\", \"Import File\", None, QtGui.QApplication.UnicodeUTF8))\n        self.pb_r2.setObjectName(_fromUtf8(\"pb_r\"))\n        self.gridLayout.addWidget(self.pb_r2, 4, 3, 1, 1)\n\n        self.header3 = QtGui.QLabel(self.scrollAreaWidgetContents)\n        self.header3.setText(QtGui.QApplication.translate(\"StreamErosionIndexguic\", \"or use MUSIC template file             Use:\", None, QtGui.QApplication.UnicodeUTF8))\n        self.header3.setObjectName(_fromUtf8(\"label\"))\n        self.gridLayout.addWidget(self.header3, 5, 0, 1, 1) \n        self.chkb_music = QtGui.QCheckBox(self.scrollAreaWidgetContents)\n        self.chkb_music.setObjectName(_fromUtf8(\"chkb_music\"))\n        self.gridLayout.addWidget(self.chkb_music, 5, 1, 1, 1)\n\n        self.label3 = QtGui.QLabel(self.scrollAreaWidgetContents)\n        self.label3.setText(QtGui.QApplication.translate(\"StreamErosionIndexguic\", \"      Select climate mlb\", None, QtGui.QApplication.UnicodeUTF8))\n        self.label3.setObjectName(_fromUtf8(\"label3\"))\n        self.gridLayout.addWidget(self.label3, 6, 0, 1, 1)\n        self.le_r3 = QtGui.QLineEdit(self.scrollAreaWidgetContents)\n        self.le_r3.setObjectName(_fromUtf8(\"le_r3\"))\n        self.gridLayout.addWidget(self.le_r3, 6, 2, 1, 1)\n        self.pb_r3 = QtGui.QPushButton(self.scrollAreaWidgetContents)\n        self.pb_r3.setText(QtGui.QApplication.translate(\"StreamErosionIndex_GUI\", \"Import File\", None, QtGui.QApplication.UnicodeUTF8))\n        self.pb_r3.setObjectName(_fromUtf8(\"pb_r3\"))\n        self.gridLayout.addWidget(self.pb_r3, 6, 3, 1, 1)\n\n        self.header4 = QtGui.QLabel(self.scrollAreaWidgetContents)\n        self.header4.setText(QtGui.QApplication.translate(\"StreamErosionIndexguic\", \"or use Templates                    Use:\", None, QtGui.QApplication.UnicodeUTF8))\n        self.header4.setObjectName(_fromUtf8(\"label\"))\n        self.gridLayout.addWidget(self.header4, 7, 0, 1, 1) \n        self.chkb_defaults = QtGui.QCheckBox(self.scrollAreaWidgetContents)\n        self.chkb_defaults.setObjectName(_fromUtf8(\"chkb_defaults\"))\n        self.gridLayout.addWidget(self.chkb_defaults, 7, 1, 1, 1)\n\n        self.city_combo = QtGui.QComboBox(self.scrollAreaWidgetContents)\n        self.city_combo.setGeometry(QtCore.QRect(250, 39, 141, 22))\n        self.city_combo.setObjectName(_fromUtf8(\"city_combo\"))\n        self.city_combo.addItem(_fromUtf8(\"\"))\n        self.city_combo.addItem(_fromUtf8(\"\"))\n        self.city_combo.addItem(_fromUtf8(\"\"))\n        self.city_combo.addItem(_fromUtf8(\"\"))\n        self.city_combo.addItem(_fromUtf8(\"\"))\n        self.city_combo.addItem(_fromUtf8(\"\"))\n        self.city_combo.addItem(_fromUtf8(\"\"))\n        self.city_combo.addItem(_fromUtf8(\"\"))\n        self.gridLayout.addWidget(self.city_combo, 9,2,1,1)\n\n\n        self.label_head3 = QtGui.QLabel(self.scrollAreaWidgetContents)\n        self.label_head3.setText(QtGui.QApplication.translate(\"ReadTableSecondary_GUI2\", \"Natural catchment properties\", None, QtGui.QApplication.UnicodeUTF8))\n        self.label_head3.setObjectName(_fromUtf8(\"lbl_head3\"))\n        self.label_head3.setFont(fontBold)\n        self.gridLayout.addWidget(self.label_head3, 9, 0, 1, 1)\n\n\n        self.label_head4 = QtGui.QLabel(self.scrollAreaWidgetContents)\n        self.label_head4.setText(QtGui.QApplication.translate(\"ReadTableSecondary_GUI2\", \"Catchment parameters\", None, QtGui.QApplication.UnicodeUTF8))\n        self.label_head4.setObjectName(_fromUtf8(\"lbl_head4\"))\n        self.gridLayout.addWidget(self.label_head4, 10, 0, 1, 1)\n\n        self.label_rainthres = QtGui.QLabel(self.scrollAreaWidgetContents)\n        self.label_rainthres.setText(QtGui.QApplication.translate(\"ReadTableSecondary_GUI2\", \"    Rainfall Threshold [mm/day]\", None, QtGui.QApplication.UnicodeUTF8))\n        self.label_rainthres.setObjectName(_fromUtf8(\"lbl_rainthres\"))\n        self.gridLayout.addWidget(self.label_rainthres, 11, 0, 1, 1)\n\n        self.le_rainthres = QtGui.QLineEdit(self.scrollAreaWidgetContents)\n        self.le_rainthres.setObjectName(_fromUtf8(\"le_rainthres\"))\n        self.gridLayout.addWidget(self.le_rainthres, 11, 2, 1, 1)\n\n        self.label_rainsoil = QtGui.QLabel(self.scrollAreaWidgetContents)\n        self.label_rainsoil.setText(QtGui.QApplication.translate(\"ReadTableSecondary_GUI2\", \"    Rainfall-Runoff - Pervious Area - Soil Storage Capacity [mm]\", None, QtGui.QApplication.UnicodeUTF8))\n        self.label_rainsoil.setObjectName(_fromUtf8(\"lbl_rainsoil\"))\n        self.gridLayout.addWidget(self.label_rainsoil, 12, 0, 1, 1)\n\n        self.le_rainsoil = QtGui.QLineEdit(self.scrollAreaWidgetContents)\n        self.le_rainsoil.setObjectName(_fromUtf8(\"le_rainsoil\"))\n        self.gridLayout.addWidget(self.le_rainsoil, 12, 2, 1, 1)\n\n        self.label_raininitial = QtGui.QLabel(self.scrollAreaWidgetContents)\n        self.label_raininitial.setText(QtGui.QApplication.translate(\"ReadTableSecondary_GUI2\", \"    Rainfall-Runoff - Pervious Area - Initial Storage [% of Capacity]\", None, QtGui.QApplication.UnicodeUTF8))\n        self.label_raininitial.setObjectName(_fromUtf8(\"lbl_raininitial\"))\n        self.gridLayout.addWidget(self.label_raininitial, 13, 0, 1, 1)\n\n        self.le_raininitial = QtGui.QLineEdit(self.scrollAreaWidgetContents)\n        self.le_raininitial.setObjectName(_fromUtf8(\"le_raininitial\"))\n        self.gridLayout.addWidget(self.le_raininitial, 13, 2, 1, 1)\n\n        self.label_rainfield = QtGui.QLabel(self.scrollAreaWidgetContents)\n        self.label_rainfield.setText(QtGui.QApplication.translate(\"ReadTableSecondary_GUI2\", \"    Rainfall-Runoff - Pervious Area - Field Capacity [mm]\", None, QtGui.QApplication.UnicodeUTF8))\n        self.label_rainfield.setObjectName(_fromUtf8(\"lbl_rainfield\"))\n        self.gridLayout.addWidget(self.label_rainfield, 14, 0, 1, 1)\n\n        self.le_rainfield = QtGui.QLineEdit(self.scrollAreaWidgetContents)\n        self.le_rainfield.setObjectName(_fromUtf8(\"le_rainfield\"))\n        self.gridLayout.addWidget(self.le_rainfield, 14, 2, 1, 1)\n\n        self.label_raininfil = QtGui.QLabel(self.scrollAreaWidgetContents)\n        self.label_raininfil.setText(QtGui.QApplication.translate(\"ReadTableSecondary_GUI2\", \"    Rainfall-Runoff - Pervious Area - Infiltration Capacity Coefficient\", None, QtGui.QApplication.UnicodeUTF8))\n        self.label_raininfil.setObjectName(_fromUtf8(\"lbl_raininfil\"))\n        self.gridLayout.addWidget(self.label_raininfil, 15, 0, 1, 1)\n\n        self.le_raininfil = QtGui.QLineEdit(self.scrollAreaWidgetContents)\n        self.le_raininfil.setObjectName(_fromUtf8(\"le_raininfil\"))\n        self.gridLayout.addWidget(self.le_raininfil, 15, 2, 1, 1)\n\n        self.label_raininfil2 = QtGui.QLabel(self.scrollAreaWidgetContents)\n        self.label_raininfil2.setText(QtGui.QApplication.translate(\"ReadTableSecondary_GUI2\", \"    Rainfall-Runoff - Pervious Area - Infiltration Capacity Exponent\", None, QtGui.QApplication.UnicodeUTF8))\n        self.label_raininfil2.setObjectName(_fromUtf8(\"lbl_raininfil2\"))\n        self.gridLayout.addWidget(self.label_raininfil2, 16, 0, 1, 1)\n\n        self.le_raininfil2 = QtGui.QLineEdit(self.scrollAreaWidgetContents)\n        self.le_raininfil2.setObjectName(_fromUtf8(\"le_raininfil2\"))\n        self.gridLayout.addWidget(self.le_raininfil2, 16, 2, 1, 1)\n\n        self.label_raindepth = QtGui.QLabel(self.scrollAreaWidgetContents)\n        self.label_raindepth.setText(QtGui.QApplication.translate(\"ReadTableSecondary_GUI2\", \"    Rainfall-Runoff - Groundwater Properties - Initial Depth [mm]\", None, QtGui.QApplication.UnicodeUTF8))\n        self.label_raindepth.setObjectName(_fromUtf8(\"lbl_raindepth\"))\n        self.gridLayout.addWidget(self.label_raindepth, 17, 0, 1, 1)\n\n        self.le_raindepth = QtGui.QLineEdit(self.scrollAreaWidgetContents)\n        self.le_raindepth.setObjectName(_fromUtf8(\"le_raindepth\"))\n        self.gridLayout.addWidget(self.le_raindepth, 17, 2, 1, 1)\n\n        self.label_rainrecharge = QtGui.QLabel(self.scrollAreaWidgetContents)\n        self.label_rainrecharge.setText(QtGui.QApplication.translate(\"ReadTableSecondary_GUI2\", \"    Rainfall-Runoff - Groundwater Properties - Daily Recharge Rate [%]\", None, QtGui.QApplication.UnicodeUTF8))\n        self.label_rainrecharge.setObjectName(_fromUtf8(\"lbl_rainrecharge\"))\n        self.gridLayout.addWidget(self.label_rainrecharge, 18, 0, 1, 1)\n\n        self.le_rainrecharge = QtGui.QLineEdit(self.scrollAreaWidgetContents)\n        self.le_rainrecharge.setObjectName(_fromUtf8(\"le_rainrecharge\"))\n        self.gridLayout.addWidget(self.le_rainrecharge, 18, 2, 1, 1)\n\n        self.label_rainbaseflow = QtGui.QLabel(self.scrollAreaWidgetContents)\n        self.label_rainbaseflow.setText(QtGui.QApplication.translate(\"ReadTableSecondary_GUI2\", \"    Rainfall-Runoff - Groundwater Properties - Daily Baseflow Rate [%]\", None, QtGui.QApplication.UnicodeUTF8))\n        self.label_rainbaseflow.setObjectName(_fromUtf8(\"lbl_rainbaseflow\"))\n        self.gridLayout.addWidget(self.label_rainbaseflow, 19, 0, 1, 1)\n\n        self.le_rainbaseflow = QtGui.QLineEdit(self.scrollAreaWidgetContents)\n        self.le_rainbaseflow.setObjectName(_fromUtf8(\"le_rainbaseflow\"))\n        self.gridLayout.addWidget(self.le_rainbaseflow, 19, 2, 1, 1)\n\n        self.label_raindeep = QtGui.QLabel(self.scrollAreaWidgetContents)\n        self.label_raindeep.setText(QtGui.QApplication.translate(\"ReadTableSecondary_GUI2\", \"    Rainfall-Runoff - Groundwater Properties - Daily Deep Seepage Rate [%]\", None, QtGui.QApplication.UnicodeUTF8))\n        self.label_raindeep.setObjectName(_fromUtf8(\"lbl_raindeep\"))\n        self.gridLayout.addWidget(self.label_raindeep, 20, 0, 1, 1)\n\n        self.le_raindeep = QtGui.QLineEdit(self.scrollAreaWidgetContents)\n        self.le_raindeep.setObjectName(_fromUtf8(\"le_raindeep\"))\n        self.gridLayout.addWidget(self.le_raindeep, 20, 2, 1, 1)\n\n\n        self.header5 = QtGui.QLabel(self.scrollAreaWidgetContents)\n        self.header5.setText(QtGui.QApplication.translate(\"StreamErosionIndexguic\", \"Partial frequency analysis\", None, QtGui.QApplication.UnicodeUTF8))\n        self.header5.setObjectName(_fromUtf8(\"label\"))\n        self.gridLayout.addWidget(self.header5, 21, 0, 1, 1) \n\n        self.labelNoY = QtGui.QLabel(self.scrollAreaWidgetContents)\n        self.labelNoY.setText(QtGui.QApplication.translate(\"StreamErosionIndexguic\", \"      Number of Years: \", None, QtGui.QApplication.UnicodeUTF8))\n        self.labelNoY.setObjectName(_fromUtf8(\"labelNoY\"))\n        self.gridLayout.addWidget(self.labelNoY, 22, 0, 1, 1)\n        self.le_NoY = QtGui.QSpinBox(self.scrollAreaWidgetContents)\n        self.le_NoY.setObjectName(_fromUtf8(\"le_NoY\"))\n        self.gridLayout.addWidget(self.le_NoY, 22, 2, 1, 1)\n\n        self.labelA = QtGui.QLabel(self.scrollAreaWidgetContents)\n        self.labelA.setText(QtGui.QApplication.translate(\"StreamErosionIndexguic\", \"      Alpha: \", None, QtGui.QApplication.UnicodeUTF8))\n        self.labelA.setObjectName(_fromUtf8(\"labelA\"))\n        self.gridLayout.addWidget(self.labelA, 23, 0, 1, 1)\n        self.le_A = QtGui.QDoubleSpinBox(self.scrollAreaWidgetContents)\n        self.le_A.setObjectName(_fromUtf8(\"le_A\"))\n        self.gridLayout.addWidget(self.le_A, 23, 2, 1, 1)\n\n        self.labelUB = QtGui.QLabel(self.scrollAreaWidgetContents)\n        self.labelUB.setText(QtGui.QApplication.translate(\"StreamErosionIndexguic\", \"Inputs from UrbanBeats?\", None, QtGui.QApplication.UnicodeUTF8))\n        self.labelUB.setObjectName(_fromUtf8(\"label\"))\n        self.gridLayout.addWidget(self.labelUB, 24, 0, 1, 1) \n        self.chkb_UB = QtGui.QCheckBox(self.scrollAreaWidgetContents)\n        self.chkb_UB.setObjectName(_fromUtf8(\"chkb_UB\"))\n        self.gridLayout.addWidget(self.chkb_UB, 24, 2, 1, 1)\n\n        \n        self.buttonBox = QtGui.QDialogButtonBox(StreamErosionIndexguic)\n        self.buttonBox.setOrientation(QtCore.Qt.Horizontal)\n        self.buttonBox.setStandardButtons(QtGui.QDialogButtonBox.Ok)\n        self.buttonBox.setObjectName(_fromUtf8(\"buttonBox\"))\n        self.verticalLayout.addWidget(self.scrollArea)\n        self.verticalLayout.addWidget(self.buttonBox)\n\n        self.retranslateUi(StreamErosionIndexguic)\n        QtCore.QObject.connect(self.buttonBox, QtCore.SIGNAL(_fromUtf8(\"accepted()\")), StreamErosionIndexguic.accept)\n        QtCore.QMetaObject.connectSlotsByName(StreamErosionIndexguic)\n\n\n    def retranslateUi(self, StreamErosionIndexguic):\n        \n        self.city_combo.setItemText(0,\"Brisbane\")\n        self.city_combo.setItemText(1,\"Sydney\")   \n        self.city_combo.setItemText(2,\"Canberra\")\n        self.city_combo.setItemText(3,\"Melbourne\")\n        self.city_combo.setItemText(4,\"Hobart\")\n        self.city_combo.setItemText(5,\"Adelaide\")\n        self.city_combo.setItemText(6,\"Perth\")\n        self.city_combo.setItemText(7,\"User defined\")\n","sub_path":"scripts/P8Modules/StreamErosionIndex_gui.py","file_name":"StreamErosionIndex_gui.py","file_ext":"py","file_size_in_byte":16963,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"580918194","text":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport argparse\nimport os\nfrom IPython.display import display\nfrom timeit import default_timer as timer\nfrom sklearn.metrics import confusion_matrix, classification_report\nfrom h1_util import load_train_data, load_test_data\nimport logistic_regression as log_reg\nfrom h1_classifiers import LogisticClassifierTwoClass, LogisticClassifier, SoftmaxClassifier, model_accuracy, run_validation\n\ndef plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Blues):\n    plt.figure()\n    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n    plt.title(title)\n    plt.colorbar()\n    plt.tight_layout()\n    plt.ylabel('True label')\n    plt.xlabel('Predicted label')\n    plt.show()\n\ndef get_digit_pair_data(X, y, i, j):\n    \"\"\" Extract classes i, j from data and return a new training set with only these and labels 0, 1 for class i and j respectively \"\"\"\n    filt = (y == i) | (y == j)    \n    X_fil = X[filt, :]\n    y_fil = y[filt]\n    y_fil = (y_fil == i).astype('int64')\n    return X_fil, y_fil\n\ndef export_dataframe(name, df):\n    result_path = 'results'\n    my_path = os.path.join(result_path, name)\n    df.to_csv(my_path, index=False)\n\ndef export_fig(name, fig):\n    result_path = 'results'\n    my_path = os.path.join(result_path, name)\n    fig.savefig(my_path)\n    \n\ndef model_stats(model, **kwargs):\n    \"\"\" Train a model and make classification report and confusion matrix and save them \"\"\"\n    X_train, y_train =  load_train_data()\n    X_test, y_test =  load_test_data()\n    model.train(X_train, y_train, **kwargs) \n    acc_train = model_accuracy(model, X_train, y_train)\n    acc_test = model_accuracy(model, X_test, y_test)\n    df_acc = pd.DataFrame(np.c_[acc_train, acc_test], columns=['train_accuracy', 'test_accuracy'])\n    export_dataframe('{0}_stats_accuracy.csv'.format(model.name.lower()), df_acc)\n    print('Train Accuracy: {0}, Test Accuracy: {1}'.format(acc_train, acc_test))\n    pred_test = model.predict(X_test)\n    confusion = confusion_matrix(y_test, pred_test)\n    cr = classification_report(y_test, pred_test)\n    print('Full Model Stats')\n    print('Classification Report')\n    print(cr)\n    print('Confusion Matrix')\n    df_confusion = pd.DataFrame(confusion)\n    print(confusion)\n    export_dataframe('{0}_confusion_matrix.csv'.format(model.name.lower()), df_confusion)\n    # plot_confusion_matrix(confusion)\n    return model\n\ndef make_logreg_statistics():\n    \"\"\" Generate simple statistics about the logistic regression implementation for the hand in report \"\"\"\n\n    pd.set_option('display.height', 1000)\n    pd.set_option('display.max_rows', 500)\n    pd.set_option('display.max_columns', 500)\n    pd.set_option('display.width', 1000)\n    model = LogisticClassifierTwoClass()\n    rounds = 50\n    print('Loading Data')\n    X_train, y_train =  load_train_data()\n    X_test, y_test =  load_test_data()\n    \n    names = ['Batch Gradient Descent', 'Mini Batch Gradient Descent', 'Scipy Optimize']\n    algs = [log_reg.batch_grad_descent, log_reg.mini_batch_grad_descent, log_reg.fast_descent]\n    params = [{'reg': 1e-4, 'rounds':rounds, 'lr':1.0},\n              {'reg': 1e-4, 'epochs':rounds, 'lr':0.1, 'batch_size':16},\n              {'reg': 1e-4, 'rounds':rounds}]\n    times = np.zeros(3)\n    acc_train = np.zeros(3)\n    acc_test = np.zeros(3)\n\n    print('Computing 2 vs 7 Statistics on fixed paramters')\n    Xbin_train, ybin_train = get_digit_pair_data(X_train, y_train, 2, 7)\n    Xbin_test, ybin_test = get_digit_pair_data(X_test, y_test, 2, 7)\n    Xbin_train_padded = np.c_[np.zeros(Xbin_train.shape[0]), Xbin_train]\n    for i, (alg, param, name) in enumerate(zip(algs, params, names)):\n        print('*** Running {0} ***'.format(name))\n        start = timer()\n        w = alg(Xbin_train_padded, ybin_train, **param)\n        end = timer()\n        time = end - start\n        model.w = w\n        train_acc = model_accuracy(model, Xbin_train, ybin_train)\n        test_acc = model_accuracy(model, Xbin_test, ybin_test)\n        times[i] = time\n        acc_train[i] =  train_acc\n        acc_test[i] =  test_acc\n        print('Alg: {0}, Rounds/Epochs: {1}, Time: {2}, Ein: {3}, Etest: {4}'.format(name, rounds, time, train_acc, test_acc))\n    df_2vs7 = pd.DataFrame(np.c_[names, times, acc_train,acc_test], columns=['Alg.', 'Time', 'Ein', 'Etest'])\n    filename_2vs7 = 'results_2_vs_7.csv'\n    print('Result of 2 vs 7 Classifier with fixed parameters stored in {0}'.format(filename_2vs7))\n    display(df_2vs7)\n    export_dataframe(filename_2vs7, df_2vs7)        \n    visualize_model(LogisticClassifierTwoClass(), Xbin_train, ybin_train)\n            \n    print('Compute Pairwise scores using mini_batch gradient descent')\n    pairwise_scores_train = np.zeros((10,10))\n    pairwise_scores_test = np.zeros((10,10))\n    pairs = [(i,j) for i in range(10) for j in range(10)]\n    model = LogisticClassifierTwoClass()\n    for i, j in pairs:\n        if j >= i: continue\n        Xbin_train, ybin_train = get_digit_pair_data(X_train, y_train, i, j)\n        Xbin_test, ybin_test = get_digit_pair_data(X_test, y_test, i, j)        \n        # model.w = log_reg.mini_batch_grad_descent(Xbin_train, ybin_train, reg=1e-4, epochs=30, lr=0.1, batch_size=16)\n        model.train(Xbin_train, ybin_train, reg=1e-4, epochs=30, lr=0.1, batch_size=16)\n        acc_train = model_accuracy(model, Xbin_train, ybin_train)\n        acc_test = model_accuracy(model, Xbin_test, ybin_test)\n        pairwise_scores_train[i][j] = acc_train\n        pairwise_scores_test[i][j] = acc_test\n    \n    df_pairwise_train = pd.DataFrame(pairwise_scores_train)\n    df_pairwise_test = pd.DataFrame(pairwise_scores_test)\n    print('Pairwise Training Accuracy')\n    display(df_pairwise_train)\n    print('Pairwise Test Accuract')\n    display(df_pairwise_test)\n    export_dataframe('pairwise_train_accuracy.csv', df_pairwise_train)\n    export_dataframe('pairwise_test_accuracy.csv', df_pairwise_test)\n    \n    print('Training full model with all vs one')\n    full_model = LogisticClassifier()\n    model_stats(full_model, **{'reg': 1e-4, 'epochs': 30, 'batch_size': 16})\n    print('Result of 2 vs 7 Classifier with fixed parameters stored in {0}'.format(filename_2vs7))\n    visualize_model(LogisticClassifier())\n\n\ndef make_softmax_statistics():\n    model = SoftmaxClassifier()\n    rounds = 50\n    print('Loading Data')\n    X_train, y_train = load_train_data()\n    X_test, y_test = load_test_data()\n    model = model_stats(model, **{'reg': 1e-4, 'epochs': 1, 'batch_size': 64})\n    visualize_model(SoftmaxClassifier())\n    return model\n                        \n\ndef visualize_model(model, X_train=None, y_train=None):\n    \"\"\" Visualize a model after training for at short time \n\n    Args:\n     model: MlModel \n     X_train: numpy array, training data\n     y_train: numpy array, training labels\n    \"\"\"\n    p1 = {'reg': 1e-4, 'epochs': 1, 'batch_size': 128}\n    # p2 = {'reg': 1e-4, 'epochs': 1, 'batch_size': 16}    \n    # p3 = {'reg': 1e-4, 'epochs': 10, 'batch_size': 16}\n    if X_train is None:\n        X_train, y_train = load_train_data()\n    model.train(X_train, y_train, **p1)\n    fig = plt.figure()\n    # ax = fig.add_axes([0.15, 0.1, 0.7, 0.3])\n    ax = fig.add_subplot(1,1,1)\n    model.visualize_model(ax)\n    export_fig('{0}_parameter_plot_{1}_{2}'.format(model.name.lower(), p1['epochs'], p1['batch_size']), fig)\n    # plt.show()\n\n\ndef best_model(model):\n    \"\"\" Find the best regularization of the model\n    All you need to do is put some regularization values in reg and\n    choose the params you want for mini_batch_gradient descent in params.\n    \"\"\"\n\n    X_train, y_train =  load_train_data()\n    X_test, y_test =  load_test_data()\n    params = {}\n    reg = [0]\n    ### YOUR CODE HERE\n    reg = [0.3**i for i in range(1, 10)]\n    ### END CODE\n    reg = sorted(reg)\n    model, acc = run_validation(model, X_train, y_train, reg, **params)\n    df_reg = pd.DataFrame(np.c_[reg, acc], columns=['reg. params', 'val. score'])\n    export_dataframe('{0}_validation_scores.csv'.format(model.name.lower()), df_reg)\n    fig = plt.figure()\n    ax = fig.add_subplot(1,1,1)    \n    ax.plot(reg, acc, 'b-*', linewidth=2, markersize=6, label='Validation Accuracy')\n    print('plot of: ', np.c_[reg, acc])\n    name = '{0}_validation_accuracy'.format(model.name.lower())\n    ax.set_title(name)\n    ax.set_xlabel('reg. params')\n    ax.set_ylabel('val. accuracy')\n    ax.legend()    \n    export_fig('{0}.png'.format(name), fig)\n    test_acc = model_accuracy(model, X_test, y_test)\n    idx = np.argmax(acc)    \n    print('Best model found: reg {0}\\nValidation accuracy: {1}\\nTest Accuracy: {2}'.format(reg[idx], acc[idx], test_acc))\n    best_result = pd.DataFrame(np.c_[reg[idx], acc[idx], test_acc], columns=['reg. value', 'validation accuracy', 'test accuracy'])\n    export_dataframe('{0}_best_result.csv'.format(model.name.lower()), best_result)\n\nif __name__=='__main__':\n    \"\"\" Add some options with argparser here \"\"\"\n    pd.set_option('display.max_rows', 500)\n    pd.set_option('display.max_columns', 500)\n    pd.set_option('display.width', 1000)\n    parser = argparse.ArgumentParser()\n    parser.add_argument('-log', default=False, help='Make Logistic Regression Stats', dest='log', action='store_true')\n    parser.add_argument('-soft', default=False, help='Make Softmax Stats', dest='soft', action='store_true')\n    parser.add_argument('-log_val', default=False, help='Make Best Logistic Regression', dest='log_val', action='store_true')\n    parser.add_argument('-soft_val', default=False, help='Make Best Softmax ', dest='soft_val', action='store_true')\n\n    # visualize_model(LogisticClassifier())\n    if not os.path.exists('results'):\n        print('create results folder')\n        os.mkdir('results')\n    \n    \n    vals = vars(parser.parse_args())\n    if vals['log']:        \n        make_logreg_statistics()\n    if vals['soft']:\n        model = make_softmax_statistics()\n    if vals['log_val']:\n        best_model(LogisticClassifier())\n    if vals['soft_val']:\n        best_model(SoftmaxClassifier())\n \n","sub_path":"S5_MachineLearning/dML/handin1/model_stats.py","file_name":"model_stats.py","file_ext":"py","file_size_in_byte":10051,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"308671713","text":"import tensorflow as tf\nfrom functools import partial\n\ncfg={\n    'conv1':      {'out_filters':32,'ksize':3,'strides':2},\n    'conv2':      {'out_filters':64,'ksize':3,'strides':2},\n    'maxpool1':   {'ksize':2,'strides':2},\n    'fc':         {'out_filters':10}\n}\n\ndef conv(x,out_filters,ksize,strides=1,name=None):\n    with tf.variable_scope(name,use_resource=True, reuse=tf.AUTO_REUSE):\n        x = tf.layers.conv2d(inputs=x, filters=out_filters, kernel_size=ksize,activation=tf.nn.relu6,name=name)\n        return x\ndef fc(x,out_filters,name=None):\n    with tf.variable_scope('flat',use_resource=True, reuse=tf.AUTO_REUSE):\n        x = tf.layers.flatten(x)\n    with tf.variable_scope(name,use_resource=True, reuse=tf.AUTO_REUSE):\n        x = tf.layers.dense(x,units=out_filters)\n        return x\ndef pool(x,ksize,strides,name=None):\n    with tf.variable_scope(name,use_resource=True, reuse=tf.AUTO_REUSE):\n        x = tf.layers.max_pooling2d(x,pool_size=2,strides=2)\n        return x\n        \nclass Demo:\n    def __init__(self, opts, is_training=True):\n        self.is_training = is_training\n        \n    def __call__(self,x):\n        fn_list = self._build_function_list()\n        tf.add_to_collection(\"activations\", x)\n        with tf.variable_scope(\"all\", use_resource=True):\n            for fn in fn_list:\n                x = fn(x)\n            return x        \n        \n    def _build_function_list(self):\n        fn_list = []\n        for key,val in cfg.items():\n            if 'conv' in key:\n                fn_list.append(partial(conv,out_filters=val['out_filters'],ksize=val['ksize'],strides=val['strides'],name=key))\n            elif 'pool' in key:\n                fn_list.append(partial(pool,ksize=val['ksize'],strides=val['strides'],name=key))\n            elif 'fc' in key:\n                fn_list.append(partial(fc,out_filters=val['out_filters'],name=key))\n        return fn_list\n    \ndef Model(opts, training, image):\n    return Demo(opts, training)(image)\n\ndef set_defaults(opts):\n    \n    if not opts.get(\"learning_rate_decay\"):\n        opts[\"learning_rate_decay\"] = [1.0, 0.1, 0.01]  \n    if not opts.get(\"base_learning_rate\"):\n        opts[\"base_learning_rate\"] = -6  \n\n    if not opts.get('epochs') and not opts.get('iterations'):\n        opts['epochs'] = 100\n    if opts.get(\"group_norm\"):\n        if not opts.get(\"groups\"):\n            opts[\"groups\"] = 32\n    if not opts.get(\"batch_size\"):\n        opts['batch_size'] = 4\n         \n    if not opts.get(\"model_size\"):\n        opts['model_size'] = 'V1'     \n         \n    if (opts['precision'] == '32.32') and not opts.get(\"shards\"):\n        opts['shards'] = 2\n         \n    opts['name'] = \"SN_bs{}\".format(opts['batch_size'])\n    if opts.get('replicas') > 1:\n        opts['name'] += \"x{}r\".format(opts['replicas'])\n    if opts['pipeline_depth'] > 1:\n        opts['name'] += \"x{}p\".format(opts['pipeline_depth'])\n    elif opts.get('gradients_to_accumulate') > 1:\n        opts['name'] += \"x{}a\".format(opts['gradients_to_accumulate'])    \n    return opts\n\ndef add_arguments(parser):\n    return parser","sub_path":"applications/tensorflow/cnns/training/Models/demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":3066,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"458311090","text":"import numpy as np\nimport scipy\nimport matplotlib.pyplot as plt\n\nfile='/home/ph290/data1/temp_data/misc_data/data_from_wanamaker_2012.csv'\n\ndata=np.genfromtxt(file,skip_header=1,delimiter=\",\",usecols=[0,1,2,3])\n\nl_wdth=2\n\nfig = plt.figure(num=None, figsize=(7, 3), dpi=150, facecolor='w', edgecolor='k')\nax = fig.add_subplot(111)\nax.set_yscale('log')\nln1=ax.plot(data[:,0],data[:,1], 'b-', linewidth=l_wdth,label='Bivalve a')\nln2=ax.plot(data[:,2],data[:,3],'r-', linewidth=l_wdth/2,label='Bivalve b')\nplt.ylim(0,1000)\nplt.xlim(1500,2010)\nplt.xlabel('year')\nplt.ylabel('increment width (logged)')\nlns=ln1+ln2\nlabs = [l.get_label() for l in lns]\n#plt.legend(lns, labs).draw_frame(False)\nplt.legend(lns, labs,prop={'size':tmp_font_size}).draw_frame(False)\n\nfor item in ([ax.xaxis.label, ax.yaxis.label]):\n\titem.set_fontsize(tmp_font_size)\n\titem.set_weight('bold')\n\n# plt.show()\nplt.savefig('/home/ph290/Desktop/delete4.pdf')\n\n","sub_path":"iclandic_mollusk_t_series_parallel.py","file_name":"iclandic_mollusk_t_series_parallel.py","file_ext":"py","file_size_in_byte":924,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"185109531","text":"from common import *\n# common tool for dataset\n\n# draw -----------------------------------\ndef im_show(name, image, resize=1):\n    H,W = image.shape[0:2]\n    cv2.namedWindow(name, cv2.WINDOW_NORMAL)\n    cv2.imshow(name, image.astype(np.uint8))\n    cv2.resizeWindow(name, round(resize*W), round(resize*H))\n\ndef draw_shadow_text(img, text, pt,  fontScale, color, thickness, color1=None, thickness1=None):\n    if color1 is None: color1=(0,0,0)\n    if thickness1 is None: thickness1 = thickness+2\n\n    font = cv2.FONT_HERSHEY_SIMPLEX\n    cv2.putText(img, text, pt, font, fontScale, color1, thickness1, cv2.LINE_AA)\n    cv2.putText(img, text, pt, font, fontScale, color,  thickness,  cv2.LINE_AA)\n\ndef draw_text(img, text, pt,  fontScale, color, thickness):\n\n    font = cv2.FONT_HERSHEY_SIMPLEX\n    cv2.putText(img, text, pt, font, fontScale, color, thickness, cv2.LINE_AA)\n\n\n##http://stackoverflow.com/questions/26690932/opencv-rectangle-with-dotted-or-dashed-lines\ndef draw_dotted_line(img, pt1, pt2, color, thickness=1, gap=20):\n\n    dist =((pt1[0]-pt2[0])**2+(pt1[1]-pt2[1])**2)**.5\n    pts= []\n    for i in  np.arange(0,dist,gap):\n        r=i/dist\n        x=int((pt1[0]*(1-r)+pt2[0]*r)+.5)\n        y=int((pt1[1]*(1-r)+pt2[1]*r)+.5)\n        p = (x,y)\n        pts.append(p)\n\n    if gap==1:\n        for p in pts:\n            cv2.circle(img,p,thickness,color,-1,cv2.LINE_AA)\n    else:\n        def pairwise(iterable):\n            \"s -> (s0, s1), (s2, s3), (s4, s5), ...\"\n            a = iter(iterable)\n            return zip(a, a)\n\n        for p, q in pairwise(pts):\n            cv2.line(img,p, q, color,thickness,cv2.LINE_AA)\n\ndef draw_dotted_poly(img, pts, color, thickness=1, gap=20):\n\n    s=pts[0]\n    e=pts[0]\n    pts.append(pts.pop(0))\n    for p in pts:\n        s=e\n        e=p\n        draw_dotted_line(img,s,e,color,thickness,gap)\n\n\ndef draw_dotted_rect(img, pt1, pt2, color, thickness=1, gap=3):\n    pts = [pt1,(pt2[0],pt1[1]),pt2,(pt1[0],pt2[1])]\n    draw_dotted_poly(img, pts, color, thickness, gap)\n\n\n## custom data transform  -----------------------------------\n\ndef tensor_to_image(tensor, mean=0, std=1):\n    image = tensor.numpy()\n    image = np.transpose(image, (1, 2, 0))\n    image = image*std + mean\n    image = image.astype(dtype=np.uint8)\n    #img = cv2.cvtColor(img , cv2.COLOR_BGR2RGB)\n    return image\n\n\ndef tensor_to_label(tensor):\n    label = tensor.numpy()*255\n    label = label.astype(dtype=np.uint8)\n    return label\n\n## transform (input is numpy array, read in by cv2)\ndef image_to_tensor(image, mean=0, std=1.):\n    image = image.astype(np.float32)\n    image = (image-mean)/std\n    image = image.transpose((2,0,1))\n    tensor = torch.from_numpy(image)   ##.float()\n    return tensor\n\ndef label_to_tensor(label, threshold=0.5):\n    label  = label\n    label  = (label>threshold).astype(np.float32)\n    tensor = torch.from_numpy(label).type(torch.FloatTensor)\n    return tensor\n\n# transform image and label\ndef randomHorizontalFlip2(image, label, u=0.5):\n\n    if random.random() < u:\n        image = cv2.flip(image,1)  #np.fliplr(img)  #cv2.flip(img,1) ##left-right\n        label = cv2.flip(label,1)  #np.fliplr(img)  #cv2.flip(img,1) ##left-right\n\n    return image, label\n\n\ndef randomShiftScaleRotate2(image, label, shift_limit=(-0.0625,0.0625), scale_limit=(-0.1,0.1), rotate_limit=(-45,45), aspect_limit = (0,0),  borderMode=cv2.BORDER_CONSTANT, u=0.5):\n    #cv2.BORDER_REFLECT_101\n\n    if random.random() < u:\n        height,width,channel = image.shape\n\n        angle  = random.uniform(rotate_limit[0],rotate_limit[1])  #degree\n        scale  = random.uniform(1+scale_limit[0],1+scale_limit[1])\n        aspect = random.uniform(1+aspect_limit[0],1+aspect_limit[1])\n        sx    = scale*aspect/(aspect**0.5)\n        sy    = scale       /(aspect**0.5)\n        dx    = round(random.uniform(shift_limit[0],shift_limit[1])*width )\n        dy    = round(random.uniform(shift_limit[0],shift_limit[1])*height)\n\n        cc = math.cos(angle/180*math.pi)*(sx)\n        ss = math.sin(angle/180*math.pi)*(sy)\n        rotate_matrix = np.array([ [cc,-ss], [ss,cc] ])\n\n        box0 = np.array([ [0,0], [width,0],  [width,height], [0,height], ])\n        box1 = box0 - np.array([width/2,height/2])\n        box1 = np.dot(box1,rotate_matrix.T) + np.array([width/2+dx,height/2+dy])\n        box0 = box0.astype(np.float32)\n        box1 = box1.astype(np.float32)\n        mat = cv2.getPerspectiveTransform(box0,box1)\n        image = cv2.warpPerspective(image, mat, (width,height),flags=cv2.INTER_LINEAR,borderMode=borderMode,borderValue=(0,0,0,))  #cv2.BORDER_CONSTANT, borderValue = (0, 0, 0))  #cv2.BORDER_REFLECT_101\n\n\n        box0 = np.array([ [0,0], [width,0],  [width,height], [0,height], ])\n        box1 = box0 - np.array([width,height])\n        box1 = np.dot(box1,rotate_matrix.T) + np.array([width+2*dx,height+2*dy])\n        box0 = box0.astype(np.float32)\n        box1 = box1.astype(np.float32)\n        mat = cv2.getPerspectiveTransform(box0,box1)\n        height2,width2  = label.shape\n        label = cv2.warpPerspective(label, mat, (width2,height2),flags=cv2.INTER_LINEAR,borderMode=borderMode,borderValue=(0,0,0,))  #cv2.BORDER_CONSTANT, borderValue = (0, 0, 0))  #cv2.BORDER_REFLECT_101\n\n    return image,label\n\n\n## sampler  -----------------------------------\n\nclass FixedSampler(Sampler):\n    def __init__(self, data, list):\n        self.num_samples = len(list)\n        self.list = list\n\n    def __iter__(self):\n        #print ('\\tcalling Sampler:__iter__')\n        return iter(self.list)\n\n    def __len__(self):\n        #print ('\\tcalling Sampler:__len__')\n        return self.num_samples\n\n\n\n\n\n\n\n\n\n# main #################################################################\nif __name__ == '__main__':\n    print( '%s: calling main function ... ' % os.path.basename(__file__))\n\n\n\n","sub_path":"car-segment/dataset/tool.py","file_name":"tool.py","file_ext":"py","file_size_in_byte":5791,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"353301563","text":"from transformers import pipeline\nimport tensorflow as tf\nimport pandas as pd\nimport numpy as np\nimport sys\n\ndef main():\n\n    # Allocate a pipeline for sentiment-analysis\n    classifier = pipeline('sentiment-analysis')\n    # Use the GPU\n    classifier.device = 0\n    classifier.ensure_tensor_on_device()\n    classifier.model\n    \n    # read data\n    tweets = pd.read_csv(sys.stdin, \"\\t\", header = None)\n    tweets = tweets.drop(0)\n    tweets.columns = ['place', 'time', 'text', 'lat', 'lon']\n    scores = classifier(list(tweets['text']))\n    tweets['sentiment'] = [5 if x['score'] < .95 else 10 if x['label'] == 'POSITIVE' else 0 for x in scores]\n    tweets.to_csv(sys.stdout, index=False)\n\n\nif __name__ == \"__main__\":\n    main()","sub_path":"NiFi/src/Twitter/twitterscore.py","file_name":"twitterscore.py","file_ext":"py","file_size_in_byte":729,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"325991685","text":"\n\n#calss header\nclass _ANTIPARTICLE():\n\tdef __init__(self,): \n\t\tself.name = \"ANTIPARTICLE\"\n\t\tself.definitions = [u'a particle that makes up antimatter, containing the same amount of matter but having the opposite electrical charge that a regular particle has']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'nouns'\n\n\n\tdef run(self, obj1 = [], obj2 = []):\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/nouns/_antiparticle.py","file_name":"_antiparticle.py","file_ext":"py","file_size_in_byte":435,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"372306046","text":"import pytest\nimport re\nfrom utils.virtualenv import recreate_venv\nfrom utils.docker import *\n\npytest_plugins = ( 'trundle_basic', 'trundle_logging', 'trundle_testbeds' )\n\ndef pytest_addoption(parser):\n    parser.addoption(\"--nms-onos-version\", action=\"store\", required=True,\n        help=\"Specify the version of the NMS ONOS to test. Required.\")\n\ndef pytest_report_header(config):\n    return \"NMS ONOS Version: \" + config.getoption(\"--nms-onos-version\")\n\n@pytest.fixture(scope=\"session\")\ndef nms_onos_version(request):\n    return request.config.getoption(\"--nms-onos-version\")\n\n@pytest.fixture(scope=\"module\")\ndef extra_logs(testbed, get_logfile):\n    def finalizer(log_file_suffix='',resourcename=''):\n        logs = testbed.get_logs(resourcename=resourcename)\n        db_logs = testbed.get_db_logs(resourcename=resourcename)\n        if log_file_suffix:\n            log_file_suffix = \"_\" + log_file_suffix\n        logfile = get_logfile('docker_container_logs{}.txt'.format(log_file_suffix))\n        logfile.write(\"{}\".format(logs))\n        if db_logs is not None:\n          db_logfile = get_logfile('docker_container_logs_db{}.txt'.format(log_file_suffix))\n          db_logfile.write(\"{}\".format(db_logs))\n    return finalizer\n\n@pytest.fixture(scope=\"module\", autouse=True)\ndef prepare_venv(tb, nms_onos_version):\n    try:\n        recreate_venv(tb.smokepc, \"smoketest\")\n    except AttributeError:\n        pass\n\n@pytest.fixture(scope=\"module\", autouse=True)\ndef prepare_docker(request, tb):\n    try:\n        remove_all_docker_images(tb.smokepc)\n        def cleanup():\n            cleandocker(tb.smokepc)\n        if request.config.doCleanup:\n            request.addfinalizer(cleanup)\n        cleanup()\n    except AttributeError:\n        pass\n\n","sub_path":"test_automation/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":1743,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"252276269","text":"\"\"\"\nLoad data of general information, structure, energy and projected density of states.\n\"\"\"\n\nimport os\n\n__all__ = ['LoadData']\n\nclass LoadData:\n\n    def __init__(self, data_root, data_dirlist):\n        self.data_root = data_root\n        self.data_dirlist = data_dirlist\n\n    def read_sys_info(self):\n        \"\"\"\n        Read file *.system-info.dat generated during initial construction of QDs.\n\t### These infos seems to be generated from build_features. \n\tIn addition to constructing the POSCAR, also generated these informations\t\n        \"\"\"\n        print(\" \")\n        self.sys_info_sid = []\n        self.core_shape = []\n        self.core_diameter = []\n        self.core_elem_natom = []\n        self.nlig = []\n        self.stoi = []\n        self.pass_prop_001_old = []\n        self.pass_prop_111_old = []\n        self.pass_prop_011_old = []\n        self.dot_type_id = []\n        self.dot_type_name = []\n        for rtid in range(len(self.data_dirlist)):\n            sysname_full = self.data_dirlist[rtid]\n            strlist = sysname_full.split('.')\n            sysname = strlist[0]\n            filein = self.data_root + '/' + sysname_full + '/' + sysname_full + '.system-info.dat'\n            print(\"read file: {0} \".format(sysname_full + '.system-info.dat'))\n            with open(filein, 'r') as f:\n                line = f.readline().rstrip('\\n')\n                nsample = int(line.split()[0])\n                f.readline()\n                f.readline() \n                for i in range(nsample):\n                    line = f.readline().rstrip('\\n')\n                    self.sys_info_sid.append([rtid, int(line.split()[1])])\n                    f.readline()\n                    f.readline()\n                    f.readline()\n                    self.core_shape.append(f.readline().rstrip('\\n'))\n                    f.readline()\n                    self.core_diameter.append(float(f.readline().rstrip('\\n')))\n                    f.readline()\n                    f.readline()\n                    f.readline()\n                    self.nlig.append(int(f.readline().rstrip('\\n')))\n                    f.readline()\n                    self.stoi.append(float(f.readline().rstrip('\\n')))\n                    for j in range(10):\n                        f.readline()\n                    self.pass_prop_001_old.append(float(f.readline().rstrip('\\n')))\n                    f.readline()\n                    self.pass_prop_111_old.append(float(f.readline().rstrip('\\n')))\n                    f.readline()\n                    self.pass_prop_011_old.append(float(f.readline().rstrip('\\n')))\n                    for j in range(5):\n                        f.readline()\n                    line = f.readline().rstrip('\\n')\n                    str1 = line.split()\n                    self.core_elem_natom.append([int(str1[0]), int(str1[1])])\n                    for j in range(23):\n                        f.readline()\n                    self.dot_type_id.append(rtid)\n                    self.dot_type_name.append(sysname_full)\n        print(\"numble of samples = {0}\".format(len(self.sys_info_sid)))\n        for i in range(len(self.sys_info_sid)-1):\n            if (self.sys_info_sid[i+1][0] == self.sys_info_sid[i][0] and \\\n                self.sys_info_sid[i+1][1] - self.sys_info_sid[i][1] !=1):\n                for j in range(self.sys_info_sid[i][1]+1, self.sys_info_sid[i+1][1]):\n                    print(\"warning: missing data from \" + self.data_dirlist[self.sys_info_sid[i][0]] \\\n                          + '.cf-' + str(j))\n\n    def read_old_struct(self):\n        \"\"\"\n        Read file *.struct-origin-poscar.dat generated during initial construction of QDs.\n        \"\"\"\n        self.old_struct_sid = []\n        self.old_struct_lc_const = []\n        self.old_struct_lc_vec = []\n        self.old_struct_elem_name = []\n        self.old_struct_elem_natom = []\n        self.old_struct_frac_coord = []\n        for rtid in range(len(self.data_dirlist)):\n            sysname_full = self.data_dirlist[rtid]\n            strlist = sysname_full.split('.')\n            sysname = strlist[0]\n            filein = self.data_root + '/' + sysname_full + '/' + sysname_full \\\n                     + '.struct-origin-poscar.dat'\n            print(\"read file: {0} \".format(sysname_full + '.struct-origin-poscar.dat'))\n            with open(filein, 'r') as f:\n                line = f.readline().rstrip('\\n')\n                nsample = int(line.split()[0])\n                for i in range(nsample):\n                    f.readline()\n                    f.readline()\n                    line = f.readline().rstrip('\\n')\n                    self.old_struct_sid.append([rtid, int(line.split()[1])])\n                    f.readline()\n                    f.readline()\n                    self.old_struct_lc_const.append(float(f.readline().rstrip('\\n')))\n                    lc_vec = []\n                    for j in range(3):\n                        line = f.readline().rstrip('\\n')\n                        str1 = line.split()\n                        lc_vec_sub = [float(str1[k]) for k in range(len(str1))]\n                        lc_vec.append(lc_vec_sub)\n                    self.old_struct_lc_vec.append(lc_vec)\n                    line = f.readline().rstrip('\\n')\n                    self.old_struct_elem_name.append(line.split())\n                    line = f.readline().rstrip('\\n')\n                    str1 = line.split()\n                    elem_natom = [int(str1[j]) for j in range(len(str1))]\n                    self.old_struct_elem_natom.append(elem_natom)\n                    f.readline()\n                    natom = sum(elem_natom)\n                    coords = []\n                    for j in range(natom):\n                        line = f.readline().rstrip('\\n')\n                        str1 = line.split()\n                        coord = [float(str1[k]) for k in range(len(str1))]\n                        coords.append(coord)\n                    self.old_struct_frac_coord.append(coords)\n        print(\"numble of samples = {0}\".format(len(self.old_struct_sid)))\n        for i in range(len(self.old_struct_sid)-1):\n            if (self.old_struct_sid[i+1][0] == self.old_struct_sid[i][0] and \\\n                self.old_struct_sid[i+1][1] - self.old_struct_sid[i][1] !=1):\n                for j in range(self.old_struct_sid[i][1]+1, self.old_struct_sid[i+1][1]):\n                    print(\"warning: missing data from \" + self.data_dirlist[self.old_struct_sid[i][0]] \\\n                          + '.cf-' + str(j))\n\n    def read_new_struct(self):\n        \"\"\"\n        Read file *.struct-relax-poscar.dat relaxed by DFT using VASP.\n        \"\"\"\n        self.new_struct_sid = []\n        self.new_struct_lc_const = []\n        self.new_struct_lc_vec = []\n        self.new_struct_elem_name = []\n        self.new_struct_elem_natom = []\n        self.new_struct_frac_coord = []\n        self.natom = []\n        for rtid in range(len(self.data_dirlist)):\n            sysname_full = self.data_dirlist[rtid]\n            strlist = sysname_full.split('.')\n            sysname = strlist[0]\n            filein = self.data_root + '/' + sysname_full + '/' + sysname_full \\\n                     + '.struct-relax-poscar.dat'\n            print(\"read file: {0} \".format(sysname_full + '.struct-relax-poscar.dat'))\n            with open(filein, 'r') as f:\n                line = f.readline().rstrip('\\n')\n                nsample = int(line.split()[0])\n                for i in range(nsample):\n                    f.readline()\n                    f.readline()\n                    line = f.readline().rstrip('\\n')\n                    self.new_struct_sid.append([rtid, int(line.split()[1])])\n                    f.readline()\n                    f.readline()\n                    self.new_struct_lc_const.append(float(f.readline().rstrip('\\n')))\n                    lc_vec = []\n                    for j in range(3):\n                        line = f.readline().rstrip('\\n')\n                        str1 = line.split()\n                        lc_vec_sub = [float(str1[k]) for k in range(len(str1))]\n                        lc_vec.append(lc_vec_sub)\n                    self.new_struct_lc_vec.append(lc_vec)\n                    line = f.readline().rstrip('\\n')\n                    self.new_struct_elem_name.append(line.split())\n                    line = f.readline().rstrip('\\n')\n                    str1 = line.split()\n                    elem_natom = [int(str1[j]) for j in range(len(str1))]\n                    self.new_struct_elem_natom.append(elem_natom)\n                    f.readline()\n                    natom = sum(elem_natom)\n                    coords = []\n                    for j in range(natom):\n                        line = f.readline().rstrip('\\n')\n                        str1 = line.split()\n                        coord = [float(str1[k]) for k in range(len(str1))]\n                        for k in range(3):\n                            if (coord[k] > 0.50):\n                                coord[k] = coord[k] - 1.0\n                        coords.append(coord)\n                    self.new_struct_frac_coord.append(coords)\n            self.natom.append(natom)\n        print(\"numble of samples = {0}\".format(len(self.new_struct_sid)))\n        for i in range(len(self.new_struct_sid)-1):\n            if (self.new_struct_sid[i+1][0] == self.new_struct_sid[i][0] and \\\n                self.new_struct_sid[i+1][1] - self.new_struct_sid[i][1] !=1):\n                for j in range(self.new_struct_sid[i][1]+1, self.new_struct_sid[i+1][1]):\n                    print(\"warning: missing data from \" + self.data_dirlist[self.new_struct_sid[i][0]] \\\n                          + '.cf-' + str(j))\n\n    def read_en_eigval(self):\n        \"\"\"\n        Read file *.en-eigval-vasp.dat calculated by DFT using VASP.\n        \"\"\"\n        self.en_eigval_sid = []\n        self.band_id = []\n        self.energy = []\n        self.eigval_orig = []\n        self.occ = []\n        self.vac = []\n        self.nband = []\n        self.ef = []\n        for rtid in range(len(self.data_dirlist)):\n            sysname_full = self.data_dirlist[rtid]\n            strlist = sysname_full.split('.')\n            sysname = strlist[0]\n            filein = self.data_root + '/' + sysname_full + '/' + sysname_full \\\n                     + '.en-eigval-vasp.dat'\n            print(\"read file: {0} \".format(sysname_full + '.en-eigval-vasp.dat'))\n            with open(filein, 'r') as f:\n                line = f.readline().rstrip('\\n')\n                nsample = int(line.split()[0])\n                lid = 2\n                for i in range(2000):\n                    line = f.readline().rstrip('\\n')\n                    str1 = line.split()\n                    lid = lid + 1\n                    if (len(str1) > 0 and str1[0] == 'vacuum'):\n                        break\n                nband = lid - 9\n                self.nband.append(nband)\n            with open(filein, 'r') as f:\n                f.readline()\n                f.readline()\n                for i in range(nsample):\n                    f.readline()\n                    line = f.readline().rstrip('\\n')\n                    self.en_eigval_sid.append([rtid, int(line.split()[1])])\n                    f.readline()\n                    f.readline()\n                    band_id = []\n                    eigval_orig = []\n                    occ = []\n                    for j in range(nband):\n                        line = f.readline().rstrip('\\n')\n                        str1 = line.split()\n                        band_id.append(int(str1[0]))\n                        eigval_orig.append(float(str1[1]))\n                        occ.append(float(str1[2]))\n                    self.band_id.append(band_id)\n                    self.eigval_orig.append(eigval_orig)\n                    self.occ.append(occ)\n                    f.readline()\n                    f.readline()\n                    line = f.readline().rstrip('\\n')\n                    self.vac.append(float(line.split()[1]))\n                    f.readline()\n                    f.readline()\n                    line = f.readline().rstrip('\\n')\n                    self.energy.append(float(line.split()[3]))\n                    f.readline()\n                    f.readline()\n                    line = f.readline().rstrip('\\n')\n                    self.ef.append(float(line.split()[2]))\n                    f.readline()\n        print(\"numble of samples = {0}\".format(len(self.en_eigval_sid)))\n        for i in range(len(self.en_eigval_sid)-1):\n            if (self.en_eigval_sid[i+1][0] == self.en_eigval_sid[i][0] and \\\n                self.en_eigval_sid[i+1][1] - self.en_eigval_sid[i][1] !=1):\n                for j in range(self.en_eigval_sid[i][1]+1, self.en_eigval_sid[i+1][1]):\n                    print(\"warning: missing data from \" + self.data_dirlist[self.en_eigval_sid[i][0]] \\\n                          + '.cf-' + str(j))\n\n    def read_pdos_ion(self):\n        \"\"\"\n        Read file *.procar-vasp.dat calculated by DFT using VASP.\n        \"\"\"\n        self.pdos_ion_sid = []\n        self.pdos_ion = []\n        for rtid in range(len(self.data_dirlist)):\n            sysname_full = self.data_dirlist[rtid]\n            strlist = sysname_full.split('.')\n            sysname = strlist[0]\n            filein = self.data_root + '/' + sysname_full + '/' + sysname_full \\\n                     + '.procar-vasp.dat'\n            print(\"read file: {0} \".format(sysname_full + '.procar-vasp.dat'))\n            with open(filein, 'r') as f:\n                line = f.readline().rstrip('\\n')\n                nsample = int(line.split()[0])\n                f.readline()\n                for i in range(nsample):\n                    f.readline()\n                    line = f.readline().rstrip('\\n')\n                    self.pdos_ion_sid.append([rtid, int(line.split()[1])])\n                    f.readline()\n                    pdos_ion = []\n                    for j in range(self.nband[rtid]):\n                        pdos_ion_sub = []\n                        for k in range(3):\n                            f.readline()\n                        for k in range(self.natom[rtid]):\n                           line = f.readline().rstrip('\\n')\n                           str1 = line.split()\n                           pdos_ion_sub.append(float(str1[4]))\n                        f.readline()\n                        f.readline()\n                        pdos_ion.append(pdos_ion_sub)\n                    self.pdos_ion.append(pdos_ion)\n                    f.readline()\n        print(\"numble of samples = {0}\".format(len(self.pdos_ion_sid)))\n        for i in range(len(self.pdos_ion_sid)-1):\n            if (self.pdos_ion_sid[i+1][0] == self.pdos_ion_sid[i][0] and \\\n                self.pdos_ion_sid[i+1][1] - self.pdos_ion_sid[i][1] !=1):\n                for j in range(self.pdos_ion_sid[i][1]+1, self.pdos_ion_sid[i+1][1]):\n                    print(\"warning: missing data from \" + self.data_dirlist[self.pdos_ion_sid[i][0]] \\\n                          + '.cf-' + str(j))\n\n    def combine_data(self):\n        \"\"\"\n        Select data with all information available and pass input variables to feature generator.\n        \"\"\"\n        print(\" \")\n        print(\"combine data...\")\n        self.record_sid = []\n        for i in range(len(self.sys_info_sid)):\n            sid = self.sys_info_sid[i]\n            if (sid in self.sys_info_sid and sid in self.old_struct_sid and sid in self.new_struct_sid \\\n                and sid in self.en_eigval_sid and sid in self.pdos_ion_sid):\n                self.record_sid.append(sid)\n            else:\n                print(\"warning: delete data sid {0}\".format(sid))\n        self.nrecord = len(self.record_sid)\n        print(\"total number of records = \", self.nrecord)\n        sys_info_labels = ['dot_type_id', 'dot_type_name', 'cf_id', 'core_shape', 'core_diameter', 'core_elem_natom', \\\n                           'nlig', 'stoi', 'pass_prop_001_old', 'pass_prop_111_old', 'pass_prop_011_old']\n        old_struct_labels = ['elem_name', 'elem_natom', 'old_lc_const', 'old_lc_vec', 'old_frac_coord']\n        new_struct_labels = ['new_lc_const', 'new_lc_vec', 'new_frac_coord']\n        en_eigval_labels = ['band_id', 'energy', 'eigval_orig', 'occ', 'vac', 'ef']\n        pdos_ion_labels = ['pdos_ion']\n        self.input_var_labels = sys_info_labels + old_struct_labels + new_struct_labels \\\n                                + en_eigval_labels + pdos_ion_labels\n        print('{0} input variables:'.format(len(self.input_var_labels)))\n        for i in range(len(self.input_var_labels)):\n            print('{0} : {1}'.format(i, self.input_var_labels[i]))\n        self.input_vars = [[] for i in range(len(self.input_var_labels))]\n        vid = 0\n        for i in range(len(self.sys_info_sid)):\n           if (self.sys_info_sid[i] in self.record_sid):\n               self.input_vars[vid].append(self.dot_type_id[i])\n               self.input_vars[vid+1].append(self.dot_type_name[i])\n               self.input_vars[vid+2].append(self.sys_info_sid[i][1])\n               self.input_vars[vid+3].append(self.core_shape[i])\n               self.input_vars[vid+4].append(self.core_diameter[i])\n               self.input_vars[vid+5].append(self.core_elem_natom[i])\n               self.input_vars[vid+6].append(self.nlig[i])\n               self.input_vars[vid+7].append(self.stoi[i])\n               self.input_vars[vid+8].append(self.pass_prop_001_old[i])\n               self.input_vars[vid+9].append(self.pass_prop_111_old[i])\n               self.input_vars[vid+10].append(self.pass_prop_011_old[i])\n        vid = vid + len(sys_info_labels)\n        for i in range(len(self.old_struct_sid)):\n           if (self.old_struct_sid[i] in self.record_sid):\n               self.input_vars[vid].append(self.old_struct_elem_name[i])\n               self.input_vars[vid+1].append(self.old_struct_elem_natom[i])\n               self.input_vars[vid+2].append(self.old_struct_lc_const[i])\n               self.input_vars[vid+3].append(self.old_struct_lc_vec[i])\n               self.input_vars[vid+4].append(self.old_struct_frac_coord[i])\n        vid = vid + len(old_struct_labels)\n        for i in range(len(self.new_struct_sid)):\n           if (self.new_struct_sid[i] in self.record_sid):\n               self.input_vars[vid].append(self.new_struct_lc_const[i])\n               self.input_vars[vid+1].append(self.new_struct_lc_vec[i])\n               self.input_vars[vid+2].append(self.new_struct_frac_coord[i])\n        vid = vid + len(new_struct_labels)\n        for i in range(len(self.en_eigval_sid)):\n           if (self.en_eigval_sid[i] in self.record_sid):\n               self.input_vars[vid].append(self.band_id[i])\n               self.input_vars[vid+1].append(self.energy[i])\n               self.input_vars[vid+2].append(self.eigval_orig[i])\n               self.input_vars[vid+3].append(self.occ[i])\n               self.input_vars[vid+4].append(self.vac[i])\n               self.input_vars[vid+5].append(self.ef[i])\n        vid = vid + len(en_eigval_labels)\n        for i in range(len(self.pdos_ion_sid)):\n           if (self.pdos_ion_sid[i] in self.record_sid):\n               self.input_vars[vid].append(self.pdos_ion[i])\n        \n        \n        \n               \n            \n        \n                      \n\n\n\n                    \n                \n                \n            \n                \n                \n                    \n                \n    \n                \n                \n            \n","sub_path":"analysis/QD_trap_py/load_data.py","file_name":"load_data.py","file_ext":"py","file_size_in_byte":19422,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"135295059","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\nAdvent of Code 2020\nDay 21, Part 2\n\"\"\"\n\nimport re\nfrom copy import deepcopy\n\n\ndef remove_from_lists(ingredients_list, allergens_list, ing, alg):\n    for ingredients, allergens in zip(ingredients_list, allergens_list):\n        if ing in ingredients:\n            ingredients.remove(ing)\n        if alg in allergens:\n            allergens.remove(alg)\n\n\ndef main():\n    with open('in.txt') as f:\n        lines = f.readlines()\n\n    ingredients_list = []\n    allergens_list = []\n\n    all_ingredients = set()\n    all_allergens = set()\n\n    for line in lines:\n        line = line.strip()\n        regex = r'([a-z, ]*) \\(contains ([a-z, ]*)\\)'\n        ingredients, allergens = re.findall(regex, line)[0]\n        ingredients = ingredients.split()\n        allergens = allergens.replace(',', '').split()\n\n        ingredients_list.append(set(ingredients))\n        allergens_list.append(set(allergens))\n\n        all_ingredients |= set(ingredients)\n        all_allergens |= set(allergens)\n\n    ingredients_dict = {}\n\n    while True:\n        change = False\n\n        i = -1\n        while True:\n            i += 1\n            if i >= len(ingredients_list):\n                break\n            ing_intersect = ingredients_list[i]\n            alg_intersect = allergens_list[i]\n            j = -1\n            while True:\n                j += 1\n                if j == i:\n                    continue\n                if j >= len(ingredients_list):\n                    break\n\n                if len(ing_intersect & ingredients_list[j]) > 0 and len(alg_intersect & allergens_list[j]) > 0:\n                    ing_intersect = ing_intersect & ingredients_list[j]\n                    alg_intersect = alg_intersect & allergens_list[j]\n                else:\n                    continue\n\n                if len(ing_intersect) == len(alg_intersect) == 1:\n                    ing, alg = ing_intersect.pop(), alg_intersect.pop()\n                    ingredients_dict[ing] = alg\n                    remove_from_lists(ingredients_list, allergens_list, ing, alg)\n                    ing_intersect = deepcopy(ingredients_list[i])\n                    alg_intersect = deepcopy(allergens_list[i])\n                    change = True\n\n        for ingredients, allergens in zip(ingredients_list, allergens_list):\n            if len(ingredients) == len(allergens) == 1:\n                ing, alg = ingredients.pop(), allergens.pop()\n                ingredients_dict[ing] = alg\n                remove_from_lists(ingredients_list, allergens_list, ing, alg)\n                change = True\n\n        if not change:\n            break\n\n    canonical = sorted(ingredients_dict.items(), key=lambda item: item[1])\n    canonical = ','.join([x[0] for x in canonical])\n\n    print(canonical)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"day21/2.py","file_name":"2.py","file_ext":"py","file_size_in_byte":2819,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"9932071","text":"#\n# Copyright 2011-2012, Intel Inc.\n#\n# This program is free software; you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation; version 2 of the License.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n# GNU Library General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program; if not, write to the Free Software\n# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.\n#\n'''\nCreated on 2 nov. 2011\n\n@author: Florent Vennetier\n'''\n\nfrom PySide.QtCore import QAbstractTableModel, QSize, Qt\nfrom PySide.QtGui import QColor\n\nfrom ObsLight.ObsLightPackageStatus import OBS_REV, OBS_STATUS, OSC_REV, OSC_STATUS, CHROOT_STATUS\nfrom ObsLight.ObsLightPackageStatus import NameColumn, StatusColumn, FSStatusColumn, SyncStatusColumn\n\n\nfrom ObsLight.ObsLightPackageStatus import ColumnHeaders, ColumnHeadersIndex\nfrom ObsLight.ObsLightPackageStatus import StatusColorsDict\nfrom ObsLight.ObsLightPackageStatus import StatusColumnCount\n\nclass PackageModel(QAbstractTableModel):\n    '''\n    QAbstractTableModel subclass to do the interface between the\n    \"packageTableWidget\" and the ObsLightManager.\n    '''\n    __emptyList = {}\n    colors = None\n\n    def __init__(self, obsLightManager, projectName):\n        QAbstractTableModel.__init__(self)\n        self.__obsLightManager = obsLightManager\n        self.__project = projectName\n        self.__pkgList = None\n        self.__getPackageList()\n\n        if self.colors is None:\n            self.__loadColors()\n\n    def __loadColors(self):\n        self.colors = {}\n        for columnLabel in StatusColorsDict.keys():\n            self.colors[columnLabel] = {}\n            for txt in StatusColorsDict[columnLabel].keys():\n                color = StatusColorsDict[columnLabel][txt]\n                self.colors[columnLabel][txt] = QColor(color)\n\n    #model\n    def rowCount(self, _parent=None):\n        return len(self.__getPackageList())\n    #model\n    def columnCount(self, _parent=None):\n        return StatusColumnCount()\n    #model\n    def data(self, index, role=Qt.DisplayRole):\n        if not index.isValid() or index.row() >= self.rowCount():\n            return None\n        if role == Qt.DisplayRole:\n            return self.displayRoleData(index.row(), index.column())\n        elif role == Qt.ForegroundRole:\n            return self.foregroundRoleData(index)\n        elif role == Qt.TextAlignmentRole:\n            return self.textAlignmentRoleData(index)\n        return None\n    #model\n    def headerData(self, section, orientation, role):\n        if role == Qt.DisplayRole:\n            if orientation == Qt.Orientation.Vertical:\n                return section\n            else:\n                return ColumnHeaders[section]\n        elif role == Qt.SizeHintRole:\n            if orientation == Qt.Orientation.Vertical:\n                pass\n            else:\n                if section == SyncStatusColumn:\n                    return QSize(32, 0)\n        return None\n\n    def getProject(self):\n        return self.__project\n\n    def resetCache(self):\n        self.__pkgList = None\n\n    def __getPackageList(self):\n        if self.getProject() is None:\n            return self.__emptyList\n\n        if self.__pkgList is None:\n            self.__pkgList = self.__obsLightManager.getPackageInfo(self.getProject())\n\n        if self.__pkgList is None:\n            return self.__emptyList\n        else:\n            return self.__pkgList\n\n    def displayRoleData(self, row, column):\n        packageList = self.__getPackageList()\n        packageName = sorted(packageList.keys())[row]\n        columnIndex = ColumnHeadersIndex[column]\n        return str(packageList[packageName].get(columnIndex, \"\"))\n\n    def foregroundRoleData(self, index):\n        row = index.row()\n        column = index.column()\n        drData = self.displayRoleData(row, column)\n\n        color = self.colors[column].get(drData, None)\n        return color\n\n    def textAlignmentRoleData(self, index):\n        column = index.column()\n        if column in range(1, self.columnCount()):\n            return Qt.AlignCenter\n#            return Qt.AlignHCenter | Qt.AlignVCenter\n        else:\n            return Qt.AlignVCenter\n\n\n#    def sort(self, Ncol, order):\n#        print \"Sort data according to column \", Ncol\n\n    def refresh(self):\n        self.resetCache()\n        self.layoutChanged.emit()\n\n    def addPackage(self, packageName):\n        '''\n        Add a package to the local project associated with this PackageModel.\n        packageName must be an existing package name on the OBS server\n        the project is associated to.\n        '''\n        self.__obsLightManager.addPackage(self.getProject(), packageName)\n        self.refresh()\n\n    def removePackage(self, packageName):\n        '''\n        Remove the package from the local project associated with this PackageModel.\n        '''\n        self.__obsLightManager.removePackage(self.getProject(), packageName)\n        self.refresh()\n","sub_path":"obslight/ObsLightGui/PackageModel.py","file_name":"PackageModel.py","file_ext":"py","file_size_in_byte":5229,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"498566787","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[2]:\n\n\n#importing Library\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nget_ipython().run_line_magic('matplotlib', 'inline')\nimport seaborn as sns\nimport plotly.express as px\nimport plotly.graph_objects as go\n\n\n# In[4]:\n\n\nnetflix=pd.read_csv('netflix_titles_IMDB.csv')\nnetflix.head()\n\n\n# In[5]:\n\n\n#getting missing values count\nmissing_values_count = netflix.isnull().sum()\nmissing_values_count\n\n\n# In[6]:\n\n\n#filling empty country field with US.\nnetflix['country'] = netflix['country'].fillna('United States')\nnetflix['country'].isnull().sum()\n\n\n# In[7]:\n\n\n#Empty rows of rating fill with TV-MA (Tv-MA is most common type of rating)\nnetflix['rating'] = netflix['rating'].fillna('TV-MA')\nnetflix['rating'].isnull().sum()\n\n\n# In[8]:\n\n\n#fill empty rows in date_added with 1st January 2020 \n#(this means if movies do not have added year on netflix than we are suggesting it got added on 1st January 2020.)\nnetflix['date_added'] =netflix['date_added'].fillna('January 1, 2020')\nnetflix['date_added'].isnull().sum()\n\n\n# In[9]:\n\n\nnetflix['month_added'] = pd.to_datetime(netflix['date_added']).dt.month_name() #Getting month name\nnetflix['year_added'] = pd.to_datetime(netflix['date_added']).dt.year #Getting year\nnetflix.head()\n\n\n# In[10]:\n\n\nnetflix.isnull().sum() #checking for null values \n\n\n# In[11]:\n\n\n#removing unwanted columns\nnetflix_clean = netflix.drop(['show_id','date_added','description'],axis=1) \nnetflix_clean.head() \n\n\n# In[12]:\n\n\n#getting counts of tv shows and movies\nmovie_tvshows_count = netflix_clean.type.value_counts() \nplt.pie(movie_tvshows_count.values,labels=movie_tvshows_count.index,autopct='%1.1f%%',radius=1.5)\nplt.legend(movie_tvshows_count.index,loc=\"upper center\", bbox_to_anchor=(1.35, 1),ncol=1)\nplt.title('TV Shows vs Movies',y=1.15,fontsize=30.0, fontweight='bold')\n\nplt.show()\n\n\n# In[13]:\n\n\n#In which year how, many contents added on Netflix\nplt.figure(figsize=(10,14))\nbins=20\nplt.hist(netflix_clean.year_added, bins, color ='blue', range=(2010,2019)) \n#y=plt.hist(netflix_clean.year_added #taking range to show past 10 years data\nplt.xticks(fontsize=15)\nplt.yticks(fontsize=15)\nplt.xlabel('Years ',fontsize=15)\nplt.ylabel('Frequency',fontsize=15)\nplt.title('shows and movies launch on netflix',fontsize=30.0, fontweight='bold')\nplt.show()\n\n\n# In[14]:\n\n\n#Heatmap to show month and year relationship in a context of content added on Netflix\nmovies_heatmap_df = netflix_clean.groupby(['year_added', 'month_added']).size().reset_index(name='count')\n\nmonth_ordered = [ 'January', 'February', 'March', 'April', 'May', 'June', 'July', 'August','September','October','November','December']\n\nmovies_heatmap_df['month_added'] = movies_heatmap_df['month_added'].astype(pd.api.types.CategoricalDtype(month_ordered))\n\nplt.figure(figsize=(30,14))\n\nsns.set(font_scale=2.25)\n\nax = pd.pivot_table(movies_heatmap_df, values= 'count', index=['month_added'], columns='year_added')\nsns.heatmap(ax, cmap=\"PuBu\", annot = True, fmt = \".1f\", annot_kws={'size':22})\n\nplt.title('content added month wise in each year',fontsize=30.0, fontweight='bold')\n\nplt.xlabel('years',fontsize=20.0, fontweight='bold')\nplt.ylabel('Month Name',fontsize=20.0, fontweight='bold')\n\n\n# In[15]:\n\n\n##animation graph showing netflix content added each year in all countries\n\nyear_country2 = netflix_clean.groupby('year_added')['country'].value_counts().reset_index(name='counts')\n\nfig = px.choropleth(year_country2, locations=\"country\", color=\"counts\", \n                    locationmode='country names',\n                    animation_frame='year_added',\n                    range_color=[0,100],\n                    color_continuous_scale=px.colors.sequential.Cividis_r\n                   )\n\nfig.update_layout(title='Comparison by country')\nfig.show()\n\n\n# In[16]:\n\n\n#netflix shows and movies of top 15 countries\n\nfiltered_countries = netflix_clean.country\nplt.figure(figsize=(13,7))\ng = sns.countplot(y = filtered_countries, order=filtered_countries.value_counts().index[:15]) \nplt.title('Top 15 Countries Contributor on Netflix',fontsize=30.0, fontweight='bold')\nplt.xlabel('Count')\nplt.ylabel('Country')\n#plt.subplot(1,2,1)\n\n\n# In[17]:\n\n\n#finding top 20 directors on netflix\n #spliting directors name from each row directors name from each row\nfiltered_directors = netflix_clean.director.str.split(', ', expand=True).stack().reset_index(level=1, drop=True)\nplt.figure(figsize=(19,10))\nplt.title('Top 20 Director on netflix',fontsize=30.0, fontweight='bold')\nsns.countplot(y = filtered_directors, order=filtered_directors.value_counts().index[:20], palette='Blues')\nplt.show()\n\n\n# In[18]:\n\n\n\n#filtering movies from Netflix dataset\nnetflix_movie_type = netflix_clean[netflix_clean.type == 'Movie']\n\n#filtering TVShows from Netflix dataset\n\nnetflix_tvshows_type = netflix_clean[netflix_clean.type == 'TV Show']\n\n##top stars on netflix(movies)\n #spliting Stars name from each row for movie\nfiltered_cast_movie = netflix_movie_type.cast.str.split(', ', expand=True).stack().reset_index(level=1, drop=True)\nplt.figure(figsize=(35,13))\nplt.subplot(1,2,1)\nplt.title('most famous movie stars on netflix',fontsize=30.0, fontweight='bold')\nsns.countplot(y = filtered_cast_movie, order=filtered_cast_movie.value_counts().index[:10],palette='rocket_r')\n\n\n##top stars on netflix(TVshows)\n #spliting Stars name from each row for TVshows\nfiltered_cast_shows = netflix_tvshows_type.cast.str.split(', ', expand=True).stack().reset_index(level=1, drop=True)\n\nplt.subplot(1,2,2)\nplt.title('most famous TVshows stars on netflix',fontsize=30.0, fontweight='bold')\nsns.countplot(y = filtered_cast_shows, order=filtered_cast_shows.value_counts().index[:10],palette='rocket_r')\n\n\n# In[19]:\n\n\n##top stars on netflix(TVshows)\n #spliting Stars name from each row for TVshows\nfiltered_genre_shows = netflix_tvshows_type.listed_in.str.split(', ', expand=True).stack().reset_index(level=1, drop=True)\n\nplt.subplot(1,2,2)\nplt.title('Top 10 genres of TVshows on netflix',fontsize=30.0, fontweight='bold')\nsns.countplot(y = filtered_genre_shows, order=filtered_genre_shows.value_counts().index[:10],palette='rocket_r')\n\n\n# In[20]:\n\n\n#comparision of movies and tvshows count according to rating\nplt.figure(figsize=(25,8))\nsns.countplot(x='rating',hue='type',data=netflix_clean)\nplt.title('comparing frequency between type and rating',fontsize=30.0, fontweight='bold')\nplt.show()\n\n\n# In[21]:\n\n\n#country wise top 10 actors\n\n##splitting countries name from each row\nnetflix['country'] = netflix.country.str.replace(\", | ,\", \",\")\nmovies_country_df = pd.DataFrame(netflix.country.str.split(',').tolist(), index=netflix.show_id).stack()\nmovies_country_df = movies_country_df.reset_index([0, 'show_id'])\nmovies_country_df.columns = ['show_id', 'country']\n\n\n\n##splitting cast's name from each row\ncast_df = netflix[['show_id', 'cast']]\ncast_df = cast_df.dropna()\ncast_df['cast'] = cast_df.cast.str.replace(\", | ,\", \",\")\ncast_df = pd.DataFrame(cast_df.cast.str.split(',').tolist(), index=cast_df.show_id).stack()\ncast_df = cast_df.reset_index([0, 'show_id'])\ncast_df.columns = ['show_id', 'cast']\n\n\n## merging countries name and cast in one data frame\ncast_df = pd.merge(movies_country_df, cast_df, on='show_id', how = 'inner')\ncast_df = cast_df.groupby(['cast','country']).size().reset_index(name='count')\ncountry_list = ['United States', 'India', 'United Kingdom', 'France', 'Canada', 'Spain']\ncast_df = cast_df[cast_df['country'].isin(country_list)]\ncast_df = cast_df.groupby(['country']).apply(lambda x: x.sort_values(['count'],ascending = False)).reset_index(drop = True)\ncast_df = cast_df.groupby(['country']).head(10)\n\n\n# In[22]:\n\n\n\nf, ax = plt.subplots(2, 3, figsize=(40, 14), sharex=False)\n\nax1 = sns.barplot(x = 'count', y = 'cast', data = cast_df[cast_df['country'] == 'United States'], palette=\"PuBu_d\", ax=ax[0, 0]).set_title(\"United States\")\nax2 = sns.barplot(x = 'count', y = 'cast', data = cast_df[cast_df['country'] == 'India'], palette=\"PuBu_d\", ax=ax[0, 1]).set_title(\"India\")\nax3 = sns.barplot(x = 'count', y = 'cast', data = cast_df[cast_df['country'] == 'United Kingdom'], palette=\"PuBu_d\", ax=ax[0, 2]).set_title(\"United Kingdom\")\nax4 = sns.barplot(x = 'count', y = 'cast', data = cast_df[cast_df['country'] == 'France'], palette=\"PuBu_d\", ax=ax[1, 0]).set_title(\"France\")\nax5 = sns.barplot(x = 'count', y = 'cast', data = cast_df[cast_df['country'] == 'Canada'], palette=\"PuBu_d\", ax=ax[1,1]).set_title(\"Canada\")\nax6 = sns.barplot(x = 'count', y = 'cast', data = cast_df[cast_df['country'] == 'Spain'], palette=\"PuBu_d\", ax=ax[1, 2]).set_title(\"Spain\")\n\nplt.setp(ax, xticks=np.arange(0,36,4))\nplt.tight_layout()\n\n\n# In[23]:\n\n\n# count of each rating type \nrating_count = netflix_clean.rating.value_counts() \nrating_count\n\n\n# In[24]:\n\n\n#season distribution according to netflix_Ratings\n##creating list of top 5 rating type \nper_season_tv_ma=[]\nper_season_tv_14=[]\nper_season_tv_pg=[]\nper_season_R=[]\nper_season_pg_13=[]\n\n\n\nfor i in range(1,15):\n    a = netflix_clean.loc[(netflix_clean['rating']=='TV-MA') & (netflix_clean.duration_in_Seasons==i)] \n    b = netflix_clean.loc[(netflix_clean['rating']=='TV-14') & (netflix_clean.duration_in_Seasons==i)] \n    c = netflix_clean.loc[(netflix_clean['rating']=='TV-PG') & (netflix_clean.duration_in_Seasons==i)] \n    d = netflix_clean.loc[(netflix_clean['rating']=='R') & (netflix_clean.duration_in_Seasons==i)] \n    e = netflix_clean.loc[(netflix_clean['rating']=='PG-13') & (netflix_clean.duration_in_Seasons==i)] \n\n    per_season_tv_ma.append(len(a))\n    per_season_tv_14.append(len(b))\n    per_season_tv_pg.append(len(c))\n    per_season_R.append(len(d))\n    per_season_pg_13.append(len(e))\n\ntrace1 = go.Scatter(x=[i for i in range(1,15)],y=per_season_tv_ma,mode='lines+markers',name=' Mature Audiences Only')\ntrace2 = go.Scatter(x=[i for i in range(1,15)],y=per_season_tv_14,mode='lines+markers',name='children under 14 years')\ntrace3 = go.Scatter(x=[i for i in range(1,15)],y=per_season_tv_pg,mode='lines+markers',name='TV parental guidelines')\ntrace4 = go.Scatter(x=[i for i in range(1,15)],y=per_season_R,mode='lines+markers',name='18+')\ntrace5 = go.Scatter(x=[i for i in range(1,15)],y=per_season_pg_13,mode='lines+markers',name='8 to 13 age group')\n\ndata=[trace1,trace2,trace3,trace4,trace5]\n\nlayout = go.Layout(title=\"Top 5 rating category per seasons\", legend=dict(x=0.1, y=1.1, orientation=\"h\"),xaxis_title=\"number of season\",\n    yaxis_title=\"count\")\n\n\n\nfig = go.Figure(data, layout=layout)\n\nfig.show()\n\n\n# In[25]:\n\n\n#Genre Text Analysis\nfrom wordcloud import WordCloud, STOPWORDS, ImageColorGenerator\n\n#splitting genre \nfiltered_genre = netflix_clean.listed_in.str.split(r\"\\,|&\", n=1, expand=True).stack().reset_index(level=1, drop=True)\ntext = list(set(filtered_genre))\n\nwordcloud = WordCloud(max_font_size=50, max_words=100,background_color=\"black\").generate(str(text))\nplt.rcParams['figure.figsize'] = (13, 13)\nplt.imshow(wordcloud,interpolation=\"bilinear\")\nplt.axis(\"off\")\nplt.show()\n\n\n# ## **Business Question**\n# \n# ### How many Movies and Tv Shows get delayed to release on Netflix ?\n# \n# ### what is the best for Netflix to attract more viewership/user by producing more movies or Tv shows ?\n# \n# \n# \n\n# In[27]:\n\n\ncounts=0\nfor i,j in zip(netflix_movie_type['release_year'].values,netflix_movie_type['year_added'].values):\n    if i!=j:\n        counts+=1\nprint(str(counts),'movies get delayed on netflix')\n\n\ncounts1=0\nfor i,j in zip(netflix_tvshows_type['release_year'].values,netflix_tvshows_type['year_added'].values):\n    if i!=j:\n        counts1+=1\nprint(str(counts1),'TVshows get delayed on netflix')\n\n\n# In[ ]:\n\n\n\n\n","sub_path":"netflix_EDA.py","file_name":"netflix_EDA.py","file_ext":"py","file_size_in_byte":11616,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"144347707","text":"from django.shortcuts import render\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom rest_framework import status\nfrom research_dss_app.models import FieldVisit,PlantHealth\nfrom research_dss_app.serializers import FieldVisitSerializer,PlantHealthSerializer\nfrom research_dss_app.modules.ndvi_module import ndvi_module\nfrom research_dss_app.modules.mapping_module import mapping_module\nfrom research_dss_app.modules.tea_bud_identification_module import tea_bud_identification_module\nfrom research_dss_app.modules import sysconf\nimport os,time,json\nfrom datetime import datetime\n# Create your views here.\n\"\"\"\nHandles the NDVI module. Inputs are the NIR and RGB map names.\nRequest type should be PUT and the output of this put method in this view class\nis the percentage of plants with good health and bad health, non plant percentage\nand the NDVI map name.\n\"\"\"\nclass NDVIMapView(APIView):\n    def post(self,request,format=None):\n        f = open(sysconf.createLogFile(request.data['project_name']),\"a+\")\n        print('[' + datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\") + '] ' + \"Started the NDVI module\")\n        sysconf.writeLogRecord('NDVI analysis is started',f)\n        #sysconf.writeLogRecord('Request data : ' + json.dumps(request.data))\n        sysconf.writeLogRecord('NDVI red, green and blue layer maps generation started',f)\n        NDVI_MAP_PATH = sysconf.getNDVIMapPath(request.data['project_name'],'ndvi_map_final')\n        sysconf.writeLogRecord('NDVI map path : ' + NDVI_MAP_PATH,f)\n        sysconf.writeLogRecord('RGB map : ' + sysconf.getRGBMapPath(request.data['project_name'],'rgb_map_final.tif'),f)\n        sysconf.writeLogRecord('NIR map : ' + sysconf.getNIRMapPath(request.data['project_name'],'nir_map_final.tif'),f)\n        # print(\"Creating the NDVI red, green and blue layer maps\",end=\"\")\n        # request.data['nir_map_name'] should be 'nir_map'\n        # request.data['rgb_map_name'] should be 'rgb'_map'\n        NDVI_MAP = ndvi_module.createNDVIMap(\n            sysconf.getNIRMapPath(request.data['project_name'],'nir_map_final.tif'),#,request.data['nir_map_name']),\n            sysconf.getRGBMapPath(request.data['project_name'],'rgb_map_final.tif'),#request.data['rgb_map_name']),\n            NDVI_MAP_PATH,f\n            )\n        # print(\"......done\")\n        sysconf.writeLogRecord('NDVI red, green and blue layer maps generation complete',f)\n        sysconf.writeLogRecord('Plant health analysis is started',f)\n        print('[' + datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\") + '] ' + \"Calculating the plant health analysis\",end=\"\")\n        HEALTH_ANALYSIS = ndvi_module.calculateHealthyAndNonHealthyPlantPercentage(NDVI_MAP)\n        print(\"......done\")\n        sysconf.writeLogRecord('Plant health analysis is complete',f)\n        sysconf.writeLogRecord('Georeferencing NDVI red, green and blue layer maps is started',f)\n        mapping_module.createGeoTiff(request.data['project_name'], 'ndvi',f)\n        sysconf.writeLogRecord('Georeferencing NDVI red, green and blue layer maps is completed',f)\n        print('[' + datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\") + '] ' + \"Saving data in the database\",end=\"\")\n        sysconf.writeLogRecord('Saving the data to database is started',f)\n        fieldVisit = FieldVisit.objects.get(real_name=request.data['project_name'])\n        plantHealthSerializer = PlantHealthSerializer(data={'project_id':fieldVisit.id,'good_plant_health':HEALTH_ANALYSIS['Good_health'],'bad_plant_health':HEALTH_ANALYSIS['Bad_health'],'non_plants':HEALTH_ANALYSIS['Non_plants']})\n        if(plantHealthSerializer.is_valid()):\n            plantHealthSerializer.save()\n            sysconf.writeLogRecord('Saving the data to database is complete',f)\n        sysconf.writeLogRecord('DB Object errors : ' + str(plantHealthSerializer.errors),f)\n        sysconf.writeLogRecord('NDVI analysis is complete',f)\n        print('[' + datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\") + '] ' + 'NDVI module is Finished')\n        f.close()\n        # print(\"......done\")\n        return Response({'results':HEALTH_ANALYSIS,'path':NDVI_MAP_PATH})\n\n\"\"\"\nHandles the mapping module. The request type should be GET. No inputs required.\nIt outputs the path of the NIR and RGB map\n\"\"\"\nclass RGBAndNIRMapView(APIView):\n    def post(self,request,operation,format=None):\n        # rgbMap = 'IMG_8764_RGB.jpg'\n        # nirMap = 'IMG_8763_RGB.jpg'\n        # resultantRGBMap = 'RGB_Ortho.jpg'\n        # resultantNIRMap = 'NIR_Ortho.jpg'\n        if(operation == 'geotag'):\n            f = open(sysconf.createLogFile(request.data['project_name']),\"a+\")\n            #Geotag nir_images\n            sysconf.writeLogRecord('NIR image geotagging has been started.',f)\n            #sysconf.writeLogRecord('Request data : ' + json.dumps(request.data))\n            print('[' + datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\") + '] ' + \"Geotagging NIR images\",end=\"\")\n            mapping_module.geotagging_images(request.data['project_name'],f)\n            print(\"......done\")\n            sysconf.writeLogRecord('NIR images geotagging is complete.',f)\n            f.close()\n            return Response({'message':'geotagging is complete'})\n        if(operation == 'mapping'):\n            #Create orthomosaics\n            f = open(sysconf.createLogFile(request.data['project_name']),\"a+\")\n            sysconf.writeLogRecord('orthomosaic generation started',f)\n            #sysconf.writeLogRecord('Request data : ' + json.dumps(request.data))\n            print('[' + datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\") + '] ' + \"When orthomosaics are generating an new terminal will execute the task and the server terminal might look like unresponsive. But don't panic. It's on the way.\")\n            mapping_module.Create_Orthomap(request.data['project_name'],request.data['mode'],f)\n            print('[' + datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\") + '] ' + request.data['mode'] + 'mapping is done')\n            # if(request.data['mode'] == 'rgb'):\n            #     print('RGB mapping......done')\n            #     mapping_module.Create_Orthomap(request.data['project_name'],'rgb')\n            # else:\n            #     print('NIR mapping......done')\n            #     mapping_module.Create_Orthomap(request.data['project_name'],'nir')\n            sysconf.writeLogRecord('orthomosaic generation completed',f)\n            f.close()\n            return Response({'message':request.data['mode'] + ' orthomosaic generation is complete'})\n            #Create NIR map\n\n        if(operation == 'align'):\n            f = open(sysconf.createLogFile(request.data['project_name']),\"a+\")\n            sysconf.writeLogRecord('Aligning images has been started',f)\n            #sysconf.writeLogRecord('Request data : ' + json.dumps(request.data))\n            sysconf.writeLogRecord(\"Raw RGB map : \" + sysconf.getRGBRawMapPath(request.data['project_name']),f)\n            sysconf.writeLogRecord(\"Raw NIR map : \" + sysconf.getNIRRawMapPath(request.data['project_name']),f)\n            sysconf.writeLogRecord(\"Resultant RGB map : \" + sysconf.getRGBMapPath(request.data['project_name'],'rgb_map_final.png'),f)\n            sysconf.writeLogRecord(\"Resultant NIR map : \" + sysconf.getNIRMapPath(request.data['project_name'],'nir_map_final.png'),f)\n            #Overlap the maps\n            print(\"Aligning images\",end=\"\")\n            print(\"Raw RGB map : \" + sysconf.getRGBRawMapPath(request.data['project_name']))\n            print(\"Raw NIR map : \" + sysconf.getNIRRawMapPath(request.data['project_name']))\n            print(\"Resultant RGB map : \" + sysconf.getNIRMapPath(request.data['project_name'],'rgb_map_final.png'))\n            print(\"Resultant NIR map : \" + sysconf.getNIRMapPath(request.data['project_name'],'nir_map_final.png'))\n\n            resultantOverlappedNIRMap = mapping_module.Image_Overlap(sysconf.getRGBRawMapPath(request.data['project_name']),sysconf.getNIRRawMapPath(request.data['project_name']),sysconf.getNIRMapPath(request.data['project_name'],'nir_map.png'),f)\n            resultantOverlappedRGBMap = mapping_module.Image_Overlap(sysconf.getNIRRawMapPath(request.data['project_name']),sysconf.getRGBRawMapPath(request.data['project_name']),sysconf.getRGBMapPath(request.data['project_name'],'rgb_map.png'),f)\n            # resultantOverlappedNIRMap = mapping_module.Image_Overlap(sysconf.getRGBRawMapPath(request.data['project_name']),sysconf.getNIRRawMapPath(request.data['project_name']),sysconf.getNIRMapPath(request.data['project_name'],'nir_map.jpg'))\n            # resultantOverlappedRGBMap = mapping_module.Image_Overlap(sysconf.getNIRRawMapPath(request.data['project_name']),sysconf.getRGBRawMapPath(request.data['project_name']),sysconf.getRGBMapPath(request.data['project_name'],'rgb_map.jpg'))\n\n            sysconf.writeLogRecord('Georeferencing RGB map image started',f)\n            mapping_module.createGeoTiff(request.data['project_name'], 'rgb',f)\n            sysconf.writeLogRecord('Georeferencing RGB map image completed',f)\n            sysconf.writeLogRecord('Georeferencing NIR map image started',f)\n            mapping_module.createGeoTiff(request.data['project_name'], 'nir',f)\n            sysconf.writeLogRecord('Georeferencing NIR map image completed',f)\n            #mapping_module.createGeoTiff(sysconf.getNIRRawMapPath(request.data['project_name']),sysconf.getNIRMapPath(request.data['project_name'],'nir_map_final.png'))\n            #mapping_module.createGeoTiff(sysconf.getNDVIMapPath(request.data['project_name']),sysconf.getNIRMapPath(request.data['project_name'],'nir_map_final.png'))\n            sysconf.writeLogRecord('orthomosaic aligning is complete',f)\n            f.close()\n            return Response({'message':'Alligning is complete','rgb_map':resultantOverlappedRGBMap,'nir_map':resultantOverlappedNIRMap})\n            # os.system('rm -r ' + sysconf.getRGBMapPath(rgbMap))\n            # os.system('rm -r ' + sysconf.getNIRMapPath(nirMap))\n            print(\"......done\")\n\n\"\"\"\nThis view class responsible for tea bud identification. Still constructing\n\"\"\"\nclass TeaBudIdentificationView(APIView):\n    def post(self,request,format=None):\n        print('[' + datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\") + '] ' + 'Tea buds identification is started')\n        f = open(sysconf.createLogFile(request.data['project_name']),\"a+\")\n        sysconf.writeLogRecord('Tea bud identification process is started',f)\n        tea_bud_identification_module.analyzeTeaBuds(request.data['project_name'],f)\n        sysconf.writeLogRecord('Georeferencing RGB cluster map image started',f)\n        mapping_module.createGeoTiff(request.data['project_name'], 'cluster',f)\n        sysconf.writeLogRecord('Georeferencing RGB cluster map image started',f)\n        sysconf.writeLogRecord('Tea bud identification process is complete',f)\n        f.close()\n        print('[' + datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\") + '] ' + 'Tea buds identification is finished')\n        # request.data['rgb_map'] should be 'rgb_map' because the name is static\n        return Response({'nir_path':sysconf.getTeaBudIndetifiedMapPath(request.data['project_name'],'rgb_processed.jpg')})\n\n\"\"\"\nResponsible for retrieving the data in the DB.\n\"\"\"\nclass FieldVisitView(APIView):\n    def get(self,request,format=None):\n        plantHealthSerializer = PlantHealthSerializer(PlantHealth.objects.all().prefetch_related('project_id'), many=True)\n        for item in plantHealthSerializer.data:\n            fieldVisitObj = FieldVisitSerializer(FieldVisit.objects.get(id=item['project_id']))\n            item['project_data'] = fieldVisitObj.data\n        return Response({'data':plantHealthSerializer.data})\n\n    def put(self,request,real_name,mode,format=None):\n        f = open(sysconf.createLogFile(real_name),\"a+\")\n        sysconf.writeLogRecord('Uploading ' + mode + ' images started',f)\n        fieldVisitObj = FieldVisit.objects.get(real_name=real_name)\n        #sysconf.writeLogRecord('Request data : ' + request.body.decode('utf-8'))\n        path = ''\n        if mode == 'rgb':\n            path = sysconf.getRGBRawImagesPath(fieldVisitObj.real_name)\n        else:\n            path = sysconf.getNIRRawImagesPath(fieldVisitObj.real_name)\n\n        for count, x in enumerate(request.FILES.getlist(\"images[]\")):\n            def process(f):\n                with open(path + '/img_' + str(count) + '.JPG', 'wb+') as destination:\n                    for chunk in f.chunks():\n                        destination.write(chunk)\n            process(x)\n        sysconf.writeLogRecord('Uploading ' + mode + ' images completed',f)\n        f.close()\n        return Response(\"File(s) uploaded!\")\n\nclass TilerView(APIView):\n    def put(self,request,format=None):\n        #odm_orthophoto.original.tiff should be replaced by 'nir_map' and 'rgb_map'\n        #because they already created\n        f = open(sysconf.createLogFile(request.data['project_name']),\"a+\")\n        sysconf.writeLogRecord('Tile generation is started',f)\n        #sysconf.writeLogRecord('Request data : ' + json.dumps(request.data))\n        response = mapping_module.createTiles(request.data['project_name'],request.data['mode'],f)\n        sysconf.writeLogRecord('Tile generation is complete',f)\n        f.close()\n        return Response({'response':response})\n\n    def post(self,request,format=None):\n        real_project_name = request.data['project_name'] + '_' + str(int(round(time.time() * 1000)))\n        f = open(sysconf.createLogFile(real_project_name),\"w+\")\n        sysconf.writeLogRecord('Inserting project data to the database.',f)\n        # sysconf.writeLogRecord('[' + datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\") + '] Inserting project data to the database')\n        # millis = int(round(time.time() * 1000))\n        fieldVisitObj = FieldVisitSerializer(data={'name':request.data['project_name'],'real_name':real_project_name})\n        #sysconf.writeLogRecord('Request data ' + json.dumps(request.data))\n        sysconf.writeLogRecord('Project real name is ' + real_project_name + '.',f)\n        sysconf.writeLogRecord('Saving project database object in database.',f)\n        #sysconf.writeLogRecord('DB object : ' + json.dumps(fieldVisitObj))\n        sysconf.writeLogRecord('Serailizer : FieldVisitSerializer',f)\n        if fieldVisitObj.is_valid():\n            fieldVisitObj.save()\n            sysconf.writeLogRecord('DB object saved successfully',f)\n        sysconf.writeLogRecord('Erros in DB object : ' + str(fieldVisitObj.errors),f)\n        sysconf.writeLogRecord('Response : ' + json.dumps({'project_real_name':real_project_name}),f)\n        sysconf.writeLogRecord('Project data insertion to database is complete',f)\n        f.close()\n        return Response({'project_real_name':real_project_name})\n","sub_path":"final-year-research-dev/BackEnd/research_dss/research_dss_app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":14712,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"123553525","text":"import os\nfrom datetime import datetime, timedelta\n\nfrom airflow import DAG\nfrom airflow.operators.bash_operator import BashOperator\nfrom airflow.operators.python_operator import PythonOperator\nfrom airflow.contrib.operators.file_to_gcs import FileToGoogleCloudStorageOperator\n\nfrom sanitisation.service import main\nfrom form_data.service import main as main_form\nfrom svg_data.main import main as main_svg\n\nfrom gcs.debugger import enable_cloud_debugger\n\nenable_cloud_debugger()\n\ndefault_args = {\n    'owner': 'Flatten.ca',\n    'depends_on_past': False,\n    'retries': 1,\n    'retry_delay': timedelta(minutes=5),\n}\n\nsanitisation_dag = DAG(\n    dag_id='sanitise',\n    start_date=datetime(2020,4,22),\n    schedule_interval='*/30 * * * *',\n    default_args=default_args,\n    catchup=True\n)\n\necho = BashOperator(\n    task_id='Echo',\n    bash_command='echo \"Running Sanitisation Script\"'\n)\necho_form = BashOperator(\n    task_id='Echo',\n    bash_command='echo \"Running Form Data Script\"'\n)\n\nsanitise = PythonOperator(\n    task_id='sanitisation',\n    python_callable=main,\n    dag=sanitisation_dag\n)\n\nform = PythonOperator(\n    task_id='form_data',\n    python_callable=main_form,\n    dag=sanitisation_dag\n)\n\nsvg = PythonOperator(\n    task_id='svg_data',\n    python_callable=main_svg,\n    dag=sanitisation_dag\n)\n\necho >> sanitise >> form >> svg","sub_path":"dags/sanitisation_dag.py","file_name":"sanitisation_dag.py","file_ext":"py","file_size_in_byte":1337,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"393081818","text":"'''\n又到了周末，小易的房间乱得一团糟。\n他希望将地上的杂物稍微整理下，使每团杂物看起来都紧凑一些，没有那么乱。\n地上一共有n团杂物，每团杂物都包含4个物品。第i物品的坐标用(ai,bi)表示，小易每次都可以将它绕着(xi,yi)逆时针旋转，\n这将消耗他的一次移动次数。如果一团杂物的4个点构成了一个面积不为0的正方形，我们说它是紧凑的。\n因为小易很懒，所以他希望你帮助他计算一下每团杂物最少需要多少步移动能使它变得紧凑。\n\n输入描述:\n第一行一个数n(1 <= n <= 100)，表示杂物的团数。\n接下来4n行，每4行表示一团杂物，每行4个数ai, bi，xi, yi, (-104 <= xi, yi, ai, bi <= 104)，表示第i个物品旋转的它本身的坐标和中心点坐标。\n\n输出描述:\nn行，每行1个数，表示最少移动次数。\n\n输入例子1:\n4\n1 1 0 0\n-1 1 0 0\n-1 1 0 0\n1 -1 0 0\n1 1 0 0\n-2 1 0 0\n-1 1 0 0\n1 -1 0 0\n1 1 0 0\n-1 1 0 0\n-1 1 0 0\n-1 1 0 0\n2 2 0 1\n-1 0 0 -2\n3 0 0 -2\n-1 1 -2 0\n\n输出例子1:\n1\n-1\n3\n3\n\n例子说明1:\n对于第一团杂物，我们可以旋转第二个或者第三个物品1次。\n'''\n\n# 一个点绕另一个点旋转角度 http://www.voidcn.com/article/p-gyblyxcw-bad.html\ndef change(i, x, y):\n    return [x + y - i[1], y - x + i[0]]\n\n# 计算两点间的距离，平方\ndef dis(a, b):\n    return (a[0] - b[0]) ** 2 + (a[1] - b[1]) ** 2\n\ndef square(a, b, c, d):\n    # 正方形的四条边和对角线\n    q = [dis(a, b), dis(a, c), dis(a, d), dis(b, c), dis(b, d), dis(c, d)]\n    # 四条边\n    q.sort()\n    # 四条边相等且不为0， 和对角线构成的是等腰三角形\n    if q[0] != 0 and q[0] == q[1] and q[1] == q[2] and q[2] == q[3] and q[4] == q[5] and q[4] == 2 * q[3]:\n        return True\n    return False\n\n# 杂物数目\nn = int(input().strip())\nfor w in range(n):\n    # 上线为100\n    best = 100\n    # 保存每一团杂物所有点可能出现的四个位置，\n    p = []\n    for i in range(4):\n        # 每个点\n        a, b, x, y = map(int, input().strip().split())\n        temp1 = [[a, b]]\n        # 每个点旋转3次，得到绕轴的四个位置，因为是90度，所以只能是4个位置\n        for j in range(3):\n            temp1.append(change(temp1[-1], x, y))\n        # 每个点所有的可能，构成一个元素，然后所有的元素构成一堆杂物每个点所有的可能\n        p.append(temp1)\n\n    for i in range(4):\n        for j in range(4):\n            for k in range(4):\n                for l in range(4):\n                    if square(p[0][i], p[1][j], p[2][k], p[3][l]):\n                        best = min(best, i + j + k + l)\n    if best == 100:\n        best = -1\n    print(best)","sub_path":"code/offer/71.003-整理房间.py","file_name":"71.003-整理房间.py","file_ext":"py","file_size_in_byte":2746,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"447711176","text":"import ROOT as rt\nimport numpy as np\nimport h5py \n#import pandas as pd\nimport sys \nimport gc\nrt.gROOT.SetBatch(rt.kTRUE)\n\ndef muno_info(pos_theta, pos_phi, pos_r, theta_mom, phi_mom, energy):\n    lowX=0.1\n    lowY=0.8\n    info  = rt.TPaveText(lowX, lowY+0.06, lowX+0.30, lowY+0.16, \"NDC\")\n    info.SetBorderSize(   0 )\n    info.SetFillStyle(    0 )\n    info.SetTextAlign(   12 )\n    info.SetTextColor(    1 )\n    info.SetTextSize(0.03)\n    info.SetTextFont (   42 )\n    info.AddText(\"#mu(pos_{#theta}^{%.2f}, pos_{#phi}^{%.2f}, pos_{r}^{%.1f}, mom_{#theta}^{%.2f}, mom_{#phi}^{%.2f}, E=%d MeV)\"%(pos_theta, pos_phi, pos_r, theta_mom, phi_mom, energy))\n    return info\n\n\ndef do_plot(event,hist,out_name,title):\n    canvas=rt.TCanvas(\"%s\"%(out_name),\"\",800,800)\n    canvas.cd()\n    canvas.SetTopMargin(0.13)\n    canvas.SetBottomMargin(0.1)\n    canvas.SetLeftMargin(0.13)\n    canvas.SetRightMargin(0.15)\n    #h_corr.Draw(\"COLZ\")\n    #h_corr.LabelsDeflate(\"X\")\n    #h_corr.LabelsDeflate(\"Y\")\n    #h_corr.LabelsOption(\"v\")\n    hist.SetStats(rt.kFALSE)\n    hist.GetXaxis().SetTitle(\"#phi(AU, 0 #rightarrow 2#pi)\")\n    hist.GetYaxis().SetTitle(\"Z(AU) (-19.5 #rightarrow 19.5 m)\")\n    hist.SetTitle(title)\n    #hist.SetTitleSize(0.1)\n    #hist.Draw(\"COLZ TEXT\")\n    hist.Draw(\"COLZ\")\n    if n3 != None:\n        Info = muno_info(n3[event][2], n3[event][3], n3[event][4], n3[event][0], n3[event][1], n3[event][5])\n        Info.Draw()\n    canvas.SaveAs(\"%s/%s.png\"%(plot_path,out_name))\n    del canvas\n    gc.collect()\n\nshow_real = False\nshow_fake = True\n\nhf = 0\nn1 = 0\nn2 = 0\nn3 = 0\nevent_list=0\nplot_path=\"\"\nif show_real:\n    hf = h5py.File('/hpcfs/juno/junogpu/fangwx/FastSim/data/data_all0.h5', 'r')\n    n1 = hf['firstHitTimeByPMT']\n    n2 = hf['nPEByPMT']\n    n3 = hf['infoMuon']\n    event_list=[1,10,100,1000,10000]\n    plot_path=\"./plots_event_display/real\"\n    print (n1.shape, n2.shape, n3.shape)\nelif show_fake:\n    hf = h5py.File('/hpcfs/juno/junogpu/fangwx/FastSim/generator/gen0822_v1.h5', 'r')\n    n1 = hf['firstHitTimeByPMT']\n    n2 = hf['nPEByPMT']\n    n1 = np.squeeze(n1)\n    n2 = np.squeeze(n2)\n    n3 = None\n    event_list=range(0,10)\n    plot_path=\"./plots_event_display/fake\"\n    print (n1.shape, n2.shape)\nelse: \n    print ('wrong config')\n    sys.exit()\n\n\nfor event in event_list:\n    if n3 != None:\n        print ('event=%d, muon Direction: theta=%f, phi=%f. Position: theta=%f, phi=%f, r=%f. Energy=%f MeV'%(event, n3[event][0], n3[event][1], n3[event][2], n3[event][3], n3[event][4], n3[event][5]))\n    nX = n1[event].shape[1]\n    nY = n1[event].shape[0]\n    \n    h_Hit = rt.TH2F('Hit_evt%d'%event, '', nX, 0, nX, nY, 0, nY)\n    h_nPE = rt.TH2F('nPE_evt%d'%event, '', nX, 0, nX, nY, 0, nY)\n    for i in range(0, nX):\n        i0 = nX-1 - i\n        for j in range(0, nY):\n            j0 = nY-1 - j\n            h_Hit.Fill(i0+0.01, j0+0.01, n1[event][j][i])\n            h_nPE.Fill(i0+0.01, j0+0.01, n2[event][j][i])\n    str1 = \"_gen\" if show_fake else \"\"\n    do_plot(event, h_Hit,'Hit_evt%d%s'%(event,str1),'First hit time of PMTs')\n    do_plot(event, h_nPE,'nPE_evt%d%s'%(event,str1),'nPE of PMTs')\n","sub_path":"DataPrepare/FastSim/junows/event_display_v1.py","file_name":"event_display_v1.py","file_ext":"py","file_size_in_byte":3111,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"290828272","text":"#BSM.py\n\ndef BSM(Smax, T ,theta, r, k = 1):\n    csr = populate_matrix(Smax, T, theta, r, k)\n    print(csr)\n    \n    \ndef populate_matrix(Smax, T, theta, r, k):\n    csr = {\"val\": [], \"col\": [], \"row_start\": [], \"B\": [], \"N\": 0.0}\n    dim = int(k * T)    \n    csr[\"N\"] = dim\n    csr[\"row_start\"].insert(0, (dim * 3) - 1) #fill last value for rowstart\n    for n in range (dim, 0, -1):\n        entry_count = 0\n        #populate 3rd equation for all rows, bar the last row in the matrix        \n        if n != dim:\n            \n            csr[\"col\"].insert(0, n + 1)\n            csr[\"val\"].insert(0, tridiagfuture(n, dim, theta, r) )\n            entry_count += 1 \n    \n        #populate 2nd equation for all rows            \n        csr[\"col\"].insert(0, n)\n        csr[\"val\"].insert(0, tridiagcurrent(n, dim, theta, r) )\n        entry_count += 1\n        \n        #populate 1st equation for all rows, bar the first row in the matrix\n        if n != 1:\n            csr[\"col\"].insert(0, n - 1)\n            csr[\"val\"].insert(0, tridiagfuture(n, dim, theta, r) )\n            entry_count += 1\n        \n        csr[\"row_start\"].insert(0, csr[\"row_start\"][0] - entry_count)        \n        csr[\"B\"].insert(0, Smax)\n            \n    return csr\n    \ndef tridiagprevious(n, t, theta, r): #-nk/2(n*o2) - r\n    return -n * ((t/(n - 1)) / 2) * ( (n * (theta ** 2)) - r) \n    \ndef tridiagcurrent(n, t, theta, r): # 1 + kr + ko2n2\n    return 1 + ((t / n) * r) + ((t / n) * (theta ** 2) * (n ** 2) )\n    \ndef tridiagfuture(n, t, theta, r): #-nk/2(n*o2) + r\n    return -n * ((t/(n + 1)) / 2) * ( (n * (theta ** 2)) + r)\n  \nif __name__ == '__main__':\n    BSM(160, 5 ,0.2, 0.01, 1)\n\n    ","sub_path":"nas_Horan_McLeod_OReilly_prog2/code/BSM.py","file_name":"BSM.py","file_ext":"py","file_size_in_byte":1664,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"262358057","text":"import numpy as np\n\nimport pytest\n\nfrom sklearn.base import clone\nfrom sklearn.datasets import make_classification, make_regression\nfrom sklearn.utils.validation import check_is_fitted\n\nfrom autosklearn.evaluation.abstract_evaluator import MyDummyClassifier, MyDummyRegressor\n\n\n@pytest.mark.parametrize(\"task_type\", ['classification', 'regression'])\ndef test_dummy_pipeline(task_type):\n    if task_type == 'classification':\n        estimator_class = MyDummyClassifier\n        data_maker = make_classification\n    elif task_type == 'regression':\n        estimator_class = MyDummyRegressor\n        data_maker = make_regression\n    else:\n        pytest.fail(task_type)\n\n    estimator = estimator_class(config=1, random_state=np.random.RandomState(42))\n    X, y = data_maker()\n    estimator.fit(X, y)\n    check_is_fitted(estimator)\n\n    assert np.shape(X)[0] == np.shape(estimator.predict(X))[0]\n\n    # make sure we comply with scikit-learn estimator API\n    clone(estimator)\n","sub_path":"test/test_evaluation/test_dummy_pipelines.py","file_name":"test_dummy_pipelines.py","file_ext":"py","file_size_in_byte":972,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"390956901","text":"# EE488C Special Topics in EE <Deep Learning and AlphaGo>\n# Fall 2016, School of EE, KAIST\n\nimport tensorflow as tf\nfrom tensorflow.examples.tutorials.mnist import input_data\n\ntf.reset_default_graph()\n\nmnist = input_data.read_data_sets('./MNIST_data', one_hot=True)\nsess = tf.InteractiveSession()\n\nwith tf.name_scope('Input'):\n    x = tf.placeholder(tf.float32, shape=[None, 784])\n    y_ = tf.placeholder(tf.float32, shape=[None, 10])\n\n# Convolutional layer\nwith tf.name_scope('Conv1'):\n    x_image = tf.reshape(x, [-1,28,28,1])\n    W_conv = tf.Variable(tf.truncated_normal([5, 5, 1, 30], stddev=0.1))\n    b_conv = tf.Variable(tf.constant(0.1, shape=[30]))\n    h_conv = tf.nn.conv2d(x_image, W_conv, strides=[1, 1, 1, 1], padding='VALID')\n    h_relu = tf.nn.relu(h_conv + b_conv)\n    h_pool = tf.nn.max_pool(h_relu, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n\n# Fully-connected layer\nwith tf.name_scope('FC'):\n    W_fc1 = tf.Variable(tf.truncated_normal([12 * 12 * 30, 500], stddev=0.1))\n    b_fc1 = tf.Variable(tf.constant(0.1, shape=[500]))\n    h_pool_flat = tf.reshape(h_pool, [-1, 12*12*30])\n    h_fc1 = tf.nn.relu(tf.matmul(h_pool_flat, W_fc1) + b_fc1)\n\n# Output layer\nwith tf.name_scope('Output'):\n    W_fc2 = tf.Variable(tf.truncated_normal([500, 10], stddev=0.1))\n    b_fc2 = tf.Variable(tf.constant(0.1, shape=[10]))\n    y_hat=tf.nn.softmax(tf.matmul(h_fc1, W_fc2) + b_fc2)\n\n# Train and Evaluate the Model\nwith tf.name_scope('CrossEntropy'):\n    cross_entropy = - tf.reduce_sum(y_*tf.log(y_hat))\n    tf.scalar_summary(\"cost\", cross_entropy)\n\nwith tf.name_scope('Training'):\n    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)\n\nwith tf.name_scope('Accuracy'):\n    correct_prediction = tf.equal(tf.argmax(y_hat,1), tf.argmax(y_,1))\n    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n    tf.scalar_summary(\"accuracy\", accuracy)\n\nsummary_op = tf.merge_all_summaries()\n\nwith tf.Session() as sess:\n    sess.run(tf.initialize_all_variables())\n    writer = tf.train.SummaryWriter(\"proj1\", graph=tf.get_default_graph())\n    print(\"=================================\")\n    print(\"|Epoch\\tBatch\\t|Train\\t|Val\\t|\")\n    print(\"|===============================|\")\n    avg_cost=0\n    batch_count = int(mnist.train.num_examples/100)\n    for j in range(5):\n        for i in range(batch_count):\n            batch = mnist.train.next_batch(100)\n            _, summary = sess.run([train_step, summary_op], feed_dict={x: batch[0], y_: batch[1]})\n            writer.add_summary(summary, j * batch_count + i)\n            if i%50 == 49:\n                train_accuracy = accuracy.eval(feed_dict={x:batch[0], y_: batch[1]})\n                val_accuracy = accuracy.eval(feed_dict=\\\n                    {x: mnist.validation.images, y_:mnist.validation.labels})\n                print(\"|%d\\t|%d\\t|%.4f\\t|%.4f\\t|\"%(j+1, i+1, train_accuracy, val_accuracy))\n    print(\"|===============================|\")\n    test_accuracy = accuracy.eval(feed_dict=\\\n        {x: mnist.test.images, y_:mnist.test.labels})\n    print(\"test accuracy=%.4f\"%(test_accuracy))\n\n","sub_path":"media/class/deeplearning_and_alphago/projects/1_mnist/tboard_for_MNIST.py","file_name":"tboard_for_MNIST.py","file_ext":"py","file_size_in_byte":3081,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"533994946","text":"from food_class import *\n# from drink_class import *\n# from combos_class import *\n\n# food_menu = [(1, 'Pizza', 6.50, 'Meal'), (2, 'Lasagna', 7.00, 'Meal'), (3, 'Ravioli', 5.50, 'Meal'),\n#              (4, 'Soup', 4.00, 'Meal'), (5, 'Spaghetti', 6.00, 'Meal')]\n# drinks_menu = [(1, 'Coke', 2.50, 'Meal'), (2, 'Water', 1.50, 'Meal'), (3, 'Prosecco', 5.00)]\n# sides_menu = [(1, 'Garlic Bread', 3.50, 'Meal'), (2, 'Mozzerella sticks', 3.50, 'Meal'), (3, 'Fries', 2.50, 'Meal'), (4, 'Jalapeno poppers', 4.50, 'Other')]\n# discount_rate = 0.5\n\n\nmain1 = Food('Pizza', 6.50, ['Bread base', 'tomato sauce', 'olives', 'herbs'])\nmain2 = Food('Lasagna', 7.00, ['Pasta dough', 'Tomato sauce', 'Lamb mince meat'])\nmain3 = Food('Ravioli', 5.50, ['Tomato sauce', 'spinach', 'cheese'])\nmain4 = Food('Soup', 4.00, ['diced veggies', 'chicken stock', 'herbs'])\nmain5 = Food('Spaghetti', 6.00, ['Spaghetti pasta', 'ragu sauce', 'cheese', 'herbs'])\ndrink1 = Drink('Coke', 2, ['caffeine'])\ndrink2 = Drink('Smoothie', 2.5, ['orange'])\n\nMenuItems.print_me_the_menu()\n\n","sub_path":"run_file.py","file_name":"run_file.py","file_ext":"py","file_size_in_byte":1042,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"294017333","text":"# License: BSD 3 clause\n\nimport unittest\n\nfrom numpy.testing import assert_almost_equal\n\nfrom tick.prox import ProxZero\nfrom tick.prox.tests.prox import TestProx\n\n\nclass Test(TestProx):\n    def test_ProxZero(self):\n        \"\"\"...Test of ProxZero\n        \"\"\"\n        coeffs = self.coeffs.copy()\n        out = coeffs.copy()\n\n        prox = ProxZero()\n        self.assertAlmostEqual(prox.value(coeffs), 0., delta=1e-14)\n        assert_almost_equal(prox.call(coeffs), out, decimal=10)\n\n        prox = ProxZero((3, 8))\n        self.assertAlmostEqual(prox.value(coeffs), 0., delta=1e-14)\n        assert_almost_equal(prox.call(coeffs), out, decimal=10)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"tick/prox/tests/prox_zero_test.py","file_name":"prox_zero_test.py","file_ext":"py","file_size_in_byte":695,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"577618016","text":"class BinarySearch(list):\n\n\tdef __init__(self, a , b):\n\t\tfor i in range(1, a+1):\n\t\t\tlist.append(self, (i*b))\n\t\tself.length = a\n\n\tdef search(self,numToSearch):\n\t    firstIndexValue = 0\n\t    lastIndex = self.length-1\n\t    found = False\n\t    count = 0\n\t    in_list = False\n\t    try:\n\t      index = self.index(numToSearch)\n\t      in_list = True\n\t    except ValueError:\n\t      index = -1\n\t      in_list = False\n\n\t    while firstIndexValue <= lastIndex and not found and in_list and numToSearch != self[lastIndex]:\n\t        middleIndex = (firstIndexValue+lastIndex)//2\n\t        mid_value = self[middleIndex]\n\t        if mid_value == numToSearch:\n\t            found = True\n\t            count +=1\n\t            index = middleIndex\n\t        else:\n\t            if numToSearch < mid_value:\n\t                lastIndex = middleIndex - 1\n\t                count +=1\n\t            else:\n\t                firstIndexValue = middleIndex + 1\n\t                count +=1\n\t    return {\"count\": count, \"index\": index}","sub_path":"Day_3/Andela labs solutions/binary_search.py","file_name":"binary_search.py","file_ext":"py","file_size_in_byte":991,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"575271750","text":"\nimport json\nimport requests\nfrom datetime import datetime \n#import octo.vers.octoversions as octoversions\nfrom pprint import pprint\n\nimport vers.octoversions\n#print(vers)\n#import vers.*\n#from . import vers\n#import vers.octoversions\n#from vers import octoversions\n#octoversions\n\n#import sets\n\n\n\ndef getApiKey():\n\tapiKeyFileName = \"tmpApiKey.txt\"\n\tkey=open(apiKeyFileName).read()\n\treturn key\n\n\napi=getApiKey() \nbase_url = 'https://octopus.ajoursystem.dk'\nmachine_url = 'https://octopus.ajoursystem.dk/api/machines'\nenv_url     = 'https://octopus.ajoursystem.dk/api/environments'\nheaders={ 'X-Octopus-ApiKey': api }\n\n\n\ndef getHttp(url):\n\t\tr = requests.get(url, headers=headers)\n\t\tif r.status_code != 200:\n\t\t\t#pprint(r.__dict__)\n\t\t\t#print('\\n', r.status_code, r.text, '\\n')\n\t\t\traise Exception('bad http response %s %s' % (r.status_code, r.text) )\n\t\treturn r\n\n\n\nclass OctoMgr:\n\n\tdef __init__(self):\n\t\tself.envs_to_machines={} # \n\t\tself.machines_to_envs={}\n\t\t\n\t\tself.env_to_clients={}\n\t\tself.all_envs=[]\n\t\tself.varset_map={}\n\t\tself.server_info = {}\n\t\tself.machineId_to_name={}\n\t\t#self.addServer('ajour4', 'ajour4',    '10.10.0.21','hestkatK..')\n\t\t#self.addServer('ajour5', 'ajour5', '192.168.15.26','morOg..')\n\t\t#self.addServer('ajour6', 'ajour6', '192.168.15.28','racerbil tr..')\n\t\t#self.addServer('ajour7', 'ajour7', '192.168.15.29','nitrogen..')\n\t\t#self.addServer('ajour8', 'ajour8', '192.168.15.30','enfemmer..')\n\t\t\n\t\t# get IP from field 'Uri' in machine!\n\t\t#self.addServer('dev', 'Dev', '192.168.15.11','123?..')\n\n\n\tdef doAll(self):\n\t\t#self.db_versions = vers.octoversions.getVersionsFromDB()\n\t\tself.getMachines(machine_url)\n\t\tself.getClientEnvs(env_url)\n\t\tself.getVarSets()\n\t\tself.getSiteVars()\n\t\tself.getSharedSiteVars()\n\t\tself.getDatabaseVars()\n\t\tself.getEventstoreVars()\n\t\tself.addWebserverToClientEnv()\n\t\tself.buildFields()\n\t\tfile = \"octovars.html\"\n\t\tself.outputHTML(file)\n\t\tif 0:\n\t\t\tself.showHTML(file)\n\n\n\tdef getMachines(self, a_machine_url):\n\t\tr = getHttp(a_machine_url)\n\n\t\ts_machines = r.text \n\t\tj_machines=json.loads(s_machines)\n\t\t#pprint(j_machines)\n\n\t\tfor machine in j_machines['Items']:\n\t\t\tmachine_name = machine['Name'] \n\t\t\tmachine_id = machine['Id']\n\t\t\tself.machineId_to_name[ machine_id ] = machine_name\n\t\t\tprint('machine: Id:%s Name:%s ' % (machine_id, machine_name ))\n\n\t\t\tif 0: # work with env stuff.\n\t\t\t\tenvs = machine['EnvironmentIds']\n\t\t\t\tprint('machine: Id:%s Name:%s #envs:%s' % (machine['Id'],  machine_name, len(envs)) )\n\t\t\t\tfor env in envs:\n\t\t\t\t\tself.addMachineToEnv(machine_name, env, machine['Id'])\n\n\t\n\t\tif 'Page.Next' in j_machines['Links']:\n\t\t\tnext_url = base_url + j_machines['Links']['Page.Next']\n\t\t\tself.getMachines(next_url)\n\t\telse:\n\t\t\tprint('no next')\n\n\n\n\tdef addMachineToEnv(self, machine_name, env, machine_id):\n\t\t# Update list of machines this env is found on:\n\t\txmachines = self.envs_to_machines.get(env, []) \n\t\txmachines.append(machine_name)\n\t\tself.envs_to_machines[env] = xmachines\n\n\t\t# arbitrarily associate the env with first machine where we encounter it:\n\t\tif len(xmachines) == 1:\n\t\t\tinfo = self.getServer(machine_name)\n\t\t\tfor var,val in info.items():\n\t\t\t\tself.addAttrToEnv(var,val,env)\n\t\t\t\tself.addAttrToEnv('env_id',env,env)\n\t\t#\n\t\t# Update the list of envs on this machine:\n\t\tthe_set = self.machines_to_envs.get(machine_id, set() )\n\t\tthe_set.add(env)\n\t\tself.machines_to_envs[ machine_id] = the_set\n\n\n\n\n\tdef addServer(self,name,big_name, ip,hint):\n\t\tserver = {'name':name, 'ip':ip, 'hint':hint,'web_srv_host':name,  }\n\t\tself.server_info[big_name] = server\n\t\t\n\tdef getServer(self,big_name):\n\t\tif not big_name in self.server_info:\n\t\t\tself.addServer(big_name,big_name,'no-ip','no-hint')\n\t\treturn self.server_info[big_name]  \n\n\n\n\n\n\n\tdef addAttrToEnv(self, var,val,env):\n\t\tif var not in ['webserver','EventstorePort','env_id','name','ip','hint']:\n\t\t\tprint('zadding:',var,val,env)\n\t\t#global env_to_clients\n\t\tdict = self.env_to_clients.get(env,{})\n\t\tdict[var] = val \n\t\tself.env_to_clients[env] = dict #client_name\n\n\tdef addClientToEnv(self, client_name, env):\n\t\t#global env_to_clients\n\t\tdict = self.env_to_clients.get(env,{})\n\t\tdict['clientname'] = client_name\n\t\tself.env_to_clients[env] = dict #client_name\n\n\n\tdef getClientEnvs(self, an_env_url):\n\t\t#print('getenv..')\n\t\t#global all_envs\n\t\tr = requests.get(an_env_url, headers=headers)\n\t\ts_envs = r.text # contents?\n\t\tj_envs=json.loads(s_envs)\n\n\t\tfor env in j_envs['Items']:\n\t\t\tclient_name = env['Name']\n\t\t\tclient_id = env['Id'] \n\t\t\tself.all_envs.append(client_id)\n\t\t\tself.addClientToEnv(client_name, client_id)\n\t\t\t#print(env['Id'], env['Name'] )\n\t\tif 'Page.Next' in j_envs['Links']:\n\t\t\tnext_url = base_url + j_envs['Links']['Page.Next']\n\t\t\tself.getClientEnvs(next_url)\n\t\telse:\n\t\t\tprint('no next')\n\n\n\n\tdef getVarSets(self):\n\t\t# https://octopus.ajoursystem.dk/api/variables\n\t\t# https://octopus.ajoursystem.dk/api/variables/names\n\t\t# https://octopus.ajoursystem.dk/api/libraryvariablesets\n\t\t# https://octopus.ajoursystem.dk/api/libraryvariablesets/LibraryVariableSets-2\n\n\t\tvarsets_url = 'https://octopus.ajoursystem.dk/api/libraryvariablesets'\n\t\tj_varset = json.loads( requests.get(varsets_url,headers=headers).text )\n\t\tfor varset in j_varset['Items']:\n\t\t\tself.varset_map[ varset['Name'] ] = varset['Links']['Variables'] #'Id']\n\t\t# variable set 'Database', 'Eventstore' 'EventstoreAuthority'\n\n\n\tdef getDatabaseVars(self):\t\t\t\t\n\t\tdb_set = self.varset_map['Database']\n\t\tprint(db_set)\n\n\t\tvar_url = base_url + db_set # 'variableset-LibraryVariableSets-2'\n\t\ttxt = requests.get (var_url,headers=headers).text\n\t\t\n\t\tshowme=open(\"showme_db\",'w')\n\t\tshowme.write(txt)\n\t\tshowme.close()\n\t\t\n\t\tj_vars = json.loads(txt) \n\t\tprint( len(j_vars['Variables']) )\n\t\tfor var in j_vars['Variables']:\n\t\t\tvarName = var['Name']\n\t\t\tval = var['Value']\n\t\t\tif varName in ('DbName','DbHost'):\n\t\t\t\tfor env in var['Scope'].get('Environment', self.all_envs):\n\t\t\t\t\tinstance_val = val\n\t\t\t\t\tif '#' in instance_val:\n\t\t\t\t\t\tsitename = self.env_to_clients[env]['clientname']\n\t\t\t\t\t\tinstance_val=instance_val.replace('#{SiteName}', sitename)\n\t\t\t\t\t#print(var['Value'])\n\t\t\t\t\tself.addAttrToEnv(varName,instance_val,env)\n\n\n\tdef getSiteVars(self):\t\t\t\t\n\t\tsss_set = self.varset_map['Site settings']\n\t\tprint(sss_set)\n\n\t\tvar_url = base_url + sss_set  \n\t\ttxt = requests.get (var_url,headers=headers).text\n\t\t\n\t\tshowme=open(\"showme2\",'w')#\n\t\tshowme.write(txt)\n\t\tshowme.close()\n\t\t\n\t\tj_vars = json.loads(txt) \n\t\tprint( len(j_vars['Variables']) )\n\t\tfor var in j_vars['Variables']:\n\t\t\tvarName = var['Name']\n\t\t\tval = var['Value']\n\t\t\tif varName in ['ApplicationPool', 'Module']: \n\t\t\t\tprint('\\nval:',val, varName)\n\t\t\t\tscope = var['Scope']\n\t\t\t\tif 'Machine' in scope:\n\t\t\t\t\tmachines = scope['Machine']\n\t\t\t\t\tprint('#machines:',len(machines))\n\t\t\t\t\tfor machine in machines:\n\t\t\t\t\t\tthe_envs = self.machines_to_envs[machine]\n\t\t\t\t\t\tprint('#envs for machine:',len(the_envs), machine, self.machineId_to_name[machine])\n\t\t\t\t\t\t#print('hmm:',self.machines_to_envs[machine])\n\t\t\t\t\t\tfor env in the_envs:\n\t\t\t\t\t\t  sitename = self.env_to_clients[env]['clientname']\n\t\t\t\t\t\t  #env_name = self.get_env_name(env)\n\t\t\t\t\t\t  val = val.replace('#{Octopus.Environment.Name}', sitename) #env_name)\n\t\t\t\t\t\t  self.addAttrToEnv(varName,val,env)\t\t\t\t\t\t\n\t\t\t\telse:\n\t\t\t\t\tthe_envs = scope.get('Environment', self.all_envs)\n\t\t\t\t\tprint( '#envs:', len(the_envs)) \n\t\t\t\t\tfor env in the_envs:\n\t\t\t\t\t\tinstance_val = val\n\t\t\t\t\t\tsitename = self.env_to_clients[env]['clientname']\n\t\t\t\t\t\t#if '#' in instance_val:\n\t\t\t\t\t\t#\tinstance_val=instance_val.replace('#{SiteName}', sitename)\n\t\t\t\t\t\t#print(var['Value'])\n\t\t\t\t\t    #env_name = self.get_env_name(env)\n\t\t\t\t\t\tinstance_val = instance_val.replace('#{Octopus.Environment.Name}', sitename) #env_name)\n\t\t\t\t\t\tself.addAttrToEnv(varName,instance_val,env)\n\t\t\t\t\t\n\t\t\t\t\t\n\tdef getSharedSiteVars(self):\t\t\t\t\n\t\tsss_set = self.varset_map['Shared site settings']\n\t\tprint(sss_set)\n\n\t\tvar_url = base_url + sss_set  \n\t\ttxt = requests.get (var_url,headers=headers).text\n\t\t\n\t\tshowme=open(\"showme2\",'w')#\n\t\tshowme.write(txt)\n\t\tshowme.close()\n\t\t\n\t\tj_vars = json.loads(txt) \n\t\tprint( len(j_vars['Variables']) )\n\t\tfor var in j_vars['Variables']:\n\t\t\tvarName = var['Name']\n\t\t\tval = var['Value']\n\t\t\tif varName in ['SiteUrl', 'SiteName', 'SiteTLD']: \n\t\t\t\tprint('\\nval:',val, varName)\n\t\t\t\tscope = var['Scope']\n\t\t\t\tif 'Machine' in scope:\n\t\t\t\t\tmachines = scope['Machine']\n\t\t\t\t\tprint('#machines:',len(machines))\n\t\t\t\t\tfor machine in machines:\n\t\t\t\t\t\tthe_envs = self.machines_to_envs[machine]\n\t\t\t\t\t\tprint('#envs for machine:',len(the_envs), machine, self.machineId_to_name[machine])\n\t\t\t\t\t\t#print('hmm:',self.machines_to_envs[machine])\n\t\t\t\t\t\tfor env in the_envs:\n\t\t\t\t\t\t  sitename = self.env_to_clients[env]['clientname']\n\t\t\t\t\t\t  #env_name = self.get_env_name(env)\n\t\t\t\t\t\t  val = val.replace('#{Octopus.Environment.Name}', sitename) #env_name)\n\t\t\t\t\t\t  self.addAttrToEnv(varName,val,env)\t\t\t\t\t\t\n\t\t\t\telse:\n\t\t\t\t\tthe_envs = scope.get('Environment', self.all_envs)\n\t\t\t\t\tprint( '#envs:', len(the_envs)) \n\t\t\t\t\tfor env in the_envs:\n\t\t\t\t\t\tinstance_val = val\n\t\t\t\t\t\tsitename = self.env_to_clients[env]['clientname']\n\t\t\t\t\t\t#if '#' in instance_val:\n\t\t\t\t\t\t#\tinstance_val=instance_val.replace('#{SiteName}', sitename)\n\t\t\t\t\t\t#print(var['Value'])\n\t\t\t\t\t    #env_name = self.get_env_name(env)\n\t\t\t\t\t\tinstance_val = instance_val.replace('#{Octopus.Environment.Name}', sitename) #env_name)\n\t\t\t\t\t\tself.addAttrToEnv(varName,instance_val,env)\n\t\t\t\t\t\n\n\tdef getEventstoreVars(self):\t\t\t\t\n\t\tes_set = self.varset_map['Eventstore']\n\t\tprint(es_set)\n\n\t\tvar_url = base_url + es_set # 'variableset-LibraryVariableSets-2'\n\t\ttxt = requests.get (var_url,headers=headers).text\n\t\t\n\t\tshowme=open(\"showme\",'w')#\n\t\tshowme.write(txt)\n\t\tshowme.close()\n\t\t\n\t\tj_vars = json.loads(txt) \n\t\tprint( len(j_vars['Variables']) )\n\t\tfor var in j_vars['Variables']:\n\t\t\tvarName = var['Name']\n\t\t\tval = var['Value']\n\t\t\tif varName in ['EventstoreAuthority', 'EventstorePort', 'EventstoreHost','EventStoreHost']: # 'EventstoreUsername',  'EventstoreConnectionString'\n\t\t\t\tprint('\\nval:',val, varName)\n\t\t\t\tscope = var['Scope']\n\t\t\t\tif 'Machine' in scope:\n\t\t\t\t\tmachines = scope['Machine']\n\t\t\t\t\tprint('#machines:',len(machines))\n\t\t\t\t\tfor machine in machines:\n\t\t\t\t\t\tthe_envs = self.machines_to_envs[machine]\n\t\t\t\t\t\tprint('#envs for machine:',len(the_envs), machine, self.machineId_to_name[machine])\n\t\t\t\t\t\t#print('hmm:',self.machines_to_envs[machine])\n\t\t\t\t\t\tfor env in the_envs:\n\t\t\t\t\t\t  self.addAttrToEnv(varName,val,env)\t\t\t\t\t\t\n\t\t\t\telse:\n\t\t\t\t\tthe_envs = scope.get('Environment', self.all_envs)\n\t\t\t\t\tprint( '#envs:', len(the_envs)) \n\t\t\t\t\tfor env in the_envs:\n\t\t\t\t\t\tinstance_val = val\n\t\t\t\t\t\t#if '#' in instance_val:\n\t\t\t\t\t\t#\tsitename = env_to_clients[env]['clientname']\n\t\t\t\t\t\t#\tinstance_val=instance_val.replace('#{SiteName}', sitename)\n\t\t\t\t\t\t#print(var['Value'])\n\t\t\t\t\t\tself.addAttrToEnv(varName,instance_val,env)\n\n\n\n\tdef addWebserverToClientEnv(self):\n\t\tix=1\n\t\tfor id,client in self.env_to_clients.items():\n\t\t\tmachine = self.envs_to_machines.get(id, ['-------']) \n\t\t\tself.addAttrToEnv('webserver',machine,id)\n\t\t\tif 0:\n\t\t\t\tprint(ix, machine, client, id)\n\t\t\tix+=1\n\t\t\tif ix < -1: # 100:\n\t\t\t\tbreak\n\t\t\t\n\n\t\t\t\t\n\n\tdef makeRow(self, file,row,elm, client_id):\n\t\t# 'site_url', bad-misleading field!\n\t\tkeys = ['clientname', 'vers-upgraded', 'dbversion' ,'web_srv_host','ip','hint','DbName', 'DbHost', 'env_id',\n\t\t'EventstoreAuthority', 'EventstorePort', 'EventstoreHost', 'EventStoreHost', \n\t\t'Module', 'ApplicationPool', 'SiteUrl', 'SiteName', 'SiteTLD' ]\n\t\tfile.write('<tr>\\n')\n\t\tfor var in keys: #,val in row.items():\n\t\t\tval = row.get(var,'-')\n\t\t\t#print('fields:',var,val)\n\t\t\tcontents = val if elm=='td' else var\n\t\t\tthe_class=''\n\t\t\tif var in [ 'EventstorePort', 'EventstoreHost', 'EventStoreHost' ]:\n\t\t\t\tthe_class = 'class=\"bad\"'\n\t\t\tif var in ['clientname']:\n\t\t\t\turl = row.get('SiteUrl') #site_url') #self.env_to_clients[]\n\t\t\t\tfile.write('  <%s %s>  <a href=\"https://%s\"> %s </a>    </%s>\\n' % (elm, the_class,url, contents,elm) )\t\t\t\n\t\t\telif var not in ['site_url','es_url']:\t\t\t\n\t\t\t\tfile.write('  <%s %s><nobr>%s</nobr></%s>\\n' % (elm, the_class,contents,elm) )\n\t\t\telse:\n\t\t\t\tfile.write('  <%s %s> ? <a href=\"%s\"> %s </a>    </%s>\\n' % (elm, the_class,contents, contents,elm) )\n\t\tfile.write('</tr>\\n')\n\t\t\n\tdef buildFields(self):\n\t\tfor id,vars in self.env_to_clients.items():\n\t\t\tsite_var = vars['clientname']\n\t\t\t\n\t\t\turl='https://%s.ajoursystem.dk' % site_var\n\t\t\tvars['site_url'] = url\n\t\t\t\n\t\t\tes_url='http://%s.ajoursystem.dk' % site_var\n\t\t\tvars['site_url'] = url\n\t\t\t#print(vars.keys())\n\t\t\t\n\t\t\tsiteUrl = vars['SiteUrl']\n\t\t\tsiteUrl = siteUrl.replace('#{SiteName}', vars['SiteName'])\n\t\t\tsiteUrl = siteUrl.replace('#{SiteTLD}', vars['SiteTLD'])\n\t\t\tvars['SiteUrl'] = siteUrl\n\n\t\t\tmissingVI = {\n\t\t\t'version': 'missing!',\n\t\t\t'created': 'still missing v-info'\n\t\t\t}\n\t\t\tversionInfo = self.db_versions.get( site_var, missingVI ) \n\t\t\tversionNr = versionInfo['version']\n\t\t\tvars['dbversion'] = versionNr\n\t\t\tvars['vers-upgraded'] = versionInfo['created'][:16]  # .replace(' ', '&nbsp;')\n\n\t\n\n\tdef outputHTML(self, html_file):\n\t\ta=open(html_file,'w')\n\n\t\ta.write('<!DOCTYPE html>\\n')\n\t\ta.write('<html>\\n')\n\t\ta.write('<head>\\n')\n\t\ta.write('<link rel=\"stylesheet\" href=\"octo.css\">\\n')\n\t\ta.write('<script src=\"https://code.jquery.com/jquery-3.2.1.min.js\" integrity=\"sha256-hwg4gsxgFZhOsEEamdOYGBf13FyQuiTwlAQgxVSNgt4=\" crossorigin=\"anonymous\"></script>')\n\t\ta.write('<script src=\"octo.js\"></script>\\n')\n\t\ta.write('</head>\\n')\n\n\t\ta.write('<body>\\n')\n\t\ta.write('<div class=\"client-list-head\">\\n')\n\t\ta.write('<div class=\"ajour-logo\"><img src=\"http://ajoursystem.com/gfx/ajour-logo.png\"/><input type=\"text\" class=\"search-text\" placeholder=\"Indtast client...\"/></div>\\n')\n\t\ta.write('<div class=\"last-updated\"><b>Last updated:</b> %s</div>' % datetime.now().isoformat())\n\t\ta.write('</div>\\n')\n\t\t\n\t\ta.write('<div class=\"client-list-body\">\\n')\n\t\ta.write('<table>\\n')\n\n\t\tix=0\n\t\tfor id,vars in self.env_to_clients.items():\n\t\t\t#print(vars.keys())\n\t\t\tif ix==0:\n\t\t\t\tself.makeRow(a,vars,'th', id)\n\n\t\t\tself.makeRow(a,vars,'td', id)\n\t\t\tix+=1\n\t\t\tif ix < -1: break\n\n\t\ta.write('</table>\\n')\n\t\ta.write('</div>\\n')\n\t\ta.write('</body>\\n')\n\t\ta.write('</html>\\n')\n\t\ta.close()\n\t\t\n\tdef showHTML(self, html_file):\n\t\tb=open(html_file).read()\n\t\tprint(b)\n\t\t\n\nmgr = OctoMgr()\nmgr.getMachines(machine_url)\nexit(0)\t\t\n\ndef extractHTML():\n\tprint(\"extracting html file...\")\n\tmgr = OctoMgr()\n\tmgr.doAll()\n\tprint(\"done extracting html file\\n\\n\")\n\n\nextractHTML()\n\n","sub_path":"octopus/octo/octo_oldcrap.py","file_name":"octo_oldcrap.py","file_ext":"py","file_size_in_byte":14287,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"364112870","text":"import yfinance as yf\nimport pandas as pd\nimport datetime as dt\nimport numpy as np\nimport math\nfrom scipy.stats import norm\n\n# Monte Carlo Simulation equations and values based off this phd paper\n# http://www.diva-portal.se/smash/get/diva2:1214365/FULLTEXT01.pdf\n\n# Resourced referenced for VaR and Expected Shortfalls calculations\n# https://towardsdatascience.com/learn-to-calculate-your-portfolios-value-at-risk-e1e2c5c68456\n# https://www.investopedia.com/articles/04/092904.asp\n# https://en.wikipedia.org/wiki/Expected_shortfall\n# https://www.mathworks.com/help/risk/value-at-risk-estimation-and-backtesting-1.html\n\n\ndef non_weighted_mean_std(data):\n    return np.mean(data), np.std(data)\n\n\ndef weighted_mean_std(data):\n    # filter out nans since average doesnt deal with them automatically\n    filtered = data[~np.isnan(data)]\n\n    alpha = 2 / (30+1)\n    weights = [(1 - alpha) ** i for i in range(len(filtered), 0, -1)]\n    average = np.average(filtered, weights=weights)\n    variance = np.average((filtered-average)**2, weights=weights)\n    return average, math.sqrt(variance)\n\n\ndef monte_carlo_basic(stock_code, period='5y', num_simulations=10, t_interval=365, mean_std_func=non_weighted_mean_std):\n    try:\n        data = yf.Ticker(stock_code+'.AX').history(period=period)['Close']\n        returns = data.pct_change()\n        expected_returns, volatility = mean_std_func(returns)\n        sampling = np.random.normal(\n            loc=0, scale=volatility, size=(t_interval+1, num_simulations))\n\n        simulation = np.zeros_like(sampling)\n        simulation[0] = data[-1]\n\n        for i in range(1, t_interval + 1):\n            simulation[i] = simulation[i - 1] * (1 + sampling[i])\n        return simulation\n    except Exception as err:\n        print(err)\n    return None\n\n\ndef monte_carlo_gbm(stock_code, period='5y', num_simulations=10, t_interval=365, mean_std_func=non_weighted_mean_std, time_step=1):\n    try:\n        data = yf.Ticker(stock_code+'.AX').history(period=period)['Close']\n        returns = data.pct_change()\n        u, std = mean_std_func(returns)\n        n_prices = math.ceil(t_interval / time_step) + 1\n\n        simulation = np.zeros((n_prices, num_simulations))\n        simulation[0] = data[-1]\n        for i in range(1, n_prices):\n            drift = (u - ((std ** 2) / 2)) * time_step\n            shock = std * \\\n                np.random.normal(0, 1, num_simulations) * (time_step ** 0.5)\n            simulation[i] = simulation[i-1] * np.exp(drift + shock)\n        return simulation\n    except Exception as err:\n        print(err)\n    return None\n\n\ndef VaR_ES_historical_sim(portfolio, n_days=1, percentile=95, period='2y'):\n    try:\n        histories = pd.DataFrame()\n\n        for code, quantity in portfolio.items():\n            histories[code] = yf.Ticker(\n                code+'.AX').history(period=period)['Close']\n\n        histories['Port_Value'] = 0\n        for code, quantity in portfolio.items():\n            histories['Port_Value'] = histories[code] * \\\n                quantity + histories['Port_Value']\n\n        histories['Perc_Change'] = histories['Port_Value'].pct_change()\n\n        value_loc_for_percentile = round(\n            len(histories) * (1 - (percentile / 100)))\n\n        sortedhistories = histories.sort_values(by=['Perc_Change'])\n\n        var_result = sortedhistories.iloc[value_loc_for_percentile +\n                                          1]['Perc_Change'] * np.sqrt(n_days)\n        var_result = None if np.isnan(var_result) else round(var_result, 5)\n\n        expected_shortfall = sortedhistories['Perc_Change'].head(\n            value_loc_for_percentile).mean(axis=0) * np.sqrt(n_days)\n        expected_shortfall = None if np.isnan(expected_shortfall\n                                              ) else round(expected_shortfall, 5)\n\n        return var_result, expected_shortfall, histories\n    except Exception as err:\n        print(err)\n    return None\n\n\ndef VaR_ES_variance_covariance(portfolio, n_days=1, percentile=95, period='2y'):\n    try:\n        histories = pd.DataFrame()\n\n        for code, quantity in portfolio.items():\n            histories[code] = yf.Ticker(\n                code+'.AX').history(period=period)['Close']\n\n        histories['Port_Value'] = 0\n        for code, quantity in portfolio.items():\n            histories['Port_Value'] = histories[code] * \\\n                quantity + histories['Port_Value']\n\n        histories['Perc_Change'] = histories['Port_Value'].pct_change()\n        u = np.mean(histories['Perc_Change'])\n        std = np.std(histories['Perc_Change'])\n\n\n        alpha = 1 - (percentile/100)\n\n        var_result = norm.ppf(alpha, loc=u, scale=std) * np.sqrt(n_days)\n        var_result = None if np.isnan(var_result) else round(var_result, 5)\n\n        # loss of portfolio\n        expected_shortfall = u + \\\n            std * (norm.pdf(norm.ppf(alpha, loc=u, scale=std) / (1-alpha), loc=u, scale=std))\n        expected_shortfall = None if np.isnan(expected_shortfall\n                                              ) else -round(expected_shortfall, 5)\n        return var_result, expected_shortfall, histories\n    except Exception as err:\n        print(err)\n    return None\n\n\ndef VaR_ES_monte_carlo(portfolio, n_days=1, percentile=95, period='2y', time_step=1, num_simulations=1000, use_weighted=False):\n    try:\n        histories = pd.DataFrame()\n        for code, quantity in portfolio.items():\n\n            histories[code] = yf.Ticker(\n                code+'.AX').history(period=period)['Close']\n\n        histories['Port_Value'] = 0\n        for code, quantity in portfolio.items():\n            histories['Port_Value'] = histories[code] * \\\n                quantity + histories['Port_Value']\n\n        histories['Perc_Change'] = histories['Port_Value'].pct_change()\n\n        # run monte carlo simulation using gbm\n        last_port_value = histories.iloc[-1]['Port_Value']\n\n        u, std = weighted_mean_std(histories['Perc_Change']) if use_weighted else non_weighted_mean_std(histories['Perc_Change'])\n        n_prices = math.ceil(n_days / time_step) + 1\n\n        simulation = np.zeros((n_prices, num_simulations))\n        simulation[0] = last_port_value\n        for i in range(1, n_prices):\n            drift = (u - ((std ** 2) / 2)) * time_step\n            shock = std * \\\n                np.random.normal(0, 1, num_simulations) * (time_step ** 0.5)\n            simulation[i] = simulation[i-1] * np.exp(drift + shock)\n\n        ending_prices = simulation[-1]\n\n        # calculate percent change between the current port value & simulated ending prices\n        simulated_endings = pd.DataFrame()\n        simulated_endings['Ending_Prices'] = simulation[-1]\n        simulated_endings['Perc_Change'] = simulated_endings / \\\n            last_port_value - 1\n\n        # sort simulations by perc_change and determine what lies at percentile\n        value_loc_for_percentile = round(\n            len(simulated_endings) * (1 - (percentile / 100)))\n        sortedSimulated = simulated_endings.sort_values(by=['Perc_Change'])\n\n        var_result = sortedSimulated.iloc[value_loc_for_percentile +\n                                          1]['Perc_Change'] * np.sqrt(n_days)\n        var_result = None if np.isnan(var_result) else round(var_result, 5)\n        expected_shortfall = sortedSimulated['Perc_Change'].head(\n            value_loc_for_percentile).mean(axis=0) * np.sqrt(n_days)\n        expected_shortfall = None if np.isnan(expected_shortfall\n                                              ) else round(expected_shortfall, 5)\n        return var_result, expected_shortfall, simulation, sortedSimulated\n    except Exception as err:\n        print(err)\n    return None\n\n\nif __name__ == '__main__':\n\n    # s1 = monte_carlo_basic(stock_code='CBA')\n    # print(s1[-1])\n    # s1 = monte_carlo_basic(\n    #     stock_code='CBA', mean_std_func=weighted_mean_std)\n    # print(s1[-1])\n\n    # print('geometric')\n    # s2 = monte_carlo_gbm(stock_code='CBA')\n    # print(s2[-1])\n\n    # s2 = monte_carlo_gbm(\n    #     stock_code='CBA', mean_std_func=weighted_mean_std)\n    # print(s2[-1])\n\n    # var, expected, data = VaR_ES_historical_sim({'CBA': 100, 'WOW': 50})\n    # print('Value at risk is ', var)\n    # print('Expected shortfall is ', expected)\n    # print(data)\n\n    # var, expected, histories = VaR_ES_variance_covariance({'CBA': 100, 'WOW': 50})\n    # print('VaR: ', var)\n    # print('Expected Shortfall ', expected)\n\n    var, expected, sim, sorted_sim = VaR_ES_monte_carlo(\n        {'CBA': 100, 'WOW': 50})\n    print('VaR: ', var)\n    print('Expected Shortfall ', expected)\n    print(sorted_sim)\n\n    pass\n","sub_path":"api/monte_carlo.py","file_name":"monte_carlo.py","file_ext":"py","file_size_in_byte":8596,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"556839787","text":"import math\n\ndef wordnumpair(l):\n    k = l.strip(\" \")\n    k = k.split(\" \")\n    word = str(k[1])\n    num = int(k[0])\n    return (num, word)\n\n\ndef load_data():\n    data = {}\n    f = open(\"input\")\n    lines = f.readlines()\n    for line in lines:\n        print(line)\n        l = line[:-1]\n        l = l.split(\"=>\")\n        e = (count, key) = wordnumpair(l[1])\n        if key not in data:\n            data[key] = [count, []]\n        else:\n            data[key][1].append(e)\n        c = l[0].split(\", \")\n        for u in c:\n            e = wordnumpair(u)\n            data[key][1].append(e)\n    return data\n\n\ndef compute(amt_needed, chem):\n    global reactions\n    global excess\n    global results\n\n    if chem == 'ORE':\n        results += amt_needed\n    else:\n        reaction_data = reactions[chem]\n        n_pack = reaction_data[0]\n        reactant_list= reaction_data[1]\n\n        if chem in excess and excess[chem] >= amt_needed:\n            excess[chem] -= amt_needed\n        else:\n            if chem in excess:\n                amt_needed -= excess[chem]\n                excess[chem] = 0\n\n            whole_units = math.ceil(amt_needed/n_pack)\n            left = (whole_units * n_pack) - amt_needed\n\n            if left > 0:\n                if chem not in excess:\n                    excess[chem] = left\n                else:\n                    excess[chem] +=  left\n            for reactant in reactant_list:\n                compute(reactant[0] * whole_units, reactant[1])\n\n\nreactions = load_data()\nexcess = {}\nresults = 0\nfor r in reactions:\n    print(r + \"<=\" + str(reactions.get(r)))\n\ncompute(1,'FUEL')\nprint(results)\nprint(excess)\n\nmax = 1000000000000\nguess = max//results\nmax = 2 * guess\nmin = 0\n\nexcess.clear()\nwhile min != max:\n    results = 0\n    compute(guess,'FUEL')\n    print(min, guess, max, results)\n    if results < 1000000000000:\n        min = guess\n        guess = min + (max - min)//2\n    else:\n        max = guess\n        guess = min+(max - min)//2\n\n\n\n\n\n\n\n\n","sub_path":"AoC2019_day14.py","file_name":"AoC2019_day14.py","file_ext":"py","file_size_in_byte":1976,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"545672811","text":"import numpy as np\nimport argparse, sys\nimport matplotlib.pyplot as plt\n\nfrom integrator1 import SimpleIntegrator, VerletIntegrator\nparser = argparse.ArgumentParser(description='Dynamika molekularna jednowymiarowa cząsteczki dwuatomowej')\n\nparser.add_argument('-n', '--n', type=int, required=True, help='liczba iteracji')\nparser.add_argument('--dt', type=float, required=True, help='krok czasowy')\nparser.add_argument('-k', '--k', type=float, required=True, help='stała siłowa')\nparser.add_argument('-r0', '--r0', type=float, required=True, help='położenie równowagowe')\nparser.add_argument('-m', '--mass', type=float,  required=True, help='masa atomu')\nparser.add_argument('-x', '--x', type=float, required=True, help='połozenie poczatkowe atomu')\n\nargs = parser.parse_args(sys.argv[1:])\n\nn = args.n\ndt = args.dt\n\nk = args.k\nr0 = args.r0\nx = args.x\nm = args.mass\n\nintegrator = VerletIntegrator(dt, m, x)\n\ntrajectory = []\nvelocity = []\nenergy = []\n\nfor _ in range(n):\n    r = x\n    F = -k*(r-r0)\n    integrator.next(F)\n    x = integrator.x\n    trajectory.append(x)\n    energy.append((k/2)*(r-r0)**2)\n    velocity.append(integrator.v)\n\nplt.plot(range(n), trajectory, range(n), energy)\nplt.legend(['Połozenie', 'Energia'])\nplt.show()\n","sub_path":"project/easy_example/two_atoms_molecule.py","file_name":"two_atoms_molecule.py","file_ext":"py","file_size_in_byte":1240,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"19353164","text":"from lasagne import *\n\n__all__ = [\n  'factory',\n  'get_input_layer',\n  'default_cls'\n]\n\ndef factory(cls):\n  return lambda *args, **kwargs: lambda input = None: cls(*args, input_layer=input, **kwargs)\n\ndef default_cls(cls):\n  def gen(depth = 3, initial_filters = 4, *args, **kwargs):\n    n_filters = [ initial_filters * (2**i) for i in range(depth) ]\n    return cls(n_filters, *args, **kwargs)\n  return gen\n\ndef get_input_layer(img_shape, input_layer):\n  if input_layer is None:\n    return layers.InputLayer(\n      shape=(None,) + img_shape,\n      name='input'\n    )\n  else:\n    return input_layer\n","sub_path":"crayimage/cosmicGAN/discriminator/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":597,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"201540268","text":"import argparse\r\nimport logging\r\n\r\nfrom .chatBotConfigParser import ChatBotConfigParser\r\nfrom .chatBotLogging import ChatBotLogging\r\n\r\nlogger = logging.getLogger(\"ChatBotBase.Lib\")\r\n\r\nclass ChatBotLib(object):\r\n    def __init__(self):\r\n        try:\r\n            self.config = ChatBotConfigParser().parser\r\n        except Exception as e:\r\n            logger.error(\"Exception in build obj for chatBotLib, exception : %s\" %(str(e)))\r\n            raise(e)\r\n        \r\n    def parseCLIArguments(self):\r\n        try:\r\n            parser = argparse.ArgumentParser()\r\n            parser.add_argument(\"option\",help=\"Spacing Mode\",choices = [\"train-rasa-nlu\",\"train-rasa-core\",\"run-train-rasa-core-online\",\"run-chatbot-local\",\"run-slack-chatbot\"])\r\n            args = parser.parse_args()\r\n            logger.info(\"User provided inputs as, %s\" %(str(args)))\r\n            return args.option\r\n        except Exception as e:\r\n            logger.error(\"Unable to parse arguments, exception : %s\" %(str(e)))\r\n            raise(e)\r\n        \r\n    def runMailServer(self):\r\n        pass","sub_path":"final_case_study_code/lib/chatBotLib.py","file_name":"chatBotLib.py","file_ext":"py","file_size_in_byte":1066,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"231306444","text":"\nimport svgwrite\nimport time\nfrom math import pi, acos, cos, asin, sin, radians, hypot, degrees, atan2\nfrom svgwrite import mm\nfrom datetime import datetime\nimport os\nimport random\nimport sys\n\nlogging = True\n# hours matched up with their angles\nhours = list(zip([3, 2, 1, 12, 11, 10, 9, 8, 7, 6, 5, 4], [((c * 30.0) - 15.0) % 360 for c in range(12)]))\n# cover_middle_hours = [1, 4, 8, 11]\n# do_left_hours = [1, 4, 8, 11, 12, 9, 5, 2, 3, 6, 7, 10]\n# do_right_hours = [1, 4, 8, 11, 12, 10, 7, 3, 2, 5, 6, 9]\ncover_middle_hours = [11, 9, 3, 1]\ndo_left_hours = []\ndo_right_hours = []\n\nflipx = False\n\nsplits = True\n\nbase_buffer = 3\n### all units are mm\nextra_outer_buffer = 7 # mm\nouter_radius = 100 # full radius of the clock\ninner_radius = 40 # radius to cover the movement piece\nshaft_radius = 5 # radius for the shaft piece to fit through\nmax_tic_width = 5 # in mm\nmin_tic_to_space_ratio = 2 # ratio of tic to between tic space, relative to 1\nbetween_hour_buffer_angle = 1 # allotted buffer room to prevent hours from seeming smushed\nspace_to_level_ratio = 1.4 # ratio of level space to level height, relative to 1\nnum_levels = 5\nlevel_length = (outer_radius - inner_radius) / (num_levels * (space_to_level_ratio + 1))\nspace_length = level_length * space_to_level_ratio\n\nwood_width = 6.4 #mm\n\n# stand = 5.7 x 6mm\n\n\n\n## [87, 87 + 8] to [113, 113 + 8]\n## [-101, -101 + 5.75]\n\nall_draws = []\n\ncreate_gif = True\n\ndef replay_for_gif():\n    if not create_gif:\n        return\n    \n    dir = './svgs/gif_%s/' % (datetime.now().strftime('%Y%m%d-%H%M%S'))\n    log('Dir = %s' % dir)\n    width = 0.5\n    os.mkdir(dir)\n    for i in range(len(all_draws)):\n        log('Creating frame %d of %d' % (i+1, len(all_draws)))\n        filename = '%s%03dframe.svg' % (dir, (i+1))\n        dwg = svgwrite.drawing.Drawing(filename=filename)\n        group = svgwrite.container.Group()\n        for draw in all_draws[:(i+1)]:\n            s = draw[0]\n            if s is 'path':\n                group.add(\n                    dwg.path(\n                        d=draw[1],\n                        fill=draw[2], \n                        stroke=draw[3], \n                        stroke_width=width))\n            elif s is 'line':\n                group.add(\n                    dwg.line(\n                        start=draw[1], \n                        end=draw[2], \n                        stroke=draw[3], \n                        stroke_width=width))\n            elif s is 'circle':\n                group.add(\n                    dwg.circle(\n                        center=draw[1], \n                        r=draw[2], \n                        stroke=draw[3], \n                        stroke_width=width,\n                        fill=draw[5]))\n        dwg.add(group)\n        dwg.save()\n    \n\ndef solve():\n    filename = 'svgs/phylo_%s.svg' % datetime.now().strftime('%Y%m%d-%H%M%S')\n    log('Creating drawing for %s' % filename)\n    dwg = svgwrite.drawing.Drawing(filename=filename)\n    \n    draw_front(MyDrawing(- outer_radius - extra_outer_buffer - 1, 0, 0.05, dwg))\n    \n    # draw_base(MyDrawing(- outer_radius - extra_outer_buffer - 1, -2, 0.05, dwg))\n    \n    # draw_back(MyDrawing(outer_radius + extra_outer_buffer + 1, 0, 0.05, dwg))\n    # \n    # draw_base(MyDrawing(outer_radius + extra_outer_buffer + 1, -2, 0.05, dwg))\n    \ndef outs(dwg, indent = False):\n    ### Outer rim of clock\n    dwg.addCircle(0, 0, outer_radius + extra_outer_buffer)\n    \n    if indent:\n        for i in range(7):\n            dwg.addCircle(0, 0, outer_radius + extra_outer_buffer - 1 - (i), 'red')\n    \nsqr_width = 56    \n\ndef draw_base(dwg):\n    \n    sep = 30\n    # dwg.addPolarArc(270 + sep, 270 - sep, outer_radius + extra_outer_buffer)\n    (x0, y0) = dwg.polarToCartesian(270 - sep, outer_radius + extra_outer_buffer)\n    (x1, y1) = dwg.polarToCartesian(270 + sep, outer_radius + extra_outer_buffer)\n    ### Do the trig to build the base\n    h = (outer_radius + extra_outer_buffer + base_buffer) / cos(radians(sep))\n    xdelta = h * sin(radians(sep))\n    x2, y2 = (- xdelta,  -outer_radius - extra_outer_buffer - base_buffer)\n    x3, y3 = (xdelta,  -outer_radius - extra_outer_buffer - base_buffer)\n    \n    dwg.addPolarArc(270 - sep, 270 + sep, outer_radius + extra_outer_buffer)\n    # print(x0, y0, x1, y1, x2, y2, x3, y3)\n    dwg.addLine(x0, y0, x2, y2)\n    dwg.addLine(x2, y2, x3, y3)\n    dwg.addLine(x3, y3, x1, y1)\n    \n    dwg.lock()\n\n\ndef draw_front(dwg):\n    outs(dwg, True)\n    \n    ### Inner rim for movement piece to fit through (6mm)\n    dwg.addCircle(0, 0, shaft_radius)\n    \n    # hlf_width = sqr_width / 2.0\n    # dwg.addRoundedSquare(sqr_width, 5, 'green')\n    # dwg.addLine(hlf_width, - hlf_width, hlf_width, hlf_width, 'green')\n    # dwg.addLine(- hlf_width, - hlf_width, hlf_width, - hlf_width, 'green')\n    # dwg.addLine(- hlf_width, - hlf_width, - hlf_width, hlf_width, 'green')\n    # dwg.addLine(- hlf_width, hlf_width, hlf_width, hlf_width, 'green')\n    \n    ### Do all the hour wedges\n    log('HOURRRS ' + str(hours))\n    for (hour, hour_angle) in hours:\n        doHourWedges(dwg, hour, hour_angle, (hour in do_right_hours), (hour in do_left_hours), (hour in cover_middle_hours))\n    \n    deterministicMerge(dwg)  \n    \n    dwg.lock()  \n    \ndef draw_back(dwg):\n    outs(dwg)\n    \n    # hlf_width = sqr_width / 2.0\n    dwg.addRoundedSquare(sqr_width, 5)\n    # dwg.addLine(hlf_width, - hlf_width, hlf_width, hlf_width)\n    # dwg.addLine(- hlf_width, - hlf_width, hlf_width, - hlf_width)\n    # dwg.addLine(- hlf_width, - hlf_width, - hlf_width, hlf_width)\n    # dwg.addLine(- hlf_width, hlf_width, hlf_width, hlf_width)\n    \n    ## 6.4mm x 10mm\n    ## [87, 87 + 8] to [113, 113 + 8]\n    ## [-101, -101 + 5.75]\n    x, y = (-25, -102)\n    xdelta = 8\n    ydelta = 6.4\n    dwg.addLine(x, y, x + xdelta, y)\n    dwg.addLine(x, y, x, y + ydelta)\n    dwg.addLine(x + xdelta, y, x + xdelta, y + ydelta)\n    dwg.addLine(x, y + ydelta, x + xdelta, y + ydelta)\n    # \n    x, y = ((0 - x) - xdelta, y)\n    dwg.addLine(x, y, x + xdelta, y)\n    dwg.addLine(x, y, x, y + ydelta)\n    dwg.addLine(x + xdelta, y, x + xdelta, y + ydelta)\n    dwg.addLine(x, y + ydelta, x + xdelta, y + ydelta)\n    \n    # square width \n    swidth = 50\n    x = 100\n    dwg.addLine(x, y, x + swidth, y)\n    dwg.addLine(x + xdelta, y + xdelta, x + swidth - xdelta, y + xdelta)\n    \n    dwg.addLine(x, y, x, y + swidth)\n    dwg.addLine(x + xdelta, y + xdelta, x + xdelta, y + swidth)\n    dwg.addLine(x, y + swidth, x + xdelta, y + swidth)\n    \n    dwg.addLine(x + swidth, y, x + swidth, y + swidth)\n    dwg.addLine(x + swidth - xdelta, y + xdelta, x + swidth - xdelta, y + swidth)\n    dwg.addLine(x + swidth - xdelta, y + swidth, x + swidth, y + swidth)\n    \n    ### Do all the hour wedges\n    log('HOURRRS ' + str(hours))\n    for (hour, hour_angle) in hours:\n        doHourWedges(dwg, hour, hour_angle, (hour in do_right_hours), (hour in do_left_hours), (hour in cover_middle_hours))\n    \n    deterministicMerge(dwg)  \n    \n    dwg.lock()  \n    \ndef deterministicMerge(dwg):\n    ### Merge the arms\n    centers = []\n    \n    # gma is center \n    centers.append((hourToAngle(12), 1))\n    \n    # merge 2 and 4 (nick, emily)\n    result = mergeArms(dwg, hourToAngle(4), hourToAngle(2), max_tic_width, 1, 1, 3)\n    centers.append((result, 3))\n    \n    # merge 8 and 10 (natalia, peter)\n    result = mergeArms(dwg, hourToAngle(10), hourToAngle(8), max_tic_width, 1, 1, 3)\n    centers.append((result, 3))\n    \n    # merge (5,6,7) (kyler, tyler, ryan)\n    result = mergeArms3(dwg, hourToAngle(7), hourToAngle(6), hourToAngle(5), max_tic_width, 1, 3)\n    centers.append((result, 3))\n    \n    connectCenters(dwg, centers)\n    \ndef connectCenters(dwg, centers):\n    log('centers: ' + str(centers))\n    centers.sort()\n    log('centers: ' + str(centers))\n    for i in range(len(centers)):\n        (angle0, level) = centers[i]\n        angle1 = centers[(i + 1) % len(centers)][0]\n        log(' angle0, level, angle1 = %f, %f, %f' % (angle0, level, angle1))\n        # connect to center\n        dwg.addPolarDoubleLine(\n            angle0, \n            inner_radius, \n            outer_radius - (level + 1) * level_length - level * space_length,\n            max_tic_width)\n        dwg.addPolarArcMeetDoubleLine(\n            angle0,\n            angle1,\n            inner_radius,\n            max_tic_width)\n\ndef mergeArms3(dwg, angle0, angle1, angle2, width, level, new_level):\n    level += 1\n    dwg.addPolarDoubleLineRight(\n        angle0, \n        outer_radius - ((new_level + 1) * level_length + new_level * space_length),\n        outer_radius - (level * level_length + (level - 1) * space_length),\n        width)\n    dwg.addPolarDoubleLineLeft(\n        angle0, \n        outer_radius - (new_level * level_length + new_level * space_length),\n        outer_radius - (level * level_length + (level - 1) * space_length),\n        width)\n    dwg.addPolarDoubleLineLeft(\n        angle1, \n        outer_radius - ((new_level) * level_length + new_level * space_length),\n        outer_radius - (level * level_length + (level - 1) * space_length),\n        width)\n    dwg.addPolarDoubleLineRight(\n        angle1, \n        outer_radius - ((new_level) * level_length + new_level * space_length),\n        outer_radius - (level * level_length + (level - 1) * space_length),\n        width)\n    dwg.addPolarDoubleLineLeft(\n        angle2, \n        outer_radius - ((new_level + 1) * level_length + new_level * space_length),\n        outer_radius - (level * level_length + (level - 1) * space_length),\n        width)\n    dwg.addPolarDoubleLineRight(\n        angle2, \n        outer_radius - (new_level * level_length + new_level * space_length),\n        outer_radius - (level * level_length + (level - 1) * space_length),\n        width)\n    dwg.addPolarArc(\n        angle0 + widthToAngle(max_tic_width, outer_radius - (new_level * level_length + new_level * space_length)) / 2.0,\n        angle1 - widthToAngle(max_tic_width, outer_radius - (new_level * level_length + new_level * space_length)) / 2.0,\n        outer_radius - (new_level * level_length + new_level * space_length))\n    dwg.addPolarArc(\n        angle1 + widthToAngle(max_tic_width, outer_radius - (new_level * level_length + new_level * space_length)) / 2.0,\n        angle2 - widthToAngle(max_tic_width, outer_radius - (new_level * level_length + new_level * space_length)) / 2.0,\n        outer_radius - (new_level * level_length + new_level * space_length))\n    dwg.addPolarArc(\n        angle0 - widthToAngle(max_tic_width, outer_radius - ((new_level + 1) * level_length + new_level * space_length)) / 2.0,\n        halfAngle(angle0, angle2) - widthToAngle(max_tic_width, outer_radius - ((new_level + 1) * level_length + new_level * space_length)) / 2.0,\n        outer_radius - ((new_level + 1) * level_length + new_level * space_length))\n    dwg.addPolarArc(\n        halfAngle(angle0, angle2) + widthToAngle(max_tic_width, outer_radius - ((new_level + 1) * level_length + new_level * space_length)) / 2.0,\n        angle2 + widthToAngle(max_tic_width, outer_radius - ((new_level + 1) * level_length + new_level * space_length)) / 2.0,\n        outer_radius - ((new_level + 1) * level_length + new_level * space_length))\n    return halfAngle(angle0, angle2)        \n    \ndef mergeArms(dwg, angle0, angle1, width, level0, level1, new_level):\n    level0 += 1\n    level1 += 1\n    dwg.addPolarDoubleLineRight(\n        angle0, \n        outer_radius - ((new_level + 1) * level_length + new_level * space_length),\n        outer_radius - (level0 * level_length + (level0 - 1) * space_length),\n        width)\n    dwg.addPolarDoubleLineLeft(\n        angle0, \n        outer_radius - (new_level * level_length + new_level * space_length),\n        outer_radius - (level0 * level_length + (level0 - 1) * space_length),\n        width)\n    dwg.addPolarDoubleLineLeft(\n        angle1, \n        outer_radius - ((new_level + 1) * level_length + new_level * space_length),\n        outer_radius - (level1 * level_length + (level1 - 1) * space_length),\n        width)\n    dwg.addPolarDoubleLineRight(\n        angle1, \n        outer_radius - (new_level * level_length + new_level * space_length),\n        outer_radius - (level1 * level_length + (level1 - 1) * space_length),\n        width)\n    dwg.addPolarArc(\n        angle0 + widthToAngle(max_tic_width, outer_radius - (new_level * level_length + new_level * space_length)) / 2.0,\n        angle1 - widthToAngle(max_tic_width, outer_radius - (new_level * level_length + new_level * space_length)) / 2.0,\n        outer_radius - (new_level * level_length + new_level * space_length))\n    dwg.addPolarArc(\n        angle0 - widthToAngle(max_tic_width, outer_radius - ((new_level + 1) * level_length + new_level * space_length)) / 2.0,\n        halfAngle(angle0, angle1) - widthToAngle(max_tic_width, outer_radius - ((new_level + 1) * level_length + new_level * space_length)) / 2.0,\n        outer_radius - ((new_level + 1) * level_length + new_level * space_length))\n    dwg.addPolarArc(\n        halfAngle(angle0, angle1) + widthToAngle(max_tic_width, outer_radius - ((new_level + 1) * level_length + new_level * space_length)) / 2.0,\n        angle1 + widthToAngle(max_tic_width, outer_radius - ((new_level + 1) * level_length + new_level * space_length)) / 2.0,\n        outer_radius - ((new_level + 1) * level_length + new_level * space_length))\n    return halfAngle(angle0, angle1)\n        \n    \ndef doHourWedges(dwg, hour, center, should_do_right=False, should_do_left=False, cover_middle=False):\n    log('Doing hour wedge ' + str(hour))\n    # dwg.addPolarLine(center + 15.0, 20, 40)\n    allotted_angle = 30.0 - between_hour_buffer_angle\n    ideal_tic_widths = hour * widthToAngle(max_tic_width, outer_radius - level_length)\n    ideal_space_widths = allotted_angle - ideal_tic_widths\n    ideal_space_widths -= allotted_angle / (hour + 1)\n    print(hour, allotted_angle, ideal_tic_widths, ideal_space_widths, min_tic_to_space_ratio)\n    if ideal_space_widths > 0 and ideal_tic_widths / ideal_space_widths <= min_tic_to_space_ratio:\n        # We have enough room to use the max_tic_width_angle\n        tic_width = max_tic_width\n    else:\n        # We don't have enough space so we shorten the angle\n        bits = allotted_angle / (hour * (min_tic_to_space_ratio + 1))\n        bits *= (min_tic_to_space_ratio + 1)\n        tic_width = bits\n        \n    start = center + between_hour_buffer_angle / 2.0\n    angles = allotted_angle / (hour + 1)\n        \n    # right barrier\n    dwg.addPolarArc(\n        center, \n        start + angles - widthToAngle(tic_width, outer_radius - level_length) / 2.0, \n        outer_radius - level_length)\n    if should_do_right:\n        dwg.addPolarArc(\n            center,\n            center + 15.0 - ((widthToAngle(max_tic_width, outer_radius - (2 * level_length) - space_length) / 2.0) if not cover_middle else 0),\n            outer_radius - (2 * level_length) - space_length)\n        dwg.addPolarArc(    \n            center, \n            start + angles - widthToAngle(tic_width, outer_radius - (level_length) - space_length) / 2.0, \n            outer_radius - (level_length) - space_length)\n        dwg.addPolarDoubleLineRight(\n            start + angles,\n            outer_radius - level_length - space_length,\n            outer_radius - level_length,\n            tic_width)\n    else: \n        dwg.addPolarArc(\n            start + angles - widthToAngle(tic_width, outer_radius - (2 * level_length) - space_length) / 2.0,\n            center + 15.0 - ((widthToAngle(max_tic_width, outer_radius - (2 * level_length) - space_length) / 2.0) if not cover_middle else 0),\n            outer_radius - (2 * level_length) - space_length)\n        dwg.addPolarDoubleLineRight(\n            start + angles,\n            outer_radius - 2 * level_length - space_length,\n            outer_radius - level_length,\n            tic_width)\n    # left barrier\n    dwg.addPolarArc(\n        start + (hour * angles) + widthToAngle(tic_width, outer_radius - level_length) / 2.0, \n        center + 30.0, \n        outer_radius - level_length)\n    if should_do_left:\n        dwg.addPolarArc(\n            center + 15.0 + ((widthToAngle(max_tic_width, outer_radius - (2 * level_length) - space_length) / 2.0) if not cover_middle else 0),\n            center + 30.0,\n            outer_radius - (2 * level_length) - space_length)\n        dwg.addPolarArc(\n            start + (hour * angles) + widthToAngle(tic_width, outer_radius - (level_length) - space_length) / 2.0, \n            center + 30.0, \n            outer_radius - (level_length) - space_length)\n        dwg.addPolarDoubleLineLeft(\n            start + (hour * angles),\n            outer_radius - level_length - space_length,\n            outer_radius - level_length,\n            tic_width)\n    else:\n        dwg.addPolarArc(\n            center + 15.0 + ((widthToAngle(max_tic_width, outer_radius - (2 * level_length) - space_length) / 2.0) if not cover_middle else 0),\n            start + (hour * angles) + widthToAngle(tic_width, outer_radius - (2 * level_length) - space_length) / 2.0,\n            outer_radius - (2 * level_length) - space_length)\n        dwg.addPolarDoubleLineLeft(\n            start + (hour * angles),\n            outer_radius - 2 * level_length - space_length,\n            outer_radius - level_length,\n            tic_width)\n    for i in range(1, hour):\n        dwg.addWedgeMeetDoubleLine(\n            start + (i * angles),\n            start + ((i + 1) * angles), \n            outer_radius - level_length - space_length, \n            outer_radius - level_length, \n            tic_width)\n        \ndef log(s):\n    if logging:\n        print(s)\n\nclass MyDrawing:\n    ### CONSTANTS ###\n    def __init__(self, xcenter, ycenter, stroke_width, dwg):\n        self.x_offset = xcenter\n        self.y_offset = ycenter\n        self.line_stroke_color = 'blue'\n        self.line_stroke_width = stroke_width * mm\n        self.group = svgwrite.container.Group()\n        self.dwg = dwg\n        \n    def lock(self):\n        self.dwg.add(self.group)\n        self.dwg.save()\n    \n    def addPolarArc(self, angle0, angle1, radius, color = None):\n        angle0 = angle0 % 360\n        angle1 = angle1 % 360\n        if angle0 == angle1:\n            return\n        angle1 = 360 if angle1 == 0 else angle1\n        if angle0 is None or angle1 is None or radius is None:\n            return\n        log('\\tAdding polar arc: %3.4fdegs to %3.4fdegs at radius %3.4f centered at (%3.4f, %3.4f)' % (angle0, angle1, radius, self.x_offset, self.y_offset))\n        if angle1 > angle0 and angle1 - angle0 > 180:\n            log('\\trecursing 1')\n            self.addPolarArc(angle0, halfAngle(angle0, angle1), radius, color)\n            self.addPolarArc(halfAngle(angle0, angle1), angle1, radius, color)\n            return\n        if angle1 < angle0 and 360 - angle1 + angle0 > 180:\n            log('\\trecursing 2')\n            self.addPolarArc(angle0, 360, radius, color)\n            self.addPolarArc(0, angle1, radius, color)\n            return\n        (x0, y0) = self.polarToCartesian(angle0, radius)\n        (x1, y1) = self.polarToCartesian(angle1, radius)\n        self.addArc(x0, y0, x1, y1, radius, color)\n                \n    ### If arc is larger than 180, this will fail\n    def addArc(self, x0, y0, x1, y1, radius, color = None):\n        log('\\t\\tAdding Cartesian Arc: (%3.4f, %3.4f) to (%3.4f, %3.4f) with radius %3.4f' % (x0, y0, x1, y1, radius))\n        \"\"\" Adds an arc that bulges to the right as it moves from p0 to p1 \"\"\"\n        x0, y0, x1, y1 = self.offset(x0, y0, x1, y1)\n        if flipx:\n            a, b = (x0, y0)\n            x0, y0 = (x1, y1)\n            x1, y1 = (a, b)\n        x0, y0, x1, y1, radius = mmToPx(x0), mmToPx(y0), mmToPx(x1), mmToPx(y1), mmToPx(radius)\n        log('\\t\\t\\tPixel coords: (%3.4f, %3.4f) to (%3.4f, %3.4f) with radius %3.4f' % (x0, y0, x1, y1, radius))\n        args = {'x0':x0, \n                'y0':-y0, \n                'xradius':radius, \n                'yradius':radius, \n                'ellipseRotation':0, #has no effect for circles\n                'x1':(x1-x0), \n                'y1':-(y1-y0)}\n        all_draws.append(\n            ('path', \n            'M %(x0)f,%(y0)f a %(xradius)f,%(yradius)f %(ellipseRotation)f 0,0 %(x1)f,%(y1)f'%args,\n            'none',\n            (self.line_stroke_color if color is None else color),\n            self.line_stroke_width))\n        self.group.add(\n            self.dwg.path(\n                d='M %(x0)f,%(y0)f a %(xradius)f,%(yradius)f %(ellipseRotation)f 0,0 %(x1)f,%(y1)f'%args,\n                fill='none', \n                stroke=(self.line_stroke_color if color is None else color), \n                stroke_width=self.line_stroke_width))\n                \n    def addRoundedSquare(self, size, radius, color = None):\n        height = mmToPx(size - 2 * radius)\n        startx, starty, _, _ = self.offset((size / 2.0) - radius, size / 2.0, 0, 0)\n        if not flipx:\n            startx = startx - size + 2*radius\n        radius = mmToPx(radius)\n        \n        log('SQUARE: starting %f, %f' % (startx, starty))\n        \n        args = {'startx':mmToPx(startx), \n                'starty':-mmToPx(starty), \n                'radius':radius, \n                'xdiff':radius, \n                'ydiff':radius,\n                'height':height}\n                \n        # \"M100,100 h200 a20,20 0 0 1 20,20 v200 a20,20 0 0 1 -20,20 h-200 a20,20 0 0 1 -20,-20 v-200 a20,20 0 0 1 20,-20 z\" />\n        strf = 'M%(startx)f,%(starty)f h%(height)f a%(radius)f,%(radius)f 0 0 1 %(xdiff)f,%(ydiff)f v%(height)f a%(radius)f,%(radius)f 0 0 1 -%(xdiff)f,%(ydiff)f h-%(height)f a%(radius)f,%(radius)f 0 0 1 -%(xdiff)f,-%(ydiff)f v-%(height)f a%(radius)f,%(radius)f 0 0 1 %(xdiff)f,-%(ydiff)f z'\n        all_draws.append(\n            ('path', \n            strf%args,\n            'none',\n            (self.line_stroke_color if color is None else color),\n            self.line_stroke_width))\n        self.group.add(\n            self.dwg.path(\n                d=strf % args,\n                fill='none', \n                stroke=(self.line_stroke_color if color is None else color), \n                stroke_width=self.line_stroke_width))\n        \n    def addCircle(self, cx, cy, radius, color = None):\n        log('\\tAdding circle: center (%3.4f, %3.4f) with radius %3.4f' % (cx, cy, radius))\n        cx, cy, _, _ = self.offset(cx, cy, 0, 0)\n        all_draws.append(\n            ('circle', \n            (cx * mm, -cy * mm),\n            radius * mm,\n            (self.line_stroke_color if color is None else color),\n            self.line_stroke_width,\n            'none'))\n        self.group.add(\n            self.dwg.circle(\n                center=(cx * mm, -cy * mm), \n                r=radius * mm, \n                stroke=(self.line_stroke_color if color is None else color), \n                stroke_width=self.line_stroke_width,\n                fill='none'))\n    \n    def addPolarLine(self, angle, r0, r1, xdelta=0.0, ydelta=0.0):\n        log('\\tAdding polar line: angle %3.4fdegs from radius %3.4f to %3.4f' % (angle, r0, r1))\n        (x0, y0) = self.polarToCartesian(angle, r0)\n        (x1, y1) = self.polarToCartesian(angle, r1)\n        return self.addLine(x0 + xdelta, y0 + ydelta, x1 + xdelta, y1 + ydelta)\n        \n    def addPolarLineWithBuffer(self, angle, r0, r1, buffer):\n        log('\\tAdding buffered polar line: angle %3.4fdegs from radius %3.4f to %3.4f with buffer %3.2f' % (angle, r0, r1, buffer))\n        xdelta, ydelta = self.perpendicularOffset(angle, buffer)\n        (x0, y0) = self.polarToCartesian(angle, r0)\n        (x1, y1) = self.polarToCartesian(angle, r1)\n        return self.addLine(x0 + xdelta, y0 + ydelta, x1 + xdelta, y1 + ydelta)\n        \n    def addLine(self, x0, y0, x1, y1, color = None):\n        x0, y0, x1, y1 = self.offset(x0, y0, x1, y1)\n        log('\\t\\tCartesian line: (%3.4f, %3.4f) to (%3.4f, %3.4f)' % (x0, y0, x1, y1))\n        all_draws.append(\n            ('line', \n            (x0 * mm, -y0 * mm),\n            (x1 * mm, -y1 * mm),\n            (self.line_stroke_color if color is None else color),\n            self.line_stroke_width))\n        self.group.add(\n            self.dwg.line(\n                start=(x0 * mm, -y0 * mm), \n                end=(x1 * mm, -y1 * mm), \n                stroke=(self.line_stroke_color if color is None else color), \n                stroke_width=self.line_stroke_width))\n        return x0, y0, x1, y1\n    \n    def addWedge(self, angle0, angle1, r0, r1, buffer):\n        log('\\tAdding wedge: angle %3.4fdegs to angle %3.4f from radius %3.4f to %3.4f with %3.4f buffer' % (angle0, angle1, r0, r1, buffer))\n        xdelta, ydelta = self.perpendicularOffset(angle0, buffer)\n        x0, y0, x1, y1 = self.addPolarLine(angle0, r0, r1, xdelta, ydelta)\n        xdelta, ydelta = self.perpendicularOffset(angle1, buffer)\n        x2, y2, x3, y3 = self.addPolarLine(angle1, r0, r1, -xdelta, -ydelta)\n        self.addArc(x0, y0, x2, y2, r0)\n        self.addArc(x1, y1, x3, y3, r1)\n        \n    def addPolarArcWithBuffer(self, angle1, angle0, r0, buffer):\n        if angle0 is None or angle1 is None or r0 is None or buffer is None:\n            return\n        log('\\tAdding polar arc with buffer: angle %3.4fdegs from radius %3.4f to %3.4f with %3.4f buffer' % (angle0, angle1, r0, buffer))\n        xdelta, ydelta = self.perpendicularOffset(angle0, buffer)\n        xdelta1, ydelta1 = self.perpendicularOffset(angle1, buffer)\n        (x0, y0) = self.polarToCartesian(angle0, r0)\n        (x2, y2) = self.polarToCartesian(angle1, r0)\n        self.addArc(x2 + xdelta1, y2 + ydelta1, x0 - xdelta, y0 - ydelta, r0)\n    \n    def addWedgeMeetDoubleLine(self, angle0, angle1, r0, r1, width):\n        self.addPolarDoubleLineLeft(angle0, r0, r1, width)\n        self.addPolarDoubleLineRight(angle1, r0, r1, width)\n        self.addPolarArcMeetDoubleLine(angle0, angle1, r0, width)\n        self.addPolarArcMeetDoubleLine(angle0, angle1, r1, width)\n    \n    def addPolarArcMeetDoubleLine(self, angle0, angle1, r, width):\n        (x0, y0) = self.polarToCartesian(angle0, r)\n        (x1, y1) = self.polarToCartesian(angle1, r)\n        x0delta, y0delta = self.perpendicularOffset(angle0, width)\n        angle0, rad0 = self.cartesianToPolar(x0 + x0delta, y0 + y0delta)\n        x1delta, y1delta = self.perpendicularOffset(angle1, width)\n        angle1, rad1 = self.cartesianToPolar(x1 - x1delta, y1 - y1delta)\n        self.addPolarArc(angle0, angle1, r)\n    \n    def addPolarDoubleLine(self, angle, r0, r1, width):\n        self.addPolarDoubleLineLeft(angle, r0, r1, width)\n        self.addPolarDoubleLineRight(angle, r0, r1, width)\n        \n    def addPolarDoubleLineLeft(self, angle, r0, r1, width):\n        log('\\tAdding polar line left: angle %3.4fdegs from radius %3.4f to %3.4f with buffer %3.2f' % (angle, r0, r1, width))\n        (x0, y0) = self.polarToCartesian(angle, r0)\n        (x1, y1) = self.polarToCartesian(angle, r1)\n        xdelta, ydelta = self.perpendicularOffset(angle, width)\n        self.addLine(x0 + xdelta, y0 + ydelta, x1 + xdelta, y1 + ydelta)\n        \n    def addPolarDoubleLineRight(self, angle, r0, r1, width):\n        log('\\tAdding polar line right: angle %3.4fdegs from radius %3.4f to %3.4f with buffer %3.2f' % (angle, r0, r1, width))\n        (x0, y0) = self.polarToCartesian(angle, r0)\n        (x1, y1) = self.polarToCartesian(angle, r1)\n        xdelta, ydelta = self.perpendicularOffset(angle, width)\n        self.addLine(x0 - xdelta, y0 - ydelta, x1 - xdelta, y1 - ydelta)\n    \n    def offset(self, x0, y0, x1, y1):\n        if flipx:\n            x0 = - x0\n            x1 = - x1\n        return (x0 + self.x_offset, y0 + self.y_offset, x1 + self.x_offset, y1 + self.y_offset)\n        \n    def perpendicularOffset(self, angle, width):\n        perp = angle + 90.0\n        return (cos(radians(perp)) * width / 2.0, sin(radians(perp)) * width / 2.0)\n        \n    def polarToCartesian(self, angle, radius):\n        return (radius * cos(radians(angle * 1.0)), radius * sin(radians(angle * 1.0)))\n    \n    def cartesianToPolar(self, x, y):\n        radius = hypot(x, y)\n        rad = atan2(y, x)\n        deg = rad * (180 / pi)\n        return deg % 360, radius\n\ndef halfAngle(angle0, angle1):\n    if angle1 >= angle0:\n        ret = ((angle1 + angle0) / 2.0) % 360\n    else:\n        ret = halfAngle(angle1, angle0 + 360)\n    return ret\n    \ndef widthToAngle(width, radius):\n    return 360 * (width / (radius * 2 * pi))\n    \ndef hourToAngle(hour):\n    hs = list(zip([3, 2, 1, 12, 11, 10, 9, 8, 7, 6, 5, 4], [((c * 30.0) - 15.0) % 360 for c in range(12)]))\n    for (i, j) in hs:\n        if i == hour:\n            return (j + 15.0) % 360\n    return None\n    \ndef mmToPx(length):\n    return 3.78 * length\n    # return 3.788975 * length\n    # return 3.543307 * length\n    \nsolve()\n\nreplay_for_gif()\n","sub_path":"phylo.py","file_name":"phylo.py","file_ext":"py","file_size_in_byte":28765,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"220130884","text":"#!/usr/bin/python3\n\nimport configparser\nimport os\n\nPATH = os.path.dirname(os.path.abspath(__file__))\nFILENAME = 'Beverage.csv'\nFILEPATH = os.path.join(PATH, FILENAME)\n\nconfig = configparser.ConfigParser()\nsection1 = config['DEFAULT']\nsection1['PATH'] = PATH\nsection1['FILENAME'] = FILENAME\nsection1['FILEPATH'] = FILEPATH\nsection1['INIT_DF'] = \"1\"\n\nwith open('config.cfg', 'w') as configfile:\n  config.write(configfile)\n\n# config.sections()\n# a = config['DEFAULT']['PATH']\n# b = config['DEFAULT']['FILENAME']\n","sub_path":"vending_machine/set_config.py","file_name":"set_config.py","file_ext":"py","file_size_in_byte":509,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"53246758","text":"\n#This slice of code is to be used after data preprocessing to run XG Boost for the same problem\n# Random Forest\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.metrics import confusion_matrix, classification_report\n\n#Split Test Train Data\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.3, random_state = 0)\n# XG Boost\nimport xgboost as xgb\n#TEST & TRAIN data\ndtrain = xgb.DMatrix(X_train, label=Y_train)\ndtest = xgb.DMatrix(X_test, label=Y_test)\nprint(Y_train.size)\nprint(Y_test.size)\n\n# Now for the XB Boost to work you need to set the parameters:\nparam = {\n    'max_depth': 15,  # the maximum depth of each tree\n    'eta':1,  # the training step for each iteration\n    'silent': 1,  # logging mode - quiet\n    'objective': 'multi:softprob',  # error evaluation for multiclass training\n    'num_class': 2}  # the number of classes that exist in this datset, there are 2 classes (opened/Notopened)\nnum_round = 10  # the number of training iterations\n\n#train the machine\nbst = xgb.train(param, dtrain, num_round)\n#Prediction\npreds = bst.predict(dtest)\nprint(preds)\n\n# output of prediction based on open, not open for the test data 1=open and 0= not_open\nimport numpy as np\nbest_preds = np.asarray([np.argmax(line) for line in preds])\nprint(best_preds)\n\n#Determine the precision of this prediction\nfrom sklearn.metrics import precision_score\nprint (precision_score(Y_test, best_preds, average='micro'))\n#Save the model\nfrom joblib import dump, load\ndump(bst, 'VahidHadavi-XGBOOST.joblib')\n\n#Validation using ROC AUC\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import roc_curve, auc,roc_auc_score\nfpr = dict()\ntpr = dict()\nroc_auc = dict()\nfor i in range(2):\n    fpr[i], tpr[i], _ = roc_curve(Y_test, best_preds)\n    roc_auc[i] = auc(fpr[i], tpr[i])\nA=roc_auc_score(Y_test, best_preds)\nprint (A)\nplt.figure()\nplt.plot(fpr[1], tpr[1])\nplt.xlim([0.0, 1.0])\nplt.ylim([0.0, 1.05])\nplt.xlabel('False Positive Rate')\nplt.ylabel('True Positive Rate')\nplt.title('Receiver operating characteristic')\nplt.show()","sub_path":"XG Boost (GitHub).py","file_name":"XG Boost (GitHub).py","file_ext":"py","file_size_in_byte":2146,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"147690801","text":"import sys\n\ndef is_adjacement(count):\n    #print(count)\n\n    first_num = count[0]\n    count_set = set(count)\n    compare_set = {first_num}\n    queue = [first_num]\n    while queue:\n        num = queue.pop(0)\n        row, col = num // 5, num % 5\n        for dr, dc in zip([-1, 1, 0, 0], [0, 0, -1, 1]):\n            nr, nc = row + dr, col + dc\n            if 0 <= nr < 5 and 0 <= nc < 5:\n                num = nr * 5 + nc\n                if num in count_set:\n                    if num in compare_set:\n                        continue\n                    compare_set.add(num)\n                    queue.append(num)\n    if len(compare_set) == len(count_set):\n        #print(compare_set, count_set)\n        return True\n    return False\n\n\ndef is_over_four(seats, student_list):\n    count = 0\n\n    for num in student_list:\n        row, col = num // 5 , num % 5\n        if seats[row][col] == 'S':\n            count += 1\n    if count >= 4:\n        #print(\"four\", student_list)\n        return True\n    return False\n\n\ndef dfs(seats, count, start):\n    result = 0\n    if len(count) == 7:\n        if is_over_four(seats, count) and is_adjacement(count):\n            return 1\n        else:\n            return 0\n    else:\n        for i in range(start, 25):\n            if len(count) <= 0 or count[-1] < i:\n                count.append(i)\n                result += dfs(seats, count, start+1)\n                #print(\"result\", result)\n                count.pop()\n        return result\n\n\ndef main():\n    seats = [input().rstrip() for _ in range(5)]\n    print(dfs(seats, [], 0))\n    \n\n\nif __name__ == \"__main__\":\n    main()","sub_path":"soohyun/python/baekjoon/0526/1941/3.py","file_name":"3.py","file_ext":"py","file_size_in_byte":1601,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"191591734","text":"#!/usr/bin/env python\nfrom __future__ import absolute_import, unicode_literals, print_function\nimport numpy\nfrom numpy import pi, cos\nfrom pymultinest.solve import solve\nimport os\nif not os.path.exists(\"chains\"): os.mkdir(\"chains\")\nfrom colossus.cosmology import cosmology\n\n\n# probability function, taken from the eggbox problem.\n\nfile = numpy.loadtxt('GilMarin_2016_CMASSDR12_measurement_monopole_post_recon.txt').T \ndata = file\nk = data[0]\n\ndef myprior(cube):\n    cube[0]=0.7*cube[0]+0.1\n    cube[1] = 3*cube[1]+0.1\n    return cube \n\n\ndef Pk_Om(Om_,b,k):\n    cosmo = cosmology.setCosmology('planck15') # Generamos los parámetros default del experimento planck15\n    z = 0.57 \n    cosmo.Om0 = Om_ # Modificamos la densidad de materia\n    return b**2*cosmo.matterPowerSpectrum(k,z) # Se regresa la función matterPowerSpectrum multiplicada por el bias al cuadrado\n\n\ndef myloglike(cube):\n    Om = cube[0]\n    b = cube[1]\n    equis = data[0] # Llamamos x al primer conjunto de datos\n    ye = data[1] # Llamamos y al segundo conjunto de datos\n    yerr = data[2]  # Llamamos yerror al tercer conjunto de datos\n    model = Pk_Om(Om, b,equis) # Calculamos el modelo utilizando la función Pk_om\n    chisq2 = (ye-model)**2/(yerr**2) + numpy.log(yerr**2) # Obtenemos chi^2\n    return -0.5*chisq2.sum()\n\n\n# number of dimensions our problem has\nparameters = [\"Om0\", \"b\"]\nn_params = len(parameters)\n# name of the output files\nprefix = \"chains/3-\"\n\n# run MultiNest\nresult = solve(LogLikelihood=myloglike, Prior=myprior, \n\tn_dims=n_params, outputfiles_basename=prefix, verbose=True)\n\nprint()\n#print('evidence: %(logZ).1f +- %(logZerr).1f' % result)\nprint()\nprint('parameter values:')\nfor name, col in zip(parameters, result['samples'].transpose()):\n\tprint('%15s : %.3f +- %.3f' % (name, col.mean(), col.std()))\n\n# make marginal plots by running:\n# $ python multinest_marginals.py chains/3-\n# For that, we need to store the parameter names:\nimport json\nwith open('%sparams.json' % prefix, 'w') as f:\n\tjson.dump(parameters, f, indent=2)\n\n","sub_path":"Semana13/DosParametrosResults/pymultinest_demo.py","file_name":"pymultinest_demo.py","file_ext":"py","file_size_in_byte":2024,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"501842393","text":"import mock\nimport pytest\n\nfrom yasmin.core import predict_output_word\nfrom yasmin.exceptions import ValidationException, OutOfVocabException\nfrom yasmin.helpers import make_type_matrix\n\n\ndef test_type_validation(wsd_instance):\n    sent = \"We areived in Berlin yesterday.\"\n    word = \"Berlin\"\n    types = ['Person', 'FakeType']\n    with pytest.raises(ValidationException):\n        wsd_instance.disambiguate(sent=sent, word=word, types=types)\n\n\n@pytest.mark.xfail()\ndef test_context_repetition(wsd_instance):\n    # This is a bit of an edge case where the context are practically the same.\n    # There isn't much that can be done without preprocessing.\n    sent = \"When we arrived in Berlin, Berlin were performing at the stadium.\"\n    word = \"Berlin\"\n    types = ['Person', 'GeographicArea']\n    res = wsd_instance.disambiguate(sent=sent, word=word, types=types)\n    assert len(res) == 2\n    city_sense, band_sense = res\n    assert city_sense['type'] == 'GeographicArea'\n    assert band_sense['type'] == 'Person'\n\n\ndef test_context_punctuation(wsd_instance):\n    sent = \"We arrived at Berlin's old Nazi airported in Tempelhoff.\"\n    word = \"Berlin\"\n    types = ['Person', 'GeographicArea']\n    res = wsd_instance.disambiguate(sent=sent, word=word, types=types)\n    assert len(res) == 1\n    sense = res[0]\n    assert sense['type'] == 'GeographicArea'\n\n\ndef test_type_matrix(wsd_instance):\n    types = {\n        'weekdays': ['monday', 'tuesday', 'wednesday', 'thursday', 'friday'],\n        'digits': ['one', 'two', 'three', 'four', 'five', 'six', 'seven']\n    }\n    matrix = make_type_matrix(model_types=types, model=wsd_instance.model)\n\n    assert sum(matrix['weekdays']) == len(types['weekdays'])\n    assert sum(matrix['digits']) == len(types['digits'])\n\n\ndef test_negative_model():\n    model = mock.Mock()\n    model.negative = False\n    with pytest.raises(RuntimeError):\n        predict_output_word(model=model, context=[])\n\n\ndef test_missing_params_of_model():\n    model = mock.Mock()\n    model.negative = True\n    model.wv = None\n    with pytest.raises(RuntimeError):\n        predict_output_word(model=model, context=[])\n\n\ndef test_model_warning():\n    model = mock.Mock()\n    model.negative = True\n    model.wv = mock.Mock()\n    model.wv.syn0 = True\n    model.syn1neg = True\n    with pytest.raises(OutOfVocabException):\n        predict_output_word(model=model, context=[])\n","sub_path":"tests/test_core.py","file_name":"test_core.py","file_ext":"py","file_size_in_byte":2376,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"638474399","text":"# emacs: -*- mode: python; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*-\n# ex: set sts=4 ts=4 sw=4 noet:\n# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##\n#\n#   See COPYING file distributed along with the datalad package for the\n#   copyright and license terms.\n#\n# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##\n\"\"\"Base class of a protocol to be used with the DataLad runner\n\"\"\"\n\nimport asyncio\nimport logging\nimport warnings\nfrom locale import getpreferredencoding\n\nfrom .exception import CommandError\nfrom .utils import ensure_unicode\n\nlgr = logging.getLogger('datalad.runner.protocol')\n\n\nclass WitlessProtocol(asyncio.SubprocessProtocol):\n    \"\"\"Subprocess communication protocol base class for `run_async_cmd`\n\n    This class implements basic subprocess output handling. Derived classes\n    like `StdOutCapture` should be used for subprocess communication that need\n    to capture and return output. In particular, the `pipe_data_received()`\n    method can be overwritten to implement \"online\" processing of process\n    output.\n\n    This class defines a default return value setup that causes\n    `run_async_cmd()` to return a 2-tuple with the subprocess's exit code\n    and a list with bytestrings of all captured output streams.\n    \"\"\"\n\n    proc_out = None\n    proc_err = None\n\n    def __init__(self, done_future=None, encoding=None):\n        \"\"\"\n        Parameters\n        ----------\n        done_future: Any\n          Ignored parameter, kept for backward compatibility (DEPRECATED)\n        encoding : str\n          Encoding to be used for process output bytes decoding. By default,\n          the preferred system encoding is guessed.\n        \"\"\"\n\n        if done_future is not None:\n            warnings.warn(\"`done_future` argument is ignored \"\n                          \"and will be removed in a future release\",\n                          DeprecationWarning)\n\n        self.fd_infos = {}\n\n        self.process = None\n        self.stdout_fileno = 1\n        self.stderr_fileno = 2\n\n        # capture output in bytearrays while the process is running\n        self.fd_infos[self.stdout_fileno] = (\"stdout\", bytearray()) if self.proc_out else (\"stdout\", None)\n        self.fd_infos[self.stderr_fileno] = (\"stderr\", bytearray()) if self.proc_err else (\"stderr\", None)\n\n        super().__init__()\n        self.encoding = encoding or getpreferredencoding(do_setlocale=False)\n\n        self._log_outputs = False\n        if lgr.isEnabledFor(5):\n            try:\n                from datalad import cfg\n                self._log_outputs = cfg.getbool('datalad.log', 'outputs', default=False)\n            except ImportError:\n                pass\n            self._log = self._log_summary\n        else:\n            self._log = self._log_nolog\n\n    def _log_nolog(self, *args):\n        pass\n\n    def _log_summary(self, fd, data):\n        fd_name = self.fd_infos[fd][0]\n        lgr.log(5, 'Read %i bytes from %i[%s]%s',\n                len(data), self.process.pid, fd_name, ':' if self._log_outputs else '')\n        if self._log_outputs:\n            log_data = ensure_unicode(data)\n            # The way we log is to stay consistent with Runner.\n            # TODO: later we might just log in a single entry, without\n            # fd_name prefix\n            lgr.log(5, \"%s| %s \", fd_name, log_data)\n\n    def connection_made(self, process):\n        self.process = process\n        lgr.log(8, 'Process %i started', self.process.pid)\n\n    def pipe_data_received(self, fd, data):\n        self._log(fd, data)\n        # store received output if stream was to be captured\n        fd_name, buffer = self.fd_infos[fd]\n        if buffer is not None:\n            buffer.extend(data)\n\n    def _prepare_result(self):\n        \"\"\"Prepares the final result to be returned to the runner\n\n        Note for derived classes overwriting this method:\n\n        The result must be a dict with keys that do not unintentionally\n        conflict with the API of CommandError, as the result dict is passed to\n        this exception class as kwargs on error. The Runner will overwrite\n        'cmd' and 'cwd' on error, if they are present in the result.\n        \"\"\"\n        return_code = self.process.poll()\n        if return_code is None:\n            raise CommandError(\n                \"Got None as a return_code for the process %i\",\n                self.process.pid)\n        lgr.log(\n            8,\n            'Process %i exited with return code %i',\n            self.process.pid, return_code)\n        # give captured process output back to the runner as string(s)\n        results = {\n            name: (\n                bytes(byt).decode(self.encoding)\n                if byt is not None\n                else '')\n            for name, byt in self.fd_infos.values()\n        }\n        results['code'] = return_code\n        return results\n\n    def process_exited(self):\n        pass\n","sub_path":"datalad/runner/protocol.py","file_name":"protocol.py","file_ext":"py","file_size_in_byte":4926,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"289476131","text":"# web-сервер\r\n# Flask -v 1.1.1\r\n\r\n# Импорты\r\nfrom flask import Flask, request, jsonify\r\nfrom classifier.spam_classifier import classify\r\nfrom classifier.config import Configuration\r\n\r\n# Создадим приложение Flask\r\napp = Flask(__name__)\r\n\r\n# Используем настройки из конфигурационного файла\r\napp.config.from_object(Configuration)\r\n\r\n# Зададим возможные методы обработки запросов\r\n@app.route('/', methods=['POST', 'GET'])\r\ndef standart_output():\r\n    return 'web-сервер: спам-фильтр'\r\n\r\n@app.route('/classify_text', methods=['POST'])\r\n\r\n\r\ndef classify_text():\r\n    \"\"\"Обрабатывает запросы к серверу и возвращает\r\n    признак сообщения: spam_l, notspam_l\"\"\"\r\n    data = request.json\r\n    text = data.get('text')\r\n    if text is None:\r\n        params = ', '.join(data.keys())\r\n        return jsonify({\r\n            'message': f'Parametr \"{params}\" is invalid'\r\n        }), 400\r\n    else:\r\n        result = classify(text)\r\n        return jsonify({'result': result})\r\n","sub_path":"skillfactory/0. real_data_science/2. spam/examples/classifier/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1143,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"640373941","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\nfrom flask import (request, jsonify)\nfrom lib.jwt import jwt_required\nfrom app.vsepic import vsepic\nfrom app.vsepic.service import StoryService\n\n\n@vsepic.route('/story/info/<int:id>', methods=['GET'])\n@jwt_required\ndef story_info(id):\n    result, data, message = StoryService.info(id=id)\n    return {\n        'result': result,\n        'data': data,\n        'message': message\n    }\n\n\n@vsepic.route('/story/list', methods=['GET'])\n@jwt_required\ndef story_list():\n    result, data, message = StoryService.list(request=request)\n    return {\n        'result': result,\n        'data': data,\n        'message': message\n    }\n\n\n@vsepic.route('/story/edit/<int:id>', methods=['PUT'])\n@jwt_required\ndef story_edit(id):\n    result, data, message = StoryService.update(id=id, request=request)\n    return {\n        'result': result,\n        'data': data,\n        'message': message\n    }\n\n\n@vsepic.route('/story/delete/<int:id>', methods=['DELETE'])\n@jwt_required\ndef story_delete(id):\n    result, data, message = StoryService.delete(id=id)\n    return {\n        'result': result,\n        'data': data,\n        'message': message\n    }\n\n\n@vsepic.route('/story/create', methods=['POST'])\n@jwt_required\ndef story_create():\n    result, data, message = StoryService.create(request=request)\n    return {\n        'result': result,\n        'data': data,\n        'message': message\n    }\n","sub_path":"app/vsepic/controller/story.py","file_name":"story.py","file_ext":"py","file_size_in_byte":1411,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"327369085","text":"import sys\n\nn = int(input())\n\n\ns = 0\ni = 1\nwhile(s < n):\n\tterm = 1 + (i - 1)\n\ts += ((1 + term)*i)/2\n\tif(s > n):\n\t\tbreak\n\ti += 1\n\nprint (i - 1)","sub_path":"vanya_and_cubes.py","file_name":"vanya_and_cubes.py","file_ext":"py","file_size_in_byte":142,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"619613494","text":"import matplotlib.pyplot as plt\nimport numpy as np\n\nimport astropy.units as u\nfrom sunpy.visualization.animator import ArrayAnimatorWCS\n\nfrom . import plotting_utils as utils\n\n__all__ = ['NDCubePlotMixin']\n\n\nclass NDCubePlotMixin:\n    \"\"\"\n    Add plotting functionality to a NDCube class.\n    \"\"\"\n\n    def plot(self, axes=None, plot_axes=None, axes_coordinates=None,\n             axes_units=None, data_unit=None, **kwargs):\n        \"\"\"\n        Visualize the `~ndcube.NDCube`.\n\n        Parameters\n        ----------\n        axes: `~astropy.visualization.wcsaxes.WCSAxes` or None:, optional\n            The axes to plot onto. If None the current axes will be used.\n\n        plot_axes: `list`, optional\n            A list of length equal to the number of pixel dimensions. This list\n            selects which cube axes are displayed on which plot axes. For a\n            image plot this list should contain ``'x'`` and ``'y'`` for the\n            plot axes and `None` for all the other elements. For a line plot it\n            should only contain ``'x'`` and `None` for all the other elements.\n\n        axes_unit: `list`, optional\n            A list of length equal to the number of world dimensions specifying\n            the units of each axis, or `None` to use the default unit for that\n            axis.\n\n        axes_coordinates: `list`, optional\n            A list of length equal to the number of pixel dimensions. For each\n            axis the value of the list should either be a string giving the\n            world axis type or `None` to use the default axis from the WCS.\n\n        data_unit: `astropy.unit.Unit`\n            The data is changed to the unit given or the ``NDCube.unit`` if not\n            given.\n        \"\"\"\n        low_level_wcs = self.wcs.low_level_wcs\n        naxis = self.wcs.pixel_n_dim\n\n        # Check kwargs are in consistent formats and set default values if not done so by user.\n        plot_axes, axes_coordinates, axes_units = utils.prep_plot_kwargs(\n            len(self.dimensions), low_level_wcs, plot_axes, axes_coordinates, axes_units)\n\n        if naxis == 1:\n            ax = self._plot_1D_cube(low_level_wcs, axes, axes_coordinates,\n                                    axes_units, data_unit, **kwargs)\n\n        elif naxis == 2:\n            ax = self._plot_2D_cube(low_level_wcs, axes, plot_axes, axes_coordinates,\n                                    axes_units, data_unit, **kwargs)\n        else:\n            ax = self._animate_cube(low_level_wcs, plot_axes=plot_axes,\n                                    axes_coordinates=axes_coordinates,\n                                    axes_units=axes_units, **kwargs)\n\n        return ax\n\n    def _plot_1D_cube(self, wcs, axes=None, axes_coordinates=None, axes_units=None,\n                      data_unit=None, **kwargs):\n        if axes is None:\n            axes = plt.subplot(projection=wcs)\n\n        if axes_coordinates is not None and axes_coordinates[0] != wcs.world_axis_physical_types[::-1][0]:\n            raise NotImplementedError(\"We need to support extra_coords here\")\n\n        default_ylabel = \"Data\"\n\n        # Derive y-axis coordinates, uncertainty and unit from the NDCube's data.\n        yerror = self.uncertainty.array if (self.uncertainty is not None) else None\n        ydata = self.data\n\n        if self.unit is None:\n            if data_unit is not None:\n                raise TypeError(\"Can only set y-axis unit if self.unit is set to a \"\n                                \"compatible unit.\")\n        else:\n            if data_unit is not None:\n                ydata = u.Quantity(ydata, unit=self.unit).to_value(data_unit)\n                if yerror is not None:\n                    yerror = u.Quantity(yerror, self.unit).to_value(data_unit)\n            else:\n                data_unit = self.unit\n\n            default_ylabel += f\" [{data_unit}]\"\n\n        # Combine data and uncertainty with mask.\n        if self.mask is not None:\n            ydata = np.ma.masked_array(ydata, self.mask)\n\n            if yerror is not None:\n                yerror = np.ma.masked_array(yerror, self.mask)\n\n        if yerror is not None:\n            # We plot against pixel coordinates\n            axes.errorbar(np.arange(len(ydata)), ydata, yerr=yerror, **kwargs)\n        else:\n            axes.plot(ydata, **kwargs)\n\n        axes.set_ylabel(default_ylabel)\n\n        utils.set_wcsaxes_format_units(axes.coords, wcs, axes_units)\n\n        return axes\n\n    def _plot_2D_cube(self, wcs, axes=None, plot_axes=None, axes_coordinates=None,\n                      axes_units=None, data_unit=None, **kwargs):\n        if axes is None:\n            axes = plt.subplot(projection=wcs, slices=plot_axes)\n\n        utils.set_wcsaxes_format_units(axes.coords, wcs, axes_units)\n\n        data = self.data\n        if data_unit is not None:\n            # If user set data_unit, convert dat to desired unit if self.unit set.\n            if self.unit is None:\n                raise TypeError(\"Can only set data_unit if NDCube.unit is set.\")\n            data = u.Quantity(self.data, unit=self.unit).to_value(data_unit)\n\n        if self.mask is not None:\n            data = np.ma.masked_array(data, self.mask)\n\n        if plot_axes.index('x') > plot_axes.index('y'):\n            data = data.T\n\n        # Plot data\n        axes.imshow(data, **kwargs)\n\n        return axes\n\n    def _animate_cube(self, wcs, plot_axes=None, axes_coordinates=None,\n                      axes_units=None, data_unit=None, **kwargs):\n        if axes_coordinates is not None and axes_coordinates[0] != wcs.world_axis_physical_types[::-1][0]:\n            raise NotImplementedError(\"We need to support extra_coords here\")\n\n        # If data_unit set, convert data to that unit\n        if data_unit is None:\n            data = self.data\n        else:\n            data = u.Quantity(self.data, unit=self.unit).to_value(data_unit)\n\n        # Combine data values with mask.\n        if self.mask is not None:\n            data = np.ma.masked_array(data, self.mask)\n\n        coord_params = {}\n        if axes_units is not None:\n            for axis_unit, coord_name in zip(axes_units, wcs.world_axis_physical_types):\n                coord_params[coord_name] = {'format_unit': axis_unit}\n\n        plot_axes = [p if p is not None else 0 for p in plot_axes]\n        ax = ArrayAnimatorWCS(data, wcs, plot_axes, coord_params=coord_params, **kwargs)\n\n        return ax\n","sub_path":"ndcube/mixins/plotting.py","file_name":"plotting.py","file_ext":"py","file_size_in_byte":6385,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"455032790","text":"import matplotlib.pyplot as plt\nimport numpy as np\nfrom matplotlib.widgets import Slider\n\n\ndef add_slider(fn, fig=None, ax=None, min_value=0, max_value=1, valinit=0.5, label='value', slider_num=0):\n    '''\n    Usage:\n    add_slider(fn, fig, ax) will add a basic slider taking values between 0 and 1.\n    fn should be a function of one argument (the value of the slider) that executes\n    the necessary changes to your plot (e.g. def fn(val): myplot.set_ydata(my_xdata**val)).\n    Note that because fn cannot take myplot as an additional argument, you'll need\n    to make sure that your myplot is in scope.\n    (e.g. myplot, = ax.plot(my_xdata, my_xdata**0.5))\n    '''\n    \n    fig.subplots_adjust(bottom=(.25 + slider_num * 0.1))\n    axmin = fig.add_axes([0.15, (0.1 + slider_num * 0.1), 0.65, 0.03])\n    sli = Slider(axmin,label, min_value, max_value, valinit=valinit)\n    sli.on_changed(fn)\n    if not hasattr(fig, 'sliders'):\n        fig.sliders = []\n    fig.sliders.append(sli)\n    return sli\n\ndef main():\n    plt.close('all')\n\n    x = np.arange(10)\n    y = x**2\n    def get_y(x, val):\n        return x**val\n\n    fig = plt.figure()\n    ax = fig.add_subplot(111)\n    l, = ax.plot(x, y)\n    def update(val):\n        l.set_ydata(get_y(x, val))\n    add_slider(update, fig, ax, min_value=0, max_value=3, valinit=2, label='power')\n    add_slider(update, fig, ax, min_value=0, max_value=3, valinit=2, label='power', slider_num=1)\n    plt.show()\n\nif (__name__) == '__main__':\n    main()\n","sub_path":"python/utils/add_slider.py","file_name":"add_slider.py","file_ext":"py","file_size_in_byte":1483,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"233953877","text":"def decode_prof(data):\n    return y.marshal.loads(y.lzma.decompress(data))\n\n\ndef encode_prof(v):\n    return y.lzma.compress(y.marshal.dumps(v))\n\n\nclass SimpleDB(object):\n    def __init__(self, path):\n        self.path = y.os.path.expanduser(path)\n\n        try:\n            y.os.makedirs(y.os.path.dirname(self.path))\n        except OSError:\n            pass\n\n    def read_db(self):\n        try:\n            y.debug('open db {bb}' + self.path + '{}')\n\n            with open(self.path, 'rb') as f:\n                return decode_prof(f.read())\n        except Exception as e:\n            if 'No such file or directory' not in str(e):\n                y.info('{br}' + str(e) + '{}, can not load db, reinitialize...')\n\n        return {}\n\n    def write_db(self, data):\n        y.info('commit {bg}' + self.path + '{}')\n        y.write_file(self.path, encode_prof(data))\n\n    def rcu(self, func):\n        self.write_db(func(self.read_db()))\n\n\ndef get_key(db, k, default):\n    if k not in db:\n        db[k] = default\n\n    return db[k]\n\n\n@y.contextlib.contextmanager\ndef open_simple_db(path):\n    db = SimpleDB(path)\n    data = db.read_db()\n    md5 = y.burn(y.json.dumps(data, sort_keys=True))\n\n    try:\n        yield data\n    finally:\n        new_md5 = y.burn(y.json.dumps(data, sort_keys=True))\n\n        if md5 == new_md5:\n            y.debug('db not changed, not write it')\n        else:\n            db.write_db(data)\n","sub_path":"ut/data_format.py","file_name":"data_format.py","file_ext":"py","file_size_in_byte":1409,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"285578791","text":"#! /usr/bin/env python\nimport abjad\nimport baca\nimport manifolds\nimport os\nimport pathlib\nimport pprint\nimport sys\n\n\nif __name__ == '__main__':\n\n    os.system('clear')\n    usage = 'usage: run.py [--nowrite|--write]'\n    write = baca.check_score_builder_commandline(sys.argv, usage)\n    directory = pathlib.Path(__file__).parent\n    template = directory / 'templates' / 'template.ly'\n\n    print('Making cells with prominences score ...')\n    score, output = manifolds.make_cells_with_prominences_score()\n    name = directory / 'scores' / 'cells_with_prominences_score'\n    title = []\n    title.append('Manifolds / Territoires')\n    title.append('unordered, prominenced pitch field cells')\n    abjad.show(score)\n    print('Writing fully labelled cells to disk ...')\n    output = pprint.pformat(output)\n    output = f'fully_labelled_cells = {output}'\n    baca.cache_output(output, 'fully_labelled_cells', __file__)\n    print()\n","sub_path":"manifolds/etc/pitch/qq_show_cells_with_prominences_score/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":924,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"535448980","text":"from storeADill import storeAList\nfrom testR import jokeMeUp\nclass Person:\n  def __init__(self, name):\n    self.name = name\n#    self.age = age\n\njoke=Person(jokeMeUp)\n#print(\"--\",\"sample:\",joke.name(5),joke.age(4),\"--\")\njoke.name()\nstoreAList(joke)\n","sub_path":"multilingual/rockstar/newdawn/info_gather-v0/wizard/Module/simpleClass.py","file_name":"simpleClass.py","file_ext":"py","file_size_in_byte":249,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"158560643","text":"import sys\n\nscript, input_encoding, error = sys.argv\n\ndef main(language_file, encoding, errors):\n    line = language_file.readline()\n\n    if line:\n        print_line(line, encoding, errors)\n        return main(language_file, encoding, errors)\n\ndef print_line(line, encoding, errors):\n    next_lang = line.strip()\n    raw_bytes = next_lang.encode(encoding, errors=errors)\n    cooked_string = raw_bytes.decode(encoding, errors=errors)\n\n    print(raw_bytes, \"<===>\", cooked_string)\n\nlanguages = open(\"languages.txt\", encoding=\"utf-8\")\n\nmain(languages, input_encoding, error)\n\n#   Breaking It\n#   1. Find strings of text encoded in other encodings and place them in the ex23.py file to see how it\n#   breaks.\n#   > I copied a chinese word encoded in big5 encoding at the end of \"languages.txt\". I tried to decode/encode it\n#   in utf-8 and big5 encoding but neither \"recognize\" the word. But... using the script to solve the point 3,\n#   it's possible read the word.\n#   2. Find out what happens when you give an encoding that doesn’t exist.\n#   > Python recognize that the encoding does not exists, stop the program and print some errors.\n#   3. Extra challenging: Rewrite this using the b'' bytes instead of the UTF-8 strings, effectively reversing\n#   the script. bytes instead of the UTF-8 strings, effectively reversing\n#   the script.\n#   > Es23_BreakingIt3.py\n#   4. If you can do that, then you can also break these bytes by removing some to see what happens.\n#   How much do you need to remove to cause Python to break? How much can you remove to\n#   damage the string output but pass Python’s decoding system.\n#   5. Use what you learned from #4 to see if you can mangle the files. What errors do you get? How\n#   much damage can you cause and get the file past Python’s decoding system?","sub_path":"Compiti-Sistemi/Es23/Es23.py","file_name":"Es23.py","file_ext":"py","file_size_in_byte":1799,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"181595552","text":"import numpy as np\nimport pickle as pkl\nimport json\n\nfrom . import units\n\ndef read_xyz(filename, unit_conversion=units.ANGSTROM_TO_AU):\n    \"\"\"\n    Read xyz file of atomic coordinates.\n\n    Args:\n        filename: xyz file to read coordinates from.\n    \"\"\"\n    X = []\n    symbols = []\n    with open(filename, 'r') as fin:\n        lines = fin.readlines()\n        natom = int(lines[0])\n        for l in lines[2:2+natom]:\n            sp = l.split()\n            symbols.append(sp[0])\n            X.append([float(s) for s in sp[1:]])\n            #X.append((float(sp[1]), float(sp[2]), float(sp[3])))\n    X = np.array(X) * unit_conversion\n    return X, np.array(symbols)\n\ndef read_traj_xyz(filename, unit_conversion=units.ANGSTROM_TO_AU, spaced=False):\n    \"\"\"\n    Read xyz, vel trajectory file.\n\n    Args:\n        filename: xyz file to read coordinates from.\n        unit_conversion: multiplicative factor to change units of file\n        spaced: True if there is a space between frames, otherwise false\n\n    Returns:\n        X, symbols: list of frames of coordinates, and list of atomic symbols\n    \"\"\"\n    X = []\n    currX = []\n    symbols = []\n    natom = 0\n    frame = -1\n    space = int(spaced)\n\n    # Extract data about all frames\n    with open(filename, 'r') as fin:\n        natom = int(fin.readline())\n        fin.readline() # skip second line\n        for i in range(natom):\n            symbols.append(fin.readline().split()[0])\n\n    # Extract coordinates\n    with open(filename, 'r') as fin:\n        for i, line in enumerate(fin):\n            mod = i%(2+natom+space)\n            if mod==0:\n                if currX:\n                    X.append(np.array(currX) * unit_conversion)\n                currX = []\n                frame += 1\n            # We don't care about second lines of frame\n            elif mod==1:\n                pass\n            # Read in lines\n            elif mod>=2 and mod<2+natom:\n                sp = line.split()\n                currX.extend(list(map(float, sp[1:])))\n\n    # Get final frame\n    if currX:\n        X.append(np.array(currX) * unit_conversion)\n                \n    return X, symbols\n\ndef read_xyz_bagel_input(filename, unit_conversion=1.0):\n    \"\"\"\n    Read coordinates from BAGEL input json file.\n\n    Args:\n        filename: BAGEL input file to read coordinates from.\n        unit_conversion: conversion factor of units, default 1.0 (a.u.)\n    \"\"\"\n    with open(filename, 'r') as fin:\n        dct = json.load(fin)\n    S = [d['atom'] for d in dct['bagel'][0]['geometry']]\n    X = np.array([d['xyz'] for d in dct['bagel'][0]['geometry']])\n    return X, S\n\ndef read_xyz_bagel_output(filename, unit_conversion=1.0):\n    \"\"\"\n    Read coordinates from BAGEL output file, returns all frames found\n\n    Args:\n        filename: BAGEL output file to read coordinates from.\n        unit_conversion: conversion factor of units, default 1.0 (a.u.)\n\n    Returns:\n        Xs: list of (natom, 3) np.arrays of coordinates\n        S: list of atomic symbols\n    \"\"\"\n    Xs = []\n    S = []\n    ibegin = -2\n    nframes = 0\n    read = False\n    with open(filename, 'r') as fin:\n        for i, line in enumerate(fin):\n            if \"*** Geometry ***\" in line:\n                ibegin = i\n                read = True\n                nframes += 1\n                Xcurr = []\n            elif i == ibegin + 1:\n                continue\n            elif read and \"atom\" in line:\n                sp = line.split()\n                if nframes == 1:\n                    S.append(sp[3][1:-2])\n                Xcurr.append([sp[7][:-1], sp[8][:-1], sp[9][:-1]])\n            elif read:\n                Xs.append(np.array(Xcurr, dtype=np.float64))\n                read = False\n    return Xs, S\n\ndef save_xyz_filehandle(X, symbols, handle, text=\"\", format_str=\"% .11E\"):\n    \"\"\"\n    Write coordinates and symbols to given file handle.\n\n    Args:\n        X: coordinates (can also append velocities or whatever)\n        symbols: atomic symbols\n        handle: file handle to write to\n        text: Helper text in second line\n        format_str: formatting for coordinates to write\n    \"\"\"\n    Xsh = np.shape(X)\n    handle.write(\"%d\\n\" % Xsh[0])\n    handle.write(text)\n    handle.write(\"\\n\")\n    for i in range(Xsh[0]):\n        handle.write(symbols[i])\n        for j in range(Xsh[1]):\n            handle.write(\"\\t\")\n            handle.write(format_str % X[i, j])\n        handle.write(\"\\n\")\n\ndef save_xyz(X, symbols, filename, unit_conversion=1.0/units.ANGSTROM_TO_AU, text=\"\", format_str=\"% .11E\"):\n    \"\"\"\n    Write coordinates and symbols to given filename.\n\n    Args:\n        X: coordinates (can also append velocities or whatever)\n        symbols: atomic symbols\n        filename: file name to write to\n        unit_conversion: unit conversion of coordinates (defaults to converting AU to Angstrom\n        text: Helper text in second line\n        format_str: formatting for coordinates to write\n    \"\"\"\n    with open(filename, 'w') as fout:\n        Xc = np.array(X) * unit_conversion\n        save_xyz_filehandle(Xc, symbols, fout, text, format_str)\n\ndef save_xyz_bagel(X, symbols, filename, unit_conversion=1.0, format_str=\"% .11E\"):\n    \"\"\"\n    Write coordinates and symbols to given filename in BAGEL's json syntax in atomic units\n\n    Args:\n        X: coordinates (can also append velocities or whatever)\n        symbols: atomic symbols\n        filename: file name to write to\n        unit_conversion: unit conversion of coordinates (defaults to converting AU to Angstrom\n        text: Helper text in second line\n        format_str: formatting for coordinates to write\n    \"\"\"\n    # Construct dict\n    Xc = np.array(X) * unit_conversion\n    with open(filename, 'w') as fout:\n        fout.write('\\t{ \"geometry\" : [\\n')\n        for i, s in enumerate(symbols):\n            fout.write(('\\t\\t{ \"atom\": \"%s\" , \"xyz\": [ ' + format_str + ', ' + format_str + ', ' + format_str + ' ] },\\n') % (s, Xc[i, 0], Xc[i, 1], Xc[i, 2]))\n        fout.write('\\t] },')\n\ndef save_traj_xyz(Xs, symbols, filename, texts=None, unit_conversion=1.0/units.ANGSTROM_TO_AU, format_str=\"% .11E\"):\n    \"\"\"\n    Write coordinates and symbols to given filename for trajectory.\n\n    Args:\n        Xs: list of X coordinates to save\n        symbols: atomic symbols\n        filename: file name to write to\n        texts: lines of text to print between atom number and symbols/coordinates\n        unit_conversion: unit conversion of coordinates (defaults to converting AU to Angstrom\n        format_str: formatting for coordinates to write\n    \"\"\"\n    if texts is None:\n        texts = [\"Frame %d\"%i for i in range(len(Xs))] \n    with open(filename, 'w') as fout:\n        for i, X in enumerate(Xs):\n            Xc = X * unit_conversion\n            save_xyz_filehandle(Xc, symbols, fout, texts[i], format_str)\n\ndef save_traj_hist(history, filename, texts=None, unit_conversion=1.0/units.ANGSTROM_TO_AU, format_str=\"% .11E\"):\n    \"\"\"\n    Write coordinates and symbols to given filename for trajectory.\n\n    Args:\n        history: list of state dicts\n        filename: file name to write to\n        texts: lines of text to print between atom number and symbols/coordinates\n        unit_conversion: unit conversion of coordinates (defaults to converting AU to Angstrom\n        format_str: formatting for coordinates to write\n    \"\"\"\n    if history[0].get('symbols', None) is None:\n        raise ValueError(\"State key 'symbols' required to output xyz.\")\n    symbols = history[0]['symbols']\n    if texts is None:\n        texts = [\"Frame %d, time %f\" % (i, frame.get('simulation_time', 0.0)) for i, frame in enumerate(history)]\n    Xs = [frame['X'] for frame in history]\n    save_traj_xyz(Xs, symbols, filename, texts, unit_conversion, format_str)\n\ndef save_traj_hist_pkl(history, filename):\n    with open(filename, 'wb') as fout:\n        pkl.dump(history, fout, protocol=2)\n        \ndef load_traj_hist_pkl(filename):\n    with open(filename, 'rb') as fin:\n        history = pkl.load(fin)\n    return history\n","sub_path":"mdprop/io.py","file_name":"io.py","file_ext":"py","file_size_in_byte":7968,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"139168048","text":"import time\nimport numpy as np\nimport pandas as pd\n\n\nclass FeatureImportances(object):\n    \"\"\"Calculates an ex post feature importance. For each feature used in a model, the method 'fi_distribution_generate'\n    bootstraps a distribution for the difference between the metric value using the test data and the metric value using\n    the test data where the values corresponding to a given feature for each observation are drawn with replacement from\n    the distribution of values for this feature. The method 'positive_fi_list' returns the list of features and their\n    corresponding means such that the 0.05 quantiles (according to the distribution found in fi_distribution_generate)\n    are positive. This method return a pandas series sorted by the mean difference in 'lift.'\n\n    This methodology for calculating feature importances is convenient when a model is already baked.\n    \"\"\"\n\n    def __init__(self, model, X, y, metric, sample_size=1):\n        self.model = model\n\n        if 0 <= sample_size <= 1:\n            self.X = X.sample(frac=sample_size, replace=True)\n        else:\n            num = int(round(len(X) * sample_size))\n            self.X = X.sample(n=num, replace=True)\n        self.y = y.loc[self.X.index]\n\n        self.metric = metric\n\n        self.reduction = {}\n\n    def fi_distribution_generate(self, draws_num):\n        start = time.time()\n        for col in self.X.columns.tolist():\n            print(col, time.time() - start)\n            col_list = []\n            for i in range(draws_num):\n                X_rand = self.X.copy()\n                X_rand[col] = X_rand[col].sample(frac=1, replace=True).values  # resample with replacement\n                y_rand = self.model(X_rand.values)  # feature_names may not contain [, ] or <\n\n                col_list.append(self.metric(self.y, self.model(self.X.values)) - self.metric(self.y, y_rand))  # feature_names may not contain [, ] or <\n            self.reduction[col] = col_list\n        return pd.DataFrame(self.reduction)\n\n    def positive_fi_list(self):\n        df = pd.DataFrame(self.reduction)\n        quantiles = pd.DataFrame(df.quantile(q=0.05, axis=0))\n        positive_effect = quantiles.loc[quantiles[0.050] > 0].index.tolist()  # generate list of features whose .05 quantile from distribution generated in previous method is greater than 0\n        return df[positive_effect].mean().sort_values(ascending=False)\n","sub_path":"estimation/prediction/model_evaluation/feature_importances/feature_importances_ex_post.py","file_name":"feature_importances_ex_post.py","file_ext":"py","file_size_in_byte":2401,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"647650635","text":"# 1.编写函数，接收两个正整数作为参数，返回一个元组，其中第一个元素为最大公约数，第二个元素为最小公倍数。\ndef func(m,n):\n     if m>n:\n             m,n = n,m\n     p = m*n\n     while m!=0:\n        r = n%m\n        n = m\n        m = r\n     return(n,p//n)\n\nprint(func(17,65))\n# 结果：(1, 1105)\n\n# 2.编写函数，接受一个字符串作为参数，计算并打印传入字符串中数字，字母，空格，以及其它的个数\nimport re\ndef func(str11):\n    digital = 0\n    letter = 0\n    blank = 0\n    other = 0\n    for i in str11:\n        if re.match('[\\d]',i) != None:\n            digital += 1\n        elif re.match('[A-Za-z]',i) != None:\n            letter += 1\n        elif re.match('\\s',i) != None:\n            blank += 1\n        else:\n            other += 1\n    print('字符串中[数字]{}个、[字母]{}个、[空格]{}个 、[其他]{}个'.format(digital, letter, blank, other))\nstr = input(\"请输入一串包含空格、字母、数字、符合的字符：\")\nfunc(str)\n# 请输入一串包含空格、字母、数字、符合的字符：1720365_lhf ';o\n# 字符串中[数字]7个、[字母]4个、[空格]1个 、[其他]3个","sub_path":"homework7/Group1/hw7_1720365.py","file_name":"hw7_1720365.py","file_ext":"py","file_size_in_byte":1191,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"40574881","text":"# (C) Datadog, Inc. 2018\n# All rights reserved\n# Licensed under a 3-clause BSD style license (see LICENSE)\n\nimport pytest\n\nfrom datadog_checks.gitlab_runner import GitlabRunnerCheck\n\nfrom .common import ALLOWED_METRICS, CONFIG, CUSTOM_TAGS, GITLAB_RUNNER_TAGS\n\n\ndef assert_check(aggregator):\n    \"\"\"\n    Basic Test for gitlab integration.\n    \"\"\"\n    aggregator.assert_service_check(\n        GitlabRunnerCheck.MASTER_SERVICE_CHECK_NAME,\n        status=GitlabRunnerCheck.OK,\n        tags=GITLAB_RUNNER_TAGS + CUSTOM_TAGS,\n        count=2,\n    )\n    aggregator.assert_service_check(\n        GitlabRunnerCheck.PROMETHEUS_SERVICE_CHECK_NAME, status=GitlabRunnerCheck.OK, tags=CUSTOM_TAGS, count=2\n    )\n    for metric in ALLOWED_METRICS:\n        if metric.startswith('ci_runner'):\n            aggregator.assert_metric(\"gitlab_runner.{}\".format(metric))\n        else:\n            aggregator.assert_metric(\"gitlab_runner.{}\".format(metric), tags=CUSTOM_TAGS, count=2)\n\n\n@pytest.mark.usefixtures(\"dd_environment\")\ndef test_check(aggregator):\n    gitlab_runner = GitlabRunnerCheck('gitlab_runner', CONFIG['init_config'], instances=CONFIG['instances'])\n\n    gitlab_runner.check(CONFIG['instances'][0])\n    gitlab_runner.check(CONFIG['instances'][0])\n\n    assert_check(aggregator)\n\n\n@pytest.mark.e2e\ndef test_e2e(dd_agent_check):\n    aggregator = dd_agent_check(CONFIG, rate=True)\n\n    assert_check(aggregator)\n","sub_path":"gitlab_runner/tests/test_integration_e2e.py","file_name":"test_integration_e2e.py","file_ext":"py","file_size_in_byte":1401,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"177074971","text":"import json\n\nreviewOutputFile = open('reviewOutput_restaurant', 'w')\n\n\nrestaurantIDList = []\nwith open('yelp_academic_dataset_business.json', 'rU') as businessFile:\n    for line in businessFile:\n        line = line.strip()\n        businessJSON = json.loads(line)\n        if 'Restaurants' in businessJSON['categories']:\n            restaurantIDList.append(businessJSON['business_id'])\n\nwith open('yelp_academic_dataset_review.json', 'rU') as reviewFile:\n    for line in reviewFile:\n        line = line.strip()\n        reviewJSON = json.loads(line)\n        if reviewJSON['business_id'] in restaurantIDList:\n            reviewText = reviewJSON['text'].encode('utf-8').replace('\\n', ' ')\n            reviewOutputFile.write(reviewText)\n            reviewOutputFile.write('\\n')\n\nreviewOutputFile.close()\nbusinessFile.close()\nreviewFile.close()","sub_path":"parseReview.py","file_name":"parseReview.py","file_ext":"py","file_size_in_byte":837,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"305455291","text":"import torch\nimport torch.nn as nn\n\nclass CharRNN(nn.Module):\n    def __init__(self, input_size, hidden_size, num_layers, ctoi, learn_init=False, dropout=0.0):\n        super().__init__()\n        self.input_size = input_size\n        self.hidden_size = hidden_size\n        self.num_layers = num_layers\n        self.ctoi = ctoi\n        self.learn_init = learn_init\n        self.dropout = dropout\n\n        if self.learn_init:\n            h0 = torch.zeros(num_layers, hidden_size)\n            self.h0 = nn.Parameter(h0, requires_grad=True)\n\n        num_emb = len(ctoi)\n        # pad token becomse zero vector\n        self.emb = nn.Embedding(num_emb, input_size, padding_idx=ctoi['<pad>'])\n        self.rnn = nn.GRU(input_size, hidden_size,\n                          num_layers=num_layers, dropout=dropout)\n        self.hid_out = nn.Linear(hidden_size, input_size)\n        self.out_emb = nn.Linear(input_size, num_emb)\n        # tying output and input embedding\n        self.out_emb.weight = self.emb.weight\n\n    def forward(self, x, h=None):\n        seq, batch = x.shape\n\n        if h is None and self.learn_init:\n            h = self.h0\n            h = h.expand(self.num_layers, batch, self.hidden_size).contiguous()\n\n        x = self.emb(x)\n        h, last_h = self.rnn(x, h)\n        o = self.hid_out(h)\n        o = self.out_emb(o)\n        return o, last_h\n","sub_path":"torchkit/language_model/models/charrnn.py","file_name":"charrnn.py","file_ext":"py","file_size_in_byte":1354,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"468870305","text":"import os,re,jieba,random\r\n\r\n\r\ndef load_text():\r\n    with open(file='../data/红楼梦/text.txt',mode='r',encoding='utf-8') as f:\r\n        text_list = []\r\n        for line in f.readlines():\r\n\r\n            # 去掉中英文符号\r\n            line_data = re.sub(\"[\\s+\\.\\!\\/_,$%^*(+\\\"\\'\\-\\“]+|[`．：:”+——！，。？?、~@#￥%……&*（）]+|[A-Za-z0-9]+\", \"\", line)\r\n            line_data = line_data.strip()\r\n            if len(line_data)>0:\r\n                text_list.append(line_data)\r\n        return text_list\r\n\r\ndef read_stop_words():\r\n    data_list = []\r\n    with open(r'../data/stopword.txt', 'r', encoding='utf-8') as f:\r\n        for line in f.readlines():\r\n            data = line.strip()\r\n            data_list.append(data)\r\n    return data_list\r\n\r\ndef fasttext_dataProcess(readFile,writeFile):\r\n    \"\"\"\r\n    此API主要是将数据中的各种标点符号移除,包括中文的,英文的各种标点符号\r\n    :param readFile:\r\n    :param writeFile:\r\n    :return:\r\n    \"\"\"\r\n    with open(file=readFile,mode='r',encoding='utf-8') as f:\r\n        data = f.read()\r\n        data = ''.join(re.findall(r\"([a-zA-Z0-9\\u4E00-\\u9FA5\\s\\r\\n_]+)\", data))\r\n        with open(file=writeFile, mode='w', encoding='utf-8') as file_w:\r\n            file_w.write(data)\r\n    print('数据处理成功------')\r\n\r\ndef spilt_data(readFile,writePath, shuffle=False,ratio=0.2):\r\n    \"\"\"\r\n    此API主要是将数据移除标点符号类的噪音数据,并且随机将数据按行切分为训练集和验证集\r\n    :param readFile:\r\n    :param shuffle:\r\n    :param ratio:\r\n    :return:\r\n    \"\"\"\r\n    with open(file=readFile,mode='r',encoding='utf-8') as f:\r\n        data = f.read()\r\n        data = ''.join(re.findall(r\"([a-zA-Z0-9\\u4E00-\\u9FA5\\s\\r\\n_]+)\", data))\r\n        data_list=data.split(sep='\\n')\r\n\r\n        n_total = len(data_list)\r\n        if ratio>0.5:\r\n            ratio=1-ratio\r\n        offset = int(n_total * ratio)\r\n        if n_total == 0 or offset < 1:\r\n            return [], data_list\r\n        if shuffle:\r\n            random.shuffle(data_list)\r\n        data_tarin = '\\n'.join(data_list[offset:])\r\n        data_valid = '\\n'.join(data_list[:offset])\r\n\r\n        # 保存数据\r\n        train_data=os.path.join(writePath, 'train.data')\r\n        valid_data = os.path.join(writePath, 'valid.data')\r\n\r\n        with open(file=train_data, mode='w', encoding='utf-8') as file_w:\r\n            file_w.write(data_tarin)\r\n        with open(file=valid_data, mode='w', encoding='utf-8') as file_w:\r\n            file_w.write(data_valid)\r\n\r\n        print('数据切割成功......')\r\n\r\nif __name__ == '__main__':\r\n    readFile='../data/fasttext_cook/cooking/cooking.txt'\r\n    writePath='../data/fasttext_cook/cooking'\r\n\r\n    spilt_data(readFile=readFile,writePath=writePath,shuffle=True,ratio=0.25)","sub_path":"utils/data_process.py","file_name":"data_process.py","file_ext":"py","file_size_in_byte":2795,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"583487002","text":"# Please make sure scikit-learn is included the conda_dependencies.yml file.\n\nimport pickle\nimport sys\nimport os\n\nfrom sklearn.datasets import load_iris\nfrom sklearn.linear_model import LogisticRegression\nfrom azureml.sdk import data_collector\n\n# initialize the logger\nrun_logger = data_collector.current_run() \n\n# create the outputs folder\nos.makedirs('./outputs', exist_ok=True)\n\nprint ('Python version: {}'.format(sys.version))\nprint ()\n\n# load Iris dataset\niris = load_iris()\nprint ('Iris dataset shape: {}'.format(iris.data.shape))\n\n# load features and labels\nX, Y = iris.data, iris.target\n\n# change regularization rate and you will likely get a different accuracy.\nreg = 0.01\n# load regularization rate from argument if present\nif len(sys.argv) > 1:\n    reg = float(sys.argv[1])\n\nprint(\"Regularization rate is {}\".format(reg))\n\n# log the regularization rate\nrun_logger.log(\"Regularization Rate\", reg)\n\n# train a logistic regression model\nclf1 = LogisticRegression(C=1/reg).fit(X, Y)\nprint (clf1)\n\naccuracy = clf1.score(X, Y)\nprint (\"Accuracy is {}\".format(accuracy))\n\n# log accuracy\nrun_logger.log(\"Accuracy\", accuracy)\n\n# serialize the model on disk in the special 'outputs' folder\nprint (\"Export the model to model.pkl\")\nf = open('./outputs/model.pkl', 'wb')\npickle.dump(clf1, f)\nf.close()\n\n# load the model back from the 'outputs' folder into memory\nprint(\"Import the model from model.pkl\")\nf2 = open('./outputs/model.pkl', 'rb')\nclf2 = pickle.load(f2)\n\n# predict a new sample\nX_new = [[3.0, 3.6, 1.3, 0.25]]\nprint ('New sample: {}'.format(X_new))\npred = clf2.predict(X_new)\nprint('Predicted class is {}'.format(pred))\n","sub_path":"iris_sklearn.py","file_name":"iris_sklearn.py","file_ext":"py","file_size_in_byte":1628,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"332355305","text":"import os\nimport json\nimport requests\nfrom tqdm import tqdm\nfrom requests.adapters import HTTPAdapter\nfrom requests.packages.urllib3.util.retry import Retry\n\nfrom nanonets.model import Model\nfrom nanonets.utils import *\n\n\nclass OCR(Model):\n\n\tdef __init__(self, api_key, categories, model_id=None):\n\t\tsuper().__init__(api_key=api_key, model_type='ObjectDetection')\n\t\tif not model_id:\n\t\t\tself.create_model(categories)\n\t\telse:\n\t\t\tself.api_key = api_key\n\t\t\tself.categories = categories\n\t\t\tself.model_id = model_id\n\t\n\tdef create_model(self, categories):\n\t\t\"\"\"\n\t\tfunction to create model given the categories and model type\n\n\t\tParameters\n\t\t----------\n\t\tcategories: List[str]\n\t\t    List of categories for our model\n\n\t\tReturns\n\t\t-------\n\t\tserver response for the request for to create model\n\t\t\"\"\"\n\n\t\turl = self.host + self.model_type + '/Model/' \n\t\tpayload = json.dumps({\"categories\": categories, \n\t\t\t\t\t\t\t  \"model_type\": \"ocr\"})\n\t\theaders = {'Content-Type': \"application/json\"}\n\t\tresponse =  requests.request(\"POST\",\n\t\t\t\t\t     url, \n\t\t\t\t\t     headers=headers, \n\t\t\t\t\t     data=payload,\n\t\t\t\t\t     auth=requests.auth.HTTPBasicAuth(self.api_key, ''))\n\t\tself.model_id = response.json()[\"model_id\"]\n\t\tprint(\"Your Model ID is: \", self.model_id)\n\n\tdef upload_image_file(self, img_path, annotation_path):\n\t\t\"\"\"\n\t\tfunction to upload a single file and it's annotation for training \n\n\t\tParameters\n\t\t----------\n\t\timg_path: str\n\t\t    path to the image we want to upload\n\n\t\tannotation_path: str\n\t\t\tpath to the annotations for the same image\n\n\t\tReturns\n\t\t-------\n\t\tserver response for the request for uploading image file and its annotation\n\t\t\"\"\"\n\t\tprocessed = [process_annotations(annotation_path)]\n\t\tannotation = json.dumps(processed)\n\t\treturn self._upload_image_file(img_path, annotation)\n\n\tdef upload_image_files(self, training_dict, batch_size=20):\n\t\t\"\"\"\n\t\tfunction to upload multiple files and their annotations \n\n\t\tParameters\n\t\t----------\n\t\ttraining_dict: Dict[str: str]\n\t\t    Dict with keys as image file paths and values as the corresponding annotation \n\t\t    paths. Annotations can be XML or JSON\n\n\t\tbatch_size (optional): int\n\t\t\tnumber of images to be uploaded per API request\n\n\t\tReturns\n\t\t-------\n\t\ta list of responses for each batch if images uploaded\n\t\t\"\"\"\n\n\t\turl = self.host + self.model_type + '/Model/' + self.model_id + '/UploadFile/'\n\t\tfiles = list(training_dict.keys())\n\t\tbatch_nb = 1\n\t\tif len(files)%batch_size == 0:\n\t\t\ttotal_batches = int(len(files)/batch_size)\n\t\telse:\n\t\t\ttotal_batches = int(int(len(files)/batch_size) + 1)\n\t\tresponses = []\n\t\twhile len(files) > 0:\n\t\t\tbatch_files = []\n\t\t\tbatch_data = []\n\t\t\tbatch, files = files[:batch_size], files[batch_size:]\n\t\t\tprint('Batch {}/{} of images'.format(batch_nb, total_batches))\t\t\t\t\n\t\t\tfor file in tqdm(batch):\n\t\t\t\timg_name = file.split('/')[-1]\n\t\t\t\tbatch_data.append(process_annotations(img_name, training_dict[file]))\n\t\t\t\tbatch_files.append(('file', (img_name, open(file, 'rb'), 'image/jpeg')))\n\t\t\tbatch_files.append(('data', ('', json.dumps(batch_data))))\n\t\t\tresponse = requests.post(url, \n\t\t\t\t\t\t auth= requests.auth.HTTPBasicAuth(self.api_key, ''), \n\t\t\t\t\t\t files=batch_files)\n\t\t\tresponses.append(response)\n\t\t\tbatch_nb+=1\n\t\treturn responses\n\n\t### UPLOAD IMAGES AND ANNOTATE THEM ON THE PLATFORM.\n\tdef upload_image_url(self, image_url, annotation_path):\n\t\tpass\n\n\t### UPLOAD IMAGES AND ANNOTATE THEM ON THE PLATFORM.\n\tdef upload_image_urls(self, training_dict):\n\t\tpass\n\n\t### FIGURE OUT ADDING URLS\n\tdef train(self, training_dict, batch_size=20):\n\t\t\"\"\"\n\t\tfunction to upload a image files and annotations and initialize the training \n\n\t\tParameters\n\t\t----------\n\t\ttraining_dict: Dict[str: str]\n\t\t    Dict with keys as image file paths and values as the corresponding annotation \n\t\t    paths. Annotations can be XML or JSON\n\n\t\tbatch_size (optional): int\n\t\t\tnumber of images to be uploaded per API request\n\n\t\tReturns\n\t\t-------\n\t\tserver response for the request for uploading files and annotation and initializing training\n\t\t\"\"\"\n\n\t\tprint(\"Uploading images ...\")\n\t\tself.upload_image_files(training_dict, batch_size=batch_size)\n\t\tprint(\"Starting training ...\")\n\t\treturn self._train()\n\n\tdef predict_for_file(self, file_path):\n\t\t\"\"\"\n\t\tfunction to get prediction for a single image file\n\n\t\tParameters\n\t\t----------\n\t\tfile_path: str \n\t\t\tpath of the image you want to get predictions for\n\n\t\tReturns\n\t\t-------\n\t\tDict with filepath as key and prediction as value \n\t\t\"\"\"\n\n\t\turl = self.host + 'OCR/Model/' + self.model_id + '/LabelFile/'\n\t\tparams = {'file': open(file_path, 'rb')}\n\t\tresponse = requests.post(url,\n\t\t\t\t\tauth=requests.auth.HTTPBasicAuth(self.api_key, ''),\n\t\t\t\t\tfiles=params) \n\t\tresult = read_prediction_response([response], 'OCR')\n\t\treturn result\n\n\tdef predict_for_url(self, image_url):\n\t\t\"\"\"\n\t\tfunction to get prediction for a single image url\n\n\t\tParameters\n\t\t----------\n\t\timage_url: str\n\t\t\turl of the image you want to get predictions for\n\n\t\tReturns\n\t\t-------\n\t\tDict with url as key and prediction as value \n\t\t\"\"\"\n\n\t\tresponse = self._predict_urls([image_url])\n\t\tresult = read_prediction_response([response], 'OCR')\n\t\treturn result\n\n\tdef predict_for_files(self, files, batch_size=20):\n\t\t\"\"\"\n\t\tfunction to get predictions for multiple files \n\n\t\tParameters\n\t\t----------\n\t\tfiles: List[str]\n\t\t    List of image file paths\n\n\t\tbatch_size (optional): int\n\t\t\tnumber of images to be uploaded per API request\n\n\t\tReturns\n\t\t-------\n\t\tDict with file names as keys and the predictions as values\n\t\t\"\"\"\n\n\t\turl = self.host + 'OCR/Model/' + self.model_id + '/LabelFile/'\n\t\tretries = Retry(total=5, backoff_factor=1, status_forcelist=[ 502, 503, 504 ])\n\t\tadapter = HTTPAdapter(max_retries=retries)\n\n\t\tsession = requests.Session()\n\t\tsession.mount(url, adapter)\n\n\t\tresponses = []\n\t\tbatch_nb = 1\n\t\tif len(files)%batch_size == 0:\n\t\t\ttotal_batches = int(len(files)/batch_size)\n\t\telse:\n\t\t\ttotal_batches = int(int(len(files)/batch_size) + 1)\n\t\twhile len(files) > 0:\n\t\t\tbatch, files = files[:batch_size], files[batch_size:]\n\t\t\tbatch_files = []\n\t\t\tbatch_data = []\n\t\t\tprint('Batch {}/{} of images'.format(batch_nb, total_batches))\t\t\t\t\n\t\t\tfor file in tqdm(batch):\n\t\t\t\timg_name = file.split('/')[-1]\n\t\t\t\tbatch_data.append({'filename': img_name})\n\t\t\t\tbatch_files.append(('file', (img_name, open(file, 'rb'), 'image/jpeg')))\n\t\t\tbatch_files.append(('data', ('', json.dumps(batch_data))))\n\t\t\tresponse = session.post(url, \n\t\t\t\t\t\tauth=requests.auth.HTTPBasicAuth(self.api_key, ''), \n\t\t\t\t\t\tfiles=batch_files)\n\t\t\tresponses.append(response)\n\t\t\tbatch_nb+=1\n\t\tresult = read_prediction_response(responses, 'OCR')\n\t\treturn result\n\n\tdef predict_for_urls(self, urls):\n\t\t\"\"\"\n\t\tfunction to get prediction for several images using their urls \n\n\t\tParameters\n\t\t----------\n\t\turls: List[str]\n\t\t\tlist of urls of the images you want to get predictions for\n\n\t\tReturns\n\t\t-------\n\t\tDict with urls as keys and predictions as values \n\t\t\"\"\"\n\n\t\tresponse = self._predict_urls(urls)\n\t\tresult = read_prediction_response([response], 'OCR')\n\t\treturn result\t\t\t\n","sub_path":"nanonets/ocr.py","file_name":"ocr.py","file_ext":"py","file_size_in_byte":6938,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"566618116","text":"def disemvowel(string): # removes vowels from a string\n    vowels = \"AEIOUYaeiouy\"\n    result = \"\"\n    for c in string:\n        if c not in vowels:\n            result += c\n        print(result)\n    return result\ndef Hasbs(string):   # if string has a 'b' or 'B' returns true, all else false\n    for c in string:\n        if c == \"b\" or c == \"B\":\n            return True\n    return False\n","sub_path":"vowels.py","file_name":"vowels.py","file_ext":"py","file_size_in_byte":386,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"64364792","text":"from urllib import urlopen\n\nurl = 'http://pbs.twimg.com/media/CaEpQQBUEAEB8rG.jpg'\nimg = urlopen(url).read()\n\nsplit_path = url.split('/')\nf_name = split_path.pop()\n\nf = open('./img/' + f_name, 'wb')\nf.write(img)\nf.close()\n","sub_path":"url.py","file_name":"url.py","file_ext":"py","file_size_in_byte":222,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"122261220","text":"# Copyright (c) 2010 OpenStack Foundation\n# Copyright 2010 United States Government as represented by the\n# Administrator of the National Aeronautics and Space Administration.\n# All Rights Reserved.\n#\n#    Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n#    not use this file except in compliance with the License. You may obtain\n#    a copy of the License at\n#\n#         http://www.apache.org/licenses/LICENSE-2.0\n#\n#    Unless required by applicable law or agreed to in writing, software\n#    distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n#    WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n#    License for the specific language governing permissions and limitations\n#    under the License.\n\n\"\"\"\nChance (Random) Scheduler implementation\n\"\"\"\n\nimport random\n\nfrom oslo_log import log as logging\n\nfrom nova.compute import rpcapi as compute_rpcapi\nimport nova.conf\nfrom nova import exception\nfrom nova.i18n import _\nfrom nova import objects\nfrom nova.scheduler import driver\n\nCONF = nova.conf.CONF\nLOG = logging.getLogger(__name__)\n\n\nclass ChanceScheduler(driver.Scheduler):\n    \"\"\"Implements Scheduler as a random node selector.\"\"\"\n\n    USES_ALLOCATION_CANDIDATES = False\n\n    def __init__(self, *args, **kwargs):\n        super(ChanceScheduler, self).__init__(*args, **kwargs)\n        LOG.warning('ChanceScheduler is deprecated in Pike and will be '\n                    'removed in a subsequent release.')\n\n    def _filter_hosts(self, hosts, spec_obj):\n        \"\"\"Filter a list of hosts based on RequestSpec.\"\"\"\n\n        ignore_hosts = spec_obj.ignore_hosts or []\n        hosts = [host for host in hosts if host not in ignore_hosts]\n        return hosts\n\n    def _schedule(self, context, topic, spec_obj, instance_uuids,\n            return_alternates=False):\n        \"\"\"Picks a host that is up at random.\"\"\"\n\n        elevated = context.elevated()\n        hosts = self.hosts_up(elevated, topic)\n        if not hosts:\n            msg = _(\"Is the appropriate service running?\")\n            raise exception.NoValidHost(reason=msg)\n\n        hosts = self._filter_hosts(hosts, spec_obj)\n        if not hosts:\n            msg = _(\"Could not find another compute\")\n            raise exception.NoValidHost(reason=msg)\n\n        # Note that we don't claim in the chance scheduler\n        num_instances = len(instance_uuids)\n        # If possible, we'd like to return distinct hosts for each instance.\n        # But when there are fewer available hosts than requested instances, we\n        # will need to return some duplicates.\n        if len(hosts) >= num_instances:\n            selected_hosts = random.sample(hosts, num_instances)\n        else:\n            selected_hosts = [random.choice(hosts)\n                    for i in range(num_instances)]\n\n        # This is the overall list of values to be returned. There will be one\n        # item per instance, and that item will be a list of Selection objects\n        # representing the selected host and zero or more alternates.\n        # NOTE(edleafe): in a multi-cell environment, this can return\n        # alternates from different cells. When support for multiple cells is\n        # implemented in select_destinations, this will have to be updated to\n        # restrict alternates to come from the same cell.\n        selections_to_return = []\n\n        # We can't return dupes as alternates, since alternates are used when\n        # building to the selected host fails.\n        if return_alternates:\n            alts_per_instance = min(len(hosts), CONF.scheduler.max_attempts)\n        else:\n            alts_per_instance = 0\n        for sel_host in selected_hosts:\n            selection = objects.Selection.from_host_state(sel_host)\n            sel_plus_alts = [selection]\n            while len(sel_plus_alts) < alts_per_instance:\n                candidate = random.choice(hosts)\n                if (candidate not in sel_plus_alts) and (\n                        candidate not in selected_hosts):\n                    # We don't want to include a selected host as an alternate,\n                    # as it will have a high likelihood of not having enough\n                    # resources left after it has an instance built on it.\n                    alt_select = objects.Selection.from_host_state(candidate)\n                    sel_plus_alts.append(alt_select)\n            selections_to_return.append(sel_plus_alts)\n        return selections_to_return\n\n    def select_destinations(self, context, spec_obj, instance_uuids,\n            alloc_reqs_by_rp_uuid, provider_summaries,\n            allocation_request_version=None, return_alternates=False):\n        \"\"\"Selects random destinations. Returns a list of list of Selection\n        objects.\n        \"\"\"\n        num_instances = spec_obj.num_instances\n        # TODO(danms): This needs to be extended to support multiple cells\n        # and limiting the destination scope to a single requested cell\n        host_selections = self._schedule(context, compute_rpcapi.RPC_TOPIC,\n                spec_obj, instance_uuids)\n        if len(host_selections) < num_instances:\n            reason = _('There are not enough hosts available.')\n            raise exception.NoValidHost(reason=reason)\n        return host_selections\n","sub_path":"nova/scheduler/chance.py","file_name":"chance.py","file_ext":"py","file_size_in_byte":5281,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"119760207","text":"import magma as m\nfrom magma.backend import verilog\nimport mantle.coreir\n\n\ndef get_mantle_define_circuit(name):\n    return getattr(mantle.coreir, \"Define{}\".format(name))\n\n\ndef bits_binop_test_factory(name, op):\n    def _test():\n        circ = get_mantle_define_circuit(name)(1)\n        assert verilog.compile(circ) == \"\"\"\nmodule {name}1 (input  I0, input  I1, output  O);\ncoreir_{op} #(.WIDTH=1) {name}1(.in0(I0), .in1(I1), .out(O));\nendmodule\n\n\"\"\".format(name=name, op=op).lstrip()\n        circ = get_mantle_define_circuit(name)(2)\n        assert verilog.compile(circ) == \"\"\"\nmodule {name}2 (input [1:0] I0, input [1:0] I1, output [1:0] O);\ncoreir_{op} #(.WIDTH=2) {name}2(.in0(I0), .in1(I1), .out(O));\nendmodule\n\n\"\"\".format(name=name, op=op).lstrip()\n    return _test\n\n\ntest_and = bits_binop_test_factory(\"And\", \"and\")\ntest_or = bits_binop_test_factory(\"Or\", \"or\")\ntest_xor = bits_binop_test_factory(\"Xor\", \"xor\")\n\n\ndef test_invert():\n    Invert1 = mantle.coreir.DefineInvert(1)\n    assert verilog.compile(Invert1) == \"\"\"\nmodule Invert1 (input  I, output  O);\ncoreir_not #(.WIDTH=1) Invert1(.in(I), .out(O));\nendmodule\n\n\"\"\".lstrip()\n    Invert2 = mantle.coreir.DefineInvert(2)\n    assert verilog.compile(Invert2) == \"\"\"\nmodule Invert2 (input [1:0] I, output [1:0] O);\ncoreir_not #(.WIDTH=2) Invert2(.in(I), .out(O));\nendmodule\n\n\"\"\".lstrip()\n\n\ndef shift_factory(direction, op, dynamic_op):\n    def _test():\n        circ = get_mantle_define_circuit(\"Shift\" + direction)(2, 1)\n        assert verilog.compile(circ) == \"\"\"\nmodule Shift{direction}2_1 (input [1:0] I, output [1:0] O);\ncoreir_{} #(.WIDTH=2, .SHIFTBITS=1) Shift{direction}2_1(.in(I), .out(O));\nendmodule\n\n\"\"\".format(op, direction=direction).lstrip()\n        circ = get_mantle_define_circuit(\"Shift\" + direction)(4, 2)\n        assert verilog.compile(circ) == \"\"\"\nmodule Shift{direction}4_2 (input [3:0] I, output [3:0] O);\ncoreir_{} #(.WIDTH=4, .SHIFTBITS=2) Shift{direction}4_2(.in(I), .out(O));\nendmodule\n\n\"\"\".format(op, direction=direction).lstrip()\n\n    def _test_dynamic():\n        circ = get_mantle_define_circuit(\"DynamicShift\" + direction)(2)\n        assert verilog.compile(circ) == \"\"\"\nmodule DynamicShift{direction}2 (input [1:0] I0, input [1:0] I1, output [1:0] O);\ncoreir_{} #(.WIDTH=2) DynamicShift{direction}2(.in0(I0), .in1(I1), .out(O));\nendmodule\n\n\"\"\".format(dynamic_op, direction=direction).lstrip()  # NOQA\n\n    return _test, _test_dynamic\n\n\ntest_shift_left, test_dynamic_shift_left = shift_factory(\"Left\", \"shl\", \"dshl\")\ntest_shift_right, test_dynamic_shift_right = shift_factory(\"Right\", \"shr\", \"dlshr\")  # NOQA\n\n\ndef binop_test_factory(name, op, signed=False):\n    def _test():\n        circ = get_mantle_define_circuit(name)(1)\n        assert verilog.compile(circ) == \"\"\"\nmodule {name}1 (input [0:0] I0, input [0:0] I1, output [0:0] O);\ncoreir_{op} #(.WIDTH=1) {name}1(.in0(I0), .in1(I1), .out(O));\nendmodule\n\n\"\"\".format(name=name, op=op).lstrip()  # NOQA\n        circ = get_mantle_define_circuit(name)(2)\n        assert verilog.compile(circ) == \"\"\"\nmodule {name}2 (input [1:0] I0, input [1:0] I1, output [1:0] O);\ncoreir_{op} #(.WIDTH=2) {name}2(.in0(I0), .in1(I1), .out(O));\nendmodule\n\n\"\"\".format(name=name, op=op).lstrip()  # NOQA\n    return _test\n\n\ntest_unsigned_add = binop_test_factory(\"UnsignedAdd\", \"add\")\ntest_unsigned_sub = binop_test_factory(\"UnsignedSub\", \"sub\")\ntest_unsigned_mul = binop_test_factory(\"UnsignedMul\", \"mul\")\ntest_unsigned_div = binop_test_factory(\"UnsignedDiv\", \"div\")\n\n# test_signed_add = binop_test_factory(\"SignedAdd\", \"+\", signed=True)\n# test_signed_sub = binop_test_factory(\"SignedSub\", \"-\", signed=True)\n# test_signed_mul = binop_test_factory(\"SignedMul\", \"*\", signed=True)\n# test_signed_div = binop_test_factory(\"SignedDiv\", \"/\", signed=True)\n\n\ndef comparison_test_factory(name, op, signed=False):\n    def _test():\n        circ = get_mantle_define_circuit(name)(1)\n        assert verilog.compile(circ) == \"\"\"\nmodule {name}1 (input [0:0] I0, input [0:0] I1, output  O);\ncoreir_{op} #(.WIDTH=1) {name}1(.in0(I0), .in1(I1), .out(O));\nendmodule\n\n\"\"\".format(name=name, op=op).lstrip()\n        circ = get_mantle_define_circuit(name)(2)\n        assert verilog.compile(circ) == \"\"\"\nmodule {name}2 (input [1:0] I0, input [1:0] I1, output  O);\ncoreir_{op} #(.WIDTH=2) {name}2(.in0(I0), .in1(I1), .out(O));\nendmodule\n\n\"\"\".format(name=name, op=op).lstrip()\n    return _test\n\n\ntest_unsigned_lt = comparison_test_factory(\"UnsignedLt\", \"ult\")\ntest_unsigned_lte = comparison_test_factory(\"UnsignedLtE\", \"ule\")\ntest_unsigned_gt = comparison_test_factory(\"UnsignedGt\", \"ugt\")\ntest_unsigned_gte = comparison_test_factory(\"UnsignedGtE\", \"uge\")\n\n# test_signed_lt = comparison_test_factory(\"SignedLt\",   \"<\",  signed=True)\n# test_signed_lte = comparison_test_factory(\"SignedLtE\", \"<=\", signed=True)\n# test_signed_gt = comparison_test_factory(\"SignedGt\",   \">\",  signed=True)\n# test_signed_gte = comparison_test_factory(\"SignedGtE\", \">=\", signed=True)\n","sub_path":"tests/bak.test_coreir.py","file_name":"bak.test_coreir.py","file_ext":"py","file_size_in_byte":4946,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"301755097","text":"\"\"\"\nUnit and regression test for the kaldo package.\n\"\"\"\n\n# Import package, test suite, and other packages as needed\nfrom kaldo.forceconstants import ForceConstants\nimport numpy as np\nfrom kaldo.phonons import Phonons\nfrom kaldo.conductivity import Conductivity\nimport pytest\n\n\n@pytest.yield_fixture(scope=\"session\")\ndef phonons():\n    print (\"Preparing phonons object.\")\n    forceconstants = ForceConstants.from_folder(folder='kaldo/tests/si-crystal',\n                                                     supercell=[3, 3, 3],\n                                                     format='eskm')\n    phonons = Phonons(forceconstants=forceconstants,\n                      kpts=[5, 5, 5],\n                      is_classic=False,\n                      temperature=300,\n                      storage='memory')\n    return phonons\n\n\ndef test_sc_conductivity(phonons):\n    cond = np.abs(np.mean(Conductivity(phonons=phonons, method='sc', max_n_iterations=71, storage='memory').conductivity\n                          .sum(axis=0).diagonal()))\n    np.testing.assert_approx_equal(cond, 255, significant=3)\n\n\ndef test_qhgk_conductivity(phonons):\n    cond = Conductivity(phonons=phonons, method='qhgk', storage='memory').conductivity.sum(axis=0)\n    cond = np.abs(np.mean(cond.diagonal()))\n    np.testing.assert_approx_equal(cond, 230, significant=3)\n\n\ndef test_rta_conductivity(phonons):\n    cond = np.abs(np.mean(Conductivity(phonons=phonons, method='rta', storage='memory').conductivity.sum(axis=0).diagonal()))\n    np.testing.assert_approx_equal(cond, 226, significant=3)\n\n\ndef test_inverse_conductivity(phonons):\n    cond = np.abs(np.mean(Conductivity(phonons=phonons, method='inverse', storage='memory').conductivity.sum(axis=0).diagonal()))\n    np.testing.assert_approx_equal(cond, 256, significant=3)","sub_path":"kaldo/tests/test_crystal.py","file_name":"test_crystal.py","file_ext":"py","file_size_in_byte":1794,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"576729487","text":"#!/usr/bin/env python\n\nimport rospy\nfrom math import fabs\nfrom dynamixel_msgs.msg import JointState\nfrom std_msgs.msg import Float64\n\nclass DXL_CONTROL():\n    def __init__(self,node_name,control_joint,max_load):\n        self.pub_dist = '/' + control_joint + '/command'\n        self.sub_dist = '/' + control_joint + '/state'\n        self.node_name = node_name\n        self.pub = rospy.Publisher(self.pub_dist,Float64,queue_size=10)\n        self.goal_pos = 0\n        self.max_load = max_load\n        self._error = 100\n        self._is_moving = True\n        rospy.init_node(self.node_name, anonymous=True)\n        rospy.Subscriber(self.sub_dist, JointState, self.transform_callback)\n\n    def transform_callback(self,data):\n        rospy.loginfo(rospy.get_name() + ': Target angle {0}'.format(self.goal_pos))\n        rospy.loginfo(rospy.get_name() + ': Goal         {0} [rad]'.format(data.goal_pos))\n        rospy.loginfo(rospy.get_name() + ': position     {0} [rad]'.format(data.current_pos))\n        rospy.loginfo(rospy.get_name() + ': velocity     {0} [rad/s]'.format(data.velocity))\n        rospy.loginfo(rospy.get_name() + ': Load         {0}'.format(data.load))\n        rospy.loginfo(rospy.get_name() + ': Moving       {0}'.format(data.is_moving))\n        rospy.loginfo(rospy.get_name() + ': Error        {0}'.format(data.error))\n\n        self._error = data.error\n        self._is_moving = data.is_moving\n\n        # If the motor has reached its limit, publish a new command.\n        # if fabs(data.load) > self.max_load:\n            # self.goal_pos = data.current_pos\n            # self.pub.publish(Float64(self.goal_pos))\n\n        #     str = \"goal pos was changed: Time: {0} Moving motor to {1}\" .format(rospy.get_time(), self.goal_pos)\n        #     rospy.loginfo(str)\n\n        # str = \"reached to GOAL: Time: {0} Moving motor to {1}\" .format(rospy.get_time(), self.goal_pos)\n        # rospy.loginfo(str)\n            \n\n    def open(self):\n        self.goal_pos = 1.5\n        # Initial movement.\n        self.pub.publish(Float64(self.goal_pos))\n        while (fabs(self._error) > 0.003):\n            print('error--', self._error)\n            rospy.sleep(0.8)\n            if(not self._is_moving):\n                break\n\n    def close(self):\n        self.goal_pos = -1.5\n        # Initial movement.\n        self.pub.publish(Float64(self.goal_pos))\n        while (fabs(self._error) > 0.003):\n            print('error--', self._error)\n            rospy.sleep(0.8)\n            if(not self._is_moving):\n                break\n\n\nif __name__ == '__main__':\n    try:\n        eef_joint = DXL_CONTROL(control_joint='joint6_controller',max_load=0.006)\n        eef_joint.open()\n        eef_joint.close()\n\n    except rospy.ROSInterruptException:\n        pass","sub_path":"dynamixel_motor/conbe/scripts/not_currently_used/dxl_move_old.py","file_name":"dxl_move_old.py","file_ext":"py","file_size_in_byte":2747,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"181241670","text":"from django.shortcuts import render, redirect\nfrom django.http import HttpResponse\nfrom .models import Simple, Original, Vote, Simplify, Comment\nfrom .forms import OriginForm, SimpForm, TestForm, CommentForm, SimplifyForm, VoteForm\nfrom django.core.urlresolvers import reverse\nfrom translate import functions, utils\n\n\ndef index(request):\n    print(\"THIS IS request\")\n    print(request)\n    print(\"THIS IS request.GET\")\n    print(request.GET)\n    #if request get contains \"original_text\" redirect\n    if 'original_text' in request.GET and request.GET[\"original_text\"]:\n        hard_text = request.GET.get(\"original_text\")\n        return redirect('result', hard_text)\n    else:\n        form = TestForm()\n        return render(request, 'index.html', {'form' : form})\n\n\ndef result(request, hard_text):\n    form = TestForm(initial={'original_text': hard_text})\n    formc = CommentForm()\n\n    #simple is simplified sentence\n    #process is: original word : top 20 words(not handled, directly from model.most_similar())\n    simple, wordnet_result, bluemix_concept, w2v_result_list, complex_word_list = utils.simplify(hard_text)\n\n\n    if 'original_text' in request.GET and request.GET[\"original_text\"]:\n        hard_text = request.GET.get(\"original_text\")\n        return redirect('result', hard_text)\n\n    elif request.method == \"POST\":\n        if 'comment' in request.POST:\n            comment = request.POST.get(\"comment\")\n            comment_form = CommentForm(request.POST)\n            history_form = SimplifyForm()\n            if comment_form.is_valid():\n                try:\n                    history = Simplify.objects.get(input=hard_text, output=simple)\n                except Simplify.DoesNotExist:\n                    history =history_form.save(commit=False)\n                    history.input = hard_text\n                    history.output=simple\n                    history.save()\n                    com = comment_form.save(commit=False)\n                    com.history = history\n                    com.comment = comment\n                    com.save()\n                    comments = Comment.objects.filter(history=history.id)\n                    return render(request, 'result.html', {'simple': simple, 'form' : form, 'formc':formc, 'com':comments, 'wordnet_result':wordnet_result, 'bluemix_concept':bluemix_concept, 'w2v_result_list': w2v_result_list, 'complex_word_list': complex_word_list})\n            else:\n                try:\n                    history = Simplify.objects.get(input=hard_text, output=simple)\n                except Simplify.DoesNotExist:\n                    return render(request, 'result.html', {'simple': simple, 'form' : form, 'formc':formc, 'wordnet_result':wordnet_result, 'bluemix_concept':bluemix_concept, 'w2v_result_list': w2v_result_list, 'complex_word_list': complex_word_list})\n                comments = Comment.objects.filter(history=history.id)\n                return render(request, 'result.html', {'simple': simple, 'form' : form, 'formc':formc, 'com':comments, 'wordnet_result':wordnet_result, 'bluemix_concept':bluemix_concept, 'w2v_result_list': w2v_result_list, 'complex_word_list': complex_word_list})\n    else:\n        try:\n            history = Simplify.objects.get(input=hard_text, output=simple)\n        except Simplify.DoesNotExist:\n            return render(request, 'result.html', {'simple': simple, 'form' : form, 'formc':formc, 'wordnet_result':wordnet_result, 'bluemix_concept':bluemix_concept, 'w2v_result_list': w2v_result_list, 'complex_word_list': complex_word_list})\n        comments = Comment.objects.filter(history=history.id)\n        return render(request, 'result.html', {'simple': simple, 'form' : form, 'formc':formc, 'com':comments, 'wordnet_result':wordnet_result, 'bluemix_concept':bluemix_concept, 'w2v_result_list': w2v_result_list, 'complex_word_list': complex_word_list})\n\n\ndef contribute(request):\n    origins = Original.objects.all()\n    return render(request, 'contribute.html', {'origins' : origins })\n\n\ndef add_simp(request, pk):\n    orig = Original.objects.get(pk=pk)\n    form = SimpForm()\n    results = Simple.objects.filter(hard=pk)\n    if request.method ==\"POST\":\n        if 'simple_text' in request.POST and request.POST[\"simple_text\"]:\n            simple_text = request.POST.get(\"simple_text\")\n            save_form = SimpForm(request.POST)\n            if save_form.is_valid():\n                simp = save_form.save(commit=False)\n                simp.simple_text = simple_text\n                simp.hard = orig\n                simp.save()\n                return render(request, 'add_simp.html', {'results': results, 'orig' : orig, 'form': form })\n        elif 'votes' in request.POST and request.POST[\"votes\"]:\n            votes = request.POST.getlist(\"votes\")\n            for vote in votes:\n                vote_form = VoteForm()\n                simple=Simple.objects.get(hard=orig, simple_text=vote)\n                try:\n                    v = Vote.objects.get(hard=orig, simple=simple)\n                    v.votes += 1\n                    v.save()\n                except Vote.DoesNotExist:\n                    v = vote_form.save(commit=False)\n                    v.hard=orig\n                    v.simple=simple\n                    v.votes=1\n                    v.save()\n            vote_result = Vote.objects.get(hard=orig, simple=simple)\n            return render(request, 'add_simp.html', {'results': results, 'orig' : orig, 'form': form})\n        else:\n            return render(request, 'add_simp.html', {'results': results, 'orig' : orig, 'form': form})\n    else:\n        return render(request, 'add_simp.html', {'results': results, 'orig' : orig, 'form': form})\n\n\ndef edit_simp(request, pk):\n    simp = Simple.objects.get(pk=pk)\n    form = SimpForm(initial={'simple_text': simp.simple_text})\n    new_simple_text = request.POST.get(\"simple_text\")\n    if request.method ==\"POST\":\n        simp.simple_text = new_simple_text\n        simp.save()\n        return redirect('add_simp', simp.hard.id )\n    else:\n        return render(request, 'edit_simp.html', {'form' : form})\n\ndef add_origin(request):\n    hard_text = request.POST.get(\"hard_text\")\n    if request.method ==\"POST\":\n        save_form = OriginForm(request.POST)\n        if save_form.is_valid():\n            orig = save_form.save(commit=False)\n            orig.hard_text = hard_text\n            orig.save()\n            origins = Original.objects.all()\n        return redirect('contribute')\n    else:\n        form = OriginForm()\n        return render(request, 'add_origin.html', { 'form':form })\n\n","sub_path":"translate/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6551,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"338971074","text":"# -*- coding: utf-8 -*-\nimport feedparser\nfrom yowsup.layers.protocol_messages.protocolentities import TextMessageProtocolEntity\nfrom pyshorteners import Shortener\nimport config\n\n\nclass NewsViews(object):\n    def __init__(self, interface_layer):\n        self.routes = [\n            # Latest news\n            ('^' + config.cmd_prefix + '(?:ultimas|últimas|noticias|notícias|news)\\s?(?P<cat>[^$]+)?$', self.get_latest_news),\n        ]\n\n    def get_latest_news(self, message, match):\n        try:\n            cat = match.group('cat').lower()\n\n            if cat in('desporto', 'sport', 'o jogo', 'ojogo', 'jogo'):\n                src = 'O Jogo'\n            elif cat in('jn', 'jornal de notícias', 'jornal de noticias', 'jornal noticias', 'jornal notícias'):\n                src = 'Jornal de Notícias'\n            else:\n                src = 'Público'\n        except AttributeError:\n            src = 'Público'\n\n        news_src = config.news_sources[src]\n        feed = feedparser.parse(news_src)\n        titles = []\n        intro = 'As últimas do \"%s\":\\n\\n' % src\n\n        shortener = Shortener('IsgdShortener')\n\n        for i, t in enumerate(feed['entries']):\n            num = i + 1\n            title = t['title']\n            link = shortener.short(t['link'])\n            category = t['category']\n\n            if i < config.max_news:\n                line = '%d. %s - %s [%s]\\n' % (num, title, link, category)\n                titles.append(line.encode('utf-8'))\n\n        titles_formatted = intro + '\\n'.join(titles)\n\n        return TextMessageProtocolEntity(titles_formatted, to=message.getFrom())\n","sub_path":"src/views/news.py","file_name":"news.py","file_ext":"py","file_size_in_byte":1605,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"351936729","text":"# List of seven letter words from:\n# http://www.poslarchive.com/math/scrabble/lists/common-7.html\n\nfrom random import randint\nimport linecache\nimport time\n\n# Puts each word in a file on a separate line\n# Use to make new word lists for hangman game\ndef split_word_list(file_name):        \n    f = open(file_name)\n    words = f.read()\n    f.close()\n    word_list = words.split()\n    f = open(file_name, 'w')\n    for line in word_list:\n        f.write(line + '\\n')\n    f.close()\n\n# Returns the number of lines in a file\n# Function taken from:\n# http://stackoverflow.com/questions/845058/how-to-get-line-count-cheaply-in-python\ndef file_length(file_name):\n    with open(file_name) as f:\n        # this line assigns variables to the tuples returned by enumerate()\n        # or something like that...\n        for line_count, line in enumerate(f):\n            pass\n    return line_count\n\n# Reads a line of a file and returns its contents\n# Used to create variable secret_word\ndef random_line(number_of_lines, file_name):\n    # linecache.getline() seems to start counting at 1 instead of zero\n    # this confuses me, because I thought I should always start at zero\n    random = randint(1, number_of_lines)\n    line = linecache.getline(file_name, random)\n    return(line)\n\n# Lets the user guess a letter\n# If the letter is in secret_word, the function returns that letter\n# If the letter is not in secret_word, the function returns False\ndef guess_letter(secret_word):\n    print('Guess a letter!')\n    user_letter = input('> ')\n    if user_letter in secret_word:\n        return(user_letter)\n    else:\n        return(False)\n\n# Returns the indexes of a letter that the user guesses\ndef find_letters(secret_word, user_letter):\n    start = 0\n    letter_indexes = []\n    while start != 'done':\n        index = secret_word.find(user_letter, start)\n        if index == -1:\n            start = 'done'\n        else:\n            letter_indexes.append(index)\n            start = index + 1\n    return(letter_indexes)\n\n# Replaces underscores in a list with letters from secret_word\ndef fill_blanks(secret_word, blanks, user_letter, letter_indexes):\n    l = list(secret_word)\n    while letter_indexes:\n        index = letter_indexes.pop()\n        blanks[index] = l[index]\n    return(blanks)\n\n# Prints a goodbye message and quits game\ndef game_over():\n    print('Game over!')\n    print('Quitting now')\n    time.sleep(1)\n    print('.')\n    time.sleep(1)\n    print('.')\n    time.sleep(1)\n    print('.')\n    time.sleep(1)\n    print('Goodbye!')\n    exit()\n    \n\n# Counts the number of wrong guesses user has made\n# If the number wrong guesses is too high, user loses\n# Holds the list of underscores/letters guessed by user\n# If user guesses all the letters, user wins\ndef game_engine(secret_word):\n    guesses = 0\n    if guesses == 0:\n        blanks = ['_', '_', '_', '_', '_', '_', '_']\n        won = False\n        while won == False:\n            user_letter = guess_letter(secret_word)\n            if user_letter != False:\n                indexes = find_letters(secret_word, user_letter)\n                blanks = fill_blanks(secret_word, blanks, user_letter, indexes)\n                print('Correct letter! Good job!')\n                print(blanks)\n                if '_' not in blanks:\n                    won = True\n            else:\n                guesses += 1\n                print('Oops, wrong letter!')\n                print(blanks)\n                print('Guesses remaining: ' + str(7 - guesses))\n                if guesses == 7:\n                    print('Sorry, you lose!')\n                    game_over()\n        print('Congratulations, you win!')\n        game_over()\n\n# Holds the functions used to run the game\ndef hangman(which_letter):\n    how_long = file_length(which_letter)\n    secret_word = random_line(how_long, which_letter)\n    print(secret_word)\n    game_engine(secret_word)\n\nhangman('seven_letter_words.txt')\n","sub_path":"hangman.py","file_name":"hangman.py","file_ext":"py","file_size_in_byte":3905,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"85676945","text":"class Solution:\n    def tribonacci(self, n: int) -> int:\n        t = [0, 1, 1]\n\n        for _ in range(3, n + 1):\n            t[0], t[1], t[2] = t[1], t[2], t[0] + t[1] + t[2]\n\n        return t[2] if n > 2 else t[n]\n\n\nif __name__ == '__main__':\n    s = Solution()\n    print(s.tribonacci(4))\n","sub_path":"leetcode/easy/n-th-tribonacci-number.py","file_name":"n-th-tribonacci-number.py","file_ext":"py","file_size_in_byte":291,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"162452278","text":"import uuid\n\nfrom random import randint\n\nfrom django.shortcuts import render\nfrom django.http import HttpResponseRedirect\n\nfrom .models import Url\n\ndef index(request):\n    if request.session.has_key(\"has_url\"):\n        url = request.session.get(\"has_url\")\n        del request.session['has_url']\n        return render(request, \"miudo/index.html\", locals())\n    return render(request, \"miudo/index.html\", {})\n\ndef make_url(request):\n    if request.method == \"POST\":\n\n        url = None # initial url\n        url_site = request.POST['url']\n        url_id = generate_key()\n\n        try:\n            url = Url.objects.get(url_id = url_id)\n            while url:\n                url_id = generate_key()\n                url = Url.objects.get(url_id = url_id)\n            create_url(request, url_id, url_site)\n            request.session[\"has_url\"] = url_id\n\n        except Url.DoesNotExist:\n            create_url(request, url_id, url_site)\n            request.session[\"has_url\"] = url_id\n\n    return HttpResponseRedirect(\"/\")\n\ndef create_url(custom_request, url_id, url_site):\n    if custom_request.user.is_authenticated():\n        url = Url.objects.create(url_id = url_id, url_site = url_site,\n                                url_author = custom_request.user)\n    else:\n        url = Url.objects.create(url_id = url_id, url_site = url_site)\n    url.save()\n\ndef generate_key():\n    to_choose = \"0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ\";\n    url_id = \"\"\n    while len(url_id) != 6:\n        i = randint(0, len(to_choose) - 1)\n        url_id += to_choose[i]\n    return url_id\n\ndef redirect_url(request, url_id=None):\n    try:\n        url = Url.objects.get(url_id = url_id)\n        url.url_clicked = url.url_clicked + 1\n        url.save()\n    except Url.DoesNotExist:\n        return render(request, \"base/page_not_found.html\", {})\n    return HttpResponseRedirect(url.url_site)\n","sub_path":"src/apps/miudo/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1890,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"125529999","text":"from Bio import Phylo\nfrom io import StringIO\n\nx = open(\"D:/Download/rosalind_ctbl.txt\",mode='r').read().strip(';\\n')\ntree = Phylo.read(StringIO(x),'newick')\nclade_list = tree.get_nonterminals()\nterminal = tree.get_terminals()\nterminal.sort(key=lambda x: x.name)\nres = []\nfor clade in clade_list:\n    temp = []\n    for ter in terminal:\n        if clade.is_parent_of(ter):\n            temp.append(1)\n        else:\n            temp.append(0)\n    res.append(temp)\n\nwith open(\"D:/Download/output.txt\",mode='w') as f:\n    for i in range(1,len(res)):\n        s = ''.join(map(str,res[i])) + '\\n'\n        f.write(s)","sub_path":"Phylogeny/Creating a Character Table.py","file_name":"Creating a Character Table.py","file_ext":"py","file_size_in_byte":607,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"423689170","text":"#!/usr/bin/env python3\n#\n# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements.  See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership.  The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); you may not use this file except in compliance\n# with the License.  You may obtain a copy of the License at\n#\n#   http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied.  See the License for the\n# specific language governing permissions and limitations\n# under the License.\n#\n# ----\n#\n# ### TBD: DOCCO\n# USAGE: run_stv.py .../Meetings/yyyymmdd\n#\n\nimport sys\nimport os.path\n\n# Ensure that we can import the \"steve\" package.\nTHIS_DIR = os.path.realpath(os.path.dirname(__file__))\nsys.path.insert(0, os.path.dirname(THIS_DIR))\nimport steve.vtypes.stv\n\n# The stv module loads the stv_tool module. Tweak it.\nstv_tool = steve.vtypes.stv.stv_tool\nstv_tool.VERBOSE = True\n\n\ndef main(mtgdir):\n    rawfile = os.path.join(mtgdir, 'raw_board_votes.txt')\n    labelfile = os.path.join(mtgdir, 'board_nominations.ini')\n\n    assert os.path.exists(rawfile)\n    assert os.path.exists(labelfile)\n\n    labelmap = stv_tool.read_labelmap(labelfile)\n    votes = stv_tool.read_votefile(rawfile).values()\n\n    # Construct a label-sorted list of names from the labelmap.\n    names = [name for _, name in sorted(labelmap.items())]\n\n    kv = {\n        'labelmap': labelmap,\n        'seats': 9,\n        }\n\n    # NOTE: for backwards-compat, the tally() function accepts a\n    # list of names with caller-defined sorting.\n    human, _ = steve.vtypes.stv.tally(votes, kv, names)\n\n    # For the comparison purposes:\n    print(human)\n    print('Done!')\n\n\nif __name__ == '__main__':\n    main(sys.argv[1])\n","sub_path":"v3/test/run_stv.py","file_name":"run_stv.py","file_ext":"py","file_size_in_byte":2006,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"341371049","text":"from heppy.framework.analyzer import Analyzer\nfrom heppy_fcc.particles.fcc.particle import Particle \nfrom heppy_fcc.particles.fcc.vertex import Vertex \nfrom heppy_fcc.tools.genbrowser import GenBrowser\n\nimport math\nimport pprint\n\nclass FCCReader(Analyzer):\n    \n    def process(self, event):\n        store = event.input\n        gen_vertices = store.get(\"GenVertex\")\n        event.gen_vertices = map(Vertex, gen_vertices)\n        gen_particles = map(Particle, store.get(\"GenParticle\"))\n        event.gen_particles = sorted( gen_particles,\n                                      key = lambda ptc: ptc.e(),\n                                      reverse=True )  \n        event.gen_particles_stable = [ptc for ptc in event.gen_particles\n                                      if ptc.status()==1 and \n                                      not math.isnan(ptc.e()) and\n                                      ptc.e()>1e-5 and \n                                      ptc.pt()>1e-5 and\n                                      not abs(ptc.pdgid()) in [12, 14, 16]]\n        # event.genbrowser = GenBrowser(event.gen_particles, event.gen_vertices)\n","sub_path":"analyzers/FCCReader.py","file_name":"FCCReader.py","file_ext":"py","file_size_in_byte":1128,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"31798532","text":"# load HL detection model from imageAI\n# open camera with openCV, analyze frame by frame\n# draw a red frame around the detected object\n# display FPS, resize image to 1/4 to improve performance\n\nfrom djitellopy import Tello\nfrom imageai.Detection.Custom import CustomObjectDetection\nimport os\nimport cv2\nimport time\n\n######################################################################\nwidth = 320  # WIDTH OF THE IMAGE\nheight = 240  # HEIGHT OF THE IMAGE\nstartCounter =0   #  0 FOR FIGHT 1 FOR TESTING\n######################################################################\n\n# load hl model detector\ndetector = CustomObjectDetection()\ndetector.setModelTypeAsYOLOv3()\ndetector.setModelPath(\"hololens-ex-60--loss-2.76.h5\") \ndetector.setJsonPath(\"detection_config.json\")\ndetector.loadModel()\n\n# CONNECT TO TELLO\nme = Tello()\nme.connect()\nme.for_back_velocity = 0\nme.left_right_velocity = 0\nme.up_down_velocity = 0\nme.yaw_velocity = 0\nme.speed = 0\n\nprint(me.get_battery())\n\nme.streamoff()\nme.streamon()\n\nwhile True:\n    # FPS process\n    start_time = time.time()\n\n    # Grab a single frame of video\n    # GET THE IMGAE FROM TELLO\n    frame_read = me.get_frame_read()\n    frame = frame_read.frame\n    img = cv2.resize(frame, (width, height))\n\n    fast_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)\n\n    detected_image, detections = detector.detectObjectsFromImage(input_image=fast_frame, input_type=\"array\", output_type=\"array\")\n\n    for detection in detections:\n        # frame for the detected object\n        (x1, y1, x2, y2) = detection[\"box_points\"]\n        x1 *= 4\n        y1 *= 4\n        x2 *= 4\n        y2 *= 4\n        cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255), 2)\n\n        # Draw a label with the detected object type below the frame\n        font = cv2.FONT_HERSHEY_DUPLEX\n        cv2.putText(frame, detection[\"name\"], (x1 + 6, y1 - 6), font, 1.0, (255, 255, 255), 1)\n\n    #display FPS\n    fpsInfo = \"FPS: \" + str(1.0 / (time.time() - start_time)) # FPS = 1 / time to process loop\n    print(fpsInfo)\n    font = cv2.FONT_HERSHEY_DUPLEX\n    cv2.putText(frame, fpsInfo, (10, 20), font, 0.4, (255, 255, 255), 1)\n\n    # Display the resulting image\n    cv2.imshow('Video', frame)\n\n    # Hit 'q' on the keyboard to quit!\n    if cv2.waitKey(1) & 0xFF == ord('q'):\n        break\n\n# Release handle to the webcam\nvideo_capture.release()\ncv2.destroyAllWindows()\n\n","sub_path":"HoloTrackerWebCam.py","file_name":"HoloTrackerWebCam.py","file_ext":"py","file_size_in_byte":2372,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"611217078","text":"# coding: utf-8\n\nfrom __future__ import absolute_import\nfrom datetime import date, datetime  # noqa: F401\n\nfrom typing import List, Dict  # noqa: F401\n\nfrom swagger_server.models.base_model_ import Model\nfrom swagger_server import util\n\n\nclass UseCodess(Model):\n    \"\"\"NOTE: This class is auto generated by the swagger code generator program.\n\n    Do not edit the class manually.\n    \"\"\"\n\n    def __init__(self, code: int=None, type: str=None, model: str=None):  # noqa: E501\n        \"\"\"UseCodess - a model defined in Swagger\n\n        :param code: The code of this UseCodess.  # noqa: E501\n        :type code: int\n        :param type: The type of this UseCodess.  # noqa: E501\n        :type type: str\n        :param model: The model of this UseCodess.  # noqa: E501\n        :type model: str\n        \"\"\"\n        self.swagger_types = {\n            'code': int,\n            'type': str,\n            'model': str\n        }\n\n        self.attribute_map = {\n            'code': 'code',\n            'type': 'type',\n            'model': 'model'\n        }\n\n        self._code = code\n        self._type = type\n        self._model = model\n\n    @classmethod\n    def from_dict(cls, dikt) -> 'UseCodess':\n        \"\"\"Returns the dict as a model\n\n        :param dikt: A dict.\n        :type: dict\n        :return: The UseCodess of this UseCodess.  # noqa: E501\n        :rtype: UseCodess\n        \"\"\"\n        return util.deserialize_model(dikt, cls)\n\n    @property\n    def code(self) -> int:\n        \"\"\"Gets the code of this UseCodess.\n\n        system return code  # noqa: E501\n\n        :return: The code of this UseCodess.\n        :rtype: int\n        \"\"\"\n        return self._code\n\n    @code.setter\n    def code(self, code: int):\n        \"\"\"Sets the code of this UseCodess.\n\n        system return code  # noqa: E501\n\n        :param code: The code of this UseCodess.\n        :type code: int\n        \"\"\"\n\n        self._code = code\n\n    @property\n    def type(self) -> str:\n        \"\"\"Gets the type of this UseCodess.\n\n        type  # noqa: E501\n\n        :return: The type of this UseCodess.\n        :rtype: str\n        \"\"\"\n        return self._type\n\n    @type.setter\n    def type(self, type: str):\n        \"\"\"Sets the type of this UseCodess.\n\n        type  # noqa: E501\n\n        :param type: The type of this UseCodess.\n        :type type: str\n        \"\"\"\n\n        self._type = type\n\n    @property\n    def model(self) -> str:\n        \"\"\"Gets the model of this UseCodess.\n\n        model  # noqa: E501\n\n        :return: The model of this UseCodess.\n        :rtype: str\n        \"\"\"\n        return self._model\n\n    @model.setter\n    def model(self, model: str):\n        \"\"\"Sets the model of this UseCodess.\n\n        model  # noqa: E501\n\n        :param model: The model of this UseCodess.\n        :type model: str\n        \"\"\"\n\n        self._model = model\n","sub_path":"python-flask-server/swagger_server/models/use_codess.py","file_name":"use_codess.py","file_ext":"py","file_size_in_byte":2829,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"19874823","text":"from objects_common.jsonObject import JsonObject\nfrom objects_common.enumType import EnumType\n\nclass TrafficParams(JsonObject):\n\n    def __init__(self, json_struct=None):\n        self.latency=0\n        self.OSNR=\"\"\n        self.estimatedPLR=\"\"\n        self.qosClass=Qosclass(0)\n        self.reservedBandwidth=\"\"\n        super(TrafficParams, self).__init__(json_struct)\n\nclass Qosclass(EnumType):\n    possible_values = ['gold', 'silver']\n    range_end = 2\n\n    def __init__(self, initial_value):\n        super(Qosclass, self).__init__(initial_value)\n","sub_path":"cop-server/objects_cop_call/trafficParams.py","file_name":"trafficParams.py","file_ext":"py","file_size_in_byte":549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"133890103","text":"#! /usr/bin/env python\n\nfrom eulercommon.modulus import ModularSolver\n\nfrom collections import defaultdict\n\nclass Choose(ModularSolver):\n\n    def __init__(self, modulus, primitive_root, *args, **kwargs):\n        super(Choose, self).__init__(modulus=modulus, primitive_root=primitive_root, *args, **kwargs)\n        self._solutions = defaultdict(lambda: None)\n\n    def choose(self, n, k):\n        if self._solutions[(n, k)] is None:\n            self._solutions[(n, k)] = self.compute_choose(n, k)\n        return self._solutions[(n, k)]\n\n    def compute_choose(self, n, k):\n        # n choose k = n! / (k! * (n-k))!\n        n_f_over_k_f = 1 # n! / k!\n        n_m_k_fact = 1 # (n-k)!\n        for i in range(1, n + 1):\n            if i <= (n - k):\n                n_m_k_fact = self.mult(n_m_k_fact, i)\n            if i > k:\n                n_f_over_k_f = self.mult(n_f_over_k_f, i)\n        return self.div(n_f_over_k_f, n_m_k_fact)\n","sub_path":"eulercommon/choose.py","file_name":"choose.py","file_ext":"py","file_size_in_byte":927,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"348937208","text":"import os\nimport time\nimport math\nimport paddle\nimport paddle.nn as nn\nfrom paddle.io import DataLoader\nimport paddle.distributed as dist\n\nfrom args import parse_args, print_args\nfrom elmo import ELMo, ELMoLoss\nfrom dataset import load_vocab, OneBillionWordDataset\n\n\n@paddle.no_grad()\ndef eval(args):\n    paddle.set_device(args.device)\n\n    if not args.init_from_ckpt:\n        raise ValueError('init_from_ckpt should be set when eval.')\n    vocab = load_vocab(args.vocab_file, args.max_characters_per_token)\n\n    elmo = ELMo(\n        args.batch_size,\n        args.char_embed_dim,\n        args.projection_dim,\n        vocab.size,\n        dropout=args.dropout,\n        num_layers=args.num_layers,\n        num_highways=args.num_highways,\n        char_vocab_size=vocab.char_size)\n    elmo.eval()\n\n    elmo_loss = ELMoLoss()\n\n    # Loads pre-trained parameters.\n    weight_state_dict = paddle.load(args.init_from_ckpt + '.pdparams')\n    elmo.set_state_dict(weight_state_dict)\n    print(\"Loaded checkpoint from %s\" % args.init_from_ckpt)\n\n    dev_dataset = OneBillionWordDataset(\n        args.dev_data_path,\n        vocab,\n        args.batch_size,\n        args.unroll_steps,\n        mode='test',\n        shuffle=False,\n        seed=args.seed)\n\n    dev_dataloader = DataLoader(dev_dataset, return_list=True, batch_size=None)\n\n    total_step = total_loss = 0\n    total_time = 0.0\n    batch_start_time = time.time()\n    for step, inputs in enumerate(dev_dataloader, start=1):\n        ids, next_ids, ids_reverse, next_ids_reverse = inputs\n        outputs = elmo([ids, ids_reverse])\n        loss = elmo_loss(outputs, [next_ids, next_ids_reverse])\n        ppl = paddle.exp(loss)\n\n        total_loss += loss.numpy()[0]\n        total_step += 1\n\n        total_time += (time.time() - batch_start_time)\n        if step % args.log_freq == 0:\n            print(\"Eval step %d - loss: %.4f - Perplexity: %.4f - %.3fs/step\" %\n                  (step, loss.numpy()[0] * args.unroll_steps, ppl.numpy()[0],\n                   total_time / args.log_freq))\n            total_time = 0.0\n        batch_start_time = time.time()\n\n    avg_loss = total_loss / total_step\n    avg_ppl = math.exp(avg_loss)\n    print(\"Eval - average loss: %.4f - average Perplexity: %.4f\" %\n          (avg_loss * args.unroll_steps, avg_ppl))\n\n\nif __name__ == '__main__':\n    args = parse_args()\n    print_args(args)\n    eval(args)\n","sub_path":"examples/language_model/elmo/run_eval.py","file_name":"run_eval.py","file_ext":"py","file_size_in_byte":2378,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"494752826","text":"import json\nfrom StringIO import StringIO\nfrom serializers import LabelDataSerializer\nfrom django.core.serializers.json import Deserializer as JSONDeserializer\nfrom django.core.serializers.python import Deserializer as PythonDeserializer\n\nSerializer = LabelDataSerializer\n\ndef Deserializer(stream_or_string, **options):\n    if isinstance(stream_or_string, basestring):\n        stream = StringIO(stream_or_string)\n    else:\n        stream = stream_or_string\n\n    data = json.load(stream)\n\n    fields = data[0][\"fields\"]\n    resources = fields[\"resources\"][0]\n    resources[\"tracking_summary_recent\"] = resources[\"tracking_summary\"][\"recent\"]\n    resources[\"tracking_summary_total\"] = resources[\"tracking_summary\"][\"total\"]\n    \n    resources[\"label_data\"] = fields[\"id\"]\n    del resources[\"tracking_summary\"]\n    del fields[\"resources\"]\n\n    fields[\"tracking_summary_recent\"] = fields[\"tracking_summary\"][\"recent\"]\n    fields[\"tracking_summary_total\"] = fields[\"tracking_summary\"][\"total\"]\n    del fields[\"tracking_summary\"]\n\n    resources_wrapper = {\n        'pk': resources[\"id\"],\n        'model': 'medsapp.Resource',\n        'fields': resources\n    }\n\n    for obj in PythonDeserializer(data, **options):\n        yield obj\n\n    for obj in PythonDeserializer([resources_wrapper], **options):\n        yield obj","sub_path":"wiser/medsapp/json_serializer.py","file_name":"json_serializer.py","file_ext":"py","file_size_in_byte":1309,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"429289662","text":"## Copyright (c) 2012-2015 Aldebaran Robotics. All rights reserved.\n## Use of this source code is governed by a BSD-style license that can be\n## found in the COPYING file.\n\"\"\" Core tools for every qisys.commands\n\n\"\"\"\n\nimport os\nimport glob\n\nfrom qisys import ui\n\ndef find(projects, package_name, directory=None, name_func=None):\n    \"\"\" Abstract find function.\n        Return path to found file with decorated name\n    \"\"\"\n\n    for project in projects:\n        decorated_name = package_name\n\n        if name_func:\n            decorated_name = name_func(package_name)\n        if directory:\n            target = os.path.join(project.path, directory)\n\n        target = os.path.join(target, decorated_name)\n        ui.debug(\"Looks into \" + target)\n        for match in glob.glob(target):\n            return match\n    return None\n","sub_path":"python/qisys/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":825,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"513143988","text":"\"\"\"Use lanczos tri-diagonalization to approximate the spectrum of any operator\nwhich has an efficient representation of its linear action on a vector.\n\"\"\"\nimport functools\nfrom math import sqrt, log2, exp\nimport time\n\nimport numpy as np\nimport scipy.linalg as scla\nimport scipy.sparse.linalg as spla\n\nfrom ..core import ptr\nfrom ..accel import prod, vdot\nfrom ..utils import int2tup\nfrom ..linalg.mpi_launcher import get_mpi_pool\nfrom ..tensor.tensor_core import einsum, einsum_path, HuskArray\nfrom ..tensor.tensor_1d import MatrixProductOperator\nfrom ..tensor.tensor_approx_spectral import (\n    construct_lanczos_tridiag_MPO,\n    PTPTLazyMPS,\n    construct_lanczos_tridiag_PTPTLazyMPS,\n)\n\n\n# --------------------------------------------------------------------------- #\n#                  'Lazy' representation tensor contractions                  #\n# --------------------------------------------------------------------------- #\n\n@functools.lru_cache(128)\ndef get_cntrct_inds_ptr_dot(ndim_ab, sysa, matmat=False):\n    \"\"\"Find the correct integer contraction labels for ``lazy_ptr_dot``.\n\n    Parameters\n    ----------\n    ndim_ab : int\n        The total number of subsystems (dimensions) in 'ab'.\n    sysa : int or sequence of int, optional\n            Index(es) of the 'a' subsystem(s) to keep.\n    matmat : bool, optional\n        Whether to output indices corresponding to a matrix-vector or\n        matrix-matrix opertion.\n\n    Returns\n    -------\n    inds_a_ket : sequence of int\n        The tensor index labels for the ket on subsystem 'a'.\n    inds_ab_bra : sequence of int\n        The tensor index labels for the bra on subsystems 'ab'.\n    inds_ab_ket : sequence of int\n        The tensor index labels for the ket on subsystems 'ab'.\n    \"\"\"\n    inds_a_ket = []\n    inds_ab_bra = []\n    inds_ab_ket = []\n\n    upper_inds = iter(range(ndim_ab, 2 * ndim_ab))\n\n    for i in range(ndim_ab):\n        if i in sysa:\n            inds_a_ket.append(i)\n            inds_ab_bra.append(i)\n            inds_ab_ket.append(next(upper_inds))\n        else:\n            inds_ab_bra.append(i)\n            inds_ab_ket.append(i)\n\n    if matmat:\n        inds_a_ket.append(2 * ndim_ab)\n\n    return tuple(inds_a_ket), tuple(inds_ab_bra), tuple(inds_ab_ket)\n\n\n@functools.lru_cache(128)\ndef prepare_lazy_ptr_dot(psi_a_shape, dims=None, sysa=0):\n    \"\"\"Pre-calculate the arrays and indexes etc for ``lazy_ptr_dot``.\n    \"\"\"\n    mat_size = psi_a_shape[1] if len(psi_a_shape) > 1 else 1\n\n    # convert to tuple so can always cache\n    sysa = int2tup(sysa)\n\n    ndim_ab = len(dims)\n    inds_a_ket, inds_ab_bra, inds_ab_ket = get_cntrct_inds_ptr_dot(\n        ndim_ab, sysa, matmat=mat_size > 1)\n\n    dims_a = tuple(d for i, d in enumerate(dims) if i in sysa)\n    if mat_size > 1:\n        dims_a = dims_a + (mat_size,)\n\n    return dims_a, inds_a_ket, inds_ab_bra, inds_ab_ket\n\n\n@functools.lru_cache(128)\ndef get_path_lazy_ptr_dot(psi_ab_tensor_shape, psi_a_tensor_shape,\n                          inds_a_ket, inds_ab_bra, inds_ab_ket):\n    return einsum_path(\n        HuskArray(psi_a_tensor_shape), inds_a_ket,\n        HuskArray(psi_ab_tensor_shape), inds_ab_bra,\n        HuskArray(psi_ab_tensor_shape), inds_ab_ket,\n        optimize='optimal',\n        memory_limit=-1,\n    )[0]\n\n\ndef do_lazy_ptr_dot(psi_ab_tensor, psi_ab_tensor_conj, psi_a_tensor,\n                    inds_a_ket, inds_ab_bra, inds_ab_ket,\n                    path, out=None):\n    \"\"\"Perform ``lazy_ptr_dot`` with the pre-calculated indexes etc from\n    ``prepare_lazy_ptr_dot``.\n    \"\"\"\n    if out is None:\n        out = np.empty_like(psi_a_tensor)\n\n    return einsum(\n        psi_a_tensor, inds_a_ket,\n        psi_ab_tensor_conj, inds_ab_bra,\n        psi_ab_tensor, inds_ab_ket,\n        optimize=path,\n        out=out,\n    )\n\n\ndef lazy_ptr_dot(psi_ab, psi_a, dims=None, sysa=0, out=None):\n    r\"\"\"Perform the 'lazy' evalution of ``ptr(psi_ab, ...) @ psi_a``,\n    that is, contract the tensor diagram in an efficient way that does not\n    necessarily construct the explicit reduced density matrix. In tensor\n    diagram notation::\n\n          ( | )\n        +-------+\n        | psi_a |   ______\n        +_______+  /      \\\n           a|      |b     |\n        +-------------+   |\n        |  psi_ab.H   |   |\n        +_____________+   |\n                          |\n        +-------------+   |\n        |   psi_ab    |   |\n        +_____________+   |\n           a|      |b     |\n            |      \\______/\n\n\n    Parameters\n    ----------\n    psi_ab : ket\n        State to partially trace and dot with another ket, with\n        size ``prod(dims)``.\n    psi_a : ket or sequence of kets\n        State to act on with the dot product, of size ``prod(dims[sysa])``, or\n        several states to act on at once, then rectangular matrix of size\n        ``(prod(dims[sysa]), nstates)``.\n    dims : sequence of int, optional\n        The sub dimensions of ``psi_ab``, inferred as bipartite if not given,\n        i.e. ``(psi_a.size, psi_ab.size // psi_a.size)``.\n    sysa : int or sequence of int, optional\n        Index(es) of the 'a' subsystem(s) to keep.\n    out : array, optional\n        If provided, the calculation is done into this array.\n\n    Returns\n    -------\n    ket\n    \"\"\"\n    if dims is None:\n        da = psi_a.shape[0]\n        d = psi_ab.size\n        dims = (da, d // da)\n    else:\n        dims = int2tup(dims)\n    sysa = int2tup(sysa)\n\n    # prepare shapes and indexes -- cached\n    dims_a, inds_a_ket, inds_ab_bra, inds_ab_ket = \\\n        prepare_lazy_ptr_dot(psi_a.shape, dims=dims, sysa=sysa)\n    psi_ab_tensor = np.asarray(psi_ab).reshape(dims)\n    psi_a_tensor = np.asarray(psi_a).reshape(dims_a)\n\n    # find the optimal path -- cached\n    path = get_path_lazy_ptr_dot(\n        psi_ab_tensor.shape, psi_a_tensor.shape,\n        inds_a_ket, inds_ab_bra, inds_ab_ket)\n\n    # perform the contraction\n    return do_lazy_ptr_dot(\n        psi_ab_tensor, psi_ab_tensor.conjugate(), psi_a_tensor,\n        inds_a_ket, inds_ab_bra, inds_ab_ket,\n        path=path, out=out).reshape(psi_a.shape)\n\n\nclass LazyPtrOperator(spla.LinearOperator):\n    \"\"\"A linear operator representing action of partially tracing a bipartite\n    state, then multiplying another 'unipartite' state.\n\n    Parameters\n    ----------\n    psi_ab : ket\n        State to partially trace and dot with another ket, with\n        size ``prod(dims)``.\n    dims : sequence of int, optional\n        The sub dimensions of ``psi_ab``.\n    sysa : int or sequence of int, optional\n        Index(es) of the 'a' subsystem(s) to keep.\n    \"\"\"\n\n    def __init__(self, psi_ab, dims, sysa):\n        self.psi_ab_tensor = np.asarray(psi_ab).reshape(dims)\n        self.psi_ab_tensor_conj = self.psi_ab_tensor.conjugate()\n        self.dims = int2tup(dims)\n        self.sysa = int2tup(sysa)\n        dims_a = [d for i, d in enumerate(dims) if i in self.sysa]\n        sz_a = prod(dims_a)\n        super().__init__(dtype=psi_ab.dtype, shape=(sz_a, sz_a))\n\n    def _matvec(self, vecs):\n        # prepare shapes and indexes -- cached\n        dims_a, inds_a_ket, inds_ab_bra, inds_ab_ket = \\\n            prepare_lazy_ptr_dot(vecs.shape, dims=self.dims, sysa=self.sysa)\n\n        # have to do this each time?\n        psi_a_tensor = np.asarray(vecs).reshape(dims_a)\n\n        # find the optimal path -- cached\n        path = get_path_lazy_ptr_dot(\n            self.psi_ab_tensor.shape, psi_a_tensor.shape,\n            inds_a_ket, inds_ab_bra, inds_ab_ket)\n\n        # perform the contraction\n        return do_lazy_ptr_dot(\n            self.psi_ab_tensor, self.psi_ab_tensor_conj, psi_a_tensor,\n            inds_a_ket, inds_ab_bra, inds_ab_ket,\n            path=path).reshape(vecs.shape)\n\n    def _matmat(self, vecs):\n        return self._matvec(vecs)\n\n    def _adjoint(self):\n        return self.__class__(self.psi_ab_tensor.conjugate(),\n                              self.dims, self.sysa)\n\n\n@functools.lru_cache(128)\ndef get_cntrct_inds_ptr_ppt_dot(ndim_abc, sysa, sysb, matmat=False):\n    \"\"\"Find the correct integer contraction labels for ``lazy_ptr_ppt_dot``.\n\n    Parameters\n    ----------\n    ndim_abc : int\n        The total number of subsystems (dimensions) in 'abc'.\n    sysa : int or sequence of int, optional\n        Index(es) of the 'a' subsystem(s) to keep, with respect to all\n        the dimensions, ``dims``, (i.e. pre-partial trace).\n    sysa : int or sequence of int, optional\n        Index(es) of the 'b' subsystem(s) to keep, with respect to all\n        the dimensions, ``dims``, (i.e. pre-partial trace).\n    matmat : bool, optional\n        Whether to output indices corresponding to a matrix-vector or\n        matrix-matrix opertion.\n\n    Returns\n    -------\n    inds_ab_ket : sequence of int\n        The tensor index labels for the ket on subsystems 'ab'.\n    inds_abc_bra : sequence of int\n        The tensor index labels for the bra on subsystems 'abc'.\n    inds_abc_ket : sequence of int\n        The tensor index labels for the ket on subsystems 'abc'.\n    inds_out : sequence of int\n        The tensor indices of the resulting ket, important as these might\n        no longer be ordered.\n    \"\"\"\n    inds_ab_ket = []\n    inds_abc_bra = []\n    inds_abc_ket = []\n    inds_out = []\n\n    upper_inds = iter(range(ndim_abc, 2 * ndim_abc))\n\n    for i in range(ndim_abc):\n        if i in sysa:\n            up_ind = next(upper_inds)\n            inds_ab_ket.append(i)\n            inds_abc_bra.append(up_ind)\n            inds_abc_ket.append(i)\n            inds_out.append(up_ind)\n        elif i in sysb:\n            up_ind = next(upper_inds)\n            inds_ab_ket.append(up_ind)\n            inds_abc_bra.append(up_ind)\n            inds_abc_ket.append(i)\n            inds_out.append(i)\n        else:\n            inds_abc_bra.append(i)\n            inds_abc_ket.append(i)\n\n    if matmat:\n        inds_ab_ket.append(2 * ndim_abc)\n        inds_out.append(2 * ndim_abc)\n\n    return (tuple(inds_ab_ket), tuple(inds_abc_bra),\n            tuple(inds_abc_ket), tuple(inds_out))\n\n\n@functools.lru_cache(128)\ndef prepare_lazy_ptr_ppt_dot(psi_ab_shape, dims, sysa, sysb):\n    \"\"\"Pre-calculate the arrays and indexes etc for ``lazy_ptr_ppt_dot``.\n    \"\"\"\n    mat_size = psi_ab_shape[1] if len(psi_ab_shape) > 1 else 1\n\n    inds_ab_ket, inds_abc_bra, inds_abc_ket, inds_out = \\\n        get_cntrct_inds_ptr_ppt_dot(len(dims), sysa, sysb, matmat=mat_size > 1)\n\n    dims_ab = tuple(d for i, d in enumerate(dims)\n                    if (i in sysa) or (i in sysb))\n    if mat_size > 1:\n        dims_ab = dims_ab + (mat_size,)\n\n    return dims_ab, inds_ab_ket, inds_abc_bra, inds_abc_ket, inds_out\n\n\n@functools.lru_cache(128)\ndef get_path_lazy_ptr_ppt_dot(psi_abc_tensor_shape, psi_ab_tensor_shape,\n                              inds_ab_ket, inds_abc_bra,\n                              inds_abc_ket, inds_out):\n    return einsum_path(\n        HuskArray(psi_ab_tensor_shape), inds_ab_ket,\n        HuskArray(psi_abc_tensor_shape), inds_abc_bra,\n        HuskArray(psi_abc_tensor_shape), inds_abc_ket,\n        inds_out,\n        optimize='optimal',\n        memory_limit=-1,\n    )[0]\n\n\ndef do_lazy_ptr_ppt_dot(psi_ab_tensor, psi_abc_tensor, psi_abc_tensor_conj,\n                        inds_ab_ket, inds_abc_bra, inds_abc_ket, inds_out,\n                        path, out=None):\n    if out is None:\n        out = np.empty_like(psi_ab_tensor)\n\n    # must have ``inds_out`` as resulting indices are not ordered\n    # in the same way as input due to partial tranpose.\n    return einsum(\n        psi_ab_tensor, inds_ab_ket,\n        psi_abc_tensor_conj, inds_abc_bra,\n        psi_abc_tensor, inds_abc_ket,\n        inds_out,\n        optimize=path,\n        out=out,\n    )\n\n\ndef lazy_ptr_ppt_dot(psi_abc, psi_ab, dims, sysa, sysb, out=None):\n    r\"\"\"Perform the 'lazy' evalution of\n    ``partial_transpose(ptr(psi_abc, ...)) @ psi_ab``, that is, contract the\n    tensor diagram in an efficient way that does not necessarily construct\n    the explicit reduced density matrix. For a tripartite system, the partial\n    trace is with respect to ``c``, while the partial tranpose is with\n    respect to ``a/b``. In tensor diagram notation::\n\n             ( | )\n        +--------------+\n        |   psi_ab     |\n        +______________+  _____\n         a|  ____   b|   /     \\\n          | /   a\\   |   |c    |\n          | | +-------------+  |\n          | | |  psi_abc.H  |  |\n          \\ / +-------------+  |\n           X                   |\n          / \\ +-------------+  |\n          | | |   psi_abc   |  |\n          | | +-------------+  |\n          | \\____/a  |b  |c    |\n         a|          |   \\_____/\n\n\n    Parameters\n    ----------\n    psi_abc : ket\n        State to partially trace, partially tranpose, then dot with another\n        ket, with size ``prod(dims)``.\n    psi_ab : ket, sequence of kets\n        State to act on with the dot product, of size\n        , or series of states to ac\n        State to act on with the dot product, of size\n        ``prod(dims[sysa] + dims[sysb])``, or several states to act on at once,\n        then rectangular matrix of size\n        ``(prod(dims[sysa] + dims[sysb]), nstates)``.\n    dims : sequence of int\n        The sub dimensions of ``psi_abc``.\n    sysa : int or sequence of int, optional\n        Index(es) of the 'a' subsystem(s) to keep, with respect to all\n        the dimensions, ``dims``, (i.e. pre-partial trace).\n    sysa : int or sequence of int, optional\n        Index(es) of the 'b' subsystem(s) to keep, with respect to all\n        the dimensions, ``dims``, (i.e. pre-partial trace).\n    out : array, optional\n        If provided, the calculation is done into this array.\n\n    Returns\n    -------\n    ket\n    \"\"\"\n    # convert to tuple so can always cache\n    dims, sysa, sysb = int2tup(dims), int2tup(sysa), int2tup(sysb)\n\n    # prepare shapes and indexes -- cached\n    dims_ab, inds_ab_ket, inds_abc_bra, inds_abc_ket, inds_out = \\\n        prepare_lazy_ptr_ppt_dot(psi_ab.shape, dims, sysa, sysb)\n    psi_ab_tensor = np.asarray(psi_ab).reshape(dims_ab)\n    psi_abc_tensor = np.asarray(psi_abc).reshape(dims)\n\n    # find the optimal path -- cached\n    path = get_path_lazy_ptr_ppt_dot(\n        psi_abc_tensor.shape, psi_ab_tensor.shape,\n        inds_ab_ket, inds_abc_bra, inds_abc_ket, inds_out)\n\n    # perform contraction\n    return do_lazy_ptr_ppt_dot(\n        psi_ab_tensor, psi_abc_tensor, psi_abc_tensor.conjugate(),\n        inds_ab_ket, inds_abc_bra, inds_abc_ket, inds_out,\n        path, out=out).reshape(psi_ab.shape)\n\n\nclass LazyPtrPptOperator(spla.LinearOperator):\n    \"\"\"A linear operator representing action of partially tracing a tripartite\n    state, partially transposing the remaining bipartite state, then\n    multiplying another bipartite state.\n\n    Parameters\n    ----------\n    psi_abc : ket\n        State to partially trace, partially tranpose, then dot with another\n        ket, with size ``prod(dims)``.\n        ``prod(dims[sysa] + dims[sysb])``.\n    dims : sequence of int\n        The sub dimensions of ``psi_abc``.\n    sysa : int or sequence of int, optional\n        Index(es) of the 'a' subsystem(s) to keep, with respect to all\n        the dimensions, ``dims``, (i.e. pre-partial trace).\n    sysa : int or sequence of int, optional\n        Index(es) of the 'b' subsystem(s) to keep, with respect to all\n        the dimensions, ``dims``, (i.e. pre-partial trace).\n    \"\"\"\n\n    def __init__(self, psi_abc, dims, sysa, sysb):\n        self.psi_abc_tensor = np.asarray(psi_abc).reshape(dims)\n        self.psi_abc_tensor_conj = self.psi_abc_tensor.conjugate()\n        self.dims = int2tup(dims)\n        self.sysa, self.sysb = int2tup(sysa), int2tup(sysb)\n        sys_ab = self.sysa + self.sysb\n        sz_ab = prod([d for i, d in enumerate(dims) if i in sys_ab])\n        super().__init__(dtype=psi_abc.dtype, shape=(sz_ab, sz_ab))\n\n    def _matvec(self, vecs):\n        # prepare shapes and indexes -- cached\n        dims_ab, inds_ab_ket, inds_abc_bra, inds_abc_ket, inds_out = \\\n            prepare_lazy_ptr_ppt_dot(vecs.shape, self.dims,\n                                     self.sysa, self.sysb)\n\n        # do each time\n        psi_ab_tensor = np.asarray(vecs).reshape(dims_ab)\n\n        # find the optimal path -- cached\n        path = get_path_lazy_ptr_ppt_dot(\n            self.psi_abc_tensor.shape, psi_ab_tensor.shape,\n            inds_ab_ket, inds_abc_bra, inds_abc_ket, inds_out)\n\n        # perform contraction\n        return do_lazy_ptr_ppt_dot(\n            psi_ab_tensor, self.psi_abc_tensor, self.psi_abc_tensor_conj,\n            inds_ab_ket, inds_abc_bra, inds_abc_ket, inds_out,\n            path).reshape(vecs.shape)\n\n    def _matmat(self, vecs):\n        return self._matvec(vecs)\n\n    def _adjoint(self):\n        return self.__class__(self.psi_abc_tensor.conjugate(), self.dims,\n                              self.sysa, self.sysb)\n\n\n# --------------------------------------------------------------------------- #\n#                         Lanczos tri-diag technique                          #\n# --------------------------------------------------------------------------- #\n\ndef inner(a, b):\n    \"\"\"Inner product between two vectors\n    \"\"\"\n    return vdot(a, b).real\n\n\ndef norm_fro(a):\n    \"\"\"'Frobenius' norm of a vector.\n    \"\"\"\n    return sqrt(inner(a, a))\n\n\ndef construct_lanczos_tridiag(\n        A,\n        K,\n        v0=None,\n        bsz=1,\n        beta_tol=1e-6,\n        seed=None):\n    \"\"\"Construct the tridiagonal lanczos matrix using only matvec operators.\n    This is a generator that iteratively yields the alpha and beta digaonals\n    at each step.\n\n    Parameters\n    ----------\n    A : matrix-like or linear operator\n        The operator to approximate, must implement ``.dot`` method to compute\n        its action on a vector.\n    K : int, optional\n        The maximum number of iterations and thus rank of the matrix to find.\n    v0 : vector, optional\n        The starting vector to iterate with, default to random.\n    bsz : int, optional\n        The block size (number of columns) of random vectors to iterate with.\n    beta_tol : float, optional\n        The 'breakdown' tolerance. If the next beta ceofficient in the lanczos\n        matrix is less that this, implying that the full non-null space has\n        been found, terminate early.\n\n    Yields\n    ------\n    alpha : sequence of float of length k\n        The diagonal entries of the lanczos matrix.\n    beta : sequence of float of length k\n        The off-diagonal entries of the lanczos matrix, with the last entry\n        the 'look' forward value.\n    scaling : float\n        How to scale the overall weights.\n    \"\"\"\n    if isinstance(A, np.matrix):\n        A = np.asarray(A)\n    d = A.shape[0]\n\n    if bsz == 1:\n        v_shp = (d,)\n    else:\n        v_shp = (d, bsz)\n\n    alpha = np.zeros(K + 1)\n    beta = np.zeros(K + 2)\n    beta[1] = sqrt(prod(v_shp))  # by construction\n\n    if v0 is None:\n        if seed is not None:\n            np.random.seed(int(time.time() * 1e7 % (2**32 - 1)) + seed)\n        V = np.random.choice([-1, 1, 1j, -1j], v_shp)\n        V /= beta[1]  # normalize\n    else:\n        V = v0.astype(np.complex128)\n        V /= norm_fro(V)  # normalize (make sure has unit variance)\n    Vm1 = np.zeros_like(V)\n\n    for j in range(1, K + 1):\n\n        Vt = A.dot(V)\n        Vt -= beta[j] * Vm1\n        alpha[j] = inner(V, Vt)\n        Vt -= alpha[j] * V\n        beta[j + 1] = norm_fro(Vt)\n\n        # check for convergence\n        if abs(beta[j + 1]) < beta_tol:\n            yield alpha[1:j + 1], beta[2:j + 2], beta[1]**2 / bsz\n            break\n\n        Vm1[...] = V[...]\n        np.divide(Vt, beta[j + 1], out=V)\n\n        if j > 3:\n            yield (np.copy(alpha[1:j + 1]),\n                   np.copy(beta[2:j + 2]),\n                   np.copy(beta[1])**2 / bsz)\n\n\ndef lanczos_tridiag_eig(alpha, beta, check_finite=True):\n    \"\"\"Find the eigen-values and -vectors of the Lanczos triadiagonal matrix.\n\n    Parameters\n    ----------\n    alpha : array of float\n        The diagonal.\n    beta : array of float\n        The k={-1, 1} off-diagonal. Only first ``len(alpha) - 1`` entries used.\n    \"\"\"\n    Tk_banded = np.empty((2, alpha.size))\n    Tk_banded[1, -1] = 0.0  # sometimes can get nan here? -> breaks eig_banded\n    Tk_banded[0, :] = alpha\n    Tk_banded[1, :beta.size] = beta\n\n    try:\n        tl, tv = scla.eig_banded(\n            Tk_banded, lower=True, check_finite=check_finite)\n\n    # sometimes get no convergence -> use dense hermitian method\n    except scla.LinAlgError:  # pragma: no cover\n        tl, tv = np.linalg.eigh(\n            np.diag(alpha) + np.diag(beta[:alpha.size - 1], -1), UPLO='L')\n\n    return tl, tv\n\n\ndef calc_trace_fn_tridiag(tl, tv, fn, pos=True):\n    return sum(fn(max(tl[i], 0.0) if pos else tl[i]) * tv[0, i]**2\n               for i in range(tl.size))\n\n\ndef ext_per_trim(x, p=0.6, s=1.0):\n    \"\"\"Extended percentile trimmed-mean. Makes the mean robust to asymmetric\n    outliers, while using all data when it is nicely clustered. This can be\n    visualized roughly as:\n\n            |--------|=========|--------|\n        x     x   xx xx xxxxx xxx   xx     x      x           x\n\n    Where the inner range contains the central ``p`` proportion of the data,\n    and the outer ranges entends this by a factor of ``s`` either side.\n\n    Parameters\n    ----------\n    x : array\n        Data to trim.\n    p : Proportion of data used to define the 'central' percentile.\n        For example, p=0.5 gives the inter-quartile range.\n    s : Include data up to this factor times the central 'percentile' range\n        away from the central percentile itself.\n\n    Returns\n    xt : array\n        Trimmed data.\n    \"\"\"\n    lb = np.percentile(x, 100 * (1 - p) / 2)\n    ub = np.percentile(x, 100 * (1 + p) / 2)\n    ib = ub - lb\n\n    x = np.array(x)\n    trimmed_x = x[(lb - s * ib < x) & (x < ub + s * ib)]\n\n    return trimmed_x\n\n\ndef _single_random_estimate(A, K, bsz, beta_tol, v0, fn, pos,\n                            tau, tol_scale, seed=None):\n    # choose normal (any LinearOperator) or MPO lanczos tridiag construction\n    if isinstance(A, MatrixProductOperator):\n        lanc_fn = construct_lanczos_tridiag_MPO\n        lanc_opts = {'max_bond': None, 'initial_bond_dim': None}\n    elif isinstance(A, PTPTLazyMPS):\n        lanc_fn = construct_lanczos_tridiag_PTPTLazyMPS\n        lanc_opts = {'max_bond': None, 'initial_bond_dim': None}\n    else:\n        lanc_fn = construct_lanczos_tridiag\n        lanc_opts = {'bsz': bsz}\n\n    # iteratively build the lanczos matrix, checking for convergence\n    estimate = None\n    for alpha, beta, scaling in lanc_fn(\n            A, K=K, beta_tol=beta_tol, seed=seed,\n            v0=v0() if callable(v0) else v0, **lanc_opts):\n\n        try:  # First bound\n            Gf = scaling * calc_trace_fn_tridiag(*lanczos_tridiag_eig(\n                alpha, beta, check_finite=False), fn=fn, pos=pos)\n        except scla.LinAlgError:\n            import warnings\n            warnings.warn(\"Approx Spectral Gf tri-eig didn't converge.\")\n            continue\n\n        # check for break-down convergence (e.g. found entire non-null)\n        if abs(beta[-1]) < beta_tol:\n            estimate = Gf\n            break\n\n        try:  # second bound\n            beta[-1] = beta[0]\n            Rf = scaling * calc_trace_fn_tridiag(*lanczos_tridiag_eig(\n                np.append(alpha, alpha[0]), beta, check_finite=False),\n                fn=fn, pos=pos)\n        except scla.LinAlgError:\n            import warnings\n            warnings.warn(\"Approx Spectral Rf tri-eig didn't converge.\")\n            continue\n\n        # check for error bound convergence\n        if abs(Rf - Gf) < 2 * tau * (abs(Gf) + tol_scale):\n            estimate = (Gf + Rf) / 2\n            break\n\n    # didn't converge, use best estimate\n    if estimate is None:\n        estimate = (Gf + Rf) / 2\n\n    return estimate\n\n\ndef approx_spectral_function(\n        A, fn,\n        tol=5e-3,\n        K=128,\n        R=1024,\n        bsz=1,\n        v0=None,\n        pos=False,\n        tau=1e-3,\n        tol_scale=1,\n        beta_tol=1e-6,\n        mean_p=0.7,\n        mean_s=1.0,\n        mpi=False):\n    \"\"\"Approximate a spectral function, that is, the quantity ``Tr(fn(A))``.\n\n    Parameters\n    ----------\n    A : matrix-like or LinearOperator\n        Operator to approximate spectral function for. Should implement\n        ``A.dot(vec)``.\n    fn : callable\n        Scalar function with with to act on approximate eigenvalues.\n    tol : float, optional\n        Convergence tolerance threshold for error on mean of repeats. This can\n        pretty much be relied on as the overall accuracy. See also\n        ``tol_scale`` and ``tau``. Default: 0.5%.\n    K : int, optional\n        The size of the tri-diagonal lanczos matrix to form. Cost of algorithm\n        scales linearly with ``K``. If ``tau`` is non-zero, this is the\n        maximum size matrix to form. Default: 100.\n    R : int, optional\n        The number of repeats with different initial random vectors to perform.\n        Increasing this should increase accuracy as ``sqrt(R)``. Cost of\n        algorithm thus scales linearly with ``R``. If ``tol`` is non-zero, this\n        is the maximum number of repeats. Default: 100.\n    bsz : int, optional\n        Number of simultenous vector columns to use at once, 1 equating to the\n        standard lanczos method. If ``bsz > 1`` then ``A`` must implement\n        matrix-matrix multiplication. This is a more performant way of\n        essentially increasing ``R``, at the cost of more memory. Default: 10.\n    v0 : vector, or callable\n        Initial vector to iterate with, sets ``R=1`` if given. If callable, the\n        function to produce a random intial vector (sequence).\n    pos : bool, optional\n        If True, make sure any approximate eigenvalues are positive by\n        clipping below 0.\n    tau : float, optional\n        The relative tolerance required for a single lanczos run to converge.\n        This needs to be small enough that each estimate with a single random\n        vector produces an unbiased sample of the operators spectrum.\n        Default: 0.05%.\n    tol_scale : float, optional\n        This sets the overall expected scale of each estimate, so that an\n        absolute tolerance can be used for values near zero. Default: 1.\n    beta_tol : float, optional\n        The 'breakdown' tolerance. If the next beta ceofficient in the lanczos\n        matrix is less that this, implying that the full non-null space has\n        been found, terminate early. Default: 1e-6.\n    mean_p : float, optional\n        Factor for robustly finding mean and err of repeat estimates,\n        see :func:`ext_per_trim`.\n    mean_s : float, optional\n        Factor for robustly finding mean and err of repeat estimates,\n        see :func:`ext_per_trim`.\n\n    Returns\n    -------\n    scalar\n        The approximate value ``Tr(fn(a))``.\n    \"\"\"\n    if (v0 is not None) and not callable(v0):\n        R = 1\n    else:\n        R = max(1, int(R / bsz))\n\n    # generate repeat estimates\n    args = (A, K, bsz, beta_tol, v0, fn, pos, tau, tol_scale)\n    if not mpi:\n        results = iter(_single_random_estimate(*args) for _ in range(R))\n    else:\n        mpi_pool = get_mpi_pool()\n        fs = [mpi_pool.submit(_single_random_estimate, *args, seed=i)\n              for i in range(R)]\n        results = iter(f.result() for f in fs)\n\n    # iterate through estimates, waiting for convergence\n    estimate = None\n    samples = []\n    for _ in range(R):\n        samples.append(next(results))\n        r = len(samples)\n\n        # wait a few iterations before checking error on mean breakout\n        if r >= 4:\n            xtrim = ext_per_trim(samples, p=mean_p, s=mean_s)\n            # sometimes everything is an outlier...\n            if xtrim.size == 0:  # pragma: no cover\n                estimate, sdev = np.mean(samples), np.std(samples)\n            else:\n                estimate, sdev = np.mean(xtrim), np.std(xtrim)\n            err = sdev / (r - 1) ** 0.5\n            if err < tol * (abs(estimate) + tol_scale):\n                return estimate\n\n    if mpi:\n        for f in fs:\n            f.cancel()\n\n    return np.mean(samples) if estimate is None else estimate\n\n\n@functools.wraps(approx_spectral_function)\ndef tr_abs_approx(*args, **kwargs):\n    return approx_spectral_function(*args, fn=abs, **kwargs)\n\n\n@functools.wraps(approx_spectral_function)\ndef tr_exp_approx(*args, **kwargs):\n    return approx_spectral_function(*args, fn=exp, **kwargs)\n\n\n@functools.wraps(approx_spectral_function)\ndef tr_sqrt_approx(*args, **kwargs):\n    return approx_spectral_function(*args, fn=sqrt, pos=True, **kwargs)\n\n\ndef xlogx(x):\n    return x * log2(x) if x > 0 else 0.0\n\n\n@functools.wraps(approx_spectral_function)\ndef tr_xlogx_approx(*args, **kwargs):\n    return approx_spectral_function(*args, fn=xlogx, **kwargs)\n\n\n# --------------------------------------------------------------------------- #\n#                             Specific quantities                             #\n# --------------------------------------------------------------------------- #\n\n\ndef choose_bsz_from_dims(dims, subsys):\n    \"\"\"Try to guess a good blocksize to ensure convergence quickly. Based\n    on some benchmarks, plus the fact that if bsz is > rank, then the lanczos\n    vectors will start to take up the most memory.\n    \"\"\"\n    rank = prod(d for i, d in enumerate(dims) if i not in subsys)\n    return min(max(1, int(rank / 8)), 128)\n\n\ndef entropy_subsys_approx(psi_ab, dims, sysa, bsz=None, **kwargs):\n    \"\"\"Approximate the (Von Neumann) entropy of a pure state's subsystem.\n\n    Parameters\n    ----------\n    psi_ab : ket\n        Bipartite state to partially trace and find entopy of.\n    dims : sequence of int, optional\n        The sub dimensions of ``psi_ab``.\n    sysa : int or sequence of int, optional\n        Index(es) of the 'a' subsystem(s) to keep.\n    bsz : int, optional\n        The size of the lanczos vector blocks to use. If None, guess a good\n        value based on the effective rank of the subsystem.\n    **kwargs\n        See :func:`approx_spectral_function`.\n    \"\"\"\n    lo = LazyPtrOperator(psi_ab, dims=dims, sysa=sysa)\n\n    if bsz is None:\n        bsz = choose_bsz_from_dims(dims, sysa)\n\n    return - tr_xlogx_approx(lo, bsz=bsz, **kwargs)\n\n\ndef tr_sqrt_subsys_approx(psi_ab, dims, sysa, bsz=None, **kwargs):\n    \"\"\"Approximate the trace sqrt of a pure state's subsystem.\n\n    Parameters\n    ----------\n    psi_ab : ket\n        Bipartite state to partially trace and find trace sqrt of.\n    dims : sequence of int, optional\n        The sub dimensions of ``psi_ab``.\n    sysa : int or sequence of int, optional\n        Index(es) of the 'a' subsystem(s) to keep.\n    bsz : int, optional\n        The size of the lanczos vector blocks to use. If None, guess a good\n        value based on the effective rank of the subsystem.\n    **kwargs\n        See :func:`approx_spectral_function`.\n    \"\"\"\n    lo = LazyPtrOperator(psi_ab, dims=dims, sysa=sysa)\n\n    if bsz is None:\n        bsz = choose_bsz_from_dims(dims, sysa)\n\n    return tr_sqrt_approx(lo, bsz=bsz, **kwargs)\n\n\ndef norm_ppt_subsys_approx(psi_abc, dims, sysa, sysb, bsz=None, **kwargs):\n    \"\"\"Estimate the norm of the partial tranpose of a pure state's subsystem.\n    \"\"\"\n    lo = LazyPtrPptOperator(psi_abc, dims=dims, sysa=sysa, sysb=sysb)\n\n    if bsz is None:\n        bsz = choose_bsz_from_dims(dims, sysa + sysb)\n\n    return tr_abs_approx(lo, bsz=bsz, **kwargs)\n\n\ndef logneg_subsys_approx(psi_abc, dims, sysa, sysb, bsz=None, **kwargs):\n    \"\"\"Estimate the logarithmic negativity of a pure state's subsystem.\n\n    Parameters\n    ----------\n    psi_abc : ket\n        Pure tripartite state, for which estimate the entanglement between\n        'a' and 'b'.\n    dims : sequence of int\n        The sub dimensions of ``psi_abc``.\n    sysa : int or sequence of int, optional\n        Index(es) of the 'a' subsystem(s) to keep, with respect to all\n        the dimensions, ``dims``, (i.e. pre-partial trace).\n    sysa : int or sequence of int, optional\n        Index(es) of the 'b' subsystem(s) to keep, with respect to all\n        the dimensions, ``dims``, (i.e. pre-partial trace).\n    bsz : int, optional\n        The size of the lanczos vector blocks to use. If None, guess a good\n        value based on the effective rank of the subsystem.\n    **kwargs\n        See :func:`approx_spectral_function`.\n    \"\"\"\n    return max(log2(norm_ppt_subsys_approx(psi_abc, dims, sysa, sysb,\n                                           bsz=bsz, **kwargs)), 0.0)\n\n\ndef negativity_subsys_approx(psi_abc, dims, sysa, sysb, bsz=None, **kwargs):\n    \"\"\"Estimate the negativity of a pure state's subsystem.\n\n    Parameters\n    ----------\n    psi_abc : ket\n        Pure tripartite state, for which estimate the entanglement between\n        'a' and 'b'.\n    dims : sequence of int\n        The sub dimensions of ``psi_abc``.\n    sysa : int or sequence of int, optional\n        Index(es) of the 'a' subsystem(s) to keep, with respect to all\n        the dimensions, ``dims``, (i.e. pre-partial trace).\n    sysa : int or sequence of int, optional\n        Index(es) of the 'b' subsystem(s) to keep, with respect to all\n        the dimensions, ``dims``, (i.e. pre-partial trace).\n    bsz : int, optional\n        The size of the lanczos vector blocks to use. If None, guess a good\n        value based on the effective rank of the subsystem.\n    **kwargs\n        See :func:`approx_spectral_function`.\n    \"\"\"\n    return max((norm_ppt_subsys_approx(psi_abc, dims, sysa, sysb,\n                                       bsz=bsz, **kwargs) - 1) / 2, 0.0)\n\n\ndef gen_bipartite_spectral_fn(exact_fn, approx_fn, pure_default):\n    \"\"\"Generate a function that computes a spectral quantity of the subsystem\n    of a pure state. Automatically computes for the smaller subsystem, or\n    switches to the approximate method for large subsystems.\n\n    Parameters\n    ----------\n    exact_fn : callable\n        The function that computes the quantity on a density matrix, with\n        signature: ``exact_fn(rho_a, rank=...)``.\n    approx_fn : callable\n        The function that approximately computes the quantity using a lazy\n        representation of the whole system. With signature\n        ``approx_fn(psi_ab, dims, sysa, **approx_opts)``.\n    pure_default : float\n        The default value when the whole state is the subsystem.\n\n    Returns\n    -------\n    bipartite_spectral_fn : callable\n        The function, with signature:\n        ``(psi_ab, dims, sysa, approx_thresh=2**12, **approx_opts)``\n    \"\"\"\n    def bipartite_spectral_fn(psi_ab, dims, sysa, approx_thresh=2**12,\n                              **approx_opts):\n        sysa = int2tup(sysa)\n        sz_a = prod(d for i, d in enumerate(dims) if i in sysa)\n        sz_b = prod(dims) // sz_a\n\n        # pure state\n        if sz_b == 1:\n            return pure_default\n\n        # also check if system b is smaller, since spectrum is same for both\n        if sz_b < sz_a:\n            # if so swap things around\n            sz_a, sz_b = sz_b, sz_a\n            sysb = [i for i in range(len(dims)) if i not in sysa]\n            sysa = sysb\n\n        # check whether to use approx lanczos method\n        if (approx_thresh is not None) and (sz_a >= approx_thresh):\n            return approx_fn(psi_ab, dims, sysa, **approx_opts)\n\n        rho_a = ptr(psi_ab, dims, sysa)\n        return exact_fn(rho_a)\n\n    return bipartite_spectral_fn\n","sub_path":"quimb/linalg/approx_spectral.py","file_name":"approx_spectral.py","file_ext":"py","file_size_in_byte":35336,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"202335172","text":"class Group(object):\n    def __init__(self, _name):\n        self.name = _name\n        self.groups = []\n        self.users = []\n\n    def add_group(self, group):\n        self.groups.append(group)\n\n    def add_user(self, user):\n        self.users.append(user)\n\n    def get_groups(self):\n        return self.groups\n\n    def get_users(self):\n        return self.users\n\n    def get_name(self):\n        return self.name\n\n\ndef is_user_in_group(user, group):\n    \"\"\"\n    Return True if user is in the group, False otherwise.\n\n    Args:\n      user(str): user name/id\n      group(class:Group): group to check user membership against\n    \"\"\"\n    if user is \"\" or group is \"\":\n        return False\n    \n    if user in group.get_users():\n        return True\n\n    for a_group in group.get_groups():\n        if is_user_in_group(user, a_group):\n            return True\n\n    return False\n\n\ndef test():\n    parent = Group(\"parent\")\n    child = Group(\"child\")\n    child2 = Group(\"child2\")\n    sub_child = Group(\"subchild\")\n    sub_child2 = Group(\"subchild2\")\n\n    sub_child_user = \"sub_child_user\"\n    sub_child.add_user(sub_child_user)\n\n    child.add_group(sub_child)\n    parent.add_group(child)\n    \n    sub_child_user2 = \"sub_child_user2\"\n    sub_child2.add_user(sub_child_user2)\n\n    child2.add_group(sub_child2)\n    parent.add_group(child2)\n\n    # Test normal cases\n    assert is_user_in_group(sub_child_user, parent) is True\n    assert is_user_in_group(sub_child_user, child) is True\n    assert is_user_in_group(\"child_user\", child) is False\n    assert is_user_in_group(sub_child_user2, parent) is True\n\n    # Test empty strings/inputs\n    assert is_user_in_group(\"\", parent) is False\n    assert is_user_in_group(sub_child_user, \"\") is False\n    assert is_user_in_group(\"\", \"\") is False\n\n    print(\"Finished testing\")\n\ntest()\n","sub_path":"active_directory.py","file_name":"active_directory.py","file_ext":"py","file_size_in_byte":1812,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"264074894","text":"from __future__ import absolute_import\nfrom __future__ import unicode_literals\n\nfrom collections import OrderedDict\n\nfrom django.test import SimpleTestCase\n\nfrom corehq.form_processor.utils.xform import build_form_xml_from_property_dict\n\n\nclass FormSubmissionBuilderTest(SimpleTestCase):\n\n    def test_property_dict_to_xml_empty(self):\n        self.assertEqual('', build_form_xml_from_property_dict({}))\n\n    def test_property_dict_to_xml_single_item(self):\n        self.assertEqual(\n            '<my-property>some value</my-property>',\n            build_form_xml_from_property_dict({'my-property': 'some value'})\n        )\n\n    def test_property_dict_to_xml_multiple_item(self):\n        props = OrderedDict()\n        props['p1'] = 'v1'\n        props['p2'] = 'v2'\n        self.assertEqual(\n            '<p1>v1</p1><p2>v2</p2>',\n            build_form_xml_from_property_dict(props)\n        )\n\n    def test_invalid_tag(self):\n        with self.assertRaises(ValueError):\n            build_form_xml_from_property_dict({'no spaces': 'are allowed'})\n","sub_path":"corehq/form_processor/tests/test_utils.py","file_name":"test_utils.py","file_ext":"py","file_size_in_byte":1044,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"633048949","text":"import streamlit as st\nimport sqlite3\nfrom sqlite3 import Error\nimport pandas as pd \nfrom datetime import datetime\nfrom automate_upload import trigger_upload\nimport os \n\ndef create_connection(db_file):\n    conn = None\n    try:\n        conn = sqlite3.connect(db_file)\n        return conn\n    except Error as e:\n        print(e)\n    return conn\n\ndef update_data(input_condition, conn, updated_data):\n    sql_statement = ''' UPDATE qnadata SET '''\n    if conn is not None:\n        sql_statement += input_condition\n        cur = conn.cursor()\n        cur.execute(sql_statement, updated_data)\n        conn.commit()\n        \ndef check_id_exists(conn):\n    if conn is not None:\n        sql_statement = ''' SELECT DISTINCT(query_id) FROM qnadata '''\n        cur = conn.cursor()\n        cur.execute(sql_statement)\n        rows = cur.fetchall()\n        return [row[0] for row in rows]\n        \ndef app():\n    conn = create_connection('qna_data.db')\n    st.title('GoldenRetriever')\n    st.header('This front end application allows you to easily update existing data in a simple and clear interface.')\n    st.markdown('View the source code [here](https://github.com/aimakerspace/goldenretriever)!')\n    st.markdown('Visit our [community](https://makerspace.aisingapore.org/community/ai-makerspace/) and ask us a question!')\n    st.markdown('Note that you are able to update ``either question or answer`` or ``both`` at the same time!')\n    st.button('Page Refresh!')\n    data_id = st.empty()\n    qn_text = st.empty()\n    ans_text = st.empty()\n    value = \" \"\n    if st.button('Reset Input Value'):\n        value = \"\"\n    \n    DATA_ID = data_id.text_input('Input the existing question id or answer id here (Compulsory)', value)\n    question = qn_text.text_area('Input the updated question here (Optional)', value) \n    answer = ans_text.text_area('Input the updated answer here (Optional)', value)\n    \n    if st.button('Update Data'):\n        if (DATA_ID.strip() == ''):\n            st.text('Please enter your data id!')\n        elif (int(DATA_ID) not in check_id_exists(conn)):\n            st.text('The data id does not exist in the database!')\n        elif (question.strip() == '' and answer.strip() == ''):\n            st.text('Please input at least a question or an answer')\n        elif (question.strip() != '' and answer.strip() == ''):\n            st.text('You are going to update a question')\n            update_data('query_str = ? WHERE query_id = ?', conn, [question, DATA_ID])\n        elif (question.strip() == '' and answer.strip() != ''):\n            st.text('You are going to update an answer')\n            update_data('ans_str = ? WHERE query_id = ?', conn, [answer, DATA_ID])\n        elif (question.strip() != '' and answer.strip() != ''):\n            st.text('You are going to update both question and answer.')\n            update_data('query_str = ?, ans_str = ? WHERE query_id = ?', conn, [question, answer, DATA_ID])\n\n    st.header('Database Preview')\n    \n    df = pd.read_sql_query(\"SELECT query_id, query_str, ans_id, ans_str FROM qnadata\", conn)\n    \n    st.write(df)\n    \n    if (st.button(\"Export Data\")):\n        now = datetime.now()\n        formatted_date = now.strftime(\"%d_%m_%Y_%H_%M_%S\") \n        display_date = now.strftime(\"%d/%m/%Y, %H:%M:%S\")\n        try:\n            df.to_csv(f'data/sample_duke_hr.csv')\n            result = trigger_upload('http://localhost:9200/', 'data/sample_duke_hr.csv', 'duke_hr')\n            st.markdown(f'You have successfully saved your data in the ``data`` folder and also in ``elk`` at {display_date}!')\n            print(result)\n            st.balloons()\n        except Exception as e:\n            print(e)\n            st.write('An error occured. Please try again!')\n    elif (st.button(\"Reset Database\")):\n        try:\n            os.remove('qna_data.db')\n            st.markdown(f'The database has been successfully resetted! Please refresh the website.')\n        except FileNotFoundError:\n            st.write('The database file does not exist! Please refresh the website!')\n        except Exception as e:\n            print(e)\n            st.write('An error occured. Please try again!')","sub_path":"app/streamlit/data_update.py","file_name":"data_update.py","file_ext":"py","file_size_in_byte":4144,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"328761517","text":"import argparse\r\nimport logging\r\nimport os\r\nimport sys\r\n\r\nimport numpy as np\r\nimport torch\r\nimport torch.nn as nn\r\nfrom torch import optim\r\nfrom tqdm import tqdm\r\nfrom sklearn import metrics\r\n\r\nfrom eval import eval_net\r\nfrom eval import eval_net1\r\nfrom eval import pixel_accuracy\r\nfrom unet import UNet\r\nfrom utils import get_ids, split_train_val, get_imgs_and_masks, batch\r\n\r\ndir_img = 'data/imgs/train/'\r\ndir_mask = 'data/masks/train_masks/'\r\ndir_checkpoint = 'checkpoints/'\r\n\r\n\r\ndef train_net(net,\r\n              device,\r\n              epochs=5,\r\n              batch_size=1,\r\n              lr=0.1,\r\n              val_percent=0.15,\r\n              save_cp=True,\r\n              img_scale=0.5):\r\n    ids = get_ids(dir_img)\r\n\r\n    iddataset = split_train_val(ids, val_percent)\r\n\r\n    logging.info('''Starting training:\r\n        Epochs:          {epochs}\r\n        Batch size:      {batch_size}\r\n        Learning rate:   {lr}\r\n        Training size:   {len(iddataset[\"train\"])}\r\n        Validation size: {len(iddataset[\"val\"])}\r\n        Checkpoints:     {save_cp}\r\n        Device:          {device.type}\r\n        Images scaling:  {img_scale}\r\n    ''')\r\n\r\n    n_train = len(iddataset['train'])\r\n    n_val = len(iddataset['val'])\r\n    optimizer = optim.Adam(net.parameters(), lr=lr)\r\n    if net.n_classes > 1:\r\n        criterion = nn.CrossEntropyLoss()\r\n    else:\r\n        criterion = nn.BCEWithLogitsLoss()\r\n\r\n    for epoch in range(epochs):\r\n        net.train()\r\n\r\n        # reset the generators\r\n        train = get_imgs_and_masks(iddataset['train'], dir_img, dir_mask, img_scale)\r\n        val = get_imgs_and_masks(iddataset['val'], dir_img, dir_mask, img_scale)\r\n\r\n        epoch_loss = 0\r\n        f1_score = 0\r\n        num = 0\r\n        with tqdm(total=n_train, desc='Epoch {epoch + 1}/{epochs}', unit='img') as pbar:\r\n            for i, b in enumerate(batch(train, batch_size)):\r\n                imgs = np.array([i[0] for i in b]).astype(np.float32)\r\n                true_masks = np.array([i[1] for i in b])\r\n\r\n                imgs = torch.from_numpy(imgs)\r\n                true_masks = torch.from_numpy(true_masks)\r\n\r\n                imgs = imgs.to(device=device)\r\n                true_masks = true_masks.to(device=device)\r\n\r\n                masks_pred = net(imgs)\r\n                # print('mask:',masks_pred.size())\r\n                # print('lab:',true_masks.size())\r\n                loss = criterion(masks_pred, true_masks)\r\n                masks_pred_np = masks_pred.detach().cpu().numpy()\r\n                true_masks_np = true_masks.detach().cpu().numpy()\r\n                epoch_loss += loss.item()\r\n\r\n                # print(\"----------------------------------------\")\r\n                # print('masks_pred',type(masks_pred),masks_pred,'\\n')\r\n                # print('true_masks',type(true_masks),true_masks,'\\n')\r\n                # print('mask:',masks_pred.size(),'\\n')\r\n                # print('lab:',true_masks.size(),'\\n')\r\n                pre_2D = np.array(masks_pred_np[0][0])\r\n                true_2D = np.array(true_masks_np[0][0])\r\n                pre_2D_threhold = pre_2D\r\n                pre_2D_threhold[pre_2D_threhold > 0.5] = 1\r\n                pre_2D_threhold[pre_2D_threhold <= 0.5] = 0\r\n                # print(\"pre_2D.shape\",pre_2D.shape,'\\n')\r\n                # print(\"true_2D.shape\" ,true_2D.shape,'\\n')\r\n                # print(\"true_2D.flatten()\",true_2D.flatten(),'\\n')\r\n                # print(\"pre_2D.flatten()\",pre_2D.flatten(),'\\n')\r\n                pixel_accuracy = (pre_2D, true_2D)\r\n                f1_score += metrics.f1_score(true_2D.flatten(), pre_2D_threhold.flatten())\r\n                num = num + 1\r\n                # print(\"----------------------------------------\")\r\n\r\n                # val_score1 = eval_net1(net,val,device,n_val)\r\n\r\n                pbar.set_postfix(**{'loss (batch)': loss.item()})\r\n\r\n                optimizer.zero_grad()\r\n                loss.backward()\r\n                optimizer.step()\r\n\r\n                pbar.update(batch_size)\r\n\r\n        if save_cp:\r\n            try:\r\n                os.mkdir(dir_checkpoint)\r\n                logging.info('Created checkpoint directory')\r\n            except OSError:\r\n                pass\r\n            torch.save(net.state_dict(),\r\n                       dir_checkpoint + 'CP_epoch{epoch + 1}.pth')\r\n            logging.info('Checkpoint {epoch + 1} saved !')\r\n\r\n        val_score = eval_net(net, val, device, n_val)\r\n        f1_score /= num\r\n        print(\"f1-score:\", f1_score, '\\n')\r\n        if net.n_classes > 1:\r\n            logging.info('Validation cross entropy: {}'.format(val_score))\r\n\r\n        else:\r\n            logging.info('Validation Dice Coeff: {}'.format(val_score))\r\n\r\n\r\ndef get_args():\r\n    parser = argparse.ArgumentParser(description='Train the UNet on images and target masks',\r\n                                     formatter_class=argparse.ArgumentDefaultsHelpFormatter)\r\n    parser.add_argument('-e', '--epochs', metavar='E', type=int, default=100,\r\n                        help='Number of epochs', dest='epochs')\r\n    parser.add_argument('-b', '--batch-size', metavar='B', type=int, nargs='?', default=1,\r\n                        help='Batch size', dest='batchsize')\r\n    parser.add_argument('-l', '--learning-rate', metavar='LR', type=float, nargs='?', default=0.1,\r\n                        help='Learning rate', dest='lr')\r\n    parser.add_argument('-f', '--load', dest='load', type=str, default=False,\r\n                        help='Load model from a .pth file')\r\n    parser.add_argument('-s', '--scale', dest='scale', type=float, default=0.5,\r\n                        help='Downscaling factor of the images')\r\n    parser.add_argument('-v', '--validation', dest='val', type=float, default=15.0,\r\n                        help='Percent of the data that is used as validation (0-100)')\r\n\r\n    return parser.parse_args()\r\n\r\n\r\ndef pretrain_checks():\r\n    imgs = [f for f in os.listdir(dir_img) if not f.startswith('.')]\r\n    masks = [f for f in os.listdir(dir_mask) if not f.startswith('.')]\r\n    if len(imgs) != len(masks):\r\n        logging.warning('The number of images and masks do not match ! '\r\n                        '{len(imgs)} images and {len(masks)} masks detected in the data folder.')\r\n\r\n\r\nif __name__ == '__main__':\r\n    logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')\r\n    args = get_args()\r\n    pretrain_checks()\r\n    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\r\n    logging.info('Using device {device}')\r\n\r\n    print(args)\r\n\r\n    # Change here to adapt to your data\r\n    # n_channels=3 for RGB images\r\n    # n_classes is the number of probabilities you want to get per pixel\r\n    #   - For 1 class and background, use n_classes=1\r\n    #   - For 2 classes, use n_classes=1\r\n    #   - For N > 2 classes, use n_classes=N\r\n    net = UNet(n_channels=3, n_classes=1)\r\n    net.cuda()\r\n    logging.info('Network:\\n'\r\n                 '\\t{net.n_channels} input channels\\n'\r\n                 '\\t{net.n_classes} output channels (classes)\\n'\r\n                 '\\t{\"Bilinear\" if net.bilinear else \"Dilated conv\"} upscaling')\r\n\r\n    if args.load:\r\n        net.load_state_dict(\r\n            torch.load(args.load, map_location=device)\r\n        )\r\n        logging.info('Model loaded from {args.load}')\r\n\r\n    net.to(device=device)\r\n    # faster convolutions, but more memory\r\n    # cudnn.benchmark = True\r\n\r\ntry:\r\n    train_net(net=net,\r\n              epochs=args.epochs,\r\n              batch_size=args.batchsize,\r\n              lr=args.lr,\r\n              device=device,\r\n              img_scale=args.scale,\r\n              val_percent=args.val / 100)\r\n    torch.save(net.state_dict(), 'MODEL.pth')\r\n\r\nexcept KeyboardInterrupt:\r\n    torch.save(net.state_dict(), 'INTERRUPTED.pth')\r\n    logging.info('Saved interrupt')\r\n    try:\r\n        sys.exit(0)\r\n    except SystemExit:\r\n        os._exit(0)\r\n","sub_path":"u_net_main(f1_score).py","file_name":"u_net_main(f1_score).py","file_ext":"py","file_size_in_byte":7870,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"363666542","text":"squares = {num: num*num for num in range(10)}\nprint(squares.items())\n\nset = {num for num in range(10) if num % 2 == 1}\nprint(set)\n\n\ndef get_odds(first = 0, last = 10, step = 1):\n    number = first\n    while number < last:\n        if number % 2 == 1:\n            yield number\n        number += step\n\ni = 0\nfor num in get_odds():\n    i += 1\n    if i == 3:\n        print(num)\n\n\ndef test(func):\n    def new(*args, **kwargs):\n        print('start')\n        result = func(*args, **kwargs)\n        print(result)\n        print('end')\n    return new\n","sub_path":"book 4 train.py","file_name":"book 4 train.py","file_ext":"py","file_size_in_byte":541,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"497720715","text":"#!/usr/bin/env python\n# _*_ coding: UTF-8 _*_\n'''\n@author: jianhua.he@tcl.com\n@date: 2016-04-01\n'''\n\nimport os, sys, time\nimport __flag_pth\nimport threading\nimport logging\n\n#the module from parent dir\nif sys.path[0] == os.path.split(os.path.realpath(__file__))[0]:\n    #print os.path.split(os.path.realpath(__file__))[0]\n    sys.path.append(__flag_pth.get_script_root_path())\n\nfrom manager import action_manager\nfrom manager import constant_state_manager\nfrom manager import adb_status_manager\nfrom manager import battery_limit_manager\nfrom manager import test_app_info_manager\nfrom manager import testing_manager\n\ntry:\n    from uiautomator import Device\nexcept:\n    try:\n        from uiautomator.uiautomator import Device\n    except:\n        logging.error(\"please install the uiautomator\")\n\n__action_num=3\nDEBUG = True\n\ndef __phone_initialize():\n    \"\"\"\n    phone state & all the apk need initialize\n    \"\"\"\n    battery_limit_manager.check_battery_limit(__action_num)\n    action_manager.print_action(__action_num)\n    device_id = adb_status_manager.AdbState().get_device_id()\n    \n    logging.debug(\"device_id=\"+device_id)\n    logging.debug(\"DEBUG = \"+str(DEBUG))\n    \n    ################# algorithm ########################\n    logging.debug(\"init: install performance tool box to device\")\n    perftoolbox = sys.path[0]+os.sep+\"libs\"+os.sep+\"PerfToolbox.apk\"#TODO sys.path[0] have issue maybe\n    return_values = os.popen(\"adb -s \"+device_id+\" install -r \"+perftoolbox).readlines()\n    for line in return_values:\n        logging.debug(line)\n    logging.info(\"init: start performance tool box service\")\n    return_values = os.popen(\"adb -s \"+device_id+\" shell am startservice -n com.hogee.perftoolbox/.LimitQuikSettingService\").readlines()\n    for line in return_values:\n        logging.debug(line)\n        \n    app_info = test_app_info_manager.get_apps_info_with_activity(constant_state_manager.get_appinfo_path())\n    \n    device = Device(device_id)#use uiautomator\n    running_count = 0\n    for app in app_info:\n        running_count += 1\n        logging.info(\"**********speed : \"+str(running_count)+\" / \"+str(len(app_info))+\" **********\")\n        pakname = test_app_info_manager.get_package_name(app)\n        adb_status_manager.AdbState().check_adb_status()\n        \n        battery_limit_manager.screen_on_off(device_id, \"on\")\n        \n        ###start app\n        thread_start = testing_manager.ThreadStartApp(app, device_id)\n        start_time = time.time()\n        thread_start.start()\n        thread_start.join(10)\n        now_time = time.time()\n        if thread_start.is_alive():\n            logging.warning(\"thread_start still alive, need force exit!\")\n            testing_manager.force_stop_process(device_id, pakname)\n            testing_manager.double_homekey_exit(device_id)\n            time.sleep(2)\n        \n        time.sleep(6)\n        \n        ###go through like permission allow\n        thread_start = ThreadGoThroughApp(device, pakname)\n        start_time = time.time()\n        thread_start.start()\n        thread_start.join(4 * 60)\n        now_time = time.time()\n        if thread_start.is_alive():\n            logging.warning(\"auto allow thread still alive, need force exit!\")\n            thread_start.set_thread_interrupt(True)\n            time.sleep(1)\n            testing_manager.force_stop_process(device_id, pakname)\n            testing_manager.double_homekey_exit(device_id)\n            time.sleep(2)\n        \n        ### initialize use monkey\n        thread_monkey = testing_manager.ThreadRunningMonkey(pakname, device_id, 1000, False)\n        monkey_start_time = time.time()\n        thread_monkey.start()\n        thread_monkey.join(120)\n        monkey_now_time = time.time()\n        time.sleep(1)\n        if thread_monkey.is_alive():\n            logging.warning(\"init monkey thread still alive, need force exit!\")\n            testing_manager.force_stop_process(device_id, pakname)\n            testing_manager.double_homekey_exit(device_id)\n            time.sleep(2)\n        \n        testing_manager.swipe_systemui_up(device_id)\n        \n        double_homekey_exit_app(device_id)\n        \n    time.sleep(4)\n    logging.info(\"init: stop performance tool box service\")\n    return_values = os.popen(\"adb -s \"+device_id+\" shell am stopservice -n com.hogee.perftoolbox/.LimitQuikSettingService\").readlines()\n    for line in return_values:\n        logging.debug(line)\n    ################# algorithm ########################\n    time.sleep(4)\n    testing_manager.clean_recent_app(device_id)\n    logging.info(\"phone initialize DONE!!!\")\n    \ndef double_homekey_exit_app(device_id):\n    logging.debug(\"double - exit home key\")\n    cmd_line1 = \"adb -s \" + device_id + \" shell input keyevent 3\"#home key#QCOM MTK same command\n    os.popen(cmd_line1)\n    time.sleep(0.05)\n    os.popen(cmd_line1)\n    time.sleep(1)\n    \n\nclass ThreadGoThroughApp(threading.Thread):\n    def __init__(self, device, pakname):\n        threading.Thread.__init__(self)\n        self.device = device\n        self.pakname = pakname\n        self.interrupt = False\n    \n    def set_thread_interrupt(self, interrupt = True):\n        self.interrupt = interrupt\n    \n    def run(self):\n        for i in range(7):\n            if self.interrupt:\n                return\n            \n            logging.info(\"auto allow some thing!\")\n            \n            self.device.watcher(\"auto_next\").when(text=u'下一步').click(text=u'下一步')\n            self.device.watcher(\"auto_next_2\").when(text=u'Next').click(text=u'Next')\n            self.device.watcher(\"auto_got_it\").when(text=u'Got it').click(text=u'Got it')\n            self.device.watcher(\"auto_accept\").when(text=u'接受').click(text=u'接受')\n            self.device.watcher(\"auto_accept_2\").when(text=u'Accept').click(text=u'Accept')\n            self.device.watcher(\"auto_cancel\").when(resourceId=self.pakname+':id/negativeButton').click(resourceId=self.pakname+':id/negativeButton')\n            self.device.watcher(\"auto_allow\").when(resourceId='com.android.packageinstaller:id/permission_message').click(resourceId='com.android.packageinstaller:id/permission_allow_button')\n            self.device.watchers.run()\n            time.sleep(2)\n        logging.info(\"auto allow OK\")\n        self.device.watcher(\"auto_next\").remove()\n        self.device.watcher(\"auto_next_2\").remove()\n        self.device.watcher(\"auto_got_it\").remove()\n        self.device.watcher(\"auto_accept\").remove()\n        self.device.watcher(\"auto_accept_2\").remove()\n        self.device.watcher(\"auto_cancel\").remove()\n        self.device.watcher(\"auto_allow\").remove()\n        \n        wm_size = constant_state_manager.WMsize()\n        x1 = wm_size.width * 0.8\n        x2 = wm_size.width * 0.1\n        y1 = wm_size.height * 0.7\n        y2 = y1\n        #cmd_line = \"adb -s \"+ device_id +\" shell input swipe \"+str(x1)+\" \"+str(y1)+\" \"+str(x2)+\" \"+str(y2)##ready TODO\n        for i in range(6):\n            logging.debug(\"swipe ...\")\n            self.device.swipe(x1, y1, x2, y2, steps=10)\n            time.sleep(2)\n            if i % 2 == 0:\n                self.auto_allow()\n            if self.interrupt:\n                return\n        \n        self.auto_allow()\n        if self.interrupt:\n            return\n        logging.info(\"swipe for initialize done\")\n        time.sleep(4)\n    \n    def auto_allow(self):\n        logging.info(\"auto allow some thing when swipe!\")\n        self.device.watcher(\"auto_experience\").when(text=u'立即体验').click(text=u'立即体验')\n        self.device.watcher(\"auto_jump\").when(text=u'跳过>').click(text=u'跳过>')\n        self.device.watcher(\"auto_jump_ad\").when(text=u'跳过广告').click(text=u'跳过广告>')\n        self.device.watcher(\"auto_next\").when(text=u'下一步').click(text=u'下一步')\n        self.device.watcher(\"auto_next_2\").when(text=u'Next').click(text=u'Next')\n        self.device.watcher(\"auto_got_it\").when(text=u'Got it').click(text=u'Got it')\n        self.device.watcher(\"auto_accept\").when(text=u'接受').click(text=u'接受')\n        self.device.watcher(\"auto_accept_2\").when(text=u'Accept').click(text=u'Accept')\n        self.device.watcher(\"auto_cancel\").when(resourceId=self.pakname+':id/negativeButton').click(resourceId=self.pakname+':id/negativeButton')\n        self.device.watcher(\"auto_allow\").when(resourceId='com.android.packageinstaller:id/permission_message').click(resourceId='com.android.packageinstaller:id/permission_allow_button')\n        \n        if self.interrupt:\n            return\n        self.device.watchers.run()\n        time.sleep(2)\n        #self.device.watchers().removeAll()##ready TODO\n        logging.info(\"auto allow OK\")\n        self.device.watcher(\"auto_experience\").remove()\n        self.device.watcher(\"auto_jump\").remove()\n        self.device.watcher(\"auto_jump_ad\").remove()\n        self.device.watcher(\"auto_next\").remove()\n        self.device.watcher(\"auto_next_2\").remove()\n        self.device.watcher(\"auto_got_it\").remove()\n        self.device.watcher(\"auto_accept\").remove()\n        self.device.watcher(\"auto_accept_2\").remove()\n        self.device.watcher(\"auto_cancel\").remove()\n        self.device.watcher(\"auto_allow\").remove()\n\ndef main():\n    __phone_initialize()\n\nif __name__ == '__main__':\n    main()\n","sub_path":"performance/python_tools/auto_performance_stress/phoneinitialize/phone_initialize.py","file_name":"phone_initialize.py","file_ext":"py","file_size_in_byte":9255,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"198650280","text":"import os\nfrom surprise import Dataset\nfrom surprise import Reader\nfrom surprise.model_selection import cross_validate\nfrom surprise.prediction_algorithms.knns import KNNWithMeans\nfrom surprise.similarities import pearson\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfile_path = os.path.expanduser('ml-latest-small/ratings_var.csv')\nreader = Reader(sep=',')\ndata = Dataset.load_from_file(file_path, reader=reader)\n# data = Dataset.load_builtin('ml-100k')\n\nsim_options = {'name': 'pearson',\n              'user_based': True\n              }\n\navg_rmse = []\navg_mae = []\nall_k = []\n\nfor i in range(2,102,2):\n  print('k = ',i)\n  all_k.append(i)\n\n  algo = KNNWithMeans(k=i, sim_options=sim_options)\n  output = cross_validate(algo, data, measures=['RMSE', 'MAE'], cv=10,  verbose=True, n_jobs=1)\n  avg_rmse.append(np.mean(output['test_rmse']))\n  avg_mae.append(np.mean(output['test_mae']))\n\nprint(\"min rmse k:\", avg_rmse.index(min(avg_rmse)))\nprint(\"min rmse:\", min(avg_rmse))\nprint(\"min mae k:\", avg_mae.index(min(avg_mae)))\nprint(\"min mae:\", min(avg_mae))\n\nplt.plot(all_k,avg_rmse)\nplt.savefig('plot/Q14_rmse_k_var.png')\nplt.clf()\n\nplt.plot(all_k,avg_mae)\nplt.savefig('plot/Q14_mae_k_var.png')\nplt.clf()\n\n\n\n\n\n","sub_path":"Q14.py","file_name":"Q14.py","file_ext":"py","file_size_in_byte":1211,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"102015908","text":"#coding:utf-8\n#user\n\nfrom flask import Blueprint, render_template, request\nfrom controller.News import News\nimport json\nnews = Blueprint('news', __name__)\n\n\n@news.route('/index/<int:id>', methods=['GET', 'POST'])\ndef index(id):\n    params = request.form\n    return json.dumps(id)\n    news_hdl = News()\n    data = news_hdl.list_show()\n    context = {\n        \"list\": data\n    }\n    return render_template('news/index.html', **context)\n\n# @news.route('/index/<int:id>', methods=['GET', 'POST'])\n# def index(id):\n#     params = request.form\n#     return json.dumps(id)","sub_path":"routes/news.py","file_name":"news.py","file_ext":"py","file_size_in_byte":565,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"423588556","text":"#!/usr/bin/env python3\n\n# Copyright (C) 2015 Commerce Technologies, Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#   http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport argparse\nimport os\n\nfrom pump_client.PumpClient import PumpClient\n\ndef get_argument_parser():\n    _fc_description = \"Client to manage and monitor Pump.\"\n\n    _query_args = [\"-q\", \"--query\"]\n    _query_kwargs = dict(action=\"store\", help=\"Specify the query Pump will use to pull records out of Drill.\", required=True, type=str)\n\n    _stream_args = [\"-s\", \"--stream\"]\n    _stream_kwargs = dict(action=\"store\", help=\"Specify the Kinesis stream that Pump will emit records to.\", required=True, type=str)\n\n    _num_records_args = [\"-n\", \"--num-records\"]\n    _num_records_kwargs = dict(action=\"store\", help=\"Specify the number of records to return in the preview.\", required=True, type=int)\n\n    _job_id_args = [\"-id\", \"--job-id\"]\n    _job_id_kwargs = dict(action=\"store\", help=\"Specify the Job ID to retrieve.\", required=True, type=str)\n\n    _show_progress_args = [\"-p\", \"--show-progress\"]\n    _show_progress_kwargs = dict(action=\"store_const\", const=True, required=False, help=\"Poll Pump for progress of job\")\n\n    _summary_only_args = [\"-t\", \"--summary-only\"]\n    _summary_only_kwargs = dict(action=\"store_true\", required=False, help=\"Only show summary information about the Job\")\n\n    _replay_flag_args = [\"-r\", \"--replay\"]\n    _replay_flag_kargs = dict(action=\"store_true\", required=False, help=\"Add replay flag to documents\")\n\n    _overwrite_flag_args = [\"-o\", \"--overwrite\"]\n    _overwrite_flag_kargs = dict(action=\"store_true\", required=False, help=\"Add overwrite flag to documents\")\n\n\n    parser = argparse.ArgumentParser(\n        prog=os.path.dirname(__file__),\n        description=_fc_description,\n        formatter_class=argparse.RawDescriptionHelpFormatter\n    )\n\n    subparsers = parser.add_subparsers()\n\n    create_job_parser = subparsers.add_parser('create-job', help=\"Enqueue a job for Pump to work on.\")\n    create_job_parser.set_defaults(which=\"create-job\")\n    create_job_parser.add_argument(*_query_args, **_query_kwargs)\n    create_job_parser.add_argument(*_stream_args, **_stream_kwargs)\n    create_job_parser.add_argument(*_show_progress_args, **_show_progress_kwargs)\n    create_job_parser.add_argument(*_replay_flag_args, **_replay_flag_kargs)\n    create_job_parser.add_argument(*_overwrite_flag_args, **_overwrite_flag_kargs)\n\n    preview_job_parser = subparsers.add_parser('preview-job', help=\"Preview a job before Pump runs it.\")\n    preview_job_parser.set_defaults(which=\"preview-job\")\n    preview_job_parser.add_argument(*_query_args, **_query_kwargs)\n    preview_job_parser.add_argument(*_num_records_args, **_num_records_kwargs)\n\n    get_job_parser = subparsers.add_parser('get-job', help=\"Retreive Job status from Pump.\")\n    get_job_parser.set_defaults(which=\"get-job\")\n    get_job_parser.add_argument(*_job_id_args, **_job_id_kwargs)\n    get_job_parser.add_argument(*_show_progress_args, **_show_progress_kwargs)\n    get_job_parser.add_argument(*_summary_only_args, **_summary_only_kwargs)\n\n    get_all_jobs_parser = subparsers.add_parser('get-all-jobs', help=\"Retreive all Jobs and their statuses from Pump.\")\n    get_all_jobs_parser.set_defaults(which=\"get-all-jobs\")    \n    get_all_jobs_parser.add_argument(*_summary_only_args, **_summary_only_kwargs)\n\n    return parser\n\nif __name__ == \"__main__\":\n    parser = get_argument_parser()\n    args = parser.parse_args()\n    if(not vars(args)):\n        parser.print_help()\n        parser.exit(1)\n        \n    pumpClient = PumpClient('http://localhost:8080/pump')\n    \n    config = dict()\n    if hasattr(args, 'which'):\n        if args.which == \"create-job\":\n            pumpClient.create_job(args.query, args.stream, args.show_progress, args.replay, args.overwrite)\n              \n        elif args.which == \"get-job\":\n            pumpClient.get_job(args.job_id, args.show_progress, args.summary_only)\n        \n        elif args.which == \"get-all-jobs\":\n            pumpClient.get_all_jobs(args.summary_only)\n        \n        elif args.which == \"preview-job\":\n            pumpClient.preview_job(args.query, args.num_records)\n        \n        else:\n            parser.print_help()\n            exit(2)\n            \n    else:\n        parser.print_help()\n        exit(2)\n        ","sub_path":"pump_client/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":4777,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"542274958","text":"from django.shortcuts import render\nfrom django.template.context import RequestContext\nfrom django.views.generic import TemplateView\nfrom apps.repositorio.models import Libro, Video, Audio\nfrom apps.administracion.models import Evento\n\nclass BuscarView(TemplateView):\n    def post(self, request, *args, **kwargs):\n        buscar = request.POST['key']\n        clase = request.POST['clase']\n        tipo = request.POST['tipo']\n        libros = Libro.objects.filter(nombre__contains = buscar, clase = clase, tipo = tipo)\n        videos = Video.objects.filter(nombre__contains = buscar, clase = clase, tipo = tipo)\n        audios = Audio.objects.filter(nombre__contains = buscar, clase = clase, tipo = tipo)\n        eventos = Evento.objects.filter(nombre__contains = buscar)\n        ctx={'libros': libros, 'videos': videos, 'audios': audios, 'eventos': eventos}\n        if request.user.is_authenticated:\n            return render(request, 'resultado_principal.html', ctx)\n        else:\n            return render(request, 'resultado.html', ctx)\n","sub_path":"Repositio/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1040,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"126917929","text":"import pygame\r\nimport random\r\nfrom circle import Circle\r\n\r\npygame.init()\r\nscreen = pygame.display.set_mode((800, 600))\r\nblack = (0, 0, 0)\r\nwhite = (255, 255, 255)\r\nred = (184, 15, 10)\r\nlime = (180, 255, 100)\r\nrunning = True\r\nmoments = []\r\ng = 0\r\nfps = pygame.time.Clock()\r\nall_circles = []\r\nc = 0\r\na = 2\r\nb = 2\r\nrad = 5\r\nflag = True\r\n\r\n# class Circle:\r\n#     def __init__(self, x, y, color, radius, thickness, direction):\r\n#         self.x = x\r\n#         self.y = y\r\n#         self.radius = radius\r\n#         self.thickness = thickness\r\n#         self.color = color\r\n#         self.direction = direction\r\n#     def make_circle(self, screen):\r\n#         pygame.draw.circle(screen, self.color, (self.x, self.y), self.radius, self.thickness)\r\n#     def move(self):\r\n#         if self.direction == 'right':\r\n#             self.x += 1\r\n#             if (self.x + self.radius) == 800:\r\n#                 self.direction = 'left'\r\n#         if self.direction == 'left':\r\n#             self.x -= 1\r\n#             if (self.x - self.radius) == 0:\r\n#                 self.direction = 'right'\r\n\r\n        \r\nfor i in range(100):\r\n        boop = Circle(i , i * 5, random.choices(range(0,255), k = 3), i + 10, 5, 'right')\r\n        all_circles.append(boop)\r\n          \r\nwhile running:\r\n    print('here')\r\n    for event in pygame.event.get():\r\n        if event.type == pygame.QUIT:\r\n            exit()  \r\n    screen.fill(black)\r\n    \r\n    for i in all_circles:\r\n        i.make_circle(screen)  \r\n        i.move()\r\n    \r\n    fps.tick(100000)\r\n    pygame.display.update()","sub_path":"1704 Лада.py","file_name":"1704 Лада.py","file_ext":"py","file_size_in_byte":1549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"517715831","text":"#!/usr/bin/env python3\n# Run script for CrabBot\n# A mess of config args and terminal polling code\n#\n# See -h or read the argparse setup for argument details\n\nimport argparse\nimport datetime\nimport logging\nimport os\nimport readline  # Only for better terminal input support, eg. history\nimport signal  # So we can send SIGINT to ourselves\nimport sys\nfrom tempfile import gettempdir  # for PID file (for easier service management)\nfrom threading import Thread\n\nimport crabbot.common\nimport crabbot.cogs.messages\nimport crabbot.cogs.quotes\nimport crabbot.cogs.voice  # comment out to disable voice commands entirely\n\npid = str(os.getpid())\npidfile = gettempdir() + '/CrabBot.pid'  # eg. so systemd's PIDFile can find a /tmp/CrabBot.pid\nwith open(pidfile, 'w') as temppidfile:\n    temppidfile.write(pid)\n\nlogging.basicConfig(filename='crabbot.log', level=logging.INFO)  # Grr, ytdl doesn't log\nlogging.info(\"________\\n\" +\n             \"Starting CrabBot at \" + str(datetime.datetime.now()) + \"\\n\"\n             \"--------\")  # Make it clear in the log when a new run starts\n                          # TODO? Might want a delimiter that is easier to write, eg. for a log parsing script\n\n# Do argparse first so that -h can print and exit before anything else happens\nparser = argparse.ArgumentParser(fromfile_prefix_chars='@', description='A silly Discord bot')\ntoken_args = parser.add_mutually_exclusive_group(required=True)\ntoken_args.add_argument('-t', '--token',\n                        help=\"The bot user's login token. Use this or -f.\")\ntoken_args.add_argument('-f', '--file', type=argparse.FileType('r'),\n                        help=\"A file with the bot user's login token as the first line. Use this or -t\")\nparser.add_argument('-p', '--prefix', default=\"!crab\",\n                    help=\"Command prefix the bot responds to\")\nparser.add_argument('--assets-path', default=\"assets/\",\n                    help=\"Path for general assets (ex. sir-places.txt)\")\nparser.add_argument('--memes-path', default=\"assets/memes\",\n                    help=\"Path for memes audio clips (and its filelist.txt)\")\nparser.add_argument('--quotes-path', default=\"../\",  # NOTE we write to this location, be careful where you put it\n                    help=\"Path containing the quotes database. Will create quotes.sqlite3 if it does not exist.\")\nparser.add_argument('--use-libav', action='store_true',\n                    help=\"Make Voice use Libav instead of FFmpeg\")\nparser.add_argument('--disable-voice', action='store_true',\n                    help=\"Disable Voice commands (can be enabled later)\")\n\nargs = parser.parse_args()\n\nif args.file is not None:\n    login = args.file.readline().rstrip()\n    args.file.close()\nelse:\n    login = args.token\n\n\nbot = crabbot.common.CrabBot(prefix=args.prefix)\n\n\ndef poll_terminal():\n    # TODO function dict instead of if/elif.\n\n    while True:  # Run thread as daemon, so Python will exit despite this loop\n        term_input = input()\n        if term_input == \"help\":\n            # TODO write help for the terminal commands\n            print(\"Uh, no. I'm gonna be annoying instead.\")\n            # NOTE could use function.__doc__ and docstrings for function help\n        elif term_input == \"quit\":\n            os.kill(int(pid), signal.SIGINT)  # discord.Client.run() quits on KeyboardInterrupt, so...\n            # This might not work on Windows? It has a special signal.CTRL_C_EVENT.\n        elif term_input.startswith(\"update_profile\"):\n            profile_args = term_input.split(' ')\n            bot._update_profile(username=profile_args[1], avatar=profile_args[2])\n        elif term_input.startswith(\"disable_voice\"):\n            logging.info(\"Disabling voice commands\")\n            bot.remove_cog(\"Voice\")\n        elif term_input.startswith(\"enable_voice\"):\n            if \"crabbot.cogs.voice\" in sys.modules:\n                logging.info(\"Enabling voice commands\")\n                bot.add_cog(crabbot.cogs.voice.Voice(bot, args.memes_path, args.use_libav))\n            else:\n                logging.info(\"Voice disabled in source. Add/uncomment import for crabbot.voice and relaunch.\")\n        elif term_input.startswith(\"update_lists\"):\n            bot.update_all_lists()\n\n\n# Start polling thread as a daemon so the program exits without waiting if ex. the bot crashes\ninput_thread = Thread(target=poll_terminal, daemon=True)\ninput_thread.start()\n\nbot.add_cog(crabbot.cogs.messages.Messages(bot, args.assets_path + \"/messages\"))\nbot.add_cog(crabbot.cogs.quotes.Quotes(bot, args.quotes_path))\n# Comment out import of voice to completely disable voice commands\nif \"crabbot.cogs.voice\" in sys.modules and args.disable_voice is False:\n    bot.add_cog(crabbot.cogs.voice.Voice(bot, args.memes_path, args.use_libav))\n\n# Blocking, must be last. See discord.py Client for more info.\nbot.run(login)\n\n# If it reaches here, CrabBot's probably logged out of Discord now\n# (CrabBot doesn't log out if it's straight terminated)\nlogging.info(\"CrabBot has recieved a SIGINT and has now exited as intended\\n\" +\n             \"————— CrabBot exited at \" + str(datetime.datetime.now()))\nprint(\"CrabBot says goodbye\")\n\n# Cleanup pidfile\ntry:\n    os.remove(pidfile)\nexcept:\n    pass  # Don't try too hard to clean up\n","sub_path":"__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":5232,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"652929241","text":"class Solution:\n    def missingNumber(self, nums: List[int]) -> int:\n        \"\"\"\n        Time complexity:\n            The time complexity of the above algorithm is O(n). In the while\n        loop, although we are not incrementing the index i when swapping the\n        numbers, this will result in more than ‘n’ iterations of the loop, but\n        in the worst-case scenario, the while loop will swap a total of ‘n-1’\n        numbers and once a number is at its correct index, we will move on to\n        the next number by incrementing i. In the end, we iterate the input\n        array again to find the first number missing from its index, so overall,\n        our algorithm will take O(n) + O(n-1) + O(n) which is asymptotically\n        equivalent to O(n).\n\n        Space complexity:\n            The algorithm runs in constant space O(1).\n        \"\"\"\n        i, n = 0, len(nums)\n\n        while i < n:\n            j = nums[i]\n            # number must be equal to its index value. If not swap.\n            if nums[i] < n and nums[i] != nums[j]:\n                nums[i], nums[j] = nums[j], nums[i]\n            else:\n                i += 1\n\n        # find the first number missing from our index\n        for i in range(n):\n            if nums[i] != i:\n                return i\n\n        return n\n","sub_path":"Problems/Grokking/5_CyclicSort/2_find_the_missing_number.py","file_name":"2_find_the_missing_number.py","file_ext":"py","file_size_in_byte":1300,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"309861556","text":"from rest_framework.response import Response\nfrom rest_framework.generics import ListAPIView\nfrom rest_framework import status\nfrom .models import Authors, PersonalWords, Words\nfrom .serializers import AuthorsSerializer, PersonalWordsSerializer, \\\n    WordsSerializer\n\n\nclass AuthorsList(ListAPIView):\n    \"\"\"List author_id: name pairs of all the authors\n    \"\"\"\n    serializer_class = AuthorsSerializer\n\n    def get_queryset(self):\n        queryset = Authors.objects.all()\n        return queryset\n\n    def list(self, request, *args, **kwargs):\n        queryset = self.get_queryset()\n        serialized_value = AuthorsSerializer(\n            queryset, many=True, context={'request': self.request}).data\n        modified_response = {}\n        for author in serialized_value:\n            modified_response[author['author_id']] = author['name']\n        return Response(modified_response, status=status.HTTP_200_OK,\n                        content_type='application/json')\n\n\nclass WordList(ListAPIView):\n    \"\"\"List top 10 words in the whole blog\n    \"\"\"\n    serializer_class = WordsSerializer\n\n    def get_queryset(self):\n        return Words.objects.all()\n\n    def list(self, request, *args, **kwargs):\n        queryset = self.get_queryset()[:10]\n        serialized_value = WordsSerializer(\n            queryset, many=True, context={'request': self.request}).data\n        modified_response = {}\n        for word_stats in serialized_value:\n            modified_response[word_stats['word']] = word_stats['word_count']\n        return Response(modified_response, status=status.HTTP_200_OK,\n                        content_type='application/json')\n\n\nclass WordsPerAuthor(ListAPIView):\n    \"\"\"List top 10 words per author\n    \"\"\"\n    serializer_class = PersonalWordsSerializer\n\n    def get_queryset(self):\n        pk = self.kwargs['author_id']\n        return PersonalWords.objects.filter(author_id=pk)\n\n    def list(self, request, *args, **kwargs):\n        queryset = self.get_queryset()[:10]\n        serialized_value = PersonalWordsSerializer(\n                queryset, many=True, context={'request': self.request}).data\n        modified_response = {}\n        for word_stats in serialized_value:\n            modified_response[word_stats['word']] = word_stats['word_count']\n        return Response(modified_response, status=status.HTTP_200_OK,\n                        content_type='application/json')\n","sub_path":"rest_api/django_app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2393,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"43808612","text":"import sys\r\nimport cv2\r\n\r\nif len(sys.argv)<2:\r\n    print('表示したいファイル名を指定してください。')\r\n    sys.exit()\r\n\r\nfile = sys.argv[1]\r\n\r\ntry:\r\n    img = cv2.imread(file)\r\n\r\n    if img is None:\r\n        raise ValueError('ファイルが見つかりません。')\r\n\r\n    img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\r\n\r\n    MAX_CORNERS = 80\r\n    QUALITY_LEVEL = 0.01\r\n    MIN_DISTANCE = 5.0\r\n\r\n    corners = cv2.goodFeaturesToTrack(img_gray, MAX_CORNERS, QUALITY_LEVEL, MIN_DISTANCE)\r\n    \r\n    for corner_info in corners:\r\n        corner_x, corner_y = corner_info.ravel()\r\n        cv2.circle(img, (corner_x, corner_y), 1, (0, 200, 255), -1)\r\n        cv2.circle(img, (corner_x, corner_y), 8, (0, 200, 255))\r\n\r\n    cv2.imshow(file, img)\r\n    cv2.waitKey(0)\r\n    cv2.destroyAllWindows()\r\nexcept ValueError as e:\r\n    print(e)\r\nexcept:\r\n    import traceback\r\n    traceback.print_exc()\r\n","sub_path":"letstry/4章/38_画像からコーナーを抽出.py","file_name":"38_画像からコーナーを抽出.py","file_ext":"py","file_size_in_byte":909,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"213470029","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\ntime_epochs = 3\nruns = 1\n\ngeo1 = np.zeros(shape=(time_epochs, runs))\ngeo2 = np.zeros(shape=(time_epochs, runs))\ngeo3 = np.zeros(shape=(time_epochs, runs))\ngeo4 = np.zeros(shape=(time_epochs, runs))\ngeo5 = np.zeros(shape=(time_epochs, runs))\ngeo6 = np.zeros(shape=(time_epochs, runs))\ngeo7 = np.zeros(shape=(time_epochs, runs))\ngeo8 = np.zeros(shape=(time_epochs, runs))\ngeo9 = np.zeros(shape=(time_epochs, runs))\ngeo10 = np.zeros(shape=(time_epochs, runs))\nbroadcast = np.zeros(shape=(time_epochs, runs))\n\ngeoP1 = np.zeros(shape=(time_epochs, runs))\ngeoP2 = np.zeros(shape=(time_epochs, runs))\ngeoP3 = np.zeros(shape=(time_epochs, runs))\ngeoP4 = np.zeros(shape=(time_epochs, runs))\ngeoP5 = np.zeros(shape=(time_epochs, runs))\ngeoP6 = np.zeros(shape=(time_epochs, runs))\ngeoP7 = np.zeros(shape=(time_epochs, runs))\ngeoP8 = np.zeros(shape=(time_epochs, runs))\ngeoP9 = np.zeros(shape=(time_epochs, runs))\ngeoP10 = np.zeros(shape=(time_epochs, runs))\nbroadcastP = np.zeros(shape=(time_epochs, runs))\n\n\nnum_mules = 92\nnum_channels = 6\nnum_Pusers = 200\nmsg_mean = 15\nttl = 180\nmax_mem = 100\nT = 360\nstartTime = 1\ndays = \"50\"\ndataset = \"Lexington\"\nbuffer_type = [\"PQ\", \"FIFO\"]\nprotocols = [\"optimistic\", \"pessimistic\", \"weighted\"]\n#fwd_strat = 1\nmetrics_file = \"metrics.txt\"\nnum_replicas = [1, 3]\nsim_round = 5\n\n\n# num_mules = 92\n# num_channels = 6\n# num_Pusers = 200\n# T = 180\n# startTime = 1\n# num_messages = 200\n# days = [\"30\"]\n# dataset = \"Lexington\"\n# buffer_types = [\"PQ\"]\n# protocols = [\"Epidemic_Smart_optimistic\", \"Epidemic_Smart_pessimistic\"]\n# # fwd_strat = [\"broadcast\", \"geo_1\", \"geo_3\", \"geo_5\"]\n# fwd_strat = \"geo_\"\n# metrics_file = \"metrics.txt\"\n\np_id = 4 # p_id = 1 for PDR, = 2 for latency, and 3 for Energy, and 4 for overhead\n\nfor i in range(msg_files):\n    for j in range(puser_files):\n        # for protocol in protocols:\n        for buffer_type in buffer_types:\n            for num_fwd in num_replicas:\n                t = 0\n                path = \"./DataMules/\" + dataset + \"/\" + days + \"/\" + str(sim_round) + \"/Link_Exists/LE_\" + str(\n                    startTime) + \\\n                       \"_\" + str(T) + \"/Epidemic_Smart_\" + protocol + \"/\" + buffer_type[0] + \"/geo_\" + str(\n                    num_replicas) + \"/mules_\" + \\\n                       str(num_mules) + \"/channels_\" + str(num_channels) + \"/P_users_\" + str(\n                    num_Pusers) + \"/msgfile_\" + str(\n                    i) + \"_\" + str(msg_mean) + \"/puserfile_\" + str(j) + \"/TTL_\" + str(ttl) + \"/BuffSize_\" + str(\n                    max_mem) + \"/\"\n\n                #path = \"DataMules/\" + dataset + \"/\" + days[i] + \"/1/Link_Exists/LE_\" + str(startTime) + \"_\" + str(T) + \"/\" + protocol + \"/\" + buffer_type + \"/\" + fwd_strat + str(num_fwd) + \"/mules_\" + str(num_mules) + \"/channels_\" + str(num_channels) + \"/P_users_\" + str(num_Pusers) + \"/\" + str(num_messages) + \"/\"\n                with open(path + metrics_file, \"r\") as f:\n                    lines = f.readlines()[1:]\n\n                for line in lines:\n                    line_arr = line.strip().split()\n                    if int(line_arr[0]) % 5 == 0:\n                        if \"optimistic\" in protocol:\n                            if num_fwd == 1:\n                                geo1[t][i] = float(line_arr[p_id])\n                            elif num_fwd == 2:\n                                geo2[t][i] = float(line_arr[p_id])\n                            elif num_fwd == 3:\n                                geo3[t][i] = float(line_arr[p_id])\n                            elif num_fwd == 4:\n                                geo4[t][i] = float(line_arr[p_id])\n                            elif num_fwd == 5:\n                                geo5[t][i] = float(line_arr[p_id])\n                            elif num_fwd == 6:\n                                geo6[t][i] = float(line_arr[p_id])\n                            elif num_fwd == 7:\n                                geo7[t][i] = float(line_arr[p_id])\n                            elif num_fwd == 8:\n                                geo8[t][i] = float(line_arr[p_id])\n                            elif num_fwd == 9:\n                                geo9[t][i] = float(line_arr[p_id])\n                            elif num_fwd == 10:\n                                geo10[t][i] = float(line_arr[p_id])\n                        elif \"pessimistic\" in protocol:\n                            if num_fwd == 1:\n                                geoP1[t][i] = float(line_arr[p_id])\n                            elif num_fwd == 2:\n                                geoP2[t][i] = float(line_arr[p_id])\n                            elif num_fwd == 3:\n                                geoP3[t][i] = float(line_arr[p_id])\n                            elif num_fwd == 4:\n                                geoP4[t][i] = float(line_arr[p_id])\n                            elif num_fwd == 5:\n                                geoP5[t][i] = float(line_arr[p_id])\n                            elif num_fwd == 6:\n                                geoP6[t][i] = float(line_arr[p_id])\n                            elif num_fwd == 7:\n                                geoP7[t][i] = float(line_arr[p_id])\n                            elif num_fwd == 8:\n                                geoP8[t][i] = float(line_arr[p_id])\n                            elif num_fwd == 9:\n                                geoP9[t][i] = float(line_arr[p_id])\n                            elif num_fwd == 10:\n                                geoP10[t][i] = float(line_arr[p_id])\n\n                        t += 1\n\n\nif p_id == 3:\n    for t in range(len(geo1)):\n        for run in range(runs):\n            geo1[t][run] = float(geo1[t][run] / 1000)\n            geo2[t][run] = float(geo2[t][run] / 1000)\n            geo3[t][run] = float(geo3[t][run] / 1000)\n            geo4[t][run] = float(geo4[t][run] / 1000)\n            geo5[t][run] = float(geo5[t][run] / 1000)\n            geo6[t][run] = float(geo6[t][run] / 1000)\n            geo7[t][run] = float(geo7[t][run] / 1000)\n            geo8[t][run] = float(geo8[t][run] / 1000)\n            geo9[t][run] = float(geo9[t][run] / 1000)\n            geo10[t][run] = float(geo10[t][run] / 1000)\n\n            geoP1[t][run] = float(geoP1[t][run] / 1000)\n            geoP2[t][run] = float(geoP2[t][run] / 1000)\n            geoP3[t][run] = float(geoP3[t][run] / 1000)\n            geoP4[t][run] = float(geoP4[t][run] / 1000)\n            geoP5[t][run] = float(geoP5[t][run] / 1000)\n            geoP6[t][run] = float(geoP6[t][run] / 1000)\n            geoP7[t][run] = float(geoP7[t][run] / 1000)\n            geoP8[t][run] = float(geoP8[t][run] / 1000)\n            geoP9[t][run] = float(geoP9[t][run] / 1000)\n            geoP10[t][run] = float(geoP10[t][run] / 1000)\n\n\nx = np.array([x for x in range(0, T +1, 30)])\nplt.xticks(fontsize=20)\nplt.yticks(fontsize=25)\nplt.xticks(np.arange(0, 181, 30))\ntitle_str = \"Messages: \" + str(num_messages) + \"    Channels: \" + str(num_channels) + \"    Primary Users: \" + str(num_Pusers)\n# title_str = \"Broadcast to everyone in range\"\nplt.title(title_str)\n# plt.xlim(0,12)\nfig_name = \"dummy.eps\"\n\nif p_id == 1:\n    plt.ylabel('Message delivery ratio', fontsize=25)\n    plt.xlabel('Time (min)', fontsize=25)\n    # plt.ylim(-0.05,1.1)\n    fig_name = \"Plots/pdr_Time_Geo.png\"\n\nif p_id == 2:\n    # plt.ylim(-1, 40)\n    plt.ylabel('Network delay (min)', fontsize=25)\n    plt.xlabel('Time (min)', fontsize=25)\n\n    fig_name = \"Plots/latency_time_Geo.png\"\n\nif p_id == 3:\n    plt.ylabel('Energy expenditure (KJ)', fontsize=25)\n    plt.xlabel('Time (min)', fontsize=25)\n    # plt.ylim(-0.01, 22)\n    fig_name = \"Plots/energy_time_Geo.png\"\n\nif p_id == 4:\n    plt.ylabel('Message overhead', fontsize=25)\n    plt.xlabel('Time (min)', fontsize=25)\n    # plt.ylim(-1, 20)\n    fig_name = \"Plots/overhead_Time_Geo.png\"\n\n\nplt.errorbar(x, geo1, 0, marker='o', markersize=10, linestyle='-', linewidth=3, color = \"red\")\n# plt.errorbar(x, geo2, 0, marker='o', markersize=10, linestyle='-', linewidth=3)\nplt.errorbar(x, geo3, 0, marker='o', markersize=10, linestyle='-', linewidth=3, color = \"blue\")\n# plt.errorbar(x, geo4, 0, marker='o', markersize=10, linestyle='-', linewidth=3)\nplt.errorbar(x, geo5, 0, marker='o', markersize=10, linestyle='-', linewidth=3, color = \"blue\")\n# plt.errorbar(x, geo6, 0, marker='o', markersize=10, linestyle='-', linewidth=3)\n# plt.errorbar(x, geo7, 0, marker='o', markersize=10, linestyle='-', linewidth=3, color = \"black\")\n# plt.errorbar(x, geo8, 0, marker='o', markersize=10, linestyle='-', linewidth=3)\n# plt.errorbar(x, geo9, 0, marker='o', markersize=10, linestyle='-', linewidth=3)\n# plt.errorbar(x, geo10, 0, marker='o', markersize=10, linestyle='-', linewidth=3, color = \"green\")\n\nplt.errorbar(x, geoP1, 0, marker='x', markersize=10, linestyle='--', linewidth=3, color = \"red\")\n# plt.errorbar(x, geoP2, 0, marker='x', markersize=10, linestyle='--', linewidth=3)\nplt.errorbar(x, geoP3, 0, marker='x', markersize=10, linestyle='--', linewidth=3, color = \"blue\")\n# plt.errorbar(x, geoP4, 0, marker='x', markersize=10, linestyle='--', linewidth=3)\nplt.errorbar(x, geoP5, 0, marker='x', markersize=10, linestyle='--', linewidth=3, color = \"blue\")\n# plt.errorbar(x, geoP6, 0, marker='x', markersize=10, linestyle='--', linewidth=3)\n# plt.errorbar(x, geoP7, 0, marker='x', markersize=10, linestyle='--', linewidth=3, color = \"black\")\n# plt.errorbar(x, geoP8, 0, marker='x', markersize=10, linestyle='--', linewidth=3)\n# plt.errorbar(x, geoP9, 0, marker='x', markersize=10, linestyle='--', linewidth=3)\n# plt.errorbar(x, geoP10, 0, marker='x', markersize=10, linestyle='--', linewidth=3, color = \"green\")\n\n\nif p_id == 1:\n    plt.legend([\"Opt-1\", \"Opt-5\", \"Opt-10\",  \"Pes-1\", \"Pes-5\", \"Pes-10\"], loc=\"upper left\", fontsize=15, ncol = 1, frameon=False)\nelif p_id == 2:\n    plt.legend([\"Opt-1\", \"Opt-5\", \"Opt-10\",  \"Pes-1\", \"Pes-5\", \"Pes-10\"], loc=\"upper left\", fontsize=15, ncol = 1, frameon=False)\nelif p_id ==3:\n    plt.legend([\"Opt-1\", \"Opt-5\", \"Opt-10\",  \"Pes-1\", \"Pes-5\", \"Pes-10\"], loc=\"upper left\", fontsize=15, ncol = 1, frameon=False)\nelif p_id ==4:\n    plt.legend([\"Opt-1\", \"Opt-5\", \"Opt-10\",  \"Pes-1\", \"Pes-5\", \"Pes-10\"], loc=\"upper left\", fontsize=15, ncol = 1, frameon=False)\n\n\nplt.tight_layout()\nplt.savefig(fig_name)\n\nplt.show()\n\n# plt.legend([\"Opt-1\", \"Opt-3\", \"Opt-5\", \"Opt-7\", \"Pes-1\", \"Pes-3\", \"Pes-5\", \"Pes-7\"], loc=\"upper left\", fontsize=15,\n#            ncol=1, frameon=False)\n\n","sub_path":"plot_geo_Time-1.py","file_name":"plot_geo_Time-1.py","file_ext":"py","file_size_in_byte":10446,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"328457043","text":"import os\nimport time\nimport requests\n\n\nfrom . import wall\nfrom .access import ourID\nfrom .owner import Owner\nfrom .post import post\nfrom .vkExceptions import getVkException\nfrom . import uniobj\n\ndef createAlbum(title, description=\"\", group_id=None,\n                privacy_view=\"all\", privacy_comment=\"all\",\n                group_upload_by_admins_only=1,\n                group_comments_disabled=0):\n    \"\"\" Создаёт альбом. \n\n        `title` - название  \n        `description` - описание альбома  \n        `group_id` - ID группы, передавать только если \n        альбом создаётся в группе  \n        Остальные параметры - приватность. Подробности тут:\n        https://vk.com/dev/photos.createAlbum\n\n        Возвращает объект `Album` \"\"\"\n    d = {\"title\": title,\n         \"description\": description,\n         \"group_id\": group_id,\n         \"privacy_view\": privacy_view,\n         \"privacy_comment\": privacy_comment}\n\n    if group_id is not None:\n        d[\"group_id\"] = group_id\n        d[\"upload_by_admins_only\"] = group_upload_by_admins_only\n        d[\"comments_disabled\"] = group_comments_disabled\n\n    resp = post(\"photos.createAlbum\", d)\n    return Album.fromJSON(resp)\n\n\ndef deleteAlbum(album_or_id, group_id=None):\n    \"\"\" Удаляет альбом с фотографиями  \n        `album_or_id` - объект `Album` или ID альбома \"\"\"\n    d = {\"album_id\": None}\n    if isinstance(album_or_id, Album):\n        d[\"album_id\"] = album_or_id.id\n        if album_or_id.owner.id < 0:\n            d[\"group_id\"] = album_or_id.owne.id\n    else:\n        d[\"album_id\"] = album_or_id\n        if group_id is not None:\n            d[\"group_id\"] = group_id\n    post(\"photos.deleteAlbum\", d)\n\n\ndef getAlbums(owner):\n    \"\"\" Возвращает фотоальбомы пользователя или группы  \n\n        `owner` - Владелец - `Owner`/`User`/`Group`/ID\"\"\"\n    resp = post(\"photos.getAlbums\", {\"owner_id\": uniobj.detect(owner)})\n    return [Album.fromJSON(item) for item in resp[\"items\"]]\n\n\ndef setAvatar(file, _square_crop=(0, 0, 9999), deletePost=True):\n    \"\"\" Загружает новое фото профиля  \n\n        `file` - открытый через `open(..., \"rb\")` файл  \n        `_square_crop` - обрезка аватарки в формате `(x,y,w)`,\n        где `x`,`y` - начальная точка, а `w` - сторона квадрата.  \n        `deletePost` - удалять пост, появляющийся после обновления авы.\n    \"\"\"\n    upload_url = post(\"photos.getOwnerPhotoUploadServer\", {\n                      \"owner_id\": ourID()})[\"upload_url\"]\n    coords = \",\".join([str(i) for i in _square_crop])\n    time.sleep(0.33)\n    p = requests.post(upload_url, data={\"_square_crop\": coords},\n                      files={\"photo\": file}).json()\n\n    if \"error\" in p:\n        raise getVkException(p[\"error_code\"])\n\n    post(\"photos.saveOwnerPhoto\", {\"server\": p[\"server\"],\n                                   \"hash\": p[\"hash\"],\n                                   \"photo\": p[\"photo\"]})\n\n    if deletePost:\n        posts = wall.get(ourID(), count=2)\n        i = 1 if p[0].pinned else 0\n        posts[i].delete()\n\n\n__all__ = [\"createAlbum\", \"deleteAlbum\", \"getAlbums\", \"delete\"]\n","sub_path":"vkApi/photos.py","file_name":"photos.py","file_ext":"py","file_size_in_byte":3415,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"546836800","text":"# -*- coding: utf-8 -*-\nimport scrapy\n\n\nclass GithubSpider(scrapy.Spider):\n    name = 'github'\n    allowed_domains = ['github.com']\n    start_urls = ['https://github.com/apache?page=1']\n\n    def parse(self, response):\n        index_list = response.xpath('//*[@id=\"org-repositories\"]/div[1]/div/ul/li/div[1]/h3/a')\n        for index in index_list:\n            url = index.xpath('./@href').extract_first()\n            url = response.urljoin(url) + '/commits/'\n            name = index.xpath('./text()').extract_first().strip()\n            yield scrapy.Request(url=url, callback=self.detail_parse2, meta={\"url\": url, \"name\": name})\n        next_url = response.xpath('//*[@id=\"org-repositories\"]/div[1]/div/div/div/a[7]/@href').extract_first()\n        next_url = response.urljoin(next_url)\n        yield scrapy.Request(url=next_url, callback=self.parse)\n\n    # def detail_parse1(self, response):\n    #     time_list = response.xpath('//*[@id=\"js-repo-pjax-container\"]/div[2]/div[1]/div[2]/div')\n    #     for times in time_list:\n    #         name = response.meta.get('name')\n    #         # parse2_url = response.meta.get('url')\n    #         parse2_url = response.xpath('//*[@id=\"js-repo-pjax-container\"]/div[1]/div/h1/strong/a/@href').extract_first()\n    #         parse2_url = response.urljoin(parse2_url) + '/commits'\n    #         commit_time = times.xpath('./text()').extract()[1].strip()\n    #         # print(parse2_url)\n    #         # print(commit_time)\n    #         yield scrapy.Request(url=parse2_url, callback=self.detail_parse2, meta={\"name\": name, \"commit_time\": commit_time, \"parse2_url\": parse2_url})\n\n    def detail_parse2(self, response):\n        table_list = response.xpath('//*[@id=\"js-repo-pjax-container\"]/div[2]/div[1]/div[2]/ol')\n\n        for tables in table_list:\n            li_list = tables.xpath('./li')\n            for table in li_list:\n                item = {}\n                item_name = response.meta.get('name')\n                parse2_url = response.meta.get('url')\n                commit_time = table.xpath('./div[1]/div/div[2]/relative-time/@datetime').extract_first()\n                commit_name = table.xpath('./div[1]/div/div[2]/relative-time/preceding-sibling::*[1]/text() | ./div[1]/div/div[2]/relative-time/preceding-sibling::*[1]/text()').extract_first()\n                btn_num = \"'\" + str(table.xpath('./div[2]/div/a/text()').extract_first().strip())\n                url = table.xpath('./div[1]/p/a[1]/@href').extract_first()\n                url = response.urljoin(url)\n                item['item_name'] = item_name\n                item['parse2_url'] = parse2_url\n                item['commit_time'] = commit_time\n                item['commit_name'] = commit_name\n                item['btn_num'] = btn_num\n                item['url'] = url\n\n                yield scrapy.Request(url=response.urljoin(url), callback=self.detail_parse3, meta={\"item\": item})\n\n    def detail_parse3(self, response):\n        item = response.meta.get('item')\n        changed_files = response.xpath('//*[@class=\"toc-diff-stats\"]//*[contains(./text(),\"file\")]/text()').extract_first().strip().split()[0]\n        additions = response.xpath('//*[@class=\"toc-diff-stats\"]//*[contains(./text(),\"addition\")]/text()').extract_first().strip().split()[0]\n        deletions = response.xpath('//*[@class=\"toc-diff-stats\"]//*[contains(./text(),\"deletion\")]/text()').extract_first().strip().split()[0]\n        item['changed_files'] = changed_files\n        item['additions'] = additions\n        item['deletions'] = deletions\n        yield item\n\n\n","sub_path":"scrapy/quotetutorail/quotetutorail/spiders/github.py","file_name":"github.py","file_ext":"py","file_size_in_byte":3552,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"527464282","text":"f = open(\"output.txt\", \"r\", encoding=\"utf-8\")\n\nlines = f.readlines()\n\nfilename = ''\nbitrate = ''\n\nfor line in lines:\n\n    if \"from\" in line:\n        start = line.find('from')\n        start = start + len('from ')\n        filename = line[start:-2]\n\n    if \"bitrate\" in line:\n        \n        start = line.find('bitrate')\n        start = start + len('bitrate: ')\n        bitrate = line[start:-6]\n    \n        if (len(filename) > 0):\n            print(filename, ',', bitrate)\n        ","sub_path":"scripts/统计所有媒体文件信息/output.py","file_name":"output.py","file_ext":"py","file_size_in_byte":480,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"579109890","text":"import cv2 as cv\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\ndef main():\r\n\r\n\t##  Affichage de la matrice image en couleur\r\n\t#matrice = cv.imread(\"Lena.png\")\r\n\t#plt.imshow(matrice[..., ::-1])\r\n\t##   On peut aussi utiliser\r\n\t#plt.imshow(cv.cvtColor(matrice, cv.COLOR_BGR2RGB))\r\n\t#plt.title(\"Image de Lena\")\r\n\t#plt.show()\r\n\r\n\r\n\r\n\t##  Affichage de la matrice image en blanc et noir\r\n\tmatrice = cv.imread(\"img/Lena.png\")\r\n\tprint(matrice.shape)\r\n\tmatrice = cv.cvtColor(matrice, cv.COLOR_BGR2GRAY)\r\n\tplt.imshow(matrice, cmap=\"gray\")\r\n\tplt.show()\r\n\r\n\r\nif __name__ == '__main__':\r\n\tmain()","sub_path":"Traitement Image/afficheImageGris.py","file_name":"afficheImageGris.py","file_ext":"py","file_size_in_byte":593,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"496911052","text":"import boto3\nfrom boto3.session import Session\nimport click\nimport logging\nfrom os.path import basename\n\n# Update the root logger to get messages at DEBUG and above\nlogging.getLogger().setLevel(logging.DEBUG)\nlogging.getLogger(\"botocore\").setLevel(logging.CRITICAL)\nlogging.getLogger(\"boto3\").setLevel(logging.CRITICAL)\nlogging.getLogger(\"urllib3.connectionpool\").setLevel(logging.CRITICAL)\nlogging.info(f\"boto3.__version__: {boto3.__version__}\")\n\n\nAWS_PROFILE=\"default\"\nAWS_DEFAULT_REGION=\"ap-southeast-2\"\nAPP_NAME = basename(__file__).split(\".\")[0]\n\n\"\"\"\nimport datetime\nimport json\nfrom time import mktime\n\nclass DefaultEncoder(json.JSONEncoder):\n  def default(self, obj):\n    if isinstance(obj, datetime.datetime):\n      return int(mktime(obj.timetuple()))\n    return json.JSONEncoder.default(self, obj)\n\njson.dumps(json_data, cls=DefaultEncoder, sort_keys=True, indent=2)\n\"\"\"\n\n################################################################################\n# Entry point\n\n@click.command()\n@click.option(\"--profile\", \"-p\", default=\"default\", help=\"AWS profile name\")\ndef main(profile):\n    session = Session(profile_name=profile)\n    this_identity = session.client(\"sts\").get_caller_identity()\n    print(f\"Started processing identity {this_identity['Arn']}\")\n\n\nif __name__ == \"__main__\": main()\n","sub_path":"_base_file_templates/simple_with_args.py","file_name":"simple_with_args.py","file_ext":"py","file_size_in_byte":1299,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"418841776","text":"from django.shortcuts import get_object_or_404, HttpResponseRedirect, render, HttpResponse\nfrom mainapp.models import Product\nfrom .models import BasketSlot\nfrom django.contrib.auth.decorators import login_required\n\n@login_required\ndef add(request, product_pk=None):\n    product = get_object_or_404(Product, pk=product_pk)\n    old_basket_slot = BasketSlot.objects.filter(user=request.user, product=product).first()\n    print(product_pk)\n    if old_basket_slot:\n        old_basket_slot.quantity += 1\n        old_basket_slot.save()\n    else:\n        new_basket_slot = BasketSlot(user=request.user, product=product)\n        new_basket_slot.save()\n\n    return HttpResponseRedirect(request.META.get('HTTP_REFERER'))\n\n@login_required\ndef remove(request, product_pk=None):\n    print(product_pk)\n    product = get_object_or_404(Product, pk=product_pk)\n    basket_slot = BasketSlot.objects.filter(user=request.user, product=product).first()\n    if basket_slot:\n        if basket_slot.quantity <= 1:\n            basket_slot.delete()\n        else:\n            basket_slot.quantity -= 1\n            basket_slot.save()\n\n    return HttpResponseRedirect(request.META.get('HTTP_REFERER'))\n\n@login_required\ndef basket(request):\n    title = 'Корзина'\n    basket = BasketSlot.objects.filter(user=request.user).all()\n    total_cost = 0\n    total_quantity = 0\n    for item in basket:\n        total_cost += item.price\n        total_quantity += item.quantity\n    content = {\n        'title': title,\n        'basket': basket,\n        'total_quantity': total_quantity,\n        'total_cost': total_cost,\n    }\n    print(basket)\n    return render(request, 'mainapp/basket.html', content)\n\n@login_required\ndef edit(request, pk):\n    bs = get_object_or_404(BasketSlot, pk=pk)\n    quantity = int(request.GET.get('quantity'))\n    if quantity > 0:\n        bs.quantity = quantity\n        bs.save()\n    else:\n        bs.delete()\n        bs.save()\n    return HttpResponse('Ok')","sub_path":"basketapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1949,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"523395381","text":"#!/usr/bin/python\n#-*- coding: utf-8 -*-\n\n# audio-offset-finder\n#\n# Copyright (c) 2014 British Broadcasting Corporation\n# Copyright (c) 2018 Abram Hindle\n# Copyright (c) 2021 Haujet Zhao\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport argparse, sys, os, shlex, subprocess\nfrom .audio_offset_finder import find_offset\n\ndef main():\n    not_exit_immediately = False\n    if len(sys.argv) == 1:\n        not_exit_immediately = True\n\n        print(f'''\nYou haven't pass any audio or video files. \nSo this short message shall guide you.\n\nThis program is used to replace the audio of a video to\nanother audio recordered in another device, such as:\n\n  * Using a camera to record a video\n  * Carry a audio recorder or phone to record a better quality audio\n  * Replace the camera audio the the higher quality audio automaticlly\n\nThe audio is normally considered longer than the video.\nSo this process can be thought as:\n    Find the offset of video sound (target) in another better quality\naudio file (scope), and replace the video sound automaticlly. \n''')\n        print(f'\\nFirst input the audio（scope）')\n        sys.argv.append(get_input_file())\n\n        print(f'\\nThen input the video（target）')\n        sys.argv.append(get_input_file())\n\n\n    parser = argparse.ArgumentParser(\n        description='''Purpose：    Get the offset of a video sound to another audio, and replace the audio. \n                       Example:     Record vlog, use a recorder for better sound, then sync the audio. ''',\n        formatter_class=argparse.ArgumentDefaultsHelpFormatter\n    )\n    parser.add_argument('Audio', type=str, help='Offset to find within')\n    parser.add_argument('Video', nargs='+',  type=str, help='Video to find the offset of（Can be more than one）')\n\n    parser.add_argument('--version', action='version', version='%(prog)s 1.0')\n    parser.add_argument('--offset', metavar='Minutes', type=int, default=0, help='Neglect how many minutes of the audio (in case your audio is very long)')\n    parser.add_argument('--trim', metavar='Minutes', type=int, default=15, help='Using how many minutes of audio as one clip to analyse with')\n    parser.add_argument('--sr', metavar='SampleRate', type=int, default=16000, help='When resample audio, what the target sample rate should be')\n    parser.add_argument('--format', metavar='Format', type=str, default='mkv', help='Output video format such as: mp4, mkv')\n    parser.add_argument('--not-generate',action='store_true', help='Do not use FFmpeg to generate new video')\n    parser.add_argument('--plotit',action='store_true', help='Show the plot picture of the analyse')\n\n    args = parser.parse_args()\n\n    slice_seconds = args.trim * 60\n    pre_offset_seconds = args.offset * 60\n    for index, video in enumerate(args.Video):\n        print(f'\\nTotal task count: {len(args.Video)}, processing No.{index + 1} : {video}')\n        sync(args.Audio, video, pre_offset_seconds, slice_seconds, args.sr, args.format, args.not_generate, args.plotit)\n\n    if not_exit_immediately:\n        input('\\nMission complete, enter to finish')\n\ndef get_input_file():\n    while True:\n        user_input = input(f'Please input the file path or drag it in: ')\n        if user_input == '':\n            continue\n        if os.path.exists(user_input.strip('\\'\"')):\n            input_file = user_input.strip('\\'\"')\n            break\n        else:\n            print('The file you input does not exitst, please retry. ')\n    return input_file\n\ndef sync(within, find_offset_of, offset, trim, sr, format, not_generate, plotit):\n    \n    for file in [within, find_offset_of]:\n        if not os.path.exists(file):\n            print(f'File do not exist, skip: {file}')\n            return False\n\n    offset, score = find_offset(within, find_offset_of, offset, sr, trim, plotit=plotit)\n\n    # ffmpeg command\n    if offset >= 0:\n        ffmpeg_cmd = f'''ffmpeg -y -hide_banner -i \"{find_offset_of}\" -ss {offset} -i \"{within}\" -map 0:v:0 -map 1:a:0 -c:v copy -c:a copy -shortest \"{find_offset_of}.sync.{\"{:.2f}\".format(offset)}.{format}\"'''\n    else:\n        delay = int(abs(offset) * 1000)\n        ffmpeg_cmd = f'''ffmpeg -y -hide_banner -i \"{find_offset_of}\" -i \"{within}\" -map 0:v:0 -map 1:a:0 -c:v copy -c:a copy -af \"adelay=delays={delay}:all=1\" -shortest \"{find_offset_of}.sync.{\"{:.2f}\".format(offset)}.{format}\"'''\n    print(f'FFmpeg command：\\n    {ffmpeg_cmd}\\n')\n\n    # Generate new video\n    if not not_generate:\n        command_arg = shlex.split(ffmpeg_cmd)\n        subprocess.run(command_arg)\n\nif __name__ == '__main__':\n    main()\n","sub_path":"audio_offset_finder/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":5058,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"339161784","text":"# partions the T7 genome into ~10000 bp segments, with 100 bp overlap between segments\n# preps input for the Salis RBS calculator (which only accepts sequences <10000 bp in length)\n\nfrom Bio import SeqIO\nfrom Bio.SeqRecord import SeqRecord\n\nt7_record = SeqIO.read(\"../../../data/t7_genome_full.fasta\", \"fasta\")\nt7_sequence = t7_record.seq\n\nlower_bound = 0\nupper_bound = 0\ngenome_partitions = []\nwhile upper_bound != len(t7_sequence):\n    upper_bound = min(lower_bound + 10000, len(t7_sequence))\n    seq_partition = t7_sequence[lower_bound:upper_bound]\n    genome_partitions.append(SeqRecord(seq_partition, \n                                       id = f\"{t7_record.id}-{lower_bound}-{upper_bound - 1}\",\n                                       name = \"\",\n                                       description = f\"{t7_record.description}, position {lower_bound}..{upper_bound - 1}\")\n                            )\n    lower_bound += 9900\n\nSeqIO.write(genome_partitions, \"t7_genome_split.fasta\", \"fasta\")","sub_path":"src/python/improved_rbs_strengths/partition_t7.py","file_name":"partition_t7.py","file_ext":"py","file_size_in_byte":995,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"278068743","text":"import numpy as np\nfrom estraine.core.math import weight_initialization\n\n\nclass LinearClassifier:\n    def __init__(self, xl, yl):\n\n        self.Xl, self.Yl = xl, yl\n        self.n_samples, self.n_features = self.Xl.shape\n        self.weight = weight_initialization(self.n_features)\n\n        self.pred = lambda x: (self.weight * x).sum()\n        self.pred_ = lambda x: (self.weight * x).sum(axis=1)\n        self.error_func = lambda x: x ** 2 if x < 0 else 0\n        self.marg = self.margin_(xl, yl)\n        self.Q = np.array(list(map(self.error_func, self.marg))).sum()\n\n    def margin(self, precedent):\n        x, y = precedent\n        pred = self.pred(x)\n        return pred * y\n\n    def margin_(self, xl, yl):\n        margin = list(map(self.margin, zip(xl, yl)))\n        return np.array(margin)\n\n    def derivative(self, x, y, h=1e-5):\n        \"\"\"\n        derivative fi(x) for chain rule\n        \"\"\"\n        df = np.ones(self.n_features)\n        for i in range(self.n_features):\n\n            weight1 = self.weight.copy()\n            weight2 = self.weight.copy()\n\n            weight1[i] = self.weight[i] + h\n            weight2[i] = self.weight[i] - h\n\n            df[i] = (self.pred(weight1) - self.pred(weight2)) / (2 * h)\n\n        x_ = self.pred(x)\n        dm = (self.margin((x_ + h, y)) - self.margin((x_ - h, y))) / (2 * h)\n\n        x = self.margin((x, 0))\n        dl = (self.error_func(x + h) - self.error_func(x - h)) / (2 * h)\n\n        return dl * dm * df\n\n    def stochastic_gradient(self, n=1e-2):\n        i = np.random.randint(self.n_samples)\n        dev = self.derivative(self.Xl[i], self.Yl[i])\n        self.weight = self.weight - n * dev\n\n    def predict(self, x):\n        return int(self.pred(x) >= 0)\n\n    def predict_(self, x):\n        return (self.pred_(x) >= 0).astype(int)\n","sub_path":"estraine/models/Linear.py","file_name":"Linear.py","file_ext":"py","file_size_in_byte":1794,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"83347186","text":"#*****************************************************#\n# This file is part of GRIDOPT.                       #\n#                                                     #\n# Copyright (c) 2015-2016, Tomas Tinoco De Rubira.    #\n#                                                     #\n# GRIDOPT is released under the BSD 2-clause license. #\n#*****************************************************#\n\nimport csv\nimport pfnet as pf\nimport numpy as np\nfrom .utils import ApplyFunc\nfrom numpy.linalg import norm\nfrom multiprocessing import Pool,cpu_count\nfrom optalg.lin_solver import new_linsolver\nfrom scipy.sparse.linalg import LinearOperator\nfrom optalg.opt_solver.opt_solver_error import *\nfrom optalg.stoch_solver import StochProblem\nfrom optalg.opt_solver import OptSolverIQP,QuadProblem\nfrom scipy.sparse import triu,bmat,coo_matrix,eye,spdiags\n            \nclass TS_DCOPF_Problem(StochProblem):\n    \"\"\"\"\n    This class represents a problem of the form\n    \n    minimize(p)   varphi_0(p) + E_r[Q(p,r)]\n    subject to    p_min <= p <= p_max\n    \n    where Q(p,r) is the optimal value of\n\n    minimize(q,w,s,z)   varphi_1(q)\n    subjcet to          G(p+q) + Rs - Aw = b\n                        Jw - z = 0\n                        p_min <= p+q <= p_max\n                        z_min <= z <= z_max\n                        0 <= s <= r.\n    \"\"\"\n\n    # Parameters\n    parameters = {'cost_factor' : 1e2,   # factor for determining gen adjustment cost\n                  'infinity' : 1e3,      # infinity\n                  'flow_factor' : 1.,    # factor for relaxing thermal limits\n                  'num_samples' : 2000}  # number of samples\n\n    def __init__(self,net):\n        \"\"\"\n        Class constructor.\n        \n        Parameters\n        ----------\n        net : PFNET Network\n        \"\"\"\n\n        # Parameters\n        self.parameters = TS_DCOPF_Problem.parameters.copy()\n\n        # Save info\n        self.total_load = sum([l.P for l in net.loads])\n        self.uncertainty = 100.*sum([g.P_std for g in net.var_generators])/sum([g.P_max for g in net.var_generators]) # % of capacity\n        self.corr_value = net.vargen_corr_value    # correlation value  ([0,1])\n        self.corr_radius = net.vargen_corr_radius  # correlation radius (# of branches)\n                \n        # Generator limits\n        for gen in net.generators:\n            gen.P_min = 0.\n            gen.P_max = np.maximum(gen.P_max,0.)\n            assert(gen.P_min <= gen.P_max)\n\n        # Branch flow limits\n        for br in net.branches:\n            if br.ratingA == 0.:\n                br.ratingA = self.parameters['infinity']\n            else:\n                br.ratingA *= self.parameters['flow_factor']\n        \n        # Counters\n        num_w = net.num_buses-net.get_num_slack_buses() # voltage angles\n        num_p = net.get_num_P_adjust_gens()             # adjustable generators\n        num_r = net.num_vargens                         # renewable generators\n        num_bus = net.num_buses                         # buses\n        num_br = net.num_branches                       # branches\n\n        # Variables\n        net.clear_flags()\n        net.set_flags(pf.OBJ_BUS,\n                      pf.FLAG_VARS,\n                      pf.BUS_PROP_NOT_SLACK,\n                      pf.BUS_VAR_VANG)\n        net.set_flags(pf.OBJ_GEN,\n                      pf.FLAG_VARS,\n                      pf.GEN_PROP_P_ADJUST,\n                      pf.GEN_VAR_P)\n        net.set_flags(pf.OBJ_VARGEN,\n                      pf.FLAG_VARS,\n                      pf.VARGEN_PROP_ANY,\n                      pf.VARGEN_VAR_P)\n        assert(net.num_vars == num_w+num_p+num_r)\n\n        # Values\n        x = net.get_var_values()\n\n        # Projections\n        Pw = net.get_var_projection(pf.OBJ_BUS,pf.BUS_VAR_VANG)\n        Pp = net.get_var_projection(pf.OBJ_GEN,pf.GEN_VAR_P)\n        Pr = net.get_var_projection(pf.OBJ_VARGEN,pf.VARGEN_VAR_P)\n        assert(Pw.shape == (num_w,net.num_vars))\n        assert(Pp.shape == (num_p,net.num_vars))\n        assert(Pr.shape == (num_r,net.num_vars))\n\n        # Power flow equations\n        pf_eq = pf.Constraint(pf.CONSTR_TYPE_DCPF,net)\n        pf_eq.analyze()\n        pf_eq.eval(x)\n        A = pf_eq.A.copy()\n        b = pf_eq.b.copy()\n\n        # Branch flow limits\n        fl_lim = pf.Constraint(pf.CONSTR_TYPE_DC_FLOW_LIM,net)\n        fl_lim.analyze()\n        fl_lim.eval(x)\n        G = fl_lim.G.copy()\n        hl = fl_lim.l.copy()\n        hu = fl_lim.u.copy()\n        assert(np.all(hl < hu))\n        \n        # Generation cost\n        cost = pf.Function(pf.FUNC_TYPE_GEN_COST,1.,net)\n        cost.analyze()\n        cost.eval(x)\n        H = (cost.Hphi + cost.Hphi.T - triu(cost.Hphi))/net.base_power # symmetric\n        g = cost.gphi/net.base_power - H*x\n        \n        # Bounds\n        l = net.get_var_values(pf.LOWER_LIMITS)\n        u = net.get_var_values(pf.UPPER_LIMITS)\n        assert(np.all(Pw*l < Pw*u))\n        assert(np.all(Pp*l < Pp*u))\n        assert(np.all(Pr*l < Pr*u))\n        \n        # Save problem data\n        self.num_w = num_w\n        self.num_p = num_p\n        self.num_r = num_r\n        self.num_bus = num_bus\n        self.num_br = num_br\n        self.p_max = Pp*u\n        self.p_min = Pp*l\n        self.w_max = self.parameters['infinity']*np.ones(self.num_w)\n        self.w_min = -self.parameters['infinity']*np.ones(self.num_w)\n        self.r_max = Pr*u\n        self.r_base = Pr*x\n        self.z_max = hu\n        self.z_min = hl \n        self.H0 = Pp*H*Pp.T\n        self.g0 = Pp*g\n        self.H1 = self.H0*self.parameters['cost_factor']\n        self.g1 = np.zeros(num_p)\n        self.G = A*Pp.T\n        self.R = A*Pr.T\n        self.A = -A*Pw.T\n        self.J = G*Pw.T\n        self.b = b\n        self.Pp = Pp\n        self.Pw = Pw\n        self.Pr = Pr\n\n        # Renewable covariance\n        from scikits.sparse.cholmod import cholesky\n        r_cov = Pr*net.create_vargen_P_sigma(self.corr_radius,self.corr_value)*Pr.T\n        self.r_cov = (r_cov+r_cov.T-triu(r_cov)).tocsc()\n        assert(self.r_cov.shape == (self.num_r,self.num_r))\n        factor = cholesky(self.r_cov)\n        L,D = factor.L_D()\n        P = factor.P()\n        PT = coo_matrix((np.ones(P.size),(P,np.arange(P.size))),shape=D.shape)\n        P = P.T\n        D = D.tocoo()\n        Dh = coo_matrix((np.sqrt(D.data),(D.row,D.col)),shape=D.shape)\n        self.L = PT*L*Dh\n        assert(np.all(D.row == D.col))\n        assert(np.all(Dh.row == Dh.col))\n        assert(np.all(D.data > 0))\n        assert(np.all(Dh.data > 0))\n        for i in range(10):\n            z = np.random.randn(self.num_r)\n            assert(norm(self.r_cov*z-self.L*self.L.T*z) < 1e-10)\n\n        # Average renewables\n        self.Er = 0\n        for i in range(self.parameters['num_samples']):\n            self.Er += (self.sample_w()-self.Er)/(i+1)\n        \n        # Checks\n        assert(np.all(self.p_min == 0))\n        assert(np.all(0 < self.r_max))\n        assert(np.all(self.z_min < self.z_max))\n        assert(np.all(self.p_min < self.p_max))\n        assert(np.all(cost.Hphi.row == cost.Hphi.col))\n        assert(np.all(cost.Hphi.data > 0))\n        assert(norm(self.A.T*np.ones(self.num_bus)) < (1e-10)*np.sqrt(self.num_bus*1.))\n        \n    def eval_EQ(self,p,samples=500,seed=None,tol=1e-4,quiet=True):\n        \"\"\"\n        Evaluates E[Q(p,r)] and its gradient. \n\n        Parameters\n        ----------\n        p : generator powers\n        samples : number of samples\n        seed : integer\n        tol : evaluation tolerance\n        quiet : flag\n        \"\"\"\n        \n        # Local vars\n        Q = 0.\n        gQ = np.zeros(self.num_p)\n\n        # Seed\n        if seed is None:\n            np.random.seed()\n        else:\n            np.random.seed(seed)\n        \n        # Problem\n        problem = self.get_problem_for_Q(p,np.ones(self.num_r))\n        \n        # Sampling loop\n        for i in range(samples):\n            \n            r = self.sample_w()\n\n            problem.u[self.num_p+self.num_w:self.num_p+self.num_w+self.num_r] = r # Important (update bound)\n            \n            q,gq = self.eval_Q(p,r,tol=tol,problem=problem)\n\n            # Show progress\n            if not quiet and i > 0:\n                print(('%d\\t%.5e' %(i,Q)))\n\n            # Infinity\n            if not q < np.inf:\n                return np.inf,None\n\n            # Update\n            Q += (q-Q)/(i+1.)\n            gQ += (gq-gQ)/(i+1.)\n                            \n        return Q,gQ\n\n    def eval_EQ_parallel(self,p,samples=500,num_procs=None,tol=1e-4,quiet=True):\n        \"\"\"\n        Evaluates E[Q(p,r)] and its gradient in parallel. \n\n        Parameters\n        ----------\n        p : generator powers\n        samples : number of samples\n        num_procs : number of parallel processes\n        tol : evaluation tolerance\n        quiet : flag\n        \"\"\"\n    \n        if not num_procs:\n            num_procs = cpu_count()\n        pool = Pool(num_procs)\n        num = int(np.ceil(float(samples)/float(num_procs)))\n        results = list(zip(*pool.map(ApplyFunc,[(self,'eval_EQ',p,num,i,tol,quiet) for i in range(num_procs)],chunksize=1)))\n        pool.terminate()\n        pool.join()\n        return [sum([val/float(num_procs) for val in vals]) for vals in results]\n        \n    def eval_Q(self,p,r,quiet=True,check=False,tol=1e-4,problem=None,return_data=False):\n        \"\"\"\n        Evaluates Q(p,r) and its gradient.\n\n        Parameters\n        ----------\n        p : generator powers\n        r : renewable powers\n        quiet : flag\n        check : flag\n        tol : evaluation tolerance \n        problem : QuadProblem\n        return_data : flag\n\n        Returns\n        -------\n        Q : value\n        gQ : gradient\n        \"\"\"\n\n        # Local vars\n        num_p = self.num_p\n        num_w = self.num_w\n        num_r = self.num_r\n        num_bus = self.num_bus\n        num_br = self.num_br\n        \n        # Check\n        assert(np.all(r > 0))\n        \n        # Problem\n        if not problem:\n            problem = self.get_problem_for_Q(p,r)\n               \n        try:\n\n            # Solver\n            solver = OptSolverIQP()\n            solver.set_parameters({'quiet':quiet,\n                                   'tol':tol})\n            \n            # Solve\n            solver.solve(problem)\n\n            # Info\n            x = solver.get_primal_variables()\n            lam,nu,mu,pi = solver.get_dual_variables()\n            k = solver.get_iterations()\n            q = x[:num_p]\n            \n            # Check\n            if check:\n                w = x[num_p:num_p+num_w]\n                s = x[num_p+num_w:num_p+num_w+num_r]\n                z = x[num_p+num_w+num_r:]\n                assert(norm(self.G*(p+q)+self.R*s-self.A*w-self.b) < (1e-6)*norm(self.b))\n                assert(norm(self.J*w-z) < (1e-6)*norm(z))\n                assert(np.all(self.p_min <= p+q))\n                assert(np.all(p+q <= self.p_max))\n                assert(np.all(0 <= s))\n                assert(np.all(s <= r))\n                assert(np.all(self.z_min <= z))\n                assert(np.all(self.z_max >= z))\n\n            # Objective\n            Q = 0.5*np.dot(q,self.H1*q)+np.dot(self.g1,q)\n            \n            # Gradient\n            gQ = -(self.H1*q+self.g1)\n\n            # Return\n            if not return_data:\n                return Q,gQ\n            else:\n\n                # Sensitivity (as linear operator)\n                dqdpT = self.get_sol_sensitivity(problem,x,lam,mu,pi) # NEED TO GENERALIZE THIS TO dxdpT\n                \n                data = {'q': q,\n                        'dqdpT': dqdpT}\n\n                return Q,gQ,data\n\n        # Errors\n        except OptSolverError_MaxIters:\n            return np.inf,None\n        except OptSolverError_LineSearch:\n            return np.inf,None\n        except Exception as e:\n            raise\n\n    def get_size_x(self):\n        \"\"\"\n        Gets size of vector of first stage variables.\n        \n        Returns\n        -------\n        size : int\n        \"\"\"\n\n        return self.num_p\n\n    def get_prop_x(self,x):\n        \"\"\"\n        Gets some property of x that is useful for monitor \n        progress.\n\n        Parameters\n        ----------\n        x : generator powers\n\n        Returns\n        -------\n        prop : float\n        \"\"\"\n        \n        return np.average(x/self.p_max)\n\n    def get_strong_convexity_constant(self):\n        \"\"\"\n        Gets strong convexity constant, which for\n        this case is lambda_min(H0)/2.\n\n        Returns\n        -------\n        c : float\n        \"\"\"\n\n        H0 = self.H0.tocoo()\n        assert(np.all(H0.row == H0.col))\n        lmin = np.min(H0.data)\n        assert(lmin > 0)\n        return lmin/2.\n\n    def get_problem_for_Q(self,p,r):\n        \"\"\"\n        Constructs second-stage quadratic problem\n        in the form\n\n        minimize(x)   (1/2)x^THx + g^Tx\n        subject to    Ax = b\n                      l <= x <= u,\n\n        where x = (q,w,s,z).\n\n        Parameters\n        ----------\n        p : generator powers\n        r : renewable powers\n\n        Returns\n        -------\n        problem : QuadProblem\n        \"\"\"\n\n        # Constatns\n        num_p = self.num_p\n        num_w = self.num_w\n        num_r = self.num_r\n        num_bus = self.num_bus\n        num_br = self.num_br\n        Ow = coo_matrix((num_w,num_w))\n        Os = coo_matrix((num_r,num_r))\n        Oz = coo_matrix((num_br,num_br))\n        Iz = eye(num_br,format='coo')\n        ow = np.zeros(num_w)\n        os = np.zeros(num_r)\n        oz = np.zeros(num_br)\n        cost_factor = self.parameters['cost_factor']\n\n        H1 = self.H1/cost_factor\n        g1 = self.g1/cost_factor\n        \n        # Form QP problem\n        H = bmat([[H1,None,None,None],  # q: gen power adjustments\n                  [None,Ow,None,None],  # w: bus voltage angles\n                  [None,None,Os,None],  # s: curtailed renewable powers\n                  [None,None,None,Oz]], # z: slack variables for thermal limits\n                 format='coo')\n        g = np.hstack((g1,ow,os,oz))\n        A = bmat([[self.G,-self.A,self.R,None],\n                  [None,self.J,None,-Iz]],format='coo')\n        b = np.hstack((self.b-self.G*p,oz))\n        l = np.hstack((self.p_min-p,\n                       self.w_min,\n                       os,\n                       self.z_min))\n        u = np.hstack((self.p_max-p,\n                       self.w_max,\n                       r,\n                       self.z_max))\n        \n        # Return\n        return QuadProblem(H,g,A,b,l,u)\n\n    def eval_F(self,x,w,tol=1e-4):\n        \"\"\"\n        Evaluates objective function for a given\n        realization of uncertainty.\n\n        Parameters\n        ----------\n        x : generator powers\n        w : renewable powers\n\n        Returns\n        -------\n        F : float\n        gF : vector\n        \"\"\"\n        \n        phi = 0.5*np.dot(x,self.H0*x)+np.dot(self.g0,x)\n        gphi = self.H0*x + self.g0\n        Q,gQ = self.eval_Q(x,w,tol=tol)\n\n        return (phi+Q,gphi+gQ)\n\n    def eval_F_approx(self,x,tol=1e-4):\n        \"\"\"\n        Evaluates certainty equivalent\n        version of objective function.\n\n        Parameters\n        ----------\n        x : generator powers\n\n        Returns\n        -------\n        F : float\n        gF : vector\n        \"\"\"\n\n        return self.eval_F(x,self.Er,tol=tol)\n\n    def eval_EF(self,x,samples=500,tol=1e-4):\n        \"\"\"\n        Evaluates expected objective function.\n\n        Parameters\n        ----------\n        x : generator powers\n        samples : number of samples\n        \n        Returns\n        -------\n        val : float\n        \"\"\"\n        \n        phi = 0.5*np.dot(x,self.H0*x)+np.dot(self.g0,x)\n        gphi = self.H0*x + self.g0\n        Q,gQ = self.eval_EQ_parallel(x,samples=samples,tol=tol)\n\n        return (phi+Q,gphi+gQ)\n\n    def project_x(self,x):\n        \"\"\"\n        Projects generator powers on the set\n        defined by generator limits.\n        \n        Parameters\n        ----------\n        x : generator powers\n\n        Returns\n        -------\n        y : vector\n        \"\"\"\n\n        return np.maximum(np.minimum(x,self.p_max),self.p_min)\n\n    def sample_w(self):\n        \"\"\"\n        Samples renewable powers.\n\n        Returns\n        -------\n        r : vector\n        \"\"\"\n\n        return np.minimum(np.maximum(self.r_base+self.L*np.random.randn(self.num_r),1e-3),self.r_max)\n\n    def save_x_info(self,x,filename):\n\n        # Local variables\n        num_p = self.num_p\n        H0 = self.H0*np.ones(num_p)\n        \n        # Check\n        assert(x.size == num_p)\n\n        # Writer\n        f = open(filename,'w')\n        writer = csv.writer(f)\n        \n        # Write\n        writer.writerow(['p','pmin','pmax','H0','g0'])\n        for i in range(num_p):\n            writer.writerow([x[i],\n                             self.p_min[i],\n                             self.p_max[i],\n                             H0[i],\n                             self.g0[i]])\n\n        # Close\n        f.close()\n\n    def show(self):\n        \"\"\"\n        Shows problem information.\n        \"\"\"\n\n        Ctot = np.sum(self.r_max)\n        Btot = np.sum(self.r_base)\n        \n        print('Stochastic Two-Stage DCOPF')\n        print('--------------------------')\n        print(('buses            : %d' %self.num_bus))\n        print(('gens             : %d' %self.num_p))\n        print(('vargens          : %d' %self.num_r))\n        print(('penetration cap  : %.2f (%% of load)' %(100.*Ctot/self.total_load)))\n        print(('penetration base : %.2f (%% of load)' %(100.*Btot/self.total_load)))\n        print(('penetration std  : %.2f (%% of local cap)' %self.uncertainty))\n        print(('correlation rad  : %d (edges)' %(self.corr_radius)))\n        print(('correlation val  : %.2f (unitless)' %(self.corr_value)))\n\n    def solve_approx(self,g_corr=None,tol=1e-4,quiet=False,samples=500,init_data=None):\n        \"\"\"\n        Solves certainty equivalent problem\n        with slope correction.\n\n        Parameters\n        ----------\n        g_corr : slope correction\n        tol : optimality tolerance\n        quiet : flag\n        samples : number of samples\n\n        Returns\n        -------\n        p : generator powers\n        \"\"\"\n        \n        # Constants\n        num_p = self.num_p\n        num_w = self.num_w\n        num_r = self.num_r\n        num_bus = self.num_bus\n        num_br = self.num_br\n        Ow = coo_matrix((num_w,num_w))\n        Os = coo_matrix((num_r,num_r))\n        Oz = coo_matrix((num_br,num_br))\n        Onp = coo_matrix((num_bus,num_p))\n        Onz = coo_matrix((num_bus,num_br))\n        ow = np.zeros(num_w)\n        os = np.zeros(num_r)\n        oz = np.zeros(num_br)\n        Iz = eye(num_br,format='coo')\n        \n        # Slope correction\n        if g_corr is None:\n            g_corr = np.zeros(num_p)\n\n        # Problem construction\n        H = bmat([[self.H0+self.H1,-self.H1,None,None,None], # p\n                  [-self.H1,self.H1,None,None,None],         # y = p + q\n                  [None,None,Ow,None,None],                  # w\n                  [None,None,None,Os,None],                  # s\n                  [None,None,None,None,Oz]],                 # z\n                 format='coo')\n        g = np.hstack((self.g0-self.g1+g_corr,self.g1,ow,os,oz))\n        A = bmat([[Onp,self.G,-self.A,self.R,Onz],\n                  [None,None,self.J,None,-Iz]],format='coo')\n        b = np.hstack((self.b,oz))\n        l = np.hstack((self.p_min,  # p\n                       self.p_min,  # y\n                       self.w_min,  # w\n                       os,          # s\n                       self.z_min)) # z\n        u = np.hstack((self.p_max,\n                       self.p_max,\n                       self.w_max,\n                       self.Er,\n                       self.z_max))\n\n        # Problem\n        if init_data is None:\n            problem = QuadProblem(H,g,A,b,l,u)\n        else:\n            problem = QuadProblem(H,g,A,b,l,u,\n                                  x=init_data['x'],\n                                  lam=init_data['lam'],\n                                  mu=init_data['mu'],\n                                  pi=init_data['pi'])\n        \n        # Solve\n        solver = OptSolverIQP()\n        solver.set_parameters({'quiet':quiet,\n                               'tol':tol})\n        solver.solve(problem)\n        results = solver.get_results()\n        x = results['x']\n        lam = results['lam']\n        mu = results['mu']\n        pi = results['pi']\n\n        # Check\n        problem.eval(x)\n        gphi = problem.gphi\n        assert(norm(gphi-A.T*lam+mu-pi) < (1e-6)*(norm(gphi)+norm(lam)+norm(mu)+norm(pi)))\n        assert(norm(mu*(u-x)) < (1e-6)*(norm(mu)+norm(u-x)))\n        assert(norm(pi*(x-l)) < (1e-6)*(norm(pi)+norm(x-l)))\n        assert(np.all(x < u))\n        assert(np.all(x > l))\n        assert(norm(A*x-b) < (1e-6)*norm(b))\n\n        # Return\n        return x[:num_p],results\n\n    def get_sol_sensitivity(self,problem,x,lam,mu,pi):\n        \"\"\"\n        Computes sensitivity of optimal q with respect to p\n        as a linear operator.\n\n        NEED TO EXTEND TO ALL x\n        \n        Parameters\n        ----------\n        problem : QuadProblem\n        x : primal\n        lam : dual var (eq constraints)\n        mu : dual var (upper bounds)\n        pi : dual var (lower bounds)\n        \n        Returns\n        -------\n        dqdpT : Linear Operator\n        \"\"\"\n\n        H = problem.H\n        g = problem.g\n        A = problem.A\n        b = problem.b\n        u = problem.u\n        l = problem.l\n\n        n = A.shape[1]\n        m = A.shape[0]\n\n        Dmu = spdiags(mu,0,n,n)\n        Dpi = spdiags(pi,0,n,n)\n        Dux = spdiags(u-x,0,n,n)\n        Dxl = spdiags(x-l,0,n,n)\n\n        In = eye(n)\n        \n        K = bmat([[H,-A.T,In,-In],\n                  [A,None,None,None],\n                  [-Dmu,None,Dux,None],\n                  [Dpi,None,None,Dxl]],\n                 format='coo')\n        KT = K.T\n        \n        Ibar = eye(self.num_p,K.shape[0])\n        \n        Onp = coo_matrix((n,self.num_p))\n        bp = bmat([[-self.G],\n                   [coo_matrix((self.num_br,self.num_p))]],\n                  format='coo')\n        up = -eye(n,self.num_p)\n        lp = -eye(n,self.num_p)\n\n        eta_p = bmat([[Onp],\n                      [bp],\n                      [-Dmu*up],\n                      [Dpi*lp]],\n                     format='coo')\n\n        linsolver = new_linsolver('mumps','unsymmetric')\n        linsolver.analyze(KT)\n        linsolver.factorize(KT)\n        \n        dqdpT = LinearOperator((self.num_p,self.num_p),\n                               lambda y : eta_p.T*linsolver.solve(Ibar.T*y))\n        \n        return dqdpT\n","sub_path":"gridopt/stochastic/two_stage_DCOPF/problem.py","file_name":"problem.py","file_ext":"py","file_size_in_byte":22643,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"230476106","text":"\"\"\"Platform for light integration.\"\"\"\nfrom .mipow import MipowDevice\n\nimport logging\n\nimport voluptuous as vol\n\nimport homeassistant.helpers.config_validation as cv\nfrom homeassistant.helpers.entity import async_generate_entity_id\n# Import the device class from the component that you want to support\nfrom homeassistant.components.light import (\n\tATTR_BRIGHTNESS,\n\tATTR_HS_COLOR,\n\tATTR_EFFECT,\n\tATTR_WHITE_VALUE,\n\tATTR_FLASH,\n\tFLASH_SHORT,\n\tFLASH_LONG,\n\tPLATFORM_SCHEMA,\n\tSUPPORT_BRIGHTNESS,\n\tSUPPORT_COLOR,\n\tSUPPORT_EFFECT,\n\tSUPPORT_WHITE_VALUE,\n\tLightEntity)\nfrom homeassistant.const import CONF_DEVICES, CONF_NAME, ATTR_BATTERY_LEVEL\nimport homeassistant.util.color as color_util\n\n_LOGGER = logging.getLogger(__name__)\n\nSUPPORT_MIPOW_LED = SUPPORT_BRIGHTNESS | SUPPORT_COLOR | SUPPORT_EFFECT | SUPPORT_WHITE_VALUE\n\nDEVICE_SCHEMA = vol.Schema({vol.Optional(CONF_NAME): cv.string})\n\nPLATFORM_SCHEMA = PLATFORM_SCHEMA.extend(\n\t{vol.Optional(CONF_DEVICES, default={}): {cv.string: DEVICE_SCHEMA}}\n)\n\nCandleEffectsMap = {\n\t\"Flash\" : 0,\n\t\"Fade\" : 1,\n\t\"Jump RGB\" : 2,\n\t\"Fade RGB\" : 3,\n\t\"Candle\" : 4,\n\t\"None\" : 255\n}\n\ndef setup_platform(hass, config, add_entities, discovery_info=None):\n\t# Add devices\n\tlights = []\n\tfor address, device_config in config[CONF_DEVICES].items():\n\t\tname = device_config[CONF_NAME];\n\t\tlight = MipowCandle(MipowDevice(address), name)\n\t\tlights.append(light)\n\n\tadd_entities(light for light in lights)\n\nclass MipowCandle(LightEntity):\n\tdef __init__(self, light, name):\n\t\tself._light = light\n\t\tself._name = name\n\t\tself._state = False\n\t\tself._brightness = 128\n\t\tself._hs_color = (1, 0)\n\t\tself._white = 0\n\t\tself._effect = None\n\t\tself._unique_id = f\"{self.__class__}.{light.mac}\"\n\t\tself._is_connected = False\n\t\tself._battery_level = None\n\t\tself._failed_updates_count = 0\n\n\t@property\n\tdef name(self):\n\t\treturn self._name\n\n\t@property\n\tdef brightness(self):\n\t\treturn self._brightness\n\n\t@property\n\tdef hs_color(self):\n\t\treturn self._hs_color\n\n\t@property\n\tdef supported_features(self):\n\t\treturn SUPPORT_MIPOW_LED\n\n\t@property\n\tdef is_on(self):\n\t\treturn self._state\n\n\t@property\n\tdef effect_list(self):\n\t\treturn [x for x in CandleEffectsMap.keys()]\n\n\t@property\n\tdef effect(self):\n\t\treturn self._effect\n\n\t@property\n\tdef white_value(self):\n\t\treturn self._white\n\n\t@property\n\tdef unique_id(self):\n\t\treturn self._unique_id\n\n\t@property\n\tdef device_state_attributes(self):\n\t\treturn {\n\t\t\t(ATTR_BATTERY_LEVEL, self._battery_level)\n\t\t}\n\n\tdef turn_on(self, **kwargs):\n\t\tbrightness = self._brightness\n\t\thsColor = self._hs_color\n\t\twhite = self._white\n\t\teffect = self._effect\n\t\teffect_delay = 0x14\n\n\t\tif ATTR_BRIGHTNESS in kwargs:\n\t\t\tbrightness = kwargs.get(ATTR_BRIGHTNESS, 255)\n\n\t\tif ATTR_HS_COLOR in kwargs:\n\t\t\thsColor = kwargs.get(ATTR_HS_COLOR)\n\n\t\tif ATTR_WHITE_VALUE in kwargs:\n\t\t\twhite = kwargs.get(ATTR_WHITE_VALUE)\n\n\t\tif ATTR_EFFECT in kwargs:\n\t\t\teffect = kwargs.get(ATTR_EFFECT)\n\t\t\tif (not effect in CandleEffectsMap):\n\t\t\t\teffect = None\n\n\t\tif ATTR_FLASH in kwargs:\n\t\t\teffect = 'Flash'\n\t\t\tflash = kwargs.get(ATTR_FLASH)\n\t\t\tif flash == FLASH_LONG:\n\t\t\t\teffect_delay = 0x30\n\t\t\telif flash == FLASH_SHORT:\n\t\t\t\teffect_delay = 0x10\n\n\t\trgbColor = color_util.color_hsv_to_RGB(\n\t\t  hsColor[0], hsColor[1], brightness / 255 * 100\n\t\t)\n\n\t\teffectId = None\n\t\tif (not effect is None):\n\t\t\teffectId = CandleEffectsMap[effect]\n\t\t\tif (effectId == 255):\n\t\t\t\teffectId = None\n\n\t\tself._connect()\n\t\tif (effectId is None):\n\t\t\tself._light.set_rgbw(rgbColor[0], rgbColor[1], rgbColor[2], white)\n\t\telse:\n\t\t\tself._light.set_effect(rgbColor[0], rgbColor[1], rgbColor[2], white, effectId, delay=effect_delay)\n\t\t\t\n\t\tself._hs_color = hsColor\n\t\tself._brightness = brightness\n\t\tself._white = white\n\t\tself._effect = effect\n\t\tself._state = True\n\n\tdef turn_off(self, **kwargs):\n\t\tself._connect()\n\t\tself._light.set_rgbw(0, 0, 0, 0)\n\t\tself._state = False\n\n\tdef _connect(self):\n\t\tif (not self._is_connected):\n\t\t\tself._light.connect()\n\t\t\tself._is_connected = True\n\t\t\tself._failed_updates_count = 0\n\n\tdef update(self):\n\t\tif (self._failed_updates_count > 10):\n\t\t\treturn\n\n\t\ttry:\n\t\t\tself._connect()\n\t\t\tresult = self._light.fetch_rgbw()\n\t\t\tself._state = result[0] != 0 or result[1] != 0 or result[2] != 0 or result[3] != 0\n\t\t\tif (self._state):\n\t\t\t\tself._white = result[0]\n\t\t\t\thsv = color_util.color_RGB_to_hsv(result[1], result[2], result[3])\n\t\t\t\tself._hs_color = hsv[:2]\n\t\t\t\tself._brightness = (hsv[2] / 100) * 255\n\n\t\t\tself._battery_level = self._light.fetch_battery_level()\t\t\n\t\texcept:\n\t\t\tself._is_connected = False\n\t\t\tself._battery_level = None\n\t\t\tself._failed_updates_count += 1","sub_path":"custom_components/mipow/light.py","file_name":"light.py","file_ext":"py","file_size_in_byte":4536,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"637279416","text":"import socket, threading\n\ndef client(soc, addr):\n    while True:\n        data = soc.recv(1024)\n        msg = data.decode()\n        print('Received from', addr, msg)\n        soc.sendall(data)\n        if msg=='/stop':\n            break\n    soc.close()\n\ndef main():\n    HOST = 'localhost'  #server ip\n    PORT = 8888         #server port\n\n    #server socket open. socket.AF_INET:주소체계(IPV4), socket.SOCK_STREAM:tcp\n    server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n    #포트 여러번 바인드하면 발생하는 에러 방지\n    server_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n\n    #바인드:오픈한 소켓에 IP와 PORT 할당\n    server_socket.bind((HOST, PORT))\n\n    #이제 accept할 수 있음을 알림\n    server_socket.listen()\n\n    print('server start')\n\n    #accept로 client의 접속을 기다리다 요청시 처리.\n    #client와 1:1통신할 작은 소켓과 연결된 상대방의 주소 반환\n    while True:\n        client_socket, addr = server_socket.accept()\n        print('Connected by', addr)\n        t = threading.Thread(target=client, args=(client_socket,addr))\n        t.start()\n\n    server_socket.close()\n\nmain()\n","sub_path":"network/echo_server_thread.py","file_name":"echo_server_thread.py","file_ext":"py","file_size_in_byte":1194,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"650982663","text":"# Задание-1: уравнение прямой вида y = kx + b задано в виде строки.\n# Определить координату y точки с заданной координатой x.\n\nequation = 'y = -12x + 11111140.2121'\nx = 2.5\n# вычислите и выведите y\n\n#простое решение именно этого уравления\nprint(\"\\n\\nЗадача №1: Найти y\")\nk = float(equation[4:7])\nb = float(equation[11:])\ny = k * x + b\nprint(\"Простое решение: y =\", y)\n\n#универсальное решение\nequation_tmp = (equation.split('='))[1] #берём правую часть уравнения\nk = (equation_tmp.split('+'))[0].replace('x','').strip() #берём левую часть, убираем х, убираем пустые символы слева и справа\nb = (equation_tmp.split('+'))[1].strip() #берём правую часть и убираем пустые символы слева и справа строки\ny = float(k) * float(x) + float(b)\nprint(\"Универсальное решение: y =\", y)\n\n# Задание-2: Дата задана в виде строки формата 'dd.mm.yyyy'.\n# Проверить, корректно ли введена дата.\n# Условия корректности:\n# 1. День должен приводиться к целому числу в диапазоне от 1 до 30(31)\n#  (в зависимости от месяца, февраль не учитываем)\n# 2. Месяц должен приводиться к целому числу в диапазоне от 1 до 12\n# 3. Год должен приводиться к целому положительному числу в диапазоне от 1 до 9999\n# 4. Длина исходной строки для частей должна быть в соответствии с форматом \n#  (т.е. 2 символа для дня, 2 - для месяца, 4 - для года)\n# Пример корректной даты\n#date = '01.11.1985'\n# Примеры некорректных дат\n#date = '01.22.1001'\n#date = '1.12.1001'\n#date = '-2.10.3001'\n\n\n#функция определения возможности конвертировать строку в целое число\ndef is_convert_to_int(str):\n    try:\n        int(str)\n        return True\n    except ValueError:\n        return False\n\n#переменная количества дней в месяце (не учитываем високосный год)\ndays_of_month = {1:31, 2:28, 3:31, 4:30, 5:31, 6:30, 7:31, 8:31, 9:30, 10:31, 11:30, 12:31}\n\n#сам скрипт. для себя написал больше условий проверки, но тут не выкладываю.\nprint(\"\\n\\nЗадача №2: проверить дату\")\ndate = input(\"Введите дату dd.mm.yyyy: \")\ndate_tmp = date.split('.')\n\n#проверяем, что все данные переводятся в тип int\nif is_convert_to_int(date_tmp[0]) and is_convert_to_int(date_tmp[1]) and is_convert_to_int(date_tmp[2]):\n\t#проверка формата \"dd.mm.yyyy\"\n\tif len(date_tmp[0]) != 2 or len(date_tmp[1]) != 2 or len(date_tmp[2]) != 4:\n\t\tprint('Формат даты введён некорректно. Введите в формате \"dd.mm.yyyy\".')\n\t#проверка года\n\telif not 1 <= int(date_tmp[2]) <= 9999:\n\t\tprint(\"Год должен быть в диапазоне от 1 до 9999\")\n\t#проверка месяца\n\telif not 1 <= int(date_tmp[1]) <= 12:\n\t\tprint(\"Месяц должен быть в диапазоне от 1 до 12\")\n\t#проверка дня\n\telif not 0 < int(date_tmp[0]) <= days_of_month[int(date_tmp[1])]:\n\t\tprint(\"В\", int(date_tmp[1]), \"месяце дата должна быть в диапазоне от 1 до\", days_of_month[int(date_tmp[1])])\n\telse:\n\t\tprint(\"Дата введена верно\")\nelse:\n\tprint(\"Не все введённые данные числовые\")\n\n# Задание-3: \"Перевёрнутая башня\" (Задача олимпиадного уровня)\n#\n# Вавилонцы решили построить удивительную башню —\n# расширяющуюся к верху и содержащую бесконечное число этажей и комнат.\n# Она устроена следующим образом — на первом этаже одна комната,\n# затем идет два этажа, на каждом из которых по две комнаты, \n# затем идёт три этажа, на каждом из которых по три комнаты и так далее:\n#         ...\n#     12  13  14\n#     9   10  11\n#     6   7   8\n#       4   5\n#       2   3\n#         1\n#\n# Эту башню решили оборудовать лифтом --- и вот задача:\n# нужно научиться по номеру комнаты определять,\n# на каком этаже она находится и какая она по счету слева на этом этаже.\n#\n# Входные данные: В первой строчке задан номер комнаты N, 1 ≤ N ≤ 2 000 000 000.\n#\n# Выходные данные:  Два целых числа — номер этажа и порядковый номер слева на этаже.\n#\n# Пример:\n# Вход: 13\n# Выход: 6 2\n#\n# Вход: 11\n# Выход: 5 3\n\nprint(\"\\n\\nЗадача №3: найти квартиру\")\n\ni = 1 #номер квадрата здания, таких как 2-5 квартары или 6-14 квартиры. В каждом квадрате\n#кол-во этажей соответствует кол-ву квартир на каждом из этих этажей.\nsearching_apartment = int(input('\\nВведите номер искомой квартиры: ')) #какую квартиру ищем\ncurrent_apartment = 0 #переменная для перебора квартир\nfloor = 0 #номер этажа\n\n#в цикле while добавляем в переменные сразу все этажи и квартиры квадратов здания в которых нет искомой квартиры\nwhile (current_apartment + i*i) < searching_apartment:\n\tcurrent_apartment += i * i\n\tfloor += i\n\ti += 1\n#циклами for ищем квартиру в квадрате в котором она находится\nfor ii in range(1,i+1):\n\tfloor += 1\n\tfor iii in range(1,i+1):\n\t\tcurrent_apartment += 1\n\t\tif current_apartment == searching_apartment:\n\t\t\tprint(f\"Этаж {floor}, квартира {iii} слева.\")\n\t\t\tbreak\n\tif current_apartment == searching_apartment:\n\t\tbreak\n\n#Изначально эту задачу хотел сделать с использованием рекурсии функции.\n#сделал правда кривовато.\n\"\"\"\nimport sys\nsys.setrecursionlimit(10000)\n\ni = 0 #\nsearching_apartment = int(input('\\nВведите номер квартиры: ')) #какую квартиру ищем\ncurrent_apartment = 0 #переменная для перебора квартир\nfloor = 0 #номер этажа\n\n#самый быстрый скрипт (ниже закоментированы скрипты менее производительные)\ndef search_apartment(storona):\n\tglobal i\n\tglobal searching_apartment\n\tglobal current_apartment\n\tglobal floor\n\ti += 1\n\tif (current_apartment + (i * i)) < searching_apartment:\n\t\tfloor += i\n\t\tcurrent_apartment += (i * i)\n\t\tsearch_apartment(i)\n\telse:\n\t\tfor ii in range(1,i+1):\n\t\t\tfloor += 1\n\t\t\tfor iii in range(1,i+1):\n\t\t\t\tcurrent_apartment += 1\n\t\t\t\tif current_apartment == searching_apartment:\n\t\t\t\t\tprint(f\"Этаж {floor}, к��артира {iii} слева.\")\n\t\t\t\t\tbreak\n\t\t\tif current_apartment == searching_apartment:\n\t\t\t\tbreak\n\nsearch_apartment(i)\n\"\"\"","sub_path":"lesson_2/hw02_hard.py","file_name":"hw02_hard.py","file_ext":"py","file_size_in_byte":7953,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"199059506","text":"#milestone27 新增開始與結束時間，serverTimeStamp，房主的uuid(這支不管token/nonce)\nimport json\nimport requests\nimport pymysql\nimport time\nimport string\nimport pytest\nfrom assistence import api\nfrom assistence import initdata\nfrom assistence import dbConnect\nfrom assistence import liveshowLib\nfrom pprint import pprint\nfrom datetime import datetime, timedelta\n\nenv = 'QA'\ntest_parameter = {}\nheader = {'Content-Type': 'application/json', 'Connection': 'Keep-alive', 'X-Auth-Token': '', 'X-Auth-Nonce': ''}\nidList = []\nmemberList = []\nliveshowId = []\n\ndef setup_module():\n    initdata.set_test_data(env, test_parameter)\n    initdata.clearLiveData(test_parameter['db'])\n    header['X-Auth-Token'] = test_parameter['backend_token']\n    header['X-Auth-Nonce'] = test_parameter['backend_nonce']\n    initdata.initIdList(test_parameter['prefix'], test_parameter['backend_token'], test_parameter['backend_nonce'], [test_parameter['broadcaster_acc'],\n    test_parameter['user_acc'], test_parameter['user1_acc']], idList)\n    liveshowLib.createMember(test_parameter['prefix'], test_parameter['backend_token'], test_parameter['backend_nonce'], memberList)\n    liveshowLib.liveshowPrepare(test_parameter['prefix'], test_parameter['db'], header, 'TrueLove teams', 2, 0, 2, 3, 351, '', \n    False, False, memberList, liveshowId) \n\ntestData = [\n    ('user取得liveshow訊息', 'user_token', 'user_nonce', 'getId', 2),\n    ('broadcaster取得liveshow訊息',  'broadcaster_token', 'broadcaster_nonce', 'getId', 2),\n    ('backend取得liveshow訊息',  'backend_token', 'backend_nonce', 'getId', 2),\n    ('liveshowID為空值',  'broadcaster_token', 'broadcaster_nonce', 'empty', 4),\n    ('liveshowID為不存在',  'broadcaster_token', 'broadcaster_nonce', 'wrongId', 4)\n]\n\nclass TestLiveshowInfo():\n    @pytest.mark.parametrize('scenario, token, nonce, action, expected', testData)         \n    def test_getInfo(self, scenario, token, nonce, action, expected):\n        actionDic = {\n            'getId': 'select liveshow_id, title, pool_id from liveshow where id = 1',\n            'empty': '',\n            'wrongId': 'errId'\n        }\n        if action == 'getId':\n            result = dbConnect.dbQuery(test_parameter['db'], actionDic[action])\n            liveshowId = result[0][0]\n        else:\n            liveshowId = actionDic[action]\n        header['X-Auth-Token'] = test_parameter[token]\n        header['X-Auth-Nonce'] = test_parameter[nonce]\n        apiName = '/api/v2/liveshow/info'\n        res = api.apiFunction(test_parameter['prefix'], header, apiName, 'get', {'liveshowId': liveshowId})\n        restext = json.loads(res.text)\n        assert res.status_code // 100 == expected\n        if expected == 2:\n            assert restext['title'] == result[0][1]\n            assert restext['masterId'] == result[0][2]\n            assert all([len(str(restext['liveStartTime'])) == 10, type(restext['liveStartTime']) == int])\n            assert all([len(str(restext['liveEndTime'])) == 10, type(restext['liveEndTime']) == int])\n            assert all([len(str(restext['serverTimeStamp'])) == 10, type(restext['serverTimeStamp']) == int])\n","sub_path":"liveShow/test_liveshowInfo.py","file_name":"test_liveshowInfo.py","file_ext":"py","file_size_in_byte":3143,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"584832012","text":"import tornado.web\nimport tornado.wsgi\nimport tornado.escape\n\nimport toml\nimport requests\n\nimport os\nimport sys\nimport hmac\nimport base64\n\nfrom OpenSSL import crypto\n\n\nif not os.path.exists('config.toml'):\n    print('No config file found---please refer to the README')\n    sys.exit(1)\n\nconfig = toml.load('config.toml')\n\n\ndef verify_signature(payload, signature, secret):\n    expected = 'sha1=' + hmac.new(secret.encode('ascii'),\n                                  payload).hexdigest()\n\n    return hmac.compare_digest(signature, expected)\n\n\nclass MainHandler(tornado.web.RequestHandler):\n    def get(self):\n        self.write(\"Hello from Tornado\")\n\n\nclass WebhookHandler(tornado.web.RequestHandler):\n    def get(self):\n        self.write('Webhook alive and listening')\n\n    def post(self):\n        if not verify_signature(self.request.body,\n                                self.request.headers.get('X-Hub-Signature'),\n                                config['github']['webhook_secret']):\n            return self.write(\n                    {'status': 'error',\n                     'message': 'Cannot validate GitHub payload with ' \\\n                                'provided WebHook secret'})\n\n        json = tornado.escape.json_decode(self.request.body)\n        payload = json[\"payload\"]\n        personal_token = config['github']['personal_access_token']\n\n        event_type = self.request.headers.get('X-GitHub-Event')\n\n\n        if event_type == 'pull_request':\n            pr = payload[\"pull_request\"]\n            gh = github.Github(personal_token)\n            repo = gh.get_repo(pr[\"repo\"][\"full_name\"])\n            commit = repo.get_commit(pr['head']['sha'])\n\n            dependent_repo = [d['triggered_repo'] for d in config['dependent_repo']\n                              if d['source_repo'] == repo]\n\n            if len(dependent_repo) == 0:\n                return self.write({'status': 'error',\n                                   'message': 'No dependent repo set for ' \\\n                                              '{}'.format(repo)})\n\n            # For now, we only support one triggered repo\n            dependent_repo = dependent_repo[0]\n\n            travis_headers = {\n                'User-Agent': 'Travis-dependent_build_server/0.0'\n            }\n            travis_api = 'https://api.travis-ci.org'\n\n            travis_token = requests.post(\n                    travis_api + '/auth/github',\n                    headers=travis_headers,\n                    json={'github_token': personal_token}).json()\n            travis_token = travis_token['access_token']\n\n            build = {\n                \"request\": {\n                    \"message\": \"Build triggered by dependent_build_server\",\n                    \"branch\": \"master\",\n                    \"config\": {\n                        \"env\": {\n                            \"matrix\": [\"CESIUM_REPO={}\".format(),\n                                       \"CESIUM_SHA={}\".format()]\n                        },\n                        \"notifications\": {\n                            \"webhooks\": [\n                                config['server']['url'] + '/travis'\n                                ],\n                        }\n                    }\n                }\n            }\n\n            r = requests.post(\n                travis_api + '/repo/' + \\\n                repo.replace('/', '%2F') + '/requests',\n                headers={'Travis-API-Version': '3',\n                         'Authorization': 'token \"{}\"'.format(travis_token)},\n                json=build)\n\n            if r.status_code != 202:\n                self.write({'status': 'error',\n                            'message': 'Failed to create Travis-CI build'})\n\n            commit.create_status(\n                    'pending',\n                    target_url='https://travis-ci.org/' + dependent_repo,\n                    description='Triggered Travis-CI build ' \\\n                                'of {}'.format(dependent_repo),\n                    context='continuous-integration/dependent-build-server')\n        else:\n            pass\n\n        self.write({'status': 'OK'})\n\n\nclass TravisHandler(tornado.web.RequestHandler):\n    def post(self):\n        payload = tornado.escape.json_decode(self.request.body)\n        signature = base64.b64decode(\n                self.request.headers.get('Signature')\n                )\n\n        status = requests.get('https://api.travis-ci.org/config').json()\n        pubkey = status['config']['notifications']['webhook']['public_key']\n\n        public_key = crypto.load_publickey(crypto.FILETYPE_PEM, pubkey)\n        certificate = crypto.X509()\n        certificate.set_pubkey(public_key)\n\n        try:\n            crypto.verify(certificate, signature, payload, 'sha1')\n        except crypto.Error:\n            return self.write({'status': 'error',\n                               'message': 'Invalid signature for Travis ' \\\n                                          'payload'})\n\n        status = (\"error\" if (payload[\"status\"] == \"1\")\n                  else \"success\")\n        commit = payload[\"commit\"]\n        repo = payload[\"repository\"][\"owner_name\"] + \"/\" + \\\n               payload[\"repository\"][\"name\"]\n\n        gh = github.Github(config[\"github\"][\"personal_access_token\"])\n        repo = gh.get_repo(repo)\n        commit = repo.get_commit(commit)\n        commit.create_status(\n                status,\n                target_url=payload[\"build_url\"],\n                description='Dependent build completed',\n                context='continuous-integration/dependent-build-server')\n\n\napplication = tornado.wsgi.WSGIApplication([\n    (r\"/\", MainHandler),\n    (r\"/webhook\", WebhookHandler),\n    (r\"/travis\", TravisHandler)\n])\n","sub_path":"dependent_build_server.py","file_name":"dependent_build_server.py","file_ext":"py","file_size_in_byte":5683,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"578031557","text":"# Week 12, ICA 1\ndef main():\n    marcel = get_goals('dionne.txt')\n    total = get_total(marcel)\n    print('Marcel Dionne scored', total, 'goals in', len(marcel), 'NHL seasons.')\n    bossy1 = [53,69,51,68,64,60,51,58,61,38]\n    store_goals(bossy1, 'bossy1.txt')\n    mike = get_data('bossy1.txt','bossy2.txt', 10)\n    display_data('Mike Bossy', mike, len(mike))\n\ndef get_goals(filename):\n    goals = [] # empty array\n    file = open(filename, 'r')\n    goals = file.readlines() # Reads contents of file into list \n    file.close()\n    index = 0\n    while index < len(goals):\n        goals[index] = int(goals[index].rstrip('\\n'))\n        index += 1\n    return goals\n\ndef get_total(a):\n    total = 0\n    for num in a:\n        total += num\n    return total\n\ndef store_goals(g, filename):\n    file = open(filename, 'w')\n    for num in g:\n        file.write(str(num)+'\\n')\n    file.close()\n\ndef get_data(file1, file2, rows):\n    data = []\n    for x in range(rows):\n        data.append([0,0])\n    goals = open(file1, 'r')\n    assists = open(file2, 'r')\n    index = 0\n    while index < rows:\n        data[index][0] = goals.readline().rstrip('\\n')\n        data[index][1] = assists.readline().rstrip('\\n')\n        index += 1\n    goals.close()\n    assists.close()\n    return data\n\ndef display_data(name, data, rows):\n    print(f'Career Stats for {name}')\n    print('\\tGoals\\tAssists')\n    print('\\t---------------')\n    for x in range(rows):\n        print(f'\\t{data[x][0]}\\t{data[x][1]}')\n\nmain()","sub_path":"ICA/Ch7Pt3.py","file_name":"Ch7Pt3.py","file_ext":"py","file_size_in_byte":1483,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"147024549","text":"import torch\r\nfrom torchtext import data\r\nfrom torchtext import datasets\r\nimport random\r\nfrom sklearn.feature_extraction.text import TfidfVectorizer\r\nfrom sklearn.naive_bayes import MultinomialNB\r\n# from bayes.classifiers import ComplementNB\r\nfrom sklearn.pipeline import make_pipeline\r\nfrom sklearn.metrics import accuracy_score\r\n# from sklearn.metrics import roc_auc_score\r\nimport nltk\r\nfrom nltk.corpus import stopwords\r\n\r\n#downloading the stopwords\r\nnltk.download('stopwords')\r\n\r\nstopset = set(stopwords.words('english'))\r\n\r\n#prepare the data\r\nSEED = 1234\r\n\r\ntorch.manual_seed(SEED)\r\ntorch.cuda.manual_seed(SEED)\r\ntorch.backends.cudnn.deterministic = True\r\n\r\n#define fields to hold the data\r\nTEXT = data.Field(tokenize='spacy')\r\nLABEL = data.LabelField(dtype=torch.float)\r\n\r\n#download IMDB dataset and do train/test splits\r\ntrain_data, test_data = datasets.IMDB.splits(TEXT, LABEL)\r\n\r\n#train/validation set split\r\ntrain_data, valid_data = train_data.split(random_state=random.seed(SEED))\r\n\r\ndef dataset_data_label_split(dataset):\r\n    dataset_data = []\r\n    dataset_label = []\r\n\r\n    for i in range(len(dataset)):\r\n        dataset_data.append(vars(dataset[i])['text'])\r\n        dataset_label.append(vars(dataset[i])['label'])\r\n\r\n    return dataset_data, dataset_label\r\n\r\n\r\ndef dataset_data_untokenizer(dataset_data):\r\n    dataset_data_str = []\r\n    for i in range(len(dataset_data)):\r\n        dataset_data_str.append(' '.join(dataset_data[i]))\r\n\r\n    return dataset_data_str\r\n\r\ntrain_data, train_label = dataset_data_label_split(train_data)\r\ntest_data, test_label = dataset_data_label_split(test_data)\r\n\r\ntrain_data_str = dataset_data_untokenizer(train_data)\r\ntest_data_str = dataset_data_untokenizer(test_data)\r\n\r\n#creating a model\r\nmodel = make_pipeline(TfidfVectorizer(stop_words=stopset, ngram_range=(1,1)),MultinomialNB()) #removing stopwords set stop_words argument to None\r\n\r\n#training the model with the train data\r\nmodel.fit(train_data_str, train_label)\r\n\r\n#creating labels for the test data\r\npredicted_labels = model.predict(test_data_str)\r\n\r\n#calculating the accuracy\r\ntest_accuracy = accuracy_score(test_label,predicted_labels)\r\n### This also works fine\r\n# acc = roc_auc_score(test_label, model.predict_proba(test_data_str))\r\n\r\n#print the accuracy in percentage\r\nprint(f'|Test Acc:{test_accuracy*100:.2f}%|')\r\n\r\n# #Evaluating using confusion matrix\r\n# from sklearn.metrics import confusion_matrix\r\n# import seaborn as sns\r\n# import matplotlib.pyplot as plt\r\n#\r\n# classes = [\"Positive\", \"Negative\"]\r\n# conf_mat = confusion_matrix(test_data, predicted_labels)\r\n# fig, ax = plt.subplots(figsize=(10,10))\r\n# sns.heatmap(conf_mat, annot=True, fmt='d',\r\n#             xticklabels=classes, yticklabels=classes)\r\n# plt.ylabel('Actual')\r\n# plt.xlabel('Predicted')\r\n# plt.show()","sub_path":"Naive_Bayes_binary_classifier.py","file_name":"Naive_Bayes_binary_classifier.py","file_ext":"py","file_size_in_byte":2784,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"595982672","text":"import ui\nimport shop\nimport GFHhg54GHGhh45GHGH\nimport chat\nimport constInfo\nimport event\nimport chr\n\nclass GuiCreator(ui.ScriptWindow):\n\tdef __init__(self):\n\t\tui.ScriptWindow.__init__(self)\n\t\tself.isLoaded = FALSE\n\t\t\n\tdef __LoadScript(self):\n\t\tself.board = ui.BoardWithTitleBar()\n\t\tself.board.SetPosition(10,10)\n\t\tself.board.SetSize(185, 190)\n\t\tself.board.AddFlag(\"movable\")\n\t\tself.board.SetTitleName(\"Gildenlager Bumser\")\n\t\tself.board.Show()\n\t\t\n\t\tself.addItem = ui.Button()\n\t\tself.addItem.SetParent(self.board)\n\t\tself.addItem.SetUpVisual(\"d:/ymir work/ui/public/xsmall_button_01.sub\")\n\t\tself.addItem.SetOverVisual(\"d:/ymir work/ui/public/xsmall_button_02.sub\")\n\t\tself.addItem.SetDownVisual(\"d:/ymir work/ui/public/xsmall_button_03.sub\")\n\t\tself.addItem.SetPosition(20,50)\n\t\tself.addItem.SetEvent(ui.__mem_func__(self.addItemToGuildstorage))\n\t\tself.addItem.SetText('addItem')\n\t\tself.addItem.Show()\n\t\t\n\t\tself.takeItem = ui.Button()\n\t\tself.takeItem.SetParent(self.board)\n\t\tself.takeItem.SetUpVisual(\"d:/ymir work/ui/public/xsmall_button_01.sub\")\n\t\tself.takeItem.SetOverVisual(\"d:/ymir work/ui/public/xsmall_button_02.sub\")\n\t\tself.takeItem.SetDownVisual(\"d:/ymir work/ui/public/xsmall_button_03.sub\")\n\t\tself.takeItem.SetPosition(70,50)\n\t\tself.takeItem.SetEvent(ui.__mem_func__(self.takeItemFromGuildstorage))\n\t\tself.takeItem.SetText('takeItem')\n\t\tself.takeItem.Show()\n\t\t\n\t\tself.isLoaded = TRUE\n\t\t\n\tdef addItemToGuildstorage(self):\n\t\tattachedSlotPos = 1\n\t\tselectedSlotPos = 117\n\t\ttab = 7\n\t\tconstInfo.GUILDSTORAGE[\"questCMD\"] = \"MOVE_ITEM#INVENTORY#\"+str(attachedSlotPos)+\"#\"+str(selectedSlotPos)+\"#\"+str(tab)\n\t\tevent.QuestButtonClick(int(constInfo.GUILDSTORAGE[\"qid\"]))\n\t\tchat.AppendChat(chat.CHAT_TYPE_INFO, \"Item eingelagert in Slot 840\")\n\t\t\n\tdef takeItemFromGuildstorage(self):\n\t\tattachedSlotPos = 117\n\t\tselectedSlotPos = 120\n\t\ttab = 7\n\t\tconstInfo.GUILDSTORAGE[\"questCMD\"] = \"TAKE_ITEM#\"+str(attachedSlotPos)+\"#\"+str(tab)\n\t\tevent.QuestButtonClick(int(constInfo.GUILDSTORAGE[\"qid\"]))\n\t\tchat.AppendChat(chat.CHAT_TYPE_INFO, \"Item rausgenommen aus Slot 840\")\n\t\t\n\tdef __del__(self):\n\t\tui.ScriptWindow.__del__(self)\n\n\tdef Destroy(self):\n\t\tself.Hide()\n\t\t\n\tdef Open(self):\n\t\tif FALSE == self.isLoaded:\n\t\t\tself.__LoadScript()\n\t\tself.SetTop()\n\t\tself.Show()\n\t\t\n\tdef OnPressEscapeKey(self):\n\t\tself.Close()\n\t\treturn TRUE\n\n\tdef OnPressExitKey(self):\n\t\tself.Close()\n\t\treturn TRUE\n\t\t\nwnd = GuiCreator()\nwnd.Open()","sub_path":"root/ultimativ.py","file_name":"ultimativ.py","file_ext":"py","file_size_in_byte":2397,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"472841346","text":"from django.conf.urls import url\nfrom .views import (\n    create_corporate_member,\n    search_corporate_member,\n    create_group,\n    search_group,\n    upload_new_members,\n    save_new_members,\n    view_agent_companies,\n    save_attachments,\n    \n)\n\nurlpatterns = [\n    url(r'^create_corporate_member/$', create_corporate_member),\n    url(r'^search_corporate_member/(?P<search_text>[a-zA-Z]+)/$',\n        search_corporate_member),\n    url(r'^create_group/$', create_group),\n    url(r'^search_group/(?P<search_text>[a-zA-Z]+)/$', search_group),\n    url(r'^upload_new_members/$', upload_new_members),\n    url(r'^save_new_members/$', save_new_members),\n    url(r'^view_agent_companies/$', view_agent_companies),\n    url(r'^save_attachments/$', save_attachments),\n]\n","sub_path":"members/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":762,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"355804187","text":"# -*- coding: UTF-8 -*-\nimport numpy as np\nimport pylab as pl\nimport time\nimport os\nx1=[]\nx2=[]\nx3=[]\ny1=[]\ny2=[]\ny3=[]\nmodule_path = os.path.dirname(__file__)\nfilename = module_path + '/record1'\nwith open(filename,'r') as f:\n\tfor s in f.readlines():\n\t\tstrlist = s.split(',')\n\t\tif len(strlist) == 4:\n\t\t\trTime = time.strptime(strlist[0],'%Y-%m-%d %H:%M:%S')\n\t\t\tstrTime = str.format(\"%02d:%02d\"%(rTime.tm_hour,rTime.tm_min))\n\t\t\tx1.append(strTime)\n\t\t\tx2.append(strTime)\n\t\t\tx3.append(strTime)\n\t\t\ty1.append(float(strlist[1]))\n\t\t\ty2.append(float(strlist[2]))\n\t\t\ty3.append(float(strlist[3]))\n\n\npl1, = pl.plot(x1, y1,'r')# use pylab to plot x and y\n\npl2, = pl.plot(x2, y2,'b')# use pylab to plot x and y\n\npl3, = pl.plot(x3, y3,'y')\n\npl.legend([pl1, pl2, pl3], ('cpu_t', 'env_h','env_t'))\n\npl.show()# show the plot on the screen","sub_path":"code/draw_temp.py","file_name":"draw_temp.py","file_ext":"py","file_size_in_byte":819,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"188595836","text":"for i in range(int(input())):\r\n    n=int(input())\r\n    l=list(map(int,input().split()))\r\n    ll=list(set(l))\r\n    lll=[]\r\n    if len(ll)!=n:\r\n        print(0)\r\n    else:\r\n        ll.sort(reverse=True)\r\n        for j in range(0,n-1):\r\n            lll.append(ll[j]-ll[j+1])\r\n        print(min(lll))\r\n","sub_path":"Codechef/EID.py","file_name":"EID.py","file_ext":"py","file_size_in_byte":298,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"611939487","text":"import numpy as np\nimport importlib\nimport matplotlib.pyplot as plt\n\nimport maze\nimport utils\nimport policies\nimport offline_benchmark as off_bench\nimport gptd\nimport td\nimport gptd_episodic_improv as improv\n\n# Plot parameters\nplt.rcParams.update({\"font.size\": 35})\nplt.rcParams.update({\"lines.linewidth\": 5})\nplt.rcParams.update({\"lines.markersize\": 10})\n\n# Reload modules, for developpement\nimportlib.reload(maze)\nimportlib.reload(utils)\nimportlib.reload(off_bench)\nimportlib.reload(gptd)\nimportlib.reload(td)\nimportlib.reload(improv)\n\n\n# ############# BUILD MAZE #############################################################################################\nenv = maze.Maze(1, 1, 0.1, 0.03, 0.01)\n\n# Set barriers\nbarrier1 = (-0.1, 0.4, -0.75, 0.8)\nbarrier2 = (0.55, 0.7, -3, 2.525)\nbarrier3 = (0.7, 1.1, -0.5, 0.6)\nbarrier4 = (0.2, 0.55, 0.5, 0.1)\nenv.add_barrier(barrier1[0], barrier1[1], barrier1[2], barrier1[3])\nenv.add_barrier(barrier2[0], barrier2[1], barrier2[2], barrier2[3])\nenv.add_barrier(barrier3[0], barrier3[1], barrier3[2], barrier3[3])\nenv.add_barrier(barrier4[0], barrier4[1], barrier4[2], barrier4[3])\n\n# Set goal regions\ngoal1 = (0, 0, 1, 0.05)\nenv.add_goal(goal1[0], goal1[1], goal1[2], goal1[3])\n\n\n# ########### SIMULATE TRAJECTORIES ####################################################################################\n# Define policy\npolicy = policies.SimplePolicy([0, 0, 0, 0, 0.8, 0, 0, 0])\n\n# Simulate trajectories\nN = 10\nT = 1000\ntrajs = utils.collect_trajectories(env, policy, T, N)\n\n# Stack trajectories in lists\nxlist = [tr[\"states\"] for tr in trajs]\nrlist = [tr[\"rewards\"] for tr in trajs]\n\n# Concatenate trajectories\nxconcat, rconcat = utils.concatenate_trajectories(xlist, rlist)\n\n\n# ########## GPTD ######################################################################################################\n# Initialize Kernel\nkernel = gptd.Gaussian_Kernel(sigma=0.1)\n\n# Parameters\ngamma = 0.95\nnu = 0.05\nsigma0 = 1\n\n# Rune GPTD\nxdict, alpha, C = gptd.iterate_gptd(xconcat, rconcat, gamma, nu, sigma0, kernel)\n\n# Vizualization\ncoords1 = np.linspace(0, 1, 100)\ncoords2 = np.linspace(0, 1, 100)\nM, S = gptd.mean_variance_matrices(xdict, coords1, coords2, alpha, C, kernel)\nmoves_x, moves_y = utils.trajectory_to_moves(xconcat)\nax1 = utils.visualization_2D_discrete(env, coords1, coords2, S, moves=None)\nax1.set_title(\"GPTD - Estimated Variance Map\")\nax2 = utils.visualization_2D_discrete(env, coords1, coords2, M, moves=(moves_x, moves_y))\nax2.set_title(\"GPTD - Estimated Value Map\")\n\n\n# ########## TD0 #######################################################################################################\n# Discretize state space\nenv.build_discretization(50, 50)\n\nV = td.temporal_difference0(env, xconcat, rconcat, gamma)\n\nax3 = utils.visualization_2D_discrete(env, env.discrete_rep[0], env.discrete_rep[1], V, moves=None)\nax3.set_title(\"TD0 - Estimated Value Map\")\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"GPTD/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2923,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"567451586","text":"from hpp.corbaserver.rbprm.hrp2 import Robot\nfrom hpp.gepetto import Viewer\nfrom tools.display_tools import *\nimport time\nprint(\"Plan guide trajectory ...\")\nfrom . import lp_stair_bauzil_hrp2_path_10 as tp\nprint(\"Done.\")\nimport time\n\npId = tp.ps.numberPaths() -1\nfullBody = Robot ()\n\n# Set the bounds for the root\nfullBody.setJointBounds (\"root_joint\", [-10.135,2, -20, 20, 0, 2.8])\n#fullBody.setConstrainedJointsBounds()\n#fullBody.setJointBounds('leg_left_1_joint',[-0.1,0.2])\n#fullBody.setJointBounds('leg_right_1_joint',[-0.2,0.1])\n# add the 6 extraDof for velocity and acceleration (see *_path.py script)\nfullBody.client.robot.setDimensionExtraConfigSpace(0)\n#~ fullBody.client.robot.setExtraConfigSpaceBounds([-tp.vMax,tp.vMax,-tp.vMax,tp.vMax,0,0,-tp.aMax,tp.aMax,-tp.aMax,tp.aMax,0,0])\nps = tp.ProblemSolver( fullBody )\n#~ ps.setParameter(\"Kinodynamic/velocityBound\",tp.vMax)\n#~ ps.setParameter(\"Kinodynamic/accelerationBound\",tp.aMax)\n#load the viewer\nv = tp.Viewer (ps,viewerClient=tp.r.client, displayCoM = True)\n\n# load a reference configuration\n#~ q_ref = fullBody.referenceConfig[::]+[0]*6\nq_ref = fullBody.referenceConfig[::]\nq_init = q_ref[::] \nfullBody.setReferenceConfig(q_ref)\nfullBody.setCurrentConfig (q_init)\nfullBody.setPostureWeights(fullBody.postureWeights[::])\n#~ fullBody.setPostureWeights(fullBody.postureWeights[::])\n#fullBody.usePosturalTaskContactCreation(True)\n\nprint(\"Generate limb DB ...\")\ntStart = time.time()\n# generate databases : \n\nnbSamples = 10000\nfullBody.addLimb(fullBody.rLegId,fullBody.rleg,fullBody.rfoot,fullBody.rLegOffset,fullBody.rLegNormal, fullBody.rLegx, fullBody.rLegy, nbSamples, \"fixedStep1\", 0.01,kinematicConstraintsPath=fullBody.rLegKinematicConstraints,kinematicConstraintsMin = 0.3)\nfullBody.runLimbSampleAnalysis(fullBody.rLegId, \"ReferenceConfiguration\", True)\nfullBody.addLimb(fullBody.lLegId,fullBody.lleg,fullBody.lfoot,fullBody.lLegOffset,fullBody.rLegNormal, fullBody.lLegx, fullBody.lLegy, nbSamples, \"fixedStep1\", 0.01,kinematicConstraintsPath=fullBody.lLegKinematicConstraints,kinematicConstraintsMin = 0.3)\nfullBody.runLimbSampleAnalysis(fullBody.lLegId, \"ReferenceConfiguration\", True)\n\n\ntGenerate =  time.time() - tStart\nprint(\"Done.\")\nprint(\"Databases generated in : \"+str(tGenerate)+\" s\")\n\n#define initial and final configurations : \nconfigSize = fullBody.getConfigSize() -fullBody.client.robot.getDimensionExtraConfigSpace()\n\n\n\n#~ q_init[0:7] = tp.ps.configAtParam(pId,0.)[0:7] # use this to get the correct orientation\nq_init[:3] = [0.1, -0.82, 0.648702]\nq_init[7:] = [ 0.0, 0.0, 0.0, 0.0,                                                  # CHEST HEAD 7-10\n        0.261799388,  0.174532925, 0.0, -0.523598776, 0.0, 0.0, 0.17, \t\t # LARM       11-17\n        0.261799388, -0.174532925, 0.0, -0.523598776, 0.0, 0.0, 0.17, \t\t # RARM       18-24\n        0.0, 0.0, -0.453785606, 0.872664626, -0.41887902, 0.0,               # LLEG       25-30\n        0.0, 0.0, -0.453785606, 0.872664626, -0.41887902, 0.0,               # RLEG       31-36\n        ];\nv(q_init)\n\n#fullBody.resetJointsBounds()\nfrom hpp.corbaserver.rbprm import  rbprmstate\nfrom hpp.corbaserver.rbprm import  state_alg\n\ndef getContactsFromConfig(q, limbs = [Robot.rLegId, Robot.lLegId]):\n    s = rbprmstate.State(fullBody, q = q, limbsIncontact = limbs)\n    rLegPn = s.getContactPosAndNormalsForLimb(Robot.rLegId)\n    lLegPn = s.getContactPosAndNormalsForLimb(Robot.lLegId)\n    return { Robot.rLegId : (rLegPn[0][0], rLegPn[1][0]),Robot.lLegId : (lLegPn[0][0], lLegPn[1][0]) }\n    \n#~ s = getContactsFromConfig ( q = fullBody.getCurrentConfig())\n\n\nfrom sl1m.planner_scenarios.stairs_10cm import *\n\npb, coms, footpos, allfeetpos, res = solve() \n\ndef gen_state(s, pId, com , num_max_sample = 1, first = False ):\n    #~ pId = 6\n    phase =  pb[\"phaseData\"][pId]\n    moving = phase[\"moving\"]\n    movingID = fullBody.lLegId\n    if moving == RF:\n        movingID = fullBody.rLegId\n    pos = allfeetpos[pId]; pos[2]+=0.01\n    com[2] += 0.5\n    print(\"com\" , com)\n    print(\"pos\" , pos.tolist())\n    #~ q = fullBody.getCurrentConfig()\n    #~ s, succ = state_alg.addNewContact(s, fullBody.rLegId, rfPos.tolist(), z.tolist())\n    sres, succ = state_alg.addNewContact(s, movingID, pos.tolist(), z.tolist(), num_max_sample= 0)\n    print(\"succes ?\", succ)\n    succCom = False\n    ite = 0\n    #~ if first:\n        #~ print \"FIRST \"\n        #~ com[2] -= 0.25\n    #~ while(not succCom and ite < 11):\n    while(not succCom and ite < 17):\n        succCom =  fullBody.projectStateToCOM(sres.sId ,com, num_max_sample)\n        com[2] -= 0.05\n        ite += 1\n    print(\"COM?\", succCom)\n    v(sres.q())\n    return sres\n    \n    \n    \nq = fullBody.getCurrentConfig()\nq[:2] = allfeetpos[0][:2].tolist()\nv(q)\ns = rbprmstate.State(fullBody, q = q, limbsIncontact = [])\nall_states = [s]\nsprev = s\nidfirst = 2\n\nfor i in range(1, len(pb[\"phaseData\"])):    \n    #~ com = (coms[i-i]  ).tolist()\n    com = (coms[i-1]  ).tolist()\n    #~ if i > 2:\n        #~ com = (coms[i-1] + (coms[i] - coms[i-1]) *0.8).tolist()\n    snew = gen_state(sprev, i , com, num_max_sample = 20, first = i == idfirst )\n    all_states += [snew]   \n    sprev = snew\n    com2 = coms[i].tolist()\n    snew2 = gen_state(snew, i , com2, num_max_sample = 20 )\n    all_states += [snew2]\n    sprev = snew2\n    \nall_states = all_states[:-1]\nconfigs = [ st.q() for st in all_states[:]]; i = 0\n\nprint(\"SID \", [s.sId for s in all_states])\n\nbeginId = 4\n\n#~ configs = configs[:-1]\n\n#~ configs = [q1, q2, q3, q4, q5]\n#~ from cPickle import dump\n\n#~ f = open('contacts_plateformes.txt','w')\n#~ dump(contacts,f)\n#~ f.close()\nax = draw_scene()\n    #~ plotQPRes(pb, res, ax=ax, plot_constraints=True)\n#~ plotQPRes(pb, res, ax=ax, plot_constraints=False)\n\npaths = []\n\ndef play_int(f_r = 100):\n    for (pid, pl) in paths:\n        for i in range(f_r):\n            frame = float(i) / float(f_r) * pl\n            v((ps.configAtParam(pid,frame)))\n            \n\n#~ def ppath(pid):\n    #~ length = \n\n#~ raise ValueError\n\n\ncomTrajs = []\nstateTrajs = []\n\ndef comTraj(c0, c1, t = 1.):\n    zero = [0.,0.,0]\n    return fullBody.generateComTraj([c0,c1],[zero],[zero],1.)\n\n\nfrom hpp.gepetto import PathPlayer\npp = PathPlayer (v)\n\nfrom time import sleep\n\ndef dq(t = 1):\n    for q in configs: v(q); sleep(t)\n\ndef nil():\n    global paths\n    global stateTrajs\n    global comTrajs\n    #~ for j in range(2,len(all_states)-2,2):    \n    for j in range(4,len(all_states),2):    \n        #~ print \"state \", j\n        pathId = fullBody.limbRRT(all_states[j].sId, all_states[j+1].sId)\n        paths += [(pathId, ps.pathLength(pathId))]\n        #~ print \"j\", j \n        if j+2 < len(all_states):\n            nPid = ps.directPath(all_states[j+1].q(),all_states[j+2].q(),False)[1]\n            paths += [(nPid, ps.pathLength(nPid))]\n            \n            p0 = comTraj(all_states[j].getCenterOfMass(),all_states[j].getCenterOfMass())\n            p1 = comTraj(all_states[j].getCenterOfMass(),all_states[j+1].getCenterOfMass())\n            p2 = comTraj(all_states[j+1].getCenterOfMass(),all_states[j+2].getCenterOfMass())\n            comTrajs += [(p0,p1,p2)]\n            stateTrajs += [(all_states[j].sId, all_states[j+2].sId)]\n            #~ comTrajs += [comTraj(all_states[j+1].getCenterOfMass(),all_states[j+2].getCenterOfMass())]\n            #~ stateTrajs += [(all_states[j+1].sId, all_states[j+2].sId)]\n        play_int()    \n    #~ for stateTraj, comTtraj in zip(stateTrajs,comTrajs):\n        #~ paths+=[fullBody.effectorRRTFromPosBetweenState(stateTraj[0],stateTraj[1],comTtraj[0],comTtraj[1],comTtraj[2])[-1]]\n        #~ pp(int(paths[-1]))\n        \ndef comrrt():\n    global paths\n    global stateTrajs\n    global comTrajs\n    #~ for j in range(2,len(all_states)-2,2):    \n    for j in range(4,len(all_states),2):    \n        #~ print \"state \", j\n        #~ pathId = fullBody.limbRRT(all_states[j].sId, all_states[j+1].sId)\n        #~ paths += [(pathId, ps.pathLength(pathId))]\n        #~ print \"j\", j \n        if j+2 < len(all_states):\n            #~ nPid = ps.directPath(all_states[j+1].q(),all_states[j+2].q(),False)[1]\n            #~ paths += [(nPid, ps.pathLength(nPid))]\n            \n            p0 = comTraj(all_states[j].getCenterOfMass(),all_states[j].getCenterOfMass())\n            p1 = comTraj(all_states[j].getCenterOfMass(),all_states[j+1].getCenterOfMass())\n            p2 = comTraj(all_states[j+1].getCenterOfMass(),all_states[j+2].getCenterOfMass())\n            comTrajs += [(p0,p1,p2)]\n            stateTrajs += [(all_states[j].sId, all_states[j+2].sId)]\n            #~ comTrajs += [comTraj(all_states[j+1].getCenterOfMass(),all_states[j+2].getCenterOfMass())]\n            #~ stateTrajs += [(all_states[j+1].sId, all_states[j+2].sId)]\n        #~ play_int()    \n    for stateTraj, comTtraj in zip(stateTrajs,comTrajs):\n        paths+=[fullBody.comRRTFromPosBetweenState(stateTraj[0],stateTraj[1],comTtraj[0],comTtraj[1],comTtraj[2])[-1]]\n        pp(int(paths[-1]))\n        \n#~ nil()\n\n#~ comrrt()\n    \n#~ play_int()\n\nbeginId = 4\n","sub_path":"sl1m/planner_scenarios/lp_stair_bauzil_10.py","file_name":"lp_stair_bauzil_10.py","file_ext":"py","file_size_in_byte":8977,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"275487300","text":"#!/usr/bin/python3 \n\"\"\"\n Author: cg\n Date: 2020/8/26 19:33\n\"\"\"\n\nfrom db.mapping.basemap import BaseMap\nfrom db.mapping.baidutieba_reply.baidutieba_reply_item import BaiduTiebaReplyItem\n\n\nclass BaiduTiebaReply(BaseMap):\n\n    @staticmethod\n    def is_instance(data):\n        return isinstance(data, BaiduTiebaReplyItem)\n\n    @staticmethod\n    def build_item(data):\n        item = BaiduTiebaReplyItem()\n        item.spiderTs = data['spiderTs']\n        item.insertTs = data['insertTs']\n        item.updateTs = data['updateTs']\n        item.title = data['title']\n        item.pUrl = data['pUrl']\n        item.vSrc = data['vSrc']\n        item.effect = data['effect']\n        item.tag = data['tag']\n        item.playerHome = data['playerHome']\n        item.playerName = data['playerName']\n        item.userName = data['userName']\n        return item","sub_path":"db/mapping/baidutieba_reply/baidutieba_reply.py","file_name":"baidutieba_reply.py","file_ext":"py","file_size_in_byte":842,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"569648560","text":"# import praw\nimport os\nimport pprint as pp\nimport boto3\nimport traceback as tb\nimport json\n\ndef capture_err(func,event,context):\n    try:\n        func(event,context)\n    except Exception as e:\n        print('ERROR captured')\n        traceback_str = tb.format_exc()\n        payload = {\n            'lambda_name': os.environ['AWS_LAMBDA_FUNCTION_NAME'],\n            'traceback': traceback_str,\n            'error_type': 'direct invocation from traceback'\n        }\n        handler_lambda_arn = os.environ['errorHandlerArn']\n        print('Sending payload to error handler lambda:\\n%s' % str(payload))\n        client = boto3.client('lambda')\n        response = client.invoke(\n            FunctionName=handler_lambda_arn,\n            InvocationType='Event',\n            Payload=json.dumps(payload).encode(),\n        )\n        print('Invoked error handler lambda\\n\\n')\n        raise(e)\n","sub_path":"AWS/data/lambda/failer/include/errors.py","file_name":"errors.py","file_ext":"py","file_size_in_byte":882,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"480406159","text":"from PySide6.QtWidgets import (\n    QMainWindow,\n    QApplication,\n    QLabel,\n    QVBoxLayout,\n    QPushButton,\n    QWidget,\n)\nfrom PySide6.QtGui import QIcon\nimport sys, os\n\nbasedir = os.path.dirname(__file__)\n\ntry:\n    from ctypes import windll  # Only exists on Windows.\n\n    myappid = \"mycompany.myproduct.subproduct.version\"\n    windll.shell32.SetCurrentProcessExplicitAppUserModelID(myappid)\nexcept ImportError:\n    pass\n\n\nclass MainWindow(QMainWindow):\n    def __init__(self):\n        super().__init__()\n\n        self.setWindowTitle(\"Hello World\")\n        layout = QVBoxLayout()\n        label = QLabel(\"My simple app.\")\n        label.setMargin(10)\n        layout.addWidget(label)\n\n        button_close = QPushButton(\"Close\")\n        button_close.setIcon(\n            QIcon(os.path.join(basedir, \"icons\", \"lightning.svg\"))\n        )\n        button_close.pressed.connect(self.close)\n        layout.addWidget(button_close)\n\n        button_maximize = QPushButton(\"Maximize\")\n        button_maximize.setIcon(\n            QIcon(os.path.join(basedir, \"icons\", \"uparrow.svg\"))\n        )\n        button_maximize.pressed.connect(self.showMaximized)\n        layout.addWidget(button_maximize)\n\n        container = QWidget()\n        container.setLayout(layout)\n\n        self.setCentralWidget(container)\n\n        self.show()\n\n\napp = QApplication(sys.argv)\napp.setWindowIcon(QIcon(os.path.join(basedir, \"icons\", \"icon.svg\")))\nw = MainWindow()\napp.exec()\n","sub_path":"pyside6-source/packaging/installer/mac/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1447,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"554198678","text":"\"\"\"CircuitPython Essentials UART Serial example\"\"\"\nimport board\nimport busio\nimport time\n\nuart = busio.UART(board.GP0, board.GP1, baudrate=9600, stop=1, parity=None)\n\n\nclass Client:\n    def __init__(self, channel, url, networkType, port, beatData, handleRec):\n        self.channel = channel\n        self.url = url\n        self.networkType = networkType\n        self.port = port\n        self.beatData = beatData\n        self.connectState = False\n        self.sendState = False\n        self.handleRec = handleRec\n\n\nclass SIMState:\n    def __init__(self, clients):\n        self.reset()\n        self.clients = clients\n    def reset(self):\n        self.functionState = True\n        self.signalState = 0\n        self.GPRSState = False\n        self.connectionMode = 0\n        self.pingState = False\n        self.bringUpWirelessConnectionState = False\n        self.local_IP_address = None\n        # \n    # def __str__(self):\n    #     return \"{}-{}-{} {}:{}:{} active:{} {} {},{} {}\".format(self.year, self.month, self.date, self.hour, self.minute, self.second, self.active,\n    #                                                             self.latitude, self.latitudeFlag, self.longitude, self.longitudeFlag)\n\nlastReceiveBuf = b''\n\ndef sim_dataReceiving(receiveBuf):\n    global lastReceiveBuf\n    # receiveBuf = uart.read()\n    if receiveBuf != None:\n        lastReceiveBuf = lastReceiveBuf+receiveBuf\n        if lastReceiveBuf.find(b'\\r\\n+RECEIVE,') != -1:\n            lastReceiveBufIndex = lastReceiveBuf.find(b'\\r\\n+RECEIVE,')\n            if lastReceiveBufIndex + len(b'\\r\\n+RECEIVE,') < len(lastReceiveBuf):\n                if lastReceiveBuf.find(b':',lastReceiveBufIndex)!= -1:\n                    lastReceiveBufLenIndex = lastReceiveBuf.find(b':\\r\\n', lastReceiveBufIndex)\n                    dataLenStr = lastReceiveBuf[lastReceiveBufIndex + len(b'\\r\\n+RECEIVE,'):lastReceiveBufLenIndex]\n                    if dataLenStr.find(b',') != -1:\n                        hasError = False\n                        try:\n                            channel = int(dataLenStr.split(b',')[0])\n                            channelRecLen = int(dataLenStr.split(b',')[1])\n                        except:\n                            hasError = True\n                        if not hasError:\n                            if len(lastReceiveBuf[lastReceiveBufLenIndex + len(':\\r\\n'):]) >= channelRecLen:\n                                channelRecBuf = lastReceiveBuf[lastReceiveBufLenIndex + len(':\\r\\n'): lastReceiveBufLenIndex + len(':\\r\\n')+channelRecLen]\n                                lastReceiveBuf = lastReceiveBuf[lastReceiveBufLenIndex + len(':\\r\\n')+channelRecLen:]\n                                return (channel, channelRecBuf)\n        # else:\n        if len(lastReceiveBuf) > 256:\n            lastReceiveBuf = lastReceiveBuf[-256:]\n    return False\n        # \\r\\n+RECEIVE,1,4:\\r\\n1234\\r\\n'\n\n\ndef simpleHandle(data):\n    if data != None:\n        print(data)\n    return\n\ndef sim_ok():\n    simpleHandle(uart.read())\n    uart.write(b'AT\\r\\n')\n    time.sleep(0.1)\n    res = uart.readline()\n    if res == b'AT\\r\\r\\n':\n        res = uart.readline()\n    if res == b'OK\\r\\n':\n        return True\n    else:\n        return False\n\n\ndef sim_checkIfOpen():\n    simpleHandle(uart.read())\n    uart.write(b'AT+CFUN?\\r\\n')\n    time.sleep(0.1)\n    res = uart.readline()\n    if res == b'AT+CFUN?\\r\\r\\n':\n        res = uart.readline()\n    if res == b'+CFUN: 1\\r\\n':\n        res = uart.readline()\n        if res == b'\\r\\n':\n            res = uart.readline()\n        if res == b'OK\\r\\n':\n            return \"function open\"\n        else:\n            return \"function error\"\n    elif res == b'+CFUN: 0\\r\\n':\n        res = uart.readline()\n        if res == b'\\r\\n':\n            res = uart.readline()\n        if res == b'OK\\r\\n':\n            return \"function close\"\n        else:\n            return \"function error\"\n    else:\n        return \"function error\"\n\n\ndef sim_close():\n    simpleHandle(uart.read())\n    uart.write(b'AT+CFUN=0\\r\\n')\n    time.sleep(0.1)\n    res = uart.readline()\n    print(res)\n    if res == b'AT+CFUN=0\\r\\r\\n':\n        res = uart.readline()\n        print(res)\n    else:\n        return False\n    if res == b'OK\\r\\n':\n        return True\n    elif res == b'+CPIN: NOT READY\\r\\n':\n        time.sleep(5)\n        res = uart.readline()\n        print(res)\n        if res == b'\\r\\n':\n            res = uart.readline()\n        if res == b'OK\\r\\n':\n            return True\n        else:\n            return False\n    else:\n        return False\n\n\ndef sim_open():\n    simpleHandle(uart.read())\n    uart.write(b'AT+CFUN=1\\r\\n')\n    time.sleep(0.1)\n    res = uart.readline()\n    print(res)\n    if res == '\\r\\n':\n        res = uart.readline()\n        print(res)\n    if res == b'AT+CFUN=1\\r\\r\\n':\n        res = uart.readline()\n        print(res)\n        if res == b'OK\\r\\n':\n            return True\n        elif res == b'+CPIN: READY\\r\\n':\n            res = uart.readline()\n            print(res)\n            if res == b'\\r\\n':\n                res = uart.readline()\n                print(res)\n            if res == b'OK\\r\\n':\n                time.sleep(7)\n                print(\"sim open:\")\n                print(uart.read())\n                return True\n            else:\n                return False\n    else:\n        return False\n\n\ndef sim_checkForSignal():\n    simpleHandle(uart.read())\n    uart.write(b'AT+CSQ\\r\\n')\n    time.sleep(0.1)\n    res = uart.readline()\n    if res == b'AT+CSQ\\r\\r\\n':\n        res = uart.readline()\n    if res.startswith(\"+CSQ:\") and res.endswith('\\r\\n'):\n        signal = res[5:-2].decode().split(',')\n        res = uart.readline()\n        if len(signal) == 2:\n            if res == b'\\r\\n':\n                res = uart.readline()\n            if res == b'OK\\r\\n':\n                return (int(signal[0].strip()), int(signal[1].strip()))\n        else:\n            return (-1, -1)\n    else:\n        return (-1, -1)\n\n\ndef sim_checkIfConnected():\n    simpleHandle(uart.read())\n    uart.write(b'AT+CGATT?\\r\\n')\n    time.sleep(0.5)\n    res = uart.readline()\n    if res == b'AT+CGATT?\\r\\r\\n':\n        res = uart.readline()\n    if res == b'+CGATT: 1\\r\\n':\n        res = uart.readline()\n        if res == b'\\r\\n':\n            res = uart.readline()\n        if res == b'OK\\r\\n':\n            return True\n    elif res == b'+CGATT: 0\\r\\n':\n        res = uart.readline()\n        if res == b'\\r\\n':\n            res = uart.readline()\n        if res == b'OK\\r\\n':\n            return False\n    elif res == b'ERROR\\r\\n':\n        return False\n    return False\n\n\ndef sim_detactFromGPRS():\n    simpleHandle(uart.read())\n    uart.write(b'AT+CGATT=0\\r\\n')\n    time.sleep(0.1)\n    res = uart.readline()\n    print(res)\n    if res == b'AT+CGATT=0\\r\\r\\n':\n        res = uart.readline()\n        print(res)\n    if res == b'\\r\\n':\n        res = uart.readline()\n        print(res)\n    if res == b'OK\\r\\n':\n       return True\n    elif res == b'ERROR\\r\\n':\n        return False\n    return False\n\n\ndef sim_attachToGPRS():\n    simpleHandle(uart.read())\n    uart.write(b'AT+CGATT=1\\r\\n')\n    time.sleep(3)\n    res = uart.readline()\n    print(res)\n    if res == b'AT+CGATT=1\\r\\r\\n':\n        res = uart.readline()\n        print(res)\n    if res == b'\\r\\n':\n        res = uart.readline()\n        print(res)\n    if res == b'OK\\r\\n':\n       return True\n    elif res == b'ERROR\\r\\n':\n        return False\n    return False\n\n\ndef sim_enableMultiConnection():\n    simpleHandle(uart.read())\n    uart.write(b'AT+CIPMUX=1\\r\\n')\n    time.sleep(0.1)\n    res = uart.readline()\n    print(res)\n    if res == b'AT+CIPMUX=1\\r\\r\\n':\n        res = uart.readline()\n        print(res)\n    if res == b'\\r\\n':\n        res = uart.readline()\n        print(res)\n    if res == b'OK\\r\\n':\n       return True\n    elif res == b'ERROR\\r\\n':\n        return False\n    return False\n\n\ndef sim_ping():\n    simpleHandle(uart.read())\n    uart.write(b'AT+CSTT\\r\\n')\n    time.sleep(0.1)\n    res = uart.readline()\n    print(res)\n    if res == b'AT+CSTT\\r\\r\\n':\n        res = uart.readline()\n        print(res)\n    if res == b'\\r\\n':\n        res = uart.readline()\n        print(res)\n    if res == b'OK\\r\\n':\n       return True\n    elif res == b'ERROR\\r\\n':\n        return False\n    return False\n\n\ndef sim_bringUpWirelessConnection():\n    simpleHandle(uart.read())\n    uart.write(b'AT+CIICR\\r\\n')\n    time.sleep(0.1)\n    res = uart.readline()\n    print(res)\n    if res == b'AT+CIICR\\r\\r\\n':\n        time.sleep(5)\n        res = uart.readline()\n        print(res)\n    if res == b'\\r\\n':\n        res = uart.readline()\n        print(res)\n    if res == b'OK\\r\\n':\n       return True\n    elif res == b'ERROR\\r\\n':\n        return False\n    return False\n\n\ndef sim_getLocalIPAddress():\n    simpleHandle(uart.read())\n    uart.write(b'AT+CIFSR\\r\\n')\n    time.sleep(0.1)\n    res = uart.readline()\n    print(res)\n    if res.endswith(b'AT+CIFSR\\r\\r\\n'):\n        res = uart.readline()\n        print(res)\n    ipList = res.strip().split(b'.')\n    if len(ipList) == 4:\n        return (int(ipList[0]), int(ipList[1]), int(ipList[2]), int(ipList[3]))\n    else:\n        return None\n\n\ndef sim_resetIP():\n    simpleHandle(uart.read())\n    uart.write(b'AT+CIPSHUT\\r\\n')\n    time.sleep(3)\n    res = uart.readline()\n    print(res)\n    if res == b'AT+CIPSHUT\\r\\r\\n':\n        res = uart.readline()\n        print(res)\n    if res == b'\\r\\n':\n        res = uart.readline()\n        print(res)\n    if res == b'SHUT OK\\r\\n':\n       return True\n    elif res == b'ERROR\\r\\n':\n        return False\n    return False\n\n\ndef sim_startConnection(channel, networkType, url, port):\n    simpleHandle(uart.read())\n    startConnectionStr = ('AT+CIPSTART='+str(channel)+',\"' +\n                          networkType+'\",\"'+url+'\",'+str(port))\n    uart.write((startConnectionStr+'\\r\\n').encode())\n    time.sleep(0.1)\n    res = uart.readline()\n    print(res)\n    if res == (startConnectionStr+'\\r\\r\\n').encode():\n        res = uart.readline()\n        print(res)\n    if res == b'OK\\r\\n':\n        res = uart.readline()\n        print(res)\n        if res == b'\\r\\n':\n            time.sleep(5.5)\n            res = uart.readline()\n            print(res)\n        if res == (str(channel)+', CONNECT OK\\r\\n').encode():\n            return True\n        elif res == (str(channel)+', CONNECT FAIL\\r\\n').encode():\n            return False\n        return False\n    elif res == b'ERROR\\r\\n':\n        res = uart.readline()\n        print(res)\n        if res == b'\\r\\n':\n            res = uart.readline()\n            print(res)\n        if res == (str(channel)+', ALREADY CONNECT\\r\\n').encode():\n            return True\n        return False\n    return False\n\n\ndef sim_dataSending(channel, dataBuf):\n    simpleHandle(uart.read())\n    dataSendingStr = 'AT+CIPSEND='+str(channel)+','+str(len(dataBuf))\n    uart.write((dataSendingStr+'\\r\\n').encode())\n    time.sleep(0.1)\n    res = uart.readline()\n    print(res)\n    if res == (dataSendingStr+'\\r\\r\\n').encode():\n        res = uart.readline()\n        time.sleep(0.1)\n        print(res)\n    if res == b'> ':\n        uart.write(dataBuf+b\"\\r\\n\")\n        return True\n    return False\n\n# def sim_dataReceiving(dataBuf):\n#     # \n#     return\n  \n# time.sleep(3+len(dataStr)/300)\n        # res = uart.read()\n        # print(res)\n        # if res.startswith(dataStr.encode()) and res.endwith((str(channel)+', SEND OK\\r\\n').encode()):\n        # return True\n        # return False\n\n# channel = 2\n# networkType = \"udp\"\n# url = \"155.138.195.23\"\n# port = 41234\n\n# sim_checkIfConnected()\n# sim_enableMultiConnection()\n# sim_ping()\n# sim_bringUpWirelessConnection()\n# sim_getLocalIPAddress()\n# sim_startConnection(channel, networkType, url, port)\n","sub_path":"selfDriving_python/sim800_uart.py","file_name":"sim800_uart.py","file_ext":"py","file_size_in_byte":11619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"410507352","text":"from django.conf.urls import url\nfrom companies import views\n\nurlpatterns = [\n    url(r'^get_related/$', views.get_related_deals),\n    url(r'^merge/$', views.merge_companies),\n    url(r'^deals/(?P<pk>[0-9]+)/$', views.get_deals_related),\n    url(r'^delete/$', views.delete_companies),\n    url(r'^change_responsible/$', views.change_responsible),\n    url(r'^get_duplicates/$', views.get_duplicates),\n    url(r'^(?P<pk>[0-9]+)/$', views.company_detail),\n    url(r'^$', views.company_list),\n]","sub_path":"companies/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":489,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"294900849","text":"from settings import *\nfrom kivy.uix.widget import Widget\nfrom kivy.graphics import Rectangle, Color\n\n\nclass Layout(Widget):\n\n    def __init__(self, **kwargs):\n        super(Layout, self).__init__(**kwargs)\n        self.id = 'layout'\n        self.w = SIZE['LAYOUT']['W']\n        self.h = SIZE['LAYOUT']['H']\n        self.color = (COLOR['LAYOUT']['R'], COLOR['LAYOUT']['G'], COLOR['LAYOUT']['B'])\n\n    def load(self):\n        with self.canvas:\n            Color(self.color[0], self.color[1], self.color[2])\n            self.background = Rectangle(size=self.size, pos=[0, 0])\n\n    @property\n    def size(self):\n        return (self.w, self.h)\n","sub_path":"lane/layout.py","file_name":"layout.py","file_ext":"py","file_size_in_byte":641,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"130679748","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport sqlite3\n\nshop_db_path = 'sources/databases/shop/all_to_the_bottom.db'\n\nconnection = sqlite3.connect(shop_db_path)\ncursor = connection.cursor()\n\ndef action_log_by_country():\n    cursor.execute(\n        '''\n        select country_code, count(*) as enc\n        from log_actions left join customers\n             on log_actions.ip_address = customers.ip_address\n        group by country_code\n        order by count(country_code) DESC\n        limit 5\n        ''')\n    return cursor.fetchall()\n\ndef fresh_fish_by_country():\n    cursor.execute(\n        '''\n        select country_code, count(*) as enc\n        from log_actions left join customers \n            on log_actions.ip_address = customers.ip_address\n        where \n            log_actions.category = 'fresh_fish'\n        order by count(country_code) DESC\n        limit 5\n        ''')\n    return cursor.fetchall()\n\ndef fresh_fish_by_time():\n    cursor.execute('''\n        select substr(datetime, 12, 2) / 6 as hrs, count(*) as enc\n        from log_actions\n        where log_actions.category = 'frozen_fish'\n        group by hrs\n        order by enc DESC\n    ''')\n    return cursor.fetchall()\n\ndef max_visits_per_hour():\n    cursor.execute('''\n        select count(*) as enc, substr(datetime, 0, 14) as dt\n        from log_actions\n        group by dt\n        order by enc DESC\n    ''')\n    return(cursor.fetchone())\n\ndef unpayed_carts():\n    cursor.execute('''\n        select count(distinct cart_id)\n        from carts \n        where is_success = 0\n    ''')\n    return cursor.fetchone()\n\ndef repeated_purchase():\n    cursor.execute('''\n        select count(*) from (\n            select distinct cart_id, ip_address\n            from carts \n            where is_success = 1\n            group by ip_address\n            having count(*) > 1\n        )\n    ''')\n    return cursor.fetchone()\n\ndef main():\n    print('1. Посетители из какой страны совершают больше всего действий на сайте?')\n    print(action_log_by_country())\n    print('\\n''\\n')\n\n    print('2. Посетители из какой страны чаще всего интересуются товарами из категории “fresh_fish”?')\n    print(fresh_fish_by_country())\n    print('\\n''\\n')\n\n    print('''3. В какое время суток чаще всего просматривают категорию “frozen_fish”?\n    (0) Ночь (00:00 - 06:00)\n    (1) Утро (06:00 - 12:00)\n    (2) День (12:00 - 18:00)\n    (3) Вечер (18:00 - 0:00)\n    ''')\n    print(fresh_fish_by_time())\n    print('\\n''\\n')\n\n    print('4. Какое максимальное число запросов на сайт за астрономический час (c 00 минут 00 секунд до 59 минут 59 секунд)?')\n    print(max_visits_per_hour())\n    print('\\n''\\n')\n\n    print('5. Товары из какой категории чаще всего покупают совместно с товаром из категории “semi_manufactures”?')\n    print('\\n')\n    print('временно недоступно')\n    print('\\n''\\n')\n\n    print('6. Сколько брошенных (не оплаченных) корзин имеется? Укажите количество таких корзин.')\n    print(unpayed_carts())\n    print('\\n''\\n')\n\n    print('7. Какое количество пользователей совершали повторные покупки?')\n    print(repeated_purchase())\n\nif __name__ == '__main__':\n    main()","sub_path":"reporter.py","file_name":"reporter.py","file_ext":"py","file_size_in_byte":3603,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"264436220","text":"import unittest\nimport pandas as pd\nimport json\nimport datetime\nimport math\nimport copy\nfrom collections import OrderedDict\n\nclass DiscreteRankTests(unittest.TestCase):\n\n    def get_param_list(self, combolist, param_str = \"\"):\n        if combolist:\n            mycombolist = copy.deepcopy(combolist)\n            level,settings = mycombolist.popitem(last = False)\n            for name, setting in settings.items():\n                my_param_str = param_str + name + \"_\"\n                self.get_param_list(mycombolist,my_param_str)\n        else:\n            self.param_list.append(param_str[:-1])\n\n\n    def go(self,config):\n        self.unittest = False\n        self.config = config\n        self.setUp()\n        self.test_rsmd_discrete()\n        self.tearDown()\n\n\n    def setUp(self):\n        try:\n            self.config\n        except AttributeError:\n            self.unittest = True\n            self.config = None\n        self.param_list = []\n\n        if self.config is None:\n            study_path = \"./test.json\"\n            with open(study_path) as json_file:\n                self.config = json.load(json_file, object_pairs_hook=OrderedDict)\n\n        self.get_param_list(self.config['batch']['parameter_combinations'])\n        self.param_set = set(self.param_list)\n        self.softAssertionErrors = []\n        self.error_path = \"./\" + self.config['parameters'][\"output_path\"] + \"error_log.txt\"\n        self.t = copy.deepcopy(self.config['tests'])\n        self.error_log = open(self.error_path, \"a+\")\n        out_path = \"./\" + self.config['parameters'][\"output_path\"] + \"discrete_rank_tests.tsv\"\n        self.output_tsv = open(out_path, \"w\")\n        self.boolean_users = OrderedDict()\n        self.rank_history = OrderedDict()\n\n        self.codes = []\n        for code,limits in self.t.items():\n            if code != \"default\":\n                self.codes.append(code)\n                rank_history_path = \"./\" + self.config['parameters'][\"output_path\"] +\"rankHistory_\" + code + \".tsv\"\n                self.rank_history[code] = pd.read_csv(rank_history_path, \"\\t\")\n                boolean_users_path = \"./\" + self.config['parameters'][\"output_path\"] +\"boolean_users_\" + code + \".tsv\"\n                self.boolean_users[code] = pd.read_csv(boolean_users_path, \"\\t\", header=None)\n                if code in self.param_set:\n                    self.param_set.remove(code)\n        for code in self.param_set:\n            if 'default' in self.t:\n                self.codes.append(code)\n                rank_history_path = \"./\" + self.config['parameters'][\"output_path\"] +\"rankHistory_\" + code + \".tsv\"\n                self.rank_history[code] = pd.read_csv(rank_history_path, \"\\t\")\n                boolean_users_path = \"./\" + self.config['parameters'][\"output_path\"] +\"boolean_users_\" + code + \".tsv\"\n                self.boolean_users[code] = pd.read_csv(boolean_users_path, \"\\t\", header=None)\n                self.t[code] = {}\n                self.t[code]['precision'] = {}\n                self.t[code]['recall'] = {}\n                self.t[code]['f1'] = {}\n                self.t[code]['accuracy'] = {}\n                self.t[code]['precision']['lower']=self.t['default']['precision']['lower']\n                self.t[code]['precision']['upper']=self.t['default']['precision']['upper']\n                self.t[code]['recall']['lower']=self.t['default']['recall']['lower']\n                self.t[code]['recall']['upper']=self.t['default']['recall']['upper']\n                self.t[code]['f1']['lower']=self.t['default']['f1']['lower']\n                self.t[code]['f1']['upper']=self.t['default']['f1']['upper']\n                self.t[code]['accuracy']['lower']=self.t['default']['accuracy']['lower']\n                self.t[code]['accuracy']['upper']=self.t['default']['accuracy']['upper']\n    def tearDown(self):\n        if self.unittest:\n            self.assertEqual([], self.softAssertionErrors)\n        self.error_log.close()\n        self.output_tsv.close()\n\n\n    def test_rsmd_discrete(self):\n        #\n        # Sort the boolean file, and then for each agent, get the rmsd between the boolean file values and the\n        # reputation system ranks, with ranks over config[\"parameters\"][\"\"reputation_system_threshold\"] clasified honest\n        # and under dishonest.\n        #\n        # RMSD = SQRT(SUM(SQR(EvaluatedGoodness-ExpectedGoodness))/N)\n        #\n        # RMSDg = SQRT(\n        # SUM(SQR(EvaluatedGoodness-ExpectedGoodness)*ExpectedGoodness) /\n        # SUM(ExpectedGoodness))\n        #\n        # RMSDb = SQRT(\n        # SUM(SQR((1-EvaluatedGoodness)-(1-ExpectedGoodness))*(1-ExpectedGoodness)) /\n        # SUM(1-ExpectedGoodness))\n        #\n        self.error_log.write(\"\\nFile:{0} \".format(self.error_path))\n        print(\"File:{0} \".format(self.error_path))\n        self.output_tsv.write(\"code\\trecall\\tprecision\\tf1\\taccuracy\")\n\n        for i, code in enumerate(self.codes):\n            self.output_tsv.write(\"\\n{0}\\t\".format(code))\n            rmsd_discrete = -1\n            rmsdg_discrete = -1\n            rmsdb_discrete = -1\n            sorted_agents = self.boolean_users[code].sort_values(self.boolean_users[code].columns[0])\n            sum_sqr_diff = 0\n            sum_sqr_diff_good = 0\n            sum_sqr_diff_bad = 0\n            sum_good = 0\n            sum_bad = 0 \n            n=0\n            true_positives = 0\n            true_negatives = 0\n            false_positives = 0\n            false_negatives = 0\n            recall = 0\n            precision = 0\n            f1 = 0\n            #for i1,day_row in self.rank_history[code].iterrows():\n\n            last_day_row = self.rank_history[code].iloc[-1]\n            for i2,agent_row in sorted_agents.iterrows():\n                agent_num = int(agent_row[sorted_agents.columns[0]])\n                agent_discrete = agent_row [sorted_agents.columns[1]]\n                agent_rank = last_day_row[self.rank_history[code].columns[agent_num + 1]]\n                if agent_rank >= 0:\n                    thresholded_rank = 0 if agent_rank < self.config[\"parameters\"][\"reputation_system_threshold\"][0] else 1\n                    if thresholded_rank == 1 and agent_discrete == 1:\n                        true_positives +=1\n                    elif thresholded_rank == 0 and agent_discrete == 0:\n                        true_negatives += 1\n                    elif thresholded_rank == 1 and agent_discrete == 0:\n                        false_positives += 1\n                    elif thresholded_rank == 0 and agent_discrete == 1:\n                        false_negatives += 1\n                    # sqr_diff = (agent_discrete-thresholded_rank)**2\n                    # sqr_diff_good = sqr_diff * agent_discrete\n                    # sqr_diff_bad = ((1-agent_discrete)-(1-thresholded_rank))**2*(1-agent_discrete)\n                    # sum_sqr_diff += sqr_diff\n                    # sum_sqr_diff_good += sqr_diff_good\n                    # sum_sqr_diff_bad += sqr_diff_bad\n                    # sum_good += agent_discrete\n                    # sum_bad += (1-agent_discrete)\n                    n += 1\n            denom = true_positives + false_negatives\n            if  denom> 0:\n                recall = true_positives / denom\n                self.output_tsv.write(\"{0}\\t\".format(recall))\n                self.error_log.write(\"\\ntime: {0} code: {1} recall: {2} lower: {3} upper: {4}\".format(\n                    datetime.datetime.now(), code, recall, self.t[code]['recall']['lower'],\n                    self.t[code]['recall']['upper']))\n                print(\"time: {0} code: {1} recall: {2} lower: {3} upper: {4}\".format(\n                    datetime.datetime.now(), code, recall, self.t[code]['recall']['lower'],\n                    self.t[code]['recall']['upper']))\n            else:\n                self.output_tsv.write(\"\\t\")\n            denom = true_positives + false_positives\n            if  denom> 0:\n                precision = true_positives / denom\n                self.output_tsv.write(\"{0}\\t\".format(precision))\n                self.error_log.write(\"\\ntime: {0} code: {1} precision: {2} lower: {3} upper: {4}\".format(\n                    datetime.datetime.now(), code, precision, self.t[code]['precision']['lower'],\n                    self.t[code]['precision']['upper']))\n                print(\"time: {0} code: {1} precision: {2} lower: {3} upper: {4}\".format(\n                    datetime.datetime.now(), code, precision, self.t[code]['precision']['lower'],\n                    self.t[code]['precision']['upper']))\n            else:\n                self.output_tsv.write(\"\\t\")\n            denom = precision + recall\n            if  denom> 0:\n                f1 = (2* precision * recall)/denom\n                self.output_tsv.write(\"{0}\\t\".format(f1))\n                self.error_log.write(\"\\ntime: {0} code: {1} f1: {2} lower: {3} upper: {4}\".format(\n                    datetime.datetime.now(), code, f1, self.t[code]['f1']['lower'],\n                    self.t[code]['f1']['upper']))\n                print(\"time: {0} code: {1} f1: {2} lower: {3} upper: {4}\".format(\n                    datetime.datetime.now(), code, f1, self.t[code]['f1']['lower'],\n                    self.t[code]['f1']['upper']))\n            else:\n                self.output_tsv.write(\"\\t\")\n            denom = true_negatives + true_positives + false_negatives + false_positives\n            if  denom> 0:\n                accuracy = (true_positives + true_negatives)/denom\n                self.output_tsv.write(\"{0}\".format(accuracy))\n                self.error_log.write(\"\\ntime: {0} code: {1} accuracy: {2} lower: {3} upper: {4}\".format(\n                    datetime.datetime.now(), code, accuracy, self.t[code]['accuracy']['lower'],\n                    self.t[code]['accuracy']['upper']))\n                print(\"time: {0} code: {1} accuracy: {2} lower: {3} upper: {4}\".format(\n                    datetime.datetime.now(), code, accuracy, self.t[code]['accuracy']['lower'],\n                    self.t[code]['accuracy']['upper']))\n\n            # if n > 0:\n            #     rmsd_discrete = math.sqrt(sum_sqr_diff/n)\n            #     self.error_log.write(\"\\ntime: {0} code: {1} rmsd_discrete: {2} lower: {3} upper: {4}\".format(\n            #         datetime.datetime.now(), code, rmsd_discrete, self.t[code]['rmsd_discrete']['lower'],\n            #         self.t[code]['rmsd_discrete']['upper']))\n            #     print(\"time: {0} code: {1} rmsd_discrete: {2} lower: {3} upper: {4}\".format(\n            #         datetime.datetime.now(), code, rmsd_discrete, self.t[code]['rmsd_discrete']['lower'],\n            #         self.t[code]['rmsd_discrete']['upper']))\n            #     print(\"n:{0},true_positives:{1},true_negatives:{2},false_positives:{3},false_negatives:{4}\".format(\n            #         n, true_positives, true_negatives, false_positives, false_negatives\n            #     ))\n            # if sum_good > 0:\n            #     rmsdg_discrete = math.sqrt(sum_sqr_diff_good/sum_good)\n            #     self.error_log.write(\"\\ntime: {0} code: {1} rmsdg_discrete: {2} lower: {3} upper: {4}\".format(\n            #         datetime.datetime.now(), code, rmsdg_discrete, self.t[code]['rmsdg_discrete']['lower'],\n            #         self.t[code]['rmsdg_discrete']['upper']))\n            #     print(\"time: {0} code: {1} rmsdg_discrete: {2} lower: {3} upper: {4}\".format(\n            #         datetime.datetime.now(), code, rmsdg_discrete, self.t[code]['rmsdg_discrete']['lower'],\n            #         self.t[code]['rmsdg_discrete']['upper']))\n            # if sum_bad > 0:\n            #     rmsdb_discrete = math.sqrt(sum_sqr_diff_bad/sum_bad)\n            #     self.error_log.write(\"\\ntime: {0} code: {1} rmsdb_discrete: {2} lower: {3} upper: {4}\".format(\n            #         datetime.datetime.now(), code, rmsdb_discrete, self.t[code]['rmsdb_discrete']['lower'],\n            #         self.t[code]['rmsdb_discrete']['upper']))\n            #     print(\"time: {0} code: {1} rmsdb_discrete: {2} lower: {3} upper: {4}\".format(\n            #         datetime.datetime.now(),code,rmsdb_discrete,self.t[code]['rmsdb_discrete']['lower'],self.t[code]['rmsdb_discrete']['upper']))\n            # try:\n            #     self.assertGreaterEqual(rmsd_discrete, self.t[code]['rmsd_discrete']['lower'])\n            # except AssertionError as e:\n            #     self.softAssertionErrors.append(str(e))\n            # try:\n            #     self.assertLessEqual(rmsd_discrete, self.t[code]['rmsd_discrete']['upper'])\n            # except AssertionError as e:\n            #     self.softAssertionErrors.append(str(e))\n            # try:\n            #     self.assertGreaterEqual(rmsdg_discrete, self.t[code]['rmsdg_discrete']['lower'])\n            # except AssertionError as e:\n            #     self.softAssertionErrors.append(str(e))\n            # try:\n            #     self.assertLessEqual(rmsdg_discrete, self.t[code]['rmsdg_discrete']['upper'])\n            # except AssertionError as e:\n            #     self.softAssertionErrors.append(str(e))\n            # try:\n            #     self.assertGreaterEqual(rmsdb_discrete, self.t[code]['rmsdb_discrete']['lower'])\n            # except AssertionError as e:\n            #     self.softAssertionErrors.append(str(e))\n            # try:\n            #     self.assertLessEqual(rmsdb_discrete, self.t[code]['rmsdb_discrete']['upper'])\n            # except AssertionError as e:\n            #     self.softAssertionErrors.append(str(e))\n            try:\n                self.assertGreaterEqual(recall, self.t[code]['recall']['lower'])\n            except AssertionError as e:\n                self.softAssertionErrors.append(str(e))\n            try:\n                self.assertLessEqual(recall, self.t[code]['recall']['upper'])\n            except AssertionError as e:\n                self.softAssertionErrors.append(str(e))\n            try:\n                self.assertGreaterEqual(precision, self.t[code]['precision']['lower'])\n            except AssertionError as e:\n                self.softAssertionErrors.append(str(e))\n            try:\n                self.assertLessEqual(precision, self.t[code]['precision']['upper'])\n            except AssertionError as e:\n                self.softAssertionErrors.append(str(e))\n            try:\n                self.assertGreaterEqual(f1, self.t[code]['f1']['lower'])\n            except AssertionError as e:\n                self.softAssertionErrors.append(str(e))\n            try:\n                self.assertLessEqual(f1, self.t[code]['f1']['upper'])\n            except AssertionError as e:\n                self.softAssertionErrors.append(str(e))\n            try:\n                self.assertGreaterEqual(accuracy, self.t[code]['accuracy']['lower'])\n            except AssertionError as e:\n                self.softAssertionErrors.append(str(e))\n            try:\n                self.assertLessEqual(accuracy, self.t[code]['accuracy']['upper'])\n            except AssertionError as e:\n                self.softAssertionErrors.append(str(e))\n\n\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"snsim/reputation_simulation/DiscreteRankTests.py","file_name":"DiscreteRankTests.py","file_ext":"py","file_size_in_byte":15026,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"6248782","text":"# Python 3.0\n#use time.time() on Linux\n\nimport re\nimport os\nimport collections\nimport time\nimport numpy as np\n#import other modules as needed\n\nalpha = 0.15\nEPS = 1E-3\n\ndef powerVector(AM, previous_p_rank):\n    while True:\n        rank = np.matmul(previous_p_rank, AM)\n        if np.linalg.norm(previous_p_rank - rank) < EPS: return rank\n        previous_p_rank = rank\n\n\ndef page_rank(mat, k):\n    #function to implement pagerank algorithm\n    #input_file - input file that follows the format provided in the assignment description\n\n\n    \"\"\"\n    N, _ = mat.shape\n    for i, row in enumerate(mat):\n        no_of_1 = np.count_nonzero(row)\n        for j, value in enumerate(row):\n            if no_of_1:\n                mat[i][j] = 1/N\n            else:\n                mat[i][j] = alpha/N if value == 0 else (1 - alpha) / no_of_1 \n                + (alpha / N)\n                \n                \"\"\"\n\n\n# Generating Initial Page Rank Iteration\n    N, _ = mat.shape\n    for i, row in enumerate(mat):\n        no_of_1 = np.count_nonzero(row)\n        for j, value in enumerate(row):\n            if no_of_1:\n                mat[i][j] = alpha/N if value == 0 else (1 - alpha) / no_of_1 + (alpha / N)\n            else:\n                mat[i][j] = 1/N\n\n\n\n# Generating Power Vector\n    p_rank = powerVector(mat, np.array([alpha for i in range(N)]))\n    #print(p_rank)\n   # return sorted(range(len(p_rank)), key=lambda x: p_rank[x])[:k]\n\n    p_rank = {i: k for i, k in enumerate(p_rank)}\n    return sorted(p_rank.items(), key=lambda x:x[1])[:k]\n    #print(p_rank)\n\n\n\n# Reading Input Files\n\ninput_file = [\"test1.txt\", \"test2.txt\"]\n#input_file = [endswith(\".txt\")]\nfor file_name in input_file:\n    print(\"\\nPage Ranks of Documents in Input File '\",file_name,\"' with Teleportation rate α = 0.15\")\n    print(\"\\nDocument List according to their Page Rank:\")\n    print(\"\\nDocuments  Page Rank\")\n    print(\"---------  ---------------\")\n\n    \n    with open(file_name) as file:\n\n        # Read number of nodes\n        node = int(file.readline())\n\n        # Read number of links\n        link = int(file.readline())\n\n       \n        Matrix = np.zeros((node, node), dtype=np.float64)\n        for x in file:\n            a, b = map(int, x.split())\n            Matrix[a][b] = 1\n\n\n\n\n    p_rank = page_rank(Matrix, 10)\n        # t1 = time.time()\n\n# Printing Top 10 documents ID with page rank\n    for doc, score in p_rank:\n        print(\"Doc\", doc,\"    \", score)\n        # print(\"Page Ranks Generated in\", '{0:.10f}'.format(t2 - t1), \"seconds\\n\")\n    print(\"\\n\")\n    \n   ","sub_path":"Information Retrieval Project/Page Rank Retrieval Using TF-IDF Scores/pagerank.py","file_name":"pagerank.py","file_ext":"py","file_size_in_byte":2539,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"540613470","text":"__author__ = 'belgau'\n\nimport time\nfrom controllers.arbitrator import Arbitrator\n\nclass BBCON():\n\n    '''\n    High level controller for the robot runtime\n    '''\n\n    def __init__(self):\n        self._behaviors = []        # all behaviors in the run\n        self._arbitrator = Arbitrator(self)\n        # public variables, used by behaviors\n        self.active_behaviors = []  # behaviors that can give motor recommendations\n        self.last_motor_rec = None  # the last motor recommendation picked by the arbitrator\n        self.object_located = False # the robot goal\n        self.timestep = 1           # used for timing certain behaviors\n\n    def add_behavior(self, b):\n        self._behaviors.append(b)\n\n    def activate_behavior(self, behavior):\n        if behavior in self._behaviors:\n            self.active_behaviors.append(behavior)\n\n    def deactivate_behavior(self, behavior):\n        if behavior in self.active_behaviors:\n            self.active_behaviors.remove(behavior)\n\n    def run_one_timestep(self):\n        # update all active behaviors\n        for b in self._behaviors:\n            b.update()\n        # check if we have any active behaviors\n        if self.active_behaviors:\n            # choose motor recommendation\n            motor_rec = self._arbitrator.choose_action()\n            self.last_motor_rec = motor_rec\n        time.sleep(0.05)\n        self.timestep += 1\n        # debug\n        print(self.timestep)\n","sub_path":"project_files/controllers/BBCON.py","file_name":"BBCON.py","file_ext":"py","file_size_in_byte":1436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"394848366","text":"from flask import Flask\nfrom flask_restful import reqparse, Api, Resource\nfrom lib.result import create_ga_screen_result\nfrom lib.util import valid_date\nimport settings\nfrom tasks.google_analitycs import GAScreenMaker\n\napp = Flask(__name__)\napi = Api(app)\n\nparser = reqparse.RequestParser()\nparser.add_argument('login', required=True)\nparser.add_argument('password', required=True)\nparser.add_argument('start_date', type=valid_date, required=True)\nparser.add_argument('end_date', type=valid_date, required=True)\nparser.add_argument('counter', required=True)\nparser.add_argument('segments')\n\n\nclass Task(Resource):\n    def get(self, task_uid):\n        celery_result = GAScreenMaker().AsyncResult(task_uid)\n        result = None\n        if celery_result.state == 'SUCCESS':\n            result = create_ga_screen_result(task_uid)\n        return {'uid': task_uid, 'state': celery_result.state, 'result': result}\n\n\nclass TaskList(Resource):\n    def post(self):\n        args = parser.parse_args()\n        celery_task = GAScreenMaker()\n        result = celery_task.delay(**args)\n        return result.id, 201\n\n\napi.add_resource(Task, '/tasks/<task_uid>')\napi.add_resource(TaskList, '/tasks')\n\nif __name__ == '__main__':\n    app.run(debug=settings.DEBUG)\n","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1247,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"72290162","text":"# method 2, math, greedy\n# sum of all prime factors, time O(n), space O(1)\n# reason p*q > p+q, so if you have (p+q) operations, always try to copy + (p-1) pase + copy + (q-1) paste\nclass Solution(object):\n    def minSteps(self, n):\n        if n == 1: return 0\n        res = 0\n        divisor = 2\n        while n > 1:\n            while n%divisor == 0:\n                n /= divisor\n                res += divisor\n            divisor += 1\n        return res\n\n\n# method 1, brute force, dfs with memo, time/space O(n^2)\nclass Solution1(object):\n    def minSteps(self, n):\n        \"\"\"\n        :type n: int\n        :rtype: int\n        \"\"\"\n        if n == 1:\n            return 0\n        if n == 2:\n            return 2\n        self.res = float('inf')\n        self.memo = {}\n        self.dfs(2, 1, 2, n)\n        return self.res\n    \n    def dfs(self, count, copy, step, n):\n        if count == n:\n            self.res = min(self.res, step)\n            return\n        if count > n:\n            return\n        if step + 1 >= self.res:   # early termination\n            return\n        if (count, copy) in self.memo and self.memo[(count, copy)] <= step:\n            return\n        self.memo[(count, copy)] = step\n        # copy + paste\n        self.dfs(count*2, count, step+2, n)\n        # paste\n        self.dfs(count+copy, copy, step+1, n)\n\n\n\"\"\"\nInitially on a notepad only one character 'A' is present. You can perform two operations on this notepad for each step:\n\nCopy All: You can copy all the characters present on the notepad (partial copy is not allowed).\nPaste: You can paste the characters which are copied last time.\n \n\nGiven a number n. You have to get exactly n 'A' on the notepad by performing the minimum number of steps permitted. Output the minimum number of steps to get n 'A'.\n\nExample 1:\n\nInput: 3\nOutput: 3\nExplanation:\nIntitally, we have one character 'A'.\nIn step 1, we use Copy All operation.\nIn step 2, we use Paste operation to get 'AA'.\nIn step 3, we use Paste operation to get 'AAA'.\n \n\nNote:\n\nThe n will be in the range [1, 1000].\n\"\"\"\n","sub_path":"0650. 2 Keys Keyboard.py","file_name":"0650. 2 Keys Keyboard.py","file_ext":"py","file_size_in_byte":2053,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"145887110","text":"import logging\nimport os\n\nfrom datetime import timedelta\n\nfrom django.conf import settings\nfrom django.utils import timezone\nfrom django_push.subscriber.models import Subscription\nfrom raven import Client\n\nfrom ...models import UniqueFeed\nfrom . import SentryCommand\n\nlogger = logging.getLogger(__name__)\n\n\nclass Command(SentryCommand):\n    \"\"\"Updates PubSubHubbub subscriptions\"\"\"\n\n    def handle_sentry(self, *args, **kwargs):\n        missing = list(UniqueFeed.objects.raw(\n            \"\"\"\n            select id, url, hub from feeds_uniquefeed u where\n                not exists (\n                    select 1 from subscriber_subscription s\n                    where\n                        u.url = s.topic and\n                        u.hub = s.hub\n                ) and\n                u.hub like 'http%%'\n            \"\"\"))\n        if len(missing):\n            logger.info(\"Subscribing to {0} feeds\".format(len(missing)))\n            for feed in missing:\n                try:\n                    Subscription.objects.subscribe(feed.url, hub=feed.hub)\n                except Exception:\n                    if settings.DEBUG or not 'SENTRY_DSN' in os.environ:\n                        raise\n                    client = Client()\n                    client.captureException()\n\n        extra = list(Subscription.objects.raw(\n            \"\"\"\n            select * from subscriber_subscription s where not exists (\n                select 1 from feeds_uniquefeed u\n                where\n                    u.url = s.topic and\n                    u.hub = s.hub and\n                    u.hub like 'http%%'\n            )\n            \"\"\"))\n        if len(extra):\n            logger.info(\"Unsubscribing from {0} feeds\".format(len(extra)))\n            for subscription in extra:\n                try:\n                    subscription.unsubscribe()\n                except Exception:\n                    if settings.DEBUG or not 'SENTRY_DSN' in os.environ:\n                        raise\n                    client = Client()\n                    client.captureException()\n\n        expiring = Subscription.objects.filter(\n            lease_expiration__lte=timezone.now() + timedelta(days=1))\n        if len(expiring):\n            logger.info(\"Renewing {0} subscriptions\".format(len(expiring)))\n            for subscription in expiring:\n                try:\n                    subscription.subscribe()\n                except Exception:\n                    if settings.DEBUG or not 'SENTRY_DSN' in os.environ:\n                        raise\n                    client = Client()\n                    client.captureException()\n","sub_path":"feedhq/feeds/management/commands/sync_pubsubhubbub.py","file_name":"sync_pubsubhubbub.py","file_ext":"py","file_size_in_byte":2607,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"255920471","text":"\n\nfrom flask_assets import Environment, Bundle\n\nfrom . import app\n\nbase_metrics_js = Bundle(\n    'metrics/js/analyticsData.js',\n    'metrics/js/analyticsGraph.js',\n    'metrics/js/dataUtils.js',\n)\nbundles={\n    'js_base_libs' : Bundle(\n        'pubswh/js/plugins.js',\n        filters='rjsmin',\n        output='gen/base_libs.js'\n    ),\n    'js_advanced_search' : Bundle(\n        'pubswh/js/searchMap.js',\n        'pubswh/js/clearFeatureControl.js',\n        'pubswh/js/advancedSearchForm.js',\n        'pubswh/js/searchFormOnReady.js',\n        filters='rjsmin',\n        output='gen/advanced_search.js'\n    ),\n    'metrics_publications_js' : Bundle(\n        base_metrics_js,\n        Bundle('metrics/js/pubsGraphController.js'),\n        filters='rjsmin',\n        output='gen/metrics_publications.js'\n    ),\n    'metrics_publications_aquisition_js' : Bundle(\n        base_metrics_js,\n        Bundle('metrics/js/pubsAcquisitionGraphController.js'),\n        filters='rjsmin',\n        output='gen/metrics_publications_acquisition.js'\n    ),\n    'metrics_publication_js': Bundle(\n        base_metrics_js,\n        Bundle('metrics/js/pubGraphController.js'),\n        filters='rjsmin',\n        output='gen/metrics_publication_js'\n    ),\n\n    'usgs_style' : Bundle(\n        'manager/less/usgs_header_footer.less',\n        filters='less,cssmin',\n        output='gen/usgs_style.css'\n    ),\n    'css_base' : Bundle(\n        'pubswh/css/normalize.css',\n        'pubswh/css/main.css',\n        filters='cssmin',\n        output='gen/min_base.css'\n    ),\n    'auth_style' : Bundle(\n        'auth/less/auth.less',\n        depends=[\n            'manager/less/usgs_header_footer.less'\n        ],\n        filters='less,cssmin',\n        output='gen/auth_style.css'\n    ),\n    'manager_style' : Bundle(\n        'manager/less/manager_custom.less',\n        depends=[\n            'manager/less/usgs_header_footer.less',\n            'manager/less/manage_publications.less',\n            'manager/less/publication.less',\n            'manager/less/bibliodata.less',\n            'manager/less/links.less',\n            'manager/less/contributors.less',\n            'manager/less/spn.less',\n            'manager/less/cataloging.less',\n            'manager/less/geospatial.less',\n            'manager/less/editSeriesTitle.less',\n            'manager/less/manageContributors.less',\n            'manager/less/manageAffiliations.less'\n        ],\n        filters='less,cssmin',\n        output='gen/manager_style.css'\n    ),\n    'metrics_style' : Bundle(\n        'metrics/less/metrics_custom.less',\n        depends=[\n            'manager/less/usgs_header_footer.less',\n        ],\n        filters='less,cssmin',\n        output='gen/metrics_style.css'\n    ),\n}\n\nassets = Environment(app)\nassets.register(bundles)\n\n","sub_path":"pubs_ui/assets.py","file_name":"assets.py","file_ext":"py","file_size_in_byte":2768,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"336035917","text":"from __future__ import absolute_import\n\nfrom django import forms\n\nfrom .models import *\nfrom .models import IES\n\nfrom localflavor.br.forms import BRZipCodeField, BRCPFField, BRPhoneNumberField, BRCNPJField\n\nfrom django.forms.extras.widgets import SelectDateWidget\n\nfrom .widget import AdminImageWidget\n\nfrom chained_selects.widgets import ChainedSelectWidget\n\nclass ChoiceTelefoneFormset(forms.ModelForm):\n    telefone = BRPhoneNumberField(label='TEL')\n\n\nclass ChoiceEnderecoFormset(forms.ModelForm):\n    cep = BRZipCodeField(label='CEP')\n\nGENERO = (('0', 'Masculino'), ('1', 'Feminino'))\n\nNACIONALIDADE = (\n    ('1', 'Brasileira'),\n    ('2', 'Brasileira - Nascido no exterior ou naturalizado'),\n    ('3', 'Estrangeira'))\n\nCORRACA = (\n    ('1', 'Branca'),\n    ('2', 'Preta'),\n    ('3', 'Parda'),\n    ('4', 'Amarela'),\n    ('5', 'Indígena'),\n    ('6', 'Não dispõe da informação'),\n    ('0', 'Não declarado'))\n\nFATORRH = (('1', 'A+'), ('2', 'A-'), ('3', 'B+'), ('4', 'B-'),\n           ('5', 'AB+'), ('6', 'AB-'), ('7', 'O+'), ('8', 'O-'))\n\n\n\n\n\nclass BairroForm(forms.ModelForm):\n\n    class Meta:\n        model = Bairro\n        fields = ('estado', 'municipio', 'bairro')\n\n    estado = forms.ModelChoiceField(queryset=Estado.objects.all())\n\n    def __init__(self, *args, **kwargs):\n        super(BairroForm, self).__init__(*args, **kwargs)\n\n        # assign a widget to second select field\n        self.fields['municipio'].widget = ChainedSelectWidget(\n            parent_name='estado',            # the name of parent field\n            app_name='adm',                  # the name of model's application\n            model_name='estado',             # the name of a model with the method\n            method_name='chained_relation',  # the name of queryset method\n        )\n\n        # edit\n        if hasattr(self, 'instance') and hasattr(self.instance, 'municipio'):\n            self.fields['estado'].initial = self.instance.municipio.estado\n            self.fields['municipio'].widget.attrs['data-initial'] = (\n                self.instance.municipio.id)\n\n\nclass PessoaForm(forms.ModelForm):\n\n    class Meta:\n        model = Pessoa\n        fields = \"__all__\"\n\n    foto = forms.ImageField(label='Foto', widget=AdminImageWidget)\n    cpf_mae = BRCPFField(required=False, label='CPF da Mãe')\n    cpf = BRCPFField(label='CPF')\n    sexo = forms.ChoiceField(\n        widget=forms.Select, choices=GENERO, label='Sexo')\n    nacionalidade = forms.ChoiceField(\n        widget=forms.Select, choices=NACIONALIDADE, label='Nacionalidade')\n    cor_raca = forms.ChoiceField(\n        widget=forms.Select, choices=CORRACA, label='Cor/Raça')\n    fator_rh = forms.ChoiceField(\n        widget=forms.Select, choices=FATORRH, label='Fator Rh')\n\n\nclass IESForm(forms.ModelForm):\n\n    class Meta:\n        model = IES\n        exclude = ['id']\n\n    cnpj_mantida = BRCNPJField(label='CNPJ da Mantida')\n    cnpj_mantenedora = BRCNPJField(label='CNPJ da Mantenedora')\n\n\n\n\nclass EnderecoForm(forms.ModelForm):\n    class Meta:\n        model = Endereco\n        fields = ('pais', 'estado', 'municipio', 'bairro', 'pessoa', 'tipo', 'logradouro', 'cep', 'complemento')\n\n    pais = forms.ModelChoiceField(queryset=Pais.objects.all())\n    estado = forms.ModelChoiceField(queryset=Estado.objects.all())\n    municipio = forms.ModelChoiceField(queryset=Municipio.objects.all())\n\n    def __init__(self, *args, **kwargs):\n        super(EnderecoForm, self).__init__(*args, **kwargs)\n\n        # assign a widget to second select field\n        self.fields['estado'].widget = ChainedSelectWidget(\n            parent_name='pais',            # the name of parent field\n            app_name='adm',                  # the name of model's application\n            model_name='pais',             # the name of a model with the method\n            method_name='chained_relation',  # the name of queryset method\n        )\n\n        self.fields['municipio'].widget = ChainedSelectWidget(\n            parent_name='estado',            # the name of parent field\n            app_name='adm',                  # the name of model's application\n            model_name='estado',             # the name of a model with the method\n            method_name='chained_relation',  # the name of queryset method\n        )\n\n        self.fields['bairro'].widget = ChainedSelectWidget(\n            parent_name='municipio',            # the name of parent field\n            app_name='adm',                  # the name of model's application\n            model_name='municipio',             # the name of a model with the method\n            method_name='chained_relation',  # the name of queryset method\n        )\n\n\n        if hasattr(self, 'instance') and hasattr(self.instance, 'estado'):\n            self.fields['pais'].initial = self.instance.estado.pais\n            self.fields['estado'].widget.attrs['data-initial'] = (\n            self.instance.estado.id)\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"adm/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":4897,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"448218204","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon May 21 02:06:17 2018\r\n\r\n@author: Vaibhav\r\n\"\"\"\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\nfrom collections import Counter\r\n\r\ntrain = pd.read_csv('train.csv')\r\ntest = pd.read_csv('test.csv')\r\n\r\nY_train = train.iloc[:,-1:].values\r\n\r\ntrain.drop(['Id','Alley','Street','Utilities', 'PoolQC', 'Fence', 'MiscFeature','FireplaceQu','SalePrice'], 1, inplace = True)\r\ntest.drop(['Id','Alley','Street','Utilities', 'PoolQC', 'Fence', 'MiscFeature','FireplaceQu'], 1, inplace = True)\r\n\r\ncombined_data = train.append(test)\r\n\r\ncombined_data['MSSubClass'] = combined_data.MSSubClass.astype(dtype = object, errors = 'ignore')\r\ncombined_data['MoSold'] = combined_data.MoSold.astype(dtype = object, errors = 'ignore')\r\ncombined_data['YrSold'] = combined_data.YrSold.astype(dtype = object, errors = 'ignore')\r\n\r\ndef plot_missing_values(dataset):\r\n    nan_values = dataset.isnull().sum()\r\n    nan_values = nan_values[nan_values>0]\r\n    nan_values.sort_values(inplace = True)\r\n#    nan_values.plot.bar()\r\n    return nan_values\r\n\r\nmissing_values = plot_missing_values(combined_data)\r\n\r\nnan_indexes = list(missing_values.index)\r\n\r\ndef fill_missing_values(dataset,missing_values_indexes):\r\n    for item in missing_values_indexes:\r\n        if(dataset.dtypes[item] != 'object'):\r\n            dataset[item].fillna(dataset[item].mean(), inplace = True)\r\n        else:\r\n #           dataset[item].fillna(dataset[item].mode()[0], inplace = True)\r\n            dataset[item].fillna(\"MISSING\", inplace = True)\r\n\r\nfill_missing_values(combined_data,nan_indexes)\r\n\r\n''' #For Future reference (observing and filling the values)\r\ntrain['LotFrontage'].plot.bar()\r\nsns.distplot(train['LotFrontage'])\r\ntrain['LotFrontage'].fillna(train['LotFrontage'].mean(), inplace = True)\r\n\r\ncount_each = Counter(train['GarageCond'])\r\ntrain['GarageCond'].fillna(train['GarageCond'].mode()[0], inplace = True)\r\n'''\r\n\r\n# Encoding categorical data\r\ndef encode_categorical(dataset):\r\n    from sklearn.preprocessing import LabelEncoder, OneHotEncoder\r\n    labelencoder= LabelEncoder()\r\n    qualitative = [q for q in dataset.columns if dataset.dtypes[q] == 'object']\r\n    list_of_categorical_indexes = []\r\n    list_of_categorical_counts = []\r\n    dataset_columns = list(dataset.columns)\r\n    \r\n    for item in qualitative:\r\n        dataset[item] = labelencoder.fit_transform(dataset[item])\r\n        list_of_categorical_indexes.append(dataset_columns.index(item))\r\n        list_of_categorical_counts.append(set(dataset[item]).__len__())\r\n    \r\n    onehotencoder = OneHotEncoder(categorical_features = list_of_categorical_indexes)    \r\n    dataset = onehotencoder.fit_transform(dataset).toarray()\r\n    dataset = pd.DataFrame(dataset)\r\n    \r\n    # Avoiding Dummy trap\r\n    modified_i = 0\r\n    for i in list_of_categorical_counts:\r\n        dataset.drop([modified_i], 1, inplace = True)\r\n        modified_i += i\r\n    return dataset\r\n\r\ncombined_data = encode_categorical(combined_data)\r\n#temp = pd.read_csv('train.csv')\r\n\r\nX_train = combined_data.iloc[:1460, :].values\r\n\r\nX_test = combined_data.iloc[1460:,:].values\r\n\r\n# Feature Scaling\r\nfrom sklearn.preprocessing import StandardScaler\r\nsc_X = StandardScaler()\r\nX_train = sc_X.fit_transform(X_train)\r\nX_test = sc_X.transform(X_test)\r\nsc_y = StandardScaler()\r\nY_train = sc_y.fit_transform(Y_train)\r\n\r\n# Fitting Random Forest Regression to the dataset\r\nfrom sklearn.ensemble import RandomForestRegressor\r\nregressor = RandomForestRegressor(n_estimators = 100, random_state = 42)\r\nregressor.fit(X_train, Y_train)\r\n\r\n# Predicting the Test set results\r\nY_pred = regressor.predict(X_test)\r\n\r\n# K-Fold Cross Validation\r\nfrom sklearn.model_selection import cross_val_score\r\naccuracies = cross_val_score(estimator = regressor, X = X_train, y = Y_train, cv = 10)\r\n\r\n# Grid Search\r\nfrom sklearn.model_selection import GridSearchCV\r\nparameters = [ {'n_estimators' : [50,100,150,200], 'min_samples_leaf' : [2,5,10,15,20]}]\r\ngrid_search = GridSearchCV(estimator = regressor, param_grid = parameters, scoring = 'mean_squared_error', cv = 10)\r\ngrid_search = grid_search.fit(X_train, Y_train)\r\nbest_accuracy = grid_search.best_score_\r\nbest_parameters = grid_search.best_params_\r\n\r\n\r\nY_pred = sc_y.inverse_transform(Y_pred)\r\nY_pred = pd.DataFrame(Y_pred)\r\nY_pred.to_csv('House_Price_RandomForest_9.0.csv', sep=',')\r\n","sub_path":"Kaggle/House Prices _ Advanced Regression/HousePrice_Prediction_RandomForest_k_fold_Grid_search.py","file_name":"HousePrice_Prediction_RandomForest_k_fold_Grid_search.py","file_ext":"py","file_size_in_byte":4383,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"155828287","text":"\"\"\"\nconway.py\nAuthor: Tess Snyder\nCredit: Mr. Dennison, Mary Feyrer, https://realpython.com/blog/python/pygame-a-primer/\nAssignment:\nWrite and submit a program that plays Conway's Game of Life, per \nhttps://github.com/HHS-IntroProgramming/Conway-Life\n\"\"\"\nfrom ggame import App, RectangleAsset\nfrom ggame import LineStyle, Color, Sprite, Sound\n\nblack = Color(0x000000, 1.0)\nwhite = Color(0xFFFFFF, 1.0) #color of dead cell\nred = Color(0xff0000, 1.0) #color of first day of life\n\nline = LineStyle(.1, black)\nnoline = LineStyle(0, black)\n\nlist1=[]\n\nscreen=RectangleAsset(600,600, line, white)\nSprite(screen, (0,0))\n\nsquare = RectangleAsset(20,20, noline, red)\n\nfor x in range(0,30):\n    for y in range(0,30):\n        s=Sprite(square, (20*x,20*y))\n        s.visible= False \n        list1.append(s)\n\n\nclass Conway(App):\n    \n    def __init__(self):\n        super().__init__()\n        self.listenMouseEvent('click', self.mouseClick)\n        self.running = False\n        self.listenKeyEvent(\"keydown\", \"space\", self.spacebar)\n        self.listenKeyEvent(\"keydown\", \"s\", self.skey)\n        \n    def mouseClick(self, event):\n        x=event.x\n        y=event.y\n        for sprite in list1:\n            xsprite =sprite.x\n            ysprite=sprite.y\n            if x<= xsprite + 10 and x>= xsprite -10 and y<=ysprite + 10 and y>= ysprite -10:\n               sprite.visible= True \n               \n    \n    def spacebar(self, event):\n        self.running = True\n        \n    def skey(self, event):\n        self.running = False\n\n    def step(self):\n        if self.running == True:\n            listred=[]\n            for spr in list1:\n                if spr.visible==True:\n                    listred.append(spr)\n            for sprite1 in list1:\n                surroundlist=[]\n                for redsprite in listred:\n                    if abs(sprite1.x-redsprite.x) <= 25 and abs(sprite1.y-redsprite.y) <= 25:\n                        surroundlist.append(redsprite)\n                num=len(surroundlist)\n                if sprite1.visible==True:\n                    num=len(surroundlist)-1\n                if num>3:\n                    sprite1.visible=False\n                elif num==3:\n                    sprite1.visible=True\n                elif num<2:\n                    sprite1.visible=False\n\nmyapp = Conway()\nmyapp.run()","sub_path":"conway.py","file_name":"conway.py","file_ext":"py","file_size_in_byte":2318,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"65676122","text":"from schema import Schema, Optional, Use, Or\nfrom datetime import datetime\n\nfrom bson import ObjectId\n\nProducts = Schema({\n    'code'          : Use(str.lower),\n    'name'          : Use(str.lower),\n    # 'type'          : Use(ObjectId),\n    Optional('type', default=None) : Or(None, Use(ObjectId)),\n    # 'mark'          : Use(ObjectId),\n    Optional('mark', default=None) : Or(None, Use(ObjectId)),\n    # 'presentation'  : Use(ObjectId),\n    Optional('presentation', default=None) : Or(None, Use(ObjectId)),\n    'cost'          : Use(float),\n    \n    'price'         : Use(float),\n\n    Optional('content')     : Use(str.lower),\n    Optional('description') : Use(str.lower),\n    # Optional('unit')        : Use(ObjectId),\n    Optional('unit', default=None) : Or(None, Use(ObjectId)),\n    Optional('points')      : Use(int),\n\n    Optional('profit',  default=20)    : Use(int),\n    Optional('round',   default=False) : Use(bool),\n    Optional('pin',     default=False) : Use(bool),\n    Optional('scale',   default=False) : Use(bool),\n    Optional('active',  default=False) : Use(bool),\n    Optional('visible', default=False) : Use(bool),\n}, ignore_extra_keys=True)","sub_path":"schemas/products.py","file_name":"products.py","file_ext":"py","file_size_in_byte":1163,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"348258123","text":"__version__ = \"1.0\"\r\n\r\nclass Producto:\r\n    def __init__(self,codigo,nombre,stock,precio):\r\n        self.codigo=codigo\r\n        self.stock=stock\r\n        self.precio=precio\r\n        self.nombre=nombre\r\n\r\n    def __eq__(self,other):\r\n        if self.codigo == other.codigo:\r\n            return True\r\n        else:\r\n            return False\r\n\r\nclass Item:\r\n    def __init__(self,cantidad,producto):\r\n        self.cantidad=cantidad\r\n        self.producto=producto\r\n\r\nclass Carro:\r\n    def __init__(self):\r\n        self.lista_de_productos=[]\r\n        self.descuento=0\r\n        self.total=0\r\n        self.direccion=\"\"\r\n\r\n    def agregar_item(self,cantidad,producto):\r\n        for item in self.lista_de_productos:\r\n            if item.producto == producto:\r\n                if cantidad==0:\r\n                    self.lista_de_productos.remove(item)\r\n                else:\r\n                    item.cantidad=cantidad\r\n                return\r\n        item=Item(cantidad,producto)\r\n        self.lista_de_productos.append(item)\r\n\r\n    def checkout(self,tipoDespacho,direccion):\r\n        self.direccion=direccion\r\n        if tipoDespacho==0:\r\n            factor=1.2\r\n        else:\r\n            factor=1.3\r\n        if self.direccion.lower().find(\"chile\")!=-1:\r\n            impuesto=1.2\r\n        else:\r\n            impuesto=1\r\n        self.descuento=0\r\n        d1=0\r\n        d2=0\r\n        v1=0\r\n        v2=0\r\n        valor=0\r\n        for item in self.lista_de_productos:\r\n            if item.producto.codigo==1:\r\n                d1+=1\r\n                v1+=item.producto.precio*item.cantidad\r\n            elif item.producto.codigo==2:\r\n                d1+=1\r\n                v1+=item.producto.precio*item.cantidad\r\n            elif item.producto.codigo==3:\r\n                d1+=1\r\n                v1+=item.producto.precio*item.cantidad\r\n            elif item.producto.codigo==4:\r\n                d2+=1\r\n                v2+=item.producto.precio*item.cantidad\r\n            elif item.producto.codigo==5:\r\n                d2+=1\r\n                v2+=item.producto.precio*item.cantidad\r\n            valor+=item.producto.precio*item.cantidad\r\n        if d1>=3:\r\n            self.descuento+=v1*0.2\r\n        if d2>=2:\r\n            self.descuento+=v2*0.15\r\n        self.total=round((valor-self.descuento)*factor*impuesto,0)\r\n        return self.total\r\n\r\n    def __repr__(self):\r\n        s=\"Carro de Compras:\\n\"\r\n        for item in self.lista_de_productos:\r\n            s=s+\"{0}: {1}\\n\".format(item.cantidad,item.producto.nombre)\r\n        return s\r\n\r\nclass Tienda:\r\n    def  __init__(self):\r\n        self.catalogo=[]\r\n        producto=Producto(1,\"Juego Pokemon Y\",10,31.51)\r\n        self.catalogo.append(producto)\r\n        producto=Producto(2,\"Nintendo 3DS\",5,190.95)\r\n        self.catalogo.append(producto)\r\n        producto=Producto(3,\"Juego Mario Kart\",10,29.96)\r\n        self.catalogo.append(producto)\r\n        producto=Producto(4,\"Playstation 4\",10,399)\r\n        self.catalogo.append(producto)\r\n        producto=Producto(5,\"Fifa 14\",10,41.95)\r\n        self.catalogo.append(producto)\r\n\r\n    def menu(self):\r\n        for producto in self.catalogo:\r\n            print(\"{0}: {1} ${2}\".format(\r\n                producto.codigo,producto.nombre,producto.precio))\r\n\r\n    def buscarProducto(self,numero):\r\n        for producto in self.catalogo:\r\n            if producto.codigo==numero:\r\n                return producto\r\n\r\nif __name__ == \"__main__\":\r\n    tienda=Tienda()\r\n    carro=Carro()\r\n    while True:\r\n        tienda.menu()\r\n        item_ingresado=input(\"Ingresa un producto a comprar (numero,cantidad): \")\r\n        if item_ingresado==\"\":\r\n            break\r\n        else:\r\n            item_ingresado=item_ingresado.split(\",\")\r\n            numero=int(item_ingresado[0])\r\n            producto=tienda.buscarProducto(numero)\r\n            cantidad=int(item_ingresado[1])\r\n            carro.agregar_item(cantidad,producto)\r\n        print(carro)\r\n\r\n    print(carro.checkout(0,\"usa\"))\r\n    print(carro.checkout(1,\"usa\"))\r\n    print(carro.checkout(0,\"Monjitas 360 D.614 chile\"))\r\n    print(carro.checkout(1,\"Monjitas 360 D.614 chile\"))\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","sub_path":"tema8_ej1/solucion.py","file_name":"solucion.py","file_ext":"py","file_size_in_byte":4129,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"72283269","text":"from django import forms\nfrom django.forms import widgets\nfrom django.utils.translation import ugettext as _\nfrom email_campaign.models import Email\n\nclass EmailForm(forms.ModelForm):\n    class Meta:\n        model = Email\n        fields = ('email', 'campaign', 'language',)\n        widgets = {\n            'campaign': widgets.HiddenInput(),\n            'language': widgets.HiddenInput(),\n        }\n\n    def __init__(self, *args, **kwargs):\n        if 'campaign' in kwargs:\n            self.campaign = kwargs.pop('campaign')\n            if not 'initial' in kwargs:\n                kwargs['initial'] = {}\n            if not 'campaign' in kwargs['initial']:\n                kwargs['initial']['campaign'] = self.campaign\n            if not 'email' in kwargs['initial']:\n                kwargs['initial']['email'] = _('Your email address')\n        else:\n            raise Exception('No campaign was selected.')\n\n        super(EmailForm, self).__init__(*args, **kwargs)\n\n    def clean_email(self):\n        email = self.cleaned_data['email'].strip()\n\n        # check if the email already exists, by filtering for it and limiting\n        # it to 1 (and only 1) item\n        exists = bool(Email.objects.filter(email=email, campaign=self.campaign)[:1])\n\n        if exists:\n            raise forms.ValidationError(_('This email address was already used.'))\n        else:\n            return email\n","sub_path":"email_campaign/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":1385,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"282631731","text":"import graphene\n\nfrom ..core.fields import FilterInputConnectionField\nfrom .bulk_mutations import (\n    CompanyBulkUpdate,\n    CompanyBulkDelete\n)\nfrom .filters import CompanyFilterInput, SuccessStoryFilterInput\nfrom .mutations.company import (\n    CompanyCreate,\n    CompanyActivate,\n    CompanyPublish,\n    CompanyUpdate,\n    CompanyUpdateAdmin,\n    CompanyUnpublish,\n    CompanyDeactivate,\n    CompanyDelete,\n    CompanyAddressCreate,\n    CompanyAddressUpdate,\n    CompanyAddressDelete,\n)\nfrom .mutations.success_story import *\nfrom .mutations.representative import (\n    RepresentativeCreate,\n    RepresentativeUpdate,\n    RepresentativeDelete,\n)\nfrom .mutations.certificate import (\n    CertificateCreate,\n    CertificateUpdate,\n    CertificateDelete,\n)\nfrom .mutations.brochure import (\n    BrochureCreate,\n    BrochureUpdate,\n    BrochureDelete,\n    BrochureIndexUpdate\n)\nfrom .mutations.images import (\n    ImageCreate,\n    ImageUpdate,\n    ImageDelete,\n    ImageIndexUpdate\n)\nfrom .mutations.video import (\n    VideoCreate,\n    VideoUpdate,\n    VideoDelete,\n    VideoIndexUpdate\n)\nfrom .mutations.social_responsibility import (\n    SocialResponsibilityCreate,\n    SocialResponsibilityUpdate,\n    SocialResponsibilityDelete,\n)\nfrom .mutations.contact import (\n    ContactCreate,\n    ContactUpdate,\n    ContactDelete,\n    ContactBulkDelete\n)\nfrom .mutations.trade_show import (\n    TradeShowCreate\n)\n\nfrom .resolvers import (\n    resolve_company,\n    resolve_companies,\n    resolve_user_company,\n    resolve_user_companies,\n    resolve_representative,\n    resolve_representatives,\n    resolve_certificate,\n    resolve_certificates,\n    resolve_brochure,\n    resolve_brochures,\n    resolve_video,\n    resolve_videos,\n    resolve_social_responsibility,\n    resolve_social_responsibilities,\n    resolve_contact,\n    resolve_contacts,\n    resolve_images,\n    resolve_image,\n    resolve_industries,\n    resolve_certificate_type,\n    resolve_industry,\n    resolve_success_story,\n    resolve_success_stories,\n    resolve_company_rosetters,\n    resolve_trade_shows,\n    resolve_trade_show,\n\n)\nfrom .sorters import (\n    CompanySortingInput, \n    ContactsSortingInput, \n    SuccessStorySortingInput, \n    SocialResponsibilitySortingInput, \n    TradeShowsInput,\n    IndexSortingInput\n)\nfrom .types import (\n    Company,\n    Representative,\n    Certificate,\n    Brochure,\n    Video,\n    SocialResponsibility,\n    Contact,\n    Images,\n    Industry,\n    CertificateType,\n    SuccessStory,\n    Roetter,\n    TradeShow,\n)\nfrom .mutations.roetter import (\n    RosetterCreate,\n    RosetterUpdate,\n    RosetterDelete,\n    RosetterBulkDelete\n)\n\n\nclass ProfileQueries(graphene.ObjectType):\n    industry = graphene.Field(\n        Industry,\n        id=graphene.Argument(\n            graphene.ID, description=\"ID of the industry.\"\n        ),\n        description=\"List of company industry.\"\n    )\n    industries = graphene.List(\n        Industry,\n        description=\"List of company industries.\"\n    )\n    certificate_type = graphene.List(\n        CertificateType,\n        description=\"List of company industries.\"\n    )\n    company = graphene.Field(\n        Company,\n        id=graphene.Argument(\n            graphene.ID, description=\"ID of the company.\"\n        ),\n        slug=graphene.Argument(\n            graphene.String, description=\"slug of the company.\"\n        ),\n        description=\"Company by ID or slug.\",\n    )\n    companies = FilterInputConnectionField(\n        Company,\n        filter=CompanyFilterInput(description=\"Filtering options for companies.\"),\n        sort_by=CompanySortingInput(description=\"Sort companies.\"),\n        description=\"List of companies.\",\n    )\n    user_company = graphene.Field(\n        Company,\n        description=\"User company profiles.\",\n    )\n    user_companies = graphene.List(\n        Company,\n        description=\"List of user company profiles.\",\n    )\n    representative = graphene.Field(\n        Representative,\n        id=graphene.Argument(\n            graphene.ID, description=\"ID of the company representative.\"\n        ),\n        description=\"Company representative by ID.\",\n    )\n    representatives = FilterInputConnectionField(\n        Representative,\n        company_id=graphene.Argument(\n            graphene.ID, description=\"ID of the profile for its representative.\"\n        ),\n        description=\"List of company representatives.\",\n    )\n    certificate = graphene.Field(\n        Certificate,\n        id=graphene.Argument(graphene.ID, description=\"ID of the company certificate.\"),\n        description=\"Company certificate by ID.\",\n    )\n    certificates = FilterInputConnectionField(\n        Certificate,\n        company_id=graphene.Argument(\n            graphene.ID, description=\"ID of the company for its certificates.\"\n        ),\n        description=\"List of company certificates.\",\n    )\n    brochure = graphene.Field(\n        Brochure,\n        id=graphene.Argument(\n            graphene.ID, description=\"ID of the company brochure.\"\n        ),\n        description=\"Company brochure by ID.\",\n    )\n    brochures = FilterInputConnectionField(\n        Brochure,\n        company_id=graphene.Argument(\n            graphene.ID, description=\"ID of the company for its brochures.\"\n        ),\n        sort_by=IndexSortingInput(description=\"Sort images.\"),\n        description=\"List of company brochures.\",\n    )\n    image = graphene.Field(\n        Images,\n        id=graphene.Argument(\n            graphene.ID, description=\"ID of the company image.\"\n        ),\n        description=\"Company image by ID.\",\n    )\n    images = FilterInputConnectionField(\n        Images,\n        company_id=graphene.Argument(\n            graphene.ID, description=\"ID of the company for its images.\"\n        ),\n        sort_by=IndexSortingInput(description=\"Sort images.\"),\n        description=\"List of company images.\",\n    )\n    video = graphene.Field(\n        Video,\n        id=graphene.Argument(\n            graphene.ID, description=\"ID of the company video.\"\n        ),\n        description=\"Company video by ID.\",\n    )\n    videos = FilterInputConnectionField(\n        Video,\n        company_id=graphene.Argument(\n            graphene.ID, description=\"ID of the company for its videos.\"\n        ),\n        sort_by=IndexSortingInput(description=\"Sort images.\"),\n        description=\"List of company videos.\",\n    )\n    social_responsibility = graphene.Field(\n        SocialResponsibility,\n        id=graphene.Argument(\n            graphene.ID, description=\"ID of the company social responsibility.\"\n        ),\n        description=\"Company social responsibility by ID.\",\n    )\n    social_responsibilities = FilterInputConnectionField(\n        SocialResponsibility,\n        company_id=graphene.Argument(\n            graphene.ID, description=\"ID of the company for its social responsibilities.\"\n        ),\n        sort_by=SocialResponsibilitySortingInput(description=\"Sort pages.\"),\n        description=\"List of company social responsibilities.\",\n    )\n    contact = graphene.Field(\n        Contact,\n        id=graphene.Argument(\n            graphene.ID, description=\"ID of the contact.\"\n        ),\n        description=\"Contact by ID.\",\n    )\n    contacts = FilterInputConnectionField(\n        Contact,\n        sort_by=ContactsSortingInput(description=\"Sort pages.\"),\n        company_id=graphene.Argument(\n            graphene.ID, description=\"ID of the company for its contacts.\"\n        ),\n        description=\"List of contacts.\",\n    )\n    success_story = graphene.Field(\n        SuccessStory,\n        id=graphene.Argument(\n            graphene.ID, description=\"ID of the success story.\"\n        ),\n        description=\"success story by ID.\",\n    )\n    success_stories = FilterInputConnectionField(\n        SuccessStory,\n        sort_by=SuccessStorySortingInput(description=\"Sort pages.\"),\n        filter=SuccessStoryFilterInput(description=\"Filtering options for companies.\"),\n        description=\"List of success stories.\",\n    )\n    company_rosetters = graphene.List(Roetter, description=\"List of rosetters.\",)\n    trade_shows = FilterInputConnectionField(\n        TradeShow,\n        company_id=graphene.Argument(\n            graphene.ID, description=\"ID of the company for its contacts.\"\n        ),\n        slug=graphene.String(),\n        sort_by=TradeShowsInput(description=\"Sort pages.\"),\n        description=\"List of success stories.\",\n    )\n    trade_show = graphene.Field(\n        TradeShow,\n        id=graphene.Argument(\n            graphene.ID, description=\"ID of the success story.\"\n        ),\n        description=\"success story by ID.\",\n    )\n    \n    def resolve_trade_shows(self, info, **kwargs):\n        return resolve_trade_shows(info, **kwargs)\n\n    def resolve_trade_show(self, info, **kwargs):\n        return resolve_trade_show(info, **kwargs)\n\n    def resolve_company_rosetters(self, info, **kwargs):\n        # from ...core.utils.random_data import create_rosetter\n        # create_rosetter()\n        return resolve_company_rosetters(info, **kwargs)\n\n    def resolve_success_story(self, info, id=None):\n        return resolve_success_story(info, id)\n\n    def resolve_success_stories(self, info, **kwargs):\n        return resolve_success_stories(info, **kwargs)\n    \n    def resolve_industry(self, info, id=None):\n        return resolve_industry(info, id)\n\n    def resolve_industries(self, info, **kwargs):\n        return resolve_industries(info, **kwargs)\n\n    def resolve_certificate_type(self, info, **kwargs):\n        return resolve_certificate_type(info, **kwargs)\n\n    def resolve_company(self, info, id=None, slug=None):\n        return resolve_company(info, id, slug)\n\n    def resolve_companies(self, info, **kwargs):\n        return resolve_companies(info, **kwargs)\n\n    def resolve_user_company(self, info):\n        return resolve_user_company(info)\n\n    def resolve_user_companies(self, info):\n        return resolve_user_companies(info)\n\n    def resolve_representative(self, info, id=None):\n        return resolve_representative(info, id)\n\n    def resolve_representatives(self, info, **kwargs):\n        return resolve_representatives(info, **kwargs)\n\n    def resolve_certificate(self, info, id=None):\n        return resolve_certificate(info, id)\n\n    def resolve_certificates(self, info, **kwargs):\n        return resolve_certificates(info, **kwargs)\n\n    def resolve_brochure(self, info, id=None):\n        return resolve_brochure(info, id)\n\n    def resolve_brochures(self, info, **kwargs):\n        return resolve_brochures(info, **kwargs)\n\n    def resolve_image(self, info, id=None):\n        return resolve_image(info, id)\n\n    def resolve_images(self, info, **kwargs):\n        return resolve_images(info, **kwargs)\n\n    def resolve_video(self, info, id=None):\n        return resolve_video(info, id)\n\n    def resolve_videos(self, info, **kwargs):\n        return resolve_videos(info, **kwargs)\n\n    def resolve_social_responsibility(self, info, id=None):\n        return resolve_social_responsibility(info, id)\n\n    def resolve_social_responsibilities(self, info, **kwargs):\n        return resolve_social_responsibilities(info, **kwargs)\n\n    def resolve_contact(self, info, id=None):\n        return resolve_contact(info, id)\n\n    def resolve_contacts(self, info, **kwargs):\n        return resolve_contacts(info, **kwargs)\n\n\nclass ProfileMutations(graphene.ObjectType):\n    company_create = CompanyCreate.Field()\n    company_activate = CompanyActivate.Field()\n    company_publish = CompanyPublish.Field()\n    company_update = CompanyUpdate.Field()\n    company_update_admin = CompanyUpdateAdmin.Field()\n    company_unpublish = CompanyUnpublish.Field()\n    company_deactivate = CompanyDeactivate.Field()\n    company_delete = CompanyDelete.Field()\n    company_address_create = CompanyAddressCreate.Field()\n    company_address_update = CompanyAddressUpdate.Field()\n    company_address_delete = CompanyAddressDelete.Field()\n    representative_create = RepresentativeCreate.Field()\n    representative_update = RepresentativeUpdate.Field()\n    representative_delete = RepresentativeDelete.Field()\n    certificate_create = CertificateCreate.Field()\n    certificate_update = CertificateUpdate.Field()\n    certificate_delete = CertificateDelete.Field()\n    brochure_create = BrochureCreate.Field()\n    brochure_update = BrochureUpdate.Field()\n    brochure_delete = BrochureDelete.Field()\n    image_create = ImageCreate.Field()\n    image_update = ImageUpdate.Field()\n    image_delete = ImageDelete.Field()\n    video_create = VideoCreate.Field()\n    video_update = VideoUpdate.Field()\n    video_delete = VideoDelete.Field()\n    social_responsibility_create = SocialResponsibilityCreate.Field()\n    social_responsibility_update = SocialResponsibilityUpdate.Field()\n    social_responsibility_delete = SocialResponsibilityDelete.Field()\n    contact_create = ContactCreate.Field()\n    contact_update = ContactUpdate.Field()\n    contact_delete = ContactDelete.Field()\n    contact_bulk_delete = ContactBulkDelete.Field()\n    company_bulk_update = CompanyBulkUpdate.Field()\n    company_bulk_delete = CompanyBulkDelete.Field()\n    success_story_create = SuccessStoryCreate.Field()\n    success_story_update = SuccessStoryUpdate.Field()\n    success_story_activate = SuccessStoryActivate.Field()\n    success_story_deactivate = SuccessStoryDeactivate.Field()\n    success_story_delete = SuccessStoryDelete.Field()\n    image_index_update = ImageIndexUpdate.Field()\n    brochure_index_update = BrochureIndexUpdate.Field()\n    video_index_update = VideoIndexUpdate.Field()\n    success_story_bulk_delete = SuccessStoryBulkDelete.Field()\n    trade_show_create = TradeShowCreate.Field()\n    rosetter_create = RosetterCreate.Field()\n    rosetter_update = RosetterUpdate.Field()\n    rosetter_delete = RosetterDelete.Field()\n    rosetter_bulk_delete = RosetterBulkDelete.Field()\n\n","sub_path":"koytola/core/graphql/profile/schema.py","file_name":"schema.py","file_ext":"py","file_size_in_byte":13804,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"106553797","text":"import os\nfrom bisect import bisect_left\nfrom pathlib import Path\nfrom typing import List, Dict, Union, Optional\n\nfrom pangtreebuild.consensus.input_types import Hbmin\nfrom pangtreebuild.datamodel.Node import NodeID\nfrom pangtreebuild.datamodel.Poagraph import Poagraph\nfrom pangtreebuild.datamodel.Sequence import SequenceID, SequencePath\nfrom pangtreebuild.output.PangenomePO import NodePO, SequencePO\nfrom pangtreebuild.tools import pathtools\nimport pangtreebuild.output.PangenomePO as PangenomePO\nimport subprocess\nfrom pangtreebuild.tools import logprocess\n\ndetailed_logger = logprocess.get_logger('details')\nglobal_logger = logprocess.get_global_logger()\n\n\nclass NoConsensusError(Exception):\n    pass\n\n\nclass ConsInfo:\n    def __init__(self,\n                 fullname: str,\n                 po_consensus_id: Optional[str] = None,\n                 assigned_sequences_ids: Optional[List[SequenceID]] = None,\n                 path: Optional[SequencePath] = None):\n        self.fullname: str = fullname\n        self.po_consensus_id: str = po_consensus_id\n        self.assigned_sequences_ids: List[SequenceID] = assigned_sequences_ids\n        self.path: SequencePath = path\n\n\ndef get_consensuses(poagraph: Poagraph, sequences_ids: List[SequenceID], output_dir: Path, job_name: str,\n                    blosum_path: Path, hbmin: Hbmin, specific_consensuses_id: Optional[List[int]] = None) -> Dict[int, ConsInfo]:\n    poa_input_path = pathtools.get_child_path(output_dir, f\"{job_name}_in_pangenome.po\")\n    poa_output_path = pathtools.get_child_path(output_dir, f\"{job_name}_out_pangenome.po\")\n\n    s = PoagraphPOTranslator(poagraph, sequences_ids)\n    poa_input_content = s.get_input_po_content()\n    with open(poa_input_path, 'w') as poa_input:\n        poa_input.write(poa_input_content)\n    b_resolved = blosum_path.resolve()\n    call(po_file_path=poa_input_path,\n         hb_file_path=poa_output_path,\n         blosum_path=blosum_path.resolve(),\n         hbmin=hbmin.value)\n    with open(poa_output_path) as poa_output:\n        poa_output_lines = poa_output.readlines()\n    os.remove(poa_input_path)\n    os.remove(poa_output_path)\n    consensus_paths = s.read_consensus_paths(poa_output_lines, specific_consensuses_id)\n    return consensus_paths\n\n\ndef call(po_file_path: Path, hb_file_path: Path, blosum_path: Path, hbmin: float) -> None:\n    poa_path = pathtools.get_child_path(Path(os.path.abspath(__file__)), '../../../bin/poa').resolve()\n    detailed_logger.info(f\"Run poa! Input: {po_file_path} Output: {hb_file_path}...\")\n    command = f\"{poa_path} -read_msa {po_file_path} -hb -po {hb_file_path} {blosum_path} -hbmin {hbmin}\"\n    poa_result = subprocess.run(command, stderr=subprocess.PIPE, shell=True)\n    poa_str_output = poa_result.stderr.decode(\"ASCII\")\n    detailed_logger.info(f\"Poa output: {poa_str_output}\")\n\n\nclass PoagraphPOTranslator:\n    def __init__(self, poagraph: Poagraph, sequences_ids: List[SequenceID]):\n        self.poagraph: Poagraph = poagraph\n        self.sequences_ids: List[SequenceID] = sequences_ids\n        self.new_to_old: Dict[NodeID, NodeID] = None\n        self.old_to_new: Dict[NodeID, NodeID] = None\n        self.seq_old_to_new: Dict[SequenceID, int] = None\n        self.seq_new_to_old: Dict[int, SequenceID] = None\n\n    def get_input_po_content(self) -> str:\n        paths_to_keep = [path\n                         for seq_id in self.sequences_ids\n                         for path in self.poagraph.sequences[seq_id].paths]\n        nodes_ids_to_keep = list(set([node_id\n                                      for path in paths_to_keep\n                                      for node_id in path]))\n        sorted_nodes_ids_to_keep = sorted(nodes_ids_to_keep)\n        self.old_to_new = {node_id: i for i, node_id in enumerate(sorted_nodes_ids_to_keep)}\n        self.new_to_old = {new_node_id: old_node_id for old_node_id, new_node_id in self.old_to_new.items()}\n        self.seq_old_to_new = {seq_id: i for i, seq_id in enumerate(self.sequences_ids)}\n        self.seq_new_to_old = {i: seq_id for seq_id, i in self.seq_old_to_new.items()}\n\n        po_nodes = [NodePO(base=self.poagraph.nodes[self.new_to_old[new_node_id]].base.value,\n                           aligned_to=self._get_aligned_node(self.new_to_old[new_node_id], sorted_nodes_ids_to_keep),\n                           in_nodes=set(),\n                           sequences_ids=[]\n                           )\n                    for new_node_id in range(len(nodes_ids_to_keep))]\n\n        sequences_weight: Dict[SequenceID, int] = self.poagraph.get_sequences_weights(self.sequences_ids)\n        po_sequences = [SequencePO(name=self.seq_new_to_old[new_seq_id],\n                                   nodes_count=self.poagraph.get_sequence_nodes_count(self.seq_new_to_old[new_seq_id]),\n                                   weight=sequences_weight[self.seq_new_to_old[new_seq_id]],\n                                   consensus_id=-1,\n                                   start_node_id=self.old_to_new[\n                                       self.poagraph.sequences[self.seq_new_to_old[new_seq_id]].paths[0][0]]\n                                   )\n                        for new_seq_id in range(len(self.sequences_ids))]\n\n        for seq_id in self.sequences_ids:\n            new_seq_id = self.seq_old_to_new[seq_id]\n            for path in self.poagraph.sequences[seq_id].paths:\n                for i, node_id in enumerate(path):\n                    new_node_id = self.old_to_new[node_id]\n                    po_nodes[new_node_id].sequences_ids.append(new_seq_id)\n                    if i > 0:\n                        new_in_node_id = self.old_to_new[path[i-1]]\n                        po_nodes[new_node_id].in_nodes.add(new_in_node_id)\n\n        for node in po_nodes:\n            node.in_nodes = sorted(node.in_nodes)\n\n        return PangenomePO.poagraph_elements_to_PangenomePO(po_nodes, po_sequences, self.poagraph.datatype)\n\n    def _get_aligned_node(self, old_node_id: NodeID, sorted_nodes_ids_to_keep: List[NodeID]) -> Union[NodeID, None]:\n        aligned_to = self.poagraph.nodes[old_node_id].aligned_to\n        if aligned_to is None:\n            return None\n        while aligned_to != old_node_id:\n            if self._is_in(sorted_list=sorted_nodes_ids_to_keep, x=aligned_to):\n                return self.old_to_new[aligned_to]\n            aligned_to = self.poagraph.nodes[aligned_to].aligned_to\n        return None\n\n    def _is_in(self, sorted_list, x):\n        i = bisect_left(sorted_list, x)\n        if i != len(sorted_list) and sorted_list[i] == x:\n            return True\n        return False\n\n    @staticmethod\n    def _extract_line_value(line: str) -> str:\n        return line.split('=')[1].strip()\n\n    def read_consensus_paths(self, po_lines: List[str], specific_consensuses_ids: Optional[List[int]] = None) -> Dict[int, ConsInfo]:\n        po_lines_iterator = iter(po_lines)\n\n        for i in range(3):\n            next(po_lines_iterator)\n\n        nodes_count = int(PoagraphPOTranslator._extract_line_value(next(po_lines_iterator)))\n        paths_count = int(PoagraphPOTranslator._extract_line_value(next(po_lines_iterator)))\n\n        consensuses_in_po_lines: Dict[int, ConsInfo] = dict()\n        current_consensus_id = -1\n        for i in range(paths_count):\n            path_name = PoagraphPOTranslator._extract_line_value(next(po_lines_iterator))\n            if len(path_name) > 7 and path_name[:7] == \"CONSENS\":\n                current_consensus_id += 1\n                if specific_consensuses_ids is None or current_consensus_id in specific_consensuses_ids:\n                    detailed_consens_info_line = next(po_lines_iterator)\n                    detailed_consens_info = self._extract_line_value(detailed_consens_info_line).split(' ')\n                    consens_nodes_count = int(detailed_consens_info[0])\n                    cons_id = int(path_name[7:])\n                    if cons_id in consensuses_in_po_lines:\n                        consensuses_in_po_lines[cons_id].fullname = path_name\n                        consensuses_in_po_lines[cons_id].po_consensus_id = f\"S{str(i)}\"\n                        consensuses_in_po_lines[cons_id].path = [None] * consens_nodes_count\n                    else:\n                        consensuses_in_po_lines[cons_id] = ConsInfo(fullname=f\"CONSENS{cons_id}\",\n                                                                    assigned_sequences_ids=[],\n                                                                    po_consensus_id=f\"S{str(i)}\",\n                                                                    path=[None] * consens_nodes_count)\n            else:\n                detailed_sequence_info_line = next(po_lines_iterator)\n                detailed_sequence_info = self._extract_line_value(detailed_sequence_info_line).split(' ')\n                assigned_consensus_id = int(detailed_sequence_info[3])\n                sequence_id = self.seq_new_to_old[i]\n                if assigned_consensus_id != -1:\n                    if assigned_consensus_id in consensuses_in_po_lines:\n                        consensuses_in_po_lines[assigned_consensus_id].assigned_sequences_ids.append(sequence_id)\n                    else:\n                        consensuses_in_po_lines[assigned_consensus_id] = ConsInfo(fullname=f\"CONSENS{assigned_consensus_id}\",\n                                                                                  assigned_sequences_ids=[sequence_id])\n\n        if not consensuses_in_po_lines:\n            raise NoConsensusError(\"No consensus found in this poagraph.\")\n\n        consensuses_paths_node_counter = {c: 0 for c in consensuses_in_po_lines.keys()}\n\n        first_node_line_idx = 5 + paths_count * 2\n        new_node_id = -1\n\n        for line_idx in range(first_node_line_idx, len(po_lines)):\n            new_node_id += 1\n            for c_id in range(len(consensuses_in_po_lines)):\n                if consensuses_in_po_lines[c_id].po_consensus_id in po_lines[line_idx]:\n                    consensuses_in_po_lines[c_id].path[consensuses_paths_node_counter[c_id]] = self.new_to_old[new_node_id]\n                    consensuses_paths_node_counter[c_id] += 1\n\n        return consensuses_in_po_lines","sub_path":"pangtreebuild/consensus/poa.py","file_name":"poa.py","file_ext":"py","file_size_in_byte":10185,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"389053545","text":"import boto3\r\nimport json\r\nimport sys\r\nfrom pprint import pprint\r\nimport io\r\nfrom io import BytesIO\r\nfrom PIL import Image, ImageDraw, ImageFont\r\n\r\ndef main(file_name='image1.jpeg'):\r\n    table_csv = get_table_csv_results(file_name)\r\n    print(table_csv)\r\n    output_file = 'output.csv'\r\n\r\n    # replace content\r\n    with open(output_file, \"wt\") as fout:\r\n        fout.write(table_csv)\r\n\r\n    # show the results\r\n    print('CSV OUTPUT FILE: ', output_file)\r\n\r\ndef get_table_csv_results(file_name):\r\n    #Amazon Textract\r\n    textract= boto3.client(\r\n        service_name='textract',\r\n        region_name='us-east-2')\r\n\r\n    #Amazon s3\r\n    s3=boto3.resource('s3')\r\n    s3object=s3.Object('formdata',file_name)      #formdata is the bucketname\r\n    s3response=s3object.get();\r\n    stream=io.BytesIO(s3response['Body'].read())\r\n    image=Image.open(stream)\r\n    try:\r\n        image_binary = stream.getvalue()\r\n        response = textract.analyze_document(Document={'Bytes': image_binary},FeatureTypes=['TABLES'])\r\n        blocks_map = {}\r\n        table_blocks = []\r\n        for block in response['Blocks']:\r\n            blocks_map[block['Id']] = block\r\n            if block['BlockType'] == \"TABLE\":\r\n                table_blocks.append(block)\r\n        csv = ''\r\n        for index, table in enumerate(table_blocks):\r\n            csv += generate_table_csv(table, blocks_map, index + 1)\r\n            csv += '\\n\\n'\r\n\r\n\r\n    except Exception as e:\r\n        print (e)\r\n    return csv;\r\n\r\ndef generate_table_csv(table_result, blocks_map, table_index):\r\n    rows = get_rows_columns_map(table_result, blocks_map)\r\n\r\n    table_id = 'Table_' + str(table_index)\r\n\r\n    # get cells.\r\n    csv = 'Table: {0}\\n\\n'.format(table_id)\r\n\r\n    for row_index, cols in rows.items():\r\n\r\n        for col_index, text in cols.items():\r\n            csv += '{}'.format(text) + \",\"\r\n        csv += '\\n'\r\n\r\n    csv += '\\n\\n\\n'\r\n    print(csv);\r\n    return csv\r\n\r\n\r\ndef get_rows_columns_map(table_result, blocks_map):\r\n    rows = {}\r\n    for relationship in table_result['Relationships']:\r\n        if relationship['Type'] == 'CHILD':\r\n            for child_id in relationship['Ids']:\r\n                cell = blocks_map[child_id]\r\n                if cell['BlockType'] == 'CELL':\r\n                    row_index = cell['RowIndex']\r\n                    col_index = cell['ColumnIndex']\r\n                    if row_index not in rows:\r\n                        # create new row\r\n                        rows[row_index] = {}\r\n                        if col_index == 2:\r\n                            rows[row_index][1]=''\r\n                    # get the text value\r\n                    rows[row_index][col_index] = get_text(cell, blocks_map)\r\n\r\n    return rows\r\n\r\ndef get_text(result, blocks_map):\r\n    text = ''\r\n    if 'Relationships' in result:\r\n        for relationship in result['Relationships']:\r\n            if relationship['Type'] == 'CHILD':\r\n                for child_id in relationship['Ids']:\r\n                    word = blocks_map[child_id]\r\n                    if word['BlockType'] == 'WORD':\r\n                        text += word['Text'] + ' '\r\n                    if word['BlockType'] == 'SELECTION_ELEMENT':\r\n                        if word['SelectionStatus'] =='SELECTED':\r\n                            text +=  'X '\r\n    return text\r\n\r\nif __name__ == \"__main__\":\r\n    main()","sub_path":"ExtractionOfTextFromImage.py","file_name":"ExtractionOfTextFromImage.py","file_ext":"py","file_size_in_byte":3345,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"519393347","text":"class Solution(object):\n    cache = {}\n    def isMatch(self, s, p):\n        \"\"\"\n        :type s: str\n        :type p: str\n        :rtype: bool\n        \"\"\"\n        length = len(s)\n        if len(p) - p.count('*') > length:\n            return False\n        dp = [True] + [False] * length\n        for item in p:\n            if item != '*':\n                for n in reversed(range(length)):\n                    dp[n+1] = dp[n] and (s[n] == item or item == '?')\n            else:\n                for n in range(1, length+1):\n                    dp[n] = dp[n] or dp[n-1]\n            dp[0] = dp[0] and item == '*'\n        return dp[-1]\n","sub_path":"LeetCode/Algorithm/Python/44-Wildcard Matching.py","file_name":"44-Wildcard Matching.py","file_ext":"py","file_size_in_byte":629,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"508253011","text":"from extractor import raid_extractor, raid_extractor_by_character, find_character, clean_date, find_character_with_attendance_stats, raid_attendance_statistics\nimport click\nimport json\nimport datetime\n\n\n@click.command()\n@click.option('-e','--extractor', is_flag=True, help='Extract data from dump.csv file')\n@click.option('-c','--character', is_flag=True, help='Results of attendence by characters')\n@click.option('-f','--find', help='Find attendence for a specific character')\n@click.option('-s','--stats', help='Find attendence for a specific character and his participation statistics')\n@click.option('-r','--raid-stats', is_flag=True, help='Find attendence statistics for raids')\n@click.option('-b','--before-date', default=datetime.datetime.now(), help='Find attendence before a specific date with format: day-month-year. Example: -b 11-09-2020')\n@click.option('-a','--after-date', default=None, help='Find attendence after a specific date with format: day-month-year. Example: -b 21-06-2020')\ndef selector(extractor, find, before_date, after_date, character, stats, raid_stats):\n\n    date_range = clean_date(after_date,before_date)\n\n    if extractor:\n        click.echo(json.dumps(raid_extractor(date_range), indent=4, sort_keys=True, ensure_ascii=False))\n\n    elif character:\n        click.echo('Results by character')\n        click.echo(json.dumps(raid_extractor_by_character(date_range), indent=4, sort_keys=True, ensure_ascii=False))\n    \n    elif find:\n        click.echo('Finding player attendance ' + find)\n        click.echo(json.dumps(find_character(find, date_range), indent=4, sort_keys=True, ensure_ascii=False))\n    \n    elif stats:\n        click.echo('Finding ' + stats + ' attendance statistics')\n        click.echo(stats + \" was {0:.0%}\".format(find_character_with_attendance_stats(stats, date_range)))\n\n    elif raid_stats:\n        click.echo('Finding attendance statistics')\n        click.echo(json.dumps(raid_attendance_statistics(date_range), indent=4, sort_keys=True, ensure_ascii=False))\n\n    else:\n        click.echo('You need to choose at least one argument. Please see --help')\n\nif __name__ == '__main__':\n    selector()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2152,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"601707871","text":"# DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER\n# Copyright (c) 2018 Juniper Networks, Inc.\n# All rights reserved.\n# Use is subject to license terms.\n#\n# Author: cklewar\n\nimport time\n\nfrom jinja2 import Template\n\nfrom lib.logmsg import LogAssignTask as logmsg\nfrom lib.logmsg import LogCommon\nfrom lib.tasks.task import Task\nimport lib.constants as c\nfrom lib.tools import Tools\n\n\nclass AssignTask(Task):\n    CHECK_SCHEMA = False\n    TASK_TYPE = c.TASK_TYPE_PROVISION\n    TASK_VERSION = 1.0\n\n    def __init__(self, sample_device=None, shared=None):\n\n        super(AssignTask, self).__init__(sample_device=sample_device, shared=shared)\n        self.logger.debug(Tools.create_log_msg(self.task_name, self.sample_device.deviceSerial,\n                                               LogCommon.IS_SUBCLASS.format(self.task_name,\n                                                                            issubclass(AssignTask, Task))))\n\n    def pre_run_task(self):\n        pass\n\n    def run_task(self):\n        self.logger.info(Tools.create_log_msg(self.task_name, self.sample_device.deviceSerial,\n                                              logmsg.ASSIGN_INIT.format(self.sample_device.deviceName)))\n        self.sample_device.deviceTasks.taskState[self.task_name] = 'Get policy'\n        policy_data = c.SRC.get_fw_policy_by_name(self.sample_device.deviceName)\n\n        if policy_data == c.SRC_RESPONSE_FAILURE:\n\n            self.sample_device.deviceTasks.taskState[self.task_name] = c.SRC_RESPONSE_FAILURE\n            self.shared[c.TASK_SHARED_STATE] = c.TASK_STATE_RESULT_FAILURE\n\n        else:\n            self.sample_device.deviceTasks.taskState[self.task_name] = 'Get Device'\n            time.sleep(c.conf.JUNOSSPACE.RestTimeout)\n            device_data = c.SRC.get_device_by_name(self.sample_device.deviceName)\n\n            if device_data == c.SRC_RESPONSE_FAILURE:\n\n                self.sample_device.deviceTasks.taskState[self.task_name] = c.SRC_RESPONSE_FAILURE\n                self.shared[c.TASK_SHARED_STATE] = c.TASK_STATE_RESULT_FAILURE\n\n            else:\n\n                time.sleep(c.conf.JUNOSSPACE.RestTimeout)\n                try:\n                    with open(c.conf.JUNOSSPACE.TemplateDir + self.grp_cfg.TASKS.Provision.Assign.AssignTemplate, 'r') as file:\n\n                        template = Template(file)\n                        BODY = template.render(moid=device_data['moid'])\n\n                except IOError as ioe:\n                    self.logger.info(Tools.create_log_msg(self.task_name, self.sample_device.deviceSerial,\n                                                          logmsg.ASSIGN_TEMPLATE_FILE_NOK.format(\n                                                              self.grp_cfg.TASKS.Provision.Assign.AssignTemplate,\n                                                              ioe.message)))\n                    self.sample_device.deviceTasks.taskState[self.task_name] = c.TASK_STATE_FAILED\n                    self.shared[c.TASK_SHARED_STATE] = c.TASK_STATE_RESULT_FAILURE\n                    return\n\n                if c.conf.JUNOSSPACE.Version == 'space151':\n                    self.sample_device.deviceTasks.taskState[self.task_name] = 'Lock policy'\n                    c.SRC.lock_fw_policy(policy_data['pid'])\n\n                time.sleep(c.conf.JUNOSSPACE.RestTimeout)\n                self.sample_device.deviceTasks.taskState[self.task_name] = 'Assign policy'\n                c.SRC.assign_device(BODY, policy_data['pid'])\n                time.sleep(c.conf.JUNOSSPACE.RestTimeout)\n\n                if c.conf.JUNOSSPACE.Version == 'space151':\n                    self.sample_device.deviceTasks.taskState[self.task_name] = 'Unlock policy'\n                    c.SRC.unlock_fw_policy(policy_data['pid'])\n                self.logger.info(Tools.create_log_msg(self.task_name, self.sample_device.deviceSerial,\n                                                      logmsg.ASSIGN_DONE.format(policy_data['pid'],\n                                                                                self.sample_device.deviceName)))\n                self.sample_device.deviceTasks.taskState[self.task_name] = c.TASK_STATE_DONE\n                self.shared[c.TASK_SHARED_STATE] = c.TASK_STATE_RESULT_DONE\n\n    def post_run_task(self):\n        pass\n","sub_path":"lib/tasks/provision/external/assign.py","file_name":"assign.py","file_ext":"py","file_size_in_byte":4291,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"400156300","text":"import sys\n\nr_d = {\n    1: 31,\n    3: 31,\n    5: 31,\n    7: 31,\n    8: 31,\n    10: 31,\n    12: 31,\n\n    4: 30,\n    6: 30,\n    9: 30,\n    11: 30,\n\n    2: 28\n}\n\nhh = int(sys.stdin.readline().strip())\n\n\ndef gui(string: str) -> str:\n    string = str(string)\n    if len(string) < 2:\n        string = '0' + string\n    return string\n\n\ndef add(num_list: str):\n    y, m, d = num_list\n    d += 1\n    if d > r_d[m]:\n        d = 1\n        m += 1\n    if m > 12:\n        m = 1\n        y += 1\n\n    print('-'.join([gui(y), gui(m), gui(d)]))\n\n\nfor _ in range(hh):\n    nums = [int(c) for c in sys.stdin.readline().strip().split(' ')]\n    add(nums)\n","sub_path":"牛客/gan/日期类.py","file_name":"日期类.py","file_ext":"py","file_size_in_byte":630,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"205780508","text":"\"\"\"Main Menu Implementation.\"\"\"\n\n\nimport constants\nfrom .menu import CLIMenu, CLIMenuItem\nfrom .level import LevelMenu\nfrom enum import Enum, unique\n\n\nclass MainMenu(CLIMenu):\n\n    \"\"\"The main menu.\"\"\"\n\n    @unique\n    class Items(Enum):\n        START_GAME = 1\n        QUIT = 2\n\n    def __init__(self, mgr):\n        \"\"\"Initialize the class.\"\"\"\n        buf = [\n            '-' * 60,\n            '',\n            'Welcome to {}'.format(constants.GAMENAME),\n            '',\n            '-' * 60,\n            '',\n            '$ ls',\n            CLIMenuItem('    start', '$ start', MainMenu.Items.START_GAME),\n            CLIMenuItem('    exit', '$ exit', MainMenu.Items.QUIT)\n        ]\n        super().__init__(mgr, buf)\n\n    def _on_choose(self, item):\n        if item == MainMenu.Items.START_GAME:\n            self._mgr.push(LevelMenu(self._mgr))\n        elif item == MainMenu.Items.QUIT:\n            # The main menu should be the last gamestate on the stack, so\n            # popping it should cause the game to exit.\n            self._mgr.pop()\n","sub_path":"menu/mainmenu.py","file_name":"mainmenu.py","file_ext":"py","file_size_in_byte":1044,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"168775133","text":"# -*- coding:utf-8 -*-\n\nclass TreeNode:\n    def __init__(self, x):\n        self.val = x\n        self.left = None\n        self.right = None\n\nclass Solution:\n    \"\"\"\n    Problem:\n    输入一棵二叉搜索树，将该二叉搜索树转换成一个排序的双向链表。\n    要求不能创建任何新的结点，只能调整树中结点指针的指向。\n    -----------------------------------------\n    Think:\n    问题转换：在不创建任何新节点的情况下，将一颗二叉搜索树，转换成一个排好序的双向链表。\n\n\n    -----------------------------------------\n    Idea:\n    1、中序遍历就是有序的。有递归和非递归版本。\n        中序遍历：先访问最左下角，然后如果没有左下角，则访问元素，然后跳转right。如果没有right\n            则继续回溯。\n        最后要进行链表重新定位指针。\n\n\n    -----------------------------------------\n    Code:\n\n\n    -----------------------\n    运行时间：\n    占用内存：\n    \"\"\"\n    def Convert(self, pRootOfTree):\n        #鲁棒性\n        if (pRootOfTree is None):\n            return None\n\n        #简单实例\n        if (pRootOfTree.left is None and pRootOfTree.right is None):\n            return pRootOfTree\n\n        #非递归中序遍历\n        help_stack = []\n        twohead_list = []\n        p = pRootOfTree\n        while (p is not None) or (len(help_stack) > 0):\n            while p is not None:    #左孩子不为空，则入栈\n                help_stack.append(p)\n                p = p.left\n\n            if len(help_stack) > 0: #栈内还存在元素\n                p = help_stack.pop()\n                twohead_list.append(p)\n                p = p.right #转向，\n\n        #链表重新定位指针\n        for i in range(len(twohead_list) - 1):\n            j = i + 1\n            twohead_list[i].right = twohead_list[j]\n            twohead_list[j].left = twohead_list[i]\n\n        return twohead_list[0]\n\nif __name__ == \"__main__\":\n    h10 = TreeNode(10)\n    h5 = TreeNode(5)\n    h12 = TreeNode(12)\n    h4 = TreeNode(4)\n    h7 = TreeNode(7)\n    h10.left = h5\n    h10.right = h12\n    h5.left = h4\n    h5.right = h7\n\n    sol = Solution()\n    twohead_list = sol.Convert(h10)\n    for i in range(len(twohead_list)):\n        print(twohead_list[i].left)\n","sub_path":"code/train/箭指Offer/src/二叉搜索树与双向链表.py","file_name":"二叉搜索树与双向链表.py","file_ext":"py","file_size_in_byte":2311,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"232511367","text":"import numpy as np\n\ngrid = np.arange(2, 30).reshape((2,14))\n\nx = np.array([1,2,4])\nx.reshape((1,3))\nx[:, np.newaxis]\n\nx = np.arange(5, 10)\ny = np.array([2,5,7,9,0,12,123,123,123,123,12,31,23])\nnp.concatenate((x,y)).reshape((5,2))\n\n\nx = np.arange(1, 10).reshape((3,3))\ny3x3 = np.arange(11, 20).reshape((3,3))\ny3x4 = np.arange(11, 23).reshape((3,4))\nnp.concatenate((x,y3x3))\nnp.concatenate((x,y3x4), axis=1)\n\nnp.vstack((x,y3x4))\nnp.hstack((x,y3x4))\n\nx = np.arange(6,29)\nx1, x2, x3 = np.split(x, [6, 20])\n\nx = np.arange(50).reshape((5,10))\nl = np.vsplit(x, [2, 3])\nl[0]\nl[1]\nl[2]\n\n\nx = np.linspace(0, np.pi, 3)\ny = np.arange(0, 9, np.pi)\nz = np.zeros((3))\nnp.multiply(x,y,out=z)\n\nx = np.linspace(0, np.pi, 10)\ny = np.zeros((20))\nnp.power(3,x,out=y[::2])\n\nx = np.arange(0,8)\nnp.add.reduce(x)\nnp.add.accumulate(x)\n\nnp.set_printoptions(linewidth=500)\nx = np.arange(1, 26)\nnp.power.outer(x,2)\nnp.multiply.outer(x,x)\n\n\n\n\nL = np.random.random(100)\nsum(L)\nnp.sum(L) # more quikly\n\n\nM = np.random.random((3, 4))\nM.max(axis=1)\n\nheights = np.array([189, 170, 189, 163, 183, 171, 185, 168, 173, 183, 173, 173, 175, 178, 183, 193, 178, 173,\n                    174, 183, 183, 168, 170, 178, 182, 180, 183, 178, 182, 188, 175, 179, 183, 193, 182, 183,\n                    177, 185, 188, 188, 182, 185])\n\nprint(\"Mean height:\", heights.mean())\nprint(\"Standard deviation:\", heights.std())\nprint(\"Minimum height: \", heights.min())\nprint(\"Maximum height:\", heights.max())\nprint('25th percentile:', np.percentile(heights, 25))\nprint('Median:', np.median(heights))\nprint('75th percentile', np.percentile(heights, 75))\n\nimport matplotlib.pyplot as plt\nimport seaborn; seaborn.set()\n\nplt.hist(heights)\nplt.title('Height Distribution of US Presidents')\n\nplt.xlabel('height (cm)')\nplt.ylabel('number')\nplt.show()\n\n\na = np.arange(3)\nb = np.arange(3)[:, np.newaxis]\na + b\n\n\nx = np.random.random((10,3))\nXmean = x.mean(0)\nnx = x - Xmean\nnx.mean(0)\n\nrand = np.random.RandomState(42)\nX = rand.rand(10, 2)\n\n\ndist_sq = np.sum((X[:,np.newaxis,:] - X[np.newaxis,:,:]) ** 2, axis=-1)\n\nX[:, np.newaxis] - X[np.newaxis, :]\ndist_sq.diagonal()\nnearest = np.argsort(dist_sq, axis=1)\nK = 2\nnearest_partition = np.argpartition(dist_sq, K+1, axis=1)\nnearest_partition[:, :K+1]\n\na = np.arange(0,10)[::-1]\na = np.array([4,1,8,2,1,4])\na1 = np.argpartition(a, 2)\nprint(a)\nprint(a1)\n\n\ntanks = np.array([\n    (5,5),\n    (7,9),\n    (10,10),\n    (3,3),\n    (3,16),\n    (14,16),\n])\n\ndiff = tanks[:, np.newaxis] - tanks[np.newaxis, :]\ndiff = diff ** 2\n\nnp.sum(diff, axis=-1)\ndist_sq = np.sum((tanks[:, np.newaxis] - tanks[np.newaxis, :]) ** 2, axis=-1)\nK = 3\nnearest_partition = np.argpartition(dist_sq, K+1, axis=1)[:, :K+1]\nnearest_sorted = np.argsort(dist_sq, axis=1)[:, :K+1]\n\nnearest_sorted[nearest_sorted[:, 0] == 5]\n","sub_path":"numpy_/1.shapes.py","file_name":"1.shapes.py","file_ext":"py","file_size_in_byte":2768,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"236611371","text":"from collections import defaultdict\n\nimport task\n\n# Question: for which variants (StateVariable,\n# InstantiatedStateVariable, Fact) do we need to store \"believers\"?\n# Working assumptions:  \n#\n# StateVariable: no need - this is a general\n# description which can always be used with arbitrary believers \n#\n# InstantiatedStateVariable: makes sense - the believers are\n# specific (grounded) arguments that we want to keep separate\n#\n# Fact: no need - this is essentially just the combination of an\n# InstantiatedStateVariable with a specific value. All info about\n# the believers is in the InstantiatedStateVariable\n\n\nclass StateVariable(object):\n    \"\"\"\n\n    \"\"\"\n\n    def __init__(self, name, parameters, value_type):\n        \"\"\"\n        \n        Arguments:\n        - `name`:\n        - `parameters`:\n        - `type`:\n        \"\"\"\n        self._name = name\n        self._parameters = parameters\n        self._value_type = value_type\n        \n    def is_equality_constraint(self):\n        return self._name == \"=\"\n\n\n\nclass InstantiatedStateVariable(object):\n    \"\"\"\n\n    \"\"\"\n\n    def __init__(self, state_variable, arguments, believers, modality=\"\"):\n        self._state_variable = state_variable\n        self._arguments = arguments\n        self._believers = believers\n        self._modality = modality\n        if believers and not modality:\n            self._modality = \"K\"\n\n    def get_believers(self):\n        return self._believers\n\n    def as_mapl_str(self, parens=True, include_believers=True):\n        elmts = [self._state_variable._name] + self._arguments\n        mapl_str = \" \".join(elmts)\n        if include_believers:\n            believers = self.get_believers()\n            if believers:\n                believers_str = \" \".join(str(believer) for believer in believers)\n                mapl_str = \"%s %s (%s)\" % (self.modality, believers_str, mapl_str)\n        if parens:\n            mapl_str = \"(%s)\" % mapl_str\n        return mapl_str\n        \n    def is_equality_constraint(self):\n        return self._state_variable.is_equality_constraint()\n\n\nclass Fact(object):\n    \"\"\"\n\n    \"\"\"\n\n    def __init__(self, instantiated_state_var, value):\n        \"\"\"\n        \n        Arguments:\n        - `instantiated_state_var`:\n        - `value`:\n        \"\"\"\n        assert isinstance(instantiated_state_var, InstantiatedStateVariable) \n        self._instantiated_state_var = instantiated_state_var\n        if isinstance(value, tuple):\n            assert len(value) == 1, \"No support for value tuples in state variables any more! Sorry!\"\n            value = value[0]\n        self._value = value\n\n    def copy(self):\n        return Fact(self._instantiated_state_var, self._value)  # when is a deep copy needed?\n    \n    def get_believers(self):\n        return self._instantiated_state_var.get_believers()\n        \n    def as_tuple(self):\n        return (self._instantiated_state_var, self.value)\n\n    def as_mapl_str(self, parens=True):\n        svar_str = self._instantiated_state_var.as_mapl_str(parens=False, include_believers=False)\n        val_str = str(self._value)\n        believers = self.get_believers()\n        if believers:\n            believers_str = \" \".join(str(believer) for believer in believers)\n            mapl_str = \"B (%s) (%s)\" % (believers_str, svar_str)\n        else:\n            mapl_str = \"%s : %s\" % (svar_str, val_str)\n        if parens:\n            mapl_str = \"(%s)\" % mapl_str\n        return mapl_str\n\n    def as_pddl_str(self, parens=True, use_direct_k=False, drop_boolean_values=False):\n        svar_name = self._instantiated_state_var._state_variable._name\n        args_str = \" \".join(self._instantiated_state_var._arguments)\n        val_str = str(self._value)\n        modality = self._instantiated_state_var._modality\n        believers = self.get_believers()\n        if believers:\n            if use_direct_k and modality == \"kval\":\n                modality = \"kvald\"\n            believers_str = \" \".join(str(believer) for believer in believers)\n            pddl_str = \"%s__%s %s %s %s\" % (modality, svar_name, believers_str, args_str, val_str)\n        else:\n            pddl_str = \"%s %s %s\" % (svar_name, args_str, val_str)\n        if parens:\n            pddl_str = \"(%s)\" % pddl_str\n        return pddl_str\n        \n\n    @classmethod\n    def ensure_fact(cls, fact_like):\n        \"\"\"fact_like is either a Fact already or a (key,value)\n        tuple. Convert into a Fact if necessary.\"\"\"\n        if isinstance(fact_like, Fact):\n            return fact_like\n        fact = Fact(*fact_like)  # whether this conversion is possible is checked in Fact.__init__()\n        return fact\n\n    @classmethod\n    def ensure_tuple(cls, fact_like):\n        \"\"\"fact_like is either a Fact already or a (key,value)\n        tuple. Convert into a tuple if necessary.\"\"\"\n        if isinstance(fact_like, Fact):\n            return fact_like._instantiated_state_var, fact_like._value\n        assert len(fact_like) == 2 and isinstance(fact_like[0], InstantiatedStateVariable)\n        key, value = fact_like\n        return key, value\n\n    def is_equality_constraint(self):\n        return self._instantiated_state_var.is_equality_constraint()\n\n        \nclass State(object):\n    \"\"\"\n\n    \"\"\"\n\n    def __init__(self, atask=None, facts=[]):\n        \"\"\"\n        \n        Arguments:\n        - `atask`: which planning task does this state belong to?\n        - `facts`: initial facts\n        \"\"\"\n        assert atask is None or isinstance(atask, task.Task)\n        self._task = atask\n        self._facts = {}  # what is the best data structure for efficient storage and retrieval?\n        for fact in facts:\n            self.add_fact(fact)\n        # the following \"views\" should be computed initally instead of\n        # stored. More costly but more open to changes while still in flux\n#         self._dict = {}  # as key/value pairs\n#         self._assignments = set()  # as Facts\n#         self._belief_partitions = defaultdict(set)  #\n#         self._evidence_partitions = {} \n\n    def add_fact(self, fact):\n        \"\"\"\n        \n        Arguments:\n        - `fact`: either a Fact or an (InstantiatedStateVariable, value) tuple\n        \"\"\"\n        key, value = Fact.ensure_tuple(fact)\n        self._facts[key] = value\n        # add to \"views\" directly here (later!)\n    \n    def get_facts(self):\n        # get from \"fact view\" (later!)\n        return (Fact(key, value) for (key, value) in self._facts.items())\n\n    def __setitem__(self, key, value):\n        assert isinstance(key, InstatiatedStateVariable)\n        fact = Fact(key, value)\n        self._facts.add(fact)\n        self._belief_partitions[key.believers].add(fact)\n        dict.__setitem__(self, key, value)\n        \n    def __delitem__(self, key):\n        if key not in self:\n            return\n        value = self[key]\n        fact = Fact(key, value)\n        self._facts.remove(fact)\n        self._belief_partitions[key.believers].remove(fact)\n        dict.__delitem__(self, key)\n\n\n\n","sub_path":"schools/css-2010/team-blue/subarchitectures/planner.sa/src/python/standalone/state.py","file_name":"state.py","file_ext":"py","file_size_in_byte":6944,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"649552731","text":"\"\"\"\nWHAT: A class which handles the logic involved with the clsAgentView\n    * validates user request body\n    * validates knowledge provider response body\n    * applies query_graph from user request body after being validated\n    * applies knowledge_graph from the knowledge provider response body after being validated\n    * creates the results which maps nodes and edges from the query_graph and knowledge_graph\nWHY: Need a class to handle data specific logic outside of clsAgentView to separate concerns\nASSUMES: 'Agent' is short for 'Explanatory Autonomous Relay Agent'\nFUTURE IMPROVEMENTS: Speed up generateResults() method\nWHO: SL 2020-09-10\n\"\"\"\n\nfrom apis.common.utils.modTextUtils import isNullOrWhiteSpace\nfrom collections import OrderedDict\n\nexplanations = {\n    0: \"reserved for textual description\",  # default\n    1: \"Disease-Gene association was identified via MAGMA_GENE (a standard method for identifying gene-condition associations from variant-level associations using proximity-based assignments from GWAS data) AND was also identified by INTEGRATED_GENETICS (an experimental method for identifying gene-condition associations using a novel combination of established methods). INTEGRATED_GENETICS is still in development and undergoing optimization; false-positives may be present.\\nConfirmation of the association with both available methods from this KP gives us high confidence in these results.\",\n    2: \"Disease-Gene association was identified via MAGMA_GENE (a standard method for identifying gene-condition associations from variant-level associations using proximity-based assignments from GWAS data), but was not confirmed by INTEGRATED_GENETICS (an experimental method that for identifying gene-condition associations using a novel combination of established methods).\\nINTEGRATED_GENETICS is still in development and undergoing optimization; false-positives may be present. Considering MAGMA_GENE is a standard method in the field, we still maintain acceptable confidence in these results.\",\n    3: \"Disease-Gene association was identified only by INTEGRATED_GENETICS (an experimental method for identifying gene-condition associations using a novel combination of established methods). Results were not confirmed using the standard MAGMA_GENE method.\\nINTEGRATED_GENETICS is still in development and undergoing optimization; false-positives may be present. For this reason, we consider these results to be the associations with lowest confidence.\",\n}\n\n\nclass clsQuery:\n    \"\"\"\n    See header\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"\n        Constructor\n        \"\"\"\n\n        # don't change these variable names, they are exposed to the client\n        self.query_graph = None  # the user supplied information\n        self.knowledge_graph = None  # the information retrieved from the outbound api call\n        self.results = None  # the created mapping between the query_graph and knowledge_graph\n\n    @staticmethod\n    def userRequestBodyIsValid(body: dict):\n        \"\"\"\n        A function to evaluate whether the JSON body received from the client conforms to the proper input standard.\n        :param body: A dictionary representing a JSON body\n        :return: Boolean: True meaning the body is valid, False meaning the body is not valid\n        \"\"\"\n\n        if type(body) != dict: return False\n        if sorted(list(body.keys())) != ['message']: return False\n\n        if type(body['message']) != dict: return False\n        if sorted(list(body['message'].keys())) != ['query_graph']: return False\n\n        if type(body['message']['query_graph']) != dict: return False\n        if sorted(list(body['message']['query_graph'].keys())) != ['edges', 'nodes']: return False\n\n        edges = body['message']['query_graph']['edges']\n        if type(edges) != list: return False\n        for edge in edges:\n            if type(edge) != dict: return False\n\n        nodes = body['message']['query_graph']['nodes']\n        if type(nodes) != list: return False\n        for node in nodes:\n            if type(node) != dict: return False\n\n        return True\n\n    @staticmethod\n    def userRequestBodyIsSupported(body: dict):\n        \"\"\"\n        A function to evaluate whether the valid response is supported by our api\n        :param body: A dictionary representing a JSON body\n        :return: Boolean: True meaning our api supports these parameters, False meaning our api does not support these parameters\n        \"\"\"\n        edges = body['message']['query_graph']['edges']\n        if len(edges) != 1: return False\n        edge = edges[0]\n        if type(edge) != dict: return False\n        if sorted(list(edge.keys())) != ['id', 'source_id', 'target_id', 'type']: return False\n        if isNullOrWhiteSpace(edge['id']): return False\n        if isNullOrWhiteSpace(edge['source_id']): return False\n        if isNullOrWhiteSpace(edge['target_id']): return False\n        if edge['type'] != 'associated': return False\n\n        nodes = body['message']['query_graph']['nodes']\n        if len(nodes) != 2: return False\n\n        geneNodeFound = False\n        diseaseNodeFound = False\n        geneId = None\n        diseaseId = None\n        validCuries = [\"EFO:0000249\", \"EFO:0000253\", \"EFO:0000270\", \"EFO:0000275\", \"EFO:0000289\", \"EFO:0000341\",\n                       \"EFO:0000378\", \"EFO:0000384\", \"EFO:0000401\", \"EFO:0000551\", \"EFO:0000612\", \"EFO:0000692\",\n                       \"EFO:0000729\", \"EFO:0001359\", \"EFO:0001360\", \"EFO:0001365\", \"EFO:0002506\", \"EFO:0003144\",\n                       \"EFO:0003761\", \"EFO:0003767\", \"EFO:0003884\", \"EFO:0003931\", \"EFO:0004190\", \"EFO:0004305\",\n                       \"EFO:0004312\", \"EFO:0004314\", \"EFO:0004326\", \"EFO:0004338\", \"EFO:0004339\", \"EFO:0004340\",\n                       \"EFO:0004342\", \"EFO:0004343\", \"EFO:0004458\", \"EFO:0004462\", \"EFO:0004465\", \"EFO:0004466\",\n                       \"EFO:0004469\", \"EFO:0004471\", \"EFO:0004501\", \"EFO:0004502\", \"EFO:0004509\", \"EFO:0004518\",\n                       \"EFO:0004530\", \"EFO:0004532\", \"EFO:0004533\", \"EFO:0004534\", \"EFO:0004535\", \"EFO:0004541\",\n                       \"EFO:0004555\", \"EFO:0004570\", \"EFO:0004574\", \"EFO:0004611\", \"EFO:0004612\", \"EFO:0004614\",\n                       \"EFO:0004615\", \"EFO:0004616\", \"EFO:0004623\", \"EFO:0004631\", \"EFO:0004683\", \"EFO:0004698\",\n                       \"EFO:0004703\", \"EFO:0004704\", \"EFO:0004705\", \"EFO:0004713\", \"EFO:0004735\", \"EFO:0004736\",\n                       \"EFO:0004741\", \"EFO:0004761\", \"EFO:0004808\", \"EFO:0004838\", \"EFO:0004844\", \"EFO:0004861\",\n                       \"EFO:0004997\", \"EFO:0005000\", \"EFO:0005001\", \"EFO:0005055\", \"EFO:0005058\", \"EFO:0005093\",\n                       \"EFO:0005094\", \"EFO:0005208\", \"EFO:0005246\", \"EFO:0005271\", \"EFO:0005665\", \"EFO:0005680\",\n                       \"EFO:0005763\", \"EFO:0006335\", \"EFO:0006336\", \"EFO:0006340\", \"EFO:0006807\", \"EFO:0006831\",\n                       \"EFO:0006832\", \"EFO:0007630\", \"EFO:0007778\", \"EFO:0007788\", \"EFO:0007789\", \"EFO:0007800\",\n                       \"EFO:0007817\", \"EFO:0007828\", \"EFO:0007878\", \"EFO:0007929\", \"EFO:0008000\", \"EFO:0008036\",\n                       \"EFO:0008037\", \"EFO:0008039\", \"EFO:0008328\", \"EFO:0008373\", \"EFO:0008455\", \"EFO:0008456\",\n                       \"EFO:0008595\", \"EFO:0008596\", \"EFO:0009270\", \"EFO:0009282\", \"EFO:0009283\", \"EFO:0009284\",\n                       \"EFO:0009718\", \"EFO:0009765\", \"EFO:0009767\", \"EFO:0009768\", \"EFO:0009769\", \"EFO:0009770\",\n                       \"EFO:0009792\", \"EFO:0009793\", \"EFO:0009881\", \"EFO:0009961\", \"EFO:0010074\", \"EFO:0010075\",\n                       \"EFO:0010076\", \"EFO:0010111\", \"EFO:0010112\", \"EFO:0010114\", \"EFO:0010115\", \"EFO:0010116\",\n                       \"EFO:0010117\", \"EFO:0010118\", \"EFO:0010119\", \"EFO:0010120\", \"EFO:0010177\", \"EFO:0010178\",\n                       \"EFO:0010465\", \"EFO:0010555\", \"EFO:1000783\", \"EFO:1000786\", \"EFO:1001506\",\n                       'MONDO:0004981', 'MONDO:0005010', 'MONDO:0005300', 'MONDO:0007455']\n        for node in nodes:\n            if sorted(list(node.keys())) == ['id', 'type'] and node['type'] == 'gene':\n                if isNullOrWhiteSpace(node['id']): return False\n                geneNodeFound = True\n                geneId = node['id']\n            elif sorted(list(node.keys())) == ['curie', 'id', 'type'] and node['type'] == 'disease':\n                if isNullOrWhiteSpace(node['id']): return False\n                if node['curie'] not in validCuries: return False\n                diseaseNodeFound = True\n                diseaseId = node['id']\n            else:\n                return False\n        if not geneNodeFound or not diseaseNodeFound: return False\n        if geneId != edge['target_id'] or diseaseId != edge['source_id']: return False\n\n        return True\n\n    @staticmethod\n    def knowledgeProviderResponseBodyIsValid(body: dict):\n        \"\"\"\n        A function to evaluate whether the JSON body received from the knowledge provider conforms to our assumptions.\n        :param body: A dictionary representing a JSON body\n        :return: Boolean: True meaning the body is valid, False meaning the body is not valid (the knowledge provider has changed their api!)\n        \"\"\"\n\n        if type(body) != dict: return False\n        if sorted(list(body.keys())) != ['knowledge_graph', 'query_graph', 'results']: return False\n\n        if type(body['knowledge_graph']) != dict: return False\n        if sorted(list(body['knowledge_graph'].keys())) != ['edges', 'nodes']: return False\n\n        edges = body['knowledge_graph']['edges']\n        if type(edges) != list: return False\n        for edge in edges:\n            if type(edge) != dict: return False\n            if sorted(list(edge.keys())) != ['id', 'score', 'score_direction', 'score_name', 'source_id', 'target_id', 'type']: return False\n\n        nodes = body['knowledge_graph']['nodes']\n        if type(nodes) != list: return False\n        for node in nodes:\n            if type(node) != dict: return False\n            if sorted(list(node.keys())) != ['id', 'type']: return False\n\n        return True\n\n    def applyQueryGraphFromUserRequestBody(self, body: dict):\n        \"\"\"\n        Assigns a portion of the JSON request body from the client to the query_graph property\n        :param body: A dictionary representing a JSON body\n        :return: None\n        \"\"\"\n        self.query_graph = body['message']['query_graph']\n\n    def applyKnowledgeGraphFromKnowledgeProviderResponseBody(self, body: dict):\n        \"\"\"\n        Assigns a portion of the JSON response body from the knowledge provider to the knowledge_graph property\n        :param body: A dictionary representing a JSON body\n        :return: None\n        \"\"\"\n        self.knowledge_graph = body['knowledge_graph']\n\n    def generateTargetIdExplanationCounts(self):\n        \"\"\"\n        Generates a dictionary of where the key is a target id and the value is another dictionary with the key as a explanation code and value is the count\n        :return: dict\n        \"\"\"\n        targetIdExplanationCounts = {}\n        for knowledgeEdge in self.knowledge_graph['edges']:\n            targetId = knowledgeEdge['target_id']\n            edgeId = knowledgeEdge['id']\n            if targetId not in targetIdExplanationCounts:\n                targetIdExplanationCounts[targetId] = {code: 0 for code in explanations.keys()}  # initialize keys with 0 as value\n            if edgeId.upper().startswith(\"MAGMA_GENE\"):\n                targetIdExplanationCounts[targetId][2] += 1\n            elif edgeId.upper().startswith(\"INTEGRATED_GENETICS\"):\n                targetIdExplanationCounts[targetId][3] += 1\n            else:\n                targetIdExplanationCounts[targetId][0] += 1\n        return targetIdExplanationCounts\n\n    @staticmethod\n    def resolveTargetIdExplanationCount(targetIdExplanationCount: dict):\n        \"\"\"\n        Returns the final explanation code based on the observed counts of the target id explanation codes\n        :param targetIdExplanationCount: A dictionary representing the key as the explanation code and the value as the observation count\n        :return: explanation code as integer\n        :raises: Attribute error if it does not resolve\n        \"\"\"\n        if targetIdExplanationCount[2] > 0 and targetIdExplanationCount[3] > 0:\n            return 1\n        elif targetIdExplanationCount[2] > 0:\n            return 2\n        elif targetIdExplanationCount[3] > 0:\n            return 3\n        elif targetIdExplanationCount[0] > 0:\n            return 0\n        else:\n            raise AttributeError(\"Target explanation codes could not resolve for: %s\" % str(targetIdExplanationCount))\n\n    def generateResults(self):\n        \"\"\"\n        Creates the results which maps nodes and edges from the query_graph and knowledge_graph.\n        This is in-lieu of the results given by the knowledge provider, those are discarded.\n        :return: None\n        \"\"\"\n        targetIdExplanationCounts = self.generateTargetIdExplanationCounts()\n\n        self.results = []\n        queryEdge = self.query_graph['edges'][0]\n        for knowledgeEdge in self.knowledge_graph['edges']:\n            result = OrderedDict()\n            result['edge_bindings'] = [{'kg_id': knowledgeEdge['id'], 'qg_id': queryEdge['id']}]\n            result['node_bindings'] = [\n                {'kg_id': knowledgeEdge['source_id'], 'qg_id': queryEdge['source_id']},\n                {'kg_id': knowledgeEdge['target_id'], 'qg_id': queryEdge['target_id']}\n            ]\n            explanationCode = self.resolveTargetIdExplanationCount(targetIdExplanationCounts[knowledgeEdge['target_id']])\n            result['Explanatory_Agent_Ranking'] = explanationCode\n            result['Explanatory_Agent_Explanation'] = explanations[explanationCode]\n            self.results.append(result)\n\n    def generateEmptyKnowledgeGraph(self):\n        \"\"\"\n        Generates an empty knowledge graph for when the userRequestBody is valid but not supported by our api\n        :return: None\n        \"\"\"\n        self.knowledge_graph = {\"edges\": [], \"nodes\": []}\n\n    def generateEmptyResults(self):\n        \"\"\"\n        Generates an empty results for when the userRequestBody is valid but not supported by our api\n        :return: None\n        \"\"\"\n        self.results = []\n","sub_path":"Agent/src/apis/v0_9/models/clsQuery.py","file_name":"clsQuery.py","file_ext":"py","file_size_in_byte":14204,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"642189787","text":"from random import randint,choice\nplaying= True\nwhile playing:\n    x=randint(1,10)\n    y=randint(1,10)\n    res=x+y\n    rus=[x+y+1,x+y+0,x+y-1,x+y+0,x+y+0]\n    ans=choice(rus)\n    print(x,\"+\",y,\"=\",ans)\n    n=(input(\"(Y/N)?\")).upper()\n\n\n    if rus==x+y+0:\n        if n=='Y':\n            print(\"Yay\")\n        elif n=='N':\n            print(\"Wrong\")\n    else:\n        if n=='Y':\n            print(\"Wrong\")\n        elif n=='N':\n            print(\"Yay\")      \n\n","sub_path":"Fundamentals/Session7/frkmad.py","file_name":"frkmad.py","file_ext":"py","file_size_in_byte":456,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"552335320","text":"from __future__ import division, absolute_import, print_function\n\n__copyright__ = \"Copyright (C) 2013 Andreas Kloeckner\"\n\n__license__ = \"\"\"\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE.\n\"\"\"\n\n\nimport numpy as np\nimport numpy.linalg as la  # noqa\nimport pyopencl as cl\nimport pyopencl.clmath  # noqa\nimport pytest\nfrom pyopencl.tools import (  # noqa\n        pytest_generate_tests_for_pyopencl as pytest_generate_tests)\n\nfrom functools import partial\nfrom meshmode.mesh.generation import (  # noqa\n        ellipse, cloverleaf, starfish, drop, n_gon, qbx_peanut, WobblyCircle,\n        make_curve_mesh, NArmedStarfish)\nfrom sumpy.visualization import FieldPlotter\nfrom pytential import bind, sym, norm\n\nimport logging\nlogger = logging.getLogger(__name__)\n\ncircle = partial(ellipse, 1)\n\n\n# {{{ geometry test\n\ndef test_geometry(ctx_factory):\n    cl_ctx = ctx_factory()\n    queue = cl.CommandQueue(cl_ctx)\n\n    nelements = 30\n    order = 5\n\n    mesh = make_curve_mesh(partial(ellipse, 1),\n            np.linspace(0, 1, nelements+1),\n            order)\n\n    from meshmode.discretization import Discretization\n    from meshmode.discretization.poly_element import \\\n            InterpolatoryQuadratureSimplexGroupFactory\n\n    discr = Discretization(cl_ctx, mesh,\n            InterpolatoryQuadratureSimplexGroupFactory(order))\n\n    import pytential.symbolic.primitives as prim\n    area_sym = prim.integral(2, 1, 1)\n\n    area = bind(discr, area_sym)(queue)\n\n    err = abs(area-2*np.pi)\n    print(err)\n    assert err < 1e-3\n\n# }}}\n\n\n# {{{ test off-surface eval\n\n@pytest.mark.parametrize(\"use_fmm\", [True, False])\ndef test_off_surface_eval(ctx_factory, use_fmm, do_plot=False):\n    logging.basicConfig(level=logging.INFO)\n\n    cl_ctx = ctx_factory()\n    queue = cl.CommandQueue(cl_ctx)\n\n    # prevent cache 'splosion\n    from sympy.core.cache import clear_cache\n    clear_cache()\n\n    nelements = 30\n    target_order = 8\n    qbx_order = 3\n    if use_fmm:\n        fmm_order = qbx_order\n    else:\n        fmm_order = False\n\n    mesh = make_curve_mesh(partial(ellipse, 3),\n            np.linspace(0, 1, nelements+1),\n            target_order)\n\n    from pytential.qbx import QBXLayerPotentialSource\n    from meshmode.discretization import Discretization\n    from meshmode.discretization.poly_element import \\\n            InterpolatoryQuadratureSimplexGroupFactory\n\n    pre_density_discr = Discretization(\n            cl_ctx, mesh, InterpolatoryQuadratureSimplexGroupFactory(target_order))\n    qbx, _ = QBXLayerPotentialSource(\n            pre_density_discr,\n            4*target_order,\n            qbx_order,\n            fmm_order=fmm_order,\n            ).with_refinement()\n\n    density_discr = qbx.density_discr\n\n    from sumpy.kernel import LaplaceKernel\n    op = sym.D(LaplaceKernel(2), sym.var(\"sigma\"), qbx_forced_limit=-2)\n\n    sigma = density_discr.zeros(queue) + 1\n\n    fplot = FieldPlotter(np.zeros(2), extent=0.54, npoints=30)\n    from pytential.target import PointsTarget\n    fld_in_vol = bind(\n            (qbx, PointsTarget(fplot.points)),\n            op)(queue, sigma=sigma)\n\n    err = cl.clmath.fabs(fld_in_vol - (-1))\n\n    linf_err = cl.array.max(err).get()\n    print(\"l_inf error:\", linf_err)\n\n    if do_plot:\n        fplot.show_scalar_in_matplotlib(fld_in_vol.get())\n        import matplotlib.pyplot as pt\n        pt.colorbar()\n        pt.show()\n\n    assert linf_err < 1e-3\n\n# }}}\n\n\n# {{{ test off-surface eval vs direct\n\ndef test_off_surface_eval_vs_direct(ctx_factory,  do_plot=False):\n    logging.basicConfig(level=logging.INFO)\n\n    cl_ctx = ctx_factory()\n    queue = cl.CommandQueue(cl_ctx)\n\n    # prevent cache 'splosion\n    from sympy.core.cache import clear_cache\n    clear_cache()\n\n    nelements = 300\n    target_order = 8\n    qbx_order = 3\n\n    mesh = make_curve_mesh(WobblyCircle.random(8, seed=30),\n                np.linspace(0, 1, nelements+1),\n                target_order)\n\n    from pytential.qbx import QBXLayerPotentialSource\n    from meshmode.discretization import Discretization\n    from meshmode.discretization.poly_element import \\\n            InterpolatoryQuadratureSimplexGroupFactory\n\n    pre_density_discr = Discretization(\n            cl_ctx, mesh, InterpolatoryQuadratureSimplexGroupFactory(target_order))\n    direct_qbx, _ = QBXLayerPotentialSource(\n            pre_density_discr, 4*target_order, qbx_order,\n            fmm_order=False,\n            target_association_tolerance=0.05,\n            ).with_refinement()\n    fmm_qbx, _ = QBXLayerPotentialSource(\n            pre_density_discr, 4*target_order, qbx_order,\n            fmm_order=qbx_order + 3,\n            _expansions_in_tree_have_extent=True,\n            target_association_tolerance=0.05,\n            ).with_refinement()\n\n    fplot = FieldPlotter(np.zeros(2), extent=5, npoints=500)\n    from pytential.target import PointsTarget\n    ptarget = PointsTarget(fplot.points)\n    from sumpy.kernel import LaplaceKernel\n\n    op = sym.D(LaplaceKernel(2), sym.var(\"sigma\"), qbx_forced_limit=None)\n\n    from pytential.qbx import QBXTargetAssociationFailedException\n    try:\n        direct_density_discr = direct_qbx.density_discr\n        direct_sigma = direct_density_discr.zeros(queue) + 1\n        direct_fld_in_vol = bind((direct_qbx, ptarget), op)(\n                queue, sigma=direct_sigma)\n\n    except QBXTargetAssociationFailedException as e:\n        fplot.show_scalar_in_matplotlib(e.failed_target_flags.get(queue))\n        import matplotlib.pyplot as pt\n        pt.show()\n        raise\n\n    fmm_density_discr = fmm_qbx.density_discr\n    fmm_sigma = fmm_density_discr.zeros(queue) + 1\n    fmm_fld_in_vol = bind((fmm_qbx, ptarget), op)(queue, sigma=fmm_sigma)\n\n    err = cl.clmath.fabs(fmm_fld_in_vol - direct_fld_in_vol)\n\n    linf_err = cl.array.max(err).get()\n    print(\"l_inf error:\", linf_err)\n\n    if do_plot:\n        #fplot.show_scalar_in_mayavi(0.1*.get(queue))\n        fplot.write_vtk_file(\"potential.vts\", [\n            (\"fmm_fld_in_vol\", fmm_fld_in_vol.get(queue)),\n            (\"direct_fld_in_vol\", direct_fld_in_vol.get(queue))\n            ])\n\n    assert linf_err < 1e-3\n\n# }}}\n\n\n# {{{ unregularized tests\n\n\ndef test_unregularized_with_ones_kernel(ctx_factory):\n    cl_ctx = ctx_factory()\n    queue = cl.CommandQueue(cl_ctx)\n\n    nelements = 10\n    order = 8\n\n    mesh = make_curve_mesh(partial(ellipse, 1),\n            np.linspace(0, 1, nelements+1),\n            order)\n\n    from meshmode.discretization import Discretization\n    from meshmode.discretization.poly_element import \\\n            InterpolatoryQuadratureSimplexGroupFactory\n\n    discr = Discretization(cl_ctx, mesh,\n            InterpolatoryQuadratureSimplexGroupFactory(order))\n\n    from pytential.unregularized import UnregularizedLayerPotentialSource\n    lpot_src = UnregularizedLayerPotentialSource(discr)\n\n    from sumpy.kernel import one_kernel_2d\n\n    expr = sym.IntG(one_kernel_2d, sym.var(\"sigma\"), qbx_forced_limit=None)\n\n    from pytential.target import PointsTarget\n    op_self = bind(lpot_src, expr)\n    op_nonself = bind((lpot_src, PointsTarget(np.zeros((2, 1), dtype=float))), expr)\n\n    with cl.CommandQueue(cl_ctx) as queue:\n        sigma = cl.array.zeros(queue, discr.nnodes, dtype=float)\n        sigma.fill(1)\n        sigma.finish()\n\n        result_self = op_self(queue, sigma=sigma)\n        result_nonself = op_nonself(queue, sigma=sigma)\n\n    assert np.allclose(result_self.get(), 2 * np.pi)\n    assert np.allclose(result_nonself.get(), 2 * np.pi)\n\n\ndef test_unregularized_off_surface_fmm_vs_direct(ctx_factory):\n    cl_ctx = ctx_factory()\n    queue = cl.CommandQueue(cl_ctx)\n\n    nelements = 300\n    target_order = 8\n    fmm_order = 4\n\n    mesh = make_curve_mesh(WobblyCircle.random(8, seed=30),\n                np.linspace(0, 1, nelements+1),\n                target_order)\n\n    from pytential.unregularized import UnregularizedLayerPotentialSource\n    from meshmode.discretization import Discretization\n    from meshmode.discretization.poly_element import \\\n            InterpolatoryQuadratureSimplexGroupFactory\n\n    density_discr = Discretization(\n            cl_ctx, mesh, InterpolatoryQuadratureSimplexGroupFactory(target_order))\n    direct = UnregularizedLayerPotentialSource(\n            density_discr,\n            fmm_order=False,\n            )\n    fmm = direct.copy(\n            fmm_level_to_order=lambda kernel, kernel_args, tree, level: fmm_order)\n\n    sigma = density_discr.zeros(queue) + 1\n\n    fplot = FieldPlotter(np.zeros(2), extent=5, npoints=100)\n    from pytential.target import PointsTarget\n    ptarget = PointsTarget(fplot.points)\n    from sumpy.kernel import LaplaceKernel\n\n    op = sym.D(LaplaceKernel(2), sym.var(\"sigma\"), qbx_forced_limit=None)\n\n    direct_fld_in_vol = bind((direct, ptarget), op)(queue, sigma=sigma)\n    fmm_fld_in_vol = bind((fmm, ptarget), op)(queue, sigma=sigma)\n\n    err = cl.clmath.fabs(fmm_fld_in_vol - direct_fld_in_vol)\n\n    linf_err = cl.array.max(err).get()\n    print(\"l_inf error:\", linf_err)\n    assert linf_err < 5e-3\n\n# }}}\n\n\n# {{{ test 3D jump relations\n\n@pytest.mark.parametrize(\"relation\", [\"sp\", \"nxcurls\", \"div_s\"])\ndef test_3d_jump_relations(ctx_factory, relation, visualize=False):\n    # logging.basicConfig(level=logging.INFO)\n\n    cl_ctx = ctx_factory()\n    queue = cl.CommandQueue(cl_ctx)\n\n    if relation == \"div_s\":\n        target_order = 3\n    else:\n        target_order = 4\n\n    qbx_order = target_order\n\n    from pytools.convergence import EOCRecorder\n    eoc_rec = EOCRecorder()\n\n    for nel_factor in [6, 10, 14]:\n        from meshmode.mesh.generation import generate_torus\n        mesh = generate_torus(\n                5, 2, order=target_order,\n                n_outer=2*nel_factor, n_inner=nel_factor)\n\n        from meshmode.discretization import Discretization\n        from meshmode.discretization.poly_element import \\\n            InterpolatoryQuadratureSimplexGroupFactory\n        pre_discr = Discretization(\n                cl_ctx, mesh,\n                InterpolatoryQuadratureSimplexGroupFactory(3))\n\n        from pytential.qbx import QBXLayerPotentialSource\n        qbx, _ = QBXLayerPotentialSource(\n                pre_discr, fine_order=4*target_order,\n                qbx_order=qbx_order,\n                fmm_order=qbx_order + 5,\n                fmm_backend=\"fmmlib\"\n                ).with_refinement()\n\n        from sumpy.kernel import LaplaceKernel\n        knl = LaplaceKernel(3)\n\n        def nxcurlS(qbx_forced_limit):\n\n            return sym.n_cross(sym.curl(sym.S(\n                knl,\n                sym.cse(sym.tangential_to_xyz(density_sym), \"jxyz\"),\n                qbx_forced_limit=qbx_forced_limit)))\n\n        x, y, z = qbx.density_discr.nodes().with_queue(queue)\n        m = cl.clmath\n\n        if relation == \"nxcurls\":\n            density_sym = sym.make_sym_vector(\"density\", 2)\n\n            jump_identity_sym = (\n                    nxcurlS(+1)\n                    - (nxcurlS(\"avg\") + 0.5*sym.tangential_to_xyz(density_sym)))\n\n            # The tangential coordinate system is element-local, so we can't just\n            # conjure up some globally smooth functions, interpret their values\n            # in the tangential coordinate system, and be done. Instead, generate\n            # an XYZ function and project it.\n            density = bind(\n                    qbx,\n                    sym.xyz_to_tangential(sym.make_sym_vector(\"jxyz\", 3)))(\n                            queue,\n                            jxyz=sym.make_obj_array([\n                                m.cos(0.5*x) * m.cos(0.5*y) * m.cos(0.5*z),\n                                m.sin(0.5*x) * m.cos(0.5*y) * m.sin(0.5*z),\n                                m.sin(0.5*x) * m.cos(0.5*y) * m.cos(0.5*z),\n                                ]))\n\n        elif relation == \"sp\":\n\n            density = m.cos(2*x) * m.cos(2*y) * m.cos(z)\n            density_sym = sym.var(\"density\")\n\n            jump_identity_sym = (\n                    sym.Sp(knl, density_sym, qbx_forced_limit=+1)\n                    - (sym.Sp(knl, density_sym, qbx_forced_limit=\"avg\")\n                        - 0.5*density_sym))\n\n        elif relation == \"div_s\":\n\n            density = m.cos(2*x) * m.cos(2*y) * m.cos(z)\n            density_sym = sym.var(\"density\")\n\n            jump_identity_sym = (\n                    sym.div(sym.S(knl, sym.normal(3).as_vector()*density_sym,\n                        qbx_forced_limit=\"avg\"))\n                    + sym.D(knl, density_sym, qbx_forced_limit=\"avg\"))\n\n        else:\n            raise ValueError(\"unexpected value of 'relation': %s\" % relation)\n\n        bound_jump_identity = bind(qbx, jump_identity_sym)\n        jump_identity = bound_jump_identity(queue, density=density)\n\n        h_max = bind(qbx, sym.h_max(qbx.ambient_dim))(queue)\n        err = (\n                norm(qbx, queue, jump_identity, np.inf)\n                / norm(qbx, queue, density, np.inf))\n        print(\"ERROR\", h_max, err)\n\n        eoc_rec.add_data_point(h_max, err)\n\n        # {{{ visualization\n\n        if visualize and relation == \"nxcurls\":\n            nxcurlS_ext = bind(qbx, nxcurlS(+1))(queue, density=density)\n            nxcurlS_avg = bind(qbx, nxcurlS(\"avg\"))(queue, density=density)\n            jtxyz = bind(qbx, sym.tangential_to_xyz(density_sym))(\n                    queue, density=density)\n\n            from meshmode.discretization.visualization import make_visualizer\n            bdry_vis = make_visualizer(queue, qbx.density_discr, target_order+3)\n\n            bdry_normals = bind(qbx, sym.normal(3))(queue)\\\n                    .as_vector(dtype=object)\n\n            bdry_vis.write_vtk_file(\"source-%s.vtu\" % nel_factor, [\n                (\"jt\", jtxyz),\n                (\"nxcurlS_ext\", nxcurlS_ext),\n                (\"nxcurlS_avg\", nxcurlS_avg),\n                (\"bdry_normals\", bdry_normals),\n                ])\n\n        if visualize and relation == \"sp\":\n            sp_ext = bind(qbx, sym.Sp(knl, density_sym, qbx_forced_limit=+1))(\n                    queue, density=density)\n            sp_avg = bind(qbx, sym.Sp(knl, density_sym, qbx_forced_limit=\"avg\"))(\n                    queue, density=density)\n\n            from meshmode.discretization.visualization import make_visualizer\n            bdry_vis = make_visualizer(queue, qbx.density_discr, target_order+3)\n\n            bdry_normals = bind(qbx, sym.normal(3))(queue)\\\n                    .as_vector(dtype=object)\n\n            bdry_vis.write_vtk_file(\"source-%s.vtu\" % nel_factor, [\n                (\"density\", density),\n                (\"sp_ext\", sp_ext),\n                (\"sp_avg\", sp_avg),\n                (\"bdry_normals\", bdry_normals),\n                ])\n\n        # }}}\n\n    print(eoc_rec)\n\n    assert eoc_rec.order_estimate() >= qbx_order - 1.5\n\n# }}}\n\n\n# You can test individual routines by typing\n# $ python test_layer_pot.py 'test_routine()'\n\nif __name__ == \"__main__\":\n    import sys\n    if len(sys.argv) > 1:\n        exec(sys.argv[1])\n    else:\n        from pytest import main\n        main([__file__])\n\n# vim: fdm=marker\n","sub_path":"test/test_layer_pot.py","file_name":"test_layer_pot.py","file_ext":"py","file_size_in_byte":15909,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"171150429","text":"# -*- coding: utf-8 -*-\nfrom django.conf import settings\nfrom django.core.exceptions import ObjectDoesNotExist, MultipleObjectsReturned\nfrom django.utils.timezone import now\nfrom django.contrib.auth import get_user_model\nfrom channels.generic.websockets import WebsocketConsumer\ntry:\n    from django.utils.encoding import smart_unicode\nexcept ImportError:\n    from django.utils.encoding import smart_text as smart_unicode\ntry:\n    import simplejson as json\nexcept ImportError:\n    import json\nimport uuid\nimport sys\nimport os\nimport traceback\nfrom jumpserver3s.models import Log\nfrom app_user.models import Server\nfrom jumpserver3s.interactive import get_redis_instance, mkdir_p, WebsocketAuth\nfrom guacamole.client import GuacamoleClient\nfrom guacamole.guacamolethreading import GuacamoleThread, GuacamoleThreadWrite\nfrom guacamole.instruction import GuacamoleInstruction as Instruction\n\n\nUser = get_user_model()\n\n\nclass GuacamoleWebsocket(WebsocketConsumer, WebsocketAuth):\n\n    http_user = True\n    #http_user_and_session = True\n    channel_session = True\n    channel_session_user = True\n\n    @property\n    def authenticate(self):\n        if self.message.user.is_authenticated:\n            print(\"authenticate: {}\".format(self.message.user.is_authenticated))\n            return True\n        else:\n            return False\n\n    def connect(self, message, id):\n        self.message.reply_channel.send({\"accept\": True})\n        prefix, label = message['path'].strip('/').split('/')\n        # print(\"prefix: {}\".format(prefix))\n        # print(\"label: {}\".format(label))\n        id = label\n        print(\"id: {}\".format(id))\n        # print(\"server: {}\".format(server))\n        user = self.message.user\n        print(\"user: {}\".format(user))\n        if not self.authenticate:\n            self.message.reply_channel.send({\"text\": json.dumps(\n                {'status': False, 'message': 'You must login to the system!'})}, immediately=True)\n            self.message.reply_channel.send({\"accept\": False})\n        else:\n            # permission auth\n            username = user.username\n            client = GuacamoleClient(\n                settings.GUACD_HOST, settings.GUACD_PORT)\n            print(\"client: {}\".format(client))\n            try:\n                data = Server.objects.get(id=id)\n                print(\"data: {}\".format(data))\n                print(\"data.credential.protocol: {}\".format(data.credential.protocol))\n                if data.credential.protocol in ['vnc', 'rdp', 'telnet']:\n                    pass\n                else:\n                    self.message.reply_channel.send({\"accept\": False})\n            except ObjectDoesNotExist:\n                # server info not exist\n                self.message.reply_channel.send({\"accept\": False})\n            cache_key = str(uuid.uuid4())\n            print(\"cache_key: {}\".format(cache_key))\n            directory_date_time = now()\n            record = os.path.join(settings.MEDIA_DIR,\"records\")\n            print(\"record: {}\".format(record))\n            recording_path = os.path.join(record, '{0}-{1}-{2}'.format(\n                directory_date_time.year, directory_date_time.month, directory_date_time.day))\n            print(\"recording_path: {}\".format(recording_path))\n            if isinstance(username, bytes):\n                username = username.decode()\n            drive_path = os.path.join(record, str(username), 'Download')\n            print(\"drive_path: {}\".format(drive_path))\n            print(\"protocol: {}\".format(data.credential.protocol))\n            \"\"\"\n            Create recording media file and drive path\n            \"\"\"\n            mkdir_p(recording_path)\n            mkdir_p(drive_path)\n\n            try:\n                client.handshake(width=data.credential.width,\n                                 height=data.credential.height,\n                                 protocol=data.credential.protocol,\n                                 hostname=data.ip,\n                                 port=data.credential.port,\n                                 username=data.credential.user.username,\n                                 password=data.credential.password,\n                                 recording_path=recording_path,\n                                 recording_name=cache_key,\n                                 create_recording_path='true',\n                                 enable_wallpaper='true',\n                                 ignore_cert='true',\n                                 enable_drive='true',\n                                 drive_path=drive_path,\n                                 create_drive_path='true',\n                                 security=\"tls\",\n                                 # console_audio='true',\n                                 # enable_audio_input='true',\n                                 # disable_audio='false',\n                                 # console='true',\n                                 enable_full_window_drag='true',\n                                 resize_method=\"reconnect\"  # display-update\n                                 )\n            except Exception as e:\n                print(e)\n                print(traceback.print_exc())\n                self.message.reply_channel.send({\"accept\": False})\n                return\n            self.message.reply_channel.send(\n                {\"text\": '0.,{0}.{1};'.format(len(cache_key), cache_key)}, immediately=True)\n\n            audit_log = Log.objects.create(user=User.objects.get(username=username), server=data, channel=self.message.reply_channel.name,\n                                           width=data.credential.width, height=data.credential.height, log=cache_key, gucamole_client_id=client._id)\n            audit_log.save()\n            guacamolethread = GuacamoleThread(self.message, client)\n            guacamolethread.setDaemon = True\n            guacamolethread.start()\n\n            guacamolethreadwrite = GuacamoleThreadWrite(self.message, client)\n            guacamolethreadwrite.setDaemon = True\n            guacamolethreadwrite.start()\n\n    def disconnect(self, message, id):\n        # close threading\n        print('disconnect')\n        try:\n            audit_log = Log.objects.get(\n                channel=self.message.reply_channel.name)\n            audit_log.is_finished = True\n            audit_log.end_time = now()\n            audit_log.save()\n            cache_key = audit_log.gucamole_client_id\n            client = GuacamoleClient(\n                settings.GUACD_HOST, settings.GUACD_PORT)\n            client.send_instruction(Instruction('select', cache_key))\n            instruction = client.read_instruction()\n            kwargs = {'width': 1024, 'height': 768, 'read_only': 'true'}\n            connection_args = [\n                kwargs.get(arg.replace('-', '_'), '') for arg in instruction.args\n            ]\n            client.send_instruction(Instruction('size', 1024, 768, 96))\n            client.send_instruction(Instruction('audio', *list()))\n            client.send_instruction(Instruction('video', *list()))\n            client.send_instruction(Instruction('image', *list()))\n            client.send_instruction(Instruction('connect', *connection_args))\n            client.send_instruction(Instruction('disconnect', *connection_args))\n        except:\n            pass\n        finally:\n            get_redis_instance().delete(id)\n            self.message.reply_channel.send({\"accept\": False})\n\n    def queue(self):\n        queue = get_redis_instance()\n        channel = queue.pubsub()\n        return queue\n\n    def receive(self, text=None, bytes=None, **kwargs):\n        self.queue().publish(self.message.reply_channel.name, text)\n\n\nclass GuacamoleMonitor(GuacamoleWebsocket, WebsocketAuth):\n\n    def connect(self, message, id):\n        self.message.reply_channel.send({\"accept\": True})\n        prefix, id = message['path'].strip('/').split('/')\n        print(\"id: {}\".format(id))\n        if not self.authenticate:\n            self.message.reply_channel.send({\"text\": json.dumps(\n                {'status': False, 'message': 'You must login to the system!'})}, immediately=True)\n            self.message.reply_channel.send({\"accept\": False})\n        else:\n            print(\"this is from connect\")\n            client = GuacamoleClient(\n                settings.GUACD_HOST, settings.GUACD_PORT)\n            log_object = Log.objects.get(id=id)\n            print(\"log_object: {}\".format(log_object))\n            cache_key = str(log_object.gucamole_client_id)\n            print(\"cache_key: {}\".format(cache_key))\n            data = log_object.server\n            print(\"data: {}\".format(data))\n            # draft version for real time monitor\n            client.send_instruction(Instruction('select', cache_key))\n            print(\"this is after send instruction\")\n            instruction = client.read_instruction()\n            print(\"instruction: {}\".format(instruction))\n            kwargs = {'width': 1024, 'height': 768, 'read_only': 'true'}\n            connection_args = [\n                kwargs.get(arg.replace('-', '_'), '') for arg in instruction.args\n            ]\n            client.send_instruction(Instruction('size', 1024, 768, 96))\n            client.send_instruction(Instruction('audio', *list()))\n            client.send_instruction(Instruction('video', *list()))\n            client.send_instruction(Instruction('image', *list()))\n            client.send_instruction(Instruction('connect', *connection_args))\n\n            # self.message.reply_channel.send({\"text\":'0.,{0}.{1};'.format(len(cache_key),cache_key)},immediately=True)\n            guacamolethread = GuacamoleThread(self.message, client)\n            guacamolethread.setDaemon = True\n            guacamolethread.start()\n\n            guacamolethreadwrite = GuacamoleThreadWrite(self.message, client)\n            guacamolethreadwrite.setDaemon = True\n            guacamolethreadwrite.start()\n\n    def disconnect(self, message, id):\n        # close threading\n        print('disconnect')\n        try:\n            log_object = Log.objects.get(id=id)\n            cache_key = log_object.gucamole_client_id\n            client = GuacamoleClient(\n                settings.GUACD_HOST, settings.GUACD_PORT)\n            client.send_instruction(Instruction('select', cache_key))\n            instruction = client.read_instruction()\n            kwargs = {'width': 1024, 'height': 768, 'read_only': 'true'}\n            connection_args = [\n                kwargs.get(arg.replace('-', '_'), '') for arg in instruction.args\n            ]\n            client.send_instruction(Instruction('size', 1024, 768, 96))\n            client.send_instruction(Instruction('audio', *list()))\n            client.send_instruction(Instruction('video', *list()))\n            client.send_instruction(Instruction('image', *list()))\n            client.send_instruction(Instruction('connect', *connection_args))\n            client.send_instruction(Instruction('disconnect', *connection_args))\n        except:\n            pass\n        finally:\n            self.message.reply_channel.send({\"accept\": False})\n","sub_path":"guacamole/consumers.py","file_name":"consumers.py","file_ext":"py","file_size_in_byte":11049,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"463799518","text":"import pandas as pd\nimport numpy as np\n\n\n\n\ndef normalize(A): #accepts ONE shower at a time\n    tot = sum(A)\n    return [i/tot for i in A]\n\n\n\ndef scale(A): #accepts ONE shower at a time\n    maxim = max(A)\n    return [i/maxim for i in A]\n\n\n\ndef meansig(A): #accepts all the showers\n    A -= np.mean(A,axis=0)\n    A /= np.std(A, axis = 0)+0.001\n    return A\n\n\n\ndef PCA(A,B): #A is the matrix containig showers with Ef.\n    #specificly A is train set, B test set\n    #both have to be projected in the same space\n    lenA = len(A)\n    lenB = len(B)\n    M = np.concatenate((A, B)) #concatenate the two lists\n    M -= np.mean(M,axis = 0)\n    #print(\"new mean {}\".format(np.mean(A,axis = 0)))\n    #print(\"Computing Conv. matrix\")\n    print(\"SHAPE\")\n    print(M.shape[0])\n    COV = np.dot(M.T,M) / M.shape[0]\n    U,S,V = np.linalg.svd(COV)\n    Mrot = np.dot(M, U) # decorrelate the data\n    Mrot_reduced = np.dot(M, U[:,0:30]) # Xrot_reduced becomes [N x 100]\n    Arot_reduced = Mrot_reduced[0:len(A),:]\n    Brot_reduced = Mrot_reduced[0:len(B),:]\n    return Arot_reduced,Brot_reduced\n\n\n\n\ndef importdata(filecsv,nCols):\n    if (nCols<400):\n        nCh = 300\n    if (nCols>1200):\n        nCh = 1200\n\n    data   = np.float32(pd.read_csv(filecsv , usecols = np.arange(0,nCh,1) , header = None).values)\n    return data\n\ndef importclasif(filecsv,nCols):\n    if (nCols<400):\n        nCh = 300\n    if (nCols>1200):\n        nCh = 1200\n    clasif = np.array(pd.read_csv(filecsv , usecols = [nCh] , header = None).values, dtype = int)\n    return clasif\n\ndef importvar(filecsv,nCols):\n    if (nCols<400):\n        nCh = 300\n    if (nCols>1200):\n        nCh = 1200\n    var = np.array( pd.read_csv(filecsv , usecols = np.arange(nCh+1,nCols,1), header = None).values , dtype = np.float32)\n\n    return var\n\n\n\ndef filterdata(data, cut1,cut2):\n    data   = data   [ np.where( np.logical_and( cut1, cut2 ) ) [0] ]\n    \n    return data\n\n\n","sub_path":"funciones.py","file_name":"funciones.py","file_ext":"py","file_size_in_byte":1909,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"126900256","text":"import cv2\nimport keras\nimport pprint\nimport sys\nimport os\nimport numpy as np\nfrom PIL import Image\nimport imageio\nimport itertools as it\npp = pprint.PrettyPrinter(depth=6)\n#from tensorflow.python.client import device_lib\n#print(device_lib.list_local_devices())\n\nclass Viewpoint(object):\n\tdef __init__(self, cam_x, cam_y, cam_w, cam_h, resize_w, resize_h):\n\t\tself.cam_x = cam_x\n\t\tself.cam_y = cam_y\n\t\tself.cam_w = cam_w\n\t\tself.cam_h = cam_h\n\n\t\tself.resize_w = resize_w\n\t\tself.resize_h = resize_h\n\n\tdef get_cam(self):\n\t\treturn (self.cam_x, self.cam_y, self.cam_w, self.cam_h)\n\t\n\tdef set_top_left(self, x, y):\n\t\tself.cam_x = x\n\t\tself.cam_y = y\n\t\n\tdef top_left(self):\n\t\treturn (self.cam_x, self.cam_y)\n\t\n\tdef bottom_right(self):\n\t\treturn (self.cam_x + self.cam_w, self.cam_y + self.cam_h)\n\t\n\tdef set_width(self, width):\n\t\tself.cam_w = width\n\t\tself.cam_h = round(width * (self.resize_h / self.resize_w))\n\t\t\n\tdef process_frame(self, frame):\n\t\t# Crop the frame, resample, and normalize\n\t\tframe_resized = cv2.resize(frame[self.cam_y:(self.cam_y+self.cam_h), self.cam_x:(self.cam_x+self.cam_w)], (self.resize_w, self.resize_h), interpolation=cv2.INTER_AREA)\n\n\t\tbatch_data = np.zeros(shape=(1, 1, self.resize_h, self.resize_w, 3), dtype=np.float32)\n\t\timage = Image.fromarray(frame_resized)\n\t\tnorm_image = np.array(image, dtype=np.float32)\n\t\tnorm_image -= 128.0\n\t\tnorm_image /= 128.0\n\t\tbatch_data[0,0,:,:,:] = np.ascontiguousarray(norm_image)\n\n\t\treturn batch_data\n\n\nmoving = False\ndef click_callback(event, x, y, flags, param):\n\tglobal refPt\n\tglobal moving\n\tif event == cv2.EVENT_LBUTTONDOWN:\n\t\trefPt[2] = refPt[2] + x - refPt[0]\n\t\trefPt[3] = refPt[3] + y - refPt[1]\n\t\trefPt[0] = x\n\t\trefPt[1] = y\n\t\tmoving = True\n\t\t\n\tif event == cv2.EVENT_LBUTTONUP or moving == True:\n\t\tif x - refPt[0] > 50:\n\t\t\trefPt[2] = x\n\t\t\trefPt[3] = y\n\t\t\n\t\tif event == cv2.EVENT_LBUTTONUP:\n\t\t\tmoving = False\n\nclass Foosbot(object):\n\tdef __init__(self, viewpoint, model_dpos_file, model_pos_file, video_file = 0):\n\t\tself.viewpoint = viewpoint\n\t\tself.video = cv2.VideoCapture(video_file)\n\n\t\t# Load the models\n\t\tself.model_dpos_file = None\n\t\tself.model_pos_file = None\n\t\tif model_dpos_file is not None:\n\t\t\tself.model_dpos_file = keras.models.load_model(model_dpos_file)\n\t\t#model_pos_file = None\n\t\tif model_pos_file is not None:\n\t\t\tself.model_pos_file = keras.models.load_model(model_pos_file)\n\n\tdef _process_frame(self, frame):\n\t\t# Evaluate the frame and output the resulting frames\n\t\tdpos = None\n\t\tpos = None\n\n\t\tif self.model_pos_file is not None:\n\t\t\t# Evalaute difference in position\n\t\t\tpos = self.model_pos_file.predict(frame)[0,:]\n\t\t\t#pos = (0.0,0.0,0.0)\n\n\t\tif self.model_dpos_file is not None:\n\t\t\t# Evalaute difference in position\n\t\t\t#dpos = self.model_dpos_file.predict(frame)[0,:]\n\t\t\tdpos = (0.0,0.0,0.0)\n\n\t\treturn (pos, dpos)\n\n\tdef run(self, display=True):\n\t\tif display:\n\t\t\tglobal refPt\n\t\t\tcv2.namedWindow(\"FoosBot\")\n\t\t\tcv2.setMouseCallback(\"FoosBot\", click_callback)\n\n\t\tcount = 0\n\t\twhile(self.video.isOpened()):\n\t\t\tif display:\n\t\t\t\tself.viewpoint.set_top_left(refPt[0], refPt[1])\n\t\t\t\tself.viewpoint.set_width(refPt[2]-refPt[0])\n\t\t\t\n\t\t\t#\tcv2.setMouseCallback(\"FoosBot\", click_callback)\n\n\t\t\t# Read in the next frame\n\t\t\tret, frame = self.video.read()\n\n\t\t\t# Crop and stretch it\n\t\t\tframe_processed = self.viewpoint.process_frame(frame)\n\n\t\t\t# Evaluate the Foosbot ML models\n\t\t\t(pos, dpos) = self._process_frame(frame_processed)\n\n\t\t\tif display:\n\t\t\t\t# Draw the frame\n\t\t\t\tfont = cv2.FONT_HERSHEY_SIMPLEX\n\t\t\t\tcv2.putText(frame,'%i: outputs: (%.2f,%.2f,%.2f)' % (count, pos[0], pos[1], pos[2]),(10,50), font, 1,(255,255,255),1,cv2.LINE_AA)\n\t\t\t\t\n\t\t\t\tcv2.rectangle(frame, self.viewpoint.top_left(), self.viewpoint.bottom_right(), (255,0,0), 2 )\n\t\t\t\t\n\t\t\t\tcv2.imshow('FoosBot',frame)\n\t\t\t\t\n\n\t\t\t# Keystroke to quit or pause\n\t\t\tkey = cv2.waitKey(1) & 0xFF\n\t\t\tif key == ord('q'):\n\t\t\t\tbreak\n\t\t\telif key == ord(' '):\n\t\t\t\tcv2.waitKey()\n\n\t\t\tcount += 1\n\n\t\tcv2.destroyAllWindows()\n\n\nrefPt = [5,5,600+5,330+5]\nview = Viewpoint(cam_x = refPt[0], cam_y = refPt[1], cam_w = refPt[2], cam_h = refPt[3], resize_w = 100, resize_h = 54)\n\n\nif( len(sys.argv) == 2 ):\n    if sys.argv[1] == \"simulate\":\n        video_file = \".\\\\..\\\\..\\\\TrainingData\\\\Raw\\\\Am1\\\\out.avi\"\n        foosbot = Foosbot( viewpoint = view, model_dpos_file = \"config12_deltapos.h5\", model_pos_file = \"config12_model_posonly.hdf\", video_file = video_file)\n        foosbot.run()\n    elif sys.argv[1] == \"run\":\n        video_file = 0 # First webcam attached to PC\n        foosbot = Foosbot( viewpoint = view, model_dpos_file = \"config12_deltapos.h5\", model_pos_file = \"config12_model_posonly.hdf\", video_file = video_file)\n        foosbot.run()\nelse:\n    print(\"run.py <simulate OR run>\")\n    \n\n","sub_path":"Code/VisualizeRunModel/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":4695,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"37174576","text":"from os import remove\nfrom coganh.config import LINE_WIDTH, P1_COLOR\nfrom coganh.player import Player\nfrom coganh.utils import boardHelper\n\nclass Chessman:\n    id: int\n    player: Player\n    def __init__(self, id, player, pos, board):\n        self.id = id\n        self.board = board\n        self.player = player\n        (self.x, self.y) = pos\n\n    def onMove(self, toPos):\n        (fromX, fromY) = (self.x, self.y)\n        (self.x, self.y) = (toX, toY) = toPos\n        (self.board[toY][toX], self.board[fromY][fromX]) = (\n            self.board[fromY][fromX], self.board[toY][toX]\n        )\n\n    def onEaten(self):\n        self.player = self.player.opposite\n\n    def canMove(self, visitted):\n        if self in visitted:\n            return False\n        visitted.add(self)\n        helper = boardHelper[self.x][self.y]\n        rt = False\n        for point in helper:\n            (x, y) = point\n            chess = self.board[y][x]\n            if chess is None:\n                rt = rt or True\n            elif chess.player is self.player:\n                rt = rt or chess.canMove(visitted)\n            if rt == True:\n                break\n        return rt\n","sub_path":"coganh/chessman.py","file_name":"chessman.py","file_ext":"py","file_size_in_byte":1156,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"130813684","text":"import random\r\n\r\ndef main():\r\n    quick = int(input(\"How many 'Quick_pick' you want to make? \"))\r\n    for i in range(0,quick):\r\n        list=[random.randint(1,45) for i in range (6)]\r\n        list.sort()\r\n        for count in range(0, len(list)):\r\n            print(list[count], end =\" \".center(1))\r\n\r\n        print(\"\".center(1))\r\n\r\n\r\n\r\nmain()","sub_path":"prac 4/quick_pick.py","file_name":"quick_pick.py","file_ext":"py","file_size_in_byte":343,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"627421710","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport os\n\n\ndef group_plt(input_k, input_dims, plt_dims, save=False, input_dir='plts', name='cells'):\n    \"\"\"\n    START.\n\n    :param input_k: input vector that contains seprate arrays to be plotted as columns\n    :param input_dims: [nlags, NX, NY]\n    :param plt_dims: your plots will have (plt_dims[0] x plt_dims[1]) shape\n    :param save: True if you want to save your figures\n    :param input_dir: the folder at which figs will be saved\n    :param name: this will be the intial word in figure file names\n    :return: a fig to be plotted and saved.\n\n    END.\n    \"\"\"\n\n    bundle_sz = int(np.prod(plt_dims))\n    plots_n = input_k.shape[1]\n    groups_n = int(np.floor(plots_n / bundle_sz)) + 1\n\n    if input_dims[0] == 1:\n        for nn in range(groups_n):\n            print('Plots in range %s to %s' % (nn*bundle_sz, (nn + 1)*bundle_sz - 1))\n            fig = plt.figure(figsize=(15, 7))\n            for i in range(bundle_sz):\n                if nn * bundle_sz + i >= plots_n:\n                    break\n                plt.subplot(plt_dims[0], plt_dims[1], i + 1)\n                k = np.reshape(input_k[:, nn * bundle_sz + i], (input_dims[2], input_dims[1]))\n                plt.imshow(k, cmap='Greys')\n                plt.title(name+' #%s ' % (nn * bundle_sz + i))\n            plt.show()\n\n            print('\\n\\n')\n            if save:\n                if not os.path.exists('./%s' % input_dir):\n                    os.makedirs('./%s' % input_dir)\n                os.path.abspath(path='')\n                fig.savefig('./'+input_dir+'/'+name+'_%s_to_%s' % (nn*bundle_sz, (nn + 1)*bundle_sz - 1))\n","sub_path":"group_plt.py","file_name":"group_plt.py","file_ext":"py","file_size_in_byte":1647,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"103690906","text":"import numpy as np\nimport random, time\nimport itertools\nimport math\nimport multiprocessing\nfrom numpy import load\n\nfrom MDP import *\nfrom JEPS import *\nfrom MILP import *\nfrom NN import *\nfrom utils import *\nfrom MCTS import *\n\ndef find_schedule(return_dict, MILP_objval, N, M, LV, GV, INPUT_CONFIGS, CONFIG, GAMMA, GAMMA_DECREASE, EPSILON, EPSILON_DECREASE, deltas, due_dates, release_dates, OBJ_FUN, layer_dims, weight_decay, NN_weights, NN_biases, NN_weights_gradients, NN_biases_gradients, PHASE, METHOD, EPOCHS, OUTPUT_DIR, TIMEOUT, MILP_TIMEOUT):\n\n    timer_start = time.time()\n\n    # Generate heuristics for Q_learning rewards\n    heur_job = heuristic_best_job(deltas, N, M, LV, GV)\n    heur_res = heuristic_best_resource(heur_job, N, M, LV)\n    heur_blocking = heuristic_blocking(deltas, N, M, LV, GV)\n    heur_rev_blocking = heuristic_reverse_blocking(deltas, N, M, LV, GV)\n\n    # policies = []\n    # for v in range(M):\n    #     policies.append(np.zeros([LV[v], N]))\n\n    # Load stored weights for the policy value function\n    if PHASE == \"load\":\n        with open('weights/'+str(layer_dims)+'-JSSP-weights.pickle','rb') as f:\n            NN_weights = pickle.load(f)\n        with open('weights/'+str(layer_dims)+'-JSSP-biases.pickle','rb') as f:\n            NN_biases = pickle.load(f)\n        with open('weights/'+str(layer_dims)+'-JSSP-weights_grad.pickle','rb') as f:\n            NN_weights_gradients = pickle.load(f)\n        with open('weights/'+str(layer_dims)+'-JSSP-biases_grad.pickle','rb') as f:\n            NN_biases_gradients = pickle.load(f)\n\n    # First epoch, used as initialization of all parameters and results\n    RL = MDP(N, M, LV, GV, release_dates, due_dates, NN_weights, NN_biases, NN_weights_gradients, NN_biases_gradients)\n    DONE = False\n    z = 0\n    RL.reset(N, M, LV, GV, release_dates, due_dates)\n    while not DONE:\n        RL, DONE = RL.step(z, N, M, LV, GV, CONFIG, GAMMA, EPSILON, deltas, heur_job, heur_res, heur_blocking, heur_rev_blocking, PHASE, METHOD)\n        z += 1\n    schedule = RL.schedule.objectives()\n    r_best = schedule.calc_reward(OBJ_FUN)\n    best_params = RL.NN.get_params()\n    best_params_grad = RL.NN.get_params_gradients()\n\n    comparing_score = schedule.Cmax + (100*schedule.Tsum)\n\n    return_dict[\"time\"] = TIMEOUT\n    return_dict[\"best_schedule\"] = schedule\n    return_dict[\"epoch_best_found\"] = 0\n    return_dict[\"epochs\"] = 0\n    return_dict[\"mdp\"] = RL\n\n    # All other epochs\n    # for epoch in range(1,EPOCHS):\n    epoch = 1\n    while True:\n\n        if epoch%1000==0:\n            EPSILON *= 1 - EPSILON_DECREASE\n\n            # write_training_batch(OUTPUT_DIR=OUTPUT_DIR, X_train=np.array(RL.NN_inputs), y_pred=np.array(RL.NN_predictions), r=r, MILP_objval=MILP_objval)\n\n        DONE = False\n        z = 0\n        RL.reset(N, M, LV, GV, release_dates, due_dates)\n        \n        # Take timesteps until processing of all jobs is finished\n        while not DONE:\n            RL, DONE = RL.step(z, N, M, LV, GV, CONFIG, GAMMA, EPSILON, deltas, heur_job, heur_res, heur_blocking, heur_rev_blocking, PHASE, METHOD)\n            z += 1\n\n        # Retrieve generated schedule and its objective value\n        schedule = RL.schedule.objectives()\n        r = schedule.calc_reward(OBJ_FUN)\n\n        # Update the weighs of the policy value function\n        RL.NN = update_NN(model=RL.NN, X_train=np.array(RL.NN_inputs), y_pred=np.array(RL.NN_predictions), weight_decay=weight_decay, GAMMA=GAMMA, loss=RL.loss, r=r, r_best=r_best, MILP_objval=MILP_objval)\n\n        # If this schedule has the best objective value found so far,\n        # update best schedule and makespan, and update policy values\n        return_dict[\"epochs\"] = epoch\n        return_dict[\"mdp\"] = RL\n        if r < r_best:\n            r_best = r\n            return_dict[\"best_schedule\"] = schedule\n            return_dict[\"epoch_best_found\"] = epoch\n\n        epoch += 1\n\n    timer_finish = time.time()\n    calc_time = timer_finish - timer_start\n\n    return_dict[\"time\"] = calc_time\n    return_dict[\"mdp\"] = RL\n\n    return return_dict\n\n# Test function, which executes the both the MILP and the NN/JEPS algorithm, and stores all relevant information\ndef test(N, M, LV, GV, INPUT_CONFIGS, CONFIG, GAMMA, GAMMA_DECREASE, EPSILON, EPSILON_DECREASE, OBJ_FUN, layer_dims, weight_decay, NN_weights, NN_biases, NN_weights_gradients, NN_biases_gradients, PHASE, METHOD, EPOCHS, MCTS_BUDGET, OUTPUT_DIR, TIMEOUT, MILP_TIMEOUT):\n\n    ins = MILP_instance(M, LV, GV, N)\n    MILP_objval = 0\n    MILP_calctime = 0\n\n    # UNCOMMENT TO RUN MILP\n    # timer_start = time.time()\n    # MILP_schedule, MILP_objval = MILP_solve(M, LV, GV, N)\n    # timer_finish = time.time()\n    # MILP_calctime = timer_finish - timer_start\n\n    deltas = []\n    due_dates = []\n    for v in range(M):\n        deltas.append(np.round(ins.lAreaInstances[v].tau))\n        due_dates.append(ins.lAreaInstances[v].d)\n    release_dates = np.zeros([N])\n\n    # MONTE CARLO TREE SEARCH\n    MCTS = Monte_Carlo(N, M, LV, GV, due_dates)\n    timer_start = time.time()\n    root = MCTS.search(MCTS_BUDGET, N, M, LV, GV, deltas, EPSILON, OBJ_FUN)\n    timer_finish = time.time()\n    MCTS_calctime = timer_finish - timer_start\n    MCTS_objval = root.objval\n\n    manager = multiprocessing.Manager()\n    return_dict = manager.dict()\n    p = multiprocessing.Process(target=find_schedule, args=(return_dict, MILP_objval, N, M, LV, GV, INPUT_CONFIGS, CONFIG, GAMMA, GAMMA_DECREASE, EPSILON, EPSILON_DECREASE, deltas, due_dates, release_dates, OBJ_FUN, layer_dims, weight_decay, NN_weights, NN_biases, NN_weights_gradients, NN_biases_gradients, PHASE, METHOD, EPOCHS, OUTPUT_DIR, TIMEOUT, MILP_TIMEOUT))\n    p.start()\n    p.join(TIMEOUT)\n    if p.is_alive():\n        print(\"Runtime error: abort mission.\\n\")\n        p.terminate()\n        p.join()\n\n    # return_dict = find_schedule(return_dict, MILP_objval, N, M, LV, GV, INPUT_CONFIGS, CONFIG, GAMMA, GAMMA_DECREASE, EPSILON, EPSILON_DECREASE, deltas, due_dates, release_dates, OBJ_FUN, layer_dims, weight_decay, NN_weights, NN_biases, NN_weights_gradients, NN_biases_gradients, PHASE, METHOD, EPOCHS, OUTPUT_DIR, TIMEOUT)\n    \n        EPOCHS = return_dict[\"epochs\"]\n        schedule = return_dict[\"best_schedule\"]\n        epoch_best_found = return_dict[\"epoch_best_found\"]\n        calc_time = return_dict[\"time\"]\n        RL = return_dict[\"mdp\"]\n\n        makespan = schedule.Cmax\n        Tsum = schedule.Tsum\n        Tmax = schedule.Tmax\n        Tn = schedule.Tn\n\n        params = RL.NN.get_params()\n        NN_weights = [params[i] for i in range(0,len(params),2)]\n        NN_biases = [params[i] for i in range(1,len(params),2)]\n        grads = RL.NN.get_params_gradients()\n        NN_weights_gradients = [grads[i] for i in range(0,len(grads),2)]\n        NN_biases_gradients = [grads[i] for i in range(1,len(grads),2)]\n\n        if PHASE == \"train\":\n            write_NN_weights(OUTPUT_DIR, M, N, LV, GV, EPSILON, layer_dims, OBJ_FUN, NN_weights, NN_biases, NN_weights_gradients, NN_biases_gradients, GAMMA, GAMMA_DECREASE)\n\n        plot_schedule(OUTPUT_DIR, schedule, N, M, LV, GV)\n        write_log(OUTPUT_DIR, PHASE, N, M, LV, GV, INPUT_CONFIGS[CONFIG], GAMMA, GAMMA_DECREASE, EPSILON, EPSILON_DECREASE, layer_dims, weight_decay, METHOD, EPOCHS, OBJ_FUN, makespan, Tsum, Tmax, Tn, calc_time, epoch_best_found, MILP_objval, MILP_calctime, MILP_TIMEOUT, MCTS_calctime, MCTS_objval)\n        # write_log(OUTPUT_DIR, PHASE, N, M, LV, GV, INPUT_CONFIGS[CONFIG], GAMMA, GAMMA_DECREASE, EPSILON, EPSILON_DECREASE, layer_dims, weight_decay, METHOD, 0, OBJ_FUN, 0, 0, 0, 0, 1, 0, MILP_objval, MILP_calctime, MILP_TIMEOUT)\n\n    #     return NN_weights, NN_biases, NN_weights_gradients, NN_biases_gradients \n    # return NN_weights, NN_biases, NN_weights_gradients, NN_biases_gradients\n\ndef main():\n    N = 5        # number of jobs\n    M = 1         # number of work stations\n    LV = [3]      # number of resources in each work station\n    GV = [6]      # number of units in each resource of each work station\n    \n    GAMMA = 0.7             # 0.7 for training, 0.3 for loading\n    EPSILON = 1.0           # 1.0 (with decrease) for training, 0.0/0.2/0.5 for loading\n\n    GAMMA_DECREASE = 0.3\n    EPSILON_DECREASE = 0.025  # 0.025 for training, 0.1 for loading\n    weight_decay = 0.00001\n\n    OBJ_FUN = {\n        \"Cmax\": 1,\n        \"Tsum\": 10,\n        \"Tmax\": 0,\n        \"Tmean\": 0,\n        \"Tn\": 0\n    }\n\n    layer_dims = [15, 11, 6, 1]\n    INPUT_CONFIGS = [\"all_vars\", \"XV\", \"minmax_large\", \"minmax_small\", \"generalizability\", \"high\", \"absolute\", \"relative\", \"generalizability_T\"]\n    CONFIG = 7\n\n    NN_weights = [np.random.rand(layer_dims[i], layer_dims[i+1]) for i in range(len(layer_dims)-1)]\n    NN_biases = [np.zeros(layer_dims[i]) for i in range(1,len(layer_dims))]\n    NN_weights_gradients = np.zeros_like(NN_weights)\n    NN_biases_gradients = np.zeros_like(NN_biases)\n\n    PHASE = \"train\"      # train / load\n    METHOD = \"NN\"       # JEPS / Q_learning / NN\n\n    EPOCHS = 30000\n    TIMEOUT = 1         # allowed computation time in sec\n    MILP_TIMEOUT = 0    # allowed computation time for MILP in sec\n    MCTS_BUDGET = 10000\n\n    OUTPUT_DIR = '../output/'\n\n    file = open(OUTPUT_DIR+\"log.csv\",'a')\n    file.write(\"METHOD\\tPHASE\\tN\\tM\\tLV\\tGV\\tCONFIG\\tEPOCHS\\tGAMMA\\tGAMMA_DECREASE\\tEPSILON\\tEPSILON_DECREASE\\tLAYER_DIMS\\tWEIGHT_DECAY\\tCMAX_WEIGHT\\tTSUM_WEIGHT\\tMAKESPAN\\tTSUM\\tTMAX\\tTN\\tTIME\\tEPOCH_BEST\\tMILP_OBJVAL\\tMILP_CALCTIME\\tMILP_TIMEOUT\\tMCTS_CALCTIME\\tMCTS_OBJVAL\")\n    file.close()\n\n    test(N, M, LV, GV, INPUT_CONFIGS, CONFIG, GAMMA, GAMMA_DECREASE, EPSILON, EPSILON_DECREASE, OBJ_FUN, layer_dims, weight_decay, NN_weights, NN_biases, NN_weights_gradients, NN_biases_gradients, PHASE, METHOD, EPOCHS, MCTS_BUDGET, OUTPUT_DIR, TIMEOUT, MILP_TIMEOUT)\n\n    # N = 5\n    # for LV in [2, 3, 5]:\n    #     test(N, M, [LV], GV, INPUT_CONFIGS, CONFIG, GAMMA, GAMMA_DECREASE, EPSILON, EPSILON_DECREASE, OBJ_FUN, layer_dims, weight_decay, NN_weights, NN_biases, NN_weights_gradients, NN_biases_gradients, PHASE, METHOD, EPOCHS, MCTS_BUDGET, OUTPUT_DIR, TIMEOUT, MILP_TIMEOUT)\n\n    # N = 7\n    # for LV in [3, 4, 6]:\n    #     test(N, M, [LV], GV, INPUT_CONFIGS, CONFIG, GAMMA, GAMMA_DECREASE, EPSILON, EPSILON_DECREASE, OBJ_FUN, layer_dims, weight_decay, NN_weights, NN_biases, NN_weights_gradients, NN_biases_gradients, PHASE, METHOD, EPOCHS, OUTPUT_DIR, TIMEOUT, MILP_TIMEOUT)\n\n    # N = 9\n    # for LV in [3, 5, 7]:\n    #     test(N, M, [LV], GV, INPUT_CONFIGS, CONFIG, GAMMA, GAMMA_DECREASE, EPSILON, EPSILON_DECREASE, OBJ_FUN, layer_dims, weight_decay, NN_weights, NN_biases, NN_weights_gradients, NN_biases_gradients, PHASE, METHOD, EPOCHS, OUTPUT_DIR, TIMEOUT, MILP_TIMEOUT)\n\n    # N = 11\n    # for LV in [4, 6, 8]:\n    #     test(N, M, [LV], GV, INPUT_CONFIGS, CONFIG, GAMMA, GAMMA_DECREASE, EPSILON, EPSILON_DECREASE, OBJ_FUN, layer_dims, weight_decay, NN_weights, NN_biases, NN_weights_gradients, NN_biases_gradients, PHASE, METHOD, EPOCHS, OUTPUT_DIR, TIMEOUT, MILP_TIMEOUT)\n\n    # N = 13\n    # for LV in [3, 5, 7, 9]:\n    #     test(N, M, [LV], GV, INPUT_CONFIGS, CONFIG, GAMMA, GAMMA_DECREASE, EPSILON, EPSILON_DECREASE, OBJ_FUN, layer_dims, weight_decay, NN_weights, NN_biases, NN_weights_gradients, NN_biases_gradients, PHASE, METHOD, EPOCHS, OUTPUT_DIR, TIMEOUT, MILP_TIMEOUT)\n\n    # N = 15\n    # for LV in [3, 5, 8, 10]:\n    #     test(N, M, [LV], GV, INPUT_CONFIGS, CONFIG, GAMMA, GAMMA_DECREASE, EPSILON, EPSILON_DECREASE, OBJ_FUN, layer_dims, weight_decay, NN_weights, NN_biases, NN_weights_gradients, NN_biases_gradients, PHASE, METHOD, EPOCHS, OUTPUT_DIR, TIMEOUT, MILP_TIMEOUT)\n\n    # N = 17\n    # for LV in [4, 6, 9, 11]:\n    #     test(N, M, [LV], GV, INPUT_CONFIGS, CONFIG, GAMMA, GAMMA_DECREASE, EPSILON, EPSILON_DECREASE, OBJ_FUN, layer_dims, weight_decay, NN_weights, NN_biases, NN_weights_gradients, NN_biases_gradients, PHASE, METHOD, EPOCHS, OUTPUT_DIR, TIMEOUT, MILP_TIMEOUT)\n\n    # N = 19\n    # for LV in [4, 6, 10, 13]:\n    #     test(N, M, [LV], GV, INPUT_CONFIGS, CONFIG, GAMMA, GAMMA_DECREASE, EPSILON, EPSILON_DECREASE, OBJ_FUN, layer_dims, weight_decay, NN_weights, NN_biases, NN_weights_gradients, NN_biases_gradients, PHASE, METHOD, EPOCHS, OUTPUT_DIR, TIMEOUT, MILP_TIMEOUT)\n\n\nif __name__ == '__main__':\n    main()","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":12275,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"237309333","text":"\"\"\"\nMethods for doing peakcalling.\n\nCopyright (c) 2019 10x Genomics, Inc. All rights reserved.\n\"\"\"\nfrom __future__ import division, print_function, absolute_import\n\nfrom collections import namedtuple\nimport numpy as np\nfrom scipy import stats\nfrom analysis.em_fitting import (\n    MLE_dispersion, MLE_geometric_parameter, weighted_mean\n)\n\n# The default count threshold to use both for initializing the parameter estimation and\n# to fall back on when there's not much count data.\nDEFAULT_THRESHOLD = 15\n\nMixedModelParams = namedtuple(\"MixedModelParams\",\n                              [\"p_zero_noise\",\n                               \"mean_bg_noise\",\n                               \"alpha_bg_noise\",\n                               \"p_signal\",\n                               \"frac_zero_noise\",\n                               \"frac_bg_noise\"])\n\n\ndef simple_threshold_estimate(count_dict, cutoff=0.25):\n    \"\"\"Does a simple estimate for the threshold count:\n    pick the threshold such that the lower noise region contains\n    cutoff (default 25%) percentage of the total reads.\n    \"\"\"\n    count_values = np.array(sorted([k for k in count_dict if k > DEFAULT_THRESHOLD]))\n    count_weights = np.array([count_dict[k] for k in count_values])\n    read_counts = count_values * count_weights\n    cum_frac = read_counts.cumsum() / read_counts.sum()\n\n    threshold = count_values[cum_frac <= cutoff][-1] \\\n        if any(cum_frac <= cutoff) else count_values[1]\n    return threshold\n\n\ndef estimate_threshold(params, odds_ratio=20, maxval=1000):\n    \"\"\"Given a set of mixed model parameters and an odds ratio,\n    find the lowest count that has at least that odds\n    ratio of being from the signal rather than a background distribution.\n    \"\"\"\n    xvals = np.arange(maxval)\n    frac_signal = (1 - params.frac_zero_noise - params.frac_bg_noise)\n    yvals_zero = stats.geom.pmf(xvals, params.p_zero_noise, loc=-1)\n    yvals_bg = stats.nbinom.pmf(xvals, 1 / params.alpha_bg_noise,\n                                1 / (1 + params.alpha_bg_noise * params.mean_bg_noise))\n    yvals_sig = stats.geom.pmf(xvals, params.p_signal, loc=-1)\n    yvals_zero = params.frac_zero_noise * yvals_zero\n    yvals_bg = params.frac_bg_noise * yvals_bg\n    yvals_sig = frac_signal * yvals_sig\n\n    yvals_total_bg = yvals_zero + yvals_bg\n\n    for val in xvals[::-1]:\n        if yvals_sig[val] / yvals_total_bg[val] < odds_ratio:\n            return val\n    return None\n\n\ndef calculate_probability_distribution(count_dict, params):\n    \"\"\"Returns the normalized probability that each count comes from the signal or the\n    noise distributions given some parameters for those.\n    Counts where both PMFs are zero are returned with a (non-normalized)\n    zero probability of coming from either.\"\"\"\n    count_values = np.array(sorted([k for k in count_dict]))\n\n    zero_noise_pmf = stats.geom.pmf(count_values, params.p_zero_noise, loc=0)\n    noise_pmf = stats.nbinom.pmf(count_values, 1 / params.alpha_bg_noise,\n                                 1 / (1 + params.alpha_bg_noise * params.mean_bg_noise))\n    signal_pmf = stats.geom.pmf(count_values, params.p_signal, loc=0)\n\n    zero_noise_pmf *= params.frac_zero_noise\n    noise_pmf *= params.frac_bg_noise\n    signal_pmf *= (1 - params.frac_zero_noise - params.frac_bg_noise)\n\n    output = np.vstack((zero_noise_pmf, noise_pmf, signal_pmf))\n    mask = output.sum(axis=0) == 0\n    output[:, mask] = 0.5\n    output /= np.sum(output, axis=0)\n    output[:, mask] = 0.0\n    return output\n\n\ndef weighted_parameter_estimates(count_dict, prob_matrix):\n    \"\"\"Given N counts and a 3 by N probability matrix with the likelihood that\n    each count is from signal or noise\n    distributions, estimate the maximum likelihood parameters for the distributions\n    \"\"\"\n    prob_zero_noise = prob_matrix[0, :]\n    prob_bg_noise = prob_matrix[1, :]\n    prob_signal = prob_matrix[2, :]\n\n    zero_noise_mask = prob_zero_noise > 0\n    bg_noise_mask = prob_bg_noise > 0\n    signal_mask = prob_signal > 0\n\n    count_values = np.array(sorted([k for k in count_dict]))\n    count_weights = np.array([count_dict[k] for k in count_values])\n\n    p_zero_noise = MLE_geometric_parameter(\n        count_values[zero_noise_mask],\n        count_weights[zero_noise_mask] *\n        prob_zero_noise[zero_noise_mask])\n    mean_bg_noise = weighted_mean(\n        count_values[bg_noise_mask],\n        count_weights[bg_noise_mask] *\n        prob_bg_noise[bg_noise_mask])\n    alpha_bg_noise = MLE_dispersion(\n        count_values[bg_noise_mask],\n        count_weights[bg_noise_mask] *\n        prob_bg_noise[bg_noise_mask])\n    p_signal = MLE_geometric_parameter(\n        count_values[signal_mask],\n        count_weights[signal_mask] *\n        prob_signal[signal_mask])\n    frac_zero_noise = (prob_zero_noise * count_weights).sum() / \\\n        (prob_matrix * count_weights).sum()\n    frac_bg_noise = (prob_bg_noise * count_weights).sum() / \\\n        (prob_matrix * count_weights).sum()\n\n    return MixedModelParams(p_zero_noise, mean_bg_noise, alpha_bg_noise, p_signal,\n                            frac_zero_noise, frac_bg_noise)\n\n\ndef normalize_params(params):\n    \"\"\"Make sure that the larger of the two NB distributions is\n    always annotated as signal\"\"\"\n    if params.p_zero_noise < params.p_signal:\n        return MixedModelParams(params.p_signal,\n                                params.mean_bg_noise,\n                                params.alpha_bg_noise,\n                                params.p_zero_noise,\n                                1 - (params.frac_zero_noise + params.frac_bg_noise),\n                                params.frac_bg_noise)\n    return params\n\n\ndef estimate_parameters(count_dict, epsilon=1e-6, maxiter=500, seed_params=None):\n    \"\"\"Uses an expectation-maximization method to estimate the mixed model parameters.\n    An initial guess is made by segmenting the count data into three regions,\n    then parameters are iteratively estimated from these.\"\"\"\n    threshold = simple_threshold_estimate(count_dict)\n\n    count_values = np.array(sorted([k for k in count_dict]))\n\n    prob_matrix = np.empty((3, len(count_values)), dtype=float)\n    prob_matrix[0, :] = count_values <= DEFAULT_THRESHOLD\n    prob_matrix[1, :] = (count_values <= threshold) & (count_values > DEFAULT_THRESHOLD)\n    prob_matrix[2, :] = count_values > threshold\n\n    params = weighted_parameter_estimates(count_dict, prob_matrix) if seed_params is None else seed_params\n    last_params = None\n    i = 0\n    # pylint: disable=unsubscriptable-object\n    while i < maxiter and (\n            last_params is None or any(\n        [abs(last_params[k] - params[k]) / params[k] > epsilon for k in\n         range(len(params))])\n    ):\n        i += 1\n        prob_matrix = calculate_probability_distribution(count_dict, params)\n        last_params = params\n        params = normalize_params(weighted_parameter_estimates(count_dict, prob_matrix))\n    return params\n\n\ndef estimate_final_threshold(count_dict, odds_ratio=20):\n    \"\"\"Provided with a count dictionary, estimate the peak threshold for the count data.\n    \"\"\"\n    if len(count_dict) < 30 or sum(count_dict.values()) < 1e5:\n        return DEFAULT_THRESHOLD, None\n    TRIES = 3\n    tries = 0\n    params = estimate_parameters(count_dict)\n\n    # if the zero inflation mean is smaller than negative binom noise mean, swap and redo\n    while 1 / params.p_zero_noise < params.mean_bg_noise and tries < TRIES:\n        new_params = MixedModelParams(params.p_signal,\n                                      1 / params.p_zero_noise,\n                                      params.alpha_bg_noise,\n                                      1 / params.mean_bg_noise,\n                                      1 - (params.frac_zero_noise + params.frac_bg_noise),\n                                      params.frac_bg_noise)\n        params = estimate_parameters(count_dict, seed_params=new_params)\n        tries += 1\n\n    return estimate_threshold(params, odds_ratio), params\n","sub_path":"lib/python/analysis/peaks.py","file_name":"peaks.py","file_ext":"py","file_size_in_byte":7960,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"415121394","text":"import sys\nimport os\nimport io\n\ndocument_root = '/home/user/myapp/documents'\n\n\ndef application(environ, start_response):\n\tstart_response('200 OK', [('Content-Type', 'text/html')])\n\n\turi = ''\n\tif (not ('REQUEST_URI' in environ)) or (not environ['REQUEST_URI']) or (environ['REQUEST_URI']=='/') or (environ['REQUEST_URI'].startswith('/?')):\n\t\turi = '/index'\n\telse:\n\t\turi = environ['REQUEST_URI'].split('?')[0]\n\t\tif len(uri)>1 and uri[-1]=='/':\n\t\t\turi = uri[0:-1]\n\n\tpath = document_root + uri + '.html'\n\n\tif not os.path.isfile(path):\n\t\treturn b'';\n\twith io.open(path, mode='r', encoding='utf-8') as content_file:\n\t\tcontent = content_file.read()\n\t\treturn content.encode('utf-8')\n\n\n# def console_start_response(status, headers):\n# \tprint 'Status: ' + status\n\n\n# if __name__ == '__main__':\n# \tenviron = dict()\n# \tenviron['REQUEST_URI'] = '/' + sys.argv[1]\n# \tprint environ['REQUEST_URI']\n# \tprint application(environ, console_start_response)\n","sub_path":"wsgi.py","file_name":"wsgi.py","file_ext":"py","file_size_in_byte":936,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"276223120","text":"\"\"\"OpenAQ Air Quality Dashboard with Flask.\"\"\"\nfrom flask import Flask, render_template, request\nfrom flask_sqlalchemy import SQLAlchemy\nimport openaq\nimport requests\n\n\n\n\"\"\"Setting an instance of the Flask App\"\"\"\nAPP = Flask(__name__)\nAPP.config['SQLALCHEMY_DATABASE_URI'] = \"sqlite:///db.sqlite3\"\nAPP.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\nDB = SQLAlchemy(APP)\nAPI = openaq.OpenAQ()\n    # Pointing to the location database will populate.\n\n@APP.route('/')\ndef root():\n    \"\"\"Base view.\"\"\"\n    return str(Record.query.filter(Record.value >=10).all)\n\n@APP.route('/refresh')\ndef refresh():\n    \"\"\"Pull fresh data from Open AQ and replace existing data.\"\"\"\n    DB.drop_all()\n    DB.create_all()\n    request = api.measurements(city='Los Angeles', parameter='pm25')\n    status, resp = request\n    date = []\n    for i in resp['results']:\n        date.append(i['date'])\n        DB.session.add(Record(datetime=date))\n    DB.session.commit()\n    return 'Data refreshed!'\n\nclass Record(DB.Model):\n    id = DB.Column(DB.Integer, primary_key=True)\n    datetime = DB.Column(DB.String(25))\n    value = DB.Column(DB.Float, nullable=False)\n\n    def __repr__(self):\n        return '<Record: {} >'.format(self.datetime, self.value)\n\nif __name__ == '__main__':\n    root()\n","sub_path":"sprint-challenge/aq_dashboard.py","file_name":"aq_dashboard.py","file_ext":"py","file_size_in_byte":1262,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"552123202","text":"\nfrom typing import final\nimport Definitions\nimport random\n\ndef userChoice(): \n    userAnswer = input(\"You have two options from this program. If you want to know how many days are left in the camp type days. if you want to know how long until you are any age type age: \")\n    if userAnswer == \"age\":\n        lowerAge = Definitions.lowerAge()\n        finalYear = random.randint(lowerAge,100)\n        Definitions.ageTo(lowerAge, finalYear)\n    elif userAnswer == \"days\":\n        daysLeft = Definitions.campDays()\n        print(f\"There are {daysLeft} days left.\")\n    else:\n        userChoice()\n\nuserChoice()","sub_path":"callupfile.py","file_name":"callupfile.py","file_ext":"py","file_size_in_byte":606,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"261923516","text":"\"\"\"pur_beurre_project URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/3.0/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.conf.urls import include, url\nfrom django.contrib import admin\nfrom django.urls import path\nfrom webapp import views\nimport debug_toolbar\n\nurlpatterns = [\n    url(r\"^$\", views.IndexView.as_view(), name=\"index\"),\n    url(r\"^webapp/\", include(\"webapp.urls\")),\n    url(r\"^userapp/\", include(\"userapp.urls\")),\n    url(\"admin/\", admin.site.urls),\n    url(r\"^legalmention/\", views.legal_mention, name=\"legalmention\"),\n    url(r\"^account/\", views.account, name=\"account\"),\n    url(r\"^saved_products/\", views.saved_products, name=\"saved_products\"),\n    path(\"accounts/\", include(\"userapp.urls\")),\n    path(\"accounts/\", include(\"django.contrib.auth.urls\")),\n    url(r\"^search/$\", views.search, name=\"search\"),\n    url(r\"^autocomplete/$\", views.ProductAutocomplete.as_view(), name=\"autocomplete\",),\n    url(r\"^search_help/$\", views.SearchHelpView.as_view(), name=\"search_help\"),\n    url(r\"^product/(?P<product_id>[0-9]+)/$\", views.product, name=\"product\"),\n    url(r\"^save_product/$\", views.save_product, name=\"save_product\"),\n    url(r\"^ajax_saved_products/\", views.ajax_saved_products, name=\"ajax_saved_products\",),\n    url(r\"^ajax_search/\", views.ajax_search, name=\"ajax_search\",),\n]\n\nurlpatterns = [url(r\"^__debug__/\", include(debug_toolbar.urls)), ] + urlpatterns\n","sub_path":"pur_beurre_project/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1914,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"144594905","text":"from __future__ import absolute_import\n\nfrom fabric.api import env, local, run\nfrom fabric.context_managers import hide\n\nfrom ..fabric.base import BaseTask, ConflictingTask\nfrom soda.misc import display\n\n\n__all__ = [\n    'display_version',\n    'update_sources',\n    'check_local_remote',\n]\n\n\nclass DisplayVersionTask(BaseTask):\n    \"\"\"Display current project revision\n    \"\"\"\n\n    name = 'display_version'\n\n    def run(self):\n        display.info('Retrieving current app version...')\n        with hide('everything'), self.user, self.in_app:\n            print(run('git log -1'))\n\n\nclass UpdateSourcesTask(ConflictingTask):\n        \"\"\"Fetch sources from default remote and checkout to revision\n        \"\"\"\n\n        name = 'update_sources'\n\n        def run(self, revision):\n            with hide('everything'), self.user, self.in_app:\n                # Get the used remote name\n                git_remote = run('git remote')\n\n                # Update the app source code\n                display.info(\n                    'Fetching source code from \"{}\"...'.format(git_remote))\n                print(run('git fetch -p {}'.format(git_remote)))\n\n                # Check working directory\n                display.info('Checking app directory...')\n                git_status = run('git status --porcelain -uno').strip()\n                if git_status:\n                    print(git_status)\n                    display.error(\n                        'App directory is dirty.', abort_task=not env.force)\n\n                # Check out to specified revision\n                display.info('Checking out to specified revision...')\n                print(run('git checkout -f {}/{}'.format(\n                    git_remote, revision)))\n\n\nclass CheckLocalRemoteTask(BaseTask):\n        \"\"\"Abort if local and remote ref don't match\n        \"\"\"\n\n        name = 'check_local_remote'\n\n        def run(self, revision):\n            display.info('Checking local and remote revisions...')\n            with hide('everything'), self.user, self.in_app:\n                # Check local revision\n                local_branch = local(\n                    'git rev-parse --abbrev-ref HEAD', capture=True)\n                if local_branch != revision:\n                    display.error(\n                        'You are currently not at {}.'.format(revision),\n                        abort_task=not env.force)\n\n                # Get the hash of the latest commit in `revision` (remote)\n                git_remote = run('git remote')\n                remote_hash = run(\n                    'git rev-parse --short --verify {}/{}'.format(\n                        git_remote, revision))\n\n                # Get the hash of the latest commit in `revision` (local)\n                local_hash = local(\n                    'git rev-parse --short --verify {}'.format(revision),\n                    capture=True\n                )\n\n            # Compare local and remote commit hashes\n            if local_hash != remote_hash:\n                display.error(\n                    'The local and remote revisions must match. \\\n                    You may need to update one of both of them.',\n                    abort_task=not env.force)\n\n            display.success('Revisions match.')\n\n\ndisplay_version = DisplayVersionTask()\nupdate_sources = UpdateSourcesTask()\ncheck_local_remote = CheckLocalRemoteTask()\n","sub_path":"soda/deploy/git.py","file_name":"git.py","file_ext":"py","file_size_in_byte":3353,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"242460155","text":"# -*- coding: utf-8 -*-\n\"\"\"Tests using pytest_resilient_circuits\"\"\"\n\nimport pytest\nfrom resilient_circuits.util import get_config_data, get_function_definition\nfrom resilient_circuits import SubmitTestFunction, FunctionResult\nfrom mock import patch\nfrom fn_qradar_integration.util.qradar_constants import PACKAGE_NAME\n\nFUNCTION_NAME = \"qradar_reference_table_add_item\"\n\n# Read the default configuration-data section from the package\nconfig_data = get_config_data(PACKAGE_NAME)\n\n# Provide a simulation of the Resilient REST API (uncomment to connect to a real appliance)\nresilient_mock = \"pytest_resilient_circuits.BasicResilientMock\"\n\nMOCK_ADD_RESPONSE = {\n  \"time_to_live\": \"999 years 0 mons 0 days 0 hours 0 mins 0.00 secs\",\n  \"timeout_type\": \"LAST_SEEN\",\n  \"number_of_elements\": 555,\n  \"creation_time\": 1570221529014,\n  \"name\": \"demo_v3\",\n  \"namespace\": \"SHARED\",\n  \"element_type\": \"ALN\",\n  \"collection_id\": 86\n}\n\nMOCK_ADD_RESPONSE_UNICODE = {\n  \"time_to_live\": \"999 years 0 mons 0 days 0 hours 0 mins 0.00 secs\",\n  \"timeout_type\": \"LAST_SEEN\",\n  \"number_of_elements\": 555,\n  \"creation_time\": 1570221529014,\n  \"name\": \"演示版vz\",\n  \"namespace\": \"SHARED\",\n  \"element_type\": \"ALN\",\n  \"collection_id\": 86\n}\n\ndef call_qradar_reference_table_add_item_function(circuits, function_params, timeout=5):\n    # Create the submitTestFunction event\n    evt = SubmitTestFunction(\"qradar_reference_table_add_item\", function_params)\n\n    # Fire a message to the function\n    circuits.manager.fire(evt)\n\n    # circuits will fire an \"exception\" event if an exception is raised in the FunctionComponent\n    # return this exception if it is raised\n    exception_event = circuits.watcher.wait(\"exception\", parent=None, timeout=timeout)\n\n    if exception_event is not False:\n        exception = exception_event.args[1]\n        raise exception\n\n    # else return the FunctionComponent's results\n    else:\n        event = circuits.watcher.wait(\"qradar_reference_table_add_item_result\", parent=evt, timeout=timeout)\n        assert event\n        assert isinstance(event.kwargs[\"result\"], FunctionResult)\n        pytest.wait_for(event, \"complete\", True)\n        return event.kwargs[\"result\"].value\n\nclass TestQradarReferenceTableAddItem:\n    \"\"\" Tests for the qradar_reference_table_add_item function\"\"\"\n\n    def test_function_definition(self):\n        \"\"\" Test that the package provides customization_data that defines the function \"\"\"\n        func = get_function_definition(PACKAGE_NAME, FUNCTION_NAME)\n        assert func is not None\n\n    mock_inputs_1 = {\n        \"qradar_reference_table_name\": \"sample text\",\n        \"qradar_reference_table_item_value\": \"sample text\",\n        \"qradar_label\": \"SOAR_Plugin_Destination_Name1\"\n    }\n\n    expected_results_1 = MOCK_ADD_RESPONSE\n\n    mock_inputs_2 = {\n        \"qradar_reference_table_name\": \"sample text\",\n        \"qradar_reference_table_item_value\": \"sample text\",\n        \"qradar_label\": \"SOAR_Plugin_Destination_Name2\"\n    }\n\n    expected_results_2 = MOCK_ADD_RESPONSE_UNICODE\n\n    @pytest.mark.parametrize(\"mock_inputs, expected_results\", [\n        (mock_inputs_1, expected_results_1),\n        (mock_inputs_2, expected_results_2)\n    ])\n    def test_success(self, circuits_app, mock_inputs, expected_results):\n        \"\"\" Test calling with sample values for the parameters \"\"\"\n\n        with patch('fn_qradar_integration.lib.reference_data.ReferenceTableFacade.ReferenceTableFacade.add_ref_element') as patched_add_element:\n            patched_add_element.return_value = expected_results\n            results = call_qradar_reference_table_add_item_function(circuits_app, mock_inputs)\n            assert(expected_results == results['content'])\n","sub_path":"fn_qradar_integration/tests/test_funct_qradar_reference_table_add_item.py","file_name":"test_funct_qradar_reference_table_add_item.py","file_ext":"py","file_size_in_byte":3675,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"464582068","text":"import boto3\nimport simplejson as json\nimport pandas as pd\nimport sqlalchemy\nimport batchproc as bp\nimport time\n\n# Redshift Credentials\n\ndatabase = {\n    'drivername': 'postgresql+psycopg2',\n    'host': 'host',\n    'username': 'rs_un',\n    'password': 'rs_pw',\n    'port': '5439',\n    'database': 'analytics',\n    }\n\n# Create Redshift Connection\n\ncon = sqlalchemy.create_engine(sqlalchemy.engine.url.URL(**database))\n\n# Connect to S3 via boto & get obejcts\n\nsession = boto3.Session(profile_name='leaflink')\ns3 = session.resource('s3')\nbucket = s3.Bucket('leafliink-data-interview-exercise')\nobjects = [obj.key for obj in bucket.objects.all() if len(obj.key) > 3]\n\n\n# Parse Objects & write to table\n\ndef extract_and_load_data(obj):\n\n    file = s3.Object('leafliink-data-interview-exercise', obj)\n    split_json = file.get()['Body'].read().decode('utf-8').splitlines()\n    data = [pd.json_normalize(json.loads(i)) for i in split_json]\n    all_data = pd.concat(data)\n\n    all_data.to_sql(\n        'table_name',\n        schema='table_schema',\n        con=con,\n        chunksize=10000,\n        method='multi',\n        if_exists='append',\n        index=False,\n        )\n\n    return obj + ' loaded at {}'.format(str(time.time()))\n\n\nif __name__ == '__main__':\n\n    # Generate process to extract & load the data & write progress to a log file.\n    log_filename = 'batch_process.log'\n    processor = bp.BatchProcessor(objects, extract_and_load_data,\n                                  log_filename=log_filename)\n\n    # apply our data-processing function, extract & load data from the s3 bucket.\n    processor.start()\n","sub_path":"leaflink.py","file_name":"leaflink.py","file_ext":"py","file_size_in_byte":1606,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"430525888","text":"#!/usr/bin/env python\n# Import necessary modules\nfrom netCDF4 import Dataset\nimport numpy as np\nimport shutil\nimport sys, getopt\n\ndef find_index(lat,lon,lat0,lon0):\n    \"\"\"\n    Find and return the (j,i) index of the input data arrays (lat,lon) \n    corresponding to the element closest to input (lat0,lon0). \n    \"\"\"\n    if(lon0 < lon.min()): \n        lon0=lon0+360.\n    elif(lon0 > lon.max()): \n        lon0=lon0-360.\n    \n    jdx = (np.abs(lat - lat0)).argmin()\n    idx = (np.abs(lon - lon0)).argmin()\n    return jdx,idx\n#test: \n#j,i = find_index(lon,lat,lon0,lat0)\n#print(j,i,lon[i],lat[j])\n#159 480 270.0 -0.280810890730407\n\ndef homogenize(var,lat,lon,lat0,lon0):\n    \"\"\"\n    Given an input 3D array var(k,j,i) compute and return a 3D array hom(k,j,i)\n    where all elements in a layer k are replaced by the same constant value\n    calculated from a 4-point average of the values of input array var around the\n    point closest to the input lat0,lon0.\n    var: 3D array (usually with time,lat,lon axis)\n    lat,lon: 1D arrays of latitudes and longitudes corresponding to the second \n    and third axis of var\n    lat0,lon0: constant latitude, longitude of the point to homogenize around\n    \"\"\"\n    j,i = find_index(lat,lon,lat0,lon0)\n    hom = np.zeros_like(var)\n    for k in range(0,var.shape[0]):\n        hom[k] = 0.25*(var[k,j,i]+var[k,j+1,i]+var[k,j,i+1]+var[k,j+1,i+1])\n    return hom\n\n\ndef main(argv):\n\n    import argparse\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--inputfile\", help=\"path to input data file\", required=True, type=str)\n    parser.add_argument(\"--outputfile\", help=\"path to output data file\", required=True, type=str)\n    parser.add_argument(\"--variable\", help=\"variable in the input data file to be homogenized\", required=True, type=str)\n    parser.add_argument(\"--lon0\", help=\"longitude of the point to be homogenized around\", type=float, default=-90)\n    parser.add_argument(\"--lat0\", help=\"lattitude of the point to be homogenized around\", type=float, default=0.0)\n    args = parser.parse_args()\n    in_file = args.inputfile\n    out_file = args.outputfile\n    varname = args.variable\n    lon0 = args.lon0\n    lat0 = args.lat0\n\n    shutil.copyfile(in_file,out_file)\n    nc_fid = Dataset(out_file, 'r+')\n\n    var= nc_fid.variables[varname][:]\n    lon= nc_fid.variables['lon'][:]\n    lat= nc_fid.variables['lat'][:]\n    hom_var = homogenize(var,lat,lon,lat0,lon0)\n    nc_fid.variables[varname][:] = hom_var\n    nc_fid.close()\n\n\nif __name__ == \"__main__\":\n   main(sys.argv[1:])\n\n#Usage examples:\n#for var in huss prra prsn psl rlds rsds tas uas vas; do  echo $var;  homogenize_netcdf_forcings.py --inputfile JRA_$var.nc  --outputfile hom_JRA_$var.nc  --variable $var --lon0=-90. --lat0=0.0 ;done\n","sub_path":"tools/homogenize_netcdf_forcings.py","file_name":"homogenize_netcdf_forcings.py","file_ext":"py","file_size_in_byte":2745,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"157478184","text":"import matplotlib.pyplot as plt\nimport matplotlib.font_manager as fm\nfrom matplotlib.animation import FuncAnimation\n\nsophia_regular_otf = \"Sofia-Regular.otf\"\nsophia_regular = fm.FontProperties(fname = sophia_regular_otf, size = 20)\n\nclass ChangeColorSackBoy:\n    def __init__(self):\n        self.fig, self.ax = plt.subplots()\n        img_mat = plt.imread(\"sackboy.png\")\n        self.img = self.ax.imshow(img_mat)\n        self.info = \" Click on a node to change it's color.\"\n\n        extent = self.img.get_extent()\n        self.width = abs(extent[0]-extent[1])\n        self.height = abs(extent[2]-extent[3])\n\n        new_extent = (extent[0], extent[1], extent[3], extent[2])\n        self.img.set_extent(new_extent)\n\n        self.ax.set_xlim([-self.width, 2*self.width])\n        self.ax.set_ylim([0, 2*self.height])\n\n        self.text = self.ax.text(0.5*self.width, 1.2*self.height, self.info, \\\n                                 fontproperties = sophia_regular, ha = \"center\", va = \"center\")\n        self.text.set_rotation_mode(\"anchor\")\n\n        self.ax.tick_params(colors = (0,0,0,0))\n\n    def show(self):\n        self.fig.show()\n\n    def animation(self, frame):\n        self.text.set_rotation(math.cos(frame)*20)\n        \n    def animate(self):\n        anim = FuncAnimation(self.fig, self.animation, frames = [i*2*math.pi/200 for i in range(201)], \\\n                             interval = 1, repeat = True)\n        plt.show(block = True)\n        return anim\n\nsackboy = ChangeColorSackBoy()\nsackboy.animate()\n","sub_path":"examples/Sackboy_animation/sackboy_anim.py","file_name":"sackboy_anim.py","file_ext":"py","file_size_in_byte":1510,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"420232202","text":"# coding: utf-8\n\nfrom __future__ import absolute_import\nfrom datetime import date, datetime  # noqa: F401\n\nfrom typing import List, Dict  # noqa: F401\n\nfrom samsara.models.base_model_ import Model\nfrom samsara.models.dispatch_route import DispatchRoute  # noqa: F401,E501\nfrom samsara import util\n\n\nclass DispatchRouteHistoricalEntry(Model):\n    \"\"\"NOTE: This class is auto generated by the swagger code generator program.\n\n    Do not edit the class manually.\n    \"\"\"\n\n    def __init__(self, changed_at_ms: int=None, route: DispatchRoute=None):  # noqa: E501\n        \"\"\"DispatchRouteHistoricalEntry - a model defined in Swagger\n\n        :param changed_at_ms: The changed_at_ms of this DispatchRouteHistoricalEntry.  # noqa: E501\n        :type changed_at_ms: int\n        :param route: The route of this DispatchRouteHistoricalEntry.  # noqa: E501\n        :type route: DispatchRoute\n        \"\"\"\n        self.swagger_types = {\n            'changed_at_ms': int,\n            'route': DispatchRoute\n        }\n\n        self.attribute_map = {\n            'changed_at_ms': 'changed_at_ms',\n            'route': 'route'\n        }\n\n        self._changed_at_ms = changed_at_ms\n        self._route = route\n\n    @classmethod\n    def from_dict(cls, dikt) -> 'DispatchRouteHistoricalEntry':\n        \"\"\"Returns the dict as a model\n\n        :param dikt: A dict.\n        :type: dict\n        :return: The DispatchRouteHistoricalEntry of this DispatchRouteHistoricalEntry.  # noqa: E501\n        :rtype: DispatchRouteHistoricalEntry\n        \"\"\"\n        return util.deserialize_model(dikt, cls)\n\n    @property\n    def changed_at_ms(self) -> int:\n        \"\"\"Gets the changed_at_ms of this DispatchRouteHistoricalEntry.\n\n        Timestamp that the route was updated, represented as Unix milliseconds since epoch.  # noqa: E501\n\n        :return: The changed_at_ms of this DispatchRouteHistoricalEntry.\n        :rtype: int\n        \"\"\"\n        return self._changed_at_ms\n\n    @changed_at_ms.setter\n    def changed_at_ms(self, changed_at_ms: int):\n        \"\"\"Sets the changed_at_ms of this DispatchRouteHistoricalEntry.\n\n        Timestamp that the route was updated, represented as Unix milliseconds since epoch.  # noqa: E501\n\n        :param changed_at_ms: The changed_at_ms of this DispatchRouteHistoricalEntry.\n        :type changed_at_ms: int\n        \"\"\"\n\n        self._changed_at_ms = changed_at_ms\n\n    @property\n    def route(self) -> DispatchRoute:\n        \"\"\"Gets the route of this DispatchRouteHistoricalEntry.\n\n\n        :return: The route of this DispatchRouteHistoricalEntry.\n        :rtype: DispatchRoute\n        \"\"\"\n        return self._route\n\n    @route.setter\n    def route(self, route: DispatchRoute):\n        \"\"\"Sets the route of this DispatchRouteHistoricalEntry.\n\n\n        :param route: The route of this DispatchRouteHistoricalEntry.\n        :type route: DispatchRoute\n        \"\"\"\n\n        self._route = route\n","sub_path":"swagger-codegen/python-flask/samsara/models/dispatch_route_historical_entry.py","file_name":"dispatch_route_historical_entry.py","file_ext":"py","file_size_in_byte":2902,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"440138740","text":"import json\nimport os\nimport queue\nimport threading\nimport time\n\nimport websocket\n\nWS_PORT = os.environ.get(\"WS_PORT\", \"35496\")\n\nchats = {}\ncontexts = {}\n\n\ndef on_message(ws, message):\n    m = json.loads(message)\n    contexts[ws.chat_id].bot.send_message(chat_id=ws.chat_id, text=m['text'])\n\n\ndef on_error(ws, error):\n    print(error)\n\n\ndef on_close(ws):\n    print(\"Connection closed\")\n\n\ndef _run(ws, username):\n    ws.send(f'{{\"id\": \"{username}\", \"text\": \"ping\"}}')\n    time.sleep(1)  # wait for the task to start.\n    while True:\n        x = chats[ws.chat_id].get()\n        if \"bye\" in x.lower():\n            break\n        data = {\"id\": username, \"text\": x}\n        json_data = json.dumps(data)\n        ws.send(json_data)\n\n    del chats[ws.chat_id]\n    ws.close()\n\n\ndef on_open(ws):\n    threading.Thread(target=_run, args=(ws, ws.username)).start()\n\n\ndef connect_ws(chat_id, username):\n    ws = websocket.WebSocketApp(\n        f\"ws://localhost:{WS_PORT}/websocket\",\n        on_message=on_message,\n        on_error=on_error,\n        on_close=on_close,\n    )\n    ws.chat_id = chat_id\n    ws.username = username\n    ws.on_open = on_open\n    ws.run_forever()\n","sub_path":"parlai/chat_service/services/telegram/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":1157,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"157738140","text":"error_list = []\nfor link in links:\n    try:\n        validate(links[link], schema)\n    except exceptions.ValidationError as ve:\n        error_list.append(links[link])\n        print(ve.message)\nfor error in error_list:\n    print(error)\n\n","sub_path":"stage1/bluetooth/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":235,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"108922804","text":"\"\"\"empty message\n\nRevision ID: dt_cleanup\nRevises: begin\nCreate Date: 2019-10-01 11:51:09.265966\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import mysql\n\n# revision identifiers, used by Alembic.\nrevision = 'dt_cleanup'\ndown_revision = 'begin'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.create_table('tags',\n    sa.Column('id', sa.Integer(), nullable=False),\n    sa.Column('name', sa.String(length=128), nullable=True),\n    sa.Column('user_id', sa.Integer(), nullable=True),\n    sa.ForeignKeyConstraint(['user_id'], ['users.id'], ),\n    sa.PrimaryKeyConstraint('id')\n    )\n    op.create_table('tags_documents',\n    sa.Column('tag_id', sa.Integer(), nullable=False),\n    sa.Column('document_id', sa.Integer(), nullable=False),\n    sa.ForeignKeyConstraint(['document_id'], ['documents.id'], ),\n    sa.ForeignKeyConstraint(['tag_id'], ['tags.id'], ),\n    sa.PrimaryKeyConstraint('tag_id', 'document_id')\n    )\n    op.drop_table('projects')\n    op.drop_table('tasks')\n    op.drop_table('user_projects')\n    op.add_column('documentlabels', sa.Column('timestamp', sa.DateTime(), nullable=True))\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_column('documentlabels', 'timestamp')\n    op.create_table('user_projects',\n    sa.Column('user_id', mysql.INTEGER(display_width=11), autoincrement=False, nullable=False),\n    sa.Column('project_id', mysql.INTEGER(display_width=11), autoincrement=False, nullable=False),\n    sa.ForeignKeyConstraint(['project_id'], ['projects.id'], name='user_projects_ibfk_1'),\n    sa.ForeignKeyConstraint(['user_id'], ['users.id'], name='user_projects_ibfk_2'),\n    sa.PrimaryKeyConstraint('user_id', 'project_id'),\n    mysql_collate='utf8mb4_0900_ai_ci',\n    mysql_default_charset='utf8mb4',\n    mysql_engine='InnoDB'\n    )\n    op.create_table('tasks',\n    sa.Column('id', mysql.INTEGER(display_width=11), autoincrement=True, nullable=False),\n    sa.Column('project_id', mysql.INTEGER(display_width=11), autoincrement=False, nullable=True),\n    sa.Column('task_content', mysql.TEXT(), nullable=True),\n    sa.Column('answer', mysql.INTEGER(display_width=11), autoincrement=False, nullable=True),\n    sa.Column('_metadata', sa.BLOB(), nullable=True),\n    sa.ForeignKeyConstraint(['project_id'], ['projects.id'], name='tasks_ibfk_1'),\n    sa.PrimaryKeyConstraint('id'),\n    mysql_collate='utf8mb4_0900_ai_ci',\n    mysql_default_charset='utf8mb4',\n    mysql_engine='InnoDB'\n    )\n    op.create_table('projects',\n    sa.Column('id', mysql.INTEGER(display_width=11), autoincrement=True, nullable=False),\n    sa.Column('name', mysql.VARCHAR(length=128), nullable=True),\n    sa.Column('created', mysql.DATETIME(), nullable=True),\n    sa.Column('active', mysql.TINYINT(display_width=1), autoincrement=False, nullable=True),\n    sa.Column('task_preface', mysql.TEXT(), nullable=True),\n    sa.CheckConstraint('(`active` in (0,1))', name='projects_chk_1'),\n    sa.PrimaryKeyConstraint('id'),\n    mysql_collate='utf8mb4_0900_ai_ci',\n    mysql_default_charset='utf8mb4',\n    mysql_engine='InnoDB'\n    )\n    op.drop_table('tags_documents')\n    op.drop_table('tags')\n    # ### end Alembic commands ###\n","sub_path":"mysite/migrations/versions/dt_cleanup_.py","file_name":"dt_cleanup_.py","file_ext":"py","file_size_in_byte":3311,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"110467320","text":"import os\nimport time \nimport datetime\nfrom datetime import date, datetime, timedelta\nimport mysql.connector                                        \n\n\n#FUNCTION THAT CHECKS WETHER TABLE 'table' exists.\ndef notExistTable(cursor,table):\n    _SQL = \"\"\"SHOW TABLES\"\"\"\n    cursor.execute(_SQL)\n    results = cursor.fetchall()\n\n    #print('All existing tables:', results) # Returned as a list of tuples\n\n    results_list = [item[0] for item in results] # Conversion to list of str\n\n    if table in results_list:\n        return False\n    else:\n        return True\n                                                  \n\n\nnotAvailable=True;\nwhile notAvailable:\n    try:\n        USER = os.getenv('MYSQL_USER', 'user')\n        PW = os.getenv('MYSQL_PASSWORD', 'confluent')\n        HOST = os.getenv('MYSQL_HOST','mysql-python')\n        DB = os.getenv('MYSQL_DATABASE','connect_test')\n        cnx = mysql.connector.connect(user=USER, password=PW, host=HOST, database=DB)\n        notAvailable=False\n    except:\n        print(\"Mysql connection not available\");\n        time.sleep(30)\n\n\ncursor = cnx.cursor()\n\n\n\n\nTABLES_NAMES = os.getenv('SINK_TOPICS',' ')\nprint ('READING TOPICS: ',TABLES_NAMES)\n\nTABLES = TABLES_NAMES.split(',')\n\nwhile True:\n    for table in TABLES:\n#        print('Accessing table: ',table)\n        if notExistTable(cursor,table):\n#            print ('   it does not exist')\n            continue\n\n        query = (\"SELECT * from \" + table)    \n        cursor.execute(query)\n        records = cursor.fetchall()\n        for row in records:\n            print (row)\n\ncursor.close()\ncnx.close()\n","sub_path":"AccessAll/app/src/accessAll.py","file_name":"accessAll.py","file_ext":"py","file_size_in_byte":1591,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"407660029","text":"from TreeNode import TreeNode\n\n\nclass BSTree:\n    def __init__(self):\n        self._root = None\n        self._size = 0\n\n    def pre_order_traversal(self):\n        def pre_order(node):\n\n            if node is None:\n                return\n            yield node.data\n            yield from pre_order(node.left_node)\n            yield from pre_order(node.right_node)\n        yield from pre_order(self._root)\n\n    def in_order_traversal(self):\n        def in_order(node):\n\n            if node is None:\n                return\n            yield from in_order(node.left_node)\n            yield node.data\n            yield from in_order(node.right_node)\n        yield from in_order(self._root)\n\n    def post_order_traversal(self):\n        def post_order(node):\n\n            if node is None:\n                return\n            yield from post_order(node.left_node)\n            yield from post_order(node.right_node)\n            yield node.data\n        yield from post_order(self._root)\n\n    @property\n    def size(self):\n        return self._size\n\n    def __contains__(self, data):\n        def _contains(node, data):\n            return (\n                False if node is None else\n                _contains(node.left_node, data) if data < node.data else\n                _contains(node.right_node, data) if data > node.data else\n                True\n            )\n        return _contains(self._root, data)\n\n    def add(self, data):\n        def _insert(node, data):\n\n            if node is None:\n                return TreeNode(data)\n\n            elif data == node.data:\n                return None\n\n            elif data < node.data:\n                node.left_node = _insert(node.left_node, data)\n\n            elif data > node.data:\n                node.right_node = _insert(node.right_node, data)\n            return node\n\n        self._root = _insert(self._root, data)\n\n        if self._root:\n            self._size += 1\n\n        return self._root is not None\n\n    def delete(self, data):\n        def _remove(node, data):\n            if node.data == data:\n                if not (node.left_node and node.right_node):\n\n                    return node.left_node or node.right_node, True\n\n                else:\n                    successor, parent = node.right_node, node\n\n                    while successor.left_node:\n\n                        successor, parent = successor.left_node, successor\n\n                    successor.left_node = node.left_node\n\n                    if parent != node:\n                        parent.left_node = successor.right_node\n                        successor.right_node = node.right_node\n\n                    return successor, True\n\n            elif data < node.data and node.left_node:\n                node.left_node, removed = _remove(node.left_node, data)\n\n                return node, removed\n\n            elif data > node.data and node.right_node:\n                node.right_node, removed = _remove(node.right_node, data)\n\n                return node, removed\n\n            return node, False\n\n        if self._root is None:\n            return False\n\n        self._root, removed = _remove(self._root, data)\n        self._size -= int(removed)\n\n        return removed\n\n    def isEmpty(self):\n        if self._root is None:\n            return True\n        else:\n            return False\n\n    def __str__(self):\n        s = \"Arbol vacío\"\n        count = 0\n\n        if self.size:\n            s = \"Pre order: [\"\n\n            for i in self.pre_order_traversal():\n                if count == 0:\n                    s += str(i)\n                else:\n                    s += \", \" + str(i)\n\n                count += 1\n\n            s += \"] \\nIn order: [\"\n            count = 0\n\n            for x in self.in_order_traversal():\n                if count == 0:\n                    s += str(x)\n                else:\n                    s += \", \" + str(x)\n\n                count += 1\n\n            s += \"] \\nPost order: [\"\n            count = 0\n\n            for y in self.post_order_traversal():\n                if count == 0:\n                    s += str(y)\n                else:\n                    s += \", \" + str(y)\n\n                count += 1\n            s += \"]\"\n\n            return (s)\n\n        else:\n            return (s)\n","sub_path":"BSTree.py","file_name":"BSTree.py","file_ext":"py","file_size_in_byte":4241,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"168626745","text":"'''\nAluno: Welton Antonio Gomes\nRA: 2840481713038\n\nProva - Tópicos Especiais em Informática\n26/04/2018\n\n'''\n# Definições gerais\nimport locale\nimport os\nimport json\nfrom operator import itemgetter, attrgetter\nlocale.setlocale(locale.LC_ALL,\"\")\n\n\n'''\nQuestão 1 - Menu:\n'''\ndef menu():\n    os.system('cls')\n    print('')\n    while(True):\n        print('------------ Pet Shop ------------')\n        print('[1] Adicionar Pet')\n        print('[2] Relatório de Pet')\n        print('[3] Relatório de Pet por Espécie')\n        print('[4] Exportar dados')\n        print('[0] Sair')\n        print('----------------------------------')\n        print('\\nDigite a opção desejada: ',end='')\n\n        try:\n            opt = int(input())\n            if(opt<0 or opt>4):\n                raise ValueError\n            else:\n                break\n        except ValueError:\n            os.system('cls')\n            print('Valor inválido. Por favor digite um valor válido.')\n\n    return opt\n\n'''\nQuestão 2 - Função Adicionar Pet:\n\n'''\n\ndef adicionar_pet(pets):\n    id = len(pets)+1\n    pet = {        \n        'nome' : '',\n        'especie' : '',\n        'raca' : '',\n        'nascimento' : '',\n        'sexo' : '',\n    }\n        \n    for key in pet:\n        os.system('cls')\n        print('------------ Criar Pet ------------')\n        print('ID: {}'.format(id))\n        print('Nome: {}'.format(pet['nome']))\n        print('Espécie: {}'.format(pet['especie']))\n        print('Raça: {}'.format(pet['raca']))\n        print('Nascimento: {}'.format(pet['nascimento']))\n        print('Sexo: {}'.format(pet['sexo']))\n        print('----------------------------------')\n        print('\\n{} do pet: '.format(key),end='')\n        pet[key] = input()\n    pet['id'] = id\n    return pet\n\n'''\nQuestão 3 - Relatório de Pets:\n\n'''\n\ndef relatorio_pet(pets,chave,especie):\n    os.system('cls')\n    print('{0:5} | {1:10} | {2:10} | {3:10} | {4:10} | {5}'.format('ID','Nome','Espécie','Raça','Nascimento','Sexo'))\n    pets_ordenado = sorted(pets, key=itemgetter(chave))\n    if(especie=='todas'):\n        for pet in pets_ordenado:        \n            print('{0:5} | {1:10} | {2:10} | {3:10} | {4:10} | {5}'.format(pet['id'],pet['nome'],pet['especie'],pet['raca'],pet['nascimento'],pet['sexo']))\n    elif(especie=='cao'):\n        for pet in pets_ordenado:\n            if(pet['especie']=='cao'):\n                print('{0:5} | {1:10} | {2:10} | {3:10} | {4:10} | {5}'.format(pet['id'],pet['nome'],pet['especie'],pet['raca'],pet['nascimento'],pet['sexo']))\n    elif(especie=='gato'):\n        for pet in pets_ordenado:\n            if(pet['especie']=='gato'):\n                print('{0:5} | {1:10} | {2:10} | {3:10} | {4:10} | {5}'.format(pet['id'],pet['nome'],pet['especie'],pet['raca'],pet['nascimento'],pet['sexo']))\n    else:\n        for pet in pets_ordenado:\n            if(pet['especie']!='gato' and pet['especie']!='cao'):\n                print('{0:5} | {1:10} | {2:10} | {3:10} | {4:10} | {5}'.format(pet['id'],pet['nome'],pet['especie'],pet['raca'],pet['nascimento'],pet['sexo']))\n    input()\n\n'''\nQuestão 4 - Relatório por espécie:\n'''\ndef relatorio_pet_especie(pets):\n    os.system('cls')\n    print('')\n    while(True):\n        print('------------ Pet Shop ------------')\n        print('[1] Cachorros')\n        print('[2] Gatos')\n        print('[3] Todos')\n        print('[4] Outros')\n        print('[0] Sair')\n        print('----------------------------------')\n        print('\\nDigite a opção desejada: ',end='')\n\n        try:\n            opt = int(input())\n            if(opt<0 or opt>4):\n                raise ValueError\n            else:\n                break\n        except ValueError:\n            os.system('cls')\n            print('Valor inválido. Por favor digite um valor válido.')\n\n    if(opt==1):\n        relatorio_pet(pets,'nome','cao')\n    elif(opt==2):\n        relatorio_pet(pets,'nome','gato')\n    elif(opt==3):\n        relatorio_pet(pets,'nome','todas')\n    elif(opt==4):\n        relatorio_pet(pets,'nome','outras')\n\n'''\nQuestão 5 - Exportar dados para um JSON:\n'''\ndef exportar(pets):\n    dir_original = os.getcwd()\n\n    dir = os.path.join(os.sep,os.environ[\"HOMEPATH\"],'Desktop','dados')\n    if(not os.path.exists(dir)):\n        os.makedirs(dir)    \n    os.chdir(dir)\n\n    f = open('arquivo.json','w')\n    json.dump(pets,f,sort_keys=False,indent=4)\n    f.close()\n\n    os.chdir(dir_original)\n\n#-----------------------------------------------------------------\n#Execução do programa:\n\n#Lista para armazenar os vários pets, inicialmente vazia\npets = []\n\nsair = False\n\nwhile not sair:\n    opt = menu()\n\n    if(opt==1):\n        pets.append(adicionar_pet(pets))\n    elif(opt==2):\n        relatorio_pet(pets,'nome','todas')\n    elif(opt==3):\n        relatorio_pet_especie(pets)\n    elif(opt==4):\n        exportar(pets)\n    else:\n        sair = True\n","sub_path":"Prova/prova1.py","file_name":"prova1.py","file_ext":"py","file_size_in_byte":4857,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"121365842","text":"from __future__ import print_function\n\n__docformat__ = 'restructedtext en'\n\nimport six.moves.cPickle as pickle\nimport gzip\nimport os\nimport sys\nimport timeit\nimport time\nimport numpy as np\nimport chromosome\nimport gene\nimport random\nimport matenc\n\nimport tensorflow as tf\n\n\ndef func(last, current):\n    return [last[0] + 1, current]\n\ndef sigmoid(arr):\n    return 1 / (1 + np.exp(-arr))\ndef relu(arr):\n    return np.where(arr>0,arr,0)\ndef dense_to_sparse(mat):\n    idx = tf.where(tf.not_equal(mat, 0))\n    sparse = tf.SparseTensor(idx, tf.gather_nd(mat, idx), mat.get_shape())\n    return sparse\ndef find_density(mat):\n    idx = tf.where(tf.not_equal(mat, 0))\n    #val = tf.gather_nd(mat, idx)\n    tup = tf.shape(mat)\n    return tf.divide( tf.shape(idx)[0],tf.multiply(tup[0],tup[1]))\n\ndef opt_compwise_multiply(mat1,mat2):\n    tempnode = dense_to_sparse( mat1 ).__mul__( mat2 )\n    return tf.scatter_nd(tempnode.indices, tempnode.values, tempnode.dense_shape)\n\n\n\nclass DeepNet(object):\n\n\n    def __init__(self, inputh, n_in, n_out, mat_enc, middle_activation = tf.nn.relu, final_activation = tf.nn.sigmoid):\n\n\n        self.n_in = n_in\n        self.n_out = n_out\n        #self.chromo = chromo\n        self.mat_enc = mat_enc\n\n        self.input = inputh\n\n        self.con_mat_var_map=  {}\n        self.wei_mat_var_map = {}\n        for key in self.mat_enc.CMatrix.keys():\n            self.con_mat_var_map[key] = tf.Variable(initial_value= self.mat_enc.CMatrix[key].astype('float32'), name = 'con_mat'+key, dtype = tf.float32)\n            self.wei_mat_var_map[key] = tf.Variable( initial_value = self.mat_enc.WMatrix[key], name = 'con_mat'+key, dtype = tf.float32)\n\n        to_effec_mat_node_map = {}\n        for key in  self.con_mat_var_map.keys():\n            to_effec_mat_node_map[key] = opt_compwise_multiply(self.con_mat_var_map[ key ],self.wei_mat_var_map[ key ])\n\n        density_map = {}\n        for key in to_effec_mat_node_map.keys():\n            density_map[key] = find_density(to_effec_mat_node_map[key])\n        self.bias_wei_arr = np.array( [ item.weight for item in self.mat_enc.Bias_conn_arr] )\n        self.bias_var = tf.Variable( initial_value = self.bias_wei_arr, name  = \"bias\", dtype = tf.float32)\n\n        input_till_H2 = None\n\n        if 'IH1' in to_effec_mat_node_map.keys():\n            input_till_H1 = middle_activation(tf.sparse_matmul( self.input, to_effec_mat_node_map['IH1'], b_is_sparse = True))\n\n        if 'IH2' in to_effec_mat_node_map.keys():\n            input_till_H2 =  tf.sparse_matmul(self.input, to_effec_mat_node_map['IH2'], b_is_sparse = True)\n\n        if 'H1H2' in to_effec_mat_node_map.keys():\n            assert( 'IH1' in to_effec_mat_node_map.keys())\n            twoh = tf.sparse_matmul( input_till_H1, to_effec_mat_node_map['H1H2'], b_is_sparse = True)\n            if 'IH2' in to_effec_mat_node_map.keys():\n                input_till_H2 =     tf.add(twoh, input_till_H2)\n\n            else:\n                input_till_H2 =  twoh\n\n        if input_till_H2 is not None:\n            input_till_H2 = middle_activation(input_till_H2)\n\n        output = None\n        if 'H2O' in to_effec_mat_node_map.keys():\n            assert('IH2' in to_effec_mat_node_map.keys() or 'H1H2' in to_effec_mat_node_map.keys())\n            threeh = tf.sparse_matmul(input_till_H2, to_effec_mat_node_map['H2O'], b_is_sparse=True)\n\n            output = threeh\n\n        if 'H1O' in to_effec_mat_node_map.keys():\n            assert ('IH1' in to_effec_mat_node_map.keys())\n            fourh = tf.sparse_matmul(input_till_H1, to_effec_mat_node_map['H1O'], b_is_sparse = True)\n\n            if output is not None:\n                output = tf.add( output, fourh)\n            else:\n                output = fourh\n\n        if 'IO' in to_effec_mat_node_map.keys():\n            assert ('IO' in to_effec_mat_node_map.keys())\n            fifth = tf.sparse_matmul(self.input, to_effec_mat_node_map['IO'] , b_is_sparse = True)\n            if output is not None:\n                output = tf.add( output, fifth)\n            else:\n                output = fifth\n\n        output = final_activation(output)\n        \"\"\"input_till_H2 = middle_activation(\n                            tf.add(\n                                tf.sparse_matmul(self.input, to_effec_mat_node_map['IH2'], b_is_sparse = True),\n                                tf.sparse_matmul( input_till_H1, to_effec_mat_node_map['H1H2'], b_is_sparse = True)\n                            )\n                        )\n\n\n        output    = final_activation(\n                        tf.add(\n                            tf.add(\n                                    tf.add(\n                                            tf.sparse_matmul(input_till_H2, to_effec_mat_node_map['H2O'], b_is_sparse = True ),\n                                            tf.sparse_matmul(input_till_H1,to_effec_mat_node_map['H1O'], b_is_sparse = True)\n                                    ),\n                                    tf.sparse_matmul(self.input, to_effec_mat_node_map['IO'] , b_is_sparse = True)\n                            ),\n\n                            self.bias_var\n                        )\n                    )\n        \"\"\"\n        self.p_y_given_x = output\n\n        half = tf.constant(0.5, dtype=self.p_y_given_x.dtype)\n        if int(self.bias_wei_arr.shape[0]) != 1:\n            self.y_pred = tf.argmax(self.p_y_given_x, axis=1)\n        else:\n            half = tf.constant(0.5, dtype=self.p_y_given_x.dtype)\n            dadum = tf.constant(0.5, dtype=self.p_y_given_x.dtype)\n            q = tf.scan(lambda last, current: current[0], elems=self.p_y_given_x, initializer=dadum)\n            s = tf.scan(lambda y, x: tf.greater_equal(x, half), elems=q, initializer=False)\n            #print(\"herehrerhehrehrehrehrhe\", s)\n            # print(\"hi\",s)\n            self.y_pred = tf.cast(s, dtype=tf.int32)\n\n        self.params = [ self.wei_mat_var_map[key] for key in self.wei_mat_var_map.keys()] + [self.bias_var]\n\n\n    def negative_log_likelihood(self, y):\n        if int(self.bias_wei_arr.shape[0])!=1:\n           dum=tf.constant(0.5,dtype=tf.float32) #dum for dummy\n           dadum=tf.constant(-1,dtype=tf.int32)# dum-dadum-dadum mast h\n           q=tf.scan(fn=func,elems=y,initializer=[dadum,dadum])\n           z=tf.transpose(tf.stack([q[0],q[1]]))\n           #print(\"hello---------------------------\")\n           w=tf.scan(lambda last,current: tf.log(self.p_y_given_x[current[0]][current[1]]),elems=z, initializer = dum)\n           #print(-tf.reduce_mean(w))\n           return -tf.reduce_mean(w)\n        else:\n\n            dum=tf.constant(0.5,dtype=tf.float64)\n            minusone=tf.constant(-1,dtype=tf.int32)\n            one=tf.constant(1,dtype=y.dtype)\n            r=tf.scan(lambda last,current:last+1,elems=y,initializer=minusone)\n\n            w=tf.scan(lambda last,current: tf.add(tf.multiply(tf.cast(y[current],dtype=self.p_y_given_x.dtype),tf.log(self.p_y_given_x[current][0])),tf.multiply(tf.cast(tf.add(one,-y[current]),dtype=self.p_y_given_x.dtype),tf.log(tf.add(tf.cast(one,dtype=self.p_y_given_x.dtype),-self.p_y_given_x[current][0])))),elems=r,initializer=dum)\n            z=-tf.reduce_mean(w)\n            return z\n\n    def errors(self, y):\n        \"\"\"Return a float representing the number of errors in the minibatch\n        over the total number of examples of the minibatch ; zero one\n        loss over the size of the minibatch\n\n        :type y: theano.tensor.TensorType\n        :param y: corresponds to a vector that gives for each example the\n                  correct label\n        \"\"\"\n\n        # check if y has same dimension of y_pred\n        \"\"\"if len(y.shape) != len(self.y_pred.shape):\n            raise TypeError(\n                'y should have the same shape as self.y_pred',\n                ('y', y.type, 'y_pred', self.y_pred.type)\n            )\n        \"\"\"\n        # check if y is of the correct datatype\n\n        if y.dtype:\n            # the T.neq operator returns a vector of 0s and 1s, where 1\n            # represents a mistake in prediction\n            r = tf.scan(lambda last, current: last + 1, elems=y, initializer=-1)\n            qn = tf.scan(lambda last, current: tf.not_equal(tf.cast(self.y_pred[current], dtype=tf.int32), y[current]),\n                         elems=r, initializer=False)\n            q = tf.cast(qn, dtype=tf.int32)\n\n            # r=tf.scan((lambda last,current: current[1]),q)\n            return tf.reduce_mean(tf.cast(q, dtype=tf.float64))\n        else:\n            raise NotImplementedError()\n\n\ndef test2():\n    for_node = [(i, 'I') for i in range(1, 4)]\n    for_node += [(i, 'O') for i in range(4, 6)]\n    st='2212211'\n    for_node +=  [(i+6,'H'+st[i]) for i in range(len(st))]\n    node_ctr = 13\n    innov_num = 25\n    dob = 0\n    node_lis = [gene.Node(x, y) for x, y in for_node]\n    for_conn = [(1, (1, 4), 0.3, True), (2, (1, 5), 0.25, False), (3, (2, 4), 0.25, False), (4, (2, 5), 0.5, False),\n                (5, (3, 4), 0.7, False), (6, (3, 5), 0.5, True), (7, (1, 6), 0.2, True), (8, (6, 4), 0.1, True),\n                (9, (2, 7), 0.1, True), (10, (7, 4), 0.15, True), (11, (1, 8), 0.5, True), (12, (8, 6), 0.7, True),\n                (13, (1, 9), 0.3, False), (14, (9, 5), 1.0, True), (15, (3, 10), 0.33, True), (16, (10, 5), 0.77, True),\n                (17, (1, 11), 0.25, True), (18, (11, 9), 0.15, True), (19, (2, 12), 0.6, True), (20, (12, 7), 0.4, True),\n                (21, (3, 12), 0.8, True), (22, (2, 9), 0.9, True), (23, (12, 4), 0.75, True), (24, (11, 5), 0.25, True),\n                ]\n    conn_lis = [gene.Conn(x, (node_lis[tup[0] - 1], node_lis[tup[1] - 1]), w, status) for x, tup, w, status in for_conn]\n    for_bias = [(4, 0.2), (5, 0.1)]\n    bias_conn_lis = [gene.BiasConn(node_lis[x - 1], y) for x, y in for_bias]\n    newchromo = chromosome.Chromosome(dob, node_lis, conn_lis, bias_conn_lis)\n    newchromo.set_node_ctr(node_ctr)\n\n    # newchromo.pp()\n    def calc_output_directly(inputarr):\n        lis = []\n        for arr in inputarr:\n            x1 = arr[0]\n            x2 = arr[1]\n            x3 = arr[2]\n            output1 = sigmoid(\n                                0.3 * x1     +\n                                0.1 * relu(\n                                            0.7 * relu(0.5 * x1) +\n                                            0.2 * x1\n                                        )    +\n                                0.15 * relu(\n                                            0.1 * x2 +\n                                            0.4 * relu(\n                                                0.6 * x2 +\n                                                0.8 * x3\n                                            )\n                                        )     +\n                                0.75 * relu(\n                                            0.6 * x2 +\n                                            0.8 * x3\n                                        )     -\n                                 0.2\n                        )\n            #output2 = sigmoid(arr[0] * 0.25 + arr[1] * 0.5 + relu(arr[2] * 0.3) * 0.6 - 0.1)\n            output2 = sigmoid(\n                0.5 * x3 +\n                1 * relu(\n                    0.15 * relu(0.25 * x1) +\n                    0.9 * x2\n                ) +\n                0.25 * relu(\n                    0.25 * x1\n                ) +\n                0.77 * relu(\n\n                    0.33 * x3\n                ) -\n                0.1\n            )\n            lis.append([output1, output2])\n        return np.array(lis)\n\n    inputarr = np.array([[0.0, 2, 1], [0.8, 1, 2]])\n    indim = 3\n    outdim = 2\n\n    #np.random\n    rng = random\n    num_data = 2\n    # inputarr = np.random.random((num_data, indim))\n    #neter = Neterr(indim, outdim, inputarr, 10, np.random)\n\n    ka = random.randint(0,2,(num_data,))\n    \"\"\"\n    targetarr = np.zeros((num_data,outdim)).astype(dtype = 'float32')\n    for i in range(num_data):\n        targetarr[i,ka[i]] = 1\n\n    print(\"target is \", targetarr)\n    \"\"\"\n    targetarr = ka.astype('int32')\n    print(targetarr.dtype)\n    inputarr = inputarr.astype('float32')\n    print(\"input type\", inputarr.dtype)\n    print(targetarr)\n    x = tf.placeholder( shape = [None, indim], dtype = tf.float32)\n    y = tf.placeholder( shape = [None,], dtype = tf.int32)\n    newmat_enc = newchromo.convert_to_MatEnc(indim,outdim)\n    newnet = DeepNet(x, indim, outdim, newmat_enc)\n    cost = newnet.negative_log_likelihood(y)\n    learning_rate = 0.05\n    optmzr = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost, var_list=newnet.params)\n    #cost = newnet.errors(y)\n    with tf.Session() as sess:\n\n        sess.run(tf.global_variables_initializer())\n        print(\"initially\")\n        print(sess.run(newnet.wei_mat_var_map['IH2']))\n        print(sess.run(newnet.con_mat_var_map['IH2']))\n        print(sess.run([optmzr,newnet.y_pred,cost], feed_dict = { x : inputarr, y : targetarr}))\n        print(sess.run(newnet.wei_mat_var_map['IH2']))\n        print(sess.run([optmzr, newnet.bias_var, cost], feed_dict={x: inputarr, y: targetarr}))\n        print(sess.run(newnet.con_mat_var_map['IH2']))\n        print(sess.run([optmzr, newnet.bias_var, newnet.errors(y)], feed_dict={x: inputarr, y: targetarr}))\n\n\n    #newchromo.modify_thru_backprop()\n\nif __name__ == '__main__':\n    test2()\n\n\n\n\n\n\n\n\n","sub_path":"deep_net.py","file_name":"deep_net.py","file_ext":"py","file_size_in_byte":13288,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"560446397","text":"import scrapy\nfrom scrapy import Request\nfrom News_Crawler.spiders.NewsSpider import NewsSpider\nfrom News_Crawler.items import Article\nfrom News_Crawler import utils\n\n\nclass TienPhongNewsSpider(NewsSpider):\n    name = \"TienPhong\"\n    allowed_domains = [\"tienphong.vn\"]\n\n    url_category_list = [\n        # (\"https://www.tienphong.vn/kinh-te-thi-truong/\", \"Tài chính\"),\n        # (\"https://www.tienphong.vn/du-lich/\", \"Du lịch\"),\n        # (\"https://www.tienphong.vn/nha-dep-phong-thuy/\", \"Nhà đẹp\"),\n        # (\"https://www.tienphong.vn/lam-dep/\", \"Thời trang\"),        \n        # (\"https://www.tienphong.vn/kinh-te-chung-khoan/\", \"Chứng khoán\"),\n        # (\"https://www.tienphong.vn/thi-truong/\", \"Bất động sản\"),\n        # (\"https://www.tienphong.vn/phap-luat/\", \"Pháp luật\"),\n        # (\"https://www.tienphong.vn/giao-duc/\", \"Giáo dục\"),\n        # (\"https://www.tienphong.vn/the-thao/\", \"Thể thao\"),\n        # (\"https://www.tienphong.vn/xe/\", \"Xe\"),\n        (\"https://www.tienphong.vn/cong-nghe-khoa-hoc/\", \"Công nghệ\")\n    ]\n\n    def start_requests(self):\n        page_idx = 1\n        for category_url, category in self.url_category_list:\n            meta = {\n                \"category\": category,\n                \"category_url_fmt\": category_url + \"?trang={}\",\n                \"page_idx\": page_idx\n            }\n            category_url = meta[\"category_url_fmt\"].format(meta[\"page_idx\"])\n            yield Request(category_url, self.parse_category, meta=meta, errback=self.errback)\n\n    def parse_category(self, response):\n        meta = response.meta\n\n        # Navigate to article\n        article_urls = response.css(\"div.cate-list-news div.other-news article>a::attr(href)\").extract()\n\n        self.logger.info(\"Parse url {}, Num Article urls : {}\".format(response.url, len(article_urls)))\n        for article_url in article_urls:\n            if utils.is_valid_url(article_url):\n                yield Request(article_url, self.parse_article, meta={\"category\": meta[\"category\"]}, errback=self.errback)\n\n        # Navigate to next page\n        if meta[\"page_idx\"] < self.page_per_category_limit and len(article_urls) > 0:\n            meta[\"page_idx\"] += 1\n            next_page = meta[\"category_url_fmt\"].format(meta[\"page_idx\"])\n            yield Request(next_page, self.parse_category, meta=meta, errback=self.errback)\n\n    def parse_article(self, response):\n        article_div = response.css(\"#main-article-body article.main-article\")\n\n        url = response.url\n        lang = self.lang\n        title = article_div.css(\"#headline ::text\").extract_first()\n        category = response.meta[\"category\"]\n        intro = article_div.css(\".cms-desc ::text\").extract_first()\n        content = ' '.join(article_div.css(\"#article-body ::text\").extract())\n        time = article_div.css(\"#article-meta .byline-dateline time::text\").extract_first()\n\n        # Transform time to uniform format\n        if time is not None:\n            time = self.transform_time_fmt(time, src_fmt=\"%d/%m/%Y %H:%M\")\n\n        self.article_scraped_count += 1\n        if self.article_scraped_count % 100 == 0:\n            self.logger.info(\"Spider {}: Crawl {} items\".format(self.name, self.article_scraped_count))\n        \n        yield Article(\n            url=url,\n            lang=lang,\n            title=title,\n            category=category,\n            intro=intro,\n            content=content,\n            time=time\n        )\n\n    def errback(self, failure):\n        self.logger.error(\"Error when send requests : \", failure.request)\n","sub_path":"build/lib/News_Crawler/spiders/TienPhongNewsSpider.py","file_name":"TienPhongNewsSpider.py","file_ext":"py","file_size_in_byte":3549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"488993485","text":"from pathlib import Path\n\nimport pytest\n\nfrom sark.dpkg import create_pkg\nfrom sark.io import HttpCache\nfrom sark.metatools import get_license\n\n\n@pytest.fixture\ndef http_cache(request):\n    # request: special object to parametrize fixtures\n    http_cache = HttpCache(request.param)\n    yield http_cache\n    try:  # remove cache before next test\n        http_cache.remove()\n    except FileNotFoundError:\n        # some tests do not create a cache; the safeguard is probably not\n        # really required because HttpCache.remove() uses glob, which returns\n        # an empty iterator when there are no files.\n        pass\n\n\n@pytest.fixture\ndef datadir():\n    return Path(\"testing/data\")\n\n\n@pytest.fixture\ndef pkg(datadir):\n    pkg_meta = {\n        \"name\": \"test\",\n        \"title\": \"test\",\n        \"licenses\": get_license(\"CC0-1.0\"),\n    }\n    return create_pkg(pkg_meta, datadir.glob(\"*.csv\"))\n","sub_path":"testing/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":893,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"339377419","text":"# -*- coding: utf-8 -*-\n# 1009 - Salary with Bonus\n\ndef main():\n    nome = input()\n    salario = float(input())\n    venda = float(input())\n    total = s + 0.15 * v\n\n    print(\"TOTAL = R$ {:.2f}\".format(total))\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"python/Beginner/1009.py","file_name":"1009.py","file_ext":"py","file_size_in_byte":250,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"564664259","text":"import json, socket, threading, sys\n\nclass Player:\n\n    def __recv_update(self):\n        bufsize = 1024\n        bufstr = \"\"\n        nextbufstr = \"\"\n        while 1:\n            try:\n                tmpstr = self.sock.recv(bufsize)\n            except:\n                self.sock = None\n                break\n            bufstr += tmpstr\n            if '\\n' not in tmpstr:\n                continue\n            bufarr = bufstr.split('\\n')\n            bufstr = bufarr[0]\n            nextbufstr = bufarr[1]\n            try:\n                self.data = json.loads(bufstr)\n            except ValueError:\n                bufstr = \"\"\n                nextbufstr = \"\"\n                continue\n            bufstr = nextbufstr\n            nextbufstr = \"\"\n\n    def __init__(self, ip, port):\n        self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        self.sock.connect((ip, int(port)))\n        self.data = None\n        thread = threading.Thread()\n        thread.run = self.__recv_update\n        thread.start()\n\n    def up(self):\n        try:\n            self.sock.send(\"up\\n\")\n        except:\n            self.sock = None\n            self.disconnect()\n\n    def left(self):\n        try:\n            self.sock.send(\"left\\n\")\n        except:\n            self.sock = None\n            self.disconnect()\n\n    def down(self):\n        try:\n            self.sock.send(\"down\\n\")\n        except:\n            self.sock = None\n            self.disconnect()\n\n    def right(self):\n        try:\n            self.sock.send(\"right\\n\")\n        except:\n            self.sock = None\n            self.disconnect()\n\n    def bomb(self):\n        try:\n            self.sock.send(\"bomb\\n\")\n        except:\n            self.sock = None\n            self.disconnect()\n\n    def coords(self):\n        if self.data:\n            return (self.data['X'], self.data['Y'])\n        return None\n\n    def board(self):\n        if self.data:\n            return self.data['Board']\n        return None\n\n    def disconnect(self):\n        sys.exit(0)\n","sub_path":"bomberman.py","file_name":"bomberman.py","file_ext":"py","file_size_in_byte":2007,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"77327605","text":"\"\"\"\nhttps://leetcode.com/problems/3sum/\n\nGiven an array S of n integers, are there elements a, b, c in S such that a + b + c = 0?\nFind all unique triplets in the array which gives the sum of zero.\n\nNote: The solution set must not contain duplicate triplets.\n\nFor example, given array S = [-1, 0, 1, 2, -1, -4],\n\nA solution set is:\n[\n  [-1, 0, 1],\n  [-1, -1, 2]\n]\n\"\"\"\n\n\ndef three_sum(nums):\n    \"\"\"\n    :type nums: List[int]\n    :rtype: List[List[int]]\n    \"\"\"\n    ans = []\n    nums.sort()  # sorting allows us to easily avoid duplicate triplets\n    length = len(nums)\n\n    # O(n^2)\n    for i in xrange(length - 2):\n        if i > 0 and nums[i] == nums[i - 1]:\n            continue  # already processed, avoid duplicate triplets\n        left = nums[i]\n        target = -left  # left + mid + right = 0\n\n        j = i + 1\n        k = length - 1\n        while j < k:\n            if j > i + 1 and nums[j] == nums[j - 1]:  # similar check to above, avoid duplicates\n                j += 1\n                continue\n            if k < len(nums) - 1 and nums[k] == nums[k + 1]:\n                k -= 1\n                continue\n\n            mid, right = nums[j], nums[k]\n            pair_sum = mid + right\n            if pair_sum == target:\n                ans.append([left, mid, right])\n                j += 1\n                k -= 1\n            elif pair_sum < target:\n                j += 1\n            else:\n                k -= 1\n    return ans\n\n\nif __name__ == \"__main__\":\n    assert three_sum([-1, 0, 1, 2, -1, -4]) == [[-1, -1, 2], [-1, 0, 1]]\n","sub_path":"lc/3sum.py","file_name":"3sum.py","file_ext":"py","file_size_in_byte":1540,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"411112790","text":"# # belajar operator aritmatika dalam python\n\n# # operator penjumlahan\n# val_a = 2\n# val_b = 2\n# sumab = val_a + val_b\n\n# print(\"hasil penjumlahan :\", sumab)\n\n# # Operator Pengurangan\n\n# vala = 3\n# valb = 4\n# sum = vala - valb\n\n# print(\"hasil pengurangan :\", sum)\n\n# # Operator Perkalian\n# nila = 10\n# nilb = 3\n# sumnil = nila * nilb\n\n# print(\"hasil Perkalian :\", sumnil)\n\n\n# # Operator pembagian\n# nil_a = 9\n# nil_b = 3\n# sumnil_ab = nil_a / nil_b\n\n# print(\"hasil pembagian :\", sumnil_ab)\n\n# # Operator Modulus\n# angka1 = 9\n# angka2 = 2\n# jumangka = angka1 % angka2\n\n# print(\"hasil modulus :\", jumangka)\n\n# # Operator pangkat\n# angka_1 = 3\n# angka_2 = 2\n# jumpangkat = angka_1 ** angka_2\n\n# print(\"hasil pangkat :\", jumpangkat)\n\n# # Pembagian bilangan bulat\n# angka3 = 5\n# angka4 = 3\n# jumangkabil = angka3 // angka4\n\n# print(\"hasil bil bulat :\", jumangkabil)\n\n\n\n# Operator pembanding\n\n# nilaia = 12\n# nilaib = 10\n\n# if(nilaia == nilaib):\n#     print(\"nilai keduanya sama\")\n# if(nilaia < nilaib) :\n#     print(\"nilaia lebih kecil dari nilaib\")\n# if(nilaia <= nilaib) :\n#     print(\"nilaia lebih kecil atau sama dengan nilaib\")\n# if(nilaia > nilaib) :\n#     print(\"nilaia lebih Besar dari nilaib\")\n# if(nilaia >= nilaib) :\n#     print(\"nilaia lebih besar sama dengan nilaib\")\n# else :\n#     print(\"nilaia tidak sama dengan nilaib\")\n\n#operator Logika \nkondisia = True\nkondisib = False\n\n# Logika AND\nLogika = kondisia and kondisib\nprint (\"%r and %r = %r\" % (kondisia,kondisib,Logika))\n\n# Logika OR\nLogika = kondisia or kondisib\nprint (\"%r or %r = %r\" % (kondisia,kondisib,Logika))\n\n# Logika Not\nLogika = not kondisia\nprint( \"not %r  = %r\" % (kondisia,kondisib))","sub_path":"operator_aritmatika.py","file_name":"operator_aritmatika.py","file_ext":"py","file_size_in_byte":1659,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"294156373","text":"# :coding: utf-8\n# :copyright: Copyright (c) 2015 ftrack\n\nimport getpass\nimport sys\nimport pprint\nimport logging\n\nimport ftrack\nimport ftrack_connect.application\n\n\nclass LaunchAction(object):\n    '''Adobe plugins discover and launch action.'''\n\n    identifier = 'ftrack-connect-launch-adobe'\n\n    def __init__(self, applicationStore, launcher):\n        '''Initialise action with *applicationStore* and *launcher*.\n\n        *applicationStore* should be an instance of\n        :class:`ftrack_connect.application.ApplicationStore`.\n\n        *launcher* should be an instance of\n        :class:`ftrack_connect.application.ApplicationLauncher`.\n\n        '''\n        super(LaunchAction, self).__init__()\n\n        self.logger = logging.getLogger(\n            __name__ + '.' + self.__class__.__name__\n        )\n\n        self.applicationStore = applicationStore\n        self.launcher = launcher\n\n    def isValidSelection(self, selection):\n        '''Return true if the selection is valid.'''\n        if (\n            len(selection) != 1 or\n            selection[0]['entityType'] != 'task'\n        ):\n            return False\n\n        return True\n\n    def register(self):\n        '''Override register to filter discover actions on logged in user.'''\n        ftrack.EVENT_HUB.subscribe(\n            'topic=ftrack.action.discover and source.user.username={0}'.format(\n                getpass.getuser()\n            ),\n            self.discover\n        )\n\n        ftrack.EVENT_HUB.subscribe(\n            'topic=ftrack.action.launch and source.user.username={0} '\n            'and data.actionIdentifier={1}'.format(\n                getpass.getuser(), self.identifier\n            ),\n            self.launch\n        )\n\n    def discover(self, event):\n        '''Return discovered applications.'''\n        if not self.isValidSelection(\n            event['data'].get('selection', [])\n        ):\n            return\n\n        items = []\n        applications = self.applicationStore.applications\n        applications = sorted(\n            applications, key=lambda application: application['label']\n        )\n\n        for application in applications:\n            applicationIdentifier = application['identifier']\n            label = application['label']\n            items.append({\n                'actionIdentifier': self.identifier,\n                'label': label,\n                'icon': application.get('icon', 'default'),\n                'applicationIdentifier': applicationIdentifier\n            })\n\n            items.append({\n                'actionIdentifier': self.identifier,\n                'label': '{label} with latest version'.format(\n                    label=label\n                ),\n                'icon': application.get('icon', 'default'),\n                'launchWithLatest': True,\n                'applicationIdentifier': applicationIdentifier\n            })\n\n        return {\n            'items': items\n        }\n\n    def launch(self, event):\n        '''Handle *event*.\n\n        event['data'] should contain:\n\n            *applicationIdentifier* to identify which application to start.\n\n        '''\n        # Prevent further processing by other listeners.\n        # TODO: Only do this when actually have managed to launch a relevant\n        # application.\n        event.stop()\n\n        applicationIdentifier = (\n            event['data']['applicationIdentifier']\n        )\n\n        context = event['data'].copy()\n        context['source'] = event['source']\n\n        # If the selected entity is an asset version, change the selection\n        # to parent task/shot instead since it is not possible to publish\n        # to an asset version in ftrack connect.\n        if context['selection'][0]['entityType'] == 'assetversion':\n            assetVersion = ftrack.AssetVersion(\n                context['selection'][0]['entityId']\n            )\n\n            entityId = assetVersion.get('taskid')\n\n            if not entityId:\n                asset = assetVersion.parent()\n                entity = asset.parent()\n\n                entityId = entity.getId()\n\n            context['selection'] = [{\n                'entityId': entityId,\n                'entityType': 'task'\n            }]\n\n        return self.launcher.launch(\n            applicationIdentifier, context\n        )\n\n\nclass ApplicationStore(ftrack_connect.application.ApplicationStore):\n\n    def _discoverApplications(self):\n        '''Return a list of applications that can be launched from this host.\n\n        An application should be of the form:\n\n            dict(\n                'identifier': 'name_version',\n                'label': 'Name version',\n                'path': 'Absolute path to the file',\n                'version': 'Version of the application',\n                'icon': 'URL or name of predefined icon'\n            )\n\n        '''\n        applications = []\n\n        if sys.platform == 'darwin':\n            prefix = ['/', 'Applications']\n\n            applications.extend(self._searchFilesystem(\n                expression=prefix + [\n                    'Adobe Photoshop CC .+', 'Adobe Photoshop CC .+.app'\n                ],\n                label='Photoshop CC {version}',\n                applicationIdentifier='photoshop_cc_{version}',\n                icon='photoshop'\n            ))\n\n            applications.extend(self._searchFilesystem(\n                expression=prefix + [\n                    'Adobe Premiere Pro CC .+', 'Adobe Premiere Pro CC .+.app'\n                ],\n                label='Premiere Pro CC {version}',\n                applicationIdentifier='premiere_pro_cc_{version}',\n                icon='premiere'\n            ))\n\n        elif sys.platform == 'win32':\n            prefix = ['C:\\\\', 'Program Files.*']\n\n            applications.extend(self._searchFilesystem(\n                expression=(\n                    prefix +\n                    ['Adobe', 'Adobe Photoshop CC .+',\n                     'Photoshop.exe']\n                ),\n                label='Photoshop CC {version}',\n                applicationIdentifier='photoshop_cc_{version}',\n                icon='photoshop'\n            ))\n\n            applications.extend(self._searchFilesystem(\n                expression=(\n                    prefix +\n                    ['Adobe', 'Adobe Premiere Pro CC .+',\n                     'Adobe Premiere Pro.exe']\n                ),\n                label='Premiere Pro CC {version}',\n                applicationIdentifier='premiere_pro_cc_{version}',\n                icon='premiere'\n            ))\n\n        self.logger.debug(\n            'Discovered applications:\\n{0}'.format(\n                pprint.pformat(applications)\n            )\n        )\n\n        return applications\n\n\nclass ApplicationLauncher(ftrack_connect.application.ApplicationLauncher):\n\n    def _getApplicationLaunchCommand(self, application, context=None):\n        '''Return *application* command based on OS and *context*.\n\n        *application* should be a mapping describing the application, as in the\n        :class:`ApplicationStore`.\n\n        *context* should provide additional information about how the\n        application should be launched.\n\n        '''\n        command = super(\n            ApplicationLauncher, self\n        )._getApplicationLaunchCommand(\n            application, context\n        )\n\n        # Figure out if the command should be started with the file path of\n        # the latest published version.\n        if command is not None and context is not None:\n            selection = context.get('selection')\n            if selection and context.get('launchWithLatest', False):\n                entity = selection[0]\n                component = None\n\n                if application['identifier'].startswith('photoshop_cc'):\n                    component = self._findLatestComponent(\n                        entity['entityId'],\n                        entity['entityType'],\n                        'psd'\n                    )\n\n                if application['identifier'].startswith('premiere_pro_cc'):\n                    component = self._findLatestComponent(\n                        entity['entityId'],\n                        entity['entityType'],\n                        'prproj'\n                    )\n\n                if component is not None:\n                    command.append(component.getFilesystemPath())\n\n        return command\n\n\ndef register(registry, **kw):\n    '''Register hooks for Adobe plugins.'''\n    applicationStore = ApplicationStore()\n\n    launcher = ApplicationLauncher(\n        applicationStore\n    )\n\n    # Create action and register to respond to discover and launch events.\n    action = LaunchAction(applicationStore, launcher)\n    action.register()\n","sub_path":"ftrack_connect_adobe_hook.py","file_name":"ftrack_connect_adobe_hook.py","file_ext":"py","file_size_in_byte":8657,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"588034842","text":"#!/usr/bin/env python3\nimport datetime\n\ncharity = \"BSRC\"\ntoday = datetime.date.today()\n\ndonor_db = {\n    'Robert Bigelow': [65300020.00, 10000000.00],\n    'Jeff Bezos': [1500000.00],\n    'Paul Allen': [4000000000.99, 20000000.00],\n    'Elon Musk': [2000000.00, 1000000.00],\n    'Richard Branson': [2000000.00, 1000000.00],\n}\n\nmenu = \"\\n\".join((\"\\nWelcome to the donors program\",\n                  \"Please choose from below options:\",\n                  \"1 - Send a Thank You\",\n                  \"2 - Create a Report\",\n                  \"3 - Send letter to all donors\",\n                  \"q - Quit\",\n                  \"###> \"))\n\nmain_prompt = menu\n\n\ndef sort_key(donor):\n    return donor[1]\n\n\ndef list_donors():\n    \"\"\"Print a list of donors\"\"\"\n    print(\"\\nList of all {} donors: \".format(len(donor_db)), '\\n', '-' * 25)\n    for donor in donor_db:\n        print(f\" \", donor)\n    print('-' * 25)\n\n\ndef thank_you_email(response, donated):\n    letter = (\n        f\"\"\"\n        Dear {response},\n        Thank you for donating to the billionaires space race charity. Your generous donation of ${donated:.2f}! will go\n        a long ways towards bringing space travel to the masses. \n\n        Thanks,\n        {charity}\n        \"\"\".format(**donor_db))\n    print(\"Writing files to current working directory\")\n    return letter\n\n\ndef send_thank_you():\n    \"\"\"Send thank you e-mails to donors\"\"\"\n    while True:\n        response = input(\"Enter the name of the donor you would like to send a thank you e-mail to \"\n                         \"\\nType 'list' to see a all our donors: \")\n        if response == 'quit':\n            break\n        if response == \"list\":\n            list_donors()\n        elif response in donor_db:\n            donation = input(\"Enter donation amount: \")\n            if donation == 'quit':\n                break\n            donated = float(donation)\n            donor_db[response].append(donated)\n            thank_you_email(response, donated)\n        else:\n            donation = input(\"{} is not a donor, adding to donor db, enter donation amount: \".format(response))\n            donated = float(donation)\n            [response] = [donated]\n            thank_you_email(response, donated)\n\n\ndef thank_you_email_all():\n    for donor, donated in donor_db.items():\n        with open(f'{donor}_{today}.txt', 'w') as file:\n            file.write(f\"\"\" \n        Dear {donor},\n        Thank you for donating to the billionaires space race charity. Your generous donation of ${sum(donated)} will go\n        a long ways towards bringing space travel to the masses. \n\n        Thanks,\n        {charity}\n        \"\"\".format(**donor_db))\n        break\n\n\ndef create_report():\n    sort_db = sorted(donor_db.items(), key=sort_key, reverse=True)\n    header_donor_name = \"Donor Name\"\n    header_total_given = \"Total Given\"\n    header_num_gifts = \"Num Gifts\"\n    header_average_gift = \"Average Gift\"\n\n    header = f\"{header_donor_name:<25} | {header_total_given:>17} | {header_num_gifts:>10} | {header_average_gift:>15}\"\n\n    print(header)\n    print(len(header) * '-')\n\n    for key, value in sort_db:\n        total_given = sum(value)\n        num_gifts = len(value)\n        average_gift = total_given / num_gifts\n        total_given_formatted = (\"{:.2f}\".format(total_given))\n        average_gift_formatted = (\"{:.2f}\".format(average_gift))\n        print(f\"{key:<25} | $ {total_given_formatted:>15} | {num_gifts:>10} | $ {average_gift_formatted:>10}\")\n\n\ndef quit():\n    print(\"Quitting\")\n    return \"exit menu\"\n\n\nmain_dispatch = {\n            \"1\": send_thank_you,\n            \"2\": create_report,\n            \"3\": thank_you_email_all,\n            \"q\": quit\n        }\n\n\ndef menu_selection(prompt, dispatch_dict):\n    while True:\n        response = input(prompt)\n        if dispatch_dict[response]() == \"exit menu\":\n            break\n\n\n# make executable\n\n\nif __name__ == \"__main__\":\n    menu_selection(main_prompt, main_dispatch)\n","sub_path":"students/jeff_scott/lesson04/mailroom2.py","file_name":"mailroom2.py","file_ext":"py","file_size_in_byte":3917,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"382300007","text":"import logging\nimport os\nfrom logging.handlers import SMTPHandler, RotatingFileHandler\n\nfrom flask.logging import default_handler\n\n\n# A wrapper class that initiate the app logging\n# It behaves like a flask extension\n# Just call its init_app() method inside your create_app() factory\n#\n#   app = Flask()\n#   app_logger  = AppLogger()\n#   app_logger.init_app(app)\n\n\nclass AppLogger:\n    def __init__(self, _app=None):\n        if _app is not None:\n            self.init_app(_app)\n\n    def init_app(self, _app):\n        _app.logger.removeHandler(default_handler)\n        if not _app.debug and not _app.testing:\n            # send ERROR by Mail if possible\n            if _app.config.get(\"MAIL_SERVER\"):\n                auth = None\n                if _app.config[\"MAIL_USERNAME\"] or _app.config[\"MAIL_PASSWORD\"]:\n                    auth = (_app.config[\"MAIL_USERNAME\"], _app.config[\"MAIL_PASSWORD\"])\n                secure = None\n                if _app.config[\"MAIL_USE_TLS\"]:\n                    secure = ()\n                mail_handler = SMTPHandler(\n                    mailhost=(_app.config[\"MAIL_SERVER\"], _app.config[\"MAIL_PORT\"]),\n                    fromaddr=\"no-reply@\" + _app.config[\"MAIL_SERVER\"],\n                    toaddrs=[_app.config[\"DEV_EMAIL\"]],\n                    subject=\"App Failure\",\n                    credentials=auth,\n                    secure=secure,\n                )\n                mail_handler.setLevel(logging.ERROR)\n                _app.logger.addHandler(mail_handler)\n\n        if _app.config.get(\"LOG_TO_STDOUT\") and _app.config[\"LOG_TO_STDOUT\"]:\n            stream_handler = logging.StreamHandler()\n            stream_handler.setLevel(logging.INFO)\n            stream_handler.setFormatter(\n                logging.Formatter(\n                    \"%(asctime)s %(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]\"\n                )\n            )\n            _app.logger.addHandler(stream_handler)\n        else:\n            # log INFO to file\n            if not os.path.exists(\"logs\"):\n                os.mkdir(\"logs\")\n            log_filename = _app.config.get(\"LOG_FILENAME\")\n            if not log_filename:\n                log_filename = \"logfile.log\"\n            file_handler = RotatingFileHandler(\n                os.path.join(\"logs\", log_filename), maxBytes=10240, backupCount=10\n            )\n            file_handler.setFormatter(\n                logging.Formatter(\n                    \"%(asctime)s %(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]\"\n                )\n            )\n            file_handler.setLevel(logging.INFO)\n            _app.logger.addHandler(file_handler)\n","sub_path":"web/app_logger.py","file_name":"app_logger.py","file_ext":"py","file_size_in_byte":2629,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"128969674","text":"# -*- coding: utf-8 -*-\n\nfrom flask.ext.script import Manager\nfrom iwx import create_app\nfrom settings import DEV as ENV\nfrom iwx.models import Roles, University, VoteScope, BazaarType, Geography\nfrom flask_security.utils import encrypt_password\nfrom datetime import datetime\nfrom flask.ext.script import Manager\nfrom flask.ext.migrate import Migrate, MigrateCommand\nimport json\n\napp = create_app(ENV)\nmanager = Manager(app)\n\nmigrate = Migrate(app, app.db)\nmanager.add_command('db', MigrateCommand)\n\n@manager.command\ndef add_su():\n    su_role = app.user_datastore.create_role(**Roles.SUPER_USER)\n    app.user_datastore.create_role(**Roles.ADMIN)\n    app.user_datastore.create_role(**Roles.USER)\n    su = app.user_datastore.create_user(**{\n        'email': 'admin@iweixiao.cn',\n        'password': encrypt_password('88888888'),\n        'name': u'超级管理员'\n    })\n    app.user_datastore.add_role_to_user(su, su_role)\n    su.confirmed_at = datetime.utcnow()\n    app.db.session.commit()\n\n\n@manager.command\ndef add_roles():\n    app.user_datastore.create_role(**Roles.IWX_ADMIN)\n    app.user_datastore.create_role(**Roles.UN_ADMIN)\n    app.user_datastore.create_role(**Roles.VISITOR)\n    app.db.session.commit()\n\n\n@manager.command\ndef add_city():\n    with open(\"city.json\", \"r\") as f:\n        data = json.load(f)\n\n    for i in range(len(data)):\n        province = dict()\n        province[\"city\"] = data[i][\"state\"]\n        pr = Geography(**province)\n        app.db.session.add(pr)\n        app.db.session.commit()\n        for item in data[i][\"cities\"]:\n            city = dict()\n            city[\"city\"] = item\n            city[\"parent_id\"] = pr.id\n            app.db.session.add(Geography(**city))\n        app.db.session.commit()\n\n\n@manager.command\ndef add_universities():\n    app.db.create_all()\n    universities = [\n    {\n        'name': u'中央民族大学',\n        'province': u'北京市',\n        'city': u'北京市'\n    }, {\n        'name': u'北京大学',\n        'province': u'北京市',\n        'city': u'北京市'\n    }, {\n        'name': u'人民大学',\n        'province': u'北京市',\n        'city': u'北京市'\n    }, {\n        'name': u'清华大学',\n        'province': u'北京市',\n        'city': u'北京市'\n    }]\n    for u in universities:\n        app.db.session.add(University(**u))\n    app.db.session.commit()\n\n\n@manager.command\ndef add_scope():\n    app.db.create_all()\n    scope = [\n        {\n            \"name\": \"所有人\",\n            \"update_time\": datetime.utcnow(),\n            \"alias\": \"EVERYONE\",\n            \"scope_type\": \"VOTE\"\n        }, {\n            \"name\": \"本校人员\",\n            \"update_time\": datetime.utcnow(),\n            \"alias\": \"SAME\",\n            \"scope_type\": \"VOTE\"\n        }, {\n            \"name\": \"签到人员\",\n            \"update_time\": datetime.utcnow(),\n            \"alias\": \"SIGN\",\n            \"scope_type\": \"ALL\"\n        }, {\n            \"name\": \"在场人员\",\n            \"update_time\": datetime.utcnow(),\n            \"alias\": \"SCENE\",\n            \"scope_type\": \"ALL\"\n        }, {\n            \"name\": \"签到人员\",\n            \"update_time\": datetime.utcnow(),\n            \"alias\": \"SIGN\",\n            \"scope_type\": \"REWARD\"\n        }, {\n            \"name\": \"持电子票入场人员\",\n            \"update_time\": datetime.utcnow(),\n            \"alias\": \"TICKET\",\n            \"scope_type\": \"REWARD\"\n        }, {\n            \"name\": \"签到人员\",\n            \"update_time\": datetime.utcnow(),\n            \"alias\": \"SIGN\",\n            \"scope_type\": \"CONTESTS\"\n        }, {\n            \"name\": \"本校人员\",\n            \"update_time\": datetime.utcnow(),\n            \"alias\": \"SAME\",\n            \"scope_type\": \"CONTESTS\"\n        }, {\n            \"name\": \"持电子票入场人员\",\n            \"update_time\": datetime.utcnow(),\n            \"alias\": \"TICKET\",\n            \"scope_type\": \"CONTESTS\"\n        }\n    ]\n    for v in scope:\n        app.db.session.add(VoteScope(**v))\n    app.db.session.commit()\n\n\n@manager.command\ndef add_bazaar_type():\n    app.db.create_all()\n    bazaar_type = [\n        {\n            \"name\": u\"求\",\n            \"alias\": \"BUY\",\n            \"create_time\": datetime.utcnow()\n        }, {\n            \"name\": u\"售\",\n            \"alias\": \"SALE\",\n            \"create_time\": datetime.utcnow()\n        }, {\n            \"name\": u\"寻\",\n            \"alias\": \"SEEK\",\n            \"create_time\": datetime.utcnow()\n        }, {\n            \"name\": u\"领\",\n            \"alias\": \"ACQUICE\",\n            \"create_time\": datetime.utcnow()\n        }, {\n            \"name\": u\"约\",\n            \"alias\": \"INVITE\",\n            \"create_time\": datetime.utcnow()\n        }\n    ]\n\n    for b in bazaar_type:\n        app.db.session.add(BazaarType(**b))\n    app.db.session.commit()\n\n@manager.command\ndef init_db():\n    app.db.create_all()\n    add_su()\n    add_universities()\n    add_scope()\n\n\nif __name__ == \"__main__\":\n    manager.run()\n","sub_path":"server/script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":4916,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"355393014","text":"import json\nimport bcrypt\nfrom rest_framework.permissions import IsAuthenticated, AllowAny\n\nfrom rest_framework.views import APIView\n\nimport jwt\n\nfrom django_server.settings import SECRET_KEY\n\nfrom django.views import View\nfrom django.http import JsonResponse, HttpResponse\nfrom braces.views import CsrfExemptMixin\nimport pymongo\n\ndb_address = \"Your Mongo address\"\n\n\n# 로그인을 위한 View\n# Request data = {'USER_ID' , 'USER_PW'}\n# Response = string\nclass SignInView(CsrfExemptMixin, APIView):\n    authentication_classes = []\n    permission_classes =  [AllowAny]\n\n    def post(self, request):\n        data = request.data\n\n        client = pymongo.MongoClient(db_address)\n        db = client.IoT_DB\n        datas = db.User\n        cursors = datas.find()\n\n        token = 0\n\n        try:\n            for cursor in cursors:\n                if cursor['USER_ID'] == data['USER_ID']:\n                    if data['USER_PW'] == cursor['USER_PW']:\n                        return HttpResponse(data['USER_ID'], status=200)  # 토큰을 담아서 응답\n\n            client.close()\n            return HttpResponse(token,status=400)\n\n        except KeyError:\n            client.close()\n            return HttpResponse(token, status=400)\n\n\n# 회원가입을 위한 View\n# Request data = {'USER_ID','USER_PW','ADDRESS','EMAIL','PHONE_NUM'}\n# Response Data = String\n\nclass SignUpView(CsrfExemptMixin, APIView):\n    authentication_classes = []\n    permission_classes =  [AllowAny]\n\n    def post(self, request):\n        data = request.data\n\n        client = pymongo.MongoClient(db_address)\n        db = client.IoT_DB\n        cursors = db.User\n\n        try:\n            Changed_PW = dict()\n            Changed_PW['USER_ID'] = data['USER_ID']\n\n            Changed_PW['USER_PW'] = data['USER_PW']\n            Changed_PW['NAME'] = data['NAME']\n            Changed_PW['ADDRESS'] = data['ADDRESS']\n            Changed_PW['EMAIL'] = data['EMAIL']\n            Changed_PW['PHONE_NUM'] = data['PHONE_NUM']\n\n            response_json = json.dumps(Changed_PW)\n            cursors.insert_one(Changed_PW)\n\n            client.close()\n            return HttpResponse(response_json,status=200)\n\n        except KeyError:\n            client.close()\n            return HttpResponse(status=400)\n\n\n# 회원가입 시 중복 아이디 체크를 위한 view\n# Request data = String(USER_ID)\n# Response = status 로 응답\nclass ValidationView(CsrfExemptMixin, APIView):\n\n    authentication_classes = []\n    permission_classes =  [AllowAny]\n    def post(self, request):\n        data = request.data\n\n        client = pymongo.MongoClient(db_address)\n        db = client.IoT_DB\n        datas = db.User\n        cursors = datas.find()\n\n        jsonRes = json.dumps(data)\n\n        for cursor in cursors:\n            if data['USER_ID'] == cursor['USER_ID']:\n                client.close()\n                return HttpResponse(status=400)\n\n        client.close()\n        return HttpResponse(jsonRes, status=200)","sub_path":"member/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2964,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"242797522","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\n------------------------------------------------\ndescribe:\n    the db interact services of enterprise\n\nusage:\n    \n\nbase_info:\n    __version__ = \"v.10\"\n    __author__ = \"mingliang.gao\"\n    __time__ = \"2020/07/11\"\n    __mail__ = \"mingliang.gao@163.com\"\n------------------------------------------------\n\"\"\"\nfrom deploy.bo.bo_base import BOBase\nfrom deploy.models.citys import CitysModel\nfrom sqlalchemy import or_, func\n\n\nclass CitysBo(BOBase):\n\n    def __init__(self):\n        super(CitysBo, self).__init__()\n\n    def new_mode(self):\n        return CitysModel()\n\n    def get_count(self):\n        q = self.session.query(func.count(CitysModel.id)).scalar()\n        return q.count()\n\n    def get_by_id(self, id):\n        if not id:\n            return {}\n\n        q = self.session.query(\n            CitysModel\n        )\n        q = q.filter(CitysModel.id == id)\n        q = q.first()\n        return q if q else {}\n\n    def get_sub_by_pid(self, pid):\n        if not pid:\n            return None\n\n        q = self.session.query(\n            CitysModel\n        )\n        q = q.filter(CitysModel.parent_id == pid)\n        q = q.all()\n        return q if q else None\n\n    def get_no_sub_citys(self):\n        q = self.session.query(\n            CitysModel\n        )\n        q = q.filter(CitysModel.is_cg == True)\n        q = q.all()\n        return q if q else {}\n","sub_path":"deploy/bo/citys.py","file_name":"citys.py","file_ext":"py","file_size_in_byte":1382,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"416559277","text":"import requests\r\n\r\ndef Zalo_run(Phone):\r\n    s = requests.session()\r\n    s.headers.update({\r\n        'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.75 Safari/537.36',\r\n        })\r\n    param = {\r\n        'a': 'pw'\r\n    }\r\n\r\n    data = {\r\n        'phone_num': Phone,\r\n        'iso_country_code': 'VN',\r\n        'continue': 'https://chat.zalo.me',\r\n    }\r\n    datas = s.post('https://id.zalo.me/account/authen?',params = param, data = data,).json()\r\n\r\n    if datas['error_code'] == -22:\r\n        return {'phone':Phone,'status':True,'message':'Live'}\r\n    elif datas['error_code'] == -31:\r\n        return {'phone':Phone,'status':False,'message':'Disable/Does not exist'}\r\n    else:\r\n        return {'phone':Phone,'status':None,'message':'An unknown error'}\r\n\r\ndef zalo_key(token):\r\n    token_list = ['5748t43fghudghdfgh433ghfudghufgh',\r\n                    'gjnjfnbvcjb9wetig49gjdfgiu2i39g',\r\n                    'fjgbdfkbjdkfjb9we348r9423fi923i',\r\n                    'kvbdfmjkbmfkbmdfkbmdfkbmdfbkmdb',\r\n                    'fgkbioke94itg3e049ig94gi49gi94g']\r\n    for kt in token_list:\r\n        if kt.strip() == token.strip():\r\n            return True\r\n    return False\r\n","sub_path":"Checkzalo.py","file_name":"Checkzalo.py","file_ext":"py","file_size_in_byte":1234,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"174902082","text":"# -*- coding: utf-8 -*-\r\n### Download files ###\r\nurl_basis = 'http://servlet.dmi.dk/bv/servlet/bv?stat=1945&map=map3&param=none'\r\n\r\nimport urllib2\r\nimport datetime\r\n\r\nimport prettytable as pt\r\nimport csv\r\n\r\nimport os\r\ncwd = os.path.dirname(os.path.abspath(__file__))\r\n\r\ndef multipleReplace(text, wordDict):\r\n    for key in wordDict:\r\n        text = text.replace(key, wordDict[key])\r\n    return text\r\n\r\n\r\n\r\nresponse = urllib2.urlopen(url_basis)\r\nhtml = response.read()\r\n\r\nsource=html.splitlines()\r\n\r\n\r\nreplace_dict = {'<br>':'','januar':'january', 'februar':'february', 'marts':'march', 'maj':'may', 'juni':'june', 'juli':'july', 'oktober':'october'}\r\n\r\ndic = {}\r\ntime_str = multipleReplace(source[4].lower(),replace_dict)\r\ntime_lst = time_str.split()[-4:]\r\ngen=(str(w) for w in time_lst)\r\ntime = ' '.join(gen)\r\ntime_datetime = datetime.datetime.strptime( time , '%d. %B %Y %H:%M' ) \r\ndic['time'] = time_datetime.isoformat()\r\n\r\nwind_angle = source[5].lower().replace('<br>','').split()[-4]   # degree\r\nwind_speed = source[5].lower().replace('<br>','').split()[-2]   # m/s\r\nwind_gust = source[6].lower().replace('<br>','').split()[-2]    # m/s\r\ntemp = source[7].lower().replace('<br>','').split()[-2]         # degree\r\nhumidity = source[8].lower().replace('<br>','').split()[-2]     # percent\r\nrain_10min = source[9].lower().replace('<br>','').split()[-2]   # mm/(10min)\r\ninsolation = source[10].lower().replace('<br>','').split()[-2]  # watt/m²\r\n\r\n## Append to csv\r\nfa = open(cwd + \"\\\\\" + \"klarup_log.csv\", \"ab\")\r\nwriter = csv.writer(fa)\r\nwriter.writerows([[time,temp,rain_10min,wind_speed,wind_gust,wind_angle,humidity,insolation]])\r\nfa.close()\r\n\r\n\r\nimport random\r\n\r\nif random.random() < 1:\r\n    from csv_remove_duplicate_rows import remove_row\r\n    remove_row(cwd + \"\\\\\" + \"klarup_log.csv\")\r\n    ","sub_path":"python26/my pys/weather/weather_log.py","file_name":"weather_log.py","file_ext":"py","file_size_in_byte":1798,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"394658275","text":"# -*- coding: utf-8 -*-\n##############################################################################\n#\n#    OpenERP, Open Source Management Solution\n#    Copyright (C) 2004-2010 Tiny SPRL (<http://tiny.be>).\n#\n#    This program is free software: you can redistribute it and/or modify\n#    it under the terms of the GNU Affero General Public License as\n#    published by the Free Software Foundation, either version 3 of the\n#    License, or (at your option) any later version.\n#\n#    This program is distributed in the hope that it will be useful,\n#    but WITHOUT ANY WARRANTY; without even the implied warranty of\n#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n#    GNU Affero General Public License for more details.\n#\n#    You should have received a copy of the GNU Affero General Public License\n#    along with this program.  If not, see <http://www.gnu.org/licenses/>.\n#\n##############################################################################\n\nfrom openerp.osv import fields, osv\nfrom datetime import datetime\n\n\nclass facebook_user(osv.osv):\n    _name = 'facebook.user'\n    _description = 'Facebook User Management'\n    \n    def _fb_comment_count(self, cr, uid, ids, field_name, arg, context=None):\n        comment = self.pool['facebook.comment']\n        return {\n            fbuser_id: comment.search_count(cr,uid, [('fbuser_id', '=', fbuser_id)], context=context)\n            for fbuser_id in ids\n        }   \n    def _fb_like_count(self, cr, uid, ids, field_name, arg, context=None):\n        like = self.pool['facebook.like']\n        return {\n            fbuser_id: like.search_count(cr,uid, [('fbuser_id', '=', fbuser_id)], context=context)\n            for fbuser_id in ids\n        }\n        \n    def _day_join(self, cr, uid, ids, field_name, arg, context=None):\n        res = {}\n        for user in self.browse(cr, uid, ids, context=context):\n            val = 0\n            if user.joindated_time != False:\n                date_now = datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n                date_nows = datetime.strptime(date_now,\"%Y-%m-%d %H:%M:%S\")\n                join_date = datetime.strptime(user.joindated_time,\"%Y-%m-%d %H:%M:%S\")\n                val =  (date_nows - join_date).days\n            res[user.id] = val\n        return res\n    \n    def _total_egm(self, cr, uid, ids, field_name, arg, context=None):\n        res = {}\n        for line in self.browse(cr, uid, ids, context=context):\n            val = 0\n            emg_like = line.fb_like_count * line.page_id.egm_like\n            egm_comment = line.fb_comments_count * line.page_id.egm_comment\n            egm_share = line.share * line.page_id.egm_comment\n            val = emg_like + egm_comment + egm_share\n            res[line.id] = val\n        return res\n    \n    def _total_positive(self, cr, uid, ids, field_name, arg, context=None):\n        res = {}\n        for line in self.browse(cr, uid, ids, context=context):\n            val = 0\n            for lines in line.comment_ids:\n                for line_tags in lines.tag_ids:\n                    if line_tags.type_id.action_id == 'Positive':\n                        val += line_tags.type_id.point\n            res[line.id] = val\n        return res\n    def _total_negative(self, cr, uid, ids, field_name, arg, context=None):\n        res = {}\n        for line in self.browse(cr, uid, ids, context=context):\n            val = 0\n            for lines in line.comment_ids:\n                for line_tags in lines.tag_ids:\n                    if line_tags.type_id.action_id == 'Negative':\n                        val += line_tags.type_id.point\n            res[line.id] = val\n        return res   \n    \n    def open_page(self, cr, uid, ids, context=None):\n        \n        for line in self.browse(cr, uid, ids, context=context):\n            url = 'https://www.facebook.com/' + line.fbuser_id\n        return { 'type': 'ir.actions.act_url','url': url,'target':'new',}  \n                         \n    _columns = {\n        'name': fields.char('FB User Name', required=True, copy=False,readonly=True, select=True),\n        'fbuser_id': fields.char('FB User ID', required=True,copy=False,readonly=True, select=True),\n        'first_name': fields.char('First Name', copy=False,readonly=True),\n        'last_name': fields.char('Last Name', copy=False,readonly=True),\n        'email': fields.char('Email', copy=False,),\n        'mobile': fields.char('Mobile', copy=False,),\n        'day_join': fields.function(_day_join, string='Day Join', type='integer'),          \n        'like_date': fields.date('Like Date',readonly=True),\n        'share_date': fields.date('Share Date',readonly=True),\n        'comment_date': fields.date('Comment Date',readonly=True),\n        'negative': fields.integer('Negative',readonly=True),\n        'not_relate': fields.integer('Not Relate',readonly=True),\n        'other': fields.integer('Other',readonly=True),\n#        'member_id': fields.many2one('crm.member', 'Member Code',),\n#        'member_status': fields.related('member_id', 'status_id', type=\"many2one\", relation=\"crm.member.status\", string=\"Member Status\", readonly=True),\n        'like_ids': fields.one2many('facebook.like', 'fbuser_id', \"Facebook like\"),\n        'comment_ids': fields.one2many('facebook.comment', 'fbuser_id', \"Facebook Comment\"),\n        'fb_comments_count': fields.function(_fb_comment_count, string='Comments', type='integer'),\n        'fb_like_count': fields.function(_fb_like_count, string='Likes', type='integer'),    \n        'total_egm': fields.function(_total_egm, string='Total EGM', type='integer',store=True),      \n        'positive': fields.function(_total_positive, string='Positive', type='integer'),  \n        'negative': fields.function(_total_negative, string='Negative', type='integer'),                     \n        'share': fields.integer('Share',readonly=True),            \n        'joindated_time': fields.datetime('Joindated Time',readonly=True),\n        'page_id': fields.many2one('facebook.page','Page Name',readonly=True),        \n    }\n\n    _order = 'joindated_time desc, id desc'\n\n\n\n# vim:expandtab:smartindent:tabstop=4:softtabstop=4:shiftwidth=4:","sub_path":"facebook_crm/facebook_user.py","file_name":"facebook_user.py","file_ext":"py","file_size_in_byte":6137,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"298052630","text":"#@+leo-ver=4-thin\n#@+node:amd_yen.20130308221230.2458:@shadow c2/docutils_ex1.py\n#coding: utf-8\n#@<< docstring >>\n#@+node:amd_yen.20130308221230.3823:<< docstring >>\n'''\n1. 由 http://docutils.sourceforge.net/docs/user/rst/cheatsheet.txt 下載 rst 格式檔案\n2. 利用本程式轉為 html, 然後以 Chrome 開啟檢查\n3. 由 url 取得文件內容\nimport urllib.request\nurl = 'http://example.com/'\nresp = urllib.request.urlopen(url)\ndata = resp.read()          # 傳回一個 'bytes' 物件\ntext = data.decode('utf-8') # 將 'bytes' 物件內容編碼成為 'string'\n參考資料: \nhttp://stackoverflow.com/questions/7243750/download-file-from-web-in-python-3\n'''\n#@-node:amd_yen.20130308221230.3823:<< docstring >>\n#@nl\n#@<< import >>\n#@+node:amd_yen.20130308221230.3824:<< import >>\n# 導入 os 模組\nimport os\n'''\n以下經由 urllib 向網路 URL 取得文件內容\n'''\n# 導入 urllib.request\nimport urllib.request\n# 導入用來處理 rst2html 的 publish_string\nfrom docutils.core import publish_string\n#@-node:amd_yen.20130308221230.3824:<< import >>\n#@nl\nrst檔案網址 = \"http://docutils.sourceforge.net/docs/user/rst/cheatsheet.txt\"\n# 開啟網址內容\n網址回覆 = urllib.request.urlopen(rst檔案網址)\n# 取得 url 所在主機的內容標頭檔案\nprint(urllib.request.urlopen(rst檔案網址).headers)\n# 取得目標網址的 byte 物件\nbyte資料 = 網址回覆.read()\n# 解碼後得到文件內容\n文件內容 = byte資料.decode('utf-8')\n# 列印出文件內容\n#print(文件內容)\n# 利用 docutils.core 模組中的 publish_string() 方法, 將 rst 內容轉為 html\n超文件內容 = publish_string(\n        source=文件內容,\n        writer_name='html',\n        settings = None,\n        settings_overrides={'output_encoding': 'unicode'}\n    )\n# 將超文件內容存成檔案\n檔案 = open(\"docutils_ex1.html\", \"w\")\n檔案.write(超文件內容)\n目前所在目錄 = os.getcwd()\nos.system(\"V:/tools/GoogleChromePortable/GoogleChromePortable.exe \"+目前所在目錄+\"/docutils_ex1.html\")\n# 關閉檔案\n檔案.close()\n#@<< comment >>\n#@+node:amd_yen.20130308221230.3825:<< comment >>\n'''\n# rst檔案1 = \"http://docutils.sourceforge.net/docs/user/rst/cheatsheet.txt\"\n# 執行 rst2html.py, rst 檔案為輸入, 而 html 檔案作為輸出\n# 將已經存在的 1.txt rst 格式檔案利用 rst2html.py 轉為 1.html\nos.system(\"V:/SciTE/python.exe V:/SciTE/scripts/rst2html.py V:/misc/rst_test/1.txt v:/misc/rst_test/1.html\")\n所產生的超文件 = \"v:/misc/rst_test/1.html\"\n# 以 Chrome 開啟所建立的 html 檔案\nos.system(\"V:/tools/GoogleChromePortable/GoogleChromePortable.exe \"+所產生的超文件)\n'''\n#@-node:amd_yen.20130308221230.3825:<< comment >>\n#@nl\n#@-node:amd_yen.20130308221230.2458:@shadow c2/docutils_ex1.py\n#@-leo\n","sub_path":"2013spring/c2/.leo_shadow/xdocutils_ex1.py","file_name":"xdocutils_ex1.py","file_ext":"py","file_size_in_byte":2775,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"518020516","text":"from django.conf.urls import url\nfrom products import views\n\nurlpatterns = [\n\n    url(\n        regex=r\"^on-sale/$\",\n        view=views.home,\n        name=\"landing_page\"\n    ),\n    \n        url(\n        regex=r\"^$\",\n        view=views.ProductsListView.as_view(),\n        name=\"deals_page\"\n    ),\n\n    url(\n        regex=r\"^all-beers/$\",\n        view=views.ProductsListView.as_view(),\n        name='product-list'\n    ),\n\n    # /stores\n    url(\n        regex=r\"^stores/$\",\n        view=views.stores,\n        name=\"api-stores\"\n    ),\n\n    # /stores/{store_id}\n    url(\n        regex=r\"^stores/(?P<store_id>[0-9]+)/$\",\n        view=views.store_by_id,\n        name=\"api-stores-id\"\n    ),\n\n    # /products\n    url(\n        regex=r\"^products/$\",\n        view=views.products,\n        name=\"api-products\"\n    ),\n\n    # /beers\n    url(\n        regex=r\"^beers/$\",\n        view=views.beers,\n        name=\"api-beers\"\n    ),\n\n    # /beers/{product_id}\n    url(\n        regex=r\"^beers/(?P<beer_id>[0-9]+)/$\",\n        view=views.beer_by_id,\n        name=\"beer_store_api\"\n    ),\n    \n        # /beers/{product_id}\n    url(\n        regex=r\"^beer_prices/(?P<beer_id>[0-9]+)/$\",\n        view=views.beer_prices_by_id,\n        name=\"beer_store_api\"\n    ),\n\n    # /beers/{product_id}\n    url(\n        regex=r\"^beers/(?P<beer_id>[0-9]+)/products/$\",\n        view=views.beer_products,\n        name=\"beer_store_api\"\n    ),\n\n    # /products/{product_id}\n    url(\n        regex=r\"^products/(?P<product_id>[0-9]+)/$\",\n        view=views.product_by_id,\n        name=\"beer_store_api\"\n    ),\n    \n    # /product_prices/{product_id}\n    url(\n        regex=r\"^product_prices/(?P<product_id>[0-9]+)/$\",\n        view=views.product_prices_by_id,\n        name=\"beer_store_api\"\n    ),\n\n    # /stores/{store_id}/products\n    url(\n        regex=r\"^stores/(?P<store_id>[0-9]+)/products/$\",\n        view=views.products_at_store,\n        name=\"beer_store_api\"\n    ),\n\n    # /products/{product_id}/stores\n    url(\n        regex=r\"^products/(?P<product_id>[0-9]+)/stores/$\",\n        view=views.stores_with_product,\n        name=\"beer_store_api\"\n    ),\n    \n    url(r'^search/?$', view=views.search, name='beer_search'),\n]\n","sub_path":"products/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2175,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"250336664","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Feb  5 20:16:56 2018\r\n\r\n@author: prerna.prakash\r\n\"\"\"\r\n\r\nimport pandas as pd\r\nimport time\r\nimport os\r\n\r\ndef timeCheck() :\r\n    return time.time()\r\n\r\ndef timeMeasure(starttime,endtime) :\r\n    timetaken = endtime-starttime\r\n    print(\"Time Taken in Seconds :\",timetaken)\r\n    \r\ndef saveExcel(FileName,Sheetname,df) :\r\n    print(FileName)\r\n    writer = pd.ExcelWriter(FileName,engine = 'xlsxwriter')\r\n    df.to_excel(writer,index = False)\r\n    writer.save()\r\n    #print(\"Your File has been saved.\")\r\n  \r\ndef setUpConfigFile(name) :\r\n    listOfConfigDetails = []\r\n    cwd = os.getcwd()\r\n    print(cwd+\"\\\\\"+name)\r\n    fobj = open(cwd+\"\\\\\"+name)   \r\n    for line in fobj:\r\n        listOfConfigDetails.append(line.rstrip())\r\n    return listOfConfigDetails\r\n\r\ndef saveExcelWithIndex(FileName,Sheetname,df) :\r\n    print(FileName)\r\n    writer = pd.ExcelWriter(FileName,engine = 'xlsxwriter')\r\n    df.to_excel(writer,index = True)\r\n    writer.save()\r\n    print(\"Your File has been saved.\")\r\n\r\ndef vlookup(dfbase,dfcopyfrom,DfBaseElement,DfCopyFromeElement,listofElements,prefix) :\r\n    print(\"Applying Vlookup.\")\r\n    i = 0\r\n    for each in dfbase[DfBaseElement] :\r\n        j = 0\r\n        for feach in dfcopyfrom[DfCopyFromeElement]:          \r\n            if str(each) in str(feach) :\r\n                for preach in listofElements :               \r\n                    dfbase.at[i,prefix+preach] = dfcopyfrom.at[j,preach]\r\n                break\r\n                \r\n            j = j+1\r\n        i = i+1\r\n    return dfbase \r\n\r\ndef mathching(col1,col2) :\r\n    \r\n    if str(col1) in str(col2) :\r\n        return True\r\n    if str(col1) == 'nan' :\r\n        return True\r\n    return False\r\n\"\"\"\r\ndef vlookupMultipleConditions(dfbase,dfcopyfrom,DfBaseElement,BaseElementList,DfCopyFromeElement,CopyElementList,listofElements,prefix) :\r\n    print(\"Applying Vlookup.\")\r\n    i = 0\r\n    for each in dfbase[DfBaseElement] :\r\n        j = 0\r\n        for feach in dfcopyfrom[DfCopyFromeElement]:          \r\n            if df.at[i,] == feach and :\r\n                for preach in listofElements :               \r\n                    dfbase.at[i,prefix+preach] = dfcopyfrom.at[j,preach]\r\n                break\r\n                \r\n            j = j+1\r\n        i = i+1\r\n    return dfbase \r\n\"\"\"\r\ndef vlookupin(dfbase,dfcopyfrom,DfBaseElement,DfCopyFromeElement,listofElements) :\r\n    i = 0\r\n    for each in dfbase[DfBaseElement] :\r\n        j = 0\r\n        for feach in dfcopyfrom[DfCopyFromeElement]: \r\n            feach = str(feach)\r\n            each = str(each)\r\n            if feach in each :\r\n                for preach in listofElements :    \r\n                    dfbase.at[i,preach] = str(dfcopyfrom.at[j,preach])\r\n                break\r\n                \r\n            j = j+1\r\n        i = i+1\r\n    return dfbase \r\n\r\ndef vlookupine(dfbase,dfcopyfrom,DfBaseElement,DfCopyFromeElement,listofElements) :\r\n    print(\"Applying Vlookup.\")\r\n    i = 0\r\n    for each in dfbase[DfBaseElement] :\r\n        j = 0\r\n        for feach in dfcopyfrom[DfCopyFromeElement]: \r\n            feach = str(feach)\r\n            each = str(each)\r\n            if feach in each :\r\n                for preach in listofElements : \r\n                    print(preach,dfbase.at[i,preach])\r\n                    dfbase.at[i,preach] = str(dfcopyfrom.at[j,preach])\r\n                break\r\n                \r\n            j = j+1\r\n        i = i+1\r\n    return dfbase \r\n\r\ndef doExclude(df,column,name) :\r\n    for each in name:\r\n        df = df[df[column]!=each]\r\n        df = df.reset_index(drop=True)\r\n    return df\r\n\r\ndef onlyInclude(df,column,name) :\r\n        df = df[df[column]==name]\r\n        df = df.reset_index(drop=True)\r\n        return df\r\n\r\ndef removeOrders(dfbase,dfdelete,DfBaseElement,DfDeleteElement) :\r\n    \r\n    count = 0\r\n    for each in dfbase[DfBaseElement] :\r\n        for feach in dfdelete[DfDeleteElement] :\r\n            if each == feach :\r\n                count = count +1\r\n                dfbase = dfbase[dfbase.SIEBEL_NUM != feach]\r\n    print(\"Number on Cancellation Orders Removed are\",count)\r\n    return dfbase \r\n    #for each in df1[\"Order No.\"] :\r\ndef takeInput(Question,ExpectedAnswer) :\r\n    answer = input(Question)\r\n    if answer != ExpectedAnswer:\r\n        print(\"Ok then.\")\r\n\r\ndef saveData (df):\r\n    takeInput(\"Do you want to save this data\",\"y\")\r\n\r\ndef AddMachine(df) :\r\n    pass\r\n\r\ndef DD(df,filepath) :\r\n    \r\n    dfnew = pd.read_excel(filepath,'Sheet2')\r\n    vlookupine(df,dfnew,['ANALYSIS'],['ANALYSIS'],['DD'])\r\n    return df\r\n    \r\n    ","sub_path":"pycel.py","file_name":"pycel.py","file_ext":"py","file_size_in_byte":4544,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"337924941","text":"import os\n\nfrom PySide import QtCore\nfrom PySide import QtGui\n\nfrom studio_usd_pipe import resources\nfrom studio_usd_pipe.core import widgets\n\n\nclass Connect(QtGui.QWidget):\n\n    def __init__(self, layout, show_icon=None, width=256, height=144):\n        super(Connect, self).__init__()\n        self.horizontalLayout = QtGui.QHBoxLayout()\n        self.horizontalLayout.setContentsMargins(5, 5, 5, 5)\n        self.horizontalLayout.setObjectName('horizontalLayout')\n        layout.addLayout(self.horizontalLayout)\n        self.button_logo = QtGui.QPushButton(self)\n        self.button_logo.setFlat(True)\n        self.button_logo.setObjectName('button_logo')\n        self.button_logo.setMinimumSize(QtCore.QSize(350, 99))\n        self.button_logo.setMaximumSize(QtCore.QSize(350, 99))                \n        logo_path = os.path.join(resources.getIconPath(), 'logo.png')        \n        widgets.image_to_button(self.button_logo, 350, 99, path=logo_path)     \n        self.horizontalLayout.addWidget(self.button_logo)       \n        spacer_item = QtGui.QSpacerItem(\n            40, 20, QtGui.QSizePolicy.Expanding, QtGui.QSizePolicy.Minimum)\n        self.horizontalLayout.addItem(spacer_item)\n        self.button_show = QtGui.QPushButton(self)\n        self.button_show.setFlat(True)\n        self.button_show.setObjectName('button_show')\n        # width=176, height=99\n        self.button_show.setMinimumSize(QtCore.QSize(width, height))\n        self.button_show.setMaximumSize(QtCore.QSize(width, height))\n        if show_icon:\n            widgets.image_to_button(self.button_show, width, height, path=show_icon)            \n        self.horizontalLayout.addWidget(self.button_show)\n        \n","sub_path":"studio_usd_pipe/resources/ui/logo_ui.py","file_name":"logo_ui.py","file_ext":"py","file_size_in_byte":1687,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"411102577","text":"from gevent import monkey;\nmonkey.patch_all()\nimport mysql.connector as connector;\nimport gevent\nimport json\nimport requests\nimport time\n\ncnx = connector.connect(user='root',password='',host='127.0.0.1',database='weixin')\ncursor = cnx.cursor()\n\ndef insert_data(ctime,aid,read_num,zan_num):\n    cursor.execute('insert into read_num(read_num,time,article_id) values(%s,%s,%s)',[read_num,ctime,aid])\n    cursor.execute('insert into zan_num(zan_num,time,article_id) values(%s,%s,%s)',[zan_num,ctime,aid])\n    cursor.execute('update focus_article set clc_read = 1, clc_zan = 1 where article_id = %s',[aid])\n    cnx.commit()\n\ndef data_query(url,aid):\n    while True:\n        try:\n            ctime = int(time.time())\n            r = requests.post(\"http://wxapi.51tools.info/wx/api.ashx?key=ch_578190611\",data = {'url':url},timeout=10)\n            j = r.json()\n            if j['msg'] != 'succ' or j['data']['readnums'] == -1:\n                continue\n            else:\n                insert_data(ctime,aid,j['data']['readnums'],j['data']['zannums'])\n                return\n        except:\n            continue\n\n\nselect = \"\"\"select article_id\n            from focus_article\n            where clc_read is null\"\"\"\n\ncursor.execute(select)\nlis = cursor.fetchall()\nprint(len(lis))\n\nfor aid, in lis:\n\n    cursor.execute('select url from article_profile where id = %s',[aid])\n    res = cursor.fetchall()\n    if len(res) == 0:\n        continue\n    url, = res[0]\n    data_query(url,aid)\n#    task = gevent.spawn(data_query,url,aid)\n#    task.start()\n    #except:\n    #    continue\n\ncursor.close()\ncnx.close()\n\n\n","sub_path":"crawler/old_get_read_zan_num.py","file_name":"old_get_read_zan_num.py","file_ext":"py","file_size_in_byte":1596,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"251653843","text":"from utils.utility import Singleton\nimport logging\nimport os\n\n\nclass Logger(metaclass=Singleton):\n    def __init__(self):\n        self.log_level = os.environ.get('LOG_LEVEL', 'INFO').upper()\n\n    def get_logger(self):\n        logger = logging.getLogger()\n        logger.setLevel(logging.getLevelName(self.log_level))\n        return logger\n","sub_path":"recommendation/utils/logs.py","file_name":"logs.py","file_ext":"py","file_size_in_byte":339,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"582062473","text":"vow=set(['A','E','I','O','U','a','e','i','o','u'])\ncon=set([chr(65+i) for i in range(26)]+[chr(97+i) for i in range(26)])-vow\ns=input()\nif(s in con or s in vow):\n  if(s in con):\n    print(\"Consonant\")\n  else:\n    print(\"Vowel\")\nelse:\n  print(\"invalid\")\n","sub_path":"code3.py","file_name":"code3.py","file_ext":"py","file_size_in_byte":253,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"229769550","text":"class Solution(object):\n    def alienOrder(self, words):\n        \"\"\"\n        :type words: List[str]\n        :rtype: str\n        \"\"\"\n        # same as the course schedule II\n        # but need to extract adj edges from adj words\n        # use edge x -> y to denote x is behind y in the dictionary\n        self.rtn = []\n        self.graph = {}\n        self.visited = {}\n        # init nodes\n        for word in words:\n            for letter in word:\n                if letter not in self.graph:\n                    self.graph[letter] = Node(letter)  # create a node for that letter\n        # init graph\n        for i in xrange(len(words[:-1])):\n            curWord = words[i]\n            nextWord = words[i + 1]\n            for j in xrange(min(len(curWord), len(nextWord))):  # this will skip [er, ert] case already\n                if curWord[j] != nextWord[j]:\n                    self.graph[nextWord[j]].adj.append(self.graph[curWord[j]])\n                    break  # DO NOT FORGET TO BREAK HERE!\n        # do DFS\n        for node in self.graph.values(): # iterate all nodes\n            if self.DFS(node):  # has cycle\n                return \"\"\n        return \"\".join(self.rtn)\n\n    def DFS(self, node):\n        if node.val not in self.visited:\n            self.visited[node.val] = -1\n            for neg in node.adj:\n                if self.DFS(neg):\n                    return True\n            self.visited[node.val] = 1 # mark as visited\n            self.rtn.append(node.val) # since all the neg nodes has been visited : self.DFS(neg)\n            return False\n        elif self.visited[node.val] == 1:\n            return False\n        else: # self.visited[node.val] == -1:\n            return True  # cycle\n\n\nclass Node(object):\n    def __init__(self, val):\n        self.val = val\n        self.adj = []  # list of nodes\n","sub_path":"269_alien_dictionary/p4_DFS.py","file_name":"p4_DFS.py","file_ext":"py","file_size_in_byte":1822,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"64377590","text":"import numpy as np\nfrom matplotlib import pyplot as plt\nimport os\nimport re\nimport sys\nimport glob\nimport matplotlib\nfrom matplotlib.ticker import (MultipleLocator, FormatStrFormatter,\n                               AutoMinorLocator)\nfrom matplotlib.colors import LogNorm\nfrom matplotlib import colors, cm\nfontsize=30\nfontsize_label=33\n\n\nmatplotlib.rcParams.update({'font.size': fontsize})\nplt.rc('text', usetex=True)\nplt.rc('font', family='serif')\ncolors = np.array([\"b\",\"r\",\"g\",\"c\",\"m\",\"darkorange\",\"k\"])\nmarkers = np.array([\"o\",\"v\",\"s\",\"+\",\"*\",\"p\",\"x\"])\nlinestyles = np.array([\":\",\"-.\",\"--\",\"-\"])\nls_dashes = np.array([[3,3,1,1],[1,1],[1,3,3,1],[2,4,2,4,2,8],[2,2,10,2],[],[4,4,2,2],[3,1,3,1],[2,4,4,2],[5,1,5,1],[7,1,7,1],[7,3,7,3],[3,1,1,1,1,1]])\n#print(files)\nfiles = glob.glob(\"Rg2_c_slice_eps-*.dat\")\nfiles.sort()\neps_WL=-0.4\nf, ax =plt.subplots(1,1, figsize=(20,10))\nk=0\nfor File in files:\n    cplot = np.loadtxt(File, unpack=True)[0]\n    Rg2plot = np.loadtxt(File, unpack=True)[1]\n    eps=float(File[-10:-4])\n    color=colors[k%len(colors)]\n    marker=markers[k%len(markers)]\n    # Ausnahme für vorher nicht benutzte Werte:\n    if (eps+0.45)**2<0.0003:\n        color=\"mediumvioletred\"\n        marker=\"X\"\n        k-=1\n    if (eps+0.55)**2<0.0003:\n        color=\"dimgray\"\n        marker=\"D\"\n        k-=1\n    ax.semilogx(cplot, Rg2plot,color=color, alpha=1, \n            #dashes=ls_dashes[k%len(ls_dashes)], \n            label=r\"$\\epsilon_{}={:.2f}$\".format(\"{ME}\",eps),\n            lw=3, basex=10, marker=marker, ms=15)\n    plt.xlabel(r\"$c$\",fontsize=fontsize_label)\n    plt.ylabel(r\"$R_g^2$\",fontsize=fontsize_label)\n    #plt.xlim(10**-3,10**0)\n    ax.xaxis.set_label_coords(0.50,-0.05)\n    ax.tick_params(left=True,right=True,bottom=True,top=True,which='major',length=10)\n    ax.tick_params(right=True, direction='in',which='both')\n    ax.tick_params(left=True,right=True,bottom=True,top=True,which='minor',length=5)\n    plt.ylim(0,200)\n    #plt.xlim(-1,0)\n    ax.tick_params(axis='x', pad=10)\n    ax.tick_params(axis='y', pad=10)\n    #minor_locator_x = AutoMinorLocator(4)\n    minor_locator_y = AutoMinorLocator(4)\n    #ax.xaxis.set_minor_locator(minor_locator_x)\n    ax.yaxis.set_minor_locator(minor_locator_y)\n    \n    k+=1    \nf.legend(prop={'size': fontsize},loc=\"upper center\", ncol=int(len(files)/2+0.5))\nplt.yticks(plt.yticks()[0][::2]) # jeden zweiten Tick löschen\nplt.subplots_adjust(left=0.07,right=0.98,top=0.835,bottom=0.08)\nfor i in np.arange(len(sys.argv)):\n    if sys.argv[i] == \"png\":\n        plt.savefig(\"../../../../../ownCloud/SS18/BA/Vortrag/Bilder/CNS_Rg2_c_epslice.png\")\nplt.show()\n\n\n","sub_path":"WangLandau/CoSolvent/N64/Rg2_T/Rg2_c_epslice_plot.py","file_name":"Rg2_c_epslice_plot.py","file_ext":"py","file_size_in_byte":2619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"571726757","text":"#pylint: disable=invalid-name, too-many-instance-attributes, too-many-arguments\n\nimport numpy as np\nimport time\nimport torch as pt\n\nfrom utilities import get_X_process\n\n\ndevice = pt.device('cpu')\n\n\nclass NNSolver():\n\n    def __init__(self, problem, name, learning_rates, gradient_steps, NN_class, K=1000,\n                 K_batch=1000, delta_t=0.01, print_every=500, method='implicit', seed=42):\n        self.problem = problem\n        self.name = name\n        self.seed = seed\n        self.method = method\n        self.K = K\n        self.K_batch = K_batch\n        self.delta_t = delta_t\n        self.sq_delta_t = pt.sqrt(pt.tensor(delta_t))\n        self.N = int(np.ceil(problem.T / delta_t))\n        self.learning_rates = learning_rates\n        self.gradient_steps = gradient_steps\n        self.NN_class = NN_class\n        self.Y_n = [NN_class(problem.d, 1, lr=self.learning_rates[n], problem=problem, seed=seed) for n in range(self.N)] + [problem.g]\n\n        # logging\n        self.runtimes = []\n        self.loss_log = []\n        self.print_every = print_every\n\n    def train(self):\n\n        X, xi = get_X_process(self.problem, self.K, self.delta_t, seed=self.seed)\n\n        self.problem.modus = 'pt'\n\n        X = pt.autograd.Variable(pt.tensor(X).float(), requires_grad=True).to(device)\n        xi = pt.tensor(xi).float().to(device)\n\n        for n in range(self.N - 1, -1, -1):\n\n            self.loss_log.append([])\n\n            if n < self.N - 1:\n                self.Y_n[n].load_state_dict(self.Y_n[n + 1].state_dict())\n\n            for l in range(self.gradient_steps[n]):\n\n                t_0 = time.time()\n\n                batch = np.random.choice(np.arange(self.K), size=self.K_batch, replace=False)\n\n                X_n = X[n, batch, :]\n                X_n_1 = X[n + 1, batch, :]\n\n\n                #loss = pt.mean((Y_n[n + 1](X_n_1).squeeze() - Y_n[n](X_n).squeeze() \n                #                + problem.h(n * delta_t, X_n, Y_n[n + 1](X_n_1).squeeze(), sigma_Z_n) * delta_t\n                #                - pt.sum(sigma_Z_n * xi[n + 1, batch, :], 1) * sq_delta_t)**2)\n                if self.method == 'implicit':\n                    Y_eval = self.Y_n[n](X_n).squeeze().sum()\n                    Y_eval.backward(retain_graph=True)\n                    grad_Y_n, = pt.autograd.grad(Y_eval, X_n, create_graph=True)\n                    if self.problem.sigma_modus == 'variable':\n                        sigma_transpose = pt.tensor(np.transpose(self.problem.sigma(X_n).detach().cpu().numpy(), [0, 2, 1])).to(device)\n                        Z_n = pt.bmm(sigma_transpose, grad_Y_n.unsqueeze(2)).squeeze(2)\n                    else:\n                        Z_n = pt.mm(self.problem.sigma(X_n).t(), grad_Y_n.t()).t()\n\n                    loss = pt.mean((self.Y_n[n + 1](X_n_1).squeeze() - self.Y_n[n](X_n).squeeze()\n                                    + self.problem.h(n * self.delta_t, X_n, self.Y_n[n](X_n).squeeze(), Z_n) * self.delta_t\n                                    - pt.sum(Z_n * xi[n + 1, batch, :], 1) * self.sq_delta_t)**2)\n\n                elif self.method == 'explicit':\n                    Y_eval = self.Y_n[n + 1](X_n_1).squeeze().sum()\n                    Y_eval.backward(retain_graph=True)\n                    grad_Y_n_1, = pt.autograd.grad(Y_eval, X_n_1, create_graph=True)\n                    if self.problem.sigma_modus == 'variable':\n                        sigma_transpose = pt.tensor(np.transpose(self.problem.sigma(X_n_1).detach().cpu().numpy(), [0, 2, 1])).to(device)\n                        Z_n_1 = pt.bmm(sigma_transpose, grad_Y_n_1.unsqueeze(2)).squeeze(2)\n                    else:\n                        Z_n_1 = pt.mm(self.problem.sigma(X_n_1).t(), grad_Y_n_1.t()).t()\n                    loss = pt.mean((self.Y_n[n + 1](X_n_1).squeeze() - self.Y_n[n](X_n).squeeze()\n                                    + self.problem.h((n + 1) * self.delta_t, X_n_1, self.Y_n[n + 1](X_n_1).squeeze(), Z_n_1) * self.delta_t)**2)\n\n                self.Y_n[n].zero_grad()\n                loss.backward(retain_graph=True)\n                self.Y_n[n].optim.step()\n\n                self.loss_log[-1].append(loss.item())\n\n                t_1 = time.time()\n\n                self.runtimes.append(t_1 - t_0)\n\n                if l % self.print_every == 0:\n                    print('%d - %d - %.4e - %.2f - est: %.2fmin' % (n, l, self.loss_log[-1][-1], self.runtimes[-1], ((sum(self.gradient_steps[:n+1]) - l) * np.mean(self.runtimes[-100:])) / 60))\n","sub_path":"NNsolver.py","file_name":"NNsolver.py","file_ext":"py","file_size_in_byte":4466,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"440315331","text":"#from myEskwela import *\nimport psycopg2, string\n\ndef index(req):\n    form = req.form; \n    acctID = form['acct'];\n    acctRole = form['acctRole']; \n \n    html = \"\"\"\n        <script>\n        $(function(){\n            $('.subject-list-item').click(function(){\n\t    \t    var subjSectID = $(this).attr('id');\n\t\t\n        \t\t//Initiate and append to QueryHandler\n\t        \t_queryHandler.initializeQuery();\n\t        \t_queryHandler.setSubjSectionID(subjSectID);\n\t\t\n        \t\t//Set Back Tracker...\n        \"\"\";\n\n    #if (acctRole == \"PARENT\"):\n    #    html += \"\"\"\n    #        $('#back-button').click(function(){\n    #            alert();\n    #        });\n    #    \"\"\"\n    #else:\n    #    html += \"\"\"\n    #            $('#back-button-cont').hide(0);\n    #    \"\"\";\n        \n    html += \"\"\"\t\n        \t\t//Go to Subject Info...\n        \t\t_queryHandler.setSubjSectionID(subjSectID);\n                _pageHandler.generate_subject_info_page();\n                \n                $('#back-button-cont').hide(0);\n        \t});\n        });\n        </script>\n        <ul data-role='listview'>\n            <li> Current Subject(s) List as of current SY</li>\n            <li data-role='list-divider'> Please select from below: </li>\n    \"\"\";\n    #print \"Here goes the war\\n\";\n    \n    #Connect to Database, currently, i'm doing a hardline... \n    connString = \"dbname=myEskwela user=postgres password=password host=localhost port=5432\"\n    conn = psycopg2.connect(connString); curr = conn.cursor();\n\n    #Get Current Term..\n    arg1 = \"SELECT current_term()\"; curr.execute(arg1);\n    data = curr.fetchone(); currentTerm = data[0];\n\n    #Getting the List of SubjectSection(s)    arg1 = None; arg2 = \"\";\n    if (acctRole == \"STUDENT\" or acctRole == \"PARENT\"):\n        arg1 = \"SELECT student_load_information(%s, %s)\"; arg2 = (acctID, currentTerm,);\n    elif (acctRole == \"FACULTY\"):\n        arg1 = \"SELECT faculty_load_information(%s, %s)\"; arg2 = (acctID, currentTerm);\n    \n    curr.execute(arg1, arg2); data = curr.fetchall();\n    for load in data:\n        info = string.split(load[0], '#');\n        html += \"<li id='\"+info[0]+\"' class='subject-list-item'><a href='#'>\";\n        html += info[1]+\"(\"+info[2]+\")<div style='font-size:80%;font-weight:normal'>\";\n        html += info[3]+\"</div></a></li>\";\n\n            #<li id='TEST1' class='subject-list-item'><a href='#'> foo</a></li>\n            #<li id='TEST2' class='subject-list-item'><a href='#'> foo</a></li>\n            #<li id='TEST3' class='subject-list-item'><a href='#'> foo</a></li>\n\n #   html += \"\"\" </ul> \"\"\";\n\n    #Closing Database Connection(s)\n    curr.close(); conn.close();\n    \n    #Return Final HTML DOM\n    return html;\n","sub_path":"scripts/m/get-subject-list-n.py","file_name":"get-subject-list-n.py","file_ext":"py","file_size_in_byte":2665,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"261585521","text":"#! /home/minys/anaconda3/bin/python\n# -*- coding: utf-8 -*-\n\nimport os.path\nimport argparse\nfrom PIL import Image\n\ndescription = \"\"\"This program convert pics into text pics. I try to preserve the original picture contrast.\"\"\"\nparser = argparse.ArgumentParser(description=description)\nparser.add_argument(\"input_file\", type=str, help=\"The input image file directory.\")\nparser.add_argument(\"--output_file\", \"-o\", dest='output_file', type=str, default=\"\", help=\"The output image file directory, if not exist, using the input_file instead.\")\nparser.add_argument(\"--resize\", \"-r\", dest=\"size\", type=str, default=\"\", help=\"Allow to resize pics by '(width)x(height)', i.e. 80x60.\")\nparser.add_argument(\"--whiten-ratio\", \"-w\", type=float, dest='white', default=0.2, help=\"Whiten(Blacken) part of the image to remove background. Default is 0.2, must be ajusted when '-W' argument apears.\")\nparser.add_argument(\"--quiet\", \"-q\", action='store_true', default=False, dest=\"quiet\", help=\"Not display result immediatly.\")\nparser.add_argument(\"--white-background\", \"-W\", action='store_true', default=False, dest='white_back', help=\"Is the background is white? Add this argument when you want to copy the output to MS Word, do not need it in the command line.\")\nargs = parser.parse_args()\n\nchar_list = list(\"$@B%8&WM#*oahkbdpqwmZO0QLCJUYXzcvunxrjft/\\|()1{}[]?-_+~<>i!lI;:,\\\"^`'. \")\n# this list is command style, more black less coverage\n# char_list.reverse()\n\ndef get_text(image, ratio=0.2, white_back=False):\n    min_pix, max_pix = image.getextrema()\n    length = len(char_list)\n    unit = 256/length\n    text = \"\"\n    if white_back:\n        char_list.reverse()\n    for i in range(0, image.size[1]): # j is height\n        for j in range(0,image.size[0]): # i is width\n            pixel_val = image.getpixel((j, i))\n            if pixel_val < max_pix * ratio:\n                text += char_list[int(pixel_val/unit)]\n            else:\n                text += ' '\n        text += '\\n'\n    return text\n    \n\nif __name__ == '__main__':    \n    input_file = args.input_file\n    output_file = args.output_file\n    size = args.size\n    quiet = args.quiet\n    white = args.white\n    white_back = args.white_back\n    image = Image.open(input_file).convert(\"L\")\n    \n    if not output_file:\n        in_path, in_name = os.path.split(input_file)\n        output_file = in_path + \"/\" + os.path.splitext(in_name)[0] + \".txt\"\n    if size:\n        try:\n            size = map(int, size.split(\"x\"))\n            image = image.resize(size)\n        except Exeption:\n            print(\"Error: wrong argument when resize pic.\")\n    \n    text = get_text(image, white, white_back)\n    with open(output_file, 'w+') as txtfile:\n        if not quiet:\n            print(text)\n        txtfile.write(text)\n\n","sub_path":"pic2text.py","file_name":"pic2text.py","file_ext":"py","file_size_in_byte":2758,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"634506501","text":"import cfg\nimport models\nimport datasets\nimport random\nfrom functions import validate, LinearLrDecay, load_params, copy_params\nfrom functions_adv import train_adv_g\nfrom utils.utils import set_log_dir, save_checkpoint, create_logger\nfrom utils.inception_score import _init_inception\nfrom utils.fid_score import create_inception_graph, check_or_download_inception\n\nimport torch\nimport os\n# os.environ['CUDA_VISIBLE_DEVICES'] = '7'\nimport numpy as np\nimport torch.nn as nn\nimport torch.nn.utils.prune as prune\nfrom tensorboardX import SummaryWriter\n\n\ndef pruning_generate(model, state_dict):\n    for (name, m) in model.named_modules():\n        if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):\n            m = prune.custom_from_mask(m, name='weight', mask=state_dict[name + \".weight_mask\"])\n\n\ndef main():\n    args = cfg.parse_args()\n    random.seed(args.random_seed)\n    torch.manual_seed(args.random_seed)\n    torch.cuda.manual_seed(args.random_seed)\n    np.random.seed(args.random_seed)\n    torch.backends.cudnn.deterministic = False\n    torch.backends.cudnn.benchmark = True\n    os.environ['PYTHONHASHSEED'] = str(args.random_seed)\n    \n    # set tf env\n    _init_inception()\n    inception_path = check_or_download_inception(None)\n    create_inception_graph(inception_path)\n\n    # import network\n    gen_net = eval('models.'+args.model+'.Generator')(args=args)\n    dis_net = eval('models.'+args.model+'.Discriminator')(args=args)\n    avg_gen_net = eval('models.'+args.model+'.Generator')(args=args)\n    initial_gen_net_weight = torch.load(os.path.join(args.init_path, 'initial_gen_net.pth'), map_location=\"cpu\")\n    initial_dis_net_weight = torch.load(os.path.join(args.init_path, 'initial_dis_net.pth'), map_location=\"cpu\")\n        \n    gen_net.load_state_dict(initial_gen_net_weight)\n    avg_gen_net.load_state_dict(initial_gen_net_weight)\n    dis_net.load_state_dict(initial_dis_net_weight)\n    \n    # set optimizer\n    gen_optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, gen_net.parameters()),\n                                     args.g_lr, (args.beta1, args.beta2))\n    dis_optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, dis_net.parameters()),\n                                     args.d_lr, (args.beta1, args.beta2))\n    gen_scheduler = LinearLrDecay(gen_optimizer, args.g_lr, 0.0, 0, args.max_iter * args.n_critic)\n    dis_scheduler = LinearLrDecay(dis_optimizer, args.d_lr, 0.0, 0, args.max_iter * args.n_critic)\n\n    # set up data_loader\n    dataset = datasets.ImageDatasetLess(args)\n    train_loader = dataset.train\n\n    if args.dataset.lower() == 'cifar10':\n        fid_stat = 'fid_stat/fid_stats_cifar10_train.npz'\n    elif args.dataset.lower() == 'stl10':\n        fid_stat = 'fid_stat/fid_stats_stl10_train.npz'\n    else:\n        raise NotImplementedError('no fid stat for %s' % args.dataset.lower())\n    assert os.path.exists(fid_stat)\n\n    # epoch number for dis_net\n    args.max_epoch = args.max_epoch * args.n_critic\n    if args.max_iter:\n        args.max_epoch = np.ceil(args.max_iter * args.n_critic / len(train_loader))\n\n    # initial\n    fixed_z = torch.cuda.FloatTensor(np.random.normal(0, 1, (25, args.latent_dim)))\n    start_epoch = 0\n    best_fid = 1e4\n\n    # set writer\n    args.path_helper = set_log_dir('logs', args.exp_name + \"_{}\".format(args.percent))\n    logger = create_logger(args.path_helper['log_path'])\n    logger.info(args)\n    writer_dict = {\n        'writer': SummaryWriter(args.path_helper['log_path']),\n        'train_global_steps': start_epoch * len(train_loader),\n        'valid_global_steps': start_epoch // args.val_freq,\n    }\n\n    pruning_generate(gen_net, torch.load(os.path.join(args.rewind_path), map_location=\"cpu\")['avg_gen_state_dict'])\n    pruning_generate(avg_gen_net, torch.load(os.path.join(args.rewind_path), map_location=\"cpu\")['avg_gen_state_dict'])\n    \n    gen_net = gen_net.cuda()\n    dis_net = dis_net.cuda()\n    avg_gen_net = avg_gen_net.cuda()\n    gen_avg_param = copy_params(gen_net)\n    logger.info(args)\n    writer_dict = {\n        'writer': SummaryWriter(args.path_helper['log_path']),\n        'train_global_steps': start_epoch * len(train_loader),\n        'valid_global_steps': start_epoch // args.val_freq,\n    }\n\n    # train loop\n    for epoch in range(int(start_epoch), int(args.max_epoch)):\n            \n        lr_schedulers = (gen_scheduler, dis_scheduler) if args.lr_decay else None\n        train_adv_g(args, gen_net, dis_net, gen_optimizer, dis_optimizer, gen_avg_param, train_loader, epoch, writer_dict, lr_schedulers, steps=args.steps, gamma=args.gamma)\n\n        if epoch and epoch % args.val_freq == 0 or epoch == int(args.max_epoch)-1:\n            backup_param = copy_params(gen_net)\n            load_params(gen_net, gen_avg_param)\n            inception_score, fid_score = validate(args, fixed_z, fid_stat, gen_net, writer_dict, epoch)\n            logger.info('Inception score: %.4f, FID score: %.4f || @ epoch %d.' % (inception_score, fid_score, epoch))\n            load_params(gen_net, backup_param)\n            if fid_score < best_fid:\n                best_fid = fid_score\n                is_best = True\n            else:\n                is_best = False\n        else:\n            is_best = False\n\n        load_params(avg_gen_net, gen_avg_param)\n        save_checkpoint({\n            'epoch': epoch + 1,\n            'model': args.model,\n            'gen_state_dict': gen_net.state_dict(),\n            'dis_state_dict': dis_net.state_dict(),\n            'avg_gen_state_dict': avg_gen_net.state_dict(),\n            'gen_optimizer': gen_optimizer.state_dict(),\n            'dis_optimizer': dis_optimizer.state_dict(),\n            'best_fid': best_fid,\n            'path_helper': args.path_helper,\n            'seed': args.random_seed\n        }, is_best, args.path_helper['ckpt_path'])\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"SNGAN/train_with_masks_adv_g_less.py","file_name":"train_with_masks_adv_g_less.py","file_ext":"py","file_size_in_byte":5857,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"191266155","text":"class Solution:\n    def maxSubArray(self, nums) -> int:\n        max = nums[0]\n        n = nums[0]\n        for i in range(1,len(nums)):\n            if(n > 0):\n                n += nums[i]\n            else:\n                n = nums[i]\n            if(max < n):\n                max = n\n        return max\nif __name__ == '__main__':\n    s = Solution()\n    print(s.maxSubArray([1]))","sub_path":"python/chuji_dp/leetCode_55.py","file_name":"leetCode_55.py","file_ext":"py","file_size_in_byte":376,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"633467839","text":"# -*- codin:utf-8 -*-\n# Author: Rui\n\n'''\n需求:\n启动程序后，让用户输入工资，然后打印商品列表\n允许用户根据商品编号购买商品\n用户选择商品后，检测余额是否够，够就直接扣款，不够就提醒\n可随时退出，退出时，打印已购买商品和余额\n'''\n\nproduct_list = [\n    ('IphoneX', '8888'),\n    ('Ipad4', '3500'),\n    ('mx8', '2399'),\n    (\"保时捷卡宴\", '870000'),\n    ('奥迪A4L', '320000'),\n    ('联想Y700', '6800'),\n    ('beats耳机', '2300'),\n    ('JBL音响', '1200')]\nshopping_list = []\n\nmoney = input(\"请输入你有多少钱:\")\nif money.isdigit(): # 如果输入的money是数字\n    money = int(money) # 把money变成int\n    while True:\n        for index, item in enumerate(product_list): # 取出商品列表和角标\n            # print(product_list.index(item), item) # 同下\n            print(index, item)\n        user_choice = input(\"请选择要购买的商品:\")\n        if user_choice.isdigit(): # 商品选择如果是数字\n            user_choice = int(user_choice)\n            if user_choice < len(product_list) and user_choice >= 0:\n                product_choice = product_list[user_choice] # 用户选择的商品\n                if int(product_choice[1]) <= money: # 买得起\n                    shopping_list.append(product_choice) # 买得起的添加到购物车\n                    money -= int(product_choice[1]) # 扣钱\n                    print(\"添加 \\033[31;1m%s\\033[0m 到你的购物车成功！你现在的余额为 \\033[31;1m%s\\033[0m\" %(product_choice[0], money))\n                else:\n                    print(\"\\033[31;1m你的余额只剩 [%s] 啦，余额不足！\\033[0m\" % money)\n            else:\n                print(\"无效的选项！！！\\033[31;1m【%s】\\033[0m\" % user_choice)\n        elif user_choice == 'q':\n            print(\"---------------shopping list------------------\")\n            for p in shopping_list:\n                print(p)\n            print(\"你的余额为：\\033[31;1m%s\\033[0m\" % money)\n            print(\"exit...\")\n            exit(0)\n        else:\n            print(\"无效选项！！！\")\n            exit(1)\nelse:\n    print(\"无效选项！！！\")\n    exit(1)\n\n","sub_path":"day2/d2-购物车.py","file_name":"d2-购物车.py","file_ext":"py","file_size_in_byte":2217,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"307197533","text":"from django.core.urlresolvers import reverse\n\nfrom rest_framework import status\n\nfrom thelastspace.test import testcases\n\n\nclass RedirectViewTests(testcases.TestCase):\n\n    def test_root(self):\n        url = reverse('feed:root')\n\n        with self.assertNumQueries(0):\n            response = self.client.get(url)\n        self.assertRedirect(response, location='http://testserver/feed/entries/')\n\n\nclass EntryViewSetTests(testcases.SearchTestCase):\n\n    def format_entry(self, entry):\n        return {\n            'source': {\n                'name': entry.source.name,\n                'link': entry.source.link,\n            },\n            'identifier': entry.identifier,\n            'title': entry.title,\n            'date': entry.date,\n            'link': entry.link,\n            'created_at': entry.created_at,\n            'updated_at': entry.updated_at,\n        }\n\n    def format_entries(self, entries):\n        return {\n            'count': len(entries),\n            'next': None,\n            'previous': None,\n            'results': [self.format_entry(entry) for entry in entries],\n        }\n\n    def format_result(self, entry):\n        return {\n            'entry': self.format_entry(entry),\n        }\n\n    def format_results(self, entries):\n        return {\n            'count': len(entries),\n            'next': None,\n            'previous': None,\n            'results': [self.format_result(entry) for entry in entries],\n        }\n\n    def test_list(self):\n        url = reverse('feed:entry-list')\n\n        # 2 queries to list the requested entries:\n        # - 1 query to count all entries\n        # - 1 query to paginate the entries\n        with self.assertNumQueries(2):\n            response = self.client.get(url)\n        self.assertEqual(response.status_code, status.HTTP_200_OK)\n        self.assertEqual(response.data, self.format_entries(self.entries))\n\n    def test_search(self):\n        url = reverse('feed:entry-search')\n\n        # 1 query to search the requested entries:\n        # - 1 query to paginate the entries\n        with self.assertNumQueries(1):\n            response = self.client.get(url, {'text': self.entries[0].title})\n        self.assertEqual(response.status_code, status.HTTP_200_OK)\n        self.assertEqual(response.data, self.format_results(self.entries[0:1]))\n","sub_path":"thelastspace/feed/tests/test_views.py","file_name":"test_views.py","file_ext":"py","file_size_in_byte":2297,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"493940211","text":"#A palindromic number reads the same both ways. The largest palindrome made from the product of two 2-digit numbers is 9009 = 91 × 99.\r\n#Find the largest palindrome made from the product of two 3-digit numbers.\r\n\r\ndef reverse_number(n):\r\n    r = 0\r\n    print(n)\r\n    while n > 0:\r\n        r *= 10\r\n        r += n % 10\r\n        n = int(n/10)\r\n    return r\r\n\r\ndef isPalindrome(n):\r\n    r = reverse_number(n)\r\n    if n == r:\r\n        return True\r\n    return False\r\n\r\npalindromes = []\r\nfor i in range(999, 100, -1):\r\n    for j in range(999 - (999-i), 100, -1):\r\n        if isPalindrome(j*i):\r\n            palindromes.append(i*j)\r\n        \r\nprint(max(palindromes))\r\n","sub_path":"004/Problem004.py","file_name":"Problem004.py","file_ext":"py","file_size_in_byte":662,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"171026310","text":"# https://projecteuler.net/problem=21\n\nhighest_number = 10000\n\n\nclass Primes:\n    primes = [2]\n    currentIndex = 0\n    high = 0\n\n    def __init__(self, high):\n        self.high = high\n\n    def __iter__(self):\n        self.currentIndex = 0\n        return self\n\n    def __next__(self):  # Python 2: def next(self)\n        if(len(self.primes) <= self.currentIndex):\n            nextPrime = self._getNextPrime()\n            self.primes.append(nextPrime)\n        self.currentIndex += 1\n        prime = self.primes[self.currentIndex - 1]\n        if prime > self.high:\n            raise StopIteration\n        else:\n            return prime\n\n    def _getNextPrime(self):\n        largestPrime = self.primes[len(self.primes)-1]\n        n = largestPrime + 1\n        if n % 2 == 0:\n            n += 1\n\n        while(True):\n            if(self._isNextPrime(n)):\n                return n\n            n += 2\n\n    def _isNextPrime(self, n):\n        for prime in self.primes:\n            if n % prime == 0:\n                return False\n        return True\n\n\ndef unique(arr):\n    return list(set(arr))\n\n\ndef is_devisor(d):\n    def _is_devisor(d, n):\n        return (d % n == 0) and (d > n)\n    return lambda n: _is_devisor(d, n)\n\n\ndef get_devisors_from_primes(arr, n):\n    combs = arr.copy()\n    new_combs = []\n    for a in range(len(combs)):\n        for b in range(a, len(combs)):\n            new_combs.append(combs[a] * combs[b])\n    combs += filter(is_devisor(n), new_combs)\n    combs = unique(combs)\n    if(len(arr) == len(combs)):\n        return sorted(arr)\n    return get_devisors_from_primes(combs, n)\n\n\ndef get_proper_divisors(n):\n    primes = Primes((n)/2)\n    prime_devisors = list(filter(is_devisor(n), primes))\n    devisors = get_devisors_from_primes(prime_devisors, n)\n    devisors.insert(0, 1)\n    return devisors\n\n\ndef get_sum_of_proper_divisors(n):\n    return sum(get_proper_divisors(n))\n\n\namicable_numbers = []\n\nfor a in range(0, highest_number):\n    b = get_sum_of_proper_divisors(a)\n    a2 = get_sum_of_proper_divisors(b)\n    if a == a2 and a != b:\n        amicable_numbers.append(a)\n        amicable_numbers.append(b)\n\nprint(sum(unique(amicable_numbers)))\n","sub_path":"python/21.py","file_name":"21.py","file_ext":"py","file_size_in_byte":2159,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"315598681","text":"\"\"\"\nNot used. Not relevant\n\"\"\"\nimport pyaudio\nimport wave\n\nFORMAT = pyaudio.paInt16\nCHANNELS = 1\nRATE = 8000\nCLICK_SECONDS = 0.02\nCHUNK = 1024\nRECORD_SECONDS = 100\nWAVE_OUTPUT_FILENAME = \"output.wav\"\n\np = pyaudio.PyAudio()\n\nstream = p.open(format=FORMAT,\n                channels=CHANNELS,\n                rate=RATE,\n                input=True,\n                frames_per_buffer=CHUNK)\n\nprint(\"* recording\")\n\nframes = []\nfor i in range(0, RECORD_SECONDS):#int(RATE / CHUNK * RECORD_SECONDS)):\n    data = stream.read(int(RATE*CLICK_SECONDS))\n    if frames:\n        \"\"\"\n        get the second half of the previous block and\n        concatenate it to the first half of the new block\n        for overlapping\n        \"\"\"\n        past_half = frames[len(frames)-1][len(data)/2:]\n        curr_half = data[:len(data)/2]\n        frames.append(past_half + curr_half)\n\n    frames.append(data)\n\nprint(\"* done recording\")\nprint(len(frames))\n\nstream.stop_stream()\nstream.close()\np.terminate()\n\nfor i in range(len(frames)):\n    wf = wave.open(\"output\"+ \"%03d\" % i+\".wav\", 'wb')\n    wf.setnchannels(CHANNELS)\n    wf.setsampwidth(p.get_sample_size(FORMAT))\n    wf.setframerate(RATE)\n    wf.writeframes(''.join(frames[i]))\n    wf.close()","sub_path":"src/legacy/mic.py","file_name":"mic.py","file_ext":"py","file_size_in_byte":1218,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"411415208","text":"# project / _config.py\n\"\"\"\n    iqorih\n\"\"\"\n\nimport os\n\n#grab the folder where the script lives\nbasedir = os.path.abspath(os.path.dirname(__file__))\n\nDATABASE = 'flasktaskr.db'\nCSRF_ENABLED = True\nSECRET_KEY = 'my_precious'\n\n#define the full path to the db\nDATABASE_PATH = os.path.join(basedir, DATABASE)\n\n#the database uri\nSQLALCHEMY_DATABASE_URI = 'sqlite:///' + DATABASE_PATH\n","sub_path":"_config.py","file_name":"_config.py","file_ext":"py","file_size_in_byte":377,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"198423753","text":"# Copyright 2020 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport re\n\nimport synthtool as s\nimport synthtool.gcp as gcp\nfrom synthtool import tmp\nfrom synthtool.languages import python\nfrom synthtool.sources import git\n\n\ngapic = gcp.GAPICBazel()\ncommon = gcp.CommonTemplates()\n\nversions = [\"v2\"]  # this package also has v1 protos, see below for note\n\n# ----------------------------------------------------------------------------\n# Generate orgpolicy GAPIC layer\n# ----------------------------------------------------------------------------\nfor version in versions:\n    library = gapic.py_library(\n        service=\"orgpolicy\",\n        version=version,\n        bazel_target=f\"//google/cloud/orgpolicy/{version}:orgpolicy-{version}-py\",\n    )\n    s.move(library, excludes=[\"setup.py\", \"README.rst\", \"docs/index.rst\"])\n\n# Rename to google-cloud-org-policy\n# TODO: use bazel option to rename package\ns.replace(\n    \"google/cloud/**/*\",\n    \"google-cloud-orgpolicy\",\n    \"google-cloud-org-policy\"\n)\n\n# ----------------------------------------------------------------------------\n#  Add templated files\n# ----------------------------------------------------------------------------\n\n# coverage is 97 to exclude orgpolicy/v1 code\ntemplated_files = common.py_library(microgenerator=True, cov_level=95)\ns.move(\n    templated_files, excludes=[\".coveragerc\",]\n)\n\n# NOTE: This library also has legacy pb2.py files for \"v1\"\n# in google/cloud/orgpolicy/v1\n# v1 only has messages (no service or RPCs).\n# v1 protos can be refreshed by running\n# `nox -s generate_protos`. See the noxfile.py.\n\n# Append generate_protos  nox session\ns.replace(\n    \"noxfile.py\",\n    \"\"\"(@nox\\.session.+?\ndef lint\\(.+?)\"\"\",\n    '''@nox.session(python=\"3.8\")\ndef generate_protos(session):\n    \"\"\"Generates the protos using protoc.\n\n    Some notes on the `google` directory:\n    1. The `_pb2.py` files are produced by protoc.\n    2. The .proto files are non-functional but are left in the repository\n       to make it easier to understand diffs.\n    3. The `google` directory also has `__init__.py` files to create proper modules.\n       If a new subdirectory is added, you will need to create more `__init__.py`\n       files.\n\n    NOTE: This is a hack and only runnable locally. You will need to have\n    the api-common-protos repo cloned. This should be migrated to use\n    bazel in the future.\n    \"\"\"\n    session.install(\"grpcio-tools\")\n    protos = [str(p) for p in (Path(\".\").glob(\"google/**/*.proto\"))]\n\n    session.run(\n        \"python\",\n        \"-m\",\n        \"grpc_tools.protoc\",\n        \"--proto_path=../api-common-protos\",\n        \"--proto_path=.\",\n        \"--python_out=.\",\n        *protos,\n    )\n\n\\g<1>''',\n)\ns.shell.run([\"nox\", \"-s\", \"blacken\"], hide_output=False)\n","sub_path":"synth.py","file_name":"synth.py","file_ext":"py","file_size_in_byte":3259,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"501333446","text":"r\"\"\"Test forward pass of GRU language model.\n\nTest target:\n- :py:meth:`lmp.model.GRUModel.forward`.\n\"\"\"\n\nimport torch\n\nfrom lmp.model import GRUModel\n\n\ndef test_input_shape_and_dtype(\n    gru_model: GRUModel,\n    batch_prev_tkids: torch.Tensor,\n):\n    r\"\"\"Input must be long tensor.\"\"\"\n\n    try:\n        gru_model = gru_model.eval()\n        gru_model(batch_prev_tkids)\n    except Exception:\n        assert False\n\n\ndef test_return_shape_and_dtype(\n    gru_model: GRUModel,\n    batch_prev_tkids: torch.Tensor,\n):\n    r\"\"\"Return float tensor with correct shape.\"\"\"\n    gru_model = gru_model.eval()\n    out = gru_model(batch_prev_tkids)\n\n    # Output float tensor.\n    assert out.dtype == torch.float\n\n    # Input shape: (B, S).\n    # Output shape: (B, S, V).\n    assert out.shape == (\n        batch_prev_tkids.shape[0],\n        batch_prev_tkids.shape[1],\n        gru_model.emb.num_embeddings,\n    )\n\n\ndef test_forward_path(\n    gru_model: GRUModel,\n    batch_prev_tkids: torch.Tensor,\n):\n    r\"\"\"Parameters used during forward must have gradients.\"\"\"\n    # Make sure model has no gradients.\n    gru_model = gru_model.train()\n    gru_model.zero_grad()\n\n    gru_model(batch_prev_tkids).sum().backward()\n\n    assert hasattr(gru_model.emb.weight.grad, 'grad')\n    assert hasattr(gru_model.pre_hid[0].weight.grad, 'grad')\n    assert hasattr(gru_model.hid.weight_ih_l0.grad, 'grad')\n    assert hasattr(gru_model.post_hid[-1].weight.grad, 'grad')\n","sub_path":"test/lmp/model/_gru/test_forward.py","file_name":"test_forward.py","file_ext":"py","file_size_in_byte":1437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"320713051","text":"import pandas as pd\nimport torch\nimport numpy as np\nimport pdb\nfrom pathlib import Path\nimport os\nfrom os.path import abspath\nfrom death.post.inputgen_planI import InputGenI, pad_collate\nfrom death.post.inputgen_planG import InputGenG, pad_collate\nfrom death.post.inputgen_planH import InputGenH\nfrom death.post.inputgen_planJ import InputGenJ\nfrom death.DNC.priorDNC import PriorDNC\nfrom torch.utils.data import DataLoader\nimport torch.nn as nn\nfrom torch.nn.modules import LSTM\nfrom torch.autograd import Variable\nimport pickle\nfrom shutil import copy\nimport traceback\nfrom collections import deque\nimport datetime\nfrom death.DNC.tsDNCtrainer import logprint\nimport pdb\nfrom death.final.losses import TOELoss, AnotherBCEWithLogits\nfrom death.final.killtime import out_of_time\nfrom death.final.metrics import ConfusionMatrixStats\nimport code\nfrom death.analysis.expectedroc import get_death_code_proportion\n\n# param_x = 7298\nparam_h = 16  # 64\nparam_L = 4  # 4\n# param_v_t = 3592 # 5952\nparam_W = 4  # 8\nparam_R = 4  # 8\nparam_N = 16  # 64\nparam_bs = 64\n# this is the empirical saturation level when positive weights are not used.\n# with lower lr, saturation can be higher\nsaturation=2000000\nval_bat_cons=2048\n\nimport torch.multiprocessing\n\ntorch.multiprocessing.set_sharing_strategy('file_system')\n\n\ndef datetime_filename():\n    return datetime.datetime.now().strftime(\"%m_%d_%X\")\n\n\ndef sv(var):\n    return var.data.cpu().numpy()\n\n\nclass dummy_context_mgr():\n    def __enter__(self):\n        return None\n\n    def __exit__(self, exc_type, exc_value, traceback):\n        return False\n\n\ndef save_model(net, optim, epoch, iteration, savestr):\n    epoch = int(epoch)\n    task_dir = os.path.dirname(abspath(__file__))\n    if not os.path.isdir(Path(task_dir) / \"saves\" / savestr):\n        os.mkdir(Path(task_dir) / \"saves\" / savestr)\n    pickle_file = Path(task_dir).joinpath(\"saves/\" + savestr + \"/seqDNC_\" + str(epoch) + \"_\" + str(iteration) + \".pkl\")\n    with pickle_file.open('wb') as fhand:\n        torch.save((net, optim, epoch, iteration), fhand)\n    print('model saved at', pickle_file)\n\n\ndef load_model(computer, optim, starting_epoch, starting_iteration, savestr):\n    task_dir = os.path.dirname(abspath(__file__))\n    save_dir = Path(task_dir) / \"saves\" / savestr\n    highestepoch = 0\n    highestiter = 0\n\n    if not os.path.isdir(Path(task_dir) / \"saves\" / savestr):\n        os.mkdir(Path(task_dir) / \"saves\" / savestr)\n\n    for child in save_dir.iterdir():\n        try:\n            epoch = str(child).split(\"_\")[3]\n            iteration = str(child).split(\"_\")[4].split('.')[0]\n        except IndexError:\n            print(str(child))\n        iteration = int(iteration)\n        epoch = int(epoch)\n        # some files are open but not written to yet.\n        if child.stat().st_size > 20480:\n            if epoch > highestepoch or (iteration > highestiter and epoch == highestepoch):\n                highestepoch = epoch\n                highestiter = iteration\n    if highestepoch == 0 and highestiter == 0:\n        print(\"nothing to load\")\n        return computer, optim, starting_epoch, starting_iteration\n    if starting_epoch==0 and starting_iteration==0:\n        pickle_file = Path(task_dir).joinpath(\n            \"saves/\" + savestr + \"/seqDNC_\" + str(highestepoch) + \"_\" + str(highestiter) + \".pkl\")\n    else:\n        pickle_file = Path(task_dir).joinpath(\n            \"saves/\" + savestr + \"/seqDNC_\" + str(starting_epoch) + \"_\" + str(starting_iteration) + \".pkl\")\n    print(\"loading model at\", pickle_file)\n    with pickle_file.open('rb') as pickle_file:\n        computer, optim, epoch, iteration = torch.load(pickle_file)\n    print('Loaded model at epoch ', highestepoch, 'iteration', iteration)\n\n    return computer, optim, highestepoch, highestiter\n\nglobal_exception_counter = 0\n\n\ndef run_one_patient(computer, input, target, optimizer, loss_type, real_criterion,\n                    binary_criterion, beta, cms, validate=False):\n    global global_exception_counter\n    patient_loss = None\n    traincms=cms[0]\n    valicms=cms[1]\n    try:\n        optimizer.zero_grad()\n\n        input = Variable(torch.Tensor(input).cuda())\n        target = Variable(torch.Tensor(target).cuda())\n        loss_type = Variable(torch.Tensor(loss_type).cuda())\n\n        # we have no critical index, becuase critical index are those timesteps that\n        # DNC is required to produce outputs. This is not the case for our project.\n        # criterion does not need to be reinitiated for every story, because we are not using a mask\n\n        patient_output = computer(input)\n        cause_of_death_output = patient_output[:, 1:]\n        cause_of_death_target = target[:, 1:]\n        # pdb.set_trace()\n        cod_loss = binary_criterion(cause_of_death_output, cause_of_death_target)\n\n        toe_output=patient_output[:,0]\n        toe_target=target[:,0]\n        toe_loss=real_criterion(toe_output,toe_target,loss_type)\n\n        total_loss=cod_loss+beta*toe_loss\n        sigoutput=torch.sigmoid(cause_of_death_output)\n\n\n        # this numerical issue destroyed this model training.\n        # loss keeps going down, but ROC is the same. I need to load an earlier epoch and restart.\n        # I need to reimplement my BCE\n        # this is a known issue for PyTorch 0.3.1 https://github.com/pytorch/pytorch/issues/2866\n\n        # this happens when the logit is exactly 1 (\\sigma(593)), and the loss is around 0.01\n        # I decide to not debug it, because the backwards signal should still be usable.\n\n        # no. the actual problem is that the death target does not conform one-hot requirement.\n        # it's not the sparsity, because the output was one\n        if cod_loss.data[0] < 0:\n            import pickle\n            # this was reached with loss negative. Why did that happen?\n            # BCE loss is supposed to be positive all the time.\n            with open(\"debug/itcc.pkl\",'wb') as f:\n                # this is a 1 Gb pickle. Somehow loading it takes forever. What?\n                pickle.dump((input, target, cause_of_death_output, cause_of_death_target),f)\n            print(cod_loss.data[0])\n            code.interact(local=locals())\n            raise ValueError\n\n        if not validate:\n            total_loss.backward()\n            optimizer.step()\n            traincms.update_one_pass(sigoutput, cause_of_death_target, cod_loss, toe_loss)\n            return float(cod_loss.data), float(toe_loss.data)\n\n        else:\n            valicms.update_one_pass(sigoutput, cause_of_death_target, cod_loss, toe_loss)\n            return float(cod_loss.data), float(toe_loss.data)\n\n        if global_exception_counter > -1:\n            global_exception_counter -= 1\n    except ValueError:\n        traceback.print_exc()\n        print(\"Value Error reached\")\n        print(datetime.datetime.now().time())\n        global_exception_counter += 1\n        if global_exception_counter == 10:\n            save_model(computer, optimizer, epoch=0, iteration=global_exception_counter)\n            raise ValueError(\"Global exception counter reached 10. Likely the model has nan in weights\")\n        else:\n            raise\n\n\n\ndef train(computer, optimizer, real_criterion, binary_criterion,\n          train, valid_dl, starting_epoch, total_epochs, starting_iter, iter_per_epoch, target_dim,\n          savestr, beta, logfile=False, kill_time=True):\n\n    valid_iterator = iter(valid_dl)\n    print_interval = 100\n    val_interval = 999\n    save_interval = 5\n    rldmax_len = 50\n    val_batch = int(val_bat_cons/param_bs)\n    running_cod_loss=deque(maxlen=rldmax_len)\n    running_toe_loss=deque(maxlen=rldmax_len)\n    traincms,validcms=ConfusionMatrixStats(target_dim-1),ConfusionMatrixStats(target_dim-1)\n    cms=(traincms,validcms)\n\n    if logfile:\n        open(logfile, 'w').close()\n\n    for name, param in computer.named_parameters():\n        logprint(logfile, name)\n        logprint(logfile, param.data.shape)\n\n    for epoch in range(starting_epoch, total_epochs):\n        for i, (input, target, loss_type) in enumerate(train):\n            i = starting_iter + i\n            if kill_time:\n                out_of_time()\n\n            if i % val_interval == 0:\n                for _ in range(val_batch):\n                    # we should consider running validation multiple times and average. TODO\n                    try:\n                        (input, target, loss_type) = next(valid_iterator)\n                    except StopIteration:\n                        valid_iterator = iter(valid_dl)\n                        (input, target, loss_type) = next(valid_iterator)\n                    cod_loss, toe_loss = run_one_patient(computer, input, target, optimizer, loss_type,\n                                          real_criterion, binary_criterion, beta, cms, validate=True)\n                # TODO this validation is not printing correctly. Way too big.\n                # this line is not printing the right value. it's not averaged.\n                logprint(logfile, \"validation. cod: %.10f, toe: %.10f, total: %.10f\" %\n                         (cod_loss, toe_loss, cod_loss + beta * toe_loss))\n                logprint(logfile, \"validate sen: %.6f, spe: %.6f, roc: %.6f\" %\n                         tuple(validcms.running_stats()))\n\n            if i < iter_per_epoch:\n                cod_loss, toe_loss = run_one_patient(computer, input, target, optimizer, loss_type,\n                                                   real_criterion, binary_criterion, beta, cms)\n                total_loss=cod_loss+toe_loss\n                running_cod_loss.appendleft(cod_loss)\n                running_toe_loss.appendleft(toe_loss)\n                if i % print_interval == 0:\n                    running_cod=np.mean(running_cod_loss)\n                    running_toe=np.mean(running_toe_loss)\n                    logprint(logfile,\n                             \"batch %4d. batch cod: %.5f, toe: %.5f, total: %.5f. running cod: %.5f, toe: %.5f, total: %.5f\" %\n                             (i, cod_loss, toe_loss, cod_loss + beta*toe_loss, running_cod, running_toe,\n                              running_cod + beta*running_toe))\n                    logprint(logfile, \"train sen: %.6f, spe: %.6f, roc: %.6f\" %\n                             tuple(traincms.running_stats()))\n            else:\n                break\n\n\n        if epoch % save_interval == 0:\n            save_model(computer, optimizer, epoch, i, savestr)\n            print(\"model saved for epoch\", epoch, \"input\", i)\n        starting_iter=0\n\n# def validationonly(savestr, beta, epoch=0, iteration=0):\n#     \"\"\"\n#\n#     :param savestr:\n#     :param epoch: default to 0 if loading the highest model\n#     :param iteration: ditto\n#     :return:\n#     \"\"\"\n#\n#     lr = 1e-3\n#     optim = None\n#     logfile = \"vallog.txt\"\n#\n#     num_workers = 8\n#     ig = InputGenH()\n#     # multiprocessing disabled, because socket request seems unstable.\n#     # performance should not be too bad?\n#     validds=ig.get_valid()\n#     validdl = DataLoader(dataset=validds,num_workers=num_workers, batch_size=param_bs, collate_fn=pad_collate)\n#     valid_iterator=iter(validdl)\n#\n#     print(\"Using\", num_workers, \"workers for validation set\")\n#     computer = SeqDNC(x=param_x,\n#                       h=param_h,\n#                       L=param_L,\n#                       v_t=param_v_t,\n#                       W=param_W,\n#                       R=param_R,\n#                       N=param_N,\n#                       bs=param_bs)\n#     # load model:\n#     print(\"loading model\")\n#     computer, optim, starting_epoch, starting_iteration = load_model(computer, optim, epoch, iteration, savestr)\n#\n#     computer = computer.cuda()\n#\n#     real_criterion = nn.SmoothL1Loss()\n#     binary_criterion = nn.BCEWithLogitsLoss()\n#\n#     # starting with the epoch after the loaded one\n#     running_loss=[]\n#     valid_batches=500\n#     for i in range(valid_batches):\n#         input, target, loss_type = next(valid_iterator)\n#         val_loss = run_one_patient(computer, input, target, None, None, loss_type,\n#                                    real_criterion, binary_criterion, beta, validate=True)\n#         if val_loss is not None:\n#             printloss = float(val_loss[0])\n#             running_loss.append((printloss))\n#         if logfile:\n#             with open(logfile, 'a') as handle:\n#                 handle.write(\"validation. count: %4d, val loss     : %.10f \\n\" %\n#                              (i, printloss))\n#         print(\"validation. count: %4d, val loss: %.10f\" %\n#               (i, printloss))\n#     print(np.mean(running_loss))\n\ndef testdl():\n\n    total_epochs = 5\n    iter_per_epoch = int(saturation/param_bs)\n    optim = None\n    starting_epoch = 0\n    starting_iteration = 0\n\n    num_workers = 32\n    ig = InputGenJ(no_underlying=True)\n    # ig = InputGenH(no_underlying=True)\n    trainds = ig.get_train()\n    validds = ig.get_valid()\n    traindl = DataLoader(dataset=trainds, batch_size=param_bs, num_workers=num_workers, collate_fn=pad_collate,pin_memory=True)\n    validdl = DataLoader(dataset=validds, batch_size=param_bs, num_workers=num_workers//2, collate_fn=pad_collate,pin_memory=False)\n\n    for i, (input, target, loss_type) in enumerate(traindl):\n        if i==100:\n            break\n\ndef main(load, savestr='default', lr=1e-3, beta=0.01, kill_time=True):\n    \"\"\"\n    :param load:\n    :param savestr:\n    :param lr:\n    :param curri:\n    :return:\n    \"\"\"\n\n\n    total_epochs = 40\n    iter_per_epoch = int(saturation/param_bs)\n    optim = None\n    starting_epoch = 0\n    starting_iteration = 0\n    logfile = \"log/dnc_\" + savestr + \"_\" + datetime_filename() + \".txt\"\n\n    num_workers = 32\n    ig = InputGenJ(no_underlying=True, death_only=True, debug=True)\n    # ig = InputGenH(no_underlying=True)\n    param_x=ig.input_dim\n    param_v_t=ig.output_dim\n    target_dim=param_v_t\n    trainds = ig.get_train()\n    validds = ig.get_valid()\n    traindl = DataLoader(dataset=trainds, batch_size=param_bs, num_workers=num_workers, collate_fn=pad_collate, pin_memory=True)\n    validdl = DataLoader(dataset=validds, batch_size=param_bs, num_workers=num_workers//2, collate_fn=pad_collate, pin_memory=True)\n\n    print(\"Generating prior\")\n    prior_probability=get_death_code_proportion(ig)\n\n    print(\"Using\", num_workers, \"workers for training set\")\n    computer = PriorDNC(x=param_x,\n                      h=param_h,\n                      L=param_L,\n                      v_t=param_v_t,\n                      W=param_W,\n                      R=param_R,\n                      N=param_N,\n                      prior=prior_probability)\n    # load model:\n    if load:\n        print(\"loading model\")\n        computer, optim, starting_epoch, starting_iteration = load_model(computer, optim, starting_epoch,\n                                                                         starting_iteration, savestr)\n\n    computer = computer.cuda()\n    if optim is None:\n        # does adamax perform better with sparse output?\n        optimizer = torch.optim.Adam(computer.parameters(), lr=lr)\n    else:\n        # print('use Adadelta optimizer with learning rate ', lr)\n        # optimizer = torch.optim.Adadelta(computer.parameters(), lr=lr)\n        optimizer = optim\n        for group in optimizer.param_groups:\n            print(\"Currently using a learing rate of \", group[\"lr\"])\n\n    # creating the positive_weights\n    # with open(\"/infodev1/rep/projects/jason/pickle/dcc.pkl\",\"rb\") as f:\n    #     # loaded here is a vector where v_i is the number of times death label i has occured\n    #     weights=pickle.load(f)\n    # negs=59652-weights\n    # weights[weights<4]=3\n    # weights=negs/weights\n    # weights=torch.from_numpy(weights).float().cuda()\n    # weights=Variable(weights)\n\n    real_criterion = TOELoss()\n    # this parameter does not appear in PyTorch 0.3.1\n    # binary_criterion = WeightedBCELLoss(pos_weight=None)\n    binary_criterion= nn.BCEWithLogitsLoss()\n    # starting with the epoch after the loaded one\n\n    train(computer, optimizer, real_criterion, binary_criterion,\n          traindl, validdl, int(starting_epoch), total_epochs,\n          int(starting_iteration), iter_per_epoch, target_dim, savestr, beta, logfile, kill_time)\n\nif __name__ == \"__main__\":\n    # testdl()\n    main(False, kill_time=False)\n\n    \"\"\"\n    12/6\n    0.0003 validation at around 8000 batches, equal to the running loss.\n    Note that after 400 batches, it's already around 0.0004. This is important, because that allows me to use early stopping.\n    Interesting. Now I vary the hyperparameter for more tests. Try radically decrease.\n    Increase of parameter size might work in favor, because overfitting is not a severe issue.\n    Small variance means I can probably lower the learning rate and feed with more data.\n    \"\"\"\n\n    \"\"\"\n    12/7\n    0.0004 is the average validation for a smaller parameter size. No batch validation reached below 0.0003.\n    For old parameter set, it happened 3 or 4 times. Pretty significant.\n    It was reached pretty early too.\n    Variance of both results cannot be identified with my eyes. \n    The next step is to increase parameter set and lower lr, train for a long time.\n    \"\"\"\n\n    \"\"\"\n    12/17\n    Loading /infodev1 bottlenecks. Not sure.\n    Lost the log. Somehow validation has problems. Training becomes very slow. What's happening? \n    I did not change anything. Sync?\n    \"\"\"\n\n    \"\"\"\n    12/20\n    It is a batchnorm problem. See the last commit message.\n    \"\"\"\n\n\n    \"\"\"\n    12/21\n    Beta is too low.\n    Maybe training two models separately is better?\n    See if adding a last output layer would help. I assume so.\n    \"\"\"\n","sub_path":"death/DNC/priortrainer.py","file_name":"priortrainer.py","file_ext":"py","file_size_in_byte":17597,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"416447275","text":"\n\nfrom test_process_thread import *\nimport time\nimport multiprocessing\n\n\ndef io():\n    write()\n    read()\n\n\ncounts = []\n\nt = time.time()\nfor x in range(10):\n    th = multiprocessing.Process(target=io)\n    th.start()\n    counts.append(th)\n\nn = 10\nwhile True:\n    for y in counts:\n        if not y.is_alive():\n            n -= 1\n    if n <= 0:\n        break\n\nprint('Process IO', time.time() - t)\n","sub_path":"pythonNet/day08/mycode_in/test/test5.py","file_name":"test5.py","file_ext":"py","file_size_in_byte":394,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"178340261","text":"import time\nfrom http.server import BaseHTTPRequestHandler, HTTPServer\nimport datetime\nimport os\n\nHOST_NAME = '192.168.1.10' # !!!REMEMBER TO CHANGE THIS!!!\nPORT_NUMBER = 8000\n\n\nclass GetInformations():\n\n    def getUptime(self):\n        uptime_seconds = 0\n        with open('/proc/uptime', 'r') as f:\n            uptime_seconds = float(f.readline().split()[0])\n\n        return uptime_seconds\n\n    def getDateTime(self):\n        return datetime.datetime.now()\n\n    def getMeminfo(self):\n        mem_total = \"\"\n        mem_usage = \"\"\n\n        with open('/proc/meminfo', 'r') as f:\n            mem_total = int(f.readline().split()[1])/1000\n            mem_free = int(f.readline().split()[1])/1000\n            mem_usage = mem_total - mem_free\n\n        return {'mem_total':mem_total,'mem_usage':mem_usage}\n\n    def getPsA(self):\n        os.system(\"ps -A > ./psfile\")\n        list_process = []\n\n        with open('./psfile', 'r') as f:\n            f.readline()\n            for line in f:\n                pid = line.split()[0]\n                name = line.split()[2]\n                list_process.append({'pid':pid,'name':name})\n        \n        os.remove(\"./psfile\")\n        return list_process\n\n    def getOsrelease(self):\n        name = \"\"\n        version = \"\"\n\n        with open('/etc/os-release', 'r') as f:\n            name = f.readline().split(\"=\")[1]\n            version = f.readline().split(\"=\")[1]\n\n        return {'name':name,'version':version}\n\n    def getCpuInfo(self):\n        model_name = \"\"\n        with open('/proc/cpuinfo', 'r') as f:\n            for line in f:\n                if \"model name\" in line:\n                    model_name = line.split(\":\")[1].strip()\n                    return model_name\n\nclass MyHandler(BaseHTTPRequestHandler):\n    def do_HEAD(s):\n        s.send_response(200)\n        s.send_header(\"Content-type\", \"text/html\")\n        s.end_headers()\n\n    def do_GET(s):\n        \"\"\"Respond to a GET request.\"\"\"\n\n        gi = GetInformations()\n        s.send_response(200)\n        s.send_header(\"Content-type\", \"text/html\")\n        s.end_headers()\n        s.wfile.write(bytes(\"<html><head>\", \"utf8\"))\n        s.wfile.write(bytes(\"<title>T1 - Lab Sisop.</title><meta http-equiv='refresh' content='5'> \", \"utf8\"))\n        s.wfile.write(bytes(\"<style>.content {max-width: 500px;margin: auto; text-align: center;}</style>\", \"utf8\"))\n        s.wfile.write(bytes(\"</head>\", \"utf8\"))\n        s.wfile.write(bytes(\"<body>\", \"utf8\"))\n\n        s.wfile.write(bytes(\"<div class='content'> <h1>T1 - LabSisop</h1>\", \"utf8\"))\n        s.wfile.write(bytes(\"<img src='https://www.oficinadanet.com.br/imagens/post/13938/capa.jpg' style='width:610px;height:420px;' >\", \"utf8\"))\n        s.wfile.write(bytes(\"</div>\", \"utf8\"))\n        # If someone went to \"http://something.somewhere.net/foo/bar/\",\n        # then s.path equals \"/foo/bar/\".\n        s.wfile.write(bytes(\"<p><b>Date and Time:</b> {}</p>\".format(gi.getDateTime().strftime(\"%m/%d/%Y, %H:%M:%S\")) , \"utf8\"))\n        s.wfile.write(bytes(\"<p><b>Uptime:</b> {} seconds </p>\".format(gi.getUptime()) , \"utf8\"))\n        s.wfile.write(bytes(\"<p><b>Model and velocity of processor:</b> {} </p>\".format(gi.getCpuInfo()) , \"utf8\"))\n\n        mem_info = gi.getMeminfo()\n        s.wfile.write(bytes(\"<p><b>Memory quantity of usage and total: </b> usage: {}MB / total: {}MB </p>\"\n            .format(mem_info[\"mem_usage\"],mem_info[\"mem_total\"]) , \"utf8\"))\n\n        os_release = gi.getOsrelease()\n        s.wfile.write(bytes(\"<p><b>System Version: </b> name: {} / version: {} </p>\"\n            .format(os_release[\"name\"],os_release[\"version\"]) , \"utf8\"))\n\n        s.wfile.write(bytes(\"<p><b>List of processes:</b></p></br>\", \"utf8\"))\n        s.wfile.write(bytes(\"<table style='width:100%'><tr><th>PID</th><th>Process Name</th></tr>\", \"utf8\"))\n\n        list_process =gi.getPsA()\n        for process in list_process:\n            s.wfile.write(bytes(\"<tr>\", \"utf8\"))\n            s.wfile.write(bytes(\"<td align='center'> {} </td>\".format(process[\"pid\"]), \"utf8\"))\n            s.wfile.write(bytes(\"<td align='center'> {} </td>\".format(process[\"name\"]), \"utf8\"))\n            s.wfile.write(bytes(\"</tr>\", \"utf8\"))   \n        s.wfile.write(bytes(\"</table>\", \"utf8\"))\n        \n        s.wfile.write(bytes(\"</body></html>\", \"utf8\"))\n\nif __name__ == '__main__':\n    server_class = HTTPServer\n    httpd = server_class((HOST_NAME, PORT_NUMBER), MyHandler)\n    print (time.asctime(), \"Server Starts - %s:%s\" % (HOST_NAME, PORT_NUMBER))\n    try:\n        httpd.serve_forever()\n    except KeyboardInterrupt:\n        pass\n    httpd.server_close()\n    print (time.asctime(), \"Server Stops - %s:%s\" % (HOST_NAME, PORT_NUMBER))\n\n","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":4656,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"535055558","text":"\n#wapp to create a file whose name is supplied by thte user\n\nimport os.path\nfile_name = input(\"enter the file_name to create \")\n\nif os.path.exists(file_name):\t\n\tprint(file_name,\"aleady exists\")\nelse:\n\tf = None\n\ttry:\n\t\tf = open(file_name,'a')\n\t\tprint(file_name,\"created\")\n\texcept Exception as e:\n\t\tprint(\"issue\",e)\n\tfinally:\n\t\tif f is not None:\n\t\t\tf.close()","sub_path":"L10/P1.py","file_name":"P1.py","file_ext":"py","file_size_in_byte":356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"540173896","text":"import re\n\n# Returns a tuple (subdomain, path) to match in handler\ndef process_uri(uri):\n  pattern_all = re.compile('/(python|rest|r)/?(.*)')\n\n  m = pattern_all.match(uri)\n  if (m != None):\n    return m.group(1, 2)\n  else:\n    uri = uri.lstrip('/')\n    return ('userguide', uri)\n\n# handler\ndef lambda_handler(event, context):\n  SUBDOMAIN_MAP = {\n    'python': 'python-docs.synapse.org/build/html',\n    'r': 'r-docs.synapse.org',\n    'rest': 'docs.synapse.org',\n    'userguide': 'user-guides.synapse.org'\n  }\n\n  request = event['Records'][0]['cf']['request']\n  uri = request['uri']\n  processed_uri = process_uri(uri)\n\n  if processed_uri[0] == 'rest':\n    # Just go on\n    return request\n  else:\n    redirect_location = \"https://{}/{}\".format(SUBDOMAIN_MAP[processed_uri[0]], processed_uri[1])\n    response = {\n      'status': '302',\n      'statusDescription': 'Found',\n      'headers': {\n        'location': [{\n          'key': 'Location',\n          'value': redirect_location\n        }]\n      }\n    }\n    return response\n","sub_path":"docsredirector.py","file_name":"docsredirector.py","file_ext":"py","file_size_in_byte":1021,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"141943968","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nimport sys\nimport random\nimport os\nimport importlib\nimport numpy as np\nimport tensorflow as tf\nimport texar as tx\n\ntry:\n    import cPickle as pickle\nexcept ImportError:  # python 3.x\n    import pickle\n\nfrom ctrl_gen_model_transformer import CtrlGenModel\n# from ctrl_gen_model import CtrlGenModel\n\nflags = tf.flags\n\nflags.DEFINE_string('config', 'config_defender_internal_layers', 'The config to use.')\n\nFLAGS = flags.FLAGS\n\nconfig = importlib.import_module(FLAGS.config)\n\nSEED = 123\nos.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\nos.environ['CUDA_VISIBLE_DEVICES'] = sys.argv[6]\nrandom.seed(SEED)\nnp.random.seed(SEED)\nrs = np.random.RandomState(123)\n\ntry:\n    tf.random.set_random_seed(123)\nexcept:\n    tf.set_random_seed(123)\n\n\ndef _main(_):\n    # Data\n    train_autoencoder = tx.data.MultiAlignedData(config.train_autoencoder)\n    dev_autoencoder = tx.data.MultiAlignedData(config.dev_autoencoder)\n    test_autoencoder = tx.data.MultiAlignedData(config.test_autoencoder)\n    train_discriminator = tx.data.MultiAlignedData(config.train_discriminator)\n    dev_discriminator = tx.data.MultiAlignedData(config.dev_discriminator)\n    test_discriminator = tx.data.MultiAlignedData(config.test_discriminator)\n    train_defender = tx.data.MultiAlignedData(config.train_defender)\n    dev_defender = tx.data.MultiAlignedData(config.dev_defender)\n    test_defender = tx.data.MultiAlignedData(config.test_defender)\n    vocab = train_autoencoder.vocab(0)\n\n    iterator = tx.data.FeedableDataIterator(\n        {\n            'train_autoencoder': train_autoencoder,\n            'dev_autoencder': dev_autoencoder,\n            'test_autoencoder': test_autoencoder,\n            'train_discriminator': train_discriminator,\n            'dev_discriminator': dev_discriminator,\n            'test_discriminator': test_discriminator,\n            'train_defender': train_defender,\n            'dev_defender': dev_defender,\n            'test_defender': test_defender,\n        })\n    batch = iterator.get_next()\n\n    # Model\n    gamma = tf.placeholder(dtype=tf.float32, shape=[], name='gamma')\n    lambda_D = tf.placeholder(dtype=tf.float32, shape=[], name='lambda_g')\n    lambda_diversity = tf.placeholder(dtype=tf.float32, shape=[], name='lambda_diver')\n    lambda_ae_ = float(config.lambda_ae_val)\n    model = CtrlGenModel(batch, vocab, lambda_ae_, gamma, lambda_D,lambda_diversity, config.model)\n\n    def defender(sess, lambda_ae_, gamma_, lambda_D_,lambda_diversity_, epoch, mode, verbose=True):\n        avg_meters_g = tx.utils.AverageRecorder(size=10)\n        step = 0\n        embedding_vecs=[]\n        dic={}\n        dic_z={}\n        dic_cls = {}\n        dic_Enc={}\n        dic_hidden = {}\n        if mode == \"train\":\n            dataset = \"train_defender\"\n            while True:\n                try:\n                    step += 1\n                    feed_dict = {\n                        iterator.handle: iterator.get_handle(sess, dataset),\n                        gamma: gamma_,\n                        lambda_D: lambda_D_,\n                        lambda_diversity: lambda_diversity_,\n                    }\n                    vals_g = sess.run(model.fetches_train_g, feed_dict=feed_dict)\n                    loss_g_ae_summary = vals_g.pop(\"loss_g_ae_summary\")\n                    loss_g_clas_summary = vals_g.pop(\"loss_g_clas_summary\")\n                    avg_meters_g.add(vals_g)\n\n                    # test_labels_val = sess.run(model.test_labels, feed_dict=feed_dict)\n                    # test_logits_val = sess.run(model.test_softmax_logits, feed_dict=feed_dict)\n                    # test_diff_val = sess.run(model.test_diff, feed_dict=feed_dict)\n                    # test_diff_clipped_val = sess.run(model.test_diff_clipped, feed_dict=feed_dict)\n                    # test_diff_val_clipped_minibatch = sess.run(model.test_diff_clipped_minibatch, feed_dict=feed_dict)\n                    # print('test_labels_val')\n                    # print(test_labels_val)\n                    # print('test_logits_val')\n                    # print(test_logits_val)\n                    # print('test_diff_val')\n                    # print(test_diff_val)\n                    # print('test_diff_clipped_val')\n                    # print(test_diff_clipped_val)\n                    # print('test_diff_val_clipped _minibatch')\n                    # print(test_diff_val_clipped_minibatch)\n                    # exit()\n\n                    if verbose and (step == 1 or step % config.defender_display == 0):\n                        print('step: {}, {}'.format(step, avg_meters_g.to_str(4)))\n                        loss_text_defender.write('step: {}, {}'.format(step, avg_meters_g.to_str(4)))\n                        loss_text_defender.write('\\n')\n\n                except tf.errors.OutOfRangeError:\n                    _ = _eval_defender(sess, lambda_ae_, gamma_, lambda_D_, lambda_diversity_, epoch,'dev_defender')\n                    break\n\n        else:\n            dataset = \"test_defender\"\n            while True:\n                try:\n                    feed_dict = {\n                        iterator.handle: iterator.get_handle(sess, dataset),\n                        gamma: gamma_,\n                        lambda_D: lambda_D_,\n                        lambda_diversity: lambda_diversity_,\n                        tx.context.global_mode(): tf.estimator.ModeKeys.EVAL\n                    }\n\n                    # vals,embedder_layer_vals,input_val,clas_embd_vals,z_vals,Enc_vals,last_hidden_vals, clas_logits = \\\n                    #     sess.run([model.fetches_eval,model.embedder_output,model.input_file,model.clas_embd,model.z_output,model.Enc,model.last_hidden,model.clas_logits],\n                    #              feed_dict=feed_dict)\n\n                    vals, input_val, last_hidden_vals, clas_logits = \\\n                        sess.run([model.fetches_eval, model.input_file, model.last_hidden, model.clas_logits],\n                                 feed_dict=feed_dict)\n                    # print(np.shape(last_hidden_vals))\n                    last_hidden_vals = np.reshape(last_hidden_vals, (len(last_hidden_vals), -1))\n                    last_hidden_vals=[[i for i in j] for j in last_hidden_vals]\n                    # print(len(last_hidden_vals))\n                    # print(len(last_hidden_vals[0]))\n\n                    # exit()\n                    # print(np.shape(last_hidden_vals))\n                    # exit()\n\n                    # print(np.shape(clas_logits))\n                    # exit()\n                    # print(tx.utils.map_ids_to_strs(input_val, vocab))\n\n                    # #GET embedder_layer_vals info\n                    # for i in range(len(input_val)):\n                    #     for num in range(len(input_val[i])):\n                    #         if input_val[i][num] != 0 and input_val[i][num] != 2:\n                    #             #----> some of the words are repeated in candidates\n                    #             # dic[input_val[i][num]]=embedder_layer_vals[i][num]\n                    #             dic[tx.utils.map_ids_to_strs(input_val[i], vocab)] = embedder_layer_vals[i][num]\n                    #\n                    # # GET z_vals info\n                    # for i in range(len(input_val)):\n                    #     dic_z[tx.utils.map_ids_to_strs(input_val[i], vocab)]=z_vals[i]\n                    #\n                    # for i in range(len(input_val)):\n                    #     dic_Enc[tx.utils.map_ids_to_strs(input_val[i], vocab)]=Enc_vals[i]\n\n                    for i in range(len(input_val)):\n                        # print(np.shape(last_hidden_vals[i]))\n                        # exit()\n                        dic_hidden[tx.utils.map_ids_to_strs(input_val[i], vocab)]=last_hidden_vals[i]\n\n\n                    # GET class embedder_layer\n                    # for i in range(len(input_val)):\n                    #     for num in range(len(input_val[i])):\n                    #         if input_val[i][num]!=0 and input_val[i][num]!=2:    #----> some of the words are repeated in candidates\n                    #             # print(tx.utils.map_ids_to_strs(input_val[i], vocab))\n                    #             # dic_cls[input_val[i][num]] = clas_embd_vals[i][num]\n                    #             # print(tx.utils.map_ids_to_strs([input_val[i][num]], vocab))\n                    #\n                    #             dic_cls[tx.utils.map_ids_to_strs([input_val[i][num]], vocab)] = clas_embd_vals[i][num]\n\n                    # print(dic_cls)\n                    # print(len(dic_cls))\n                    # print('---------------------------------')\n                    # # print(dic_z)\n                    # print(len(dic_z))\n\n                except tf.errors.OutOfRangeError:\n                    # print(len(dic))\n                    # with open('{}/gen_embd_output.p'.format(config.lambda_path), 'wb') as fp:\n                    #     pickle.dump(dic, fp, protocol=pickle.HIGHEST_PROTOCOL)\n                    #\n                    # print(len(dic_z))\n                    # with open('{}/z_output.p'.format(config.lambda_path), 'wb') as fp:\n                    #     pickle.dump(dic_z, fp, protocol=pickle.HIGHEST_PROTOCOL)\n                    #\n                    # print(len(dic_cls))\n                    # with open('{}/class_embd_output.p'.format(config.lambda_path), 'wb') as fp:\n                    #     pickle.dump(dic_cls, fp, protocol=pickle.HIGHEST_PROTOCOL)\n                    #\n                    # print(len(dic_Enc))\n                    # with open('{}/enc_output.p'.format(config.lambda_path), 'wb') as fp:\n                    #     pickle.dump(dic_Enc, fp, protocol=pickle.HIGHEST_PROTOCOL)\n\n                    print(len(dic_hidden))\n                    with open('{}/hidden_output.p'.format(config.lambda_path), 'wb') as fp:\n                        pickle.dump(dic_hidden, fp, protocol=pickle.HIGHEST_PROTOCOL)\n\n                    print(config.restore_file)\n                    exit()\n                    break\n\n    def _eval_defender(sess, lambda_ae_, gamma_, lambda_D_,lambda_diversity_,epoch, dataset):\n        avg_meters_g = tx.utils.AverageRecorder(size=10)\n        iterator.restart_dataset(sess, dataset)\n        while True:\n            try:\n                feed_dict = {\n                    iterator.handle: iterator.get_handle(sess, dataset),\n                    gamma: gamma_,\n                    lambda_D: lambda_D_,\n                    lambda_diversity: lambda_diversity_,\n                }\n\n                vals_g = sess.run(model.fetches_train_g, feed_dict=feed_dict)\n\n                loss_g_ae_summary = vals_g.pop(\"loss_g_ae_summary\")\n                loss_g_clas_summary = vals_g.pop(\"loss_g_clas_summary\")\n                batch_size = vals_g.pop('batch_size')\n                avg_meters_g.add(vals_g, weight=batch_size)\n\n            except tf.errors.OutOfRangeError:\n                print('EPOCH: {}, {}'.format(epoch, avg_meters_g.to_str(4)))\n                loss_text_defender.write('epoch: {}, {}'.format(epoch, avg_meters_g.to_str(4)))\n                loss_text_defender.write('\\n')\n                loss_text_defender.write('\\n')\n                loss_text_defender.write('\\n')\n                break\n\n        return True\n\n    # neal_path='test'\n    # tf.gfile.MakeDirs(neal_path)\n    # os.chmod('test' ,0o777)\n    # tf.gfile.MakeDirs('test/tets2')\n    tf.gfile.MakeDirs(config.sample_path)\n    tf.gfile.MakeDirs(config.checkpoint_path)\n\n    # Runs the logics\n    with tf.Session() as sess:\n        sess.run(tf.global_variables_initializer())\n        sess.run(tf.local_variables_initializer())\n        sess.run(tf.tables_initializer())\n\n        saver = tf.train.Saver(max_to_keep=None)\n        print(config.restore_file)\n        if config.restore_file:\n            print('Restore from: {}'.format(config.restore_file))\n            saver.restore(sess, config.restore_file)\n\n        iterator.initialize_dataset(sess)\n        #\n        # gamma_ = 1.0\n        # lambda_D_ = 0\n        # lambda_diversity_=0\n\n        gamma_ = 1.0\n        lambda_D_ = config.lambda_D_val\n        lambda_diversity_=config.lambda_diversity_val\n        # gamma_decay = 0.5  # Gumbel-softmax temperature anneal rate\n\n        # Train sentiment transfer\n        loss_file = os.path.join(config.loss_path, 'test_not_impo.txt')\n        loss_text_defender=open(loss_file,'w')\n        # for epoch in range(0, config.full_nepochs):\n        #     print('gamma: {}, lambda_ae: {}, lambda_D: {}, lambda_diversity: {}'.format(gamma_, lambda_ae_, lambda_D_,lambda_diversity_))\n        #     loss_text_defender.write('gamma: {}, lambda_ae: {}, lambda_D: {}, lambda_diversity: {}'.format(gamma_, lambda_ae_, lambda_D_,lambda_diversity_))\n        #     loss_text_defender.write('\\n')\n        #\n        #     iterator.restart_dataset(sess, ['train_defender'])\n        #     defender(sess, lambda_ae_, gamma_, lambda_D_,lambda_diversity_, epoch, mode='train')\n        #     if epoch>10:\n        #         saver.save(sess, os.path.join(config.checkpoint_path,\n        #         'full_lambdaAE%s_lambdaD%s_lambdaDiv%s_ckpt'%(config.lambda_ae_val,config.lambda_D_val,config.lambda_diversity_val)), epoch)\n        #     # if epoch > 4:\n        #     #     gamma_ = max(0.001, gamma_ * 0.5)\n        #     # if epoch > 0:\n        #     #     lambda_diversity_ = max(0.001, float(lambda_diversity_) *2.0)\n        #     #     # Test sentiment transfer\n        iterator.restart_dataset(sess, 'test_defender')\n        defender(sess, lambda_ae_, gamma_, lambda_D_,lambda_diversity_, 10, mode='test')\n\n\nif __name__ == '__main__':\n    tf.app.run(main=_main)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"fakeddit/main_defender_internal_layers.py","file_name":"main_defender_internal_layers.py","file_ext":"py","file_size_in_byte":13719,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}
+{"seq_id":"67836638","text":"__author__ = 'xiaoma'\n\n\n\n'''\n========================超重点1==============================\n多进程socketServer服务器模型\n2。启动python脚本的同时启动了一个主进程，由此主进程监听某个端口，接受客户端的请求。\n2。主进程在每一次接收到客户端请求时，都创建一个子进程，主进程等待子进程执行完毕。\n3。子进程再创建一个孙子进程。孙子进程创建完毕后，子进程立即退出；主进程等待子进程执行完毕，可再次接受下一个客户端请求。\n4。由新创建的孙子进程处理客户端的请求。孙子进程的父进程（之前的子进程）已经退出，孙子进程由Linux的init进程进行统一管理。\n\n\n注意一定要有子进程的回收机制！\n在某些系统上，子进程的创建可能会有限制，在高负载情况下会有问题。\n'''\n\n\n\nimport socket\nimport os\nimport time\nimport sys\n\ndef client_deal(client,client_info):\n    while True:\n        msg = client.recv(1024)\n        if msg == '':\n            client.close()\n            break\n        print('收到信息： %s  from : %s' % msg,client_info)\n        client.send('服务器处理后回显： %s' % msg)\n        if msg == 'q' or msg =='Q':\n            client.close()\n            break\n    print('孙子进程%s执行完毕' % os.getpid())\n\ndef client_process(client,client_info):\n    pid = os.fork()\n\n    if pid == 0:\n        ppid = os.fork()\n        if ppid == 0:\n            client_deal(client,client_info)\n        else:\n            print('子进程%s退出' % os.getpid())\n            sys.exit()  #os._exit()似乎也可以\n\n    else:\n        client.close()\n        os.wait()\n        print('主进程%s等侍子进程创建完毕' % os.getppid())\n\n\nif __name__ == \"__main__\":\n\n    tcp_server = socket.socket(socket.AF_INET,socket.SOCK_STREAM)\n    tcp_server.bind(('127.0.0.2',9999))\n    tcp_server.listen(5)\n\n    while True:\n        print('进程%s,等待客户端连接' % os.getpid())\n        client,client_info = tcp_server.accept()\n        print('客户端%s 连接成功，开始进行处理。')\n        client_process(client,client_info)\n\n    tcp_server.close()","sub_path":"python_study/socket/socketServer4.py","file_name":"socketServer4.py","file_ext":"py","file_size_in_byte":2174,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"66"}