diff --git "a/1329.jsonl" "b/1329.jsonl" new file mode 100644--- /dev/null +++ "b/1329.jsonl" @@ -0,0 +1,558 @@ +{"seq_id": "13886113367", "text": "import csv\nimport datetime\nimport hashlib\nimport struct\nfrom collections import defaultdict\n\n\ndef Byte(byte_stream, size):\n return byte_stream[:size]\n\n\ndef Char(byte_stream, size):\n return struct.unpack('>' + str(size) + 'c', byte_stream[:size])\n\n\ndef Word(byte_stream, size):\n if size % 2 == 0:\n return struct.unpack('>' + str(size // 2) + 'H', byte_stream[:size])\n else:\n raise IOError('Bytestream not multiple of 2')\n\n\ndef Short(byte_stream, size):\n if size % 2 == 0:\n return struct.unpack('>' + str(size // 2) + 'h', byte_stream[:size])\n else:\n raise IOError('Bytestream not multiple of 2')\n\n\ndef Long(byte_stream, size):\n if size % 4 == 0:\n return struct.unpack('>' + str(size // 4) + 'l', byte_stream[:size])\n else:\n raise IOError('ByteStream not multiple of 4')\n\n\ndef Float(byte_stream, size):\n if size % 4 == 0:\n return struct.unpack('>' + str(size // 4) + 'f', byte_stream[:size])\n else:\n raise IOError('ByteStream not multiple of 4')\n\n\ndef Double(byte_stream, size):\n if size % 8 == 0:\n return struct.unpack('>' + str(size // 8) + 'd', byte_stream[:size])\n else:\n raise IOError('Bytestream not multiple of 8')\n\n\ndef Date(byte_stream, size):\n if size == 4:\n return struct.unpack('>hBB', byte_stream[:size])\n else:\n raise IOError('ByteStream not length of 4')\n\n\ndef Time(byte_stream, size):\n if size == 4:\n return struct.unpack('>BBBB', byte_stream[:size])\n else:\n raise IOError('ByteStream not length of 4')\n\n\ndef pString(byte_stream, size):\n return struct.unpack('>' + str(size) + 'p', byte_stream[:size])\n\n\ndef cString(byte_stream, size):\n return struct.unpack('>' + str(size) + 's', byte_stream[:size])\n\n\ndef Thumb(byte_stream, size):\n if size == 10:\n return struct.unpack('>iiBB', byte_stream[:size])\n else:\n raise IOError('Bytestream not length of 10')\n\n\ndef Bool(byte_stream, size):\n return struct.unpack('>' + str(size) + '?', byte_stream[:size])\n\n\ndef User(byte_stream, size):\n return byte_stream[:size]\n\n\nstructUnpacker = {1: Byte,\n 2: Char,\n 3: Word,\n 4: Short,\n 5: Long,\n 7: Float,\n 8: Double,\n 10: Date,\n 11: Time,\n 18: pString,\n 19: cString,\n 12: Thumb,\n 13: Bool,\n 0: User}\n\n\nclass FSAFile(object):\n SIGNATURE = b\"ABIF\"\n\n def __init__(self, byte_stream, malform_check=True):\n self._channels = {}\n self.raw = byte_stream\n self.signature = struct.unpack('>4s', byte_stream[0:4])[0]\n self.version = struct.unpack('>h', byte_stream[4:6])[0]\n self._channels = None\n self._hash = None\n\n if self.signature != FSAFile.SIGNATURE:\n raise IOError('WARNING: Not a valid ABIF File.')\n\n self.tdir = FSADir(byte_stream, offset=6)\n\n self.directories = defaultdict(dict)\n\n # Unpack FSA File.\n for i in range(0, self.tdir.numElements):\n directory = FSADir(byte_stream, self.tdir.dataOffset + i * 28)\n self.directories[directory.name][directory.number] = directory\n\n if malform_check:\n for k in ['DyeW', 'DATA']:\n if k not in self.directories:\n raise IOError(\"ABIF Malformed\")\n\n def dump_to_csv(self, filename):\n with open(filename, 'w') as f:\n w = csv.writer(f)\n for dir_key in self.directories:\n directory = self.directories[dir_key]\n for entry_key in directory:\n row = [dir_key, entry_key]\n row += list(directory[entry_key].data)\n w.writerow(row)\n\n def _compute_hash(self):\n return hashlib.md5(self.raw).hexdigest()\n\n @property\n def hash(self):\n if not self._hash:\n self._hash = self._compute_hash()\n return self._hash\n\n @property\n def channels(self):\n if not self._channels:\n # Colors ordered so that as they are popped off they match the given channel.\n colors = ['orange', 'red', 'yellow', 'green', 'blue']\n wavelength_keys = list(self.directories['DyeW'])\n # Backwards compatibility of FSA files requires that the 5th channel, if used, is labeled\n # as 105 in the data directory.\n if len(wavelength_keys) == 5:\n data_keys = [1, 2, 3, 4, 105]\n else:\n data_keys = sorted(wavelength_keys)\n self._channels = [{'data': self.directories['DATA'][data_keys[k]].data,\n 'wavelength': self.directories['DyeW'][wavelength_keys[k]].data[0],\n 'color': colors.pop()\n } for k in range(len(wavelength_keys))]\n return self._channels\n\n @property\n def sample_name(self):\n # Sample Label\n return self.directories['SpNm'][1].data[0].decode('ascii')\n\n @property\n def plate(self):\n # Plate Label\n return self.directories['CTID'][1].data[0].replace(b\"\\x00\", b\"\").decode('ascii')\n\n @property\n def well(self):\n well = self.directories['TUBE'][1].data[0].decode('ascii')\n # Normalize well label so that integer portion is zero-padded.\n if int(well[1:]) < 10:\n well_letter = well[0]\n well_integer = well[1]\n well = f'{well_letter}0{well_integer}'\n return well\n\n @property\n def date_run(self):\n date = datetime.datetime(*(sum((self.directories['RUND'][1].data, self.directories['RUNT'][1].data), ())))\n return date\n\n @property\n def ce_machine(self):\n # Name of CE machine on which the plate was run.\n ce_machine = self.directories['MCHN'][1].data[0].decode('ascii')\n return ce_machine\n\n @property\n def plate_size(self):\n # Size of the plate, either 384 or 96.\n plate_size = self.directories['PSZE'][1].data[0]\n return plate_size\n\n @property\n def offscale_indices(self):\n # Indices of data points detected by the machine where the signal is saturated.\n if 'Satd' in self.directories:\n offscale_indices = list(self.directories['Satd'][1].data)\n else:\n offscale_indices = []\n return offscale_indices\n\n @property\n def polymer_expiration(self):\n exp_date = datetime.datetime.strptime(self.directories['SMED'][1].data[0], '%b %d, %Y')\n return exp_date\n\n @property\n def polymer_lot_number(self):\n lot_num = int(self.directories['SMLt'][1].data[0])\n return lot_num\n\n @property\n def voltage(self):\n voltage = list(self.directories['DATA'][5].data)\n return voltage\n\n @property\n def current(self):\n current = list(self.directories['DATA'][6].data)\n return current\n\n @property\n def power(self):\n power = list(self.directories['DATA'][7].data)\n return power\n\n @property\n def temperature(self):\n temperature = list(self.directories['DATA'][8].data)\n return temperature\n\n\nclass FSADir(object):\n \"\"\"\n Given a full bytestream and an offset, unpack the directory found within an FSA file.\n \"\"\"\n def __init__(self, bytestream, offset):\n self.name = struct.unpack('>4s', bytestream[offset: offset + 4])[0].decode('ascii')\n self.number = struct.unpack('>i', bytestream[offset + 4: offset + 8])[0]\n self.elementType = struct.unpack('>h', bytestream[offset + 8: offset + 10])[0]\n self.elementSize = struct.unpack('>h', bytestream[offset + 10: offset + 12])[0]\n self.numElements = struct.unpack('>i', bytestream[offset + 12: offset + 16])[0]\n self.dataSize = struct.unpack('>i', bytestream[offset + 16: offset + 20])[0]\n self.dataOffset = struct.unpack('>i', bytestream[offset + 20: offset + 24])[0]\n self.dataHandle = struct.unpack('>i', bytestream[offset + 24: offset + 28])[0]\n if self.dataSize > 4:\n self.data = structUnpacker.get(self.elementType, User)(bytestream[self.dataOffset:], self.dataSize)\n else:\n self.data = structUnpacker.get(self.elementType, User)(bytestream[offset + 20: offset + 24], self.dataSize)\n\n def __repr__(self):\n if len(self.data) > 25:\n return ''.format(str(self.name) + str(self.number), str(self.data[0:25]) + \"...\")\n else:\n return ''.format(str(self.name) + str(self.number), str(self.data[0:25]))\n", "repo_name": "EPPIcenter/MicroSPAT", "sub_path": "src/microspat-py/app/microspat/fsa_tools/FSAExtractor.py", "file_name": "FSAExtractor.py", "file_ext": "py", "file_size_in_byte": 8700, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "95", "api": [{"api_name": "struct.unpack", "line_number": 13, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 18, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 25, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 32, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 39, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 46, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 53, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 60, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 66, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 70, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 75, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 81, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 110, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 111, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 120, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 134, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 143, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 191, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 217, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 217, "usage_type": "attribute"}, {"api_name": "struct.unpack", "line_number": 251, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 252, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 253, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 254, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 255, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 256, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 257, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 258, "usage_type": "call"}]} +{"seq_id": "32053157480", "text": "\nimport json\n\nCLOCK = 19520\nADJUST_HIGH = 0\nADJUST_LOW = CLOCK // 2\n\ndef decode(pulses):\n\tresult = []\n\tcurrent_byte = 0\n\tbit_count = 0\n\tlevel = True\n\tfor pulse in pulses:\n\t\tif level:\n\t\t\t# falling edge, round down prev high\n\t\t\tnew_bits = (pulse + ADJUST_HIGH) // CLOCK\n\t\t\tfor _ in range(new_bits):\n\t\t\t\tcurrent_byte <<= 1\n\t\t\t\tcurrent_byte |= 1\n\t\t\tbit_count += new_bits\n\t\telse:\n\t\t\t# rising edge, round up prev low\n\t\t\tnew_bits = (pulse + ADJUST_LOW) // CLOCK\n\t\t\tcurrent_byte <<= new_bits\n\t\t\tbit_count += new_bits\n\t\tlevel = not level\n\t\tif bit_count == 10:\n\t\t\tresult.append((current_byte >> 1) & 0xFF)\n\t\t\tcurrent_byte = 0\n\t\t\tbit_count = 0\n\t\tif bit_count > 10:\n\t\t\tresult.append(0x1000 + bit_count)\n\t\t\treturn result\n\tcurrent_byte <<= 10 - bit_count\n\tresult.append((current_byte >> 1) & 0xFF)\n\treturn result\n\nif __name__ == \"__main__\":\n with open(\"irdata.json\") as f:\n for item in json.load(f)[\"data\"]:\n if item[\"id\"].startswith(\"mw\"):\n decoding = decode(item[\"A\"][1:])\n print(\" \".join(\"%02X\" % x for x in decoding), end=\"\\t\")\n print(item[\"id\"])\n\n", "repo_name": "dmcomm/irplot", "sub_path": "decode_witches.py", "file_name": "decode_witches.py", "file_ext": "py", "file_size_in_byte": 1104, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "95", "api": [{"api_name": "json.load", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "28463637700", "text": "import unittest\r\n\r\nimport requests\r\nfrom parameterized import parameterized\r\n\r\nfrom 获取用户信息.api.user_api import TestUser\r\nfrom 获取用户信息.read_json.read_user_json import ReadJson\r\n\r\n\r\ndef get_data():\r\n datas = ReadJson(\"use.json\").read_uesr_json()\r\n arr = []\r\n for data in datas.values():\r\n arr.append((data.get(\"headers\"),\r\n data.get(\"params\"),\r\n data.get(\"url\"),\r\n data.get(\"status_code\")))\r\n return arr\r\n\r\n\r\nclass TestUserCase(unittest.TestCase):\r\n @parameterized.expand(get_data())\r\n def test_user(self, headers, params, url, status_code):\r\n # headers = {\r\n # \"Content-Type\": \"application/json\",\r\n # \"Authorization\": \"Token 0f3f4f9e398819eb92f6dae3abe7958c50670338\"\r\n # }\r\n # params = {\"uid\": \"111579943374652448\"}\r\n # url = \"http://test.zaitakugeek.cn:8000/user/profile\"\r\n r = TestUser().user_api(headers, params, url)\r\n # r = requests.get(headers=headers,url=url,params=params)\r\n print(r.json())\r\n print(r.status_code)\r\n try:\r\n self.assertEqual(status_code, r.status_code)\r\n except AssertionError:\r\n raise\r\n\r\n\r\nif __name__ == '__main__':\r\n TestUserCase().test_user()\r\n", "repo_name": "cai9987/test_automation", "sub_path": "接口自动化/获取用户信息/case/user_case.py", "file_name": "user_case.py", "file_ext": "py", "file_size_in_byte": 1292, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "95", "api": [{"api_name": "获取用户信息.read_json.read_user_json.ReadJson", "line_number": 11, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 21, "usage_type": "attribute"}, {"api_name": "获取用户信息.api.user_api.TestUser", "line_number": 30, "usage_type": "call"}, {"api_name": "parameterized.parameterized.expand", "line_number": 22, "usage_type": "call"}, {"api_name": "parameterized.parameterized", "line_number": 22, "usage_type": "name"}]} +{"seq_id": "2259817697", "text": "import pandas as pd \nimport numpy as np\nimport sys\nimport os\nimport pathlib\n\nanss = [11,12,13,14,15,16,17]\ntotal = pd.DataFrame({'id':range(20000)})\nfor i in anss:\n\ttotal['y'+str(i)] = pd.read_csv('ans/ans_'+str(i)+'.csv')['label'].values\n\ntotal['ans'] = [0]*len(total)\nfor i in anss:\n\ttotal['ans']+=total['y'+str(i)]\n\non_thres = (total['ans'].values == len(anss)/2).astype(np.int)\nfor idx in range(len(on_thres)):\n\tif on_thres[idx]==1:\n\t\tprint(idx)\n\nprint(on_thres.sum())\n\ny_test = (total['ans'].values >= (len(anss)/2) ).astype(np.int)\n\nif os.path.dirname(sys.argv[1])!='': \n\tif not os.path.isdir(os.path.dirname(sys.argv[1])):\n\t\tdirname = os.path.dirname(sys.argv[1])\n\t\todir = pathlib.Path(dirname)\n\t\todir.mkdir(parents=True, exist_ok=True)\n\nwith open(sys.argv[1], 'w') as f:\n\tf.write('id,label')\n\tf.write('\\n')\n\tfor i in range(y_test.shape[0]):\n\t\tf.write(str(i))\n\t\tf.write(',')\n\t\tf.write(str(y_test[i]))\n\t\tf.write('\\n')", "repo_name": "zytyz/ML2019SPRING", "sub_path": "hw6/voting.py", "file_name": "voting.py", "file_ext": "py", "file_size_in_byte": 923, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "95", "api": [{"api_name": "pandas.DataFrame", "line_number": 8, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 16, "usage_type": "attribute"}, {"api_name": "numpy.int", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 26, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 28, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 31, "usage_type": "attribute"}]} +{"seq_id": "39205203186", "text": "#!/usr/bin/env python3\n\nimport numpy as np\nimport os\nimport sys\nimport matplotlib.pyplot as plt\nfrom util import savefig, get_path\n\nxlim = (-4, 2)\nylim = (-3, 3)\nx1 = np.linspace(*xlim, 500)\ny1 = np.linspace(*ylim, 500)\n\nx, y = np.meshgrid(x1, y1)\n\n\ndef f(y):\n return y\n\n\ndef rk1(f, y, h):\n k1 = f(y)\n y += h * k1\n return y\n\n\n# Midpoint\ndef rk2_midpoint(f, y, h):\n k1 = f(y)\n k2 = f(y + h * k1 / 2)\n y += h * k2\n return y\n\n# Ralston's third-order\ndef rk3_ralston(f, y, h):\n k1 = f(y)\n k2 = f(y + h * k1 / 2)\n k3 = f(y + h * k2 * 3 / 4)\n y += h * (2 * k1 + 3 * k2 + 4 * k3) / 9\n return y\n\n# Heun's second-order\ndef rk2(f, y, h):\n k1 = f(y)\n k2 = f(y + h * k1)\n y += h * (k1 + k2) / 2\n return y\n\n# Heun's third-order\ndef rk3(f, y, h):\n k1 = f(y)\n k2 = f(y + h * k1 / 3)\n k3 = f(y + h * k2 * 2 / 3)\n y += h * (k1 + 3 * k3) / 4\n return y\n\n\n\ndef rk4(f, y, h):\n k1 = f(y)\n k2 = f(y + h * k1 / 2)\n k3 = f(y + h * k2 / 2)\n k4 = f(y + h * k3)\n y += h * (k1 + 2 * k2 + 2 * k3 + k4) / 6\n return y\n\n\ndef rk(p, f, y, h):\n return [rk1, rk2, rk3, rk4][p - 1](f, y, h)\n\n\nfig, ax = plt.subplots(figsize=(2, 2))\nz = x + y * 1j\n\nfor p, c, lbl in [\n (1, 'C0', r'p=1'),\n (2, 'C1', r'p=2'),\n (3, 'C2', r'p=3'),\n (4, 'C3', r'p=4'),\n]:\n r = np.abs(rk(p, f, 1, z))\n ax.plot([], [], c=c, label=lbl)\n ax.contour(x1, y1, r, levels=[1.], colors=c)\n ax.contourf(x1, y1, r, levels=[0., 1.], colors='k', alpha=0.1)\n\nax.set_xlim(*xlim)\nax.set_ylim(*ylim)\nax.set_xlabel(r'$\\mathrm{Re}(h\\lambda)$')\nax.set_ylabel(r'$\\mathrm{Im}(h\\lambda)$')\nax.set_aspect('equal')\nax.set_xticks(range(xlim[0], xlim[1] + 1))\nax.set_yticks(range(ylim[0], ylim[1] + 1))\nax.axvline(x=0, lw=0.5, ls='-', c='k', zorder=-5)\nax.axhline(y=0, lw=0.5, ls='-', c='k', zorder=-5)\nax.legend(bbox_to_anchor=(1.4,1))\nsavefig(fig)\nplt.close(fig)\n", "repo_name": "pkarnakov/am205", "sub_path": "media/unit3/media/stabregion.py", "file_name": "stabregion.py", "file_ext": "py", "file_size_in_byte": 1888, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "95", "api": [{"api_name": "numpy.linspace", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "numpy.abs", "line_number": 81, "usage_type": "call"}, {"api_name": "util.savefig", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}]} +{"seq_id": "14077921119", "text": "from django.db import migrations\nfrom django.utils.timezone import now\n\n\ndef update_weather(apps, schema_editor):\n Weather = apps.get_model(\"weather\", \"Weather\")\n for weather in Weather.objects.all():\n weather.is_forecast = weather.date_time > now()\n weather.save()\n\n\nclass Migration(migrations.Migration):\n dependencies = [\n (\"weather\", \"0003_weather_is_forecast\"),\n ]\n\n operations = [migrations.RunPython(update_weather)]\n", "repo_name": "ollz272/garden-server", "sub_path": "apps/weather/migrations/0004_set_default_weather.py", "file_name": "0004_set_default_weather.py", "file_ext": "py", "file_size_in_byte": 460, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "95", "api": [{"api_name": "django.utils.timezone.now", "line_number": 8, "usage_type": "call"}, {"api_name": "django.db.migrations.Migration", "line_number": 12, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 12, "usage_type": "name"}, {"api_name": "django.db.migrations.RunPython", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 17, "usage_type": "name"}]} +{"seq_id": "18486634883", "text": "from selenium import webdriver\n\nfrom webdriver_manager.chrome import ChromeDriverManager\nfrom selenium.webdriver.chrome.service import Service as ChromeService\n\nfrom webdriver_manager.firefox import GeckoDriverManager\nfrom selenium.webdriver.firefox.service import Service as FirefoxService\n\n\nLAUNCH_DRIVER = 'remote' # 'local' >> use webdriver_manager :: 'remote' >> use webdriver.Remote - for grid\n\n\nclass DriverFactory:\n\n @staticmethod\n def get_driver(browser):\n if browser == 'chrome':\n options = webdriver.ChromeOptions()\n options.add_argument(\"--start-maximized\")\n options.add_argument(\"--disable-infobars\")\n options.add_argument(\"--disable-extensions\")\n options.add_argument('--disable-notifications') # notification handler\n options.add_argument('--ignore-certificate-errors')\n options.add_argument(\"--no-sandbox\")\n options.add_argument(\"--verbose\")\n\n if LAUNCH_DRIVER == 'local':\n return webdriver.Chrome(ChromeDriverManager().install(), options=options) # local\n elif LAUNCH_DRIVER == 'remote':\n options.set_capability(\"browserName\", \"chrome\") # grid\n return webdriver.Remote(\"http://localhost:4444/wd/hub\", options=options) # grid\n\n elif browser == 'firefox':\n options = webdriver.FirefoxOptions()\n options.add_argument(\"--start-maximized\")\n options.add_argument(\"--disable-infobars\")\n options.add_argument(\"--disable-extensions\")\n options.add_argument('--disable-notifications') # notification handler\n options.add_argument('--ignore-certificate-errors')\n options.add_argument(\"--no-sandbox\")\n options.add_argument(\"--verbose\")\n\n if LAUNCH_DRIVER == 'local':\n # service = FirefoxService(executable_path=GeckoDriverManager().install()) # local future\n # return webdriver.Firefox(service=service, options=options) # local future\n return webdriver.Firefox(executable_path=GeckoDriverManager().install(), options=options) # local\n elif LAUNCH_DRIVER == 'remote':\n options.set_capability(\"browserName\", \"firefox\") # grid\n return webdriver.Remote(\"http://localhost:4444/wd/hub\", options=options) # grid\n\n raise Exception('Provide valid driver name')", "repo_name": "KarolZajkowski/TAB_UI_testing", "sub_path": "Page_Object_Pattern/utils/driver_factory.py", "file_name": "driver_factory.py", "file_ext": "py", "file_size_in_byte": 2433, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "95", "api": [{"api_name": "selenium.webdriver.ChromeOptions", "line_number": 18, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 18, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 28, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 28, "usage_type": "name"}, {"api_name": "webdriver_manager.chrome.ChromeDriverManager", "line_number": 28, "usage_type": "call"}, {"api_name": "selenium.webdriver.Remote", "line_number": 31, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 31, "usage_type": "name"}, {"api_name": "selenium.webdriver.FirefoxOptions", "line_number": 34, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 34, "usage_type": "name"}, {"api_name": "selenium.webdriver.Firefox", "line_number": 46, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 46, "usage_type": "name"}, {"api_name": "webdriver_manager.firefox.GeckoDriverManager", "line_number": 46, "usage_type": "call"}, {"api_name": "selenium.webdriver.Remote", "line_number": 49, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 49, "usage_type": "name"}]} +{"seq_id": "32788304973", "text": "from collections import deque\r\n#Breadth First Search\r\ndef BFS(G, node1, node2): #finds if there is a path between 2 nodes\r\n Q = deque([node1])\r\n marked = {node1 : True} #already been to first node\r\n for node in G.adj:\r\n if node != node1:\r\n marked[node] = False #all the other nodes have yet to be visited\r\n while len(Q) != 0: #while queue is not empty\r\n current_node = Q.popleft()\r\n for node in G.adj[current_node]:\r\n if node == node2:\r\n return True\r\n if not marked[node]: #if node is not already visited, add it to the queue to explore its neighbors and mark it as visited\r\n Q.append(node)\r\n marked[node] = True\r\n return False", "repo_name": "matpetro/python", "sub_path": "Graphs/BFS.py", "file_name": "BFS.py", "file_ext": "py", "file_size_in_byte": 751, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "95", "api": [{"api_name": "collections.deque", "line_number": 4, "usage_type": "call"}]} +{"seq_id": "27934877855", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Jul 10 13:30:21 2019\r\n\r\n@author: Adi\r\n\"\"\"\r\n\r\n\r\n\r\n### Packages\r\n\r\nimport psycopg2\r\n\r\n\r\n### Global variables\r\n\r\n#Database connection parameters\r\nnazwa_bazy = 'database_name'\r\nuzytkownik = 'user_name' \r\nhaslo = 'password' \r\n\r\n\r\n\r\n################################ PART I - HEXAGONAL GRID ##################################\r\n\r\n\r\n#Function arguments\r\n\r\nsrid = 4326 # EPSG Code - Coordinate System\r\noffset_y = 0.1 # Side length of the hexagon\r\nwarstwa_siatki = \"hex_grid_name\" # The name of the grid you want to create\r\nwarstwa_zasiegu = \"coverage_layer\" # The name of the layer for which area you want to draw the grid\r\n\r\n\r\n\r\ndef siatka_heksagonalna(srid, offset_y, warstwa_siatki, warstwa_zasiegu):\r\n \r\n \"\"\"\r\n \r\n The function receives from the user following arguments: srid - coordinate system, offset_y - side length of the hexagon, warstwa_siatki - name of the layer storing the grid of regular hexagons, warstwa_zasiegu - the name of the layer for which area you want to draw the grid. \r\n \r\n \r\n \"\"\"\r\n ### Connecting with database\r\n \r\n try:\r\n \r\n conn = psycopg2.connect(host=\"localhost\",database=nazwa_bazy, user=uzytkownik, password=haslo)\r\n print('OK')\r\n \r\n except:\r\n print('Something went wrong...')\r\n \r\n \r\n ### Creating a cursor\r\n \r\n cur = conn.cursor()\r\n \r\n \r\n \r\n ## Query - range\r\n \r\n query3 = \"\"\"select min(ST_XMin(geom)) as minX , max(ST_XMax(geom)) as maxX, min(ST_YMin(geom)) as minY, max(ST_YMax(geom)) as maxY from {warstwa_zasiegu};\"\"\".format(warstwa_zasiegu = warstwa_zasiegu)\r\n \r\n \r\n cur.execute(query3)\r\n b = cur.fetchone()\r\n \r\n minX,maxX,minY,maxY= list(b)\r\n \r\n \r\n \r\n offset_x = offset_y*pow(3,1/2)/2 # offset_x - the height of an equilateral triangle\r\n \r\n offset2_x = offset_x*2 # real shift, horizontal difference between hexagons\r\n \r\n \r\n ldX,ldY = minX - offset_x, minY-0.5*offset_y\r\n lgX,lgY = minX- offset_x, minY+0.5*offset_y\r\n sgX, sgY = minX, minY + offset_y\r\n pgX,pgY = minX + offset_x, minY+0.5*offset_y\r\n pdX,pdY = minX + offset_x, minY-0.5*offset_y\r\n sdX, sdY = minX, minY - offset_y\r\n \r\n \r\n offset2_y = offset_y*3 # real shift - vertical difference between hexagons\r\n \r\n \r\n ## Calculation of the maximum value for the SQL function - generate_series\r\n \r\n zasiegX = abs(maxX - minX) # difference max - min horizontally - layer extent \r\n offsety_w_x = int(round(zasiegX/offset2_x + 1,0)) # the number of regular hexagons that should cover the horizontal layer / 1 in addition\r\n ilosc_x = offsety_w_x * offset2_x # calculation of the boundary range for hexagons - necessary for the SQL function\r\n \r\n zasiegY = abs(maxY - minY) # analogous steps for vertical coverage\r\n offsety_w_y = int(round(zasiegY/offset2_y + 1,0))\r\n ilosc_y = offsety_w_y * offset2_y \r\n \r\n \r\n ## Query creating table in the database storing regular hexagons\r\n \r\n query2= \"\"\"\r\n create TABLE {warstwa_siatki} (gid serial not null primary key);\r\n SELECT addgeometrycolumn('{warstwa_siatki}','geom', {srid}, 'POLYGON', 2);\r\n \r\n INSERT INTO {warstwa_siatki} (geom)\r\n SELECT st_translate(geom, x_series, y_series)\r\n from generate_series(0, {ilosc_x}, {roznica_x}) AS x_series,\r\n generate_series(0, {ilosc_y}, {roznica_y}) as y_series,\r\n \r\n (\r\n SELECT ST_setSRID('POLYGON(({ldX} {ldY},{lgX} {lgY},{sgX} {sgY},{pgX} {pgY},{pdX} {pdY},{sdX} {sdY},{ldX} {ldY}))'::geometry,{srid}) as geom\r\n UNION\r\n SELECT ST_Translate(st_setSRID('POLYGON(({ldX} {ldY},{lgX} {lgY},{sgX} {sgY},{pgX} {pgY},{pdX} {pdY},{sdX} {sdY},{ldX} {ldY}))'::geometry,{srid}), {offset_x}, {offset2_y}) as geom\r\n \r\n ) as two_hex;\r\n \r\n \"\"\".format(srid=srid, minX = minX, maxX=maxX, minY=minY, maxY=maxY,ldX=ldX, ldY=ldY, lgX=lgX, lgY=lgY, sgX=sgX, sgY=sgY, pgX=pgX, pgY=pgY, pdX=pdX,pdY=pdY,sdX=sdX,sdY=sdY, roznica_x = offset2_x, roznica_y = offset2_y, offset_x=offset_x, offset2_y = 1.5*offset_y, ilosc_x = ilosc_x, ilosc_y = ilosc_y, warstwa_siatki = warstwa_siatki)\r\n \r\n \r\n \r\n try:\r\n cur.execute(query2)\r\n print('OK')\r\n except:\r\n print('Something went wrong')\r\n \r\n \r\n \r\n #Commit for table creating operation\r\n conn.commit()\r\n \r\n conn.close()\r\n\r\n\r\n############################ PART II ######################################\r\n########### STATISTICAL ANALYSIS BASED ON HEXAGONES ############\r\n\r\n\r\n#Function arguments\r\n\r\n\r\nwarstwa_siatki = \"hex_grid_name\" # The name of the grid you created\r\nwarstwa_zasiegu = \"coverage_layer\" # The name of the layer you want to calculate statistics based on\r\nwarstwa_docelowa = \"target_layer\" # The name of layer you want to create - it will contain collectd hexagonal stats\r\npole_wagowe= 0 # Argument with the default value of zero, do not change it in the first version of the code\r\n\r\n\r\n\r\ndef statystyki_heksagony(warstwa_zasiegu, warstwa_docelowa, pole_wagowe, warstwa_siatki):\r\n \"\"\"\r\n \r\n The function calculates statistics based on the drawn hexagons.\r\n \r\n \r\n \"\"\"\r\n \r\n \r\n #### Connecting with database\r\n \r\n try:\r\n \r\n conn = psycopg2.connect(host=\"localhost\",database=nazwa_bazy, user=uzytkownik, password=haslo)\r\n print('OK')\r\n\r\n except:\r\n print('Something went wrong...')\r\n \r\n \r\n \r\n cur = conn.cursor() # Creating cursor\r\n \r\n \r\n query = \"select distinct(st_geometrytype(geom)) from {};\".format(warstwa_zasiegu) # query retrieving the layer geometry type\r\n cur.execute(query)\r\n typ_warstwy = cur.fetchone()\r\n typ_warstwy=str(list(typ_warstwy)) # string conversion - character-by-character comparison\r\n \r\n \r\n ### POINT LAYER ###\r\n \r\n if typ_warstwy == \"['ST_Point']\" or \"['ST_MultiPoint']\":\r\n if pole_wagowe != 0:\r\n query2 = \" \"\r\n print(\"first if\")\r\n else:\r\n try:\r\n query2 = \"select a.gid, a.geom, count(b.geom) INTO {warstwa_docelowa} from {warstwa_siatki} as a, {warstwa_zasiegu} as b where st_intersects(a.geom, b.geom)=true group by a.gid; alter table {warstwa_docelowa} ADD PRIMARY KEY (gid); \".format(warstwa_docelowa = warstwa_docelowa, warstwa_zasiegu = warstwa_zasiegu, warstwa_siatki = warstwa_siatki)\r\n cur.execute(query2)\r\n conn.commit()\r\n conn.close()\r\n print(\"All rigtht\")\r\n except:\r\n print(\"Something went wrong for point...\")\r\n else: \r\n print('does not see ST_Point...')\r\n \r\n \r\n \r\n ### LINE LAYER ###\r\n \r\n if typ_warstwy == \"['ST_MultiLineString']\":\r\n if pole_wagowe != 0:\r\n query2 = \" \"\r\n print(\"first if\")\r\n else:\r\n try:\r\n query2 = \"select sum(st_length(st_intersection(a.geom,b.geom))),a.geom, a.gid INTO {} from {warstwa_siatki} as a, {} as b where st_intersects(b.geom, a.geom) group by a.gid;\".format(warstwa_docelowa, warstwa_zasiegu, warstwa_siatki=warstwa_siatki)\r\n cur.execute(query2)\r\n conn.commit()\r\n conn.close()\r\n print(\"All right\")\r\n except:\r\n print(\"Something went wrong for line...\")\r\n else: \r\n print('does not see ST_MultiLineString...')\r\n \r\n \r\n \r\n ### POLYGON LAYER ###\r\n \r\n if typ_warstwy == \"['ST_MultiPolygon']\":\r\n if pole_wagowe != 0:\r\n query2 = \" \"\r\n print(\"first if\")\r\n else:\r\n try:\r\n query2 = \"select sum(st_area(st_intersection(a.geom,b.geom))),a.geom, a.gid INTO {warstwa_docelowa} from {warstwa_siatki} as a, {warstwa_zasiegu} as b where st_intersects(b.geom, a.geom) group by a.gid;\".format(warstwa_docelowa = warstwa_docelowa, warstwa_zasiegu = warstwa_zasiegu, warstwa_siatki=warstwa_siatki)\r\n cur.execute(query2)\r\n conn.commit()\r\n conn.close()\r\n print(\"All right\")\r\n except:\r\n print(\"Something went wrong for polygon...\")\r\n else: \r\n print('does not see ST_MultiPolygon...')\r\n \r\n \r\n \r\n \r\n \r\n", "repo_name": "AdiKom95/hexagons", "sub_path": "hexagons_procedural_01.py", "file_name": "hexagons_procedural_01.py", "file_ext": "py", "file_size_in_byte": 8285, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "95", "api": [{"api_name": "psycopg2.connect", "line_number": 48, "usage_type": "call"}, {"api_name": "psycopg2.connect", "line_number": 163, "usage_type": "call"}]} +{"seq_id": "21484984203", "text": "#!/usr/bin/env python\n# -*- coding=utf8 -*-\n\n\nimport pytest\n\n\nclass Solution:\n\n def trailingZeroes1(self, n: int) -> int:\n \"\"\"\n https://leetcode.com/problems/factorial-trailing-zeroes/discuss/196311/topic\n\n 2和5相乘为0，本质求2和5的个数，最终转换为5的个数，有几对2*5结尾就有几个0。\n\n 递归公式：f(n) = n/5 + f(n/5)\n \"\"\"\n if (n < 5):\n return 0\n if (n < 10):\n return 1\n return n // 5 + self.trailingZeroes1(n // 5)\n\n def trailingZeroes2(self, n: int) -> int:\n res = 0\n while n > 0:\n n //= 5\n res += n\n return res\n\n\n@pytest.mark.parametrize((\"param\", \"ret\"), [(3, 0),\n (5, 1),\n (10, 2),\n (30, 7)])\ndef test1(param, ret):\n solution = Solution()\n assert solution.trailingZeroes1(param) is ret\n assert solution.trailingZeroes2(param) is ret\n", "repo_name": "helloocc/algorithm", "sub_path": "172_factorial-trailing-zeroes.py", "file_name": "172_factorial-trailing-zeroes.py", "file_ext": "py", "file_size_in_byte": 1036, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "95", "api": [{"api_name": "pytest.mark.parametrize", "line_number": 32, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 32, "usage_type": "attribute"}]} +{"seq_id": "17154299402", "text": "from flask_restful import Resource, Api\nfrom flask_restful import reqparse\nfrom flask import jsonify\nfrom flask import abort\nimport extra.auth as auth\nfrom models import Character\n\ncharacters_parser = reqparse.RequestParser()\ncharacters_parser.add_argument('title', required=True)\ncharacters_parser.add_argument('name', required=True)\ncharacters_parser.add_argument('city', required=True)\ncharacters_parser.add_argument('age', required=True, type=int)\ncharacters_parser.add_argument('info', required=True)\ncharacters_parser.add_argument('ispublic', required=False, type=bool)\n\n\nclass CharactersListApi(Resource):\n def __init__(self, auth):\n super(CharactersListApi, self).__init__()\n self._auth = auth\n\n def get(self):\n characters = Character.query.all()\n return jsonify(characters=[i.serialize for i in characters])\n\n def post(self):\n if not self._auth.is_authorized():\n abort(401)\n args = characters_parser.parse_args()\n characters = Character.add(args['name'], args['title'], args['city'],\n args['age'], args['info'], args['ispublic'], self._auth.get_user())\n return jsonify(characters.serialize)\n\n\nclass CharactersApi(Resource):\n\n def __init__(self, auth):\n super(CharactersApi, self).__init__()\n self._auth = auth\n\n def get(self, id):\n characters = Character.query.filter_by(id=id).first()\n if not characters:\n abort(404)\n return jsonify(characters.serialize)\n\n def delete(self, id):\n if not self._auth.is_authorized():\n abort(401)\n characters = Character.query.filter_by(id=id).first()\n if characters.user_id != self._auth.get_user().id:\n abort(403)\n Character.delete(characters)\n return jsonify({\"deleted\": True})\n", "repo_name": "Maria173/web-server-gameroll", "sub_path": "api/news_api.py", "file_name": "news_api.py", "file_ext": "py", "file_size_in_byte": 1837, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "95", "api": [{"api_name": "flask_restful.reqparse.RequestParser", "line_number": 8, "usage_type": "call"}, {"api_name": "flask_restful.reqparse", "line_number": 8, "usage_type": "name"}, {"api_name": "flask_restful.Resource", "line_number": 17, "usage_type": "name"}, {"api_name": "extra.auth", "line_number": 20, "usage_type": "name"}, {"api_name": "models.Character.query.all", "line_number": 23, "usage_type": "call"}, {"api_name": "models.Character.query", "line_number": 23, "usage_type": "attribute"}, {"api_name": "models.Character", "line_number": 23, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 24, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 28, "usage_type": "call"}, {"api_name": "models.Character.add", "line_number": 30, "usage_type": "call"}, {"api_name": "models.Character", "line_number": 30, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 32, "usage_type": "call"}, {"api_name": "flask_restful.Resource", "line_number": 35, "usage_type": "name"}, {"api_name": "extra.auth", "line_number": 39, "usage_type": "name"}, {"api_name": "models.Character.query.filter_by", "line_number": 42, "usage_type": "call"}, {"api_name": "models.Character.query", "line_number": 42, "usage_type": "attribute"}, {"api_name": "models.Character", "line_number": 42, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 44, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 45, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 49, "usage_type": "call"}, {"api_name": "models.Character.query.filter_by", "line_number": 50, "usage_type": "call"}, {"api_name": "models.Character.query", "line_number": 50, "usage_type": "attribute"}, {"api_name": "models.Character", "line_number": 50, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 52, "usage_type": "call"}, {"api_name": "models.Character.delete", "line_number": 53, "usage_type": "call"}, {"api_name": "models.Character", "line_number": 53, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 54, "usage_type": "call"}]} +{"seq_id": "17352181049", "text": "'''\n集成分类器方法有：bagging(boosting aggregating，自举汇聚法)、随机森林(random forest)、boosting等\nboosting也可细分为很多种，其中比较流行的一种是AdaBoost(adaptive boosting, 自适应boosting)\nAdaBoost一般流程为：\n1、收集数据\n2、准备数据\n3、分析数据\n4、训练算法：AdaBoost的大部分时间用在训练上，分类器将多次在同一数据集上训练弱分类器\n5、测试算法：计算分类的错误率\n6、使用算法\n以下是利用多个单层决策树和adaboost算法，在小数据上的运用实例\n'''\n\nfrom numpy import *\nimport matplotlib.pyplot as plt\n\ndef loadSimpData():\n datMat = matrix([[ 1. , 2.1],\n [ 2. , 1.1],\n [ 1.3, 1. ],\n [ 1. , 1. ],\n [ 2. , 1. ]])\n classLabels = [1.0, 1.0, -1.0, -1.0, 1.0]\n return datMat,classLabels\n\n# 通过阈值比较对数据进行分类\ndef stumpClassify(dataMatrix,dimen,threshVal,threshIneq):\n \"\"\"\n [summary]:单层决策树分类函数,根据某一特征进行分类\n \n Arguments:\n dataMatrix -- 数据矩阵\n dimen -- 选取第几列,对特征进行抽取\n threshVal -- 阀值\n threshIneq -- 比较关系(lt)\n \n Returns:\n retArray [numpy.ndarray]-- 分类结果\n \"\"\"\n # 初始化retArray为1,m行1列全为1\n retArray = ones((shape(dataMatrix)[0],1))\n # 置于-1,进行分类\n if threshIneq == 'lt':\n retArray[dataMatrix[:,dimen] <= threshVal] = -1.0 # 该列的值<=threshVal的，全部置为-1\n else:\n retArray[dataMatrix[:,dimen] > threshVal] = -1.0\n return retArray\n \n# 构建单层决策树（决策树的简化版本），是一种弱分类器算法\ndef buildStump(dataArr,classLabels,D):\n \"\"\"\n [summary]:找到数据集上最佳的单层决策树\n 将最小错误率minError设为+∞ \n 对数据集中的每一个特征（第一层循环）:\n 对每个步长（第二层循环）:\n 对每个不等号（第三层循环）:\n 建立一棵单层决策树并利用加权数据集对它进行测试\n 如果错误率低于minError，则将当前单层决策树设为最佳单层决策树\n 返回最佳单层决策树\n \n Arguments:\n dataArr -- 数据矩阵\n classLabels -- 数据标签\n D -- 样本权重\n \n Returns:\n bestStump - 最佳单层决策树信息\n minError - 最小误差\n bestClasEst - 最佳的分类结果\n \"\"\"\n dataMatrix = mat(dataArr); labelMat = mat(classLabels).T #.T就是对一个矩阵的转置\n m,n = shape(dataMatrix)\n numSteps = 10.0; bestStump = {}; bestClasEst = mat(zeros((m,1)))\n minError = inf # 最小误差初始化为正无穷大\n for i in range(n):\n rangeMin = dataMatrix[:,i].min(); rangeMax = dataMatrix[:,i].max() # 找到特征中最小的值和最大值\n stepSize = (rangeMax-rangeMin)/numSteps # 步长，按步长选择列的最佳分割值\n for j in range(-1,int(numSteps)+1):# 第二层循环：按一定步长，遍历当前特征的特征值\n for inequal in ['lt', 'gt']: # 大于和小于的情况， 第三层循环：在大于和小于之间切换不等式\n threshVal = (rangeMin + float(j) * stepSize) # 根据阈值对数据进行分类，得到预测分类值\n # 计算分类结果\n predictedVals = stumpClassify(dataMatrix,i,threshVal,inequal) # 结果为m行1列的二维，值为-1或者1\n # 初始化误差矩阵\n errArr = mat(ones((m,1)))\n # 分类正确的,赋值为0,其他依然为1\n errArr[predictedVals == labelMat] = 0\n weightedError = D.T*errArr # 计算总误差乘以D，结果为一个值\n # nn=\"split: dim %d, thresh %.2f, thresh ineqal: %s, the weighted error is %.3f\" % (i, threshVal, inequal, weightedError)\n # print(nn)\n # 找到误差最小的分类方式\n if weightedError < minError:\n minError = weightedError\n bestClasEst = predictedVals.copy()\n bestStump['dim'] = i # 最佳分割维度\n bestStump['thresh'] = threshVal # 最佳分割值\n bestStump['ineq'] = inequal # 最佳分割方法：le/ge\n return bestStump,minError,bestClasEst\n\ndatMat,classLabels=loadSimpData()\nD=mat(ones((5,1))/5)\nbestStump,minError,bestClasEst=buildStump(datMat,classLabels,D)\n# print(\"***********************\")\n# print(bestStump)\n# print(minError)\n# print(bestClasEst)\n\n#INPUT:dataArr:训练集 classLabels:训练集的标签 numIt:弱分类器最多的个数\n#OUPUT:weakClassArr:弱分类器的线性组合\ndef adaBoostTrainDS(dataArr,classLabels,numIt=40):\n \"\"\"\n [summary]:\n 对每次迭代：\n 利用buildStump()函数找到最佳的单层决策树\n 将最佳单层决策树加入到单层决策树数组\n 计算alpha\n 计算新的权重向量D\n 更新累计类别估计值\n 如果错误率等于0.0，则退出循环\n \n Arguments:\n dataArr {[type]} -- 数据\n classLabels {[type]} -- 标签\n \n Keyword Arguments:\n numIt {int} -- 迭代次数 (default: {40})\n \n Returns:\n weakClassArr\n aggClassEst\n \"\"\"\n weakClassArr = []\n m = shape(dataArr)[0]\n D = mat(ones((m,1))/m) # 初始权重1/m，概率分布向量,元素之和为1。D在迭代中增加错分数据的权重\n aggClassEst = mat(zeros((m,1))) # 记录每个数据点的类别估计累计值\n for i in range(numIt):\n # 构建单层决策树\n bestStump,error,classEst = buildStump(dataArr,classLabels,D)\n # print(\"D:\",D.T)\n # 根据公式计算弱学习算法权重alpha,使error不等于0,因为分母不能为0\n alpha = float(0.5*log((1.0-error)/max(error,1e-16))) # 1/2*In((1-error)/error),分类器的权重。\n bestStump['alpha'] = alpha # 存储弱学习算法权重\n weakClassArr.append(bestStump) # 弱分类器的列表，存储单层决策树 \n # print(\"classEst: \",classEst.T)\n expon = multiply(-1*alpha*mat(classLabels).T,classEst) # 根据数学公式更改权重\n D = multiply(D,exp(expon)) # 为下一次迭代计算新的D\n D = D/D.sum() # 下一个分类的各样本的权重D(i+1)\n # 所有分类器的计算训练错误，如果为0，则提前退出循环（使用中断）\n aggClassEst += alpha*classEst\n # print(\"aggClassEst: \",aggClassEst.T)\n aggErrors = multiply(sign(aggClassEst) != mat(classLabels).T,ones((m,1))) # sign()函数：如果数字为正数，则返回 1；如果数字为 0，则返回零 (0)；如果数字为负数，则返回 -1\n errorRate = aggErrors.sum()/m\n # print(\"total error: \",errorRate)\n if errorRate == 0.0: break # 两种情况停止:(1)40个弱分类器的组合 (2)分类误差为0\n # return weakClassArr\n return weakClassArr,aggClassEst # plotROC()函数\n\n# print(adaBoostTrainDS(datMat,classLabels,9))\n\n\n'''\n参数：多个待分类样例 dataToClass，多个弱分类器 classifierArr\n'''\ndef adaClassify(datToClass,classifierArr):\n dataMatrix = mat(datToClass)\n m = shape(dataMatrix)[0]\n aggClassEst = mat(zeros((m,1)))\n for i in range(len(classifierArr)):\n classEst = stumpClassify(dataMatrix,classifierArr[i]['dim'],\\\n classifierArr[i]['thresh'],\\\n classifierArr[i]['ineq']) \n aggClassEst += classifierArr[i]['alpha']*classEst\n # print(aggClassEst)\n return sign(aggClassEst)\n\ndatArr,labelArr=loadSimpData()\nclassifierArr=adaBoostTrainDS(datArr,labelArr,30)\n# print(adaClassify([0,0],classifierArr))\n\ndef loadDataSet(fileName): \n numFeat = len(open(fileName).readline().split('\\t'))\n dataMat = []; labelMat = []\n fr = open(fileName)\n for line in fr.readlines():\n lineArr =[]\n curLine = line.strip().split('\\t')\n for i in range(numFeat-1):\n lineArr.append(float(curLine[i]))\n dataMat.append(lineArr)\n labelMat.append(float(curLine[-1]))\n return dataMat,labelMat\n\n# datArr,labelArr=loadDataSet('horseColicTraining2.txt')\n# classifierArr=adaBoostTrainDS(datArr,labelArr,10)\n\n# testArr,testLabelArr=loadDataSet('horseColicTest2.txt')\n# prediction10=adaClassify(testArr,classifierArr)\n# errArr=mat(ones((67,1))) # 有67行数据\n# sum=errArr[prediction10!=mat(testLabelArr).T].sum()\n# print(sum)\n\n'''\n参数：分类器的预测强度(即每个特征对应的类别累计估计值)，数据标签\n'''\ndef plotROC(predStrengths, classLabels):\n import matplotlib.pyplot as plt\n cur = (1.0,1.0) # 绘制光标的位置\n ySum = 0.0 # 用于计算AUC的值\n numPosClas = sum(array(classLabels)==1.0) # 正例的数目,这里是178\n yStep = 1/float(numPosClas); # 纵坐标表示实际正例中被正确识别的概率\n xStep = 1/float(len(classLabels)-numPosClas) # 横坐标表示实际反例中被错误识别的概率\n sortedIndicies = predStrengths.argsort() # 获取排序索引：由小到大\n fig = plt.figure()\n fig.clf()\n ax = plt.subplot(111)\n print(sortedIndicies.tolist()[0])\n # 循环遍历所有值，在每个点绘制线段\n for index in sortedIndicies.tolist()[0]: # tolist()作用：将矩阵（matrix）和数组（array）转化为列表。\n if classLabels[index] == 1.0: \n delX = 0; delY = yStep # delX是横坐标变化值，delY是纵坐标变化值\n else:\n delX = xStep; delY = 0\n ySum += cur[1] # 把每一小段的y值相加，最后乘以xStep就是面积AUC\n # 从cur到（cur[0]-delX，cur[1]-delY）绘制线\n ax.plot([cur[0],cur[0]-delX],[cur[1],cur[1]-delY], c='b') # 从右上到左下画线\n cur = (cur[0]-delX,cur[1]-delY) # 画完线之后，当前点作为光标起点\n ax.plot([0,1],[0,1],'b--') # 画对角线\n plt.xlabel('False positive rate'); plt.ylabel('True positive rate')\n plt.title('ROC curve for AdaBoost horse colic detection system')\n ax.axis([0,1,0,1]) # 设置坐标轴范围\n plt.savefig('ROC.png')\n plt.show()\n print(\"the Area Under the Curve is: \",ySum*xStep)\n\ndatArr,labelArr=loadDataSet('horseColicTraining2.txt')\nclassifierArr,aggClassEst=adaBoostTrainDS(datArr,labelArr,10)\nplotROC(aggClassEst.T,labelArr)", "repo_name": "Zheng-shuang/Machine-Learning-in-Action", "sub_path": "AdaBoost/adaboost.py", "file_name": "adaboost.py", "file_ext": "py", "file_size_in_byte": 10715, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "95", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 212, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 212, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 214, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 214, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 227, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 227, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 227, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 228, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 228, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 230, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 230, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 231, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 231, "usage_type": "name"}]} +{"seq_id": "19951339757", "text": "from django.urls import path\nfrom .views import index, products, contractors, documents, operations, storage_items, products_to_xls, \\\n contractors_to_xls, remove_marked_objects, import_products, consolidated_report, motion_report\n\napp_name = 'main'\n\nurlpatterns = [\n path('', index, name='index'),\n path('products/', products, name='products'),\n path('contractors/', contractors, name='contractors'),\n path('documents/', documents, name='documents'),\n path('operations/', operations, name='operations'),\n path('storage_items/', storage_items, name='storage_items'),\n path('products_to_xls/', products_to_xls, name='products_to_xls'),\n path('contractors_to_xls/', contractors_to_xls, name='contractors_to_xls'),\n path('remove_marked_objects/', remove_marked_objects, name='remove_marked_objects'),\n path('import_products/', import_products, name='import_products'),\n path('consolidated_report/', consolidated_report, name='consolidated_report'),\n path('motion_report/', motion_report, name='motion_report')\n]\n", "repo_name": "SergeyLebidko/MiniStorage", "sub_path": "main/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1048, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 12, "dataset": "github-code", "pt": "95", "api": [{"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "views.index", "line_number": 8, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "views.products", "line_number": 9, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "views.contractors", "line_number": 10, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "views.documents", "line_number": 11, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "views.operations", "line_number": 12, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "views.storage_items", "line_number": 13, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "views.products_to_xls", "line_number": 14, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "views.contractors_to_xls", "line_number": 15, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "views.remove_marked_objects", "line_number": 16, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "views.import_products", "line_number": 17, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "views.consolidated_report", "line_number": 18, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "views.motion_report", "line_number": 19, "usage_type": "argument"}]} +{"seq_id": "72216135992", "text": "#importing art.py file\nimport art\nprint(art.logo)\n\n#start encode function\ndef cipher(text):\n to_return=\"\"\n for letter in text:\n if ord(letter)>=97 and ord(letter)<=122:\n if ord(letter)+shift<123:\n to_return+=chr(ord(letter)+shift)\n else:\n add=ord(letter)+shift-123\n to_return+=chr(97+int(add))\n else:\n to_return+=letter\n \n return to_return;\n\n\n#start the main code\n\nrestart='y'\n\nwhile restart=='y':\n\n direction = input(\"Type 'encode' to cipher, type 'decode' to decrypt:\\n\")\n text = input(\"Type your message:\\n\").lower()\n shift = int(input(\"Type the shift number:\\n\"))\n shift%=26\n\n if direction=='encode':\n print(cipher(text))\n elif direction==\"decode\":\n shift=26-shift\n print(cipher(text))\n else:\n print(\"wrong option\")\n exit\n\n restart=input(\"Do you want to restart the program (y/n) \")\n\n", "repo_name": "surendraLongre/caesar-cipher", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 959, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "95", "api": [{"api_name": "art.logo", "line_number": 3, "usage_type": "attribute"}]} +{"seq_id": "35692321366", "text": "import cherrypy\nimport json\n\nfrom ratelimit import *\nfrom services import ShortURL\n\n\n@cherrypy.expose\nclass ShortURLAPIv1(object):\n \"\"\"Create shortened URLs and convert them back.\n\n REST URIs for v1:\n - [GET] /shorturl/v1/ RETURNS data including both URLs, based on short URL.\n - [POST] /shorturl/v1/ RETURNS data including both URLs, based on original URL.\n\n The REST URIs are rate limited (2 requests per second) using the ratelimit module\n found here: https://pypi.python.org/pypi/ratelimit/1.1.0.\n\n Motivation for the REST URI architecture came from GOOG.LE URL shortener. The idea is to let\n METHODS tell us what we need to do (and not nest deeply). Thus, the REST URIs are simple and\n return the same data (with just different inputs). The return data could be modified in future\n versions.\n \"\"\"\n\n ACCEPTED_CONTENT_TYPES = dict(json='application/json', plain='text/plain')\n\n def __init__(self):\n \"\"\"Initialization instantiations.\"\"\"\n self.shorturl = ShortURL()\n\n\n def process_params(self, params):\n \"\"\"Support AJAX requests that are JSON formatted.\n\n This method is needed in order to support AJAX requests if a `Content-Type` header is\n declared. I have opted to require AJAX callers declare this because it is more beneficial\n to be explicit. Wait, explicit, go figure ;)\n \"\"\"\n header_content_type = cherrypy.request.headers.get('Content-Type')\n if not params:\n if header_content_type == self.ACCEPTED_CONTENT_TYPES.get('plain'):\n params = json.loads(cherrypy.request.body.read().decode('utf-8'))\n elif header_content_type == self.ACCEPTED_CONTENT_TYPES.get('json'):\n params = cherrypy.request.json\n else:\n raise cherrypy.HTTPError(400, 'ERROR_INCORRECT_OR_MISSING_PARAM')\n elif params and (header_content_type in self.ACCEPTED_CONTENT_TYPES.values()):\n # supports GETs\n params = json.loads(list(params.keys())[0])\n\n return params\n\n\n def retrieve_param(self, identifier, params):\n \"\"\"Retrieve the specified parameter.\"\"\"\n identified_param = params.get(identifier)\n\n if not identified_param:\n raise cherrypy.HTTPError(400, 'ERROR_INCORRECT_OR_MISSING_PARAM')\n\n return identified_param\n\n\n @rate_limited(2)\n def OPTIONS(self, *args, **kwargs):\n \"\"\"Accept CORS preflight check if 'Content-Type' header is not standard for AJAX.\n \n The BODY of OPTIONS is not important. Accepts any params (needed).\n\n per https://developer.mozilla.org/en-US/docs/Web/HTTP/Access_control_CORS#Overview\n \"...the specification mandates that browsers \"preflight\" the request, soliciting\n supported methods from the server with an HTTP OPTIONS request method...\"\n\n per http://api.jquery.com/jquery.ajax/ (regarding `contentType`)\n \"Note: For cross-domain requests, setting the content type to anything other than\n application/x-www-form-urlencoded, multipart/form-data, or text/plain will trigger\n the browser to send a preflight OPTIONS request to the server.\"\n\n Important REQEUST HEADERS to look at\n (example per jQuery `contentType: 'application/json'`):\n Request Headers:\n ACCESS-CONTROL-REQUEST-HEADERS: content-type\n ACCESS-CONTROL-REQUEST-METHOD: GET\n\n See corsheaders() in the main configuration. They \"answer\" the \"preflight\" request. \n \"\"\"\n return\n\n\n @cherrypy.tools.json_in()\n @cherrypy.tools.json_out()\n @rate_limited(2)\n def GET(self, **kwargs):\n \"\"\"Return a set of data based on the original URL.\n\n successful example:\n\n {\n 'short_url': 'http://52.8.43.12/qM',\n 'original_url': 'http://example.com/hello-there/testing',\n 'created': '2017-01-05 02:57:10.366'\n }\n \"\"\"\n params = kwargs\n try:\n params = self.process_params(params)\n short_url = self.retrieve_param('short_url', params)\n except cherrypy._cperror.HTTPError as e:\n return self.shorturl.standardize_error(e)\n\n return self.shorturl.short_to_original(short_url)\n\n\n @cherrypy.tools.json_in()\n @cherrypy.tools.json_out()\n @rate_limited(2)\n def POST(self, **kwargs):\n \"\"\"Return a set of data based on the original URL.\n\n successful example:\n\n {\n 'short_url': 'http://52.8.43.12/qM',\n 'original_url': 'http://example.com/hello-there/testing',\n 'created': '2017-01-05 02:57:10.366'\n }\n \"\"\"\n params = kwargs\n try:\n params = self.process_params(params)\n original_url = self.retrieve_param('original_url', params)\n except cherrypy._cperror.HTTPError as e:\n return self.shorturl.standardize_error(e)\n\n return self.shorturl.original_to_short(original_url)\n", "repo_name": "gsafcik/ShortURL", "sub_path": "services/shorturl_api_v1.py", "file_name": "shorturl_api_v1.py", "file_ext": "py", "file_size_in_byte": 5020, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "95", "api": [{"api_name": "services.ShortURL", "line_number": 29, "usage_type": "call"}, {"api_name": "cherrypy.request.headers.get", "line_number": 39, "usage_type": "call"}, {"api_name": "cherrypy.request", "line_number": 39, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 42, "usage_type": "call"}, {"api_name": "cherrypy.request.body.read", "line_number": 42, "usage_type": "call"}, {"api_name": "cherrypy.request", "line_number": 42, "usage_type": "attribute"}, {"api_name": "cherrypy.request", "line_number": 44, "usage_type": "attribute"}, {"api_name": "cherrypy.HTTPError", "line_number": 46, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 49, "usage_type": "call"}, {"api_name": "cherrypy.HTTPError", "line_number": 59, "usage_type": "call"}, {"api_name": "cherrypy._cperror", "line_number": 108, "usage_type": "attribute"}, {"api_name": "cherrypy.tools.json_in", "line_number": 90, "usage_type": "call"}, {"api_name": "cherrypy.tools", "line_number": 90, "usage_type": "attribute"}, {"api_name": "cherrypy.tools.json_out", "line_number": 91, "usage_type": "call"}, {"api_name": "cherrypy.tools", "line_number": 91, "usage_type": "attribute"}, {"api_name": "cherrypy._cperror", "line_number": 132, "usage_type": "attribute"}, {"api_name": "cherrypy.tools.json_in", "line_number": 114, "usage_type": "call"}, {"api_name": "cherrypy.tools", "line_number": 114, "usage_type": "attribute"}, {"api_name": "cherrypy.tools.json_out", "line_number": 115, "usage_type": "call"}, {"api_name": "cherrypy.tools", "line_number": 115, "usage_type": "attribute"}, {"api_name": "cherrypy.expose", "line_number": 8, "usage_type": "attribute"}]} +{"seq_id": "69867179503", "text": "from functools import partial\nfrom class_fungsi import StopWordRemovalTransformer, LemmatizeTransformer, DocEmbeddingVectorizer\nfrom sklearn.model_selection import RandomizedSearchCV, cross_val_score\nimport numpy as np\nfrom urllib.request import urlopen, urlretrieve\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.naive_bayes import BernoulliNB\nimport tarfile\nimport pandas as pd\nimport os.path\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.feature_selection import SelectKBest, mutual_info_classif\nfrom sklearn.feature_extraction.text import CountVectorizer\nimport string\n\nSYMBOLS = \" \".join(string.punctuation).split(\" \") + [\"-\", \"...\", \"”\", \"”\"]\n\ndef iText(file):\n text = file[-1].strip() \n label = file[-1].strip() \n data_frame = (text, label)\n\n return data_frame\n\n\ndef fetch_URLSpam(data_home='data'):\n URL_LINGSPAM = 'http://nlp.cs.aueb.gr/software_and_datasets/lingspam_public.tar.gz'\n if not os.path.exists(data_home + '/lingspam_public.tar.gz'):\n urlretrieve(URL_LINGSPAM, data_home + '/lingspam_public.tar.gz')\n df = pd.DataFrame(columns=['text', 'spam?'])\n with tarfile.open(mode=\"r:gz\", name=data_home+'/lingspam_public.tar.gz') as f:\n # We load only the raw texts. \n folder = 'lingspam_public/bare/'\n files = [name for name in f.getnames() if name.startswith(folder) and name.endswith('.txt')]\n for name in files:\n m = f.extractfile(name)\n df = df.append({'text':str(m.read(), 'utf-8'), \n 'spam?':1 if 'spmsg' in name else 0}, \n ignore_index=True)\n return df \n\n\ndef create_pipelines_URLSpam():\n stop = ('stop', StopWordRemovalTransformer())\n lemma = ('lemma', LemmatizeTransformer())\n binz = ('binarizer', CountVectorizer())\n we = ('document embedding', DocEmbeddingVectorizer())\n sel = ('fsel', SelectKBest(score_func=mutual_info_classif, k=100))\n clf = ('cls', BernoulliNB()) # Binary features in the original paper. \n return Pipeline([binz, sel, clf]), \\\n Pipeline([stop, binz, sel, clf]), \\\n Pipeline([lemma, binz, sel, clf]), \\\n Pipeline([stop, lemma, binz, sel, clf]), \\\n Pipeline([stop, lemma, we, sel, clf])\n\n\ndef fetch_spambase(data_home='data'):\n URL_SPAMBASE = 'https://archive.ics.uci.edu/ml/machine-learning-databases/spambase/'\n\n columns = []\n with urlopen(URL_SPAMBASE + 'spambase.names') as f:\n content = f.readlines()\n for line in content:\n if str(line,'utf-8').startswith(('word_freq', 'char_freq', 'capital_run')):\n columns.append(str(line,'utf-8').split(':')[0]) \n columns.append('spam?')\n df = pd.read_csv(URL_SPAMBASE + 'spambase.data', header=None)\n df.columns = columns\n return df\n\ndef create_pipeline_spambase():\n clf = ('cls', BernoulliNB()) # Has binary and frequencies. \n return Pipeline([clf])\n\n", "repo_name": "zakyyusuff/zakar", "sub_path": "zakar.py", "file_name": "zakar.py", "file_ext": "py", "file_size_in_byte": 2938, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "91", "api": [{"api_name": "string.punctuation", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.path.exists", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 28, "usage_type": "name"}, {"api_name": "urllib.request.urlretrieve", "line_number": 29, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 30, "usage_type": "call"}, {"api_name": "tarfile.open", "line_number": 31, "usage_type": "call"}, {"api_name": "class_fungsi.StopWordRemovalTransformer", "line_number": 44, "usage_type": "call"}, {"api_name": "class_fungsi.LemmatizeTransformer", "line_number": 45, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.CountVectorizer", "line_number": 46, "usage_type": "call"}, {"api_name": "class_fungsi.DocEmbeddingVectorizer", "line_number": 47, "usage_type": "call"}, {"api_name": "sklearn.feature_selection.SelectKBest", "line_number": 48, "usage_type": "call"}, {"api_name": "sklearn.feature_selection.mutual_info_classif", "line_number": 48, "usage_type": "name"}, {"api_name": "sklearn.naive_bayes.BernoulliNB", "line_number": 49, "usage_type": "call"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 50, "usage_type": "call"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 51, "usage_type": "call"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 52, "usage_type": "call"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 53, "usage_type": "call"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 54, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 61, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 67, "usage_type": "call"}, {"api_name": "sklearn.naive_bayes.BernoulliNB", "line_number": 72, "usage_type": "call"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 73, "usage_type": "call"}]} +{"seq_id": "5607786393", "text": "# -*- encoding: utf-8 -*-\n'''\n@File : cycleGAN.py\n@Time : 2023/03/10 13:14:04\n@Author : zwt \n@Version : 1.0\n@Contact : 1030456532@qq.com\n'''\n\n# here put the import lib\n\nfrom collections import OrderedDict\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass RedisualBlock(nn.Module):\n \"\"\"\n Don't change the input shape.\n \"\"\"\n def __init__(self, input_channel) -> None:\n super().__init__()\n\n conv_block = [\n # Use the boundary as the axis of symmetry reflection to padding it.\n # \n # 5 4 5 6 5\n # 1 2 3 2 1 2 3 2 2 1 2 3 2\n # 4 5 6 => 5 4 5 6 5 => 5 4 5 6 5\n # 7 8 9 8 7 8 9 8 8 7 8 9 8\n # 5 4 5 6 5\n # \n nn.ReflectionPad2d(1), \n nn.Conv2d(input_channel, input_channel, 3),\n # ** Is more suit for style transform missions (cycle gan style transform). **\n nn.InstanceNorm2d(input_channel),\n nn.ReLU(inplace=True), \n nn.ReflectionPad2d(1), \n nn.Conv2d(input_channel, input_channel, 3), \n nn.InstanceNorm2d(input_channel)\n ]\n\n self.conv_block = nn.Sequential(*conv_block)\n\n def forward(self, x):\n return x + self.conv_block(x)\n\n\nclass Generator(nn.Module):\n def __init__(self, input_channel, output_channel, redisual_block_nums=9) -> None:\n super().__init__()\n\n # ReflectionPad2d 3 with kernel_size 7. Then the output shape will not changed.\n # input.\n model = [\n nn.ReflectionPad2d(3), \n nn.Conv2d(input_channel, 64, 7), \n nn.InstanceNorm2d(64), \n nn.ReLU(inplace=True)\n ]\n\n # down sample.\n input_channel = 64\n for _ in range(2):\n model += [\n nn.Conv2d(input_channel, input_channel * 2, 3, stride=2, padding=1), \n nn.InstanceNorm2d(input_channel * 2), \n nn.ReLU(inplace=True)\n ]\n input_channel = input_channel * 2\n \n # redisual block.\n for _ in range(redisual_block_nums):\n model += [RedisualBlock(input_channel)]\n\n # up sample.\n for _ in range(2):\n model += [\n nn.ConvTranspose2d(input_channel, input_channel // 2, 3, stride=2, padding=1, output_padding=1), \n nn.InstanceNorm2d(input_channel // 2),\n nn.ReLU(inplace=True)\n ]\n input_channel = input_channel // 2\n \n # output.\n \"\"\"\n saturated neurons: sigmoid , tanh. etc\n\n one-sided saturations: relu , leaky relu. etc\n\n 1. solve the vanishing gradients problems.\n \"\"\"\n model += [\n nn.ReflectionPad2d(3),\n nn.Conv2d(64, output_channel, 7),\n nn.Tanh()\n ]\n\n self.model = nn.Sequential(*model)\n\n def forward(self, x):\n return self.model(x)\n\nclass Discriminator(nn.Module):\n def __init__(self) -> None:\n super().__init__()\n\nif __name__ == '__main__':\n G = Generator(3, 32)\n R = RedisualBlock(3)\n print(G)\n print(R)\n\n import torch\n input = torch.zeros((4, 3, 256, 256))\n output = G(input)\n r_output = R(input)\n print(f\"input.shape: {input.shape} output.shape: {output.shape} r: {r_output.shape}\")", "repo_name": "zwtttttt/GAN_ZOO", "sub_path": "cycleGAN/model/cycleGAN.py", "file_name": "cycleGAN.py", "file_ext": "py", "file_size_in_byte": 3423, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "95", "api": [{"api_name": "torch.nn.Module", "line_number": 16, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.nn.ReflectionPad2d", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 32, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 33, "usage_type": "name"}, {"api_name": "torch.nn.InstanceNorm2d", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 35, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 36, "usage_type": "name"}, {"api_name": "torch.nn.ReflectionPad2d", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 37, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 38, "usage_type": "name"}, {"api_name": "torch.nn.InstanceNorm2d", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 39, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 42, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 48, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.nn.ReflectionPad2d", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 55, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 56, "usage_type": "name"}, {"api_name": "torch.nn.InstanceNorm2d", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 57, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 58, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 65, "usage_type": "name"}, {"api_name": "torch.nn.InstanceNorm2d", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 66, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 67, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 78, "usage_type": "name"}, {"api_name": "torch.nn.InstanceNorm2d", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 79, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 80, "usage_type": "name"}, {"api_name": "torch.nn.ReflectionPad2d", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 93, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 94, "usage_type": "name"}, {"api_name": "torch.nn.Tanh", "line_number": 95, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 95, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 98, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 103, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 103, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 114, "usage_type": "call"}]} +{"seq_id": "37127761822", "text": "\"\"\"\nCurve fitting routines\n\ncurve + bounds\noptimization algorithm\ncost function\n\"\"\"\n\nfrom __future__ import annotations\n\nfrom typing import Callable\n\nimport numpy as np\nimport pandas as pd\n\n\ndef fit_parametric_power_curve(\n x: np.ndarray | pd.Series,\n y: np.ndarray | pd.Series,\n curve: Callable,\n optimization_algorithm: Callable,\n cost_function: Callable,\n bounds: tuple[\n tuple[float, float],\n tuple[float, float],\n tuple[float, float],\n tuple[float, float],\n tuple[float, float],\n ],\n return_params: bool = False,\n):\n \"\"\"\n Fit curve to filtered power-windspeed data.\n\n Args:\n x(:obj:`numpy.ndarray` | `pandas.Series`): independent variable\n y(:obj:`numpy.ndarray` | `pandas.Series`): dependent variable\n curve(:obj:`Callable`): function/lambda name for power curve desired default is curves.logistic5param\n optimization_algorithm(Function): scipy.optimize style optimization algorithm\n cost_function(:obj:`Callable`): Python function that takes two np.array 1D of real numbers and returns a real numeric\n cost.\n bounds(:obj:`tuple[tuple[float, float], tuple[float, float], tuple[float, float], tuple[float, float], tuple[float, float]]`):\n bounds on parameters for power curve, default is for logistic5param, with power in kw and windspeed in m/s\n return_params(:obj:`bool`): If True, return a tuple of (Callable, scipy.optimize.fit), and if\n False return only the Callable.\n\n Returns:\n Callable(np.array -> np.array): function handle to optimized power curve\n \"\"\"\n\n # Build opt function as a closure on \"x\" and \"y\"\n def f(opt_params):\n return cost_function(curve(x, *opt_params), y)\n\n # Run the optimization algorithm\n fit = optimization_algorithm(f, bounds)\n\n # Create closure of curve function with fit params\n def fit_curve(x_2):\n return curve(x_2, *fit.x)\n\n # Return values based on flag\n if return_params:\n return lambda x_2: fit_curve, fit\n else:\n return fit_curve\n\n\n\"\"\"\nCost Functions\n\"\"\"\n\n\ndef least_squares(x: np.ndarray | pd.Series, y: np.ndarray | pd.Series):\n \"\"\"Least Squares loss function\n\n Args:\n x(:obj:`np.ndarray` | `pandas.Series`): 1-D array of numbers representing x\n y(:obj:`np.ndarray` | `pandas.Series`): 1-D array of numbers representing y\n\n Returns:\n The least square of x and y.\n \"\"\"\n return np.sum((x - y) ** 2)\n", "repo_name": "NREL/OpenOA", "sub_path": "openoa/utils/power_curve/parametric_optimize.py", "file_name": "parametric_optimize.py", "file_ext": "py", "file_size_in_byte": 2503, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 155, "dataset": "github-code", "pt": "91", "api": [{"api_name": "numpy.ndarray", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pandas.Series", "line_number": 18, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pandas.Series", "line_number": 19, "usage_type": "attribute"}, {"api_name": "typing.Callable", "line_number": 20, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 21, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 22, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 74, "usage_type": "attribute"}, {"api_name": "pandas.Series", "line_number": 74, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 84, "usage_type": "call"}]} +{"seq_id": "2189697464", "text": "import osmosis_aws_driver.data_S3_plugin as ocean_s3\n# General imports\nimport sys\nimport os\n#import glob\nimport pandas as pd\nimport hashlib\n\n#%% Logging\nimport logging\nloggers_dict = logging.Logger.manager.loggerDict\n \nlogger = logging.getLogger()\nlogger.handlers = []\n\n# Set level\nlogger.setLevel(logging.DEBUG)\n\n# Create formatter\n\n#FORMAT = \"%(asctime)s - %(levelno)s - %(module)-15s - %(funcName)-15s - %(message)s\"\nFORMAT = \"%(asctime)s L%(levelno)s: %(message)s\"\n\nDATE_FMT = \"%Y-%m-%d %H:%M:%S\"\nformatter = logging.Formatter(FORMAT, DATE_FMT)\n\n# Create handler and assign\nhandler = logging.StreamHandler(sys.stderr)\nhandler.setFormatter(formatter)\nlogger.handlers = [handler]\nlogger.critical(\"Logging started\")\n\n\nimport warnings\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\n#%% IO\n\n# The working directory is the repo root\nlogging.debug(\"Current working directory: {}\".format(os.getcwd()))\n\n# The source catalog\nFNAME_SOURCE_CATALOG = \"Original/OceanDataSets_master catalog clean.csv\"\n# The current catalog stores the updated state\nFNAME_CURRENT_CATALOG = r\"Master catalog current.csv\"\nPATH_SOURCE_CATALOGUE = os.path.join(os.getcwd(),'catalog', FNAME_SOURCE_CATALOG)\nPATH_CURRENT_CATALOGUE = os.path.join(os.getcwd(),'catalog', FNAME_CURRENT_CATALOG)\nassert os.path.exists(PATH_SOURCE_CATALOGUE), \"{}\".format(PATH_SOURCE_CATALOGUE)\nassert os.path.exists(PATH_CURRENT_CATALOGUE), \"{}\".format(PATH_CURRENT_CATALOGUE)\n\n#%% Load the data catalogue\ndf = pd.read_csv(PATH_CURRENT_CATALOGUE)\n\ntotal_GB = sum(df.loc[:,'SizeGB'])\nlogging.debug(\"Loaded data catalogue with {} records representing {:0.0f} GB\".format(len(df),total_GB))\nlogging.debug(\"{} files have been flagged as already uploaded to S3.\".format(sum(df['uploaded'])))\nerrors = df[df['error'] != 'No error']['error'].value_counts()\nlogging.debug(\"{} files have been flagged with an upload error.\".format(sum(errors)))\n\nprint(\"Error summary:\")\nfor err in errors.iteritems():\n print('\\t',*err)\n\nres = df.head()\ndf = df[0:5]\n\n\n#%% Create the connection via the wrapper\n\n# The `osmosis-aws-driver`, imported here as `ocean_s3` is a wrapper for Boto3.\n\n\n# config = dict()\n# config['region'] = 'eu-central-1'\nconfig = None\nocn_s3 = ocean_s3.S3_Plugin(config)\n\n#%% List buckets\n\nfor i,b in enumerate(ocn_s3.list_buckets()):\n print(i,b['Name'])\n\n#%% Get the bucket\nbucketname =\"data-catalogue-r00\"\n#bucket = ocn_s3.s3_client.head_bucket(Bucket=bucketname)\nbucket = ocn_s3.s3_resource.Bucket(bucketname)\n\n#%% Get the files\ns3files = {obj.key:obj for obj in bucket.objects.all()}\n\n# Select a subset of files\nthese_keys = list(s3files.keys())[:2]\nfor f in these_keys:\n meta_data = s3files[f].Object().metadata\n print(f, meta_data)\n\ntotal_GB=sum([s3files[f].size for f in s3files])/1000/1000/1000\n\nlogging.debug(\"{} files on {}, {:0.2f} GB\".format(len(s3files),bucketname,total_GB))\n\n\n#%%\n\n#%%\nfor row in df.iterrows():\n print(row)\n\n#%%\ndf['uploaded']\n\n#%% Register the dataset onto blockchain", "repo_name": "oceanprotocol-archive/mantaray", "sub_path": "ipython_scripts/0_notebooks_verify/Superceded/s00_test_connections.py", "file_name": "s00_test_connections.py", "file_ext": "py", "file_size_in_byte": 2978, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "95", "api": [{"api_name": "logging.Logger", "line_number": 11, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 17, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 25, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 28, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 28, "usage_type": "attribute"}, {"api_name": "warnings.simplefilter", "line_number": 35, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 40, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 52, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 55, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 56, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 58, "usage_type": "call"}, {"api_name": "osmosis_aws_driver.data_S3_plugin.S3_Plugin", "line_number": 76, "usage_type": "call"}, {"api_name": "osmosis_aws_driver.data_S3_plugin", "line_number": 76, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 99, "usage_type": "call"}]} +{"seq_id": "29002799837", "text": "import pandas as pd\nfrom textblob import TextBlob\nfrom sklearn.feature_extraction.text import CountVectorizer\nimport matplotlib.pyplot as plt\n\n\n# Load the clustered tweets data\ndf = pd.read_csv('../data/tweets_clustered.csv')\n\n# Perform sentiment analysis on each tweet\ndf['sentiment'] = df['text'].apply(lambda x: TextBlob(x).sentiment.polarity)\n\n# Identify top keywords for each cluster\nclusters = df['cluster'].unique()\nvectorizer = CountVectorizer(stop_words='english')\nfor cluster in clusters:\n cluster_df = df[df['cluster'] == cluster]\n cluster_texts = cluster_df['text'].tolist()\n vectorized_texts = vectorizer.fit_transform(cluster_texts)\n feature_names = vectorizer.get_feature_names_out()\n cluster_keywords = [feature_names[idx] for idx in vectorized_texts.sum(axis=0).argsort()[::-1][:10]]\n print(f\"Cluster {cluster} Keywords: {cluster_keywords}\")\n\n# Visualize sentiment distribution within each cluster\nfor cluster in clusters:\n cluster_df = df[df['cluster'] == cluster]\n plt.hist(cluster_df['sentiment'], bins=20, range=(-1, 1), alpha=0.5, label=f'Cluster {cluster}')\n plt.xlabel('Sentiment Score')\n plt.ylabel('Count')\n plt.title(f'Sentiment Distribution - Cluster {cluster}')\n plt.legend()\n plt.show()", "repo_name": "Lujaina-E/Fifa2022-BERT", "sub_path": "model/exploratory-analysis-2.py", "file_name": "exploratory-analysis-2.py", "file_ext": "py", "file_size_in_byte": 1255, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "95", "api": [{"api_name": "pandas.read_csv", "line_number": 8, "usage_type": "call"}, {"api_name": "textblob.TextBlob", "line_number": 11, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.CountVectorizer", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}]} +{"seq_id": "72116941744", "text": "###### 자바스크립트에서 한글 깨짐 현상 방지용 ################################\nimport sys\nimport io\nsys.stdout = io.TextIOWrapper(sys.stdout.detach(), encoding = 'utf-8')\nsys.stderr = io.TextIOWrapper(sys.stderr.detach(), encoding = 'utf-8')\n###########################################################################\n\n# print('삼성전자 전일종가 알려줘')\n\nimport requests\n\n# 네이버 클로바 API - 15초/4원 과금\n\ndata = open('./uploads/' + sys.argv[1], \"rb\") # STT를 진행하고자 하는 음성 파일\n\nLang = \"Kor\" # Kor / Jpn / Chn / Eng\nURL = \"https://naveropenapi.apigw.ntruss.com/recog/v1/stt?lang=\" + Lang\n \nID = \"sb21okbjmh\" # 인증 정보의 Client ID\nSecret = \"iFVd8KWihMzJnXRFGbpBQ6vutUT0FhmLzTSAnklV\" # 인증 정보의 Client Secret\n \nheaders = {\n \"Content-Type\": \"application/octet-stream\", # Fix\n \"X-NCP-APIGW-API-KEY-ID\": ID,\n \"X-NCP-APIGW-API-KEY\": Secret,\n}\nresponse = requests.post(URL, data=data, headers=headers)\nrescode = response.status_code\n\nif(rescode == 200):\n #print (response.text)\n print(response.text.split('\":\"')[1].split('\"')[0].replace(' ',''))\n # 공백 없이 전부\nelse:\n print(\"Error : \" + response.text)\n\n", "repo_name": "charles098/2021_Fall_Capston_Design2", "sub_path": "server/api_codes/naverCloba.py", "file_name": "naverCloba.py", "file_ext": "py", "file_size_in_byte": 1210, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "91", "api": [{"api_name": "sys.stdout", "line_number": 4, "usage_type": "attribute"}, {"api_name": "io.TextIOWrapper", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.stdout.detach", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 5, "usage_type": "attribute"}, {"api_name": "io.TextIOWrapper", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.stderr.detach", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 14, "usage_type": "attribute"}, {"api_name": "requests.post", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "17047223782", "text": "from django.conf.urls import url\n\nfrom dashboard.views import *\n\n\nurlpatterns = [\n url(r'^$', dashboard_view, name=\"dashboard_view\"),\n url(r'^settings', setitngs_view, name=\"setitngs_view\"),\n url(r'^save-settings', save_settings_action, name=\"save_settings_action\"),\n url(r'^activate-trade-bot/(?P.+)', activate_trade_bot_action, name=\"activate_trade_bot_action\"),\n url(r'^deactivate-trade-bot/(?P.+)', deactivate_trade_bot_action, name=\"deactivate_trade_bot_action\"),\n url(r'^delete-trade-bot/(?P.+)', delete_trade_bot_action, name=\"delete_trade_bot_action\"),\n url(r'^activate-custom-bot/(?P.+)', activate_custom_bot_action, name=\"activate_custom_bot_action\"),\n url(r'^deactivate-custom-bot/(?P.+)', deactivate_custom_bot_action, name=\"deactivate_custom_bot_action\"),\n url(r'^delete-custom-bot/(?P.+)', delete_custom_bot_action, name=\"delete_custom_bot_action\")\n]\n", "repo_name": "jthomaskerr/Haas-Dradis", "sub_path": "dashboard/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 946, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "91", "api": [{"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "42492485354", "text": "import matplotlib.pyplot\nimport sys\nimport numpy as np\nimport os\nimport copy\nimport stitch.mesospim\nimport stitch.Rigid\n\nme = \"b.py\"\nshifts = []\nwhile True:\n sys.argv.pop(0)\n if len(sys.argv) and len(sys.argv[0]) > 1 and sys.argv[0][0] == '-':\n if sys.argv[0][1] == 'h':\n usg()\n elif sys.argv[0][1] == 'f':\n sys.argv.pop(0)\n if len(sys.argv) == 0:\n sys.stderr.write(\"%s: not enough arguments for -f\\n\" % me)\n sys.exit(1)\n shift_path = sys.argv[0]\n with open(shift_path) as file:\n for line in file:\n x, y, z, corr = line.split()\n shifts.append((int(x), int(y), int(z)))\n else:\n sys.stderr.write(\"%s: unknown option '%s'\\n\" % (me, sys.argv[0]))\n sys.exit(2)\n else:\n break\ntry:\n (tx, ty), (nx, ny, nz), (ox,\n oy), path = stitch.mesospim.read_tiles(sys.argv)\nexcept ValueError:\n tx = ty = 2\n nx = ny = nz = 200\n ox = oy = 10\n path = sys.argv\ndtype = np.dtype(\" nz - 1:\n z = nz - 1\n s = src[i][::stride[0], ::stride[1], z]\n y = ty * (ny - oy) - y\n extent = (x, x + nx, y - ny, y)\n cmap = 'Greens' if se[0] == i else 'Greys'\n if art[i] is not None:\n art[i].remove()\n vmin = np.quantile(s, 0.1)\n vmax = np.quantile(s, 0.9)\n art[i] = matplotlib.pyplot.imshow(s.T,\n alpha=0.5,\n cmap=cmap,\n vmin=vmin,\n vmax=vmax,\n extent=extent)\n\n\ndef press(event):\n n = len(src)\n key = event.key\n if key == \"down\":\n se[0] += 1\n if se[0] == n:\n se[0] = 0\n for i in range(n):\n draw(i)\n fig.canvas.draw()\n elif key == \"up\":\n se[0] -= 1\n if se[0] == -1:\n se[0] = n - 1\n for i in range(n):\n draw(i)\n fig.canvas.draw()\n elif key == \"right\":\n zslice[0] += stride[2]\n for i in range(n):\n draw(i)\n fig.canvas.draw()\n elif key == \"left\":\n zslice[0] -= stride[2]\n for i in range(n):\n draw(i)\n fig.canvas.draw()\n elif key == \"h\":\n positions[se[0]][0] -= stride[0]\n draw(se[0])\n fig.canvas.draw()\n elif key == \"l\":\n positions[se[0]][0] += stride[0]\n draw(se[0])\n fig.canvas.draw()\n elif key == \"j\":\n positions[se[0]][1] -= stride[1]\n draw(se[0])\n fig.canvas.draw()\n elif key == \"k\":\n positions[se[0]][1] += stride[1]\n draw(se[0])\n fig.canvas.draw()\n elif key == \"i\":\n positions[se[0]][2] -= stride[2]\n draw(se[0])\n fig.canvas.draw()\n elif key == \"n\":\n positions[se[0]][2] += stride[2]\n draw(se[0])\n fig.canvas.draw()\n elif key == \"z\":\n if art[se[0]] == None:\n draw(se[0])\n fig.canvas.draw()\n else:\n art[se[0]].remove()\n art[se[0]] = None\n fig.canvas.draw()\n elif key == \"R\":\n stride[:] = [2 * e for e in stride]\n for i in range(n):\n draw(i)\n fig.canvas.draw()\n elif key == \"r\":\n stride[:] = [max(1, e // 2) for e in stride]\n for i in range(n):\n draw(i)\n fig.canvas.draw()\n elif key == \"q\":\n sys.exit(0)\n elif key == \"s\":\n print()\n with open(\".shifts\", \"w\") as file:\n for (x, y, z), (x0, y0, z0) in zip(positions, positions0):\n print(x, y, z)\n file.write(\"%d %d %d %.16e\\n\" % (x - x0, y - y0, z - z0, 0.0))\n\n\nfor i in range(len(src)):\n draw(i)\nfig.canvas.mpl_connect('key_press_event', press)\nfig.tight_layout()\nmatplotlib.pyplot.show()\n", "repo_name": "cselab/stitch", "sub_path": "poc/viewer/a.py", "file_name": "a.py", "file_ext": "py", "file_size_in_byte": 4992, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "95", "api": [{"api_name": "sys.argv.pop", "line_number": 12, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 12, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 13, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 14, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 16, "usage_type": "attribute"}, {"api_name": "sys.argv.pop", "line_number": 17, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 17, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 18, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 19, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 19, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 20, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 21, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 27, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 27, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 27, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 28, "usage_type": "call"}, {"api_name": "stitch.mesospim.mesospim.read_tiles", "line_number": 33, "usage_type": "call"}, {"api_name": "stitch.mesospim.mesospim", "line_number": 33, "usage_type": "attribute"}, {"api_name": "stitch.mesospim", "line_number": 33, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 33, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.dtype", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.memmap", "line_number": 41, "usage_type": "call"}, {"api_name": "stitch.mesospim.Rigid.place", "line_number": 55, "usage_type": "call"}, {"api_name": "stitch.mesospim.Rigid", "line_number": 55, "usage_type": "attribute"}, {"api_name": "stitch.mesospim", "line_number": 55, "usage_type": "name"}, {"api_name": "copy.copy", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot.subplots", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot", "line_number": 60, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "numpy.quantile", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.quantile", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot.imshow", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot", "line_number": 90, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 168, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot.show", "line_number": 181, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot", "line_number": 181, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 181, "usage_type": "name"}]} +{"seq_id": "39587158846", "text": "import pandas as pd\nimport torch\nfrom torch.utils.data import Dataset\n\n\ndf = pd.read_csv(\"Data/Clean_Flipkart_Product.csv\")\ndf.dropna(inplace=True)\n\nclass Vocabulary:\n \n '''\n __init__ method is called by default as soon as an object of this class is initiated\n we use this method to initiate our vocab dictionaries\n '''\n def __init__(self, freq_threshold, max_size):\n '''\n freq_threshold : the minimum times a word must occur in corpus to be treated in vocab\n max_size : max source vocab size. Eg. if set to 10,000, we pick the top 10,000 most frequent words and discard others\n '''\n #initiate the index to token dict\n ## -> padding, used for padding the shorter sentences in a batch to match the length of longest sentence in the batch\n ## -> start token, added in front of each sentence to signify the start of sentence\n ## -> End of sentence token, added to the end of each sentence to signify the end of sentence\n ## -> words which are not found in the vocab are replace by this token\n self.itos = {0: '', 1:'', 2:'', 3: ''}\n #initiate the token to index dict\n self.stoi = {k:j for j,k in self.itos.items()} \n \n self.freq_threshold = freq_threshold\n self.max_size = max_size\n \n '''\n __len__ is used by dataloader later to create batches\n '''\n def __len__(self):\n return len(self.itos)\n \n '''\n a simple tokenizer to split on space and converts the sentence to list of words\n '''\n @staticmethod\n def tokenizer(text):\n return [tok.lower().strip() for tok in text.split(' ')]\n \n '''\n build the vocab: create a dictionary mapping of index to string (itos) and string to index (stoi)\n output ex. for stoi -> {'the':5, 'a':6, 'an':7}\n '''\n def build_vocabulary(self, sentence_list):\n #calculate the frequencies of each word first to remove the words with freq < freq_threshold\n frequencies = {} #init the freq dict\n idx = 4 #index from which we want our dict to start. We already used 4 indexes for pad, start, end, unk\n \n #calculate freq of words\n for sentence in sentence_list:\n for word in self.tokenizer(sentence):\n if word not in frequencies.keys():\n frequencies[word]=1\n else:\n frequencies[word]+=1\n \n \n #limit vocab by removing low freq words\n frequencies = {k:v for k,v in frequencies.items() if v>self.freq_threshold} \n \n #limit vocab to the max_size specified\n frequencies = dict(sorted(frequencies.items(), key = lambda x: -x[1])[:self.max_size-idx]) # idx =4 for pad, start, end , unk\n \n #create vocab\n for word in frequencies.keys():\n self.stoi[word] = idx\n self.itos[idx] = word\n idx+=1\n \n \n '''\n convert the list of words to a list of corresponding indexes\n ''' \n def numericalize(self, text):\n #tokenize text\n tokenized_text = self.tokenizer(text)\n numericalized_text = []\n for token in tokenized_text:\n if token in self.stoi.keys():\n numericalized_text.append(self.stoi[token])\n else: #out-of-vocab words are represented by UNK token index\n numericalized_text.append(self.stoi[''])\n \n return numericalized_text\n\n\nclass CustomDataset(Dataset):\n '''\n Initiating Variables\n df: the training dataframe\n source_column : the name of source text column in the dataframe\n transform : If we want to add any augmentation\n freq_threshold : the minimum times a word must occur in corpus to be treated in vocab\n source_vocab_max_size : max source vocab size\n '''\n \n def __init__(self, df, source_column,freq_threshold = 3,\n source_vocab_max_size = 10000 , transform=None):\n \n self.df = df\n self.transform = transform\n \n #get source and target texts\n self.source_texts = self.df[source_column]\n \n \n ##VOCAB class has been created above\n #Initialize source vocab object and build vocabulary\n self.source_vocab = Vocabulary(freq_threshold, source_vocab_max_size)\n self.source_vocab.build_vocabulary(self.source_texts.tolist())\n\n \n def __len__(self):\n return len(self.df)\n \n '''\n __getitem__ runs on 1 example at a time. Here, we get an example at index and return its numericalize source and\n target values using the vocabulary objects we created in __init__\n '''\n def __getitem__(self, index):\n source_text = self.source_texts[index]\n \n if self.transform is not None:\n source_text = self.transform(source_text)\n \n #numericalize texts ['','cat', 'in', 'a', 'bag',''] -> [1,12,2,9,24,2]\n numerialized_source = [self.source_vocab.stoi[\"\"]]\n numerialized_source += self.source_vocab.numericalize(source_text)\n numerialized_source.append(self.source_vocab.stoi[\"\"])\n \n #convert the list to tensor and return\n return torch.tensor(numerialized_source), torch.tensor(self.df.y[index])\n\n\ndataset = CustomDataset(df, \"clean_review\")\n\n\n# Learn the vocabulary for the source language\ndata_words = df['clean_review2'].values.tolist()\n\ndel df\nfrom gensim.models import FastText\nfasttext_model = FastText(data_words, vector_size= 100, window=5, min_count=5, workers=4,sg=1)", "repo_name": "Ankit-Gupta-11/Aspect-Based-Sentiment-Analysis", "sub_path": "CustomDataset.py", "file_name": "CustomDataset.py", "file_ext": "py", "file_size_in_byte": 5649, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "95", "api": [{"api_name": "pandas.read_csv", "line_number": 6, "usage_type": "call"}, {"api_name": "torch.utils.data.Dataset", "line_number": 92, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 137, "usage_type": "call"}, {"api_name": "gensim.models.FastText", "line_number": 148, "usage_type": "call"}]} +{"seq_id": "23299741461", "text": "import configparser\nimport os\nimport smtplib\nimport sys\nimport time\nfrom datetime import datetime\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\nfrom email.utils import formataddr\n\nimport requests\nfrom bs4 import BeautifulSoup\n\n\nclass Log:\n # 오늘 일자로 파일 이름을 생성 한다.!!\n def __init__(self):\n self.file_name = f'./log{datetime.today().strftime(\"%Y-%m-%d\")}.txt'\n\n # 현재시간을 기준으로 로그를 파일에 작성 및 \n def add_log(self, comment: str):\n if os.path.isfile(self.file_name) is True:\n with open(self.file_name, 'a', encoding='utf-8') as f:\n log_str = f'{datetime.today().strftime(\"%Y/%m/%d %H:%M:%S : \")}{comment}'\n f.write(log_str + '\\n')\n print(log_str)\n return True\n return False\n\n # 새로운 파일 생성\n def new_log_file(self):\n if os.path.isfile(self.file_name) is False:\n with open(self.file_name, 'w', encoding='utf-8') as f:\n pass\n return True\n return False\n\n\nclass Properties:\n\n def __init__(self):\n self.file_name = f'./config.ini'\n\n def new_config_file(self):\n if os.path.isfile(self.file_name) is False:\n with open(self.file_name, 'w', encoding='utf-8') as f:\n f.write('')\n\n return True\n return False\n\n def set(self):\n config = configparser.ConfigParser() ## 클래스 객체 생성\n\n config[\"DEFAULT\"] = {\"google_gmail_id\": \"myid@gmail.com\", \"google_app_pw\": \"xxxxyyyyzzzzqqqq\"}\n config[\"MAIL_TEXT\"] = {\"title\": \"안녕하세요 OO 입니다.\", \"header\": \"안녕하세요 OO 입니다.\", \"footer\": \"문의사항 있으면 연락주세요\"}\n\n with open(self.file_name, \"w\", encoding='utf-8') as f:\n config.write(f)\n\n\nclass Boho:\n def send_mail(id: str, pw: str, article: str, new_num: int, to_ad: str, title: str, header: str, footer: str):\n from_addr = formataddr(('SOCH', id))\n\n # 받는사람\n to_addr = formataddr(('담당자', to_ad))\n\n session = None\n try:\n # SMTP 세션 생성\n session = smtplib.SMTP('smtp.gmail.com', 587)\n # session.set_debuglevel(True)\n\n # SMTP 계정 인증 설정\n session.ehlo()\n session.starttls()\n session.login(id, pw)\n\n # 메일 콘텐츠 설정\n message = MIMEMultipart(\"mixed\")\n\n # 메일 송/수신 옵션 설정\n message.set_charset('utf-8')\n message['From'] = from_addr\n message['To'] = to_addr\n message['Subject'] = f\"{title} (\" + str(new_num) + \"건)\"\n # 메일 콘텐츠 - 내용\n body = f\"

{header}


\" + article + \"

\" + footer + \"
\"\n bodyPart = MIMEText(body, 'html', 'utf-8')\n message.attach(bodyPart)\n\n # 메일 발송\n session.sendmail(from_addr, to_addr, message.as_string())\n\n except Exception as e:\n return 9\n\n finally:\n if session is not None:\n session.quit()\n\n def get_text_list(file_name: str):\n if os.path.isfile(file_name) is False:\n nf = open(file_name, 'w', encoding='utf-8')\n nf.close()\n\n f = open(file_name, 'r', encoding='utf-8')\n search = '\\n'\n return_list = [word.strip(search) for word in f.readlines()]\n return return_list if len(return_list) > 0 else None\n\n def file_set_article(file_name: str, articles: list):\n f = open(file_name, 'w', encoding='utf-8')\n for i in articles:\n f.writelines(i + '\\n')\n f.close()\n\n def get_data(url: str):\n response = requests.get(url)\n articles_list = []\n line = ''\n if response.status_code == 200:\n html = response.text.strip()\n soup = BeautifulSoup(html, 'html.parser')\n\n articles = soup.select('table > tbody > tr > td')\n for article in enumerate(articles, start=1):\n\n if int(article[0]) % 5 != 3:\n line += article[1].text.strip() + ' '\n\n if int(article[0]) % 5 == 2:\n line += 'URL : https://www.boho.or.kr' + article[1].find(\"a\")[\"href\"] + ' '\n\n if int(article[0]) % 5 == 0:\n articles_list.append(line)\n line = ''\n continue\n\n return articles_list\n\n def what_is_new_article(article_list: list, new_article_list: list):\n if article_list is None:\n return list(set(new_article_list))\n return sorted(list(set(new_article_list) - set(article_list)))\n\n def article_to_html(newest_article: list):\n text = ''\n for i in newest_article:\n text += i + '
'\n return text\n\n\nlog = Log()\nproperties = Properties()\n\nprint('''\n.______ _______. _______. .______ ______ __ __ ______ \n| _ \\ / | / | | _ \\ / __ \\ | | | | / __ \\ \n| |_) | | (----` | (----` ______| |_) | | | | | | |__| | | | | | \n| / \\ \\ \\ \\ |______| _ < | | | | | __ | | | | | \n| |\\ \\----.----) | .----) | | |_) | | `--' | | | | | | `--' | \n| _| `._____|_______/ |_______/ |______/ \\______/ |__| |__| \\______/ v1.3\n\n''')\n\n\nif log.new_log_file():\n pass\n\nmail_list = Boho.get_text_list(file_name='./mail_list.txt')\nBoho.get_text_list(file_name='./article_lists.txt')\n\nlog.add_log(comment='소스코드 https://github.com/TwoIceFIsh/RSS-Boho')\nlog.add_log(comment='설명 https://twoicefish-secu.tistory.com/428')\n\nif properties.new_config_file() is True:\n log.add_log(comment='[-] 새로운 설정 파일이 생성 되었습니다!')\n log.add_log(comment='[-] 설정 후 실행해 주세요.')\n log.add_log(comment=f'[-] =======================================')\n log.add_log(comment=f'[-] {os.path.join(os.path.dirname(__file__),\"config.ini\")}')\n log.add_log(comment=f'[-] {os.path.join(os.path.dirname(__file__),\"mail_list.txt\")}')\n log.add_log(comment=f'[-] =======================================')\n properties.set()\n os.system('pause')\n sys.exit()\n\npropertiesq = configparser.ConfigParser() ## 클래스 객체 생성\npropertiesq.read('./config.ini', encoding='utf-8')\ndefault = propertiesq['DEFAULT']\nmail_text = propertiesq['MAIL_TEXT']\n\nif 'myid@gmail.com' == default['google_gmail_id'] or 'xxxxyyyyzzzzqqqq' == default['google_app_pw']:\n log.add_log(comment=f'[!] 자신만의 설정값으로 변경해 주세요')\n log.add_log(comment=f'[-] {os.path.join(os.path.dirname(__file__),\"config.ini\")}')\n os.system('pause')\n sys.exit()\n\nif mail_list is None:\n log.add_log(comment=f'[!] 이메일 리스트가 비어 있습니다. 추가해주세요')\n log.add_log(comment=f'[-] {os.path.join(os.path.dirname(__file__),\"mail_list.txt\")}')\n print(f'''\n 작성예시({os.path.join(os.path.dirname(__file__),\"mail_list.txt\")})\n \n asdfadsf@gmail.com\n sdijovjid@test.com\n sdjico@sdco.net\n \n ...\n \n ''')\n os.system('pause')\n sys.exit()\nelse:\n for i in mail_list:\n if '@' not in i or '.' not in i:\n log.add_log(comment=f'[!] {i} 올바른 이메일 형식이 아닙니다. 확인해 주세요')\n print(f'''\n 작성예시({os.path.join(os.path.dirname(__file__),\"mail_list.txt\")})\n \n asdfadsf@gmail.com\n sdijovjid@test.com\n sdjico@sdco.net\n \n ...\n \n ''')\n os.system('pause')\n sys.exit()\n\nwhile True:\n log.add_log(comment=f'[-] ======RSS-Boho Start======')\n\n # 신규 게시물 확인\n article_list = Boho.get_text_list(file_name='./article_lists.txt')\n new_article_list = Boho.get_data(url='https://www.boho.or.kr/kr/bbs/list.do?menuNo=205020&bbsId=B0000133')\n newest_article = Boho.what_is_new_article(article_list=article_list, new_article_list=new_article_list)\n\n # 이메일 목록을 획득 및 메일 발송\n if len(newest_article) > 0:\n log.add_log(comment=f'=====================================')\n log.add_log(comment=f'[-] {len(newest_article)}건의 신규 게시물이 발견 되었습니다 ')\n article_text = Boho.article_to_html(newest_article=newest_article)\n for i in enumerate(newest_article, start=1):\n log.add_log(comment=f'[{i[0]}] {i[1].split(\":\")[0].replace(\" URL \", \"\")}(New)')\n log.add_log(comment=f'=====================================')\n\n # 메일리스트 확인\n mail_list = Boho.get_text_list(file_name='./mail_list.txt')\n if mail_list is None:\n log.add_log(comment=f'[!] 이메일 리스트가 비어 있습니다. 추가해주세요')\n log.add_log(comment=f'[!] 15분후에 메일 발송을 시도 합니다.')\n log.add_log(comment=f'[!] (신규 게시글 정보 업데이트 스킵)')\n else:\n\n pid = default['google_gmail_id']\n ppw = default['google_app_pw']\n header = mail_text['header']\n footer = mail_text['footer']\n title = mail_text['title']\n for to in mail_list:\n message = Boho.send_mail(id=pid, pw=ppw, article=article_text, new_num=len(newest_article), to_ad=to,\n title=title, header=header, footer=footer)\n if message == 9:\n log.add_log(comment=f'[!] Google ID 및 Google API PW를 일치하지 않거나 존재하지 않습니다 확인해 주세요')\n log.add_log(comment=f'[-] {os.path.join(os.path.dirname(__file__),\"config.ini\")}')\n os.system('pause')\n sys.exit()\n\n else:\n log.add_log(comment=f'[-] {mail_list}에게 메일을 발송했습니다.')\n Boho.file_set_article(file_name='./article_lists.txt', articles=new_article_list)\n log.add_log(comment=f'[-] 신규 게시글 정보 업데이트를 완료했습니다.')\n log.add_log(comment=f'{os.path.join(os.path.dirname(__file__),\"article_lists.txt\")}')\n log.add_log(comment=f'[-] 루틴 종료 15분후에 재탐색을 실시 합니다.')\n else:\n log.add_log(comment=f'[-] 새롭게 발견된 기사가 없습니다.')\n log.add_log(comment=f'[-] 루틴 종료 15분후에 재탐색을 실시 합니다.')\n\n time.sleep(900)\n", "repo_name": "TwoIceFIsh/RSS-Boho", "sub_path": "RSS-Boho.py", "file_name": "RSS-Boho.py", "file_ext": "py", "file_size_in_byte": 10819, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "95", "api": [{"api_name": "datetime.datetime.today", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 18, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "datetime.datetime.today", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 24, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "configparser.ConfigParser", "line_number": 53, "usage_type": "call"}, {"api_name": "email.utils.formataddr", "line_number": 64, "usage_type": "call"}, {"api_name": "email.utils.formataddr", "line_number": 67, "usage_type": "call"}, {"api_name": "smtplib.SMTP", "line_number": 72, "usage_type": "call"}, {"api_name": "email.mime.multipart.MIMEMultipart", "line_number": 81, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path", "line_number": 104, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 120, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 125, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 182, "usage_type": "call"}, {"api_name": "os.path", "line_number": 182, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 182, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 183, "usage_type": "call"}, {"api_name": "os.path", "line_number": 183, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 183, "usage_type": "call"}, {"api_name": "os.system", "line_number": 186, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 187, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 189, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 196, "usage_type": "call"}, {"api_name": "os.path", "line_number": 196, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 196, "usage_type": "call"}, {"api_name": "os.system", "line_number": 197, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 198, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 202, "usage_type": "call"}, {"api_name": "os.path", "line_number": 202, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 202, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 204, "usage_type": "call"}, {"api_name": "os.path", "line_number": 204, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 204, "usage_type": "call"}, {"api_name": "os.system", "line_number": 213, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 214, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 220, "usage_type": "call"}, {"api_name": "os.path", "line_number": 220, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 220, "usage_type": "call"}, {"api_name": "os.system", "line_number": 229, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 230, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 267, "usage_type": "call"}, {"api_name": "os.path", "line_number": 267, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 267, "usage_type": "call"}, {"api_name": "os.system", "line_number": 268, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 269, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 275, "usage_type": "call"}, {"api_name": "os.path", "line_number": 275, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 275, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 281, "usage_type": "call"}]} +{"seq_id": "29657852404", "text": "import cv2\r\n\r\nvc = cv2.VideoCapture('/media/root/tmp/2018-09-20_11_50_54.mp4')\r\nc=1\r\na=0\r\nif vc.isOpened():\r\n rval,frame=vc.read()\r\nelse:\r\n rval = False\r\n\r\ntimeF = 3\r\n\r\n# munster _000032_000019_leftImg8bit.png\r\n# lindau _000000_000019_leftImg8bit.png\r\nwhile rval:\r\n rval,frame = vc.read()\r\n if(c%timeF == 0):\r\n strzyx = 'outpic/'+'munster_'+ '%06d' % a +'_000019_leftImg8bit.png'\r\n frame = cv2.resize(frame,(960,480))\r\n cv2.imwrite(str(strzyx),frame)\r\n a = a + 1\r\n c = c+1\r\n # print('1536822933796196.mp4.pic/'+'3_'+str(a) +'.png')\r\n print(a)\r\n if 174==a:\r\n exit(0)\r\n pass\r\n cv2.waitKey(1)\r\nvc.release()\r\n", "repo_name": "zyxcambridge/data_utils_python", "sub_path": "jiequ.py", "file_name": "jiequ.py", "file_ext": "py", "file_size_in_byte": 678, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "91", "api": [{"api_name": "cv2.VideoCapture", "line_number": 3, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "30636388362", "text": "import logging\nfrom django.contrib.gis.geos import Point\nfrom django.test import TestCase\nfrom django.contrib.gis.db.models import functions\nfrom django.contrib.gis.measure import Distance\n\nfrom . import models\n\nlogging.basicConfig(format='%(asctime)s %(levelname)-7s %(thread)-5d %(filename)s:%(lineno)s | %(funcName)s | %(message)s', datefmt='%H:%M:%S')\nlogging.getLogger().setLevel(logging.DEBUG)\nlogging.disable(logging.NOTSET)\nlogging.getLogger('environ').setLevel(logging.INFO)\n\n\nclass GeoTests(TestCase):\n def test_distance(self):\n location_manager = models.Location.objects\n location_manager.create(point=Point(x=float(-119), y=float(35), srid=models.DEFAULT_SRID))\n item = location_manager.create(point=Point(x=float(-118), y=float(34), srid=models.DEFAULT_SRID))\n queryset = location_manager.all()\n\n point = Point(x=float(-119), y=float(34), srid=models.DEFAULT_SRID)\n distance = Distance(km=100)\n\n queryset = queryset.filter(point__distance_lte=(point, distance))\n queryset = queryset.annotate(distance=functions.Distance('point', point))\n\n logging.debug(\"queryset[0].distance: %s\", queryset[0].distance)\n self.assertEqual(Distance(m=92184.53310623), queryset[0].distance)\n self.assertEqual([item], list(queryset))\n\n", "repo_name": "wooyek/docker-geodjango", "sub_path": "sample/awesome-project/geoapp/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 1307, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "95", "api": [{"api_name": "logging.basicConfig", "line_number": 9, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 10, "usage_type": "attribute"}, {"api_name": "logging.disable", "line_number": 11, "usage_type": "call"}, {"api_name": "logging.NOTSET", "line_number": 11, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 12, "usage_type": "attribute"}, {"api_name": "django.test.TestCase", "line_number": 15, "usage_type": "name"}, {"api_name": "django.contrib.gis.geos.Point", "line_number": 18, "usage_type": "call"}, {"api_name": "django.contrib.gis.geos.Point", "line_number": 19, "usage_type": "call"}, {"api_name": "django.contrib.gis.geos.Point", "line_number": 22, "usage_type": "call"}, {"api_name": "django.contrib.gis.measure.Distance", "line_number": 23, "usage_type": "call"}, {"api_name": "django.contrib.gis.db.models.functions.Distance", "line_number": 26, "usage_type": "call"}, {"api_name": "django.contrib.gis.db.models.functions", "line_number": 26, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 28, "usage_type": "call"}, {"api_name": "django.contrib.gis.measure.Distance", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "19913362334", "text": "# Modified from https://github.com/meetshah1995/pytorch-semseg/blob/801fb20054/ptsemseg/models/segnet.py\nimport torch.nn as nn\n\nclass ConvBNRelu(nn.Sequential):\n def __init__(self, in_channels, out_channels, kernel_size, stride, padding, bias=True, dilation=1, batch_norm=True, activation=True):\n conv_mod = nn.Conv2d(int(in_channels), int(out_channels), kernel_size=kernel_size, padding=padding, stride=stride, bias=bias, dilation=dilation)\n if batch_norm:\n cbr_unit = [conv_mod, nn.BatchNorm2d(int(out_channels)) ]\n else:\n cbr_unit = [ conv_mod ]\n\n if activation==True:\n cbr_unit.append( nn.ReLU(inplace=True) )\n super(ConvBNRelu, self).__init__(*cbr_unit)\n\nclass SegnetDown(nn.Module):\n def __init__(self, in_channels, out_channels, num_convs=2, batch_norm=True):\n super(SegnetDown, self).__init__()\n layers = [ConvBNRelu(in_channels, out_channels, kernel_size=3, stride=1, padding=1, batch_norm=batch_norm)]\n for _ in range(num_convs-1):\n layers.append( ConvBNRelu(out_channels, out_channels, kernel_size=3, stride=1, padding=1, batch_norm=batch_norm) )\n\n self.layers = nn.Sequential( *layers ) \n self.maxpool_with_argmax = nn.MaxPool2d(2, 2, return_indices=True)\n\n def forward(self, inputs):\n outputs = self.layers(inputs)\n ori_shape = outputs.size()\n outputs, indices = self.maxpool_with_argmax(outputs)\n return outputs, indices, ori_shape\n\nclass SegnetUp(nn.Module):\n def __init__(self, in_channels, out_channels, num_convs=2, outer_most=False, batch_norm=True):\n super(SegnetUp, self).__init__()\n if outer_most:\n batch_norm = False\n activation = False\n else:\n activation = True\n\n layers = []\n for _ in range(num_convs-1):\n layers.append( ConvBNRelu(in_channels, in_channels, kernel_size=3, stride=1, padding=1, batch_norm=batch_norm) )\n # remove relu if it is the outer most layer\n layers.append( ConvBNRelu(in_channels, out_channels, kernel_size=3, stride=1, padding=1, batch_norm=batch_norm, activation=activation) )\n self.unpool = nn.MaxUnpool2d(2, 2)\n self.layers = nn.Sequential( *layers ) \n \n def forward(self, inputs, indices, ori_shape):\n outputs = self.unpool(input=inputs, indices=indices, output_size=ori_shape)\n outputs = self.layers(outputs)\n return outputs\n\n", "repo_name": "zju-vipa/KamalEngine", "sub_path": "kamal/vision/models/segmentation/segnet/layer.py", "file_name": "layer.py", "file_ext": "py", "file_size_in_byte": 2472, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 91, "dataset": "github-code", "pt": "95", "api": [{"api_name": "torch.nn.Sequential", "line_number": 4, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 4, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 6, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 6, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 8, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 8, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 13, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 16, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 32, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 32, "usage_type": "name"}, {"api_name": "torch.nn.MaxUnpool2d", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 46, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 47, "usage_type": "name"}]} +{"seq_id": "40419366914", "text": "import pandas as pd\nimport argparse\nimport sys\nimport os\n\nDEFAULT_SIZE = 120_000\n\nCATEGORIES = {\n 'autos': 'Autos & Vehicles',\n 'comedy': 'Comedy',\n 'education': 'Education',\n 'entertainment': 'Entertainment',\n 'film': 'Film & Animation',\n 'gaming': 'Gaming',\n 'howto': 'Howto & Style',\n 'music': 'Music',\n 'news': 'News & Politics',\n 'nonprofits': 'Nonprofits & Activism',\n 'people': 'People & Blogs',\n 'pets': 'Pets & Animals',\n 'science': 'Science & Technology',\n 'sports': 'Sports',\n 'travel': 'Travel & Events'\n }\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(\n description='Sample videos for the corpus based on retrieved data.',\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n )\n parser.add_argument(\n 'lang', type=str, help='Language code (ja, en, ...)'\n )\n parser.add_argument(\n 'subdata', type=str,\n help='Filename of the retrieved subtitle data, e.g. sub/en/en'\n )\n parser.add_argument(\n '--outdir', type=str, default='sub', help='Dirname to save results'\n )\n parser.add_argument(\n '--outname', type=str, default=None,\n help='Output filename (default: LANG_sample.csv)'\n )\n parser.add_argument(\n '--size', '-n', type=int, default=DEFAULT_SIZE,\n help=f'Sample size (default: {DEFAULT_SIZE})'\n )\n parser.add_argument(\n '--dry-run', action='store_true',\n help=f'Do not output the sample, only count available data'\n )\n\n sub = parser.add_argument_group('Subtitle conditions (matches any)')\n sub.add_argument(\n '--auto', action=argparse.BooleanOptionalAction, default=False,\n help='Auto subtitles in target language (default: false)'\n )\n sub.add_argument(\n '--manual', action=argparse.BooleanOptionalAction, default=True,\n help='Manual subtitles in target language (default: true)'\n )\n\n lang = parser.add_argument_group('Language conditions (matches any)')\n lang.add_argument(\n '--video-lang', action=argparse.BooleanOptionalAction, default=True,\n help='Video in the target language (default: true)'\n )\n lang.add_argument(\n '--any-lang', action=argparse.BooleanOptionalAction, default=False,\n help='No conditions on language (default: false)'\n )\n lang.add_argument(\n '--sub-lang', action=argparse.BooleanOptionalAction, default=False,\n help='Manual subtitles only in target language (default: false)'\n )\n lang.add_argument(\n '--sub-lang-video-lang-na', action=argparse.BooleanOptionalAction,\n default=False,\n help=('Manual subtitles only in target language and video language is N/A '\n '(default: false)')\n )\n\n parser.add_argument(\n '--categories', '-c', nargs='+', choices=CATEGORIES, default=None,\n help='Limit to certain categories (default: all)'\n )\n\n return parser.parse_args()\n\n\ndef main(args):\n df = pd.read_csv(\n args.subdata,\n dtype={\n 'videoid': str, 'auto': bool, 'sub': bool, 'nsub': int, 'categories': str,\n 'duration': int, 'view_count': int, 'upload_date': int, 'channel_id': str,\n 'uploader_id': str, 'language': str\n }\n )\n\n assert len(args.lang) == 2, args.lang\n\n cond = False\n assert args.auto or args.manual\n if args.manual:\n cond |= df['sub']\n if args.auto:\n cond |= df['auto']\n\n assert (\n args.any_lang or args.video_lang or args.sub_lang or args.sub_lang_video_lang_na\n )\n if not args.any_lang:\n lang_cond = False\n\n if args.video_lang:\n lang_cond |= df['language'].str.startswith(args.lang)\n\n if args.sub_lang:\n lang_cond |= df['sub'] & (df['nsub'] == 1)\n elif args.sub_lang_video_lang_na:\n lang_cond |= df['sub'] & (df['nsub'] == 1) & df['language'].isna()\n\n cond &= lang_cond\n\n if args.categories is not None:\n categories = set(map(CATEGORIES.get, args.categories))\n cat_cond = df['categories'].apply(lambda c: c in categories)\n cond &= cat_cond\n\n valid = df.loc[cond]\n\n sys.stderr.write(\n f'All data: {len(df)}\\n'\n f'Valid data: {len(valid)}\\n'\n )\n if len(valid) < args.size:\n sys.stderr.write(\n f'Warning: Data is smaller than the requested sample '\n f'size {len(valid)} < {args.size}.\\n'\n )\n\n if not args.dry_run:\n filename = os.path.join(\n args.outdir, args.lang,\n args.outname or f'{args.lang}_sample.csv'\n )\n\n # Data is ordered randomly (by video id):\n valid[:args.size].to_csv(filename, index=None)\n\n print(f\"Saved {args.lang.upper()} sample to {filename}.\")\n\n\nif __name__ == '__main__':\n args = parse_args()\n main(args)\n", "repo_name": "adno/jtubespeech-subtitles", "sub_path": "scripts/sample.py", "file_name": "sample.py", "file_ext": "py", "file_size_in_byte": 4926, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "95", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 28, "usage_type": "call"}, {"api_name": "argparse.ArgumentDefaultsHelpFormatter", "line_number": 30, "usage_type": "attribute"}, {"api_name": "argparse.BooleanOptionalAction", "line_number": 57, "usage_type": "attribute"}, {"api_name": "argparse.BooleanOptionalAction", "line_number": 61, "usage_type": "attribute"}, {"api_name": "argparse.BooleanOptionalAction", "line_number": 67, "usage_type": "attribute"}, {"api_name": "argparse.BooleanOptionalAction", "line_number": 71, "usage_type": "attribute"}, {"api_name": "argparse.BooleanOptionalAction", "line_number": 75, "usage_type": "attribute"}, {"api_name": "argparse.BooleanOptionalAction", "line_number": 79, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 94, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 135, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 135, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 140, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 140, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 146, "usage_type": "call"}, {"api_name": "os.path", "line_number": 146, "usage_type": "attribute"}]} +{"seq_id": "26458487493", "text": "from flask import Flask, request, render_template\n\napp = Flask(__name__)\n\n# Initialize the variables with some default values\nprogress = \"0%\"\nrow = \"n/a\"\nlink = \"n/a\"\nfunction = \"n/a\"\nstart_time = \"n/a\"\n\n@app.route('/update_progress', methods=['POST'])\ndef update_progress():\n global progress\n new_value = request.form['new_value']\n progress = new_value\n return \"progress updated successfully\"\n\n@app.route('/update_row', methods=['POST'])\ndef update_row():\n global row\n new_value = request.form['new_value']\n row = new_value\n return \"row updated successfully\"\n\n@app.route('/update_link', methods=['POST'])\ndef update_link():\n global link\n new_value = request.form['new_value']\n link = new_value\n return \"link updated successfully\"\n\n@app.route('/update_function', methods=['POST'])\ndef update_function():\n global function\n new_value = request.form['new_value']\n function = new_value\n return \"function updated successfully\"\n\n@app.route('/update_starttime', methods=['POST'])\ndef update_starttime():\n global start_time\n new_value = request.form['new_value']\n start_time = new_value\n return \"start time updated successfully\"\n\n@app.route('/display_variables')\ndef display_variables():\n return f\"Progress: {progress}\\nRow: {row}\\nLink: {link}\\nFunction: {function}\\nStart Time: {start_time}\"\n\n@app.route('/')\ndef index():\n rendered = render_template('index.html', progress=progress, row=row, link=link, function=function, start_time=start_time)\n return rendered\n\nif __name__ == \"__main__\":\n app.run(debug=True)", "repo_name": "maxtalwar/Dev-Report", "sub_path": "dev_report/github_urls_present/flask_app.py", "file_name": "flask_app.py", "file_ext": "py", "file_size_in_byte": 1576, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "95", "api": [{"api_name": "flask.Flask", "line_number": 3, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 15, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 15, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 22, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 22, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 29, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 29, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 36, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 36, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 43, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 43, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "39173738083", "text": "import errno\nimport logging\nimport os.path\nimport sys\n\nfrom hadoop import confparse\nfrom desktop.lib.security_util import get_components\n\nif sys.version_info[0] > 2:\n open_file = open\nelse:\n open_file = file\n\nLOG = logging.getLogger()\n\n\nSITE_PATH = None\nSITE_DICT = None\n\n_CNF_HBASE_THRIFT_KERBEROS_PRINCIPAL = 'hbase.thrift.kerberos.principal'\n_CNF_HBASE_THRIFT_SPNEGO_PRINCIPAL = 'hbase.thrift.spnego.principal'\n_CNF_HBASE_AUTHENTICATION = 'hbase.security.authentication'\n_CNF_HBASE_REGIONSERVER_THRIFT_FRAMED = 'hbase.regionserver.thrift.framed'\n\n_CNF_HBASE_IMPERSONATION_ENABLED = 'hbase.thrift.support.proxyuser'\n_CNF_HBASE_USE_THRIFT_HTTP = 'hbase.regionserver.thrift.http'\n_CNF_HBASE_USE_THRIFT_SSL = 'hbase.thrift.ssl.enabled'\n\n\n\ndef reset():\n global SITE_DICT\n SITE_DICT = None\n\n\ndef get_conf():\n if SITE_DICT is None:\n _parse_site()\n return SITE_DICT\n\n\ndef get_server_principal():\n thrift_principal = get_conf().get(_CNF_HBASE_THRIFT_KERBEROS_PRINCIPAL, None)\n principal = get_conf().get(_CNF_HBASE_THRIFT_SPNEGO_PRINCIPAL, thrift_principal)\n components = get_components(principal)\n if components is not None:\n return components[0]\n\n\ndef get_server_authentication():\n return get_conf().get(_CNF_HBASE_AUTHENTICATION, 'NOSASL').upper()\n\ndef get_thrift_transport():\n use_framed = get_conf().get(_CNF_HBASE_REGIONSERVER_THRIFT_FRAMED)\n if use_framed is not None:\n if use_framed.upper() == \"TRUE\":\n return \"framed\"\n else:\n return \"buffered\"\n else:\n #Avoid circular import\n from hbase.conf import THRIFT_TRANSPORT\n return THRIFT_TRANSPORT.get()\n\ndef is_impersonation_enabled():\n #Avoid circular import\n from hbase.conf import USE_DOAS\n return get_conf().get(_CNF_HBASE_IMPERSONATION_ENABLED, 'FALSE').upper() == 'TRUE' or USE_DOAS.get()\n\ndef is_using_thrift_http():\n #Avoid circular import\n from hbase.conf import USE_DOAS\n return get_conf().get(_CNF_HBASE_USE_THRIFT_HTTP, 'FALSE').upper() == 'TRUE' or USE_DOAS.get()\n\ndef is_using_thrift_ssl():\n return get_conf().get(_CNF_HBASE_USE_THRIFT_SSL, 'FALSE').upper() == 'TRUE'\n\n\ndef _parse_site():\n global SITE_DICT\n global SITE_PATH\n\n #Avoid circular import\n from hbase.conf import HBASE_CONF_DIR\n SITE_PATH = os.path.join(HBASE_CONF_DIR.get(), 'hbase-site.xml')\n try:\n data = open_file(SITE_PATH, 'r').read()\n except IOError as err:\n if err.errno != errno.ENOENT:\n LOG.error('Cannot read from \"%s\": %s' % (SITE_PATH, err))\n return\n data = \"\"\n\n SITE_DICT = confparse.ConfParse(data)\n\n", "repo_name": "cloudera/hue", "sub_path": "apps/hbase/src/hbase/hbase_site.py", "file_name": "hbase_site.py", "file_ext": "py", "file_size_in_byte": 2516, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 988, "dataset": "github-code", "pt": "91", "api": [{"api_name": "sys.version_info", "line_number": 9, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "desktop.lib.security_util.get_components", "line_number": 45, "usage_type": "call"}, {"api_name": "hbase.conf.THRIFT_TRANSPORT.get", "line_number": 63, "usage_type": "call"}, {"api_name": "hbase.conf.THRIFT_TRANSPORT", "line_number": 63, "usage_type": "name"}, {"api_name": "hbase.conf.USE_DOAS.get", "line_number": 68, "usage_type": "call"}, {"api_name": "hbase.conf.USE_DOAS", "line_number": 68, "usage_type": "name"}, {"api_name": "hbase.conf.USE_DOAS.get", "line_number": 73, "usage_type": "call"}, {"api_name": "hbase.conf.USE_DOAS", "line_number": 73, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 85, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 85, "usage_type": "name"}, {"api_name": "hbase.conf.HBASE_CONF_DIR.get", "line_number": 85, "usage_type": "call"}, {"api_name": "hbase.conf.HBASE_CONF_DIR", "line_number": 85, "usage_type": "name"}, {"api_name": "errno.ENOENT", "line_number": 89, "usage_type": "attribute"}, {"api_name": "hadoop.confparse.ConfParse", "line_number": 94, "usage_type": "call"}, {"api_name": "hadoop.confparse", "line_number": 94, "usage_type": "name"}]} +{"seq_id": "72026123511", "text": "import os\nimport time \nimport subprocess \nfrom BallCatch import BallCatch\nfrom agent import Agent\nfrom torch.utils.tensorboard import SummaryWriter \n\n\ndef agent_train():\n game_name = \"BallCatch\"\n run_time = time.strftime('%m_%d_%H_%M_%S', time.localtime(time.time()))\n log_dir = os.path.join(os.path.join(os.path.expanduser('~')), 'Desktop/tensorboard_Data')\n\n port = 6006 \n\n subprocess.Popen(f\"tensorboard --logdir={log_dir} --port={port} --reload_multifile=true\", shell=True)\n\n log_dir = log_dir + '/' + game_name + '_' + str(run_time)\n\n env = BallCatch()\n\n state_size = env.state_n\n action_size = env.action_n\n\n hidden_size = 256\n learning_rate = 0.001 \n memory_size = 10000 \n batch_size = 64\n gamma = 0.99 \n\n agent = Agent(state_size=state_size, action_size=action_size,\n hidden_size=hidden_size, learning_rate=learning_rate,\n memory_size=memory_size, batch_size=batch_size,\n gamma=gamma)\n \n\n # Set up TensorBoard output\n writer = SummaryWriter(log_dir=log_dir)\n\n\n num_episode = 1000\n\n for i_episode in range(num_episode):\n state, info = env.reset()\n done = False\n truncated = False \n total_length = 1 \n total_reward = 0 \n\n while not(done or truncated):\n action = agent.act(state)\n next_state, reward, done, truncated, info = env.step(action)\n\n total_reward += reward \n total_length +=1 \n\n agent.remember(state, action, reward, next_state, done)\n\n state = next_state\n agent.replay()\n\n \n if done:\n agent.decay_epsilon() \n\n\n writer.add_scalar(\"reward\", total_reward, i_episode) \n writer.add_scalar(\"length\", total_length, i_episode)\n writer.add_scalar(\"reward_rate\", total_reward/total_length, i_episode)\n writer.add_scalar(\"epsilion\", agent.epsilon, i_episode)\n\n print(\"Episode: {}, total_reward: {:.2f}, epsilon: {:.2f}, length: {}\".format(i_episode, total_reward, agent.epsilon, total_length))\n\n env.close()\n writer.close() \n\nif __name__ == \"__main__\":\n agent_train() \n", "repo_name": "k1seul/comp_RL", "sub_path": "agent_train.py", "file_name": "agent_train.py", "file_ext": "py", "file_size_in_byte": 2174, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "95", "api": [{"api_name": "time.strftime", "line_number": 11, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 11, "usage_type": "call"}, {"api_name": "time.time", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 12, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 16, "usage_type": "call"}, {"api_name": "BallCatch.BallCatch", "line_number": 20, "usage_type": "call"}, {"api_name": "agent.Agent", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.utils.tensorboard.SummaryWriter", "line_number": 38, "usage_type": "call"}, {"api_name": "agent.act", "line_number": 51, "usage_type": "call"}, {"api_name": "agent.remember", "line_number": 57, "usage_type": "call"}, {"api_name": "agent.replay", "line_number": 60, "usage_type": "call"}, {"api_name": "agent.decay_epsilon", "line_number": 64, "usage_type": "call"}, {"api_name": "agent.epsilon", "line_number": 70, "usage_type": "attribute"}, {"api_name": "agent.epsilon", "line_number": 72, "usage_type": "attribute"}]} +{"seq_id": "74189361913", "text": "\"\"\"\nTools are singular physical objects or process grouped physical\nobjects that are used to perform a specific task\n\nthey have multiple taxonomies\nProcess, what sorta processes this tool can be used for\nAutomation, what level of automation this tool contains for accomplishing the process?\nApproach,\nViewpoint\n\"\"\"\nfrom catalog import Catalog\nfrom colorful.fields import RGBColorField\nfrom django.conf import settings\nfrom django.db import models\nfrom django.urls import reverse\nfrom django.utils.translation import ugettext_lazy as _\nfrom django_extensions.db.models import TimeStampedModel, TitleDescriptionModel\nfrom markdownx.models import MarkdownxField\nfrom tagulous.models import TagField, TagTreeModel\n\nfrom utils.models import StateMachineMixin\n\nfrom tools.exceptions import ToolAvailabilityException, ToolClearanceException\nfrom tools.querysets import ToolHistoryQuerySet, UserToolQuerySet\n\n\nclass ToolTaxonomy(TagTreeModel):\n \"\"\"A generic way of describing a tool, the top level is the base taxonomy\"\"\"\n\n # order = models.IntegerField(blank=False, default=0)\n\n # Published state\n # Allows users to submit new taxonomies that are evaluated and approved\n class State(Catalog):\n _attrs = \"value\", \"label\"\n in_review = 0, _(\"in review\")\n approved = 1, _(\"approved\")\n rejected = 2, _(\"rejected\")\n\n state = models.PositiveSmallIntegerField(\n _(\"State\"), choices=State._zip(\"value\", \"label\"), default=State.in_review.value\n )\n\n color = RGBColorField(null=True, blank=True)\n\n class Meta:\n verbose_name = _(\"Tool Taxonomy\")\n verbose_name_plural = _(\"Tool Taxonomies\")\n\n class TagMeta:\n force_lowercase = False\n space_delimiter = False\n\n def get_color(self):\n if not self.color and self.parent:\n # try to get color from parent (this can cascade)\n return self.parent.get_color()\n else:\n return self.color\n\n def get_absolute_url(self):\n return reverse(\"tools:taxonomy_detail\", kwargs={\"path\": self.path})\n\n\nclass ToolStates(Catalog):\n _attrs = \"value\", \"label\", \"badge_type\"\n none = \"none\", _(\"None\"), None\n available = \"available\", _(\"Available\"), \"success\"\n in_use = \"in_use\", _(\"In Use\"), \"warning\"\n disabled = \"disabled\", _(\"Disabled\"), \"danger\"\n\n\nclass ToolTransitions(Catalog):\n _attrs = \"value\", \"label\", \"source\", \"dest\"\n create = 0, _(\"Create\"), ToolStates.none.value, ToolStates.available.value\n borrow = 1, _(\"Borrow\"), ToolStates.available.value, ToolStates.in_use.value\n return_ = 2, _(\"Return\"), ToolStates.in_use.value, ToolStates.available.value\n decommission = 3, _(\"Decommission\"), \"*\", ToolStates.disabled.value\n reinstate = 4, _(\"Reinstate\"), ToolStates.disabled.value, ToolStates.available.value\n\n\nclass UserTool(StateMachineMixin, TitleDescriptionModel, TimeStampedModel):\n \"\"\"A tool owned by a User\"\"\"\n\n States = ToolStates\n\n Transitions = ToolTransitions\n\n class Visibility(Catalog):\n _attrs = \"value\", \"label\", \"card_class\"\n private = 0, _(\"Private\"), \"tool-private border-danger\"\n cleared = 1, _(\"Cleared Users\"), \"tool-cleared-only border-success\"\n public = 2, _(\"Public\"), \"tool-public\"\n\n class Clearance(Catalog):\n _attrs = \"value\", \"label\"\n none = 0, _(\"Available to all\")\n owner = 1, _(\"Owner cleared users only\")\n cleared = 2, _(\"Cleared users can approve anyone\")\n\n user = models.ForeignKey(\n settings.AUTH_USER_MODEL, on_delete=models.PROTECT, related_name=\"tools\"\n )\n description = MarkdownxField(blank=True)\n state = models.CharField(\n max_length=10,\n choices=States._zip(\"value\", \"label\"),\n default=States.none.value,\n editable=False,\n )\n taxonomies = TagField(to=ToolTaxonomy, blank=True, related_name=\"tools\")\n visibility = models.PositiveSmallIntegerField(\n _(\"Visibility\"),\n choices=Visibility._zip(\"value\", \"label\"),\n default=Visibility.public.value,\n help_text=_(\"The level of user visibility for this tool\"),\n )\n clearance = models.PositiveSmallIntegerField(\n _(\"Clearance\"),\n choices=Clearance._zip(\"value\", \"label\"),\n default=Clearance.none.value,\n help_text=_(\"Who is allowed to clear a user to use this tool\"),\n )\n\n objects = UserToolQuerySet.as_manager()\n\n class Meta:\n ordering = (\"-created\",)\n get_latest_by = \"created\"\n verbose_name = _(\"Tool\")\n verbose_name_plural = _(\"Tools\")\n\n class StateMachine(StateMachineMixin.StateMachine):\n states = [{\"name\": state.value} for state in ToolStates]\n transitions = [\n {\"trigger\": trigger, \"source\": source, \"dest\": dest}\n for trigger, source, dest in ToolTransitions._zip(\"name\", \"source\", \"dest\")\n ]\n after_state_change = \"record_transition\"\n\n def __str__(self):\n return self.title\n\n def get_absolute_url(self):\n return reverse(\"tools:detail\", kwargs={\"pk\": self.pk})\n\n def record_transition(self, event):\n if not event.kwargs.get(\"skip_save\", False):\n self.save()\n self.history.create(\n user=event.kwargs.get(\"user\"), action=self.Transitions(event.event.name, \"name\").value\n )\n\n def check_clearance(self, user):\n return self.permissions.filter(cleared_user=user).exists()\n\n def user_can_grant_clearance(self, user):\n \"\"\"See if we're allowed to grant clearance\"\"\"\n level = self.Clearance(self.clearance)\n if level == self.Clearance.none:\n return True\n if level == self.Clearance.owner:\n return self.user == user\n if level == self.Clearance.cleared:\n return self.user == user or self.check_clearance(user)\n\n def user_can_borrow(self, user):\n return self._meta.model.objects.borrowable_to_user(user).filter(pk=self.pk).exists()\n\n def prepare_borrow(self, event):\n \"\"\"Do validation before allowing a user to borrow a tool\"\"\"\n user = event.kwargs.get(\"user\")\n if not self.user_can_borrow(user):\n raise ToolClearanceException(\"%s isn't allowed to borrow this tool\" % user)\n # Is this needed?\n if not self.is_available():\n raise ToolAvailabilityException()\n\n @property\n def cover_photo(self):\n try:\n return self.photos.latest()\n except ToolPhoto.DoesNotExist:\n return None\n\n\nclass ToolHistory(TimeStampedModel):\n tool = models.ForeignKey(UserTool, on_delete=models.CASCADE, related_name=\"history\")\n user = models.ForeignKey(\n settings.AUTH_USER_MODEL,\n on_delete=models.PROTECT,\n blank=True,\n null=True,\n related_name=\"tool_history\",\n )\n action = models.PositiveSmallIntegerField(choices=UserTool.Transitions._zip(\"value\", \"label\"))\n\n objects = ToolHistoryQuerySet.as_manager()\n\n class Meta:\n ordering = (\"-created\",)\n get_latest_by = \"created\"\n verbose_name = _(\"Tool History\")\n verbose_name_plural = _(\"Tool Histories\")\n\n def __str__(self):\n action = UserTool.Transitions(self.action).label\n return f\"{self.tool} - {action}\"\n\n\nclass ClearancePermission(TimeStampedModel):\n tool = models.ForeignKey(UserTool, on_delete=models.CASCADE, related_name=\"permissions\")\n cleared_by_user = models.ForeignKey(\n settings.AUTH_USER_MODEL, on_delete=models.PROTECT, related_name=\"given_tool_permissions\"\n )\n cleared_user = models.ForeignKey(\n settings.AUTH_USER_MODEL, on_delete=models.PROTECT, related_name=\"tool_permissions\"\n )\n\n class Meta:\n ordering = (\"-created\",)\n get_latest_by = \"created\"\n unique_together = ((\"tool\", \"cleared_user\"),)\n verbose_name = _(\"Clearance\")\n verbose_name_plural = _(\"Clearances\")\n\n def __str__(self):\n return f\"{self.cleared_by_user} cleared {self.cleared_user} ({self.tool})\"\n\n\nclass ToolPhoto(TimeStampedModel):\n tool = models.ForeignKey(UserTool, on_delete=models.CASCADE, related_name=\"photos\")\n uploading_user = models.ForeignKey(\n settings.AUTH_USER_MODEL, on_delete=models.PROTECT, related_name=\"uploaded_photos\"\n )\n file = models.FileField()\n title = models.CharField(max_length=255, blank=True)\n\n class Meta:\n ordering = (\"-created\",)\n get_latest_by = \"created\"\n", "repo_name": "bkmakerspace/toolhub", "sub_path": "tools/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 8419, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "95", "api": [{"api_name": "tagulous.models.TagTreeModel", "line_number": 27, "usage_type": "name"}, {"api_name": "catalog.Catalog", "line_number": 34, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 36, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 37, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 38, "usage_type": "call"}, {"api_name": "django.db.models.PositiveSmallIntegerField", "line_number": 40, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 40, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 41, "usage_type": "call"}, {"api_name": "colorful.fields.RGBColorField", "line_number": 44, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 47, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 48, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 62, "usage_type": "call"}, {"api_name": "catalog.Catalog", "line_number": 65, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 67, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 68, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 69, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 70, "usage_type": "call"}, {"api_name": "catalog.Catalog", "line_number": 73, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 75, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 76, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 77, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 78, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 79, "usage_type": "call"}, {"api_name": "utils.models.StateMachineMixin", "line_number": 82, "usage_type": "name"}, {"api_name": "django_extensions.db.models.TitleDescriptionModel", "line_number": 82, "usage_type": "name"}, {"api_name": "django_extensions.db.models.TimeStampedModel", "line_number": 82, "usage_type": "name"}, {"api_name": "catalog.Catalog", "line_number": 89, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 91, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 92, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 93, "usage_type": "call"}, {"api_name": "catalog.Catalog", "line_number": 95, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 97, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 98, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 99, "usage_type": "call"}, {"api_name": "django.db.models.ForeignKey", "line_number": 101, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 101, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 102, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 102, "usage_type": "name"}, {"api_name": "django.db.models.PROTECT", "line_number": 102, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 102, "usage_type": "name"}, {"api_name": "markdownx.models.MarkdownxField", "line_number": 104, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 105, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 105, "usage_type": "name"}, {"api_name": "tagulous.models.TagField", "line_number": 111, "usage_type": "call"}, {"api_name": "django.db.models.PositiveSmallIntegerField", "line_number": 112, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 112, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 113, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 116, "usage_type": "call"}, {"api_name": "django.db.models.PositiveSmallIntegerField", "line_number": 118, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 118, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 119, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 122, "usage_type": "call"}, {"api_name": "tools.querysets.UserToolQuerySet.as_manager", "line_number": 125, "usage_type": "call"}, {"api_name": "tools.querysets.UserToolQuerySet", "line_number": 125, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 130, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 131, "usage_type": "call"}, {"api_name": "utils.models.StateMachineMixin.StateMachine", "line_number": 133, "usage_type": "attribute"}, {"api_name": "utils.models.StateMachineMixin", "line_number": 133, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 145, "usage_type": "call"}, {"api_name": "tools.exceptions.ToolClearanceException", "line_number": 174, "usage_type": "call"}, {"api_name": "tools.exceptions.ToolAvailabilityException", "line_number": 177, "usage_type": "call"}, {"api_name": "django_extensions.db.models.TimeStampedModel", "line_number": 187, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 188, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 188, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 188, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 189, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 189, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 190, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 190, "usage_type": "name"}, {"api_name": "django.db.models.PROTECT", "line_number": 191, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 191, "usage_type": "name"}, {"api_name": "django.db.models.PositiveSmallIntegerField", "line_number": 196, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 196, "usage_type": "name"}, {"api_name": "tools.querysets.ToolHistoryQuerySet.as_manager", "line_number": 198, "usage_type": "call"}, {"api_name": "tools.querysets.ToolHistoryQuerySet", "line_number": 198, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 203, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 204, "usage_type": "call"}, {"api_name": "django_extensions.db.models.TimeStampedModel", "line_number": 211, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 212, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 212, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 212, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 213, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 213, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 214, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 214, "usage_type": "name"}, {"api_name": "django.db.models.PROTECT", "line_number": 214, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 214, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 216, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 216, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 217, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 217, "usage_type": "name"}, {"api_name": "django.db.models.PROTECT", "line_number": 217, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 217, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 224, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 225, "usage_type": "call"}, {"api_name": "django_extensions.db.models.TimeStampedModel", "line_number": 231, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 232, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 232, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 232, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 233, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 233, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 234, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 234, "usage_type": "name"}, {"api_name": "django.db.models.PROTECT", "line_number": 234, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 234, "usage_type": "name"}, {"api_name": "django.db.models.FileField", "line_number": 236, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 236, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 237, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 237, "usage_type": "name"}]} +{"seq_id": "71973553583", "text": "import os, uuid\nfrom azure.storage.queue import QueueServiceClient, QueueClient, QueueMessage\n\nconnect_str = os.getenv('AZURE_STORAGE_CONNECTION_STRING')\n\nqueue_name = \"testqueue\"\nqueue_client1 = QueueClient.from_connection_string(connect_str, \"testqueue1\")\nqueue_client2 = QueueClient.from_connection_string(connect_str, \"testqueue2\")\n# Receive messages one-by-one\nwhile(True):\n messages1 = queue_client1.receive_messages()\n messages2 = queue_client2.receive_messages()\n for msg in messages1:\n print(msg.content)\n # do the task\n queue_client1.delete_message(msg)\n for msg in messages2:\n print(msg.content)\n # do the task\n queue_client2.delete_message(msg)\n\n", "repo_name": "GihanMora/Armitage_project", "sub_path": "crawl_n_depth/Simplified_System/azure_test1.py", "file_name": "azure_test1.py", "file_ext": "py", "file_size_in_byte": 712, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "91", "api": [{"api_name": "os.getenv", "line_number": 4, "usage_type": "call"}, {"api_name": "azure.storage.queue.QueueClient.from_connection_string", "line_number": 7, "usage_type": "call"}, {"api_name": "azure.storage.queue.QueueClient", "line_number": 7, "usage_type": "name"}, {"api_name": "azure.storage.queue.QueueClient.from_connection_string", "line_number": 8, "usage_type": "call"}, {"api_name": "azure.storage.queue.QueueClient", "line_number": 8, "usage_type": "name"}]} +{"seq_id": "74650609903", "text": "import datetime\nimport logging\nfrom abc import ABC, abstractmethod\n\nfrom lib.models.etl import Pipeline\nfrom lib.providers.state import BaseStateProvider\n\n\nclass Process(ABC):\n \"\"\"\n Процесс загрузки данных оперирующий несколькими входными параметрами:\n - Хранилище состояния, откуда берется сохраненное состояние по каждой итерации\n для начала следующей итерации с нужного места\n - Экстрактор, позволящий достававть данные из хранилища\n - Трансформер, преобразующий данные в пригодный вид для последующей загрузки\n - Загрузчик, склыдвающий в быстрое хранилище подготовленные данные\n \"\"\"\n\n @abstractmethod\n def run(self):\n \"\"\"Основная точка входа для процесса загрузки данных\n Выполняет итерацию загрузки данных, основываясь на фильтрах и текущем со��тоянии\"\"\"\n\n\nclass ETLProcess(Process):\n def __init__(\n self,\n pipeline: Pipeline,\n state_provider: BaseStateProvider,\n logger: logging.Logger = None,\n ):\n self.pipeline = pipeline\n\n self.extractor = pipeline.extractor\n self.transformer = pipeline.transformer\n self.loader = pipeline.loader\n\n self.state = state_provider\n self.logger = logger\n self.__init_logger()\n\n async def run(self):\n ids = set()\n for f in self.pipeline.filters:\n self.log(f\"search by {f.state_key}\")\n state = await self.__get_state(f.state_key)\n self.log(f\"state: {state}\")\n\n # Выборка всех ID сущности по фильтру\n current_filter_result = self.extractor.extract(\n query=f.query, args={f.param: state}\n )\n if current_filter_result:\n # Сборка уникальных ID\n ids.update({r.id for r in current_filter_result})\n\n # Сохранение последней обработанной даты в фильтре\n last_filter_dt = max(\n [r.modified for r in current_filter_result]\n )\n await self.state.set(f.state_key, str(last_filter_dt))\n self.log(f\"{f.state_key} now is {last_filter_dt}\")\n\n self.log(f\"Collected {len(ids)} ids\")\n\n if ids:\n # Выборка пачками через генератор данные по сущностям\n db_gen = self.extractor.extract_generator(\n query=self.pipeline.collect_query,\n args={\"ids\": tuple(str(id_) for id_ in ids)},\n model=self.pipeline.model,\n )\n # Загрузка преобразованных данных\n for chunk in db_gen:\n transformed_data = [\n self.transformer.transform(c) for c in chunk\n ]\n result = self.loader.load(transformed_data)\n self.log(result)\n self.log(f\"Loaded {len(chunk)} rows\")\n\n self.log(\"=\" * 50)\n\n def log(self, msg: str, level=None):\n \"\"\"Обертка логирования для упрощенного доступа\"\"\"\n self.logger.log(level=level or logging.INFO, msg=msg)\n\n async def __get_state(self, state_key: str):\n value = await self.state.get(state_key)\n if value:\n return datetime.datetime.fromisoformat(value)\n return datetime.datetime.min\n\n def __init_logger(self):\n if not self.logger:\n self.logger = logging.getLogger(__name__)\n", "repo_name": "async-python/Async_API_sprint_2", "sub_path": "etl/lib/etl_process.py", "file_name": "etl_process.py", "file_ext": "py", "file_size_in_byte": 4019, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "91", "api": [{"api_name": "abc.ABC", "line_number": 9, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 19, "usage_type": "name"}, {"api_name": "lib.models.etl.Pipeline", "line_number": 28, "usage_type": "name"}, {"api_name": "lib.providers.state.BaseStateProvider", "line_number": 29, "usage_type": "name"}, {"api_name": "logging.Logger", "line_number": 30, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 86, "usage_type": "attribute"}, {"api_name": "datetime.datetime.fromisoformat", "line_number": 91, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 91, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 92, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 96, "usage_type": "call"}]} +{"seq_id": "4936421536", "text": "import os\n\nfrom PIL import Image\nimport io\nimport base64\n\n\nfrom pubnub.callbacks import SubscribeCallback\nfrom pubnub.enums import PNStatusCategory, PNOperationType\nfrom pubnub.enums import PNReconnectionPolicy\nfrom pubnub.pnconfiguration import PNConfiguration\nfrom pubnub.pubnub import PubNub\n\nENTRY = \"Earth\"\nCHANNEL = \"awesomeChannel\"\n\npnconfig = PNConfiguration()\npnconfig.publish_key = \"pub-c-10b9f92d-a0e2-4d07-bef2-8b84aee0c2a3\"\npnconfig.subscribe_key = \"sub-c-12ea3aa6-ed25-11ea-a728-4ec3aefbf636\"\npnconfig.uuid = \"serverUUID-SUB\"\npnconfig.reconnect_policy = PNReconnectionPolicy.LINEAR\n\npubnub = PubNub(pnconfig)\n\n\nclass MySubscribeCallback(SubscribeCallback):\n def presence(self, pubnub, event):\n print(\"[PRESENCE: {}]\".format(event.event))\n print(\"uuid: {}, channel: {}\".format(event.uuid, event.channel))\n\n def status(self, pubnub, status):\n \n if status.category == PNStatusCategory.PNUnexpectedDisconnectCategory:\n print(\"PN Unexpected Disconnect\")\n pubnub.reconnect()\n\n if status.category == PNStatusCategory.PNConnectedCategory:\n print(\"PN Connected\")\n\n if status.category == PNStatusCategory.PNReconnectedCategory:\n print(\"PN Re-Connected\")\n pubnub.subscribe().channels('devChannel').execute()\n\n if status.category == PNStatusCategory.PNDecryptionErrorCategory:\n print(\"PN Decryption Error\")\n \n \n def message(self, pubnub, event):\n print(\"[MESSAGE received]\")\n\n print(\"message : {}\".format(event.message[\"message\"]))\n\nclass HandleDisconnectsCallback(SubscribeCallback):\n def status(self, pubnub, status):\n if status.category == PNStatusCategory.PNUnexpectedDisconnectCategory:\n print(\"Fuck you\")\n # internet got lost, do some magic and call reconnect when ready\n pubnub.reconnect()\n elif status.category == PNStatusCategory.PNTimeoutCategory:\n # do some magic and call reconnect when ready\n pubnub.reconnect()\n print(\"Fuck you\")\n\n else:\n print(\"Bitch im working\")\n \n def presence(self, pubnub, presence):\n pass\n \n def message(self, pubnub, event):\n print(\"[MESSAGE received]\")\n print(\"{} : {}\".format(event.message[\"name\"], event.message[\"message\"]))\n s = event.message[\"message\"]\n s = s[2:-1]\n f = io.BytesIO(base64.b64decode(s))\n pilimage = Image.open(f)\n pilimage.save(\"recovered.jpg\")\n\n def signal(self, pubnub, signal):\n pass\n\ndisconnect_listener = HandleDisconnectsCallback()\n \npubnub.add_listener(disconnect_listener)\n\n\npubnub.subscribe().channels(CHANNEL).with_presence().execute()\n\nprint(\"***************************************************\")\nprint(\"* Waiting for updates to The Guide about {}... *\".format(ENTRY))\nprint(\"***************************************************\")\n", "repo_name": "Ravenking7675/Screen-Sharing-using-PubSub-", "sub_path": "sub.py", "file_name": "sub.py", "file_ext": "py", "file_size_in_byte": 2873, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "91", "api": [{"api_name": "pubnub.pnconfiguration.PNConfiguration", "line_number": 17, "usage_type": "call"}, {"api_name": "pubnub.enums.PNReconnectionPolicy.LINEAR", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pubnub.enums.PNReconnectionPolicy", "line_number": 21, "usage_type": "name"}, {"api_name": "pubnub.callbacks", "line_number": 23, "usage_type": "name"}, {"api_name": "pubnub.pubnub.PubNub", "line_number": 23, "usage_type": "call"}, {"api_name": "pubnub.callbacks.SubscribeCallback", "line_number": 26, "usage_type": "name"}, {"api_name": "pubnub.enums.PNStatusCategory.PNUnexpectedDisconnectCategory", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pubnub.enums.PNStatusCategory", "line_number": 33, "usage_type": "name"}, {"api_name": "pubnub.callbacks.reconnect", "line_number": 35, "usage_type": "call"}, {"api_name": "pubnub.callbacks", "line_number": 35, "usage_type": "name"}, {"api_name": "pubnub.enums.PNStatusCategory.PNConnectedCategory", "line_number": 37, "usage_type": "attribute"}, {"api_name": "pubnub.enums.PNStatusCategory", "line_number": 37, "usage_type": "name"}, {"api_name": "pubnub.enums.PNStatusCategory.PNReconnectedCategory", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pubnub.enums.PNStatusCategory", "line_number": 40, "usage_type": "name"}, {"api_name": "pubnub.callbacks.subscribe", "line_number": 42, "usage_type": "call"}, {"api_name": "pubnub.callbacks", "line_number": 42, "usage_type": "name"}, {"api_name": "pubnub.enums.PNStatusCategory.PNDecryptionErrorCategory", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pubnub.enums.PNStatusCategory", "line_number": 44, "usage_type": "name"}, {"api_name": "pubnub.callbacks.SubscribeCallback", "line_number": 53, "usage_type": "name"}, {"api_name": "pubnub.enums.PNStatusCategory.PNUnexpectedDisconnectCategory", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pubnub.enums.PNStatusCategory", "line_number": 55, "usage_type": "name"}, {"api_name": "pubnub.callbacks.reconnect", "line_number": 58, "usage_type": "call"}, {"api_name": "pubnub.callbacks", "line_number": 58, "usage_type": "name"}, {"api_name": "pubnub.enums.PNStatusCategory.PNTimeoutCategory", "line_number": 59, "usage_type": "attribute"}, {"api_name": "pubnub.enums.PNStatusCategory", "line_number": 59, "usage_type": "name"}, {"api_name": "pubnub.callbacks.reconnect", "line_number": 61, "usage_type": "call"}, {"api_name": "pubnub.callbacks", "line_number": 61, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 75, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 75, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 76, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 76, "usage_type": "name"}, {"api_name": "pubnub.callbacks.add_listener", "line_number": 84, "usage_type": "call"}, {"api_name": "pubnub.callbacks", "line_number": 84, "usage_type": "name"}, {"api_name": "pubnub.callbacks.subscribe", "line_number": 87, "usage_type": "call"}, {"api_name": "pubnub.callbacks", "line_number": 87, "usage_type": "name"}]} +{"seq_id": "35882727559", "text": "import os\nimport sys\nimport pickle\nimport argparse\nimport ipaddress\nimport socket\nimport time\nfrom pathlib import Path\n\n\nServerPort = 8089\n\ndef recvStr(sock):\n data = sock.recv(4)\n numBytes = int.from_bytes(data, \"big\")\n data = sock.recv(numBytes)\n return data.decode(\"utf-8\")\n\n\ndef sendStr(sock, s):\n data = s.encode(\"utf-8\")\n sent = sock.send(len(data).to_bytes(4, \"big\"))\n assert sent == 4\n sent = sock.send(data)\n assert sent == len(data)\n\n\ndef sendFile(sock, filePath):\n ''' Send a big file over socket. Should pair with function recvFile '''\n if not sock:\n raise socket.error\n length = filePath.stat().st_size\n sentByteNum = sock.send(length.to_bytes(4, \"big\"))\n assert sentByteNum == 4\n chunckSize = 64 * 1024\n totalSent = 0\n lastProcess = 0\n startTime = time.perf_counter()\n lastTime = startTime\n lastSent = 0\n with filePath.open(\"rb\") as f:\n while True:\n data = f.read(chunckSize) # Send a small chunk of files\n if not data:\n break\n sendByteNum = sock.send(data)\n assert sendByteNum == len(data)\n totalSent += sendByteNum\n progress = int(totalSent/length * 20)\n if progress > lastProcess:\n curTime = time.perf_counter()\n deltaTime = curTime - lastTime\n deltaSent = totalSent - lastSent\n speed = deltaSent / deltaTime / 1024\n lastSent = totalSent\n lastTime = curTime\n lastProcess = progress\n\n sys.stdout.write('\\r')\n # the exact output you're looking for:\n sys.stdout.write(\"[%-20s] %3d%% %.0f KB/s\" % ('='*progress, 5*progress, speed))\n sys.stdout.flush()\n deltaTime = curTime - startTime\n aveSpeed = length / deltaTime / 1024\n print(\"\\nCost time %.1fs, average speed %.0f KB/s\" % (deltaTime, aveSpeed))\n\n\ndef pickOneHost(hostDict):\n hostIp = None\n phoneChecked = False\n while hostDict:\n ipList = list(hostDict.keys())\n if len(hostDict) > 1:\n print(\"There're %d previous ip scan results:\" % len(hostDict))\n for i, ip in enumerate(ipList):\n print(\"(%d) %s %s\" % (i, ip, hostDict[ip]))\n key = input(\"Please choose one to try:\")\n try:\n idx = int(key)\n ip = ipList[idx]\n except Exception:\n return None\n else:\n ip = ipList[0]\n\n # Try to connect\n while True:\n s = socket.socket()\n try:\n s.connect((ip, ServerPort))\n sendStr(s, \"Identify yourself\")\n recvStr(s)\n hostIp = ip\n break\n except Exception:\n if not phoneChecked:\n print(\"Cannot connect %s %s. Please make sure the phone app is turned on.\" % (ip, hostDict[ip]))\n input(\"Press enter to continue...\")\n phoneChecked = True\n else:\n break\n finally:\n s.close()\n\n if not hostIp:\n print(\"Removed %s %s from scan result\" % (ip, hostDict[ip]))\n hostDict.pop(ip)\n else:\n break\n\n return hostIp\n\n\ndef main(argv):\n # Parse the command line\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-network\", help=\"The ip network to scan\", default=\"192.168.0.0/255.255.255.0\")\n parser.add_argument(\"-ip\", help=\"The ip address to send file\")\n parser.add_argument(\"file\", help=\"file to send\")\n\n args = parser.parse_args(argv)\n filePath = Path(args.file)\n if not filePath.is_file():\n print(\"local file %s doesn't exist\" % str(filePath))\n return 1\n\n hostDict = dict() # Element (ip, hostName)\n cachePath = Path(os.path.expanduser('~'))/\"ipScanCache.pickle\"\n if cachePath.is_file():\n with cachePath.open(\"rb\") as f:\n hostDict = pickle.load(f)\n\n if args.ip:\n s = socket.socket()\n try:\n s.connect((args.ip, ServerPort))\n sendStr(s, \"Identify yourself\")\n msg = recvStr(s)\n if not msg.startswith(\"This is a file receiver at\"):\n return 1\n hostName = msg.split(\"This is a file receiver at\")[1].strip()\n hostDict[args.ip] = hostName\n except Exception:\n print(\"Cannot connect %s\" % args.ip)\n return 1\n finally:\n s.close()\n\n ip = pickOneHost(hostDict)\n\n if not ip:\n # Cannot connect with previous scan result. Need a new scan\n for addr in ipaddress.IPv4Network(args.network):\n s = socket.socket()\n s.settimeout(0.2)\n try:\n print(\"Scan %s\" % str(addr))\n s.connect((str(addr), ServerPort))\n s.settimeout(1)\n sendStr(s, \"Identify yourself\")\n msg = recvStr(s)\n if not msg.startswith(\"This is a file receiver at\"):\n s.close()\n continue\n hostName = msg.split(\"This is a file receiver at\")[1]\n hostDict[str(addr)] = hostName\n print(\"Found host %s\" % hostName)\n except Exception:\n pass\n s.close()\n if not hostDict:\n print(\"Cannot find any device in the range %s\" % args.network)\n return 1\n ip = pickOneHost(hostDict)\n assert ip\n # Save the hostDict\n with cachePath.open(\"wb\") as f:\n pickle.dump(hostDict, f)\n\n s = socket.socket()\n try:\n s.connect((ip, ServerPort))\n sendStr(s, \"Please download file\")\n msg = recvStr(s)\n if msg != \"What's the file name?\":\n return 1\n sendStr(s, filePath.name)\n print(\"Sending file to %s %s...\" % (ip, hostDict[ip]))\n sendFile(s, filePath)\n msg = recvStr(s)\n if msg != \"File received\":\n return 1\n print(\"Sent file %s\" % str(filePath))\n except Exception as e:\n print(\"Something wrong happend during transfering file\")\n print(e)\n s.close()\n\n\nif __name__ == \"__main__\":\n main(sys.argv[1:])\n", "repo_name": "Yuhe-Wang/DevTools", "sub_path": "scripts/sendfile.py", "file_name": "sendfile.py", "file_ext": "py", "file_size_in_byte": 6297, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "95", "api": [{"api_name": "socket.error", "line_number": 31, "usage_type": "attribute"}, {"api_name": "time.perf_counter", "line_number": 38, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 51, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 59, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 59, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 61, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 61, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 62, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 62, "usage_type": "attribute"}, {"api_name": "socket.socket", "line_number": 88, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 116, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 122, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path.expanduser", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path", "line_number": 128, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 131, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 134, "usage_type": "call"}, {"api_name": "ipaddress.IPv4Network", "line_number": 153, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 154, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 178, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 180, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 201, "usage_type": "attribute"}]} +{"seq_id": "41350149915", "text": "# encoding: utf-8\n\n\"\"\"\n\n@author: linchart\n@file: config.py\n@version: 1.0\n@time : 2019/1/20\n\n\"\"\"\n\nimport torch\n\ndevice = torch.device(\"cuda: 0\" if torch.cuda.is_available() else \"cpu\")\n\nTRAIN_BATCH_SIZE = 32\nDIM = 200\nHIDDEN_SIZE = 128\nNUM_LAYER = 1\ndrop_out = 0.3\nepochs = 30\nsilent = False\nlabel_class = 2\nbidirectional = True\nLR = 0.001\n\nfinetune_epochs=500\nfinetune_batch_size = 20\n\n\ntrain_file = '../output/train_char.csv'\ntest_file = '../output/test_char1.csv'\npredict_file = '../input/other_test_data_deal.csv'\n# predict_file = '../input/finetune_test_data.csv'\nfinetune_train_file = '../input/finetune_train_data.csv'\nfinetune_valid_file = '../input/finetune_validation_data.csv'\nfinetune_test_file = '../input/finetune_test_data.csv'\n\n\n\nvector_file = '../output/chars.vector'\n\n# model_path = '../output/model_best.pth.tar'\nmodel_path = '../output/finetune_model_best.pth.tar'\n", "repo_name": "linchart/NLP", "sub_path": "bigru_attention/config.py", "file_name": "config.py", "file_ext": "py", "file_size_in_byte": 884, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "91", "api": [{"api_name": "torch.device", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 14, "usage_type": "attribute"}]} +{"seq_id": "36766709943", "text": "from flask import Flask\nfrom flask import render_template\nfrom flask import request, jsonify\nfrom flask import current_app as app\nfrom application.models import Category, Product, Managers, Users, Orders_Desc, Order_Details\nfrom .database import db\nfrom datetime import datetime\n\n@app.route(\"//cart_addition/prod_\")\ndef define_item_qty(usr, pid):\n p = Product.query.filter(Product.PID == pid).first()\n c = Category.query.filter(Category.CID == p.CID).first()\n return render_template(\"add_to_cart.html\", user=usr, prod=p, cat = c)\n\n@app.route(\"/add_item\", methods=['POST'])\ndef add_to_cart():\n uname = request.json['uid']\n pid = request.json['pid']\n qty = request.json['qty']\n\n status = \"\"\n u = Users.query.filter(Users.Uname == uname).first()\n active_order = None\n for o in u.orders:\n if o.Status == 1:\n active_order = o\n print(active_order.OID)\n if active_order is None:\n try:\n new_order = Orders_Desc(Status = 1, Uname = uname)\n db.session.add(new_order)\n db.session.flush()\n \n new_line_item = Order_Details(OID = new_order.OID, PID = pid, Qty = qty)\n db.session.add(new_line_item)\n db.session.flush()\n except:\n status = \"Invalid addition\"\n print(\"Rolling back\")\n db.session.rollback()\n else:\n status=\"success\"\n db.session.commit()\n print(\"Commit\")\n else:\n try: \n new_line_item = Order_Details(OID = active_order.OID, PID = pid, Qty = qty)\n db.session.add(new_line_item)\n db.session.flush()\n except:\n status = \"This item exists in your cart\\n You can change your quantity by editing the cart\"\n print(\"Rolling back\")\n db.session.rollback()\n else:\n status=\"success\"\n db.session.commit()\n print(\"Commit\")\n \n return jsonify(stat=status)\n\n@app.route(\"//cart\")\ndef show_cart(usr):\n u = Users.query.filter(Users.Uname == usr).first()\n active_order = None\n for o in u.orders:\n if o.Status == 1:\n active_order = o\n if active_order is None:\n return render_template(\"cart.html\", user=usr, order=None)\n else:\n details = None\n #details = Product.query.join(Order_Details, Product.PID == Order_Details.PID).filter(Order_Details.OID == active_order.OID)\n details = db.session.query(Order_Details, Product).join(Product, Product.PID == Order_Details.PID).filter(Order_Details.OID == active_order.OID).all()\n if len(details) == 0:\n return render_template(\"cart.html\", user=usr, order=None) \n return render_template(\"cart.html\", user=usr, order=details)\n \n@app.route(\"/update_item\", methods = ['POST'])\ndef update_order():\n oid = request.json['oid']\n pid = request.json['pid']\n qty = request.json['qty']\n\n status = \"\"\n item = Order_Details.query.filter(Order_Details.OID == oid, Order_Details.PID == pid).first()\n \n if item is None:\n status = \"failure\"\n else:\n try:\n item.Qty = qty;\n db.session.flush()\n\n except Exception as e:\n status = \"Invalid request\"\n print(\"Rolling back\")\n \n else:\n status=\"success\"\n db.session.commit()\n print(\"Commit\")\n \n return jsonify(stat=status)\n\n@app.route(\"/del_item\", methods = ['POST'])\ndef del_from_order():\n oid = request.json['oid']\n pid = request.json['pid']\n\n item = Order_Details.query.filter(Order_Details.OID == oid, Order_Details.PID == pid).first()\n\n try:\n if item is not None:\n \n db.session.delete(item)\n db.session.commit()\n return jsonify(stat='success')\n else:\n return jsonify(error='Product not found')\n except Exception as e:\n db.session.rollback()\n return jsonify(error='An error occurred while deleting the product')\n \n@app.route(\"/checkout_cart\", methods = ['POST'])\ndef checkout():\n oid = request.json['oid']\n iso_datetime = request.json['date']\n datetime_obj = datetime.strptime(iso_datetime, '%Y-%m-%dT%H:%M:%S.%fZ')\n\n status = \"\"\n order = Orders_Desc.query.filter(Orders_Desc.OID == oid).first()\n \n if order is None:\n status = \"failure\"\n else:\n try:\n order.Status = 0\n order.Date = datetime_obj\n db.session.flush()\n \n except Exception as e:\n status = \"Invalid request\"\n print(\"Rolling back\")\n \n else:\n status=\"success\"\n db.session.commit()\n update_stock(oid)\n print(\"Commit\")\n \n return jsonify(stat=status)\n\ndef update_stock(oid):\n order_items = Order_Details.query.filter(Order_Details.OID == oid).all()\n for item in order_items:\n try:\n prod = Product.query.filter(Product.PID == item.PID).first()\n prod.Stock = prod.Stock - item.Qty\n db.session.flush()\n #print(prod.PID, prod.Stock)\n \n except Exception as e:\n status = \"Invalid request\"\n print(\"Rolling back\")\n \n status=\"success\"\n db.session.commit()\n #print(\"here----Commit\")\n \n return jsonify(stat=status)\n\n \n", "repo_name": "subhashree211002/Grocery-Store", "sub_path": "application/user_actions.py", "file_name": "user_actions.py", "file_ext": "py", "file_size_in_byte": 5466, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "91", "api": [{"api_name": "application.models.Product.query.filter", "line_number": 11, "usage_type": "call"}, {"api_name": "application.models.Product.query", "line_number": 11, "usage_type": "attribute"}, {"api_name": "application.models.Product", "line_number": 11, "usage_type": "name"}, {"api_name": "application.models.Product.PID", "line_number": 11, "usage_type": "attribute"}, {"api_name": "application.models.Category.query.filter", "line_number": 12, "usage_type": "call"}, {"api_name": "application.models.Category.query", "line_number": 12, "usage_type": "attribute"}, {"api_name": "application.models.Category", "line_number": 12, "usage_type": "name"}, {"api_name": "application.models.Category.CID", "line_number": 12, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 13, "usage_type": "call"}, {"api_name": "flask.current_app.route", "line_number": 9, "usage_type": "call"}, {"api_name": "flask.current_app", "line_number": 9, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 17, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 17, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 18, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 18, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 19, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 19, "usage_type": "name"}, {"api_name": "application.models.Users.query.filter", "line_number": 22, "usage_type": "call"}, {"api_name": "application.models.Users.query", "line_number": 22, "usage_type": "attribute"}, {"api_name": "application.models.Users", "line_number": 22, "usage_type": "name"}, {"api_name": "application.models.Users.Uname", "line_number": 22, "usage_type": "attribute"}, {"api_name": "application.models.Orders_Desc", "line_number": 30, "usage_type": "call"}, {"api_name": "database.db.session.add", "line_number": 31, "usage_type": "call"}, {"api_name": "database.db.session", "line_number": 31, "usage_type": "attribute"}, {"api_name": "database.db", "line_number": 31, "usage_type": "name"}, {"api_name": "database.db.session.flush", "line_number": 32, "usage_type": "call"}, {"api_name": "database.db.session", "line_number": 32, "usage_type": "attribute"}, {"api_name": "database.db", "line_number": 32, "usage_type": "name"}, {"api_name": "application.models.Order_Details", "line_number": 34, "usage_type": "call"}, {"api_name": "database.db.session.add", "line_number": 35, "usage_type": "call"}, {"api_name": "database.db.session", "line_number": 35, "usage_type": "attribute"}, {"api_name": "database.db", "line_number": 35, "usage_type": "name"}, {"api_name": "database.db.session.flush", "line_number": 36, "usage_type": "call"}, {"api_name": "database.db.session", "line_number": 36, "usage_type": "attribute"}, {"api_name": "database.db", "line_number": 36, "usage_type": "name"}, {"api_name": "database.db.session.rollback", "line_number": 40, "usage_type": "call"}, {"api_name": "database.db.session", "line_number": 40, "usage_type": "attribute"}, {"api_name": "database.db", "line_number": 40, "usage_type": "name"}, {"api_name": "database.db.session.commit", "line_number": 43, "usage_type": "call"}, {"api_name": "database.db.session", "line_number": 43, "usage_type": "attribute"}, {"api_name": "database.db", "line_number": 43, "usage_type": "name"}, {"api_name": "application.models.Order_Details", "line_number": 47, "usage_type": "call"}, {"api_name": "database.db.session.add", "line_number": 48, "usage_type": "call"}, {"api_name": "database.db.session", "line_number": 48, "usage_type": "attribute"}, {"api_name": "database.db", "line_number": 48, "usage_type": "name"}, {"api_name": "database.db.session.flush", "line_number": 49, "usage_type": "call"}, {"api_name": "database.db.session", "line_number": 49, "usage_type": "attribute"}, {"api_name": "database.db", "line_number": 49, "usage_type": "name"}, {"api_name": "database.db.session.rollback", "line_number": 53, "usage_type": "call"}, {"api_name": "database.db.session", "line_number": 53, "usage_type": "attribute"}, {"api_name": "database.db", "line_number": 53, "usage_type": "name"}, {"api_name": "database.db.session.commit", "line_number": 56, "usage_type": "call"}, {"api_name": "database.db.session", "line_number": 56, "usage_type": "attribute"}, {"api_name": "database.db", "line_number": 56, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 59, "usage_type": "call"}, {"api_name": "flask.current_app.route", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.current_app", "line_number": 15, "usage_type": "name"}, {"api_name": "application.models.Users.query.filter", "line_number": 63, "usage_type": "call"}, {"api_name": "application.models.Users.query", "line_number": 63, "usage_type": "attribute"}, {"api_name": "application.models.Users", "line_number": 63, "usage_type": "name"}, {"api_name": "application.models.Users.Uname", "line_number": 63, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 69, "usage_type": "call"}, {"api_name": "application.models.Product", "line_number": 73, "usage_type": "argument"}, {"api_name": "database.db.session.query", "line_number": 73, "usage_type": "call"}, {"api_name": "application.models.Order_Details", "line_number": 73, "usage_type": "argument"}, {"api_name": "database.db.session", "line_number": 73, "usage_type": "attribute"}, {"api_name": "database.db", "line_number": 73, "usage_type": "name"}, {"api_name": "application.models.Product.PID", "line_number": 73, "usage_type": "attribute"}, {"api_name": "application.models.Order_Details.PID", "line_number": 73, "usage_type": "attribute"}, {"api_name": "application.models.Order_Details.OID", "line_number": 73, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 75, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 76, "usage_type": "call"}, {"api_name": "flask.current_app.route", "line_number": 61, "usage_type": "call"}, {"api_name": "flask.current_app", "line_number": 61, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 80, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 80, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 81, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 81, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 82, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 82, "usage_type": "name"}, {"api_name": "application.models.Order_Details.query.filter", "line_number": 85, "usage_type": "call"}, {"api_name": "application.models.Order_Details.query", "line_number": 85, "usage_type": "attribute"}, {"api_name": "application.models.Order_Details", "line_number": 85, "usage_type": "name"}, {"api_name": "application.models.Order_Details.OID", "line_number": 85, "usage_type": "attribute"}, {"api_name": "application.models.Order_Details.PID", "line_number": 85, "usage_type": "attribute"}, {"api_name": "database.db.session.flush", "line_number": 92, "usage_type": "call"}, {"api_name": "database.db.session", "line_number": 92, "usage_type": "attribute"}, {"api_name": "database.db", "line_number": 92, "usage_type": "name"}, {"api_name": "database.db.session.commit", "line_number": 100, "usage_type": "call"}, {"api_name": "database.db.session", "line_number": 100, "usage_type": "attribute"}, {"api_name": "database.db", "line_number": 100, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 103, "usage_type": "call"}, {"api_name": "flask.current_app.route", "line_number": 78, "usage_type": "call"}, {"api_name": "flask.current_app", "line_number": 78, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 107, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 107, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 108, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 108, "usage_type": "name"}, {"api_name": "application.models.Order_Details.query.filter", "line_number": 110, "usage_type": "call"}, {"api_name": "application.models.Order_Details.query", "line_number": 110, "usage_type": "attribute"}, {"api_name": "application.models.Order_Details", "line_number": 110, "usage_type": "name"}, {"api_name": "application.models.Order_Details.OID", "line_number": 110, "usage_type": "attribute"}, {"api_name": "application.models.Order_Details.PID", "line_number": 110, "usage_type": "attribute"}, {"api_name": "database.db.session.delete", "line_number": 115, "usage_type": "call"}, {"api_name": "database.db.session", "line_number": 115, "usage_type": "attribute"}, {"api_name": "database.db", "line_number": 115, "usage_type": "name"}, {"api_name": "database.db.session.commit", "line_number": 116, "usage_type": "call"}, {"api_name": "database.db.session", "line_number": 116, "usage_type": "attribute"}, {"api_name": "database.db", "line_number": 116, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 117, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 119, "usage_type": "call"}, {"api_name": "database.db.session.rollback", "line_number": 121, "usage_type": "call"}, {"api_name": "database.db.session", "line_number": 121, "usage_type": "attribute"}, {"api_name": "database.db", "line_number": 121, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 122, "usage_type": "call"}, {"api_name": "flask.current_app.route", "line_number": 105, "usage_type": "call"}, {"api_name": "flask.current_app", "line_number": 105, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 126, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 126, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 127, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 127, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 128, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 128, "usage_type": "name"}, {"api_name": "application.models.Orders_Desc.query.filter", "line_number": 131, "usage_type": "call"}, {"api_name": "application.models.Orders_Desc.query", "line_number": 131, "usage_type": "attribute"}, {"api_name": "application.models.Orders_Desc", "line_number": 131, "usage_type": "name"}, {"api_name": "application.models.Orders_Desc.OID", "line_number": 131, "usage_type": "attribute"}, {"api_name": "database.db.session.flush", "line_number": 139, "usage_type": "call"}, {"api_name": "database.db.session", "line_number": 139, "usage_type": "attribute"}, {"api_name": "database.db", "line_number": 139, "usage_type": "name"}, {"api_name": "database.db.session.commit", "line_number": 147, "usage_type": "call"}, {"api_name": "database.db.session", "line_number": 147, "usage_type": "attribute"}, {"api_name": "database.db", "line_number": 147, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 151, "usage_type": "call"}, {"api_name": "flask.current_app.route", "line_number": 124, "usage_type": "call"}, {"api_name": "flask.current_app", "line_number": 124, "usage_type": "name"}, {"api_name": "application.models.Order_Details.query.filter", "line_number": 154, "usage_type": "call"}, {"api_name": "application.models.Order_Details.query", "line_number": 154, "usage_type": "attribute"}, {"api_name": "application.models.Order_Details", "line_number": 154, "usage_type": "name"}, {"api_name": "application.models.Order_Details.OID", "line_number": 154, "usage_type": "attribute"}, {"api_name": "application.models.Product.query.filter", "line_number": 157, "usage_type": "call"}, {"api_name": "application.models.Product.query", "line_number": 157, "usage_type": "attribute"}, {"api_name": "application.models.Product", "line_number": 157, "usage_type": "name"}, {"api_name": "application.models.Product.PID", "line_number": 157, "usage_type": "attribute"}, {"api_name": "database.db.session.flush", "line_number": 159, "usage_type": "call"}, {"api_name": "database.db.session", "line_number": 159, "usage_type": "attribute"}, {"api_name": "database.db", "line_number": 159, "usage_type": "name"}, {"api_name": "database.db.session.commit", "line_number": 167, "usage_type": "call"}, {"api_name": "database.db.session", "line_number": 167, "usage_type": "attribute"}, {"api_name": "database.db", "line_number": 167, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 170, "usage_type": "call"}]} +{"seq_id": "436484551", "text": "from pytz import common_timezones_set\nimport StrategyLearner\nimport ManualStrategy \nimport datetime as dt \nimport time\nimport marketsimcode\nimport pandas as pd\nimport matplotlib.pyplot as plt \t\n\ndef run_experiment(): \n\n \n sd = dt.datetime(2008,1,1)\n ed = dt.datetime(2009,12,31)\n sv = 100000\n\n # Strategy Learner - impact = 0.0005\n qlearner = StrategyLearner.StrategyLearner(verbose = False, impact=0.0005)\n qlearner.add_evidence(symbol=\"JPM\",sd=dt.datetime(2008,1,1),ed=dt.datetime(2009,12,31),sv=100000)\n q_trades = qlearner.testPolicy(symbol=\"JPM\",sd=dt.datetime(2008,1,1),ed=dt.datetime(2009,12,31),sv=100000)\n q_port_vals1 = marketsimcode.compute_portvals(q_trades,'JPM',sv,0,0.0005)\n q_port_vals_normed1=q_port_vals1/q_port_vals1.iloc[0,:]\n\n\n # Strategy Learner - impact = 0.005\n qlearner = StrategyLearner.StrategyLearner(verbose = False, impact=0.005)\n qlearner.add_evidence(symbol=\"JPM\",sd=dt.datetime(2008,1,1),ed=dt.datetime(2009,12,31),sv=100000)\n q_trades = qlearner.testPolicy(symbol=\"JPM\",sd=dt.datetime(2008,1,1),ed=dt.datetime(2009,12,31),sv=100000)\n q_port_vals2 = marketsimcode.compute_portvals(q_trades,'JPM',sv,0,0.005)\n q_port_vals_normed2=q_port_vals2/q_port_vals2.iloc[0,:]\n\n # Strategy Learner - impact = 0.05\n qlearner = StrategyLearner.StrategyLearner(verbose = False, impact=0.05)\n qlearner.add_evidence(symbol=\"JPM\",sd=dt.datetime(2008,1,1),ed=dt.datetime(2009,12,31),sv=100000)\n q_trades = qlearner.testPolicy(symbol=\"JPM\",sd=dt.datetime(2008,1,1),ed=dt.datetime(2009,12,31),sv=100000)\n q_port_vals3 = marketsimcode.compute_portvals(q_trades,'JPM',sv,0,0.05)\n q_port_vals_normed3=q_port_vals3/q_port_vals3.iloc[0,:]\n\n\n \n \n\n\n \n\n # Benchmark_orders = q_trades.copy()\n # Benchmark_orders[:]=0\n # Benchmark_orders.iloc[0]=1000\n\n \n # Benchmark_port_vals = marketsimcode.compute_portvals(Benchmark_orders,'JPM',sv,9.95,0.005)\n # q_port_vals = marketsimcode.compute_portvals(q_trades,'JPM',sv,9.95,0.005)\n # manual_port_vals = marketsimcode.compute_portvals(manual_trades,'JPM',sv,9.95,0.005)\n\n\n q_cr1,q_adr1,q_sddr1,q_sr1 = marketsimcode.get_statistics(q_port_vals1) \n q_port_vals_normed1=q_port_vals1/q_port_vals1.iloc[0,:]\n\n q_cr2,q_adr2,q_sddr2,q_sr2 = marketsimcode.get_statistics(q_port_vals2) \n q_port_vals_normed2=q_port_vals2/q_port_vals2.iloc[0,:]\n\n q_cr3, q_adr3,q_sddr3,q_sr3 = marketsimcode.get_statistics(q_port_vals3) \n q_port_vals_normed3=q_port_vals3/q_port_vals3.iloc[0,:]\n\n \n #sys.stdout = open(\"p6_results.txt\", \"w\")\n print()\n print(f\" IN SAMPLE Date Range: {sd} to {ed}\") \n print('------------------------------------------------------------------------------------------------------')\t\n print(' Startegy Learner Impact 0.0005 0.005 0.05')\n print('------------------------------------------------------------------------------------------------------')\t \t \t\t \t \t\t\t \t\t \t\t\t \t\t\t \t \t\t \t \t\t \t \t\t\t \t\t \t\t\t \t\t\t \t \n print(f\"Sharpe Ratio {'%08.6f'%q_sr1[0]} {'%08.6f'%q_sr2[0]} {'%08.6f'%q_sr3[0]}\") \n print('------------------------------------------------------------------------------------------------------')\t\t\t \t \t\t \t \t\t\t \t\t \t\t\t \t\t\t \t \t\t \t \t\t \t \t\t\t \t\t \t\t\t \t\t\t \t \t\t \t \t\t \t \t\t\t \t\t \t\t\t \t\t\t \t \n print(f\"Cumulative Return {'%08.6f'%q_cr1[0]} {'%08.6f'%q_cr2[0]} {'%08.6f'%q_cr3[0]}\") \t\t \t \t\t \t \t\t\t \t\t \t\t\t \t\t\t \t \n print('------------------------------------------------------------------------------------------------------')\t\t \t \t\t \t \t\t\t \t\t \t\t\t \t\t\t \t \t\t \t \t\t \t \t\t\t \t\t \t\t\t \t\t\t \t \n print(f\"Standard Deviation {'%08.6f'%q_sddr1[0]} {'%08.6f'%q_sddr2[0]} {'%08.6f'%q_sddr3[0]}\")\n print('------------------------------------------------------------------------------------------------------') \t\t \t \t\t \t \t\t\t \t\t \t\t\t \t\t\t \t \n print(f\"Average Daily Return {'%08.6f'%q_adr1[0]} {'%08.6f'%q_adr2[0]} {'%08.6f'%q_adr3[0]}\") \n print('------------------------------------------------------------------------------------------------------') \t\t \t \t\t \t \t\t\t \t\t \t\t\t \t\t\t \t \n print(f\"Final Portfolio Value: {'%09.2f'%q_port_vals1.iloc[-1][0]} {'%09.2f'%q_port_vals2.iloc[-1][0]} {'%09.2f'%q_port_vals3.iloc[-1][0]}\")\n print('------------------------------------------------------------------------------------------------------')\t\n \t \t \t\t \t \t\t\t \t\t \t\t\t \t\t\t \t \n df_temp = pd.concat( \t\t \t \t\t \t \t\t\t \t\t \t\t\t \t\t\t \t \n [q_port_vals_normed1,q_port_vals_normed2, q_port_vals_normed3], keys=[\"Strategy Learner\",\"Manual Strategy\", \"Benchmark\"], axis=1 \t\t \t \t\t \t \t\t\t \t\t \t\t\t \t\t\t \t \n ) \t\t \t \t\t \t \t\t\t \t\t \t\t\t \t\t\t \t \n ax = df_temp.plot(title=\"Strategy Learner vs Impact \",color=['y','r','purple'], grid=True, fontsize=12)\n ax.legend(['Impact = 0.0005',\"Impact = 0.005\",'Impact = 0.05'])\n ax.set_xlabel(\"Date\")\n ax.set_ylabel(\"Normalized daily portfolio value\")\n fig = ax.get_figure()\n fig.savefig('images/impact.png')\n plt.close()\n \n\ndef test():\n print()\n print()\n print('********************** Starting Experiment 2 **********************')\n run_experiment()\n print('********************** End of Experiment 2 **********************')\n print()\n print()\n\n\ndef author(): \t\t \t \t\t \t \t\t\t \t\t \t\t\t \t\t\t \t \n \"\"\" \t\t \t \t\t \t \t\t\t \t\t \t\t\t \t\t\t \t \n :return: The GT username of the student \t\t \t \t\t \t \t\t\t \t\t \t\t\t \t\t\t \t \n :rtype: str \t\t \t \t\t \t \t\t\t \t\t \t\t\t \t\t\t \t \n \"\"\" \t\t \t \t\t \t \t\t\t \t\t \t\t\t \t\t\t \t \n return \"ybouzekraoui3\"\nif __name__ == \"__main__\":\n test()\n", "repo_name": "Younes43/Trading-Strategy-Evaluation", "sub_path": "experiment2.py", "file_name": "experiment2.py", "file_ext": "py", "file_size_in_byte": 5945, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "91", "api": [{"api_name": "datetime.datetime", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 14, "usage_type": "call"}, {"api_name": "StrategyLearner.StrategyLearner", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 20, "usage_type": "call"}, {"api_name": "marketsimcode.compute_portvals", "line_number": 21, "usage_type": "call"}, {"api_name": "StrategyLearner.StrategyLearner", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 28, "usage_type": "call"}, {"api_name": "marketsimcode.compute_portvals", "line_number": 29, "usage_type": "call"}, {"api_name": "StrategyLearner.StrategyLearner", "line_number": 33, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 35, "usage_type": "call"}, {"api_name": "marketsimcode.compute_portvals", "line_number": 36, "usage_type": "call"}, {"api_name": "marketsimcode.get_statistics", "line_number": 56, "usage_type": "call"}, {"api_name": "marketsimcode.get_statistics", "line_number": 59, "usage_type": "call"}, {"api_name": "marketsimcode.get_statistics", "line_number": 62, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}]} +{"seq_id": "21024983278", "text": "from collections import Counter\nimport glob\nimport os\nimport re\nfrom math import log2\nimport pandas as pd\nfrom nltk.util import ngrams\nimport csv\n\ndef unigrams(words): return Counter(words)\n\ndef bigrams(words): return Counter(ngrams(words,2))\n\ndef H(elements):\n N = sum(elements)\n return -sum([k * log2(k/N + (k==0)) for k in elements])\n\ndef calcuate_llr(bigram, BIGRAMS,UNIGRAMS, N_BI, N_UNI):\n k_11 = BIGRAMS[bigram] / N_UNI\n px = UNIGRAMS[bigram[0]]/N_BI\n py = UNIGRAMS[bigram[1]]/N_BI\n k_22 = 1 - (px+py-k_11)\n k_21 = px-k_11\n k_12 = py - k_11\n sumOfEvents = k_11+k_12+k_21+k_22;\n H_all = H((k_11,k_12,k_21,k_22))\n H_rows = H((k_11+k_12,k_21+k_22))\n H_cols = H((k_11+k_21,k_12+k_22))\n llr = 2*sumOfEvents * (H_rows + H_cols - H_all)\n return llr;\n\ndataPath = 'dump/'\nregexp = re.compile(\"\\w+\")\nfiles = glob.glob(os.path.join(dataPath,\"*.txt\"))\nWORDS = []\nWORDS2 = []\ni = 0\ngrammarClasses=set([\"subst\", \"depr\", \"num\", \"numcol\", \"adj\", \"ppron12\", \"ppron3\", \"siebie\", \"ger\", \"pact\", \"ppas\", \"prep\"])\nfor file in files:\n print(\"reading csv file: \" + file)\n with open(file) as csvfile:\n reader = csv.reader(csvfile, delimiter=\"\\t\")\n print(\"file read, getting words from csv\")\n for row in reader:\n if len(row) < 3: continue\n word = str(row[1]).lower()\n if regexp.match(word):\n grammarClass = str(row[2]).split(\":\").pop(0)\n word = word+\":\"+grammarClass\n WORDS.append(word)\n if(set([grammarClass]) & grammarClasses != set()):\n WORDS2.append(word)\nprint(\"creating unigrams 1\")\nUNIGRAMS1 = unigrams(WORDS)\nN_UNI1 = sum(UNIGRAMS1.values())\nprint(\"creating bigrams 1\")\nBIGRAMS1 = bigrams(WORDS)\nN_BI1 = sum(BIGRAMS1.values())\n\nprint(\"creating unigrams 2\")\nUNIGRAMS2 = unigrams(WORDS2)\nN_UNI2 = sum(UNIGRAMS2.values())\nprint(\"creating bigrams 2\")\nBIGRAMS2 = bigrams(WORDS2)\nN_BI2 = sum(BIGRAMS2.values())\n\nprint(\"saving results1 to files\")\nbig1 = open(\"bigrams1.txt\",\"w+\")\nllr1 = open(\"llr1.txt\",\"w+\")\nresults = open(\"results.txt\",\"w+\")\nfor bigram in BIGRAMS1.most_common():\n big1.write(\"{}\\t{}\\n\".format(bigram[0], bigram[1]))\n llr = calcuate_llr(bigram[0],BIGRAMS1, UNIGRAMS1, N_BI1, N_UNI1)\n llr1.write(\"{}\\t{}\\n\".format(bigram[0], llr))\n left = bigram[0][0].split(\":\").pop(1).split(\"'\").pop(0)\n right = bigram[0][1].split(\":\").pop(1).split(\"'\").pop(0)\n if(left == \"subst\") and ((right == \"subst\") or (right == \"adj\")):\n results.write(\"{}\\t{}\\n\".format(bigram[0], llr))\nllr1.close()\nbig1.close()\nresults.close()\n\nprint(\"saving results2 to file\")\nbig2 = open(\"bigrams2.txt\",\"w+\")\nllr2 = open(\"llr2.txt\",\"w+\")\nfor bigram in BIGRAMS2.most_common():\n big2.write(\"{}\\t{}\\n\".format(bigram[0], bigram[1]))\n llr = calcuate_llr(bigram[0],BIGRAMS2, UNIGRAMS2, N_BI2, N_UNI2)\n llr2.write(\"{}\\t{}\\n\".format(bigram[0], llr))\nllr2.close()\nbig2.close()", "repo_name": "marcinosypka/pjn", "sub_path": "5/analyzeText.py", "file_name": "analyzeText.py", "file_ext": "py", "file_size_in_byte": 2948, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "91", "api": [{"api_name": "collections.Counter", "line_number": 10, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 12, "usage_type": "call"}, {"api_name": "nltk.util.ngrams", "line_number": 12, "usage_type": "call"}, {"api_name": "math.log2", "line_number": 16, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 33, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "csv.reader", "line_number": 42, "usage_type": "call"}]} +{"seq_id": "5767034190", "text": "import torch\nfrom torch.utils.data import DataLoader\nimport math\nfrom sentence_transformers import models, losses\nfrom sentence_transformers import LoggingHandler, SentenceTransformer, util, InputExample\nfrom sentence_transformers.evaluation import EmbeddingSimilarityEvaluator, LabelAccuracyEvaluator\nimport logging\nfrom datetime import datetime\nimport sys\nimport re\nimport numpy as np\nimport argparse\nfrom utils import read_sts\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--model',dest='name_model', type=str, help='bert/roberta/gpt...', default='bert-base-cased')\nparser.add_argument('--dataset', dest='dataset', type=str, help='name of dataset', default='')\nparser.add_argument('--path-data',dest='path_data', type=str , help='path of folder data', default='')\nparser.add_argument('--pooling',dest='pooling', type=str, help='mean/max', default='mean')\nparser.add_argument('--use-topic',dest='use_topic', type=str, help='Use Trasformer-topic (True/False)', default='False')\nparser.add_argument('--train-topic',dest='train_topic', type=str, help='Update topic embed during training (True/False)', default='False')\nparser.add_argument('--train-file',dest='train_file', type=str, default='')\nparser.add_argument('--dev-file', dest='dev_file', type=str, default='')\nparser.add_argument('--test-file',dest='test_file', type=str, default='')\nparser.add_argument('--batch-size', dest='batch_size', type=int, default=8)\nparser.add_argument('--epochs', dest='num_epochs', type=int, default=2)\nparser.add_argument('--num-topics', dest='num_topic', type=int, default=0)\nargs = parser.parse_args()\n\n\n\nuse_topic = True if args.use_topic.lower() != 'false' else False\ntrain_topic = True if args.train_topic.lower() != 'false' else False\n\n# model_save_path = args.path_data+'/'+args.dataset+'_'+args.name_model+'_'+str(args.use_topic)+'_'+str(args.train_topic)+'_'+args.pooling +\"_\"+ str(args.num_topic)\n\n# topic set up\nif use_topic is True:\n model_save_path = args.path_data+'/'+args.dataset+'_'+args.name_model+'_'+str(args.use_topic)+'_'+str(args.train_topic)+'_'+args.pooling +\"_\"+ str(args.num_topic)\n W = torch.tensor(np.load(args.path_data+'/W_gensim_t'+ str(args.num_topic) + '.npy'), dtype = torch.float) \n word_embedding_model = models.Transformer_Topic(args.name_model, topic_weight = W, train_topic = train_topic, max_seq_length = 512)\n transfer_layer = models.Features_transfer(word_embedding_model.get_word_embedding_dimension(), word_embedding_model.get_word_embedding_dimension())\n pooling_model = models.Pooling(transfer_layer.get_word_embedding_dimension(), pooling_mode_mean_tokens=True, pooling_mode_cls_token=False, pooling_mode_max_tokens=False)\n model = SentenceTransformer(modules=[word_embedding_model, transfer_layer, pooling_model])\nelse:\n model_save_path = args.path_data+'/'+args.dataset+'_'+args.name_model+'_'+str(args.use_topic)+'_'+str(args.train_topic)+'_'+args.pooling\n word_embedding_model = models.Transformer(args.name_model, max_seq_length = 256)\n pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(), pooling_mode_mean_tokens=True, pooling_mode_cls_token=False, pooling_mode_max_tokens=False)\n model = SentenceTransformer(modules=[word_embedding_model, pooling_model])\n\nmodel.cuda()\n\ntrain_samples = []\ntrain_data = read_sts(args.path_data+'/'+args.train_file)\nfor sample in train_data:\n train_samples.append(InputExample(texts=[sample[1], sample[2]], label=float(sample[0])/5.0))\n \ntrain_dataloader = DataLoader(train_samples, shuffle=True, batch_size=args.batch_size)\ntrain_loss = losses.CosineSimilarityLoss(model=model)\n\ndev_samples = []\nif args.dev_file == '':\n dev_samples = train_samples[0:1000]\nelse:\n train_data = read_sts(args.path_data+'/'+args.dev_file)\n for sample in train_data:\n dev_samples.append(InputExample(texts=[sample[1], sample[2]], label=float(sample[0])/5.0))\n\nevaluator = EmbeddingSimilarityEvaluator.from_input_examples(dev_samples, name='sts-dev')\n\nwarmup_steps = math.ceil(len(train_dataloader) * args.num_epochs * 0.1) #10% of train data for warm-up\nlogging.info(\"Warmup-steps: {}\".format(warmup_steps))\n\n# Train the model\nmodel.fit(train_objectives=[(train_dataloader, train_loss)],\n evaluator=evaluator,\n epochs=args.num_epochs,\n evaluation_steps=1000,\n warmup_steps=warmup_steps,\n output_path=model_save_path\n )\n\n\ntest_samples = []\ntrain_data = read_sts(args.path_data+'/'+args.test_file)\nfor sample in train_data:\n test_samples.append(InputExample(texts=[sample[1], sample[2]], label=float(sample[0])/5.0))\n \nmodel = SentenceTransformer(model_save_path)\nmodel.cuda()\ntest_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(test_samples, batch_size=args.batch_size, name='sts-test')\ntest_evaluator(model, output_path=model_save_path)\ntest_evaluator.predict(model, output_path=model_save_path)", "repo_name": "binhdt95/SubTST", "sub_path": "sts.py", "file_name": "sts.py", "file_ext": "py", "file_size_in_byte": 4923, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "91", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 40, "usage_type": "attribute"}, {"api_name": "sentence_transformers.models.Transformer_Topic", "line_number": 41, "usage_type": "call"}, {"api_name": "sentence_transformers.models", "line_number": 41, "usage_type": "name"}, {"api_name": "sentence_transformers.models.Features_transfer", "line_number": 42, "usage_type": "call"}, {"api_name": "sentence_transformers.models", "line_number": 42, "usage_type": "name"}, {"api_name": "sentence_transformers.models.Pooling", "line_number": 43, "usage_type": "call"}, {"api_name": "sentence_transformers.models", "line_number": 43, "usage_type": "name"}, {"api_name": "sentence_transformers.SentenceTransformer", "line_number": 44, "usage_type": "call"}, {"api_name": "sentence_transformers.models.Transformer", "line_number": 47, "usage_type": "call"}, {"api_name": "sentence_transformers.models", "line_number": 47, "usage_type": "name"}, {"api_name": "sentence_transformers.models.Pooling", "line_number": 48, "usage_type": "call"}, {"api_name": "sentence_transformers.models", "line_number": 48, "usage_type": "name"}, {"api_name": "sentence_transformers.SentenceTransformer", "line_number": 49, "usage_type": "call"}, {"api_name": "utils.read_sts", "line_number": 54, "usage_type": "call"}, {"api_name": "sentence_transformers.InputExample", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 58, "usage_type": "call"}, {"api_name": "sentence_transformers.losses.CosineSimilarityLoss", "line_number": 59, "usage_type": "call"}, {"api_name": "sentence_transformers.losses", "line_number": 59, "usage_type": "name"}, {"api_name": "utils.read_sts", "line_number": 65, "usage_type": "call"}, {"api_name": "sentence_transformers.InputExample", "line_number": 67, "usage_type": "call"}, {"api_name": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.from_input_examples", "line_number": 69, "usage_type": "call"}, {"api_name": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator", "line_number": 69, "usage_type": "name"}, {"api_name": "math.ceil", "line_number": 71, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 72, "usage_type": "call"}, {"api_name": "utils.read_sts", "line_number": 85, "usage_type": "call"}, {"api_name": "sentence_transformers.InputExample", "line_number": 87, "usage_type": "call"}, {"api_name": "sentence_transformers.SentenceTransformer", "line_number": 89, "usage_type": "call"}, {"api_name": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.from_input_examples", "line_number": 91, "usage_type": "call"}, {"api_name": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator", "line_number": 91, "usage_type": "name"}]} +{"seq_id": "51474751", "text": "\n\n__all__ = '''\n per_page_transform__url\n\n per_page_transform__page\n extract_mzitu_com__per_pages\n '''.split()\n\n#from .home_page_transform import NotFoundError, find\nfrom .DATA import (\n website_per_page_img_url_regex\n ,echo_proxy_query_key\n\t\t,min_width_num_pages\n\t\t,max_width_num_pages\n )\nfrom ._configure_ import timeout\nfrom nn_ns.internet.webpage.fetch_webpage import fetch_webpage\nfrom bs4 import BeautifulSoup\n#from seed.helper.repr_input import repr_helper\n#import re\nfrom urllib.parse import urlparse #, urlunparse\nfrom pathlib import PurePosixPath as Path\n\n\n#proxy_url_prefix = 'http://api.hahacn.com/other/getimg2?url='\n#proxy_url_prefix = 'http://127.0.0.1:8000/echo_image/?url='\n#proxy_url_prefix_less = 'http://127.0.0.1:8000/echo_image/'\ndef per_page_transform__url(old_url, proxy_url_prefix_less):\n html_page = fetch_webpage(old_url, timeout=timeout)\n return per_page_transform__page(html_page, proxy_url_prefix_less)\n\n\ndef extract_mzitu_com__per_pages(html_page):\n # html_page -> (html_title, [img_url])\n #\n soup = BeautifulSoup(html_page, 'lxml')\n [html_title] = soup.head.find_all('title')\n html_title = html_title.get_text()\n\n [class_main_image] = soup.find_all('div', {'class': 'main-image'})\n [img_tag] = class_main_image.find_all('img')\n img_url = img_tag['src']\n img_url = img_url.lower()\n\n m = website_per_page_img_url_regex.fullmatch(img_url)\n if m is None: raise Exception(img_url)\n str_IMG_NUMBER = m['IMG_NUMBER']\n #assert str_IMG_NUMBER == '01'\n # /169493/ disordered\n\n begin = m.start('IMG_NUMBER')\n end = m.end('IMG_NUMBER')\n init = img_url[:begin]\n tail = img_url[end:]\n\n\n [class_page_navigate] = soup.find_all('div', {'class': 'pagenavi'})\n children = class_page_navigate.find_all('a')\n children = list(children)\n assert len(children) >= 3\n\n last = children[-1]\n last2 = children[-2]\n #print(last.name)\n #print(repr(last))\n assert last.name == 'a'\n assert last2.name == 'a'\n assert '下一页' in last.get_text()\n str_IMG_TOTAL = last2.get_text()\n IMG_TOTAL = int(str_IMG_TOTAL)\n assert IMG_TOTAL < 10**max_width_num_pages\n\n img_urls = []\n for i in range(0, IMG_TOTAL):\n n = i+1\n str_IMG_NUMBER = f'{n:0>2}'\n #assert len(str_IMG_NUMBER) == 2\n assert min_width_num_pages <= len(str_IMG_NUMBER) <= max_width_num_pages\n img_url = init + str_IMG_NUMBER + tail\n img_urls.append(img_url)\n return html_title, img_urls\n\n\n\n\n\nnew_html_begin = r'''\n\n\n\n \n\n\n\n\n
\n
\n